Interdecadal Variability in Ocean Memory of the Maritime Continent and Its Effect on Asian-Australian Monsoon Prediction

Interdecadal Variability in Ocean Memory of the Maritime Continent and Its Effect on Asian-Australian Monsoon Prediction | 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 Research Article Interdecadal Variability in Ocean Memory of the Maritime Continent and Its Effect on Asian-Australian Monsoon Prediction Simeng han, Zhiwei Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4708586/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2024 Read the published version in Climate Dynamics → Version 1 posted 5 You are reading this latest preprint version Abstract Ocean memory is crucial for improving climate models and enhancing the accuracy of climate predictions. However, due to the changes in ocean memory over the past few decades, its impact on monsoon predictions remains unclear. The persistence of sea surface temperature (SST) anomalies, as a key indicator of ocean memory, can regulate the local air-sea coupling processes affecting the Asian-Australian monsoon (A-AM), thereby significantly influencing climate predictions for Asia, Australia, and the entire Indo-Pacific region. Based on observational and numerical modeling evidence, the study finds that within the context of interdecadal variation in ocean memory, the seasonal persistence of Maritime Continent (MC) SST anomalies is more pronounced during the strong memory epoch (1982–1999), sustaining the anomalous western North Pacific anti-cyclone (WNPAC) through a stronger Matsuno-Gill response during the decaying phase of El Niño-Southern Oscillation (ENSO), thereby enhancing the overall strength of the A-AM system during the monsoon year. Additionally, the connection between ENSO and the A-AM is strengthened. By contrast, these air-sea coupling processes have weakened during the weak memory epoch (2000–2017), making it more difficult to capture the characteristics of the A-AM. The early 21st-century decline in MC ocean memory reduced the prediction skills of the leading mode of the A-AM. Above all, this study emphasizes the significant impact of ocean memory on monsoon prediction skills, providing new insight into seeking more reliable sources of predictability for the A-AM. Ocean memory Asian-Australian monsoon Maritime continent Multi-model ensemble hindcasts Predictability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction The predictability of the climate system largely originates from the slow evolution of the lower boundary forcing (Shukla 1985 ). The ocean, being the Earth's largest heat reservoir, can impact the atmosphere for months or even years due to the memory effect of its upper heat content. (Namias et al. 1970; Frankignoul and Hasselmann 1977 ; Deser et al. 2003 ; Smith et al. 2012 ). Therefore, ocean memory serves as a critical link between oceanic anomalies and subsequent atmospheric responses, which is the basic premise of seasonal prediction in the tropics (McCartney 1997 ; Shukla 1998 ; Chang et al. 2006b ; Vidard et al. 2007 ). For instance, the seasonal footprinting mechanism suggests that the extratropical ocean can store atmospheric circulation signals and may impact the tropics up to one year in advance (Vimont et al. 2001 , 2003a , b ). The coupled oceanic-atmospheric bridge and chain-coupled bridge proposed by Li et al. (2016, 2019 ) also emphasize that the memory effect of oceanic bridge prolongs anomalous atmospheric signals into subsequent seasons. Therefore, the memory nature of the ocean makes it possible to perform skillful seasonal predictions of anomalous climatic phenomena, makes it possible to perform skillful seasonal prediction for anomalous climatic phenomena (Wu et al. 2009 , 2015 ; Li et al. 2017 ; Gao et al. 2019 ; Desbruyeres et al. 2021; Zhang et al. 2022 ). In most research, the role of ocean memory typically refers to the sustained impact of sea surface anomalies (SSTAs) on the atmospheric circulation system, serving as a crucial source of predictability for seasonal climate variability in many terrestrial regions (Smith et al. 2012 ; Zhang et al. 2016 ). Of note, the persistence of SST plays a pivotal role in accurately predicting monsoon variability (Wang et al. 2015 ). The persistent SST dipole pattern in the South Atlantic-Pacific during winter can serve as a "charger" to maintain the November–December Southern Hemisphere annular mode signals, which is a precursor signal for seasonal prediction of the East Asian winter monsoon (EAWM) (Wu et al. 2015 ). The tripole SSTAs in the North Pacific and the widespread negative SSTAs in the North Atlantic are considered important predictors of the intensity of the EAWM (Yu et al. 2018 ). The ocean memory effect also plays a crucial role in the prediction of the Asian summer monsoon. It allows the spring North Atlantic Oscillation (NAO)-induced tripole SST pattern in the North Atlantic to persist into summer, providing a physical basis for the seasonal prediction of the East Asian summer monsoon (EASM) (Wu et al. 2009 ). This persistence extends the lead time and enhances the accuracy of monsoon predictions. Statistical analyses suggest that the significant persistence of Indian Ocean SST prior to the onset of the EASM offers predictive potential of the monsoon (Li et al. 2017 ). Furthermore, Sahai et al. ( 2003 ) discovered that the behavior of the ensuing Indian summer monsoon precipitation (ISMR) can be predicted nine months in advance using global SST only, and the sustained SSTAs during the monsoon period significantly impact the effectiveness of seasonal prediction for ISMR (DAS et al. 2013 ). In addition, the ocean memory effects have been applied to predict cross-seasonal precipitation over the Arabian Peninsula and the extent of Antarctic sea ice, among other phenomena (Libera et al. 2021 ; Wei et al. 2023 ). The atmospheric responses triggered by these precursor signals can persist for extended periods, thereby enhancing the predictive capability for regional climates. Previous studies have mainly focused on the impact of ocean memory on the prediction of individual monsoon regions. Nevertheless, the ocean memory effect on the Asian-Australian monsoon (A-AM), the largest monsoon region, has not been well documented and investigated. The A-AM constitutes a crucial component of the global atmospheric circulation (Wang et al. 2000 ). Many studies have investigated the links between A-AM and tropical SST variability, one of the major sources of seasonal predictability. The interannual variation of the A-AM is primarily characterized by the quasi-biennial oscillation of the troposphere (Meehl et al. 1987) and is closely linked to the phase changes of the El Niño-Southern Oscillation (ENSO) (Wang et al. 2003 , 2007, 2008 ). Some studies emphasize that ENSO typically persists from JJA to DJF but loses its memory in springtime due to the abrupt development of the monsoon, leading to a decrease in the skill of ENSO-based forecasting techniques (Webster and Yang, 1992; Lau and Yang, 1997 ). Additionally, air-sea interaction in the western Pacific warm pool has been identified as a significant factor regulating A-AM variability and its seasonal predictability (Wang et al. 2003 , 2004 ; Kumar et al. 2005 ; Wu and Kirtman 2005 ). In the absence of atmosphere-warm ocean interaction, atmospheric circulation models struggle to capture the changes in A-AM (Sperber et al. 1996; Wang et al. 2004 ; Liang et al. 2009 ; Chowdary et al. 2010 ; Lee et al. 2011 ). The MC region, situated within the western Pacific warm pool, serves as the pivotal "crossroads" of the A-AM system through cross-equatorial meridional airflow (Xie 1998 ). Moreover, as ocean memory is measured by the difference between the actual response and the response in steady balance with the instantaneous wind stress, the largest ocean memory is found in the MC region over the tropical Pacific (Neelin et al. 1998 ; Dijkstra 2006 ). Robust interaction between tropical convection and air-sea coupling in the MC area can induce climate anomalies in the A-AM region through the Gill-type response and meridional propagation of Rossby waves (Gill 1980 ; Nitta 1987; Meehl 1987 ; Sardeshmukh and Hoskins 1988 ; Lau et al. 1997 ; Hoerling and Kumar 2002; Moon and Ha 2003 ; Chang et al. 2005). Although MC SST is significantly influenced by ENSO, the ENSO-independent MC SSTA pattern can still significantly impact anomalous circulation in the A-AM region (Chen et al. 2020 ; Zhu et al. 2022 ). The variation in convective activity over the MC can trigger an East Asia–Pacific/Pacific–Japan (EAP/PJ)-like anomalous wave train after removing the ENSO and IOD signals, leading to significant positive precipitation anomaly over the Yangtze River valley (Xu et al. 2017). The above results provide important clues for predicting A-AM anomalies and associated summer precipitation anomalies in China. However, owing to the shallowing of the mixed layer depth (MLD) in the context of global warming, the reduction in thermal inertia of the upper ocean has declined the oceanic memory effect (Shi et al. 2022 ), especially in the MC region, shortening the lead time of skillful persistence-based predictions of sea surface thermal conditions (Luo et al. 2011 ; Hervieux et al. 2019 ; Shi et al.2022). Despite the consensus on the reduced ocean memory in future warming scenarios, it remains unclear whether such changes have already become apparent in observations. Therefore, this study endeavors to address three questions: Has ocean memory changed in the MC region in the last few decades? Will these memory changes affect the prediction skills of the A-AM? What are the physical mechanisms causing this impact? To answer the above questions, the paper is organized as follows. Section 2 briefly describes the data and methods used in this study. Section 3 outlines the criteria for dividing periods with strong and weak ocean memory. Section 4 analyzes interdecadal differences in the leading modes of the interannual variability of the A-AM during two periods. Section 5 contrasts the differences in prediction skills of the A-AM between the two periods. Section 6 delves into the physical mechanisms underlying the differences in prediction skills. Finally, a summary and discussion are provided in Section 7 . 2. Data and methodology a. Data The observed datasets utilized in this study include: (1) monthly mean SST data from the National Oceanic and Atmospheric Administration Extended Reconstructed SST version 5; (2) ocean stratification data from the Institute of Atmospheric Physic global ocean stratification gridded product; (3) the wind fields and the vertical pressure velocity field from the NCEP/DOE AMIP II Reanalysis; (4) monthly precipitation data provided by the Global Precision Climatology Project, v2.3 (Adler et al. 2003 ). The ensemble hindcasts (i.e., retrospective prediction experiments aimed at predicting past events) utilized in this study are obtained from the North American Multi-Model Ensemble (NMME) and cover the period from 1982 to 2018. The NMME is a collaborative initiative that integrates seasonal forecasting systems, incorporating coupled atmospheric-ocean-sea ice-land models from various modeling centers in the United States and Canada (Kirtman et al. 2014). We select four models from the NMME that provide wind field hindcasts. These models include CanCM4i, CanSIPS-IC3, CanSIPSv2, and GEM-NEMO. To calculate variable anomalies, we subtracted climatology based on hindcast data from individual models and computed ensemble averages for each model using uniform weights. Table 1 provides detailed information on the period covered, ensemble size, and lead months for each of these models. In this research, lead time is defined as the number of months between the start time of prediction and the midpoint of the forecast month. For example, a forecast for January has a 0.5-month lead, for February a 1.5-month lead, and so forth. Table 1 Detailed information of NMME climate models. Model Period Ensemble size Lead times(months) CanCM4i Jan 1981-Dec 2018 10 0.5–11.5 CanSIPS-IC3 Jan 1980-Dec 2021 20 0.5–11.5 CanSIPSv2 Jan 1981-Dec 2018 20 0.5–11.5 GEM-NEMO Jan 1981-Dec 2018 10 0.5–11.5 b. Method The application of season-reliant Empirical Orthogonal Function (S-EOF) analysis allows for the extraction of the dominant mode of the A-AM, capturing its major spatiotemporal characteristics in seasonal evolution (Wang and An, 2005; Wang et al. 2008 b). The monsoon year is defined from the summer of year 0 (referred to as June to August (0) [JJA(0)]) to the spring of the following year (March to May (1) [MAM(1)]) (Meehl, 1987 ; Yasunari, 1991), thus comprising four consecutive seasonal anomalies. This approach facilitates the characterization of the seasonal evolution patterns of A-AM in the monsoon year. In this study, we adopt the definition of ocean memory proposed by Hui et al. (2022) and define the 1-year autocorrelation of annual mean SSTAs as an indicator for evaluating ocean memory in the MC region. The SSTA anomalies are derived for a common reference period 1982–2018. This indicator allows us to classify years with either strong or weak memory. Furthermore, we employ the time correlation coefficient (ACC) and root mean square error (RMSE) to evaluate the prediction skills of both the individual models and the multi-model ensemble (MME). The calculation formulas for these metrics are as follows: $$\:\begin{array}{c}AC{\text{C}}_{\text{i}}=\frac{{\sum\:}_{\text{j}=1}^{\text{N}}\left({\text{o}}_{\text{i},\text{j}}-\stackrel{-}{{\text{x}}_{\text{i}}}\right)\left({\text{f}}_{\text{i},\text{j}}-\stackrel{-}{{\text{f}}_{\text{i}}}\right)}{\sqrt{{\sum\:}_{\text{j}=1}^{\text{N}}{\left({\text{o}}_{\text{i},\text{j}}-\stackrel{-}{{\text{o}}_{\text{j}}}\right)}^{2}}\times\:\sqrt{{\sum\:}_{\text{j}=1}^{\text{N}}{\left({\text{f}}_{\text{i},\text{j}}-\stackrel{-}{{\text{f}}_{\text{i}}}\right)}^{2}}}\#\left(1\right)\end{array}$$ $$\:\begin{array}{c}{\text{R}\text{M}\text{S}\text{E}}_{\text{i}}=\sqrt{\frac{1}{\text{N}}\sum\:_{\text{j}=1}^{\text{N}}{\left({\text{f}}_{\text{j}}-{\text{o}}_{\text{j}}\right)}^{2}}\#\left(2\right)\end{array}$$ where O is the verification field anomaly, and F is the forecast anomaly. In this study, to investigate the response of local winds over the WNP to anomalous MC adiabatic heating, numerical experiments are conducted using the linear baroclinic model (LBM) developed by Watanabe and Kimoto ( 2000 ). The primitive equation model was linearized about a prescribed three-dimensional basic state to simulate the dry dynamical response to non-adiabatic heating. The model employed horizontal and vertical resolutions of T42 and 20 levels in the sigma-coordinate. Bi-harmonic diffusion was applied in the model, with an e-folding time of 1 hour for the maximum wave number and 1000 days for vertical diffusion (Sekizawa et al. 2021 ). In this study, the average response integrated from day 31 to day 45 was considered as the steady response. 3. Division of periods with strong and weak ocean memory In recent decades, as increased greenhouse gas emissions amplify the Earth's radiative imbalance, the oceans take up about 93% of the extra heat in the climate system, which has intensified the rate of ocean warming (Li et al. 2012 ; Johnson and Lyman 2020 ). Changes in ocean temperature and salinity have led to variations in ocean density, with a general trend toward increased stratification observed globally (Capotondi et al. 2012 ; Li et al. 2020 ). Meanwhile, the mixed layer in the northern part of the MC region and tropical western Pacific has shown a downward trend during 2006–2021 (Roch et al. 2023 ). One potential cause is the increased freshwater flux, significantly decreasing density in the upper ocean, leading to a more stable mixed layer and enhanced stratification (Ke-xin and Fei 2022 ). This has exacerbated the differences between the upper-ocean mixed layer and the deep ocean, reducing the vertical mixing between surface and deep waters (Capotondi et al. 2012 ). Consequently, as solar radiation primarily heats the surface layer, less heat is transferred to the deep ocean, leading to a more pronounced warming of the surface waters. Over the past four decades, the enhanced upper ocean stratification has played a predominant role in shallowing the MLD, thereby reducing the thermal inertia of the upper ocean and affecting ocean memory (Li et al. 2017 ; Sung et al. 2023 ; Cai et al. 2023 ; Jo et al. 2022 ; Nagano et al. 2022 ; Liu et al. 2024 ). Therefore, based on the established conclusions above, to identify regions where ocean stratification changes significantly affect ocean memory and SST response, we analyzed the correlation between SST and upper ocean stratification at each grid point in the MC region from 1980 to 2018. By conducting sensitivity experiments controlled for data resolution, we identified the region with the strongest correlation between the surface ocean and the underlying water masses as the MC region (122.5° -147.5°E, 2.5°S-17.5°N) (red box in Fig. 1 ) in this study. Following the definition of ocean memory by Hui et al. (2022), we consider the year-to-year ocean memory index as the 1-year lag autocorrelation of the SSTAs in a 21-year sliding window. Note that different windows for running mean are also applied and the results are essentially the same. Figure 2 shows that in the early 21st century, the memory index of MC began to fall below the 95% significance threshold and exhibited a downward trend. This suggests that ocean memory in the MC region has been deteriorating since the early 21st century. Due to constraints of the length of hindcasts, we delineated the period from 1982 to 1999 as the epoch characterized by strong ocean memory and the period from 2000 to 2017 as the epoch marked by weak memory. Here we hypothesize that the prediction skills of the leading modes of A-AM may vary correspondingly between periods of strong and weak ocean memory. In the following section, preceding the comparison of prediction skills, we first examine the dominant modes of A-AM and their differences in spatial patterns during these two periods. 4. Interdecadal variation in the two leading modes of A-AM variability for periods with strong and weak memory To extract the leading modes of interannual variability of the A-AM under different memory conditions and analyze the differences in their predictability sources, season-reliant EOF (S-EOF) decomposition is performed using the 850-hPa meridional wind component as the primary variable because it is more reliable than a divergent circulation component (Wang et al. 2008 ). Figure 3 displays the spatial patterns of the first S-EOF mode (S-EOF1) during the monsoon year. It should be noted that the characteristic vectors were derived exclusively from the 850-hPa wind anomalies. The 500-hPa vertical velocity anomalies and the 850-hPa meridional wind anomalies were obtained using regression of the corresponding fields with reference to the first principal component (PC1). For the S-EOF1, the anomalous atmospheric circulation features include sinking motion over the MC and rising motion over the Philippine Sea during JJA (0). The easterly anomalies over the Indian Ocean strengthen from SON(0) to DJF(0/1) and then weaken in MAM(1). The tropical WNP anomalous anticyclone begins near the northern Philippines in SON(0), rapidly intensifies, and extends eastward into the following summer (JJA(1)). The anomalous circulation patterns for S-EOF1 are similar during the two epochs. However, there are differences in D(0)JF(1) and subsequent MAM(1), particularly in years of strong memory (1982–1999), where the anomalous western North Pacific anti-cyclone (WNPAC) is more persistent and stronger. This means that the first leading mode of A-AM exhibited stronger interannual variability with more typical years during 1982–1999, and the change of wind field can be more explained by the leading mode captured by S-EOF, which is conducive to prediction improvement. The associated easterly anomalies from Indonesia to the equatorial Indian Ocean during MAM(1) do not exhibit the rapid decay observed in periods of weak memory, implying a weaker ENSO-WNP relationship in recent decades. This viewpoint may diverge from the result of Wang et al. ( 2008 ) that the coupling between A-AM and ENSO has strengthened since the 1970s. This indicates that the relationship between A-AM and ENSO has not continued to strengthen since the early 21st century, which may not be conducive to accurate prediction of the dominant modes of A-AM. The seasonal evolution of the second S-EOF mode (S-EOF2) unveils notable interdecadal variations in circulation anomalies across the A-AM region. From 1982 to 1999, roughly one year prior to the El Niño mature phase, westerly anomalies were already established over the western equatorial Pacific, closely associated with cyclonic anomalies in the Philippine Sea (Fig. 4 ). These anomalies gradually intensified over the subsequent three seasons, and as El Niño matures, these cyclonic anomalies transform into anticyclonic anomalies and reach their full development. During periods of strong memory, the cyclonic anomalies in the Philippine Sea and anomalous WNPAC are more pronounced. Additionally, robust westerly anomalies extending from the Arabian Sea and the MC to the Western Pacific are evident. However, although precursor signals are present during periods of weak memory, this feature is noticeably weakened. The variations in the S-EOF2 pattern imply that the precursor role of the A-AM in leading El Niño has reduced in the early 21st century. 5. Prediction skills and biases of the A-AM in NMME models In the preceding section, we identified spatial disparities in the dominant modes of the A-AM between the two periods. In this section, we will further evaluate whether differences exist in the prediction skills of the A-AM leading modes during these two epochs. We used the ACC and RMSE between the reconstructed principal components (RPCs) generated by projecting the model prediction field onto the observational S-EOF patterns and the observed PCs as metrics for prediction skills assessment. Both individual model and MME predictions are compared for the first two modes of the monsoon variability. Here we pay more attention to the results of MME. Figure 5 illustrates the variation of hindcast skills as a function of lead time. For the prediction of the first leading mode of the A-AM variability during 1982–1999, ACC skill scores remained above the 99% significance threshold before the lead time of 3.5 months, with RMSE less than 0.9, and the MME outperformed individual models. However, the ACC of MME skill scores only exceeded the 95% significance threshold within a lead time of 1.5 months during 2000–2017, and thereafter the prediction skills rapidly decreased, with RMSE greater than 0.9 across all lead times. If an ACC value of 0.5 is used as a standard of skillful predictions, we find that the MME forecast is skillful up to about 4.5-month lead time during 1982–1999, which is much longer than the skillful lead time of 2.5 months seen in 2000–2017. Additionally, the RMSE skill values are lower across all lead times compared to the period of weak memory. This indicates that the MME prediction skills for the leading modes of the A-AM are severely constrained during periods of weak memory. Figure 6 illustrates a more pronounced difference in the prediction performance of the S-EOF2. During 1982–1999, ACC skill scores remain stable and significant for lead times up to 9.5 months, with low RMSE (around 0.4–0.7), indicating an advantage in a lead time of 9.5 or 10.5 months. The temporal correlations between observed PCs and predicted RPCs remain around 0.9 until an 8.5-month lead time. In contrast, for the years 2000–2017, the performance of the individual model and the MME deteriorates, with ACC prediction skills ranging between 0.6 and 0.7 and RMSE exceeding 0.8 before lead times of 8.5 months. Comparatively, the prediction skills for the S-EOF2 during 1982–1999 are superior to that during 2000–2017. Accordingly, the capacity of NMME models to simulate the spatiotemporal characteristics of the first two main modes of A-AM is significantly stronger than that for the periods of weak memory. 6. Mechanism for the different prediction skills of the A-AM In this section, we will investigate the dominant factors to the causes of different prediction skills of the leading modes of the A-AM from two perspectives: (a) remote impact from tropical SST forcing and (b) local air-sea interaction processes, including the seasonal persistence of MC SSTA and the corresponding structure of atmospheric circulation during two periods. a. Teleconnections of SST in the tropical equatorial Pacific during two epochs To explain the spatial differences between the leading modes of A-AM in section 3 and the corresponding differences in prediction techniques in section 4, we will explore their mechanisms from the perspective of tropical SST signals. As an early signal, ENSO contributes to the better performance of multi-model prediction in capturing the spatiotemporal variations of the leading modes of the A-AM. (Wang et al. 2003 ). Figure 7 shows the seasonal mean SSTA regressed to PC1. It is evident that the warming of Niño3.4 is stronger before the year 2000 and the warming of the eastern equatorial Pacific and Indian Ocean persists until the following summer, enhancing the lagged relationship between A-AM and ENSO. This indicates a synchronization between the seasonal evolution of the S-EOF1 and the temporal transitions of ENSO, potentially leading to more pronounced abnormal Walker circulation. During the decaying phase of ENSO, El Niño events dissipated more rapidly during the period from 2000 to 2017. In MAM(1), the eastern equatorial Pacific SST has begun to transform into a cold anomaly, and the warm anomaly in the central and eastern equatorial Pacific completely disappears by the following summer. To evaluate whether there are differences in the ability of the NMME models to capture the seasonal evolution of tropical Pacific SST in years with strong and weak memory, leading to differences in predictions for the A-AM system. Figure 7 and Fig. 8 show the regression of the first two PCs and NMME pattern projection coefficients with increasing leading time onto tropical Pacific SSTAs for the two epochs. Here we focus on the results of the MME. It is observed that S-EOF1 of A-AM corresponds to the decaying phase of ENSO. During 1982–1999, warming in the eastern equatorial Pacific persists until MAM(1) or even JJA(1). The MME captures the decaying phase of ENSO well within a lead time of 1–3 months with a PCC of around 0.9. During 2000–2017, ENSO events decay rapidly, and cooling in the eastern Pacific begins from MAM(1), becoming a cold phase by JJA(1), weakening the lagged relationship between A-AM and ENSO. Moreover, compared to 1982–1999, the predictive skills are weaker, particularly at a lead time of 5 months when the skill becomes negative and essentially fails. It is worth noting that during the El Niño developing phase, MME exhibits a higher pattern correlation with the seasonal evolution of SSTAs. However, during the El Niño decaying phase (JJA(1)), the similarity to observations rapidly decreases, especially for weak memory epoch, indicating a significant impact of SST forcing on the accuracy of model predictions. The pattern of S-EOF2 mainly corresponds to the developing phase of ENSO (Fig. 8 ). During periods with a strong memory, warming amplitudes in the equatorial central-eastern Pacific are more intense, corresponding to a stronger westerly anomalies in S-EOF2 observed in JJA(1) and a precursor signal for ENSO. The relationship between ENSO and S-EOF2 has changed Remarkably. As depicted in Table 2 , during 1982–1999, the lagged correlation coefficient between the second principal component (PC2) and the Niño3.4 index was found to be 0.827, indicating a significant relationship at a confidence level of at least 99% according to a Pearson correlation test. In contrast, during 2000–2017, this lagged correlation coefficient decreased to only 0.33, which did not reach statistical significance at a confidence level of at least 95%. During the period from 1982 to 1999, the model simulation accurately captured the transition of the Eastern Pacific from a near-normal SST mode to warming during the ENSO developing phase (PCC around 0.92–0.99). From 2000 to 2017, the warming amplitude during the developing stages of ENSO was lower compared to that observed in the preceding period, and the MME overestimated the warming amplitude of the eastern equatorial Pacific (PCC around 0.58–0.85). When the MME struggles to accurately capture the evolution characteristics of tropical SST during the mature and decay phases of ENSO, it will correspondingly lead to a decline in the prediction skills of the A-AM. Table 2 Lead-lag correlations between Niño 3.4 and the first and second S-EOF mode principal components. (0) represents the reference year, and (1) represents the following year. The correlation coefficients that are statistically significant at a 95% confidence level are in boldface. PC1(0) PC2(0) 1982–1999 2000–2017 1982–1999 2000–2017 \(\:\text{Ni}\stackrel{\text{\sim}}{\text{n}}\text{o}\) -3.4 SSTA(0) 0.924 0.836 0.023 0.085 \(\:\text{Ni}\stackrel{\text{\sim}}{\text{n}}\text{o}\) -3.4 SSTA(1) -0.026 -0.103 0.827 0.331 To further compare the lead-lag correlation between the two leading modes of A-AM and ENSO in periods with strong and weak memory, Fig. 9 displays the lead-lag correlation coefficients between four PCs and seasonal (three-month mean) Nino3.4 index for the pre-2000 and post-2000 epochs. In the period 1982–1999, the highest positive correlation coefficient is in D(0)JF(1). The differences between the two epochs in the first mode are minor, both showing a strong positive correlation with Niño 3.4 SSTA. However, the lead-lag correlation coefficients significantly differ for the S-EOF2. During 1982–1999, there is a steady increase in the lead-lag correlations from JJA(-1) to MAM(2), with a maximum correlation of 0.8. This is consistent with the results of Wang et al. ( 2008 ). The lowest negative correlation (-0.31) lags El Niño by one year, marking the transition from La Niña to El Niño events. The overall lead-lag correlation coefficients are much higher during the strong memory period (1982–1999) compared to the weak memory period (2000–2017). In the next section, we aim to explain why the association between ENSO and A-AM is stronger during the strong memory epoch from the perspective of MC ocean memory. b. Influence of ocean memory changes in the MC region In Section 4, we have identified that the differences in predictability sources of the leading modes of the A-AM primarily arise from variations in the strength of the anomalous anticyclone in WNP during El Niño mature winter to the following spring. The WNPAC, as a crucial subsystem of the A-AM, serves as a critical link between El Niño in the Central-Eastern Pacific and the A-AM (Wang et al. 2000 , 2003 ). Therefore, we hypothesize that the interdecadal variability in the ocean memory of the MC alters the persistence of seasonal evolution of SSTA, destabilizing local air-sea interactions and impacting the forcing of WNPAC anomalies. The depth of the upper-ocean MLD plays a crucial role in the persistence of SST anomalies on seasonal to interannual time scales (Frankignoul and Hasselmann 1977 ; de Coёtlogon and Frankignoul 2003). In previous studies, it was found that the upper MLD is thickest in winter and thinnest in summer. Consequently, the strongest ocean memory is in winter and spring (Namias et al. 1988 ). Thermal anomalies stored in the winter mixed layer weaken during summer and become partially re-entrained into the mixed layer during the following winter. This suggests that the mean seasonal cycle of MLD can induce winter-to-winter memory or persistence of SST anomalies through a "re-emergence" mechanism (Namias and Born 1974 ; Alexander and Deser 1995 ; Alexander et al. 2000 ; Deser et al. 2003 ). Therefore, to investigate interdecadal variations in the influence of the annual cycle of MC SST persistence on the intensity of the anomalous WNPAC, and to identify the intrinsic memory of the local ocean by isolating the specific effects of local SST anomalies on the subsequent seasonal evolution of SST without the confounding influence of ENSO, we conducted a partial correlation analysis between the regional averaged MC SST index (MC SSTI) in D(0)JF(1) and local SSTA during D(0)JF(1) to JJA(1) after removing the effect of the concurrent Nino3.4 index, as shown in Fig. 10 . During the mature phase of ENSO, the MC SST exhibits a consistent cold SSTA mode. The cold SSTA in the period with strong memory is slightly stronger than that in the period with weak memory. Notably, the consistent cold SSTA in the MC region persists during the period with strong memory in MAM(1), whereas it cannot be sustained during the period with weak memory. In the subsequent seasons, the consistent cold SSTA in the MC region remains well-maintained in the period with strong memory, but the intensity of the cold SSTA is weaker compared to the previous period with weak memory. It is worth mentioning that the interdecadal variation of SSTAs in the MC region in MAM(1) is the most prominent among the four seasons. Therefore, in the subsequent discussion, we will focus on the analysis of spring when ENSO signals rapidly decline as a representative season, as the characteristics of other seasons are similar and will not be further elaborated. Our analysis suggests that the maintenance of the anomalous WNPAC is influenced by different modes of SST anomalies in the MC region during two periods. To examine this hypothesized mechanism, we conducted partial regression of the 500-hPa geopotential height and wind anomalies in MAM(1) with the MC SSTI during two distinct epochs (Fig. 11 ). Our analysis reveals that in the strong memory epoch, an anomalous anticyclone centered on the Philippine Sea is accompanied by the cold SSTA in the MC region, indicating that the Matsuno/Gill-type response, resulting from the abnormal atmospheric thermal forcing caused by the cold SSTA, strengthens the anticyclonic circulation. However, during the period of weak memory, the cold SSTA in the MC region during D(0)JF(1) cannot be sustained until the following MAM(1), leading to an insignificant anticyclone anomaly. This suggests that the passive response of the atmosphere to SST is weakened. To further elucidate the impact of the MC SSTA on the enhanced anomalous anticyclonic circulation, we performed partial regression analysis using the MC SSTI, to examine the velocity potential and divergent wind at both 850-hPa and 250-hPa levels (Fig. 12 ). Our finding indicates that the cold SSTA in the MC region is accompanied by convergence in the upper troposphere and divergence in the lower troposphere near the Philippine Sea. During the period of weak memory, when the cold SSTA weakens, the intensity of upper tropospheric convergence and lower tropospheric divergence also weakens, thereby diminishing its role in maintaining the WNPAC in MAM(1). Likewise, the spatial pattern of the partial regression field for precipitation anomalies during MAM(1) aligns with the partial correlation field of SSTA. To further validate the impact of the interdecadal variation of the MC SSTA pattern on the anomalous WNPAC, the LBM experiment is conducted to analyze its underlying mechanisms. Heating rates are estimated here by the precipitation anomalies with forcing prescribed over the area 122.5° -147.5°E, 2.5°S-17.5°N. The independent effect of MC SSTA on the intensity of WNPAC during years with strong and weak memory is compared. Figure 13 displays the steady response of the 500-hPa geopotential height field and winds. It is evident that the 31–45 days average results in the LBM experiment closely resemble the observed results. During periods of strong memory, a stronger anticyclonic circulation anomaly is observed near the Philippine Sea in the 500-hPa wind field, with significant positive geopotential height anomalies. This feature is greatly diminished and less pronounced during the later period. Additionally, it is found that the cold MC SSTA caused upper tropospheric convergence and lower tropospheric divergence in the Philippine Sea to its northwest through a Gill-type response (Fig. 14 a-c) (Gill 1980 ). Compared to the atmospheric circulation pattern during periods of weak memory (Fig. 14 d-e), this effect is more pronounced during periods with significant ocean memory. Therefore, since the 1980s, under the influence of memory changes, there have been interdecadal variations in the seasonal evolution of the MC SSTA and its driving effects on anomalous WNPAC. The physical mechanism underlying these variations is illustrated in Fig. 15 . During periods of strong memory, the cold SSTA in the MC region exhibits strong persistence from the decaying phase of El Niño, allowing abnormal atmospheric signals to be maintained for a longer duration. The convection restrained by the local cooling SSTA is robust, facilitating a stronger coupling between the anomalous WNPAC and the cold MC SSTA through a positive wind–evaporation–SST (WES) feedback (Wang et al. 2000 ) mechanism. Through the Gill-type response, the MC SSTA exerts a more prominent role in promoting the maintenance of the anomalous WNPAC, thereby reinforcing the A-AM system. On the other hand, the sustained development of anomalous WNPAC also helps to strengthen the connection between ENSO and A-AM systems, facilitating the transmission of SST signals in the central and eastern equatorial Pacific, thereby enhancing the predictive skills of the A-AM system. However, during periods of weak memory, the cold SSTA in the MC region during the mature phase of ENSO cannot be well sustained in the subsequent seasons due to decreased SST persistence, leading to a less significant independent effect on the WNP anomalous anticyclone. Consequently, the weakened intensity of the A-AM system and the diminished coupling relationship with ENSO result in lower prediction skills compared to periods of strong memory. 7. Conclusion and discussion Ocean memory within the MC regions has undergone a notable interdecadal shift in the early 21st century, characterized by a decline in the persistence of SSTAs, which has affected the prediction skills of the dominant modes of A-AM. Higher prediction skills of the leading modes of A-AM are typically associated with strong ocean memory. During periods of strong ocean memory, the prediction skills are significantly enhanced, while during periods of weak ocean memory, their skills tend to deteriorate. The key conclusions drawn from this study are summarized as follows: 1. Since the 1980s, accelerated ocean warming and increased upper ocean stratification have exacerbated mixed layer shallowing, reduced thermal inertia of the upper ocean, and led to interdecadal variations in ocean memory within the MC region. Using the defined memory index (the 1-year lag autocorrelation in a 21-year sliding window of annual mean SSTAs), a significant turning point was observed in the early 21st century, followed by a continuous decline in ocean memory. Consequently, to better understand the impact of varying ocean memory in the MC region on the prediction skills of A-AM, the period from 1982 to 1999 is categorized as a period of strong memory within the MC region, while the period from 2000 to 2017 is delineated as a period of weak memory. 2. Comparing hindcast outcomes from the NMME models at all lead times, it is indicated that the NMME models exhibited superior prediction skills of the leading modes of the A-AM during a strong memory epoch. For the S-EOF1, if an ACC value of 0.5 is used as a standard of skillful predictions, we find that the MME forecast is skillful up to about 4.5-month lead time during 1982-1999, which is much longer than the skillful lead time of 2.5 months seen in 2000-2017. Additionally, the RMSE skill scores are lower across all lead times compared to the period of weak memory. As for the S-EOF2, the MME forecasts during the strong memory epoch demonstrate the superior performance of the A-AM predictions at all lead times in terms of both ACC and RMSE skill scores. During the weak memory period, the ACC skill scores decreased by 27% and RMSE exceeded 0.8 before lead times of 8.5 months. Accordingly, the superior performance of NMME models to simulate the spatiotemporal characteristics during a strong memory epoch is evident for the leading modes of the A-AM, shedding light on the potential relationship between the A-AM prediction skills and the ocean memory in the MC region. 3. Through differences in the spatial patterns of the main modes of the A-AM from S-EOF analysis, we identified that the interdecadal disparities in sources of predictability of the leading mode between the two periods primarily stemmed from the intensity of the anomalous WNPAC and the tightness of the connection with ENSO. Changes in ocean memory have affected the seasonal persistence of SSTA in the MC region since the early 21st century. In the periods with a strong memory, MC SSTA exhibits strong seasonal continuity independent of ENSO, providing a more stable ocean boundary condition for persistent atmospheric anomalous circulation during the spring and summer seasons as the ENSO effect declines. Based on observational and LBM experimental evidence, it has been found that during a strong memory epoch, considering the significant ocean memory in the MC region, the seasonal continuity of the cold SSTA mode contributes to the maintenance and promotion of the anomalous WNPAC during the ENSO decay phase. The coupling between the anomalous WNPAC and the underlying cold SSTA is strengthened by pronounced positive WES feedback and stronger suppression of local convection. Consequently, the Gill-type response on the northwestern side of the MC region more effectively maintains anomalous anticyclones in the Northwest Pacific, enhancing the strength of the A-AM system during the monsoon year. This facilitates a better connection between ENSO and the A-AM system, thereby enhancing the transmission of equatorial Pacific SST signals to A-AM and improving the average prediction skills of the A-AM system throughout the entire monsoon year. Conversely, during periods of weak memory, the cold SSTA in the MC region fails to be well maintained due to the decreased seasonal persistence of MC SSTA, weakening the effect of local air-sea coupling. As a result, the strength of the A-AM system and its coupling with ENSO is weakened, leading to lower prediction skills of the A-AM compared to periods of strong memory. In summary, this study presents observed evidence and numerical experiment results to underscore the substantial influence of ocean memory in the MC region on the prediction proficiency of the A-AM, which has never been noticed before. In the case of interdecadal changes in memory, both the MC SSTA cooling and anomalous WNPAC persist longer in post-El Niño seasons during periods of strong memory, indicating that the positive feedback of these interannual ocean-atmosphere anomaly interactions is strengthened. The cold SSTA pattern exerts a more robust maintenance and promotion effect on the anomalous WNPAC via the Gill-type response, bolstering the overall strength of the A-AM system during the monsoon year. Consequently, the overall prediction skills of A-AM are improved. However, after the early 21st century, the decline in ocean memory in the MC region has weakened this impact, rendering it challenging for models to capture the spatiotemporal characteristics in the seasonal evolution of the A-AM. Therefore, it is imperative to identify more favorable predictive factors to address the monsoon prediction challenges brought about by the decline in ocean memory under the influence of global warming. Although the explanations for changes in ocean memory are given in the current study, the respective degree of contributions of natural variability and anthropogenic forcing to the observed changes in ocean memory remains uncertain. Future research will provide a more detailed explanation for observed interdecadal changes in ocean memory through diagnostic analysis. Additionally, the predictors of the A-AM and relevant physical mechanisms may also change. Will ocean memory continue to deteriorate or improve under future emission scenarios? How will changes in ocean memory affect the sources of predictability for the A-AM system? These questions remain open for future research. Declarations Acknowledgments This research was jointly supported by the National Natural Science Foundation of China (NSFC) Major Research Plan on West-Pacific Earth System Multi-spheric Interactions (Grant No. 92158203), the Ministry of Science and Technology of China (Grant No. 2023YFF0805100), and the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0102). Author contributions Zhiwei Wu and Simeng Han designed the study. The data collection, data analysis, and model designments were performed by Simeng Han. The first draft of the manuscript was written by Simeng Han and all authors reviewed the manuscript. Conflict of interest The authors declare no competing interests. Data available The NMME datasets used in this study are freely available from the IRI website (http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/). NCEP–NCAR Reanalysis data from their website at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. The NOAA Extended Reconstruction SSTs version 5 (ERSSTv5) is available at the website of https://climatedataguide.ucar.edu/climate-data/sst-data-noaa-extended-reconstruction-ssts-version. Ocean stratification data from their website at http://www.ocean.iap.ac.cn/pages/dataService/dataService.html?navAnchor=dataService. The GPCP precipitation dataset is available at https://psl.noaa.gov/data/gridded/data.gpcp.html. References Adler RF, et al (2003) The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979present). J Hydrometeor 4:1147–1167. https://doi.org/10.1175/ 1525-7541(2003)004,1147:TVGPCP.2.0.CO;2 Alexander, M. A., and C. 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J Climate 35: 7949–7964. https://doi.org/10.1175/JCLI-D-21-0783.1 Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in Climate Dynamics → Version 1 posted Editorial decision: Major Revision 25 Aug, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers invited by journal 16 Jul, 2024 Editor assigned by journal 15 Jul, 2024 First submitted to journal 08 Jul, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4708586","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327450429,"identity":"f54b0d2e-7f54-45c1-961a-98d8e96bea6d","order_by":0,"name":"Simeng han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACfvb+hw8+/rNhlmdvPkCcFsmeM8yGM9jS2A17jiUQp8XgRg6bNAfbYX6GGzkGRLrszNlj0gw8adKMM3I+3njDYCen20BAB2N7X7J1gYSNMTvP282WcxiSjc0OENDCzHPA8PYMg7RkxvbcbdI8DAcStxHSwiaRYCDNk3C4vuFAzjPitPBI5BhJ8xw4zMxwAhgORGmR4DmWbDizIY0ZGMjGlnMMiPCL/fHmgw8+NoCj8uGNNxV2cgS1oFlJbNQgaSFVxygYBaNgFIwIAACn00S3icoapgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-0761-0824","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Simeng","middleName":"","lastName":"han","suffix":""},{"id":327450430,"identity":"59c1cc7c-dfe4-401c-bd8c-00f8f35889d0","order_by":1,"name":"Zhiwei Wu","email":"","orcid":"https://orcid.org/0000-0002-8163-2215","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-07-09 02:33:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4708586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4708586/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00382-024-07487-6","type":"published","date":"2024-12-18T15:58:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62113318,"identity":"ecf0b4f0-d997-43a5-91c7-5bed6743b7cd","added_by":"auto","created_at":"2024-08-09 12:24:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":843304,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of correlation coefficients between Brunt–Väisälä frequency in the depth range of 0-400m and SST in MC from 1982 to 2018. Correlation coefficients exceeding the 5% significance level are indicated by black dots. The red box area represents the key areas of MC ocean memory changes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/8126134052c0a947fb65e3ad.png"},{"id":62112848,"identity":"cc1fbf54-9f56-41e6-96d7-eab1fabd2821","added_by":"auto","created_at":"2024-08-09 12:16:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161300,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual variation of ocean memory index in MC region.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/c44d37ce4ed978717d1cb8a0.png"},{"id":62112857,"identity":"b779f301-6241-488b-b6d0-7e4ca4fe9d09","added_by":"auto","created_at":"2024-08-09 12:16:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2740459,"visible":true,"origin":"","legend":"\u003cp\u003e(a)Seasonal evolution of spatial patterns for the first leading S-EOF mode of interannual variability of the A-AM System, including 850-hPa meridional wind, regressed zonal wind (wind Vectors, m/s), and regressed 500-hPa vertical (pressure) velocity anomalies (color shading, 10\u003csup\u003e-2\u003c/sup\u003ehPa\u003csup\u003e-1\u003c/sup\u003e). (b) As in (a), but for 2000-2017. Regression coefficients exceeding the 5% significance level are indicated by black dots. Only wind anomalies exceeding 5% significance level are shown.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/279fbbf0ed246b50385ac60d.png"},{"id":62112851,"identity":"384ded20-ee75-4c0f-9dc3-b570b732f124","added_by":"auto","created_at":"2024-08-09 12:16:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1860029,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Figure. 3 but for the second S-EOF mode.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/fd8ef3a1100d72986b3db9ab.png"},{"id":62112855,"identity":"abee1ddb-14da-4eaa-ba6b-ba7a7b7de387","added_by":"auto","created_at":"2024-08-09 12:16:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":633205,"visible":true,"origin":"","legend":"\u003cp\u003e(a) ACC and (b) RMSE skill between the observed and predicted PCs of the first S-EOF mode for 1982-1999. (c) and (d) Similar to (a) and (b), but for 2000-2017. The light gray dashed line represents the 95% significance level, and the dark gray dashed line represents the 99% significance level.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/7b89be8108e4a0976d6f3766.png"},{"id":62112850,"identity":"583bd9cc-c3f1-4eaf-b9ad-d73335cc7b9a","added_by":"auto","created_at":"2024-08-09 12:16:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":571064,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Figure. 5, but for the second S-EOF mode.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/414dc541dae2ba1e4d35f890.png"},{"id":62113319,"identity":"9a9e4e8c-7aee-4b72-8e29-6b45ac2ee724","added_by":"auto","created_at":"2024-08-09 12:24:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1058439,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of the first S-EOF eigenvector of seasonal SST obtained from NCEP, and NMME seasonal forecasts as a function of lead times of 1, 3, and 5 months, (a) for 1982-1999, (b) for 2000-2017.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/4f6634ecd38b95321e6a852a.png"},{"id":62114417,"identity":"293a1bf7-43e3-450b-9a58-c6d0880b0ae1","added_by":"auto","created_at":"2024-08-09 12:40:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":975287,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Figure. 7, but for the second S-EOF mode.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/a638694514e4e8b89f59bb86.png"},{"id":62112859,"identity":"da16a8fc-a1c9-4e27-b604-4b805be4448d","added_by":"auto","created_at":"2024-08-09 12:16:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":208993,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Lead-lag correlations between Niño 3.4 index and the first S-EOF mode principal component for 1982-1999 and 2000-2017. (b) Same as (a), but for the second S-EOF mode.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/aae3b738d615894565564a7e.png"},{"id":62113321,"identity":"64faf1f4-b58f-410a-92d4-6d3fd74de653","added_by":"auto","created_at":"2024-08-09 12:24:58","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1437669,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Partial correlation of SSTA in MC region from D(0)JF(1) to JJA(1) for the period 1982–1999 and (b) 2000-2017. Coefficients exceeding the 95% significance level are indicated by white dots.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/5969d4af143dffe3f1090e81.png"},{"id":62113324,"identity":"00e34744-59d0-4fd9-9844-fd745569a9e5","added_by":"auto","created_at":"2024-08-09 12:24:59","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":620740,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of the partial correlation fields of MAM(1) Z500 anomalies (shade, gpm) and 500-hPa wind anomalies (vectors, m/s) against the MC SSTI for the period 1982–1999 (a) and 2000-2017(b). (c) is the difference field between (a) and (b). Coefficients exceeding the 95% significance level are indicated by white dots.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/103eeffa4b82e3dd945788d2.png"},{"id":62113818,"identity":"9b70392d-c382-41e2-bf87-d029d086c8a1","added_by":"auto","created_at":"2024-08-09 12:32:58","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1471629,"visible":true,"origin":"","legend":"\u003cp\u003ePartial regression fields of MAM(1) 850-hPa velocity potential anomalies (color shading, mm/day) and divergent wind (vectors, m/s) against the MC SSTI for the period 1982–1999 (a) and 2000-2017(b). (c) is the difference field between (a) and (b). (d)–(f) As in (a)-(c), but for the 250-hPa level.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/5b57b0465c3b04b73c89e56e.png"},{"id":62112862,"identity":"8f69c9bc-4b46-4b0d-ab0e-9f40e8e1fe2c","added_by":"auto","created_at":"2024-08-09 12:16:59","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":501558,"visible":true,"origin":"","legend":"\u003cp\u003e(a)-(b) Responses of 500-hPa winds (vectors, m/s) and Z500 (shading, gpm) in two LBM experiments, (c) is the difference field between (a) and (b). (d)-(f) As in (a)-(c), but for the zonally averaged (115°-130°E) meridional overturning circulation.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/7ccd2dcd317df5a937f3fe82.png"},{"id":62112861,"identity":"c8c1153f-61b1-499e-a9f1-45068d20d006","added_by":"auto","created_at":"2024-08-09 12:16:59","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1285087,"visible":true,"origin":"","legend":"\u003cp\u003e(a)-(b) Responses of 850-hPa divergent wind (vectors, m/s) and velocity potential (shading, 106m2s−1) in the LBM experiment. (d)-(f) As in (a)-(c), but for 250-hPa.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/a1af35ba7ef15ac0537a0d22.png"},{"id":62113323,"identity":"20ccec76-7604-4242-924c-b998b1e70ce1","added_by":"auto","created_at":"2024-08-09 12:24:58","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":85384,"visible":true,"origin":"","legend":"\u003cp\u003eThe schematic diagram of the impact of interdecadal changes in MC ocean memory during MAM(1). The red and blue elliptical shadows represent the warm SSTA and cold SSTA, respectively. The blue oval dashed line represents the memory decline of MC SSTA. The blue arrow represents the Gill-type response. The black and gray arrows indicate the circulation cycle, while the gray indicates a weaker connection. The green elliptical arrow indicates anomalous WNPAC, while the light green ellipse and blue dashed arrow indicate an insignificant relationship between MC SSTA and anomalous WNPAC.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/e543b1cb097a1b07806e07db.png"},{"id":72202857,"identity":"466fb43b-37ef-4338-b5e9-f889f1826bac","added_by":"auto","created_at":"2024-12-23 16:16:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12162665,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4708586/v1/515b0e84-5af3-415c-a6df-38b29ffcd3e7.pdf"}],"financialInterests":"","formattedTitle":"Interdecadal Variability in Ocean Memory of the Maritime Continent and Its Effect on Asian-Australian Monsoon Prediction","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe predictability of the climate system largely originates from the slow evolution of the lower boundary forcing (Shukla \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The ocean, being the Earth's largest heat reservoir, can impact the atmosphere for months or even years due to the memory effect of its upper heat content. (Namias et al. 1970; Frankignoul and Hasselmann \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Deser et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, ocean memory serves as a critical link between oceanic anomalies and subsequent atmospheric responses, which is the basic premise of seasonal prediction in the tropics (McCartney \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Shukla \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e; Vidard et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For instance, the seasonal footprinting mechanism suggests that the extratropical ocean can store atmospheric circulation signals and may impact the tropics up to one year in advance (Vimont et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003a\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003eb\u003c/span\u003e). The coupled oceanic-atmospheric bridge and chain-coupled bridge proposed by Li et al. (2016, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also emphasize that the memory effect of oceanic bridge prolongs anomalous atmospheric signals into subsequent seasons.\u003c/p\u003e \u003cp\u003eTherefore, the memory nature of the ocean makes it possible to perform skillful seasonal predictions of anomalous climatic phenomena, makes it possible to perform skillful seasonal prediction for anomalous climatic phenomena (Wu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Desbruyeres et al. 2021; Zhang et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In most research, the role of ocean memory typically refers to the sustained impact of sea surface anomalies (SSTAs) on the atmospheric circulation system, serving as a crucial source of predictability for seasonal climate variability in many terrestrial regions (Smith et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Of note, the persistence of SST plays a pivotal role in accurately predicting monsoon variability (Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The persistent SST dipole pattern in the South Atlantic-Pacific during winter can serve as a \"charger\" to maintain the November\u0026ndash;December Southern Hemisphere annular mode signals, which is a precursor signal for seasonal prediction of the East Asian winter monsoon (EAWM) (Wu et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The tripole SSTAs in the North Pacific and the widespread negative SSTAs in the North Atlantic are considered important predictors of the intensity of the EAWM (Yu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The ocean memory effect also plays a crucial role in the prediction of the Asian summer monsoon. It allows the spring North Atlantic Oscillation (NAO)-induced tripole SST pattern in the North Atlantic to persist into summer, providing a physical basis for the seasonal prediction of the East Asian summer monsoon (EASM) (Wu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This persistence extends the lead time and enhances the accuracy of monsoon predictions. Statistical analyses suggest that the significant persistence of Indian Ocean SST prior to the onset of the EASM offers predictive potential of the monsoon (Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, Sahai et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) discovered that the behavior of the ensuing Indian summer monsoon precipitation (ISMR) can be predicted nine months in advance using global SST only, and the sustained SSTAs during the monsoon period significantly impact the effectiveness of seasonal prediction for ISMR (DAS et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, the ocean memory effects have been applied to predict cross-seasonal precipitation over the Arabian Peninsula and the extent of Antarctic sea ice, among other phenomena (Libera et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The atmospheric responses triggered by these precursor signals can persist for extended periods, thereby enhancing the predictive capability for regional climates.\u003c/p\u003e \u003cp\u003ePrevious studies have mainly focused on the impact of ocean memory on the prediction of individual monsoon regions. Nevertheless, the ocean memory effect on the Asian-Australian monsoon (A-AM), the largest monsoon region, has not been well documented and investigated. The A-AM constitutes a crucial component of the global atmospheric circulation (Wang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Many studies have investigated the links between A-AM and tropical SST variability, one of the major sources of seasonal predictability. The interannual variation of the A-AM is primarily characterized by the quasi-biennial oscillation of the troposphere (Meehl et al. 1987) and is closely linked to the phase changes of the El Ni\u0026ntilde;o-Southern Oscillation (ENSO) (Wang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, 2007, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Some studies emphasize that ENSO typically persists from JJA to DJF but loses its memory in springtime due to the abrupt development of the monsoon, leading to a decrease in the skill of ENSO-based forecasting techniques (Webster and Yang, 1992; Lau and Yang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Additionally, air-sea interaction in the western Pacific warm pool has been identified as a significant factor regulating A-AM variability and its seasonal predictability (Wang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wu and Kirtman \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In the absence of atmosphere-warm ocean interaction, atmospheric circulation models struggle to capture the changes in A-AM (Sperber et al. 1996; Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chowdary et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The MC region, situated within the western Pacific warm pool, serves as the pivotal \"crossroads\" of the A-AM system through cross-equatorial meridional airflow (Xie \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Moreover, as ocean memory is measured by the difference between the actual response and the response in steady balance with the instantaneous wind stress, the largest ocean memory is found in the MC region over the tropical Pacific (Neelin et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Dijkstra \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Robust interaction between tropical convection and air-sea coupling in the MC area can induce climate anomalies in the A-AM region through the Gill-type response and meridional propagation of Rossby waves (Gill \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Nitta 1987; Meehl \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Sardeshmukh and Hoskins \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Lau et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Hoerling and Kumar 2002; Moon and Ha \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Chang et al. 2005). Although MC SST is significantly influenced by ENSO, the ENSO-independent MC SSTA pattern can still significantly impact anomalous circulation in the A-AM region (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The variation in convective activity over the MC can trigger an East Asia\u0026ndash;Pacific/Pacific\u0026ndash;Japan (EAP/PJ)-like anomalous wave train after removing the ENSO and IOD signals, leading to significant positive precipitation anomaly over the Yangtze River valley (Xu et al. 2017). The above results provide important clues for predicting A-AM anomalies and associated summer precipitation anomalies in China.\u003c/p\u003e \u003cp\u003eHowever, owing to the shallowing of the mixed layer depth (MLD) in the context of global warming, the reduction in thermal inertia of the upper ocean has declined the oceanic memory effect (Shi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), especially in the MC region, shortening the lead time of skillful persistence-based predictions of sea surface thermal conditions (Luo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hervieux et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shi et al.2022). Despite the consensus on the reduced ocean memory in future warming scenarios, it remains unclear whether such changes have already become apparent in observations. Therefore, this study endeavors to address three questions: Has ocean memory changed in the MC region in the last few decades? Will these memory changes affect the prediction skills of the A-AM? What are the physical mechanisms causing this impact?\u003c/p\u003e \u003cp\u003eTo answer the above questions, the paper is organized as follows. Section 2 briefly describes the data and methods used in this study. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the criteria for dividing periods with strong and weak ocean memory. Section 4 analyzes interdecadal differences in the leading modes of the interannual variability of the A-AM during two periods. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e5\u003c/span\u003e contrasts the differences in prediction skills of the A-AM between the two periods. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e6\u003c/span\u003e delves into the physical mechanisms underlying the differences in prediction skills. Finally, a summary and discussion are provided in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e"},{"header":"2. Data and methodology","content":"\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea. Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe observed datasets utilized in this study include: (1) monthly mean SST data from the National Oceanic and Atmospheric Administration Extended Reconstructed SST version 5; (2) ocean stratification data from the Institute of Atmospheric Physic global ocean stratification gridded product; (3) the wind fields and the vertical pressure velocity field from the NCEP/DOE AMIP II Reanalysis; (4) monthly precipitation data provided by the Global Precision Climatology Project, v2.3 (Adler et al. \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe ensemble hindcasts (i.e., retrospective prediction experiments aimed at predicting past events) utilized in this study are obtained from the North American Multi-Model Ensemble (NMME) and cover the period from 1982 to 2018. The NMME is a collaborative initiative that integrates seasonal forecasting systems, incorporating coupled atmospheric-ocean-sea ice-land models from various modeling centers in the United States and Canada (Kirtman et al. 2014). We select four models from the NMME that provide wind field hindcasts. These models include CanCM4i, CanSIPS-IC3, CanSIPSv2, and GEM-NEMO. To calculate variable anomalies, we subtracted climatology based on hindcast data from individual models and computed ensemble averages for each model using uniform weights. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides detailed information on the period covered, ensemble size, and lead months for each of these models. In this research, lead time is defined as the number of months between the start time of prediction and the midpoint of the forecast month. For example, a forecast for January has a 0.5-month lead, for February a 1.5-month lead, and so forth.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed information of NMME climate models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnsemble size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLead times(months)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanCM4i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJan 1981-Dec 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u0026ndash;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanSIPS-IC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJan 1980-Dec 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u0026ndash;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanSIPSv2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJan 1981-Dec 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u0026ndash;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEM-NEMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJan 1981-Dec 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u0026ndash;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eb. Method\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe application of season-reliant Empirical Orthogonal Function (S-EOF) analysis allows for the extraction of the dominant mode of the A-AM, capturing its major spatiotemporal characteristics in seasonal evolution (Wang and An, 2005; Wang et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003eb). The monsoon year is defined from the summer of year 0 (referred to as June to August (0) [JJA(0)]) to the spring of the following year (March to May (1) [MAM(1)]) (Meehl, \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e; Yasunari, 1991), thus comprising four consecutive seasonal anomalies. This approach facilitates the characterization of the seasonal evolution patterns of A-AM in the monsoon year.\u003c/p\u003e\n\u003cp\u003eIn this study, we adopt the definition of ocean memory proposed by Hui et al. (2022) and define the 1-year autocorrelation of annual mean SSTAs as an indicator for evaluating ocean memory in the MC region. The SSTA anomalies are derived for a common reference period 1982\u0026ndash;2018. This indicator allows us to classify years with either strong or weak memory. Furthermore, we employ the time correlation coefficient (ACC) and root mean square error (RMSE) to evaluate the prediction skills of both the individual models and the multi-model ensemble (MME). The calculation formulas for these metrics are as follows:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}AC{\\text{C}}_{\\text{i}}=\\frac{{\\sum\\:}_{\\text{j}=1}^{\\text{N}}\\left({\\text{o}}_{\\text{i},\\text{j}}-\\stackrel{-}{{\\text{x}}_{\\text{i}}}\\right)\\left({\\text{f}}_{\\text{i},\\text{j}}-\\stackrel{-}{{\\text{f}}_{\\text{i}}}\\right)}{\\sqrt{{\\sum\\:}_{\\text{j}=1}^{\\text{N}}{\\left({\\text{o}}_{\\text{i},\\text{j}}-\\stackrel{-}{{\\text{o}}_{\\text{j}}}\\right)}^{2}}\\times\\:\\sqrt{{\\sum\\:}_{\\text{j}=1}^{\\text{N}}{\\left({\\text{f}}_{\\text{i},\\text{j}}-\\stackrel{-}{{\\text{f}}_{\\text{i}}}\\right)}^{2}}}\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}{\\text{R}\\text{M}\\text{S}\\text{E}}_{\\text{i}}=\\sqrt{\\frac{1}{\\text{N}}\\sum\\:_{\\text{j}=1}^{\\text{N}}{\\left({\\text{f}}_{\\text{j}}-{\\text{o}}_{\\text{j}}\\right)}^{2}}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere O is the verification field anomaly, and F is the forecast anomaly.\u003c/p\u003e\n\u003cp\u003eIn this study, to investigate the response of local winds over the WNP to anomalous MC adiabatic heating, numerical experiments are conducted using the linear baroclinic model (LBM) developed by Watanabe and Kimoto (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). The primitive equation model was linearized about a prescribed three-dimensional basic state to simulate the dry dynamical response to non-adiabatic heating. The model employed horizontal and vertical resolutions of T42 and 20 levels in the sigma-coordinate. Bi-harmonic diffusion was applied in the model, with an e-folding time of 1 hour for the maximum wave number and 1000 days for vertical diffusion (Sekizawa et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the average response integrated from day 31 to day 45 was considered as the steady response.\u003c/p\u003e"},{"header":"3. Division of periods with strong and weak ocean memory","content":"\u003cp\u003eIn recent decades, as increased greenhouse gas emissions amplify the Earth's radiative imbalance, the oceans take up about 93% of the extra heat in the climate system, which has intensified the rate of ocean warming (Li et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Johnson and Lyman \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Changes in ocean temperature and salinity have led to variations in ocean density, with a general trend toward increased stratification observed globally (Capotondi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meanwhile, the mixed layer in the northern part of the MC region and tropical western Pacific has shown a downward trend during 2006–2021 (Roch et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One potential cause is the increased freshwater flux, significantly decreasing density in the upper ocean, leading to a more stable mixed layer and enhanced stratification (Ke-xin and Fei \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This has exacerbated the differences between the upper-ocean mixed layer and the deep ocean, reducing the vertical mixing between surface and deep waters (Capotondi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consequently, as solar radiation primarily heats the surface layer, less heat is transferred to the deep ocean, leading to a more pronounced warming of the surface waters. Over the past four decades, the enhanced upper ocean stratification has played a predominant role in shallowing the MLD, thereby reducing the thermal inertia of the upper ocean and affecting ocean memory (Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sung et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nagano et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, based on the established conclusions above, to identify regions where ocean stratification changes significantly affect ocean memory and SST response, we analyzed the correlation between SST and upper ocean stratification at each grid point in the MC region from 1980 to 2018. By conducting sensitivity experiments controlled for data resolution, we identified the region with the strongest correlation between the surface ocean and the underlying water masses as the MC region (122.5° -147.5°E, 2.5°S-17.5°N) (red box in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in this study. Following the definition of ocean memory by Hui et al. (2022), we consider the year-to-year ocean memory index as the 1-year lag autocorrelation of the SSTAs in a 21-year sliding window. Note that different windows for running mean are also applied and the results are essentially the same. Figure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that in the early 21st century, the memory index of MC began to fall below the 95% significance threshold and exhibited a downward trend. This suggests that ocean memory in the MC region has been deteriorating since the early 21st century. Due to constraints of the length of hindcasts, we delineated the period from 1982 to 1999 as the epoch characterized by strong ocean memory and the period from 2000 to 2017 as the epoch marked by weak memory. Here we hypothesize that the prediction skills of the leading modes of A-AM may vary correspondingly between periods of strong and weak ocean memory. In the following section, preceding the comparison of prediction skills, we first examine the dominant modes of A-AM and their differences in spatial patterns during these two periods.\u003c/p\u003e "},{"header":"4. Interdecadal variation in the two leading modes of A-AM variability for periods with strong and weak memory","content":"\u003cp\u003eTo extract the leading modes of interannual variability of the A-AM under different memory conditions and analyze the differences in their predictability sources, season-reliant EOF (S-EOF) decomposition is performed using the 850-hPa meridional wind component as the primary variable because it is more reliable than a divergent circulation component (Wang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the spatial patterns of the first S-EOF mode (S-EOF1) during the monsoon year. It should be noted that the characteristic vectors were derived exclusively from the 850-hPa wind anomalies. The 500-hPa vertical velocity anomalies and the 850-hPa meridional wind anomalies were obtained using regression of the corresponding fields with reference to the first principal component (PC1). For the S-EOF1, the anomalous atmospheric circulation features include sinking motion over the MC and rising motion over the Philippine Sea during JJA (0). The easterly anomalies over the Indian Ocean strengthen from SON(0) to DJF(0/1) and then weaken in MAM(1). The tropical WNP anomalous anticyclone begins near the northern Philippines in SON(0), rapidly intensifies, and extends eastward into the following summer (JJA(1)). The anomalous circulation patterns for S-EOF1 are similar during the two epochs. However, there are differences in D(0)JF(1) and subsequent MAM(1), particularly in years of strong memory (1982–1999), where the anomalous western North Pacific anti-cyclone (WNPAC) is more persistent and stronger. This means that the first leading mode of A-AM exhibited stronger interannual variability with more typical years during 1982–1999, and the change of wind field can be more explained by the leading mode captured by S-EOF, which is conducive to prediction improvement. The associated easterly anomalies from Indonesia to the equatorial Indian Ocean during MAM(1) do not exhibit the rapid decay observed in periods of weak memory, implying a weaker ENSO-WNP relationship in recent decades. This viewpoint may diverge from the result of Wang et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) that the coupling between A-AM and ENSO has strengthened since the 1970s. This indicates that the relationship between A-AM and ENSO has not continued to strengthen since the early 21st century, which may not be conducive to accurate prediction of the dominant modes of A-AM.\u003c/p\u003e\u003cp\u003eThe seasonal evolution of the second S-EOF mode (S-EOF2) unveils notable interdecadal variations in circulation anomalies across the A-AM region. From 1982 to 1999, roughly one year prior to the El Niño mature phase, westerly anomalies were already established over the western equatorial Pacific, closely associated with cyclonic anomalies in the Philippine Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These anomalies gradually intensified over the subsequent three seasons, and as El Niño matures, these cyclonic anomalies transform into anticyclonic anomalies and reach their full development. During periods of strong memory, the cyclonic anomalies in the Philippine Sea and anomalous WNPAC are more pronounced. Additionally, robust westerly anomalies extending from the Arabian Sea and the MC to the Western Pacific are evident. However, although precursor signals are present during periods of weak memory, this feature is noticeably weakened. The variations in the S-EOF2 pattern imply that the precursor role of the A-AM in leading El Niño has reduced in the early 21st century.\u003c/p\u003e"},{"header":"5. Prediction skills and biases of the A-AM in NMME models","content":"\u003cp\u003eIn the preceding section, we identified spatial disparities in the dominant modes of the A-AM between the two periods. In this section, we will further evaluate whether differences exist in the prediction skills of the A-AM leading modes during these two epochs. We used the ACC and RMSE between the reconstructed principal components (RPCs) generated by projecting the model prediction field onto the observational S-EOF patterns and the observed PCs as metrics for prediction skills assessment. Both individual model and MME predictions are compared for the first two modes of the monsoon variability. Here we pay more attention to the results of MME. Figure\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the variation of hindcast skills as a function of lead time. For the prediction of the first leading mode of the A-AM variability during 1982\u0026ndash;1999, ACC skill scores remained above the 99% significance threshold before the lead time of 3.5 months, with RMSE less than 0.9, and the MME outperformed individual models. However, the ACC of MME skill scores only exceeded the 95% significance threshold within a lead time of 1.5 months during 2000\u0026ndash;2017, and thereafter the prediction skills rapidly decreased, with RMSE greater than 0.9 across all lead times. If an ACC value of 0.5 is used as a standard of skillful predictions, we find that the MME forecast is skillful up to about 4.5-month lead time during 1982\u0026ndash;1999, which is much longer than the skillful lead time of 2.5 months seen in 2000\u0026ndash;2017. Additionally, the RMSE skill values are lower across all lead times compared to the period of weak memory. This indicates that the MME prediction skills for the leading modes of the A-AM are severely constrained during periods of weak memory.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates a more pronounced difference in the prediction performance of the S-EOF2. During 1982\u0026ndash;1999, ACC skill scores remain stable and significant for lead times up to 9.5 months, with low RMSE (around 0.4\u0026ndash;0.7), indicating an advantage in a lead time of 9.5 or 10.5 months. The temporal correlations between observed PCs and predicted RPCs remain around 0.9 until an 8.5-month lead time. In contrast, for the years 2000\u0026ndash;2017, the performance of the individual model and the MME deteriorates, with ACC prediction skills ranging between 0.6 and 0.7 and RMSE exceeding 0.8 before lead times of 8.5 months. Comparatively, the prediction skills for the S-EOF2 during 1982\u0026ndash;1999 are superior to that during 2000\u0026ndash;2017. Accordingly, the capacity of NMME models to simulate the spatiotemporal characteristics of the first two main modes of A-AM is significantly stronger than that for the periods of weak memory.\u003c/p\u003e"},{"header":"6. Mechanism for the different prediction skills of the A-AM","content":"\u003cp\u003eIn this section, we will investigate the dominant factors to the causes of different prediction skills of the leading modes of the A-AM from two perspectives: (a) remote impact from tropical SST forcing and (b) local air-sea interaction processes, including the seasonal persistence of MC SSTA and the corresponding structure of atmospheric circulation during two periods.\u003c/p\u003e \u003cp\u003e \u003cem\u003ea. Teleconnections of SST in the tropical equatorial Pacific during two epochs\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo explain the spatial differences between the leading modes of A-AM in section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and the corresponding differences in prediction techniques in section 4, we will explore their mechanisms from the perspective of tropical SST signals. As an early signal, ENSO contributes to the better performance of multi-model prediction in capturing the spatiotemporal variations of the leading modes of the A-AM. (Wang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the seasonal mean SSTA regressed to PC1. It is evident that the warming of Ni\u0026ntilde;o3.4 is stronger before the year 2000 and the warming of the eastern equatorial Pacific and Indian Ocean persists until the following summer, enhancing the lagged relationship between A-AM and ENSO. This indicates a synchronization between the seasonal evolution of the S-EOF1 and the temporal transitions of ENSO, potentially leading to more pronounced abnormal Walker circulation. During the decaying phase of ENSO, El Ni\u0026ntilde;o events dissipated more rapidly during the period from 2000 to 2017. In MAM(1), the eastern equatorial Pacific SST has begun to transform into a cold anomaly, and the warm anomaly in the central and eastern equatorial Pacific completely disappears by the following summer.\u003c/p\u003e \u003cp\u003eTo evaluate whether there are differences in the ability of the NMME models to capture the seasonal evolution of tropical Pacific SST in years with strong and weak memory, leading to differences in predictions for the A-AM system. Figure\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e show the regression of the first two PCs and NMME pattern projection coefficients with increasing leading time onto tropical Pacific SSTAs for the two epochs. Here we focus on the results of the MME. It is observed that S-EOF1 of A-AM corresponds to the decaying phase of ENSO. During 1982\u0026ndash;1999, warming in the eastern equatorial Pacific persists until MAM(1) or even JJA(1). The MME captures the decaying phase of ENSO well within a lead time of 1\u0026ndash;3 months with a PCC of around 0.9. During 2000\u0026ndash;2017, ENSO events decay rapidly, and cooling in the eastern Pacific begins from MAM(1), becoming a cold phase by JJA(1), weakening the lagged relationship between A-AM and ENSO. Moreover, compared to 1982\u0026ndash;1999, the predictive skills are weaker, particularly at a lead time of 5 months when the skill becomes negative and essentially fails. It is worth noting that during the El Ni\u0026ntilde;o developing phase, MME exhibits a higher pattern correlation with the seasonal evolution of SSTAs. However, during the El Ni\u0026ntilde;o decaying phase (JJA(1)), the similarity to observations rapidly decreases, especially for weak memory epoch, indicating a significant impact of SST forcing on the accuracy of model predictions.\u003c/p\u003e \u003cp\u003eThe pattern of S-EOF2 mainly corresponds to the developing phase of ENSO (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). During periods with a strong memory, warming amplitudes in the equatorial central-eastern Pacific are more intense, corresponding to a stronger westerly anomalies in S-EOF2 observed in JJA(1) and a precursor signal for ENSO. The relationship between ENSO and S-EOF2 has changed Remarkably. As depicted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, during 1982\u0026ndash;1999, the lagged correlation coefficient between the second principal component (PC2) and the Ni\u0026ntilde;o3.4 index was found to be 0.827, indicating a significant relationship at a confidence level of at least 99% according to a Pearson correlation test. In contrast, during 2000\u0026ndash;2017, this lagged correlation coefficient decreased to only 0.33, which did not reach statistical significance at a confidence level of at least 95%. During the period from 1982 to 1999, the model simulation accurately captured the transition of the Eastern Pacific from a near-normal SST mode to warming during the ENSO developing phase (PCC around 0.92\u0026ndash;0.99). From 2000 to 2017, the warming amplitude during the developing stages of ENSO was lower compared to that observed in the preceding period, and the MME overestimated the warming amplitude of the eastern equatorial Pacific (PCC around 0.58\u0026ndash;0.85). When the MME struggles to accurately capture the evolution characteristics of tropical SST during the mature and decay phases of ENSO, it will correspondingly lead to a decline in the prediction skills of the A-AM.\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\u003eLead-lag correlations between Ni\u0026ntilde;o 3.4 and the first and second S-EOF mode principal components. (0) represents the reference year, and (1) represents the following year. The correlation coefficients that are statistically significant at a 95% confidence level are in boldface.\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=\"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 \u003cdiv align=\"left\" 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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePC1(0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePC2(0)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1982\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1982\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2000\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Ni}\\stackrel{\\text{\\sim}}{\\text{n}}\\text{o}\\)\u003c/span\u003e\u003c/span\u003e-3.4 SSTA(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.924\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.836\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Ni}\\stackrel{\\text{\\sim}}{\\text{n}}\\text{o}\\)\u003c/span\u003e\u003c/span\u003e-3.4 SSTA(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.827\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.331\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\u003eTo further compare the lead-lag correlation between the two leading modes of A-AM and ENSO in periods with strong and weak memory, Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays the lead-lag correlation coefficients between four PCs and seasonal (three-month mean) Nino3.4 index for the pre-2000 and post-2000 epochs. In the period 1982\u0026ndash;1999, the highest positive correlation coefficient is in D(0)JF(1). The differences between the two epochs in the first mode are minor, both showing a strong positive correlation with Ni\u0026ntilde;o 3.4 SSTA. However, the lead-lag correlation coefficients significantly differ for the S-EOF2. During 1982\u0026ndash;1999, there is a steady increase in the lead-lag correlations from JJA(-1) to MAM(2), with a maximum correlation of 0.8. This is consistent with the results of Wang et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The lowest negative correlation (-0.31) lags El Ni\u0026ntilde;o by one year, marking the transition from La Ni\u0026ntilde;a to El Ni\u0026ntilde;o events. The overall lead-lag correlation coefficients are much higher during the strong memory period (1982\u0026ndash;1999) compared to the weak memory period (2000\u0026ndash;2017). In the next section, we aim to explain why the association between ENSO and A-AM is stronger during the strong memory epoch from the perspective of MC ocean memory.\u003c/p\u003e \u003cp\u003e \u003cem\u003eb. Influence of ocean memory changes in the MC region\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn Section 4, we have identified that the differences in predictability sources of the leading modes of the A-AM primarily arise from variations in the strength of the anomalous anticyclone in WNP during El Ni\u0026ntilde;o mature winter to the following spring. The WNPAC, as a crucial subsystem of the A-AM, serves as a critical link between El Ni\u0026ntilde;o in the Central-Eastern Pacific and the A-AM (Wang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Therefore, we hypothesize that the interdecadal variability in the ocean memory of the MC alters the persistence of seasonal evolution of SSTA, destabilizing local air-sea interactions and impacting the forcing of WNPAC anomalies.\u003c/p\u003e \u003cp\u003eThe depth of the upper-ocean MLD plays a crucial role in the persistence of SST anomalies on seasonal to interannual time scales (Frankignoul and Hasselmann \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; de Coёtlogon and Frankignoul 2003). In previous studies, it was found that the upper MLD is thickest in winter and thinnest in summer. Consequently, the strongest ocean memory is in winter and spring (Namias et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Thermal anomalies stored in the winter mixed layer weaken during summer and become partially re-entrained into the mixed layer during the following winter. This suggests that the mean seasonal cycle of MLD can induce winter-to-winter memory or persistence of SST anomalies through a \"re-emergence\" mechanism (Namias and Born \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Alexander and Deser \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Alexander et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Deser et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Therefore, to investigate interdecadal variations in the influence of the annual cycle of MC SST persistence on the intensity of the anomalous WNPAC, and to identify the intrinsic memory of the local ocean by isolating the specific effects of local SST anomalies on the subsequent seasonal evolution of SST without the confounding influence of ENSO, we conducted a partial correlation analysis between the regional averaged MC SST index (MC SSTI) in D(0)JF(1) and local SSTA during D(0)JF(1) to JJA(1) after removing the effect of the concurrent Nino3.4 index, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e10\u003c/span\u003e. During the mature phase of ENSO, the MC SST exhibits a consistent cold SSTA mode. The cold SSTA in the period with strong memory is slightly stronger than that in the period with weak memory. Notably, the consistent cold SSTA in the MC region persists during the period with strong memory in MAM(1), whereas it cannot be sustained during the period with weak memory. In the subsequent seasons, the consistent cold SSTA in the MC region remains well-maintained in the period with strong memory, but the intensity of the cold SSTA is weaker compared to the previous period with weak memory. It is worth mentioning that the interdecadal variation of SSTAs in the MC region in MAM(1) is the most prominent among the four seasons. Therefore, in the subsequent discussion, we will focus on the analysis of spring when ENSO signals rapidly decline as a representative season, as the characteristics of other seasons are similar and will not be further elaborated.\u003c/p\u003e \u003cp\u003eOur analysis suggests that the maintenance of the anomalous WNPAC is influenced by different modes of SST anomalies in the MC region during two periods. To examine this hypothesized mechanism, we conducted partial regression of the 500-hPa geopotential height and wind anomalies in MAM(1) with the MC SSTI during two distinct epochs (Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Our analysis reveals that in the strong memory epoch, an anomalous anticyclone centered on the Philippine Sea is accompanied by the cold SSTA in the MC region, indicating that the Matsuno/Gill-type response, resulting from the abnormal atmospheric thermal forcing caused by the cold SSTA, strengthens the anticyclonic circulation. However, during the period of weak memory, the cold SSTA in the MC region during D(0)JF(1) cannot be sustained until the following MAM(1), leading to an insignificant anticyclone anomaly. This suggests that the passive response of the atmosphere to SST is weakened. To further elucidate the impact of the MC SSTA on the enhanced anomalous anticyclonic circulation, we performed partial regression analysis using the MC SSTI, to examine the velocity potential and divergent wind at both 850-hPa and 250-hPa levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Our finding indicates that the cold SSTA in the MC region is accompanied by convergence in the upper troposphere and divergence in the lower troposphere near the Philippine Sea. During the period of weak memory, when the cold SSTA weakens, the intensity of upper tropospheric convergence and lower tropospheric divergence also weakens, thereby diminishing its role in maintaining the WNPAC in MAM(1). Likewise, the spatial pattern of the partial regression field for precipitation anomalies during MAM(1) aligns with the partial correlation field of SSTA.\u003c/p\u003e \u003cp\u003eTo further validate the impact of the interdecadal variation of the MC SSTA pattern on the anomalous WNPAC, the LBM experiment is conducted to analyze its underlying mechanisms. Heating rates are estimated here by the precipitation anomalies with forcing prescribed over the area 122.5\u0026deg; -147.5\u0026deg;E, 2.5\u0026deg;S-17.5\u0026deg;N. The independent effect of MC SSTA on the intensity of WNPAC during years with strong and weak memory is compared. Figure\u0026nbsp;\u003cspan refid=\"Fig24\" class=\"InternalRef\"\u003e13\u003c/span\u003e displays the steady response of the 500-hPa geopotential height field and winds. It is evident that the 31\u0026ndash;45 days average results in the LBM experiment closely resemble the observed results. During periods of strong memory, a stronger anticyclonic circulation anomaly is observed near the Philippine Sea in the 500-hPa wind field, with significant positive geopotential height anomalies. This feature is greatly diminished and less pronounced during the later period. Additionally, it is found that the cold MC SSTA caused upper tropospheric convergence and lower tropospheric divergence in the Philippine Sea to its northwest through a Gill-type response (Fig.\u0026nbsp;\u003cspan refid=\"Fig25\" class=\"InternalRef\"\u003e14\u003c/span\u003ea-c) (Gill \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Compared to the atmospheric circulation pattern during periods of weak memory (Fig.\u0026nbsp;\u003cspan refid=\"Fig25\" class=\"InternalRef\"\u003e14\u003c/span\u003ed-e), this effect is more pronounced during periods with significant ocean memory.\u003c/p\u003e \u003cp\u003eTherefore, since the 1980s, under the influence of memory changes, there have been interdecadal variations in the seasonal evolution of the MC SSTA and its driving effects on anomalous WNPAC. The physical mechanism underlying these variations is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig26\" class=\"InternalRef\"\u003e15\u003c/span\u003e. During periods of strong memory, the cold SSTA in the MC region exhibits strong persistence from the decaying phase of El Ni\u0026ntilde;o, allowing abnormal atmospheric signals to be maintained for a longer duration. The convection restrained by the local cooling SSTA is robust, facilitating a stronger coupling between the anomalous WNPAC and the cold MC SSTA through a positive wind\u0026ndash;evaporation\u0026ndash;SST (WES) feedback (Wang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) mechanism. Through the Gill-type response, the MC SSTA exerts a more prominent role in promoting the maintenance of the anomalous WNPAC, thereby reinforcing the A-AM system. On the other hand, the sustained development of anomalous WNPAC also helps to strengthen the connection between ENSO and A-AM systems, facilitating the transmission of SST signals in the central and eastern equatorial Pacific, thereby enhancing the predictive skills of the A-AM system. However, during periods of weak memory, the cold SSTA in the MC region during the mature phase of ENSO cannot be well sustained in the subsequent seasons due to decreased SST persistence, leading to a less significant independent effect on the WNP anomalous anticyclone. Consequently, the weakened intensity of the A-AM system and the diminished coupling relationship with ENSO result in lower prediction skills compared to periods of strong memory.\u003c/p\u003e"},{"header":"7. Conclusion and discussion","content":"\u003cp\u003eOcean memory within the MC regions has undergone a notable interdecadal shift in the early 21st century, characterized by a decline in the persistence of SSTAs, which has affected the prediction skills of the dominant modes of A-AM. Higher prediction skills of the leading modes of A-AM are typically associated with strong ocean memory. During periods of strong ocean memory, the prediction skills are significantly enhanced, while during periods of weak ocean memory, their skills tend to deteriorate. The key conclusions drawn from this study are summarized as follows:\u003c/p\u003e\n\u003cp\u003e1. Since the 1980s, accelerated ocean warming and increased upper ocean stratification have exacerbated mixed layer shallowing, reduced thermal inertia of the upper ocean, and led to interdecadal variations in ocean memory within the MC region. Using the defined memory index (the 1-year lag autocorrelation in a 21-year sliding window of annual mean SSTAs), a significant turning point was observed in the early 21st century, followed by a continuous decline in ocean memory. Consequently, to better understand the impact of varying ocean memory in the MC region on the prediction skills of A-AM, the period from 1982 to 1999 is categorized as a period of strong memory within the MC region, while the period from 2000 to 2017 is delineated as a period of weak memory.\u003c/p\u003e\n\u003cp\u003e2. Comparing hindcast outcomes from the NMME models at all lead times, it is indicated that the NMME models exhibited superior prediction skills of the leading modes of the A-AM during a strong memory epoch. For the S-EOF1, if an ACC value of 0.5 is used as a standard of skillful predictions, we find that the MME forecast is skillful up to about 4.5-month lead time during 1982-1999, which is much longer than the skillful lead time of 2.5 months seen in 2000-2017. Additionally, the RMSE skill scores are lower across all lead times compared to the period of weak memory. As for the S-EOF2, the MME forecasts during the strong memory epoch demonstrate the superior performance of the A-AM predictions at all lead times in terms of both ACC and RMSE skill scores. During the weak memory period, the ACC skill scores decreased by 27% and RMSE exceeded 0.8 before lead times of 8.5 months. Accordingly, the superior performance of NMME models to simulate the spatiotemporal characteristics during a strong memory epoch is evident for the leading modes of the A-AM, shedding light on the potential relationship between the A-AM prediction skills and the ocean memory in the MC region.\u003c/p\u003e\n\u003cp\u003e3. Through differences in the spatial patterns of the main modes of the A-AM from S-EOF analysis, we identified that the interdecadal disparities in sources of predictability of the leading mode between the two periods primarily stemmed from the intensity of the anomalous WNPAC and the tightness of the connection with ENSO. Changes in ocean memory have affected the seasonal persistence of SSTA in the MC region since the early 21st century. In the periods with a strong memory, MC SSTA exhibits strong seasonal continuity independent of ENSO, providing a more stable ocean boundary condition for persistent atmospheric anomalous circulation during the spring and summer seasons as the ENSO effect declines. Based on observational and LBM experimental evidence, it has been found that during a strong memory epoch, considering the significant ocean memory in the MC region, the seasonal continuity of the cold SSTA mode contributes to the maintenance and promotion of the anomalous WNPAC during the ENSO decay phase. The coupling between the anomalous WNPAC and the underlying cold SSTA is strengthened by pronounced positive WES feedback and stronger suppression of local convection. Consequently, the Gill-type response on the northwestern side of the MC region more effectively maintains anomalous anticyclones in the Northwest Pacific, enhancing the strength of the A-AM system during the monsoon year. This facilitates a better connection between ENSO and the A-AM system, thereby enhancing the transmission of equatorial Pacific SST signals to A-AM and improving the average prediction skills of the A-AM system throughout the entire monsoon year. Conversely, during periods of weak memory, the cold SSTA in the MC region fails to be well maintained due to the decreased seasonal persistence of MC SSTA, weakening the effect of local air-sea coupling. As a result, the strength of the A-AM system and its coupling with ENSO is weakened, leading to lower prediction skills of the A-AM compared to periods of strong memory.\u003c/p\u003e\n\u003cp\u003eIn summary, this study presents observed evidence and numerical experiment results to underscore the substantial influence of ocean memory in the MC region on the prediction proficiency of the A-AM, which has never been noticed before. In the case of interdecadal changes in memory, both the MC SSTA cooling and anomalous WNPAC persist longer in post-El Ni\u0026ntilde;o seasons during periods of strong memory, indicating that the positive feedback of these interannual ocean-atmosphere anomaly interactions is strengthened. The cold SSTA pattern exerts a more robust maintenance and promotion effect on the anomalous WNPAC via the Gill-type response, bolstering the overall strength of the A-AM system during the monsoon year. Consequently, the overall prediction skills of A-AM are improved. However, after the early 21st century, the decline in ocean memory in the MC region has weakened this impact, rendering it challenging for models to capture the spatiotemporal characteristics in the seasonal evolution of the A-AM. Therefore, it is imperative to identify more favorable predictive factors to address the monsoon prediction challenges brought about by the decline in ocean memory under the influence of global warming.\u003c/p\u003e\n\u003cp\u003eAlthough the explanations for changes in ocean memory are given in the current study, the respective degree of contributions of natural variability and anthropogenic forcing to the observed changes in ocean memory remains uncertain. Future research will provide a more detailed explanation for observed interdecadal changes in ocean memory through diagnostic analysis. Additionally, the predictors of the A-AM and relevant physical mechanisms may also change. Will ocean memory continue to deteriorate or improve under future emission scenarios? How will changes in ocean memory affect the sources of predictability for the A-AM system? These questions remain open for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis research was jointly supported by the National Natural Science Foundation of China (NSFC) Major Research Plan on West-Pacific Earth System Multi-spheric Interactions (Grant No. 92158203), the Ministry of Science and Technology of China (Grant No. 2023YFF0805100), and the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0102).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eZhiwei Wu and\u0026nbsp;Simeng\u0026nbsp;Han\u0026nbsp;designed the study. The data collection, data analysis, and model designments were performed by\u0026nbsp;Simeng\u0026nbsp;Han. The first draft of the manuscript was written by\u0026nbsp;Simeng\u0026nbsp;Han\u0026nbsp;and all authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData available\u0026nbsp;\u003c/strong\u003eThe NMME datasets used in this study are freely available from the IRI website (http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/). NCEP\u0026ndash;NCAR Reanalysis data from their website at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. The NOAA Extended Reconstruction SSTs version 5 (ERSSTv5) is available at the website of https://climatedataguide.ucar.edu/climate-data/sst-data-noaa-extended-reconstruction-ssts-version. Ocean stratification data from their website at http://www.ocean.iap.ac.cn/pages/dataService/dataService.html?navAnchor=dataService. The GPCP precipitation dataset is available at https://psl.noaa.gov/data/gridded/data.gpcp.html.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdler RF, et al (2003) The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979present). J Hydrometeor 4:1147\u0026ndash;1167. https://doi.org/10.1175/ 1525-7541(2003)004,1147:TVGPCP.2.0.CO;2\u003c/li\u003e\n\u003cli\u003eAlexander, M. A., and C. Deser (1995) A Mechanism for the Recurrence of Wintertime Midlatitude SST Anomalies. J Phys Oceanogr 25: 122\u0026ndash;137. https://doi.org/10.1175/1520-0485(1995)025\u0026lt;0122:AMFTRO\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003cli\u003eAlexander MA, Scott JD, Deser C (2000) Processes that influence sea surface temperature and ocean mixed layer depth variability in a coupled model. 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Environ Res Lett 17:124012.https://doi.org/10.1088/1748-9326/aca2c0\u003c/li\u003e\n\u003cli\u003eZhu, J., Z. Guan, and X. Wang (2022) Variations of Summertime SSTA Independent of ENSO in the Maritime Continent and Their Possible Impacts on Rainfall in the Asian\u0026ndash;Australian Monsoon Region. J Climate 35: 7949\u0026ndash;7964. https://doi.org/10.1175/JCLI-D-21-0783.1\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ocean memory, Asian-Australian monsoon, Maritime continent, Multi-model ensemble hindcasts, Predictability","lastPublishedDoi":"10.21203/rs.3.rs-4708586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4708586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOcean memory is crucial for improving climate models and enhancing the accuracy of climate predictions. However, due to the changes in ocean memory over the past few decades, its impact on monsoon predictions remains unclear. The persistence of sea surface temperature (SST) anomalies, as a key indicator of ocean memory, can regulate the local air-sea coupling processes affecting the Asian-Australian monsoon (A-AM), thereby significantly influencing climate predictions for Asia, Australia, and the entire Indo-Pacific region. Based on observational and numerical modeling evidence, the study finds that within the context of interdecadal variation in ocean memory, the seasonal persistence of Maritime Continent (MC) SST anomalies is more pronounced during the strong memory epoch (1982\u0026ndash;1999), sustaining the anomalous western North Pacific anti-cyclone (WNPAC) through a stronger Matsuno-Gill response during the decaying phase of El Ni\u0026ntilde;o-Southern Oscillation (ENSO), thereby enhancing the overall strength of the A-AM system during the monsoon year. Additionally, the connection between ENSO and the A-AM is strengthened. By contrast, these air-sea coupling processes have weakened during the weak memory epoch (2000\u0026ndash;2017), making it more difficult to capture the characteristics of the A-AM. The early 21st-century decline in MC ocean memory reduced the prediction skills of the leading mode of the A-AM. Above all, this study emphasizes the significant impact of ocean memory on monsoon prediction skills, providing new insight into seeking more reliable sources of predictability for the A-AM.\u003c/p\u003e","manuscriptTitle":"Interdecadal Variability in Ocean Memory of the Maritime Continent and Its Effect on Asian-Australian Monsoon Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 12:16:53","doi":"10.21203/rs.3.rs-4708586/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-08-25T23:35:42+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-16T06:15:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-16T04:00:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T14:36:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2024-07-08T22:32:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e198b157-532c-4f6b-a657-0f1e9594133c","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:13:09+00:00","versionOfRecord":{"articleIdentity":"rs-4708586","link":"https://doi.org/10.1007/s00382-024-07487-6","journal":{"identity":"climate-dynamics","isVorOnly":false,"title":"Climate Dynamics"},"publishedOn":"2024-12-18 15:58:35","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-08-09 12:16:53","video":"","vorDoi":"10.1007/s00382-024-07487-6","vorDoiUrl":"https://doi.org/10.1007/s00382-024-07487-6","workflowStages":[]},"version":"v1","identity":"rs-4708586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4708586","identity":"rs-4708586","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}