A four-dimensional variational (4D-Var) data assimilation system for a regional dust model (RAMS/CFORS-4DVAR; RC4) is applied to an adjoint inversion of a heavy dust event over eastern Asia during 20 March–4 April 2007. The vertical profiles of the dust extinction coefficients derived from NIES Lidar network are directly assimilated, with validation using observation data. Two experiments assess impacts of observation site selection: Experiment A uses five Japanese observation sites located downwind of dust source regions; Experiment B uses these and two other sites near source regions. Assimilation improves the modeled dust extinction coefficients. Experiment A and Experiment B assimilation results are mutually consistent, indicating that observations of Experiment A distributed over Japan can provide comprehensive information related to dust emission inversion. Time series data of dust AOT calculated using modeled and Lidar dust extinction coefficients improve the model results. At Seoul, Matsue, and Toyama, assimilation reduces the root mean square differences of dust AOT by 35–40%. However, at Beijing and Tsukuba, the RMS differences degrade because of fewer observations during the heavy dust event. Vertical profiles of the dust layer observed by CALIPSO are compared with assimilation results. The dense dust layer was trapped at potential temperatures (θ) of 280–300 K and was higher toward the north; the model reproduces those characteristics well. Latitudinal distributions of modeled dust AOT along the CALIPSO orbit paths agree well with those of CALIPSO dust AOT, OMI AI, and MODIS coarse-mode AOT, capturing the latitude at which AOTs and AI have high values. Assimilation results show increased dust emissions over the Gobi Desert and Mongolia; especially for 29–30 March, emission flux is about 10 times greater. Strong dust uplift fluxes over the Gobi Desert and Mongolia cause the heavy dust event. Total optimized dust emissions are 57.9 Tg (Experiment A; 57.8% larger than before assimilation) and 56.3 Tg (Experiment B; 53.4% larger).

We introduce a Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) framework that directly accounts for model error in transport and chemistry, and we integrate a portfolio of data assimilation analyses obtained using multiple forward chemical transport models in a state-of-the-art ensemble Kalman filter data assimilation system. The data assimilation simultaneously optimizes both concentrations and emissions of multiple species through ingestion of a suite of measurements (ozone, NO₂, CO, HNO₃) from multiple satellite sensors. In spite of substantial model differences, the observational density and accuracy was sufficient for the assimilation to reduce the multi-model spread by 20 %–85 % for ozone and annual mean bias by 39 %–97 % for ozone in the middle troposphere, while simultaneously reducing the tropospheric NO₂ column biases by more than 40 % and the negative biases of surface CO in the Northern Hemisphere by 41 %–94 %. For tropospheric mean OH, the multi-model mean meridional hemispheric gradient was reduced from 1.32±0.03 to 1.19±0.03, while the multi-model spread was reduced by 24 %–58 % over polluted areas. The uncertainty ranges in the a posteriori emissions due to model errors were quantified in 4 %–31 % for NO_x and 13 %–35 % for CO regional emissions. Harnessing assimilation increments in both NO_x and ozone, we show that the sensitivity of ozone and NO₂ surface concentrations to NO_x emissions varied by a factor of 2 for end-member models, revealing fundamental differences in the representation of fast chemical and dynamical processes. A systematic investigation of model ozone response and analysis increment in MOMO-Chem could benefit evaluation of future prediction of the chemistry–climate system as a hierarchical emergent constraint.

A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1° × 1.1°). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations. Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under- and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts. AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0. 90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

An intense dust storm occurred during 19–20 May 2007 over the Taklimakan Desert in northwestern China. Over the following days, the space-borne lidar CALIOP tracked an optically thin, highly elevated, horizontally extensive dust veil that was transported intercontinentally over eastern Asia, the Pacific Ocean, North America, and the Atlantic Ocean. A global aerosol transport model (SPRINTARS) simulated the dust veil quite well and provided a three-dimensional view of the intercontinental dust transport. The SPRINTARS simulation revealed that the dust veil traveled at 4–10 km altitudes with a thickness of 1–4 km along the isentropic surface between 310 and 340 K. The transport speed was about 1500 km/day. The estimated dust amount exported to the Pacific was 30.8 Gg, of which 65% was deposited in the Pacific and 18% was transported to the North Atlantic. These results imply that dust veils can fertilize open oceans, add to background dust, and affect the radiative budget at high altitudes through scattering and absorption. The injection mechanism that lifts dust particles into the free atmosphere is important for understanding the formation of the dust veil and subsequent long-range transport. We used a regional dust transport model (RC4) to analyze the dust emission and injection over the source region. The RC4 analysis revealed that strong northeasterly surface winds associated with low pressures invaded the Taklimakan Desert through the eastern corridor. These winds then formed strong upslope wind along the high, steep mountainsides of the Tibetan Plateau and blew large amounts of dust into the air. The updraft lifted the dust particles farther into the upper troposphere (about 9 km above mean sea level, MSL), where westerlies are generally present. The unusual terrain surrounding the Taklimakan Desert played a key role in the injection of dust to the upper troposphere to form the dust veil.

Acidifying species in precipitation can have severe impacts on ecosystems. The chemical composition of precipitation is directly related to the amount of precipitation; accordingly, it is difficult to identify long-term variation in chemical concentrations. The ratio of the nitrate (NO₃⁻) to non-sea-salt sulfate (nss-SO₄²⁻) concentration in precipitation on an equivalent basis (hereinafter, Ratio) is a useful index to investigate the relative contributions of these acidifying species. To identify the long-term record of acidifying species in precipitation over East Asia, the region with the highest emissions worldwide, we compiled ground-based observations of the chemical composition of precipitation over China, Korea, and Japan from 2001 to 2015 based on the Acid Deposition Monitoring Network in East Asia (EANET). The spatial coverage was limited, but additional monitoring data for Japan, southern China, and northern China around Beijing were utilized. The period of analysis was divided into three phases: Phase I (2001–2005), Phase II (2006–2010), and Phase III (2011–2015). The behaviors of NO₃⁻ and nss-SO₄²⁻ concentrations and hence the Ratio in precipitation were related to these precursors. The anthropogenic NO_x and SO₂ emissions and the NO_x ∕ SO₂ emission ratio were analyzed. Further, satellite observations of the NO₂ and SO₂ column density to capture the variation in emissions were applied. We found that the long-term trend in the NO₃⁻ concentration in precipitation was not related to the variation in NO_x emission and the NO₂ column. In comparison, the nss-SO₄²⁻ concentration in precipitation over China, Korea, and Japan was partially connected to the changes in SO₂ emissions from China, but the trends were not significant. The long-term trends of Ratio over China, Korea, and Japan were nearly flat during Phase I, increased significantly during Phase II, and were essentially flat again during Phase III. This variation in Ratio in East Asia clearly corresponded to the NO_x ∕ SO₂ emission ratio and the NO₂ ∕ SO₂ column ratio in China. The initial flat trend during Phase I was due to increases in both NO_x and SO₂ emissions in China, the significantly increasing trend during Phase II was triggered by the increase in NO_x emissions and decrease in SO₂ emissions in China, and the return to a flat trend during Phase III was caused by declines in both NO_x and SO₂ emissions in China. These results suggest that emissions in China had a significant impact not only on China but also on downwind precipitation chemistry during the 15-year period of 2001–2015. In terms of wet deposition, the NO₃⁻ wet deposition over China, Korea, and Japan did not change dramatically, but the nss-SO₄²⁻ wet deposition declined over China, Korea, and Japan from Phase II to III. These declines were caused by a strong decrease in the nss-SO₄²⁻ concentration in precipitation accompanied by a reduction in SO₂ emission from China, which counteracted the increase in precipitation. These findings indicated that the acidity of precipitation shifted from sulfur to nitrogen.

We verified an algorithm using multi-wavelength Mie-Raman lidar (MMRL) observations to retrieve four aerosol components (black carbon (BC), sea salt (SS), air pollution (AP), and mineral dust (DS)) with in-situ aerosol measurements, and determined the seasonal variation of aerosol components in Fukuoka, in the western region of Japan. PM_2.5, PM₁₀, and mass concentrations of BC and SS components are derived from in-situ measurements. MMRL provides the aerosol extinction coefficient (α), particle linear depolarization ratio (δ), backscatter coefficient (β), and lidar ratio (S) at 355 and 532 nm, and the attenuated backscatter coefficient (β_att) at 1064 nm. We retrieved vertical distributions of extinction coefficients at 532 nm for four aerosol components (BC, SS, AP, and DS) using 1α₅₃₂ + 1β₅₃₂ + 1β_att,1064 + 1δ₅₃₂ data of MMRL. The retrieved extinction coefficients of the four aerosol components at 532 nm were converted to mass concentrations using the theoretical computed conversion factor assuming the prescribed size distribution, particle shape, and refractive index for each aerosol component. MMRL and in-situ measurements confirmed that seasonal variation of aerosol optical properties was affected by internal/external mixing of various aerosol components, in addition to hygroscopic growth of water-soluble aerosols. MMRL overestimates BC mass concentration compared to in-situ observation using the pure BC model. This overestimation was reduced drastically by introducing the internal mixture model of BC and water-soluble substances (Core-Gray Shell (CGS) model). This result suggests that considering the internal mixture of BC and water-soluble substances is essential for evaluating BC mass concentration in this area. Systematic overestimation of BC mass concentration was found during summer, even when we applied the CGS model. The observational facts based on in-situ and MMRL measurements suggested that misclassification of AP as CGS particles was due to underestimation of relative humidity (RH) by the numerical model in lidar analysis, as well as mismatching of the optical models of AP and CGS assumed in the retrieval with aerosol properties in the actual atmosphere. The time variation of lidar-derived SS was generally consistent with in-situ measurement; however, we found some overestimation of SS during dust events. The cause of this SS overestimation is mainly due to misclassifying internally mixing DS as SS, implying that to consider internal mixing between DS and water-soluble substances leads to better estimation. The time-variations of PM_2.5 and PM₁₀ generally showed good agreement with in-situ measurement although lidar-derived PM_2.5 and PM₁₀ overestimated in dust events.