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Seasonal strength of terrestrial net ecosystem CO2 exchange from North America is underestimated in global inverse modeling
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  • Yuyan Cui,
  • Li Zhang,
  • Andrew R Jacobson,
  • Matthew S Johnson,
  • Sajeev Philip,
  • David Baker,
  • Frederic Chevallier,
  • Andrew E Schuh,
  • Junjie Liu,
  • Sean Crowell,
  • Helene Peiro,
  • Feng Deng,
  • Sourish Basu,
  • Kenneth J Davis
Yuyan Cui
The Pennsylvania State University

Corresponding Author:[email protected]

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Li Zhang
The Pennsylvania State University
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Andrew R Jacobson
NOAA/GML/CIRES
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Matthew S Johnson
NASA Ames Research Center
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Sajeev Philip
NASA Ames Research Center
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David Baker
Colorado State University
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Frederic Chevallier
Laboratoire des Sciences du Climat et de l'Environnement (LSCE)
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Andrew E Schuh
Colorado State University
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Junjie Liu
Jet Propulsion Laboratory
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Sean Crowell
University of Oklahoma
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Helene Peiro
University of Oklahoma
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Feng Deng
University of Toronto
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Sourish Basu
NASA GSFC GMAO / University of Maryland
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Kenneth J Davis
The Pennsylvania State University
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Abstract

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.