A seamless coastal to global ocean pCO2 and pH reconstruction at a 0.25-degree resolution with extrapolation to the near future

Abstract

Observational networks monitoring marine carbon variables are established to meet the critical need to estimate ocean CO2 uptake, as well as assessing its consequences on ocean health through changes in carbonate chemistry (ocean acidification). Despite considerable efforts over the past decades, data coverage is still sparse over large ocean regions, prompting the implementation of mapping methods to gap-fill carbon datasets over the globe. Different statistical approaches have been proposed with the aim to generate reconstructions of the complete marine CO2 system at high spatial-temporal resolutions. Following this goal, we first introduce a global reconstruction of surface ocean partial pressure of CO2 (pCO2) at monthly and 0.25-degree resolutions over the period 1985-2021. This high-resolution pCO2 product is derived from ensemble neural network models interpolating monthly gridded observation-based data from Surface Ocean CO2 ATlas (SOCAT). We will assess the ability of the proposed pCO2 ensemble (1) to derive long-term time series of pCO2 and associated 1-sigma uncertainty per 0.25-degree grid cell for each month, (2) to reproduce temporal and horizontal gradients of coastal pCO2 observations in comparison with a coarser spatial resolution, (3) to estimate surface ocean pH and air-sea CO2 fluxes. Furthermore, we will present an extension of the ensemble neural network models, which is referred to as a new module extrapolating pCO2 to several years ahead. The extended ensemble-based approach will ultimately be used to project global ocean CO2 uptake and ocean acidification with low latency.