ENSO teleconnections with the NAE sector during December in CMIP5/CMIP6 models: impacts of the atmospheric mean state

ENSO teleconnections with the NAE sector during December in CMIP5/CMIP6 models: impacts of the atmospheric mean state | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article ENSO teleconnections with the NAE sector during December in CMIP5/CMIP6 models: impacts of the atmospheric mean state Davide Sabatani, Silvio Gualdi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5560758/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jun, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 13 You are reading this latest preprint version Abstract This study investigates how the atmospheric mean state influences the El Niño-Southern Oscillation (ENSO) teleconnections with the North Atlantic-European (NAE) region, using ERA5 and CMIP5/CMIP6 models. By isolating the contributions of heating anomalies in the Niño 3.4 and Tropical Western-Eastern Indian Ocean (TWEIO) regions, we find that in November, the Niño 3.4 teleconnection dominates, projecting onto the positive phase of the North Atlantic Oscillation (NAO). In December, the TWEIO teleconnection prevails, reinforcing the positive NAO via a zonal wavenumber-3 Rossby wave train originating from SouthEast Asia (SEA). Models that fail to simulate the December ENSO teleconnection with the NAE exhibit a weak Rossby wave source in SEA and overly strong subtropical Pacific and Atlantic jet streams, which trap Rossby waves at lower latitudes, affecting the remote atmospheric response over the NAE. This waveguide bias is likely driven by a cold bias in the northern Pacific and Atlantic, a common mean-state error in climate models. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Atmospheric science Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The El Niño-Southern Oscillation (ENSO) is the dominant mode of variability at interannual timescales and constitutes one of the most prominent sources of predictability at seasonal 1 , 2 to annual timescales 3 . The Pacific-North-American (PNA) pattern stands out as the most recognized ENSO teleconnection with the extratropics, reaching its peak during winter 4 , 5 . The ENSO signal in the North Atlantic and European (NAE) sector is generally less robust in terms of amplitude, spatial structure, and significance compared to the canonical PNA pattern 6 , 7 . Nevertheless, it remains one of the major sources of predictability in this region. Recent studies have addressed the complexity and seasonality of ENSO teleconnection with the NAE 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 . Dynamical mechanisms have been proposed to explain the propagation of ENSO teleconnection with the NAE sector, including both tropospheric 7 , 11 , 15 , 17 and stratospheric pathways 15 , 16 , 18 , 19 . The ENSO-induced atmospheric circulation anomalies in the NAE features a meridional geopotential dipole, resembling the negative phase of the North Atlantic Oscillation (NAO) in February and March, and its positive phase in November and December 7 . The transition of the ENSO-related signal in the NAE region has been linked to an interfering teleconnection triggered by heating anomalies located in the tropical Indian Ocean (IO) 7 , 9 , 11 , 13 , 19 , 20 . These anomalies, linked to the Indian Ocean Dipole (IOD) mode, can initiate a wavenumber-3 Rossby wave-train, which projects onto the positive phase of the NAO in December 11 . General Circulation Models (GCMs) exhibit limitations in accurately simulating the November and December ENSO teleconnection with the NAE region 7 , 13 , 20 , 21 . By analyzing historical simulations from models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), a previous study 13 demonstrated that a poor December ENSO teleconnection with the NAE may be linked to a weak Indian Ocean precipitation dipole, suggesting limitations in the simulation of ENSO-IOD coupling. Further research 7 showed that while seasonal hindcasts capture the January–February ENSO teleconnection with the NAE region reasonably well, the November–December teleconnections are often underestimated. This discrepancy may stem from model biases in simulating the interference between teleconnections originating from the Indian and Pacific Oceans. To our knowledge, only a very limited number of studies analyze how model biases affect the simulation of ENSO and TWEIO teleconnections with the NAE region. For instance, previous research 22 found that errors in the atmospheric response to ENSO in the northeastern Pacific could be partially attributed to biases in the jet stream, which can alter the propagation of Rossby waves from the tropical Pacific to the North Pacific. In contrast, it was concluded that a poor NAE response to ENSO is primarily due to biases in the tropical Rossby wave source located in the Caribbean Sea. These results are consistent with other studies emphasizing the influence of the Caribbean Sea and Gulf of Mexico Rossby wave sources on the NAE sector 8 , 17 , 23 , 24 . Using a bias correction method on simulated divergence and temperature tendencies, a previous work 25 demonstrated that model biases generally do not affect the ENSO-induced atmospheric response in the tropics and North Pacific. Using an intermediate-complexity general circulation model, it was suggested that a stronger Pacific jet stream can meridionally bend ENSO-induced Rossby waves, shifting the upper tropospheric geopotential response equatorward 26 . Likewise, low-frequency variability in Pacific SSTs has been shown to influence the propagation of ENSO-driven Rossby waves by modifying the background flow 14 . The present study serves as a continuation of previous analyses 13 , utilizing historical simulations from both the CMIP5 27 and CMIP6 28 datasets. The main objectives of this work are twofold: 1) to evaluate the ability of state-of-the-art coupled models to simulate ENSO teleconnection with the NAE during winter, and 2) to examine the influence of the models’ atmospheric mean state on the simulation of ENSO teleconnection with the NAE. Results ENSO teleconnection in reanalysis Figure 1 shows the upper tropospheric (200 hPa) geopotential height and WAF anomalies regressed against the Niño 3.4 index from November to February. During November (Fig. 1 a) and December (Fig. 1 b), the ENSO teleconnection with the North Pacific is weaker compared to January (Fig. 1 c) and February (Fig. 1 d). Over the North Atlantic sector, a positive NAO-like signal emerges in November and persists into December, with its response shifted meridionally relative to the canonical NAO pattern. In November, the northern center of action, represented by an anomalous Icelandic low, is more pronounced than its southern counterpart, the anomalous Azores high. By December, the Icelandic low fades, suggesting a weaker ENSO teleconnection compared to November. Table 1 Contribution of the Indian and Pacific Ocean heating anomalies to the NAO phase across different reference datasets in November. Regression of the November Eastern Atlantic Gradient (EAG) of 200 hPa geopotential height anomalies [(25°N-40°N, 25°W-5°E) and (50°N-65°N, 50°W-10°W)] against the Niño 3.4 (EAG (Niño3.4) ), the pure Niño 3.4 (EAG (P−Niño3.4) ), and the pure TWEIO (EAG (P−TWEIO) ) indices for the period 1979–2024. Values of the regression coefficient p* are also shown. The percentages in the last column indicate the contribution of the EAG (P−TWEIO) to the EAG (Niño3.4) . Regression values shown in bold are significant at the 90% level of confidence with respect to a two-tailed t-test. EAG (Niño3.4) EAG (P−Niño3.4) EAG (P−TWEIO) p* p*EAG (P−TWEIO) / EAG (Niño3.4) ERA5 61.2 44.3 22.4 0.76 28% ERA5 + GPCP 65.6 48.0 22.8 0.77 27% NCAR-NCEP 59.7 56.6 6.0 0.52 5% NCAR-NCEP + GPCP 65.3 48.3 22.0 0.77 26% Table 2 Contribution of the Indian and Pacific Ocean heating anomalies to the NAO phase across different reference datasets in December. As in Table 1 but for December. EAG (Niño3.4) EAG (P−Niño3.4) EAG (P−TWEIO) p* p*EAG (P−TWEIO) / EAG (Niño3.4) ERA5 35.6 6.2 53.6 0.55 82% ERA5 + GPCP 36.9 5.8 56.3 0.55 84% NCAR-NCEP 45.6 37.7 17.3 0.45 17% NCAR-NCEP + GPCP 37.9 7.0 56.0 0.55 81% The extratropical ENSO teleconnection involves strong wave flux in the subtropical Western Pacific regions in November (Fig. 1 a) and December (Fig. 1 b). During December the wave flux weakens in the North Atlantic but strengthens in the western and central Pacific at mid-latitudes (Fig. 1 b). The canonical PNA teleconnection pattern appears in January (Fig. 1 c) and February (Fig. 1 d), involving a strengthening of the Aleutian low and positive geopotential anomalies extending over the North American sector. During February, the atmospheric response in the NAE sector significantly projects onto the negative phase of the NAO (Fig. 1 d). Figure 2 illustrates the 200 hPa geopotential height and WAF anomalies regressed against the pure TWEIO and pure Niño 3.4 indices for November and December. Starting from November, the pure TWEIO regression shows a strong wave activity flux originating from SouthEast Asia (SEA: 20°N-40°N, 80°E-140°E; black box in Fig. 6 a) (Fig. 2 a). The wave-train exhibits an anomalous trough located in the SEA, followed by an anomalous ridge over the Japanese sector. In December (Fig. 2 b), the pattern exhibits a wave-train characterized by alternating geopotential height anomalies with a zonal wavelength of approximately 120°, corresponding to a zonal wavenumber of 3. The meridional scale of the wave spans the 30°N-60°N latitude band, indicating a meridional wavenumber of approximately 6. The zonal and meridional wavenumbers are consistent with the atmospheric waveguide (Figure S4c), which exhibits total stationary wavenumbers of 5–7 over the SEA region. This wave eventually reaches the NAE sector and projects onto the positive phase of the NAO. Conversely, the pure ENSO teleconnection with the NAE is stronger in November (Fig. 2 c) than in December (Fig. 2 d). In November, the upper tropospheric atmospheric response to the pure ENSO signal resembles the positive phase of the NAO (Fig. 2 c). In December the circulation anomalies weaken, and a positive geopotential anomaly develops stretching from northwestern North America to the North Atlantic (Fig. 2 d). The monthly Niño 3.4 regressions, shown in Fig. 1 , can be seen as a linear combination of the pure Niño 3.4 and TWEIO regressions, illustrated in Fig. 2 . The ENSO signal in the NAE during November (Fig. 1 a) seems to be dominated by the pure ENSO teleconnection (Fig. 2 c). In December, the pure TWEIO signal strengthens while the pure ENSO signal weakens over the NAE, and the circulation anomalies over the NAE appear to be primarily attributable to heating anomalies in the TWEIO region. The monthly contribution of the pure TWEIO index to the Niño 3.4 regressions can be further analyzed by calculating the regression coefficient p* (Eq. 3a). The regression coefficient for 1950–2014 is 0.86 for November, 0.58 for December, 0.41 for January, and 0.30 for February. Additionally, our analysis of precipitation anomalies regressed against the Niño 3.4 index during the extended winter season (Figure S1 ) indicates that heating anomalies in the Indian Ocean weaken during February, resulting in a weakening of the pure TWEIO teleconnection. Although the regression coefficient in November is larger compared to later months, the partial regression analysis indicates that the pure ENSO teleconnection has a stronger influence than the pure TWEIO teleconnection over the NAE region (Figs. 2 a and 2 c). This finding is not consistent with previous research 11 , which suggested that the pure TWEIO contribution dominates over the pure Niño 3.4 contribution in driving the NAO-like pattern during November. We believe that this discrepancy may be, at least in part, explained by the use of different reference datasets and periods. To test the first hypothesis, we applied partial regression analysis to four datasets — ERA5, ERA5 with GPCP, NCAR-NCEP, and NCAR-NCEP with GPCP — covering the common period 1979–2024. For each dataset, we calculated the Eastern Atlantic Gradient (EAG; dashed boxes in Fig. 1 a) index, defined as the difference between the average 200 hPa geopotential height anomalies in the two regions (25°N-40°N, 25°W-5°E) and (50°N-65°N, 50°W-10°W) 7 . Using partial regression analysis, we find that: EAG (Niño3.4) = EAG (P−Niño3.4) + p*EAG (P−TWEIO) , (1) where EAG (Niño3.4) , EAG (P−Niño3.4) , and EAG (P−TWEIO) represent the regressions of the EAG index onto the Niño 3.4, pure Niño 3.4, and pure TWEIO indices, respectively. The EAG index has been shown to better capture the NAO-like pattern compared to the traditional NAO index 7 . This is because the regions used to define the EAG index are centered approximately 5 degrees south of the points canonically used for the NAO index. However, the results remain similar to those obtained using the NAO index from a previous study 11 (not shown). The values for terms in Eq. 1 are summarized in Tables 1 and 2 for November and December, respectively. For November, when combining ERA5 and NCAR-NCEP with GPCP, the pure TWEIO EAG contributes approximately 28% and 26% to the Niño 3.4 EAG, respectively (Table 1 ). This percentage is relatively similar when using ERA5 alone (27%) but decreases drastically when using the NCAR-NCEP dataset (5%). In December (Table 2 ), the ENSO teleconnection projects onto the positive phase of the NAO, albeit more weakly than in November (Table 1 ). The pure TWEIO EAG accounts for approximately 80% of the Niño 3.4 EAG across all datasets, except for NCAR-NCEP, which shows a lower and statistically insignificant TWEIO contribution of around 17%. As a result, in December, the pure TWEIO signal prevails over the pure ENSO signal. The discrepancies observed when using the NCAR-NCEP reanalysis compared to other datasets underscore its limitations for calculating precipitation-based indices. To examine the second hypothesis, specifically the sensitivity to different time periods, we used a re-sampling method for the month of November. We randomly selected 45 years from the complete ERA5 dataset (covering 1940–2024) and the NCAR-NCEP dataset (covering 1948–2024) to align with the temporal coverage of the GPCP dataset (covering 1979–2024). From each of the ERA5 and NCAR-NCEP reanalysis products, we generated 10000 sub-samples. We then calculated the frequency of instances where p*EAG (P−TWEIO) exceeded EAG (P−Niño3.4 ) . This frequency constitutes the probability that the ENSO teleconnection is dominated by the pure TWEIO signal. The probability is 12% and 3% for the ERA5 and NCAR-NCEP datasets respectively, supporting a weak contribution of the pure TWEIO teleconnection with the NAE during November. We conclude that, for the considered period and reference dataset, the influence of the pure TWEIO teleconnection on circulation anomalies in the NAE dominates over that of the pure ENSO teleconnection in December, while the opposite is true for November and February. ENSO teleconnection in CMIP models The ability of CMIP5 and CMIP6 models to simulate the ENSO teleconnection is assessed through Taylor diagrams 29 (Fig. 3 ) of the monthly 200 hPa geopotential height anomaly patterns regressed onto the Niño 3.4 index in the NAE sector (here defined as the domain 20°N-80°N, 60°W-0° and shown by the black box in Fig. 4 a). The diagrams have been computed for November (Fig. 3 a), December (Fig. 3 b), and February (Fig. 3 c). January was excluded from the analysis due to the weak and statistically insignificant ENSO signal in the NAE region during this month (Fig. 1 c). Sensitivity tests demonstrate that the results are not dependent on the choice of the domain (not shown). Table 3 summarizes the key findings from the Taylor diagrams for each month. Table 3 Summary of the Taylor diagram results stratified over CMIP5 and CMIP6 models. Values of pattern correlation and normalized standard deviation between the CMIP5/CMIP6 MME relative to ERA5. The two rightmost columns indicate the percentage of CMIP5 and CMIP6 models with negative pattern correlation relative to ERA5. CMIP5 MME corr CMIP6 MME corr CMIP5 MME std CMIP6 MME std CMIP5 models with negative corr CMIP6 models with negative corr November 0.70 0.76 0.34 0.46 26% 20% December 0.39 0.67 0.40 0.55 38% 30% February 0.58 0.77 0.61 0.65 10% 4% Overall, the models show better agreement with ERA5 in November and February than in December. Noteworthy, the results in Table 3 indicate an improvement in the simulation of ENSO teleconnection with the NAE in CMIP6 models compared to CMIP5, particularly in December. However, it's important to acknowledge that the number of CMIP6 models is 30% higher than that of CMIP5, which may influence the MME mean performance. By computing the spatial correlation between the reanalysis and simulated ENSO teleconnection patterns in the NAE region during December, we identified clusters of “good” and “bad” models. Good models are those that produce teleconnection patterns with a positive correlation exceeding 0.6 with the ERA5 patterns in November and December. Conversely, models with a pattern correlation below − 0.35 in December are classified as bad models. The classification of clustered models, stratified across CMIP5 and CMIP6, is presented in Table 4 . Table 4 Categorization of CMIP models based on clustering. CMIP5 and CMIP6 models that meet the criterion of good (correlation of ENSO teleconnection pattern in the NAE > 0.6 in November and December) and bad (correlation of ENSO teleconnection pattern in the NAE < -0.35 in December) models. CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm). CMIP5 CMIP6 Bad models giss-e2-h, hadcm3, csiro-mk3-6-0, cmcc-cm, cesm1-fastchem, cmcc-cesm CNRM-CM6-1-HR, MPI-ESM1-2-HR Good models noresm1-me ICON-ESM-LR, CIESM, NorCPM1, CESM2, BCC-ESM1, MPI-ESM-1-2-HAM, HadGEM3-GC31-MM The clustering process used to select good models ensures the inclusion of those that, to some extent, can simulate the ENSO teleconnection in November and December. As a part of a sensitivity test, we have also selected good models based on the pattern correlation during December only (with a threshold of 0.75)*. Analogous results to those presented here were obtained (not shown). We, then, analyze the fields obtained by averaging regression patterns and mean state over good models and bad models, especially for December. *In this case, the models that fall into the category of good models are: CESM2, NorCPM1, hadgem2-cc, MPI-ESM1-2-LR, CNRM-CM6-1, IITM-ESM, ICON-ESM-LR, EC-Earth3-Veg-LR, cnrm-cm5, cnrm-cm5-2, HadGEM3-GC31-MM, noresm1-me. Figures 4 a and 4 b show the averaged regressions of the December 200 hPa geopotential height anomalies onto the Niño 3.4 index for the two clusters. Bad models not only fail to capture the positive NAO-like phase in the NAE region but also exhibit a weak geopotential response in the subtropical East Asian and western Pacific sectors. This deficit is also evident when taking the difference of the regressions shown in Figs. 4 a and 4 b (Figure S2a). These findings suggest that the ENSO inter-basin teleconnection with the IO might contribute to this inter-model diversity. Figures 4 c and 4 d show the average precipitation anomalies regressed against the Niño 3.4 index for the clustered groups. Both model groups simulate the ENSO-related precipitation patterns in the Pacific sector reasonably well when compared to the reference dataset (Figure S1 b). However, significant discrepancies are observed in the IO basin, where bad models fail at reproducing the east-west precipitation dipole associated with the ENSO inter-basin teleconnection (see also Figure S2b for the pattern difference). It is important to note that bad models tend to reproduce a double Inter-Tropical Convergence Zone (ITCZ) bias, marked by excessively strong convergence zones in the South Pacific, Atlantic, and Indian Oceans (Figure S3). The TRWSs in reanalysis and CMIP models Results from ERA5 (top row in Figure S4), good models (middle row in Figure S4), and bad models (bottom row in Figure S4) reveal three main waveguides extending from subtropical Africa, the western Pacific, and the subtropical Atlantic: the Sub-Saharan Africa (SSA: 10°N-30°N, 10°E-40°E; black box in Fig. 6 b) waveguide, the subtropical South Asian waveguide, and the subtropical Atlantic waveguide. The lower tropospheric convergence (divergence) associated with anomalous convection (subsidence) in the tropics is balanced in the upper troposphere by divergent (convergent) flow. This mechanism interacts with the atmospheric waveguide and constitutes an essential trigger for the initiation of quasi-stationary planetary Rossby wave-trains 30 , 31 . Figure 5 shows the 200 hPa TRWS and irrotational wind anomalies regressed against the Niño 3.4 index for the reference dataset (Fig. 5 a) and the clustered groups (Figs. 5 b and 5 c). In ERA5, the Niño 3.4 heating anomalies induce upper tropospheric divergence, resulting in a negative horseshoe-like TRWS pattern (Fig. 5 a). In the Indian Ocean, upper tropospheric divergence (convergence) over the western (eastern) basin interacts with the African (Asian) waveguide. This mechanism, in turn, generates a negative (positive) TRWS located in the SSA (SEA) region. Two secondary Rossby wave sources are also identified north of the Gulf of Mexico and east of the Caribbean Sea, primarily driven by the anomalous rearrangement of the Walker and Hadley circulations in response to Niño 3.4 heating anomalies. Hence, five key regions involving significant TRWSs have been identified: the SSA (10°N-30°N, 10°E-40°E; black box in Fig. 6 b), the SEA (20°N-40°N, 80°E-140°E; black box in Fig. 6 a and Fig. 9 a), the Tropical Pacific (TP: 0°-30°N, 180°E-120°W; black box in Fig. 9 c), the Caribbean Sea (CS: 5°-25°N, 70°W-20°W; black box in Fig. 9 e), and the Gulf of Mexico (GM: 25°N-45°N, 110°W-60°W; black box in Fig. 9 g). Many studies have investigated the role of TRWSs in triggering Rossby waves over the tropical and subtropical Pacific, the Caribbean Sea, and the Gulf of Mexico 8 , 16 , 17 , 22 , 23 , 24 . However, much less is known about the TRWSs in the SEA and SSA regions. To fill this gap, we first defined two indices by averaging the 200 hPa TRWS time series over the SEA and SSA regions, indicated by the black boxes in Figs. 6 a and 6 b, respectively. Subsequently, we regressed the 200 hPa geopotential height anomalies against the pure TRWS indices (i.e., independent of the Niño 3.4 index), as shown in Fig. 6 . Sensitivity tests indicate that the results shown in Fig. 6 are not affected by the size of the boxes used to compute the indices (not shown). The SEA TRWS explains a zonal wavenumber-3 Rossby wave-train, consistently with the regression based on the pure TWEIO index (Fig. 2 b). The regression patterns show a strong wave flux originating from the SEA, where an anomalous trough appears. The wave-train reaches the NAE sector, where geopotential anomalies resemble the positive phase of the NAO. Therefore, we argue that in December, the SEA TRWS plays a key role in modulating the atmospheric response to ENSO in the NAE sector. The regression of 200 hPa geopotential anomalies and WAF onto the pure SSA TRWS index (Fig. 6 b) shows a wave-train similar to that in Fig. 2 b and Fig. 6 a. However, a key difference emerges in the North Atlantic, where strong WAF originates from the Caribbean Sea, arches northeastward, and then propagates back to the SSA. This analysis suggests that the TRWS in the SSA sector is a response of a Rossby wave originating from the Caribbean Sea. A comparison of the upper tropospheric TRWS simulated by good models (Fig. 5 b) and bad models (Fig. 5 c) shows that the latter significantly underestimate TRWS in the SSA and SEA regions. The difference in the SEA is primarily attributed to a weaker upper tropospheric anomalous divergent wind field (Figure S5b) rather than discrepancies in the climatological absolute vorticity gradient (Figure S5a). In contrast, no substantial differences in the TRWS field are observed across the tropical and subtropical Pacific, the Caribbean Sea, and the Gulf of Mexico. As a further step, we have calculated for each model and the reanalysis the ENSO-related December 200 hPa TRWS averaged over the five key regions previously identified: the SSA, the SEA, the TP, the GM, and the CS. The values of the averaged TRWS in each of the five regions are plotted in Fig. 7 against the spatial correlation coefficients of the ENSO-related 200 hPa geopotential height anomalies in the NAE sector (see Fig. 3 b). In the models, the relationship of the averaged TRWS over the SSA (Fig. 7 a) exhibits a significant correlation, suggesting that models failing to simulate the positive NAO phase during an El Niño event in December also underestimate the SSA TRWS. This finding is consistent with the hypothesis that the atmospheric anomalies linked to the positive NAO phase may propagate to the tropics over the African subcontinent, thereby generating the negative SSA TRWS. Also, for the SEA case (Fig. 7 b), models with a weak TRWS tend to simulate a poor December ENSO teleconnection with the NAE. The simulated relations for the other three key regions, namely the TP (Fig. 7 c), the GM (Fig. 7 d), and the CS (Fig. 7 e), were found to be insignificant at the 95% level of confidence, confirming that a missed positive NAO response cannot be attributed to differences of TRWSs in these regions. However, the following question arises from the previous analysis: Why do bad models fail to reproduce the SSA TRWS, despite their ability to simulate the Caribbean Sea TRWS? This point will be addressed in the following section. The role of the atmospheric waveguide Previous studies have highlighted the influence of the jet stream on the propagation of atmospheric Rossby wave 14 , 26 . Here, we examine whether differences in the simulated climatological jet stream can explain variations in the December ENSO teleconnection with the NAE across different models. Figure 8 shows the differences in the December mean states of upper tropospheric zonal wind and SST between the MME means of bad and good models. Compared to good models, bad models exhibit subtropical Pacific and Atlantic jet streams that are 20–30% stronger and shifted equatorward (Fig. 8 a). These stronger waveguides are likely linked to a cold SST bias extending across the northern Pacific and Atlantic basins (Fig. 8 b). The cold bias, in fact, tends to enhance (reduce) the meridional SST gradient at low (high) latitudes, thereby strengthening (weakening) upper-level zonal winds through thermal wind balance. Additionally, the stronger Pacific (Atlantic) waveguide, coupled with a colder North Pacific (Atlantic) surface mean state, is also evident in the ensemble mean of bad models relative to the reference (Figure S6). The weak and statistically not significant differences in tropical Pacific SST mean states (Fig. 8 b) suggest that substantial variations in the seasonal phase and amplitude of simulated ENSO events are unlikely. At the surface, the Polar high, Aleutian and Icelandic lows are stronger in bad models (Figure S7). These patterns are in agreement with the difference in the lower tropospheric wind field, which exhibits a cyclonic (anti-cyclonic) pattern over the North Pacific and Atlantic (Arctic). The pressure systems detected in Figure S7 show a quasi-barotropic structure (not shown) and resembles the leading mode of variability of the extratropical circulation, namely the Arctic Oscillation (AO) 32 , 33 . To assess the impact of the stronger jet streams on the propagation of planetary waves during December, we implement the Rossby wave ray tracing algorithm in two configurations: using the ERA5 200 hPa zonal wind December climatology, hereafter referred to as \(\:\stackrel{̄}{\:u}\) ERA5 . using \(\:\stackrel{̄}{u}\) ERA5 superposed to the difference between bad and good models of the 200 hPa zonal wind climatology, hereafter referred to as \(\:\stackrel{̄}{u}\) BAD − GOOD . The rays are initiated from the TRWSs located in four key regions previously identified: the SEA, the TP, the CS and the GM. The SSA TRWS has been excluded because, as discussed in Section 3.3, we interpret it as a result of atmospheric anomalies propagating from the Euro-Atlantic sector to tropical Africa. The starting points for the Rossby wave ray algorithm are chosen as the grid point exhibiting maxima/minima of TRWS. The ray position and group velocity are calculated and then stepped forward in time for 10 days for zonal wavenumbers spanning from 1 to 5. The results are shown in Fig. 9 for the \(\:\stackrel{̄}{u}\) ERA5 (left panels) and \(\:\stackrel{̄}{u}\) ERA5 + \(\:\stackrel{̄}{\:u}\) BAD − GOOD (right panels) configurations. When interpreting the results presented in Fig. 9 , it is important to keep in mind that the wave-tracking algorithm relies on the linear theory for stationary barotropic Rossby waves. This theoretical basis limits the conclusions we can draw, and thus the results should be interpreted with some caution 6 . As Fig. 9 a shows, Rossby wave-trains of zonal wavenumber-3 (green scatters) follow a track that originated in the SEA region, crosses the Pacific, and eventually reaches the NAE sector. This is consistent with the results from the partial regression analysis of 200 hPa geopotential height anomalies (Figs. 2 b and 6 a). By superposing a stronger subtropical Pacific jet stream onto the reference basic flow, the trajectory of Rossby waves is affected, particularly those of zonal wavenumber-3 (Fig. 9 b). In ERA5, Rossby wave trains with zonal wavenumbers 2 (yellow scatters) and 3 (green scatters) originating from the TP successfully propagate to the NAE sector (Fig. 9 c). However, as shown in Fig. 9 d, the stronger Atlantic and Pacific jet streams constrain wavenumber-3 Rossby waves to lower latitudes, potentially altering the Euro-Atlantic atmospheric response to ENSO. Similarly, zonal wavenumber-1 to 3 Rossby waves originating from the CS (Fig. 9 e) and GM (Fig. 9 g) regions can freely propagate toward the northern Atlantic and European sectors in ERA5. Interestingly, waves with zonal wavenumbers-4 and 5 forced from the CS sector, as well as waves with zonal wavenumbers-2 and 3 from the GM sector, eventually reach the Sub-Saharan region. This finding is consistent with the hypothesis that the SSA TRWS is not a direct ENSO-driven source but rather a response to atmospheric anomalies propagating from the Euro-Atlantic region back to the tropics. In bad models, the stronger Atlantic waveguide appears to alter the ray paths of wavenumber-3 waves originating from the CS (Fig. 9 f) and GM (Fig. 9 h) regions, constraining them to lower latitudes. This finding may explain why bad models fail to produce a strong upper-tropospheric atmospheric response (Fig. 4 b) and TRWS (Fig. 5 c) in the SSA region, despite their ability to simulate the TRWSs over the CS and GM sectors. Furthermore, a comparison of the permitted total wavenumbers in the atmospheric waveguide of bad models (Figure S4i) with the reference (Figure S4c) and good models (Figure S4f) reveals the presence of a “forbidden area” (i.e., a region of imaginary total stationary wavenumber) over the northeastern sector of North America in bad models. The location of the forbidden region supports the hypothesis that Rossby waves originating from the CS and GM are effectively trapped at lower latitudes, preventing their poleward propagation. These findings indicate that bad models, which significantly underestimate the SEA TRWS linked to ENSO inter-basin teleconnections, are also characterized by stronger Pacific and Atlantic waveguides. The associated intensification of the subtropical jet streams leads to an equatorward shift of the refraction latitudes of zonal wavenumber-3 Rossby waves originating from the tropics, potentially altering the remote atmospheric response in the Euro-Atlantic region. Discussion This study examines how the atmospheric mean states of climate models influence the ENSO teleconnection with the North Atlantic and European sector in winter. The results obtained from ERA5 show that the ENSO teleconnection with the NAE region projects onto the positive phase of the NAO in November and December. Using partial regression analysis and a subsampling technique across multiple reanalysis datasets, we demonstrated that in November, the NAO-like pattern is primarily driven by the pure ENSO teleconnection. This finding contrasts with the results of previous research, which suggested that the November positive NAO is primarily driven by the pure TWEIO signal 7 , 11 . Tropical rainfall variability in reanalyses is influenced by observational constraints, which improved significantly with the advent of satellite data in the late 1970s. Before this period, reanalysis-derived precipitation over tropical oceans is increasingly uncertain due to sparse observational coverage. Additionally, intraseasonal variability in the Indian Ocean, particularly the Madden-Julian Oscillation (MJO), introduces further discrepancies among datasets on monthly timescales, potentially affecting our teleconnection indices in the pre-1979 decades. However, we have shown that the dominance of the pure ENSO teleconnection over the pure TWEIO signal remains robust even in the post-1979 decades. Furthermore, decadal to multi-decadal variability, such as that associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO), can modulate the contribution of the pure TWEIO teleconnection to the NAE. To explore this, we computed the November contribution of the pure TWEIO teleconnection to the ENSO teleconnection (see the rightmost column in Table 1 ) using a 35-year sliding window applied to ERA5 over the 1940–2024 period. The results, presented in Figure S8, reveal a strong non-stationary signal, even in the post-1979 decades, with a strong decrease in the pure TWEIO contribution after 1985. Another possible dynamical explanation for the dominance of the pure ENSO signal in November is that the IOD SST anomalies, which peak in October, exhibit a 2-months delayed connection with the atmospheric circulation over the NAE via a precipitation dipole over the Indian Ocean 34 . Consequently, the November TWEIO precipitation index may not fully be influenced by the October IOD SSTs, thereby reducing the impact of the pure TWEIO signal on the NAE region. By December circulation anomalies appear to be predominantly influenced by the pure TWEIO teleconnection. The TWEIO heating anomalies are associated with the initiation of a zonal wavenumber-3 Rossby wave-train originating from the SEA region and extending to the NAE sector. In the North Atlantic, this wave-train spatially projects onto the positive phase of the NAO, further supporting the role of the TWEIO in influencing the atmospheric response to ENSO in the NAE sector 7 , 9 , 11 , 13 , 19 , 20 , 21 , 35 . Through a clustering analysis, we identified models that can reasonably simulate ENSO teleconnections with the NAE region (“good models”), distinguishing them from those that cannot (“bad models”). Results indicate that bad models tend to underestimate the Indian Ocean heating dipole, often concurrent with ENSO episodes, leading to a weak tropical Rossby wave source in the SEA sector. In turn this wave source is directly linked to the initiation of a zonal wavenumber-3 Rossby wave-train that impacts the NAE sector with a positive NAO-like fingerprint; thus, its weakness results in feeble teleconnections. Previous studies have highlighted the existence of a secondary tropical Rossby wave source situated over the Caribbean Sea, associated with the Atlantic waveguide 8 , 17 , 22 , 23 , 24 . According to our results, no significant differences are detected in the simulated TRWSs over the Gulf of Mexico and Caribbean Sea between bad and good models. The mean state analysis of clustered groups reveals that bad models tend to simulate a stronger Pacific and Atlantic waveguides. These patterns reflect the jet stream bias commonly observed in many climate models 26 , 36 , 37 , 38 , 39 , 40 . The stronger subtropical Pacific and Atlantic jet streams are related via thermal wind balance to a colder northern Pacific and Atlantic Oceans, respectively, which mirror a common systematic error in older-generation models 40 , 41 , 42 , 43 , usually characterized by a poor resolution in the ocean. Using a ray tracing algorithm based on linear theory, we have shown that, in December, a stronger subtropical Pacific and Atlantic jet streams are associated with a southward displacement of the refraction latitudes of zonal wavenumber-3 Rossby waves. As a result, Rossby waves originating from the tropics are meridionally bent and may, therefore, fail to reach the NAE sector. This could explain why bad models fail to capture the ENSO-induced remote atmospheric response over the Euro-Atlantic and Sub-Saharan sectors, despite their ability to simulate the TRWSs over the Gulf of Mexico and Caribbean Sea. In conclusion, in the analyzed CMIP models, two key sources of errors concerning the simulation of the December ENSO teleconnection with the North Atlantic-European sector have been identified: i) an underestimated SEA TRWS, which might be the consequence of a poorly simulated ENSO inter-basin connection with the Indian Ocean; ii) stronger Pacific and Atlantic waveguides, in turn related via thermal wind balance to overly cold Pacific and Atlantic Oceans, respectively. This study clearly demonstrates the strong impact that models’ systematic errors have on the simulation of teleconnections. Addressing these biases would significantly improve the models' ability to simulate the atmospheric extratropical teleconnection patterns and thus the skill of climate predictions in mid-latitude. Data and Methods Models The historical simulations from 48 and 37 models participating in phases 5 and 6 of CMIP, respectively, are used. To distinguish between CMIP6 and CMIP5 models, the names of CMIP6 models are written in uppercase letters, while CMIP5 model names are written in lowercase letters (e.g., CESM2 and cmcc-cm). The lists of CMIP6 and CMIP5 models can be found in Tables 5 and 6 , respectively. Table 5 CMIP6 models. List of the 48 CMIP6 models analyzed in this study, along with their respective modeling centers, countries, and the ensemble members used. CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm). Model Name (CMIP6) Modeling Center Nation Ensemble member ACCESS-CM2 CSIRO-ARCCSS Australia r1i1p1f1 ACCESS-ESM1-5 CSIRO-ARCCSS Australia r1i1p1f1 AWI-CM-1-1-MR AWI Germany r1i1p1f1 BCC-ESM1 BCC/CMA China r1i1p1f1 CESM2 NCAR USA r1i1p1f1 CESM2-FV2 NCAR USA r1i1p1f1 CESM2-WACCM NCAR USA r1i1p1f1 CESM2-WACCM-FV2 NCAR USA r1i1p1f1 CMCC-ESM2 CMCC Italy r1i1p1f1 CMCC-CM2-HR4 CMCC Italy r1i1p1f1 CMCC-CM2-SR5 CMCC Italy r1i1p1f1 CIESM THU China r1i1p1f1 CNRM-CM6-1 CNRM France r1i1p1f2 CNRM-CM6-1-HR CNRM France r1i1p1f2 CanESM5-CanOE CCCma Canada r1i1p2f1 E3SM-1-0 E3SM-Project USA r1i1p1f1 E3SM-1-1 E3SM-Project USA r1i1p1f1 E3SM-1-1-ECA E3SM-Project USA r1i1p1f1 EC-Earth3-AerChem EC-Earth Consortium Europe r1i1p1f1 EC-Earth3-CC EC-Earth Consortium Europe r1i1p1f1 EC-Earth3-Veg-LR EC-Earth Consortium Europe r1i1p1f1 FGOALS-f3-L CAS China r1i1p1f1 FGOALS-g3 CAS China r1i1p1f1 FIO-ESM-2-0 FIO-QLNM China r1i1p1f1 GFDL-ESM4 GFDL USA r1i1p1f1 GISS-E2-1-H NASA GISS USA r1i1p1f1 HadGEM3-GC31-LL MOHC UK r1i1p1f1 HadGEM3-GC31-MM MOHC UK r1i1p1f3 ICON-ESM-LR MPI-M Germany r1i1p1f1 IITM-ESM CCCR-IITM India r1i1p1f1 INM-CM4-8 INM Russia r1i1p1f1 INM-CM5-0 INM Russia r1i1p1f1 IPSL-CM6A-LR IPSL France r1i1p1f1 KACE-1-0-G KIOST South Korea r1i1p1f1 KIOST-ESM KIOST South Korea r1i1p1f1 MCM-UA-1-0 UA USA r1i1p1f1 MIROC-ES2H JAMSTEC Japan r1i1p4f2 MIROC-ES2L JAMSTEC Japan r1i1p1f2 MIROC6 JAMSTEC Japan r1i1p1f1 MPI-ESM-1-2-HAM MPI-M Germany r1i1p1f1 MPI-ESM1-2-HR MPI-M Germany r1i1p1f1 MPI-ESM1-2-LR MPI-M Germany r1i1p1f1 MRI-ESM2-0 MRI Japan r1i1p1f1 NESM3 NUIST China r1i1p1f1 NorCPM1 NCC Norway r1i1p1f1 NorESM2-MM NCC Norway r1i1p1f1 SAM0-UNICON SNU South Korea r1i1p1f1 TaiESM1 RCEC Taiwan r1i1p1f1 Table 6 CMIP5 models. List of the 37 CMIP5 models analyzed in this study, along with their respective modeling centers, countries, and the ensemble members used. CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm). Model Name (CMIP5) Modeling Center Nation Ensemble member access1-0 CSIRO-BOM Australia r1i1p1 access1-3 CSIRO-BOM Australia r1i1p1 bcc-csm1-1 BCC/CMA China r1i1p1 bcc-csm1-1-m BCC/CMA China r1i1p1 bnu-esm BNU China r1i1p1 cancm4 CCCma Canada r1i1p1 canesm2 CCCma Canada r1i1p1 ccsm4 NCAR USA r1i1p1 cesm1-fastchem NCAR USA r1i1p1 cesm1-bgc NCAR USA r1i1p1 cesm1-cam5 NCAR USA r1i1p1 cesm1-waccm NCAR USA r1i1p1 cmcc-cesm CMCC Italy r1i1p1 cmcc-cm CMCC Italy r1i1p1 cmcc-cms CMCC Italy r1i1p1 cnrm-cm5 CNRM-CERFACS France r1i1p1 cnrm-cm5-2 CNRM-CERFACS France r1i1p1 csiro-mk3-6-0 CSIRO-QCCCE Australia r1i1p1 csiro-mk3l-1-2 CSIRO-QCCCE Australia r1i1p1 fgoals-g2 CAS China r1i1p1 fgoals-s2 CAS China r1i1p1 fio-esm FIO China r1i1p1 gfdl-cm3 GFDL USA r1i1p1 giss-e2-g NASA GISS USA r1i1p1 giss-e2-r NASA GISS USA r1i1p1 hadcm3 MOHC UK r1i1p1 hadgem2-cc MOHC UK r1i1p1 hadgem2-es MOHC UK r1i1p1 inmcm4 INM Russia r1i1p1 ipsl-cm5a-lr IPSL France r1i1p1 miroc5 JAMSTEC Japan r1i1p1 mpi-esm-lr MPI-M Germany r1i1p1 mpi-esm-mr MPI-M Germany r1i1p1 mpi-esm-p MPI-M Germany r1i1p1 mri-cgm3 MRI Japan r1i1p1 noresm1-me NCC Norway r1i1p1 noresm1-m NCC Norway r1i1p1 The model data include the monthly upper tropospheric geopotential height (200 hPa), upper tropospheric zonal and meridional winds (200 hPa), total precipitation, sea surface temperature, Sea Level Pressure (SLP), and lower tropospheric zonal and meridional winds (925 hPa). Following the methodology of a previous study 13 , only one ensemble member (i.e., 'r1i1p1f1' and 'r1i1p1', when available) is included to ensure equal weightage. Other members were included for those models not having the first realization of the ensemble. The last 64 years of model simulations were analyzed, with CMIP6 data assessed for the period 1950–2014 and CMIP5 data for the period 1940–2004. Similar results are obtained when analyzing CMIP6 data for the period 1940–2004 (not shown). Reanalysis The reference dataset used in this study is the ERA5 reanalysis product 44 , which includes monthly data of upper tropospheric geopotential height (200 hPa), upper tropospheric zonal and meridional winds (200 hPa), and total precipitation. The dataset covering the period from 1950 to 2014 is analyzed, and similar results are obtained when using the dataset spanning from 1940 to 2004 (not shown). To assess the sensitivity of the results to the reference dataset, additional data from the NCEP/NCAR Reanalysis 1 product 45 and the Global Precipitation Climatology Project (GPCP) Version 2 dataset 46 are used. Both model and reference datasets are bilinearly interpolated to a common grid with a horizontal resolution of 2.5°x2.5°. Linear and partial regression analysis Given that teleconnections primarily originate from heating anomalies produced by intense convective activities in tropical regions, monthly precipitation anomalies are used to calculate the indices instead of SST anomalies 7 , 11 , 47 . The normalized Niño 3.4 precipitation index, hereafter referred to as Niño 3.4 index, is obtained by averaging the standardized monthly precipitation anomalies over the Niño 3.4 region (5°S-5°N, 170°E-120°W; black box in Fig. 1 a). To assess the link between heating anomalies located in the Indian Ocean and ENSO teleconnections, the normalized Tropical Western-Eastern Indian Ocean (TWEIO; black boxes in Fig. 2 a) precipitation index is used. Following previous studies 11 , 47 , the TWEIO index is defined as the difference of standardized precipitation anomalies averaged over the western (10°S-10°N, 40°E-80°E) and eastern (10°S-10°N, 100°E-140°E) Indian Ocean. Linear regression analysis is applied to monthly data, with linearly detrended anomaly fields regressed onto normalized indices. The resulting regression patterns are consistent with those obtained from composite analysis (not shown). The partial regression method is implemented to separate the contributions of different processes from an anomaly field. The TWEIO and Niño 3.4 indices can be decomposed as follows: α TWEIO (t) = β TWEIO (t) + p*α Niño3.4 (t) , (2a) α Niño3.4 (t) = β Niño3.4 (t) + s*α TWEIO (t) , (2b) where α TWEIO and α Niño3.4 denote the TWEIO and Niño 3.4 indices, respectively, β TWEIO represents the pure TWEIO index (i.e., independent of the Niño 3.4 index), and β Niño3.4 represents the pure Niño 3.4 index (i.e., independent of the TWEIO index). The regression coefficients p* and s* are computed as follows: p * = \(\:\frac{{\sum\:}_{t=0}^{N}\left[{\alpha\:}_{TWEIO}\right(t\left){\alpha\:}_{Niño3.4}\right(t\left)\right]}{{\sum\:}_{t=0}^{N}{\alpha\:}_{Niño3.4}^{2}\left(t\right)}\) (3a) s * = \(\:\frac{{\sum\:}_{t=0}^{N}\left[{\alpha\:}_{TWEIO}\right(t\left){\alpha\:}_{Niño3.4}\right(t\left)\right]}{{\sum\:}_{t=0}^{N}{\alpha\:}_{TWEIO}^{2}\left(t\right)}\) (3b) The Niño 3.4 regression patterns can be expressed as a linear combination of the regressions against the pure Niño 3.4 and TWEIO indices, as follows: R(x) = R 0 (x) + p*P 0 (x) (4) where R(x) is the Niño 3.4 regression, R 0 (x) is the pure Niño 3.4 regression, P 0 (x) is the pure TWEIO regression, and p* is the regression coefficient shown in Eq. 3a. From now on, regressions against the pure TWEIO and pure Niño 3.4 indices will also be referred to as pure TWEIO and ENSO teleconnections, respectively. The significance testing for ERA5 and CMIP models is assessed by implementing a two-tailed t-test, while for the Multi-Model Ensemble (MME) mean a Student’s one-sample t-test is implemented. Tropical Rossby wave source analysis The Tropical Rossby Wave Source (TRWS) formula is used to investigate the anomalous source of upper tropospheric vorticity, which is responsible for the initiation of planetary Rossby waves 8 , 16 . The TRWS is calculated as the advection of climatological absolute vorticity by anomalous irrotational wind 48 , 49 : $$\:TRWS\:=\:-\upsilon\:{\prime\:}ᵪ\cdot\:\nabla\:(\stackrel{̄}{\zeta\:}+f)$$ 5 In Eq. 5 , \(\:\upsilon\:{\prime\:}ᵪ\) represents the anomalous irrotational wind, \(\:\stackrel{̄}{\zeta\:}\) and \(\:f\) are the relative and planetary climatological vorticities, respectively. The irrotational wind is calculated by first integrating the velocity potential ꭓ from the divergence field, and then deriving it to obtain the irrotational wind, similarly to previous research 16 . The advection of the climatological absolute vorticity by anomalous irrotational flow is the dominant source of upper tropospheric vorticity in the tropics 49 . We recognize that the term representing the vorticity tendency by vortex stretching or shrinking, the Extratropical Rossby Wave Source (ERWS) 49 , is excluded from our analysis. However, analogous conclusions hold when considering the ERWS (not shown). Atmospheric waveguide analysis The meridional gradient of climatological upper tropospheric absolute vorticity and zonal wind serve as a proxy for the atmospheric waveguide 30 . The total stationary wavenumber, indicating the permitted wavenumbers of stationary Rossby waves along a given waveguide, is calculated using the linear theory for barotropic Rossby waves as follows: $$\:K=\sqrt{(\beta\:/\stackrel{̄}{u})\:}$$ 6 where \(\:\beta\:\) is the meridional gradient of absolute vorticity and \(\:\stackrel{̄}{u}\) is the climatological upper tropospheric zonal wind. Regions of negative \(\:K\) 2 , which indicate imaginary wavenumbers, are regarded as “forbidden regions” for the meridional propagation of stationary Rossby waves and therefore serve as effective refraction zones. Wave activity analysis The propagation of stationary Rossby wave packets is assessed using the Wave Activity Flux (WAF) formula 50 . This formula diagnoses Rossby wave propagation independently of its phase and parallel to its local group velocity under the geostrophic approximation. It is a widely used tool in extratropical teleconnection studies 11 , 13 , 51 . The zonal and meridional WAF components are: $$\:{\:WAF}_{x}=\:\frac{pcos\varphi\:}{2\stackrel{̄}{U}}\left(\frac{\stackrel{̄}{u}}{{a}^{2\:}co{s}^{2}\varphi\:}\left[({\partial\:}_{x}\psi\:{\prime\:}{)}^{2\:}-\:\psi\:{\prime\:}\:{{\partial\:}^{2}}_{x}\psi\:{\prime\:}\right]+\frac{\stackrel{̄}{v}}{{a}^{2\:}cos\varphi\:}({\partial\:}_{x}\psi\:{\prime\:}{\partial\:}_{y}\psi\:{\prime\:}-\psi\:{\prime\:}{{\partial\:}^{2}}_{xy}\psi\:{\prime\:})\right)$$ 7a $$\:{WAF}_{y}=\:\frac{pcos\varphi\:}{2\stackrel{̄}{U}}\left(\frac{\stackrel{̄}{u}}{{a}^{2\:}cos\varphi\:}\left[{\partial\:}_{x}\psi\:{\prime\:}{\partial\:}_{y}\psi\:{\prime\:}-\psi\:{\prime\:}{{\partial\:}^{2}}_{xy}\psi\:{\prime\:}\right]+\frac{\stackrel{̄}{v}}{{a}^{2\:}}\left[({\partial\:}_{y}\psi\:{\prime\:}{)}^{2\:}-\:\psi\:{\prime\:}\:{{\partial\:}^{2}}_{y}\psi\:{\prime\:}\right]\right)$$ 7b where p is normalized pressure, \(\:\stackrel{̄}{u}\) and \(\:\stackrel{̄}{v}\) are the climatological zonal and meridional winds, \(\:\stackrel{̄}{U}\) is the absolute value of the vector \(\:\overrightarrow{\stackrel{̄}{U}}=(\stackrel{̄}{u},\:\stackrel{̄}{v})\) , a is the Earth radius, and \(\:{\partial\:}_{x}\) and \(\:{\partial\:}_{y}\) are the partial derivatives along the zonal and meridional directions, respectively. The term 𝜓’ stands for the anomalous geostrophic streamfunction, defined as the anomalous geopotential 𝜑’ divided by the Coriolis parameter f. Both TRWS and WAF are calculated at the 200 hPa level in the upper troposphere, where Rossby wave sources typically reach their maximum intensity 6 . Rossby wave ray tracing algorithm An algorithm based on linear theory and the assumption of stationarity 30 , 52 is applied to investigate the pathway of Rossby wave rays 6 , 8 . The zonal and meridional group velocity of stationary barotropic Rossby waves are: $$\:{c}_{gx}=\frac{2[{\stackrel{̄}{u}]}^{2\:}{{k}^{\:}}^{2\:}}{(\beta\:-{{\partial\:}^{2}}_{y}[\stackrel{̄}{u}\left]\right)}\:$$ 8a $$\:{c}_{gy}=\frac{2\left[{\stackrel{̄}{u}]}^{2\:}{k}^{\:}\right(\frac{\beta\:-\:{{\partial\:}^{2}}_{y}\left[\stackrel{̄}{u}\right]}{\stackrel{̄}{\left[u\right]}}\:-\:{k}^{2\:}{)}^{1/2}}{(\beta\:-{{\partial\:}^{2}}_{y}[\stackrel{̄}{u}\left]\right)}\:\:$$ 8b where \(\:\left[\stackrel{̄}{u}\right]\) is the zonal average of the climatological zonal wind, \(\:{\partial\:}_{y}\) is the partial derivative along the meridional direction, k is the zonal wavenumber, and β is the meridional gradient of the Coriolis parameter. The zonal group velocity, c gx , is consistently positive and it increases at a faster rate than c gy , indicating that barotropic Rossby waves propagate more predominantly in the zonal direction 30 , 52 . We consider the barotropic case because these waves propagate more easily, thereby influencing the remote response 6 . From a starting point, typically identified within a region exhibiting a local TRWS maximum, the group velocities and position are determined. Subsequently, they are projected forward in time by 1 hour over a span of 10 days 6 , 8 . The grid-point group velocities and positions are calculated using nearest-neighbor interpolation of the fields described in Equations 8a and 8b, following the approach of previous research 6 , 8 . Declarations Data availability The data analyzed in this study is freely available at the link http://esgf-index1.ceda.ac.uk, which is maintained by the Earth System Grid Federation (ESGF). Code availability statement The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author. Acknowledgements One of the authors (SG) acknowledges the financial support from Spoke 4 of the ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data, and Quantum Computing, funded by the European Union under NextGenerationEU. Author Contributions DS conceptualized the study, carried out the analysis, and drafted the manuscript. SG contributed to the discussion, the implemented methodology, and the revision and writing of the manuscript. Competing interests The authors declare no competing interests. References Doblas‐Reyes, F. J., García‐Serrano, J., Lienert, F., Biescas, A. P., & Rodrigues, L. R. (2013). Seasonal climate predictability and forecasting: status and prospects. Wiley Interdisciplinary Reviews: Climate Change , 4 (4), 245-268. Domeisen, D. I., Butler, A. H., Fröhlich, K., Bittner, M., Müller, W. A., & Baehr, J. (2015). Seasonal predictability over Europe arising from El Niño and stratospheric variability in the MPI-ESM seasonal prediction system. Journal of Climate , 28 (1), 256-271. Dunstone, Nick, et al. "Skilful predictions of the winter North Atlantic Oscillation one year ahead." Nature Geoscience 9.11 (2016): 809-814. Horel, J. D., and J. M. Wallace (1981), Planetary-scale atmospheric phenomena associated with the Southern Oscillation, Mon. Weather Rev., 103, 813–829. vAN LooN, H. A. R. R. Y., and Roland A. Madden. "The Southern Oscillation. Part I: Global associations with pressure and temperature in northern winter." Monthly Weather Review 109.6 (1981): 1150-1162. Scaife, Adam A., et al. "Tropical rainfall, Rossby waves and regional winter climate predictions." Quarterly Journal of the Royal Meteorological Society 143.702 (2017): 1-11. Molteni, Franco, and Anca Brookshaw. "Early-and late-winter ENSO teleconnections to the Euro-Atlantic region in state-of-the-art seasonal forecasting systems." Climate Dynamics (2023): 1-20. Ayarzagüena, B., Ineson, S., Dunstone, N. J., Baldwin, M. P., & Scaife, A. A. (2018). Intraseasonal effects of el niño–southern oscillation on North Atlantic climate. Journal of Climate , 31 (21), 8861-8873. King, Martin P., et al. "Importance of late fall ENSO teleconnection in the Euro-Atlantic sector." Bulletin of the American Meteorological Society 99.7 (2018): 1337-1343. Mezzina, J. García-Serrano, I. Bladé, F. Kucharski, Dynamics of the enso teleconnection and nao variability in the north atlantic–european late winter, Journal of Climate 33 (3) (2020) 907–923. Abid, M. A., Kucharski, F., Molteni, F., Kang, I. S., Tompkins, A. M., & Almazroui, M. (2021). Separating the Indian and Pacific Ocean impacts on the Euro-Atlantic response to ENSO and its transition from early to late winter. Journal of Climate , 34 (4), 1531-1548. King MP, Li C, Sobolowski S (2021) Resampling of ENSO teleconnections: accounting for cold-season evolution reduces uncertainty in the North Atlantic. Weather Clim Dyn 2:759–776. https://doi.org/10.5194/wcd-2-759-2021 Joshi, M. K., Abid, M. A., & Kucharski, F. (2021). The role of an Indian Ocean heating dipole in the ENSO teleconnection to the North Atlantic European region in early winter during the twentieth century in reanalysis and CMIP5 simulations. Journal of Climate , 34 (3), 1047-1060. Benassi, Marianna, et al. "El Niño teleconnection to the Euro-Mediterranean late-winter: the role of extratropical Pacific modulation." Climate Dynamics 58.7 (2022): 2009-2029. Geng, Xin, Jiuwei Zhao, and Jong-Seong Kug. "ENSO-driven abrupt phase shift in North Atlantic oscillation in early January." npj Climate and Atmospheric Science 6.1 (2023): 80. Mezzina, Bianca, et al. "Multi-model assessment of the late-winter extra-tropical response to El Niño and La Niña." Climate Dynamics 58.7 (2022): 1965-1986. Mezzina, B., García-Serrano, J., Ambrizzi, T., Matei, D., Manzini, E., & Bladé, I. (2023). Tropospheric pathways of the late-winter ENSO teleconnection to Europe. Climate Dynamics , 60 (11), 3307-3317. Domeisen, D. I., Garfinkel, C. I., & Butler, A. H. (2019). The teleconnection of El Niño Southern Oscillation to the stratosphere. Reviews of Geophysics , 57 (1), 5-47. Hardiman, Steven C., et al. "Predictability of European winter 2019/20: Indian Ocean dipole impacts on the NAO." Atmospheric Science Letters 21.12 (2020): e1005. Abid, M. A., Kucharski, F., Molteni, F., & Almazroui, M. (2023). Predictability of Indian Ocean precipitation and its North Atlantic teleconnections during early winter. npj Climate and Atmospheric Science , 6 (1), 17. Molteni, Franco, et al. "Boreal-winter teleconnections with tropical Indo-Pacific rainfall in HighResMIP historical simulations from the PRIMAVERA project." Climate Dynamics 55 (2020): 1843-1873. Li, R. K., Woollings, T., O’Reilly, C., & Scaife, A. A. (2020). Effect of the North Pacific tropospheric waveguide on the fidelity of model El Niño teleconnections. Journal of Climate , 33 (12), 5223-5237. Hardiman, S. C., Dunstone, N. J., Scaife, A. A., Smith, D. M., Ineson, S., Lim, J., & Fereday, D. (2019). The impact of strong El Niño and La Niña events on the North Atlantic. Geophysical Research Letters , 46 (5), 2874-2883. Rodríguez-Fonseca, Belén, et al. "A review of ENSO influence on the North Atlantic. A non-stationary signal." Atmosphere 7.7 (2016): 87. Tyrrell, Nicholas L., and Alexey Yu Karpechko. "Minimal impact of model biases on northern hemisphere ENSO teleconnections." Weather and Climate Dynamics Discussions 2020 (2020): 1-16 Di Carlo, E., Ruggieri, P., Davini, P., Tibaldi, S., & Corti, S. (2022). ENSO teleconnections and atmospheric mean state in idealised simulations. Climate Dynamics , 59 (11), 3287-3304. Taylor, Karl E., Ronald J. Stouffer, and Gerald A. Meehl. "An overview of CMIP5 and the experiment design." Bulletin of the American meteorological Society 93.4 (2012): 485-498. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development , 9 (5), 1937-1958. Taylor, Karl E. "Summarizing multiple aspects of model performance in a single diagram." Journal of geophysical research: atmospheres 106.D7 (2001): 7183-7192. Hoskins, Brian J., and Tercio Ambrizzi. "Rossby wave propagation on a realistic longitudinally varying flow." Journal of Atmospheric Sciences 50.12 (1993): 1661-1671 Trenberth, K. E., Branstator, G. W., Karoly, D., Kumar, A., Lau, N. C., & Ropelewski, C. (1998). Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. Journal of Geophysical Research: Oceans , 103 (C7), 14291-14324. Ambaum, Maarten HP, Brian J. Hoskins, and David B. Stephenson. "Arctic oscillation or North Atlantic oscillation?." Journal of Climate 14.16 (2001): 3495-3507. Thompson, David WJ, and John M. Wallace. "Annular modes in the extratropical circulation. Part I: Month-to-month variability." Journal of climate 13.5 (2000): 1000-1016. Raganato, A., Abid, M. A., & Kucharski, F. (2025). The Combined Link of the Indian Ocean Dipole and ENSO with the North Atlantic–European Circulation during Early Boreal Winter in Reanalysis and the ECMWF SEAS5 Hindcast. Journal of Climate , 38 (2), 445-460. Molteni, Franco, Timothy N. Stockdale, and Frédéric Vitart. "Understanding and modelling extra-tropical teleconnections with the Indo-Pacific region during the northern winter." Climate dynamics 45 (2015): 3119-3140. Iqbal, Waheed, Wai-Nang Leung, and Abdel Hannachi. "Analysis of the variability of the North Atlantic eddy-driven jet stream in CMIP5." Climate Dynamics 51 (2018): 235-247. Curtis, Paul Edwin, Paulo Ceppi, and Giuseppe Zappa. "Role of the mean state for the Southern Hemispheric jet stream response to CO2 forcing in CMIP6 models." Environmental Research Letters 15.6 (2020): 064011. Athanasiadis, Panos J., et al. "Mitigating climate biases in the midlatitude North Atlantic by increasing model resolution: SST gradients and their relation to blocking and the jet." Journal of Climate 35.21 (2022): 6985-7006. Liu, Anqi, Ying Huang, and Danqing Huang. "Inter‐model spread of the simulated winter surface air temperature over the Eurasian continent and the physical linkage to the jet streams from the CMIP6 models." Journal of Geophysical Research: Atmospheres 127.22 (2022): e2022JD037172 Wang, C., Zhang, L., Lee, S. K., Wu, L., & Mechoso, C. R. (2014). A global perspective on CMIP5 climate model biases. Nature Climate Change , 4 (3), 201–205. https://doi.org/10.1038/nclimate2118 Wang, Chenqi, Liwei Zou, and Tianjun Zhou. "SST biases over the Northwest Pacific and possible causes in CMIP5 models." Science China Earth Sciences 61 (2018): 792-803. Feng, J., Lian, T., Chen, D., & Li, Y. (2021). The cause of the large cold bias in the northwestern Pacific Ocean. Geophysical Research Letters , 48 (18), e2021GL094616. Zhang, Qibei, et al. "Understanding models' global sea surface temperature bias in mean state: from CMIP5 to CMIP6." Geophysical Research Letters 50.4 (2023): e2022GL10088 Hersbach H, Bell B, Berrisford P et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049. https://doi.org/ 10.1002/qj.3803 Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996 Adler, Robert F., et al. "The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present)." Journal of hydrometeorology 4.6 (2003): 1147-1167. Abid, M. A., Ashfaq, M., Kucharski, F., Evans, K. J. & Almazroui, M. Tropical Indian Ocean mediates ENSO influence over central Southwest Asia during the wet season. Geophys. Res. Lett. 47, e2020GL089308. https://doi.org/10.1029/2020GL089308 (2020). Sardeshmukh PD, Hoskins BJ (1988) The generation of global rotational flow by steady idealized tropical divergence. J Atmos Sci 45:1228–1251. https://doi.org/10.1175/1520-0469(1988)0452.0.CO;2 Qin, Jianchun, and Walter A. Robinson. "On the Rossby wave source and the steady linear response to tropical forcing." Journal of Atmospheric Sciences 50.12 (1993): 1819-1823. Takaya, Koutarou, and Hisashi Nakamura. "A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow." Journal of the Atmospheric Sciences 58.6 (2001): 608-627. Ruggieri, Paolo, et al. "Atlantic multidecadal variability and North Atlantic jet: a multimodel view from the decadal climate prediction project." Journal of Climate 34.1 (2021): 347-360. Hoskins BJ, Karoly DJ. 1981. The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci. 38: 1179–1196. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Jun, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Accepted 23 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers agreed at journal 12 Apr, 2025 Editor assigned by journal 07 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviews received at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5560758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434881567,"identity":"7be66fe8-664c-40d5-94fe-14f9aa43b672","order_by":0,"name":"Davide Sabatani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYDCCA0DEA2JIgLkScvxA8jAQ8xCjhbEBSBpLNkC14NRzAGYgRAtD4gagCDMDHmv4jp8xPPCmgsGef3bz8wc/d1gwbr6Re/BwQQWDjD0OLZJncgwOzjnDkDjjzjHDxt4zEsxmN/ISDs84g9thBgfSEg7ztjEkMNxIMGzgbZNgM7uRYwASwa3l/DOgln8M9vI30j82/m2T4DGeQUjLjeQDh3kbGBg33MgxbAbaImEgQUCL5I3HBw7OOSaRuPHOmcLZsm1AHWfeGAD9IsHDcwBHiJ1PbP7wpsbGXu52+4aPb9vq6vvbc4w/F1TY2LM34LAGAiSIEBkFo2AUjIJRQDwAAOR9X61830EBAAAAAElFTkSuQmCC","orcid":"","institution":"University of Bologna","correspondingAuthor":true,"prefix":"","firstName":"Davide","middleName":"","lastName":"Sabatani","suffix":""},{"id":434881569,"identity":"c4d00129-f55b-4b5d-98e0-7fcc6b859384","order_by":1,"name":"Silvio Gualdi","email":"","orcid":"","institution":"CMCC Foundation- Euro-Mediterranean Center on Climate Change","correspondingAuthor":false,"prefix":"","firstName":"Silvio","middleName":"","lastName":"Gualdi","suffix":""}],"badges":[],"createdAt":"2024-12-02 02:38:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5560758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5560758/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41612-025-01064-2","type":"published","date":"2025-06-17T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79412443,"identity":"34b58be5-2171-4625-955f-549d4346dd61","added_by":"auto","created_at":"2025-03-28 06:26:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2539607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eENSO teleconnection in the reference dataset during the extended boreal winter season. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eERA5 200 hPa geopotential height anomalies (Z200) and WAF regressed against the normalized Niño 3.4 precipitation index for (a) November, (b) December, (c) January, and (d) February. Shading indicates the regression of geopotential height anomalies (units m), while vectors represent the WAF (units m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/s\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) with a unit vector of 0.2 m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/s\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e. The black solid box indicates the domain (5°S-5°N, 170°E-120°W) used to compute the Niño 3.4 index. The black dashed boxes indicate the domains, namely (25°N-40°N, 25°W-5°E) and (50°N-65°N, 50°W-10°W), used to compute the Eastern Atlantic Gradient (EAG) indices listed in Tables 1 and 2. The light-gray stippling indicates the grid points in which the regression is significant at the 95% level of confidence with respect to a two-tailed t-test. The units of regression are specified per standard deviation.\u003c/em\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/cd4bd9700ec9a64ea7d0e349.jpg"},{"id":79412442,"identity":"8c34edbb-b419-4f70-85ee-f43c9a947c4c","added_by":"auto","created_at":"2025-03-28 06:26:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2409427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePure ENSO and TWEIO teleconnections in the reference dataset during boreal November and December. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eERA5 200 hPa geopotential height anomalies (Z200) and WAF regressed against the pure (i.e., independent of ENSO variability) TWEIO precipitation index for (a) November, (b) December. (c)-(d) as in (a)-(b) but for the pure (i.e., independent of TWEIO variability) Niño 3.4 regression. Shading indicates the regression of geopotential height anomalies (units m), while vectors represent the WAF (units m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/s\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) with a unit vector of 0.2 m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/s\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e. The black solid boxes in Figure 2a indicate the western (10°S-10°N, 40°E-80°E) and eastern (10°S-10°N, 100°E-140°E) Indian Ocean domains used to compute the TWEIO index. The black solid box in Figure 2c indicates the domain (5°S-5°N, 170°E-120°W) used to compute the Niño 3.4 index. The light-gray stippling indicates the grid points in which the regression is significant at the 95% level of confidence with respect to a two-tailed t-test. The units of regression are specified per standard deviations.\u003c/em\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/d391097d03e55be80a920917.jpg"},{"id":79412454,"identity":"426f6117-4f5c-4f3a-9cc5-af749ff356dc","added_by":"auto","created_at":"2025-03-28 06:26:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1023815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePerformance of the ENSO teleconnection with the NAE in CMIP models. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eTaylor diagram based on the 200 hPa geopotential height anomalies regressed against the Niño 3.4 index over the NAE sector (20°N-80°N;60°W-0°) for (a) November, (b) December, and (c) February. The black star represents the reference dataset, the black-filled square indicates the MME of CMIP6 models, and the white-filled square represents the MME of CMIP5 models. The color-filled symbols denote individual CMIP6 models, while the white-filled symbols denote individual CMIP5 models. The horizontal axis represents the standard deviation of the spatial pattern normalized over the reference standard deviation. The outer semicircle denotes the pattern correlation. Note the different sizes of the x-axis in (c) compared to (a)-(b). CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/efbb9085904051738ea3e685.jpg"},{"id":79412446,"identity":"bf0eeab7-fc8a-44d8-8160-1b43f33f17e6","added_by":"auto","created_at":"2025-03-28 06:26:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2546022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eENSO teleconnection and ENSO-related precipitation anomalies stratified by clustered groups. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eEnsemble verage of 200 hPa geopotential height anomalies (Z200, units m) regressed against the normalized Niño 3.4 precipitation index for (a) ensemble mean of good models (GOOD) and (b) ensemble mean of bad models (BAD). (c)-(d) as in (a)-(b), but for the regression of precipitation anomalies (units 10\u003c/em\u003e\u003csup\u003e\u003cem\u003e-5\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e kg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003es). The black box in Figure 4a indicates the NAE domain (20°N-80°N, 60°W-0°) used to compute the pattern correlation for the Taylor diagrams in Figure 3.\u003c/em\u003e \u003cem\u003eThe light-gray stippling indicates the grid points in which the average is significant at the 95% level of confidence with respect to a one-sample t-test. The units of regression are specified per standard deviations.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/e9d04727b5598fabaee18109.jpg"},{"id":79412476,"identity":"027093f1-4eab-44c8-851c-32272935cfd9","added_by":"auto","created_at":"2025-03-28 06:26:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1097933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eENSO-related Tropical Rossby Wave Source (TRWS) in ERA5 and clustered groups. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eDecember 200 hPa TRWS and irrotational wind regressed against the normalized Niño 3.4 precipitation index for (a) ERA5, (b) ensemble mean of good models (GOOD), and (c) ensemble mean of bad models (BAD). Shading indicates the regression of TRWS anomalies (units 10\u003c/em\u003e\u003csup\u003e\u003cem\u003e-11\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e s\u003c/em\u003e\u003csup\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), while vectors represent the irrational wind (units m/s) with a unit vector of 0.5 m/s. Only TRWS that is significant at the 95% level of confidence with respect to two-tailed (ERA5) and one-sample (MME) t-tests is shown. Winds are displayed at the 90% level of confidence with respect to two-tailed (ERA5) and one-sample (MME) t-tests. The units of regression are specified per standard deviations.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/fcfc1bf737a909b8c4f84f0c.jpg"},{"id":79412465,"identity":"d53d5e80-945d-4ab6-a2df-9d51dd737713","added_by":"auto","created_at":"2025-03-28 06:26:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1251846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUpper tropospheric atmospheric response to Rossby wave sources in the SouthEast Asia (SEA) and Sub-Saharan Africa (SSA) regions. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eERA5 December 200 hPa geopotential height anomalies (Z200) and WAF regressed against the pure a) SEA (20°N-40°N, 80°E-140°E), and b) SSA (10°N-30°N, 10°E-40°E) TRWS indices. Shading indicates the regression of geopotential height anomalies (units m), while vectors represent the WAF (units m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/s\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) with a unit vector of 0.2 m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/s\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e. The black box in Figure 6a indicates the domain used to compute the SEA index. The black box in Figure 6b indicates the domain used to compute the SSA index. The light-gray stippling indicates the grid points in which the regression is significant at the 95% level of confidence with respect to two-tailed t-test. The units of regression are specified per standard deviations.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/ddda29e832945dfa8a265b09.jpg"},{"id":79412452,"identity":"1be656bf-18cf-438a-b5f5-c42587ccb19b","added_by":"auto","created_at":"2025-03-28 06:26:28","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":75611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eRelationship between the simulation of ENSO teleconnection with the NAE and the TRWSs averaged over five key regions in CMIP models. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eDecember multi-model relation of the spatial correlation of the ENSO-related 200 hPa geopotential height anomalies over the NAE region (see Figure 3b) and TRWS averaged over (a) the SSA (10°N-30°N, 10°E-40°E), (b) the SEA (20°N-40°N, 80°E-140°E), (c) the TP (0°-30°N, 180°E-120°W), (d) the CS (0°-30°N, 80°W-20°W), and (e) the GM (25°N-45°N, 120°W-60°W). The black star represents the reference dataset, the black-filled square indicates the MME of CMIP6 models, and the white-filled square represents the MME of CMIP5 models. The color-filled symbols denote individual CMIP6 models, while the white-filled symbols denote individual CMIP5 models. The gray dashed line indicates the best linear fit. The Pearson correlation is shown in the top-left corner, with values shown in bold if they are significant at the 95% level of confidence. CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/80dfdc9c6f0409e5545b3be0.jpg"},{"id":79412468,"identity":"ce9898a5-cb33-4ffa-9078-38733d63f4e9","added_by":"auto","created_at":"2025-03-28 06:26:28","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":964048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eJet stream and surface ocean mean states stratified by clustered groups.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Difference between the ensemble averages of bad and good models (BAD - GOOD) of the December climatology, denoted by the overbar, for (a) 200 hPa zonal wind (units m/s) and (b) sea surface temperature (units °C). The contour lines in (a) represent the 200 hPa zonal wind climatology for good models (units m/s), while the contour lines in (b) represent the SST climatology for good models (units °C). The light-gray stippling indicates the grid points in which the difference is significant at the 95% level of confidence with respect to one-sample t-test.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/e1de03b495f7294ed66c8a0e.jpg"},{"id":79412464,"identity":"ec07b22a-9448-4e9f-80c4-47907a33d667","added_by":"auto","created_at":"2025-03-28 06:26:28","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3689447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe influence of the atmospheric mean state on Rossby wave ray paths.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e December ERA5 200 hPa TRWS regressed against the normalized Niño 3.4 precipitation index (shading with 10\u003c/em\u003e\u003csup\u003e\u003cem\u003e-11\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e s\u003c/em\u003e\u003csup\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) and Rossby rays (colored scatters) for different zonal wavenumber k, starting from the gridpoint of maximum TRWS in the SEA (20°N-40°N, 80°E-140°E) for (a) ERA5 (\u003c/em\u003e\u003csub\u003e\u003cem\u003eū\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e\u003cem\u003eERA5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) and (b) ERA5 superposed to the difference of zonal wind climatology between bad and good models (\u003c/em\u003e\u003csub\u003e\u003cem\u003eū\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e\u003cem\u003eERA5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+\u003c/em\u003e\u003csub\u003e\u003cem\u003eū\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e\u003cem\u003eBAD - GOOD\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e). (c)-(d), (e)-(f), and (g)-(h) as in (a)-(b) but for the TP (0°-30°N, 180°E-120°W), the CS (5°-25°N, 70°W-20°W), and the GM (25°N-45°N, 110°W-60°W) sectors, respectively. The boxes in the left panels highlight the key areas where the rays are initiated and where TRWS-based indices are computed.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/1469f6b9042387db727afba3.jpg"},{"id":85231392,"identity":"ec22bfe1-4cdb-4edc-b37f-1c2b9ff26cf2","added_by":"auto","created_at":"2025-06-23 16:07:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17474518,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/d812a38b-f564-4095-9cc6-f2440c28b3a3.pdf"},{"id":79412458,"identity":"cd17a514-c7c2-4333-8f5c-91cc616cb1a0","added_by":"auto","created_at":"2025-03-28 06:26:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10689410,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5560758/v1/7405d2134ae007c87923ffdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ENSO teleconnections with the NAE sector during December in CMIP5/CMIP6 models: impacts of the atmospheric mean state","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe El Ni\u0026ntilde;o-Southern Oscillation (ENSO) is the dominant mode of variability at interannual timescales and constitutes one of the most prominent sources of predictability at seasonal\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e to annual timescales\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The Pacific-North-American (PNA) pattern stands out as the most recognized ENSO teleconnection with the extratropics, reaching its peak during winter\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The ENSO signal in the North Atlantic and European (NAE) sector is generally less robust in terms of amplitude, spatial structure, and significance compared to the canonical PNA pattern\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Nevertheless, it remains one of the major sources of predictability in this region.\u003c/p\u003e \u003cp\u003eRecent studies have addressed the complexity and seasonality of ENSO teleconnection with the NAE\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Dynamical mechanisms have been proposed to explain the propagation of ENSO teleconnection with the NAE sector, including both tropospheric\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and stratospheric pathways\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ENSO-induced atmospheric circulation anomalies in the NAE features a meridional geopotential dipole, resembling the negative phase of the North Atlantic Oscillation (NAO) in February and March, and its positive phase in November and December\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The transition of the ENSO-related signal in the NAE region has been linked to an interfering teleconnection triggered by heating anomalies located in the tropical Indian Ocean (IO)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These anomalies, linked to the Indian Ocean Dipole (IOD) mode, can initiate a wavenumber-3 Rossby wave-train, which projects onto the positive phase of the NAO in December\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGeneral Circulation Models (GCMs) exhibit limitations in accurately simulating the November and December ENSO teleconnection with the NAE region\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. By analyzing historical simulations from models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), a previous study\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e demonstrated that a poor December ENSO teleconnection with the NAE may be linked to a weak Indian Ocean precipitation dipole, suggesting limitations in the simulation of ENSO-IOD coupling. Further research\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e showed that while seasonal hindcasts capture the January\u0026ndash;February ENSO teleconnection with the NAE region reasonably well, the November\u0026ndash;December teleconnections are often underestimated. This discrepancy may stem from model biases in simulating the interference between teleconnections originating from the Indian and Pacific Oceans.\u003c/p\u003e \u003cp\u003eTo our knowledge, only a very limited number of studies analyze how model biases affect the simulation of ENSO and TWEIO teleconnections with the NAE region. For instance, previous research\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e found that errors in the atmospheric response to ENSO in the northeastern Pacific could be partially attributed to biases in the jet stream, which can alter the propagation of Rossby waves from the tropical Pacific to the North Pacific. In contrast, it was concluded that a poor NAE response to ENSO is primarily due to biases in the tropical Rossby wave source located in the Caribbean Sea. These results are consistent with other studies emphasizing the influence of the Caribbean Sea and Gulf of Mexico Rossby wave sources on the NAE sector\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing a bias correction method on simulated divergence and temperature tendencies, a previous work\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e demonstrated that model biases generally do not affect the ENSO-induced atmospheric response in the tropics and North Pacific. Using an intermediate-complexity general circulation model, it was suggested that a stronger Pacific jet stream can meridionally bend ENSO-induced Rossby waves, shifting the upper tropospheric geopotential response equatorward\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Likewise, low-frequency variability in Pacific SSTs has been shown to influence the propagation of ENSO-driven Rossby waves by modifying the background flow\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study serves as a continuation of previous analyses\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, utilizing historical simulations from both the CMIP5\u003csup\u003e27\u003c/sup\u003e and CMIP6\u003csup\u003e28\u003c/sup\u003e datasets. The main objectives of this work are twofold: 1) to evaluate the ability of state-of-the-art coupled models to simulate ENSO teleconnection with the NAE during winter, and 2) to examine the influence of the models\u0026rsquo; atmospheric mean state on the simulation of ENSO teleconnection with the NAE.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eENSO teleconnection in reanalysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the upper tropospheric (200 hPa) geopotential height and WAF anomalies regressed against the Ni\u0026ntilde;o 3.4 index from November to February. During November (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and December (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), the ENSO teleconnection with the North Pacific is weaker compared to January (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and February (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Over the North Atlantic sector, a positive NAO-like signal emerges in November and persists into December, with its response shifted meridionally relative to the canonical NAO pattern. In November, the northern center of action, represented by an anomalous Icelandic low, is more pronounced than its southern counterpart, the anomalous Azores high. By December, the Icelandic low fades, suggesting a weaker ENSO teleconnection compared to November.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eContribution of the Indian and Pacific Ocean heating anomalies to the NAO phase across different reference datasets in November.\u003c/b\u003e Regression of the November Eastern Atlantic Gradient (EAG) of 200 hPa geopotential height anomalies [(25\u0026deg;N-40\u0026deg;N, 25\u0026deg;W-5\u0026deg;E) and (50\u0026deg;N-65\u0026deg;N, 50\u0026deg;W-10\u0026deg;W)] against the Ni\u0026ntilde;o 3.4 (EAG\u003csub\u003e(Ni\u0026ntilde;o3.4)\u003c/sub\u003e), the pure Ni\u0026ntilde;o 3.4 (EAG\u003csub\u003e(P\u0026minus;Ni\u0026ntilde;o3.4)\u003c/sub\u003e), and the pure TWEIO (EAG\u003csub\u003e(P\u0026minus;TWEIO)\u003c/sub\u003e) indices for the period 1979\u0026ndash;2024. Values of the regression coefficient p* are also shown. The percentages in the last column indicate the contribution of the EAG\u003csub\u003e(P\u0026minus;TWEIO)\u003c/sub\u003e to the EAG\u003csub\u003e(Ni\u0026ntilde;o3.4)\u003c/sub\u003e. Regression values shown in bold are significant at the 90% level of confidence with respect to a two-tailed t-test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;TWEIO)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep*\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep*EAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;TWEIO)\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e/ EAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e61.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e44.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e22.4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.76\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e28%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERA5\u0026thinsp;+\u0026thinsp;GPCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e65.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e48.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e22.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.77\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e27%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCAR-NCEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e59.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e56.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e6.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.52\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCAR-NCEP\u0026thinsp;+\u0026thinsp;GPCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e65.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e48.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e22.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.77\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e26%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eContribution of the Indian and Pacific Ocean heating anomalies to the NAO phase across different reference datasets in December.\u003c/b\u003e As in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e but for December.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;TWEIO)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep*\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep*EAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;TWEIO)\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e/ EAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e35.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e53.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.55\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e82%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERA5\u0026thinsp;+\u0026thinsp;GPCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e36.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e56.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.55\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e84%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCAR-NCEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e45.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e17.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.45\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e17%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCAR-NCEP\u0026thinsp;+\u0026thinsp;GPCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e37.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e7.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e56.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.55\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e81%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe extratropical ENSO teleconnection involves strong wave flux in the subtropical Western Pacific regions in November (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and December (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). During December the wave flux weakens in the North Atlantic but strengthens in the western and central Pacific at mid-latitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The canonical PNA teleconnection pattern appears in January (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and February (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), involving a strengthening of the Aleutian low and positive geopotential anomalies extending over the North American sector. During February, the atmospheric response in the NAE sector significantly projects onto the negative phase of the NAO (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the 200 hPa geopotential height and WAF anomalies regressed against the pure TWEIO and pure Ni\u0026ntilde;o 3.4 indices for November and December. Starting from November, the pure TWEIO regression shows a strong wave activity flux originating from SouthEast Asia (SEA: 20\u0026deg;N-40\u0026deg;N, 80\u0026deg;E-140\u0026deg;E; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The wave-train exhibits an anomalous trough located in the SEA, followed by an anomalous ridge over the Japanese sector. In December (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), the pattern exhibits a wave-train characterized by alternating geopotential height anomalies with a zonal wavelength of approximately 120\u0026deg;, corresponding to a zonal wavenumber of 3. The meridional scale of the wave spans the 30\u0026deg;N-60\u0026deg;N latitude band, indicating a meridional wavenumber of approximately 6. The zonal and meridional wavenumbers are consistent with the atmospheric waveguide (Figure S4c), which exhibits total stationary wavenumbers of 5\u0026ndash;7 over the SEA region. This wave eventually reaches the NAE sector and projects onto the positive phase of the NAO.\u003c/p\u003e\u003cp\u003eConversely, the pure ENSO teleconnection with the NAE is stronger in November (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) than in December (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). In November, the upper tropospheric atmospheric response to the pure ENSO signal resembles the positive phase of the NAO (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). In December the circulation anomalies weaken, and a positive geopotential anomaly develops stretching from northwestern North America to the North Atlantic (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eThe monthly Ni\u0026ntilde;o 3.4 regressions, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, can be seen as a linear combination of the pure Ni\u0026ntilde;o 3.4 and TWEIO regressions, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The ENSO signal in the NAE during November (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) seems to be dominated by the pure ENSO teleconnection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). In December, the pure TWEIO signal strengthens while the pure ENSO signal weakens over the NAE, and the circulation anomalies over the NAE appear to be primarily attributable to heating anomalies in the TWEIO region.\u003c/p\u003e \u003cp\u003eThe monthly contribution of the pure TWEIO index to the Ni\u0026ntilde;o 3.4 regressions can be further analyzed by calculating the regression coefficient p* (Eq.\u0026nbsp;3a). The regression coefficient for 1950\u0026ndash;2014 is 0.86 for November, 0.58 for December, 0.41 for January, and 0.30 for February. Additionally, our analysis of precipitation anomalies regressed against the Ni\u0026ntilde;o 3.4 index during the extended winter season (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) indicates that heating anomalies in the Indian Ocean weaken during February, resulting in a weakening of the pure TWEIO teleconnection.\u003c/p\u003e \u003cp\u003eAlthough the regression coefficient in November is larger compared to later months, the partial regression analysis indicates that the pure ENSO teleconnection has a stronger influence than the pure TWEIO teleconnection over the NAE region (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This finding is not consistent with previous research\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, which suggested that the pure TWEIO contribution dominates over the pure Ni\u0026ntilde;o 3.4 contribution in driving the NAO-like pattern during November. We believe that this discrepancy may be, at least in part, explained by the use of different reference datasets and periods.\u003c/p\u003e \u003cp\u003eTo test the first hypothesis, we applied partial regression analysis to four datasets \u0026mdash; ERA5, ERA5 with GPCP, NCAR-NCEP, and NCAR-NCEP with GPCP \u0026mdash; covering the common period 1979\u0026ndash;2024. For each dataset, we calculated the Eastern Atlantic Gradient (EAG; dashed boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) index, defined as the difference between the average 200 hPa geopotential height anomalies in the two regions (25\u0026deg;N-40\u0026deg;N, 25\u0026deg;W-5\u0026deg;E) and (50\u0026deg;N-65\u0026deg;N, 50\u0026deg;W-10\u0026deg;W)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Using partial regression analysis, we find that:\u003c/p\u003e \u003cp\u003e \u003cem\u003eEAG\u003c/em\u003e \u003csub\u003e \u003cem\u003e(Ni\u0026ntilde;o3.4)\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;EAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;p*EAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;TWEIO)\u003c/em\u003e\u003c/sub\u003e, (1)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;Ni\u0026ntilde;o3.4)\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;TWEIO)\u003c/em\u003e\u003c/sub\u003e represent the regressions of the EAG index onto the Ni\u0026ntilde;o 3.4, pure Ni\u0026ntilde;o 3.4, and pure TWEIO indices, respectively. The EAG index has been shown to better capture the NAO-like pattern compared to the traditional NAO index\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This is because the regions used to define the EAG index are centered approximately 5 degrees south of the points canonically used for the NAO index. However, the results remain similar to those obtained using the NAO index from a previous study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e (not shown). The values for terms in Eq.\u0026nbsp;1 are summarized in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for November and December, respectively.\u003c/p\u003e \u003cp\u003eFor November, when combining ERA5 and NCAR-NCEP with GPCP, the pure TWEIO EAG contributes approximately 28% and 26% to the Ni\u0026ntilde;o 3.4 EAG, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This percentage is relatively similar when using ERA5 alone (27%) but decreases drastically when using the NCAR-NCEP dataset (5%).\u003c/p\u003e \u003cp\u003eIn December (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the ENSO teleconnection projects onto the positive phase of the NAO, albeit more weakly than in November (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The pure TWEIO EAG accounts for approximately 80% of the Ni\u0026ntilde;o 3.4 EAG across all datasets, except for NCAR-NCEP, which shows a lower and statistically insignificant TWEIO contribution of around 17%. As a result, in December, the pure TWEIO signal prevails over the pure ENSO signal. The discrepancies observed when using the NCAR-NCEP reanalysis compared to other datasets underscore its limitations for calculating precipitation-based indices.\u003c/p\u003e \u003cp\u003eTo examine the second hypothesis, specifically the sensitivity to different time periods, we used a re-sampling method for the month of November. We randomly selected 45 years from the complete ERA5 dataset (covering 1940\u0026ndash;2024) and the NCAR-NCEP dataset (covering 1948\u0026ndash;2024) to align with the temporal coverage of the GPCP dataset (covering 1979\u0026ndash;2024). From each of the ERA5 and NCAR-NCEP reanalysis products, we generated 10000 sub-samples. We then calculated the frequency of instances where \u003cem\u003ep*EAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;TWEIO)\u003c/em\u003e\u003c/sub\u003e exceeded \u003cem\u003eEAG\u003c/em\u003e\u003csub\u003e\u003cem\u003e(P\u0026minus;Ni\u0026ntilde;o3.4\u003c/em\u003e)\u003c/sub\u003e. This frequency constitutes the probability that the ENSO teleconnection is dominated by the pure TWEIO signal. The probability is 12% and 3% for the ERA5 and NCAR-NCEP datasets respectively, supporting a weak contribution of the pure TWEIO teleconnection with the NAE during November.\u003c/p\u003e \u003cp\u003eWe conclude that, for the considered period and reference dataset, the influence of the pure TWEIO teleconnection on circulation anomalies in the NAE dominates over that of the pure ENSO teleconnection in December, while the opposite is true for November and February.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eENSO teleconnection in CMIP models\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe ability of CMIP5 and CMIP6 models to simulate the ENSO teleconnection is assessed through Taylor diagrams\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) of the monthly 200 hPa geopotential height anomaly patterns regressed onto the Ni\u0026ntilde;o 3.4 index in the NAE sector (here defined as the domain 20\u0026deg;N-80\u0026deg;N, 60\u0026deg;W-0\u0026deg; and shown by the black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe diagrams have been computed for November (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), December (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), and February (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). January was excluded from the analysis due to the weak and statistically insignificant ENSO signal in the NAE region during this month (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Sensitivity tests demonstrate that the results are not dependent on the choice of the domain (not shown). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the key findings from the Taylor diagrams for each month.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of the Taylor diagram results stratified over CMIP5 and CMIP6 models.\u003c/b\u003e Values of pattern correlation and normalized standard deviation between the CMIP5/CMIP6 MME relative to ERA5. The two rightmost columns indicate the percentage of CMIP5 and CMIP6 models with negative pattern correlation relative to ERA5.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIP5 MME corr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMIP6 MME corr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCMIP5 MME std\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCMIP6 MME std\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCMIP5 models with negative corr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCMIP6 models with negative corr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, the models show better agreement with ERA5 in November and February than in December. Noteworthy, the results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicate an improvement in the simulation of ENSO teleconnection with the NAE in CMIP6 models compared to CMIP5, particularly in December. However, it's important to acknowledge that the number of CMIP6 models is 30% higher than that of CMIP5, which may influence the MME mean performance.\u003c/p\u003e \u003cp\u003eBy computing the spatial correlation between the reanalysis and simulated ENSO teleconnection patterns in the NAE region during December, we identified clusters of \u0026ldquo;good\u0026rdquo; and \u0026ldquo;bad\u0026rdquo; models. Good models are those that produce teleconnection patterns with a positive correlation exceeding 0.6 with the ERA5 patterns in November and December. Conversely, models with a pattern correlation below \u0026minus;\u0026thinsp;0.35 in December are classified as bad models. The classification of clustered models, stratified across CMIP5 and CMIP6, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCategorization of CMIP models based on clustering.\u003c/b\u003e CMIP5 and CMIP6 models that meet the criterion of good (correlation of ENSO teleconnection pattern in the NAE\u0026thinsp;\u0026gt;\u0026thinsp;0.6 in November and December) and bad (correlation of ENSO teleconnection pattern in the NAE \u0026lt; -0.35 in December) models. CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIP5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMIP6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egiss-e2-h, hadcm3, csiro-mk3-6-0, cmcc-cm, cesm1-fastchem, cmcc-cesm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCNRM-CM6-1-HR, MPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enoresm1-me\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICON-ESM-LR, CIESM, NorCPM1, CESM2, BCC-ESM1, MPI-ESM-1-2-HAM, HadGEM3-GC31-MM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe clustering process used to select good models ensures the inclusion of those that, to some extent, can simulate the ENSO teleconnection in November and December. As a part of a sensitivity test, we have also selected good models based on the pattern correlation during December only (with a threshold of 0.75)*. Analogous results to those presented here were obtained (not shown). We, then, analyze the fields obtained by averaging regression patterns and mean state over good models and bad models, especially for December.\u003c/p\u003e \u003cp\u003e \u003cem\u003e*In this case, the models that fall into the category of good models are: CESM2, NorCPM1, hadgem2-cc, MPI-ESM1-2-LR, CNRM-CM6-1, IITM-ESM, ICON-ESM-LR, EC-Earth3-Veg-LR, cnrm-cm5, cnrm-cm5-2, HadGEM3-GC31-MM, noresm1-me.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb show the averaged regressions of the December 200 hPa geopotential height anomalies onto the Ni\u0026ntilde;o 3.4 index for the two clusters. Bad models not only fail to capture the positive NAO-like phase in the NAE region but also exhibit a weak geopotential response in the subtropical East Asian and western Pacific sectors. This deficit is also evident when taking the difference of the regressions shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb (Figure S2a). These findings suggest that the ENSO inter-basin teleconnection with the IO might contribute to this inter-model diversity.\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ed show the average precipitation anomalies regressed against the Ni\u0026ntilde;o 3.4 index for the clustered groups. Both model groups simulate the ENSO-related precipitation patterns in the Pacific sector reasonably well when compared to the reference dataset (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). However, significant discrepancies are observed in the IO basin, where bad models fail at reproducing the east-west precipitation dipole associated with the ENSO inter-basin teleconnection (see also Figure S2b for the pattern difference). It is important to note that bad models tend to reproduce a double Inter-Tropical Convergence Zone (ITCZ) bias, marked by excessively strong convergence zones in the South Pacific, Atlantic, and Indian Oceans (Figure S3).\u003c/p\u003e\n\u003ch3\u003eThe TRWSs in reanalysis and CMIP models\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eResults from ERA5 (top row in Figure S4), good models (middle row in Figure S4), and bad models (bottom row in Figure S4) reveal three main waveguides extending from subtropical Africa, the western Pacific, and the subtropical Atlantic: the Sub-Saharan Africa (SSA: 10\u0026deg;N-30\u0026deg;N, 10\u0026deg;E-40\u0026deg;E; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) waveguide, the subtropical South Asian waveguide, and the subtropical Atlantic waveguide. The lower tropospheric convergence (divergence) associated with anomalous convection (subsidence) in the tropics is balanced in the upper troposphere by divergent (convergent) flow. This mechanism interacts with the atmospheric waveguide and constitutes an essential trigger for the initiation of quasi-stationary planetary Rossby wave-trains\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the 200 hPa TRWS and irrotational wind anomalies regressed against the Ni\u0026ntilde;o 3.4 index for the reference dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and the clustered groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In ERA5, the Ni\u0026ntilde;o 3.4 heating anomalies induce upper tropospheric divergence, resulting in a negative horseshoe-like TRWS pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In the Indian Ocean, upper tropospheric divergence (convergence) over the western (eastern) basin interacts with the African (Asian) waveguide. This mechanism, in turn, generates a negative (positive) TRWS located in the SSA (SEA) region. Two secondary Rossby wave sources are also identified north of the Gulf of Mexico and east of the Caribbean Sea, primarily driven by the anomalous rearrangement of the Walker and Hadley circulations in response to Ni\u0026ntilde;o 3.4 heating anomalies. Hence, five key regions involving significant TRWSs have been identified: the SSA (10\u0026deg;N-30\u0026deg;N, 10\u0026deg;E-40\u0026deg;E; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), the SEA (20\u0026deg;N-40\u0026deg;N, 80\u0026deg;E-140\u0026deg;E; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), the Tropical Pacific (TP: 0\u0026deg;-30\u0026deg;N, 180\u0026deg;E-120\u0026deg;W; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ec), the Caribbean Sea (CS: 5\u0026deg;-25\u0026deg;N, 70\u0026deg;W-20\u0026deg;W; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ee), and the Gulf of Mexico (GM: 25\u0026deg;N-45\u0026deg;N, 110\u0026deg;W-60\u0026deg;W; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003eg).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003eMany studies have investigated the role of TRWSs in triggering Rossby waves over the tropical and subtropical Pacific, the Caribbean Sea, and the Gulf of Mexico\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, much less is known about the TRWSs in the SEA and SSA regions. To fill this gap, we first defined two indices by averaging the 200 hPa TRWS time series over the SEA and SSA regions, indicated by the black boxes in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, respectively. Subsequently, we regressed the 200 hPa geopotential height anomalies against the pure TRWS indices (i.e., independent of the Ni\u0026ntilde;o 3.4 index), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Sensitivity tests indicate that the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e are not affected by the size of the boxes used to compute the indices (not shown).\u003c/p\u003e \u003cp\u003eThe SEA TRWS explains a zonal wavenumber-3 Rossby wave-train, consistently with the regression based on the pure TWEIO index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The regression patterns show a strong wave flux originating from the SEA, where an anomalous trough appears. The wave-train reaches the NAE sector, where geopotential anomalies resemble the positive phase of the NAO. Therefore, we argue that in December, the SEA TRWS plays a key role in modulating the atmospheric response to ENSO in the NAE sector.\u003c/p\u003e \u003cp\u003eThe regression of 200 hPa geopotential anomalies and WAF onto the pure SSA TRWS index (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) shows a wave-train similar to that in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea. However, a key difference emerges in the North Atlantic, where strong WAF originates from the Caribbean Sea, arches northeastward, and then propagates back to the SSA. This analysis suggests that the TRWS in the SSA sector is a response of a Rossby wave originating from the Caribbean Sea.\u003c/p\u003e \u003cp\u003eA comparison of the upper tropospheric TRWS simulated by good models (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and bad models (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) shows that the latter significantly underestimate TRWS in the SSA and SEA regions. The difference in the SEA is primarily attributed to a weaker upper tropospheric anomalous divergent wind field (Figure S5b) rather than discrepancies in the climatological absolute vorticity gradient (Figure S5a). In contrast, no substantial differences in the TRWS field are observed across the tropical and subtropical Pacific, the Caribbean Sea, and the Gulf of Mexico.\u003c/p\u003e \u003cp\u003eAs a further step, we have calculated for each model and the reanalysis the ENSO-related December 200 hPa TRWS averaged over the five key regions previously identified: the SSA, the SEA, the TP, the GM, and the CS. The values of the averaged TRWS in each of the five regions are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e against the spatial correlation coefficients of the ENSO-related 200 hPa geopotential height anomalies in the NAE sector (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the models, the relationship of the averaged TRWS over the SSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ea) exhibits a significant correlation, suggesting that models failing to simulate the positive NAO phase during an El Ni\u0026ntilde;o event in December also underestimate the SSA TRWS. This finding is consistent with the hypothesis that the atmospheric anomalies linked to the positive NAO phase may propagate to the tropics over the African subcontinent, thereby generating the negative SSA TRWS.\u003c/p\u003e \u003cp\u003eAlso, for the SEA case (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), models with a weak TRWS tend to simulate a poor December ENSO teleconnection with the NAE. The simulated relations for the other three key regions, namely the TP (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), the GM (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), and the CS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ee), were found to be insignificant at the 95% level of confidence, confirming that a missed positive NAO response cannot be attributed to differences of TRWSs in these regions. However, the following question arises from the previous analysis: \u003cem\u003eWhy do bad models fail to reproduce the SSA TRWS, despite their ability to simulate the Caribbean Sea TRWS?\u003c/em\u003e This point will be addressed in the following section.\u003c/p\u003e\n\u003ch3\u003eThe role of the atmospheric waveguide\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePrevious studies have highlighted the influence of the jet stream on the propagation of atmospheric Rossby wave\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Here, we examine whether differences in the simulated climatological jet stream can explain variations in the December ENSO teleconnection with the NAE across different models.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the differences in the December mean states of upper tropospheric zonal wind and SST between the MME means of bad and good models. Compared to good models, bad models exhibit subtropical Pacific and Atlantic jet streams that are 20\u0026ndash;30% stronger and shifted equatorward (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). These stronger waveguides are likely linked to a cold SST bias extending across the northern Pacific and Atlantic basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). The cold bias, in fact, tends to enhance (reduce) the meridional SST gradient at low (high) latitudes, thereby strengthening (weakening) upper-level zonal winds through thermal wind balance. Additionally, the stronger Pacific (Atlantic) waveguide, coupled with a colder North Pacific (Atlantic) surface mean state, is also evident in the ensemble mean of bad models relative to the reference (Figure S6). The weak and statistically not significant differences in tropical Pacific SST mean states (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eb) suggest that substantial variations in the seasonal phase and amplitude of simulated ENSO events are unlikely.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the surface, the Polar high, Aleutian and Icelandic lows are stronger in bad models (Figure S7). These patterns are in agreement with the difference in the lower tropospheric wind field, which exhibits a cyclonic (anti-cyclonic) pattern over the North Pacific and Atlantic (Arctic). The pressure systems detected in Figure S7 show a quasi-barotropic structure (not shown) and resembles the leading mode of variability of the extratropical circulation, namely the Arctic Oscillation (AO)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo assess the impact of the stronger jet streams on the propagation of planetary waves during December, we implement the Rossby wave ray tracing algorithm in two configurations:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eusing the ERA5 200 hPa zonal wind December climatology, hereafter referred to as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{\\:u}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003eERA5\u003c/sub\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eusing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{u}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003eERA5\u003c/sub\u003e superposed to the difference between bad and good models of the 200 hPa zonal wind climatology, hereafter referred to as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{u}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003eBAD \u0026minus; GOOD\u003c/sub\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe rays are initiated from the TRWSs located in four key regions previously identified: the SEA, the TP, the CS and the GM. The SSA TRWS has been excluded because, as discussed in Section 3.3, we interpret it as a result of atmospheric anomalies propagating from the Euro-Atlantic sector to tropical Africa. The starting points for the Rossby wave ray algorithm are chosen as the grid point exhibiting maxima/minima of TRWS. The ray position and group velocity are calculated and then stepped forward in time for 10 days for zonal wavenumbers spanning from 1 to 5. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e for the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{u}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003eERA5\u003c/sub\u003e (left panels) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{u}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003eERA5\u003c/sub\u003e +\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{\\:u}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003eBAD \u0026minus; GOOD\u003c/sub\u003e (right panels) configurations. When interpreting the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e, it is important to keep in mind that the wave-tracking algorithm relies on the linear theory for stationary barotropic Rossby waves. This theoretical basis limits the conclusions we can draw, and thus the results should be interpreted with some caution\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ea shows, Rossby wave-trains of zonal wavenumber-3 (green scatters) follow a track that originated in the SEA region, crosses the Pacific, and eventually reaches the NAE sector. This is consistent with the results from the partial regression analysis of 200 hPa geopotential height anomalies (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). By superposing a stronger subtropical Pacific jet stream onto the reference basic flow, the trajectory of Rossby waves is affected, particularly those of zonal wavenumber-3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn ERA5, Rossby wave trains with zonal wavenumbers 2 (yellow scatters) and 3 (green scatters) originating from the TP successfully propagate to the NAE sector (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). However, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ed, the stronger Atlantic and Pacific jet streams constrain wavenumber-3 Rossby waves to lower latitudes, potentially altering the Euro-Atlantic atmospheric response to ENSO. Similarly, zonal wavenumber-1 to 3 Rossby waves originating from the CS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ee) and GM (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003eg) regions can freely propagate toward the northern Atlantic and European sectors in ERA5. Interestingly, waves with zonal wavenumbers-4 and 5 forced from the CS sector, as well as waves with zonal wavenumbers-2 and 3 from the GM sector, eventually reach the Sub-Saharan region. This finding is consistent with the hypothesis that the SSA TRWS is not a direct ENSO-driven source but rather a response to atmospheric anomalies propagating from the Euro-Atlantic region back to the tropics.\u003c/p\u003e \u003cp\u003eIn bad models, the stronger Atlantic waveguide appears to alter the ray paths of wavenumber-3 waves originating from the CS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003ef) and GM (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003eh) regions, constraining them to lower latitudes. This finding may explain why bad models fail to produce a strong upper-tropospheric atmospheric response (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) and TRWS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) in the SSA region, despite their ability to simulate the TRWSs over the CS and GM sectors. Furthermore, a comparison of the permitted total wavenumbers in the atmospheric waveguide of bad models (Figure S4i) with the reference (Figure S4c) and good models (Figure S4f) reveals the presence of a \u0026ldquo;forbidden area\u0026rdquo; (i.e., a region of imaginary total stationary wavenumber) over the northeastern sector of North America in bad models. The location of the forbidden region supports the hypothesis that Rossby waves originating from the CS and GM are effectively trapped at lower latitudes, preventing their poleward propagation.\u003c/p\u003e \u003cp\u003eThese findings indicate that bad models, which significantly underestimate the SEA TRWS linked to ENSO inter-basin teleconnections, are also characterized by stronger Pacific and Atlantic waveguides. The associated intensification of the subtropical jet streams leads to an equatorward shift of the refraction latitudes of zonal wavenumber-3 Rossby waves originating from the tropics, potentially altering the remote atmospheric response in the Euro-Atlantic region.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study examines how the atmospheric mean states of climate models influence the ENSO teleconnection with the North Atlantic and European sector in winter.\u003c/p\u003e \u003cp\u003eThe results obtained from ERA5 show that the ENSO teleconnection with the NAE region projects onto the positive phase of the NAO in November and December. Using partial regression analysis and a subsampling technique across multiple reanalysis datasets, we demonstrated that in November, the NAO-like pattern is primarily driven by the pure ENSO teleconnection. This finding contrasts with the results of previous research, which suggested that the November positive NAO is primarily driven by the pure TWEIO signal\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTropical rainfall variability in reanalyses is influenced by observational constraints, which improved significantly with the advent of satellite data in the late 1970s. Before this period, reanalysis-derived precipitation over tropical oceans is increasingly uncertain due to sparse observational coverage. Additionally, intraseasonal variability in the Indian Ocean, particularly the Madden-Julian Oscillation (MJO), introduces further discrepancies among datasets on monthly timescales, potentially affecting our teleconnection indices in the pre-1979 decades. However, we have shown that the dominance of the pure ENSO teleconnection over the pure TWEIO signal remains robust even in the post-1979 decades.\u003c/p\u003e \u003cp\u003eFurthermore, decadal to multi-decadal variability, such as that associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO), can modulate the contribution of the pure TWEIO teleconnection to the NAE. To explore this, we computed the November contribution of the pure TWEIO teleconnection to the ENSO teleconnection (see the rightmost column in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) using a 35-year sliding window applied to ERA5 over the 1940–2024 period. The results, presented in Figure S8, reveal a strong non-stationary signal, even in the post-1979 decades, with a strong decrease in the pure TWEIO contribution after 1985.\u003c/p\u003e \u003cp\u003eAnother possible dynamical explanation for the dominance of the pure ENSO signal in November is that the IOD SST anomalies, which peak in October, exhibit a 2-months delayed connection with the atmospheric circulation over the NAE via a precipitation dipole over the Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Consequently, the November TWEIO precipitation index may not fully be influenced by the October IOD SSTs, thereby reducing the impact of the pure TWEIO signal on the NAE region.\u003c/p\u003e \u003cp\u003eBy December circulation anomalies appear to be predominantly influenced by the pure TWEIO teleconnection. The TWEIO heating anomalies are associated with the initiation of a zonal wavenumber-3 Rossby wave-train originating from the SEA region and extending to the NAE sector. In the North Atlantic, this wave-train spatially projects onto the positive phase of the NAO, further supporting the role of the TWEIO in influencing the atmospheric response to ENSO in the NAE sector\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThrough a clustering analysis, we identified models that can reasonably simulate ENSO teleconnections with the NAE region (“good models”), distinguishing them from those that cannot (“bad models”). Results indicate that bad models tend to underestimate the Indian Ocean heating dipole, often concurrent with ENSO episodes, leading to a weak tropical Rossby wave source in the SEA sector. In turn this wave source is directly linked to the initiation of a zonal wavenumber-3 Rossby wave-train that impacts the NAE sector with a positive NAO-like fingerprint; thus, its weakness results in feeble teleconnections.\u003c/p\u003e \u003cp\u003ePrevious studies have highlighted the existence of a secondary tropical Rossby wave source situated over the Caribbean Sea, associated with the Atlantic waveguide\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. According to our results, no significant differences are detected in the simulated TRWSs over the Gulf of Mexico and Caribbean Sea between bad and good models.\u003c/p\u003e \u003cp\u003eThe mean state analysis of clustered groups reveals that bad models tend to simulate a stronger Pacific and Atlantic waveguides. These patterns reflect the jet stream bias commonly observed in many climate models\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The stronger subtropical Pacific and Atlantic jet streams are related via thermal wind balance to a colder northern Pacific and Atlantic Oceans, respectively, which mirror a common systematic error in older-generation models\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, usually characterized by a poor resolution in the ocean.\u003c/p\u003e \u003cp\u003eUsing a ray tracing algorithm based on linear theory, we have shown that, in December, a stronger subtropical Pacific and Atlantic jet streams are associated with a southward displacement of the refraction latitudes of zonal wavenumber-3 Rossby waves. As a result, Rossby waves originating from the tropics are meridionally bent and may, therefore, fail to reach the NAE sector. This could explain why bad models fail to capture the ENSO-induced remote atmospheric response over the Euro-Atlantic and Sub-Saharan sectors, despite their ability to simulate the TRWSs over the Gulf of Mexico and Caribbean Sea.\u003c/p\u003e \u003cp\u003eIn conclusion, in the analyzed CMIP models, two key sources of errors concerning the simulation of the December ENSO teleconnection with the North Atlantic-European sector have been identified: i) an underestimated SEA TRWS, which might be the consequence of a poorly simulated ENSO inter-basin connection with the Indian Ocean; ii) stronger Pacific and Atlantic waveguides, in turn related via thermal wind balance to overly cold Pacific and Atlantic Oceans, respectively.\u003c/p\u003e \u003cp\u003eThis study clearly demonstrates the strong impact that models’ systematic errors have on the simulation of teleconnections. Addressing these biases would significantly improve the models' ability to simulate the atmospheric extratropical teleconnection patterns and thus the skill of climate predictions in mid-latitude.\u003c/p\u003e \u003c/div\u003e"},{"header":"Data and Methods","content":"\u003ch2\u003eModels\u003c/h2\u003e\u003cp\u003eThe historical simulations from 48 and 37 models participating in phases 5 and 6 of CMIP, respectively, are used. To distinguish between CMIP6 and CMIP5 models, the names of CMIP6 models are written in uppercase letters, while CMIP5 model names are written in lowercase letters (e.g., CESM2 and cmcc-cm). The lists of CMIP6 and CMIP5 models can be found in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCMIP6 models.\u003c/b\u003e List of the 48 CMIP6 models analyzed in this study, along with their respective modeling centers, countries, and the ensemble members used. CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Name (CMIP6)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModeling Center\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnsemble member\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSIRO-ARCCSS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSIRO-ARCCSS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAWI-CM-1-1-MR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAWI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCC-ESM1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCC/CMA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2-FV2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2-WACCM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2-WACCM-FV2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMCC-ESM2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMCC-CM2-HR4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIESM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNRM-CM6-1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNRM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNRM-CM6-1-HR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNRM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanESM5-CanOE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p2f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3SM-1-0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3SM-Project\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3SM-1-1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3SM-Project\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3SM-1-1-ECA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3SM-Project\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-Earth3-AerChem\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth Consortium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-Earth3-CC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth Consortium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-Earth3-Veg-LR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth Consortium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOALS-f3-L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOALS-g3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIO-ESM-2-0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIO-QLNM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGISS-E2-1-H\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNASA GISS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHadGEM3-GC31-LL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOHC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHadGEM3-GC31-MM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOHC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICON-ESM-LR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIITM-ESM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCR-IITM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINM-CM4-8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINM-CM5-0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKACE-1-0-G\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIOST\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIOST-ESM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIOST\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCM-UA-1-0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIROC-ES2H\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAMSTEC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p4f2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIROC-ES2L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAMSTEC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAMSTEC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-ESM-1-2-HAM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNESM3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNUIST\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorCPM1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorESM2-MM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM0-UNICON\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaiESM1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCEC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaiwan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1f1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCMIP5 models.\u003c/b\u003e List of the 37 CMIP5 models analyzed in this study, along with their respective modeling centers, countries, and the ensemble members used. CMIP6 models are represented in uppercase letters (e.g., CESM2), while CMIP5 models are shown in lowercase letters (e.g., cmcc-cm).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Name (CMIP5)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModeling Center\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnsemble member\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaccess1-0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSIRO-BOM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaccess1-3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSIRO-BOM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebcc-csm1-1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCC/CMA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebcc-csm1-1-m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCC/CMA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebnu-esm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBNU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecancm4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecanesm2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eccsm4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecesm1-fastchem\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecesm1-bgc\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecesm1-cam5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecesm1-waccm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecmcc-cesm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecmcc-cm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecmcc-cms\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecsiro-mk3l-1-2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSIRO-QCCCE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efgoals-g2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efgoals-s2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efio-esm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egfdl-cm3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egiss-e2-g\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNASA GISS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egiss-e2-r\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNASA GISS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehadcm3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOHC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehadgem2-cc\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOHC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehadgem2-es\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOHC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einmcm4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eipsl-cm5a-lr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiroc5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAMSTEC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003empi-esm-lr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003empi-esm-mr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003empi-esm-p\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emri-cgm3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enoresm1-me\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enoresm1-m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er1i1p1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe model data include the monthly upper tropospheric geopotential height (200 hPa), upper tropospheric zonal and meridional winds (200 hPa), total precipitation, sea surface temperature, Sea Level Pressure (SLP), and lower tropospheric zonal and meridional winds (925 hPa). Following the methodology of a previous study\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, only one ensemble member (i.e., 'r1i1p1f1' and 'r1i1p1', when available) is included to ensure equal weightage. Other members were included for those models not having the first realization of the ensemble. The last 64 years of model simulations were analyzed, with CMIP6 data assessed for the period 1950–2014 and CMIP5 data for the period 1940–2004. Similar results are obtained when analyzing CMIP6 data for the period 1940–2004 (not shown).\u003c/p\u003e\u003ch3\u003eReanalysis\u003c/h3\u003e\u003cp\u003eThe reference dataset used in this study is the ERA5 reanalysis product\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, which includes monthly data of upper tropospheric geopotential height (200 hPa), upper tropospheric zonal and meridional winds (200 hPa), and total precipitation. The dataset covering the period from 1950 to 2014 is analyzed, and similar results are obtained when using the dataset spanning from 1940 to 2004 (not shown). To assess the sensitivity of the results to the reference dataset, additional data from the NCEP/NCAR Reanalysis 1 product\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and the Global Precipitation Climatology Project (GPCP) Version 2 dataset\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e are used. Both model and reference datasets are bilinearly interpolated to a common grid with a horizontal resolution of 2.5°x2.5°.\u003c/p\u003e\u003ch2\u003eLinear and partial regression analysis\u003c/h2\u003e\u003cp\u003eGiven that teleconnections primarily originate from heating anomalies produced by intense convective activities in tropical regions, monthly precipitation anomalies are used to calculate the indices instead of SST anomalies\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The normalized Niño 3.4 precipitation index, hereafter referred to as Niño 3.4 index, is obtained by averaging the standardized monthly precipitation anomalies over the Niño 3.4 region (5°S-5°N, 170°E-120°W; black box in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). To assess the link between heating anomalies located in the Indian Ocean and ENSO teleconnections, the normalized Tropical Western-Eastern Indian Ocean (TWEIO; black boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) precipitation index is used. Following previous studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, the TWEIO index is defined as the difference of standardized precipitation anomalies averaged over the western (10°S-10°N, 40°E-80°E) and eastern (10°S-10°N, 100°E-140°E) Indian Ocean.\u003c/p\u003e\u003cp\u003eLinear regression analysis is applied to monthly data, with linearly detrended anomaly fields regressed onto normalized indices. The resulting regression patterns are consistent with those obtained from composite analysis (not shown). The partial regression method is implemented to separate the contributions of different processes from an anomaly field. The TWEIO and Niño 3.4 indices can be decomposed as follows:\u003c/p\u003e\u003cp\u003e \u003cem\u003eα\u003c/em\u003e \u003csub\u003e \u003cem\u003eTWEIO\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(t) = β\u003c/em\u003e \u003csub\u003e \u003cem\u003eTWEIO\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(t) + p*α\u003c/em\u003e \u003csub\u003e \u003cem\u003eNiño3.4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(t)\u003c/em\u003e, (2a)\u003c/p\u003e\u003cp\u003e \u003cem\u003eα\u003c/em\u003e \u003csub\u003e \u003cem\u003eNiño3.4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(t) = β\u003c/em\u003e \u003csub\u003e \u003cem\u003eNiño3.4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(t) + s*α\u003c/em\u003e \u003csub\u003e \u003cem\u003eTWEIO\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(t)\u003c/em\u003e, (2b)\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003eTWEIO\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003eNiño3.4\u003c/em\u003e\u003c/sub\u003e denote the TWEIO and Niño 3.4 indices, respectively, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eTWEIO\u003c/em\u003e\u003c/sub\u003e represents the \u003cem\u003epure\u003c/em\u003e TWEIO index (i.e., independent of the Niño 3.4 index), and \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eNiño3.4\u003c/em\u003e\u003c/sub\u003e represents the \u003cem\u003epure\u003c/em\u003e Niño 3.4 index (i.e., independent of the TWEIO index). The regression coefficients \u003cem\u003ep*\u003c/em\u003e and \u003cem\u003es*\u003c/em\u003e are computed as follows:\u003c/p\u003e\u003cp\u003ep\u003csup\u003e*\u003c/sup\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\sum\\:}_{t=0}^{N}\\left[{\\alpha\\:}_{TWEIO}\\right(t\\left){\\alpha\\:}_{Niño3.4}\\right(t\\left)\\right]}{{\\sum\\:}_{t=0}^{N}{\\alpha\\:}_{Niño3.4}^{2}\\left(t\\right)}\\)\u003c/span\u003e\u003c/span\u003e (3a)\u003c/p\u003e\u003cp\u003es\u003csup\u003e*\u003c/sup\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\sum\\:}_{t=0}^{N}\\left[{\\alpha\\:}_{TWEIO}\\right(t\\left){\\alpha\\:}_{Niño3.4}\\right(t\\left)\\right]}{{\\sum\\:}_{t=0}^{N}{\\alpha\\:}_{TWEIO}^{2}\\left(t\\right)}\\)\u003c/span\u003e\u003c/span\u003e (3b)\u003c/p\u003e\u003cp\u003eThe Niño 3.4 regression patterns can be expressed as a linear combination of the regressions against the pure Niño 3.4 and TWEIO indices, as follows:\u003c/p\u003e\u003cp\u003e \u003cem\u003eR(x) = R\u003c/em\u003e \u003csub\u003e \u003cem\u003e0\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(x) + p*P\u003c/em\u003e \u003csub\u003e \u003cem\u003e0\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(x)\u003c/em\u003e (4)\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eR(x)\u003c/em\u003e is the Niño 3.4 regression, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(x)\u003c/em\u003e is the pure Niño 3.4 regression, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(x)\u003c/em\u003e is the pure TWEIO regression, and \u003cem\u003ep*\u003c/em\u003e is the regression coefficient shown in Eq.\u0026nbsp;3a. From now on, regressions against the pure TWEIO and pure Niño 3.4 indices will also be referred to as pure TWEIO and ENSO teleconnections, respectively.\u003c/p\u003e\u003cp\u003eThe significance testing for ERA5 and CMIP models is assessed by implementing a two-tailed t-test, while for the Multi-Model Ensemble (MME) mean a Student’s one-sample t-test is implemented.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eTropical Rossby wave source analysis\u003c/h2\u003e\u003cp\u003eThe Tropical Rossby Wave Source (TRWS) formula is used to investigate the anomalous source of upper tropospheric vorticity, which is responsible for the initiation of planetary Rossby waves\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The TRWS is calculated as the advection of climatological absolute vorticity by anomalous irrotational wind\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:TRWS\\:=\\:-\\upsilon\\:{\\prime\\:}ᵪ\\cdot\\:\\nabla\\:(\\stackrel{̄}{\\zeta\\:}+f)$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cp\u003eIn Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\upsilon\\:{\\prime\\:}ᵪ\\)\u003c/span\u003e\u003c/span\u003e represents the anomalous irrotational wind, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{\\zeta\\:}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\)\u003c/span\u003e\u003c/span\u003e are the relative and planetary climatological vorticities, respectively. The irrotational wind is calculated by first integrating the velocity potential ꭓ from the divergence field, and then deriving it to obtain the irrotational wind, similarly to previous research\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The advection of the climatological absolute vorticity by anomalous irrotational flow is the dominant source of upper tropospheric vorticity in the tropics\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. We recognize that the term representing the vorticity tendency by vortex stretching or shrinking, the Extratropical Rossby Wave Source (ERWS)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, is excluded from our analysis. However, analogous conclusions hold when considering the ERWS (not shown).\u003c/p\u003e\u003ch2\u003eAtmospheric waveguide analysis\u003c/h2\u003e\u003cp\u003eThe meridional gradient of climatological upper tropospheric absolute vorticity and zonal wind serve as a proxy for the atmospheric waveguide\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The total stationary wavenumber, indicating the permitted wavenumbers of stationary Rossby waves along a given waveguide, is calculated using the linear theory for barotropic Rossby waves as follows:\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:K=\\sqrt{(\\beta\\:/\\stackrel{̄}{u})\\:}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is the meridional gradient of absolute vorticity and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{u}\\)\u003c/span\u003e\u003c/span\u003e is the climatological upper tropospheric zonal wind. Regions of negative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:K\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e, which indicate imaginary wavenumbers, are regarded as “forbidden regions” for the meridional propagation of stationary Rossby waves and therefore serve as effective refraction zones.\u003c/p\u003e\u003ch2\u003eWave activity analysis\u003c/h2\u003e\u003cp\u003eThe propagation of stationary Rossby wave packets is assessed using the Wave Activity Flux (WAF) formula\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This formula diagnoses Rossby wave propagation independently of its phase and parallel to its local group velocity under the geostrophic approximation. It is a widely used tool in extratropical teleconnection studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The zonal and meridional WAF components are:\u003c/p\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\:WAF}_{x}=\\:\\frac{pcos\\varphi\\:}{2\\stackrel{̄}{U}}\\left(\\frac{\\stackrel{̄}{u}}{{a}^{2\\:}co{s}^{2}\\varphi\\:}\\left[({\\partial\\:}_{x}\\psi\\:{\\prime\\:}{)}^{2\\:}-\\:\\psi\\:{\\prime\\:}\\:{{\\partial\\:}^{2}}_{x}\\psi\\:{\\prime\\:}\\right]+\\frac{\\stackrel{̄}{v}}{{a}^{2\\:}cos\\varphi\\:}({\\partial\\:}_{x}\\psi\\:{\\prime\\:}{\\partial\\:}_{y}\\psi\\:{\\prime\\:}-\\psi\\:{\\prime\\:}{{\\partial\\:}^{2}}_{xy}\\psi\\:{\\prime\\:})\\right)$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e7a\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{WAF}_{y}=\\:\\frac{pcos\\varphi\\:}{2\\stackrel{̄}{U}}\\left(\\frac{\\stackrel{̄}{u}}{{a}^{2\\:}cos\\varphi\\:}\\left[{\\partial\\:}_{x}\\psi\\:{\\prime\\:}{\\partial\\:}_{y}\\psi\\:{\\prime\\:}-\\psi\\:{\\prime\\:}{{\\partial\\:}^{2}}_{xy}\\psi\\:{\\prime\\:}\\right]+\\frac{\\stackrel{̄}{v}}{{a}^{2\\:}}\\left[({\\partial\\:}_{y}\\psi\\:{\\prime\\:}{)}^{2\\:}-\\:\\psi\\:{\\prime\\:}\\:{{\\partial\\:}^{2}}_{y}\\psi\\:{\\prime\\:}\\right]\\right)$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e7b\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere p is normalized pressure, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{u}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{v}\\)\u003c/span\u003e\u003c/span\u003e are the climatological zonal and meridional winds, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{U}\\)\u003c/span\u003e\u003c/span\u003e is the absolute value of the vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overrightarrow{\\stackrel{̄}{U}}=(\\stackrel{̄}{u},\\:\\stackrel{̄}{v})\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003ea\u003c/em\u003e is the Earth radius, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\partial\\:}_{x}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\partial\\:}_{y}\\)\u003c/span\u003e\u003c/span\u003e are the partial derivatives along the zonal and meridional directions, respectively. The term 𝜓’ stands for the anomalous geostrophic streamfunction, defined as the anomalous geopotential 𝜑’ divided by the Coriolis parameter \u003cem\u003ef.\u003c/em\u003e Both TRWS and WAF are calculated at the 200 hPa level in the upper troposphere, where Rossby wave sources typically reach their maximum intensity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eRossby wave ray tracing algorithm\u003c/h2\u003e\u003cp\u003eAn algorithm based on linear theory and the assumption of stationarity\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e is applied to investigate the pathway of Rossby wave rays\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The zonal and meridional group velocity of stationary barotropic Rossby waves are:\u003c/p\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{c}_{gx}=\\frac{2[{\\stackrel{̄}{u}]}^{2\\:}{{k}^{\\:}}^{2\\:}}{(\\beta\\:-{{\\partial\\:}^{2}}_{y}[\\stackrel{̄}{u}\\left]\\right)}\\:$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e8a\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{c}_{gy}=\\frac{2\\left[{\\stackrel{̄}{u}]}^{2\\:}{k}^{\\:}\\right(\\frac{\\beta\\:-\\:{{\\partial\\:}^{2}}_{y}\\left[\\stackrel{̄}{u}\\right]}{\\stackrel{̄}{\\left[u\\right]}}\\:-\\:{k}^{2\\:}{)}^{1/2}}{(\\beta\\:-{{\\partial\\:}^{2}}_{y}[\\stackrel{̄}{u}\\left]\\right)}\\:\\:$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e8b\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[\\stackrel{̄}{u}\\right]\\)\u003c/span\u003e\u003c/span\u003e is the zonal average of the climatological zonal wind, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\partial\\:}_{y}\\)\u003c/span\u003e\u003c/span\u003e is the partial derivative along the meridional direction, \u003cem\u003ek\u003c/em\u003e is the zonal wavenumber, and β is the meridional gradient of the Coriolis parameter. The zonal group velocity, \u003cem\u003ec\u003c/em\u003e\u003csub\u003egx\u003c/sub\u003e, is consistently positive and it increases at a faster rate than \u003cem\u003ec\u003c/em\u003e\u003csub\u003egy\u003c/sub\u003e, indicating that barotropic Rossby waves propagate more predominantly in the zonal direction\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. We consider the barotropic case because these waves propagate more easily, thereby influencing the remote response\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. From a starting point, typically identified within a region exhibiting a local TRWS maximum, the group velocities and position are determined. Subsequently, they are projected forward in time by 1 hour over a span of 10 days\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The grid-point group velocities and positions are calculated using nearest-neighbor interpolation of the fields described in Equations 8a and 8b, following the approach of previous research\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study is freely available at the link http://esgf-index1.ceda.ac.uk, which is maintained by the Earth System Grid Federation (ESGF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the authors (SG) acknowledges the financial support from Spoke 4 of the ICSC \u0026ndash; Centro Nazionale di Ricerca in High Performance Computing, Big Data, and Quantum Computing, funded by the European Union under NextGenerationEU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDS conceptualized the study, carried out the analysis, and drafted the manuscript. SG contributed to the discussion, the implemented methodology, and the revision and writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eDoblas‐Reyes, F. J., Garc\u0026iacute;a‐Serrano, J., Lienert, F., Biescas, A. P., \u0026amp; Rodrigues, L. R. (2013). Seasonal climate predictability and forecasting: status and prospects. \u003cem\u003eWiley Interdisciplinary Reviews: Climate Change\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(4), 245-268.\u003c/li\u003e\n\u003cli\u003eDomeisen, D. I., Butler, A. H., Fr\u0026ouml;hlich, K., Bittner, M., M\u0026uuml;ller, W. A., \u0026amp; Baehr, J. (2015). Seasonal predictability over Europe arising from El Ni\u0026ntilde;o and stratospheric variability in the MPI-ESM seasonal prediction system. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 256-271.\u003c/li\u003e\n\u003cli\u003eDunstone, Nick, et al. \u0026quot;Skilful predictions of the winter North Atlantic Oscillation one year ahead.\u0026quot; \u003cem\u003eNature Geoscience\u003c/em\u003e 9.11 (2016): 809-814.\u003c/li\u003e\n\u003cli\u003eHorel, J. D., and J. M. Wallace (1981), Planetary-scale atmospheric phenomena associated with the Southern Oscillation, Mon. Weather Rev., 103, 813\u0026ndash;829.\u003c/li\u003e\n\u003cli\u003evAN LooN, H. A. R. R. Y., and Roland A. Madden. \u0026quot;The Southern Oscillation. Part I: Global associations with pressure and temperature in northern winter.\u0026quot; \u003cem\u003eMonthly Weather Review\u003c/em\u003e 109.6 (1981): 1150-1162.\u003c/li\u003e\n\u003cli\u003eScaife, Adam A., et al. \u0026quot;Tropical rainfall, Rossby waves and regional winter climate predictions.\u0026quot; \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e 143.702 (2017): 1-11.\u003c/li\u003e\n\u003cli\u003eMolteni, Franco, and Anca Brookshaw. \u0026quot;Early-and late-winter ENSO teleconnections to the Euro-Atlantic region in state-of-the-art seasonal forecasting systems.\u0026quot; \u003cem\u003eClimate Dynamics\u003c/em\u003e (2023): 1-20.\u003c/li\u003e\n\u003cli\u003eAyarzag\u0026uuml;ena, B., Ineson, S., Dunstone, N. J., Baldwin, M. P., \u0026amp; Scaife, A. A. (2018). Intraseasonal effects of el ni\u0026ntilde;o\u0026ndash;southern oscillation on North Atlantic climate. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(21), 8861-8873.\u003c/li\u003e\n\u003cli\u003eKing, Martin P., et al. \u0026quot;Importance of late fall ENSO teleconnection in the Euro-Atlantic sector.\u0026quot; \u003cem\u003eBulletin of the American Meteorological Society\u003c/em\u003e 99.7 (2018): 1337-1343.\u003c/li\u003e\n\u003cli\u003eMezzina, J. García-Serrano, I. Bladé, F. Kucharski, Dynamics of the enso teleconnection and nao variability in the north atlantic\u0026ndash;european late winter, Journal of Climate 33 (3) (2020) 907\u0026ndash;923. \u003c/li\u003e\n\u003cli\u003eAbid, M. A., Kucharski, F., Molteni, F., Kang, I. S., Tompkins, A. M., \u0026amp; Almazroui, M. (2021). Separating the Indian and Pacific Ocean impacts on the Euro-Atlantic response to ENSO and its transition from early to late winter. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 1531-1548.\u003c/li\u003e\n\u003cli\u003eKing MP, Li C, Sobolowski S (2021) Resampling of ENSO teleconnections: accounting for cold-season evolution reduces uncertainty in the North Atlantic. Weather Clim Dyn 2:759\u0026ndash;776. https://doi.org/10.5194/wcd-2-759-2021\u003c/li\u003e\n\u003cli\u003eJoshi, M. K., Abid, M. A., \u0026amp; Kucharski, F. (2021). The role of an Indian Ocean heating dipole in the ENSO teleconnection to the North Atlantic European region in early winter during the twentieth century in reanalysis and CMIP5 simulations. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(3), 1047-1060.\u003c/li\u003e\n\u003cli\u003eBenassi, Marianna, et al. \u0026quot;El Ni\u0026ntilde;o teleconnection to the Euro-Mediterranean late-winter: the role of extratropical Pacific modulation.\u0026quot; \u003cem\u003eClimate Dynamics\u003c/em\u003e 58.7 (2022): 2009-2029.\u003c/li\u003e\n\u003cli\u003eGeng, Xin, Jiuwei Zhao, and Jong-Seong Kug. \u0026quot;ENSO-driven abrupt phase shift in North Atlantic oscillation in early January.\u0026quot; \u003cem\u003enpj Climate and Atmospheric Science\u003c/em\u003e 6.1 (2023): 80.\u003c/li\u003e\n\u003cli\u003eMezzina, Bianca, et al. \u0026quot;Multi-model assessment of the late-winter extra-tropical response to El Ni\u0026ntilde;o and La Ni\u0026ntilde;a.\u0026quot; \u003cem\u003eClimate Dynamics\u003c/em\u003e 58.7 (2022): 1965-1986.\u003c/li\u003e\n\u003cli\u003eMezzina, B., Garc\u0026iacute;a-Serrano, J., Ambrizzi, T., Matei, D., Manzini, E., \u0026amp; Blad\u0026eacute;, I. (2023). Tropospheric pathways of the late-winter ENSO teleconnection to Europe. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(11), 3307-3317.\u003c/li\u003e\n\u003cli\u003eDomeisen, D. I., Garfinkel, C. I., \u0026amp; Butler, A. H. (2019). The teleconnection of El Ni\u0026ntilde;o Southern Oscillation to the stratosphere. \u003cem\u003eReviews of Geophysics\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(1), 5-47.\u003c/li\u003e\n\u003cli\u003eHardiman, Steven C., et al. \u0026quot;Predictability of European winter 2019/20: Indian Ocean dipole impacts on the NAO.\u0026quot; \u003cem\u003eAtmospheric Science Letters\u003c/em\u003e 21.12 (2020): e1005.\u003c/li\u003e\n\u003cli\u003eAbid, M. A., Kucharski, F., Molteni, F., \u0026amp; Almazroui, M. (2023). Predictability of Indian Ocean precipitation and its North Atlantic teleconnections during early winter. \u003cem\u003enpj Climate and Atmospheric Science\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 17.\u003c/li\u003e\n\u003cli\u003eMolteni, Franco, et al. \u0026quot;Boreal-winter teleconnections with tropical Indo-Pacific rainfall in HighResMIP historical simulations from the PRIMAVERA project.\u0026quot; \u003cem\u003eClimate Dynamics\u003c/em\u003e 55 (2020): 1843-1873.\u003c/li\u003e\n\u003cli\u003eLi, R. K., Woollings, T., O\u0026rsquo;Reilly, C., \u0026amp; Scaife, A. A. (2020). Effect of the North Pacific tropospheric waveguide on the fidelity of model El Ni\u0026ntilde;o teleconnections. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(12), 5223-5237.\u003c/li\u003e\n\u003cli\u003eHardiman, S. C., Dunstone, N. J., Scaife, A. A., Smith, D. M., Ineson, S., Lim, J., \u0026amp; Fereday, D. (2019). The impact of strong El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events on the North Atlantic. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(5), 2874-2883.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Fonseca, Bel\u0026eacute;n, et al. \u0026quot;A review of ENSO influence on the North Atlantic. A non-stationary signal.\u0026quot; \u003cem\u003eAtmosphere\u003c/em\u003e 7.7 (2016): 87.\u003c/li\u003e\n\u003cli\u003eTyrrell, Nicholas L., and Alexey Yu Karpechko. \u0026quot;Minimal impact of model biases on northern hemisphere ENSO teleconnections.\u0026quot; \u003cem\u003eWeather and Climate Dynamics Discussions\u003c/em\u003e 2020 (2020): 1-16\u003c/li\u003e\n\u003cli\u003eDi Carlo, E., Ruggieri, P., Davini, P., Tibaldi, S., \u0026amp; Corti, S. (2022). ENSO teleconnections and atmospheric mean state in idealised simulations. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(11), 3287-3304.\u003c/li\u003e\n\u003cli\u003eTaylor, Karl E., Ronald J. Stouffer, and Gerald A. Meehl. \u0026quot;An overview of CMIP5 and the experiment design.\u0026quot; \u003cem\u003eBulletin of the American meteorological Society\u003c/em\u003e 93.4 (2012): 485-498.\u003c/li\u003e\n\u003cli\u003eEyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., \u0026amp; Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. \u003cem\u003eGeoscientific Model Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 1937-1958.\u003c/li\u003e\n\u003cli\u003eTaylor, Karl E. \u0026quot;Summarizing multiple aspects of model performance in a single diagram.\u0026quot; \u003cem\u003eJournal of geophysical research: atmospheres\u003c/em\u003e 106.D7 (2001): 7183-7192.\u003c/li\u003e\n\u003cli\u003eHoskins, Brian J., and Tercio Ambrizzi. \u0026quot;Rossby wave propagation on a realistic longitudinally varying flow.\u0026quot; \u003cem\u003eJournal of Atmospheric Sciences\u003c/em\u003e 50.12 (1993): 1661-1671\u003c/li\u003e\n\u003cli\u003eTrenberth, K. E., Branstator, G. W., Karoly, D., Kumar, A., Lau, N. C., \u0026amp; Ropelewski, C. (1998). Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e(C7), 14291-14324.\u003c/li\u003e\n\u003cli\u003eAmbaum, Maarten HP, Brian J. Hoskins, and David B. Stephenson. \u0026quot;Arctic oscillation or North Atlantic oscillation?.\u0026quot; \u003cem\u003eJournal of Climate\u003c/em\u003e 14.16 (2001): 3495-3507.\u003c/li\u003e\n\u003cli\u003eThompson, David WJ, and John M. Wallace. \u0026quot;Annular modes in the extratropical circulation. Part I: Month-to-month variability.\u0026quot; \u003cem\u003eJournal of climate\u003c/em\u003e 13.5 (2000): 1000-1016.\u003c/li\u003e\n\u003cli\u003eRaganato, A., Abid, M. A., \u0026amp; Kucharski, F. (2025). The Combined Link of the Indian Ocean Dipole and ENSO with the North Atlantic\u0026ndash;European Circulation during Early Boreal Winter in Reanalysis and the ECMWF SEAS5 Hindcast. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(2), 445-460.\u003c/li\u003e\n\u003cli\u003eMolteni, Franco, Timothy N. Stockdale, and Fr\u0026eacute;d\u0026eacute;ric Vitart. \u0026quot;Understanding and modelling extra-tropical teleconnections with the Indo-Pacific region during the northern winter.\u0026quot; \u003cem\u003eClimate dynamics\u003c/em\u003e 45 (2015): 3119-3140.\u003c/li\u003e\n\u003cli\u003eIqbal, Waheed, Wai-Nang Leung, and Abdel Hannachi. \u0026quot;Analysis of the variability of the North Atlantic eddy-driven jet stream in CMIP5.\u0026quot; \u003cem\u003eClimate Dynamics\u003c/em\u003e 51 (2018): 235-247.\u003c/li\u003e\n\u003cli\u003eCurtis, Paul Edwin, Paulo Ceppi, and Giuseppe Zappa. \u0026quot;Role of the mean state for the Southern Hemispheric jet stream response to CO2 forcing in CMIP6 models.\u0026quot; \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e 15.6 (2020): 064011.\u003c/li\u003e\n\u003cli\u003eAthanasiadis, Panos J., et al. \u0026quot;Mitigating climate biases in the midlatitude North Atlantic by increasing model resolution: SST gradients and their relation to blocking and the jet.\u0026quot; \u003cem\u003eJournal of Climate\u003c/em\u003e 35.21 (2022): 6985-7006.\u003c/li\u003e\n\u003cli\u003eLiu, Anqi, Ying Huang, and Danqing Huang. \u0026quot;Inter‐model spread of the simulated winter surface air temperature over the Eurasian continent and the physical linkage to the jet streams from the CMIP6 models.\u0026quot; \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e 127.22 (2022): e2022JD037172\u003c/li\u003e\n\u003cli\u003eWang, C., Zhang, L., Lee, S. K., Wu, L., \u0026amp; Mechoso, C. R. (2014). A global perspective on CMIP5 climate model biases. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 201\u0026ndash;205. https://doi.org/10.1038/nclimate2118\u003c/li\u003e\n\u003cli\u003eWang, Chenqi, Liwei Zou, and Tianjun Zhou. \u0026quot;SST biases over the Northwest Pacific and possible causes in CMIP5 models.\u0026quot; \u003cem\u003eScience China Earth Sciences\u003c/em\u003e 61 (2018): 792-803. \u003c/li\u003e\n\u003cli\u003eFeng, J., Lian, T., Chen, D., \u0026amp; Li, Y. (2021). The cause of the large cold bias in the northwestern Pacific Ocean. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(18), e2021GL094616.\u003c/li\u003e\n\u003cli\u003eZhang, Qibei, et al. \u0026quot;Understanding models\u0026apos; global sea surface temperature bias in mean state: from CMIP5 to CMIP6.\u0026quot; \u003cem\u003eGeophysical Research Letters\u003c/em\u003e 50.4 (2023): e2022GL10088\u003c/li\u003e\n\u003cli\u003eHersbach H, Bell B, Berrisford P et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999\u0026ndash;2049. https://doi.org/ 10.1002/qj.3803\u003c/li\u003e\n\u003cli\u003eKalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996\u003c/li\u003e\n\u003cli\u003eAdler, Robert F., et al. \u0026quot;The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979\u0026ndash;present).\u0026quot; \u003cem\u003eJournal of hydrometeorology\u003c/em\u003e 4.6 (2003): 1147-1167.\u003c/li\u003e\n\u003cli\u003eAbid, M. A., Ashfaq, M., Kucharski, F., Evans, K. J. \u0026amp; Almazroui, M. Tropical Indian Ocean mediates ENSO influence over central Southwest Asia during the wet season. Geophys. Res. Lett. 47, e2020GL089308. https://doi.org/10.1029/2020GL089308 (2020).\u003c/li\u003e\n\u003cli\u003eSardeshmukh PD, Hoskins BJ (1988) The generation of global rotational flow by steady idealized tropical divergence. J Atmos Sci 45:1228\u0026ndash;1251. https://doi.org/10.1175/1520-0469(1988)045\u0026lt;1228:TGOGRF\u0026gt;2.0.CO;2 \u003c/li\u003e\n\u003cli\u003eQin, Jianchun, and Walter A. Robinson. \u0026quot;On the Rossby wave source and the steady linear response to tropical forcing.\u0026quot; \u003cem\u003eJournal of Atmospheric Sciences\u003c/em\u003e 50.12 (1993): 1819-1823.\u003c/li\u003e\n\u003cli\u003eTakaya, Koutarou, and Hisashi Nakamura. \u0026quot;A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow.\u0026quot; \u003cem\u003eJournal of the Atmospheric Sciences\u003c/em\u003e 58.6 (2001): 608-627.\u003c/li\u003e\n\u003cli\u003eRuggieri, Paolo, et al. \u0026quot;Atlantic multidecadal variability and North Atlantic jet: a multimodel view from the decadal climate prediction project.\u0026quot; \u003cem\u003eJournal of Climate\u003c/em\u003e 34.1 (2021): 347-360.\u003c/li\u003e\n\u003cli\u003eHoskins BJ, Karoly DJ. 1981. The steady linear response of a spherical atmosphere to thermal and orographic forcing. \u003cem\u003eJ. Atmos. Sci.\u003c/em\u003e 38: 1179\u0026ndash;1196.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5560758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5560758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study investigates how the atmospheric mean state influences the El Niño-Southern Oscillation (ENSO) teleconnections with the North Atlantic-European (NAE) region, using ERA5 and CMIP5/CMIP6 models. By isolating the contributions of heating anomalies in the Niño 3.4 and Tropical Western-Eastern Indian Ocean (TWEIO) regions, we find that in November, the Niño 3.4 teleconnection dominates, projecting onto the positive phase of the North Atlantic Oscillation (NAO). In December, the TWEIO teleconnection prevails, reinforcing the positive NAO via a zonal wavenumber-3 Rossby wave train originating from SouthEast Asia (SEA). Models that fail to simulate the December ENSO teleconnection with the NAE exhibit a weak Rossby wave source in SEA and overly strong subtropical Pacific and Atlantic jet streams, which trap Rossby waves at lower latitudes, affecting the remote atmospheric response over the NAE. This waveguide bias is likely driven by a cold bias in the northern Pacific and Atlantic, a common mean-state error in climate models.","manuscriptTitle":"ENSO teleconnections with the NAE sector during December in CMIP5/CMIP6 models: impacts of the atmospheric mean state","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 06:26:22","doi":"10.21203/rs.3.rs-5560758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-23T15:28:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T15:15:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33247794279677955880053488536596292625","date":"2025-04-15T10:19:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203944728012047768527862651052681999284","date":"2025-04-14T17:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176109566169787224091682376317965069521","date":"2025-04-13T15:33:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339204665512421553272422678408124119080","date":"2025-04-12T10:31:04+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T01:36:59+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"246654317895453259313419073425220400317","date":"2025-03-28T10:17:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-27T14:01:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136903315809745162569855193463253094477","date":"2025-03-27T09:32:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T07:20:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T07:19:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Climate and Atmospheric Science","date":"2025-03-21T15:35:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8bfdaa20-ad5a-460f-bfa8-6fa2d0b5da34","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46306762,"name":"Earth and environmental sciences/Climate sciences"},{"id":46306763,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"}],"tags":[],"updatedAt":"2025-06-23T16:01:50+00:00","versionOfRecord":{"articleIdentity":"rs-5560758","link":"https://doi.org/10.1038/s41612-025-01064-2","journal":{"identity":"npj-climate-and-atmospheric-science","isVorOnly":false,"title":"npj Climate and Atmospheric Science"},"publishedOn":"2025-06-17 15:57:40","publishedOnDateReadable":"June 17th, 2025"},"versionCreatedAt":"2025-03-28 06:26:22","video":"","vorDoi":"10.1038/s41612-025-01064-2","vorDoiUrl":"https://doi.org/10.1038/s41612-025-01064-2","workflowStages":[]},"version":"v1","identity":"rs-5560758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5560758","identity":"rs-5560758","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}