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Quantifying the Impact of Atmospheric Transport Uncertainty on CO 2 Surface Flux Estimates
Author(s) -
Schuh Andrew E.,
Jacobson Andrew R.,
Basu Sourish,
Weir Brad,
Baker David,
Bowman Kevin,
Chevallier Frédéric,
Crowell Sean,
Davis Kenneth J.,
Deng Feng,
Denning Scott,
Feng Liang,
Jones Dylan,
Liu Junjie,
Palmer Paul I.
Publication year - 2019
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/2018gb006086
Subject(s) - environmental science , flux (metallurgy) , carbon cycle , atmospheric sciences , latitude , climatology , annual cycle , carbon dioxide in earth's atmosphere , carbon dioxide , physics , geology , chemistry , ecology , astronomy , organic chemistry , ecosystem , biology
We show that transport differences between two commonly used global chemical transport models, GEOS‐Chem and TM5, lead to systematic space‐time differences in modeled distributions of carbon dioxide and sulfur hexafluoride. The distribution of differences suggests inconsistencies between the transport simulated by the models, most likely due to the representation of vertical motion. We further demonstrate that these transport differences result in systematic differences in surface CO 2 flux estimated by a collection of global atmospheric inverse models using TM5 and GEOS‐Chem and constrained by in situ and satellite observations. While the impact on inferred surface fluxes is most easily illustrated in the magnitude of the seasonal cycle of surface CO 2 exchange, it is the annual carbon budgets that are particularly relevant for carbon cycle science and policy. We show that inverse model flux estimates for large zonal bands can have systematic biases of up to 1.7 PgC/year due to large‐scale transport uncertainty. These uncertainties will propagate directly into analysis of the annual meridional CO 2 flux gradient between the tropics and northern midlatitudes, a key metric for understanding the location, and more importantly the processes, responsible for the annual global carbon sink. The research suggests that variability among transport models remains the largest source of uncertainty across global flux inversion systems and highlights the importance both of using model ensembles and of using independent constraints to evaluate simulated transport.