This study investigates the accuracy of aerosol optical thickness (AOT) forecasts and analyses during a whole year, by assimilating AOT retrievals from the Naval Research Laboratory (NRL) Moderate Resolution Imaging Spectroradiometer (MODIS) into the aerosol-coupled Non-hydrostatic ICosahedral Atmospheric Model. We explore the impact of data assimilation on aerosol direct radiative effect (DRE), taking into account the interactions between aerosol particles and radiation. Evaluation against the assimilated MODIS AOT data shows an improvement in the AOT fields. The root mean square error (RMSE) dropped from 0.027 in the free-run to 0.018 (a 33% reduction) for the forecast and to 0.017 (a 37% reduction) for the analysis, while the correlation coefficient rose from 0.640 (free-run) to 0.911 (forecast) and 0.986 (analysis), respectively. Furthermore, the most significant improvements were observed during the peak biomass burning period from August to October. This enhanced performance is further certified independently by Aerosol Robotic Network (AERONET) observations, which show a reduction in RMSE from 0.050 (free-run) to 0.038 (forecast) and 0.040 (analysis), alongside a marked rise in correlation coefficient to 0.810 and 0.884, respectively. The forecast DRE under clear-sky condition at the TOA is-2.69 f 2.02 W/m2 and at the surface is-4.04 f 2.96 W/m2. Under all-sky conditions, aerosol DRE are influenced by clouds, the forecast DRE under all-sky condition at the TOA is-1.45 f 1.26 W/m2 and at the surface is-2.74 f 1.98 W/m2.

Cesium-137 is one of the most concerned radionuclides produced by fission energy, which has a long half-life, considerable biological toxicity and complex transport behaviors. Wet deposition is a key process in determining the atmospheric transport of 137Cs aerosols following the Fukushima Daiichi nuclear power plant (FDNPP) accident. However, our understanding of this process remains insufficient because of the complex size distributions of the 137Cs aerosols and related atmospheric transport processes. This study investigates the wet deposition behavior of observed 137Cs aerosols by developing a wet deposition model that considers multiple continuous size distributions in the online-coupled WRF-Chem. The wet deposition efficiency is calculated based on Köhler's theory. Ten log-normal size distributions are constructed, with geometric mean diameters (dm) derived from observation. These distributions are applied to simulate the 137Cs transport following the FDNPP accident. The results reveal that the established model successfully reproduces the 137Cs transport and wet deposition. Below-cloud deposition is dominant over in-cloud deposition for 137Cs aerosols with dm ≤ 2.0 μm. In-cloud deposition is also important when dm > 2.0 μm, whereas dry deposition is predominant for dm > 6.4 μm. As for the atmospheric concentration, the influence of aerosol size is substantial during weak rain near the source. 137Cs aerosols with dm ≈ 2.8 μm best reproduce both the cumulative deposition and atmospheric concentration at the same time. The results indicate the importance of considering multiple log-normal size distributions and fog deposition.

Abstract. The vertical distribution of distinct aerosol components fundamentally governs atmospheric shortwave heating rates and radiative effects. However, traditional data assimilation (DA) methods typically rely on total aerosol optical thickness (AOT) or extinction, which fails to constrain the specific aerosol composition and often leads to the misallocation of aerosol between scattering and absorbing species. To address this limitation, we develop a component-resolved four-dimensional local ensemble transform Kalman filter (4D-LETKF) system with spatial and observational constraints within the WRF-Chem model. This novel system assimilates CALIOP-MODIS synergistic retrievals of species-specific extinction profiles (dust, sea salt, black carbon, and water-soluble aerosols) to explicitly optimize the three-dimensional distributions of individual components over East Asia. Results demonstrate that this DA system successfully reconstructs the complex vertical layering of multi-component aerosols. Notably, it effectively corrects severe underestimations of elevated black carbon (BC) plumes, capturing persistent free-tropospheric BC layers over South Asia that traditional models typically miss. Independent validations against ground-based AERONET and AD-Net lidar observations confirm significant improvements not only in total AOT and extinction but also in the single scattering albedo (SSA). By independently adjusting the mass of scattering and absorbing species, the system corrects local biases in aerosol optical properties. Consequently, the optimized component-specific profiles profoundly affect the atmospheric shortwave radiative heating. The elevated BC plumes induces a pronounced mid-tropospheric warming accompanied by a reduction in lower-tropospheric heating due to the attenuation of downward solar radiation. This study highlights the importance of component-specific vertical constraints for accurately assessing aerosol-induced atmospheric heating and its vertical structure.

Abstract Nitrous oxide (N2O) is a strong greenhouse gas that contributes significantly to global warming and causes depletion of ozone in the stratosphere. Recent observational records show an unprecedented acceleration in atmospheric N₂O growth, reaching 1.15 ppb yr− 1 in 2019–2023, a significant increase compared to 0.68 ppb yr− 1 in 2001–2005. This surge in growth rate is particularly pronounced over tropical regions, and has been measured most prominently at the southern-most island of Japan (Hateruma). In this study, we use N2O observations from globally distributed multi-institutional networks and the MIROC4-ACTM inversion framework to quantify N2O emissions and identify key regions that are driving the recent acceleration. Our results suggest that the major Asian countries, Brazil, Central and Northern Africa, and the Contiguous United States have increased emission sources in the recent 2.5 decades (1998–2023). Further, there has been an increase in land N2O emissions, at a rate of 106 GgN yr− 1 per year during 1998–2002 to 2019–2023 (1Gg = 109g). The inversion inferred trends are consistent with increased fertiliser use and manure production to support extensive agriculture, and terrestrial ecosystem model results. The emissions from oceanic regions did not show significant increases in N2O (rate: 7 ± 2 GgN yr− 1 per year) in our inverse model setup. Our results underscore the importance for improved climate mitigation strategies and emissions reduction policies by increasing nitrogen-fertiliser use efficiency in agricultural land.

The complexity of ozone chemistry makes it difficult to quantify the extent to which different national ozone reduction scenarios affect the country that implemented the strategy, neighboring countries, and the world. To better understand ozone sensitivity to national/regional emission scenarios, the zero-out method was applied for the anthropogenic emission of ozone precursors, i.e., nitrogen oxides (NOx), in nine regions and three countries worldwide. In this study, ozone simulations were performed at a high global resolution of 56 km via the use of a nonhydrostatic icosahedral atmosphere model coupled with chemistry (NICAM-Chem). The model performance in predicting surface ozone was evaluated with ground-based measurements worldwide. The correlation was moderate to high, but both bias and uncertainty were high but remained within the ranges of values obtained from other global chemistry models. Experiments based on zero-out methods for assessing anthropogenic NOx emissions revealed that the domestic impacts of anthropogenic NOx emission reduction on ozone reduction were the largest in India (14 ppbv, regional and annual average) and China (11 ppbv, regional and annual average). The influence of anthropogenic NOx emission reduction in China on transboundary ozone concentrations was notable, with a reduction in ozone of 7 ppbv (regional and annual average) in Japan and even yielding ozone reductions in North America (1 ppbv, regional and annual average) and western Europe (1 ppbv, regional and annual average). Sensitivity experiments with a 20% reduction in anthropogenic NOx emissions were also conducted, and the results were generally consistent with those of previous studies. However, the results for a 20% reduction in anthropogenic NOx emissions partially differed from the results for a 100% reduction in anthropogenic NOx emissions because of the high nonlinearity of ozone chemistry. For example, in Japan, a 20% reduction in domestic NOx emissions resulted in a larger reduction in ozone levels than that obtained with a 20% reduction in Chinese NOx emissions. This indicates the inherent uncertainty resulting from experimental settings that assume a substantial reduction, such as a 100% decrease in the NOx flux. Understanding the impact of emission reduction in a given country on other countries is important for accelerating future international collaboration on ozone reduction for ensuring climate and human health.

This study presents the development of the 2nd version of the multi-wavelength and multi-pixel method, MWPM2, for land aerosol remote sensing, and shows its validation studies with data of GOSAT-2/CAI-2 satellite-borne imager in the period from 2019 through 2022. The imager has unique near-ultraviolet bands of 340 and 380nm with a 460m-spatial resolution. The study found that the MWPM2 retrieval of CAI-2 data is capable of simultaneous retrievals of aerosol optical thickness, single scattering albedo, PM2.5, and black carbon concentration (BC) with enough precision within the GOSAT-2 mission success criteria when the retrieval results are filtered by a suitable quality control system.The developed system was then applied to CAI-2 imaging of the Indian land area over two-year periods to depict the horizontal distribution and seasonal variation of BC, which were found to be consistent with those of surface measurements by the ARFINET network.

Abstract. Due to very few reports of δ13CO2 (the stable carbon isotopic ratio of CO2) observations in the stratosphere, its variations are not well understood. In order to elucidate stratospheric δ13CO2 variations and their governing mechanisms, we have collected stratospheric air samples using balloon-borne cryogenic samplers over Japan since 1985 and analyzed them for δ13CO2. To obtain precise δ13CO2 values, we incorporated the mass-independent fractionation of 17O and 18O in the δ13CO2 calculation. δ13CO2 has decreased through time in the mid-stratosphere with an average rate of change of −0.026 ± 0.001 ‰ yr−1 for the period 1985–2020, consistent with that in the troposphere. However, mid-stratospheric δ13CO2 values did not show a time delay compared to the tropical tropospheric values. This could be explained by the production of CO2 by CH4 oxidation and the gravitational separation of 13CO2 and 12CO2. To confirm this hypothesis, we used a two-dimensional model to simulate the stratospheric δ13CO2 values while accounting for these processes. The results indicate that these two effects strongly impact the vertical distribution of δ13CO2. We newly defined “stratospheric potential δ13C” (δ13CP) as a quasi-conservative parameter incorporating the kinetic isotope effect of CH4 oxidation and gravitational separation, and we found that δ13CP in the mid-latitude mid-stratosphere decreases over time with about a 5 yr lag relative to the tropical upper troposphere. This fact strongly supports that stratospheric δ13CO2 variations are governed by the airborne production of 13C-depleted CO2 by CH4 oxidation, the gravitational separation, and the propagation of the decreasing tropospheric δ13CO2 trend into the stratosphere.

This paper reviews studies of atmospheric climate forcers, namely, greenhouse gases (GHGs) and aerosols, and their impacts on radiation and clouds in the Arctic during the Arctic Challenge for Sustainability II (ArCS II) project conducted between 2020 and 2024. In GHG research, we measured atmospheric mixing ratios of CO2, CH4, and N2O and their isotope ratios, as well as O-2/N-2 ratios from the ground, ship (research vessel Mirai), and commercial aircraft over the northern high latitudes. We showed that the rapid increase of CH4 mixing ratios after similar to 2018 could be attributed to elevated microbial CH4 emissions. The increase of N2O after similar to 2011 was attributable to an increase of N2O emissions from soil treated with chemical fertilizer. In aerosol research, we provided a scientific basis for constructing unified black carbon (BC) datasets in the Arctic and estimated the contributions to Arctic BC from biomass-burning and anthropogenic sources in various regions. We showed the abundance of highly active ice nucleating particles (INPs) increased with rising surface temperatures above 0 degrees C and evaluated impacts of Arctic dust that serve as INP on clouds. We found that marine biota were likely the sources of cloud condensation nuclei and INPs over the remote Arctic Ocean during periods of high biological activity.

Nitrous oxide (N₂O) is a strong greenhouse gas that contributes significantly to global warming and causes depletion of ozone in the stratosphere. Recent observational records show an unprecedented acceleration in atmospheric N₂O growth, reaching 1.15 ppb yr-1 in 2019–2023, a significant increase compared to 0.68 ppb yr-1 in 2001–2005. This surge in growth rate is particularly pronounced over tropical regions. In this study, we use N₂O observations from globally distributed multi-institutional networks and the MIROC4-ACTM inversion framework to quantify N₂O emissions and identify key regions that are driving the recent acceleration. Our results suggest that the major Asian countries, Brazil, Central and Northern Africa, and the Coterminous United States have increased emission sources in the recent 2.5 decades (1998-2023). Further, the increase in land N2O emissions, at a rate of 106 GgN yr-2 during 1998-2002 to 2019-2023 (1Gg=109g), has been clearly associated with the use of nitrogen fertilisers to support extensive agriculture, as inferred from a terrestrial ecosystem model, statistics of nitrogen fertiliser use and inversion results. The emissions from oceanic regions did not show significant increases in N2O emissions (rate: 7±2 GgN yr-2). Our results underscore the importance for improved climate mitigation strategies and emissions reduction policies by increasing nitrogen-fertiliser use efficiency (NUE) in agricultural land.

Due to very few reports of delta 13CO2 (the stable carbon isotopic ratio of CO2) observations in the stratosphere, its variations are not well understood. In order to elucidate stratospheric delta 13CO2 variations and their governing mechanisms, we have collected stratospheric air samples using balloon-borne cryogenic samplers over Japan since 1985 and analyzed them for delta 13CO2. To obtain precise delta 13CO2 values, we incorporated the mass-independent fractionation of 17O and 18O in the delta 13CO2 calculation. delta 13CO2 has decreased through time in the mid-stratosphere with an average rate of change of -0.026 +/- 0.001 parts per thousand yr-1 for the period 1985-2020, consistent with that in the troposphere. However, mid-stratospheric delta 13CO2 values did not show a time delay compared to the tropical tropospheric values. This could be explained by the production of CO2 by CH4 oxidation and the gravitational separation of 13CO2 and 12CO2. To confirm this hypothesis, we used a two-dimensional model to simulate the stratospheric delta 13CO2 values while accounting for these processes. The results indicate that these two effects strongly impact the vertical distribution of delta 13CO2. We newly defined "stratospheric potential delta 13C" (delta 13CP) as a quasi-conservative parameter incorporating the kinetic isotope effect of CH4 oxidation and gravitational separation, and we found that delta 13CP in the mid-latitude mid-stratosphere decreases over time with about a 5 yr lag relative to the tropical upper troposphere. This fact strongly supports that stratospheric delta 13CO2 variations are governed by the airborne production of 13C-depleted CO2 by CH4 oxidation, the gravitational separation, and the propagation of the decreasing tropospheric delta 13CO2 trend into the stratosphere.

Establishing interlaboratory compatibility among measurements of stable isotope ratios of atmospheric methane (δ13C-CH4 and δD-CH4) is challenging. Significant offsets are common because laboratories have different ties to the VPDB or SMOW-SLAP scales. Umezawa et al. (2018) surveyed numerous comparison efforts for CH4 isotope measurements conducted from 2003 to 2017 and found scale offsets of up to 0.5 ‰ for δ13C-CH4 and 13 ‰ for δD-CH4 between laboratories. This exceeds the World Meteorological Organisation Global Atmospheric Watch (WMO-GAW) network compatibility targets of 0.02 ‰ and 1 ‰ considerably. We employ a method to establish scale offsets between laboratories using their reported CH4 isotope measurements on atmospheric samples. Our study includes data from eight laboratories with experience in high-precision isotope ratio mass spectrometry (IRMS) measurements for atmospheric CH4. The analysis relies exclusively on routine atmospheric measurements conducted by these laboratories at high-latitude stations in the Northern and Southern Hemispheres, where we assume each measurement represents sufficiently well-mixed air at the latitude for direct comparison. We use two methodologies for interlaboratory comparisons: (I) assessing differences between time-adjacent observation data and (II) smoothing the observed data using polynomial and harmonic functions before comparison. The results of both methods are consistent, and with a few exceptions, the overall average offsets between laboratories align well with those reported by Umezawa et al. (2018). This indicates that interlaboratory offsets remain robust over multi-year periods. The evaluation of routine measurements allows us to calculate the interlaboratory offsets from hundreds, in some cases thousands of measurements. Therefore, the uncertainty in the mean interlaboratory offset is not limited by the analytical error of a single analysis but by real atmospheric variability between the sampling dates and stations. Using the same method, we assess this uncertainty by investigating measurements from four high-latitude sites analysed by the INSTAAR laboratory. After applying the derived interlaboratory offsets, we present a harmonised time series for δ13C-CH4 and δD-CH4 at high northern and southern latitudes, covering the period from 1988 to 2023.

Emissions from South Asia (SA) represent a critical source of aerosols on the Tibetan Plateau (TP), and aerosols can significantly reduce the surface solar energy. To enhance the precision of aerosol forecasting and its radiative effects in SA and the TP, we employed a four-dimensional local ensemble transform Kalman filter (4D-LETKF) aerosol data assimilation (DA) system. This system was utilized to assimilate Himawari-8 aerosol optical thickness (AOT) into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to depict one SA air pollution outbreak event in spring 2018. Sensitivity tests for the assimilation system were conducted first to tune temporal localization lengths. Comparisons with independent Moderate Resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET) observations demonstrate that the AOT analysis and forecast fields have more reasonable diurnal variations by assimilating all the observations within a 12 h window, which are both better than assimilating the hourly observations in the current assimilation time slot. Assimilation of the entire window of observations with aerosol radiative effect activation significantly improves the prediction of downward solar radiation compared to the free-run experiment. The AOT assimilation with aerosol radiative effect activation led to a reduction in aerosol concentrations over SA, resulting in increased surface radiation, temperature, boundary layer height, and atmospheric instability. These changes facilitated air uplift, promoting aerosol transport from SA to the southeastern TP and leading to an increase in AOT in this region.

Asian over-land aerosols are complexities due to a mixture of anthropogenic air pollutants and natural dust. The accuracy of the aerosol optical thickness (AOT) retrieved from the satellite is crucial to their application in the aerosol data assimilation system. Fusion of AOTs with high spatiotemporal resolution from next-generation geostationary satellites such as Fengyun-4B (FY-4B) and Himawari-9, provides a new high-quality dataset capturing the aerosol spatiotemporal variability for data assimilation. This study develops a complete fusion algorithm to estimate the optimal AOT over-land in Asia from September 2022 to August 2023 at 10 km x 10 km resolution with high efficiency. The data fusion involves four steps: (1) investigating the spatiotemporal variability of FY-4B AOT within the past 1 h and 12 km radius calculation domain; (2) utilizing the aerosol spatiotemporal variability characteristics to estimate FY-4B pure and hourly merged AOTs; (3) performing bias corrections for FY-4B and Himwari-9 hourly merged AOT for different observation times and seasons considering pixel -level errors for each satellite; (4) fusing the bias -corrected FY -4B and Himawari-9 hourly merged AOT based on maximum -likelihood estimation (MLE) method. Compared to the original FY -4B AOT, validation with AERONET observation confirms that the root mean square error (RMSE) of hourly merged FY -4B AOT decreases by around 40.6 % and the correlation coefficient (CORR) increases by about 27.8 %. Compared to FY -4B and Himawari-9 merged AOT, the fused AOT significantly decreases (increases) RMSE (CORR) by around 24.7 % (7.3 %) and 20.2 % (5.6 %). In addition, fused AOT is double the number of single -sensor merged AOT. Fusion aerosol map accurately describes the spatial and temporal variations in Asian regions controlled by air pollution and dust storms. Further studies are required for other landscapes with different satellite combinations to promote the application in the data assimilation system.

A record-breaking east Asian dust storm over recent years occurred in March 2021. The & Aring;ngstr & ouml;m exponent (AE), which measures the wavelength dependence of aerosol optical thickness (AOT), is significantly sensitive to large aerosols such as dust. Due to the lack of observations during dust storms and the accuracy of the satellite-retrieved AE depending on the instrument and retrieval algorithm, it is possible to estimate the dust storm emission using the time-lagged ground-based AE observations. In this study, the hourly AEs observed by the Aerosol Robotic Network (AERONET) are assimilated with the ensemble Kalman smoother (EnKS) and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to optimize simulated dust emissions from 14 to 23 March 2021. The results demonstrate that the additional inclusion of AE can optimize the size distribution of dust emissions and the associated total flux depending on the covariance between time-lagged AE observations and simulated dust emissions in each size bin. Compared to the experiment only assimilating AOT, validation by independent observations from the Skynet Observation NETwork (SONET) shows that assimilating additional AE information reduces the root mean square error (RMSE) of simulated AOT and AE by approximately 17 % and 61 %, respectively. The temporal variation in both simulated AOT and AE is improved through assimilating additional AE information. The assimilation of AOT and AE also makes the magnitude and variations in aerosol vertical extinctions more comparable to the independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations in both westward and eastward pathways of dust transport. The optimized dust emissions in the Gobi Desert during this period is estimated to be 52.63 Tg and reached a peak value of 3837 kt h-1 at 07:00 UTC on 14 March.

In accordance with progression in current capabilities towards high-resolution approaches, applying a convective-permitting resolution to global aerosol models helps comprehend how complex cloud–precipitation systems interact with aerosols. This study investigates the impacts of a double-moment bulk cloud microphysics scheme, i.e., NICAM Double-moment bulk Water 6 developed in this study (NDW6-G23), on the spatiotemporal distribution of aerosols in the Nonhydrostatic ICosahedral Atmospheric Model as part of the version-19 series (NICAM.19) with 14 km grid spacing. The mass concentrations and optical thickness of the NICAM-simulated aerosols are generally comparable to those obtained from in situ measurements. However, for some aerosol species, especially dust and sulfate, the differences between experiments of NDW6 and of the NICAM single-moment bulk module with six water categories (NSW6) were larger than those between experiments with different horizontal resolutions (14 and 56 km grid spacing), as shown in a previous study. The simulated aerosol burdens using NDW6 are generally lower than those using NSW6; the net instantaneous radiative forcing due to aerosol–radiation interaction (IRFari) is estimated to be −1.36 W m−2 (NDW6) and −1.62 W m−2 (NSW6) in the global annual mean values at the top of the atmosphere (TOA). The net effective radiative forcing due to anthropogenic aerosol–radiation interaction (ERFari) is estimated to be −0.19 W m−2 (NDW6) and −0.23 W m−2 (NSW6) in the global annual mean values at the TOA. This difference among the experiments using different cloud microphysics modules, i.e., 0.26 W m−2 or 16 % difference in IRFari values and 0.04 W m−2 or 16 % difference in ERFari values, is attributed to a different ratio of column precipitation to the sum of the column precipitation and column liquid cloud water, which strongly determines the magnitude of wet deposition in the simulated aerosols. Since the simulated ratios in the NDW6 experiment are larger than those of the NSW6 result, the scavenging effect of the simulated aerosols in the NDW6 experiment is larger than that in the NSW6 experiment. A large difference between the experiments is also found in the aerosol indirect effect (AIE), i.e., the net effective radiative forcing due to aerosol–cloud interaction (ERFaci) from the present to preindustrial days, which is estimated to be −1.28 W m−2 (NDW6) and −0.73 W m−2 (NSW6) in global annual mean values. The magnitude of the ERFaci value in the NDW6 experiment is larger than that in the NSW6 result due to the differences in both the Twomey effect and the susceptibility of the simulated cloud water to the simulated aerosols between NDW6 and NSW6. Therefore, this study shows the importance of the impacts of the cloud microphysics module on aerosol distributions through both aerosol wet deposition and the AIE.

Wet scavenging was critical in the atmospheric transport of Cs-137 aerosols following the Fukushima accident. The aerosol size diversity and related microphysical processes produce complex behaviors during wet scavenging. Such behaviors are difficult to investigate using traditional simplified size distributions, resulting in inaccurate modeling. This study establishes an improved size-resolved wet scavenging model that considers the activation process. Using this model, five monodisperse simulations with five representative observed diameters with realistic solubility setting are performed to investigate the spatiotemporal wet scavenging behaviors of Cs-137 aerosols. One polydisperse simulation with an empirical size distribution is also validated against the observation. The results reveal that Cs-137 aerosols with diameters of 0.6 and 2.0 mu m are mainly subject to below-cloud scavenging, which makes a significant contribution to low-deposition areas (<300 kBq/m(2)). For Cs-137 aerosols with diameters of 6.4, 15, and 30 mu m, in-cloud scavenging dominates, and the resulting depositions make significant contributions in high -deposition areas. The polydisperse results satisfy the criteria for good performance and better agree with the size, and deposition observations than the five monodisperse simulations, whereas for the concentration, the results show a similar RANK2 with the best mono1 and mono2 cases and reach the satisfactory criteria. These findings reveal the complex behavior and wet scavenging process of multi -mode Cs-137 aerosols, improving our understanding and modeling.

AbstractMethane (CH4) emission reduction to limit warming to 1.5 °C can be tracked by analyzing CH4 concentration and its isotopic composition (δ13C, δD) simultaneously. Based on reconstructions of the temporal trends, latitudinal, and vertical gradient of CH4 and δ13C from 1985 to 2020 using an atmospheric chemistry transport model, we show (1) emission reductions from oil and gas exploitation (ONG) since the 1990s stabilized the atmospheric CH4 growth rate in the late 1990s and early 2000s, and (2) emissions from farmed animals, waste management, and coal mining contributed to the increase in CH4 since 2006. Our findings support neither the increasing ONG emissions reported by the EDGARv6 inventory during 1990–2020 nor the large unconventional emissions increase reported by the GAINSv4 inventory since 2006. Total fossil fuel emissions remained stable from 2000 to 2020, most likely because the decrease in ONG emissions in some regions offset the increase in coal mining emissions in China.

AbstractThe computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high‐resolution aerosol assimilation system. A 2‐dimensional variational method (2DVar) was implemented in a non‐hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar‐orbital satellites. The model results were evaluated against ground‐based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite‐estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite‐retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground‐based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes.