Tropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric carbon dioxide (CO2) have greatly increased spatial coverage over tropical regions, providing the potential for improved estimates of terrestrial fluxes. Despite this advancement, the spread among satellite-based and in-situ atmospheric CO2 flux inversions over northern tropical Africa (NTA), spanning 0-24◦N, remains large. Satellite-based estimates of an annual source of 0.8-1.45 PgC yr−1 challenge our understanding of tropical and global carbon cycling. Here, we compare posterior mole fractions from the suite of inversions participating in the Orbiting Carbon Observatory 2 (OCO-2) Version 10 Model Intercomparison Project (v10 MIP) with independent in-situ airborne observations made over the tropical Atlantic Ocean by the NASA Atmospheric Tomography (ATom) mission during four seasons. We develop emergent constraints on tropical African CO2 fluxes using flux-concentration relationships defined by the model suite. We find an annual flux of 0.14 ± 0.39 PgC yr−1 (mean and standard deviation) for NTA, 2016-2018. The satellite-based flux bias suggests a potential positive concentration bias in OCO-2 B10 and earlier version retrievals over land in NTA during the dry season. Nevertheless, the OCO-2 observations provide improved flux estimates relative to the in situ observing network at other times of year, indicating stronger uptake in NTA during the wet season than the in-situ inversion estimates.

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.

Multiple 50-member ensemble simulations with the Community Earth System Model version 2 are performed to estimate the coupled climate responses to the 2019-2020 Australian wildfires and COVID-19 pandemic policies. The climate response to the pandemic is found to be weak generally, with net top-of-atmosphere radiative anomalies of +0.23+-0.14 W m/2driving a gradual global warming of 0.05+-0.04 K by the end of 2022. While regional anomalies are detectible in aerosol burdens and clear-sky radiation, few significant anomalies exist in other fields due to internal variability. In contrast, the simulated response to Australian wildfires is a strong and rapid cooling, peaking at -0.95+-0.15 W m/2 in late 2019 with an anomalous global cooling of 0.06+-0.04 K by mid-2020. Transport of fire aerosols throughout the Southern Hemisphere increases albedo and drives a strong interhemispheric radiative contrast, with simulated responses that are consistent generally with those to a Southern Hemisphere volcanic eruption.

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.

We present an integrated analysis of measurements from ozonesonde, ozone (O3) Differential Absorption Lidar (DIAL), ceilometer, surface monitors, and space-borne observations in conjunction with the regional chemical transport model Weather Research and Forecast Model with Chemistry (WRF-Chem) to investigate the effect of biomass burning emissions on the vertical distribution of ozone and aerosols during an episode of the 2016 Southeastern United States wildfires. The ceilometer and DIAL measurements capture the vertical extent of the smoke plumes affecting the surface and upper air over Huntsville, AL. The model evaluation results suggest a scaling factor of 3-4 for the wildfire aerosol emissions to better match observed aerosol optical depth (AOD), fine particulate matter (PM2.5), and DIAL aerosol extinction. We use the scaled emissions together with WRF-Chem tendency diagnostics to quantify the fire impacts and characterize the processes affecting the vertical ozone budget downstream of the wildfires. During the daytime at Huntsville on 12 and 13 November, we estimate that fire emissions contribute 12-32 μg/m3 (44-70%) to hourly surface PM2.5 and 7-8 ppb/10 hrs (30-37%) to the surface ozone increase (∆O3), respectively. Net chemical ozone production (PO3) is the main contributor to upper-air ozone, which reaches 17-19 ppb/10 hrs with 14-25% contribution from fire sources. Vertical mixing and advection are the major drivers of changes in surface ozone. Model analysis indicates that advection dominates fire-related ∆O3 below 1 km on 12 November, while local photochemistry dominates on 13 November. These results quantify the different mechanisms through which fires can influence the vertical ozone budget and point out uncertainties in fire inventories that need to be addressed in light of the increasing role of wildfires on air quality.

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.

Australian fires are a primary driver of variability in Australian atmospheric composition and contribute significantly to regional and global carbon budgets. However, biomass burning emissions from Australia remain highly uncertain. In this work, we use surface in situ, ground-based total column and satellite total column observations to evaluate the ability of two global models (GEOS-Chem and ACCESS-UKCA) and three global biomass burning emission inventories (FINN1.5, GFED4s, and QFED2.4) to simulate carbon monoxide (CO) in the Australian atmosphere. We find that emissions from northern Australia savanna fires are substantially lower in FINN1.5 than in the other inventories. Model simulations driven by FINN1.5 are unable to reproduce either the magnitude or the variability of observed CO in northern Australia. The remaining two inventories perform similarly in reproducing the observed variability, although the larger emissions in QFED2.4 combined with an existing high bias in the southern hemisphere background lead to large CO biases. We therefore recommend GFED4s as the best option of the three for global modelling studies with focus on Australia or the southern hemisphere. Near fresh fire emissions, the higher resolution ACCESS-UKCA model is better able to simulate surface CO than GEOS-Chem, while GEOS-Chem captures more of the observed variability in the total column and remote surface air measurements. We also show that existing observations in Australia can only partially constrain global model estimates of biomass burning. Continuous measurements in fire-prone parts of Australia are needed, along with updates to global biomass burning inventories that are validated with Australian data.

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.

The vertical accumulation of ozone and aerosol during an episode of the 2016 Southeastern United States Wildfires is analyzed by integrating a regional chemical transport model with ozonesonde, O$_3$ Differential Absorption Lidar (DIAL), ceilometer, surface monitors, and satellite products. The results indicate that measurements capture the vertical extent of the smoke plumes affecting the surface and upper air over Huntsville, AL, and also the enhanced ozone lamina in the plumes. Sensitivity simulations and tendency diagnostics characterize the chemical and physical processes affecting the vertical profiles downstream of the wildfires. The model results show that the net chemical ozone production (PO$_3$) dominates the daytime ozone accumulation by up to 19 ppb/10 hrs in the upper air over Huntsville. At the surface, the negative PO$_3$ is offset by positive O$_3$ contributions from vertical mixing and advection. Fire emissions increase the vertical ozone by affecting local chemical reactions, transportation, and vertical exchange. The dominant processes exhibit daily, diurnal, and vertical variability. Quantitatively, fire emissions increase the daytime positive PO$_3$ by up to 25\% in the upper air, and increase the daytime PM2.5 by up to 77\%. The capability of the regional model for reproducing the observations is explored. Increasing the fire aerosol emissions improves the model performance on domain-averaged PM2.5. The model captures the well-mixed aerosol in the boundary layer but fails to fully reproduce the densest plumes seen in the DIAL and satellite. The discrepancies are associated with poor satellite observing condition due to clouds and with uncertainties in emission inventories.