CMIP6 Model Evaluation for Wind Speed Responses to IOD during Monsoon Season over the Indian Ocean

CMIP6 Model Evaluation for Wind Speed Responses to IOD during Monsoon Season over the Indian Ocean | 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 CMIP6 Model Evaluation for Wind Speed Responses to IOD during Monsoon Season over the Indian Ocean Ramakant Prasad, Prashant Kumar, Anshu Yadav, Anurag Singh, Divya Sardana, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4275922/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Indian Ocean Dipole (IOD) exerts a substantial influence over wind speed (WS) in the Tropical Indian Ocean (TIO), yet climate model's potential to replicate the observed IOD impact on WS has not been estimated. This study assesses 24 CMIP6 models to determine their ability to replicate the impact of the Indian Ocean Dipole (IOD) on wind speed (WS) in the Tropical Indian Ocean (TIO) during the JJA season from 1958 to 2014. The observation data for WS is obtained from fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5). In the CMIP6 models, IOD portrays a crucial role to simulate WS across the tropical Indian Ocean. The efficacy of models is computed based on three skill metric criteria such as interannual variability score (IVS), M-Score, and root mean square error (RMSE). A total rank has been evaluated based on the three-skill metrics. The top ten best performing models are CESM2, EC-Earth3, ACCESS-ESM1-5, EC-Earth3-Veg-LR, MMM, NorESM2-LM, CESM2-WACCM, EC-Earth3-Veg, MPI-ESM1-2-HR, and FIO-ESM2-0. Despite this, significant biases are found in the CMIP6 models, indicating a moderate overall ability to capture WS responses to IOD over the Indian Ocean. CMIP6 Wind Speed Indian Ocean IOD pIOD nIOD Model evaluation NIO Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Wind Speed (WS) and its patterns are crucial in the North Indian Ocean (NIO), particularly due to its significant association with the Indian monsoon. Additionally, WS and its patterns are immensely associated with Indian Ocean Dipole (IOD) which is a vital climate driver in the Tropical Indian Ocean (TIO). Positive IOD (pIOD) instigates cooler SSTs over the tropical southeastern Indian Ocean bounded between 90 o E-110 o E, 10 o S-equator and warmer over the tropical western Indian Ocean bounded between 50 o E-70 o E, 10 o S-10 o N. Positive phase of IOD (pIOD) is connected with higher rainfall and negative phase of IOD (nIOD) is associated with lesser rainfall in India (McKenna et al. 2020 ). In CMIP5 and CMIP6 climate models, there is an uncertainty of wind pattern as it is highly influenced by IOD. It is a major challenge to replicate IOD in these models as it is affected by internal forcings as well as external forcings. Therefore, it is essential to evaluate CMIP6 models to understand the wind speed responses to IOD. The relentless increase in WS over the globe is contemplated to have disastrous effects (Young et al., 2011 , 2012 ; Zheng et al.,2016; Young and Ribal, 2019 ; Lyddon et al. 2019 ; Deng et al. 2021 ), which emphasizes to have a better insight of WS so that socio-economic calamities could be averted. Wind speed (WS) is a primary oceanic variable of interest (Yu et al. 2020 ; Kusuru et al. 2022 ) that yields better comprehension of numerous characteristics of ocean dynamics, such as the ocean’s response to tropical cyclones, wind energy, wave height, wind direction, air-sea fluxes, and ocean currents (Pasquero et al. 2020; Wang et al. 2021 ; Ye et al. 2020 ; Kusuru et al. 2022 ). These are the essential components in anticipating and alleviating the repercussions of climate change, which is one of the largest global challenges confronted by society nowadays, as the ocean is the major guiding force in controlling the climate of the Earth (Bigg 2003 ; Benestad 2006; Barry and Chorley 2009 ). WS plays a pivotal role in determining the sea level rise (Andrée et al. 2022 ), and it is of great concern to engineers, scientists, climate modelers, and the Government due to its catastrophic effect on the inhabitants along the shoreline and their economic activities, coastal layouts, marine ecosystems and onshore environments (Kumar et al. 2021; Li et al. 2022 ; Magnan et al. 2022 ; Chen et al. 2023 ; Roy et al. 2023 ). The downtrend of surface wind speed is observed in Australia utilizing data measured by terrestrial anemometers from 1975 to 2006 (Mc Vicar et al. 2008). The slowdown of WS is visualized in Canada for the period 1953–2006 using data measured at nonstandard anemometer (Wan et al. 2010 ). The declining trend of WS is noticed in Turkey during 1975–2006 using weather stations data (Dadaser-Celik and Cengiz, 2014 ). The downward trend in WS is noted in USA using National Climate Data Centre datasets from 1973–2000 (Pryor et al. 2009). The decreasing trend of WS in Spain and Portugal is visualized using data from land-based stations from 1961–2011 (Azorin-Molina et al. 2014 ). The slowdown in WS is shown in Netherlands using 101 years data from five dutch stations (Cusack 2013 ). The slowdon in WS is observed in India on at least eight sites out of eleven sites chosen using data from India Meteorological Department (Pune) (Jhajharia et al. 2009 ). The slowdown in WS is noticed in China for the different periods(Guo et al. 2011; Lin et al. 2013; Chen et al. 2013 ; Wu et al. 2017 ). The decline in WS is observed in South Korea using ground-observed daily data from Korean Meteorological Administration during 1954–2013 (Kim and Paik 2015 ). The TIO displays coupled ocean-atmosphere interactions yielding interannual climate variability known as Indian Ocean Dipole (IOD) which is similar to ENSO to a certain degree (Saji et al. 1999). IOD has strong influences on the lives of millions residing along Indian-Ocean rim countries (Saji and Yamagata 2003 ). pIOD events are forecasted to increase three times in twenty first century (one event in 6.3 years) in comparison to twentieth century (one event in 17.3 years) due to excessive greenhouse gas emissions (Cai et al. 2014 ). It has strong association with Indian summer monsoon precipitation (McKenna et al. 2020 ). During pIOD and nIOD (independent of ENSO), SST anomalies in the eastern part of IO are due to wind anomalies, which subsequently affect oceanic dynamics and cause anomalies in western part of IO (Hong et al. 2008 ). Variations in ocean wave height during monsoon season over BoB and AS are associated with changes in WS which is primarily affected by IOD (Kumar et al. 2019 ). During IOD events, large changes in the zonal component of surface wind field over TIO are visualized and utmost changes in zonal wind are observed over equatorial central and eastern IO, where correlation with DMI is found greater than 0.6 (Saji et al. 1999). The correlation coefficients between significant wave height (SWH) anomalies and DMI are weaker at most places of NIO and SWH anomalies attain maximum in the same season as DMI in all types of IOD events. In the eastern equatorial NIO, IOD events affects the SWH through the wind changes (Fu et al. 2018 ). Several studies have explored the historical trends of WS in CMIP5 and CMIP6 models (Krishnan and Bhaskaran 2020 ; Shen et al. 2021 ; Shen et al. 2022 ; Miao et al. 2023 ; Li et al. 2024 ). Utilizing data from CMIP6 simulations, it is observed that the global annual mean of near surface wind speed increased during 1850–1967 and decreased for the period 1968–2014(Shen et al. 2021 ). Using model simulations from CMIP6 and reanalysis data, it is found that the recent trends of annual mean near-surface wind speed (10m) is decreasing in Northern Hemisphere and increasing in the Southern Hemisphere during 1980–2010 and opposite trend is observed for the period 2010 to 2019 (Deng et al. 2021 ). Using CMIP6 models simulations and ERA5 reanalysis datasets for the period 1981–2010, it is analyzed that WS is strengthened during summer and winter season in South China Sea whereas in the East China Sea, WS is increased in summer and decreased in winter (Deng et al. 2024 ). Global mean of near-surface wind speed has shown declining trend (Vautard et al. 2012 ; Tobin et al. 2014 ; Dunn et al. 2016 ; Azorin-Molina et al. 2017 ; Zha et al. 2020 ). The global surface wind speed has shown an increasing trend using Cross-Calibrated, Multi-Platform (CCMP) data for the period 1988 to 2011 where rate of increase is maximum during 1991–2007 (Zheng et al. 2016 ). Studies have been conducted to interpret trends of WS utilizing CMIP5 and CMIP6 models (Taylor et al. 2012 ; Eyring et al. 2016 a). Using WS data simulated from CMIP6 models, global near-surface wind speed is expected to decrease during 2021–2100 (Shen et al. 2022 ). WS data simulated from CMIP6 models reveals an upward trend in northern BoB and a downward trend in southern BoB during 2026–2100 (Krishnan and Bhaskaran 2020 ). In the middle and late twenty-first century, substantial decrease in WS averaged over China in winter and annual is anticipated whereas opposite trend is contemplated in summer using wind data simulated from CMIP 6 models (Wu et al. 2020 ). There has been an increasing trend of WS in the Caribbean and during the twenty-first century, it is expected to increase at faster rate using CMIP6 simulations data of WS (Bustos-Usta and Torres-Parra 2023). Nevertheless, in the literature, CMIP6 model evaluation on the basis of WS response to IOD has not been analyzed yet. Therefore, it is imperative to examine the CMIP6 models’ performance based on the IOD amplitude associated with WS. It can provide a deep insight to understand the WS change associated with IOD in CMIP6 models. This study investigates the efficacy of 24 CMIP6 models to replicate the observed IOD associations with WS for the period 1958 to 2014 during the monsoon season. Linear regression analysis has been carried out to fetch individual model’s WS response to IOD and analyze the outcomes with the observation datasets. In addition, an assessment is done on the basis of three distinct skill metric parameters. In totality, the paper is organized as follows. Section 2 details on the data and methodology. Section-3 consists of the climatology, variability, and outcomes of the CMIP6 model finding for the Indian Ocean. Section 4 elaborates the discussion and conclusion. 2. Data and Methodology 2.1 Data Table 1 presents 24 CMIP6 historical models utilized in this study to analyze their efficiency. The analysis of WS over the Indian Ocean is based on the output of models during the JJA season. CMIP datasets are developed by the Earth System Grid Federation (ESGF), and dataset storage is hosted by various partners of ESGF (Williams et al. 2015). Monthly historical model datasets are utilized from the CMIP6 archive from 1958 to 2014, which are available at URL https://esgf-node.llnl.gov/projects/cmip6/ . For CMIP6 analysis, the study utilized datasets from the first ensemble member-run of variant label “r1i1p1f1”, where r1 representsrealization index, i1 initialization index, p1 physics index, and f1 forcing index (Taylor et al. 2018 ; Tian et al. 2020). The CMIP6 models are interpolated to a common 1° × 1° grid to make analysis simple as models display dissimilar horizontal resolutions. Bilinear interpolation has been used. The original resolution is provided in Table 1 . Table 1 24 CMIP6 models used in this study Model Number Model Name Country Resolution (lon by lat) Ocean Component Atmosphere Component 1. ACCESS-CM2 Australia 360 × 300 ACCESS-OM2 MetUM-HadGEM3-GA7.1 2. ACCESS-ESM1-5 Australia 360 × 300 ACCESS-OM2 HadGAM2 3. BCC-CSM2-MR China 360 × 232 MOM4 BCC_AGCM3_MR 4. CAMS-CSM1-0 China 360 × 200 MOM4 ECHAM5_CAMS 5. CanESM5 Canada 361 × 290 NEMO3.4.1 CanAM5 6. CAS-ESM2-0 China 362 × 196 LICOM2.0 IAP AGCM 5.0 7. CESM2-WACCM USA 320 × 384 POP2 WACCM6 8. CIESM China 720 × 560 CIESM-OM CIESM-AM 9. CMCC-CM2-SR5 Italy 362 × 292 NEMO3.6 CAM5.3 10. CMCC-ESM2 Italy 362 × 292 NEMO3.6 CAM5.3 11. EC-Earth3 Europe 362 × 292 NEMO3.6 IFS cy36r4 12. EC-Earth3-Veg Europe 362 × 292 NEMO3.6 IFS cy36r4 13. EC-Earth3-Veg-LR Europe 362 × 292 NEMO3.6 IFS cy36r4 14. FGOALS-g3 China 360 × 218 LICOM3.0 GAMIL3 15. FIO-ESM-2-0 China 320 × 384 POP2-W CAM4 16. GFDL-ESM4 USA 720 × 576 GFDL-OM4p5 GFDL-AM4.1 17. IPSL-CM6A-LR France 362 × 332 NEMO-OPA LMDZ 18. MIROC6 Japan 360 × 256 COCO4.9 CCSR AGCM 19. MPI-ESM1-2-HR Germany 802 × 404 MPIOM1.63 ECHAM6.3 20. MPI-ESM1-2-LR Germany 256 × 220 MPIOM1.63 ECHAM6.3 21. MRI-ESM2-0 Japan 360 × 364 MRI.COM4.4 MRI-AGCM3.5 22. NorESM2-LM Norway 360 × 384 MICOM CAM-OSLO 23. NorESM2-MM Norway 360 × 384 MICOM CAM-OSLO 24. TaiESM1 Taiwan 320×384 POP2 TaiAM1 Wind speed (u-component and v-component at 10-m height) datasets are extracted from ERA5 for the 57-year period from 1958–2014. ERA5 is the newly released reanalysis product (Hersbach, 2016; Hersbach et al. 2020 ; He et al. 2021 ) which provides hourly estimates of data for large numbers of oceanic variables from January 1940 to till date. It is an upgraded version of the ERA-interim reanalysis product, which has advanced spatial resolutions (31 kilometers) and temporal resolutions (hourly) (Dee et al. 2011 ). The spatial and temporal resolutions of WS data are 0.5° × 0.5° and 6 hourly, respectively. Here, ERA5 is utilized as observation data to correlate with the CMIP6 models. 2.2 DMI Indices The study utilizes the Dipole Mode Index (DMI), which is a simple index time series to understand the intensity of IOD. The DMI is described as the anomalous SST gradient between the western Indian Ocean bounded between 50 o E-70 o E, 10 o S-10 o N, and the eastern Indian Ocean bounded between 90 o E-110 o E, 10 o S-equator (Saji et al. 1999). The SST anomalies are averaged over the region 50 o E-70 o E, 10 o S-10 o N for the tropical western IO and over the area 90 o E-110 o E, 10 o S-equator for the tropical south-eastern IO. It is computed employing the SST data from ERA5 reanalysis datasets from 1958 to 2014. Additionally, during the same period, the DMI is determined for CMIP6 models utilizing the SST anomalies of each model. Further, the DMI for each model is normalized and detrended during the same period. 2.3 Methodology 2.3.1 Bilinear Interpolation Bilinear interpolation is the most popular technique employed for climate grid interpolation and is defined as linear interpolation along two directions. This is the effective method to interpolate the data values in 2-D. This technique can be applied easily when the destination and source grids are rectilinear in shape whereas it fails for the unstructured and curvilinear grids as it may comprise of complex algorithms to obtain the points neighboring the location at which it is to be interpolated. It is employed to compute the functional value at a point located inside a rectangular grid at which functional values are known. Bilinear interpolation has been implemented in the Climate Data Operators software. This technique assumes that the function changes linearly between adjacent grid points in both the x and y directions. A point Q is given inside the rectangular grid with known functional values, and the four nearest grid points to Q are obtained. These four points make a square inside which Q is present. The distances between each of the four grid points and Q are evaluated. At the four grid points, these distances are utilized to weigh the functional values. The value of the function at Q is interpolated by employing the weighted mean of the functional values at the four grid points. The weights are proportional to the inverse of the distances between the grid points and Q. The above-mentioned procedure is repeated for each point that requires to be interpolated. 2.3.2 Linear Regression The climate indices that correspond to observation data, each model, and their MMM during JJA season are normalized and detrended. The effect of IOD over WS is estimated by employing simple linear regression, wherein the JJA mean of WS is regressed onto the detrended and normalized IOD index as: $$WS=a0+a1*IOD$$ 1 Where \(a0\) is the intercept and \(a1\) is the regression coefficient, which are evaluated using least square methods. The regression coefficient expresses the type of relationship along with the strength the predictor variable has with the response variable. Coefficients for negative and positive relationships have negative and positive signs, respectively. The spatial distributions of the regression coefficient are depicted as WS response to IOD. The statistical significance of the regression coefficients for observation data, each model, and their MMM are done using a two-tailed Student’s t-test (Joshi et al. 2020 ). The test statistic is given by \({T}_{x}=x\sqrt{m-2}/\sqrt{1-{x}^{2}}\) where x represents the correlation coefficient and T x is the student t-value having (m‒2) degrees of freedom. When | T x | > t α/2 for α level of significance and t α/2 as the critical value, it is implied that a significant linear relationship exists between the variables. The areas with statistical significance passing the α level of significance are displayed by hatching. 2.3.3 Skill Metrics Statistical evaluation of 24 CMIP6 models is examined utilizing diversified skill metrics such as interannual variability skill score (IVS), M-Score, and root mean square error (RMSE) against the observation WS response to IOD during JJA season from 1958 to 2014. RMSE represents error index statistics, which outlines the differences between the model data and observed data. For the individual model, WS response to IOD, RMSE is estimated as: $${ RMSE}_{PQ}=\sqrt{⟨{(P-Q)}^{2}⟩}$$ 2 Where P and Q are represented as the WS response to IOD corresponding to the model and reanalysis data, respectively. The angular brackets indicate the spatial mean over the Indian Ocean. Lower RMSE value implies better performance of the model and, conversely. The interannual variability skill score (IVS) investigates the interannual variability of the simulations with respect to that of the observations (Gleckler et al. 2008 ; Scherrer 2011 ; Chen et al. 2011 ; Jiang et al. 2015 ). IVS is a symmetric variability statistic utilized to quantify the similarity of interannual variation between observation data and simulation data. It is defined as: $$IVS={\left( {\frac{{ST{D_m}}}{{ST{D_o}}} - \frac{{ST{D_o}}}{{ST{D_m}}}} \right)^2}$$ 3 Where STD o and STD m represent the spatial standard deviations of the observation and simulation WS response to IOD, respectively. Smaller value of IVS exhibits a more accurate performance of datasets in simulating the interannual variability. In addition, the Arcsin–Mielke measure or the M-Score (Mielke Jr 1991 ; Watterson 1996 ; Watterson et al. 2014 ) describes a non-dimensional skill score based on the Mean Squared Error (MSE) normalized by the spatial variance. The M-Score value is estimated, as defined in Watterson et al. (2015) given by: $$M=\frac{2}{\pi }\arcsin \left( {1 - \frac{{MSE}}{{{V_x}+{V_y}+{{({G_x} - {G_y})}^2}}}} \right) \times 1000$$ 4 Where G x and G y represent the spatial average of the model and observation fields, respectively; V x and V y are the spatial variances of the model and observation fields, respectively; MSE is the mean square error between the observation field X and the model field Y; 2/π is a normalizing factor for the arcsin term which goes from 0 to π/2. The range of M-Score varies from zero (or even below) which specifies no skill, to a theoretical maximum of one thousand corresponding to MSE as zero. The findings of the above given three metrics has been measured in Table 2 . Based on these skill metrics, each model is ranked accordingly, and a total rank is determined by taking an arithmetic mean of the above three metric rankings. Table 2 Root-Mean‐Square Errors (RMSEs), Interannual Variability Skill Score (IVS), and M-Score of individual CMIP6 Models against the Observation WS response to IOD for JJAduring 1958–2014. Model Number Model Name RMSE (m) IVS M-Score 1. ACCESS-CM2 0.14 0.71093667 734 2. ACCESS-ESM1-5 0.12104 0.17384852 771 3. BCC-CSM2-MR 0.13207 1.423356 731 4. CAMS-CSM1-0 0.12656 2.8282686 722 5. CanESM5 0.12784 0.3716274 753 6. CAS-ESM2-0 0.15936 0.04339706 705 7. CESM2-WACCM 0.12756 0.03274063 776 8. CIESM 0.13519 0.06714264 764 9. CMCC-CM2-SR5 0.15595 0.15980088 733 10. CMCC-ESM2 0.16655 0.45438047 725 11. EC-Earth3 0.11946 0.15963516 775 12. EC-Earth3-Veg 0.11489 0.7505581 773 13. EC-Earth3-Veg-LR 0.11791 0.47738758 771 14. FGOALS-g3 0.12385 0.48509501 759 15. FIO-ESM-2-0 0.14196 0.01751805 749 16. GFDL-ESM4 0.12806 0.23423694 756 17. IPSL-CM6A-LR 0.12738 0.84925931 747 18. MIROC6 0.15494 0.42029531 744 19. MPI-ESM1-2-HR 0.12257 0.44461574 769 20. MPI-ESM1-2-LR 0.14252 0.15143353 731 21. MRI-ESM2-0 0.15518 0.00863992 723 22. NorESM2-LM 0.1268 0.08342173 764 23. NorESM2-MM 0.17321 0.75503671 722 24. TaiESM1 0.15788 0.34018162 736 25. MMM 0.10625 0.71443445 894 3. Results 3.1 IOD indices, multi-model ensemble mean (MMM) and model spread Figure 1 (a) represents the detrended and normalized time series of DMI indices of MMM of 24 CMIP6 models, observed dataset, accompanied by CMIP6 model spread from 1958 to 2014 during JJA season. Multi-model ensemble mean helps to curtail the errors in the outputs of each model (Kharin et al. 2001 ; Knutti et al. 2010 ; Warner et al. 2011). DMI indices of MMM is calculated by taking the arithmetic mean of 24 CMIP6 DMI indices. The DMI index determined from the observed data displays the occurrence of positive IOD (pIOD) events years (1961; 1963; 1967; 1972; 1982; 1983; 1994; 1997; 2006; 2007; 2012) and negative IOD (nIOD) events years (1958; 1960; 1975; 1984; 1985; 1990; 1992; 1995; 1996; 1998; 2005; 2010). Overall, the range of the observed DMI time series is in uniformity with CMIP6 model spread. Figure 1 (b) depicts the corresponding interannual standard deviations of DMI indices before detrending and normalizing. Fluctuations of the DMI index from year-to-year are exhibited by all the models with varying amplitudes. The standard deviation in the DMI index of the MMM is greater than 0.6°C. Two models (CMCC-CM2-SR5 and CMCC-ESM2) exhibit stronger IOD amplitude (> 1°C). The standard deviations of models EC-Earth3 and MPI-ESM1-2-HR are close to standard deviation of MMM. Minimum variation of IOD amplitude is visualized in ACCESS-CM2 having a magnitude of approximately 0.3°C. 3.2 Climatology, Variability and Bias In Fig. 2 , the climatology of WS from 1958 to 2014 during the JJA season over IO is displayed for ERA5 (observation data), CMIP6, and multi-model ensemble mean (MMM). MMM refers to arithmetic mean across individual models. MMM works as a useful mechanism in climate model evaluation by measuring uncertainty, lowering down model biases, consensus and analyzing model spread,which brings out potential knowledge through a robust integration of climate model outcomes. In all the CMIP6 models, the climatology pattern reveals strong positive trend of WS over Southern Indian Ocean (SIO) whereas mild increase is observed over the TIO and BoB. Most of the models detect a significant increase in WS over coastal region of the Arabian Sea. The climatology pattern of observation data illustrates a mild increase in WS over SIO, TIO, BoB and the coastal region of eastern AS and a substantial increase over western AS. Overall, the climatology of observation data and CMIP6 MMM replicates the pattern well in AS, BoB, and TIO whereas MMM overestimates the trend over SIO. In totality, the climatological pattern of observation data and CMIP6 MMM has a good spatial correlation. The variability pattern of WS over JJA season during 1958‒2014 is displayed in Fig. 3 for ERA5 (observation data), CMIP6, and MMM. The variability in WS is expressed by the interannual standard deviation. Strong variations in WS have been detected by most of the CMIP6 models over SIO and BoB. Few models have identified strong variations in WS over TIO, whereas others have shown mild variations over the same region. In all the CMIP6 models, AS has observed very mild variations in WS. The variability of observation data exhibits strong fluctuations over SIO and TIO, significant variations over BoB, and negligible changes over AS. Overall, there is consistency between observation data and CMIP6 MMM over BoB. In totality, the variability pattern of CMIP6 MMM underestimates over TIO, and SIO and overestimates over southern AS, left to southern tip of India . The biases in the variability of WS of CMIP6 MMM (i.e., MMM minus ERA5) during JJA season for the period 1958–2014 is depicted in Fig. 4 . CMIP6 models are underestimating the observation (ERA5) in most parts of SIO and overestimating in nortehr parts. Strong negative bias is observed over SIO, moderate positive bias is seen along African coast and over southern AS, left to the southern tip of India and mild negative bias is obtained over BoB and northern AS. Some models exhibit strong positive bias (~ 0.4–0.6 m/s) in BoB, while others show moderate biases upto 0.2 m/s. In CMIP6 MMM, minimal biases (0-0.2 m/s) are observed over AS and BoB, maximal biases (0.4–0.8 m/s) are visualized over SIO, and moderate biases (0.2–0.4 m/s) are noted over TIO. 3.3 Model Evaluation 3.3.1 WS response to IOD The spatial pattern of regression coefficients of WS onto the DMI index for CMIP6 models, their MMM, and ERA5 dataset during JJA season is depicted in Fig. 5 . Hatching indicates regions with statistical significance at the 5% level. For MMM, an ensemble mean of all the regression coefficients is displayed. By combining the outputs of multiple models through a MMM, it is possible to reduce the impact of individual biases or errors, and obtain a more robust estimate. Based on the ERA5, IOD events instigate significant enhancement in WS over the eastern tropical IO and mild increase over BoB, while a significant reduction in WS is apparent over the equatorial region of IO below the southern tip of India, which extends towards the westward over AS and slightly eastward towards BoB. Overall, WS response to IOD across the Indian Ocean is moderately captured by various individual models. However, some models (CESM2, CESM2-WACCM, CMCC-CM2-SR5, CMCC-ESM2, FIO-ESM-2-0, MRI-ESM2-O, NorESM2-MM and TaiESM1) overestimate the observed pattern. While some (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-O, MPI-ESM1-2-HR, and MPI-ESM1-2-LR) underestimate the observed pattern. ACCESS-ESM1-5, EC-Earth3, EC-Earth3-Veg, EC-Earth3-Veg-LR, GFDL-ESM4, and IPSL-CM6A-LR demonstrate a similar WS response pattern to the observation data. The correlation coefficients between the CMIP6 models and the observation WS regression pattern range from 0.58 to 0.75 (Moderate correlation coefficient is observed between the observation and CMIP6 models WS pattern). The MMM regression pattern of WS exhibits maximum correlation with that of the observation pattern. Overall, MMM performs better than individual models and exhibits minimal biases in the representation of the WS regression pattern. 3.3.2 Model evaluation based on skill metrics In this section, CMIP6 model performance for WS response is assessed using three skill metrics as RMSE, IVS, and M-Score. Based on skill metric parameters, each model is ranked according to their performance (as shown in Fig. 6 ). Rankings are graphically represented using a portrait diagram in Fig. 6 , which offers a concise summary of the CMIP6 global historical simulation of model WS response compared to the observed WS response. For an overall evaluation, a total ranking is also determined from the arithmetic average of the three rankings (last column of Fig. 6 ). Among all the CMIP6 models, EC-Earth3-Veg exhibits the least RMSE value, followed by EC-Earth3-Veg-LR, EC-Earth3, ACCESS-ESM1-5, MPI-ESM1-2-HR, FGOALS-g3, CAMS-CSM1-0, NorESM2-LM, IPSL-CM6A-LR, CESM2, CanESM5 and GFDL-ESM4. Overall, the RMSE values of the model simulations relative to the observation WS responses are minimal, thereby indicating that most of the CMIP6 models appropriately capture the WS response to IOD during the monsoon season. The MMM exhibits the lowest RMSE value compared to CMIP6 models, and thus outperforms the individual models as expected. IVS values are calculated for each model based on WS response to IOD over the Indian Ocean (see Table 2 ). Among all the CMIP6 models, MRI-ESM2-0, FIO-ESM2-0, CESM2, CAS-ESM2-0, CESM2-WACCM, NorESM2-LM, MPI-ESM1-2-LR, EC-Earth3, CMCC-CM2-SR5, and ACCESS-ESM1-5 are ranked as top ten models in the evaluation of IVS for WS response. The top twenty models in evaluating IVS for WS response exhibit lower value than the MMM IVS value. Four models such as CAMS-CSM1-0, BCC-CSM2-MR, IPSL-CM6A-LR, and NorESM2-MM show high IVS values, thus poorly simulate the interannual variability of monsoon WS response to IOD. Further, M-Score evaluated for the WS response to IOD among the CMIP6 models ranges between 700 to 800. A higher value of M-Score corresponds to a better performance of the CMIP6 models. CESM2, EC-Earth3, EC-Earth3-Veg, EC-Earth3-Veg-LR, ACCESS-ESM1-5, MPI-ESM1-2-HR, NorESM2-LM, CESM2-WACCM, FGOALS-g3, and GFDL-ESM4 models are top ten models for the evaluation of M-Score. The lowest M-Score prevails for NorESM2-MM, CAMS-CSM1-0, and MRI-ESM2-0. Overall, it is observed that the performance of CMIP6 models varies differently in individual skill metric parameters (RMSE, IVS, and M-Score). Specifically, CMIP6 models exhibit a range of performances in the representation of WS response to IOD based on different skill metric parameters. Besides, ranking model’s performance based on one single parameter would not be sufficient to give a detailed outcome. Therefore, an arithmetic average of the rank of these skill metrics has been considered to determine the total rank of individual models, which will provide a synthesis of the WS response to IOD for each model. Based on the total ranking, the top best-performing models are CESM2, EC-Earth3, ACCESS-ESM1-5, EC-Earth3-Veg-LR, MMM, NorESM2-LM, CESM2-WACCM, EC-Earth3-Veg, MPI-ESM1-2-HR, and FIO-ESM2-0 (see Fig. 6 ). 4. Conclusion and Discussion This study estimated 24 CMIP6 models efficiency to simulate WS response to IOD during JJA season over the Indian Ocean based on historical simulations from 1958–2014. The range of the observed DMI time series is consistent with CMIP6 model spread. Minimum variation of IOD amplitude is observed in the model ACCESS-CM2 having a magnitude of approximately 0.3°C whereas maximum IOD amplitude is displayed by CMCC-ESM2 having greater than 1°C. The performance of the individual model varies, whereas MMM accuracy is close to observation data. The climatology and variability have been evaluated. It is found that the climatological pattern of observation data and CMIP6 MMM are in consonance and have a good correlation, whereas the variability pattern of CMIP6 MMM underestimates over northern AS, TIO, and SIO and overestimates over southern AS. In the case of bias in variability, minimum is observed over BoB and AS, maximum over SIO, and moderate over TIO. WS responses to IOD are analyzed by using simple linear regression analysis. Some models overestimate the observed pattern, some underestimate the observed pattern while few match with the observed trends. Overall, WS response to IOD across the Indian Ocean is moderately captured by various individual models, whereas MMM appropriately captures to the observed pattern. The efficiency of CMIP6 models changes differently in individual skill metric parameters (RMSE, IVS, and M-Score). On the basis of total rankings, the top ten best-performing models are CESM2, EC-Earth3, ACCESS-ESM1-5, EC-Earth3-Veg-LR, MMM, NorESM2-LM, CESM2-WACCM, EC-Earth3-Veg, MPI-ESM1-2-HR, and FIO-ESM2-0. Declarations Ethics approval and consent to participate (kindly mention the name of the Ethics Committee and the Ethical Approval Number) Not Applicable. Consent for publication I, the undersigned, give my consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (“Material”) to be published in the above Journal and Article. Availability of data and materials The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no known competing financial interests. Funding The present study is supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under core research grant project file no. (CRG/2021/003654). Authors' contributions Ramakant Prasad: Data curation, Investigation, Methodology, Writing- Original draft preparation, Anshu Yadav: Investigation, Writing- Original draft preparation, Conceptualization, Divya Sardana: Data curation, Investigation, Validation, Prashant Kumar: Conceptualization, Visualization, Methodology, Supervision, Writing-Reviewing and Editing, Anurag Singh : Supervision, Writing-Reviewing and Editing, Yukiharu Hisaki: Conceptualization, Visualization, Writing-Reviewing and Editing, Rajni: Visualization, Writing- Reviewing and Editing; Acknowledgement The present study is supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under core research grant project file no. (CRG/2021/003654). References Andrée, E., Drews, M., Su, J., Larsen, M. A. D., Drønen, N., Madsen, K. S., 2022. Simulating wind-driven extreme sea levels: Sensitivity to wind speed and direction. Weather and Climate Extremes. 36, 100422. https://doi.org/10.1016/j.wace.2022.100422. Azorin-Molina, C., Dunn, R. J. H., Mears, C. A., Berrisford, P., McVicar, T. R., & Nicolas, J. P., 2017. Surface winds [in “State of the Climate in 2016”]. Bull. Amer. Meteor. Soc. 98(8), S37-S39. Azorin-Molina, C., Vicente-Serrano, S. M., McVicar, T. R., Jerez, S., Sanchez-Lorenzo, A., López-Moreno, J. I., ..., Espírito-Santo, F., 2014. Homogenization and assessment of observed near-surface wind speed trends over Spain and Portugal, 1961–2011. Journal of Climate. 27(10), 3692-3712. https://doi.org/10.1175/JCLI-D-13-00652.1. Barry, R. G., Chorley, R. J., 2009. Atmosphere, weather and climate. Routledge. https://doi.org/10.4324/9780203871027. Bi, D., Dix, M., Marsland, S., O’farrell, S., Sullivan, A., Bodman, R., ..., Heerdegen, A., 2020. Configuration and spin-up of ACCESS-CM2, the new generation Australian community climate and earth system simulator coupled model. Journal of Southern Hemisphere Earth Systems Science. 70(1), 225-251. https://doi.org/10.1071/ES19040. Bigg, G. R., 2003. The oceans and climate, second ed. Cambridge University Press. Bormann, H., 2011. Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Climatic Change. 104(3), 729-753. https://doi.org/10.1007/s10584-010-9869-7. Bustos Usta, D. F., Torres Parra, R. R., 2023. Projected wind changes in the Caribbean Sea based on CMIP6 models. Climate Dynamics. 60(11), 3713-3727. https://doi.org/10.1007/s00382-022-06535-3. Cai, W., Santoso, A., Wang, G., Weller, E., Wu, L., Ashok, K., ... & Yamagata, T., 2014. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature. 510(7504), 254-258. https://doi.org/10.1038/nature13327. Chen, H., He, Z., Xie, Q., Zhuang, W., 2023. Performance of CMIP6 models in simulating the dynamic sea level: Mean and interannual variance. Atmospheric and Oceanic Science Letters. 16(1), 100288. https://doi.org/10.1016/j.aosl.2022.100288. Chen, L., Li, D., Pryor, S. C., 2013. Wind speed trends over China: quantifying the magnitude and assessing causality. International Journal of Climatology. 33(11), 2579-2590. https://doi.org/10.1002/joc.3613. Chen, W., Jiang, Z., Li, L., 2011. Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. Journal of Climate. 24(17), 4741-4756. https://doi.org/10.1175/2011JCLI4102.1. Cusack, S., 2013. A 101 year record of windstorms in the Netherlands. Climatic Change. 116 (3 4), 693-704. 10.1007/s10584-012-0527-0. Dadaser‐Celik, F., Cengiz, E., 2014. Wind speed trends over Turkey from 1975 to 2006. International Journal of Climatology. 34(6), 1913-1927. https://doi.org/10.1002/joc.3810. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., ..., Vitart, F., 2011. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society. 137(656), 553-597. https://doi.org/10.1002/qj.828. Deng, K., Azorin-Molina, C., Minola, L., Zhang, G., Chen, D., 2021. Global near-surface wind speed changes over the last decades revealed by reanalyses and CMIP6 model simulations. Journal of Climate. 34(6), 2219-2234. https://doi.org/10.1175/JCLI-D-20 0310.1. Deng, K., Yang, S., Liu, W., Li, H., Chen, D., Lian, T., ..., Shen, C., 2024. The offshore wind speed changes in China: an insight into CMIP6 model simulation and future projections. Climate Dynamics. 1-15. https://doi.org/10.1007/s00382-023-07066-1. Dunn, R. J. H., Azorin-Molina, C., Mears, C. A., Berrisford, P., McVicar, T. R., 2016. Surface winds (in “State of the climate in 2015”). Bull Am Meteorol Soc. 97(8), S38-S40. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., Taylor, K. E., 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development. 9(5), 1937 1958. https://doi.org/10.5194/gmd-9-1937-2016. Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., ..., Williamson, M. S., 2019. Taking climate model evaluation to the next level. Nature Climate Change. 9(2), 102-110. https://doi.org/10.1038/s41558-018-0355-y. Fu, C., Wang, D., Yang, L., Luo, Y., Zhou, F., Priyadarshana, T., Yao, J., 2018. The relationship between significant wave height and Indian Ocean Dipole in the equatorial North Indian Ocean. Ocean Dynamics. 68, 689-699. https://doi.org/10.1007/s10236-018-1154-8. Gleckler, P. J., Taylor, K. E., & Doutriaux, C., 2008. Performance metrics for climate models. Journal of Geophysical Research: Atmospheres. 113(D6). D06104. https://doi.org/10.1029/2007JD008972. He, Y., Wang, K., & Feng, F., 2021. Improvement of ERA5 over ERA-Interim in simulating surface incident solar radiation throughout China. Journal of Climate. 34(10), 3853-3867. https://doi.org/10.1175/JCLI-D-20-0300.1. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., ..., Thépaut, J. N., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society. 146(730), 1999-2049. https://doi.org/10.1002/qj.3803. Hong, C. C., Lu, M. M., Kanamitsu, M., 2008. Temporal and spatial characteristics of positive and negative Indian Ocean dipole with and without ENSO. Journal of Geophysical Research: Atmospheres. 113(D8). https://doi.org/10.1029/2007JD009151. Jhajharia, D., Shrivastava, S. K., Sarkar, D. S. A. S., & Sarkar, S., 2009. Temporal characteristics of pan evaporation trends under the humid conditions of northeast India. Agricultural and Forest Meteorology. 149(5), 763-770. https://doi.org/10.1016/j.agrformet.2008.10.024. Jiang, Z., Li, W., Xu, J., & Li, L., 2015. Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. Journal of Climate. 28(21), 8603-8619. https://doi.org/10.1175/JCLI-D-15-0099.1. Joshi, M. K., Rai, A., Kulkarni, A., & Kucharski, F., 2020. Assessing changes in characteristics of hot extremes over India in a warming environment and their driving mechanisms. Scientific Reports. 10(1), 2631. https://doi.org/10.1038/s41598-020-59427-z. Kaur, S., Kumar, P., Min, S. K., Patra, A., Wang, X. L., 2022. CMIP5 model evaluation for extreme ocean wave height responses to ENSO. Climate Dynamics. 1-15. https://doi.org/10.1007/s00382-021-06039-6. Kharin, V. V., Zwiers, F. W., & Gagnon, N., 2001. Skill of seasonal hindcasts as a function of the ensemble size. Climate Dynamics. 17(11), 835-843. https://doi.org/10.1007/s003820100149. Kim, I. W., Oh, J., Woo, S., Kripalani, R. H., 2019. Evaluation of precipitation extremes over the Asian domain: observation and modelling studies. Climate Dynamics. 52, 1317-1342. https://doi.org/10.1007/s00382-018-4193-4. Kim, J., Paik, K. 2015. Recent recovery of surface wind speed after decadal decrease: a focus on South Korea. Climate Dynamics. 45, 1699-1712. https://doi.org/10.1007/s00382-015 2546-9 . Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., Meehl, G. A. 2010. Challenges in combining projections from multiple climate models. Journal of Climate. 23(10), 2739-2758. https://doi.org/10.1175/2009jcli3361.1. Krishnan, A., Bhaskaran, P. K., 2020. Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal. Climate Dynamics. 55(9-10), 2667-2687. https://doi.org/10.1007/s00382-020-05406-z. Kumar, P., Kaur, S., Weller, E., Min, S. K., 2019. Influence of natural climate variability on the extreme ocean surface wave heights over the Indian Ocean. Journal of Geophysical Research: Oceans. 124(8), 6176-6199. https://doi.org/10.1029/2019JC015391. Kumar, P., Sardana, D., 2021. Regional sea level changes in the Indian shelf sea and its association with SST anomalies. Regional Studies in Marine Science. 47, 101992. https://doi.org/10.1016/j.rsma.2021.101992. Kushwaha, P., Pandey, V. K., Kumar, P., Sardana, D., 2024. CMIP6 Model Evaluation for Mean and Extreme Precipitation Over India. Pure and Applied Geophysics. 1-24. https://doi.org/10.1007/s00024-023-03409-5. Kusuru, D., Imandi, R., Turlapaty, A. C., Thakur, M., 2022. A Gaussian Gamma mixture model for Indian ocean surface wind speed. In OCEANS 2022-Chennai (pp. 1-5). IEEE. https://doi.org/10.1109/OCEANSChennai45887.2022.9775268. Landerer, F. W., Gleckler, P. J., & Lee, T., 2014. Evaluation of CMIP5 dynamic sea surface height multi-model simulations against satellite observations. Climate dynamics. 43, 1271-1283. https://doi.org/10.1007/s00382-013-1939-x. Laurila, T. K., Sinclair, V. A., Gregow, H., 2021. Climatology, variability, and trends in near surface wind speeds over the North Atlantic and Europe during 1979–2018 based on ERA5. International Journal of Climatology. 41(4), 2253-2278. https://doi.org/10.1002/joc.6957. Li, J. L. F., Tsai, Y. C., Xu, K. M., Lee, W. L., Jiang, J. H., Yu, J. Y., ..., Stephens, G., 2022. Inferring the linkage of sea surface height anomalies, surface wind stress and sea surface temperature with the falling ice radiative effects using satellite data and global climate models. Environmental Research Communications. 4(12), 125004. https://doi.org/10.1088/2515-7620/aca3fe. Li, J., Zhao, Y., Wang, M., Tan, W., Yin, J., 2024. Projected Changes of Wind Energy Input to Surface Waves in the North Indian Ocean Based on CMIP6. Atmosphere. 15(1), 139. https://doi.org/10.3390/atmos15010139. Lyddon, C. E., Brown, J. M., Leonardi, N., Plater, A. J., 2019. Increased coastal wave hazard generated by differential wind and wave direction in hyper-tidal estuaries. Estuarine, Coastal and Shelf Science. 220, 131-141. https://doi.org/10.1016/j.ecss.2019.02.042. Lyu, K., Zhang, X., Church, J. A., 2020. Regional dynamic sea level simulated in the CMIP5 and CMIP6 models: Mean biases, future projections, and their linkages. Journal of Climate. 33(15), 6377-6398. https://doi.org/10.1175/JCLI-D-19-1029.1. Lyu, K., Zhang, X., Church, J. A., Hu, J., 2016. Evaluation of the interdecadal variability of sea surface temperature and sea level in the Pacific in CMIP3 and CMIP5 models. International Journal of Climatology. 36(11), 3723-3740. https://doi.org/10.1002/joc.4587. Magnan, A. K., Oppenheimer, M., Garschagen, M., Buchanan, M. K., Duvat, V. K., Forbes, D. L., ..., Pörtner, H. O., 2022. Sea level rise risks and societal adaptation benefits in low lying coastal areas. Scientific reports. 12(1), 10677. https://doi.org/10.1038/s41598-022 14303-w . Malloy, J. W., Krahenbuhl, D. S., Bush, C. E., Balling, R. C., Santoro, M. M., White, J. R., ..., Cerveny, R. S., 2015. A surface wind extremes (“wind lulls” and “wind blows”) climatology for central North America and adjoining oceans (1979–2012). Journal of Applied Meteorology and Climatology. 54(3), 643-657. https://doi.org/10.1175/JAMC D-14-0009.1 . McKenna, S., Santoso, A., Gupta, A. S., Taschetto, A. S., Cai, W., 2020. Indian Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO. Scientific reports. 10(1), 11500. https://doi.org/10.1038/s41598-020-68268-9. McVicar, T. R., Van Niel, T. G., Li, L. T., Roderick, M. L., Rayner, D. P., Ricciardulli, L., Donohue, R. J., 2008. Wind speed climatology and trends for Australia, 1975–2006: Capturing the stilling phenomenon and comparison with near‐surface reanalysis output. Geophysical Research Letters. 35(20). https://doi.org/10.1029/2008GL035627. Miao, H., Xu, H., Huang, G., Yang, K., 2023. Evaluation and future projections of wind energy resources over the Northern Hemisphere in CMIP5 and CMIP6 models. Renewable Energy. 211, 809-821. https://doi.org/10.1016/j.renene.2023.05.007. Mielke Jr, P. W., 1991. The application of multivariate permutation methods based on distance functions in the earth sciences. Earth-Science Reviews. 31(1), 55-71. https://doi.org/10.1016/0012-8252(91)90042-E. Pasquero, C., Desbiolles, F., Meroni, A. N., 2021. Air‐sea interactions in the cold wakes of tropical cyclones. Geophysical Research Letters. 48(2), e2020GL091185. https://doi.org/10.1029/2020GL091185 . Patra, A., Bhaskaran, P. K., 2017. Temporal variability in wind–wave climate and its validation with ESSO-NIOT wave atlas for the head Bay of Bengal. Climate Dynamics. 49, 1271 1288. https://doi.org/10.1007/s00382-016-3385-z. Pryor, S. C., & Ledolter, J. (2010). Addendum to “Wind speed trends over the contiguous United States”. Journal of Geophysical Research: Atmospheres, 115(D10). https://doi.org/10.1029/2009JD013281 . Raju, K. S., Kumar, D. N., 2020. Review of approaches for selection and ensembling of GCMs. Journal of Water and Climate Change. 11(3), 577-599. http://doi.org/10.2166/wcc.2020.128 . Rashmi, R., Polnikov, V., Pogarskii, F., Gomorev, I., Samiksha, V., Vethamony, P., 2016. Long term variability of the wind field over the Indian Ocean based on ERA-Interim reanalysis. Atmosphere-Ocean. 54(5), 505-518. https://doi.org/10.1080/07055900.2016.1158147. Roy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., Shit, M., 2023. Effects of climate change and sea-level rise on coastal habitat: Vulnerability assessment, adaptation strategies and policy recommendations. Journal of Environmental Management. 330, 117187. https://doi.org/10.1016/j.jenvman.2022.117187. Saji, N. H., Yamagata, T. J. C. R., 2003. Possible impacts of Indian Ocean dipole mode events on global climate. Climate research. 25(2), 151-169. https://doi.org/10.3354/cr025151. Sardana, D., Kumar, P., Rajni., 2023. CMIP6 model evaluation for sea surface height responses to ENSO. Climate Dynamics. 1-19. https://doi.org/10.1007/s00382-023-06997 z . Scherrer, S. C., 2011. Present‐day interannual variability of surface climate in CMIP3 models and its relation to future warming. International Journal of Climatology. 31(10), 1518 1529. https://doi.org/10.1002/joc.2170. Shanas, P. R., Kumar, V. S., 2015. Trends in surface wind speed and significant wave height as revealed by ERA‐Interim wind wave hindcast in the Central Bay of Bengal. International Journal of Climatology. 35(9), 2654-2663. https://doi.org/10.1002/joc.4164. Shanas, P. R., Sanil Kumar, V., 2014. Temporal variations in the wind and wave climate at a location in the eastern Arabian Sea based on ERA-Interim reanalysis data. Natural hazards and earth system sciences. 14(5), 1371-1381. https://doi.org/10.5194/nhess-14 1371-2014 . Shen, C., Zha, J., Li, Z., Azorin‐Molina, C., Deng, K., Minola, L., Chen, D., 2022. Evaluation of global terrestrial near‐surface wind speed simulated by CMIP6 models and their future projections. Annals of the New York Academy of Sciences. 1518(1), 249-263. https://doi.org/10.1111/nyas.14910. Shen, C., Zha, J., Zhao, D., Wu, J., Fan, W., Yang, M., Li, Z., 2021. Estimating centennial-scale changes in global terrestrial near-surface wind speed based on CMIP6 GCMs. Environmental Research Letters. 16(8), 084039. https://doi.org/10.1088/1748 9326/ac1378 . Shen, C., Zha, J., Zhao, D., Wu, J., Fan, W., Yang, M., Li, Z., 2021. Estimating centennial-scale changes in global terrestrial near-surface wind speed based on CMIP6 GCMs. Environmental Research Letters. 16(8), 084039. https://doi.org/10.1088/1748 9326/ac1378 . Taylor, K. E., Juckes, M., Balaji, V., Cinquini, L., Denvil, S., Durack, P. J., ..., Stockhause, M., 2018. CMIP6 global attributes, DRS, filenames, directory structure, and CV’s. PCMDI Document. https://goo.gl/v1drZl . Taylor, K. E., Stouffer, R. J., Meehl, G. A., 2012. An overview of CMIP5 and the experiment design. Bulletin of the American meteorological Society. 93(4), 485-498. https://doi.org/10.1175/BAMS-D-11-00094.1. Tian, B., Dong, X., 2020. The double‐ITCZ bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation. Geophysical Research Letters. 47(8), e2020GL087232. https://doi.org/10.1029/2020GL087232. Tobin, I., Berrisford, P., Dunn, R. J. H., Vautard, R., McVicar, T. R., 2014. Land surface wind speed [in “State of the Climate in 2013”]. Bull. Amer. Meteor. Soc. 95(7), S28-S29. Troccoli, A., Muller, K., Coppin, P., Davy, R., Russell, C., Hirsch, A. L., 2012. Long-term wind speed trends over Australia. Journal of Climate. 25(1), 170-183. https://doi.org/10.1175/2011JCLI4198.1. Vautard, R., McVicar, T. R., Thepaut, J. N., Roderic, M. L., 2012. Global climate; atmospheric circulation; surface winds) land surface winds and atmospheric evaporative demand (in “State of the Climate in 2011”). Bull Am Meteorol Soc. 93(7), S6-S38. Wan, H., Wang, X. L., Swail, V. R., 2010. Homogenization and trend analysis of Canadian near surface wind speeds. Journal of Climate. 23(5), 1209-1225. https://doi.org/10.1175/2009JCLI3200.1. Wang, C., Zhang, L., Lee, S. K., Wu, L., Mechoso, C. R., 2014. A global perspective on CMIP5 climate model biases. Nature Climate Change. 4(3), 201-205. https://doi.org/10.1038/nclimate2118. Wang, Y., Zou, R., Liu, F., Zhang, L., Liu, Q., 2021. A review of wind speed and wind power forecasting with deep neural networks. Applied Energy. 304, 117766. https://doi.org/10.1016/j.apenergy.2021.117766. Warner, T. T., 2010. Numerical weather and climate prediction. Cambridge university press. https://doi.org/10.1017/CBO9780511763243. Watterson, I. G., 1996. Non‐dimensional measures of climate model performance. International Journal of Climatology: A Journal of the Royal Meteorological Society. 16(4), 379-391. https://doi.org/10.1002/(SICI)1097-0088(199604)16:43.0.CO;2-U. Watterson, I. G., 2015. Improved simulation of regional climate by global models with higher resolution: skill scores correlated with grid length. Journal of Climate. 28(15), 5985 6000. https://doi.org/10.1175/JCLI-D14-00702.1. Watterson, I. G., 2019. Indices of climate change based on patterns from CMIP5 models, and the range of projections. Climate Dynamics. 52(3), 2451-2466. https://doi.org/10.1007/s00382-018-4260-x. Watterson, I. G., Bathols, J., Heady, C., 2014. What influences the skill of climate models over the continents?. Bulletin of the American Meteorological Society. 95(5), 689-700. https://doi.org/10.1175/BAMS-D-12-00136.1. Williams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D., Evans, B., ..., Trenham, C., 2016. A global repository for planet-sized experiments and observations. Bulletin of the American Meteorological Society. 97(5), 803-816. https://doi.org/10.1175/BAMS-D-15 00132.1. Williams, D. N., Taylor, K. E., Cinquini, L., Evans, B., Kawamiya, M., Lautenschlager, M., ..., Contributors, E. S. G. F., 2011. The Earth System Grid Federation: Software framework supporting CMIP5 data analysis and dissemination. ClIVAR Exchanges. 56(2), 40-42. Wu, J., Shi, Y., Xu, Y., 2020. Evaluation and projection of surface wind speed over China based on CMIP6 GCMs. Journal of Geophysical Research: Atmospheres. 125(22), e2020JD033611. https://doi.org/10.1029/2020JD033611. Wu, J., Zha, J., Zhao, D., 2017. Evaluating the effects of land use and cover change on the decrease of surface wind speed over China in recent 30 years using a statistical downscaling method. Climate Dynamics. 48, 131-149. https://doi.org/10.1007/s00382 016-3065-z . Ye, M., Wu, J., Liu, W., He, X., Wang, C., 2020. Dependence of tropical cyclone damage on maximum wind speed and socioeconomic factors. Environmental Research Letters. 15(9), 094061. https://doi.org/10.1088/1748-9326/ab9be2. Young, I. R., Ribal, A., 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science. 364(6440), 548-552. https://doi.org/10.1126/science.aav95. Young, I. R., Vinoth, J., Zieger, S., Babanin, A. V., 2012. Investigation of trends in extreme value wave height and wind speed. Journal of Geophysical Research: Oceans. 117(C11). https://doi.org/10.1029/2011JC007753. Young, I. R., Zieger, S., Babanin, A. V., 2011. Global trends in wind speed and wave height. Science. 332(6028), 451-455. https://doi.org/10.1126/science.1197219. Yu, L., Zhong, S., Sun, B., 2020. The climatology and trend of surface wind speed over Antarctica and the Southern Ocean and the implication to wind energy application. Atmosphere. 11(1), 108. https://doi.org/10.3390/atmos11010108. Zha, J., Wu, J., Zhao, D., Fan, W., 2020. Future projections of the near-surface wind speed over eastern China based on CMIP5 datasets. Climate Dynamics. 54(3-4), 2361-2385. https://doi.org/10.1007/s00382-020-05118-4. Zheng, C. W., Pan, J., Li, C. Y., 2016. Global oceanic wind speed trends. Ocean & Coastal Management. 129, 15-24. https://doi.org/10.1016/j.ocecoaman.2016.05.001. Zheng, C. W., Pan, J., Li, C. Y., 2016. Global oceanic wind speed trends. Ocean & Coastal Management. 129, 15-24. https://doi.org/10.1016/j.ocecoaman.2016.05.001. 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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-4275922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294283120,"identity":"4ecf0296-1bf1-4c1a-ae64-7fad7b8d8376","order_by":0,"name":"Ramakant Prasad","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Ramakant","middleName":"","lastName":"Prasad","suffix":""},{"id":294283121,"identity":"868847f1-a718-4121-a2b7-95de423dd19f","order_by":1,"name":"Prashant Kumar","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8480-7490","institution":"National Institute of Technology, Delhi","correspondingAuthor":true,"prefix":"","firstName":"Prashant","middleName":"","lastName":"Kumar","suffix":""},{"id":294283122,"identity":"2ef96a95-7a70-400b-971f-cf1443c97b87","order_by":2,"name":"Anshu Yadav","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Anshu","middleName":"","lastName":"Yadav","suffix":""},{"id":294283123,"identity":"642e7952-dc41-44bf-bbff-50d4363abaf3","order_by":3,"name":"Anurag Singh","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Anurag","middleName":"","lastName":"Singh","suffix":""},{"id":294283124,"identity":"00f5ad54-7352-416c-9815-dd593942efe9","order_by":4,"name":"Divya Sardana","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"","lastName":"Sardana","suffix":""},{"id":294283125,"identity":"dbf2449a-8be7-4c84-ab5e-baccd3f4f6cf","order_by":5,"name":"Yukiharu Hisaki","email":"","orcid":"","institution":"University of the Ryukyus: Ryukyu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yukiharu","middleName":"","lastName":"Hisaki","suffix":""}],"badges":[],"createdAt":"2024-04-16 12:11:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4275922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4275922/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55369715,"identity":"82e4e38e-149f-43fd-9a8b-407bbac35be1","added_by":"auto","created_at":"2024-04-26 11:08:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":572196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). \u003c/strong\u003eDetrended and normalized time series of DMI indices of CMIP6 MMM represented by black solid line, observation dataset represented by red solid line, and CMIP6 model spread of the time series represented by yellow shading during JJA from 1958‒2014, \u003cstrong\u003e(b) \u003c/strong\u003einterannual standard deviation of DMI index (before detrending and normalizing) for CMIP6 models, MMM, and observation data. Horizontal dashed lines in (a) represent ± 0.5 standard deviation.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4275922/v1/913aa1d88915a88d1215bd76.jpeg"},{"id":55369713,"identity":"f3d9b7ae-d366-43d6-9bdb-7257805d4720","added_by":"auto","created_at":"2024-04-26 11:08:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255878,"visible":true,"origin":"","legend":"\u003cp\u003eClimatology of wind speed for 24 CMIP6 models, their MMM, \u0026nbsp;and ERA5 data during 1958-2014 during JJA over the Indian Ocean.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4275922/v1/73ade0f1db94188f0cfc3433.png"},{"id":55369716,"identity":"a0e0b0d2-0b65-4720-95da-14645d0725e1","added_by":"auto","created_at":"2024-04-26 11:08:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":227829,"visible":true,"origin":"","legend":"\u003cp\u003eVariability of wind speed for 24 CMIP6 models, their MMM, and ERA5 data over the period 1958-2014 during JJA over the Indian Ocean.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4275922/v1/a76aaba94bad72e6daeda64f.png"},{"id":55369717,"identity":"ee0b5452-5b79-41ce-a1bc-0f45a9789e18","added_by":"auto","created_at":"2024-04-26 11:08:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211399,"visible":true,"origin":"","legend":"\u003cp\u003eBias in the variability of wind speed during 1958-2014 for JJA over the Indian Ocean.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4275922/v1/2b7e1a1fb705f24e9cddf27e.png"},{"id":55369718,"identity":"2a7817ed-60af-4122-9679-8d51a6971e5f","added_by":"auto","created_at":"2024-04-26 11:08:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":339586,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the regression coefficients of WS onto the DMI index during JJA from 1958–2014 for CMIP6 models, MMM, and ERA5 data. Each panel of the figure contains the correlation coefficient of WS regression pattern of the model with that of observation data in brackets. Hatching represents statistically significant regions at the 5% level. The unit of regression coefficients is m per standard deviation.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4275922/v1/28b6b89fe8d90c5e525d8ebb.png"},{"id":55369714,"identity":"b8cf67f5-5ffa-4b1c-950b-88e2718fa955","added_by":"auto","created_at":"2024-04-26 11:08:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":73754,"visible":true,"origin":"","legend":"\u003cp\u003ePortrait diagram is shown for the rankings based on RMSE, IVS, and M-Score of individual CMIP6 model against the observation WS response to IOD based on the evaluations made in Table 2 during JJA from 1958–2014. Total rank is obtained by taking an arithmetic average of the three metric rankings.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4275922/v1/b68fa501beebe95850ccba1e.png"},{"id":56208588,"identity":"d7219d63-949b-486b-9e64-e53ad518d15e","added_by":"auto","created_at":"2024-05-10 00:12:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1885352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4275922/v1/e0c0b05d-ac7c-48f7-be15-5e645faf96ad.pdf"}],"financialInterests":"","formattedTitle":"CMIP6 Model Evaluation for Wind Speed Responses to IOD during Monsoon Season over the Indian Ocean","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWind Speed (WS) and its patterns are crucial in the North Indian Ocean (NIO), particularly due to its significant association with the Indian monsoon. Additionally, WS and its patterns are immensely associated with Indian Ocean Dipole (IOD) which is a vital climate driver in the Tropical Indian Ocean (TIO). Positive IOD (pIOD) instigates cooler SSTs over the tropical southeastern Indian Ocean bounded between 90\u003csup\u003eo\u003c/sup\u003eE-110\u003csup\u003eo\u003c/sup\u003eE, 10\u003csup\u003eo\u003c/sup\u003eS-equator and warmer over the tropical western Indian Ocean bounded between 50\u003csup\u003eo\u003c/sup\u003eE-70\u003csup\u003eo\u003c/sup\u003eE, 10\u003csup\u003eo\u003c/sup\u003eS-10\u003csup\u003eo\u003c/sup\u003eN. Positive phase of IOD (pIOD) is connected with higher rainfall and negative phase of IOD (nIOD) is associated with lesser rainfall in India (McKenna et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In CMIP5 and CMIP6 climate models, there is an uncertainty of wind pattern as it is highly influenced by IOD. It is a major challenge to replicate IOD in these models as it is affected by internal forcings as well as external forcings. Therefore, it is essential to evaluate CMIP6 models to understand the wind speed responses to IOD. The relentless increase in WS over the globe is contemplated to have disastrous effects (Young et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zheng et al.,2016; Young and Ribal, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lyddon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which emphasizes to have a better insight of WS so that socio-economic calamities could be averted.\u003c/p\u003e \u003cp\u003eWind speed (WS) is a primary oceanic variable of interest (Yu et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kusuru et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that yields better comprehension of numerous characteristics of ocean dynamics, such as the ocean\u0026rsquo;s response to tropical cyclones, wind energy, wave height, wind direction, air-sea fluxes, and ocean currents (Pasquero et al. 2020; Wang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ye et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kusuru et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These are the essential components in anticipating and alleviating the repercussions of climate change, which is one of the largest global challenges confronted by society nowadays, as the ocean is the major guiding force in controlling the climate of the Earth (Bigg \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Benestad 2006; Barry and Chorley \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). WS plays a pivotal role in determining the sea level rise (Andr\u0026eacute;e et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and it is of great concern to engineers, scientists, climate modelers, and the Government due to its catastrophic effect on the inhabitants along the shoreline and their economic activities, coastal layouts, marine ecosystems and onshore environments (Kumar et al. 2021; Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Magnan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Roy et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe downtrend of surface wind speed is observed in Australia utilizing data measured by terrestrial anemometers from 1975 to 2006 (Mc Vicar et al. 2008). The slowdown of WS is visualized in Canada for the period 1953\u0026ndash;2006 using data measured at nonstandard anemometer (Wan et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The declining trend of WS is noticed in Turkey during 1975\u0026ndash;2006 using weather stations data (Dadaser-Celik and Cengiz, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The downward trend in WS is noted in USA using National Climate Data Centre datasets from 1973\u0026ndash;2000 (Pryor et al. 2009). The decreasing trend of WS in Spain and Portugal is visualized using data from land-based stations from 1961\u0026ndash;2011 (Azorin-Molina et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The slowdown in WS is shown in Netherlands using 101 years data from five dutch stations (Cusack \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The slowdon in WS is observed in India on at least eight sites out of eleven sites chosen using data from India Meteorological Department (Pune) (Jhajharia et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The slowdown in WS is noticed in China for the different periods(Guo et al. 2011; Lin et al. 2013; Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The decline in WS is observed in South Korea using ground-observed daily data from Korean Meteorological Administration during 1954\u0026ndash;2013 (Kim and Paik \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe TIO displays coupled ocean-atmosphere interactions yielding interannual climate variability known as Indian Ocean Dipole (IOD) which is similar to ENSO to a certain degree (Saji et al. 1999). IOD has strong influences on the lives of millions residing along Indian-Ocean rim countries (Saji and Yamagata \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). pIOD events are forecasted to increase three times in twenty first century (one event in 6.3 years) in comparison to twentieth century (one event in 17.3 years) due to excessive greenhouse gas emissions (Cai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It has strong association with Indian summer monsoon precipitation (McKenna et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). During pIOD and nIOD (independent of ENSO), SST anomalies in the eastern part of IO are due to wind anomalies, which subsequently affect oceanic dynamics and cause anomalies in western part of IO (Hong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Variations in ocean wave height during monsoon season over BoB and AS are associated with changes in WS which is primarily affected by IOD (Kumar et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). During IOD events, large changes in the zonal component of surface wind field over TIO are visualized and utmost changes in zonal wind are observed over equatorial central and eastern IO, where correlation with DMI is found greater than 0.6 (Saji et al. 1999). The correlation coefficients between significant wave height (SWH) anomalies and DMI are weaker at most places of NIO and SWH anomalies attain maximum in the same season as DMI in all types of IOD events. In the eastern equatorial NIO, IOD events affects the SWH through the wind changes (Fu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have explored the historical trends of WS in CMIP5 and CMIP6 models (Krishnan and Bhaskaran \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shen et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Miao et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Utilizing data from CMIP6 simulations, it is observed that the global annual mean of near surface wind speed increased during 1850\u0026ndash;1967 and decreased for the period 1968\u0026ndash;2014(Shen et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using model simulations from CMIP6 and reanalysis data, it is found that the recent trends of annual mean near-surface wind speed (10m) is decreasing in Northern Hemisphere and increasing in the Southern Hemisphere during 1980\u0026ndash;2010 and opposite trend is observed for the period 2010 to 2019 (Deng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using CMIP6 models simulations and ERA5 reanalysis datasets for the period 1981\u0026ndash;2010, it is analyzed that WS is strengthened during summer and winter season in South China Sea whereas in the East China Sea, WS is increased in summer and decreased in winter (Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Global mean of near-surface wind speed has shown declining trend (Vautard et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tobin et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dunn et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Azorin-Molina et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zha et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The global surface wind speed has shown an increasing trend using Cross-Calibrated, Multi-Platform (CCMP) data for the period 1988 to 2011 where rate of increase is maximum during 1991\u0026ndash;2007 (Zheng et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have been conducted to interpret trends of WS utilizing CMIP5 and CMIP6 models (Taylor et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Eyring et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003ea). Using WS data simulated from CMIP6 models, global near-surface wind speed is expected to decrease during 2021\u0026ndash;2100 (Shen et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). WS data simulated from CMIP6 models reveals an upward trend in northern BoB and a downward trend in southern BoB during 2026\u0026ndash;2100 (Krishnan and Bhaskaran \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the middle and late twenty-first century, substantial decrease in WS averaged over China in winter and annual is anticipated whereas opposite trend is contemplated in summer using wind data simulated from CMIP 6 models (Wu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There has been an increasing trend of WS in the Caribbean and during the twenty-first century, it is expected to increase at faster rate using CMIP6 simulations data of WS (Bustos-Usta and Torres-Parra 2023).\u003c/p\u003e \u003cp\u003eNevertheless, in the literature, CMIP6 model evaluation on the basis of WS response to IOD has not been analyzed yet. Therefore, it is imperative to examine the CMIP6 models\u0026rsquo; performance based on the IOD amplitude associated with WS. It can provide a deep insight to understand the WS change associated with IOD in CMIP6 models.\u003c/p\u003e \u003cp\u003eThis study investigates the efficacy of 24 CMIP6 models to replicate the observed IOD associations with WS for the period 1958 to 2014 during the monsoon season. Linear regression analysis has been carried out to fetch individual model\u0026rsquo;s WS response to IOD and analyze the outcomes with the observation datasets. In addition, an assessment is done on the basis of three distinct skill metric parameters.\u003c/p\u003e \u003cp\u003eIn totality, the paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e details on the data and methodology. Section-3 consists of the climatology, variability, and outcomes of the CMIP6 model finding for the Indian Ocean. Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e4\u003c/span\u003e elaborates the discussion and conclusion.\u003c/p\u003e"},{"header":"2. Data and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents 24 CMIP6 historical models utilized in this study to analyze their efficiency. The analysis of WS over the Indian Ocean is based on the output of models during the JJA season. CMIP datasets are developed by the Earth System Grid Federation (ESGF), and dataset storage is hosted by various partners of ESGF (Williams et al. 2015). Monthly historical model datasets are utilized from the CMIP6 archive from 1958 to 2014, which are available at URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://esgf-node.llnl.gov/projects/cmip6/\u003c/span\u003e\u003cspan address=\"https://esgf-node.llnl.gov/projects/cmip6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. For CMIP6 analysis, the study utilized datasets from the first ensemble member-run of variant label \u0026ldquo;r1i1p1f1\u0026rdquo;, where r1 representsrealization index, i1 initialization index, p1 physics index, and f1 forcing index (Taylor et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tian et al. 2020). The CMIP6 models are interpolated to a common 1\u0026deg; \u0026times; 1\u0026deg; grid to make analysis simple as models display dissimilar horizontal resolutions. Bilinear interpolation has been used. The original resolution is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e24 CMIP6 models used in this study\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=\"char\" char=\"\u0026times;\" 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 \u003cp\u003eModel Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003cp\u003e(lon by lat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOcean Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAtmosphere Component\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACCESS-OM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMetUM-HadGEM3-GA7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACCESS-OM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHadGAM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMOM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBCC_AGCM3_MR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAMS-CSM1-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMOM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eECHAM5_CAMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e361 \u0026times; 290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEMO3.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCanAM5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e362 \u0026times; 196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLICOM2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIAP AGCM 5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCESM2-WACCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e320 \u0026times; 384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWACCM6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCIESM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e720 \u0026times; 560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCIESM-OM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCIESM-AM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e362 \u0026times; 292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEMO3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAM5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC-ESM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e362 \u0026times; 292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEMO3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAM5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e362 \u0026times; 292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEMO3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIFS cy36r4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e362 \u0026times; 292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEMO3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIFS cy36r4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3-Veg-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e362 \u0026times; 292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEMO3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIFS cy36r4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGOALS-g3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLICOM3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGAMIL3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIO-ESM-2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e320 \u0026times; 384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOP2-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAM4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e720 \u0026times; 576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFDL-OM4p5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGFDL-AM4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e362 \u0026times; 332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEMO-OPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMDZ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOCO4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCSR AGCM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e802 \u0026times; 404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMPIOM1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eECHAM6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e256 \u0026times; 220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMPIOM1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eECHAM6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMRI.COM4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMRI-AGCM3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorESM2-LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMICOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAM-OSLO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorESM2-MM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e360 \u0026times; 384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMICOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAM-OSLO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaiESM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaiwan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e320\u0026times;384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTaiAM1\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\u003eWind speed (u-component and v-component at 10-m height) datasets are extracted from ERA5 for the 57-year period from 1958\u0026ndash;2014. ERA5 is the newly released reanalysis product (Hersbach, 2016; Hersbach et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; He et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) which provides hourly estimates of data for large numbers of oceanic variables from January 1940 to till date. It is an upgraded version of the ERA-interim reanalysis product, which has advanced spatial resolutions (31 kilometers) and temporal resolutions (hourly) (Dee et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The spatial and temporal resolutions of WS data are 0.5\u0026deg; \u0026times; 0.5\u0026deg; and 6 hourly, respectively. Here, ERA5 is utilized as observation data to correlate with the CMIP6 models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 DMI Indices\u003c/h2\u003e \u003cp\u003eThe study utilizes the Dipole Mode Index (DMI), which is a simple index time series to understand the intensity of IOD. The DMI is described as the anomalous SST gradient between the western Indian Ocean bounded between 50\u003csup\u003eo\u003c/sup\u003eE-70\u003csup\u003eo\u003c/sup\u003eE, 10\u003csup\u003eo\u003c/sup\u003eS-10\u003csup\u003eo\u003c/sup\u003eN, and the eastern Indian Ocean bounded between 90\u003csup\u003eo\u003c/sup\u003eE-110\u003csup\u003eo\u003c/sup\u003eE, 10\u003csup\u003eo\u003c/sup\u003eS-equator (Saji et al. 1999). The SST anomalies are averaged over the region 50\u003csup\u003eo\u003c/sup\u003eE-70\u003csup\u003eo\u003c/sup\u003eE, 10\u003csup\u003eo\u003c/sup\u003eS-10\u003csup\u003eo\u003c/sup\u003eN for the tropical western IO and over the area 90\u003csup\u003eo\u003c/sup\u003eE-110\u003csup\u003eo\u003c/sup\u003eE, 10\u003csup\u003eo\u003c/sup\u003eS-equator for the tropical south-eastern IO. It is computed employing the SST data from ERA5 reanalysis datasets from 1958 to 2014. Additionally, during the same period, the DMI is determined for CMIP6 models utilizing the SST anomalies of each model. Further, the DMI for each model is normalized and detrended during the same period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methodology\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Bilinear Interpolation\u003c/h2\u003e \u003cp\u003eBilinear interpolation is the most popular technique employed for climate grid interpolation and is defined as linear interpolation along two directions. This is the effective method to interpolate the data values in 2-D. This technique can be applied easily when the destination and source grids are rectilinear in shape whereas it fails for the unstructured and curvilinear grids as it may comprise of complex algorithms to obtain the points neighboring the location at which it is to be interpolated. It is employed to compute the functional value at a point located inside a rectangular grid at which functional values are known. Bilinear interpolation has been implemented in the Climate Data Operators software. This technique assumes that the function changes linearly between adjacent grid points in both the x and y directions. A point Q is given inside the rectangular grid with known functional values, and the four nearest grid points to Q are obtained. These four points make a square inside which Q is present. The distances between each of the four grid points and Q are evaluated. At the four grid points, these distances are utilized to weigh the functional values. The value of the function at Q is interpolated by employing the weighted mean of the functional values at the four grid points. The weights are proportional to the inverse of the distances between the grid points and Q. The above-mentioned procedure is repeated for each point that requires to be interpolated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Linear Regression\u003c/h2\u003e \u003cp\u003eThe climate indices that correspond to observation data, each model, and their MMM during JJA season are normalized and detrended. The effect of IOD over WS is estimated by employing simple linear regression, wherein the JJA mean of WS is regressed onto the detrended and normalized IOD index as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$WS=a0+a1*IOD$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(a0\\)\u003c/span\u003e\u003c/span\u003e is the intercept and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(a1\\)\u003c/span\u003e\u003c/span\u003e is the regression coefficient, which are evaluated using least square methods. The regression coefficient expresses the type of relationship along with the strength the predictor variable has with the response variable. Coefficients for negative and positive relationships have negative and positive signs, respectively. The spatial distributions of the regression coefficient are depicted as WS response to IOD.\u003c/p\u003e \u003cp\u003eThe statistical significance of the regression coefficients for observation data, each model, and their MMM are done using a two-tailed Student\u0026rsquo;s t-test (Joshi et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The test statistic is given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{x}=x\\sqrt{m-2}/\\sqrt{1-{x}^{2}}\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cem\u003ex\u003c/em\u003e represents the correlation coefficient and \u003cem\u003eT\u003c/em\u003e\u003csub\u003ex\u003c/sub\u003e is the student t-value having (m‒2) degrees of freedom. When |\u003cem\u003eT\u003c/em\u003e\u003csub\u003ex\u003c/sub\u003e| \u0026gt; t\u003csub\u003eα/2\u003c/sub\u003e for α level of significance and t\u003csub\u003eα/2\u003c/sub\u003e as the critical value, it is implied that a significant linear relationship exists between the variables. The areas with statistical significance passing the α level of significance are displayed by hatching.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Skill Metrics\u003c/h2\u003e \u003cp\u003eStatistical evaluation of 24 CMIP6 models is examined utilizing diversified skill metrics such as interannual variability skill score (IVS), M-Score, and root mean square error (RMSE) against the observation WS response to IOD during JJA season from 1958 to 2014. RMSE represents error index statistics, which outlines the differences between the model data and observed data. For the individual model, WS response to IOD, RMSE is estimated as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${ RMSE}_{PQ}=\\sqrt{\u0026lang;{(P-Q)}^{2}\u0026rang;}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere P and Q are represented as the WS response to IOD corresponding to the model and reanalysis data, respectively. The angular brackets indicate the spatial mean over the Indian Ocean. Lower RMSE value implies better performance of the model and, conversely.\u003c/p\u003e \u003cp\u003eThe interannual variability skill score (IVS) investigates the interannual variability of the simulations with respect to that of the observations (Gleckler et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Scherrer \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). IVS is a symmetric variability statistic utilized to quantify the similarity of interannual variation between observation data and simulation data. It is defined as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$IVS={\\left( {\\frac{{ST{D_m}}}{{ST{D_o}}} - \\frac{{ST{D_o}}}{{ST{D_m}}}} \\right)^2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eSTD\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eSTD\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e represent the spatial standard deviations of the observation and simulation WS response to IOD, respectively. Smaller value of IVS exhibits a more accurate performance of datasets in simulating the interannual variability.\u003c/p\u003e \u003cp\u003eIn addition, the Arcsin\u0026ndash;Mielke measure or the M-Score (Mielke Jr \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Watterson \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Watterson et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) describes a non-dimensional skill score based on the Mean Squared Error (MSE) normalized by the spatial variance. The M-Score value is estimated, as defined in Watterson et al. (2015) given by:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$M=\\frac{2}{\\pi }\\arcsin \\left( {1 - \\frac{{MSE}}{{{V_x}+{V_y}+{{({G_x} - {G_y})}^2}}}} \\right) \\times 1000$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere G\u003csub\u003ex\u003c/sub\u003e and G\u003csub\u003ey\u003c/sub\u003e represent the spatial average of the model and observation fields, respectively; V\u003csub\u003ex\u003c/sub\u003e and V\u003csub\u003ey\u003c/sub\u003e are the spatial variances of the model and observation fields, respectively; MSE is the mean square error between the observation field X and the model field Y; 2/π is a normalizing factor for the arcsin term which goes from 0 to π/2. The range of M-Score varies from zero (or even below) which specifies no skill, to a theoretical maximum of one thousand corresponding to MSE as zero. The findings of the above given three metrics has been measured in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on these skill metrics, each model is ranked accordingly, and a total rank is determined by taking an arithmetic mean of the above three metric rankings.\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\u003eRoot-Mean‐Square Errors (RMSEs), Interannual Variability Skill Score (IVS), and M-Score of individual CMIP6 Models against the Observation WS response to IOD for JJAduring 1958\u0026ndash;2014.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM-Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71093667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17384852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.423356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAMS-CSM1-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.8282686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3716274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04339706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCESM2-WACCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03274063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCIESM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06714264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15980088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC-ESM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45438047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15963516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7505581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3-Veg-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47738758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGOALS-g3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48509501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIO-ESM-2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01751805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23423694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84925931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42029531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44461574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15143353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00863992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorESM2-LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08342173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorESM2-MM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75503671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaiESM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34018162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71443445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 IOD indices, multi-model ensemble mean (MMM) and model spread\u003c/h2\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e (a) represents the detrended and normalized time series of DMI indices of MMM of 24 CMIP6 models, observed dataset, accompanied by CMIP6 model spread from 1958 to 2014 during JJA season. Multi-model ensemble mean helps to curtail the errors in the outputs of each model (Kharin et al. \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Knutti et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Warner et al. 2011). DMI indices of MMM is calculated by taking the arithmetic mean of 24 CMIP6 DMI indices. The DMI index determined from the observed data displays the occurrence of positive IOD (pIOD) events years (1961; 1963; 1967; 1972; 1982; 1983; 1994; 1997; 2006; 2007; 2012) and negative IOD (nIOD) events years (1958; 1960; 1975; 1984; 1985; 1990; 1992; 1995; 1996; 1998; 2005; 2010). Overall, the range of the observed DMI time series is in uniformity with CMIP6 model spread.\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e (b) depicts the corresponding interannual standard deviations of DMI indices before detrending and normalizing. Fluctuations of the DMI index from year-to-year are exhibited by all the models with varying amplitudes. The standard deviation in the DMI index of the MMM is greater than 0.6\u0026deg;C. Two models (CMCC-CM2-SR5 and CMCC-ESM2) exhibit stronger IOD amplitude (\u0026gt;\u0026thinsp;1\u0026deg;C). The standard deviations of models EC-Earth3 and MPI-ESM1-2-HR are close to standard deviation of MMM. Minimum variation of IOD amplitude is visualized in ACCESS-CM2 having a magnitude of approximately 0.3\u0026deg;C.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Climatology, Variability and Bias\u003c/h2\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the climatology of WS from 1958 to 2014 during the JJA season over IO is displayed for ERA5 (observation data), CMIP6, and multi-model ensemble mean (MMM). MMM refers to arithmetic mean across individual models. MMM works as a useful mechanism in climate model evaluation by measuring uncertainty, lowering down model biases, consensus and analyzing model spread,which brings out potential knowledge through a robust integration of climate model outcomes.\u003c/p\u003e\n\u003cp\u003eIn all the CMIP6 models, the climatology pattern reveals strong positive trend of WS over Southern Indian Ocean (SIO) whereas mild increase is observed over the TIO and BoB. Most of the models detect a significant increase in WS over coastal region of the Arabian Sea. The climatology pattern of observation data illustrates a mild increase in WS over SIO, TIO, BoB and the coastal region of eastern AS and a substantial increase over western AS. Overall, the climatology of observation data and CMIP6 MMM replicates the pattern well in AS, BoB, and TIO whereas MMM overestimates the trend over SIO. In totality, the climatological pattern of observation data and CMIP6 MMM has a good spatial correlation.\u003c/p\u003e\n\u003cp\u003eThe variability pattern of WS over JJA season during 1958‒2014 is displayed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for ERA5 (observation data), CMIP6, and MMM. The variability in WS is expressed by the interannual standard deviation. Strong variations in WS have been detected by most of the CMIP6 models over SIO and BoB. Few models have identified strong variations in WS over TIO, whereas others have shown mild variations over the same region. In all the CMIP6 models, AS has observed very mild variations in WS. The variability of observation data exhibits strong fluctuations over SIO and TIO, significant variations over BoB, and negligible changes over AS. Overall, there is consistency between observation data and CMIP6 MMM over BoB. In totality, the variability pattern of CMIP6 MMM underestimates over TIO, and SIO and overestimates over southern AS, left to southern tip of India .\u003c/p\u003e\n\u003cp\u003eThe biases in the variability of WS of CMIP6 MMM (i.e., MMM minus ERA5) during JJA season for the period 1958\u0026ndash;2014 is depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. CMIP6 models are underestimating the observation (ERA5) in most parts of SIO and overestimating in nortehr parts. Strong negative bias is observed over SIO, moderate positive bias is seen along African coast and over southern AS, left to the southern tip of India and mild negative bias is obtained over BoB and northern AS. Some models exhibit strong positive bias (~\u0026thinsp;0.4\u0026ndash;0.6 m/s) in BoB, while others show moderate biases upto 0.2 m/s. In CMIP6 MMM, minimal biases (0-0.2 m/s) are observed over AS and BoB, maximal biases (0.4\u0026ndash;0.8 m/s) are visualized over SIO, and moderate biases (0.2\u0026ndash;0.4 m/s) are noted over TIO.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Model Evaluation\u003c/h2\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.1 WS response to IOD\u003c/h2\u003e\n\u003cp\u003eThe spatial pattern of regression coefficients of WS onto the DMI index for CMIP6 models, their MMM, and ERA5 dataset during JJA season is depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Hatching indicates regions with statistical significance at the 5% level. For MMM, an ensemble mean of all the regression coefficients is displayed. By combining the outputs of multiple models through a MMM, it is possible to reduce the impact of individual biases or errors, and obtain a more robust estimate. Based on the ERA5, IOD events instigate significant enhancement in WS over the eastern tropical IO and mild increase over BoB, while a significant reduction in WS is apparent over the equatorial region of IO below the southern tip of India, which extends towards the westward over AS and slightly eastward towards BoB. Overall, WS response to IOD across the Indian Ocean is moderately captured by various individual models. However, some models (CESM2, CESM2-WACCM, CMCC-CM2-SR5, CMCC-ESM2, FIO-ESM-2-0, MRI-ESM2-O, NorESM2-MM and TaiESM1) overestimate the observed pattern. While some (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-O, MPI-ESM1-2-HR, and MPI-ESM1-2-LR) underestimate the observed pattern. ACCESS-ESM1-5, EC-Earth3, EC-Earth3-Veg, EC-Earth3-Veg-LR, GFDL-ESM4, and IPSL-CM6A-LR demonstrate a similar WS response pattern to the observation data.\u003c/p\u003e\n\u003cp\u003eThe correlation coefficients between the CMIP6 models and the observation WS regression pattern range from 0.58 to 0.75 (Moderate correlation coefficient is observed between the observation and CMIP6 models WS pattern). The MMM regression pattern of WS exhibits maximum correlation with that of the observation pattern. Overall, MMM performs better than individual models and exhibits minimal biases in the representation of the WS regression pattern.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.2 Model evaluation based on skill metrics\u003c/h2\u003e\n\u003cp\u003eIn this section, CMIP6 model performance for WS response is assessed using three skill metrics as RMSE, IVS, and M-Score. Based on skill metric parameters, each model is ranked according to their performance (as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Rankings are graphically represented using a portrait diagram in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, which offers a concise summary of the CMIP6 global historical simulation of model WS response compared to the observed WS response. For an overall evaluation, a total ranking is also determined from the arithmetic average of the three rankings (last column of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAmong all the CMIP6 models, EC-Earth3-Veg exhibits the least RMSE value, followed by EC-Earth3-Veg-LR, EC-Earth3, ACCESS-ESM1-5, MPI-ESM1-2-HR, FGOALS-g3, CAMS-CSM1-0, NorESM2-LM, IPSL-CM6A-LR, CESM2, CanESM5 and GFDL-ESM4. Overall, the RMSE values of the model simulations relative to the observation WS responses are minimal, thereby indicating that most of the CMIP6 models appropriately capture the WS response to IOD during the monsoon season. The MMM exhibits the lowest RMSE value compared to CMIP6 models, and thus outperforms the individual models as expected. IVS values are calculated for each model based on WS response to IOD over the Indian Ocean (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Among all the CMIP6 models, MRI-ESM2-0, FIO-ESM2-0, CESM2, CAS-ESM2-0, CESM2-WACCM, NorESM2-LM, MPI-ESM1-2-LR, EC-Earth3, CMCC-CM2-SR5, and ACCESS-ESM1-5 are ranked as top ten models in the evaluation of IVS for WS response. The top twenty models in evaluating IVS for WS response exhibit lower value than the MMM IVS value. Four models such as CAMS-CSM1-0, BCC-CSM2-MR, IPSL-CM6A-LR, and NorESM2-MM show high IVS values, thus poorly simulate the interannual variability of monsoon WS response to IOD.\u003c/p\u003e\n\u003cp\u003eFurther, M-Score evaluated for the WS response to IOD among the CMIP6 models ranges between 700 to 800. A higher value of M-Score corresponds to a better performance of the CMIP6 models. CESM2, EC-Earth3, EC-Earth3-Veg, EC-Earth3-Veg-LR, ACCESS-ESM1-5, MPI-ESM1-2-HR, NorESM2-LM, CESM2-WACCM, FGOALS-g3, and GFDL-ESM4 models are top ten models for the evaluation of M-Score. The lowest M-Score prevails for NorESM2-MM, CAMS-CSM1-0, and MRI-ESM2-0.\u003c/p\u003e\n\u003cp\u003eOverall, it is observed that the performance of CMIP6 models varies differently in individual skill metric parameters (RMSE, IVS, and M-Score). Specifically, CMIP6 models exhibit a range of performances in the representation of WS response to IOD based on different skill metric parameters. Besides, ranking model\u0026rsquo;s performance based on one single parameter would not be sufficient to give a detailed outcome. Therefore, an arithmetic average of the rank of these skill metrics has been considered to determine the total rank of individual models, which will provide a synthesis of the WS response to IOD for each model. Based on the total ranking, the top best-performing models are CESM2, EC-Earth3, ACCESS-ESM1-5, EC-Earth3-Veg-LR, MMM, NorESM2-LM, CESM2-WACCM, EC-Earth3-Veg, MPI-ESM1-2-HR, and FIO-ESM2-0 (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion and Discussion","content":"\u003cp\u003eThis study estimated 24 CMIP6 models efficiency to simulate WS response to IOD during JJA season over the Indian Ocean based on historical simulations from 1958\u0026ndash;2014. The range of the observed DMI time series is consistent with CMIP6 model spread. Minimum variation of IOD amplitude is observed in the model ACCESS-CM2 having a magnitude of approximately 0.3\u0026deg;C whereas maximum IOD amplitude is displayed by CMCC-ESM2 having greater than 1\u0026deg;C. The performance of the individual model varies, whereas MMM accuracy is close to observation data. The climatology and variability have been evaluated. It is found that the climatological pattern of observation data and CMIP6 MMM are in consonance and have a good correlation, whereas the variability pattern of CMIP6 MMM underestimates over northern AS, TIO, and SIO and overestimates over southern AS. In the case of bias in variability, minimum is observed over BoB and AS, maximum over SIO, and moderate over TIO.\u003c/p\u003e \u003cp\u003eWS responses to IOD are analyzed by using simple linear regression analysis. Some models overestimate the observed pattern, some underestimate the observed pattern while few match with the observed trends. Overall, WS response to IOD across the Indian Ocean is moderately captured by various individual models, whereas MMM appropriately captures to the observed pattern. The efficiency of CMIP6 models changes differently in individual skill metric parameters (RMSE, IVS, and M-Score). On the basis of total rankings, the top ten best-performing models are CESM2, EC-Earth3, ACCESS-ESM1-5, EC-Earth3-Veg-LR, MMM, NorESM2-LM, CESM2-WACCM, EC-Earth3-Veg, MPI-ESM1-2-HR, and FIO-ESM2-0.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate (kindly mention the name of the Ethics Committee and the Ethical Approval Number)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eI, the undersigned, give my consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (\u0026ldquo;Material\u0026rdquo;) to be published in the above Journal and Article.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no known \u003cem\u003ecompeting\u003c/em\u003e financial \u003cem\u003einterests.\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe present study is supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under core research grant project file no. (CRG/2021/003654).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eRamakant Prasad:\u0026nbsp;\u003c/strong\u003eData curation, Investigation, Methodology, Writing- Original draft preparation,\u003cstrong\u003e\u0026nbsp;Anshu Yadav:\u0026nbsp;\u003c/strong\u003eInvestigation, Writing- Original draft preparation, Conceptualization, \u003cstrong\u003eDivya Sardana:\u003c/strong\u003e Data curation, Investigation, Validation, \u003cstrong\u003ePrashant Kumar:\u003c/strong\u003e Conceptualization, Visualization, Methodology, Supervision, Writing-Reviewing and Editing, \u003cstrong\u003eAnurag Singh\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Supervision, Writing-Reviewing and Editing, \u003cstrong\u003eYukiharu\u0026nbsp;Hisaki:\u003c/strong\u003e Conceptualization, Visualization, Writing-Reviewing and Editing, \u003cstrong\u003e\u0026nbsp;Rajni:\u003c/strong\u003e Visualization, Writing- Reviewing and Editing;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe present study is supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under core research grant project file no. (CRG/2021/003654).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndr\u0026eacute;e, E., Drews, M., Su, J., Larsen, M. A. D., Dr\u0026oslash;nen, N., Madsen, K. S., 2022. Simulating wind-driven extreme sea levels: Sensitivity to wind speed and direction. Weather and Climate Extremes. 36, 100422. https://doi.org/10.1016/j.wace.2022.100422.\u003c/li\u003e\n\u003cli\u003eAzorin-Molina, C., Dunn, R. J. H., Mears, C. A., Berrisford, P., McVicar, T. R., \u0026amp; Nicolas, J. P., 2017. Surface winds [in \u0026ldquo;State of the Climate in 2016\u0026rdquo;]. Bull. Amer. Meteor. Soc. 98(8), S37-S39.\u003c/li\u003e\n\u003cli\u003eAzorin-Molina, C., Vicente-Serrano, S. M., McVicar, T. R., Jerez, S., Sanchez-Lorenzo, A., L\u0026oacute;pez-Moreno, J. I., ..., Esp\u0026iacute;rito-Santo, F., 2014. Homogenization and assessment of observed near-surface wind speed trends over Spain and Portugal, 1961\u0026ndash;2011. Journal of Climate. 27(10), 3692-3712. https://doi.org/10.1175/JCLI-D-13-00652.1.\u003c/li\u003e\n\u003cli\u003eBarry, R. G., Chorley, R. J., 2009. Atmosphere, weather and climate. Routledge. https://doi.org/10.4324/9780203871027.\u003c/li\u003e\n\u003cli\u003eBi, D., Dix, M., Marsland, S., O\u0026rsquo;farrell, S., Sullivan, A., Bodman, R., ..., Heerdegen, A., 2020. Configuration and spin-up of ACCESS-CM2, the new generation Australian community climate and earth system simulator coupled model. Journal of Southern Hemisphere Earth Systems Science. 70(1), 225-251. https://doi.org/10.1071/ES19040.\u003c/li\u003e\n\u003cli\u003eBigg, G. R., 2003. The oceans and climate, second ed. Cambridge University Press. \u003c/li\u003e\n\u003cli\u003eBormann, H., 2011. Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Climatic Change. 104(3), 729-753. https://doi.org/10.1007/s10584-010-9869-7.\u003c/li\u003e\n\u003cli\u003eBustos Usta, D. F., Torres Parra, R. R., 2023. Projected wind changes in the Caribbean Sea based on CMIP6 models. Climate Dynamics. 60(11), 3713-3727. https://doi.org/10.1007/s00382-022-06535-3.\u003c/li\u003e\n\u003cli\u003eCai, W., Santoso, A., Wang, G., Weller, E., Wu, L., Ashok, K., ... \u0026amp; Yamagata, T., 2014. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature. 510(7504), 254-258. https://doi.org/10.1038/nature13327.\u003c/li\u003e\n\u003cli\u003eChen, H., He, Z., Xie, Q., Zhuang, W., 2023. Performance of CMIP6 models in simulating the dynamic sea level: Mean and interannual variance. Atmospheric and Oceanic Science Letters. 16(1), 100288. https://doi.org/10.1016/j.aosl.2022.100288.\u003c/li\u003e\n\u003cli\u003eChen, L., Li, D., Pryor, S. C., 2013. Wind speed trends over China: quantifying the magnitude and assessing causality. International Journal of Climatology. 33(11), 2579-2590. https://doi.org/10.1002/joc.3613.\u003c/li\u003e\n\u003cli\u003eChen, W., Jiang, Z., Li, L., 2011. Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. Journal of Climate. 24(17), 4741-4756. https://doi.org/10.1175/2011JCLI4102.1.\u003c/li\u003e\n\u003cli\u003eCusack, S., 2013. A 101 year record of windstorms in the Netherlands. Climatic Change. 116 (3 4), 693-704. 10.1007/s10584-012-0527-0.\u003c/li\u003e\n\u003cli\u003eDadaser‐Celik, F., Cengiz, E., 2014. Wind speed trends over Turkey from 1975 to 2006. International Journal of Climatology. 34(6), 1913-1927. https://doi.org/10.1002/joc.3810.\u003c/li\u003e\n\u003cli\u003eDee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., ..., Vitart, F., 2011. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society. 137(656), 553-597. https://doi.org/10.1002/qj.828.\u003c/li\u003e\n\u003cli\u003eDeng, K., Azorin-Molina, C., Minola, L., Zhang, G., Chen, D., 2021. Global near-surface wind speed changes over the last decades revealed by reanalyses and CMIP6 model simulations. Journal of Climate. 34(6), 2219-2234. https://doi.org/10.1175/JCLI-D-20 0310.1.\u003c/li\u003e\n\u003cli\u003eDeng, K., Yang, S., Liu, W., Li, H., Chen, D., Lian, T., ..., Shen, C., 2024. The offshore wind speed changes in China: an insight into CMIP6 model simulation and future projections. Climate Dynamics. 1-15. https://doi.org/10.1007/s00382-023-07066-1.\u003c/li\u003e\n\u003cli\u003eDunn, R. J. H., Azorin-Molina, C., Mears, C. A., Berrisford, P., McVicar, T. R., 2016. Surface winds (in \u0026ldquo;State of the climate in 2015\u0026rdquo;). Bull Am Meteorol Soc. 97(8), S38-S40.\u003c/li\u003e\n\u003cli\u003eEyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., Taylor, K. E., 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development. 9(5), 1937 1958. https://doi.org/10.5194/gmd-9-1937-2016.\u003c/li\u003e\n\u003cli\u003eEyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., ..., Williamson, M. S., 2019. Taking climate model evaluation to the next level. Nature Climate Change. 9(2), 102-110. https://doi.org/10.1038/s41558-018-0355-y.\u003c/li\u003e\n\u003cli\u003eFu, C., Wang, D., Yang, L., Luo, Y., Zhou, F., Priyadarshana, T., Yao, J., 2018. The relationship between significant wave height and Indian Ocean Dipole in the equatorial North Indian Ocean. Ocean Dynamics. 68, 689-699. https://doi.org/10.1007/s10236-018-1154-8.\u003c/li\u003e\n\u003cli\u003eGleckler, P. J., Taylor, K. E., \u0026amp; Doutriaux, C., 2008. Performance metrics for climate models. Journal of Geophysical Research: Atmospheres. 113(D6). D06104. https://doi.org/10.1029/2007JD008972.\u003c/li\u003e\n\u003cli\u003eHe, Y., Wang, K., \u0026amp; Feng, F., 2021. Improvement of ERA5 over ERA-Interim in simulating surface incident solar radiation throughout China. Journal of Climate. 34(10), 3853-3867. https://doi.org/10.1175/JCLI-D-20-0300.1. \u003c/li\u003e\n\u003cli\u003eHersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor\u0026aacute;nyi, A., Mu\u0026ntilde;oz‐Sabater, J., ..., Th\u0026eacute;paut, J. N., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society. 146(730), 1999-2049. https://doi.org/10.1002/qj.3803.\u003c/li\u003e\n\u003cli\u003eHong, C. C., Lu, M. M., Kanamitsu, M., 2008. Temporal and spatial characteristics of positive and negative Indian Ocean dipole with and without ENSO. Journal of Geophysical Research: Atmospheres. 113(D8). https://doi.org/10.1029/2007JD009151.\u003c/li\u003e\n\u003cli\u003eJhajharia, D., Shrivastava, S. K., Sarkar, D. S. A. S., \u0026amp; Sarkar, S., 2009. Temporal characteristics of pan evaporation trends under the humid conditions of northeast India. Agricultural and Forest Meteorology. 149(5), 763-770. https://doi.org/10.1016/j.agrformet.2008.10.024.\u003c/li\u003e\n\u003cli\u003eJiang, Z., Li, W., Xu, J., \u0026amp; Li, L., 2015. Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. Journal of Climate. 28(21), 8603-8619. https://doi.org/10.1175/JCLI-D-15-0099.1.\u003c/li\u003e\n\u003cli\u003eJoshi, M. K., Rai, A., Kulkarni, A., \u0026amp; Kucharski, F., 2020. Assessing changes in characteristics of hot extremes over India in a warming environment and their driving mechanisms. Scientific Reports. 10(1), 2631. https://doi.org/10.1038/s41598-020-59427-z. \u003c/li\u003e\n\u003cli\u003eKaur, S., Kumar, P., Min, S. K., Patra, A., Wang, X. L., 2022. CMIP5 model evaluation for extreme ocean wave height responses to ENSO. Climate Dynamics. 1-15. https://doi.org/10.1007/s00382-021-06039-6.\u003c/li\u003e\n\u003cli\u003eKharin, V. V., Zwiers, F. W., \u0026amp; Gagnon, N., 2001. Skill of seasonal hindcasts as a function of the ensemble size. Climate Dynamics. 17(11), 835-843. https://doi.org/10.1007/s003820100149.\u003c/li\u003e\n\u003cli\u003eKim, I. W., Oh, J., Woo, S., Kripalani, R. H., 2019. Evaluation of precipitation extremes over the Asian domain: observation and modelling studies. Climate Dynamics. 52, 1317-1342. https://doi.org/10.1007/s00382-018-4193-4.\u003c/li\u003e\n\u003cli\u003eKim, J., Paik, K. 2015. Recent recovery of surface wind speed after decadal decrease: a focus on South Korea. Climate Dynamics. 45, 1699-1712. https://doi.org/10.1007/s00382-015 \u003cu\u003e2546-9\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eKnutti, R., Furrer, R., Tebaldi, C., Cermak, J., Meehl, G. A. 2010. Challenges in combining projections from multiple climate models. Journal of Climate. 23(10), 2739-2758. https://doi.org/10.1175/2009jcli3361.1.\u003c/li\u003e\n\u003cli\u003eKrishnan, A., Bhaskaran, P. K., 2020. Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal. Climate Dynamics. 55(9-10), 2667-2687. https://doi.org/10.1007/s00382-020-05406-z.\u003c/li\u003e\n\u003cli\u003eKumar, P., Kaur, S., Weller, E., Min, S. K., 2019. Influence of natural climate variability on the extreme ocean surface wave heights over the Indian Ocean. Journal of Geophysical Research: Oceans. 124(8), 6176-6199. https://doi.org/10.1029/2019JC015391.\u003c/li\u003e\n\u003cli\u003eKumar, P., Sardana, D., 2021. Regional sea level changes in the Indian shelf sea and its association with SST anomalies. Regional Studies in Marine Science. 47, 101992. https://doi.org/10.1016/j.rsma.2021.101992.\u003c/li\u003e\n\u003cli\u003eKushwaha, P., Pandey, V. K., Kumar, P., Sardana, D., 2024. CMIP6 Model Evaluation for Mean and Extreme Precipitation Over India. Pure and Applied Geophysics. 1-24. https://doi.org/10.1007/s00024-023-03409-5.\u003c/li\u003e\n\u003cli\u003eKusuru, D., Imandi, R., Turlapaty, A. C., Thakur, M., 2022. A Gaussian Gamma mixture model for Indian ocean surface wind speed. In OCEANS 2022-Chennai (pp. 1-5). IEEE. https://doi.org/10.1109/OCEANSChennai45887.2022.9775268.\u003c/li\u003e\n\u003cli\u003eLanderer, F. W., Gleckler, P. J., \u0026amp; Lee, T., 2014. Evaluation of CMIP5 dynamic sea surface height multi-model simulations against satellite observations. Climate dynamics. 43, 1271-1283. https://doi.org/10.1007/s00382-013-1939-x.\u003c/li\u003e\n\u003cli\u003eLaurila, T. K., Sinclair, V. A., Gregow, H., 2021. Climatology, variability, and trends in near surface wind speeds over the North Atlantic and Europe during 1979\u0026ndash;2018 based on ERA5. International Journal of Climatology. 41(4), 2253-2278. https://doi.org/10.1002/joc.6957.\u003c/li\u003e\n\u003cli\u003eLi, J. L. F., Tsai, Y. C., Xu, K. M., Lee, W. L., Jiang, J. H., Yu, J. Y., ..., Stephens, G., 2022. Inferring the linkage of sea surface height anomalies, surface wind stress and sea surface temperature with the falling ice radiative effects using satellite data and global climate models. Environmental Research Communications. 4(12), 125004. https://doi.org/10.1088/2515-7620/aca3fe.\u003c/li\u003e\n\u003cli\u003eLi, J., Zhao, Y., Wang, M., Tan, W., Yin, J., 2024. Projected Changes of Wind Energy Input to Surface Waves in the North Indian Ocean Based on CMIP6. Atmosphere. 15(1), 139. https://doi.org/10.3390/atmos15010139.\u003c/li\u003e\n\u003cli\u003eLyddon, C. E., Brown, J. M., Leonardi, N., Plater, A. J., 2019. Increased coastal wave hazard generated by differential wind and wave direction in hyper-tidal estuaries. Estuarine, Coastal and Shelf Science. 220, 131-141. https://doi.org/10.1016/j.ecss.2019.02.042.\u003c/li\u003e\n\u003cli\u003eLyu, K., Zhang, X., Church, J. A., 2020. Regional dynamic sea level simulated in the CMIP5 and CMIP6 models: Mean biases, future projections, and their linkages. Journal of Climate. 33(15), 6377-6398. https://doi.org/10.1175/JCLI-D-19-1029.1.\u003c/li\u003e\n\u003cli\u003eLyu, K., Zhang, X., Church, J. A., Hu, J., 2016. Evaluation of the interdecadal variability of sea surface temperature and sea level in the Pacific in CMIP3 and CMIP5 models. International Journal of Climatology. 36(11), 3723-3740. https://doi.org/10.1002/joc.4587. \u003c/li\u003e\n\u003cli\u003eMagnan, A. K., Oppenheimer, M., Garschagen, M., Buchanan, M. K., Duvat, V. K., Forbes, D. L., ..., P\u0026ouml;rtner, H. O., 2022. Sea level rise risks and societal adaptation benefits in low lying coastal areas. Scientific reports. 12(1), 10677. \u003cu\u003ehttps://doi.org/10.1038/s41598-022\u003c/u\u003e \u003cu\u003e14303-w\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eMalloy, J. W., Krahenbuhl, D. S., Bush, C. E., Balling, R. C., Santoro, M. M., White, J. R., ..., Cerveny, R. S., 2015. A surface wind extremes (\u0026ldquo;wind lulls\u0026rdquo; and \u0026ldquo;wind blows\u0026rdquo;) climatology for central North America and adjoining oceans (1979\u0026ndash;2012). Journal of Applied Meteorology and Climatology. 54(3), 643-657. https://doi.org/10.1175/JAMC \u003cu\u003eD-14-0009.1\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eMcKenna, S., Santoso, A., Gupta, A. S., Taschetto, A. S., Cai, W., 2020. Indian Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO. Scientific reports. 10(1), 11500. https://doi.org/10.1038/s41598-020-68268-9.\u003c/li\u003e\n\u003cli\u003eMcVicar, T. R., Van Niel, T. G., Li, L. T., Roderick, M. L., Rayner, D. P., Ricciardulli, L., Donohue, R. J., 2008. Wind speed climatology and trends for Australia, 1975\u0026ndash;2006: Capturing the stilling phenomenon and comparison with near‐surface reanalysis output. Geophysical Research Letters. 35(20). https://doi.org/10.1029/2008GL035627.\u003c/li\u003e\n\u003cli\u003eMiao, H., Xu, H., Huang, G., Yang, K., 2023. Evaluation and future projections of wind energy resources over the Northern Hemisphere in CMIP5 and CMIP6 models. Renewable Energy. 211, 809-821. https://doi.org/10.1016/j.renene.2023.05.007.\u003c/li\u003e\n\u003cli\u003eMielke Jr, P. W., 1991. The application of multivariate permutation methods based on distance functions in the earth sciences. Earth-Science Reviews. 31(1), 55-71. https://doi.org/10.1016/0012-8252(91)90042-E.\u003c/li\u003e\n\u003cli\u003ePasquero, C., Desbiolles, F., Meroni, A. N., 2021. Air‐sea interactions in the cold wakes of tropical cyclones. Geophysical Research Letters. 48(2), e2020GL091185. https://doi.org/10.1029/2020GL091185\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003ePatra, A., Bhaskaran, P. K., 2017. Temporal variability in wind\u0026ndash;wave climate and its validation with ESSO-NIOT wave atlas for the head Bay of Bengal. Climate Dynamics. 49, 1271 1288. https://doi.org/10.1007/s00382-016-3385-z.\u003c/li\u003e\n\u003cli\u003ePryor, S. C., \u0026amp; Ledolter, J. (2010). Addendum to \u0026ldquo;Wind speed trends over the contiguous United States\u0026rdquo;. Journal of Geophysical Research: Atmospheres, 115(D10). https://doi.org/10.1029/2009JD013281\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eRaju, K. S., Kumar, D. N., 2020. Review of approaches for selection and ensembling of GCMs. Journal of Water and Climate Change. 11(3), 577-599. http://doi.org/10.2166/wcc.2020.128\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eRashmi, R., Polnikov, V., Pogarskii, F., Gomorev, I., Samiksha, V., Vethamony, P., 2016. Long term variability of the wind field over the Indian Ocean based on ERA-Interim reanalysis. Atmosphere-Ocean. 54(5), 505-518. https://doi.org/10.1080/07055900.2016.1158147. \u003c/li\u003e\n\u003cli\u003eRoy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., Shit, M., 2023. Effects of climate change and sea-level rise on coastal habitat: Vulnerability assessment, adaptation strategies and policy recommendations. Journal of Environmental Management. 330, 117187. https://doi.org/10.1016/j.jenvman.2022.117187.\u003c/li\u003e\n\u003cli\u003eSaji, N. H., Yamagata, T. J. C. R., 2003. Possible impacts of Indian Ocean dipole mode events on global climate. Climate research. 25(2), 151-169. https://doi.org/10.3354/cr025151.\u003c/li\u003e\n\u003cli\u003eSardana, D., Kumar, P., Rajni., 2023. CMIP6 model evaluation for sea surface height responses to ENSO. Climate Dynamics. 1-19. \u003cu\u003ehttps://doi.org/10.1007/s00382-023-06997\u003c/u\u003e \u003cu\u003ez\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eScherrer, S. C., 2011. Present‐day interannual variability of surface climate in CMIP3 models and its relation to future warming. International Journal of Climatology. 31(10), 1518 1529. https://doi.org/10.1002/joc.2170.\u003c/li\u003e\n\u003cli\u003eShanas, P. R., Kumar, V. S., 2015. Trends in surface wind speed and significant wave height as revealed by ERA‐Interim wind wave hindcast in the Central Bay of Bengal. International Journal of Climatology. 35(9), 2654-2663. https://doi.org/10.1002/joc.4164.\u003c/li\u003e\n\u003cli\u003eShanas, P. R., Sanil Kumar, V., 2014. Temporal variations in the wind and wave climate at a location in the eastern Arabian Sea based on ERA-Interim reanalysis data. Natural hazards and earth system sciences. 14(5), 1371-1381. \u003cu\u003ehttps://doi.org/10.5194/nhess-14\u003c/u\u003e \u003cu\u003e1371-2014\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eShen, C., Zha, J., Li, Z., Azorin‐Molina, C., Deng, K., Minola, L., Chen, D., 2022. Evaluation of global terrestrial near‐surface wind speed simulated by CMIP6 models and their future projections. Annals of the New York Academy of Sciences. 1518(1), 249-263. https://doi.org/10.1111/nyas.14910.\u003c/li\u003e\n\u003cli\u003eShen, C., Zha, J., Zhao, D., Wu, J., Fan, W., Yang, M., Li, Z., 2021. Estimating centennial-scale changes in global terrestrial near-surface wind speed based on CMIP6 GCMs. Environmental Research Letters. 16(8), 084039. https://doi.org/10.1088/1748 \u003cu\u003e9326/ac1378\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eShen, C., Zha, J., Zhao, D., Wu, J., Fan, W., Yang, M., Li, Z., 2021. Estimating centennial-scale changes in global terrestrial near-surface wind speed based on CMIP6 GCMs. Environmental Research Letters. 16(8), 084039. \u003cu\u003ehttps://doi.org/10.1088/1748\u003c/u\u003e \u003cu\u003e9326/ac1378\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eTaylor, K. E., Juckes, M., Balaji, V., Cinquini, L., Denvil, S., Durack, P. J., ..., Stockhause, M., 2018. CMIP6 global attributes, DRS, filenames, directory structure, and CV\u0026rsquo;s. PCMDI Document. \u003cu\u003ehttps://goo.gl/v1drZl\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eTaylor, K. E., Stouffer, R. J., Meehl, G. A., 2012. An overview of CMIP5 and the experiment design. Bulletin of the American meteorological Society. 93(4), 485-498. https://doi.org/10.1175/BAMS-D-11-00094.1.\u003c/li\u003e\n\u003cli\u003eTian, B., Dong, X., 2020. The double‐ITCZ bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation. Geophysical Research Letters. 47(8), e2020GL087232. https://doi.org/10.1029/2020GL087232.\u003c/li\u003e\n\u003cli\u003eTobin, I., Berrisford, P., Dunn, R. J. H., Vautard, R., McVicar, T. R., 2014. Land surface wind speed [in \u0026ldquo;State of the Climate in 2013\u0026rdquo;]. Bull. Amer. Meteor. Soc. 95(7), S28-S29.\u003c/li\u003e\n\u003cli\u003eTroccoli, A., Muller, K., Coppin, P., Davy, R., Russell, C., Hirsch, A. L., 2012. Long-term wind speed trends over Australia. Journal of Climate. 25(1), 170-183. https://doi.org/10.1175/2011JCLI4198.1. \u003c/li\u003e\n\u003cli\u003eVautard, R., McVicar, T. R., Thepaut, J. N., Roderic, M. L., 2012. Global climate; atmospheric circulation; surface winds) land surface winds and atmospheric evaporative demand (in \u0026ldquo;State of the Climate in 2011\u0026rdquo;). Bull Am Meteorol Soc. 93(7), S6-S38.\u003c/li\u003e\n\u003cli\u003eWan, H., Wang, X. L., Swail, V. R., 2010. Homogenization and trend analysis of Canadian near surface wind speeds. Journal of Climate. 23(5), 1209-1225. https://doi.org/10.1175/2009JCLI3200.1.\u003c/li\u003e\n\u003cli\u003eWang, C., Zhang, L., Lee, S. K., Wu, L., Mechoso, C. R., 2014. A global perspective on CMIP5 climate model biases. Nature Climate Change. 4(3), 201-205. https://doi.org/10.1038/nclimate2118.\u003c/li\u003e\n\u003cli\u003eWang, Y., Zou, R., Liu, F., Zhang, L., Liu, Q., 2021. A review of wind speed and wind power forecasting with deep neural networks. Applied Energy. 304, 117766. https://doi.org/10.1016/j.apenergy.2021.117766.\u003c/li\u003e\n\u003cli\u003eWarner, T. T., 2010. Numerical weather and climate prediction. Cambridge university press. https://doi.org/10.1017/CBO9780511763243.\u003c/li\u003e\n\u003cli\u003eWatterson, I. G., 1996. Non‐dimensional measures of climate model performance. International Journal of Climatology: A Journal of the Royal Meteorological Society. 16(4), 379-391. https://doi.org/10.1002/(SICI)1097-0088(199604)16:4\u0026lt;379::AID-JOC18\u0026gt;3.0.CO;2-U.\u003c/li\u003e\n\u003cli\u003eWatterson, I. G., 2015. Improved simulation of regional climate by global models with higher resolution: skill scores correlated with grid length. Journal of Climate. 28(15), 5985 6000. https://doi.org/10.1175/JCLI-D14-00702.1.\u003c/li\u003e\n\u003cli\u003eWatterson, I. G., 2019. Indices of climate change based on patterns from CMIP5 models, and the range of projections. Climate Dynamics. 52(3), 2451-2466. https://doi.org/10.1007/s00382-018-4260-x. \u003c/li\u003e\n\u003cli\u003eWatterson, I. G., Bathols, J., Heady, C., 2014. What influences the skill of climate models over the continents?. Bulletin of the American Meteorological Society. 95(5), 689-700. https://doi.org/10.1175/BAMS-D-12-00136.1.\u003c/li\u003e\n\u003cli\u003eWilliams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D., Evans, B., ..., Trenham, C., 2016. A global repository for planet-sized experiments and observations. Bulletin of the American Meteorological Society. 97(5), 803-816. https://doi.org/10.1175/BAMS-D-15 00132.1.\u003c/li\u003e\n\u003cli\u003eWilliams, D. N., Taylor, K. E., Cinquini, L., Evans, B., Kawamiya, M., Lautenschlager, M., ..., Contributors, E. S. G. F., 2011. The Earth System Grid Federation: Software framework supporting CMIP5 data analysis and dissemination. ClIVAR Exchanges. 56(2), 40-42.\u003c/li\u003e\n\u003cli\u003eWu, J., Shi, Y., Xu, Y., 2020. Evaluation and projection of surface wind speed over China based on CMIP6 GCMs. Journal of Geophysical Research: Atmospheres. 125(22), e2020JD033611. https://doi.org/10.1029/2020JD033611.\u003c/li\u003e\n\u003cli\u003eWu, J., Zha, J., Zhao, D., 2017. Evaluating the effects of land use and cover change on the decrease of surface wind speed over China in recent 30 years using a statistical downscaling method. Climate Dynamics. 48, 131-149. https://doi.org/10.1007/s00382 \u003cu\u003e016-3065-z\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eYe, M., Wu, J., Liu, W., He, X., Wang, C., 2020. Dependence of tropical cyclone damage on maximum wind speed and socioeconomic factors. Environmental Research Letters. 15(9), 094061. https://doi.org/10.1088/1748-9326/ab9be2.\u003c/li\u003e\n\u003cli\u003eYoung, I. R., Ribal, A., 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science. 364(6440), 548-552. https://doi.org/10.1126/science.aav95.\u003c/li\u003e\n\u003cli\u003eYoung, I. R., Vinoth, J., Zieger, S., Babanin, A. V., 2012. Investigation of trends in extreme value wave height and wind speed. Journal of Geophysical Research: Oceans. 117(C11). https://doi.org/10.1029/2011JC007753.\u003c/li\u003e\n\u003cli\u003eYoung, I. R., Zieger, S., Babanin, A. V., 2011. Global trends in wind speed and wave height. Science. 332(6028), 451-455. https://doi.org/10.1126/science.1197219.\u003c/li\u003e\n\u003cli\u003eYu, L., Zhong, S., Sun, B., 2020. The climatology and trend of surface wind speed over Antarctica and the Southern Ocean and the implication to wind energy application. Atmosphere. 11(1), 108. https://doi.org/10.3390/atmos11010108.\u003c/li\u003e\n\u003cli\u003eZha, J., Wu, J., Zhao, D., Fan, W., 2020. Future projections of the near-surface wind speed over eastern China based on CMIP5 datasets. Climate Dynamics. 54(3-4), 2361-2385. https://doi.org/10.1007/s00382-020-05118-4.\u003c/li\u003e\n\u003cli\u003eZheng, C. W., Pan, J., Li, C. Y., 2016. Global oceanic wind speed trends. Ocean \u0026amp; Coastal Management. 129, 15-24. https://doi.org/10.1016/j.ocecoaman.2016.05.001.\u003c/li\u003e\n\u003cli\u003eZheng, C. W., Pan, J., Li, C. Y., 2016. Global oceanic wind speed trends. Ocean \u0026amp; Coastal Management. 129, 15-24. https://doi.org/10.1016/j.ocecoaman.2016.05.001.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CMIP6, Wind Speed, Indian Ocean, IOD, pIOD, nIOD, Model evaluation, NIO","lastPublishedDoi":"10.21203/rs.3.rs-4275922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4275922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndian Ocean Dipole (IOD) exerts a substantial influence over wind speed (WS) in the Tropical Indian Ocean (TIO), yet climate model's potential to replicate the observed IOD impact on WS has not been estimated. This study assesses 24 CMIP6 models to determine their ability to replicate the impact of the Indian Ocean Dipole (IOD) on wind speed (WS) in the Tropical Indian Ocean (TIO) during the JJA season from 1958 to 2014. The observation data for WS is obtained from fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5). In the CMIP6 models, IOD portrays a crucial role to simulate WS across the tropical Indian Ocean. The efficacy of models is computed based on three skill metric criteria such as interannual variability score (IVS), M-Score, and root mean square error (RMSE). A total rank has been evaluated based on the three-skill metrics. The top ten best performing models are CESM2, EC-Earth3, ACCESS-ESM1-5, EC-Earth3-Veg-LR, MMM, NorESM2-LM, CESM2-WACCM, EC-Earth3-Veg, MPI-ESM1-2-HR, and FIO-ESM2-0. Despite this, significant biases are found in the CMIP6 models, indicating a moderate overall ability to capture WS responses to IOD over the Indian Ocean.\u003c/p\u003e","manuscriptTitle":"CMIP6 Model Evaluation for Wind Speed Responses to IOD during Monsoon Season over the Indian Ocean","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 11:08:52","doi":"10.21203/rs.3.rs-4275922/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a5186f22-b563-4ce9-a4d2-4ca3ee6e2f42","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-10T00:04:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 11:08:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4275922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4275922","identity":"rs-4275922","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}