The historical evolution of Earth’s energy imbalance can be quantified by changes in the global ocean heat content. However, historical reconstructions of ocean heat content often neglect a large volume of the deep ocean, due to sparse observations of ocean temperatures below 2000 m. Here, we provide a global reconstruction of historical changes in full-depth ocean heat content based on interpolated subsurface temperature data using an autoregressive artificial neural network, providing estimates of total ocean warming for the period 1946-2019. We find that cooling of the deep ocean and a small heat gain in the upper ocean led to no robust trend in global ocean heat content from 1960-1990, implying a roughly balanced Earth energy budget within −0.16 to 0.06 W m−2 over most of the latter half of the 20th century. However, the past three decades have seen a rapid acceleration in ocean warming, with the entire ocean warming from top to bottom at a rate of 0.63 ± 0.13 W m−2. These results suggest a delayed onset of a positive Earth energy imbalance relative to previous estimates, although large uncertainties remain.

Abstract. Nitrate is a critical ingredient for life in the ocean because, as the most abundant form of fixed nitrogen in the ocean, it is an essential nutrient for primary production. The availability of marine nitrate is principally determined by biological processes, each having a distinct influence on the N isotopic composition of nitrate (nitrate δ15N) – a property that informs much of our understanding of the marine N cycle as well as marine ecology, fisheries, and past ocean conditions. However, the sparse spatial distribution of nitrate δ15N observations makes it difficult to apply this useful property in global studies or to facilitate robust model–data comparisons. Here, we use a compilation of published nitrate δ15N measurements (n=12 277) and climatological maps of physical and biogeochemical tracers to create a surface-to-seafloor, 1∘ resolution map of nitrate δ15N using an ensemble of artificial neural networks (EANN). The strong correlation (R2>0.87) and small mean difference (<0.05 ‰) between EANN-estimated and observed nitrate δ15N indicate that the EANN provides a good estimate of climatological nitrate δ15N without a significant bias. The magnitude of observation-model residuals is consistent with the magnitude of seasonal to interannual changes in observed nitrate δ15N that are not captured by our climatological model. The EANN provides a globally resolved map of mean nitrate δ15N for observational and modeling studies of marine biogeochemistry, paleoceanography, and marine ecology.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Nitrate is a critical ingredient for life in the ocean because, as the most abundant form of fixed nitrogen in the ocean, it is an essential nutrient for primary production. The availability of marine nitrate is principally determined by biological processes, each having a distinct influence on the N isotopic composition of nitrate (nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N) &amp;#8211; a property that informs much of our understanding of the marine N cycle as well as marine ecology, fisheries, and past ocean conditions. However, the sparse spatial distribution of nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N observations makes it difficult to apply this useful property in global studies, or to facilitate robust model-data comparisons. Here, we use a compilation of published nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N measurements (n&amp;#8201;=&amp;#8201;12277) and climatological maps of physical and biogeochemical tracers to create a surface-to-seafloor, 1&amp;#176; resolution map of nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N using an Ensemble of Artificial Neural Networks (EANN). The strong correlation (R&lt;sub&gt;2&lt;/sub&gt;&amp;#8201;&gt;&amp;#8201;0.87) and small mean difference (&lt;&amp;#8201;0.05&amp;#8201;&amp;#8240;) between EANN-estimated and observed nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N indicates that the EANN provides a good estimate of climatological nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N without a significant bias. The magnitude of observation-model residuals is consistent with the magnitude of seasonal-decadal changes in observed nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N that are not captured by our climatological model. As such, these observation-constrained results provide a globally-resolved map of mean nitrate &lt;i&gt;&amp;#948;&lt;/i&gt;&lt;sup&gt;15&lt;/sup&gt;N for observational and modeling studies of marine biogeochemistry, paleoceanography, and marine ecology.&lt;/p&gt;

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.

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