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In this reply to the discussion of our paper (Kumar et al. in Nat Hazards 116:2437–2455, 2023), we attempt to rebut the claims made regarding the use of the maximum sustained wind speed of a cyclone event irrespective of its location, inappropriate implementation of fitting methods, and inconsistent use of future projected sea surface temperature to historical records. While the discussion highlights certain observations about our results, the application of such techniques for evaluating PMTC parameters (adhering to AERB criteria) requires further research. [ABSTRACT FROM AUTHOR]

Globe's primary issue is global warming, water temperatures have accompanied it as the sea surface temperature, and it is the primary attribute to balance the energy on the earth's surface. Sea surface temperature prediction is vital to climate forecast. Downwelling currents carry some of this heat to the ocean's bottom layers, which are also heating, covering far behind the increase in sea surface temperature. In deep learning models, the correct loss function will try to reduce the error and converge fast. The proposed improved loss function correctly estimates how close the predictions made by the long short-term memory match the observed values in the training data. This research considers location-specific sea surface temperature predictions using the improved loss function in the long short-term memory neural network at six different locations around India for daily, weekly, and monthly time horizons. Most existing research concentrated on periodic forecasts, but this paper focused on daily, weekly, and monthly predictions. The improved loss function—long short-term memory, achieved 98.7% accuracy, and this improved loss function overcomes the limitations of the existing techniques and reduces the processing time to ~ 0.35 s. In this research, the sea surface temperature prediction using the improved loss function in the long short-term memory neural network gives better results than the standard prediction models and other existing techniques by considering the long-time dependencies and obtaining features from the spatial data. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of the frequency of lightning strikes associated with thunderstorm and precipitation distributions over smooth oceanic surface relative to that over solid earth surface in the tropical region. Long-term (1998–2014) data retrieved from the Lightning Imaging Sensors (LIS) of the Tropical Rainfall Measuring Mission (TRMM) satellite shows lightning flash counts over Indian landmass found to be 9.1 times more than those over the smooth oceanic surface of the Arabian Sea and the Bay of Bengal. On the other hand, the annual variation of rainfall-to-lightning ratio (RLR) is found to be 0.8 over Indian landmass, whereas it is 10 over the oceanic surfaces. We discuss the convective strength of thunderstorm distributions over land and oceanic regions by examining the relationships of RLR to the Bowen ratio, sea surface temperature (SST), and maximum air temperature over land, maximum updraft speed, and Aerosol Optical Depth (AOD) and cloud ice water content. The RLR shows high positive Pearson's correlations with the Bowen ratio, maximum updraft speed, AOD, and cloud ice water content over land region relative to those measured over the oceanic region. The RLR also shows negative correlations with SST, maximum updraft speed, AOD over the oceanic region. The results are applicable in understanding of the convective characteristics of thunderstorm distributions and lightning flashes over the tropical regions of the world. [ABSTRACT FROM AUTHOR]

Global warming is the average temperature of the earth's surface which has increased over the past century by about 0.6 °C. This rising temperature may vary precipitation patterns, more frequent droughts, rise in sea level and intense storms and can be termed as climate change. To understand uneven precipitation pattern for monsoon dominated region like India, there is a need to study cloud processes at high resolution with the help of cloud microphysical properties. Ocean is the major and primary source of cloud whereas local water bodies and re-evaporated water over land could be secondary source. Paper focuses over ocean (Arabian Sea) as well as over land (central India) to know the effect of global warming on cloud microphysical properties such as cloud effective radius and cloud liquid water content. Warming signal in terms of rise in sea surface temperature (0.1 °C/decade) as well as rise in surface air temperature (0.05 °C/decade) are observed over Arabian Sea and central India respectively. Satellite data show an increasing (0.5 µm/decade) trend in cloud effective radius over Arabian Sea, whereas it decreases (− 0.1 µm/decade) over central India. Increasing trend in temperature and cloud properties is may be due to warming signal. Aerosol concentration over ocean and land further helped to understand cloud processes with cloud microphysical properties. Paper will focus on the effect of warming signal in cloud properties over Arabian Sea and central India. This type of high resolution study may help to understand cloud processes which in turn may help to understand precipitation patterns. [ABSTRACT FROM AUTHOR]

The aquarium industry in India relies mainly on ornamental fishes harvested from nature mostly confined to fresh water sources followed by brackish and to a little extent from marine waters. Lobsters, especially spiny forms have unique blend of colors with peculiar shape, movements and are distinct from popular aquarium varieties. They also meet quality criteria required for aquarium pets other than aesthetic value. Lobsters are calm, non-voracious, non-aggressive, less cannibalistic and adaptable in aquarium conditions. The above factors favor introduction of lobsters as a candidate species that will add a new look for marine aquarium. In India, all along the maritime states and union territories; Puducherry, Daman and Diu, lobsters are mostly landed as by catch by trawl nets. Lobsters below 150 g are banned from exporting in India and fetch low monetary value in domestic market. A study was conducted to test the efficacy of spiny and sand lobsters for using in marine aquarium. The details on husbandry practices emphasizing major water quality parameters and food and feeding experimented by introducing spiny and sand lobsters in marine aquarium is presented in this paper. The major water quality parameters recorded from lobster aquaria are, sea water temperature 29.07±0.04 to 29.98 ±0.020C, salinity 34.47±0.51 to 39±0.94 ppt, dissolved oxygen 4.82 ± 0.48 mg/l to 5.53 ± 0.28 ml/l, PH 7.97 ±0.18 to 8.31 ±0.19 and ammonia 0.004 ± 0.002 to 0.48 ± 0.64 mg/l, nitrite2 0.004 ± 0.002 to 0.11 ± 0.14 mg/l and nitrate ranged from 1.06 ± 0.06 to 1.93 ±2.68 mg/l. In this experiments, 100% survival was achieved for all the lobsters suggesting suitability of spiny and sand lobsters in marine ornamental aquarium. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon rainfall (ISMR) during June to September contributes most of the annual rainfall over India and plays an important role in Indian agriculture and thus the economy. It exhibits high spatio-temporal variabilities forced from both internal and external factors, which are important for better understanding and prediction of ISMR. Since the internal factors, mainly in the form of intraseasonal oscillations set a limit to the predictability, the major focus is given to the external forcing factors including the coupled air–sea interactions, sea surface temperature variations, snow cover, etc. This paper mainly aims to review the results of recent research analysis on ISMR variability and the major climate factors that determine the variability. Focus is given on the contributions from the coupled ocean–atmosphere processes in the Indian and Pacific Oceans to the ISMR variability [(primarily the El Niño Southern Oscillation (ENSO)] and Indian Ocean Dipole (IOD). Several studies were carried out in recent decades to explore the ISMR variabilities and their influences from tropical oceans. The studies, which focused the impact of ENSO and IOD on the ISMR variability have been considered in exploring their relationships and observed changes in recent decades. In the backdrop of varying relationship of ISMR with ENSO and IOD in the regional scale, it is important to study further the regional teleconnection of ISMR variabilities with oceanic factors, especially from the Indian and Pacific Ocean basin. [ABSTRACT FROM AUTHOR]

Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10latitude with Maharashtra's monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model's performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2. [ABSTRACT FROM AUTHOR]

Drought is a function of time as well as climate variables such as temperature and precipitation. The process of drought forming is slow, and it manifests at different time scales, which adversely affects the economy of a country. The identification and characterization of droughts at various spatiotemporal scales are of great importance. It helps in the planning and management of water resources, policymaking, and agribusiness industries. In the present paper, the Cauvery River basin is chosen as a study area to analyze the changes in the frequency distribution of extreme droughts and duration, with the combined effect of evapotranspiration and rainfall. The drought indices such as Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) are implemented on monthly rainfall data and potential evapotranspiration of resolution 0.25° × 0.25° long./lat. for the period 1931–2010. The results reveal that the frequency of the extreme droughts over the basin has significantly increased over the post-era of global warming. The increased rate of extreme droughts is particularly evident in downstream of the basin, mainly due to the increase in temperature and deficit rainfall. Further, the implementation of continuous wavelet transform reveals that SPI at 3-(SPI-3) and 12-(SPI-12) month scale are associated with extended reconstruction of sea surface temperature (ERSST) in anti-phase and in-phase, respectively. It is concluded that the in-phase association of SPI-12 and ERSST enhances the drought situation compared to the anti-phase link of SPI-3 and ERSST. [ABSTRACT FROM AUTHOR]

We present in this paper the interannual variability of seasonal temperature and rainfall in the Indian meteorological subdivisions (IMS) for boreal winter and summer seasons that take in to account the varying length of the seasons. Our study reveals that accounting for the variations in the length of the seasons produces stronger teleconnections between the seasonal anomalies of surface temperature and rainfall over India with corresponding sea surface temperature anomalies of the tropical Oceans (especially over the northern Indian and the equatorial Pacific Oceans) compared to the same teleconnections from fixed length seasons over the IMS. It should be noted that the IMS show significant spatial heterogeneity in these teleconnections. [ABSTRACT FROM AUTHOR]

Joseph, D.; Liya, V.B.; Rojith, G.; Zacharia, P.U., and Grinson, G., 2019. Time series analysis of CMIP5 model and observed sea surface temperature anomaly along Indian coastal zones. In: Jithendran, K.P.; Saraswathy, R.; Balasubramanian, C.P.; Kumaraguru Vasagam, K.P.; Jayasankar, V.; Raghavan, R.; Alavandi, S.V., and Vijayan, K.K. (eds.), BRAQCON 2019: World Brackishwater Aquaculture Conference. Journal of Coastal Research, Special Issue No. 86, pp. 239–247. Coconut Creek (Florida), ISSN 0749-0208. Analysis of the time series Sea Surface Temperature (SST) variations is a key element in understanding the climate change impacts on the phenology, trophodynamics, distribution, and catch of commercial marine fish species. As SST projections are mostly model dependent, the discrepancy in the model and observed values needs to be elucidated so as to derive accurate interpretations and conclusions. In this paper, the decadal and seasonal variations of SST anomaly over the four coastal zones of India were analysed. The selected period of the study is from 1968 to 2017 and the data obtained from the International Comprehensive Ocean Atmosphere Data Sets (ICOADS) and Max Planck Institute Earth System Model (MPI-ESM) was used. The Coupled Model Intercomparison Project 5 (CMIP5) model data were used for comparison with the observed value. The linear trend of observed and modelled values was inferred using the least square method for four seasons. The seasonal variation of SST anomaly of observed data in four coastal zones of India reveals that Northeast zone exhibits the least trend of warming in all seasons, whereas Northwest zone shows the highest trend of warming. SST anomaly of the first decade in all zones exhibited negative values in all seasons, while last decade shows positive value, indicating warming trend. In other decades, no uniform SST trend for all seasons was observed. However, the Northeast zone is exceptional with negative anomaly during all decades. The warming trend was observed in all coasts for both model and observed values. The model and observed SST anomaly follows an almost similar trend, but with noticeable differences in values among both. Owing to the differences in the model and observed values, it could be emphasised that error corrections needs to be applied in futuristic SST projections and related studies of Indian fisheries. [ABSTRACT FROM AUTHOR]

India experienced several unprecedented floods in the recent decades. The increase in the extreme rainfall events (EREs) is the primary cause for these floods, manifesting its societal impacts. The daily downscaled and bias corrected (DBC) Coupled Model Intercomparison Project Phase 6 (CMIP6) rainfall and sea surface temperature (SST) are prepared for the Indian region and are utilized to examine the characteristics of EREs. The DBC products capture the characteristic features of EREs for the baseline period, which inspired us to assess the EREs over India in CMIP6 future projections. Consistent with the observations, DBC product shows ~ 8% of Indian land found to experienced extremely heavy rainfall associated with the long duration EREs in the baseline period. However, area and extreme rainfall thresholds are projected to increase by about 18(13)% and 58(50)%, respectively in the far future under SSP5-8.5 (SSP2-4.5) emission scenario relative to the baseline period. A two-fold-65(62)% increase in long-duration EREs compared to the short-duration EREs and substantial warming ~ 2.4(2.9) oC of Indian Ocean SSTs in the far future under SSP5-8.5 (SSP2-4.5) emission scenario compared to baseline period are reported. These findings may provide fundamental insights to formulate national climate change adaptation policies for the EREs. [ABSTRACT FROM AUTHOR]

This paper describes a novel simulation of active and break spells of the Indian summer monsoon (ISM) using a relatively high-resolution regional coupled ocean-atmosphere climate model (RCM) run at 10-km grid spacing. Similar to what is seen in observations, the RCM-simulated active (break) spells are characterized by stronger (weaker) rainfall over central India and anomalous low-level atmospheric flow that enhances (weakens) the climatological flow pattern. Highlights of this study include the improved spatiotemporal structure, propagation characteristics, and amplitude of the intraseasonal variations of the ISM rainfall in the RCM simulation as compared with some of the more recent simulations conducted with global models at coarser spatial resolutions. This study's RCM simulation also displays associated variations in the upper ocean, with active (break) spells of the ISM coinciding with colder (warmer) sea surface temperatures (SSTs) in both the Arabian Sea and the Bay of Bengal. These SST anomalies are mainly sustained by corresponding net heat flux anomalies on the ocean surface. The active (break) spells are further associated with shoaling (deepening) of the mixed layer depth, which is critical for the SST response to heat flux. All of these simulated features of intraseasonal variations of the ISM have been seen in earlier observational studies, which further confirms the fidelity of the model simulation and the importance of coupled air-sea interactions and upper-ocean stratification. [ABSTRACT FROM AUTHOR]

In this study, we investigate the connection between planetary equatorial waves, modulated by the Indian Ocean dipole (IOD) and El Niño Southern Oscillation (ENSO), and the interannual variabilities of the salinity distribution in the Bay of Bengal (BoB) in October–December (OND), along with its associated dynamics, using satellite and reanalysis datasets. In OND 2010 and 2016 (1994, 1997, 2006, and 2019), positive (negative) sea surface salinity anomalies (SSSAs) were distributed in the eastern equatorial Indian Ocean (EIO) and Andaman Sea. Moreover, the southward movement of negative (positive) SSSAs along the eastern Indian coast was observed. This phenomenon was caused by large-scale anomalous currents associated with zonal wind over the EIO. During OND 2010 and 2016 (1994, 1997, 2006, and 2019), due to anomalous westerlies (easterlies) over the EIO and anomalous downwelling (upwelling) Kelvin waves, the strengthened (weakened) Wyrtki jet and the basin-scale anomalous cyclonic (anticyclonic) circulation in the BoB gave rise to positive (negative) SSSAs within the eastern EIO and Andaman Sea. In addition, the intensified (weakened) eastern Indian coastal currents led to the southward movement of negative (positive) SSSAs. It is worth noting that downwelling Kelvin waves reached the western coast of India during OND 2010 and 2016, while upwelling Kelvin waves were only confined to the eastern coast of India during OND 1994, 1997, 2006, and 2019. Furthermore, westward salinity signals associated with reflected westward Rossby waves could modulate the spatial pattern of salinity. The distribution of salinity anomalies could potentially influence the formation of the barrier layer, thereby impacting the sea surface temperature variability and local convection. [ABSTRACT FROM AUTHOR]

Current knowledge suggests a drought Indian monsoon (perhaps a severe one) when the El Nino Southern Oscillation and Pacific Decadal Oscillation each exhibit positive phases (a joint positive phase). For the monsoons, which are exceptions in this regard, we found northeast India often gets excess pre‐monsoon rainfall. Further investigation reveals that this excess pre‐monsoon rainfall is produced by the interaction of the large‐scale circulation associated with the joint phase with the mountains in northeast India. We posit that a warmer troposphere, a consequence of excess rainfall over northeast India, drives a stronger monsoon circulation and enhances monsoon rainfall over central India. Hence, we argue that pre‐monsoon rainfall over northeast India can be used for seasonal monsoon rainfall prediction over central India. Most importantly, its predictive value is at its peak when the Pacific Ocean exhibits a joint positive phase and the threat of extreme drought monsoon looms over India. Plain Language Summary: Monsoon brings rain over India. But some years are droughts. These drought monsoon years are historically associated with warmer sea surface temperatures (SSTs) in the eastern Pacific and cooler SST in the northern Pacific. This motivated scientists to predict drought monsoons when we observe a warm eastern and cold northern Pacific Ocean. However, in some years, the monsoon is not drought despite the SST anomalies in the Pacific suggesting so. We find that, in such years, rainfall over northeastern India during pre‐monsoon months is often excessive. So we argue that when the Pacific Ocean state suggests a drought monsoon over India (central region) but if pre‐monsoon rainfall over northeastern India is excessive, then we can rely less on the drought signal of the Pacific Ocean. Key Points: Pre‐monsoon rainfall over northeastern India is a potential indicator of false alarms of monsoon drought over central Indian regionAssociation between northeastern India pre‐monsoon rainfall and monsoon rainfall over central India oscillates multidecadallySea surface temperature anomalies in the Pacific are a key driver of pre‐monsoon rainfall over the northeastern India [ABSTRACT FROM AUTHOR]

In this study, rainfall estimates by the Tropical Rainfall Measuring Mission are used to understand the spatiotemporal structures of convection in the intraseasonal time scale and their intensity during the boreal summer over South Asia. A quantitative analysis on how these intraseasonal modes modulate the central Indian rainfall is also provided. Two dominantmodes of variability with periodicities of 10-20 and 20-60 days are found, with the latter strongly modulated by sea surface temperature. The 20-60-day mode shows northward propagation from the equatorial Indian Ocean linkedwith eastward-propagatingmodes of convective systems over the tropics.The 10-20-daymode shows a complex space-time structure with a northwestward-propagating anomalous pattern emanating from the Indonesian coast. This pattern is found to be interacting with a structure emerging from higher latitudes propagating southeastward, the development ofwhich is attributed to the vertical shear of the zonalwind. The two modes exhibit profound variability in their intensity on the interannual time scale and they contribute a comparable amount to the daily rainfall variability in a season. The intensity of the 20-60 and 10-20-day modes shows a significantly strong inverse and direct relationship with the all-India June-September rainfall, respectively. This study establishes that the probability of the occurrence of substantial rainfall over central India increases significantly if the two intraseasonal modes simultaneously exhibit positive anomalies over the region. The results presented in this paper will provide a pathway to understand, using observations and numerical model simulations, intraseasonal variability and its relative contribution to the Indian summer monsoon. It can also be used for model evaluation. [ABSTRACT FROM AUTHOR]

In this paper, the impact of the SST drifts in the ECMWF system 3 forecasting system on the simulated monsoon climatology is investigated. It is shown that hindcasts initialized in February show paradoxically better climatological rainfall in the early monsoon season compared to the hindcasts initialized in the May. The differences in rainfall and SST evolution in the two hindcast sets point to the SST differences as the crucial factor that improves the February initialized hindcasts. Further experiments with the atmospheric component of the ECMWF system 3 forecasting system confirm this by showing similar rainfall biases in the early monsoon season as the hindcasts initialized in the May. This study points to the potential beneficial impacts of reducing systematic biases in the atmospheric components forecasting systems, and of an anomaly initialization technique to improve Indian Monsoon forecasts. [ABSTRACT FROM AUTHOR]

This paper analyzes the role of the Indian Ocean (IO) and the atmosphere biases in generating and sustaining large-scale precipitation biases over Central India (CI) during the Indian summer monsoon (ISM) in the climate forecast system version 2 (CFSv2) hindcasts that are produced by initializing the system each month from January 1982 to March 2011. The CFSv2 hindcasts are characterized by a systematic dry monsoon bias over CI that deteriorate with forecast lead-times and coexist with a wet bias in the tropical IO suggesting a large-scale interplay between coupled ocean-atmosphere and land biases. The biases evolving from spring-initialized forecasts are analyzed in detail to understand the evolution of summer biases. The northward migration of the Inter Tropical Convergence Zone (ITCZ) that typically crosses the equator in the IO sector during April in nature is delayed in the hindcasts when the forecast system is initialized in early spring. Our analyses show that the delay in the ITCZ coexists with wind and SST biases and the associated processes project onto the seasonal evolution of the coupled ocean-atmosphere features. This delay in conjunction with the SST and the wind biases during late spring and early summer contributes to excessive precipitation over the ocean and leading to a deficit in rainfall over CI throughout the summer. Attribution of bias to a specific component in a coupled forecast system is particularly challenging as seemingly independent biases from one component affect the other components or are affected by their feedbacks. In the spring-initialized forecasts, the buildup of deeper thermocline in association with warmer SSTs due to the enhanced Ekman pumping in the southwest IO inhibits the otherwise typical northward propagation of ITCZ in the month of April. Beyond this deficiency in the forecasts, two key ocean-atmosphere coupled mechanisms are identified; one in the Arabian Sea, where a positive windstress curl bias in conjunction with warmer SSTs lead to a weakening of Findlater jet and the other in the east equatorial IO where a remote forcing by the predominantly westerly bias in the western-central equatorial IO in the summer strengthen the seasonal downwelling Kelvin wave that in turn deepens the thermocline in the eastern IO. The equatorial Kelvin wave continues as a coastal Kelvin wave and disperses as Rossby waves off Sumatra and induces positive SST and precipitation biases in the eastern and southern Bay of Bengal. This study shows that the biases that first appear in winds lead to a cascade of coupled processes that exacerbate the subsequent biases by modulating the evolution of seasonal processes such as the annual Kelvin and Rossby waves and the cross-equatorial vertically integrated moisture transport. While this analysis does not offer any particular insights into improving the ISM forecasts, it is a foundational first step towards this goal. [ABSTRACT FROM AUTHOR]

We describe the use of regional relaxation ("nudging") experiments carried out in initialised hindcasts to shed light on the contribution from particular regions to the errors developing in the Asian Summer Monsoon. Results so far confirm previous hypotheses that errors in the Maritime Continent region contribute substantially to the East Asia Summer Monsoon (EASM) circulation errors through their effects on the Western North Pacific Subtropical High. Locally forced errors over the Indian region also contribute to the EASM errors. Errors arising over the Maritime Continent region also affect the circulation and sea surface temperatures in the Equatorial Indian Ocean region, contributing to a persistent error pattern resembling a positive Indian Ocean Dipole phase. This is associated with circulation errors over India and the strengthening and extension of the westerly jet across southeast Asia and the South China Sea into the Western Pacific, thereby affecting the ASM circulation and rainfall patterns as a whole. However, errors developing rapidly in the deeper equatorial Indian Ocean, apparently independently of the atmosphere errors, are also contributing to this bias pattern. Preliminary analysis of nudging increments over the Maritime Continent region suggests that these errors may at least partly be related to deficiencies in the convection and boundary layer parametrisations. [ABSTRACT FROM AUTHOR]

Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6. [ABSTRACT FROM AUTHOR]

The Met Office Global Coupled Model (GC) and the NCEP Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of time scales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems, in order to provide insight into how they develop across time scales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer time scales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases, so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just eight days, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2, and is different during the early part of the monsoon season from during the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the Boreal Summer IntraSeasonal Oscillation (BSISO) index as a measure of whether the large-scale dynamics favours increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles. [ABSTRACT FROM AUTHOR]

This study attempts to build nonstationary indices for assessing meteorological drought in two different climate zones in India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop nonstationary models using the generalized additive model in location, scale, and shape (GAMLSS) for the period, 1951–2004. A comparative study has been conducted to assess the statistical performance of stationary and nonstationary models on various time scales (3, 6, 12, and 24 months). The best model is selected to conduct copula-based bivariate drought analysis. For this purpose, drought properties such as drought severity, duration, and peak are calculated. The annual and seasonal rainfall departures are also analyzed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The nonstationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The nonstationary drought index shows better consistency with historical drought and flood events than the stationary index. Cooccurrence and joint return periods are calculated and compared with univariate return periods. A significant difference is observed between bivariate and univariate return periods, and more risk is detected in Saurashtra and Kutch than in Coastal Karnataka. The impacts of rainfall and drought on the yield of major crops in study areas are also analyzed. The yield loss rate of bajra significantly correlates with the nonstationary standardized precipitation index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. This new aspect of drought analysis provides feasible results in both arid and humid regions in a changing environment. El Niño-Southern Oscillation (ENSO) refers to changes in ocean temperatures and atmospheric pressure in the Pacific Ocean, while Indian Ocean Dipole (IOD) refers to differences in sea surface temperatures in the Indian Ocean. Both ENSO and IOD can affect rainfall and drought conditions in India, making them essential factors to consider in understanding and predicting drought events. The nonstationary drought index developed using climate indices could accurately assess the important aspects of drought, like how severe it was, how long it lasted, and when it reached its peak. By evaluating the return period of a specific drought severity and duration, decision makers can assess the likelihood of experiencing such an event in a given year or over a specific period. It helps to prioritize resources, plan adaptation measures, and design drought management strategies to mitigate potential impacts. If specific ENSO or IOD conditions are associated with prolonged drought periods, water resource managers can implement proactive measures like water conservation initiatives, irrigation scheduling, and crop diversification. This research examines the impact of rainfall and drought on major crop yields (bajra in arid regions and rice in humid-tropical regions). This knowledge can aid farmers and policymakers in predicting crop losses, optimizing irrigation strategies, and implementing timely interventions to minimize agricultural productivity losses. [ABSTRACT FROM AUTHOR]

In this paper, the east-west tropical 'Walker circulation' and its linear association with sea surface temperature (SST) of the Nino 3 region and Indian summer monsoon rainfall (ISMR) have been investigated. 'Walker circulation'/'reverse Walker circulation' is primarily forced by SSTs of the equatorial Pacific Ocean. In this study, velocity potential field of 0.21 sigma level over the tropics was considered as proxy of the zonal tropical circulation ('Walker circulation'/'reverse Walker circulation'). Principal component analysis of the monsoon season tropical velocity potential data of 0.21 sigma level for the two periods 1951-1980 and 1981-2010, was done separately. We find that earlier, two different patterns of the velocity potential field, forced by probably two distinct modes of El Nino episodes, were associated with the ISMR. These two El Nino episodes, respectively, correspond to the strong El Nino events, where in warming was extended up to the date line (primarily zonal) and the moderate El Nino events in which, warming having north south extension, was limited to the eastern Pacific Ocean only. However, in recent years, only the first pattern of the velocity potential field, induced by the strong El Nino events (warming extending up to the date line), was correlated with the ISMR. Further, in the later period (1981-2010), velocity potential field at 0.21 sigma level over the tropical Pacific and Indian Oceans, which appeared to be primarily driven by SST anomalies of the equatorial Pacific Ocean in the first period, was found to be significantly correlated with the extra tropical circulation anomalies also. Therefore, modulation of the ISMR through velocity potential field over the tropical Indian and Pacific Oceans, in the later period, may have additional significant impact of the extra-tropical circulation anomalies. This might have led weak correlation between the ISMR and SSTs of the Nino 3 region, which is actually being observed in recent years. [ABSTRACT FROM AUTHOR]

Aquaculture plays a significant role in food security and provides livelihoods and employment for millions of people among coastal communities worldwide. However, the growing aquaculture sector has also created debates around its long-term ecological sustainability, economic viability, potential social inequalities and governance issues. We investigated the perceived challenges and opportunities to achieving sustainable mud crab aquaculture in tropical coastal regions by using the case study of coastal mud crab farms in Andhra Pradesh, India. Informed by perceptions and indicative financial data from a sample of stakeholders we investigated the potential economic outcomes under different scenarios representing varying yield levels, risk factors and project time periods. The main risks identified by the stakeholders were associated with the limited supply of mud crab seeds and the lack of access to governmental and non-governmental support schemes. There are no financial buffers, therefore major disease outbreaks or extreme weather conditions caused by climate change would lead to a loss of livelihoods. This paper also highlights the most critical factor determining the level of success of mud crab farming being the crab survival rate which is influenced by a variety of factors including increasing sea surface temperature. The results of this study show that small-scale mud crab farming has fewer risks and higher flexibility involved than large-scale mud crab farming. It could be an economically sustainable enterprise and serve as a tool for poverty alleviation in developing countries if microfinance support and training are available. • Small-scale mud crab aquaculture can be economically, environmentally and socially sustainable in tropical coastal regions. • The sustainability of mud crab aquaculture expansion depends on access to affordable loans, training and sustainable feed. • Ensuring a high survival rate of mud crabs, influenced by climate change and disease , is critical for aquaculture success. [ABSTRACT FROM AUTHOR]

Previous studies suggest that the winter surface freshening in the southeastern Arabian Sea (SEAS) contributes to the development of very high Sea Surface Temperatures (SST) thereby influencing the following summer monsoon onset. Here, we use forced and coupled simulations with a regional ocean general circulation model to explore the SEAS Sea Surface Salinity (SSS) variability mechanisms and impact on the monsoon. Both configurations capture the main SEAS oceanographic features, and confirm that the winter SSS decrease results from horizontal advection of Bay of Bengal freshwater by the cyclonic circulation around India during fall. A coupled model sensitivity experiment where salinity has no effect on mixing indicates that the salinity stratification reduces the SEAS mixed layer cooling by vertical processes by 3 °C seasonally. Salinity however enhances mixed layer cooling by a similar amount through concentrating negative winter surface heat fluxes into a thinner mixed layer, resulting in no climatological impact on SST and summer monsoon rainfall. The Indian Ocean Dipole (IOD) is the main driver of the winter SEAS SSS interannual variability (r ~ 0.8). Salty anomalies generated in the western Bay of Bengal during fall by positive IOD events are indeed transported by the cyclonic climatological coastal circulation, reaching the SEAS in winter. By this time, warm IOD-induced SST anomalies in the SEAS are already decaying, and the SEAS SSS anomalies hence do not contribute to their development. Overall, our model results suggest a weak climatological and interannual impact of the SEAS winter freshening on local SST and following monsoon onset. [ABSTRACT FROM AUTHOR]

The southwest monsoon delivers over 70% of India's annual rainfall and is crucial to the success of agriculture across much of South Asia. Monsoon precipitation is known to be sensitive to sea surface temperature (SST) in the Bay of Bengal (BoB). Here, we use a configuration of the Unified Model of the Met Office coupled to an ocean mixed layer model to investigate the role of upper-ocean features in the BoB on southwest monsoon precipitation. We focus on the pronounced zonal and meridional SST gradients characteristic of the BoB; the zonal gradient in particular has an as-yet unknown effect on monsoon rainfall. We find that the zonal SST gradient is responsible for a 50% decrease in rainfall over the southern BoB (approximately 5 mm day−1), and a 50% increase in rainfall over Bangladesh and northern India (approximately 1 mm day−1). This increase is remotely forced by a strengthening of the monsoon Hadley circulation. The meridional SST gradient acts to decrease precipitation over the BoB itself, similarly to the zonal SST gradient, but does not have comparable effects over land. The impacts of barrier layers and high-salinity subsurface water are also investigated, but neither has significant effects on monsoon precipitation in this model; the influence of barrier layers on precipitation is felt in the months after the southwest monsoon. Models should accurately represent oceanic processes that directly influence BoB SST, such as the BoB cold pool, in order to faithfully represent monsoon rainfall. [ABSTRACT FROM AUTHOR]

In India, around 7 million people depend on fishing for their livelihoods. They are assisted with a reliable and fast brief forecast for the areas of fish aggregations. Habitat mapping is critical in supporting strategic choices on fish usage and protection. In conjunction with techniques for machine learning, remote control has made comprehensive fish mapping on relevant scales possible. In machine learning, supervised algorithms are utilized to make forecasts from datasets, when data is accessible without relating output factors. In this research work, Ocean Surface Temperature (OST) and Satellite derived Chlorophyl material are the basic inputs to generating the information of Potential Fishing Zone (PFZ). The 16 features and additional financial derivative features are used for accurate future prediction of PFZ. The unwanted and missing data are removed using effective pre-processing techniques. Among the various methods available for forecasting nonlinear phenomena, the Neural Network is the best and the efficient method to get a forecast. Therefore, the Function Fitting Neural Network (FFNN) technique is mainly used to predicting the Integrated Potential Fishing Zone (IPFZ). The practical analyses are performed by analysing the 80% -20%, 60% -40% and future data in terms of various parameters. From the results, it is proved that the suggested FFNN achieved 90% of accuracy, where the existing neural network achieved 86% of accuracy by implementing with financial derivative features for the 80% -20% of available dataset. [ABSTRACT FROM AUTHOR]

Prediction of the seasonal monsoon rainfall over India relies largely on the well-known relationship with El Niño and Southern Oscillation (ENSO) and is possible because reasonably reliable seasonal predictions of ENSO are now available. Usually, the cold phase of ENSO is associated with above-normal monsoon rainfall and the warm phase of ENSO with below-normal rainfall. There are, however, exceptions: years in the cold phase of ENSO with below-normal monsoon rainfall and even drought conditions. We term these exceptional events 'rogue La Niñas'. Clearly, an explanation of these exceptional cases will improve the predictive skill. Here we show that for the part of the Arabian Sea, east of the upwelling region and north of the equatorial belt (60°–70°E, 10°–23°N), the correlation of outgoing longwave radiation with Indian summer monsoon rainfall is even higher than that with the equatorial central Pacific associated with ENSO. Convection over this region is triggered by ENSO, but is modulated by the underlying sea surface temperature (SST). There is a minimum of SST of about 28.1°C above which the convection over the Arabian Sea is high enough and there are no rogue La Niñas. Furthermore, we show that, in this region, the SST of June–September is related to the SST of April–May. When April–May SST is >29.6°C, June–September mean SST is always >28.1°C and there are no rogue La Niñas; the monsoon rainfall is always normal or above normal as expected with a La Niña. Thus the chance of a rogue La Niña can be predicted from the April–May SST of the Arabian Sea. [ABSTRACT FROM AUTHOR]

Northern Hemispheric summer monsoons were more intense during the mid-Holocene (MH ~ 6000 years ago) and coincided with a northward shift of the Intertropical Convergence Zone (ITCZ) compared to the pre-industrial (PI) climate. Ancient civilizations in the Indus valley, Mesopotamia, and Egypt appear to have flourished during this period, thanks to abundant water availability. This study exploits a high-resolution variable grid global atmosphere model to understand the role of orbital forcing and ocean surface conditions in strengthening the monsoons and shifting the ITCZ northward over Africa, India, and East Asia during the MH. The combined impact of orbital forcing and sea surface temperature (SST) boundary conditions led to a change in monsoon rainfall of around 42, 30, 21, and 41% over Africa, East Asia, India, and northwest India (NWI) relative to the PI conditions. Changes in orbital parameters alone account for more than 36 and 26% of total rainfall increases in Africa and East Asia. Over the Indian subcontinent, the strengthening of monsoon was primarily a combined effect of SST and orbital forcing. In contrast, the SST boundary condition alone could explain the 39% of rainfall increase over NWI, where the Indus valley civilization once existed. Through moisture budget analysis, the study further illustrates the role of dynamic and thermodynamic factors responsible for the changes in monsoon precipitation. The enhanced monsoon resulted in a northward shift of ITCZ by around 3°N, 1.9°N, and 2.5°N over Africa, East Asia, and India, respectively, compared to its PI position. Analogous to the precipitation changes, orbital forcing mostly mediated ITCZ changes across Africa and East Asia, but the combined impact of orbital forcing and SST was responsible for the changes over India. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, precipitation outputs from retrospective seasonal forecasts made by nine General Circulation Models ( GCMs) are used to investigate historical Indian summer monsoon seasonal rainfall variability and predictability over India. The observed data is taken from the India Meteorological Department whereas GCMs are obtained from the International Research Institute for Climate and Society, Columbia University, the National Center for Environmental Prediction, and the Japan Agency for Marine Earth Science and Technology. The study focusses on June- September precipitation hindcasts initialized from the 1 May. First, the mean climatology, variance of interannual variability ( IAV), and long-term trends for the nine GCMs were evaluated. Then Empirical Orthogonal Function ( EOF) is used to extract major climate modes and spectral analyses method is used to investigate the temporal properties of the leading principal components. It is found that the models are able to reproduce the climatology and IAV to varying degrees. The EOF and spectral analyses of models hindcast reveal that these models are capable of predicting the observed precipitation variability to some extent. In order to study the remote response, the correlation co-efficient between model predicted rainfall and sea surface temperature ( SST) have been calculated. The results suggest that the models show exaggerated remote response to ENSO SST forcing and the Indian Ocean Dipole Mode index has less predictive skill compared to ENSO. The correlation values between the model predicted Monsoon Hadley Index ( MHI) and observed MHI reveals that only a few of them could exhibit large scale circulation features well. [ABSTRACT FROM AUTHOR]

The present paper presents a diagnostic study of two recent monsoon years, of which one is dry monsoon year (2009) and the other is wet monsoon year (2010). The study utilized the IMD gridded rainfall data set in addition to the Reynolds SST, NCEP-NCAR reanalysis wind and temperature products, and NOAA OLR. The study revealed that the months July and August are the most crucial months to decide whether the ISMR is wet or dry. However, during July 2009, most of the Indian subcontinent received more than 60 % in the central and western coastal regions. In a wet monsoon year, about 35-45 % of rainfall is contributed during June and July in most parts of India. During these years, the influence of features in the Pacific Ocean played vital role on the Indian summer monsoon rainfall. During 2009, Pacific SST was above normal in nino regions, characteristic of the El Nino structure; however, during 2010, the nino regions were clearly below normal temperature, indicating the La Nina pattern. The associated atmospheric general circulation through equatorial Walker and regional Hadley circulation modulates the tropospheric temperature, and hence the organized convective cloud bands. These cloud bands show different characteristics in northward propagation during dry and wet years of ISMR. During a dry year, the propagation speed and magnitudes are considerably higher than during a wet monsoon year. [ABSTRACT FROM AUTHOR]

The Indian Ocean Dipole (IOD) can affect the El Niño-Southern Oscillation (ENSO) state of the following year, in addition to the well-known preconditioning by equatorial Pacific Warm Water Volume (WWV), as suggested by a study based on observations over the recent satellite era (1981-2009). The present paper explores the interdecadal robustness of this result over the 1872-2008 period. To this end, we develop a robust IOD index, which well exploits sparse historical observations in the tropical Indian Ocean, and an efficient proxy of WWV interannual variations based on the temporal integral of Pacific zonal wind stress (of a historical atmospheric reanalysis). A linear regression hindcast model based on these two indices in boreal fall explains 50 % of ENSO peak variance 14 months later, with significant contributions from both the IOD and WWV over most of the historical period and a similar skill for El Niño and La Niña events. Our results further reveal that, when combined with WWV, the IOD index provides a larger ENSO hindcast skill improvement than the Indian Ocean basin-wide mode, the Indian Monsoon or ENSO itself. Based on these results, we propose a revised scheme of Indo-Pacific interactions. In this scheme, the IOD-ENSO interactions favour a biennial timescale and interact with the slower recharge-discharge cycle intrinsic to the Pacific Ocean. [ABSTRACT FROM AUTHOR]

The Quaternary sediments of the Aramda Reef Member of the Chaya Formation exposed in the Mojap coast near Mithapur, Gujarat are characterized by well-developed coralline algal build-ups. These algal build-ups are exceptionally rich in coralline algae and corals. In the present paper, thirteen species belonging to eight genera of coralline algae are described. Out of these, seven species ( Titanoderma nataliae, Lithophyllum nitorum, Lithophyllum quadratum, Spongites sp. Brandano et al., 2005, Sporolithon lvovicum, Mesophyllum fructiferum and Lithothamnion praefruticulosum) are the new records for India. Four species ( Titanoderma pustulatum, Sporolithon intermedium, Mesophyllum commune and Phymatolithon sp.) are first time recorded from the study area. Among the major framework builders of coralline algae of the Aramda Reef Member are Lithophyllum, Titanoderma, Sporolithon, Mesophyllum and Lithothamnion. Two associations of the coralline algal assemblages can be distinguished: one indicating shallow water, high-energy conditions is developed in the upper part, while the other suggesting deposition in low-energy conditions is characteristic of the lower part of the succession. These algal associations, together with their growth-forms (encrusting, warty to fruticose, layered) come from stratigraphically separate beds. They indicate that the temperature, depth and hydrodynamic energy conditions also fluctuated during deposition of the Aramda Reef Member. It is concluded that the lower Hapalidiaceae-Sporolithaceae association dominated during warmer interval, whereas the upper lithophylloids association flourished in relatively low-temperature conditions. The associated corals indicate that minimum winter sea surface temperature remained above 18°-20° C. [ABSTRACT FROM AUTHOR]

Ecological associations are the inter-relationship between the species and their environment. Oceanographic processes like upwelling events and formation of eddies, rings, and fronts have been monitored using National Oceanic and Atmospheric Administration Advanced Very High Resolution (NOAA AVHRR) and Indian Remote Sensing Satellite-P4-Ocean Colour Monitor (IRS-OCM) data. Sea Surface Temperature (SST) and chlorophyll concentration (CC) images were derived from AVHRR and OCM, respectively. Upwelling event was monitored using AVHRR-SST by detecting the differences in surface water temperature. The formation of eddies, rings, cyclonic eddies, and anti-cyclonic eddies and their biological responses were studied using CC. Eddies and rings were found with high phytoplankton production in the form of bloom, which provide grazing ground for fishes. The anti-cyclonic eddies were found with very low CC, indicating the biological deserts in the ocean. The impacts of these processes on fish catch were studied using fishing operations data procured from Fishery Survey of India. In this paper, the occurrence of different oceanographic processes, their persistence, and relevance with catch statistics of fishery resources in the study area are discussed. The study explains the potentials of satellite remote sensing to establish the habitat linkage between oceanographic processes and fishery resources. [ABSTRACT FROM AUTHOR]

Tropical cyclone 'Tauktae', in May 2021, was the strongest pre-monsoon cyclone that formed in the Arabian Sea after Kandla in 1998. It turned into an extremely severe cyclonic storm undergoing rapid intensification under favourable conditions. The current study is an effort to understand the role of warm ocean conditions favourable for the genesis and intensification of extremely severe cyclonic storm 'Tauktae'. Very high sea surface temperature anomaly (0.8–1.6°C) and high tropical cyclone heat potential (120–140 kJ/cm2) over the tropical cyclone genesis point and along the tropical cyclone track provided the conditions for the rapid intensification of the tropical cyclone 'Tauktae'. High sea surface temperature and tropical cyclone heat potential enhanced the accumulated cyclone energy of tropical cyclone 'Tauktae', which is very high when compared to the climatological mean. The presence of warm core eddies was seen in the area, where the tropical cyclone had rapidly intensified from a very severe cyclone to an extremely severe cyclonic storm from 16 to 17 May 2021. High sea surface temperature, tropical cyclone heat potential, and warm-core eddies create warm ocean conditions that provided continuous energy in the form of sensible and latent heat flux from the ocean surface to the atmosphere. Our analysis shows that along with the favourable atmospheric conditions, the excessively warm ocean led to the genesis and intensification of tropical cyclone 'Tauktae'. As the Arabian Sea continues to warm, it is inevitable to monitor and understand its effect on tropical cyclone genesis and intensification, which can open the way to predict and mitigate the catastrophic effect of such extreme weather events over India. [ABSTRACT FROM AUTHOR]

As subsystems of the Asian summer monsoon, summer precipitation variations in India and the northern part of eastern China (NEC) are physically connected. This study noted that the connection has been significantly enhanced after 1999 compared to 1979-98, which is due to the strengthened water vapor transportation connection between the two regions. That is associated with interdecadal variations of the combined effects of El Niño-Southern Oscillation (ENSO) and sea surface temperature anomalies (SSTAs) over the tropical Indian Ocean (TIO) on the northwest Pacific subtropical high (NWPSH) and the Indo-Pacific Walker cell. Against the background of La Niña, the strengthened NWPSH and Indo-Pacific Walker cell favor water vapor transport to India and the NEC since 1999. Accordingly, summer precipitation in the two regions increases simultaneously, leading to the enhancement of the summer precipitation teleconnection between them. In addition, the influence of TIO SSTAs and the Indian Ocean dipole (IOD) on Indo-Pacific circulations decreases, which further enhances the relative importance of ENSO on the summer precipitation in the two regions. However, during 1979-98, La Niña SSTAs has weak effects on the NWPSH and Indo-Pacific Walker cell, the negative TIO SSTAs significantly weaken NWPSH, and the negative IOD also obstructs the westward extension of the Indo-Pacific Walker cell. Circulations and water vapor transportation related to the Indian Ocean and NEC summer precipitation are inconsistent, resulting in a weak precipitation teleconnection between them. The above conclusions are also validated by extreme case analysis and CMIP6 models. [ABSTRACT FROM AUTHOR]

The rapid industrial and economic development in India leads to high level of pollution in environment. Due to increased level of aerosols oceans are warming. This paper highlights on aerosols effect on sea surface wind and sea surface temperature by using remote sensing data. The windplays important role in Aerosol Optical Depth (AOD) and radiative forcing and is analyzed using National Centers for Environmental Prediction (NCEP) monthly wind data. The radiative forcing observed is much higher and up to 60% of total AOD during summer monsoon. SeaSurface Temperature (SST) is another important parameter in ocean atmosphere system and a key variable in coupling the atmosphere and ocean. The SST has changed during the change of atmospheric pattern and it plays an important role in aerosols mechanism. The study observed that during summer monsoon SST over the South Eastern Tropical Indian Ocean (SETIO) much higher than Western Tropical Indian Ocean (WTIO) and it plays important role in aerosols mechanism. [ABSTRACT FROM AUTHOR]

The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993–2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability. [ABSTRACT FROM AUTHOR]

This study uses the 30 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project phase-6 (CMIP6) to examine the simulations of the surplus/deficit Indian summer monsoon rainfall (ISMR) and its associated air-sea interactions on intraseasonal to interannual timescales. The majority of the CMIP6 models simulate the seasonal mean state of ISMR over the Indian mainland with systematic biases. Best performing models (BPM; AWI-ESM-1-1-LR, BCC-CSM2-MR, BCC-ESM1, CNRM-CM6-1, CNRM-ESM2-1, GFDL-CM4, INM-CM5-0, MIROC-ES2L, MIROC6, TaiESM1) well simulated the seasonal mean precipitation with Taylor skill score > 0.75 and normalized root mean square error (NRMSE) is < 0.7. However, the models are failed to simulate precipitation over the orographic regions (Western Ghats). Improving the simulations of low-level winds and sea surface temperature (SST) with high spatial resolutions would provide better precipitation simulations. B-MME (multimodel ensemble mean of BPM) can capture the negative IOD-like (Indian Ocean Dipole) pattern during deficit monsoon years and fail to capture the positive IOD-like pattern during surplus monsoon years. Models overestimate the moisture transport from the West Indian Ocean to the sub-continent of India during deficit monsoons, which plays a crucial role in modulating the precipitation and its associated intraseasonal variability. The present analysis identified that during deficit monsoon years, the faster moving 20–100 days oscillations are evident; however, these oscillations are sluggish during surplus monsoon years, which affects the duration of convection activity and causes dry conditions over the regions. During surplus monsoon years, the Bay of Bengal (Arabian Sea) responds strongly (slowly) to the atmosphere than the deficit monsoon years. However, models are fail to represent the ocean's response to the atmosphere over the Bay of Bengal. The freshwater forcing improvement in the models simulates the ocean to atmosphere response over the Indian region. The present study further suggests that the improved simulation of the Indian summer monsoon (ISM) variability by the GCMs is possible by improving the ocean and atmosphere feedback mechanisms, sensitivities of the models among internal variables, and orographic features necessary for the accurate simulation of intraseasonal variability. [ABSTRACT FROM AUTHOR]

The onset of the Asian summer monsoon (ASM) starts the rainy season in one of the most world populous regions. The present study found that the stepwise ASM onset process (ASMOP) in the Bay of Bengal, South China Sea (SCS), and India exhibits three distinct interannual modes induced by different sea surface temperature anomalies (SSTAs). The first mode features are in‐phase variation of ASMOP in southern Asia, which originates from the ENSO‐related SSTAs and anomalous circulations in the tropical Indo‐Pacific Ocean. The second mode indicates the anomalous ASMOP in India and depends on the southwestern Indian SSTAs that alter the low‐level cross‐equatorial flow over the Arabian Sea. In the third mode, the western North Pacific SSTAs and their resultant circulation anomalies regulate ASMOP in the northern SCS. These extra‐equatorial SSTAs and their associated air‐sea interaction serve as an additional source of seasonal predictability for ASMOP besides El Niño–Southern Oscillation events. Plain Language Summary: The onset of the Asian summer monsoon (ASM) begins the rainy season in South and East Asia, one of the world's most populous regions. In general, the ASM onset process (ASMOP) follows a stepwise progression over the Bay of Bengal (BOB), South China Sea (SCS), and India, in turn. The year‐by‐year relationship between the monsoon onset dates over these areas helps to predict the arrival of the monsoon in Asian countries. However, there is no consensus on the year‐by‐year relationship of ASMOP among these areas. The present study used a unified definition to revisit the distinct modes of interannual variation in ASMOP. The El Niño–Southern Oscillation (ENSO)–associated tropical sea surface temperature anomalies (SSTAs) in the Indian and Pacific oceans can modify ASMOP over BOB, the southern SCS, and India simultaneously. Besides the influence of ENSO, the SSTAs in the southwestern Indian Ocean can regulate ASMOP in India. At the same time, the SSTAs modulate ASMOP in the northern SCS in the western North Pacific. Results from numerical experiments verified that air–sea interaction is critical for the effect of extra‐equatorial SSTAs on ASMOP, which provides an additional source of seasonal predictability of ASMOP besides ENSO factors. Key Points: The Asian summer monsoon onset process (ASMOP) shows distinct interannual modes in the tropicsEl Niño–Southern Oscillation determines the interannual variability in ASMOP over India, the Bay of Bengal, and the southern South China Sea (SCS)The southwestern Indian and western Pacific sea surface temperature anomalies modulate ASMOP over India and the northern SCS via air–sea interaction, respectively [ABSTRACT FROM AUTHOR]

The present study evaluates the skill of seasonal forecasts of temperatures over India during April to June using the Monsoon Mission Coupled Forecasting System (MMCFS) model hindcasts, which are initialized with February initial conditions. Model hindcast data of 1981–2017 period have been used for the analysis. The India Meteorological Department (IMD) gridded temperature dataset has been used for model verifications. The MMCFS model captures the annual cycle of temperatures reasonably well, but with a higher mean and smaller variability compared to observations. The model hindcasts show a significant skill for seasonal forecasts of temperatures over most of northwest and central India. Empirical Orthogonal Function (EOF) analysis suggests that the model captures temporal and spatial characteristics of different modes of maximum temperatures but with less accuracy. The model teleconnections of maximum temperatures with Indian Ocean sea surface temperatures (SSTs) and El Niño–Southern Oscillation (ENSO) are weakly represented. The model is also found capable of predicting the spatial distribution of heat wave characteristics such as heat wave frequency (HWF) and heat wave duration (HWD) reasonably well. The present study suggests that the MMCFS Model can be used to generate a useful outlook of hot weather seasonal temperatures and heat waves over India. [ABSTRACT FROM AUTHOR]

Forecasts of Indian summer monsoon rainfall (ISMR: June to September, JJAS) are issued prior to the onset of rainy season. Thus, an assessment of both potential and actual forecast skills for Indian summer monsoon rainfall should be based on a longer lead time. Based upon the European Center for Medium Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system (SEAS5), two lead times are considered: one with an April initial condition (IC) and the other with a May IC from 1981 through 2019 (39 years). Our results show that SEAS5 successfully represents the spatial patterns and variations in the mean JJAS precipitation in the ISMR region compared with the observed rainfall patterns. However, there seem to be significant discrepancies in the simulation of mean precipitation, particularly over topographical regions. SEAS5 is capable of reproducing the observed annual precipitation cycle in India. Moreover, the model is able to better predict the realistic ISMR teleconnections with El Niño-Southern Oscillation and the Indian Ocean Dipole at May ICs. The resulting forecasts across the region are characterized by moderate significant potential and actual skill in both leads, and it decreases as lead time increases. The predictability of SEAS5 is directly related to its ability to correctly predict the forcing of the tropical sea surface temperature and its teleconnections. In spite of this, both lead forecasts have a significant number of unpredicted events and false alarms. This study highlights model discrepancies, shows poor performance in predicting ISMR, and highlights the need for further research on this crucial issue of social relevance. [ABSTRACT FROM AUTHOR]

The post‐monsoon (October–November–December) tropical cyclone (TC) over the Bay of Bengal is one of the most devastating natural disasters causing economic and human losses over India and its neighbouring countries. This study discusses a hybrid statistical/dynamical model developed to forecast the post‐monsoon cyclone activities over the Bay of Bengal, where 80% of the TCs of the North Indian Ocean are originated. In the hybrid model, the coupled model CFSv2 predicts the large‐scale climate indices, and the principal component regression (PCR) model is used to relate these indices with the TC frequency. A solid concurrent relation between the cyclonic disturbance frequencies and various large‐scale variables is noted. The dynamical variable, for example, the zonal wind, acts as a precursor variable. We identified three concurrent predictors (ocean heat content over the Bay of Bengal, sea surface temperature (SST) over the Indian Ocean, and SST over the tropical central Pacific regions) and two precursor predictors (low‐level wind at equatorial Indian ocean and strength of upper‐level easterly jet over African coast) influencing the cyclonic disturbance frequencies over the Bay of Bengal. The concurrent predictors are calculated from the CFSv2 hindcast/forecast output and the precursor predictors are calculated from the reanalysis data. The predictors influencing the cyclonic disturbance over the Bay of Bengal are also influencing the cyclonic storms. Hence, the same predictors are used for developing a hybrid model for cyclonic disturbance and storm frequencies. A significant inter‐correlation among different predictors is observed and the PCR model avoids these inter‐correlations and, in this method, PCs are estimated on the predictors to make them orthogonal to each other. The hybrid model achieved a significant skill for seasonal cyclone forecast over the Bay of Bengal. Results suggest the potential for using the hybrid model for the operational seasonal forecasting of post‐monsoon cyclone activity over the Bay of Bengal. [ABSTRACT FROM AUTHOR]

The changing climate poses significant stress on the yield of wheat, which is a major grain crop in Gangetic Plain and therefore on the food security of this densely populated region. Here, we aim to assess the effects of different climate parameters on wheat yield in the last four decades. The redundancy analysis (RDA) shows that the climatic factors could explain up to 35% of the variations in the wheat yield. The negative correlation with precipitation, Palmer Drought Severity Index (PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI) in winter months might be associated with extreme wetting that delays the yield during the seeding and late growth stages. We recorded a positive and statistically significant correlation between wheat yield and previous year winter mean temperature. The analysis shows that the increase in temperature during the seedling and the late growth stage may result in maximum yield. The linear relationship with the yield in the study area is statistically significant with temperature rise during 1971–2011. The yield of wheat shows a significant positive relationship with both air temperature and sea surface temperature (SST) during winter months in and around India and over the seas regulating winter climate. This indicates that winter temperature may have a direct role in modulating the yield of wheat in the Gangetic Plain demanding further implications of temperature rise on wheat production in future. [ABSTRACT FROM AUTHOR]

Drifting buoys (DBs) are widely deployed to observe near-surface ocean currents and sea surface temperature. The National Institute of Ocean Technology (NIOT), Chennai, India, had indigenised the DB with the Indian satellite (INSAT) in 2012. This paper describes the results of various studies conducted by NIOT using the indigenised DBs and also describes unique features attempted in indigenised DBs to measure the near-surface ocean current with 24 position acquisitions per day to capture small-scale surface eddies and the use of real-time geostationary satellite communication every hour. Additionally, the surface currents observed with indigenous DB are compared with DBs available in the market, Marlin-Yug (coefficient of determination R2 > 0.88) and forecast using the ocean surface current analyses real-time (coefficient of determination R2 > 0.90). Our results show that the DBs in the Bay of Bengal are carried with the East India Coastal Current in the March–May periods and the in-situ observations by the DB provide accurate surface current observations than satellite-based data. Furthermore, the new observations near the world's largest tidal mangrove and delta system, the Sundarbans and Bengal Delta, will help in further enhancing our understanding of the spatiotemporal variability in the region in terms of coastal currents and its influence on marine environments. [ABSTRACT FROM AUTHOR]

The summer monsoon rainfall affects most of the population of the Indian subcontinent. A slight variation of strength and spatial distribution in monsoon rains has huge societal impacts. So understanding the significance of its variability and the factors that control this variability is an imperative task. The Indian monsoon (also regarded as a global phenomenon) varies with the global climatic components like geopotential height, sea surface temperature, outgoing long-wave radiation and mean sea level pressure. The paper presents a statistical analysis of rainfall (over India) and climatic components (over the Arabian Peninsula) and the relationship between them. The period (1979–2013) shows a decrease in monsoon rainfall over the country. During this period, there was an increase in the tropospheric temperature over the Arabian Peninsula. Rainfall showed a direct relationship with the tropospheric temperature and thickness that gets stronger during the period (1979–2013). Except for the northeastern region of India, the rest showed a strong positive correlation with the middle and upper tropospheric temperature over the Arabian Peninsula while the lower tropospheric temperature showed a weak relationship. However, this correlation gets weakened during the El Nino and La Nina years. The findings will be helpful to enhance the Indian rainfall variation and the climatic factors responsible for this variation. [ABSTRACT FROM AUTHOR]

An effort is made to implement a regional earth system model (RESM); ROM, over CORDEX-South Asia (SA). The added value of RESM is assessed for mean precipitation, its variability (intraseasonal to interannual), extremes, and associated processes. In this regard, ROM's fields are compared with the respective fields of its standalone version (REMO), the models belonging coupled model intercomparison project (CMIP5 and CMIP6), and regional climate models of CORDEX-CORE simulations. RESM shows substantial improvement for most of the Indian monsoon's aspects; however, the magnitude of the value addition varies spatiotemporally and also with different aspects.. The improved representation of intraseasonal variability (active-break spell's duration and intensity) and Interannual variability attributed to improved mean seasonal precipitation. Additionally, correct representation of sea surface temperature, Indian Ocean Dipole, and its underlying dynamics also contribute to improving the mean precipitation. The notable improvement is seen especially over the south-eastern regions of the Bay of Bengal (BoB) and South-Central India, where increasing (decreasing) low-pressure systems over Central India (BoB) are noticed as a consequence of air-sea coupling, leading to enhanced (reduced) precipitation over Central India (BoB), reducing dry (wet) bias found in REMO and the other models. Despite substantial improvements, RESM has a systematic wet bias in the mean precipitation associated with a warm bias over the western coast of the Arabian Sea. An overestimation of very high extreme precipitation due to the enhanced contribution of low-pressure systems indicates the model's limitations, suggesting the need for further tuning of the RESM. [ABSTRACT FROM AUTHOR]

The Bay of Bengal (BoB) sea surface temperatures (SSTs) are found to be negatively correlated with the Indian summer monsoon (ISM) rainfall at seasonal, monthly and daily time scales. It is intriguing that the ISM seasonal mean rainfall is reported to be decreasing in the recent decades while no prominent warming has been observed over the BoB. We posit that the reason why BoB warming is not apparent is because BoB surface waters stay largely above the convective threshold of ~ 28 °C and further warming may be perceivable only in terms of an atmospheric response. This is consistent with prominent increase in the latent heat flux (LHF) over BoB during the pre-monsoon season. However such an increasing trend in LHF is not seen for the monsoon season. The increased surface evaporation leading to an increase in LHF over BoB enhances convection aloft which drives a stronger low level pre-monsoon circulation. We hypothesize that the dynamic response of the atmosphere associated with the pre-monsoon circulation changes driven by the BoB warming can explain, at least in part, the observed ISM rainfall trend. This hypothesis is tested by diagnosing SST-forced simulations with the Community Atmosphere Model version 6. The results suggest that the BoB warming does generate dynamic and thermodynamic responses which enhance moisture convergence (divergence) and convection over BoB (central India) during the pre-monsoon season. As a consequence, evapotranspiration is enhanced over the Indian subcontinent leaving the ground drier and colder. The net result is a weaker land–ocean thermal contrast and a weaker monsoon circulation and hence reduced seasonal rainfall totals. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) are prolonged warm sea condition events that can have a destructive impact on marine ecosystems. The documentation of MHWs and assessment of their impacts is largely confined to a few regional seas or to global mean studies. The north Indian Ocean received almost no attention in this regard despite the fact that this ocean basin, particularly the Arabian Sea, has been warming at the most rapid pace among the other tropical basins in recent decades. This study shows the characteristics of MHW events for the Arabian Sea during 1982–2019. Our analysis shows that the duration (frequency) of MHWs exhibits a rapidly increasing trend of ∼20 d per decade (1.5–2 events per decade) in the northern Arabian Sea and the southeastern Arabian Sea close to the west coast of India, which is a multifold increase in MHW days (frequency) from the 80s. Notably, since the beginning of the satellite record, the years 2010 and 2016 have exhibited the maximum number of heatwave days when more than 75 % of days of the pre-monsoon and summer monsoon season experience heatwaves. The accelerated trend of the heatwave days is found to be driven by the rapid rise in the mean sea surface temperature (SST) of the Arabian Sea in the recent decade. Moreover, longer heatwave days are also associated with dominant climate modes. Among them, the Indian Ocean Basin Mode via the decaying phase of El Niño is the most influential mode contributing to more than 70 %–80 % of observed heatwave days in this basin. Further analysis of the most prolonged observed heatwave during April–June 2010 indicates that surface heat flux associated with the weaker latent heat loss and the shallow mixed layer was the primary cause of this event. Further, we note that the pre-monsoon cyclonic storms in the Arabian Sea often contribute to the waning of such heatwaves in the basin. [ABSTRACT FROM AUTHOR]

Research characterising the effects of future climate change on the marine environment remains heavily focussed on that of temperate regions and organisms. Furthermore, little is known of these effects on the early life stages of many marine species. Tropical regions are already experiencing an increase in sea surface temperature and decrease in sea surface salinity, conditions favoured by pathogenic bacteria such as Vibrio spp. The early life stages of crabs are known to be particularly vulnerable to both the direct physiological effects of climate change and exposure to harmful microorganisms, yet there are limited data on these effects on juveniles of many tropical crustacean species. This study assessed the metabolic responses of mud crab (Scylla serrata) juveniles to warming and/or freshening in the presence or absence of pathogenic bacteria in southwest India. Juvenile crabs were exposed to either ambient (28 °C/30 PSU) or one of three projected climate change regimes (28 °C/20 PSU (freshening), 32 °C/30 PSU (warming), 32 °C/20 PSU (warming + freshening)) for 10 days, in either the presence or absence of the pathogenic bacteria Vibrio parahaemolyticus. Results show that simulated climate change conditions, especially freshening, caused a significant increase in oxygen consumption rates (MO2), and that these were further increased when juveniles were exposed to V. parahaemolyticus. These results suggest that the effects of future climate change conditions could have significant implications for the conservation of wild stocks and commercial farming of this species in South Asia. [ABSTRACT FROM AUTHOR]

This study presents an artificial neural network (ANN) approach to estimate the drought events in the Indian state of Madhya Pradesh, also known as central India. Here, sea surface temperature (SST) data obtained from centennial observation-based estimates (COBE) and seasonal rainfall station data (June–July–August) obtained from the India Meteorological Department for the period of 1971–2013 were employed to develop two different ANN models, aiming to forecast the standardized precipitation evapotranspiration index (SPEI) for four stations in central India. Preliminary results from the first ANN model (derived from SST only) showed that correlation between observed SPEI and SST ranged between 0.585 and 0.773. For the second ANN model (derived from both rainfall and SST), the correlation increased and ranged between 0.960 and 0.979. The root mean square error was in the range of 0.619–0.823 and 0.202–0.280 for the first and second ANN model, respectively. Furthermore, probabilistic forecasts of SPEI were made, and the relative operating characteristics scores are examined. The results indicate that the forecasted SPEI values are suitable for drought prediction and that an ANN offers a framework for forecasting the SPEI drought index. [ABSTRACT FROM AUTHOR]