Abstract
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.
Highlights
The historical evolution of Earth’s energy imbalance can be quantified by changes in the global ocean heat content
Passive transport methods[10,16] that propagate surface temperature anomalies to the deep ocean using steady-state ocean circulation patterns provide internally consistent estimates of fulldepth ocean heat content (OHC) that can be directly compared to artificial neural network (ARANN), after adjusting their baselines to coincide with the ARANN estimate during the Argo era (2005 onwards) (Fig. 1a)
The ARANN reconstruction of full-depth OHC provides an internally consistent framework for monitoring energy imbalance (EEI) over time, showing that the Earth energy budget was in quasi-equilibrium, with substantial decadal variability, for the four decades from 1950 to 1990
Summary
The historical evolution of Earth’s energy imbalance can be quantified by changes in the global ocean heat content. 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. Dynamical data-assimilation models offer an alternative approach to objective mapping and provide full-depth estimates of OHC14,15, but data sparsity means these models are poorly constrained at depth, leading to large cross-model variance[15] Another approach based on the passive transport of surface temperature anomalies into the interior ocean[10,16] can reconstruct full-depth temperature anomalies and OHC changes, but relies on the potentially incorrect assumption of steady-state circulation[16] and is sensitive to the initial condition used in the simulation[10,16] and to poorly known surface ocean temperatures dating back several millennia[10]. An interpolation product based on in situ temperature data that covers the deep ocean below 2000 m, allowing for a full-depth OHC estimate, remains crucial to reliably estimating historical changes in EEI7,17
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