Large volcanic eruptions drive significant climate perturbations through major anomalies in radiative fluxes and the resulting widespread cooling of the surface and upper ocean. Recent studies suggest that these eruptions also drive important variability in air‐sea carbon and oxygen fluxes. By simulating the Earth system using two initial‐condition large ensembles, with and without the aerosol forcing associated with the Mt. Pinatubo eruption in June 1991, we isolate the impact of this volcanic event on physical and biogeochemical properties of the ocean. The Mt. Pinatubo eruption forced significant anomalies in surface fluxes and the ocean interior inventories of heat, oxygen, and carbon. Pinatubo‐driven changes persist for multiple years in the upper ocean and permanently modify the ocean's heat, oxygen, and carbon inventories. Positive anomalies in oxygen concentrations emerge immediately post‐eruption and penetrate into the deep ocean. In contrast, carbon anomalies intensify in the upper ocean over several years post‐eruption, and are largely confined to the upper 150 m. In the tropics and northern high latitudes, the change in oxygen is dominated by surface cooling and subsequent ventilation to mid‐depths, while the carbon anomaly is associated with solubility changes and eruption‐generated El Niño—Southern Oscillation variability. We do not find significant impact of Pinatubo on oxygen or carbon fluxes in the Southern Ocean; but this may be due to Southern Hemisphere aerosol forcing being underestimated in Community Earth System Model 1 simulations.
Abstract. Using the Large Enemble Testbed, a collection of 100 members from four independent Earth system models, we test three general-purpose Machine Learning (ML) approaches to understand their strengths and weaknesses in statistically reconstructing full-coverage surface ocean pCO2 from sparse in situ data. To apply the Testbed, we sample the full-field model pCO2 as real-world pCO2 collected from 1982–2016 for each ensemble member. We then use ML approaches to reconstruct the full-field and compare with the original model full-field pCO2 to assess reconstruction skill. We use feed forward neural network (NN), XGBoost (XGB), and random forest (RF) approaches to perform the reconstructions. Our baseline is the NN, since this approach has previously been shown to be a successful method for pCO2 reconstruction. The XGB and RF allow us to test tree-based approaches. We perform comparisons to a test set, which consists of 20% of the real-world sampled data that are withheld from training. Statistical comparisons with this test set are equivalent to that which could be derived using real-world data. Unique to the Testbed is that it allows for comparison to all the "unseen" points to which the ML algorithms extrapolate. When compared to the test set, XGB and RF both perform better than NN based on a suite of regression metrics. However, when compared to the unseen data, degradation of performance is large with XGB and even larger with RF. Degradation is comparatively small with NN, indicating a greater ability to generalize. Despite its larger degradation, in the final comparison to unseen data, XGB slightly outperforms NN and greatly outperforms RF, with lowest mean bias and more consistent performance across Testbed members. All three approaches perform best in the open ocean and for seasonal variability, but performance drops off at longer time scales and in regions of low sampling, such as the Southern Ocean and coastal zones. For decadal variability, all methods overestimate the amplitude of variability and have moderate skill in reconstruction of phase. For this timescale, the greater ability of the NN to generalize allows it to slightly outperform XGB. Taking into account all comparisons, we find XGB to be best able to reconstruct surface ocean pCO2 from the limited available data.
As a result of anthropogenic activities, the global ocean is losing oxygen and gaining carbon. Observations indicate that the ocean's oxygen inventory has declined by about 2% in the 5 decades following 1960 as the upper ocean warms and stratifies (Ito et al., 2017;Schmidtko et al., 2017). This oxygen loss has major consequences for nutrient cycling, compression of marine ecosystem habitats, and global fisheries (Deutsch et al., 2015;Gruber, 2011;Keeling et al., 2010). Since pre-industrial times, the ocean has absorbed ∼170 Pg of anthropogenic carbon from the atmosphere (Canadell et al., 2021), which is beneficial for the mitigation of anthropogenic warming, but harmful to some organisms through the related decline in pH, known as ocean acidification.These long-term changes in ocean oxygen and carbon are superimposed on large interannual to multi-decadal variability, challenging the attribution of reported trends (
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