We have analyzed TROPOMI data over the Copperbelt mining region (Democratic Republic of Congo and Zambia). Despite high background values, we find that annual 2019-2022 means of TROPOMI NO2 show local enhancements consistent with six point sources (mines and cities) where high-emission industrial activities take place. We have quantified annual NO2 emissions for the six sources, identified temporal trends in these emissions, and found strong correlations with mine/refinery production data. CAMS-GLOB-ANT v5 inventory emissions are lower than TROPOMI-derived emissions by 61-96 % and lack the temporal trends observed in TROPOMI and mine/oil refinery production. Lack of TROPOMI SO2 enhancements over the point sources analyzed indicates SO2 capture and transformation into sulfuric acid, a profitable byproduct. These results demonstrate the potential for satellite monitoring of mining/oil refining activity which impacts the air quality of local communities. This is particularly important for Africa, where mining is increasing aggressively.

Modeling atmospheric chemistry across scales with the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) leverages a new regional refinement capability within the NCAR Community Earth System Model (CESM). This framework allows for simultaneous simulation at human-relevant and policy-relevant scales at the same time as hemispheric and global-scales. Here, we present the development of a regional refined grid over the Australian region and assess initial simulations for the 2019/2020 wildfire season. The Australian wildfire events of 2019/2020 saw large local emissions of pollution with wide-scale impacts across the southeast of the continent, where smoke and haze degraded air quality for many days for millions of people. Hemispheric transport of pollution at low and lofted altitudes also occurred, creating an atmospheric signature over New Zealand and South America, and had a direct influence on climate. These multiple-scale impacts from wildfire in the Australian region make it ideal for testing the new capability of multi-scale modeling.

There have been many extreme fire seasons in Maritime Southeast Asia (MSEA) over the last two decades, a trend which will likely continue, if not accelerate, due to climate change. Fires, in turn, are a major driver of atmospheric carbon monoxide (CO) variability, especially in the Southern Hemisphere. Previous studies have explored the relationship between climate variability and fire counts, burned area, and atmospheric CO through regression models that use climate mode indices as predictor variables. Here we model the connections between climate variability and atmospheric CO at a level of complexity not yet studied and make accurate predictions of atmospheric CO (a proxy for fire intensity) at useful lead times. To do this, we develop a regularization-based statistical modeling framework that can accommodate multiple lags of a single climate index, which we show to be an important feature in explaining CO. We use this framework to present advancements over previous modeling efforts, such as the inclusion of outgoing longwave radiation (OLR) anomalies, the use of weekly data, and a stability analysis that adds weight to the scientific interpretation of selected model terms. We find that the El Ni\ {n}o Southern Oscillation (ENSO), the Dipole Mode Index (DMI), and OLR (as a proxy for the Madden-Julian Oscillation) at various lead times are the most significant predictors of atmospheric CO in MSEA. We further show that the model gives accurate predictions of atmospheric CO at leads times of up to 6 months, making it a useful tool for fire season preparedness.

Emissions of nitrogen oxides (NOx = NO + NO2) in the United States have declined significantly during the past three decades. However, satellite observations since 2009 indicate total column NO2 is no longer declining even as bottom-up inventories suggest continued decline in emissions. Multiple explanations have been proposed for this discrepancy including 1) the increasing relative importance of non-urban NOx to total column NO2, 2) differences between background and urban NOx lifetimes, and 3) that the actual NOx emissions are declining more slower after 2009. Here we use a deep learning model trained by NOx emissions and surface observations of ozone to assess consistency between the reported NOx trends between 2005-2014 and observations of surface ozone. We find that the 2005-2014 trend from older satellite-derived emission estimates produced at low spatial resolution best reproduce ozone in low NOx emission (background) regions, reflecting the blending of urban and background NOx in these low-resolution top-down analyses. The trend from higher resolution satellite-based estimates, which are more capable of capturing the urban emission signature, is in better agreement with ozone in high NOx emission regions, and is consistent with the trend based on surface observations of NO2. In contrast, the 2005-2014 trend from the US Environmental Protection Agency (EPA) National Emission Inventory (NEI) results in an underestimate of ozone. Our results confirm that the satellite-derived trends reflect anthropogenic and background influences and that the 2005-2014 trend in the NEI inventory is overestimating recent reductions in NOx emissions.

Fire emissions are an important component of global models, which help to understand the influence of sources, transport and chemistry on atmospheric composition. Global fire emission inventories can vary substantially due to the assumptions made in the emission creation process, including the defined vegetation type, fire detection, fuel loading, fraction of vegetation burned and emissions factors. Here, we focus on the uncertainty in emission factors and the resulting impact on modeled composition. Our study uses the Community Atmosphere Model with chemistry (CAM-chem) to model atmospheric composition for 2014, a year chosen for the relatively quiet El Niño Southern Oscillation activity. We focus on carbon monoxide (CO), a trace gas emitted from incomplete combustion and also produced from secondary oxidation of volatile organic compounds (VOCs). Fire is a major source of atmospheric CO and VOCs. Modeled CO from four fire emission inventories (CMIP6/GFED4s, QFED2.5, GFAS1.2 and FINN1.5) are compared after being implemented in CAM-chem. Multiple sensitivity tests are performed based on CO and VOC emission factor uncertainties. We compare model output in the 14 basis regions defined by the Global Fire Emissions Database (GFED) team and evaluate against CO observations from the Measurements of Pollution in the Troposphere (MOPITT) satellite-based instrument. For some regions, emission factor uncertainty spans the results found by using different inventories. Finally, we use modeled ozone (O3) to briefly investigate how emission factor uncertainty influences the atmospheric oxidative environment. Overall, accounting for emission factor uncertainty when modeling atmospheric chemistry can lend a range of uncertainty to simulated results.

Fire emissions are a major contributor to atmospheric composition, affecting atmospheric oxidizing capacity and air quality. Transported amounts from Northern Hemisphere boreal fires can reach the pristine Arctic atmosphere as well as impact air quality in populated regions. Carbon monoxide (CO) is a useful trace gas emitted from fires that can be used to link extreme fire events with climate variability. We use our recently developed statistical tool to investigate the climate drivers of satellite measured CO variability in two Northern Hemisphere boreal fire regions: northwest Canada and Siberia. Our focus is on quantifying the ability of climate mode indices for the Pacific, Atlantic, Indian and Arctic Oceans in predicting CO amounts in these regions. Climate mode indices El Niño Southern Oscillation (ENSO), Tropical North Atlantic (TNA), the Dipole Mode Index (DMI) and the Arctic Oscillation (AO) are used to develop statistical models of column CO interannual variability from the Measurements of Pollution In The Troposphere (MOPITT) satellite instrument, for the time period covering 2001-2017. In addition, we assess the ability of fire emission inventories to reproduce CO, including the Fire Inventory from NCAR (FINN), the NASA Quick Fire Emissions Dataset (QFED) and the Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS). These are implemented in the NCAR Community Atmosphere Model with chemistry (CAM-chem) and subsequently evaluated against MOPITT CO observations. Emission uncertainty contribution to inter-inventory differences are quantified, and the modeled contribution of fires to CO interannual variability is determined.

Atmospheric carbon monoxide (CO) has been decreasing globally for the last two decades. Recently, positive fire trends in Northern Hemisphere boreal regions may have impacted the decreasing CO. Additionally, time-varying air quality policies will have different impacts on atmospheric composition and related trends. Aerosols are co-emitted with CO from both fires and anthropogenic sources. Consequently, a combined trend analysis of CO and aerosol optical depth (AOD) measurements from space can help elucidate the drivers of regional differences in the CO trend. We use valuable long-term records from two instruments aboard the Terra satellite. Measurements of Pollution in the Troposphere (MOPITT) CO and AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument are examined hemispherically and in sub-regions to determine trends between 2002 and 2018. The records are further split into two sub-periods in order to examine temporal stability in the trend values. We also assess the CO trends in monthly percentile values to use seasonal information when interpreting trend contributions. Our focus is on four major population centers: Southeast USA, Europe, Northeast China and North India, as well as biomass burning regions in both hemispheres. Our results show that globally, CO declines faster in the first half of the record compared to the second half. Both atmospheric species are important when interpreting trends in the smaller regions. Northern Hemisphere boreal fire regions show a regime-shift in their seasonality for both CO and AOD, which may counteract the downward trend in CO. Anthropogenic regions with minimal air quality management such as North India become more globally relevant as the global CO trend weakens. We also find clear evidence of the atmospheric impact of policy choices. Overall, we observe that local changes in biomass burning and air quality can counteract the global downward trend in CO.

There have been many extreme fire seasons in Maritime Southeast Asia (MSEA) over the last two decades, a trend which will likely continue or accelerate due to climate change. Fires, in turn, are a major driver of atmospheric carbon monoxide (CO) variability, especially in the Southern Hemisphere. Here we attempt to maximize the amount of CO variability that can be explained via human-interpretable statistical models that use only climate mode indices as predictor variables. We expand upon previous work through the complexity at which we study the connections between climate mode indices and atmospheric CO (a proxy for fire intensity). Specifically, we present three modeling advancements. First, we analyze five different climate modes at a weekly timescale, which increases explained variability by 15% over models on a monthly timescale. Second, we accommodate multiple lead times for each climate mode index, finding that some indices have very different effects on CO at different lead times. Finally, we model the interactions between climate mode indices at weekly timescales, which provides a framework for studying these interactions at a higher level of complexity than previous work. Furthermore, we perform a stability analysis and show that our model for the MSEA region is robust, which adds weight to the scientific interpretation of the selected model terms. We believe that the complex relationships quantified here will be useful for scientists studying modes of variability in MSEA and for forecasters looking to maximize the information they glean from climate modes.

Changes in aerosol optical depth, both positive and negative, are observed across the globe during the 21rst Century. However, attribution of these changes to specific sources is largely uncertain as there are multiple contributing natural and anthropogenic sources that produce aerosols either directly or through secondary chemical reactions. Here we show that satellite-based changes in small-mode AOD between 2002 and 2019 observed in data from MISR can primarily be explained by changes, either directly or indirectly, in combustion emissions. We quantify combustion emissions using MOPITT total column CO observations and the adjoint of the GEOS-Chem global chemistry and transport model. The a priori fire emissions are taken from the Global Fire Emission Data base with small fires (GFED4s) but with fixed a priori for non-fire emissions. Aerosol precursor and direct emissions are updated by re-scaling them with the monthly ratio of the CO posterior to prior emissions. The correlation between modeled and observed AOD improves from a mean of 0.15 to 0.81 for the four industrial regions considered and from 0.52 to 0.75 for the four wildfire-dominant regions considered. Using these updated emissions in the GEOS-Chem global chemistry transport model, our results indicate that surface PM2.5 have declined across many regions of the globe during the 21rst century. For example, PM2.5 over China has declined by ~30% with smaller fractional declines in E. USA and Europe (from fossil emissions) and in S. America (from fires). These results highlight the importance of forest management and cleaner combustion sources in improving air-quality.

Isoprene is a hydrocarbon emitted in large quantities by terrestrial vegetation. It is a precursor to several air quality and climate pollutants including ozone. Emission rates vary with plant species and environmental conditions. This variability can be modelled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN). MEGAN parameterizes isoprene emission rates as a vegetation-specific standard rate which is modulated by scaling factors that depend on meteorological and environmental driving variables. Recent experiments have identified large uncertainties in the MEGAN temperature response parameterization, while the emission rates under standard conditions are poorly constrained in some regions due to a lack of representative measurements and uncertainties in landcover. In this study, we use Bayesian model-data fusion to optimize the MEGAN temperature response and standard emission rates using satellite- and ground-based observational constraints. Optimization of the standard emission rate with satellite constraints reduced model biases but was highly sensitive to model input errors and drought stress and was found to be inconsistent with ground-based constraints at an Amazonian field site, reflecting large uncertainties in the satellite-based emissions. Optimization of the temperature response with ground-based constraints increased the temperature sensitivity of the model by a factor of five at an Amazonian field site but had no impact at a UK field site, demonstrating significant ecosystem-dependent variability of the isoprene emission temperature sensitivity. Ground-based measurements of isoprene across a wide range of ecosystems will be key for obtaining an accurate representation of isoprene emission temperature sensitivity in global biogeochemical models.

In this study, we examine the ability of the data assimilation of global satellite-based carbon monoxide (CO) observations to constrain high-latitude boreal wildfire emissions. We compare the optimized emissions from inversions using CO measurements from the Measurement of Pollution in the Troposphere (MOPITT) and Infrared Atmospheric Sounding Interferometer (IASI). We found that both inversions yield generally consistent posterior CO emissions globally; however, distinct differences are observed for the episodic 2017 Canadian wildfires. The 3-day global coverage of MOPITT limits its ability to accurately optimize emissions, while the daily global coverage of IASI provides a moderate improvement despite its lower surface sensitivity. Through a series of observing system simulation experiments (OSSEs), we show that the temporal coverage of IASI most strongly influenced the posterior estimates, while the differences in vertical sensitivities of MOPITT and IASI have a minor contribution.

Wildfire emissions can vary substantially between different inventories due to the assumptions made in the emission creation process, including the defined vegetation type, fire detection, fuel loading, fraction of vegetation burned and emissions factors. Here, we focus on the uncertainty in emission factors and the resulting impact on modeled composition. We use the Community Atmosphere Model with chemistry (CAM-chem) to simulate 2014 atmospheric composition and focus on carbon monoxide (CO), a trace gas emitted from incomplete combustion and also produced from secondary oxidation of volatile organic compounds (VOCs). Fire is a major source of atmospheric CO and VOCs. Multiple simulations are compared, from an ensemble using four fire emission inventories (CMIP6/GFED4s, QFED2.5, GFAS1.2 and FINN1.5) and a range of sensitivity tests based on CO and VOC emission factor uncertainties. We compare model output and evaluate against CO observations from the Measurements of Pollution in the Troposphere (MOPITT) satellite-based instrument. For some regions, emission factor uncertainty spans the results found by using different inventories. Finally, we use modeled ozone (O3) to investigate how emission factor uncertainty influences the atmospheric oxidative environment. Overall, accounting for emission factor uncertainty lends a range of uncertainty to simulated results.