Quantifying errors in ocean carbon reconstructions in the context of marine carbon dioxide removal (mCDR) - Preprint

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Aug 6, 2025, 6:23:52 AM8/6/25

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https://essopenarchive.org/users/915901/articles/1321083-quantifying-errors-in-ocean-carbon-reconstructions-in-the-context-of-marine-carbon-dioxide-removal-mcdr

Authors

Thea Hatlen Heimdal,

Galen A McKinley,

Abby P Shaum,

Viviana Acquaviva,

Amanda R Fay,

Adrienne J. Sutton

04 August 2025

Abstract

The Intergovernmental Panel on Climate Change (IPCC) suggests that carbon dioxide removal (CDR) will be needed to stabilize global atmospheric temperatures at <2 °C above the pre-industrial baseline. The ocean sequesters anthropogenic carbon without direct intervention through the background ocean carbon sink, a process with large variability in both time and space that complicates its quantification. A major challenge for the effective implementation of marine CDR (mCDR) technologies will be to quantify its additionality above background ocean carbon uptake. Here, we quantify reconstruction errors in one machine learning method for monthly mean surface ocean pCO2 using a high-resolution (0.1°x0.1°) model as a testbed. We sub-sample this model based on the observational coverage of pCO2 in SOCAT for years 2020-2021 and compare the pCO2 reconstructions to the model full-field pCO2 ‘truth’. We focus on three potential mCDR deployment areas: the Gulf, the Bering Sea and the Caribbean Sea. We show that reconstruction RMSEs are generally >4 μatm in the Caribbean Sea and the Gulf and >10 μatm within the Bering Sea, which is larger than expected mCDR signals (<2 μatm). By simulating the addition of globally distributed floats with no pCO2 bias, increasing the 2020-2021 coverage from 0.5% to 0.7%, reconstruction errors ≤2 μatm are achieved regionally within 30°S and 30°N in the open ocean, but not in the three study areas. We conclude that current estimates of the background ocean carbon sink must be significantly improved before mCDR additionality can be accurately quantified.