We examine the spatio-temporal variability of aerosol loading in the recent decade (2005-2014) over the North American Monsoon (NAM) region. Emerging patterns are characterized using aerosol optical depth (AOD) retrievals from the NASA Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) instrument along with a suite of satellite retrievals of atmospheric and land-surface properties. We selected 20 aerosol hotspots and classified them into fire, anthropogenic, dust, and NAM alley clusters based on the dominant driver influencing aerosol variability. We then analyzed multivariate statistics of associated anomalies during pre-, monsoon, and post-monsoon periods. Our results show a decrease in aerosol loading for the entire NAM region, confirming previous reports of a declining AOD trend over the continental United States. This is evident during pre-monsoon and monsoon for fire and anthropogenic clusters, which are associated with a decrease in the lower and upper quartile of fire counts and carbon monoxide, respectively. The overall pattern is obfuscated in the NAM alley, especially during monsoon and post-monsoon seasons. While the NAM alley is mostly affected by monsoon precipitation, the frequent occurrence of dust storms in the area modulates this trend. We find that aerosol loading in the dust cluster is associated with observed vegetation index and has only slightly decreased in the recent decade.

This study uses more than a decade's worth of data across Arizona to characterize the spatiotemporal distribution, frequency, and source of extreme aerosol events, defined as when the concentration of a species on a particular day exceeds that of the average plus two standard deviations for that given month. Depending on which of eight sites studied, between 5% and 7% of the total days exhibited an extreme aerosol event due to either extreme levels of PM₁₀, PM_2.5, and/or fine soil. Grand Canyon exhibited the most extreme event days (120, i.e., 7% of its total days). Fine soil is the pollutant type that most frequently impacted multiple sites at once at an extreme level. PM₁₀, PM_2.5, fine soil, non-Asian dust, and Elemental Carbon extreme events occurred most frequently in August. Nearly all Asian dust extreme events occurred between March and June. Extreme Elemental Carbon events have decreased as a function of time with statistical significance, while other pollutant categories did not show any significant change. Extreme events were most frequent for the various pollutant categories on either Wednesday or Thursday, but there was no statistically significant difference in the number of events on any particular day or on weekends versus weekdays.

This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of compact phase space retrievals of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (R2). Those CO forecast improvements were significant at the 95 % level.

Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by g 1/4 g ¯35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.

 We present an inverse-modeling analysis of CO emissions using column CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) instrument and a global chemical transport model (GEOS-CHEM). We first focus on the information content of MOPITT CO column retrievals in terms of constraining CO emissions associated with biomass burning and fossil fuel/biofuel use. Our analysis shows that seasonal variation of biomass-burning CO emissions in Africa, South America, and Southeast Asia can be characterized using monthly mean MOPITT CO columns. For the fossil fuel/biofuel source category the derived monthly mean emission estimates are noisy even when the error statistics are accurately known, precluding a characterization of seasonal variations of regional CO emissions for this source category. The derived estimate of CO emissions from biomass burning in southern Africa during the June–July 2000 period is significantly higher than the prior estimate (prior, 34 Tg; posterior, 13 Tg). We also estimate that emissions are higher relative to the prior estimate in northern Africa during December 2000 to January 2001 and lower relative to the prior estimate in Central America and Oceania/Indonesia during April–May and September–October 2000, respectively. While these adjustments provide better agreement of the model with MOPITT CO column fields and with independent measurements of surface CO from National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory at background sites in the Northern Hemisphere, some systematic differences between modeled and measured CO fields persist, including model overestimation of background surface CO in the Southern Hemisphere. Characterizing and accounting for underlying biases in the measurement model system are needed to improve the robustness of the top-down estimates.

During the 1997 to 1998 El Niño, drought conditions triggered widespread increases in fire activity, releasing CH4 and CO2 to the atmosphere. We evaluated the contribution of fires from different continents to variability in these greenhouse gases from 1997 to 2001, using satellite-based estimates of fire activity, biogeochemical modeling, and an inverse analysis of atmospheric CO anomalies. During the 1997 to 1998 El Niño, the fire emissions anomaly was 2.1 +/- 0.8 petagrams of carbon, or 66 +/- 24% of the CO2 growth rate anomaly. The main contributors were Southeast Asia (60%), Central and South America (30%), and boreal regions of Eurasia and North America (10%).

Expanding networks of all-weather, high-time-resolution GPS networks with surface meteorology throughout the southwestern United States and Mexico offers opportunities for improved understanding of the North American monsoon convection and forecasting of organized convective events and associated hazards.

We investigate regional sources contributing to CO during the Korea United States Air Quality (KORUS-AQ) campaign conducted over Korea (1 May to 10 June 2016) using 17 tagged CO simulations from the Community Atmosphere Model with chemistry (CAM-chem). The simulations use three spatial resolutions, three anthropogenic emission inventories, two meteorological fields, and nine emission scenarios. These simulations are evaluated against measurements from the DC-8 aircraft and Measurements Of Pollution In The Troposphere (MOPITT). Results show that simulations using bottom-up emissions are consistently lower (bias: −34 to −39%) and poorer performing (Taylor skill: 0.38–0.61) than simulations using alternative anthropogenic emissions (bias: −6 to −33%; Taylor skill: 0.48–0.86), particularly for enhanced Asian CO and volatile organic compound (VOC) emission scenarios, suggesting underestimation in modeled CO background and emissions in the region. The ranges of source contributions to modeled CO along DC-8 aircraft from Korea and southern (90°E to 123°E, 20°N to 29°N), middle (90°E to 123°E, 29°N to 38.5°N), and northern (90°E to 131.5°E, 38.5°N to 45°N) East Asia (EA) are 6–13%, ~5%, 16–28%, and 9–18%, respectively. CO emissions from middle and northern EA can reach Korea via transport within the boundary layer, whereas those from southern EA are transported to Korea mainly through the free troposphere. Emission contributions from middle EA dominate during continental outflow events (29–51%), while Korean emissions play an overall more important role for ground sites (up to 25–49%) and plumes within the boundary layer (up to 25–44%) in Korea. Finally, comparisons with four other source contribution approaches (FLEXPART 9.1 back trajectory calculations driven by Weather Research and Forecasting (WRF) WRF inert tracer, China signature VOCs, and CO to CO2 enhancement ratios) show general consistency with CAM-chem.