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Cold extremes have large impacts on human society. Understanding the physical processes dominating the changes in cold extremes is crucial for a reliable projection of future climate change. The observed cold extremes have decreased during the last several decades, and this trend will continue under future global warming. Here, we quantitatively identify the contributions of dynamic (changes in large-scale atmospheric circulation) and thermodynamic (rising temperatures resulting from global warming) effects to East Asian cold extremes in the past several decades and in a future warm climate by using two sets of large-ensemble simulations of climate models. We show that the dynamic component accounts for over 80 % of the cold-month (coldest 5 % boreal winter months) surface air temperature (SAT) anomaly over the past 5 decades. However, in a future warm climate, the thermodynamic change is the main contributor to the decreases in the intensity and occurrence probability of East Asian cold extremes, while the dynamic change is also contributive. The intensity of East Asian cold extremes will decrease by around 5 °C at the end of the 21st century, in which the thermodynamic (dynamic) change contributes approximately 75 % (25 %). The present-day (1986–2005) East Asian cold extremes will almost never occur after around 2035, and this will happen 10 years later due solely to thermodynamic change. The upward trend of a positive Arctic Oscillation-like sea level pressure pattern dominates the changes in the dynamic component. The finding provides a useful reference for policymakers in climate change adaptation activities. [ABSTRACT FROM AUTHOR]

Reliable attribution of Asian summer monsoon variations to aerosol forcing is critical to reducing uncertainties in future projections of regional water availability, which is of utmost importance for risk management and adaptation planning in this densely populated region. Yet, simulating the monsoon remains a challenge for climate models that suffer from long-standing biases, undermining their reliability in attributing anthropogenically forced changes. We analyze a suite of climate model experiments to identify a link between model biases and monsoon responses to Asian aerosols and associated physical mechanisms, including the role of large-scale circulation changes. The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model's ability to simulate the spatio-temporal variability in the climatological monsoon winds, clouds, and precipitation across Asia, which modulates the magnitude and efficacy of aerosol–cloud–precipitation interactions, an important component of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response. We found a striking contrast between the early- and late-summer aerosol-driven changes ascribable to opposite signs and seasonal evolution of the biases in the two regions. A realistic simulation of the evolution of the large-scale atmospheric circulation is crucial to realize the full extent of the aerosol impact over Asia. These findings provide important implications for better understanding and constraining the diversity and inconsistencies of model responses to aerosol changes over Asia in historical simulations and future projections. [ABSTRACT FROM AUTHOR]

Organic aerosols from marine sources over the western Pacific Ocean of East Asia were investigated using an online coupled regional chemistry–climate model RIEMS-Chem for the entire year 2014. Model evaluation against a wide variety of observations from research cruises and in situ measurements demonstrated a good skill of the model in simulating temporal variation and spatial distribution of particulate matter with aerodynamic diameter less than 2.5 and 10 µm (PM 2.5 and PM 10), black carbon (BC), organic carbon (OC), sodium, and aerosol optical depth (AOD) in the marine atmosphere. The inclusion of marine organic aerosols improved model performance on OC concentration by reducing model biases of up to 20 %. The regional and annual mean near-surface marine organic aerosol (MOA) concentration was estimated to be 0.27 µgm-3 , with the maximum in spring and the minimum in winter, and contributed 26 % of the total organic aerosol concentration on average over the western Pacific. Marine primary organic aerosol (MPOA) accounted for the majority of marine organic aerosol (MOA) mass, and the MPOA concentration exhibited the maximum in autumn and the minimum in summer, whereas marine secondary organic aerosol (MSOA) was approximately 1–2 orders of magnitude lower than MPOA, having a distinct summer maximum and a winter minimum. MOA induced a direct radiative effect (DRE MOA) of - 0.27 Wm-2 and an indirect radiative effect (IRE MOA) of - 0.66 Wm-2 at the top of the atmosphere (TOA) in terms of annual and oceanic average over the western Pacific, with the highest seasonal mean IRE MOA up to - 0.94 Wm-2 in spring. IRE MOA was stronger than, but in a similar magnitude to, the IRE due to sea salt aerosol on average, and it was approximately 9 % of the IRE due to anthropogenic aerosols in terms of annual mean over the western Pacific. This ratio increased to 19 % in the northern parts of the western Pacific in autumn. This study reveals an important role of MOA in perturbing cloud properties and shortwave radiation fluxes in the western Pacific of East Asia. [ABSTRACT FROM AUTHOR]

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea–United States Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by - 64 % but overestimates nitrate by + 36 %. The largest underestimate in sulfate occurs during the pollution event, for which models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by + 100 % against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of 5 increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape, resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model's inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model, which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation, implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66 % to 54 %. Locally produced sulfate increased from 1 % to 25 % of locally produced PM2.5 , implying that local emissions controls could have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction, which affects the aerosol liquid water abundance and chemistry driving sulfate–nitrate–ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and this results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate + nitrate + ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) plays a central role in the chemistry of inorganic secondary aerosols in the atmosphere. The largest emission sector for NH3 is agriculture, where NH3 is volatilized from livestock wastes and fertilized soils. Although the NH3 volatilization from soils is driven by the soil temperature and moisture, many atmospheric chemistry models prescribe the emission using yearly emission inventories and climatological seasonal variations. Here we evaluate an alternative approach where the NH3 emissions from agriculture are simulated interactively using the process model FANv2 (Flow of Agricultural Nitrogen, version 2) coupled to the Community Atmospheric Model with Chemistry (CAM-chem). We run a set of 6-year global simulations using the NH3 emission from FANv2 and three global emission inventories (EDGAR, CEDS and HTAP) and evaluate the model performance using a global set of multi-component (atmospheric NH3 and NH4+ , and NH4+ wet deposition) in situ observations. Over East Asia, Europe and North America, the simulations with different emissions perform similarly when compared with the observed geographical patterns. The seasonal distributions of NH3 emissions differ between the inventories, and the comparison to observations suggests that both FANv2 and the inventories would benefit from more realistic timing of fertilizer applications. The largest differences between the simulations occur over data-scarce regions. In Africa, the emissions simulated by FANv2 are 200 %–300 % higher than in the inventories, and the available in situ observations from western and central Africa, as well as NH3 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument, are consistent with the higher NH3 emissions as simulated by FANv2. Overall, in simulating ammonia and ammonium concentrations over regions with detailed regional emission inventories, the inventories based on these details (HTAP, CEDS) capture the atmospheric concentrations and their seasonal variability the best. However these inventories cannot capture the impact of meteorological variability on the emissions, nor can these inventories couple the emissions to the biogeochemical cycles and their changes with climate drivers. Finally, we show with sensitivity experiments that the simulated time-averaged nitrate concentration in air is sensitive to the temporal resolution of the NH3 emissions. Over the CASTNET monitoring network covering the US, resolving the NH3 emissions hourly instead monthly reduced the positive model bias from approximately 80 % to 60 % of the observed yearly mean nitrate concentration. This suggests that some of the commonly reported overestimation of aerosol nitrate over the US may be related to unresolved temporal variability in the NH3 emissions. [ABSTRACT FROM AUTHOR]

Global coupled chemistry–climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea–United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42 % in the control run and by 12 % with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80 % for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O 3 datasets and observationally constrained box model simulations of OH and HO 2. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29 % for CO, 18 % for ozone, 11 % for HO 2 , and 27 % for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O 3 , with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NO x controls, can improve ozone pollution over East Asia. [ABSTRACT FROM AUTHOR]

The Taklamakan desert is an important dust source for the global atmospheric dust budget and a cause of the dust weather in East Asia. The characterization of Taklamakan dust in the source region is still very limited. To fill this gap, the DAO (dust aerosol observation) was conducted in April 2019 in Kashi, China. The Kashi site is about 150 km from the western rim of the Taklamakan desert and is strongly impacted by desert dust aerosols, especially in spring time, i.e., April and May. According to sun–sky photometer measurements, the aerosol optical depth (at 500 nm) varied in the range of 0.07–4.70, and the Ångström exponent (between 440 and 870 nm) in the range of 0.0–0.8 in April 2019. In this study, we provide the first profiling of the 2α+3β+3δ parameters of Taklamakan dust based on a multiwavelength Mie–Raman polarization lidar. For Taklamakan dust, the Ångström exponent related to the extinction coefficient (EAE, between 355 and 532 nm) is about 0.01 ± 0.30, and the lidar ratio is found to be 45 ± 7 sr (51 ± 8–56 ± 8 sr) at 532 (355) nm. The particle linear depolarization ratios (PLDRs) are about 0.28–0.32 ± 0.07 at 355 nm, 0.36 ± 0.05 at 532 nm and 0.31 ± 0.05 at 1064 nm. Both lidar ratios and depolarization ratios are higher than the typical values of Central Asian dust in the literature. The difference is probably linked to the fact that observations in the DAO campaign were collected close to the dust source; therefore, there is a large fraction of coarse-mode and giant particles (radius >20 µ m) in the Taklamakan dust. Apart from dust, fine particles coming from local anthropogenic emissions and long-range transported aerosols are also non-negligible aerosol components. The signatures of pollution emerge when dust concentration decreases. The polluted dust (defined by PLDR 532≤0.30 and EAE 355-532≥0.20) is featured with reduced PLDRs and enhanced EAE 355-532 compared to Taklamakan dust. The mean PLDRs of polluted dust generally distributed in the range of 0.20–0.30. Due to the complexity of the nature of the involved pollutants and their mixing state with dust, the lidar ratios exhibit larger variabilities compared to those of dust. The study provides the first reference of novel characteristics of Taklamakan dust measured by Mie–Raman polarization lidar. The data could contribute to complementing the dust model and improving the accuracy of climate modeling. [ABSTRACT FROM AUTHOR]

This study compares the performance of 12 regional chemical transport models (CTMs) from the third phase of the Model Inter-Comparison Study for Asia (MICS-Asia III) on simulating the particulate matter (PM) over East Asia (EA) in 2010. The participating models include the Weather Research and Forecasting model coupled with Community Multiscale Air Quality (WRF-CMAQ; v4.7.1 and v5.0.2), the Regional Atmospheric Modeling System coupled with CMAQ (RAMS-CMAQ; v4.7.1 and v5.0.2), the Weather Research and Forecasting model coupled with chemistry (WRF-Chem; v3.6.1 and v3.7.1), Goddard Earth Observing System coupled with chemistry (GEOS-Chem), a non-hydrostatic model coupled with chemistry (NHM-Chem), the Nested Air Quality Prediction Modeling System (NAQPMS) and the NASA-Unified WRF (NU-WRF). This study investigates three model processes as the possible reasons for different model performances on PM. (1) Models perform very differently in the gas–particle conversion of sulfur (S) and oxidized nitrogen (N). The model differences in sulfur oxidation ratio (50 %) are of the same magnitude as that in SO42- concentrations. The gas–particle conversion is one of the main reasons for different model performances on fine mode PM. (2) Models without dust emission modules can perform well on PM 10 at non-dust-affected sites but largely underestimate (up to 50 %) the PM 10 concentrations at dust sites. The implementation of dust emission modules in the models has largely improved the model accuracies at dust sites (reduce model bias to -20 %). However, both the magnitude and distribution of dust pollution are not fully captured. (3) The amounts of modeled depositions vary among models by 75 %, 39 %, 21 % and 38 % for S wet, S dry, N wet and N dry depositions, respectively. Large inter-model differences are found in the washout ratios of wet deposition (at most 170 % in India) and dry deposition velocities (generally 0.3–2 cm s -1 differences over inland regions). [ABSTRACT FROM AUTHOR]

Recent studies suggested significant impacts of boreal cryosphere changes on wintertime air stagnation and haze pollution extremes in China. However, the underlying mechanisms of such a teleconnection relationship remains unclear. Here we use the Whole Atmosphere Community Climate Model (WACCM) to investigate dynamic processes leading to atmospheric circulation and air stagnation responses to Arctic sea ice changes. We conduct four climate sensitivity experiments by perturbing sea ice concentrations (SIC) and corresponding sea surface temperature (SST) in autumn and early winter over the whole Arctic and three subregions in the climate model. The results indicate distinct responses in circulation patterns and regional ventilation to the region-specific Arctic changes, with the largest increase of both the probability (by 132 %) and the intensity (by 30 %) of monthly air stagnation extremes being found in the experiment driven by SIC and SST changes over the Pacific sector of the Arctic (the East Siberian and Chukchi seas). The increased air stagnation extremes are mainly driven by an amplified planetary-scale atmospheric teleconnection pattern that resembles the negative phase of the Eurasian (EU) pattern. Dynamical diagnostics suggest that convergence of transient eddy forcing in the vicinity of Scandinavia in winter is largely responsible for the amplification of the teleconnection pattern. Transient eddy vorticity fluxes dominate the transient eddy forcing and produce a barotropic anticyclonic anomaly near Scandinavia and wave train propagation across Eurasia to the downstream regions in East Asia. The piecewise potential vorticity inversion analysis reveals that this long-range atmospheric teleconnection of Arctic origin takes place primarily via the middle and upper troposphere. The anomalous ridge over East Asia in the middle and upper troposphere worsens regional ventilation conditions by weakening monsoon northwesterlies and enhancing temperature inversions near the surface, leading to more and stronger air stagnation and pollution extremes over eastern China in winter. Ensemble projections based on state-of-the-art climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) corroborate this teleconnection relationship between high-latitude environmental changes and midlatitude weather extremes, though the tendency and magnitude vary considerably among each participating model. [ABSTRACT FROM AUTHOR]

In this study, a full description and comprehensive evaluation of a global–regional nested model, the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM), is presented for the first time. Not only are the global budgets and distribution explored, but comparisons of the nested simulation over China against multiple datasets are investigated, which benefit from access to Chinese air quality monitoring data from 2013 to the present and the "Model Inter-Comparison Study for Asia" project. The model results and analysis can help reduce uncertainties and aid with understanding model diversity with respect to assessing global and regional aerosol effects on climate and human health, especially over East Asia and areas affected by East Asia. For the global simulation, the 1-year simulation for 2014 shows that the IAP-AACM is within the range of other models. Overall, it reasonably reproduced spatial distributions and seasonal variations of trace gases and aerosols in both surface concentrations and column burdens (mostly within a factor of 2). The model captured spatial variation for carbon monoxide well with a slight underestimation over ocean, which implicates the uncertainty of the ocean source. The simulation also matched the seasonal cycle of ozone well except for the continents in the Northern Hemisphere, which was partly due to the lack of stratospheric–tropospheric exchange. For aerosols, the simulation of fine-mode particulate matter (PM 2.5) matched observations well. The simulation of primary aerosols (normalized mean biases, NMBs, are within ±0.64) is better than that of secondary aerosols (NMB values are greater than 1.0 in some regions). For the nested regional simulation, the IAP-AACM shows the superiority of higher-resolution simulation using the nested domain over East Asia. The model reproduced variation of sulfur dioxide (SO2), nitrogen dioxide (NO2), and PM 2.5 accurately in typical cities, with correlation coefficients (R) above 0.5 and NMBs within ±0.5. Compared with the global simulation, the nested simulation exhibits an improved ability to capture the high temporal and spatial variability over China. In particular, the R values for SO2 , NO2 and PM 2.5 are increased by ∼0.15 , ∼0.2 , and ∼0.25 respectively in the nested grid. Based on the evaluation and analysis, future model improvements are suggested. [ABSTRACT FROM AUTHOR]

In order to optimize surface CO2 fluxes at grid scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the ensemble Kalman smoother (EnKS) to optimize the surface CO2 fluxes. The smoothing operator is associated with the atmospheric transport model to constitute a persistence dynamical model to forecast the surface CO2 flux scaling factors. In this implementation, the "signalto-noise" problem can be avoided; plus, any useful observed information achieved by the current assimilation cycle can be transferred into the next assimilation cycle. Thus, the surface CO2 fluxes can be optimized as a whole at the grid scale in CFI-CMAQ. The performance of CFI-CMAQ was quantitatively evaluated through a set of Observing System Simulation Experiments (OSSEs) by assimilating CO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The results showed that the CO2 concentration assimilation using EnKF could constrain the CO2 concentration effectively, illustrating that the simultaneous assimilation of CO2 concentrations can provide convincing CO2 initial analysis fields for CO2 flux inversion. In addition, the CO2 flux optimization using EnKS demonstrated that CFI-CMAQ could, in general, reproduce true fluxes at grid scales with acceptable bias. Two further sets of numerical experiments were conducted to investigate the sensitivities of the inflation factor of scaling factors and the smoother window. The results showed that the ability of CFI-CMAQ to optimize CO2 fluxes greatly relied on the choice of the inflation factor. However, the smoother window had a slight influence on the optimized results. CFICMAQ performed very well even with a short lag-window (e.g. 3 days). [ABSTRACT FROM AUTHOR]