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Abstract. The Southern Ocean is highly under-sampled for the purpose of assessing total carbon uptake and its variability. Since this region dominates the mean global ocean sink for anthropogenic carbon, understanding temporal change is critical. Underway measurements of pCO2 collected as part of the Drake Passage Time-series (DPT) program that began in 2002 inform our understanding of seasonally changing air–sea gradients in pCO2, and by inference the carbon flux in this region. Here, we utilize available pCO2 observations to evaluate how the seasonal cycle, interannual variability, and long-term trends in surface ocean pCO2 in the Drake Passage region compare to that of the broader subpolar Southern Ocean. Our results indicate that the Drake Passage is representative of the broader region in both seasonality and long-term pCO2 trends, as evident through the agreement of timing and amplitude of seasonal cycles as well as trend magnitudes both seasonally and annually. The high temporal density of sampling by the DPT is critical to constraining estimates of the seasonal cycle of surface pCO2 in this region, as winter data remain sparse in areas outside of the Drake Passage. An increase in winter data would aid in reduction of uncertainty levels. On average over the period 2002–2016, data show that carbon uptake has strengthened with annual surface ocean pCO2 trends in the Drake Passage and the broader subpolar Southern Ocean less than the global atmospheric trend. Analysis of spatial correlation shows Drake Passage pCO2 to be representative of pCO2 and its variability up to several hundred kilometers away from the region. We also compare DPT data from 2016 and 2017 to contemporaneous pCO2 estimates from autonomous biogeochemical floats deployed as part of the Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM) so as to highlight the opportunity for evaluating data collected on autonomous observational platforms. Though SOCCOM floats sparsely sample the Drake Passage region for 2016–2017 compared to the Drake Passage Time-series, their pCO2 estimates fall within the range of underway observations given the uncertainty on the estimates. Going forward, continuation of the Drake Passage Time-series will reduce uncertainties in Southern Ocean carbon uptake seasonality, variability, and trends, and provide an invaluable independent dataset for post-deployment assessment of sensors on autonomous floats. Together, these datasets will vastly increase our ability to monitor change in the ocean carbon sink.
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This content will become publicly available on April 13, 2026
Targeting bias in algorithm optimization improves reconstructions of surface ocean pCO2
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.
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- Award ID(s):
- 2019625
- PAR ID:
- 10611081
- Publisher / Repository:
- Machine Learning: Earth
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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