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 90oE-110oE, 10oS-equator and warmer over the tropical western Indian Ocean bounded between 50oE-70oE, 10oS-10oN. 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. 2016a). 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.