ALBERT

Description: Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical properties, which are not available under cloudy conditions. Here we set the first steps to overcome the long-standing limitation that aerosols cannot be constrained using satellite remote sensing under cloudy conditions. We introduce a unique data assimilation method that uses cloud droplet number (Nd) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent Nd observations and with in situ aerosol measurements below shallow cumulus clouds. The impacts of a single assimilation on aerosol and cloud forecasts extend beyond 24 h. Unlike previous methods, this technique can directly improve predictions of near-surface fine mode aerosols responsible for human health impacts and low-cloud radiative forcing. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates, and air-quality, weather, and climate predictions.

Description: An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM2.5 predictions reduced at over 90% of the stations and AOD errors reduced at 100% of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.

Description: We evaluate a regional-scale simulation with the WRF-Chem model for the VAMOS (Variability of the American Monsoon Systems) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx), which sampled the Southeast Pacific's persistent stratocumulus deck. Evaluation of VOCALS-REx ship-based and three aircraft observations focuses on analyzing how aerosol loading affects marine boundary layer (MBL) dynamics and cloud microphysics. We compare local time series and campaign-averaged longitudinal gradients, and highlight differences in model simulations with (W) and without (NW) wet deposition processes. The higher aerosol loadings in the NW case produce considerable changes in MBL dynamics and cloud microphysics, in accordance with the established conceptual model of aerosol indirect effects. These include increase in cloud albedo, increase in MBL and cloud heights, drizzle suppression, increase in liquid water content, and increase in cloud lifetime. Moreover, better statistical representation of aerosol mass and number concentration improves model fidelity in reproducing observed spatial and temporal variability in cloud properties, including top and base height, droplet concentration, water content, rain rate, optical depth (COD) and liquid water path (LWP). Together, these help to quantify confidence in WRF-Chem's modeled aerosol-cloud interactions, especially in the activation parameterization, while identifying structural and parametric uncertainties including: irreversibility in rain wet removal; overestimation of marine DMS and sea salt emissions, and accelerated aqueous sulfate conversion. Our findings suggest that WRF-Chem simulates marine cloud-aerosol interactions at a level sufficient for applications in forecasting weather and air quality and studying aerosol climate forcing, and may do so with the reliability required for policy analysis.

Description: Particles containing black carbon (BC), a strong absorbing substance, exert a rather uncertain direct and indirect radiative forcing in the atmosphere. To investigate the mass concentration and absorption properties of BC particles over Central Europe, the model WRF-Chem was used at a resolution of 12 km in conjunction with a high resolution BC emission inventory (EUCAARI 42-Pan-European Carbonaceous Aerosol Inventory; 1/8° × 1/16°). The model simulation was evaluated using measurements of equivalent soot carbon, absorption coefficients and particle number concentrations at 7 sites within the German Ultrafine Aerosol Network, PM10 mass concentrations from the dense measurement network of the German Federal Environmental Agency at 392 monitoring stations, and aerosol optical depth from MODIS and AERONET. A distinct time period (25 March to 10 April 2009) was chosen, during which the clean marine air mass prevailed in the first week and afterwards the polluted continental air mass mainly from south-east dominated with elevated daily average BC concentration up to 4 μg m−3. The simulated PM10 mass concentration, aerosol number concentration and optical depth were in a good agreement with the observations, while the modelled BC mass concentrations were found to be a factor of 2 lower than the observations. Together with backtrajectories, detailed model bias analyses suggested that the current BC emission in countries to the east and south of Germany might be underestimated by a factor of 5, at least for the simulation period. Running the model with upscaled BC emissions in these regions led to a smaller model bias and a better correlation between model and measurement. On the contrary, the particle absorption coefficient was positively biased by about 20% even when the BC mass concentration was underestimated by around 50%. This indicates that the internal mixture treatment of BC in the WRF-Chem optical calculation is unrealistic in our case, which over amplifies the light absorption by BC containing particles. By adjusting the modeled mass absorption cross-section towards the measured values, the simulation of particle light absorption of BC was improved as well. Finally, the positive direct radiative forcing of BC particles at top of the atmosphere was estimated to be in the range of 0 to +4 W m−2 over Germany for the model run with improved BC mass concentration and adjusted BC light absorption cross-section. This treatment lowered the positive forcing of BC by up to 70%, compared with the internal mixing treatment of BC in the model simulation.

Description: Particulate matter (PM) mass concentrations, seasonal cycles, source sector and source region contributions in Central Asia (CA) are analyzed for the period April 2008–July 2009 using the Sulfur Transport and dEposition Model (STEM) chemical transport model and modeled meteorology from the Weather Research and Forecasting (WRF) model. Predicted Aerosol Optical Depth (AOD) values (annual mean value ∼0.2) in CA vary seasonally with lowest values in the winter. Surface PM2.5 concentrations (annual mean value ∼10 μg m−3) also exhibit a seasonal cycle, with peak values and largest variability in the spring/summer, and lowest values and variability in the winter (hourly values from 2–90 μg m−3). Surface concentrations of black carbon (BC) (mean value ∼0.1 μg m−3) show peak values in the winter. The simulated values are compared to surface measurements of AOD, and PM2.5, PM10, BC, organic carbon (OC) mass concentrations at two regional sites in the Kyrgyz Republic (Lidar Station Teplokluchenka (LST) and Bishkek). The predicted values of AOD and PM mass concentrations and their seasonal cycles are fairly well captured. The carbonaceous aerosols are underpredicted in winter, and analysis suggests that the winter heating emissions are underestimated in the current inventory. Dust, from sources within and outside CA, is a significant component of the PM mass and drives the seasonal cycles of PM and AOD. On an annual basis, the power and industrial sectors are found to be the most important contributors to the anthropogenic portion of PM2.5. Residential combustion and transportation are shown to be the most important sectors for BC. Biomass burning within and outside the region also contributes to elevated PM and BC concentrations. The analysis of the transport pathways and the variations in particulate matter mass and composition in CA demonstrate that this region is strategically located to characterize regional and intercontinental transport of pollutants. Aerosols at these sites are shown to reflect dust, biomass burning and anthropogenic sources from Europe, South, East and CA, and Russia depending on the time period. Simulations for a reference 2030 emission scenario based on pollution abatement measures already committed to in current legislation show that PM2.5 and BC concentrations in the region increase, with BC growing more than PM2.5 on a relative basis. This suggests that both the health impacts and the climate warming associated with these particles may increase over the next decades unless additional control measures are taken. The importance of observations in CA to help characterize the changes that are rapidly taking place in the region are discussed.

Description: An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system when WRF-Chem forecasts are performed with a detailed sectional model (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA. A first set of simulations is performed comparing the assimilation impacts of operational MODIS dark target retrievals to observationally constrained ones (i.e. calibrated with AERONET data), the latter ones showing higher error reductions and increased fraction of improved PM2.5 and AOD ground-based monitors. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.

Description: The winter haze is a growing problem in North China, but the causes have not been well understood. The chemistry version of the Weather Research and Forecasting model (WRF-Chem) was applied in North China to examine how the PM2.5 concentrations change in response to changes in emissions (sulfur dioxide (SO2), black carbon (BC), organic carbon (OC), ammonia (NH3), and nitrogen oxides (NOx)), as well as meteorology (temperature, relative humidity (RH), and wind speeds) changes in winter. From 1960 to 2010, the dramatic changes in emissions lead to +260 % increases in sulfate, +320 % increases in nitrate, +300 % increases in ammonium, +160 % increases in BC and 50 % increases in OC. The responses of PM2.5 to individual emission specie indicate that the simultaneous increases in SO2, NH3 and NOx emissions dominated the increases in PM2.5 concentrations. PM2.5 is more sensitive to changes in SO2 and NH3 as compared to changes in NOx emissions. In addition, OC also accounts for a large fraction in PM2.5 changes. These results provide some implications for haze pollution control. The responses of PM2.5 concentrations to temperature increases are dominated by changes in wind fields and mixing heights. PM2.5 is not sensitive to temperature increases and RH decreases, compared to changes in wind speed and aerosol feedbacks. From 1960 to 2010, aerosol feedbacks have been significantly enhanced, due to higher aerosol loadings. The discussions in this study indicate that dramatic changes in emissions are the main cause of increasing haze events in North China, and long-term trends in atmospheric circulations maybe another important cause since PM2.5 is shown to be sensitive to wind speed and aerosol feedbacks. More studies are necessary to get a better understanding of the aerosol-circulation interactions.

Description: Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

Description: A diverse collection of models are used to simulate the marine boundary layer in the Southeast Pacific region during the period of the October–November 2008 VOCALS REx field campaign. Regional models simulate the period continuously in boundary-forced free-running mode, while global forecast models and GCMs are run in forecast mode. The models are compared to extensive observations along a line at 20° S extending westward from the South American coast. Most of the models simulate cloud and aerosol characteristics and gradients across the region that are recognizably similar to observations, despite the complex interaction of processes involved in the problem, many of which are parameterized or poorly resolved. Some models simulate the regional low cloud cover well, though many models underestimate MBL depth near the coast. Most models qualitatively simulate the observed offshore gradients of SO2, sulfate aerosol, CCN concentration in the MBL, and the related gradient in cloud droplet concentrations, but there are large quantitative intermodel differences in both means and gradients of these quantities. Most models underestimate large CCN (at 0.1% supersaturation) in the MBL and free troposphere. The GCMs also have difficulty simulating coastal gradients in CCN and cloud droplet number concentration. The overall performance of the models demonstrates their potential utility in simulating aerosol-cloud interactions in the MBL, though quantitative estimation of aerosol-cloud interactions and aerosol indirect effects of MBL clouds with these models remains uncertain.

Description: The online coupled Weather Research and Forecasting-Chemistry (WRF-Chem) model was applied to simulate a haze event that happened in January 2010 in the North China Plain (NCP), and was validated against various types of measurements. The evaluations indicate that WRF-Chem provides reliable simulations for the 2010 haze event in the NCP. This haze event is mainly caused by high emissions of air pollutants in the NCP and stable weather conditions in winter. Secondary inorganic aerosols also played an important role and cloud chemistry had important contributions. Air pollutants outside Beijing contributed about 47.8 % to the PM2.5 levels in Beijing during this haze event, and most of them are from south Hebei, Shandong and Henan provinces. In addition, aerosol feedback has important impacts on surface temperature, Relative Humidity (RH) and wind speeds, and these meteorological variables affect aerosol distribution and formation in turn. In Shijiazhuang, Planetary Boundary Layer (PBL) decreased about 300 m and PM2.5 increased more than 20 μg m-3 due to aerosol feedback. Feedbacks associated to Black Carbon (BC) account for about 50 % of the PM2.5 increases and 50 % of the PBL decreases in Shijiazhuang, indicating more attention should be paid to BC from both air pollution control and climate change perspectives.