Simulated Aerosol Optical Depth Research Articles

Light Precipitation rather than Total Precipitation Determines Aerosol Wet Removal.

Precipitation scavenging is an important sink for both organic and inorganic pollutants in the atmosphere. However, there has been controversy over the relative importance of different precipitation characteristics (i.e., the precipitation amount, intensity, frequency, and duration) in the removal of air pollutants. It is critical to reach a consensus on which precipitation characteristics are most significant for aerosol wet removal. In this study, the analysis of multisource in situ observations of aerosol wet deposition worldwide indicates that the precipitation frequency plays a first-order role in scavenging aerosols. This is because large amounts of air pollutants are efficiently removed at precipitation initiation. As the duration and amount of precipitation increase, the scavenging efficiency decreases sharply. Consequently, it is featured that light precipitation, due to its high frequency of occurrence, rather than total precipitation, determines climatological aerosol wet deposition. To further confirm this, a state-of-the-art global climate model with convection resolved is modified to reduce the light precipitation frequency in both stratiform and convective cloud regimes. Results show widespread increases in aerosol optical depth (AOD) due to the reduced wet deposition. The spatial distribution of aerosol wet deposition changes resembles that of changes in the light precipitation frequency rather than that of total precipitation changes. These findings are consistent with those from the Coupled Model Intercomparison Project Phase 6 multimodel simulations, which show that the intermodel uncertainty in simulated AOD correlates more strongly with the uncertainty in light precipitation frequency than with that in total precipitation.

Optical and physical characteristics of aerosols over Asia: AERONET, MERRA-2 and CAMS

A comprehensive study on the regional and spatial distributions of aerosol columnar optical and physical characteristics utilizing high-quality Aerosol Robotic Network (AERONET) datasets over Asia (Central, South, South-East, and East Asia), along with spatiotemporal collocated validation of two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated aerosol optical depth (AOD) and Ångström exponent (AE) on annual and seasonal scales, is conducted for the first time. AOD is the highest over South Asia in each season, followed by South-East, East, and Central Asia. Annual regional average AOD at 0.50 μm over South Asia is 0.61. Combined influence of both fine mode anthropogenic aerosol emissions from fossil fuel combustion and biomass burning, and coarse mode dust aerosols from seasonal transport lead to higher AOD and total volume concentration (TVC), and exhibit significant spatiotemporal variations. Coarse volume fraction (CVF) and effective radius (Reff) are higher over Central Asia due to the dominance of coarse dust aerosols. East and South-East Asia are dominated by fine mode aerosols and result in higher fine volume concentration (FVC), AE, fine mode fraction (FMF), and lower Reff. Compared to the other regions of the globe (except Africa), AOD is higher over Asia, with higher spatiotemporal variabilities in AOD, AE, FMF, and TVC. Over Asia, the agreement between AERONET and CAMS AOD is better than that of AERONET and MERRA-2 AOD. For high AOD conditions, underestimation in model AODs is higher, and lower fraction of model AODs satisfy the Global Climate Observing System (GCOS) requirement over all regions, and these are more pronounced over Asia. Biases in model AODs are higher over Asia compared to the other regions of the globe, and lower over North America, Europe, and Australia. Higher bias in model AEs compared to AODs over all the regions shows substantial challenges in simulating spectral AOD and appropriate contributions of fine and coarse mode aerosols. These findings over a global aerosol hotspot region, Asia, along with other regions of the globe are crucial for accurate simulation and fine-tuning of aerosol characteristics by regional and global models, and for reducing uncertainties in the assessment of radiative and climate impact of aerosols.

Investigation of observed dust trends over the Middle East region in NASA Goddard Earth Observing System (GEOS) model simulations

Abstract. Satellite observations and ground-based measurements have indicated a high variability in the aerosol optical depth (AOD) in the Middle East region in recent decades. In the period that extends from 2003 to 2012, observations show a positive AOD trend of 0.01–0.04 per year or a total increase of 0.1–0.4 per decade. This study aimed to investigate if the observed trend was also captured by the NASA Goddard Earth Observing System (GEOS) model. To this end, we examined changes in the simulated dust emissions and dust AOD during this period. To understand the factors driving the increase in AOD in this region we also examined meteorological and surface parameters important for dust emissions, such as wind fields and soil moisture. Two GEOS model simulations were used in this study: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis (with meteorological and aerosol AOD data assimilated) and MERRA-2 Global Modeling Initiative (GMI) Replay (with meteorology constrained by the MERRA-2 reanalysis but without aerosol assimilation). We did not find notable changes in the modeled 10 m wind speed and soil moisture. However, analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data did show an average decrease of 8 % per year in the region encompassing Syria and Iraq, which prompted us to quantify the effects of vegetation on dust emissions and AOD in the Middle East region. This was done by performing a sensitivity experiment in which we enhanced dust emissions in grid cells where the NDVI decreased. The simulation results supported our hypothesis that the loss of vegetation cover and the associated increase in dust emissions over Syria and Iraq can partially explain the increase in AOD downwind. The model simulations indicated dust emissions need to be 10-fold larger in those grid cells in order to reproduce the observed AOD and trend in the model.

Evaluation of WRF-Chem-simulated meteorology and aerosols over northern India during the severe pollution episode of 2016

Abstract. We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µm) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s−1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r>0.9). We evaluate modelled PM2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM2.5 during the severe episode. Delhi experiences some of the highest daily mean PM2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM2.5 levels during severe pollution episodes.

How well do Earth system models reproduce the observed aerosol response to rapid emission reductions? A COVID-19 case study

Abstract. The spring 2020 COVID-19 lockdowns led to a rapid reduction in aerosol and aerosol precursor emissions. These emission reductions provide a unique opportunity for model evaluation and to assess the potential efficacy of future emission control measures. We investigate changes in observed regional aerosol optical depth (AOD) during the COVID-19 lockdowns and use these observed anomalies to evaluate Earth system model simulations forced with COVID-19-like reductions in aerosols and greenhouse gases. Most anthropogenic source regions do not exhibit statistically significant changes in satellite retrievals of total or dust-subtracted AOD, despite the dramatic economic and lifestyle changes associated with the pandemic. Of the regions considered, only India exhibits an AOD anomaly that exceeds internal variability. Earth system models reproduce the observed responses reasonably well over India but initially appear to overestimate the magnitude of response in East China and when averaging over the Northern Hemisphere (0–70∘ N) as a whole. We conduct a series of sensitivity tests to systematically assess the contributions of internal variability, model input uncertainty, and observational sampling to the aerosol signal, and we demonstrate that the discrepancies between observed and simulated AOD can be partially resolved through the use of an updated emission inventory. The discrepancies can also be explained in part by characteristics of the observational datasets. Overall our results suggest that current Earth system models have potential to accurately capture the effects of future emission reductions.

Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)

Abstract. The Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, which controls the sources, sinks, and chemistry of aerosols within the Goddard Earth Observing System (GEOS), recently underwent a major refactoring and update, including a revision of the emissions datasets and the addition of brown carbon. A 4-year benchmark simulation utilizing the new version of the model code, termed GOCART Second Generation (GOCART-2G) and coupled to the Goddard Earth Observing System (GEOS) model, was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development. A comparison of simulated aerosol optical depth between GOCART-2G and MODIS retrievals indicates the model captures the overall spatial pattern and seasonal cycle of aerosol optical depth but overestimates aerosol extinction over dusty regions and underestimates aerosol extinction over Northern Hemisphere boreal forests, requiring further investigation and tuning of emissions. This MODIS-based analysis is corroborated by comparisons to MISR and selected AERONET stations; however, discrepancies between the Aqua and Terra satellites indicate there is a diurnal component to biases in aerosol optical depth over southern Asia and northern Africa. Despite the underestimate of aerosol optical depth in biomass burning regions in GEOS, there is an overestimate in the surface mass of organic carbon in the United States, especially during the summer months. Over Europe, GOCART-2G is unable to match the summertime peak in aerosol optical depth, opposing the observed late fall and early spring peaks in surface mass concentration. A comparison of the vertical profile of attenuated backscatter to observations from CALIPSO indicates the GEOS model is capable of capturing the vertical profile of aerosol; however, the mid-troposphere plumes of dust in the North Atlantic and smoke in the southeastern Atlantic are perhaps too low in altitude. The results presented highlight priorities for future development with GOCART-2G, including improvements for dust, biomass burning aerosols, and anthropogenic aerosols.

Monitoring biomass burning aerosol transport using CALIOP observations and reanalysis models: a Canadian wildfire event in 2019

Abstract. In May–June 2019, smoke plumes from wildfires in Alberta, Canada, were advected all the way to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the spaceborne lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) were used. The plumes were located with the help of a trajectory analysis, and the masses of smoke aerosols were retrieved from the CALIOP observations. The accuracy of the CALIOP mass retrievals was compared with the accuracy of ground-based lidars/ceilometer near the source in North America and after the long-range transport in Europe. Overall, CALIOP and the ground-based lidars/ceilometer produced comparable results. Over North America the CALIOP layer mean mass was 30 % smaller than the ground-based estimates, whereas over southern Europe that difference varied between 12 % and 43 %. Finally, the CALIOP mass retrievals were compared with simulated aerosol concentrations from two reanalysis models: MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) and CAMS (Copernicus Atmospheric Monitoring System). The simulated total column aerosol optical depths (AODs) and the total column mass concentration of smoke agreed quite well with CALIOP observations, but the comparison of the layer mass concentration of smoke showed significant discrepancies. The amount of smoke aerosols in the model simulations was consistently smaller than in the CALIOP retrievals. These results highlight the limitations of such models and more specifically their limitation to reproduce properly the smoke vertical distribution. They indicate that CALIOP is a useful tool monitoring smoke plumes over secluded areas, whereas reanalysis models have difficulties in representing the aerosol mass in these plumes. This study shows the advantages of spaceborne aerosol lidars, e.g., being of paramount importance to monitor smoke plumes, and reveals the urgent need of future lidar missions in space.

Expanding the simulation of East Asian super dust storms: physical transport mechanisms impacting the western Pacific

Abstract. Dust models are widely applied over the East Asian region for the simulation of dust emission, transport, and deposition. However, due to the uncertainties in estimates of dust transport, these methods still lack the necessary precision to capture the complexity of transboundary dust events. This study demonstrates an improvement in the Community Multiscale Air Quality (CMAQ) model dust treatment during long-range transport of dust from northwestern China to the South China Sea (SCS). To accomplish this, we considered a super dust storm (SDS) event in March 2010 and evaluated the dust scheme by including adjustments to the recent calibration (Dust_Refined_1) and bulk density (Dust_Refined_2) refinements individually and in combination (Dust_Refined_3). The Dust_Refined_3 normalized mean bias of PM10 was −30.65 % for the 2010 SDS event, which was lower in magnitude compared to Dust_Refined_1 (−41.18 %) and Dust_Refined_2 (−49.88 %). Indeed, Dust_Refined_3 improved the simulated aerosol optical depth (AOD) value during significant dust cases, e.g., in March 2005, March 2006, and April 2009. Dust_Refined_3 also showed more clearly that, in March 2010, a “double plume” (i.e., one plume originating from the Taiwan Strait and the other from the western Pacific) separated by the Central Mountain Range (CMR) of Taiwan affected dust transport on the island of Dongsha in the SCS. On 15–21 April 2021, both CMAQ simulations and satellite data highlighted the influence of Typhoon Surigae on dust transport to downwind Taiwan and the western Pacific Ocean (WPO). The CMAQ Dust_Refined_3 simulations further revealed that many dust aerosols were removed over the WPO due to Typhoon Surigae. Hence, the model indicated a near-zero dust particle concentration over the WPO, which was significantly different from previous dust transport episodes over the Taiwan region. Therefore, our study suggested an effective method to improve dust management of CMAQ under unique topographical and meteorological conditions.

Measurement report: Assessing the impacts of emission uncertainty on aerosol optical properties and radiative forcing from biomass burning in peninsular Southeast Asia

Abstract. Despite significant advancements in improving the dataset for biomass burning (BB) emissions over the past few decades, uncertainties persist in BB aerosol emissions, impeding the accurate assessment of simulated aerosol optical properties (AOPs) and direct radiative forcing (DRF) during wildfire events in global and regional models. This study assessed AOPs (including aerosol optical depth (AOD), aerosol absorption optical depth (AAOD), and aerosol extinction coefficients (AECs)) and DRF using eight independent BB emission inventories applied to the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) during the BB period (March 2019) in peninsular Southeast Asia (PSEA), where the eight BB emission inventories were the Global Fire Emissions Database version 4.1s (GFED), Fire INventory from NCAR version 1.5 (FINN1.5), the Fire Inventory from NCAR version 2.5 MOS (MODIS fire detections; FINN2.5 MOS), the Fire Inventory from NCAR version 2.5 MOSVIS (MODIS + VIIRS fire detections; FINN2.5 MOSVIS), Global Fire Assimilation System version 1.2s (GFAS), Fire Energetics and Emissions Research version 1.0 (FEER), Quick Fire Emissions Dataset version 2.5 release 1 (QFED), and Integrated Monitoring and Modelling System for Wildland FIRES project version 2.0 (IS4FIRES), respectively. The results show that in the PSEA region, organic carbon (OC) emissions in the eight BB emission inventories differ by a factor of about 9 (0.295–2.533 Tg M−1), with 1.09 ± 0.83 Tg M−1 and a coefficient of variation (CV) of 76 %. High-concentration OC emissions occurred primarily in savanna and agricultural fires. The OC emissions from the GFED and GFAS are significantly lower than the other inventories. The OC emissions in FINN2.5 MOSVIS are approximately twice as high as those in FINN1.5. Sensitivity analysis of AOD simulated by WRF-Chem to different BB emission datasets indicated that the FINN scenarios (v1.5 and 2.5) significantly overestimate AOD compared to observation (VIIRS), while the other inventories underestimate AOD in the high-AOD (HAOD; AOD > 1) regions range from 15–22.5∘ N, 97–110∘ E. Among the eight schemes, IS4FIRES and FINN1.5 performed better in terms of AOD simulation consistency and bias in the HAOD region when compared to AERONET sites. The AAOD in WRF-Chem during the PSEA wildfire period was assessed, using satellite observations (TROPOMI) and AERONET data, and it was found that the AAOD simulated with different BB schemes did not perform as well as the AOD. The significant overestimation of AAOD by FINN (v1.5 and 2.5), FEER, and IS4FIRES schemes in the HAOD region, with the largest overestimation for FINN2.5 MOSVIS. FINN1.5 schemes performed better in representing AAOD at AERONET sites within the HAOD region. The simulated AOD and AAOD from FINN2.5 MOSVIS always show the best correlation with the observations. AECs simulated by WRF-Chem with all the eight BB schemes trends were consistent with CALIPSO in the vertical direction (0.5 to 4 km), demonstrating the efficacy of the smoke plume rise model used in WRF-Chem to simulate smoke plume heights. However, the FINN (v1.5 and 2.5) schemes overestimated AECs, while the other schemes underestimated it. In the HAOD region, BB aerosols exhibited a daytime shortwave radiative forcing of −32.60 ± 24.50 W m−2 at the surface, positive forcing (1.70 ± 1.40 W m−2) in the atmosphere, and negative forcing (−30.89 ± 23.6 W m−2) at the top of the atmosphere. Based on the analysis, FINN1.5 and IS4FIRES are recommended for accurately assessing the impact of BB on air quality and climate in the PSEA region.

Control of the Dust Vertical Distribution over Western Africa by Convection and Scavenging

Saharan dust represents more than 50% of the total desert dust emitted around the globe and its radiative effect significantly affects the atmospheric circulation at a continental scale. Previous studies on dust vertical distribution and the Saharan Air Layer (SAL) showed some shortcomings that could be attributed to imperfect representation of the effects of deep convection and scavenging. The authors investigate here the role of deep convective transport and scavenging on the vertical distribution of mineral dust over Western Africa. Using multi-year (2006–2010) simulations performed with the variable-resolution (zoomed) version of the LMDZ climate model. Simulations are compared with aerosol amounts recorded by the Aerosol Robotic Network (AERONET) and with vertical profiles of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements. LMDZ allows a thorough examination of the respective roles of deep convective transport, convective and stratiform scavenging, boundary layer transport, and advection processes on the vertical mineral dust distribution over Western Africa. The comparison of simulated dust Aerosol Optical Depth (AOD) and distribution with measurements suggest that scavenging in deep convection and subsequent re-evaporation of dusty rainfall in the lower troposphere are critical processes for explaining the vertical distribution of desert dust. These processes play a key role in maintaining a well-defined dust layer with a sharp transition at the top of the SAL and in establishing the seasonal cycle of dust distribution. This vertical distribution is further reshaped offshore in the Inter-Tropical Convergence Zone (ITCZ) over the Atlantic Ocean by marine boundary layer turbulent and convective transport and wet deposition at the surface.

Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties

Abstract. Accurate representation of aerosol optical properties is essential for the modeling and remote sensing of atmospheric aerosols. Although aerosol optical properties are strongly dependent upon the aerosol size distribution, the use of detailed aerosol microphysics schemes in global atmospheric models is inhibited by associated computational demands. Computationally efficient parameterizations for aerosol size are needed. In this study, airborne measurements over the United States (DISCOVER-AQ) and South Korea (KORUS-AQ) are interpreted with a global chemical transport model (GEOS-Chem) to investigate the variation in aerosol size when organic matter (OM) and sulfate–nitrate–ammonium (SNA) are the dominant aerosol components. The airborne measurements exhibit a strong correlation (r=0.83) between dry aerosol size and the sum of OM and SNA mass concentration (MSNAOM). A global microphysical simulation (GEOS-Chem-TOMAS) indicates that MSNAOM and the ratio between the two components (OM/SNA) are the major indicators for SNA and OM dry aerosol size. A parameterization of the dry effective radius (Reff) for SNA and OM aerosol is designed to represent the airborne measurements (R2=0.74; slope = 1.00) and the GEOS-Chem-TOMAS simulation (R2=0.72; slope = 0.81). When applied in the GEOS-Chem high-performance model, this parameterization improves the agreement between the simulated aerosol optical depth (AOD) and the ground-measured AOD from the Aerosol Robotic Network (AERONET; R2 from 0.68 to 0.73 and slope from 0.75 to 0.96). Thus, this parameterization offers a computationally efficient method to represent aerosol size dynamically.

Volcanic Drivers of Stratospheric Sulfur in GFDL ESM4

AbstractStratospheric injections of sulfur dioxide from major volcanic eruptions perturb the Earth's global radiative balance and dominate variability in stratospheric sulfur loading. The atmospheric component of the GFDL Earth System Model (ESM4.1) uses a bulk aerosol scheme and previously prescribed the distribution of aerosol optical properties in the stratosphere. To quantify volcanic contributions to the stratospheric sulfur cycle and the resulting climate impact, we modified ESM4.1 to simulate stratospheric sulfate aerosols prognostically. Driven by explicit volcanic emissions of aerosol precursors and non‐volcanic sources, we conduct ESM4.1 simulations from 1989 to 2014, with a focus on the Mt. Pinatubo eruption. We evaluate our interactive representation of the stratospheric sulfur cycle against data from Moderate Resolution Imaging Spectroradiometer, Multi‐angle Imaging SpectroRadiometer, Advanced Very High Resolution Radiometer, High Resolution Infrared Radiation Sounder, and Stratospheric Aerosol and Gas Experiment II. To assess the key processes associated with volcanic aerosols, we performed a sensitivity analysis of sulfate burden from the Mt. Pinatubo eruption by varying injection heights, emission amount, and stratospheric sulfate's dry effective radius. We find that the simulated stratospheric sulfate mass burden and aerosol optical depth in the model are sensitive to these parameters, especially volcanic SO2 injection height, and the optimal combination of parameters depends on the metric we evaluate.

Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol–Cloud Microphysical–Radiation Interactions

In this work, we report the ongoing implementation of online-coupled aerosol–cloud microphysical–radiation interactions in the Brazilian global atmospheric model (BAM) and evaluate the initial results, using remote-sensing data for JFM 2014 and JAS 2019. Rather than developing a new aerosol model, which incurs significant overheads in terms of fundamental research and workforce, a simplified aerosol module from a preexisting global aerosol–chemistry–climate model is adopted. The aerosol module is based on a modal representation and comprises a suite of aerosol microphysical processes. Mass and number mixing ratios, along with dry and wet radii, are predicted for black carbon, particulate organic matter, secondary organic aerosols, sulfate, dust, and sea salt aerosols. The module is extended further to include physically based parameterization for aerosol activation, vertical mixing, ice nucleation, and radiative optical properties computations. The simulated spatial patterns of surface mass and number concentrations are similar to those of other studies. The global means of simulated shortwave and longwave cloud radiative forcing are comparable with observations with normalized mean biases ≤11% and ≤30%, respectively. Large positive bias in BAM control simulation is enhanced with the inclusion of aerosols, resulting in strong overprediction of cloud optical properties. Simulated aerosol optical depths over biomass burning regions are moderately comparable. A case study simulating an intense biomass burning episode in the Amazon is able to reproduce the transport of smoke plumes towards the southeast, thus showing a potential for improved forecasts subject to using near-real-time remote-sensing fire products and a fire emission model. Here, we rely completely on remote-sensing data for the present evaluation and restrain from comparing our results with previous results until a complete representation of the aerosol lifecycle is implemented. A further step is to incorporate dry deposition, in-cloud and below-cloud scavenging, sedimentation, the sulfur cycle, and the treatment of fires.

Investigation of aerosol direct radiative forcing during a dust storm using a regional climate model over Turkiye

Dust particles originating from arid zones can be transported long distances and change the radiation budget. Understanding atmospheric aerosols and their radiative forcing is important to determine their global and regional climate effects. The aim of this study is to simulate the dust transport event that occurred on March 22, 2018, using RegCM-4.5 and investigate the radiative forcing effect of Saharan dust particles. The model was run with the 4 dust bin chemistry option and the direct effect of aerosols on the regional radiation balance was investigated. Validation of the model was made by a comparison between simulated aerosol optical depth (AOD) and MODIS satellite data. The radiative forcing at the top and surface was calculated at all sky conditions. During the dust episode, the shortwave radiative forcing was ranging between -3 and -50 W/m2 at surface and -2 and -22 W/m2 at top of atmosphere while the longwave radiative forcing was between 0.5 and 8 W/m2 at surface and 0.2 and 2 W/m2 at top of atmosphere over Turkiye. In Istanbul it produced a cooling up to -18 W/m2 at surface and -9 W/m2 at top of atmosphere, a warming up to 3.5 W/m2 at surface and 0.6 W/m2 at top of atmosphere.

Statistical constraints on climate model parameters using a scalable cloud-based inference framework

Abstract Atmospheric aerosols influence the Earth’s climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining the parameters of expensive climate models by comparing model outputs with observations. Using the C3.AI Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with 2 weeks’ worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every 3 hr and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods.

Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020

Investigating spatiotemporal variations of atmospheric aerosols is important for climate change and environmental research. Although satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Implementation of Atmospheric Correct) algorithm provides a unique opportunity to represent global aerosol loading with high spatiotemporal resolution, accurate assessment of long-term aerosol loading countrywide is still challenging due to its non-random missingness. This study aimed to develop an adaptive spatiotemporal high-resolution imputation modeling framework for AOD that incorporates random forest models and multisource data (the simulated AOD, meteorological, and surface condition data) to support full-coverage long- and short-term aerosol studies in China. Aided by the time-stratified approach, the imputation model was constructed for each day, and the MAIAC AOD was used as the target variable. The proposed approach could effectively capture the massive spatiotemporal variability in a large amount of data and deliver full-coverage AODs with high accuracies at a daily timescale (i.e., overall validation R2 against ground-level AOD measurements of 0.77). We then employed the proposed approach to impute the daily MAIAC retrieved AOD towards complete coverage for China for 2003–2020. Due to the complete coverage, the spatial pattern of monthly/seasonal/yearly mean AOD imputations has better representativeness than that of original MAIAC retrievals. Comparison analysis shows that the monthly/seasonal/yearly aerosol loading over most of China tends to be underestimated by temporal aggregates of original satellite-retrieved AODs. Such underestimation is particularly severe in summer and over the North China Plain (the amount of underestimation >0.2). Consequently, our full-coverage AOD imputations can advance scientific research and environmental management by supporting national and local complete pictures of both short-term episodes and long-term trends in atmospheric aerosols.

Retrieved XCO2 Accuracy Improvement by Reducing Aerosol-Induced Bias for China’s Future High-Precision Greenhouse Gases Monitoring Satellite Mission

China is developing the High-precision Greenhouse gases Monitoring Satellite (HGMS), carrying a high-spectral-resolution lidar (HSRL) for aerosol vertical profiles and imaging grating spectrometers for CO2 measurements at the same time. By providing simultaneous evaluation of the aerosol scattering effect, HGMS would reduce the bias of the XCO2 retrievals from the passive sensor. In this work, we propose a method to reduce aerosol-induced bias in XCO2 retrievals for the future HGMS mission based on the correlation analysis among simulated radiance, XCO2 bias, and aerosol optical depth (AOD) ratio. We exercise the method with the Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals and AOD ratio inferred from the OCO-2 O2 A-band aerosol parameters at 755 nm and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD at 532 nm at several Total Carbon Column Observing Network (TCCON) sites in Europe. The results showed that 80% of measurements from OCO-2 were improved, and data from six TCCON sites show an average of 2.6 ppm reduction in mean bias and a 68% improvement in accuracy. We demonstrate the advantage of fused active–passive observation of the HGMS for more accurate global XCO2 measurements in the future.

Description and evaluation of the tropospheric aerosol scheme in the Integrated Forecasting System (IFS-AER, cycle 47R1) of ECMWF

Abstract. This article describes the Integrated Forecasting System aerosol scheme (IFS-AER) used operationally in the IFS cycle 47R1, which was operated by the European Centre for Medium Range Weather Forecasts (ECMWF) in the framework of the Copernicus Atmospheric Monitoring Services (CAMS). It represents an update of the Rémy et al. (2019) article, which described cycle 45R1 of IFS-AER in detail. Here, we detail only the parameterisations of sources and sinks that have been updated since cycle 45R1, as well as recent changes in the configuration used operationally within CAMS. Compared to cycle 45R1, a greater integration of aerosol and chemistry has been achieved. Primary aerosol sources have been updated, with the implementation of new dust and sea salt aerosol emission schemes. New dry and wet deposition parameterisations have also been implemented. Sulfate production rates are now provided by the global chemistry component of IFS. This paper aims to describe most of the updates that have been implemented since cycle 45R1, not just the ones that are used operationally in cycle 47R1; components that are not used operationally will be clearly flagged. Cycle 47R1 of IFS-AER has been evaluated against a wide range of surface and total column observations. The final simulated products, such as particulate matter (PM) and aerosol optical depth (AOD), generally show a significant improvement in skill scores compared to results obtained with cycle 45R1. Similarly, the simulated surface concentration of sulfate, organic matter and sea salt aerosol are improved by cycle 47R1 compared to cycle 45R1. Some biases persist, such as the surface concentrations of nitrate and organic matter being simulated too high. The new wet and dry deposition schemes that have been implemented into cycle 47R1 have a mostly positive impact on simulated AOD, PM and speciated aerosol surface concentration.

Description and evaluation of a secondary organic aerosol and new particle formation scheme within TM5-MP v1.2

Abstract. We have implemented and evaluated a secondary organic aerosol scheme within the chemistry transport model TM5-MP in this work. In earlier versions of TM5-MP the secondary organic aerosol (SOA) was emitted as Aitken-sized particle mass emulating the condensation. In the current scheme we simulate the formation of secondary organic aerosol from oxidation of isoprene and monoterpenes by ozone and hydroxyl radicals, which produce semi-volatile organic compounds (SVOCs) and extremely low-volatility compounds (EVOCs). Subsequently, SVOCs and ELVOCs can condense on particles. Furthermore, we have introduced a new particle formation mechanism depending on the concentration of ELVOCs. For evaluation purposes, we have simulated the year 2010 with the old and new scheme; we see an increase in simulated production of SOA from 39.9 Tg yr−1 with the old scheme to 52.5 Tg yr−1 with the new scheme. For more detailed analysis, the particle mass and number concentrations and their influence on the simulated aerosol optical depth are compared to observations. Phenomenologically, the new particle formation scheme implemented here is able to reproduce the occurrence of observed particle formation events. However, the modelled concentrations of formed particles are clearly lower than in observations, as is the subsequent growth to larger sizes. Compared to the old scheme, the new scheme increases the number concentrations across the observation stations while still underestimating the observations. The organic aerosol mass concentrations in the US show a much better seasonal cycle and no clear overestimation of mass concentrations anymore. In Europe the mass concentrations are lowered, leading to a larger underestimation of observations. Aerosol optical depth (AOD) is generally slightly increased except in the northern high latitudes. This brings the simulated annual global mean AOD closer to the observational estimate. However, as the increase is rather uniform, biases tend to be reduced only in regions where the model underestimates the AOD. Furthermore, the correlations with satellite retrievals and ground-based sun-photometer observations of AOD are improved. Although the process-based approach to SOA formation causes a reduction in model performance in some areas, overall the new scheme improves the simulated aerosol fields.