Estimating historical air-sea CO2 fluxes: Incorporating physical
knowledge within a data-only approach
Abstract
The ocean plays a critical role in reducing human impact on the global
climate by absorbing and sequestering CO2 from the atmosphere. To
quantify the ocean’s role in the global carbon budget, we need surface
ocean pCO2 across space and time, but only sparse observations exist.
The typical approach to reconstructing pCO2 is to train a machine
learning approach on a subset of the pCO2 data and available physical
and biogeochemical observations. Though the variables are all related to
the pCO2, these approaches are often perceived as black boxes, as it is
unclear how inputs are physically linked to pCO2 outputs. Here, we add
physics by incorporating our knowledge of the direct effect of
temperature on surface ocean pCO2. We use the machine learning algorithm
XGBoost to develop a function between satellite and in-situ observations
and the difference between observed pCO2 and the pCO2 that would exist
if temperature variations were the only driver of variability. We show
the resulting model is physically consistent, and performs at least as
well as other data approaches. Uncertainty in the reconstructed pCO2 and
its impact on the estimated CO2 fluxes are quantified. Uncertainty in
piston velocity drives flux uncertainties. The historical reconstructed
CO2 fluxes show larger interannual variability than the smoother neural
network approaches, but a lesser trend since 2005. We estimate an
air-sea flux of -2.3 +/- 0.5 PgC/yr for 1990-2018, agreeing with other
data products and the Global Ocean Carbon Budget models of 2021 estimate
of -2.3 +/- 0.4 PgC/yr.