Model For Simulating Aerosol Interactions And Chemistry Research Articles

Assessment of the impact of atmospheric aerosols and meteorological data assimilation on simulation of the weather over India during summer 2015

This study investigates the sensitivity of climate model-predicted surface radiation and surface meteorological parameters to atmospheric aerosols and meteorological data assimilation (meteorological initial and boundary conditions) utilizing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For this purpose, three sets of simulations including WRFChemCntrl (WRFChem without meteorological data assimilation), WRFChemDA (WRFChem with meteorological data assimilation) and WRFDA (WRF only with meteorological data assimilation) were performed over the South Asian domain. A 12-hourly cyclic 3-dimensional variational (3DVAR) meteorological data assimilation (DA) of in-situ meteorological observations was used to generate WRFChemDA and WRFDA reanalyses, during summer (March–May) 2015. The Carbon Bond Mechanism-Z (CBMZ) gas-phase chemistry with Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) was selected for the chemistry simulations. A reduction in incoming shortwave radiation (∼10–60 W/m2) over the Indian landmass and a slight increase (∼10–20 W/m2) over the surrounding oceanic region due to the influence of aerosols in WRF-Chem simulations was observed, suggesting that aerosols effects, were simulated by the model successfully. These effects of aerosols were further evident in the model output of other parameters such as outgoing longwave radiation at the surface, 2-m temperature (T2), 2-m relative humidity (RH2), and planetary boundary layer height (PBLH). Our results also show that the inclusion of aerosols in the WRFDA simulations (i.e WRFChemDA) improved the prediction of incoming shortwave radiation but deteriorated some of the meteorological parameters. However, an improved agreement between model simulated radiation, T2, RH2 and PBLH and observations was evident in WRFChemDA output compared to WRFChemCntrl output, reinforcing DA induced improvements in meteorological parameters for a given model set-up.

Chloride (HCl ∕ Cl−) dominates inorganic aerosol formation from ammonia in the Indo-Gangetic Plain during winter: modeling and comparison with observations

Abstract. The Winter Fog Experiment (WiFEX) was an intensive field campaign conducted at Indira Gandhi International Airport (IGIA) Delhi, India, in the Indo-Gangetic Plain (IGP) during the winter of 2017–2018. Here, we report the first comparison in South Asia of high-temporal-resolution simulation of ammonia (NH3) along with ammonium (NH4+) and total NHx (i.e., NH3+ NH4+) using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and measurements made using the Monitor for AeRosols and Gases in Ambient Air (MARGA) at the WiFEX research site. In the present study, we incorporated the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme into WRF-Chem. Despite simulated total NHx values and variability often agreeing well with the observations, the model frequently simulated higher NH3 and lower NH4+ concentrations than the observations. Under the winter conditions of high relative humidity (RH) in Delhi, hydrogen chloride (HCl) was found to promote the increase in the particle fraction of NH4+ (which accounted for 49.5 % of the resolved aerosol in equivalent units), with chloride (Cl−) (29.7 %) as the primary anion. By contrast, the absence of chloride (HCl / Cl−) chemistry in the standard WRF-Chem model results in the prediction of sulfate (SO42-) as the dominant inorganic aerosol anion. To understand the mismatch associated with the fraction of NHx in the particulate phase (NH4+ / NHx), we added HCl / Cl− to the model and evaluated the influence of its chemistry by conducting three sensitivity experiments using the model: no HCl, base case HCl (using a published waste burning inventory), and 3 × base HCl run. We found that 3 × base HCl increased the simulated average NH4+ by 13.1 µg m−3 and NHx by 9.8 µg m−3 concentration while reducing the average NH3 by 3.2 µg m−3, which is more in accord with the measurements. Thus HCl / Cl− chemistry in the model increases total NHx concentration, which was further demonstrated by reducing NH3 emissions by a factor of 3 (−3 × NH3_EMI) in the 3 × base HCl simulation. Reducing NH3 emissions in the 3 × base HCl simulation successfully addressed the discrepancy between measured and modeled total NHx. We conclude that modeling the fate of NH3 in Delhi requires a correct chemistry mechanism accounting for chloride dynamics with accurate inventories of both NH3 and HCl emissions.

Development and Evaluation of E3SM‐MOSAIC: Spatial Distributions and Radiative Effects of Nitrate Aerosol

AbstractNitrate aerosol plays an important role in affecting regional air quality as well as Earth's climate. However, it is not well represented or even neglected in many global climate models. In this study, we couple the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) module with the four‐mode version of the Modal Aerosol Module (MAM4) in DOE's Energy Exascale Earth System Model version 2 (E3SMv2) to simulate nitrate aerosol and its radiative effects. We find that nitrate aerosol simulated by E3SMv2‐MAM4‐MOSAIC is sensitive to the treatment of gaseous HNO3 transfer to/from interstitial particles related to accommodation coefficients of HNO3 () on dust and non‐dust particles. We compare three different treatments of HNO3 transfer: (a) a treatment (MTC_SLOW) that uses a low in the mass transfer coefficient (MTC) calculation; (b) a dust‐weighted MTC treatment (MTC_WGT) that uses a high on non‐dust particles; and (c) a dust‐weighted MTC treatment that also splits coarse mode aerosols into the coarse dust and sea salt sub‐modes in MOSAIC (MTC_SPLC). MTC_WGT and MTC_SPLC increase the global annual mean (2005–2014) nitrate burden from 0.096 (MTC_SLOW) to 0.237 and 0.185 Tg N, respectively, mostly in the coarse mode. MTC_WGT and MTC_SPLC also produce stronger nitrate direct radiative forcing (−0.048 and −0.051 W m−2, respectively) and indirect forcing (−0.33 and −0.35 W m−2, respectively) than MTC_SLOW (−0.021 and −0.24 W m−2). MTC_WGT and MTC_SPLC improve nitrate surface concentrations over remote oceans based on limited observations and vertical profiles of nitrate concentrations against aircraft measurements below 400 hPa.

3DVAR Aerosol Data Assimilation and Evaluation Using Surface PM2.5, Himawari-8 AOD and CALIPSO Profile Observations in the North China

Based on the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme of the Weather Research and Forecasting model coupled with online Chemistry (WRF-Chem) and the three-dimensional variational (3DVAR) assimilation method, a 3DVAR data assimilation (DA) system for aerosol optical depth (AOD) and aerosol concentration observations was developed. A case study on assimilating the Himawari-8 satellite AOD and/or fine particulate matter (PM2.5) was conducted to investigate the improvement of DA on the analysis accuracy and forecast skills of the spatial distribution characteristics of aerosols, especially in the vertical dimension. The aerosol extinction coefficient (AEC) profile data from The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), surface PM2.5 and Himawari-8 AOD measurements were used for verification. One control experiment (without DA) and two DA experiments including a PM2.5 DA experiment denoted by Da_PM and a combined PM2.5 and AOD DA experiment denoted by Da_AOD_PM were conducted. Both DA experiments had positive effects on the surface PM2.5 mass concentration forecast skills for more than 60 h. However, the Da_PM showed a slight improvement in the analysis accuracy of the AOD distribution compared with the control experiment, while the Da_AOD_PM showed a considerable improvement. The Da_AOD_PM had the best positive effect on the AOD forecast skills. The correlation coefficient (CORR), root mean square error (RMSE), and mean fraction error (MFE) of the 24 h AOD forecasts for the Da_AOD_PM were 0.73, 0.38, and 0.54, which are 0.09 (14.06%), 0.08 (17.39%), and 0.22 (28.95%) better than that of the control experiment, and 0.05 (7.35%), 0.06 (13.64%), and 0.19 (26.03%) better than that of the Da_PM, respectively. Moreover, improved performance for the Da_AOD_PM occurred when the AEC profile was used for verification, as when the AOD was used for verification. The Da_AOD_PM successfully simulated the first increasing and then decreasing trend of the aerosol extinction coefficients below 1 km, while neither the control nor the Da_PM did. This indicates that assimilating AOD can effectively improve the analyses and forecast accuracy of the aerosol structure in both the horizontal and vertical dimensions, thereby compensating for the limitations associated with assimilating traditional surface aerosol observations alone.

A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations

Abstract. This paper presents a three-dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived AOT data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from 23 to 29 November 2018, during which a heavy air pollution event occurred in northern China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM2.5 concentrations to be closer to the real distribution in northern China, which is of great value for studying heavy air pollution events.

Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model

Based on the three-dimensional variational assimilation (3DVAR) algorithm, the extinction coefficient profiles of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite were assimilated for the first time on the basis of the aerosol variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the Weather Research and Forecasting-Chemistry (WRF-Chem) model. A light aerosol pollution episode (case1) and a heavy pollution event (case2) were selected to simulate and predict using the WRF-Chem model. Two sets of data were assimilated: CALIPSO derived aerosol extinction coefficient profiles and surface PM mass concentration data obtained from ground monitoring stations across China. Then, various aerosol data sources including ground-based lidar in Beijing area, Himawari-8 (H-8) satellite aerosol optical depth (AOD) and surface PM monitors were used for comprehensive evaluate the impact of CALIPSO data assimilation on the model performance. The results showed that simultaneous assimilation of the CALIPSO extinction coefficient profiles and surface PM mass concentration data can effectively improve the model aerosol initial conditions and forecasting accuracy of PM2.5, PM10 and AOD. The positive impact on PM2.5 and PM10 forecasting by the assimilation can be maintained for more than 14 h for the case1 and 24 h for the case2, and the initial field of AOD in assimilation experiment has been greatly improved comparing the model control run (cn, without data assimilation). For the simulation of the case2, the assimilation of CALIPSO extinction coefficient profiles reduced the root mean square error (RMSE), mean fraction (MFE) and bias (BIAS) of the AOD in the initial field of the model by 0.08 (19.5%), 0.34 (55.7%) and 0.16 (48.5%) compared to the cn experiment, respectively. When the CALIPSO extinction coefficient profiles and surface PM mass concentration data were assimilated simultaneously, the reduction of the RMSE, MFE and BIAS of the AOD was 0.6 (14.6%), 0.32 (52.5%) and 0.12 (36.4%), respectively. Additionally, aerosol extinction coefficient are remarkably underestimated in the cn experiment, particularly during heavy aerosol pollution case. Assimilating surface PM data can improve the extinction coefficient distribution below 500 m. The simulated aerosol extinction coefficient distribution at 1 km to 2 km can be significantly improved in both cases by assimilating the CALIPSO extinction coefficient profiles, and the value of model simulated extinction coefficient is nearly doubled compared to the cn experiment. Finally, the evaluation with independent ground-based lidar measurements indicates that combined assimilation of surface PM mass concentration and CALIPSO extinction coefficient profile can effectively improve the aerosol distribution near the ground and in the upper layer, and thus generate a more accurate 3D aerosol analysis and forecast field.

Radiative Forcing of Nitrate Aerosols From 1975 to 2010 as Simulated by MOSAIC Module in CESM2‐MAM4

AbstractWe incorporate the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) module in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model Version 6 with interactive chemistry (CAM6‐chem), and couple it with the four mode version of the Modal Aerosol Module (MAM4). The MOSAIC module is used to simulate the thermodynamics of the gas‐aerosol mass exchange, with a special focus on simulating nitrate aerosol. By comparing against ground and satellite observations, we found that the MOSAIC/MAM4 scheme performs reasonably well in simulating spatiotemporal distributions of aerosols, including nitrate aerosol. We conducted a series of model experiments with and without nitrate aerosols, and examined the radiative effect (RE) associated with nitrate aerosols in 1975, 2000, and 2010, and accessed the radiative forcing (RF) of nitrate aerosols between the present day and pre‐industrial periods. Comparing with the nitrate aerosol RE, we predicted relatively small RF of anthropogenic nitrate aerosol from aerosol‐radiation interactions (RFari: −0.014 W m−2) and large RF from aerosol‐cloud interactions (RFaci: −0.219 W m−2). Regional signatures of nitrate RE/RF are noticeable and important: for instance, very small changes in REari in Europe and USA, but 2.8–3 times increases in REari in India and China from 1975 to 2010, while REaci/RFaci in China is a warming effect due to the competing effect between sulfate and nitrate aerosols as cloud condensation nuclei.

Humidity Dependence of the Condensational Growth of α-Pinene Secondary Organic Aerosol Particles.

The influence of relative humidity (RH) on the condensational growth of organic aerosol particles remains incompletely understood. Herein, the RH dependence was investigated via a series of experiments for α-pinene ozonolysis in a continuously mixed flow chamber in which recurring cycles of particle growth occurred every 7 to 8 h at a given RH. In 5 h, the mean increase in the particle mode diameter was 15 nm at 0% RH and 110 nm at 75% RH. The corresponding particle growth coefficients, representing a combination of the thermodynamic driving force and the kinetic resistance to mass transfer, increased from 0.35 to 2.3 nm2 s-1. The chemical composition, characterized by O:C and H:C atomic ratios of 0.52 and 1.48, respectively, and determined by mass spectrometry, did not depend on RH. The Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) was applied to reproduce the observed size- and RH-dependent particle growth by optimizing the diffusivities Db within the particles of the condensing molecules. The Db values increased from 5 α-1 × 10-16 at 0% RH to 2 α-1 × 10-12 cm-2 s-1 at 75% RH for mass accommodation coefficients α of 0.1 to 1.0, highlighting the importance of particle-phase properties in modeling the growth of atmospheric aerosol particles.

Time-dependent 3D simulations of tropospheric ozone depletion events in the Arctic spring using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem)

Abstract. Tropospheric bromine release and ozone depletion events (ODEs) as they commonly occur in the Arctic spring are studied using a regional model based on the open-source software package Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For this purpose, the MOZART (Model for Ozone and Related chemical Tracers)–MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) chemical reaction mechanism is extended by bromine and chlorine reactions as well as an emission mechanism for reactive bromine via heterogeneous reactions on snow surfaces. The simulation domain covers an area of 5040 km×4960 km, centered north of Utqiaġvik (formerly Barrow), Alaska, and the time interval from February through May 2009. Several simulations for different strengths of the bromine emission are conducted and evaluated by comparison with in situ and ozone sonde measurements of ozone mixing ratios as well as by comparison with tropospheric BrO vertical column densities (VCDs) from the Global Ozone Monitoring Experiment-2 (GOME-2) satellite instrument. The base bromine emission scheme includes the direct emission of bromine due to bromide oxidation by ozone. Results of simulations with the base emission rate agree well with the observations; however, a simulation with 50 % faster emissions performs somewhat better. The bromine emission due to bromide oxidation by ozone is found to be important to provide an initial seed for the bromine explosion. Bromine release due to N2O5 was found to be important from February to mid March but irrelevant thereafter. A comparison of modeled BrO with in situ and multi-axis differential optical absorption spectroscopy (MAX-DOAS) data hints at missing bromine release and recycling mechanisms on land or near coasts. A consideration of halogen chemistry substantially improves the prediction of the ozone mixing ratio with respect to the observations. Meteorological nudging is essential for a good prediction of ODEs over the 3-month period.

Development and Evaluation of Chemistry-Aerosol-Climate Model CAM5-Chem-MAM7-MOSAIC: Global Atmospheric Distribution and Radiative Effects of Nitrate Aerosol.

An advanced aerosol treatment, with a focus on semivolatile nitrate formation, is introduced into the Community Atmosphere Model version 5 with interactive chemistry (CAM5‐chem) by coupling the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) with the 7‐mode Modal Aerosol Module (MAM7). An important feature of MOSAIC is dynamic partitioning of all condensable gases to the different fine and coarse mode aerosols, as governed by mode‐resolved thermodynamics and heterogeneous chemical reactions. Applied in the free‐running mode from 1995 to 2005 with prescribed historical climatological conditions, the model simulates global distributions of sulfate, nitrate, and ammonium in good agreement with observations and previous studies. Inclusion of nitrate resulted in ∼10% higher global average accumulation mode number concentrations, indicating enhanced growth of Aitken mode aerosols from nitrate formation. While the simulated accumulation mode nitrate burdens are high over the anthropogenic source regions, the sea‐salt and dust modes respectively constitute about 74% and 17% of the annual global average nitrate burden. Regional clear‐sky shortwave radiative cooling of up to −5 W m−2 due to nitrate is seen, with a much smaller global average cooling of −0.05 W m−2. Significant enhancements in regional cloud condensation nuclei (at 0.1% supersaturation) and cloud droplet number concentrations are also attributed to nitrate, causing an additional global average shortwave cooling of −0.8 W m−2. Taking into consideration of changes in both longwave and shortwave radiation under all‐sky conditions, the net change in the top of the atmosphere radiative fluxes induced by including nitrate aerosol is −0.7 W m−2.

Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China

Abstract. The Gridpoint Statistical Interpolation data assimilation (DA) system was developed for the four size bin sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol mechanism in the Weather Research and Forecasting-Chemistry (WRF-Chem) model. The forward and tangent linear operators for the aerosol optical depth (AOD) analysis were derived from WRF-Chem aerosol optical code. We applied three-dimensional variational DA to assimilate the multi-wavelength AOD, ambient aerosol scattering coefficient, and aerosol absorption coefficient, measured by the sun–sky photometer, nephelometer, and aethalometer, respectively. These measurements were undertaken during a dust observation field campaign at Kashi in northwestern China in April 2019. The results showed that the DA analyses decreased the model aerosols' low biases; however, it had some deficiencies. Assimilating the surface particle concentration increased the coarse particles in the dust episodes, but AOD and the coefficients for aerosol scattering and absorption were still lower than those observed. Assimilating aerosol scattering coefficient separately from AOD improved the two optical quantities. However, it caused an overestimation of the particle concentrations at the surface. Assimilating the aerosol absorption coefficient yielded the highest positive bias in the surface particle concentration, aerosol scattering coefficient, and AOD. The positive biases in the DA analysis were caused by the forward operator underestimating aerosol mass scattering and absorption efficiency. As compensation, the DA system increased particle concentrations excessively to fit the observed optical values. The best overall improvements were obtained from the simultaneous assimilation of the surface particle concentration and AOD. The assimilation did not substantially change the aerosol chemical fractions. After DA, the clear-sky aerosol radiative forcing at Kashi was −10.4 W m−2 at the top of the atmosphere, which was 55 % higher than the radiative forcing value before DA.

Urbanization-Induced Land and Aerosol Impacts on Storm Propagation and Hail Characteristics

AbstractChanges in land surface and aerosol characteristics from urbanization can affect dynamic and microphysical properties of severe storms, thus affecting hazardous weather events resulting from these storms such as hail and tornadoes. We examine the joint and individual effects of urban land and anthropogenic aerosols of Kansas City on a severe convective storm observed during the 2015 Plains Elevated Convection At Night (PECAN) field campaign, focusing on storm evolution, convective intensity, and hail characteristics. The simulations are carried out at the cloud-resolving scale (1 km) using a version of WRF-Chem in which the spectral-bin microphysics (SBM) is coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). It is found that the urban land effect of Kansas City initiated a much stronger convective cell and the storm got further intensified when interacting with stronger turbulence induced by the urban land. The urban land effect also changed the storm path by diverting the storm toward the city, mainly resulting from enhanced urban land-induced convergence in the urban area and around the urban–rural boundaries. The joint effect of urban land and anthropogenic aerosols enhances occurrences of both severe hail and significant severe hail by ~20% by enhancing hail formation and growth from riming. Overall the urban land effect on convective intensity and hail is relatively larger than the anthropogenic aerosol effect, but the joint effect is more notable than either of the individual effects, emphasizing the importance of considering both effects in evaluating urbanization effects.

Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM<sub>2.5</sub> forecasts across China

Abstract. The authors developed a three-dimensional variational (3-DVAR) aerosol extinction coefficient (AEC) and aerosol mass concentration (AMC) data assimilation (DA) system for aerosol variables in the Weather Research and Forecasting–Chemistry (WRF–Chem) model with the WRF–Chem using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) scheme. They establish an AEC observation operator and its corresponding adjoint based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) equation and investigate the use of lidar AEC and surface AMC DA to forecast mass concentration (MC) profiles of PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) across China. Two sets of data were assimilated: AEC profiles captured by five conventional Mie scattering lidars (positioned in Beijing, Shijiazhuang, Taiyuan, Xuzhou, and Wuhu) and PM2.5 and PM10 MC data obtained from over 1500 ground environmental monitoring stations across China. Three DA experiments (i.e., a PM2.5 (PM10) DA experiment, a lidar AEC DA experiment, and a simultaneous PM2.5 (PM10) and lidar AEC DA experiment) with a 12 h assimilation period and a 24 h forecast period were conducted. The PM2.5 (PM10) DA reduced the root mean square error (RMSE) of the surface PM2.5 MC in the initial field of the model by 38.6 µg m−3 (64.8 %). When lidar AEC data were assimilated, this reduction was 10.5 µg m−3 (17.6 %), and a 38.4 µg m−3 (64.4 %) reduction occurred when the two data sets were assimilated simultaneously, although only five lidars were available within the simulation region (approximately 2.33 million km2 in size). The RMSEs of the forecasted surface PM2.5 MC 24 h after the DA period in the three DA experiments were reduced by 6.1 µg m−3 (11.8 %), 1.5 µg m−3 (2.9 %), and 6.5 µg m−3 (12.6 %), respectively, indicating that the assimilation and hence the optimization of the initial field have a positive effect on the PM2.5 MC forecast performance over a period of 24 h after the DA period.

Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies

Abstract. To improve the operational air quality forecasting over China, a new aerosol or gas-phase pollutants assimilation capability is developed within the WRFDA system using the three-dimensional variational (3DVAR) algorithm. In this first application, the interface for the MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme is built with the potential for flexible extension. Based on the new WRFDA-Chem system, five experiments assimilating different surface observations, including PM2.5, PM10, SO2, NO2, O3, and CO, are conducted for January 2017 along with a control experiment without data assimilation (DA). Results show that the WRFDA-Chem system evidently improves the air quality forecasting. From the analysis aspect, the assimilation of surface observations reduces the bias and RMSE in the initial condition (IC) remarkably; from the forecast aspect, better forecast performances are acquired up to 24 h, in which the experiment assimilating the six pollutants simultaneously displays the best forecast skill overall. With respect to the impact of the DA cycling frequency, the responses toward IC updating are found to be different among the pollutants. For PM2.5, PM10, SO2, and CO, the forecast skills increase with the DA frequency. For O3, although improvements are acquired at the 6 h cycling frequency, the advantage of more frequent DA could be consumed by the disadvantages of the unbalanced photochemistry (due to inaccurate precursor NOx ∕ VOC (volatile organic compound) ratios) or the changed titration process (due to changed NO2 concentrations but not NO) from assimilating the existing observations (only O3 and NO2, but no VOC and NO). As yet the finding is based on the 00:00 UTC forecast for this winter season only, and O3 has strong diurnal and seasonal variations. More experiments should be conducted to draw further conclusions. In addition, considering one aspect (IC) in the model is corrected by DA, the deficiencies of other aspects (e.g., chemical reactions) could be more evident. This study explores the model deficiencies by investigating the effects of assimilating gaseous precursors on the forecast of related aerosols. Results show that the parameterization (uptake coefficients) in the newly added sulfate–nitrate–ammonium (SNA)-relevant heterogeneous reactions in the model is not fully appropriate although it best simulates observed SNA aerosols without DA; since the uptake coefficients were originally tuned under the inaccurate gaseous precursor scenarios without DA, the biases from the two aspects (SNA reactions and IC DA) were just compensated. In future chemistry development, parameterizations (such as uptake coefficients) for different gaseous precursor scenarios should be adjusted and verified with the help of the DA technique. According to these results, DA ameliorates certain aspects by using observations as constraints and thus provides an opportunity to identify and diagnose the model deficiencies; it is useful especially when the uncertainties of various aspects are mixed up and the reaction paths are not clearly revealed. In the future, besides being used to improve the forecast through updating IC, DA could be treated as another approach to explore necessary developments in the model.

A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China

A three-dimensional variational (3DVAR) data assimilation (DA) system is presented here based on a size-resolved sectional aerosol model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. The use of this approach means that both gaseous pollutants such as SO2, NO2, CO, and O3 as well as particulate matter (PM2.5, PM10) observational data can be assimilated simultaneously. Two one-month parallel simulation experiments were conducted, one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre (CNEMC) and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts. Results show that, in the first place, use of the DA system can provide a more accurate model initial field. The root-mean-square error of PM2.5, PM10, SO2, NO2, CO, and O3 mass concentrations in analysis field fell by 29.27 μg m–3 (53.5%), 34.5 μg m–3 (50.9%), 30.36 μg m–3 (64.2%), 8.91 μg m–3 (39.5%), 0.46 mg m–3 (47.4%), and 15.11 μg m–3 (51.0%), respectively, compared to a background field without assimilation. At the same time, mean fraction error was reduced by 42.6%, 53.1%, 45.2%, 43.1%, 69.9%, and 48.8%, respectively, while the correlation coefficient increased by 0.51, 0.55, 0.48, 0.38, 0.47, 0.65, respectively. Secondly, the results of this analysis reveal variable benefits from assimilation on different pollutants. DA significantly improves PM2.5, PM10, and CO forecasts leading to positive effects that last more than 48 h. The positive effects of DA on SO2 and O3 forecasts last up to 8 h but that remains relatively poor for NO2 forecasts. Thirdly, the influence of assimilation varies in different areas. It is possible that the positive effects of DA on PM2.5 and PM10 forecasts can last more than 48 h across most regions of China. Indeed, DA significantly improves SO2 forecasts within 48 h over north China, and much longer CO assimilation benefits (48 h) are found in most regions apart from north and east China and across the Sichuan Basin. DA is able to improve O3 forecasts within 48 h across China with the exception of southwest and northwest regions and the O3 DA benefits in southern China are more evident, while from a spatial distribution perspective, NO2 DA benefits remain relatively poor.

Retrospective analysis of 2015–2017 wintertime PM<sub>2.5</sub> in China: response to emission regulations and the role of meteorology

Abstract. To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing

A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO3− are the most significant. The correlation coefficient between PM2.5 concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 μg·m−3, which respectively compared to those without DA.

Sensitivity of simulated chemical concentrations and aerosol‐meteorology interactions to aerosol treatments and biogenic organic emissions in WRF/Chem

AbstractCoupled air quality and climate models can predict aerosol concentrations and properties, as well as aerosol direct and indirect effects that depend on aerosol chemistry and microphysics treatments. In this study, Weather Research and Forecasting with Chemistry (WRF/Chem) simulations are conducted over continental U.S. (CONUS) for January and July 2001 with the same gas‐phase mechanism (CB05) but three aerosol modules (Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model (MADE/SORGAM), Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), and Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID)) to examine the impacts of aerosol treatments on predictions of aerosols and their effects on cloud properties and radiation. The simulations with the three aerosol modules give similar domain mean predictions of surface PM2.5 concentrations but exhibit a strong spatial variation in magnitudes with large differences in eastern U.S. Large discrepancies are found in the predicted concentrations of sulfate and organic matter due to different treatments in secondary inorganic and secondary organic aerosol (SOA) formation. In particular, the nucleation calculation in MADE/SORGAM causes mass buildup of sulfate which results in much higher sulfate concentrations that those predicted by WRF/Chem with the other two aerosol modules. Different PM mass concentrations and size representations lead to differences in the predicted aerosol number concentrations. The above differences in PM concentrations lead to large differences in simulated condensation nuclei (CCN) and cloud properties in both months. The simulated ranges of domain mean are (1.9–14.3) × 109 m−3 and (1.4–5.4) × 109 m−3 for PM2.5 number concentration, (1.6–3.9) × 108 cm−2 and (1.9–3.9) × 108 cm−2 for CCN, 102.9–208.2 cm−3 and 143.7–202.2 cm−3 for column cloud droplet number concentration (CDNC), and 4.5–6.4 and 3.6–6.7 for cloud optical depths (COT) in January and July, respectively. The sensitivity simulation for July 2001 using online biogenic emissions increases isoprene concentrations but decreases terpene concentrations, leading to a domain mean increase in O3 (1.5 ppb) and a decrease in biogenic SOA (−0.07 µg m−3) and PM2.5 (−0.2 µg m−3). Anthropogenic emissions contribute to O3, biogenic SOA (BSOA), and PM2.5 concentrations by 38.0%, 44.2%, and 53.6% domain mean and by up to 78.5%, 89.7%, and 96.3%, respectively, indicating that a large fraction of BSOA is controllable through controlling atmospheric oxidant levels in CONUS. Anthropogenic emissions also contribute to a decrease in downward shortwave flux at ground surface (−5.8 W m−2), temperature at 2 m (−0.05°C), wind speed at 10 m (−0.02 m s−1), planetary boundary layer height (−6.6 m), and precipitation (−0.08 mm), as well as an increase in CCN (+5.7 × 10−7 cm−2), in‐cloud CDNC (+40.4 cm−3), and COT (+0.6). This work indicates the need for an accurate representation of several aerosol processes such as SOA formation and aerosol‐cloud interactions in simulating aerosol direct and indirect effects in the online‐coupled models.

Black carbon simulations using a size‐ and mixing‐state‐resolved three‐dimensional model: 1. Radiative effects and their uncertainties

AbstractThis study quantifies how uncertainties in the size distribution and mixing state parameters of black carbon (BC) emissions translate into the uncertainties in BC radiative effects by using a particle‐size‐ and mixing‐state‐resolved three‐dimensional model, the Weather Research and Forecasting model with chemistry (WRF‐chem) with the Aerosol Two‐dimensional bin module for foRmation and Aging Simulation (ATRAS) and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). The WRF‐chem/ATRAS‐MOSAIC model can explicitly calculate BC processes in the atmosphere, such as BC aging due to condensation and coagulation and the resulting enhancement of absorption and cloud condensation nuclei activity, with 12 size and 10 BC mixing state bins (128 bins in total). Fifteen model simulations perturbing the emission parameters within their uncertainties are conducted over East Asia (spring 2009) to understand which parameters and processes are important and which are associated with the uncertainty in evaluating BC radiative effects. The simulations reveal a large variability (uncertainty) of BC optical and radiative variables over the East Asian region (the variability is 58–99%), which corresponds to ranges of BC radiative effect of 1.6–2.8 W m−2 at the top of the atmosphere and from −5.2 to −2.1 W m−2 at the surface over East Asia. BC optical and radiative variables are 3 to 5 times sensitive to the size and the mixing state in emissions than BC mass concentrations (the variability is 20%). The two main causes of the difference in sensitivity are the reduction of the variability of BC mass concentrations by coagulation and the enhancement of the variability of BC absorption by resolving BC mixing state. These complicated responses of aerosol processes can be calculated for the first time using a detailed aerosol model such as ATRAS. The results suggest that the following two points are important in the estimation of BC radiative effects: (1) reduction of the uncertainties in the aerosol size distribution and mixing state in emissions and (2) improvement of the representation of BC mixing state and absorption enhancement in aerosol models because most models do not treat them sufficiently.

Black carbon simulations using a size‐ and mixing‐state‐resolved three‐dimensional model: 2. Aging timescale and its impact over East Asia

AbstractThis study evaluates the aging timescale and the cloud condensation nuclei (CCN) activity of black carbon (BC) over East Asia and its outflow region using a size‐ and mixing‐state‐resolved three‐dimensional model, the Weather Research and Forecasting model with chemistry (WRF‐chem) with the Aerosol Two‐dimensional bin module for foRmation and Aging Simulation (ATRAS) and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). The WRF‐chem/ATRAS‐MOSAIC model explicitly calculates BC aging (condensation and coagulation) and removal processes, with 12 size and 10 BC mixing state bins (128 bins in total). The model reveals large spatial and temporal variability of the BC aging timescale and the CCN activity of BC‐containing particles over East Asia (spring 2009) with their strong size and supersaturation dependence. The BC aging timescale differs from 0.19 to 3.1 days (period and domain average at an altitude of 1 km), depending on the choice of size (mass or number) and supersaturation (1.0% or 0.1%). As a result, almost 100% of BC‐containing particles are CCN‐active at a supersaturation of 1.0%, whereas 20–50% of BC‐containing particles are CCN‐inactive at a supersaturation of 0.1%, with a strong size dependence. These results show the importance of resolving BC aging processes and their dependence on size and supersaturation in models for more accurate simulations of BC concentrations and their distribution and lifetime. A sensitivity simulation without resolving BC mixing state shows the underestimation of total BC mass concentrations by 5–10% and the BC mass concentrations in the CCN‐inactive particles (at a supersaturation of 0.1%) by 40–60% over the outflow region (at 150°E), compared with the simulation resolving BC mixing state. Because BC aging speed is very slow at 150°E and eastward, the change in BC mass and its CCN activity by resolving BC mixing state will continue over long distances and may have a large impact on BC transport from East Asia to remote regions such as North America and the Arctic.