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CSIR Fourth Paradigm Institute

(Formerly CSIR Centre for Mathematical Modelling and Computer Simulation)

A constituent laboratory of Council of Scientific & Industrial Research (CSIR).

Ministry of Science and Technology, Government of India.

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Modelling for science, for a better future - some recent outcomes

by S. Swetha, K. V. Ramesh & V. Rakesh 

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.

 

by Ashish Routray, Shivaji Singh Patel, Devajyoti Dutta, K. B. R. R. Hari Prasad, K. C. Gouda, Rajeev Bhatla, V. S. Prasad

An effort was made to investigate the impact of the assimilation of microwave imager (MI) radiance observations on the simulation of tropical cyclones (TCs) using the high-resolution NCUM-4DVAR assimilation method. Two numerical experiments were performed: a control experiment (CTL) (assimilated global telecommunication system [GTS] observations) and satellite observations (SAT) (assimilated GTS plus global microwave imager [GMI] and special sensor microwave imager and sounder [SSMIS] satellite MI radiance). Assimilation of MI radiance is observed to be capable of depicting the structure, track and intensity of storms. The analysis increments of temperature and geopotential height suggested that the SAT experiment effectively adjusted the core region of TCs and systematically corrected the position in the first guess of the model. The strength of large-scale moisture transport from the underlying oceanic surface and helicity around the storms are well simulated by the SAT with the axisymmetric eye of TCs. The premature intensification of TCs is simulated by CTL, whereas the progression of the intensity of storms is relatively well captured in the SAT experiment. Dry air invading the inner core of a TC can disrupt the energy cycle and reduce storm intensity, as seen in the CTL simulations. Similarly, the negative value of diabatic heating appears around the centre of the storm with an increase of altitudes in CTL simulations, which impedes the intensity of TCs. This feature suggested that CTL simulations do not adequately bring out the intensity and vertical structure of the TCs. The track forecast of storms is considerably improved in SAT simulations compared to CTL. The forecast skill of rainfall is also relatively improved in SAT simulations. Overall assimilation of MI radiances enhanced the model's forecast skill to simulate structure, movement, intensity and precipitation associated with the storms.

 
Source: https://doi.org/10.1002/qj.4984 

by S Swetha & K V Ramesh

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.

 
Source: https://doi.org/10.1007/s12040-025-02532-2 
 

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Vision: 

To synergize the Transdisciplinary Pan-CSIR expertise and build a unified platform that embodies a rich set of big data enabling technologies and services with optimized performance to facilitate data intensive scientific discovery in the country.

Mission:

To Pioneer data driven interdisciplinary research in diverse fields through state-of-the-art data science ecosystem and impactful industrial partnerships for the betterment of Society.

Mandate: 

To develop cutting edge data science products as a horizontal across the CSIR Themes and position as an Institute of Excellence in Bigdata and Artificial Intelligence.

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