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In order to fully understand current and future climate impacts from rising carbon emissions, it is crucial to accurately quantify the air-sea CO2 flux and the ocean carbon sink in space and time. Air-sea flux estimates from observation-based data products used in the Global Carbon Budget show a large spread, and suggest a stronger carbon sink than global ocean biogeochemistry models (GOBMs) in the last decade. Output from GOBMs and Earth system models (ESMs) can be used as ‘testbeds’ to better understand current estimates of ocean carbon uptake in time and space through sub-sampling experiments. Recent testbed studies show improvement in reconstruction skill with increasing observational coverage, but the direction (over- vs. underestimation) and magnitude of bias for ocean carbon uptake vary significantly. Here, we use a collection of CMIP6 ESMs as a testbed to better understand the causes of the spread of sink estimates in observation-based products. Specifically, we assess how the choice of hyperparameters for the machine learning algorithm and the testbed structure impact reconstruction skill of surface ocean pCO2 (spCO2) using the pCO2-Residual method. We find that, when negative mean squared error (nMSE) is used as error metric during hyperparameter optimization, the reconstruction significantly underestimates spCO2 over 2017-2022, irrespective of which CMIP6 ESM is used as a testbed; this results in an overestimation of the global ocean sink, assessed through comparison to the ‘testbed truth’. If hyperparameters are selected based on bias as the error metric, this trend of increasingly negative bias is eliminated. When applied to real-world SOCAT data, this leads to a significantly weaker global ocean carbon sink in 2021-2022 (up to ~ 0.5 Pg C/yr), and less divergence from GOBM estimates. This suggests that the increasingly stronger sink showed by the pCO2-Residual method in recent years might not represent a real trend, but may be due to algorithmic design choices in the context of sparse and biased observational coverage. 

Abstract In climate studies, it is crucial to distinguish between changes caused by natural variability and those resulting from external forcing. Here we use a suite of numerical experiments based on the ECCO‐Darwin ocean biogeochemistry model to separate the impact of the atmospheric carbon dioxide (CO₂) growth rate and climate on the ocean carbon sink — with a goal of disentangling the space‐time variability of the dominant drivers. When globally integrated, the variable atmospheric growth rate and climate exhibit similar magnitude impacts on ocean carbon uptake. At local scales, interannual variability in air‐sea CO₂flux is dominated by climate. The implications of our study for real‐world ocean observing systems are clear: in order to detect future changes in the ocean sink due to slowing atmospheric CO₂growth rates, better observing systems and constraints on climate‐driven ocean variability are required. 

Abstract The ocean has absorbed about 25% of the carbon emitted by humans to date. To better predict how much climate will change, it is critical to understand how this ocean carbon sink will respond to future emissions. Here, we examine the ocean carbon sink response to low emission (SSP1-1.9, SSP1-2.6), intermediate emission (SSP2-4.5, SSP5-3.4-OS), and high emission (SSP5-8.5) scenarios in CMIP6 Earth System Models and in MAGICC7, a reduced-complexity climate carbon system model. From 2020–2100, the trajectory of the global-mean sink approximately parallels the trajectory of anthropogenic emissions. With increasing cumulative emissions during this century (SSP5-8.5 and SSP2-4.5), the cumulative ocean carbon sink absorbs 20%–30% of cumulative emissions since 2015. In scenarios where emissions decline, the ocean absorbs an increasingly large proportion of emissions (up to 120% of cumulative emissions since 2015). Despite similar responses in all models, there remains substantial quantitative spread in estimates of the cumulative sink through 2100 within each scenario, up to 50 PgC in CMIP6 and 120 PgC in the MAGICC7 ensemble. We demonstrate that for all but SSP1-2.6, approximately half of this future spread can be eliminated if model results are adjusted to agree with modern observation-based estimates. Considering the spatial distribution of air-sea CO₂fluxes in CMIP6, we find significant zonal-mean divergence from the suite of newly-available observation-based constraints. We conclude that a significant portion of future ocean carbon sink uncertainty is attributable to modern-day errors in the mean state of air-sea CO₂fluxes, which in turn are associated with model representations of ocean physics and biogeochemistry. Bringing models into agreement with modern observation-based estimates at regional to global scales can substantially reduce uncertainty in future role of the ocean in absorbing anthropogenic CO₂from the atmosphere and mitigating climate change. 

Abstract. As the largest active carbon reservoir on Earth, the ocean is a cornerstone of the global carbon cycle, playing a pivotal role in modulating ocean health and regulating climate. Understanding these crucial roles requires access to a broad array of data products documenting the changing chemistry of the global ocean as a vast and interconnected system. This review article provides a comprehensive overview of 60 existing ocean carbonate chemistry data products, encompassing compilations of cruise datasets, derived gap-filled data products, model simulations, and compilations thereof. It is intended to help researchers identify and access data products that best align with their research objectives, thereby advancing our understanding of the ocean's evolving carbonate chemistry. 

Abstract The El Niño‐Southern Oscillation (ENSO) in the equatorial Pacific is the dominant mode of global air‐sea carbon dioxide (CO₂) flux interannual variability (IAV). Air‐sea CO₂fluxes are driven by the difference between atmospheric and surface ocean pCO₂, with variability of the latter driving flux variability. Previous studies found that models in Coupled Model Intercomparison Project Phase 5 (CMIP5) failed to reproduce the observed ENSO‐related pattern of CO₂fluxes and had weak pCO₂IAV, which were explained by both weak upwelling IAV and weak mean vertical dissolved inorganic carbon (DIC) gradients. We assess whether the latest generation of CMIP6 models can reproduce equatorial Pacific pCO₂IAV by validating models against observations‐based data products. We decompose pCO₂IAV into thermally and non‐thermally driven anomalies to examine the balance between these competing anomalies, which explain the total pCO₂IAV. The majority of CMIP6 models underestimate pCO₂IAV, while they overestimate sea surface temperature IAV. Insufficient compensation of non‐thermal pCO₂to thermal pCO₂IAV in models results in weak total pCO₂IAV. We compare the relative strengths of the vertical transport of temperature and DIC and evaluate their contributions to thermal and non‐thermal pCO₂anomalies. Model‐to‐observations‐based product comparisons reveal that modeled mean vertical DIC gradients are biased weak relative to their mean vertical temperature gradients, but upwelling acting on these gradients is insufficient to explain the relative magnitudes of thermal and non‐thermal pCO₂anomalies.