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  • Data-based estimates of interannual sea–air CO<sub>2</sub> flux variations 1957–2020 and their relation to environmental drivers
Title: Data-based estimates of interannual sea–air CO<sub>2</sub> flux variations 1957–2020 and their relation to environmental drivers
Abstract. This study considersyear-to-year and decadal variations in as well as secular trendsof the sea–air CO2 flux over the 1957–2020 period,as constrained by the pCO2 measurements from the SOCATv2021 database.In a first step,we relate interannual anomalies in ocean-internal carbon sources and sinksto local interannual anomalies insea surface temperature (SST), the temporal changes in SST (dSST/dt),and squared wind speed (u2),employing a multi-linear regression.In the tropical Pacific, we find interannual variability to be dominated by dSST/dt,as arising from variations in the upwelling of colder and more carbon-rich waters into the mixed layer.In the eastern upwelling zones as well as in circumpolar bands in the high latitudes of both hemispheres,we find sensitivity to wind speed,compatible with the entrainment of carbon-rich water during wind-driven deepening of the mixed layerand wind-driven upwelling.In the Southern Ocean,the secular increase in wind speed leads to a secular increase in the carbon source into the mixed layer,with an estimated reduction in the sink trend in the range of 17 % to 42 %.In a second step,we combined the result of the multi-linear regression andan explicitly interannual pCO2-based additive correctioninto a “hybrid” estimate of the sea–air CO2 flux over the period 1957–2020.As a pCO2 mapping method,it combines (a) the ability of a regression to bridge data gaps and extrapolate intothe early decades almost void of pCO2 databased on process-related observablesand (b) the ability of an auto-regressive interpolation to follow signalseven if not represented in the chosen set of explanatory variables.The “hybrid” estimate can be applied as an ocean flux prior foratmospheric CO2 inversions covering the whole period of atmospheric CO2 data since 1957.  more » « less
Award ID(s):
1948955
NSF-PAR ID:
10336025
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Biogeosciences
Volume:
19
Issue:
10
ISSN:
1726-4189
Page Range / eLocation ID:
2627 to 2652
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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