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High‐resolution climate projections are valuable resources for understanding the regional impacts of climate change and developing appropriate adaptation/mitigation strategies. In this study, we developed a 10‐km gridded hydrometeorological dataset over India by dynamic downscaling of the bias‐corrected Community Earth System Model (CESMv1) climate projections under RCP8.5 scenario using the state‐of‐the‐art Weather Research and Forecasting (WRF) model. The downscaled CESM dataset (DSCESM) is archived in the World Data Center for Climate (WDCC) portal at three temporal resolutions (daily, monthly and monthly climatology) for current (2006–2015), mid‐century (2041–2050) and end‐century (2091–2100) periods. The dataset includes 2‐m air temperature, total accumulated precipitation, wind speed, relative humidity, sensible and latent heat fluxes, along with surface shortwave and outgoing longwave radiation. All the DSCESM variables were evaluated against reanalysis data and station observations for the period 2006–2015. This dataset can help us quantitatively understand regional climate change in India. It can also be used in conjunction with agricultural, hydrological, fire and other application models for climate change impact assessment on various sectors to help develop effective adaptation/mitigation strategies. [ABSTRACT FROM AUTHOR]

The present study focuses on characterizing the time–frequency aspects of seasonal temperatures in India by integrating the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm with the Hilbert–Huang transform (HHT) decomposition method. The investigation also explores the connections between maximum temperature (Tmax) and minimum temperature (Tmin) with global climate oscillations, such as the El Nino Southern Oscillation (ENSO), Sunspot Number (SN), and Pacific Decadal Oscillations (PDO). The findings indicate that intra and inter-decadal modes play a pivotal role in influencing temperature series across various seasons, with notable changes observed in the amplitudes of inter-decadal modes for seasonal Tmin and Tmax. The analysis of intrinsic mode functions (IMFs) reveals that IMF2 closely align to ENSO with a periodicity of 5–7 years, IMF3 to the sunspot cycle with a frequency of approximately 11 years, and IMF5 to PDO with a long periodicity exceeding 60 years. The association between the IMF components of Tmin and Tmax temperature series and the three climate indices is most evident for low-frequency modes, demonstrating a consistent evolution of trend components. [ABSTRACT FROM AUTHOR]

The present study deals with the observation of Rn-222 emanation process in terms of its exhalation rate for 48 h from sub-surface soil at 3 h intervals, using a continuous Rn-222 monitoring system RAD7 at Sidho-Kanho-Birsha University campus in Purulia district, located in the easternmost part of the Chotanagpur Plateau, India based on local weather conditions, over five different seasons. For every season, the diurnal variations of the Rn-222 exhalation rate increase after sunset, reaching to its maximum at mid-night and thereafter starts decreasing until it falls to the minimum around noon time. Local meteorological parameters such as ambient temperature, relative humidity, atmospheric pressure were also recorded during the observation period to study the possible influences of these parameters on the soil gas Rn-222 emission rates using Pearson's correlation matrix. There exists a strong positive correlation between relative humidity and Rn-222 exhalation rate and a positive correlation between atmospheric pressure and Rn-222 exhalation rate for all the observation periods. On the other hand, negative correlations between the atmospheric temperature and the Rn-222 exhalation rate exist for all the seasons. [ABSTRACT FROM AUTHOR]

Understanding the trend of seasonal rainfall in the context of climate change is crucial for the maintenance of regional water resources management. The present study examines the seasonal rainfall trend in the Mahi River basin, India by using the wavelet transform and clustering method. Daily gridded rainfall data (0.25° × 0.25° spatial resolution) for 51 grids from 1970 to 2020 have been taken from India Meteorological Department (IMD). The monthly, seasonal, and annual statistic has been analyzed for 51 years. We have also plotted the relationship between precipitation trend–elevation. After that, homogeneous precipitation regions are delineated with hierarchical clustering analysis. Results reveal that seasonal precipitation over the basin clusters into 4 subregions for monsoon and 3 subregions for winter, pre-monsoon, and post-monsoon seasons. After the regionalization of the subregions, the periodicity and the inter-seasonal relationship were analyzed using continuous wavelet transform (CWT). In addition, it was clear that the cross-correlation between pre-monsoon and monsoon seasons had a significant periodic change in the last 30 years over the basin. The basin is located adjacent to the Arabian sea, which makes the basin more sensitive to a natural event like ENSO, it can cause changes in ocean temperatures and atmospheric circulation patterns that affect precipitation patterns during the study period. It is expected that some natural phenomena like monsoon variability, low-level jet streams, and many more also have an impact on the rainfall patterns of the basin. Thus, the understanding of seasonal precipitation variation provides a practical reference for water resources management, agricultural planning, and a forecast of precipitation in different regions and river basins of India which may give a better climate change indication. [ABSTRACT FROM AUTHOR]

This study investigates the incidence of heat wave and cold wave condition during 2000 – 2022 in the Bathinda district of South-Western region of Punjab. Notable spikes in heat wave (HW) activity were observed in 2002 and 2022 with 29 and 27 days respectively. Similarly, for severe heat waves (SHW), 2010 and 2022 witnessed the highest frequencies recording 16 and 18 days respectively. Conversely, cold wave (CW) events peaked in 2005 and 2008 with 10 and 11 days respectively. Notably, 2008 also observed the highest frequency of severe cold wave (SCW) days with 15 days. However, results revealed decline in cold wave days towards the latter years, while severe cold wave days also exhibited decreasing frequencies like 2015 and 2016 recorded zero CW and SCW days. One key finding highlights a substantial correlation between land surface temperature (LST) and maximum air temperature during heat wave periods (R² = 0.83), indicating LST’s efficacy as an indicator for monitoring temperature trends during heat wave events. [ABSTRACT FROM AUTHOR]

Hydro-meteorological parameters significantly influence groundwater storage in unconfined aquifers. Although the unconfined aquifer is closer to the surface, the dry conditions have an impact on the water level. The relationship between the hydro-meteorological parameters and the unconfined groundwater level (GWL) has not received much attention or study. Hence, the impact of hydro-meteorological parameters (e.g. rainfall (RF), air temperature (T), and potential evapotranspiration (PEV)) on the GWL is studied over West Bengal, India. The region is categorized into four zones (zone-1, zone-2, zone-3, and zone-4), considering the geo-hydrological scenario. GWL raised by ≈ 1.63 m, 3.51 m, 3 m, and 2.24 m owing to the RF of ≈ 880 mm, 953 mm, 1083 mm, and 1593 mm during monsoon in zone-1, zone-2, zone-3, and zone-4, respectively. The groundwater table is 6.9 m, 95.2 m, 22.3 m, and 80.2 m in the respective zones during winter. The groundwater flows seasonally from zone-2 to zone-1 and zone-3 and, similarly, from zone-4 to zone-3 and zone-1. In zone-1, as RF increased in monsoon, the shallow GWL occurred, and PEV caused water loss. PEV and T have a dependency on deeper GWL in other seasons. PEV and T are critical factors in zone-2's GWL depletion in all seasons. Winter, pre-monsoon, and post-monsoon showed a correlation between the deeper GWL and PEV and T in zone-3 and zone-4. When sufficient RF occurred, the deeper GWL enhanced to a shallower GWL. In monsoon, recharge of the unconfined aquifer occurred owing to RF. The deeper GWL developed in post-monsoon because of lower RF and increased PEV and T. [ABSTRACT FROM AUTHOR]

Studying atmospheric temperature characteristics is crucial under climate change, as it helps us to understand the changing patterns in temperature that have significant implications for the environment, ecosystems, and human well-being. This study presents the comprehensive analysis of the spatiotemporal variability of scaling behavior of daily temperature series across the whole Indian mainland, using a Multifractal Detrended Fluctuation Analysis (MFDFA). The analysis considered 1° × 1° datasets of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and diurnal temperature range (DTR) (TDTR = Tmax − Tmin) from 1951 to 2016 to compare their scaling behavior for the first time. Our results indicate that the Tmin series exhibits the highest persistence (with the Hurst exponent ranging from 0.849 to unity, and a mean of 0.971), and all four-temperature series display long-term persistence and multifractal characteristics. The variability of the multifractal characteristics is less significant in North–Central India, while it is highest along the western coast of India. Moreover, the assessment of multifractal characteristics of different temperature series during the pre- and post-1976–1977 period of the Pacific climate shift reveals a notable decrease in multifractal strength and persistence in the post-1976–1977 series across all regions. Moreover, for the detection of climate change and its dominant driver, we propose a new rolling window multifractal (RWM) framework by evaluating the temporal evolution of the spectral exponents and the Hurst exponent. This study successfully captured the regime shifts during the periods of 1976–1977 and 1997–1998. Interestingly, the earlier climatic shift primarily mitigated the persistence of the Tmax series, whereas the latter shift significantly influenced the persistence of the Tmean series in the majority of temperature-homogeneous regions in India. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Accurate and reliable air temperature forecasts are necessary for predicting and responding to thermal disasters such as heat strokes. Forecasts from Numerical Weather Prediction (NWP) models contain biases which require post-processing. Studies assessing the skill of probabilistic post-processing techniques (PPTs) on temperature forecasts in India are lacking. This study aims to evaluate probabilistic post-processing approaches such as Nonhomogeneous Gaussian Regression (NGR) and Bayesian Model Averaging (BMA) for improving daily temperature forecasts from two NWP models, namely, the European Centre for Medium Range Weather Forecasts (ECMWF) and the Global Ensemble Forecast System (GEFS), across the Indian subcontinent. Apart from that, the effect of probabilistic PPT on heatwave prediction skills across India is also evaluated. Results show that probabilistic PPT comprehensively outperform traditional approaches in forecasting temperatures across India at all lead times. In the Himalayan regions where the forecast skill of raw forecasts is low, the probabilistic techniques are not able to produce skillful forecasts even though they perform much better than traditional techniques. The NGR method is found to be the best performing PPT across the Indian region. Post-processing Tmax forecasts using the NGR approach was found to considerably improve the heatwave prediction skill across highly heatwave prone regions in India. The outcomes of this study will be helpful in setting up improved heatwave prediction and early warning systems in India. [ABSTRACT FROM AUTHOR]

During the pre- and post-monsoon season, the eastern and western coasts are highly vulnerable to cyclones. The tropical cyclone "Tauktae" formed in the Arabian Sea on 14 May 2021 and moved along the west coast of India, and landfall occurred on 17 May 2021. During the cyclone, the maximum wind speed was 220 km/h with a pressure of 935 mb affecting meteorological, atmospheric parameters, and weather conditions of the northern and central parts of India causing devastating damage. Analysis of satellite, Argo, and ground data show pronounced changes in the oceanic, atmospheric, and meteorological parameters associated during the formation and landfall of the cyclone. During cyclone generation (before landfall), the air temperature (AT) was maximum (30.51 °C), and winds (220 km/h) were strong with negative omega values (0.3). The relative humidity (RH) and rainfall (RF) were observed to be higher at the location of the cyclone formation in the ocean and over the landfall location, with an average value of 81.28% and 21.45 mm/day, respectively. The concentration of total column ozone (TCO), CO volume mixing ratio (COVMR), H2O mass mixing ratio (H2O MMR), aerosol parameters (AOD, AE) and air quality parameter (PM) was increased over land and along the cyclone track, leading to a deterioration in the air quality. The strong wind mixes the air mass from the surroundings to the local anthropogenic emissions, and causing strong mixing of the aerosols. The detailed results show a pronounced change in the ocean, land, meteorological, and atmospheric parameters showing a strong land–ocean-atmosphere coupling associated with the cyclone. [ABSTRACT FROM AUTHOR]

Agriculture is affected due to the impact of climate change on soil, land, water and the atmosphere. Agriculture is also a significant contributor to greenhouse gas emissions, directly or indirectly. The challenge lies in the reduction of emissions without impacting food production and yield. With the increase in air temperature and extreme events of rainfall as the probable indicators of climate change, there can be impacts on various components of the agroecosystem, which include crop yield, soil quality, pest and disease infestation, crop loss, irrigation water demand, etc. This will, in turn, impact food security. The development of novel adaptation techniques and strategies for agriculture and surveillance of resources is needed to tackle the adverse effects of climate change. This article discusses the impacts of climate change on various components of the agroecosystem and how to achieve resilience for sustainable development. Various national-level programmes for climate change resilience in agriculture have been implemented in India to incorporate climate information into agricultural development by integrating climate services into practices and policies for future food security. [ABSTRACT FROM AUTHOR]

The isotopic composition (δ18O and δ2H) of precipitation is widely used as a moisture source tracer. In north‐west India, the δ18O, δ2H and D‐excess values of precipitation correlate mainly with air temperature; however, the moisture sources of water vapour are unclear. Therefore, we collected daily precipitation isotope data (n = 425) from 13 rain gauge stations in northwest India in 2013. We established a regional meteoric water line (MWL) for northwest India and local MWLs for all 13 rain gauge stations separately. We observed an altitudinal gradient of about 0.11‰/100 m in slope and about 1.22‰/100 m in intercept from the western to the northeast part of the study area. The isotopic composition of precipitation shows spatial and temporal variability across the study area. We found higher Deuterium excess values during winter (December–February) and lower during the monsoon season (July–September), which may be associated with the different moisture sources, namely, the Bay of Bengal, Arabian Sea and Westerly disturbances. Our results suggest local moisture recycling may also occur during the study period. The present study can enhance the knowledge of the isotopic evolution of precipitation and moisture sources in northwest India. [ABSTRACT FROM AUTHOR]

Nowcasting and short‐range predictions of severe weather events require frequent observations of atmospheric thermodynamic state. To meet these requirements, India has launched a series of advanced weather satellites in geostationary platform, with Indian National Satellite System (INSAT)‐3D and INSAT‐3DR as first two satellites placed in this orbit. Both satellites are designed for meteorological observations, particularly the vertical profiles of temperature and humidity at hourly intervals. As these satellites are relatively new, there is a need to validate and make accuracy assessment of their retrievals prior to scientific analyses. These sounders have a spatial resolution of 10 km, and measurements are available from 2014 to date. This study makes use of collocated measurements of INSAT‐3D and INSAT‐3DR with those from 18 radiosondes, Atmospheric Infrared Sounder (AIRS), and reanalysis data within the troposphere (1,000–100 hPa) to validate INSAT data. We analyzed the data for India land regions, and found an average bias of about 2–3 K (1–1.5 K) in most regions, when INSAT‐3D (3DR) is compared to AIRS measurements. Compared to the reanalysis data, INSAT‐3D (3DR) shows a bias within 2.5–5 K (2–3 K) in lower troposphere, 1.5–2.5 K (1–2 K) in mid‐troposphere and 1.5–2.5 (<2 K) in upper troposphere. Similar biases were also estimated with radiosonde measurements. In general, the validation shows that temperature profiles are generally accurate within 1–1.5 K, and INSAT‐3DR retrievals are better than that of INSAT‐3D. Nevertheless, a better cloud filter together with an advanced radiative transfer model can further improve the algorithm used for retrieval. Plain Language Summary: Monitoring temperature at different atmospheric levels is crucial for detecting changes in weather patterns, which can have detrimental effects on crops, especially during crucial growth stages, resulting in reduced yields. Additionally, monitoring temperature helps in predicting extreme weather events such as storms, heatwaves and droughts, which demand additional energy and resources. The Indian Space Research Organization has developed two advanced weather monitoring satellites, Indian National Satellite System (INSAT)‐3D and INSAT‐3DR, which provide high temporal resolution temperature profiles owing to its observation from a geostationary orbit. However, the satellite data are collected remotely, and accuracy of the data can be affected by various factors including atmospheric conditions and the sensor characteristics. Therefore, a thorough validation is necessary before the data to be used for scientific studies. Our study validates the temperature measurements from INSAT‐3D and INSAT‐3DR within the troposphere (1,000–100 hPa) using collocated measurements from ground‐based, satellite, and reanalysis data. The validation shows that the temperature profiles are accurate within 1–1.5 K and further improvements can be made by using a better cloud‐filtering algorithm together with an advanced radiative transfer model. [ABSTRACT FROM AUTHOR]

In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models. [ABSTRACT FROM AUTHOR]

Although, the aerosol-cloud interactions and its impact on daily to seasonal radiation/temperature has been well observed over South Asia in last two decade, the role of aerosol-cloud interactions on cloud occurrence trends (and surface temperature) is yet not evident. Here, evidence of aerosol-induced control on cloud occurrence trends over the Northern Bay of Bengal (NBOB) during the monsoon onset period is presented. In last 15 years, increased aerosol emissions over North India have led to an increase in aerosol loading at an elevated altitude of 1–3 km over the NBOB outflow region in monsoon onset period. This elevated aerosol loading induces increases the air temperature at 1–2 km altitude and stabilizes the lower troposphere over the region in recent years. The enhanced atmospheric stability in the region caused low-level cloud occurrences (below 3 km) to increase in recent years by ~20%, potentially contributing to the observed non-intuitive cooling trends in sea surface temperatures. These aerosol-cloud-climate observations emphasize the crucial need for improved aerosol representations in coupled ocean-atmosphere models for accurate predictions of climate change over South Asia. [ABSTRACT FROM AUTHOR]

• MacCormack scheme based Numerical Model for simulating temperature profile in landfill. • Model Considers waste stratification as new and old as aerobic and anaerobic. • Includes depth Variability in density, moisture content & hydraulic conductivity. • A correlation of 0.8 and 0.73 is obtained in calibration & validation. • Landfill temperature rises at all depths and in all season. Elevated Landfill temperatures have an undesirable effect on landfill cover, stability, slope and leachate migration pattern. Thus, to predict the temperature profile in the landfill a distributed numerical model using MacCormack finite difference method is developed. The developed model considers stratification of the upper and lower layers of the waste as new and old waste by assigning different values of heat generation for aerobic and anaerobic processes. Further, as the new layers of the waste get accumulated over the older layers, the density, moisture content and hydraulic conductivity of the underlying waste layers get modified. The mathematical model utilizes a predictor–corrector approach with a Dirichlet boundary at the surface and no flow condition at the bottom. The developed model is applied to the Gazipur site located in Delhi in India. A correlation coefficient of 0.8 and 0.73 is obtained between the simulated and observed temperatures in calibration and validation respectively. The result shows that the temperature at all the depths and in all the seasons was found to be higher than the atmospheric temperature. The maximum difference of 333 °C was observed in December, and the minimum difference of 22 °Cs was observed in June. The temperature rise is higher in the upper waste layers as it undergoes aerobic degradation. The locus of the maximum temperature gets modified with moisture movement. Since the developed model shows a good agreement with the field observation, it can be used to predict the temperature variation within the landfill under different climatic conditions. [ABSTRACT FROM AUTHOR]

• Diurnal and seasonal variations of ML features on a long term basis. • Long term decrease in ML height impacted by atmospheric instability changes. • Increasing occurrences of stratiform rain and decrease in convective rain. The present study deals with the variation in height and width of the melting layer (ML) in relation to atmospheric parameters such as near-surface temperature (2 m), zero-degree isotherm height, cloud liquid water content (CLWC), and prevailing instability expressed in terms of convective available potential energy (CAPE) during the pre-monsoon (March-May) and monsoon (June-September) seasons over a tropical location Kolkata (22.57° N, 88.37° E), India. Stratiform rains which are characterized by the presence of ML, are prevalent over Kolkata during both pre-monsoon and monsoon seasons. The ML height is observed to be dependent on the near-surface temperature, zero-degree isotherm height, and atmospheric instability. Again, CLWC controls the melting layer width. Also, the diurnal and seasonal variations of ML height and width have been investigated over the present location. In addition, a long-term change in ML height, zero-degree isotherm altitude, and atmospheric instability (CAPE) have been observed over this location. The decreasing CAPE has been found to be responsible for increasing (decreasing) stratiform (convective) precipitation over the years. [ABSTRACT FROM AUTHOR]

Soil CO2 efflux (Fsoil) is a significant contributor of labile CO2 to the atmosphere. The Himalayas, a global climate hotspot, condense several climate zones on account of their elevational gradients, thus, creating an opportunity to investigate the Fsoil trends in different climate zones. Presently, the studies in the Indian Himalayan region are localized to a particular forest type, climate zone, or area of interest, such as seasonal variation. We used a portable infrared gas analyzer to investigate the Fsoil rates in Himalayan tropical to alpine scrub forest along a 3100-m elevational gradient. Several study parameters such as seasons, forest types, tree species identity, age of trees, distance from tree base, elevation, climatic factors, and soil physico-chemical and enzymatic parameters were investigated to infer their impact on Fsoil regulation. Our results indicate the warm and wet rainy season Fsoil rates to be 3.8 times higher than the cold and relatively dry winter season. The tropical forest types showed up to 11 times higher Fsoil rates than the alpine scrub forest. The temperate Himalayan blue pine and tropical dipterocarp sal showed significant Fsoil rates, while the alpine Rhododendron shrubs the least. Temperature and moisture together regulate the rainy season Fsoil maxima. Spatially, Fsoil rates decreased with distance from the tree base (ρ = − 0.301; p < 0.0001). Nepalese alder showed a significant positive increase in Fsoil with stem girth (R2 = 0.7771; p = 0.048). Species richness (r, 0.81) and diversity (r, 0.77) were significantly associated with Fsoil, while elevation and major edaphic properties showed a negative association. Surface litter inclusion presented an elevation-modulated impact. Temperature sensitivity was exorbitantly higher in the sub-tropical pine (Q10, 11.80) and the alpine scrub (Q10, 9.08) forests. We conclude that the rise in atmospheric temperature and the reduction in stand density could enhance the Fsoil rates on account of increased temperature sensitivity. [ABSTRACT FROM AUTHOR]

The study examined the dynamic impact of climate such as Carbon dioxide (CO2) emissions, temperature, and rainfall on agricultural yield in India from 1980 to 2017 by applying the nonlinear autoregressive distributed lag (NARDL) model. The empirical evidence supports a long- and short-run asymmetry effect, implying that the positive impact of the climatic variables on agricultural yield is distinct from the adverse effects on both the long- and short-run equilibrium. In addition, CO2 emissions and average atmospheric temperature have diverse impacts on agriculture yield. [ABSTRACT FROM AUTHOR]

Mathematical validation and comparison of coal milling operations of a horizontal ball tube mill and a vertical bowl mill located in two distinct coal based power plants in India was carried out. The coal mill model was developed and the unknown model coefficients were obtained numerically by genetic algorithm (GA). The input variables of mass flow rates of raw coal and air and temperature of inlet primary air were obtained from the on-site data. The output variables of mill current and mill outlet temperature as obtained by GA were compared with on-site data for the two mills and show good agreement. The ball tube mill requires less mass flow rate of primary air and raw coal per MW of power produced than the bowl mill. It contains larger mass of raw and pulverized coal but less grinding heat is generated in this mill. [ABSTRACT FROM AUTHOR]

The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) substantially influences the land climate conditions through the co-variability of multiple climate variables, which in turn affect the structural and functional characteristics of terrestrial vegetation. Our study explored the varying responses of vegetation photosynthesis in India to the SST variations in the tropical Indian Ocean during the summer monsoon. To characterise the terrestrial photosynthetic activity, we used solar-induced chlorophyll fluorescence (SIF). Our results demonstrated a significant negative SST-SIF relationship during the onset phase of the summer monsoon: the SIF anomalies in the northern and central Indian regions decrease when strong warm SST anomalies persist in the tropical Indian Ocean. Further, SIF anomalies increase with cold anomalies of SST. However, the negative SST anomalies in the tropical Indian Ocean are less impactful on SIF anomalies relative to the positive SST anomalies. The observed statistically significant SST–SIF link is feasible through atmospheric teleconnections. During monsoon onset, positive SST anomalies in the tropical Indian Ocean favour weakened monsoon flow, decreasing moisture transport from the ocean to the Indian mainland. The resultant water deficiency, along with the high air temperature, created a stress condition and reduced the photosynthetic rate, thus demonstrating negative SIF anomalies across India. Conversely, negative SST anomalies strengthened monsoon winds in the onset period and increased moisture availability across India. Negative air temperature anomalies also dampen water stress conditions and increased photosynthetic activity, resulting in positive SIF anomalies. The identified SST-SIF relationship would be beneficial to generate a simple framework that aids in the detection of the probable impact on vegetation growth across India associated with the rapidly varying climate conditions in the Indian Ocean. [ABSTRACT FROM AUTHOR]

Water quality parameters were analysed in complex aquatic ecosystem of two major harbour waters (Ennore Fish Landing Centre and Royapuram Fishing Harbour), along the Chennai district. Monthly variations in water physicochemical parameters such as atmospheric temperature (26–34°C), surface water temperature (23.2–33.5°C), pH (7.3–8.12), salinity (28.0–33.5 psu) and dissolved oxygen (3.2–5.6 mg/L) were analysed in the selected harbours. The inorganic nutrients such as nitrate (0.042–0.18 mg/L), nitrite (0.070–0.096 mg/L) phosphate (0.078–0.197 mg/L), silicate (0.480–0.790 mg/L) and ammonia (0.040–0.158 mg/L) were estimated in selected study site. The water quality parameters have obtained important seasonal and spatial variations. The increased harbour activities in both the selected harbour have been made the noticeable water quality changes. The obtained results evidently insisting the necessity of continuous monitoring of harbour waters. The present work was contributed in the harbour water quality monitoring and impact assessment for further policy mitigation measures. [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]

A study on annual and seasonal (winter, spring, summer, and autumn) trend analysis of maximum and minimum temperature at the spatial and temporal scales had been carried out during January, 1981 to December, 2020 for agro-climatic zone- III of Bihar, India. Non-parametric statistical techniques viz. Mann-Kendall (MK) test with Sen’s slope estimator, and Pettitt Mann–Whitney (PMW) test had been carried out to examine the annual and seasonal trends at 5% level of significance. The monthly air temperature (°C) from the period January, 1981 to December, 2020 had been recorded for the spatial and temporal trend analysis. The results of the nonparametric tests showed statistically significant increasing trends in summer, autumn, and spring seasons for minimum temperature at most the spatial horizons while the trends for maximum temperature showed statistically non-significant decreasing trends at all the spatial horizons. The rate of change of the minimum temperature obtained from Sen’s slope estimator was found to be higher in summer (0.021°C year-1), autumn (0.054°C year-1), and spring (0.041°C year-1) seasons at Bhojpur district. In case of the maximum temperature, the rate of change of temperature was observed to be higher in annual (-0.020°C year-1), winter (-0.012°C year-1), summer (-0.019°C year-1), and autumn (-0.013°C year-1) seasons. The most probable year of changes in the observed temperature were occurred in between 1992–2010. In conclusion, it was clear that the magnitudes of the minimum temperature trends and variability is greater than the maximum temperature for all 6 spatial horizons. [ABSTRACT FROM AUTHOR]

• Mixed mode UV sheet tent house solar dryer using natural convection is investigated. • Potato chips are dried in a natural convection solar dryer with different thickness. • For 2.5 mm and 5.0 mm thick sliced potatoes, solar drying took 6h less time than OSD. • Upper and lower trays ranged from 34.2°C to 59.7°C and 33.6°C to 57.1°C respectively. • All the performance parameters are superior for 2.5 mm thick sliced potatoes. A mixed mode natural convection ultraviolet (UV) tent house solar dryer is designed and installed at University Teaching Department, Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh (21°27′N, 81°43′E) India for drying potato slices. This solar dryer is equipped with a solar flat plate collector (area 1.12 m2), covered with 5.0 mm thick glass and consists of two trays of the same size (0.57 m X 1.11 m) in a drying chamber on which equal quantities of potato slices were placed. The local atmospheric conditions including relative humidity, atmospheric temperatures, solar intensity, and wind velocity have been recorded for analyzing various parameters. The solar intensity and atmospheric temperature ranges from 134 to 993 W/m2 and 36.2 °C to 44.9 °C respectively. The initial moisture content value of 85.25% reaches 14.75% for 2.5 mm thick potato slices and for 5.0 mm thickness, the initial & final moisture content values are 85.10% & 14.90% respectively. The upper and lower tray temperature varies from 34.2 °C to 59.7 °C and 33.6 °C to 57.1 °C respectively. The open sun drying took 6 h longer than the solar natural convection drying. Moisture ratio for 2.5 mm thickness and 5.0 mm thickness lies 0.475–0.001 and 0.650–0.003 respectively. The maximum dryer efficiency is 26.62% and 21.61% for 2.5 mm and 5.0 mm thick potato slices respectively. All the drying kinetics is superior in the 2.5 mm thickness of the potato in process of drying using natural convection. [ABSTRACT FROM AUTHOR]

The present study focuses on climatological distributions of Monsoon Depressions (MDs) during different phases of Monsoon Intraseasonal Oscillation (MISO) and their relationship with different observed meteorological and oceanic parameters from buoys. The MISO is represented as a cyclic process with eight phases to show the northward propagation of the rainfall band. Almost 60% of MDs occur during the third and fourth phases of MISO over the north Bay of Bengal (BoB) and central India. Interestingly, a similar climatological composite of SST for different MISO phases does not precisely match the spatial precipitation pattern over the BoB. Instead, the oscillation is shown over a confined area near the east coast and to the north of 15 ∘ N. The lagged (6–12 days) impact of SST is well recognized in central and west BoB. The characteristics of MISO are explained through a detailed investigation of two contrasting years (2017 and 2018) in terms of the number of MDs. The analysis showed possible impacts of intensity and track of depressions on the MISO in terms of its intensity and successive phases. The intraseasonal signal of surface salinity is mostly negative during the weak phases of MISO (7, 8, and 1) with a slight lag (10–15 days) because of a freshwater flux resulting from the previous wet period. Similarly, during the active phases (phase 3 to phase 6), the salinity signal becomes positive for the lagged impact of higher evaporation during the current and prior phases. A consecutive repetition of phases 4 to 6, associated with different MDs concurrence with increasing ocean heat content (OHC), is noted during both years. The ISO in subsurface (25–60 m) temperature is stronger in 2018, and upward propagation of temperature anomaly helps in formations of more depressions than in 2017. In addition, six years (2013–2018) of subsurface temperature showed that MDs formation followed the warmer subsurface. The ISO signals for different meteorological parameters, Air Temperature, Sea Level Pressure, and Wind Speed are comparatively stronger (by amplitude) in 2018 than in 2017, resulting in higher variability of MISO and more MDs. [ABSTRACT FROM AUTHOR]

Forest fires are caused naturally by lightning, high atmospheric temperatures, and dryness. Forest fires have ramifications for both climatic conditions and anthropogenic ecosystems. According to various research studies, there has been a noticeable increase in the frequency of forest fires in India. Between 1 January and 31 March 2022, the country had 136,604 fire points. They activated an alerting system that indicates the location of a forest fire detected using MODIS sensor data from NASA Aqua and Terra satellite images. However, the satellite passes the country only twice and sends the information to the state forest departments. The early detection of forest fires is crucial, as once they reach a certain level, it is hard to control them. Compared with the satellite monitoring and detection of fire incidents, video-based fire detection on the ground identifies the fire at a faster rate. Hence, an unmanned aerial vehicle equipped with a GPS and a high-resolution camera can acquire quality images referencing the fire location. Further, deep learning frameworks can be applied to efficiently classify forest fires. In this paper, a cheaper UAV with extended MobileNet deep learning capability is proposed to classify forest fires (97.26%) and share the detection of forest fires and the GPS location with the state forest departments for timely action. [ABSTRACT FROM AUTHOR]

A high surface air temperature (SAT) record over Northwestern India was reported in April 2022. This study examines the contribution of interannual variability on Indian SAT and possible reasons for the extreme SAT during April 2022. Result shows that the interannual variability of SAT is captured by the first two leading modes using the empirical orthogonal function (EOF) analysis. Both of them show highest values over the Northwestern India and are simultaneous energetic during 2022. The EOF1 is related to the Indian Ocean Basin Mode (IOBM) and an anomalous anti-cyclone in the troposphere, while the Rossby wave train from the North Atlantic to the north of India controls the EOF2. The active IOBM and strong Rossby wave source account for the extreme SAT over northwestern India during April 2022. In addition, the Indian regional mean SAT during April is well represented by the indices of IOBM and tropospheric anomalous anti-cyclone, which can help to improve the prediction of SAT over India. [ABSTRACT FROM AUTHOR]

In the present study, the efficacy of the North American Multi-Model Ensemble (NMME) seasonal prediction models initialized by February month in simulating the climatological features and inter-annual variability of Summer (March through May; MAM) Surface Air Temperature (SSAT) over India is validated with the observations during 1981–2018. Among the six models, the CanCM4i model can capture the observed increasing trend of maximum, minimum, and mean temperatures over the Indian landmass with slight variation (trend values: 0.84 °C/38 years, 1.1 °C/38 years, and 0.96 °C/38 years). But, statistical analysis reveals that the CanCM4i model has more clod bias than the rest of the models (with less mean bias and RMSE) and in good agreement (slightly) with the observations. Most of the NMME models unable to simulating the extremely warm and cold events over the Indian region. Around 20–30% of extreme events are captured well like in the observations than in the normal years (high threat score). Furthermore, the dominant leading modes (by EOF1 and EOF2) of climate variability over India indicate that all NMME models underestimate (overestimate) the total variance of the first (second) dominant mode of SSAT than the observation except the Gem-NEMO model. Gem-NEMO model has a better total variance for EOF1 (63.84%) and EOF2 (11.23%) than other models. The IC3 (IC3-CanCM4, IC3-CanSIPS, IC3-Gem5-NEMO) models reasonably captured the association between principal component-1 and sea surface temperature (SST) anomalies over the eastern Pacific and positively over the south Atlantic region as in the observations. The NMME models have shown the SST anomalies over the eastern Pacific, Indian, and Atlantic Oceans, which are against to the observations except for the Gem-NEMO model. Thus, there is a large diversity in the representation of climatology features, inter-annual variability, and two dominant modes of variability of SSAT over India in present generation coupled seasonal prediction models. [ABSTRACT FROM AUTHOR]

Tropical forests sequester six times higher carbon than that released by humans annually into the atmosphere. These biodiversity-rich tropical forests have high net primary productivity (NPP), which differs among constituent plant communities. Tropical moist deciduous forests occupy 179,335 km2 of India's geographical area and constitute 44% of the country's total protected area (PA) forests. The productivity of these forests has neither been estimated specifically nor precisely. We measured the annual NPP of three predominant distinct community types, viz., mixed (DM), sal (SM), and teak (TP), in a tropical moist deciduous forest in northern India. The NPP was estimated from tree biomass data collected from nine long-term ecological research (LTER) plots of 1 ha each representing the above three community types. The estimated annual NPP were 10.28, 6.25, and 9.79 Mg ha−1 year−1 in DM; 8.93, 7.09, and 10.59 Mg ha−1 year−1 in SM; and 14.57, 7.14, and 13.56 Mg ha−1 year−1 in TP for the years 2010, 2011, and 2012, respectively. The NPP was correlated with tree density, height and DBH, species richness, diversity, microclimatic and edaphic variables, and leaf area index (LAI) using principal component analysis (PCA) and generalized linear modeling (GLM). Air temperature and humidity were strongly related to NPP in all the community types, while "complementarity" and "selection effects" contributed to the NPP in both the sal and mixed forest communities with equal importance, and the NPP in teak plantation ould point to "dominance effect." [ABSTRACT FROM AUTHOR]

Climate change is widely recognized as a major threat to public health. The Intergovernmental Panel on Climate Change's Sixth Assessment Report (IPCC AR6), assessing different Shared Socioeconomic Pathway scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), projects that relative to 1850–1900, the global temperature is rising and would exceed 2 °C during the twenty-first century under the high (SSP3-7.0) and very high (SSP5-8.5) greenhouse gas (GHG) emission scenarios considered. Populations within tropical and subtropical regions are more likely to experience increased vulnerability towards heat stress. In this study, a summary of some of the important aspects of climate change and human health has been presented. The effects of climate change on India's energy demand, employment, labor market and benefits have also been highlighted. Finally, we have discussed the national policies implemented or action underway to mitigate climate change and improve public health and have also provided some recommendations to carry forward. The current study overviewing the nexus between climate change and public health has a major aim to provide a perspective towards strengthening the health system in Indian cities. [ABSTRACT FROM AUTHOR]

The present work is to describe the electrochemical-thermal profiles of a Li-ion cell of a battery pack for an electric vehicle application considering a realistic drive-cycle. For this investigation, a tropical country like India is considered. The atmospheric temperature varies at different locations of India, and hence, a range of temperatures (25–55°C) is taken into consideration for the study. An electrochemical-thermal model featuring physics laws has been considered as it can provide all the electrochemical kinetics and side reactions to get more accurate results. The two-way coupled electrochemical-thermal model consisting of a 1-dimensional electrochemical model for heat generation and a 2D thermal model to show temperature distribution over the cell is developed. Thermal contact resistance is modeled which is formed in between the battery electrode coatings and current collectors due to imperfect contact at the time of electrode fabrication. This model is first tested with standard charge–discharge profiles at different C-rates, and thereafter, it is validated with the experimental results. Finally, the model is tested with a standard drive-cycle called modified Indian driving cycle, and the results are analyzed. Comments are made on the state of charge (SoC) of Li-ion cell as well as SoC of electrodes. The temperature profiles of the cell show a range of ambient temperatures, and results are reported. [ABSTRACT FROM AUTHOR]

Atmospheric warming results in an intensification of annual precipitation over the globe but large uncertainties remain regionally and at seasonal scales, especially for extremes. Using 29 models from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), we investigate future seasonal changes in extreme precipitation (under the scenario SSP5‐8.5) and how it compares to changes in mean precipitation. Over land, we find a strong intensification of the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere and over India during the monsoon. Extreme intensity decreases in the subtropics for some seasons, including over large regions around the Mediterranean basin and Southern Africa, and these drying patterns are not apparent in annual results. The key finding is that the CMIP6 multi‐model mean always shows that seasonal changes in mean and extreme precipitation align where there is high model agreement. That is, in all seasons by the end of the 21st century, extremes intensify in regions where mean precipitation increases and decline where mean precipitation decreases. This should not hide inherent uncertainties associated, namely the large range of changes intensity that can be found across the models, and an important modulation of the changes by internal variability. Yet, this study shows that the multi‐model mean shows broad consistency such that future seasonal changes in mean precipitation could be used to infer future changes in extremes (and vice versa), thus providing valuable information for risk planning and mitigation strategies. Plain Language Summary: The rise in surface air temperature due to human influence results in an intensification of the most extreme events of precipitation over the globe. Although this is shown by both observations and Earth System Models at the global scale, more uncertainties remain regionally and at the seasonal scales. In this study, we use a large ensemble of state‐of‐the‐art Earth System Models and investigate future seasonal changes in extreme precipitation and how it compares to changes in total precipitation. Our key finding is that, in all seasons by the end of the 21st century, extremes intensify in regions where mean precipitation increases and decline where mean precipitation decreases. This is found locally and in regions where there is high agreement between the models with projected changes of a same sign. Hence, when future seasonal changes in precipitation can be assessed with high confidence, we find that changes in totals and extremes align in the future. Our results demonstrate that there is potential added value from examining concomitant projected changes in totals and extremes of precipitation on seasonal and regional scales which could help better inform decision‐making in relevant climate‐sensitive sectors. Key Points: We present projected seasonal changes in Coupled Model Intercomparison Project (CMIP6) total and extreme precipitation and show that they scale into the futureCMIP6 multi‐model mean seasonal change is of same sign for total and extreme precipitation when there is high agreement between modelsThis is found locally (at the grid cell scale) and regionally and is generally supported by the 29 individual models [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]

The previous studies on temperature trends are based on temperatures observed at a fixed hour (maximum and minimum temperatures). In this study, trends in sub-daily (hourly) temperatures are examined using quality temperature data from 66 Indian stations spread over the country. The season April–May–June and the data period of 1975–2012 have been considered for this analysis. Analysis was made to see the differences in two phases, 1975–1993 and 1994–2012. Most parts of the country were warmer throughout the day during the period 1994–2012 compared to the earlier period 1975–1993. Increase in daytime temperatures over many stations in Central India is subdued due to the cooling effect caused by the increase in aerosol content. Turbidity has shown a significant increasing trend over central Indian station, and it is 0.06 per decade. A statistically significant (at the 5% significant level) warming trend in diurnal temperature is observed during night/early morning hours. Warm hours showed an increasing trend in all four homogenous regions with a significant increase over northwest India ~ 1.5 h per decade and northeast India ~ 2 h per decade. The increase in extreme temperature hours may lead to an increase in the duration of heat wave events in the future. [ABSTRACT FROM AUTHOR]

The spatially widely distributed and temporally protracted Santonian–Campanian stratigraphic records of the Sillakkudi Formation of the Ariyalur Group, Cauvery Basin, southern India represent an onland exposure of a Gilbert‐type delta. Here, we present geochemical, mineralogical, and petrographic analyses of two composite stratigraphic sections, and evaluate the climatic, tectonic, and other factors that contributed to the spatio‐emporal uniqueness of this formation. A range of low to intensive weathering, simultaneous exhumation/erosion, physical transport, and chemical weathering, prevalent at different parts of the source region and or the existence of multiple channels that drained varied source regions and hence varied lithologies at exhumation surfaces which in turn experienced seasonally varied flow conditions were interpreted. The prevalence of physical erosion during the initial stages of the deposition of the Sillakkudi Formation that progressed towards higher intensities of chemical weathering, in tune with the increase in atmospheric temperature and sea‐level rise during the later stages of the formation, is evidenced. Occurrences of positive and negative Eu anomalies and a progression from active to passive tectonic setting of samples, concomitant with change from tectonic control to relative sea‐level and the sediment influx controlled nature of deposition from Santonian–Campanian is recorded. Together, three sets of related factors namely, tectonics, increase of atmospheric temperature and sea‐level rise operated independently, and the enforced transience of transformation of the source terrain from active to passive margin, the transformation of depositional setting from fluvial to estuarine/intertidal to open marine, and shifting of principal loci of deposition from onland to offshore, are documented. The causal link between the progressive increase of sea‐level control over the depositional system during the Santonian–Campanian as recorded by this study could be due to the rise in atmospheric temperature that in turn might have been influenced by the movement of the Indian Plate from low latitudes towards the equatorial region. The second implication of the results is that the studies attempting to document tectonic setting, provenance, and weathering, with the help of geochemical discriminant diagrams, should also consider the spatial and temporal changes in the source area as well as depositional setting. [ABSTRACT FROM AUTHOR]

The experiment was carried out in the laboratories of the Department of Food Engineering and the Department of Plantation Spices Medicinal & Aromatic Crops, and the Central Horticultural Laboratories, Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India during 2018-2019 and 2019-2020. With an objective of study the performance of drying methods at different temperatures on physical and quality parameters of dry chilli. The experiment was conducted in Randomized Complete Block Design (RCBD) replicated thrice with seven treatments of different drying methods viz., sun drying, solar drying and oven drying at 50°C, 55°C, 60°C, 65°C and 70°C. The experimental results were maximum fruit length (7.00 cm), fruit diameter (10.22 mm), chroma (38.13), minimum total color difference (11.82) and browning index minimum (85.81) were found in the oven drying at 65oC and the fruit weight (0.81 g) was maximum observed in the sun drying method. However, the minimum bulk density (0.31 g cc-1) and the maximum dehydration ratio (3.88) were detected by oven drying at 70°C. The highest ß-carotene content (224.41 µg 100 g-1) was observed during oven drying at 50°C, where as minimum ß carotene content was recorded by sun drying. In sun drying method low quality due to oxidation as the dried chilli was exposed directly to light and sun. An increase in drying air temperature and long period of drying time has a negative effect on quality of dry chilli. [ABSTRACT FROM AUTHOR]

The isotopic values of rainfall in the southern parts of India exhibit strong seasonality. Such a contrast in isotopic values arises mainly due to the seasonal reversal of the monsoon winds. During the summer monsoon season, moisture is primarily transported from the Arabian Sea, while during the winter, it is sourced mainly from the Bay of Bengal. Additionally, atmospheric processes contribute to these differences. We collected precipitation samples from a southern Indian site and two neighboring island locations to study their isotopic characteristics from intra‐seasonal to seasonal timescale. Oxygen isotopes in the marine environment seem to respond differently to the surface and tropospheric temperature than their counterparts from the land region. The seasonal isotopic gradient in the marine environment appears to be modulated by the tropospheric temperature anomaly. Oxygen isotopic values showed a strong association with the Webster‐Yang monsoon index. Two distinct clusters were formed when the oxygen isotopes were plotted against the above‐mentioned monsoon index. The formation of these clusters indicates the precipitation isotopes' response to moisture dynamics, which significantly change during the transition phase of the southwest to the northeast monsoon. Finally, we examine the potential of the precipitation isotopic records in studying the monsoon process, viz, the use of precipitation isotopes in characterizing the thermodynamical properties of the atmosphere and estimating the summer monsoon withdrawal phase. Plain Language Summary: Atmospheric parameters such as temperature, rainfall, humidity, wind circulation, etc., vary significantly across the seasons. Such kind of variations or the seasonality also varies from place to place. The heavy to light isotopic distributions of hydrogen and oxygen atoms in atmospheric water also differ considerably from summer to winter, termed isotopic seasonality (defined here as Δδ18O). This feature manifests well in southern peninsular India, wherein the summer monsoon season is characterized by higher isotopic values relative to the winter season. Monsoon circulation is south‐westerly during the summer and north‐easterly during the winter. These two opposing circulation regimes cause isotopic seasonality in south peninsular India. Meteorological parameters such as rainfall, wind speed, etc., display sharp changes during the summer monsoon onset in early June. But they change rather slowly during the transition from summer to winter monsoon season around late September–early October. As a result, the estimation of the summer monsoon withdrawal date may have considerable uncertainty. It is proposed that applying the isotopic technique may be potentially helpful in reducing such an uncertainty. Key Points: Precipitation isotopic time series at the northern Indian Ocean reveal strong seasonality, driven by monsoon thermodynamicsTwo well‐defined clusters are formed when δ18O is plotted against the Webster‐Yang circulation indexThe formation of clusters is indicative of isotopic response to moisture dynamics [ABSTRACT FROM AUTHOR]

In the absence of effective drying techniques, a lot of food gets wasted as there is a lack of post-harvest processes. In India, most of the agricultural produces like paddy, maize, wheat, corn, oil seeds, pulses, chillies, etc. require a temperature range of 50–80 °C for effective drying. Hence, in these conditions, solar drying techniques seem to be the most economical; also, it is safe and eco-friendly. Various types of solar dryers are used across India and worldwide; these are direct solar dryer, green house dryer and indirect solar dryer. Nowadays, indirect type solar dryers are most commonly used because of their several advantages over direct solar dryers. In case of indirect type solar dryers, the products to be dried are kept inside a separate compartment known as drying chamber. Hot air is obtained from the solar collectors either by direct heating method or by using a secondary heating medium and then supplied to the drying chamber for heating of the products. This paper presents a detailed review of various innovative designs of indirect type solar dryers and compares the performance of different types of dryer configuration in terms of collector efficiency, dryer efficiency, drying time and maximum air temperature. Also, the effects of various operating parameters on the thermal performance of such dryers have been discussed. [ABSTRACT FROM AUTHOR]

The article presents the discussion on recent warming of the earth and the imminent consequences in the form of accelerated climatic variability threatening humankind. Topics include COP countries reaffirming the Paris Agreement goal of limiting the increase in the global average temperature; and Himalayan birch, a prominent broadleaf tree growing mostly in harsh and frigid climatic conditions in the Himalayas.

In recent decades, a significant rise in extreme rainfall events has been reported across India, accompanied by large-scale flood/drought-like conditions and catastrophic loss of life. Large-scale climate variability modes like the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) tend to influence the surface air temperature and rainfall variability over India. In what follows, the complete and independent influences of ENSO using Niño3.4, Niño3, and Niño4 indices, and IOD using Dipole Mode Index (DMI) on seasonal mean and extreme surface air temperature and rainfall over India are examined. A non-stationary generalized extreme value (GEV) distribution is implemented to analyze the seasonal extremes over the period 1979‒2019. Niño3.4 induces deficit rainfall over regions such as the Gangetic plains, the entire Deccan plateau, and western India in boreal summer. In autumn, strong positive seasonal mean rainfall responses are evident in peninsular India and eastern parts of Madhya Pradesh (MP). A dipole pattern evident in north India during summer reverses its polarity by autumn. Yet, Niño3 significantly reduces the intensity of rainfall over large parts of MP in summer. Contrarily, Niño4 strengthens the rainfall in similar regions, thereby significantly impacting the rainfall variability over India. Likewise, positive (negative) phases of the IOD lead to wet (dry) conditions over northwestern India in summers and central India in autumn. Overall, a coherent inverse relationship between rainfall and daily maximum temperature is observed. For Niño3.4 independent of IOD (Niño3.4|IOD), a weaker intensity in rainfall is found in northern India compared to the original Niño3.4 response. However, the IOD independent of Niño3.4 (IOD|Niño3.4) rainfall response is weaker in northern India and stronger in central India compared to original IOD responses. Importantly, a composite analysis of rainfall and temperature anomalies during different phase combinations of ENSO and IOD also shows that the IOD mitigates the influence of ENSO in boreal summer and fall whenever such events occur in-phase. [ABSTRACT FROM AUTHOR]

Temperature perturbations from the total solar eclipse of January 15, 2010, were monitored from surface to stratosphere (30 km) over Kadapa (14.28° N, 78.42° E), India, a semi-arid region. During the maximum solar eclipse, the entire upper tropospheric and lower stratospheric (UTLS) region reflected the cooling effect. On the eclipse day, the surface temperature showed 2 °C cooling compared to the control day, though surface pressure did not vary. During the eclipse peak, temperature above 30 km altitude decreased by 10 °C compared to the control days. Two inversions in temperature profiles, first around 1 km, near the atmospheric boundary layer, and second near 13 km altitude, were noted after eclipse onset. The diurnal variations in the tropopause temperature, height, and width of the tropical tropopause layer (TTL) were also examined on eclipse and control days. At the time of maximum obscuration on the eclipse day, cold point tropopause (CPT) temperature decreased by ~ 2 °C, and its height decreased by ~ 1.1 km. However, the lower boundary of the TTL did not reflect any significant variations in response to the eclipse. In addition, wave-like oscillations were noted in the UTLS region during the eclipse period. A wave-like pattern with a wavelength of 4–5 km was seen to occur in the stratospheric region (17–22 km) during the maximum eclipse phase. At the end of the eclipse period, the wave pattern speared in the troposphere also (up to ~ 12 km) with a reduced wavelength of 2–3 km. [ABSTRACT FROM AUTHOR]

Climatological variations and trends in aerosol parameters were studied over six different regions identified as different source regions of aerosol emissions over India. Analysis was carried out on monthly mean satellite data for total aerosol optical depth (AOD), black carbon AOD (BC-AOD), single-scattering albedo (SSA) and Ångström exponent (ANG), in addition to NCEP reanalysis data for air temperature and relative humidity, for two decades (2000–2019) over the six different Indian regions. Through analysis of seasonal and annual climatology, it was found that region R3 (Indo-Gangetic Plain) was the most aerosol-laden area, as evidenced by a high value of BC-AOD and AOD. A high mean value of ANG indicated an increase in natural aerosol emissions over region R1 (Northeast India). Single-scattering albedo was highest in region R4 (South India). The aerosol changes over all regions showed a lower enhancing trend in total AOD and BC-AOD in the second decade (2010–2019) as compared with the first decade (2000–2009), which was attributed to the improvements in vehicle technology and use of more refined fuels in the domestic sector. The decreasing trend in Ångström exponent and AOD indicates that the anthropogenic aerosol loading decreased in the second decade relative to the first decade. [ABSTRACT FROM AUTHOR]

India is a developing country with a huge population which means our need is only going to be increasing. The residential floor area is going to be doubled and the energy consumption tripled. Around 34% of the energy in the housing sector is consumed just for heating, cooling, and ventilation of the building. The increase in global warming is causing a drastic change in the environment and the atmospheric temperature. This is causing an increase in the demand for AC and heaters. This brings the question of what should be done. One way out is to increase the thermal efficiency of the building envelope. When the thermal efficiency is increased the temperature outside will not affect the inside temperature to a large extent. It also improves the lighting and ventilation of the building. India never had a code for the building envelope until 2018 Eco Niwas Samitha 2018 was launched to maximize the RETV value for a building. So, this project would also like to increase its awareness in society and also develop different methods to include it during the planning and construction of a building. [ABSTRACT FROM AUTHOR]

The designing of solar thermal systems need accurate information on global solar radiation (GSR). In the present study, six machine learning models, for example, random forest, k‐nearest neighbors, Gaussian process regression, support vector machine, multilayer perception, and XGBoost, are developed for GSR prediction with only air temperature as input for different climatic zones of India. The performance of machine learning models is also compared with some well‐known empirical models. The results show that generally, the performance of the machine learning models is better than empirical models, though, for a few climatic zones, empirical models give a better prediction. The top‐performing models are k‐nearest neighbors and XGBoost. Thus, we highly recommend temperature‐based models to predict GSR in the regions of India where only air temperature data are available. The accurate information of future GSR can be easily obtained by combining future air temperature forecasts with KNN/XGBoost models. These models can be extremely helpful in designing solar thermal systems in those regions where solar radiation facility is not available. [ABSTRACT FROM AUTHOR]

The paper contributes to the growing literature highlighting the significance of assessing risk and vulnerability, micro-level perceptions and adaptation decision-making in building resilience of farm communities to climate change in dryland region of India. To select a region for grassroots enquiry, spatial differential in risk to climate change was assessed in Rajasthan, using IPCC AR5 framework. Among the highly vulnerable and risky districts, Bikaner district was selected for elicitation of micro-level imperatives. Rising atmospheric temperature, inter-seasonal displacements of rainfall and recurrence of extreme events were perceived by the farmers resulting in resource degradation, production risks and erosion of households' socio-economic dynamics. As risk preventive measures, suitable adjustment in agricultural practices, natural resource management, shift to off-farm activities and other relief measures were adopted by the farmers. Farmer's choice of adaptation was influenced by several climatic, socio-economic and infrastructural & institutional factors in varying degree. Moreover, several financial, economic, infrastructural and informational bottlenecks to adaptations were reported during household survey and FGDs. The results suggest that capturing grassroots evidence is crucial for directing locally tailored adaptation strategies, along with the improving deficiencies in the developmental pathways for climate-resilient agriculture. [ABSTRACT FROM AUTHOR]

The signal of temperature change has emerged from background variations in most tropical regions in boreal summer over decadal‐centennial timescales but not in northern‐central India (NCI). In this study, we investigated the reason for the limited temperature change in NCI. We found that internal variability, largely caused by the Interdecadal Pacific Oscillation (IPO) on a ∼20‐year timescale, has the potential to mask the temperature change signal. Besides, local response to external forcing, linked to non‐greenhouse gas (GHG) forcings, strongly overrides GHG warming in NCI, which results in little trend in the temporal evolution of external variability. The internal variability related to IPO and the limited warming arising from the competition between multiple forcings result in the smallest signal‐to‐noise ratio and thus, the temperature change signal fails to emerge from the background variations. Plain Language Summary: Earth's surface has warmed considerably since 1850s, which can be largely attributed to anthropogenic greenhouse gas emissions. When human activities (e.g., greenhouse gas emissions) cause the local temperature change to exceed the inherent variation of the climate system, the region will face more extreme climate events. Although temperature has experienced warming almost all around the globe, here we show that surface air temperature in boreal summer in northern‐central India has not gone through significant changes. The timing of transition is important for policymakers. Thus, we analyzed the reasons for this and show that it reflects the combined effects of natural processes and local anthropogenic forcings. Key Points: The external forcing exhibits a cooling trend in northern‐central India (NCI), whereas most global regions have warmed considerablyCooling effects of non‐greenhouse gas forcings that override global warming lead to a cooling trend in NCIThe internal variability related to the Interdecadal Pacific Oscillation and the limited warming cause the smallest signal‐to‐noise ratio [ABSTRACT FROM AUTHOR]

The present study proposes a new chaos theory-based local approximation technique for the spatial downscaling of rainfall from GCM outputs. The approach involves: (1) reconstruction of relevant climate data in the state space using an embedding technique; and (2) use of a neighbor search procedure in the reconstructed space and local approximation for downscaling. The approach is implemented for downscaling of monthly rainfall at a spatial resolution of 0.25° × 0.25° across India. The effectiveness of the approach is first demonstrated using the NCEP/NCAR reanalysis dataset (1979–2014) and then tested using the outputs from five CMIP6 GCMs (CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1) (1961–2014). Eight climate variables (eastward wind, northward wind, relative humidity, specific humidity, surface temperature, air temperature, sea level pressure, and geopotential height) are considered as predictors, with the observed rainfall data from the India Meteorological Department (IMD) as the predictand. Three statistical measures, namely correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), and normalized root mean square error (NRMSE), are used for evaluation. The results indicate that the local approximation downscaling approach yields promising results for both reanalysis dataset and GCM outputs. Among the five GCMs, the CMCC-ESM2 model yields the best results. Comparison of the results from the chaos theory-based approach with those of the linear regression-based approach indicates better performance of the former. The proposed framework is general and can be applied for any climate variable, GCM, and region. • A chaos theory approach is proposed for spatial rainfall downscaling of GCM outputs. • It involves embedding of climate data, neighbor search, and local approximation. • It is implemented for spatial downscaling of monthly rainfall across India. • Performance is assessed for reanalysis dataset and outputs from five GCMs. • Chaos theory-based downscaling approach outperforms linear regression method. [ABSTRACT FROM AUTHOR]

The eastern sub-Himalayan region of India (which belongs to the Cwa zone in Koppen’s classification) experiences severe cold waves during winter which causes a loss in seed vigor. Though most of the studies on cold stress deal with atmospheric temperatures, the seed vigor is majorly affected by the temperature of the soil. Therefore, the vigor loss of tomato and green gram were investigated under low-temperature stress. The analysis of locally available soil temperature showed a median value of 20.3 ± 0.1 °C. When the seeds were subjected to this temperature in vitro, a loss in vigor (70–75%) was observed. This was due to the reduced fluidity of the membrane which caused electrolyte leakage. In this regard, the indigenous polysaccharides-producing microbes act as an eco-friendly priming agent to recover the lost vigor. However, seed treatment with Bacillus and Pseudomonas strains didn’t affect the germination-related factors but aided in recovering 30–70% of the lost vigor by enhancing the growth of seedlings. The mode of vigor recovery was the production of indole-acetic acid. This approach can be used to quicken the nursery period of tomato and green gram exposed to low soil temperatures prevalent in the sub-Himalyan terai region. [ABSTRACT FROM AUTHOR]