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.

The long-term net sink of carbon (C), nitrogen (N) and greenhouse gases (GHGs) in the northern permafrost region is projected to weaken or shift under climate change. But large uncertainties remain, even on present-day GHG budgets. We compare bottom-up (data-driven upscaling, process-based models) and top-down budgets (atmospheric inversion models) of the main GHGs (CO2, CH4, and N2O) and lateral fluxes of C and N across the region over 2000-2020. Bottom-up approaches estimate higher land to atmosphere fluxes for all GHGs compared to top-down atmospheric inversions. Both bottom-up and top-down approaches respectively show a net sink of CO2 in natural ecosystems (-31 (-667, 559) and -587 (-862, -312), respectively) but sources of CH4 (38 (23, 53) and 15 (11, 18) Tg CH4-C yr-1) and N2O (0.6 (0.03, 1.2) and 0.09 (-0.19, 0.37) Tg N2O-N yr-1) in natural ecosystems. Assuming equal weight to bottom-up and top-down budgets and including anthropogenic emissions, the combined GHG budget is a source of 147 (-492, 759) Tg CO2-Ceq yr-1 (GWP100). A net CO2 sink in boreal forests and wetlands is offset by CO2 emissions from inland waters and CH4 emissions from wetlands and inland waters, with a smaller additional warming from N2O emissions. Priorities for future research include representation of inland waters in process-based models and compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more in-situ flux observations and improved resolution in upscaling.

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.

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.

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE), and their relationship with environmental drivers, are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state-of-the-science flux products: NEE constrained by surface and space-based atmospheric CO2 measurements over 2010–2015 and satellite up-scaled GPP from FluxSat over 2001-2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems are dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models.

Top-down estimates of CO2 fluxes are typically constrained by either surface-based or space-based CO2 observations. Both of these measurement types have spatial and temporal gaps in observational coverage that can lead to biases in inferred fluxes. Assimilating both surface-based and space-based measurements concurrently in a flux inversion framework improves observational coverage and reduces sampling biases. This study examines the consistency of flux constraints provided by these different observations and the potential to combine them by performing a series of six-year (2010–2015) CO2 flux inversions. Flux inversions are performed assimilating surface-based measurements from the in situ and flask network, measurements from the Total Carbon Column Observing Network (TCCON), and space-based measurements from the Greenhouse Gases Observing Satellite (GOSAT), or all three datasets combined. Combining the datasets results in more precise flux estimates for sub-continental regions relative to any of the datasets alone. Combining the datasets also improves the accuracy of the posterior fluxes, based on reduced root-mean-square differences between posterior-flux-simulated CO2 and aircraft-based CO2 over midlatitude regions (0.35–0.50~ppm) in comparison to GOSAT (0.39–0.57~ppm), TCCON (0.52–0.63~ppm), or in situ and flask measurements (0.45–0.53~ppm) alone. These results suggest that surface-based and GOSAT measurements give complementary constraints on CO2 fluxes in the northern extratropics and can be combined in flux inversions to improve observational coverage. This stands in contrast with many earlier attempts to combine these datasets and suggests that improvements in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm have significantly improved the consistency of space-based and surface-based flux constraints.

The carbon cycle displays strong sensitivity to short term variations in environmental conditions, and it is key to understand how these variations are linked with variations in CO2 fluxes. Previously, atmospheric observations of CO2 have been sparse in many regions of the globe, making it challenging to evaluate these relationships. However, the OCO-2 satellite, launched in July 2014, provides new insight into global CO2 fluxes, particularly in regions that were previously difficult to monitor. In this study, we combine OCO-2 observations with a geostatistical inverse model to explore data-driven relationships between inferred CO2 flux patterns and environmental drivers. We further use year 2016 as an initial case study to explore the applicability of the geostatistical approach to large satellite-based inverse problems. We estimate daily, global CO2 fluxes at the model grid scale and find that a combination of air temperature, daily precipitation, and photosynthetically active radiation (PAR) best describe patterns in CO2 fluxes in most biomes across the globe. PAR is an adept predictor of fluxes across mid-to-high latitudes, whereas a combined set of daily air temperature and precipitation shows strong explanatory power across tropical biomes. However, we are unable to quantify a larger number of relationships between environmental drivers and CO2 fluxes using OCO-2 due to the limited sensitivity of total column satellite observations to detailed surface processes. Overall, we estimate a global net biospheric flux of -1.73 ± 0.53 GtC in year 2016, in close agreement with recent inverse modeling studies using OCO-2 retrievals as observational constraints.

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.

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.

The ACT-America Earth Venture mission conducted five airborne campaigns across four seasons from 2016-2019, to study the transport and fluxes of Greenhouse gases across the eastern United States (US). Unprecedented spatial sampling of atmospheric tracers (CO2, CO, and COS) related to biospheric processes offers opportunities to improve our qualitative and quantitative understanding of seasonal and spatial patterns of biospheric carbon uptake. Here, we examine co-variation of boundary layer enhancements of CO2, CO, and COS across three diverse regions: the crop-dominated Midwest, evergreen-dominated South, and deciduous broadleaf-dominated Northeast. To understand the biogeochemical processes controlling these tracers, we compare the observed co-variation to simulated co-variation resulting from model- and satellite- constrained surface carbon fluxes. We found indication of a common terrestrial biogenic sink of CO2 and COS and secondary production of CO from biogenic sources in summer throughout the eastern US. Stomatal conductance likely drives fluxes through diffusion of CO2 and COS into leaves and emission of biogenic volatile organic compounds into the atmosphere. ACT-America airborne campaigns filled a critical sampling gap in the southern US, providing information about seasonal carbon uptake in southern temperate forests, and demanding a deeper investigation of underlying biological processes and climate sensitivities. Satellite- constrained carbon fluxes capture much of the observed seasonal and spatial variability, but underestimate the magnitude of net CO2 and COS depletion in the Southeast, indicating a stronger than expected net sink in late summer.

Since the 1960s, carbon cycling in the high-latitude northern forest (HLNF) has experienced dramatic changes: most of the forest is greening and net carbon uptake from the atmosphere has increased. During the same time period, the COseasonal cycle amplitude (SCA) has almost doubled. Disentangling complex processes that drive these changes has been challenging. In this study, we substitute spatial sensitivity to temperature for time to quantify the impact of temperature increase on Gross Primary Production (GPP), total ecosystem respiration (TER), the fraction of Photosynthetic Active Radiation (fPAR), and the resulted contribution of these changes in amplifying the COSCA over the HLNF since 1960s. We use the spatial heterogeneity of GPP inferred from solar-induced chlorophyll Fluorescence in combination with net ecosystem exchange (NEE) inferred from column COobservations made between 2015 and 2017 from NASA’s Orbiting Carbon Observatory -2. We find that three quarters of the spatial variations in GPP and in the fPAR absorbed by the HLNF can be explained by the spatial variation in the growing season mean temperature (GSMT). The long term hindcast captures both the magnitude and spatial variability of the trends in observed fPAR. We estimate that between 1960 and 2010, the increase in GSMT enhanced both GPP and the SCA of NEE by ~20%. The calculated enhancement of NEE due to increase in GSMT contributes 56–72% of the trend in the CO SCA at high latitudes, much larger than simulations by most biogeochemical models.