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The appearance of COVID-19 in December, 2019 in China and its rapid spread all over the globe, forced the governments to severely curb the social and economic activities of their respective countries. Barring the essential services, most of the business activities and transport sectors have been suspended and an unprecedented lockdown imposed over major economies in the world. South-East Asian regions, such as India and China, were no different. As a result, the pollutant level has gone down over these regions, and the air quality improved somewhat better than it was before the lockdown. This study uses satellite retrievals and attempts to estimate the extent of the reduction of major pollutants, like carbon monoxide (CO), nitrogen dioxide (NO 2 ) and sulfur dioxide (SO 2 ) in India and China during January to April, 2020. We have calculated anomalies of pollutants during the lockdown period relative to their long-term records. NO 2 , which has significant emissions from the transport sector, is reduced on an average by 17% over India and 25% over China. SO 2 , which mainly emits from power plants, shows significant reductions (approx. 17%) especially over the Eastern sector of India. CO is found to be reduced by 6.5% over north-central China. The differential reduction was attributed to man made versus natural activities. This study is helpful to policy makers in mitigating the air-pollution on a long-term perspective.

Amongst all the anthropogenically produced greenhouse gases (GHGs), carbon dioxide (CO 2 ) and methane (CH 4 ) are the most important, owing to their maximum contribution to the net radiative forcing of the Earth. India is undergoing rapid economic development, where fossil fuel emissions have increased drastically in the last three decades. Apart from the anthropogenic activities, the GHGs dynamics in India are governed by the biospheric process and monsoon circulation; however, these aspects are not well addressed yet. Towards this, we have measured CO 2 and CH 4 concentration at Sinhagad, located on the Western Ghats in peninsular India. The average concentrations of CO 2 and CH 4 observed during the study period are 406.05 ± 6.36 and 1.97 ± 0.07 ppm (µ ± 1σ), respectively. They also exhibit significant seasonal variabilities at this site. CH 4 (CO 2 ) attains its minimum concentration during monsoon (post-monsoon), whereas CO 2 (CH 4 ) reaches its maximum concentration during pre-monsoon (post-monsoon). CO 2 poses significant diurnal variations in monsoon and post-monsoon. However, CH 4 exhibits a dual-peak like pattern in pre-monsoon. The study suggests that the GHG dynamics in the western region of India are significantly influenced by monsoon circulation, especially during the summer season.

Changes in tropical wetland, ruminant or rice emissions are thought to have played a role in recent variations in atmospheric methane (CH 4 ) concentrations. India has the world’s largest ruminant population and produces ~ 20% of the world’s rice. Therefore, changes in these sources could have significant implications for global warming. Here, we infer India’s CH 4 emissions for the period 2010–2015 using a combination of satellite, surface and aircraft data. We apply a high-resolution atmospheric transport model to simulate data from these platforms to infer fluxes at sub-national scales and to quantify changes in rice emissions. We find that average emissions over this period are 22.0 (19.6–24.3) Tg yr −1 , which is consistent with the emissions reported by India to the United Framework Convention on Climate Change. Annual emissions have not changed significantly (0.2 ± 0.7 Tg yr −1 ) between 2010 and 2015, suggesting that major CH 4 sources did not change appreciably. These findings are in contrast to another major economy, China, which has shown significant growth in recent years due to increasing fossil fuel emissions. However, the trend in a global emission inventory has been overestimated for China due to incorrect rate of fossil fuel growth. Here, we find growth has been overestimated in India but likely due to ruminant and waste sectors. India’s methane emissions have been quantified using atmospheric measurements to provide an independent comparison with reported emissions. Here Ganesan et al. find that derived methane emissions are consistent with India’s reports and no significant trend has been observed between 2010–2015.

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.

The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO2) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO2 fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO2 exchanges independently, and it obtains CO2 fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr−1 using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr−1 by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO2 fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency.

The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO2) and methane (CH4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH4 and CO2 using zoomed versions (abbreviated as \"ZAs\") of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of  ∼ 0.66° in longitude and  ∼ 0.51° in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH4 and CO2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50° in longitude and 1.27° in latitude (abbreviated as \"STs\"), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH4 annual gradients between stations in SEA, with reduction of RMSE by 16–20% compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO2 suggests that the CO2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH4 and CO2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions.

This study investigates the seasonal and diurnal variations of soil CO 2 flux (Fc) and the impact of meteorological variables on its dynamics. The study took place in the subtropical forest ecosystem of Kaziranga National Park (KNP), from November 2019 to March 2020. The highest Fc (6.24 gC m −2 day −1 ) was observed in the pre-monsoon season (March), and the lowest (0.85 gC m −2 day −1 ) in winter (February), with the mean value of 2.19 ± 0.84 gC m −2 day −1 . Fc is primarily influenced by changes in air temperature (Tair), soil temperature (Tsoil), solar radiation (Rg), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). This is evident from the strong positive correlations of Fc with Tair, Tsoil, Rg, VPD, and PAR (correlation coefficients being 0.75, 0.67, 0.37, 0.59, and 0.37, respectively; all significant at 99% level) indicating their critical role in driving soil respiration. Conversely, relative humidity (RH) and atmospheric pressure (Pair) negatively affect Fc. Soil moisture (SoilM) influenced Fc to some extent, but its effect was less pronounced compared to Tair, Tsoil, and Rg. Diurnal variations revealed higher Fc during the daytime (between 10:00 and 14:00 IST) and the lowest in the night-time (between 05:30 and 07:00 IST). These findings underline the strong seasonal and diurnal controls of environmental factors on soil respiration enhancing our understanding of carbon dynamics in subtropical forest ecosystems.

Accurate renditions of country-scale methane (CH 4 ) emissions are critical in understanding the regional CH 4 budget and essential for adapting national climate mitigation policies to curtail the atmospheric build-up of this greenhouse gas with high warming potential. India housing 30% of the Asian population is currently appraised as a region of CH 4 source based on the inventories. To date, there have not been many reported efforts to estimate the regional CH 4 emissions using direct measurements of boundary layer CH 4 concentrations at multiple locations over India. Here, 2 years (2017–2018) of in situ CH 4 observations from three distantly placed stations over the peninsular India is combined with state-of-the-art inversion using a Lagrangian particle dispersion model for the estimation of CH 4 emission. This study updates CH 4 emission over the peninsular India (land area south of 21.5°N) as ~ 10.63 Terra gram (Tg) CH 4 year −1 , which is 0.13 Tg CH 4 year −1 higher than the existing inventory-based emission. On seasonal scale, the changes from the existing CH 4 emission inventories are 0.12, 0.05, 0.055 and 0.28 Tg CH 4 year −1 during winter, pre-monsoon, monsoon and post-monsoon seasons respectively. Spatial distributions of seasonal variability of posterior emissions suggest an enhancement over the eastern region of peninsular India compared to the western part. The study with observations from three stations over the peninsular India provides an update on the inventory-based estimation of CH 4 emissions and urges the importance of more observations over the Indian region for the accurate estimation of fluxes.

We present shipborne measurements of surface ozone (O3), carbon monoxide (CO), and methane (CH4) over the Bay of Bengal (BoB), the first time such measurements have been performed during the summer monsoon season, as a part of the Continental Tropical Convergence Zone (CTCZ) experiment during 2009. O3, CO, and CH4 mixing ratios exhibited significant spatial and temporal variability in the ranges of 8-54nmolmol-1, 50-200nmolmol-1, and 1.57-2.15µmolmol-1, with means of 29.7±6.8nmolmol-1, 96±25nmolmol-1, and 1.83±0.14µmolmol-1, respectively. The average mixing ratios of trace gases over BoB in air masses from central/northern India (O3: 30±7nmolmol-1; CO: 95±25nmolmol-1; CH4: 1.86±0.12µmolmol-1) were not statistically different from those in air masses from southern India (O3: 27±5nmolmol-1; CO: 101±27nmolmol-1; CH4: 1.72±0.14µmolmol-1). Spatial variability is observed to be most significant for CH4 with higher mixing ratios in the air masses from central/northern India, where higher CH4 levels are seen in the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) data. O3 mixing ratios over the BoB showed large reductions (by ∼ 20nmolmol-1) during four rainfall events. Temporal changes in the meteorological parameters, in conjunction with O3 vertical profile, indicate that these low-O3 events are associated with downdrafts of free-tropospheric O3-poor air masses. While the observed variations of O3 and CO are successfully reproduced using the Weather Research and Forecasting model with Chemistry (WRF-Chem), this model overestimates mean concentrations by about 6 and 16% for O3 and CO, respectively, generally overestimating O3 mixing ratios during the rainfall events. An analysis of modelled O3 along air mass trajectories show mean en route O3 production rate of about 4.6nmolmol-1day-1 in the outflow towards the BoB. Analysis of the various tendencies from model simulations during an event on 10 August 2009, reproduced by the model, shows horizontal advection rapidly transporting O3-rich air masses from near the coast across the BoB. This study fills a gap in the availability of trace gas measurements over the BoB and, when combined with data from previous campaigns, reveals large seasonal amplitude ( ∼ 39 and ∼ 207nmolmol-1 for O3 and CO, respectively) over the northern BoB.

The terrestrial biosphere plays a pivotal role in removing carbon from the atmosphere. The removal processes are primarily affected by the presence of extreme temperature in the atmosphere. Little information is available on carbon removal response by the terrestrial biosphere during extreme temperature events over the Indian region. India has witnessed frequent and intense heatwaves in the recent past, and future projections about the frequency of heatwave occurrence suggest a further increase in the changing climate scenario. This study used surface CO2 flux observations and satellite retrieved columnar and mid-tropospheric CO2 concentrations to understand atmospheric CO2 variability and its transport patterns with anomalously high-temperature events such as heatwave conditions over India. Intensification of temperature up to 32 °C has increased the atmosphere-biosphere CO2 fluxes (carbon sink). But further intensification in temperature (> 32–33 °C), like those observed during heatwaves, tends to drive the ecosystem to act as a CO2 source into the atmosphere due to reduced ability to absorb atmospheric CO2. Such excess CO2 fluxes may lead to change in the atmospheric CO2 concentration via atmospheric circulation or the vertical transport of the air masses from the near-surface to the upper levels in the atmosphere. The satellite observed CO2 concentration is elevated by 2–3 ppm during the heatwave conditions over India. The impact of extreme temperature on the biospheric sink capability in the carbon cycle, leading to an increase in the atmospheric CO2 concentration, is one of the significant outcomes of this study.