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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]

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]

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]

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]

• 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]

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]

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]

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]

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]

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 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 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]

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]

Decadal climate prediction has been recognized as the important information for policy makers for agriculture, health and energy sectors and the general public for any near‐term planning. The modelling community is determined to put forward a reliable near‐term climate forecasting system, to predict annual to decadal state and variability of climate. However, deriving reliable information from the decadal prediction/hindcast is still a challenge. This study examines the decadal hindcast simulations of surface air temperature (SAT) over India in seven different ocean–atmosphere coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Each decadal hindcast is available for the next 10‐years period from the initialized climate states of 1961–2006. The performance of models is assessed using different evaluation metrics, such as absolute mean difference, root mean square error, skill score and uncertainty in terms of the range of hindcasts. The multimodel ensemble mean displayed considerable skill in representing the spatial distribution of SAT over the Indian region except over the western Himalaya and Northeast India. Our results indicate that the decadal hindcasts skills improved noticeably when quantile mapping (QM) approach is used for the bias corrections. The major improvements are seen in terms of reducing absolute mean difference and uncertainty, regardless of lead time and region. The present study advocates that QM approach is useful not only for reducing bias but also for improving the decadal hindcast skill for SAT over India in the coupled models. [ABSTRACT FROM AUTHOR]

In warm and hot climates, ceiling fans and/or air conditioners (ACs) are used to maintain thermal comfort. Ceiling fans provide air movement near the skin, which enhances the evaporation of sweat, reduces heat stress, and enhances thermal comfort. This is also called the cooling effect. However, AC usage behaviour and the effects of elevated air speed through the use of ceiling fans on indoor operative temperature during AC usage are not widely studied. This study investigated the optimum AC (cooling) set point temperature and air velocity necessary for maintaining thermal comfort while achieving energy conservation, in mixed-mode buildings in India, through field studies by using used custom-built Internet of Things (IOT) devices. In the current study, the results indicate a 79% probability that comfort conditions can be maintained by achieving a temperature drop of 3K. If this drop can be achieved, as much as possible, through passive measures, the duration of AC operation and its energy consumption are reduced, at least by 67.5 and 58.4%, respectively. During the air-conditioned period, there is a possibility that the cooing effect is reduced because of increase in operative temperature due to ceiling fan operation. Therefore, the optimum solution is to maintain the highest AC set point and minimum fan speed setting that are acceptable. [ABSTRACT FROM AUTHOR]

Efficient heat recovery from low-grade heat sources is an achievable scientific frontier in the coming decade. One of the promising technologies in waste heat recovery is the organic Rankine cycle (ORC) system. Major obstacles to low-grade heat recovery include economic viability, scale, system efficiency, and exergy efficiency. In low temperature ORC systems, the cost of condenser becomes significant and hence air-cooled condenser is a feasible option. However, the performance of the air-cooled condenser is sensitive to the variation in ambient air conditions. India, being a tropical country, have villages and cities whose average temperature varies from -2°C in winter to 50°C in summer. The present work involves the study of heat transfer effects on the condenser using common organic working fluids and quantifying the consequences of environmental temperature variations. The constant thermal evaporator load is supplied with a medium-enthalpy heat source at 100 and 150°C. The atmospheric temperature associated with heat rejection from the condenser is varied from 273K to 313K for 5 working fluids. A MATLAB model of the ORC system is developed to study the effect of condenser performance on the first and second law efficiency of the ORC system. The MATLAB model is used to investigate the effect of varying inlet temperatures of the working fluid on the performance of ORC system to choose an organic working fluid suitable for Indian climatic conditions. R365 mfc, and R1233zd exhibit higher expander work and show higher heat rejection. At higher heat source temperatures of 150°C, the simulation shows a higher second law efficiency for the working fluid R1224yd(Z), which has critical temperature closed to the heat source temperature. [ABSTRACT FROM AUTHOR]

Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year's data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill. [ABSTRACT FROM AUTHOR]

This paper presents the experimental investigation of an earthto-air heat exchanger for heating purposes in the Patna region of India, using an experimental test rig. In the view of the author, real field experiments have several limitations such as lack of repeatability and uncontrolled conditions. It also takes more time for the response of parameters that depends on nature and climate. Moreover, earth-to-air heat exchangers may be expensive to fabricate and require more land area. Thus, in this work authors executed their experimental work in indoor controllable environments to investigate the thermal performance of an earth-to-air heat exchanger. The actual soil conditions were created and maintained the temperature at 26°C throughout the soil in the vicinity of pipes. Three horizontal PVC pipes of equal lengths and diameters of 0.0285 m, 0.038 m and 0.0485 m were installed in the test rig. The experiments were performed for different inlet air velocities at ambient air temperature. This study acknowledges that the maximum rise in outlet temperature occurs at a lower speed for smaller pipes. Also, the maximum effectiveness of 0.83 was observed at 2 m/s for the smallest diameter pipe. [ABSTRACT FROM AUTHOR]

A geothermal climate-change observatory at Choutuppal (17.29 oN, 78.92°E) in south India was put into operation in July 2009 to study the inter-relationships between subsurface, ground-surface and surface-air temperature. The observatory is the first of its kind in a low latitude region with a significant monsoon season. Subsurface temperature measurements were obtained at different times of the year in two boreholes at the observatory drilled into granite to 21 m and 210 m depths respectively; a continuous record of shallow ground temperatures was acquired at six different depths to 1.2 m next to the deeper boreholes. Meteorological parameters recorded at the same site include surface air temperature (SAT), relative humidity, precipitation, solar radiation, wind speed and direction. Analysis of ground-air temperature data for the first five years (2009–2014) reveals the following. (i) Air temperature changes diffuse into the granitic subsurface with associated attenuation of high frequency components and phase lag characteristic of conductive heat transfer. (ii) Ground temperatures measured in the 1.2 m and 21 m deep holes track the diurnal and seasonal changes in SAT respectively, (iii) The ground is warmer than air on average; the difference is not constant but varies from −2.1 to +7.6 °C with a mean of 3.4 ± 1.8 (SD) oC, (iv) Ground-air temperature difference is influenced by incoming solar radiation and monsoon precipitation. On average, the ground warms by 2.5 °C for 100 Wm−2 increase in average incident solar radiation. The study highlights the importance of long-term monitoring to test ground-air temperature tracking critical to using borehole temperatures to infer climate change. • Five-year record shows strong coupling between ground and air temperature at daily, annual and multi-year time scales. • Ground is warmer than air. Annual ground temperatures are 3.6 to 4.7 °C higher than air, daily variations are -2.1 to +7.6 °C. • On average, the ground-air temperature difference increases by 2.5 °C for every 100 Wm-2 increase in incident solar radiation on average. • The monsoon period and subsequent development of vegetative ground cover lessens the ground-air temperature difference. [ABSTRACT FROM AUTHOR]

The onset of the Indian summer monsoon (ISM) marks the arrival of the rainy season and affects billions of people. Therefore, exploring the factors influencing ISM onset is essential, especially sea surface temperature (SST) anomalies. In addition to the well-known El Niño-Southern Oscillation in the tropical Pacific, in this study, we find that the SST anomalies in the extratropical Pacific (Victoria mode, VM) can significantly impact ISM onset. The positive phase of the May VM causes monsoon onset delay, and two distinct mechanisms linking these phenomena are identified. The tropical pathway involves divergent circulation and equatorial Rossby waves. The VM-associated SST gradient causes low-level divergence and suppressed rainfall around the Indo-China Peninsula. Such suppressed rainfall induces anomalous anticyclone circulation by exciting the equatorial Rossby wave response, which prevents the establishment of monsoonal southwesterly winds and moisture transport, therefore delaying ISM onset. The extratropical pathway involves the Rossby wave train and land–sea thermal contrast. The VM triggers an eastward Rossby wave train via upper-level divergence around the Gulf of Alaska. This wave train propagates downstream and generates anomalous negative geopotential heights and low air temperatures to the northwest of India, which reduces the land–sea thermal contrast and delays ISM onset. Our result suggest that the ISM onset predictions should consider the role of May VM. • The North Pacific Victoria Mode delays the onset of the Indian summer monsoon via the tropical and extratropical pathways. • The tropical pathway includes the large-scale divergent circulation and the equatorial Rossby wave response. • The extratropical pathway includes the stationary Rossby wave train and the land-sea thermal contrast. [ABSTRACT FROM AUTHOR]

This study performs the multifractal characterization of reference evapotranspiration (ET0) and its controlling factors of five locations in India with climatic diversity. First, the ET0 and the predictor variables like minimum air temperature ( T min ) , maximum air temperature ( T max ) and average wind speed (AW) of five stations are analysed using multifractal detrended fluctuation analysis (MFDFA). The investigation could detect long-term persistence and multifractality of different series, irrespective of the climatic condition and geographical location. Higher persistence (>0.8) is noted in the ET0 and T min series indicating higher predictability in all the stations and highest multifractality was noted for the highly complex wind speed time series. Further, the ET0 estimates by Hargreaves Samani (HS) and Droogers and Allen (DA) methods differs in their persistence and multifractal properties, controlled by the geographic location of the station. Subsequently, multifractal cross correlation analysis (MFCCA) is used to investigate the correlations between ET0 and other variables. MFCCA analysis showed that, for all the time series considered, the joint scaling exponent is roughly the average of individual scaling exponents, and base width of the joint spectra is lower than that of individual series, validating two universal properties of multifractal cross correlation studies for agro-meteorological datasets. [ABSTRACT FROM AUTHOR]

In this study, a dynamically downscaled regional climate model (RegCM4.3) is used to study the Indian summer monsoon (ISM) surface air temperature over the South‐Asia CORDEX domain using six convection schemes during 1986–2010. The spatial and temporal variability of mean surface air temperature has been analysed with reference to the India Meteorological Department (IMD) analysis data using various statistical scores. The sensitivity experiments in selecting the best convective parameterized schemes have been performed in simulating the surface air temperature during the summer monsoon season (June–September) over India and its five sub‐regions such as Northwest India, Northcentral India, West Peninsular India, Eastern Peninsular India, and Southern Peninsular India. The model results show the tendency of overestimation of surface air temperature mainly in four cumulus parameterization schemes (CPSs) that is, Tiedtke, Emanuel, Mix98, and Mix99 of RegCM4.3 during the JJAS, where Grell and Kuo CPSs show better agreement with the IMD data. Overall, Grell CPS has a close resemblance to the observation data with a minimum root mean square error, mean absolute error, lowest mean absolute percentage error (MAPE), and higher correlation coefficient. The model simulated results have also been investigated further using modified Nash Sutcliffe efficiency and modified Willmott's degree of index. These analyses confirm the potentiality of the Grell CPS followed by the Kuo CPS in simulating interannual variability of the surface air temperature over Indian and its five sub‐regions. The MAPE in Grell and Kuo CPSs are 0.004 and 0.013°C during monsoon season over India, respectively. The inter‐scheme difference in simulating surface air temperature is linked with the generation of low cloud convection and warming‐induced atmospheric moisture advection in the schemes. Therefore, Emanuel, Tiedtke, and Mix98 CPSs have shown a persistent nature of overestimation in surface air temperature variability during JJAS. It is also inferred that after removing the systematic mean bias from the RegCM4.3 model simulated outputs; the skill of Emanuel, Mix98, and Mix99 could be useful over the Indian subcontinent except for the southern peninsular region. [ABSTRACT FROM AUTHOR]

Human-induced land use land cover changes particularly from forestland to cropland and pastures have increased over the times in tropical regions. The present study aims to understand the impact of enhanced forest conditions on regional weather over central India using a coupled land-atmospheric model in NASA-Unified WRF (NU-WRF) system during monsoon and post-monsoon seasons for the year 2013. In this study, two sets of simulations were carried out over the Indian region: (i) Control (Ctl) run that represents the cropland (normal) scenario and (ii) experimental (Exp) run that represents enhanced forest conditions, where the cropland cover was replaced by evergreen broadleaf forest over the targeted region(central India). Enhanced forest conditions resulted in a decrease of near-surface (2 m) air temperature and an increase in near-surface (2 m) specific humidity during both the seasons, which is attributed to an increase in latent heat flux and a decrease in sensible heat flux. An increase of surface roughness length due to enhanced forest conditions led to a reduction in low-level winds during both the seasons. An increase in both moisture convergence and evapotranspiration led to an increase in precipitation during the monsoon season, while an increase in moisture divergence led to a slight decrease in precipitation during the post-monsoon season. Furthermore, the effect of enhanced forest conditions on regional weather, except for precipitation and low-level circulation patterns, is more pronounced during the post-monsoon season than during the monsoon season. [ABSTRACT FROM AUTHOR]

Compared to any single hydroclimatic variable, joint extremes of multiple variables impact more heavily on the society and ecosystem. In this study, we developed new joint extreme indices (JEIs) using temperature and precipitation, and investigated its spatio-temporal variation with observed records across Indian mainland. Analysis shows an alarming rate of change in the spatial extent of some of the joint extreme phenomena, tending to remain above normal. For example, above normal hot nights and wet days events expands at a rate of 0.61% per year considering entire Indian mainland. If the historical trend continues at the same rate, consecutive cold and wet day events will drop below the threshold of mean value observed in the base line period (1981–2010) everywhere in the country by the end of the twenty-first century. In contrast, the entire country will be covered by hot nights and wet days events only (frequency of occurrence will cross the threshold of mean value observed in the base line period). This observation is also supported by the CMIP6 climate model outputs. It is further revealed that extremes of any single variable, i.e. either precipitation or temperature (e.g., Extreme Wet Days, Consecutive Wet Days, Hot Nights, and Cold Spell Duration Index), do not manifest such an alarming spatial expansion/contraction. This indicates that the consideration of the joint indices of hydroclimatic variables is more informative for the climate change impact analysis. [ABSTRACT FROM AUTHOR]

A case‐crossover analysis is used as a simple but powerful tool for estimating the effect of short‐term environmental factors such as extreme temperatures or poor air quality on mortality. The environment on the day of each death is compared to the one or more "control days" in previous weeks, and higher levels of exposure on death days than control days provide evidence of an effect. Current state‐of‐the‐art methodology and software (integrated nested Laplace approximation [INLA]) cannot be used to fit the most flexible case‐crossover models to large datasets, because the likelihood for case‐crossover models cannot be expressed in a manner compatible with this methodology. In this paper, we develop a flexible and scalable modeling framework for case‐crossover models with linear and semiparametric effects which retains the flexibility and computational advantages of INLA. We apply our method to quantify nonlinear associations between mortality and extreme temperatures in India. An R package implementing our methods is publicly available. [ABSTRACT FROM AUTHOR]

The analysis of field observations of thunderclouds in India is provided. The characteristics of the cloud collection obtained from the radar and lightning detection network in the area of the city of Aurangabad (the Maharashtra province, India) are considered. The maximum radar reflectivity of thunderstorm cells, the volume of the supercooled part above the 0°C isotherm, the intensity and precipitation flux, and the frequency of electric discharges are obtained. It is shown that the frequency of electric discharges is linked with some radar parameters of thunderclouds, and this relationship depends on the stage of cloud development. Comparative field data for other regions are given. [ABSTRACT FROM AUTHOR]

We examine here the relative influence of natural and anthropogenic forcing on surface air temperature (SAT) variability over the Indian subcontinent (IND) through the analysis of the normalized data of maximum (Tmax), minimum (Tmin), and mean (Tmean/INDSAT) temperatures, total solar irradiance (TSI), El-Nino Southern Oscillations (ENSO), Pacific Decadal Oscillations (PDO), North Atlantic Oscillations (NAO), sea surface temperature (SST), all-India rainfall (AIR), and CO2 using artificial neural networks (ANN). Sensitivity analysis reveals the dependency of each of the natural (e.g. TSI, ocean-atmospheric circulations [OAC, e.g. ENSO, PDO, NAO], SST, AIR) and anthropogenic (CO2) parameters on SAT as follows: (i) SAT is highly sensitive to continuous emission of anthropogenic CO2, (ii) intensive influence of TSI at 11- and 22-year cycles, and (iii) intermittent influence of OAC and SST at low intensive solar radiance. These dependent parameters are used to develop a model to predict future temperatures using multi-perceptron nonlinear feed-forward and back-propagation schemes with a NARX network encoded Bayesian regularization (BR) training algorithm. We used TSI, CO2, OAC, SST, and AIR data as input and Tmean/INDSAT as target (supervised learning). In order to ensure the stable and best possible result, we trained the network successively with different numbers (1–10) of neurons and delay parameters. Optimum mean square error (MSE) and R values (minimum MSE and maximum R value) with eight neurons were found appropriate for the present analysis. We therefore, chose eight neurons to develop a model for training and testing the data and obtained MSE = 0.00039976 and R = 0.98933. This model was validated from 2001 to 2007 and predicted the temperatures from 2001 to 2019 with error ≤ 20%. [ABSTRACT FROM AUTHOR]

In this paper, the performance of a PVT (photovoltaic thermal) air collector with V-groove absorber in the air channel is theoretically and experimentally examined under different climate conditions of Ghaziabad city, India, in summer, June 2019. A low power brushless DC fan is fixed at the input of the air-channel to ensure the circulation of air through the V-groove. The energy balance equation is developed with the matrix-inversion method to estimate the PV (photovoltaic) cell and air output temperatures. The theoretical results obtained using mathematical modeling has been compared with that of the real-time experimental results. The electrical, thermal, and overall efficiencies for the theoretical and experimental studies are found in the range of 10.39–10.26%, 41.78–41.57%, and 52.17–51.81%, respectively. It has been observed that the theoretical results are coherent with experimental results for the proposed model achieving an accuracy of 98.98%, 99.43%, and 99.31% for electrical, thermal, and overall efficiencies, respectively. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) is an important indicator of climatic change and a critical thermal metric to assess the impact on agriculture and human health. This study investigates the seasonal, annual and decadal changes in the spatio‐temporal trend in DTR and air temperatures (maximum: Tmax and minimum: Tmin) during 1951–2016 and solar radiation (Srad) during 1984–2016 over 14 different agro‐climatic zones (ACZs) in India. The changes in the DTR trend between two time periods:1951–2016 and 1991–2016 (recent period) are also assessed. The results indicate an overall increasing trend in DTR (0.038°C/decade), Tmax (0.078°C/decade, significant), Tmin (0.049°C/decade) during 1951–2016 and Srad (0.10 MJ/m2/day/decade) during 1984–2016. However, a decreasing trend in DTR (−0.02°C/decade) and a significant increasing trend in Tmin (0.210°C/decade) was noted during 1991–2016. The decadal changes showed an evident decline in DTR during the recent period since 1991. The relative increase in Tmin (0.21°C/decade, significant) compared to Tmax (0.18°C/decade) resulted in a decreasing DTR trend. This was evident across the 5 out of the 14 agro‐climatic zones for the 1991–2016 period. The seasonal analysis showed a significant (95%) increasing trend in DTR during pre‐monsoon and monsoon (1951–2016), and a negative trend for the post‐monsoon and monsoon since 1991. There were also interesting spatial differences found with the ACZs in the north‐west, parts of Gangetic plain, north‐east, and central India exhibiting negative DTR trends. The effect of Srad is larger on Tmax than Tmin; therefore, the decrease in Srad in parts of Gangetic plain likely contributed to a smaller increase in Tmax relative to Tmin and led to a decreasing trend in DTR. At the same time, the west coast, east coast, and southern region show positive trends. The observational analysis finds a distinct increase in the Tmin and also highlights the need for future assessments to continue investigate the causes of these spatio‐temporal changes found in this study. [ABSTRACT FROM AUTHOR]

This work re-explores the seasonal change of atmospheric carbon dioxide (CO2) concentration, a subject of renewable interest over half a century, in both time and frequency domain and identifies the atmospheric surface pressure and the surface air temperature as key factors for the temporal variation. The annual variability of CO2 dry-air mole fraction derived from the National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA-JPL) Orbiting Carbon Observatory-2 (OCO-2) database for the region bounded by 22° N to 23° N and 86° E to 89° E was found to increase in summer, to decrease in winter and to exhibit a tendency of increase at the end of the year consistently for the years 2016 and 2017 whereas the corresponding variations of solar-induced fluorescence, surface pressure and air temperature showed partial consistency with that. The trends of these findings were cross-checked with the long-time gross variations of CO2, Normalized Difference Vegetation Index, surface pressure and surface temperature for 25 urban regions within approximately 20° N to 29° N and 69° E to 89° E obtained from NASA-Giovanni database for a period of 2010 to 2017. The periodic variations of these parameters were pin-pointed by Fourier transform. Field measurements were carried out for ground level and column-averaged CO2 concentrations in parts per million (ppm) at the region around Kolkata city (22.55° N, 88.35° E). Based on the above findings, a simple atmospheric model is developed for vertical redistribution of CO2 molecules under changed temperature and pressure causing a changed atmospheric path for radiation absorption and an apparent change in the gas concentration at any specific altitude. [ABSTRACT FROM AUTHOR]

• Purpose of ETC aided hybrid solar dryer is to provide prominent heat. • The designed dryer is easy to operate and is found to be eco - friendly. • Drying kinetics of cassava is determined using IBM SPSS 23 statistical package. • Proximate analysis reveals that ETC dried cassava is better than sun dried cassava. A prototype hybrid Photovoltaic Thermal (PV – T) solar dryer aided with Evacuated Tube Collector (ETC) is presented for drying of cassava slices under the meteorological conditions of Thanjavur, Tamilnadu, India. Further the morphological, structural and physical properties of thin layer drying kinetics are studied and compared with natural sun drying. The designed dryer reduces the moisture content of cassava from 91.5% (wb) to 10.67% (wb) which is considered to be a safe level for storage in 8 h. There is a wide scope to save the conventional fuel by adopting the designed solar dryer as the air temperature inside the chamber rise is 30–40 °C than the ambient temperature. Non – linear regression analysis is performed to find out the drying kinetics of cassava using IBM SPSS 23 statistical package. Two - term model is proved to be the most suitable model for predicting thin-layer drying behaviour of cassava slices with R2 = 0.982, χ2 = 0.017553 for hybrid dryer and R2 = 0.998, χ2 = 0.001247 for open sun drying. XRD results exhibit 'A' type crystalline pattern that indicates semi–crystalline nature of cassava for both hybrid and sun dried cassava. Structural, morphological and proximate composition analysis reveals that the quality of hybrid dried cassava is better in terms of physical and chemical compositions than sun dried cassava. The proposed hybrid dryer is capable of producing high standard dried products for exporting and can make a good profit. [ABSTRACT FROM AUTHOR]

Background: Potassium (K) availability in soil and plant uptake is restrained by the dynamic interactions among the different pools of K. Aims: To understand these interactions, a study was undertaken to assess the quantity–intensity (Q/I) and buffering characteristics of rainfed maize (Zea mays L.) growing soils. Ten contrasting soils were evaluated for K partitioning changes in exchangeable K (ΔEK) and non‐exchangeable K (ΔNEK) pools in the soil‐solution phase and buffering characteristics using a modified version of Q/I approach. Results: The partitioned Q/I isotherms showed strong adsorption with the increase in K concentration ratio (CRK) and the changes due to ΔEK were higher than changes due to ΔNEK. Total buffering capacity (PBCK) significantly correlated (r = 0.92, p <0.01) with clay content with a major share contributed by buffering capacity owing to non‐exchangeable K (PBCΔNEKK) rather than exchangeable K (PBCΔEKK). The fixation capacity (β) factor, the magnitude of added K converted into a non‐exchangeable pool, ranged from 41 to 63%, whereas release (α) factor, the magnitude of added K converted to the exchangeable pool, ranged from 19 to 36%. Both threshold solution K (CKr) and threshold exchangeable K (EKr) values were found to be high in Satran clay loam (S2) and lower in Doon silty clay loam (S3) soils. The equilibrium exchangeable K (EKo) was found close to minimum exchangeable K (Emin) in Doon silty clay loam (S3) and Babaweyl sandy clay loam (S1) soils and overall Emin constituted about 8.94 to 0.57% of the EKo. Conclusion: It may be concluded that K Q/I isotherm partitioning provides a valuable insight to assess the dynamic relations. The ratio of α/β (K recharge index) could be used to evaluate the K enrichment capacity of soil to K additions while EKr and Emin can be potentially useful in the elucidation of exchangeable K as K fertility index especially in soils with poor K fertilizer management. [ABSTRACT FROM AUTHOR]

Introduction: Chlorophyll a concentration proxies the phytoplankton biomass which directly involves in signifying the production functions of aquatic ecosystem. Thus, it is imperative to understand their spatio-temporal kinetics in lotic environment with reference to regional climatic variabilities in the tropical inland waters. Objective: In-situ studies were conducted to examine the changes in phytoplankton biomass in lower Ganga basin as influenced by various environmental parameters under regional climatic variability during 2014-2016. Methods: Firstly, the most key influential environmental parameters on riverine Chl-a concentration were determined. Then the direct cascading effect of changing climatic variables on key environmental parameters were derived through modeling and quantified probable changes in mean Chl-a concentration in the lower stretch of river. Results: Only five environmental parameters namely water temperature, total dissolved solid, salinity, total alkalinity and pH were key factors influencing Chl-a (Multiple R²: 0.638, P < 0.05). Present estimates indicate that if the present rate of regional climatic variability over the last 3 decades (mean air temperature + 0.24 °C, total annual rainfall -196.3 mm) remain consistent over the next three decades (2015-2045), an increase in mean Chl-a by + 170 µgL-1 may likely be expected grossly reaching about 475.94 µg L-1 by the year 2045 or more. Conclusions: The present study is first such comprehending a gross hint towards the probable ecosystem response with an alternative model based methodology in data-deficient situations. Subsequently, the output would also be of great benefit for increase water governance and developing strategy protocol for sustainable water management for greater ecosystem services. [ABSTRACT FROM AUTHOR]

Sedimentation in lakes occurs from the time of its formation and continues till the final deposition. The rate of sedimentation varies depending upon climate, geology, soil cover and relief. In the present study, an attempt has been made to investigate the distribution and variation of sedimentation rate in Indian lakes. For this purpose, Indian lakes are classified into three zones viz., North Indian lakes (NIL), Central Indian lakes (CIL), South Indian lakes (SIL) and 27 sampling points distributed throughout India has been considered among all three zones. Correlation of rate of sedimentation data with catchment area, surface area and lithology is done in order to understand the controlling factors, rainfall and temperature data are also taken into consideration to know the effect on the sedimentation rate. In all three zones, the highest precipitation observed 2488, 1800 and 4000 mm/yr in the Nainital lake, Chilika lake and Pookot lake, respectively and temperature fluctuations in the northern region of India is higher than the central and southern region. The results showed that the NIL have relatively higher sedimentation rate compared to the CIL and SIL due to slopy terrain, higher precipitation, temperature and lithological inconsistency in northern Indian region whereas all these factors are less effective in other two zones with a minor contribution from other independent factors. NIL have ~0.634-0.804 cm/yr and ~0.636-1.117 cm/yr more sedimentation rate than CIL and SIL, respectively. [ABSTRACT FROM AUTHOR]

A spatio-temporal analysis of representative concentration pathways (RCPs)-based projections of surface air temperatures under different combinations of global circulation models (GCMs) and RCPs for Satluj River Basin has been presented. The projections of temperature anomalies have been obtained for several meteorological stations in the basin using 16 different combinations of emission scenarios and global climate models (GCMs). For each combination, the projections of temperature anomalies have been analysed for two future time periods centred at 2030 and 2050. The results of the analysis conducted herein clearly indicate that the temperature will rise for all the future time scales with the maximum increase being projected under RCP8.5 compared to the baseline 1986–2005. However, there is large inter-model variability in the projections of temperature anomalies under different RCPs. Under RCP 8.5, the average temperature in Satluj basin is projected to rise by around 4°C by mid of the twenty-first century. The projections of temperature anomalies analysed herein could be potentially used for the evaluation of hydrological impacts of climate change in Satluj River Basin—a key basin in the Himalayan region from the view point of global warming. [ABSTRACT FROM AUTHOR]

Persimmon (Diospyros kaki) is an important, underutilized fruit crop of India. The thin layer drying of persimmon slices would make it a perfect snack that can be used in various recipes. The drying process of persimmon slices was optimized using Box–Behnken design in response surface methodology. Three categorical independent drying variables, such as temperature (50–70°C), air velocity (0.5–1.5 m/s), and sample thickness (4–8 mm), were considered for the study. The response variables include drying time, ascorbic acid content, browning index, and rehydration ratio were investigated. The air temperature and sample thickness showed a significant effect on drying time. Browning index and rehydration ratio were significantly affected by air temperature and air velocity. As per the given experimental design constraints, the optimum solutions obtained were air temperature (58°C), air velocity (1.40 m/s), and slice thickness (4 mm). The predicted responses for the optimized combination of the drying process were drying time (442.55 min), ascorbic acid (8.61 mg/g dry matter), browning index (0.396 absorbance unit), and rehydration ratio (5.47%). Therefore, this study would be beneficial for scaling up industrial drying of persimmon slices for commercial production to ensure smooth processing and achieve a profitable path. [ABSTRACT FROM AUTHOR]

A modeling effort is considered to understand the effect of dust aerosol storms in the Indian summer monsoon (ISM) onset propagation over the Indian subcontinent. The study found how the dust aerosol loading during the pre-monsoon period over the Indian subcontinent positively impacts the advancing ISM onset. The heavy loading of dust aerosol from middle east Asia to the northwest (NW) Indian region boosts the temperature gradient over the mid-troposphere. This sudden rise in the mid-tropospheric atmospheric temperature triggers the low-pressure belt over the monsoon trough. It amplifies the Elevated Heat Pump mechanism and loads enough dust for the dust storm over north India. The formation of the monsoon trough over the Indo-Gangetic Plain (IGP) pulls the moisture-loaded wind towards the Indian subcontinent and the IGP, followed by the onset of monsoon over Kerala within a week. Due to this storm, the intense heat gradient in the troposphere also induces the monsoon propagation and the monsoon advancement from Kerala to BoB within a short time. However, it makes up for the naturally delayed monsoon on time. Study shows how the pre-monsoon dust storm modulates the Indian summer monsoon (ISM) propagation over the Indian region. The onset of the monsoon was delayed in a particular year, and the pre-monsoon dust storm over northwest India changed the regular monsoon mechanism and generated an entirely different mechanism. The elevated heat pump theory applies that pushes the monsoon advancement faster than usual. A dust storm over north India causes a sudden rise in the mid-tropospheric temperature gradient and more rapid monsoon advancement than usual. • The initiation of the Indian summer monsoon is predominantly influenced by dust storms during the pre-monsoon period. • A rapid increase in the mid-tropospheric heat gradient aligns ITCZ and Hadley cell before monsoon onset. • Pre-monsoon dust storm, faster monsoon linked to elevated heat pump effect. [ABSTRACT FROM AUTHOR]