[1] In ensemble Kalman filter, space localization is used to reduce the impact of long-distance sampling errors in the ensemble estimation of the forecast error covariance. When two variables are not physically correlated, their error covariance is still estimated by the ensemble and, therefore, it is dominated by sampling errors. We introduce a "variable localization" method, zeroing out such covariances between unrelated variables to the problem of assimilating carbon dioxide concentrations into a dynamical model using the local ensemble transform Kalman filter (LETKF) in an observing system simulation experiments (OSSE) framework. A system where meteorological and carbon variables are simultaneously assimilated is used to estimate surface carbon fluxes that are not directly observed. A range of covariance structures are explored for the LETKF, with emphasis on configurations allowing nonzero error covariance between carbon variables and the wind field, which affects transport of atmospheric CO 2 , but not between CO 2 and the other meteorological variables. Such variable localization scheme zeroes out the background error covariance among prognostic variables that are not physically related, thus reducing sampling errors. Results from the identical twin experiments show that the performance in the estimation of surface carbon fluxes obtained using variable localization is much better than that using a standard full covariance approach. The relative improvement increases when the surface fluxes change with time and model error becomes significant.

Abstract. Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-Asia III Topic 3 study design, including descriptions of participating models and model inputs, the experimental designs, and results of model evaluation, are presented. Six modeling groups from China, Korea and the United States submitted results from seven applications of online coupled chemistry–meteorology models. Results are compared to meteorology and air quality measurements, including data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) and the Acid Deposition Monitoring Network in East Asia (EANET). The correlation coefficients between the multi-model ensemble mean and the CARE-China observed near-surface air pollutants range from 0.51 to 0.94 (0.51 for ozone and 0.94 for PM2.5) for January 2010. However, large discrepancies exist between simulated aerosol chemical compositions from different models. The coefficient of variation (SD divided by the mean) can reach above 1.3 for sulfate in Beijing and above 1.6 for nitrate and organic aerosols in coastal regions, indicating that these compositions are less consistent from different models. During clean periods, simulated aerosol optical depths (AODs) from different models are similar, but peak values differ during severe haze events, which can be explained by the differences in simulated inorganic aerosol concentrations and the hygroscopic growth efficiency (affected by varied relative humidity). These differences in composition and AOD suggest that future models can be improved by including new heterogeneous or aqueous pathways for sulfate and nitrate formation under hazy conditions, a secondary organic aerosol (SOA) formation chemical mechanism with new volatile organic compound (VOCs) precursors, yield data and approaches, and a more detailed evaluation of the dependence of aerosol optical properties on size distribution and mixing state. It was also found that using the ensemble mean of the models produced the best prediction skill. While this has been shown for other conditions (for example, the prediction of high-ozone events in the US (McKeen et al., 2005)), this is to our knowledge the first time it has been shown for heavy haze events.

[1] We perform every 6 h a simultaneous data assimilation of surface CO 2 fluxes and atmospheric CO 2 concentrations along with meteorological variables using the Local Ensemble Transform Kalman Filter (LETKF) within an Observing System Simulation Experiments framework. In this paper, we focus on the impact of advanced variance inflation methods and vertical localization of column CO 2 data on the analysis of CO 2 . With both additive inflation and adaptive multiplicative inflation, we are able to obtain encouraging multiseasonal analyses of surface CO 2 fluxes in addition to atmospheric CO 2 and meteorological analyses. Furthermore, we examine strategies for vertical localization in the assimilation of simulated CO 2 from GOSAT that has nearly uniform sensitivity from the surface to the upper troposphere. Since atmospheric CO 2 is forced by surface fluxes, its short-term variability should be largest near the surface. We take advantage of this by updating observed changes only into the lower tropospheric CO 2 rather than into the full column. This results in a more accurate analysis of CO 2 in terms of both RMS error and spatial patterns. Assimilating synthetic CO 2 ground-based observations and CO 2 retrievals from GOSAT and AIRS with the enhanced LETKF, we obtain an accurate estimation of the evolving surface fluxes even in the absence of any a priori information. We also test the system with a longer assimilation window and find that a short window with an efficient treatment for wind uncertainty is beneficial to flux inversion. Since this study assumes a perfect forecast model, future research will explore the impact of model errors.

[1] Vertical dust flux parameterizations were assessed by implementing three different dust emission schemes, namely, those of Marticorena and Bergametti (1995), Shao (1999), andShao (2004) (hereinafter referred to as MB, LS, and S04 schemes, respectively) in Weather Research and Forecasting with Chemistry (WRF/Chem). Through sensitivity tests, the scattering of vertical dust fluxes resulting from different parameterizations was shown even under the condition of same horizontal sand flux. The difference between the estimated vertical dust fluxes of three emission schemes ranges from the order of 10 1 for sand to the order of 10 2 for clay. The MB scheme generally produces higher dust emissions than the LS and S04 schemes, and the difference is the greatest for clay because the MB scheme considers vertical dust flux to be related to clay content, while the LS and S04 schemes consider it to be inversely proportional to surface hardness. To investigate the performance of each dust emission scheme in the simulation of Asian dust events, a case study was carried out for a severe Asian dust event that took place between 30 March and 1 April 2007. Simulation results reproduced the outbreak and transport pattern of dust plumes satisfactorily. However, the estimated dust emission amounts in each scheme differed greatly, particularly in loamy soil. The total dust emission amounts averaged for the main dust source region in this Asian dust event for five consecutive days are 84 Tg, 149 Tg, and 532 Tg for the LS, S04, and MB schemes, respectively.

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[1] Inference of surface CO 2 fluxes from atmospheric CO 2 observations requires information about large-scale transport and turbulent mixing in the atmosphere, so transport errors and the statistics of the transport errors have significant impact on surface CO 2 flux estimation. In this paper, we assimilate raw meteorological observations every 6 hours into a general circulation model with a prognostic carbon cycle (CAM3.5) using the Local Ensemble Transform Kalman Filter (LETKF) to produce an ensemble of meteorological analyses that represent the best approximation to the atmospheric circulation and its uncertainty. We quantify CO 2 transport uncertainties resulting from the uncertainties in meteorological fields by running CO 2 ensemble forecasts within the LETKF-CAM3.5 system forced by prescribed surface fluxes. We show that CO 2 transport uncertainties are largest over the tropical land and the areas with large fossil fuel emissions, and are between 1.2 and 3.5 ppm at the surface and between 0.8 and 1.8 ppm in the column-integrated CO 2 (with OCO-2-like averaging kernel) over these regions. We further show that the current practice of using a single meteorological field to transport CO 2 has weaker vertical mixing and stronger CO 2 vertical gradient when compared to the mean of the ensemble CO 2 forecasts initialized by the ensemble meteorological fields, especially over land areas. The magnitude of the difference at the surface can be up to 1.5 ppm.

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[1] This study is our first step toward the generation of 6 hourly 3-D CO 2 fields that can be used to validate CO 2 forecast models by combining CO 2 observations from multiple sources using ensemble Kalman filtering. We discuss a procedure to assimilate Atmospheric Infrared Sounder (AIRS) column-averaged dry-air mole fraction of CO 2 (Xco 2 ) in conjunction with meteorological observations with the coupled Local Ensemble Transform Kalman Filter (LETKF)-Community Atmospheric Model version 3.5. We examine the impact of assimilating AIRS Xco 2 observations on CO 2 fields by comparing the results from the AIRS-run, which assimilates both AIRS Xco 2 and meteorological observations, to those from the meteor-run, which only assimilates meteorological observations. We find that assimilating AIRS Xco 2 results in a surface CO 2 seasonal cycle and the N-S surface gradient closer to the observations. When taking account of the CO 2 uncertainty estimation from the LETKF, the CO 2 analysis brackets the observed seasonal cycle. Verification against independent aircraft observations shows that assimilating AIRS Xco 2 improves the accuracy of the CO 2 vertical profiles by about 0.5-2 ppm depending on location and altitude. The results show that the CO 2 analysis ensemble spread at AIRS Xco 2 space is between 0.5 and 2 ppm, and the CO 2 analysis ensemble spread around the peak level of the averaging kernels is between 1 and 2 ppm. This uncertainty estimation is consistent with the magnitude of the CO 2 analysis error verified against AIRS Xco 2 observations and the independent aircraft CO 2 vertical profiles.