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We identified a set of bias-corrected and downscaled Coupled Model Intercomparison Project 6 (CMIP6) models capable of accurately simulating the observed mean Indian summer monsoon rainfall, extreme rain events (EREs) and their respective interannual variability. The future changes in EREs projected by these models for four climate change scenarios—Shared Socioeconomic Pathways (SSPs), 1–2.6, 2–4.5, 3–7.0 and 5–8.5 were estimated using percentile thresholds. Under the highest emission scenario, SSP5-8.5, at the end of the century, summer monsoon season total rainfall exhibits a 1.1-fold increase, while extreme rainfall intensity demonstrates a more substantial rise of 1.3-fold. The spatial variability of seasonal total rainfall increases by 1.2-fold compared to the baseline period, with an even more pronounced 2.1-fold increase in the spatial variability of extreme rainfall (R99p). These findings underscore the significant amplification of rainfall variability and intensity under the most extreme climate scenario. The intensity and frequency of very extreme rainfall events (vEREs) were also found to increase, though with a substantial inter-model spread. Under SSP5-8.5, extreme rainfall intensity scales with temperature at 1.5 to 2 times the Clausius-Clapeyron (CC) rate. While mid-century scenarios show minimal variations in extreme rainfall intensity from the historical period, end-century projections reveal significant shifts; an increase in north India and a decrease in south India due to cloud-induced cooling effects.

Earlier proxy‐observational studies, and a sole modeling study, suggest that the Indian Ocean Dipole (IOD), an important global climate driver, exhibited multi‐scale temporal variability during the Last Millennium (LM; CE 0851–1849, with relatively high number of strong positive IOD events during the Little Ice Age (LIA; CE 1550–1749), and strong negative IOD events during the Medieval Warm Period (MWP; CE 1000–1199). Using nine model simulations from the PMIP3, we study the IOD variability during the LM after due validation of the simulated current day (CE 1850–2005) IOD variability. Majority of the models simulate relatively higher number of positive IOD events during the MWP, and negative IOD events in the LIA, commensurate with simulated background conditions. However, higher number of strong positive IOD events are simulated relative to the negative IODs during the LIA, in agreement with proxy‐observations, apparently owing to increased coupled feedback during positive IODs. Plain Language Summary The Indian Ocean Dipole (IOD) is a natural climate phenomenon in the tropical Indian Ocean with significant global impacts. Positive IOD (pIOD) events are apparently occurring more frequently in recent decades, which may also be due to under‐sampling associated with limited observations span. Analyzing outputs for last millennium (CE 850–1850) from climate models, validated for historical period, helps in generating relatively longer‐period the paleo‐IOD records. Our analysis of simulations of the last thousand years from multiple models indicates relatively more positive (negative) IOD events in medieval warm period—CE 1000–1200 (Little Ice Age—CE 1550–1749). While during the ICA, background conditions similar to a negative IOD were simulated, models also simulate an increase in relatively‐stronger positive IOD events in its latter part, in agreement with a proxy‐climate record. The simulated centennial changes in positive and negative IOD frequencies are associated with changes in coupled ocean‐atmospheric feedback mechanisms. Key Points Change in the Indian Ocean mean state from the medieval warm period (MWP) to the Little Ice Age (LIA) Despite negative IOD‐like background conditions in the LIA, models and paleo‐data show more stronger positive IODs then There are significant changes in feedback mechanisms of IODs from the MWP to LIA

Biological modelling approach adopted by the Ocean Carbon-Cycle Model Intercomparison Project (OCMIP-II) provided amazingly simple but surprisingly accurate rendition of the annual mean carbon cycle for the global ocean. Nonetheless, OCMIP models are known to have seasonal biases which are typically attributed to their bulk parameterisation of compensation depth. Utilising the criteria of surface Chl a-based attenuation of solar radiation and the minimum solar radiation required for production, we have proposed a new parameterisation for a spatially and temporally varying compensation depth which captures the seasonality in the production zone reasonably well. This new parameterisation is shown to improve the seasonality of CO2 fluxes, surface ocean pCO2, biological export and new production in the major upwelling zones of the Indian Ocean. The seasonally varying compensation depth enriches the nutrient concentration in the upper ocean yielding more faithful biological exports which in turn leads to accurate seasonality in the carbon cycle. The export production strengthens by ∼ 70 % over the western Arabian Sea during the monsoon period and achieves a good balance between export and new production in the model. This underscores the importance of having a seasonal balance in the model export and new productions for a better representation of the seasonality of the carbon cycle over upwelling regions. The study also implies that both the biological and solubility pumps play an important role in the Indian Ocean upwelling zones.

The Indian Ocean dipole (IOD) mode events are one of the most fascinating interannual ocean-atmosphere phenomenon in the tropical Indian Ocean. The zonal contrast of tropical ocean conditions foists enormous unfavorable impacts on the regional weather and climate. Until recently, the predictability of IOD events was limited to only a season in advance which poses a huge socioeconomic loss in the affected regions. We explore the DCPP-CMIP6 decadal hindcast models for their predictability of IOD events at different years after their initialization. Two CMIP6 models viz., CanESM5 and NorCPM1 (to some extent) have quite significant prediction skills for IOD events even after eight to ten years of initialization. We study these two models to probe the features that impact the IOD predictability. Customized version of model components, refined ocean-atmosphere coupler and sophisticated data assimilation system are the major attributes to their skill for IOD predictability. It is evident from CanESM5 simulations that Southern Ocean provides notable signals through Antarctic circumpolar currents that propagate to tropical Indian Ocean and influence the IOD events.

The cyclones over Bay of Bengal (BoB) have varied socio-economic impacts and meteorological importance. There are considerable uncertainties in predicting the track and intensity of cyclonic systems in the BoB. The present study examines the cyclogenesis characteristics over the BoB and addresses the regional impacts and their importance in terms of intensification of cyclones. An analysis of cyclone track data from 1971–2013 reveals that the cyclones generated in Andaman Sea (a regional sea of BoB) and propagating through central BoB sustain maximum life time. Furthermore, within the BoB, the cyclones originated from Andaman Sea are the most intensified and characterized by highest cyclogenesis potential index. Interestingly, we have found that higher value of mid-tropospheric relative humidity over Andaman Sea during the cyclone period is enhancing the cyclone’s intensity. Climatologically also the Andaman Sea is dominated by higher values of mid-tropospheric relative humidity compared to other regions of BoB. There is no significant distinction between Andaman Sea and rest of the BoB for other meteorological and oceanic parameters that supports cyclogenesis. Climatologically dominant east–west asymmetry in mid-tropospheric relative humidity is enhancing the intensity of cyclones from Andaman Sea. The results will be helpful in understanding the processes of cyclone intensification and useful in the statistical and dynamical prediction of cyclones.

IntroductionThis study investigates the biogeochemical impact of Godavari River discharge (GRD) on the Bay of Bengal (BoB), focusing on the formation of an intense and shallow oxygen minimum zone (OMZ) near the river mouth during the fall season. Unlike the BoB’s typical intermediate-depth OMZ, this subsurface ( 40-200 m) phenomenon is attributed to the interplay of GRD-driven nutrient enrichment, coastal upwelling, enhanced productivity, and subsequent organic matter decomposition.Data and MethodsOur analysis using the Biogeochemical-Argo floats and World Ocean Atlas 2018 data reveals that a clear shoaling and intensification of the OMZ in the fall season. Further, a comparative analysis at two geographically distinct locations highlighted the pivotal role of GRD.Results, Discussion, and ImplicationsThe location directly influenced by GRD exhibited significantly higher chlorophyll-a blooms, net primary production during the southwest monsoon, and pronounced oxygen consumption during the fall compared to the other. Our analysis suggests that GRD fuels primary productivity, leading to organic matter abundance and intense oxygen depletion in the subsurface layers, driving the observed shallow OMZ. Understanding the complex interplay between GRD, stratification, upwelling, and biogeochemical processes is crucial for predicting the impact of altered riverine inputs on coastal ecosystems, greenhouse gas emissions, and the overall health of the coastal BoB.

The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread.