सीएसआईआर-राष्ट्रीय डेटा विज्ञान एवं कृत्रिम बुद्धिमत्ता संस्थान

Marine heatwaves (MHWs), defined as anomalously warm sea surface temperature (SST) events, are analyzed over the Indian Ocean using NOAA OISST V2 (0.25° resolution) from 1981 to 2022, focusing on frequency, heatdays, maximum intensity, and mean intensity. Spatial analysis reveals MHW hotspots in the central-southwestern Indian Ocean, Bay of Bengal, and northern Arabian Sea, with 1–3 events per year lasting 3–15 days. Basin-wide mean maximum and mean intensities are 1.1 °C and 0.9 °C, respectively. Significant upward trends are found in all MHW metrics, including frequency (0.73 events/decade), heatdays (1.7 days/decade), mean intensity (0.11 °C/decade), and maximum intensity (0.14 °C/decade), with the Arabian Sea exhibiting the largest rise in heatdays (17.79 days/decade). A comparison of the periods 1981–2000 and 2001–2022 shows increased frequency and duration in the latter, though with marginally lower intensities. Empirical Orthogonal Function (EOF) analysis explains 28% and 17% of the spatial variance in frequency and duration, respectively. Partial correlation and kernel-based methods identify the El Niño–Southern Oscillation (ENSO), particularly positive Niño3.4 anomalies, as the dominant driver of MHW variability, strongly correlated with frequency (r = 0.59) and intensity (r = 0.5). Granger causality and Convergent Cross Mapping confirm ENSO’s particularly Niño3.4’s causal influence and reveal bidirectional feedback with MHWs heatdays, linked to Walker circulation driven changes in zonal winds and thermocline depth. The Pacific–Japan (PJ) pattern exerts secondary influence, while the Indian Ocean Dipole (IOD) plays a limited role. This underscores the importance of incorporating such drivers in predictive models and regional adaptation strategies.

This study quantifies CMIP6 models’ performance in simulating marine heatwaves (MHWs) over the Indian Ocean based on MHW properties like frequency, heatdays, and intensity. Spatial and temporal variations were assessed using spatial skill scores (SS score) and M2 scores, with model skills quantified by the performance score (PS), ranging from 0 to 100. The top three qualifying models, CMCC-ESM2, CMCC-CM2-SR5, and GFDL-ESM4, demonstrated notable accuracy in capturing the spatial and temporal features of MHWs close to observations for the period 1981–2014. The models are reliable in simulating the trend in intensity (both mean and maximum intensity) and frequency. However, selected models tended to underestimate MHW properties with a maximum deviation from observations of ~62% for MHW frequency and MHW intensity and the least deviation of only ~28% for MHW heatdays. Surface flux analysis revealed an overestimation of upwelling latent heat flux, longwave flux, and sensible heat flux, leading to a cold bias, particularly in the Southern Indian Ocean region, which could be a possible reason for the model’s underestimation of MHW properties. Future projections for the middle and end century (2041–2100) under SSP585 showed an alarming increase in MHW frequency, heatdays, and intensities, with MHW heatdays reaching a near-permanent state by the end of the century (2100). Over a period of 60 years, the MHW frequency displays an increasing trend of ~0.4 events annually, MHW heatdays increasing at ~1.5 events annually, MHW maximum intensity increasing at ~0.14°C and mean intensity increasing at ~0.1°C annually for the highest emission scenario SSP585.

Coastal Odisha, strategically positioned between the Bay of Bengal and a vast inland landmass, is highly susceptible to climate change and variability impacts. This study comprehensively examines interannual temperature variability and its relationship with mean seasonal temperature using 54 years of homogenised surface meteorological data (1969–2022) from seven WMO-standard meteorological stations. To assess inter-annual variability in association with mean seasonal temperature and precipitation, a difference of standard deviations (SD) of two periods (e.g. 1969–1996 and 1995–2022) along with the relation between running 28-year values of SD and means of seasonal temperature for the period from 1969 to 2022, F-ratio test along with slope and correlation values have been conducted. By comparing two distinct periods (1969–1996 and 1995–2022), the analysis reveals significant climatic shifts, including rising mean temperatures and increased interannual variability at most stations, but some of the stations show an increase in mean temperature with decreased or fluctuating variability. These trends are linked to a heightened frequency of extreme weather events, cascading impacts on agriculture, water resources, health, and infrastructure. Notably, the findings identify localised patterns where warming is accompanied by increased or reduced variability, offering nuanced insights into the region’s complex dynamics. Explicitly noted the limitations of prior studies (e.g. low station density, short temporal scales, lack of focus on interannual variability),this study contributes novel insights to climate research in Odisha by employing long-term, high-resolution station-level data and advanced homogenisation techniques. The relationship between temperature and interannual variability is unique and the inverse relationship between the mean temperature and the variance of temperature is disrupted at most of the stations in coastal Odisha. Its findings hold critical implications for future research as a foundation for regional climate modelling, extreme event prediction, and studying climate variability’s socio-economic impacts. Actionable recommendations, including targeted disaster risk reduction measures, adaptive agricultural practices, and resilient infrastructure planning, underscore policy relevance. By addressing gaps in regional climate knowledge, this work provides a roadmap for integrating localised climate insights into state-level and national climate adaptation frameworks, ensuring preparedness for the evolving climate challenges in coastal Odisha.