We employed an atmospheric transport model to attribute column‐averaged CO₂ mixing ratios (X_CO2) observed by Greenhouse gases Observing SATellite (GOSAT) to emissions due to large sources such as megacities and power plants. X_CO2 enhancements estimated from observations were compared to model simulations implemented at the spatial resolution of the satellite observation footprint (0.1° × 0.1°). We found that the simulated X_CO2 enhancements agree with the observed over several continental regions across the globe, for example, for North America with an observation to simulation ratio of 1.05 ± 0.38 (p < 0.1), but with a larger ratio over East Asia (1.22 ± 0.32; p < 0.05). The obtained observation‐model discrepancy (22%) for East Asia is comparable to the uncertainties in Chinese emission inventories (~15%) suggested by recent reports. Our results suggest that by increasing the number of observations around emission sources, satellite instruments like GOSAT can provide a tool for detecting biases in reported emission inventories.

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.

We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH₄ yr⁻¹ for Russia, 12.8 to 14.3 Tg CH₄ yr⁻¹ for temperate South America, and 43.2 to 44.9 Tg CH₄ yr⁻¹ for contiguous USA, and the values decrease from 54 to 51.3 Tg CH₄ yr⁻¹ for China, 26.2 to 25.5 Tg CH₄ yr⁻¹ for Europe, and by 12.4 Tg CH₄ yr⁻¹ for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC.

The dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO₂ emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH₄ yr⁻¹) of the methane emission growth rate of China (0.87 Tg CH₄ yr⁻¹) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.

There are several studies showing a skillful empirical prediction of the All India Summer Monsoon Rainfall (AISMR) based on various combination of parameters as the predictors. However, the southwest monsoon rainfall over Kerala, a meteorological subdivision of India, bears a considerably low correlation coefficient with the AISMR. This implies that the existing predictors in the long‐range forecast models of the AISMR do not have much influence on the Kerala Summer Monsoon Rainfall (KSMR). This study attempts to examine the relationship of some ocean and atmospheric parameters with the rainfall and to formulate a linear multiple regression model for the long‐range forecast over a small area like the Kerala. Parameters having significant correlation (significant at 1% level) with the KSMR were identified for the period 1961–1994. The consistency of the relationship between these parameters and the KSMR was checked by doing a 21‐year sliding window correlation (significant at 5% level). Using a stepwise regression method, seven predictors, explaining a significant amount of variance in the KSMR were selected and a linear multiple regression model was developed. The parameters that explain the high inter‐annual variability of the KSMR are specific humidity, sensible heat net flux, relative humidity, zonal wind at 70 and 10 hPa, meridional wind and geopotential height. The characteristics of the forecast and its reliability were studied by various statistical techniques such as, Durbin Watson statistics and variance inflation factor. The model has a multiple correlation of 0.943 and coefficient of determination of 88.8%. The root mean square error (RMSE) was 6.60% (15.80%), bias (BIAS) was − 0.26% (6.20%), absolute error (ABSE) was 5.33% (13.15%) of mean rainfall for the training (test) period respectively. Climatological predictions were also made and the RMSE was (17.90%), BIAS (−5.40%) and ABSE (15.16%) of mean rainfall. The selected parameters were at least 2 months prior to the monsoon season and hence have predictive value. Copyright © 2007 Royal Meteorological Society