Terminal heat stress is a major constraint limiting wheat productivity in tropical and subtropical regions. This study aimed to assess genetic diversity and identify key traits and genotypes associated with terminal heat stress tolerance using multivariate and stress-index-based approaches. A diverse panel of 500 wheat genotypes, comprising 119 indigenous and 381 exotic entries, were evaluated under three field sowing conditions (timely, late, and very late) to impose natural terminal heat stress. Data were recorded for important phenological, physiological, and yield-related traits. Principal component analysis effectively differentiated genotypes along a tolerance–susceptibility gradient under very late sown conditions. Very late sowing (VLS) imposed severe terminal heat stress, resulting in a grain yield reduction of 58.33% and 59.41% during 2022–23 and 2023–24, respectively, with an average reduction of 58.89% compared with timely sowing. Based on their ability to maintain grain yield under terminal heat stress, as reflected by low Stress susceptibility index (SSI) and Tolerance index (TOL) values and high mean productivity (MP), Stress tolerance index (STI), Grain mean productivity (GMP), Yield index (YI), and Yield stability index (YSI) values, 22 superior genotypes (13 indigenous and 9 exotic) were identified. These genotypes were further classified based on SSI. The heat-tolerant group included genotypes such as 21HTWYT-31, 40SAWSN-3160, DBW-173, PHSL-10, NEST-20-30, 40SAWSN-3080, MP-1323, NEST-20-39, and HD-2967©. The identified genotypes and associated traits constitute valuable genetic resources for breeding wheat cultivars with enhanced terminal heat tolerance.

Climate reanalyses combine historical observational data with advanced modeling techniques to create long-term, consistent climate datasets. Such global datasets are produced by several international centers and have a large and diverse range of applications. The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system and coordinates the generation and exchange of weather, climate, and water information across its members. The WMO has successfully coordinated the production and provision of weather forecasts from international operational centers, from short range to medium range, to seasonal and decadal time scales. In June 2024, the WMO approved the inclusion of global climate reanalysis in their WMO Integrated Processing and Prediction System (WIPPS), which means it is now formally an operational activity within WIPPS. This will ensure that such vital datasets already produced operationally by participating centers are delivered to users in a unified, WIPPS-compliant framework, with regular traceable updates in a timely fashion. The lead center coordinating this activity will facilitate intercomparison of global reanalysis products from several centers, with the provision of comparable data on identical grids, and graphical products and visualization tools.

Subseasonal forecasts are routinely produced by different prediction centers around the globe, offering actionable lead time for decisions across climate-sensitive sectors. However, the uptake of these forecasts is still limited. We argue that this gap persists not because of a single barrier but because of a set of obstacles: insufficient understanding of how and where subseasonal information enters real decision processes; limited knowledge of when forecasts can be trusted; weak methods for communicating and incorporating uncertainty into operational workflows; and few demonstrated success stories linking forecast use to improved outcomes. In this Perspective, we synthesize the evidence accumulated during the Subseasonal-to-Seasonal Prediction (S2S) Project and identify the critical gaps that limit the transition from prediction science to climate services, and present the research-and-implementation agenda of SAGE (Subseasonal Applications for Agriculture and Environment), a five-year project under the World Weather Research Programme (WWRP). SAGE prioritizes user needs to advance in the understanding on how and where information at subseasonal timescales is and can be used. We outline key scientific questions for the broader community, describe the phased research plan and its expected outcomes. SAGE encourages the entire community of scientists and practitioners to contribute to this agenda.

Utilizing reanalysis and satellite observations, the present study investigates the interactions and redistribution of aerosols during a Very Severe Cyclonic Storm (VSCS) Vardah (6-13 December, 2016) in the Bay of Bengal (BoB). The detailed analysis focuses on the effects of aerosols on the tropical cyclone (TC) induced precipitation, including an examination of aerosol loading, changes in their distribution during the passage of the cyclone. As cyclone Vardah matured from a Severe Cyclonic Storm (SCS) to VSCS, a gradual reduction in the Precipitation Rate (PR) was observed, accompanied by an increasing trend of Lower Tropospheric Stability (LTS). Winds originating from the northeastern Himalayan region and transporting aerosols from the aerosol-rich Indo-Gangetic Plain (IGP) carried a substantial amount of aerosol particles toward the cyclone, resulting in a significant influx into its circulation. Even more aerosol loading was recorded over the central and western BoB during the SCS and VSCS stages of TC Vardah, respectively. This could be due to the strong drag of winds from the north-eastern Himalayan region towards the cyclone as it approached the coastal region. Investigation of the spatial distribution of aerosols and precipitation rate during all three stages (i.e., Cyclonic Storm (CS), SCS, and VSCS) revealed that the presence of aerosols played a significant role in suppressing precipitation before cyclone Vardah made landfall. Additionally, spatio-temporal anomalies of AOD showed a sharp contrast, with anthropogenic aerosols depleted near the storm core due to wet scavenging, while natural aerosols such as sea salt were enhanced along the storm track, highlighting the cyclone's dual role as both a cleanser and redistributor of aerosols. Further, our analysis revealed that TC Vardah deposited a significant amount of aerosols over Chennai, bringing it from the ocean. These results make an important contribution to understanding the redistribution of aerosols and their impact on precipitation induced by cyclones over the BoB.

Tropical cyclone 'Amphan,' originating over the Bay of Bengal, made landfall as a well-defined low-pressure system across northern Bangladesh and the surrounding region around midnight on May 21, 2020. This research focuses on the influence of cumulus parameterization schemes (CPS) within the Advanced Research Weather Research Forecast (WRF-ARW) model for tropical cyclone 'Amphan.' Parameters such as maximum sustained wind (MSW), mean sea level pressure (MSLP), potential vorticity (PV), vertical integrated moisture transport (VIMT), and rainfall are investigated in this paper. This study employs the National Center for Environmental Prediction (NCEP)'s global operational analysis and prediction products at a 1 degrees x1 degrees resolution for initial and boundary conditions. The analyses compare different cumulus parameterization schemes Kain Fritsch (KF) Scheme, Betts-Miller-Janjic (BMJ), Grell-3, combined with the Yonsei University (YSU) planetary boundary layer (PBL) scheme and NCEP operational cloud microphysics scheme (Ferrier), against observed datasets from the Indian Meteorological Department (IMD) and ERA5 reanalysis datasets. Using the mass flux type scheme for cumulus parameterization, the results of MSW and MSLP with the Grell-3 scheme in WRF-ARW model are closely aligned with IMD observations during intense stages of tropical cyclone 'Amphan'. The findings highlight the efficacy of the Grell-3 cumulus parameterization scheme in capturing the large-scale Latitude versus height structure of PV. This research investigates the vertically integrated moisture transport (VIMT) for the SuCS 'Amphan' and its correlation with rainfall over BoB from 16-21, May 2020 using WRF-ARW simulated datasets and ERA5 reanalysis datasets. The Grell-3 scheme exhibits notable differences in the area-averaged VIMT and corresponding rainfall. This research contributes to enhancing the understanding and prediction accuracy of tropical cyclones through comprehensive WRF-ARW model analyses.

This study uses the 2023/2024 winter as a case study to analyze California precipitation response in atmospheric model simulations to illustrate the nuances in the response to variations in sea surface temperature (SST) anomalies during strong El Niño‐Southern Oscillation (ENSO) events. The 2024 El Niño exhibited a wet signal over California, but it was notably weaker. Observed SST anomalies for this winter included a contribution from SST warming trends and pronounced Atlantic warming that was much larger than attributable to SST warming trends alone. Sensitivity experiments indicated that SST trends suppressed California precipitation by disrupting traditional El Niño teleconnection patterns, particularly in southern California. Atlantic warming contributed to North Pacific anticyclonic anomalies, opposing typical El Niño‐induced wet conditions. These findings highlight the role of inter‐basin SST interactions and of trends in shaping atmospheric responses and underscore the importance of regional and global SST variations in modulating ENSO teleconnections.

Abstract We use a version of the NOAA Climate Forecast System with enhanced (up to 1‐m) ocean model vertical resolution to investigate the mean diurnal cycles of upper ocean temperature and currents. The model sea surface temperature diurnal cycle agrees well with a global observational analysis. The simulated time‐depth profiles of temperature and current also match closely observations from densely instrumented moorings in the tropical Pacific. Our analyses provide new insights into subsurface ocean diurnal cycles. Significant temperature diurnal range occurs, with seasonal modulation, at depths greater than 10 m across broad areas of the subtropical and midlatitude oceans. Significant current diurnal cycles are evident below 30 m across parts of the tropics, including in areas where deep‐cycle turbulence has been observed.

The present research investigates the dynamics and underlying causes contributing to the exceptional intensity of Super Cyclonic Storm (SuCS) Amphan (16th to 21st May 2020) over the Bay of Bengal (BoB), as well as its impact on aerosol redistribution along the four cities of eastern coast and north-eastern India. Notably, the SuCS was formed during the first phase of the COVID-19 lockdown in India, giving it a unique aspect of study and analysis. Our analysis based on 30 years of climatology data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis reveals 'positive' monthly anomalous winds (0.8 to 1.6 m/s) prevailed over the central BoB for May 2020. The present study further found the evolution of 'barrier layer thickness'(BLT) leading up to landfall, noting a thickening trend from 8 to 3 days before landfall, contributing to maintaining warmer sea surface temperatures near the coast. Additionally, utilizing European Centre for Medium-Range Weather Forecasts (ECMWF), reanalysis version-5 (ERA-5) data, a mean positive sea surface temperature (SST) anomaly of 0.8 to 1 degrees C was observed 'before' cyclone period (10-15 May 2020) near the cyclogenesis point. A detailed examination of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cross-section plots during the cyclone's intensification stage reveals the presence of highaltitude clouds composed primarily of ice crystals. Further, analysis also indicates that the cyclone transported Sea-salt PM2.5 aerosols from the ocean, dispersing them in the landfall region.The aerosol optical Depth (AOD) data obtained from the National Aeronautics and Space Administration's (NASA) 'Clouds and the Earth's Radiant Energy System (CERES)' mission and MERRA-2 were also analysed, revealing that the cyclone redistributed aerosols over the Bengal basin region (mainly over 'Kolkata') and three other nearby cities along the track of the cyclone (i.e., Bhubaneswar (Odisha) Agartala (Tripura) and Shillong (Meghalaya) respectively).

The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system, with an intergovernmental mandate for coordinating the generation and exchange of weather, climate, and water information across its members. WMO has played a vital role in coordinating production and dissemination of weather forecasts from short to medium range whereby global weather forecasts from large operational centers are made available to all WMO members to serve needs of stakeholders at the local level. In recent decades, there has also been an increasing demand for similar forecasts on longer lead times that include prediction on subseasonal, seasonal, and annual to decadal leads. To address the increasing requirements for forecast services by members, WMO has been actively accrediting and coordinating the essential forecast infrastructure that includes provision of forecasts from WMO designated Global Producing Centers and collection of forecasts by Lead Centers to facilitate the dissemination of information and products to WMO members and relevant nongovernmental organizations. Although the basic ingredients of the infrastructure are now in place, the uptake of the forecast information has been suboptimal. To engage the community in developing solutions to enhance the utilization of available information, this paper summarizes the WMO infrastructure for long-range forecasts, particularly for seasonal time scale, and follows with a discussion of current issues that are hindering their uptake. Finally, a set of proposals to advance the utilization of the available information from the WMO long-lead forecast infrastructure are discussed.

Realistic representation of monthly sea level anomalies in coastal regions has been a challenge for global ocean reanalyses. This is especially the case in coastal regions where sea levels are influenced by western boundary currents such as near the U.S. Atlantic Coast and the Gulf of Mexico. For these regions, most ocean reanalyses compare poorly to observations. Problems in reanalyses include errors in data assimilation and horizontal resolutions that are too coarse to simulate energetic currents like the Gulf Stream and Loop Current System. However, model capabilities are advancing with improved data assimilation and higher resolution. Here, we show that some current-generation ocean reanalyses produce monthly sea level anomalies with improved skill when compared to satellite altimetry observations of sea surface heights. Using tide gauge observations for coastal verification, we find the highest skill associated with the GLORYS12 and HYCOM ocean reanalyses. Both systems assimilate altimetry observations and have eddy-resolving horizontal resolutions (1/12°). We found less skill in three other ocean reanalyses (ACCESS-S2, ORAS5, and ORAP6) with coarser, though still eddy-permitting, resolutions (1/4°). The operational reanalysis from ECMWF (ORAS5) and their pilot reanalysis (ORAP6) provide an interesting comparison because the latter assimilates altimetry globally and with more weight, as well as assimilating ocean observations over continental shelves. We find these attributes associated with improved skill near many tide gauges. We also assessed an older reanalysis (CFSR), which has the lowest skill likely due to its lower resolution (1/2°) and lack of altimetry assimilation. ACCESS-S2 likewise does not assimilate altimetry, although its skill is much better than CFSR and only somewhat lower than ORAS5. Since coastal flooding is influenced by sea level anomalies, the recent development of skilful ocean reanalyses on monthly timescales may be useful for better understanding the physical processes associated with flood risks.

The present study investigates the role of coastal downwelling in the intensification of tropical cyclones before landfall near the coastal Bay of Bengal (BoB) during the post monsoon season. Four major cyclones (Phailin, Hudhud, Titli, and Gaja), whose maximum intensity is equivalent to a very severe cyclonic storm or higher, with a wind speed of more than 64 knots, are considered in this study. It was found that higher Sea Surface Temperature (SST) (similar to 30 C-degrees) conditions prevailed near the coastal BoB for all four cyclones a week before landfall. This was primarily attributed to coastal downwelling, which was identified by a positive sea level anomaly along the west coast of the Bay of Bengal during the post-monsoon season. This phenomenon results in the deepening of the isothermal layer, leading to an increase in Ocean Heat Content and ultimately contributing to the sustained higher SST in the nearby region of their landfall. The low-saline freshwater influx advected by the southward coastal current also helps to maintain the warm temperature of the upper layer. Coastal downwelling raises the ocean heat content near the coast, which provides the energy needed to intensify cyclones before landfall. These findings show the importance of coastal downwelling when simulating the short-term evolution of near-shore oceanic conditions, allowing us to improve the prediction of changes in tropical cyclone intensity prior to landfall.

The difference in observed atmospheric anomalies over the Northern Hemisphere winter between 2021-22 and 2020-21 La Nina years indicated a tripole pattern consisting of a Japan cyclone, a Bering Sea anticyclone, and a cyclone over the North American continent. This feature, however, was not replicated in the North American Multi-Model Ensemble (NMME) forecasts. A set of model sensitivity experiments was performed to better understand the cause of this discrepancy. The results revealed the possible role of the influence of sea surface temperature (SST) anomalies, particularly over the Indian Ocean, on the observed circulation differences that was further modulated by internal atmospheric variability. The failure in predicting circulation changes in NMME was next attributed to the errors in SST predictions over the Indian Ocean and highlights the need for improvements in SST forecasts over this region. The tropical SST anomalies associated with the El Nino-Southern Oscillation (ENSO) are known to influence the global atmospheric circulation and are the major source of skill in U.S. seasonal predictions. As the cold phase of ENSO, La Nina features below-normal SST anomalies and suppressed convection over the equatorial central and eastern Pacific. Such a tropical heating distribution favors the formation of the atmospheric circulation pattern that has a roughly opposite effect on U.S. surface climate compared to El Nino, the warm phase of ENSO, although the effect is not strictly symmetric. For the recent two La Nina boreal winters of 2020-21 and 2021-22, the observed circulation patterns differed, but dynamical seasonal prediction failed to replicate this feature. Understanding the cause for the discrepancy of circulation changes between prediction and observations is of fundamental importance for the improvement of seasonal forecasts. Toward this, we designed numerical experiments that are forced with observed and predicted SST anomalies over different ocean basins. The results show that it is the errors in SST prediction over the Indian Ocean that contributed to the failure in the prediction of circulation changes, highlighting the importance of skillful prediction of SST over this region. The difference in observed atmospheric anomalies for 2022 versus 2021 La Nina boreal winters featured a Northern Hemisphere tripole pattern Indian Ocean SST contributed to the formation of observed tripole pattern, with internal atmospheric variability modulating its magnitude Errors in SST predictions over the Indian Ocean led to the failure in predictions of the circulation changes in NMME forecasts

The urban population of India has increased fivefold during the last five decades. Monitoring and estimation of urban sprawl are crucial for urban planning, land and water resource management, facility allocation, etc. The present study is aimed at making an in-depth analysis of urban dynamics in the Bengaluru Urban District by using three different indices: the built-up density index (BUDI), the annual urban spatial expansion index (AUSEI), and the annual built-up change index (ABCI), along with temporal satellite data and GIS. The study reveals that an exponential outward urban expansion has taken place in the study area from 1993 to 2020, mainly due to the development of the co-working industry, migration of people from cities to outer areas, high population growth, rapid and significant growth in the IT field, economic growth, and developments of road networks. The analysis shows that about 21.08

Abstract In light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria.

AbstractAs an update on the current NOAA/NCEP operational ocean reanalysis systems, a new system named GLobal Ocean Reanalysis (GLORe) is recently built up based on the JEDI‐SOCA 3DVar scheme. In this study, the quality of GLORe is assessed in initializing ENSO predictions using the NOAA Unified Forecast System (UFS). In details, initialized by GLORe, 9‐month ensemble hindcasts are conducted from each May/November during 1982–2021. The ENSO prediction skill is compared to the current NOAA operational system CFSv2, suggesting that UFS initialized with GLORe has an improved skill in ENSO predictions. By conducting another set of hindcasts with UFS and the same initializations as CFSv2, it is found that the skill improvement is largely attributed to the ocean initialization with GLORe, but with some contributions from model improvements as well. The effect of ocean initializations is further confirmed by the superiority of GLORe over CFSR as validated against an objective analysis.

The present study delineates the role of ocean conditions in the genesis and rapid intensification (RI) of a very severe cyclonic storm (VSCS) 'Titli' (2018). The tropical cyclone (TC) formed over the warm waters of the east-central Bay of Bengal during 08-13 October 2018. According to the India Meteorological Department (IMD), the cyclone was the most damaging storm to hit any coast of India in the year 2018, making it a special case of analysis. In the present study, 10 m winds , Sea Surface Temperature (SST), Latent heat flux, and relative vorticity (RV) during the lifespan of the cyclone are studied using ECMWF reanalysis V5 (ERA5) prepared by European Centre for Medium Range Weather Forecasts (ECMWF). Further, the Tropical Cyclone Heat Potential (TCHP) data generated by the Indian National Centre for Ocean Information Services (INCOIS) in Hyderabad is used to study the important information about the oceanic conditions of the TC. The investigation of the TC's sea surface temperature data from satellites reveals that a relatively warmer SST prevailed during the cyclone's occurrence, which may have been the primary factor in the TC's rapid intensification. Further, the latent Heat flux (LHF) and TCHP values were also found high in conjunction with SST values. Our in-depth analysis reveals that the 10 m winds embedded into the TC were extremely strong, exceeding 12 m/s prior to the landfall. A positive and large value of RV was found when the TC was about to hit the coast. This may be one of the reasons behind the 'catastrophic landfall' of the cyclone.

The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean’s role in Earth’s climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements.

NOAA Climate Prediction Center (CPC) has generated a 100-member ensemble of Atmospheric Model Intercomparison Project (AMIP) simulations from 1979 to present using the GFSv15 with FV3 dynamical core. The intent of this study is to document a development in an infrastructure capability with a focus to demonstrate the quality of these new simulations is on par with the previous GFSv2 AMIP simulations. These simulations are part of CPC’s efforts to attribute observed seasonal climate variability to SST forcings and get updated once a month by available observed SST. The performance of these simulations in replicating observed climate variability and trends, together with an assessment of climate predictability and the attribution of some climate events is documented. A particular focus of the analysis is on the US climate trend, Northern Hemisphere winter height variability, US climate response to three strong El Niño events, the analysis of signal to noise ratio (SNR), the anomaly correlation for seasonal climate anomalies, and the South Asian flooding of 2022 summer, and thereby samples wide aspects that are important for attributing climate variability. Results indicate that the new model can realistically reproduce observed climate variability and trends as well as extreme events, better capturing the US climate response to extreme El Niño events and the 2022 summer South Asian record-breaking flooding than GFSv2. The new model also shows an improvement in the wintertime simulation skill of US surface climate, mainly confined in the Northern and Southeastern US for precipitation and in the east for temperature.