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Barystatic sea level rise caused by the addition of freshwater to the ocean from melting ice can in principle be recorded by a reduction in seawater salinity, but detection of this signal has been hindered by sparse data coverage and the small trends compared to natural variability. Here, we develop an autoregressive machine learning method to estimate salinity changes in the global ocean from 2001-2019 that reduces uncertainties in ocean freshening trends by a factor of four compared to previous estimates. We find that the ocean mass rose by 13,000±3,000 Gt from 2001-2019, implying a barystatic sea level rise of 2.0±0.5 mm/yr. Combined with sea level rise of 1.3±0.1 mm/yr due to ocean thermal expansion, these results suggest that global mean sea level rose by 3.4±0.6 mm/yr from 2001-2019. These results provide an important validation of remote-sensing measurements of ocean mass changes, global sea level rise, and global ice budgets. 

Datasets and code for global ocean heat analysis using an autoregressive artificial neural network (ARANN).Cooling of the global ocean below 2000 m counteracted some of the warming of the shallow ocean over much of the late 20th century. This trend has shifted to warming, leading the deep ocean to absorb a meaningful fraction of total ocean heat during the 21st century. 

Abstract Barystatic sea level rise (SLR) caused by the addition of freshwater to the ocean from melting ice can in principle be recorded by a reduction in seawater salinity, but detection of this signal has been hindered by sparse data coverage and the small trends compared to natural variability. Here, we develop an autoregressive machine learning method to estimate salinity changes in the global ocean from 2001 to 2019 that reduces uncertainties in ocean freshening trends by a factor of four compared to previous estimates. We find that the ocean mass rose by 13,000 ± 3,000 Gt from 2001 to 2019, implying a barystatic SLR of 2.0 ± 0.5 mm/yr. Combined with SLR of 1.3 ± 0.1 mm/yr due to ocean thermal expansion, these results suggest that global mean sea level rose by 3.4 ± 0.6 mm/yr from 2001 to 2019. These results provide an important validation of remote‐sensing measurements of ocean mass changes, global SLR, and global ice budgets. 

Abstract Historical estimates of ocean heat content (OHC) are important for understanding the climate sensitivity of the Earth system and for tracking changes in Earth’s energy balance over time. Prior to 2004, these estimates rely primarily on temperature measurements from mechanical and expendable bathythermograph (BT) instruments that were deployed on large scales by naval vessels and ships of opportunity. These BT temperature measurements are subject to well-documented biases, but even the best calibration methods still exhibit residual biases when compared with high-quality temperature datasets. Here, we use a new approach to reduce biases in historical BT data after binning them to a regular grid such as would be used for estimating OHC. Our method consists of an ensemble of artificial neural networks that corrects biases with respect to depth, year, and water temperature in the top 10 m. A global correction and corrections optimized to specific BT probe types are presented for the top 1800 m. Our approach differs from most prior studies by accounting for multiple sources of error in a single correction instead of separating the bias into several independent components. These new global and probe-specific corrections perform on par with widely used calibration methods on a series of metrics that examine the residual temperature biases with respect to a high-quality reference dataset. However, distinct patterns emerge across these various calibration methods when they are extrapolated to BT data that are not included in our cross-instrument comparison, contributing to uncertainty that will ultimately impact estimates of OHC. 

Historical estimates of ocean heat content (OHC) are important for understanding the climate sensitivity of the Earth system, and for tracking changes in the Earth’s energy balance over time. Prior to 2004, these estimates rely primarily on temperature measurements from mechanical and expendable bathythermograph (BT) instruments that were deployed on large scales by naval vessels and ships of opportunity. These BT temperature measurements are subject to well-documented biases, but even the best calibration methods still exhibit residual biases when compared to high-quality temperature datasets. Here, we use a new approach to reduce biases in historical BT data after binning them to a regular grid such as would be used for estimating OHC. Our method consists of an ensemble of artificial neural networks that corrects biases with respect to depth, year, and water temperature in the top 10 meters. A global correction, as well as corrections optimized to specific BT probe types are presented for the top 1800 m. Our approach differs from most prior studies by accounting for multiple sources of error in a single correction, instead of separating the bias into several independent components. These new global and probe-specific corrections perform on par with widely-used calibration methods on a series of metrics that examine the residual temperature biases with respect to a high-quality reference dataset. However, distinct patterns emerge across these various calibration methods when they are extrapolated to BT data not included in our cross-instrument comparison, contributing to uncertainty that will ultimately impact estimates of OHC. 

Abstract. Nitrate is a critical ingredient for life in the ocean because, as the mostabundant form of fixed nitrogen in the ocean, it is an essential nutrientfor primary production. The availability of marine nitrate is principallydetermined by biological processes, each having a distinct influence on theN isotopic composition of nitrate (nitrate δ15N) – a propertythat informs much of our understanding of the marine N cycle as well asmarine ecology, fisheries, and past ocean conditions. However, the sparsespatial distribution of nitrate δ15N observations makes itdifficult to apply this useful property in global studies or to facilitaterobust model–data comparisons. Here, we use a compilation of publishednitrate δ15N measurements (n=12 277) and climatological mapsof physical and biogeochemical tracers to create a surface-to-seafloor,1∘ resolution map of nitrate δ15N using an ensembleof artificial neural networks (EANN). The strong correlation (R2>0.87) and small mean difference (<0.05 ‰) between EANN-estimated and observed nitrateδ15N indicate that the EANN provides a good estimate ofclimatological nitrate δ15N without a significant bias. Themagnitude of observation-model residuals is consistent with the magnitude of seasonal to interannual changes in observed nitrate δ15N that are notcaptured by our climatological model. The EANN provides a globally resolved map of mean nitrate δ15Nfor observational and modeling studies of marine biogeochemistry,paleoceanography, and marine ecology.