Improving statistical prediction and revealing nonlinearity of ENSO using observations of ocean heat content in the tropical Pacific

It is well-known that the upper ocean heat content (OHC) variability in the tropical Pacific contains valuable information about dynamics of El Niño–Southern Oscillation (ENSO). Here we combine sea surface temperature (SST) and OHC indices derived from the gridded datasets to construct a phase space for data-driven ENSO models. Using a Bayesian optimization method, we construct linear as well as nonlinear models for these indices. We find that the joint SST-OHC optimal models yield significant benefits in predicting both the SST and OHC as compared with the separate SST or OHC models. It is shown that these models substantially reduces seasonal predictability barriers in each variable – the spring barrier in the SST index and the winter barrier in the OHC index. We also reveal the significant nonlinear relationships between the ENSO variables manifesting on interannual scales, which opens prospects for improving yearly ENSO forecasting.

The Interannual Variability of Shallow Meridional Overturning Circulation and its association with the South-West Indian Ocean Heat Content variability

The present study examines interannual variability of Shallow Meridional Overturning Circulation (SMOC) using century long reanalysis data. The strength of the transport associated with SMOC is calculated by meridional overturning streamfunction. The interannual variability in SMOC is found maximum between the 5oS and 15oS and displaying strong signals after 1940s. A year for which the meridional overturning streamfunction detrended anomaly is greater (lesser) its standard deviation is identified as strong (weak) SMOC year. For strong (weak) SMOC year composite displayed more (less) southward transport (~2.5 Sv) and shown excess (less) subduction over the South Indian Ocean. During strong (weak) years, the excess (less) southward heat transport (~0.25PW) leads to reduction (increase) in the upper 200m Ocean Heat Content (OHC) and sea level over the Southwest Indian Ocean (SWIO). The results obtained are well supported by tide gauge and satellite measured sea level data for the available period. Further analysis reveals that the SMOC variability is primarily driven by change in zonal wind stress south of the equator and displayed association with the Southern Oscillation Index. The Ocean model-based sensitivity experiments confirms that the OHC variability over SWIO is closely associated with the SMOC variability and is primarily driven by local wind forcing as a response to El Niño Southern Oscillation. However, the role of remote forcing from Pacific through Oceanic pathway over SWIO is absent. Study attempts to provide a comprehensive view on the interannual variability of SMOC and its linkage to OHC variability over SWIO during last century.

Quantifying spread in spatio-temporal changes of upper-ocean heat content estimates: an internationally coordinated comparison

The Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ~64% of that in the upper 700 m. Differences in upper ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008 – with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups – to evaluate the spatio-temporal spread in upper OHCA estimates arising from two choices: firstly, those arising from instrumental bias adjustments; and secondly those arising from mathematical (i.e. mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are not only found in the poorly-observed Southern Ocean but also on the well-observed Northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.

Trends, variability and predictive skill of the ocean heat content in North Atlantic: an analysis with the EC-Earth3 model

This study investigates linear trends, variability and predictive skill of the upper ocean heat content (OHC) in the North Atlantic basin. This is a region where strong decadal variability superimposes the externally forced trends, introducing important differences in the local warming rates and leading in the case of the Central Subpolar North Atlantic to an overall long-term cooling. Our analysis aims to better understand these regional differences, by investigating how internal and forced variability contribute to local trends, exploring also their role on the local prediction skill. The analysis combines the study of three ocean reanalyses to document the uncertainties related to observations with two sets of CMIP6 experiments performed with the global coupled climate model EC-Earth3: a historical ensemble to characterise the forced signals, and a retrospective decadal prediction system to additionally characterise the contributions from internal climate variability. Our results show that internal variability is essential to understand the spatial pattern of North Atlantic OHC trends, contributing decisively to the local trends and providing high levels of predictive skill in the Eastern Subpolar North Atlantic and the Irminger and Iceland Seas, and to a lesser extent in the Labrador Sea. Skill and trends in other areas like the Subtropical North Atlantic, or the Gulf Stream Extension are mostly externally forced. Large observational and modeling uncertainties affect the trends and interannual variability in the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain the very poor local predictive skill.

Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives

Global ocean heat content (OHC) is generally estimated using gridded, model and reanalysis data; its change is crucial to understanding climate anomalies and ocean warming phenomena. However, Argo gridded data have short temporal coverage (from 2005 to the present), inhibiting understanding of long-term OHC variabilities at decadal to multidecadal scales. In this study, we utilized multisource remote sensing and Argo gridded data based on the long short-term memory (LSTM) neural network method, which considers long temporal dependence to reconstruct a new long time-series OHC dataset (1993–2020) and fill the pre-Argo data gaps. Moreover, we adopted a new machine learning method, i.e., the Light Gradient Boosting Machine (LightGBM), and applied the well-known Random Forests (RFs) method for comparison. The model performance was measured using determination coefficients (R2) and root-mean-square error (RMSE). The results showed that LSTM can effectively improve the OHC prediction accuracy compared with the LightGBM and RFs methods, especially in long-term and deep-sea predictions. The LSTM-estimated result also outperformed the Ocean Projection and Extension neural Network (OPEN) dataset, with an R2 of 0.9590 and an RMSE of 4.45 × 1019 in general in the upper 2000 m for 28 years (1993–2020). The new reconstructed dataset (named OPEN-LSTM) correlated reasonably well with other validated products, showing consistency with similar time-series trends and spatial patterns. The spatiotemporal error distribution between the OPEN-LSTM and IAP datasets was smaller on the global scale, especially in the Atlantic, Southern and Pacific Oceans. The relative error for OPEN-LSTM was the smallest for all ocean basins compared with Argo gridded data. The average global warming trends are 3.26 × 108 J/m2/decade for the pre-Argo (1993–2004) period and 2.67 × 108 J/m2/decade for the time-series (1993–2020) period. This study demonstrates the advantages of LSTM in the time-series reconstruction of OHC, and provides a new dataset for a deeper understanding of ocean and climate events.

A Theory of the Spring Persistence Barrier on ENSO. Part III: The role of Tropical Pacific Ocean Heat Content

In this paper, we investigate the relationship between upper ocean heat content (OHC) and El Niño-Southern Oscillation (ENSO) sea surface temperature (SST) anomalies mainly using the neutral recharge oscillator (NRO) model both analytically and numerically. Previous studies showed that spring OHC, which leads SST by 6-12 months, represents a major source of predictability for ENSO. It is suggested that this seasonality is caused by the seasonally varying growth rate in SST anomalies. Moreover, a shortened ENSO period will lead to a reduced SST predictability from OHC, with the most significant decrease occurring in the latter half of the calendar year. The cross-correlation relationship between OHC and ENSO SST anomalies is further identified in damped and self-excited version of the recharge oscillator model. Finally, we suggest that the seasonal growth rate of ENSO anomalies is the cause of the seasonality in the effectiveness of OHC as a predictor in ENSO forecasting. We also explain the shorter lead time between spring OHC and ENSO SST anomalies after the turn of the 21st century in terms of the apparent higher frequency of the ENSO period.

20th century cooling of the deep ocean contributed to delayed acceleration of Earth’s energy imbalance

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.

A Comparison of the Variability and Changes in Global Ocean Heat Content from Multiple Objective Analysis Products During the Argo Period

Ocean heat content (OHC) is key to estimating the energy imbalance of the earth system. Over the past two decades, an increasing number of OHC studies were conducted using oceanic objective analysis (OA) products. Here we perform an intercomparison of OHC from eight OA products with a focus on their robust features and significant differences over the Argo period (2005-2019), when the most reliable global scale oceanic measurements are available. For the global ocean, robust warming in the upper 2000 m is confirmed. The 0-300 m layer shows the highest warming rate but is heavily modulated by interannual variability, particularly the El Niño–Southern Oscillation. The 300-700 m and 700-2000 m layers, on the other hand, show unabated warming. Regionally, the Southern Ocean and mid-latitude North Atlantic show a substantial OHC increase, and the subpolar North Atlantic displays an OHC decrease. A few apparent differences in OHC among the examined OA products were identified. In particular, temporal means of a few OA products that incorporated other ocean measurements besides Argo show a global-scale cooling difference, which is likely related to the baseline climatology fields used to generate those products. Large differences also appear in the interannual variability in the Southern Ocean and in the long-term trends in the subpolar North Atlantic. These differences remind us of the possibility of product-dependent conclusions on OHC variations. Caution is therefore warranted when using merely one OA product to conduct OHC studies, particularly in regions and on timescales that display significant differences.