Fires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfire. We evaluated historical patterns of MODIS active fires in African tropical forests from 2003-2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year-to-year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015-2016 El Niño. These results contrast sharply with the drier African woodlands and savannas, where fires have been steadily decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon storage and their local implications for biodiversity and human livelihoods.

Previous studies done for the TROPOMI mission (e.g. Meister et al (2017), Hu et al (2016), Landgraf et al (2016)) has shown that inhomogeneous illumination within the field of view causes distortions in the instrument spectral response function (ISRF). The differences in the distorted ISRF and the ISRF measured in the pre-flight calibration lead to retrieval biases. It is known through laboratory trials that these distortions increase with wavelength and various methods have been employed to mitigate these issues, including continuous within-slit scanning and temporal oversampling. Recently, a hardware solution, called a slit homogenizer, has shown promise in reducing distortions due to inhomogeneous illumination. In this work, we used an instrument model of the Geostationary Carbon Cycle Observatory, GeoCarb, to simulate observations in the 0.76um, 1.6um, 2.05um, and 2.3um spectral bands with varying illuminations. We found that the posterior scatter and bias of Greenhouse Gas retrievals grow linearly with the coefficient of variation of illumination within a footprint. We also found that the ISRF distortion effects of inhomogeneous illumination are significantly reduced through the use of a slit homogenizer, though not completely resolved.

OCO-2, launched in 2014, uses reflected solar spectra and other retrieved geophysical variables to estimate (“retrieve”) the column averaged dry air mole fraction of CO2, termed XCO2. A critical issue in satellite estimates of trace greenhouse gasses and remote sensing at large is the error distribution of an estimated target variable which arises from instrument artifacts as well as the under-determined nature of the retrieval of the quantities of interest. A large portion of the error is often incurred during inference from measurement of retrieved physical variables. These residual errors are typically corrected using ground truth observations of the target variable or some other truth proxy. Previous studies used multilinear regression to model the error distribution with a few covariates from the retrieved state vector, sometimes termed “features.” This presentation will cover the bias correction of XCO2 error attributed to retrieved covariates with a novel approach utilizing explainable Machine Learning methods (XAI) on simulated sounding retrievals from GeoCarb. Utilization of non-linear models (Zhou, Grassotti 2020) or models that can capture non-linearity implicitly (Lorente et al. 2021) have been shown to improve on linear methods in operation. Our approach uses a gradient boosted decision tree ensemble method, XGBoost, that captures non-linear relations between input features and the target variable. XGBoost also incorporates regularization to prevent overfitting, while also remaining resilient to noise and large outliers – a feature missing from other ensemble DT methods. Decision Tree based models provide inherent feature importance that allows for high interpretability. We also approach post training analysis with model agnostic, explainable methods (XAI). XAI methods allow for rigorous insight into the causes of a model’s decision (Gilpin et al. 2018). By applying these techniques, we will demonstrate our approach provides reduced residual errors relative to the operational method as well as yielding an uncertainty estimate in bias corrected XCO2, which is currently not treated separately from the posterior uncertainty estimate derived from the retrieval algorithm.

The 2020 California fire season yielded the largest fires in California history. TROPOMI nitrogen dioxide (NO2) and carbon monoxide (CO) ratios (i.e. mole density ratios; MDR) have been previously used to distinguish fire combustion among different vegetation types around the world. Here, we analyze the MDR from TROPOMI overpasses to identify fire characteristics over two regions of California where two wildfires were reported. We show that MDR calculated with TROPOMI NO2 and CO columns can be used to assess fire characteristics at the scale of counties through identifying the smoldering fires of the northern region and flaming fire of the southern region with a shift in the combustion at the end of September resulting from a change in fuel types. Improvement in the land cover maps and use of hourly data could help to better attribute MDR to specific fuel source categories.

The ability of current global models to simulate the transport of CO2 by mid-latitude, synoptic-scale weather systems (i.e. CO2 weather) is important for inverse estimates of regional and global carbon budgets but remains unclear without comparisons to targeted measurements. Here, we evaluate ten models that participated in the Orbiting Carbon Observatory-2 model intercomparison project (OCO-2 MIP version 9) with intensive aircraft measurements collected from the Atmospheric Carbon Transport (ACT)-America mission. We quantify model-data differences in the spatial variability of CO2 mole fractions, mean winds, and boundary layer depths in 27 mid-latitude cyclones spanning four seasons over the central and eastern United States. We find that the OCO-2 MIP models are able to simulate observed CO2 frontal differences with varying degrees of success in summer and spring, and most underestimate frontal differences in winter and autumn. The models may underestimate the observed boundary layer-to-free troposphere CO2 differences in spring and autumn due to model errors in boundary layer height. Attribution of the causes of model biases in other seasons remains elusive. Transport errors, prior fluxes, and/or inversion algorithms appear to be the primary cause of these biases since model performance is not highly sensitive to the CO2 data used in the inversion. The metrics presented here provide new benchmarks regarding the ability of atmospheric inversion systems to reproduce the CO2 structure of mid-latitude weather systems. Controlled experiments are needed to link these metrics more directly to the accuracy of regional or global flux estimates.

Past studies have demonstrated that synoptic weather events play an important role in the spatial and temporal variations of atmospheric carbon dioxide ( within and above the boundary layer. In this study, we investigate the spatial variability of column average CO2 dry air mole fraction (XCO2) due to the impact of synoptic-scale transport using retrievals from the Orbiting Carbon Observatory-2 for 66 summer cold frontal cases over the conterminous U.S. and Mexico above 20°N from 2015 to 2019. The results show that cold fronts in summer are in general agreement with data from the Atmospheric Chemistry and Transport (ACT-America) field campaign observations, which are significantly different compared to non-frontal spatial distributions in summer, though with reduced magnitude due to their nature as a column average as opposed to an in situ measurements in the boundary layer and free troposphere.

Sources and sinks of the two most important greenhouse gases CO2 and CH4 at regional to continental scales remain poorly understood. In our previous work, the WRF-VPRM, a weather-biosphere-online-coupled model in which the biogenic CO2 fluxes are handled by the Vegetation Photosynthesis and Respiration Model (VPRM), was further developed by coupling with the CarbonTracker global CO2 simulation and incorporating optimized terrestrial CO2 flux parameterization (Hu et al., 2021; Hu et al., 2020). In this work, an enhanced version of WRF-VPRM by including CH4 (referred to as WRF-GHG hereafter) is further developed by coupling with the Copernicus Atmosphere Monitoring Service (CAMS) CH4 global simulation for the initial and boundary conditions and the WetCHARTs wetland CH4 emissions and NEI2017 anthropogenic CH4 emissions, which dominate emissions over the contiguous United States (CONUS). Yearly WRF-GHG simulations are conducted for year 2018 and 2019 over CONUS at a horizontal grid spacing of 12 km to examine the impact of 2019 abnormal mid-west precipitation on CO2 and CH4 fluxes and atmospheric concentrations, with the simulation for 2018 serving as a baseline for comparison, similarly to Yin et al (2020). Simulated CO2 and CH4 are evaluated using remotely sensed data from Total Carbon Column Observing Network (TCCON), OCO-2, TROPOMI, and in-situ measurements from the GLOBALVIEW obspack data. WRF-GHG has been shown to capture the monthly variation of column-averaged CO2 concentrations (XCO2) and episodic variations associated with frontal passages. In this work, we will show that TCCON XCH4 shows mild seasonal variation and more prominent episodic variations, which are captured by WRF-GHG. As a case study, the 2019 May flood delayed growing season in mid-west and the typical spring and summer drawdown of atmospheric CO2 by 1-3 weeks. Obspack and TROPOMI data indicate higher CH4 in the mid-west in July and August, in 2019 relative to 2018, which we hypothesize is related to the abnormal precipitation in 2019 in the region that induces more wetland CH4 emissions. The WRF-GHG model significantly underestimates CH4 concentration in mid-west in summer 2019 when the WetCHARTs wetland CH4 emissions are driven by ERA-Interim reanalysis precipitation, which is known to be underestimated. An updated WetCHARTs wetland CH4 emissions driven by the PRISM precipitation data are currently being produced at JPL, which are expected to reduce the WRF-GHG CH4 bias, as wetland fluxes are highly sensitive to inundation from precipitation.

We evaluate terrestrial net ecosystem-atmosphere exchange (NEE) of CO2 from nine global inversion systems that inferred fluxes from four CO2 observational sources. We use 98 flights in the central and eastern U.S. from the ACT-America aircraft mission to conduct this sub-continental, seasonal-scale evaluation. We use Lagrangian particle dispersion modeling (FLEXPARTv10.4-ERA-Interim) to compare observed and simulated regional biogenic CO2 mole fractions. We find a positive bias (modeled CO2 > observed) in the summer and negative bias (modeled CO2 < observed) in dormant seasons across most flux products, suggesting that the seasonal strength of CO2 NEE is underestimated in these inverse models. Fluxes inferred from OCO-2 v9 satellite land nadir/glint observations yield an error level that is similar to fluxes inferred from in-situ data. Large bias errors are observed in the croplands and eastern forests. Future experiments are needed to determine if these seasonal biases are associated with biases in net annual flux estimates.

Enhanced CO2 mole fraction bands were often observed immediately ahead of cold front during the Atmospheric Carbon and Transport (ACT)-America mission and their formation mechanism is undetermined. Improved understanding and correct simulation of these CO2 bands are needed for unbiased inverse CO2 flux estimation. Such CO2 bands are hypothesized to be related to nighttime CO2 respiration and investigated in this study using WRF-VPRM, a weather-biosphere-online-coupled model, in which the biogenic fluxes are handled by the Vegetation Photosynthesis and Respiration Model (VPRM). While the default VPRM satisfactorily parameterizes gross ecosystem exchange, its treatment of terrestrial respiration as a linear function of temperature was inadequate as respiration is a nonlinear function of temperature and also depends on the amount of biomass and soil wetness. An improved ecosystem respiration parameterization including enhanced vegetation index, a water stress factor, and a quadratic temperature dependence is incorporated into WRF-VPRM and evaluated in a year-long simulation before applied to the investigation of the frontal CO2 band on 4 August 2016. The evaluation shows that the modified WRF-VPRM increases ecosystem respiration during the growing season, and improves model skill in reproducing nighttime near-surface CO2 peaks. A nested-domain WRF-VPRM simulation is able to capture the main characteristics of the 4 August CO2 band and informs its formation mechanism. Nighttime terrestrial respiration leads to accumulation of near-surface CO2 in the region. As the cold front carrying low-CO2 air moves southeastward, and strong photosynthesis depletes CO2 further southeast of the front, a CO2 band develops immediately ahead of the front.