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A compilation of radiocarbon measurements is used to characterize deep-sea overturning since the last ice age.

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 seriesmore »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.« less

The ocean is a reservoir for CFC-11, a major ozone-depleting chemical. Anthropogenic production of CFC-11 dramatically decreased in the 1990s under the Montreal Protocol, which stipulated a global phase out of production by 2010. However, studies raise questions about current overall emission levels and indicate unexpected increases of CFC-11 emissions of about 10 Gg ⋅ yr −1 after 2013 (based upon measured atmospheric concentrations and an assumed atmospheric lifetime). These findings heighten the need to understand processes that could affect the CFC-11 lifetime, including ocean fluxes. We evaluate how ocean uptake and release through 2300 affects CFC-11 lifetimes, emission estimates, and the long-term return of CFC-11 from the ocean reservoir. We show that ocean uptake yields a shorter total lifetime and larger inferred emission of atmospheric CFC-11 from 1930 to 2075 compared to estimates using only atmospheric processes. Ocean flux changes over time result in small but not completely negligible effects on the calculated unexpected emissions change (decreasing it by 0.4 ± 0.3 Gg ⋅ yr −1 ). Moreover, it is expected that the ocean will eventually become a source of CFC-11, increasing its total lifetime thereafter. Ocean outgassing should produce detectable increases in global atmospheric CFC-11 abundances by themore »mid-2100s, with emission of around 0.5 Gg ⋅ yr −1 ; this should not be confused with illicit production at that time. An illustrative model projection suggests that climate change is expected to make the ocean a weaker reservoir for CFC-11, advancing the detectable change in the global atmospheric mixing ratio by about 5 yr.« less

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 ofmore »marine biogeochemistry,paleoceanography, and marine ecology.« less