Variability of North Atlantic CO2 fluxes for the 2000–2017 period estimated from atmospheric inverse analyses
QE1-996.5
550
Ecology
Geology
551
01 natural sciences
Life
13. Climate action
QH501-531
14. Life underwater
QH540-549.5
0105 earth and related environmental sciences
DOI:
10.5194/bg-18-4549-2021
Publication Date:
2021-08-09T08:42:49Z
AUTHORS (6)
ABSTRACT
Abstract. We present new estimates of the regional North Atlantic
(15–80∘ N) CO2 flux for the 2000–2017 period
using atmospheric CO2 measurements from the NOAA long-term surface site
network in combination with an atmospheric carbon cycle data assimilation
system (GEOS-Chem–LETKF, Local Ensemble Transform Kalman Filter). We assess the sensitivity of flux estimates to
alternative ocean CO2 prior flux distributions and to the
specification of uncertainties associated with ocean fluxes. We present a
new scheme to characterize uncertainty in ocean prior fluxes, derived from a
set of eight surface pCO2-based ocean flux products, and which
reflects uncertainties associated with measurement density and
pCO2-interpolation methods. This scheme provides improved model
performance in comparison to fixed prior uncertainty schemes, based on
metrics of model–observation differences at the network of surface sites.
Long-term average posterior flux estimates for the 2000–2017
period from our GEOS-Chem–LETKF analyses are −0.255 ± 0.037 PgC yr−1 for the subtropical basin (15–50∘ N) and
−0.203 ± 0.037 PgC yr−1 for the subpolar region (50–80∘ N, eastern boundary at 20∘ E). Our basin-scale
estimates of interannual variability (IAV) are 0.036 ± 0.006 and 0.034 ± 0.009 PgC yr−1 for subtropical and subpolar
regions, respectively. We find statistically significant trends in carbon
uptake for the subtropical and subpolar North Atlantic of −0.064 ± 0.007 and −0.063 ± 0.008 PgC yr−1 decade−1; these trends are
of comparable magnitude to estimates from surface ocean pCO2-based
flux products, but they are larger, by a factor of 3–4, than trends estimated from
global ocean biogeochemistry models.
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