Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020)

Abstract

Abstract. Here we present a global and regionally resolved terrestrial net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020). It is estimated using the NASA Carbon Monitoring System Flux (CMS-Flux) top-down flux inversion system that assimilates column CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT) and NASA's Observing Carbon Observatory 2 (OCO-2). The regional monthly fluxes are readily accessible as tabular files, and the gridded fluxes are available in NetCDF format. The fluxes and their uncertainties are evaluated by extensively comparing the posterior CO2 mole fractions with CO2 observations from aircraft and the NOAA marine boundary layer reference sites. We describe the characteristics of the dataset as the global total, regional climatological mean, and regional annual fluxes and seasonal cycles. We find that the global total fluxes of the dataset agree with atmospheric CO2 growth observed by the surface-observation network within uncertainty. Averaged between 2010 and 2018, the tropical regions range from close to neutral in tropical South America to a net source in Africa; these contrast with the extra-tropics, which are a net sink of 2.5±0.3 Gt C/year. The regional satellite-constrained NBE estimates provide a unique perspective for understanding the terrestrial biosphere carbon dynamics and monitoring changes in regional contributions to the changes of atmospheric CO2 growth rate. The gridded and regional aggregated dataset can be accessed at https://doi.org/10.25966/4v02-c391 (Liu et al., 2020).

Covariation of Airborne Biogenic Tracers (CO2, COS, and CO) Supports Stronger Than Expected Growing Season Photosynthetic Uptake in the Southeastern US

“…Global top-down inversion studies leveraging surface-based CO 2 stations in northern latitudes (CarbonTracker, CT2019) indicate strong and persistent CO 2 uptake in North America (NA) of ∼0.6 Pg C from 2001 to 2018 (https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/), driven by temperate ecosystems in the eastern US (east of the Rockies) and in southern Canada (Peters et al, 2007). Recent inversion efforts that incorporate satellite-based CO 2 observations support these estimates for temperate eastern North America, showing a statistically significant sink of similar magnitude (∼0.5 Pg C) over the period 2010-2018 (Liu, Baskaran, et al, 2020). These results are encouraging as we move toward combined surface-and satellite-based inversion approaches to improve spatially and temporally integrated constraints of net CO 2 exchange at regional and global scales, and advance regional-scale understanding of terrestrial CO 2 sinks (e.g., Byrne, Liu, Bloom, et al, 2020;Byrne, Liu, Lee, et al, 2020).…”

Changes in global terrestrial live biomass over the 21st century

“…Process-based models that include the effect of the CO 2 fertilization equally distribute the net carbon gain between tropical and northern ecosystems (8,16). Interhemispheric atmospheric CO 2 analyses indicate that the tropics is either carbon neutral or a small net source (17), while the northern ecosystems are an increasing net sink (3,(18)(19)(20). Resolving these discrepancies from multiple approaches requires reducing the uncertainty in estimates of carbon loss and gain regionally.…”

Evaluation of CarbonTracker's Inverse Estimates of North American Net Ecosystem Exchange of CO2 From Different Observing Systems Using ACT‐America Airborne Observations

“…Atmospheric CO 2 measurements, on which the top-down method relies, can contribute powerful constraints to the bottom-up methods (e.g., Ogle et al, 2015). Different atmospheric CO 2 measurement platforms such as boundary-layer CO 2 mole fractions from ground-based networks (e.g., Andrews et al, 2014;Miles et al, 2012) and column-averaged CO 2 mole fractions (XCO 2 ) from satellites (e.g., Liu et al, 2020), aim to complement each other. Measurement biases, atmospheric transport errors, or representation errors, however, may cause difficulty in assimilating these measurements within the optimization process.…”