Extreme climate events are becoming more frequent, with poorly understood implications for carbon sequestration by terrestrial ecosystems. A better understanding will critically depend on accurate and precise quantification of ecosystems responses to these events. Taking the 2019 US Midwest floods as a case study, we investigate current capabilities for tracking regional flux anomalies with “top-down” inversion analyses that assimilate atmospheric CO2 observations. For this analysis, we develop a regionally nested version of the NASA Carbon Monitoring System-Flux (CMS-Flux) that allows high resolution atmospheric transport (0.5° × 0.625°) over a North America domain. Relative to a 2018 baseline, we find US Midwest growing season net carbon uptake is reduced by 11-57 TgC (3-16%) for 2019 (inversion mean estimates across experiments). These estimates are found to be consistent with independent “bottom-up” estimates of carbon uptake based on vegetation remote sensing. We then investigate current limitations in tracking regional carbon emissions and removals by ecosystems using “top-down” methods. In a set of observing system simulation experiments, we show that the ability to recover regional carbon flux anomalies is still limited by observational coverage gaps for both in situ and satellite observations. Future space-based missions that allow for daily observational coverage across North America would largely mitigate these observational gaps, allowing for improved top-down estimates of ecosystem responses to extreme climate events.

While the preindustrial ocean was assumed to be in equilibrium with the atmosphere, the modern ocean is a carbon sink, resulting from natural variability and anthropogenic perturbations, such as fossil fuel emissions and changes in riverine exports over the past two centuries. Here we use a suite of sensitivity experiments based on the ECCO-Darwin global-ocean biogeochemistry model to evaluate the response of air-sea CO2 flux and carbon cycling to present-day lateral fluxes of carbon, nitrogen, and silica. We generate a daily export product by combining point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral fluxes from 5171 watersheds worldwide. From 2000 to 2019, carbon exports increase CO2 outgassing by 0.22 Pg C yr-1 via the solubility pump, while nitrogen exports increase the ocean sink by 0.17 Pg C yr-1 due to phytoplankton fertilization. On regional scales, exports to the Tropical Atlantic and Arctic Ocean are dominated by organic carbon, which originates from terrestrial vegetation and peats and increases CO2 outgassing (+10 and +20%, respectively). In contrast, Southeast Asia is dominated by nitrogen from anthropogenic sources, such as agriculture and pollution, leading to increased CO2 uptake (+7%). Our results demonstrate that the magnitude and composition of riverine exports, which are determined in part from upstream watersheds and anthropogenic perturbations, substantially impact present-day regional-to-global-ocean carbon cycling. Ultimately, this work stresses that lateral fluxes must be included in ocean biogeochemistry and Earth System Models to better constrain the transport of carbon, nutrients, and metals across the land-ocean-aquatic-continuum.

This study explores an optimal inversion strategy for assimilating the Orbiting Carbon Observatory-2 (OCO-2) column-averaged atmospheric CO2 concentration (XCO2) observations to constrain air-sea CO2 fluxes. The performance of different inversion set-ups is evaluated through Observing System Simulation Experiments (OSSEs) by comparing the optimized fluxes with assumed true fluxes. The results indicate that the conventional inversion, simultaneously optimizing terrestrial biosphere and air-sea fluxes, reduces root mean square errors (RMSEs) in regional monthly air-sea fluxes by up to 22–24% and 6–10% in the low (<40°) and high (>40°) latitudes, respectively, with up to 22% error reduction in global annual air-sea fluxes. These limited adjustments are associated with an order of magnitude higher variability of terrestrial biosphere fluxes compared to the air-sea fluxes. To isolate ocean signals within XCO2 variations, we employ a sequential inversion, first optimizing terrestrial biosphere fluxes with land XCO2 data and then optimizing air-sea fluxes with ocean XCO2 data while prescribing the optimized terrestrial biosphere fluxes. This approach achieves an 11% additional error reduction in global annual air-sea fluxes and a 33% further RMSE reduction in monthly air-sea fluxes in the southern high latitudes. However, we find that potential biases (+0.2 ppm) in ocean XCO2 measurements over this region could induce a 24% RMSE increase despite the application of sequential inversion. Our results show that sequential inversion is a promising technique for improving seasonal air-sea flux estimates in the Southern Ocean but mitigation of OCO-2 measurement biases is required for practical applications.

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.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes due in large part to their different lifetimes. Here, we discuss two key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed. Second, it demonstrated empirically that the response of atmospheric composition to emissions changes is heavily modulated by factors including carbon cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality.

Efforts to slow the transmission of COVID-19 led to rapid, global ancillary reductions in air pollutant emissions. Here, we quantify the resulting decreases in global NOx emissions and their consequent impact on the production of global tropospheric ozone using a multi-constituent data assimilation system. Total anthropogenic NOx emissions were reduced by at least 15% globally and 18-25% for Europe, North America, and the Middle East in April and May 2020. The efficacy of these reductions in altering ozone concentrations varied substantially in both space and time, with differences driven by local meteorology and chemical production efficiency. Globally, the total tropospheric ozone burden dropped by about 6 TgO 3 (∼2%) in May-June 2020, largely due to emission reductions in Asia and the Americas. Our results show a clear and global atmospheric imprint from COVID-19 mitigation, which altered the atmospheric oxidative capacity, climate radiative forcing, and human health.

The Atmospheric Carbon and Transport (ACT) – America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. Two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five, six-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO2 and CH4 fluxes and atmospheric GHG transport realizations. The ACT-America data set and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise sub-continental GHG flux estimates.

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.

2019 was both the hottest and driest year on record for Australia, leading to large forest fires in the southeast from November 2019 to January 2020. However, in early 2020, the fires and hot-dry conditions dissipated with above average rainfall and below average temperatures along Australia’s southeast coast. In this study, we utilize space-based measurements of trace gases (TROPOMI XCO, OCO-2 XCO2) and vegetation function (OCO-2 SIF, MODIS NDVI) to quantify the carbon cycle anomalies resulting from drought and fire in southeast Australia during the 2019/2020 growing season. During the austral spring, we find anomalous reductions in primary productivity and large biomass burning emissions in excess of bottom-up estimates from GFAS. This is then followed by a remarkable recovery and greening during early 2020, coincident with cooler and wetter conditions. We will further discuss different behaviors of recovery over fire-devasted and non-fire regions. This study showcases the capability of combining observations from multiple satellites to monitor the carbon and ecosystem anomalies resulting from extreme events. Finally, we will discuss the remaining challenges in monitoring the carbon cycle from space.