Parameterization of below-cloud scavenging for polydisperse fine mode aerosols as a function of rain intensity

The below-cloud aerosol scavenging process by precipitation is one of the most important mechanisms to remove aerosols from the atmosphere. Due to its complexity and dependence on both aerosol and raindrop sizes, wet scavenging process has been poorly treated, especially during the removal of fine particles. This makes the numerical simulation of below-cloud scavenging in large-scale aerosol models unrealistic. To consider the slip effects of submicron particles, a simplified expression for the diffusion scavenging was developed by approximating the Cunningham slip correction factor. The derived analytic solution was parameterized as a simple power function of rain intensity under the assumption of the lognormal size distribution of particles. The resultant approximated expression was compared to the observed data and the results of previous studies including a 3D atmospheric chemical transport model simulation. Compared with the default GEOS-Chem coefficient of 0.00106R^0.61 and the observation-based coefficient of 0.0144R^0.9268, the coefficient of a and b in Λ_m = aR^b spread in the range of 0.0002- 0.1959 for a and 0.3261- 0.525 for b over a size distribution of GSD of 1.3-2.5 and a geometric mean diameter of 0.01- 2.5 µm. Overall, this study showed that the scavenging coefficient varies widely by orders of magnitude according to the size distribution of particles and rain intensity. This study also demonstrated that the obtained simplified expression could consider the theoretical approach of aerosol polydispersity. Our proposed analytic approach showed that results can be effectively applied for reduced computational burden in atmospheric modeling.

Contributions of Traffic and Industrial Emission Reductions to the Air Quality Improvement after the Lockdown of Wuhan and Neighboring Cities Due to COVID-19

Wuhan was locked down from 23 January to 8 April 2020 to prevent the spread of the novel coronavirus disease 2019 (COVID-19). Both public and private transportation in Wuhan and its neighboring cities in Hubei Province were suspended or restricted, and the manufacturing industry was partially shut down. This study collected and investigated ground monitoring data to prove that the lockdowns of the cities had significant influences on the air quality in Wuhan. The WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) model was used to evaluate the emission reduction from transportation and industry sectors and associated air quality impact. The results indicate that the reduction in traffic emission was nearly 100% immediately after the lockdown between 23 January and 8 February and that the industrial emission tended to decrease by about 50% during the same period. The industrial emission further deceased after 9 February. Emission reduction from transportation and that from industry was not simultaneous. The results imply that the shutdown of industry contributed significantly more to the pollutant reduction than the restricted transportation.

Assimilation of Both Column- and Layer-Integrated Dust Opacity Observations in the Martian Atmosphere

A new dust data assimilation scheme has been developed for the UK version of the Laboratoire de Météorologie Dynamique Martian General Circulation Model. The Analysis Correction scheme (adapted from the UK Met Office) is applied with active dust lifting and transport to analyze measurements of temperature, and both column-integrated dust optical depth (CIDO), τ _ref (rescaled to a reference level), and layer-integrated dust opacity (LIDO). The results are shown to converge to the assimilated observations, but assimilating either of the dust observation types separately does not produce the best analysis. The most effective dust assimilation is found to require both CIDO (from Mars Odyssey/THEMIS) and LIDO observations, especially for Mars Climate Sounder data that does not access levels close to the surface. The resulting full reanalysis improves the agreement with both in-sample assimilated CIDO and LIDO data and independent observations from outside the assimilated data set. It is thus able to capture previously elusive details of the dust vertical distribution, including elevated detached dust layers that have not been captured in previous reanalyzes. Verification of this reanalysis has been carried out under both clear and dusty atmospheric conditions during Mars Years 28 and 29, using both in-sample and out of sample observations from orbital remote sensing and contemporaneous surface measurements of dust opacity from the Spirit and Opportunity landers. The reanalysis was also compared with a recent version of the Mars Climate Database (MCD v5), demonstrating generally good agreement though with some systematic differences in both time mean fields and day-to-day variability.

Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic

In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from −0.15–0.26 to 0.17–0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April–September 2008 were 2.18–3.70 μg m−3 and 0.85–1.68 μg m−3 over the Arctic, respectively. These corresponded to an increase of 140–180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.

Global Spatial and Temporal Variation of the Combined Effect of Aerosol and Water Vapour on Solar Radiation

This study aims to calculate the combined and individual effects of the optical thickness of aerosols (AOT) and precipitable water vapour (PWV) on the solar radiation reaching the Earth’s surface at a global scale and to analyse its spatial and temporal variation. For that purpose, a novel but validated methodology is applied to CERES SYN1deg products for the period 2000–2019. Spatial distributions of AOT and PWV effects, both individually and combined, show a close link with the spatial distributions of AOT and PWV. The spatially averaged combined effect results in a −13.9% reduction in irradiance, while the average AOT effect is −2.3%, and the PWV effect is −12.1%. The temporal analysis focuses on detecting trends in the anomalies. The results show overall positive trends for AOT and PWV. Consequently, significant negative overall trends are found for the effects. However, significant positive trends for the individual AOT and the combined AOT-PWV effects are found in specific regions, such as the eastern United States, Europe or Asia, indicating successful emission control policies in these areas. This study contributes to a better understanding of the individual and combined effects of aerosols and water vapour on solar radiation at a global scale.

Spatial and Temporal Variation of Aerosol and Water Vapour Effects on Solar Radiation in the Mediterranean Basin during the Last Two Decades

This study aims to calculate and analyse the spatial and temporal variation of aerosol optical thickness (AOT) and precipitable water vapour (PWV) and their effects on solar radiation at the surface in the Mediterranean basin, one of the maritime areas with the largest aerosol loads in the world. For the achievement of this objective, a novel and validated methodology was applied. Satellite data, specifically CERES (Clouds and the Earth’s Radiant Energy System) SYN1deg products during the period 2000–2018, were used. Results show that the spatial distribution of AOT and PWV are closely linked to the spatial distributions of its effects on solar radiation. These effects are negative, indicating a reduction of solar radiation reaching the surface due to aerosol and water vapour effects. This reduction ranges between 2% and 8% for AOT, 11.5% and 15% for PWV and 14% and 20% for the combined effect. The analysis of the temporal distribution has focused on the detection of trends from their anomalies. This study has contributed to a better understanding of AOT and PWV effects on solar radiation over the Mediterranean basin, one of the most climatically sensitive regions of the planet, and highlighted the importance of water vapour.

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 PM_2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM_2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM_2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM_2.5 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 PM_2.5 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.

Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.

Observation-Based Decomposition of Radiative Perturbations and Radiative Kernels

The Clouds and the Earth's Radiant Energy System (CERES)-partial radiative perturbation [PRP (CERES-PRP)] methodology applies partial-radiative-perturbation-like calculations to observational datasets to directly isolate the individual cloud, atmospheric, and surface property contributions to the variability of the radiation budget. The results of these calculations can further be used to construct radiative kernels. A suite of monthly mean observation-based inputs are used for the radiative transfer, including cloud properties from either the diurnally resolved passive-sensor-based CERES synoptic (SYN) data or the combination of the CloudSat cloud radar and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. The CloudSat/CALIPSO cloud profiles are incorporated via a clustering method that obtains monthly mean cloud properties suitable for accurate radiative transfer calculations. The computed fluxes are validated using the TOA fluxes observed by CERES. Applications of the CERES-PRP methodology are demonstrated by computing the individual contributions to the variability of the radiation budget over multiple years and by deriving water vapor radiative kernels. The calculations for the former are used to show that an approximately linear decomposition of the total flux anomalies is achieved. The observation-based water vapor kernels were used to investigate the accuracy of the GCM-based NCAR CAM3.0 water vapor kernel. Differences between our observation-based kernel and the NCAR one are marginally larger than those inferred by previous comparisons among different GCM kernels.

A Possible Role of Dust in Resolving the Holocene Temperature Conundrum

Climate models generally fail to produce a warmer (by as much as 0.5 °C) early to mid-Holocene than the pre-industrial in the global annual temperature, which has been termed the Holocene temperature conundrum. Here we use a fully coupled atmosphere-ocean general circulation model to test whether the conundrum can be partially resolved by considering the fact that atmospheric dust loading was much reduced during the early to mid-Holocene. Our experiments show that the global annual mean surface temperature increases by 0.30 °C and 0.23 °C for the mid-Holocene (6 ka) and early Holocene (9 ka), respectively, if the dust is completely removed. The temperature increase scales almost linearly with the fraction of dust being removed, with the 50% dust reduction experiment for the 6 ka being the only one deviating from the linear trend. The indirect effect of dust, which is highly uncertain and is not included in the model, may further enhance the warming. Therefore, the neglect of dust reduction in the Holocene in climate models could contribute significantly to the model-data discrepancy, especially in the Northern Hemisphere.

Cloud Occurrences and Cloud Radiative Effects (CREs) from CCCM and CloudSat Radar-Lidar Products

Two kinds of radar-lidar synergy cloud products are compared and analyzed in this study; CERES-CALIPSO-CloudSat-MODIS (CCCM) product and CloudSat radar-lidar (RL) product such as GEOPROF-LIDAR and FLXHR-LIDAR. Compared to GEOPROF-LIDAR, CCCM has more low-level (< 1 km) clouds over tropical oceans because CCCM uses a more relaxed threshold of Cloud-Aerosol Discrimination (CAD) score for Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask (VFM) product. In contrast, GEOPROF-LIDAR has more mid-level (1-8 km) clouds than CCCM at high latitudes (> 40°). The difference occurs when hydrometeors are detected by CALIPSO lidar but are undetected by CloudSat radar, which may be related to precipitation. In the comparison of cloud radiative effects (CREs), global mean differences between CCCM and FLXHR-LIDAR are mostly smaller than 5 W m-2, while noticeable regional differences are found over three regions. First, CCCM has larger shortwave (SW) and longwave (LW) CREs than FXLHR-LIDAR along the west coasts of Africa and America. This might be caused by missing small-scale marine boundary layer clouds in FLXHR-LIDAR. Second, over tropical oceans where precipitation frequently occurs, SW and LW CREs from FLXHR-LIDAR are larger than those from CCCM partly because FLXHR-LIDAR algorithm includes the contribution of rainwater to total liquid water path. Third, over midlatitude storm-track regions, CCCM shows larger SW and LW CREs than FLXHR-LIDAR, due to CCCM biases caused by larger cloud optical depth or higher cloud effective height.

Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM2.5 Predictions

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM_2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM_2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At nonassimilated sites, improvements (higher correlation coefficients and lower mean bias errors (MBE) and root-mean-square errors (RMSE)) are also found in PM_2.5, PM₁₀, and AOD predictions. Using the constrained aerosol fields, we estimate that the PM_2.5 concentrations in January 2013 might have caused 7550 premature deaths in Jing-Jin-Ji areas, which are 2% higher than the estimates using unconstrained aerosol fields. We also estimate that the daytime monthly mean anthropogenic aerosol radiative forcing (ARF) to be -29.9W/m² at the surface, 27.0W/m² inside the atmosphere, and -2.9W/m² at the top of the atmosphere. Our estimates update the previously reported overestimations along Yangtze River region and underestimations in North China. This GSI-WRF-Chem system would also be potentially useful for air quality forecasting in China.

Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only.

Asian aerosols: current and year 2030 distributions and implications to human health and regional climate change

Aerosol distributions in Asia calculated over a 4-year period and constrained by satellite observations of aerosol optical depth (AOD) are presented. Vast regions in Asia that include > 80% of the population have PM2.5 concentrations that exceed on an annual basis the WHO guideline of 10 microg/m3, often by factors of 2 to 4. These high aerosol loadings also have important radiative effects, causing a significant dimming at the surface, and mask approximately 45% of the warming by greenhouse gases. Black carbon (BC) concentrations are high throughout Asia, representing 5-10% of the total AOD, and contributing significantly to atmospheric warming (its warming potential is approximately 55% of that due to CO2). PM levels and AODs in year 2030, estimated based on simulations that consider future changes in emissions, are used to explore opportunities for win-win strategies built upon addressing air quality and climate change together. It is found that in 2030 the PM2.5 levels in significant parts of Asia will increase and exacerbate health impacts; but the aerosols will have a larger masking effect on radiative forcing, due to a decrease in BC and an increase in SO2 emissions.