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Using the Community Earth System Model (CESM)-Large Ensemble (LE) surface air temperature (SAT) data, we investigate the multidecadal changes in SAT variability over Central Indian landmass, particularly the Indo-Gangetic (IG) river basin. This region comes under the active influence of the Indian summer monsoon, and during the summer monsoon months (JJA), we observe an amplified cooling (

Coronavirus Disease 2019 (COVID‐19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID‐19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID‐19 epicenters. Daily COVID‐19 count has strong covariability with local temperature, which accounts approximately 65–85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID‐19 cases are clustered at temperature and humidity ranging within 27–32°C and 25–45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID‐19 growth at the initial phase. The results are highly significant for predicting future COVID‐19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO2 emission is alarmingly high in South Asia (India) and entails high risk of climate change and extreme hot summer. Zoonotic viruses are sensitive to warming induced climate change; COVID‐19 epicenters are collocated on CO2 emission hotspots. The COVID‐19 count distribution peaks at 31.0°C, which is 1.0°C higher than current (2020) and historical (1961–1990) mean, value. Approximately, 72% of the COVID‐19 cases are clustered at severe to record‐breaking hot extremes of historical temperature distribution spectrum. Therefore, extreme climate change has important role in the spread of COVID‐19 pandemic. Hence, a strenuous mitigation measure to abate greenhouse gas (GHG) emission is essential to avoid such pandemics in future., Key Points The survival and growth of SARS‐CoV‐2 in the environment have tighter association with ambient temperature and relative humidityApproximately 72% of the COVID‐19 cases in India are clustered in severe record‐breaking hot extreme of historical temperature distribution spectrumExtreme climate change has an important role in the spread of COVID‐19 pandemic in India

The Greater Hinggan Forest was the world’s largest stand of evergreens, along the Black Dragon River (also known as Amur), which forms the border between Chinese Manchuria and Soviet Siberia. Black Dragon fire ranks as one of the worst environmental disasters of the 20th century and it burned about 18 million acres of conifer forest. In the 2nd week of May, 1987, we observe more than 10K rise in brightness temperature over a wide region in the China-Russia border. The weekly mean NDVI data shows the changes in greenness after the forest fire broke out. The NDVI value is positive with persistent greenness and vegetation in the Amur River valley, but from the 2nd week of May onwards the reddish patch appears to spread over the entire region, indicates the burned areas. In addition, we observe the impact of Black Dragon forest fire on tropospheric ozone concentration, aerosol index away from the location over North Pacific Ocean. A clear increase in atmospheric pollutants can be noticed after the forest fire event and the long range transports are confirmed with 72 hours NOAA HYSPLIT forward trajectory analysis.

Since 1960s, India experiences a series of extreme drought and flood events during the summer months. The Humid Subtropical Climatic Zone (HSTC), which comprises the Indo-Gangetic Plain region of India, is highly vulnerable to the climatic extremities. This region is the home for approximately 40% of total population and yields approximately 50% of total agricultural production of India. We investigate the historical variation in dry/wet conditions and project the future changes in extreme events under two different scenarios of the Coupled Model Inter-comparison Project 5 models. First, the model parameters, that is, precipitation (P) and temperature (T) are bias corrected with respect to observation data and finally six models are selected, which are in right phase with the observation for composite analysis. Next, we calculate the potential evapotranspiration (PET) and the Standardized Potential Evapotranspiration Index to characterize the extreme events. Both P and PET are projected to increase in the HSTC zone; however, both the wet and dry conditions demonstrate a persistent increase in future. In relative terms, P increases faster than PET along the Gangetic Plain region (wet condition) and decreases in the southern and eastern part of the region (dry condition). The mitigating effect (RCP4.5 scenario) of precipitation increase will be overridden by strengthened PET and extreme dry condition project markedly under RCP8.5 scenario. The features are consistent with the increase/ decrease in multimodel mean SPEI, consistent with the spatial pattern of P−PET. The area affected due to wet and dry events will be relatively higher under the RCP4.5 and RCP8.5 scenario, respectively.

In this study, we investigate the spatiotemporal characteristics of drought in India and its impact on agriculture during the summer season (April–September). In the analysis, we use Standardized Precipitation Evapotranspiration Index (SPEI) datasets between 1982 and 2012 at the six-monthly timescale. Based on the criterion SPEI < −1, we obtain a map of the number of occurrences of drought and find that the humid subtropical Upper Middle Gangetic Plain (UMGP) region is highly drought-prone, with an occurrence frequency of 40%–45%. This UMGP region contributes at least 18%–20% of India’s annual cereal production. Not only the probability of drought, but the UMGP region has become increasingly drought-prone in recent decades. Moreover, cereal production in the UMGP region has experienced a gradual declining trend from 2000 onwards, which is consistent with the increase in drought-affected areas from 20%–25% to 50%–60%, before and after 2000, respectively. A higher correlation coefficient (−0.69) between the cereal production changes and drought-affected areas confirms that at least 50% of the agricultural (cereal) losses are associated with drought. While analyzing the individual impact of precipitation and surface temperature on SPEI at 6 month timescale [SPEI (6)] we find that, in the UMGP region, surface temperature plays the primary role in the lowering of the SPEI. The linkage is further confirmed by correlation analysis between SPEI (6) and surface temperature, which exhibits strong negative values in the UMGP region. Higher temperatures may have caused more evaporation and drying, which therefore increased the area affected by drought in recent decades.

The summer surface air temperature (SAT) in the Humid Subtropical Climate Zone in India, exhibits a significant cooling trend (~−3 °C/40 yrs.) in CRU observational data during 1961–2000. Here we investigate the contribution of internal and external factors, which are driving this cooling trend. Using the Community Earth System Model-Large Ensemble (CESM-LE), we analyze the historical climate change in presence of internal climate variability. Most of the model ensemble members could reproduce this amplified cooling (

The Coronavirus Disease 2019 (COVID-19) pandemic poses a serious threat to global health system and economy. It was first reported in Wuhan, China, and later appeared in Central Asia, Europe, North America, and South America. The spatial COVID-19 distribution pattern highly resembles the global population distribution and international travel routes. We select 48 cities in 16 countries across 4 continents having infection counts higher than 10,000 (by 25 April 2020) as the COVID-19 epicenters. At the initial stage, daily COVID-19 counts co-varies strongly with local temperature and humidity, which are clustered within 0–10 °C and 70–95%, respectively. Later, it spreads in colder (−15 °C) and warmer (25 °C) countries, due to faster adaptability in diverse environmental conditions. We introduce a combined temperature-humidity profile, which is essential for prediction of COVID-19 cases based on environmental conditions. The COVID-19 epicenters are collocated on global CO2 emission hotspots and its distribution maximizes at 7.49 °C, which is 1.35 °C/2.44 °C higher than current (2020)/historical (1961–1990) mean. Approximately 75% of the COVID-19 cases are clustered at severe-extreme end of historical temperature distribution spectrum, which establish its tighter and possible association with extreme climate change. A strong mitigation measure is essential to abate the GHG emissions, which may reduce the probability of such pandemics in the future.

Food consumption puts pressure on natural resources and arable land. In the present study, we examined the dynamics of land requirements for food in South Africa from 1961 to 2007 and investigated the relationships between dietary patterns, yield, cropping intensity, population and the area of required land using the thought experiment method. Strong population growth and the development of agricultural technology (indicated by yield) accounted for more than a 2.5-fold increase in the total land requirements for food from 1961 to 2007. Before the 1990s, the increase in crop yields enabled constant land requirements, whereas, after the 1990s, the combined effect of agricultural technology and population growth, together with a small contribution from dietary changes, led to an increase in the land requirements for food. Our findings confirm that the variation in land requirements for food is a complex, non-linear function of agricultural production techniques, population growth and dietary patterns and show that the complex relationship between dietary pattern changes, and economic development challenges future predictions of land requirements for food in South Africa.

This study evaluates the performances of three global satellite datasets (Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l’ observation de la Terre (SPOT) of the Mongolian Plateau, where in situ observation is insufficient to assess vegetation dynamics on terrestrial systems. We give a comprehensive assessment of the historical changes in vegetation dynamics by using comparative and correlation methods on the three archives using two indices: the growing season’s Normalized Difference Vegetation Index (NDVI) and the Start of the Season Index (SOS). The main findings are: (1) MODIS and SPOT have generally better comparability and consistency in the spatial-temporal trends of NDVI and SOS than AVHRR in this area; (2) all the three archives exhibit better consistency in Inner Mongolia than in Mongolia; (3) integration data analysis of AVHRR (1982–1997) and SPOT (1998–2012) shows that the dynamics of vegetation growth has three distinct phases: enhanced before 1994; a flatter/slightly decreasing trend before 2001; and, then, a rapid recovery between 2001 and 2012 with remarkable spatial heterogeneity, with Inner Mongolia experiencing a significant greening in vegetation NDVI compared with no obvious changes in Mongolia; (4) the temporal average SOS showed no significant “earlier spring” onset during the past 31 years, on the middle and northern Mongolian Plateau.

Drawn from multiple reanalysis datasets, an increasing trend and westward shift in the number of Potential Vorticity intrusion events over the Pacific are evident. The increased frequency can be linked to a long-term trend in upper tropospheric equatorial westerly wind and subtropical jets during boreal winter to spring. These may be resulting from anomalous warming and cooling over the western Pacific warm pool and the tropical eastern Pacific, respectively. The intrusions brought dry and ozone rich air of stratospheric origin deep into the tropics. In the tropical upper troposphere, interannual ozone variability is mainly related to convection associated with El Niño/Southern Oscillation. Zonal mean stratospheric overturning circulation organizes the transport of ozone rich air poleward and downward to the high and midlatitudes leading there to higher ozone concentration. In addition to these well described mechanisms, we observe a long-term increasing trend in ozone flux over the northern hemispheric outer tropical (10–25°N) central Pacific that results from equatorward transport and downward mixing from the midlatitude upper troposphere and lower stratosphere during PV intrusions. This increase in tropospheric ozone flux over the Pacific Ocean may affect the radiative processes and changes the budget of atmospheric hydroxyl radicals.

China’s pig industry is experiencing a dramatic increase to meet increasing consumption demand. How these changes influence the limited arable land resources through consuming grain as feed has not been clearly understood. In this manuscript, we calculate the arable land requirement for pig industry (LRP) from 2001 to 2013 and forecast future demand towards 2050 from the point of production, in order to quantify the pressure in different scenarios. The results indicate that the LRP has increased from 22.0 Million Ha in 2001 to 31.6 Million Ha in 2013. LRP will be 23.7-29.4 Million Ha in 2030 and 11.6-18.7 Million Ha in 2050 according to different scenarios. Logarithmic Mean Divisia Index (LMDI) decomposition method is assessed to the effect of population, consumption and technology for three time periods e.g. 2010-2030; 2030-2050 and 2010-2050. And technology will become primary reason. These findings could help optimizing the relationships between limited arable land resources and development of pig industry, and promote sustainable development of the pig industry.