Improved latitudinal carbon budgets from global airborne surveys
Abstract
Robust information on the spatial distribution of global carbon fluxes is required to project the future trajectory of carbon-climate feedback effects and atmospheric CO 2 concentrations. Estimates of the latitudinal partitioning of carbon fluxes from top-down atmospheric CO 2 inverse models currently diverge widely, because of methodological limitations or systematic biases in models or observations. We use airborne CO 2 observations from the NASA Atmospheric Tomography Mission to evaluate and refine inverse model estimates from the Orbiting Carbon Observatory version 10 Model Intercomparison Project of total CO 2 exchange for the two-year period of June 2016–May 2018. Applying emergent concentration-flux relationships as constraints reduces zonal total flux uncertainties by 46 to 56% relative to the full v10 MIP ensemble and by 17 to 28% relative to the subset excluding satellite observations over ocean. Subtracting independent estimates of fossil-fuel emissions and air-sea gas exchange results in residual land fluxes with a large northern extratropical sink, a small southern extratropical sink, and a small tropical source. The airborne-derived tropical land source disagrees with a large tropical land sink from process-based terrestrial models combined with estimates of land use emissions and river fluxes, representing an important challenge for our understanding of the global carbon cycle. The large implied northern extratropical sink can be explained either by underestimated land uptake by process models or a combination of process model bias and overestimated fossil fuel emissions.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (22)
Britton B. Stephens
NSF National Center for Atmospheric Research, Earth Observing Laboratory
Yuming Jin
NSF National Center for Atmospheric Research, Earth Observing Laboratory
Colm Sweeney
National Oceanic and Atmospheric Administration Global Monitoring Laboratory
Kathryn McKain
National Oceanic and Atmospheric Administration Global Monitoring Laboratory
Benjamin Gaubert
NSF National Center for Atmospheric Research, Earth Observing Laboratory
David F. Baker
Cooperative Institute for Research in the Atmosphere, Colorado State University
Sourish Basu
NASA Goddard Space Flight Center, Global Modeling and Assimilation Office
Michael Bertolacci
School of Physics, Mathematics and Computing, Mathematics and Statistics, University of Western Australia
Frédéric Chevallier
Laboratoire des Sciences du Climat et de L’Environnement Institut Pierre Simon Laplace, Université Paris-Saclay
Róisín Commane
Department of Earth and Environmental Sciences, Lamont Doherty Earth Observatory
Sean Crowell
Department of Earth and Environmental Sciences, University of Rochester
Feng Deng
National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Wuhan Institute of Physics and Mathematics
Matthew S. Johnson
Earth Science Division, NASA Ames Research Center
Ralph F. Keeling
Geosciences Research Division, Scripps Institution of Oceanography
Junjie Liu
Institute of Molecular Physiology
Zhiqiang Liu
Suman Maity
Japan Agency for Marine-Earth Science and Technology, Yokohama Institute for Earth Sciences
Eric J. Morgan
Geosciences Research Division, Scripps Institution of Oceanography
Prabir Patra
Japan Agency for Marine-Earth Science and Technology, Yokohama Institute for Earth Sciences
Sajeev Philip
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi
Steven C. Wofsy
School of Engineering and Applied Sciences, Harvard University
Andrew Zammit-Mangion
School of Mathematics and Applied Statistics, University of Wollongong