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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
A. Bagnell ◽  
T. DeVries
Keyword(s):  
20Th Century ◽  
Heat Content ◽  
Deep Ocean ◽  
Ocean Warming ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Energy Imbalance ◽  
Delayed Onset ◽  
The Past ◽  
Historical Reconstructions

AbstractThe 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 new project to monitor the Ocean Heat Content and the Earth Energy imbalance from space: MOHeaCAN

2020 ◽  
Author(s):  
Michaël Ablain ◽  
Benoit Meyssignac ◽  
Alejandro Blazquez ◽  
Marti Florence ◽  
Rémi Jugier ◽  
...  
Keyword(s):  
Time Scales ◽  
Radiant Energy ◽  
Heat Content ◽  
Deep Ocean ◽  
Heat Fluxes ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Energy Imbalance ◽  
The Earth

<p>The Earth Energy Imbalance (EEI) is a key indicator to understand the Earth’s changing. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m-2, compared to the amount of energy entering and leaving the climate system of ~340 W.m-2. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. It implies that an accuracy of <0.3 W.m-2 at decadal time scales is necessary to evaluate the long term mean EEI associated with anthropogenic forcing. Ideally an accuracy of <0.1 W.m-2 at decadal time scales is desirable if we want to monitor future changes in EEI. The ocean heat content (OHC) is a very good proxy to estimate EEI as ocean concentrates the vast majority of the excess of energy (~93%) associated with EEI. Several methods exist to estimate OHC:</p><ul><li>the direct measurement of in situ temperature based on temperature/Salinity profiles (e.g. ARGO floats),</li> <li>the measurement of the net ocean surface heat fluxes from space (CERES),</li> <li>the estimate from ocean reanalyses that assimilate observations from both satellite and in situ instruments,</li> <li>the measurement of the thermal expansion of the ocean from space based on differences between the total sea-level content derived from altimetry measurements and the mass content derived from GRACE data (noted “Altimetry-GRACE”).</li> </ul><p>To date, the best results are given by the first method based on ARGO network. However ARGO measurements do no sample deep ocean below 2000 m depth and marginal seas as well as the ocean below sea ice. Re-analysis provides a more complete estimation but large biases in the polar oceans and spurious drifts in the deep ocean mask a significant part of the OHC signal related to EEI. The method based on estimation of ocean net heat fluxes (CERES) is not appropriate for OHC calculation due to a too strong uncertainty (±15 W.m-2). </p><p>In the MOHeaCAN project supported by ESA, we are being developed the “Altimetry-GRACE” approach  which is promising since it provides consistent spatial and temporal sampling of the ocean, it samples the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. Consequently, it complements the OHC estimation from ARGO.  However, to date the uncertainty in OHC from this method is close to 0.5 W.m-2, and thus greater than the requirement of 0.3 W.m-2 needed to a good EEI estimation. Therefore the scientific objective of the MOHeaCan project is  to improve these estimates :</p><ol><li>by developing novel algorithms in order to reach the challenging target for the uncertainty quantification of 0.3 W. m−2;</li> <li>by estimating realistic OHC uncertainties thanks to an error budget of measurements applying a rigorous mathematical formalism;</li> <li>by developing a software prototype systems that allow to perform sensitivities studies and OHC product and its uncertainty generation;</li> <li>by assessing our estimation by performing comparison against independent estimates based on ARGO network, and based on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere.</li> </ol>

Monitoring the Ocean Heat Content and the Earth Energy imbalance from space altimetry and space gravimetry: the MOHeaCAN product

2021 ◽  
Author(s):  
Marti Florence ◽  
Ablain Michaël ◽  
Fraudeau Robin ◽  
Jugier Rémi ◽  
Meyssignac Benoît ◽  
...  
Keyword(s):  
Time Scales ◽  
Radiant Energy ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Argo Data ◽  
Data Set ◽  
Energy Imbalance ◽  
Temporal Sampling ◽  
The Earth

<p>The Earth Energy Imbalance (EEI) is a key indicator to understand climate change. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m<sup>-2</sup>, compared to the amount of energy entering and leaving the climate system of ~340 W.m<sup>-2</sup>. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m<sup>-2</sup>. It implies that an accuracy of <0.3 W.m<sup>-2</sup> at decadal time scales is necessary to evaluate the long term mean EEI associated with anthropogenic forcing. Ideally an accuracy of <0.1 W.m<sup>-2</sup> at decadal time scales is desirable if we want to monitor future changes in EEI.</p><p>In the frame of the MOHeaCAN project supported by ESA, the EEI indicator is deduced from the global change in Ocean Heat Content (OHC) which is a very good proxy of the EEI since the ocean stores 93% of the excess of heat  gained by the Earth in response to EEI. The OHC is estimated from space altimetry and gravimetry missions (GRACE). This “Altimetry-Gravimetry'' approach is promising because it provides consistent spatial and temporal sampling of the ocean, it samples nearly the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. Consequently, it complements the OHC estimation from the ARGO network. </p><p>The MOHeaCAN product contains monthly time series (between August 2002 and June 2017) of several variables, the main ones being the regional OHC (3°x3° spatial resolution grids), the global OHC and the EEI indicator. Uncertainties are provided for variables at global scale, by propagating errors from sea level measurements (altimetry) and ocean mass content (gravimetry). In order to calculate OHC at regional and global scales, a new estimate of the expansion efficiency of heat at global and regional scales have been performed based on the global ARGO network. </p><p>A scientific validation of the MOHeaCAN product has also been carried out performing thorough comparisons against independent estimates based on ARGO data and on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere. The mean EEI derived from MOHeaCAN product is 0.84 W.m<sup>-2</sup> over the whole period within an uncertainty of ±0.12 W.m<sup>-2</sup> (68% confidence level - 0.20 W.m<sup>-2</sup> at the 90% CL). This figure is in agreement (within error bars at the 90% CL) with other EEI indicators based on ARGO data (e.g. OHC-OMI from CMEMS) although the best estimate is slightly higher. Differences from annual to inter-annual scales have also been observed with ARGO and CERES data. Investigations have been conducted to improve our understanding of the benefits and limitations of each data set to measure EEI at different time scales.</p><p><strong>The MOHeaCAN product from “altimetry-gravimetry” is now available</strong> and can be downloaded at https://doi.org/10.24400/527896/a01-2020.003. Feedback from interested users on this product are welcome.</p>

scholarly journals Measuring Global Ocean Heat Content to Estimate the Earth Energy Imbalance

2019 ◽  
Vol 6 ◽  
Author(s):  
Benoit Meyssignac ◽  
Tim Boyer ◽  
Zhongxiang Zhao ◽  
Maria Z. Hakuba ◽  
Felix W. Landerer ◽  
...  
Keyword(s):  
Heat Content ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Energy Imbalance ◽  
The Earth

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

2021 ◽  
Vol 13 (19) ◽  
pp. 3799
Author(s):  
Hua Su ◽  
Tian Qin ◽  
An Wang ◽  
Wenfang Lu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Time Series ◽  
Heat Content ◽  
Ocean Warming ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Gridded Data ◽  
Light Gradient

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.

Estimating Global Ocean Heat Content Changes in the Upper 1800 m since 1950 and the Influence of Climatology Choice*

2014 ◽  
Vol 27 (5) ◽  
pp. 1945-1957 ◽  
Author(s):  
John M. Lyman ◽  
Gregory C. Johnson
Keyword(s):  
Heat Content ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Ocean Temperature ◽  
Vertical Separation ◽  
Ocean Heat Uptake ◽  
Expendable Bathythermograph ◽  
Heat Uptake ◽  
The Mean

Abstract Ocean heat content anomalies are analyzed from 1950 to 2011 in five distinct depth layers (0–100, 100–300, 300–700, 700–900, and 900–1800 m). These layers correspond to historic increases in common maximum sampling depths of ocean temperature measurements with time, as different instruments—mechanical bathythermograph (MBT), shallow expendable bathythermograph (XBT), deep XBT, early sometimes shallower Argo profiling floats, and recent Argo floats capable of worldwide sampling to 2000 m—have come into widespread use. This vertical separation of maps allows computation of annual ocean heat content anomalies and their sampling uncertainties back to 1950 while taking account of in situ sampling advances and changing sampling patterns. The 0–100-m layer is measured over 50% of the globe annually starting in 1956, the 100–300-m layer starting in 1967, the 300–700-m layer starting in 1983, and the deepest two layers considered here starting in 2003 and 2004, during the implementation of Argo. Furthermore, global ocean heat uptake estimates since 1950 depend strongly on assumptions made concerning changes in undersampled or unsampled ocean regions. If unsampled areas are assumed to have zero anomalies and are included in the global integrals, the choice of climatological reference from which anomalies are estimated can strongly influence the global integral values and their trend: the sparser the sampling and the bigger the mean difference between climatological and actual values, the larger the influence.

scholarly journals Consistency of the current global ocean observing systems from an Argo perspective

Ocean Science ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 547-557 ◽  
Author(s):  
K. von Schuckmann ◽  
J.-B. Sallée ◽  
D. Chambers ◽  
P.-Y. Le Traon ◽  
C. Cabanes ◽  
...  
Keyword(s):  
Sea Level ◽  
Heat Content ◽  
Deep Ocean ◽  
Global Ocean ◽  
Regional Variability ◽  
Tropical Ocean ◽  
Steric Sea Level ◽  
Ocean Observing ◽  
Ocean Observing Systems ◽  
Observing Systems

Abstract. Variations in the world's ocean heat storage and its associated volume changes are a key factor to gauge global warming and to assess the earth's energy and sea level budget. Estimating global ocean heat content (GOHC) and global steric sea level (GSSL) with temperature/salinity data from the Argo network reveals a positive change of 0.5 ± 0.1 W m−2 (applied to the surface area of the ocean) and 0.5 ± 0.1 mm year−1 during the years 2005 to 2012, averaged between 60° S and 60° N and the 10–1500 m depth layer. In this study, we present an intercomparison of three global ocean observing systems: the Argo network, satellite gravimetry from GRACE and satellite altimetry. Their consistency is investigated from an Argo perspective at global and regional scales during the period 2005–2010. Although we can close the recent global ocean sea level budget within uncertainties, sampling inconsistencies need to be corrected for an accurate global budget due to systematic biases in GOHC and GSSL in the Tropical Ocean. Our findings show that the area around the Tropical Asian Archipelago (TAA) is important to closing the global sea level budget on interannual to decadal timescales, pointing out that the steric estimate from Argo is biased low, as the current mapping methods are insufficient to recover the steric signal in the TAA region. Both the large regional variability and the uncertainties in the current observing system prevent us from extracting indirect information regarding deep-ocean changes. This emphasizes the importance of continuing sustained effort in measuring the deep ocean from ship platforms and by beginning a much needed automated deep-Argo network.

scholarly journals Observational Constraint on Greenhouse Gas and Aerosol Contributions to Global Ocean Heat Content Changes

2020 ◽  
Vol 33 (24) ◽  
pp. 10579-10591
Author(s):  
Elodie Charles ◽  
Benoit Meyssignac ◽  
Aurélien Ribes
Keyword(s):  
Greenhouse Gas ◽  
Radiative Forcing ◽  
Climate Models ◽  
Ghg Emissions ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Detection And Attribution ◽  
Aerosol Forcing ◽  
Bivariate Method

AbstractObservations and climate models are combined to identify an anthropogenic warming signature in the upper ocean heat content (OHC) changes since 1971. We apply a new detection and attribution analysis developed by Ribes et al. that uses a symmetric treatment of the magnitude and the pattern of the climate response to each radiative forcing. A first estimate of the OHC response to natural, anthropogenic, greenhouse gas, and other forcings is derived from a large ensemble of CMIP5 simulations. Observational datasets from historical reconstructions are then used to constrain this estimate. A spatiotemporal observational mask is applied to compare simulations with actual observations and to overcome reconstruction biases. Results on the 0–700-m layer from 1971 to 2005 show that the global OHC would have increased since 1971 by 2.12 ± 0.21 × 107 J m−2 yr−1 in response to GHG emissions alone. But this has been compensated for by other anthropogenic influences (mainly aerosol), which induced an OHC decrease of 0.84 ± 0.18 × 107 J m−2 yr−1. The natural forcing has induced a slight global OHC decrease since 1971 of 0.13 ± 0.09 × 107 J m−2 yr−1. Compared to previous studies we have separated the effect of the GHG forcing from the effect of the other anthropogenic forcing on OHC changes. This has been possible by using a new detection and attribution (D&A) method and by analyzing simultaneously the global OHC trends over 1957–80 and over 1971–2005. This bivariate method takes advantage of the different time variation of the GHG forcing and the aerosol forcing since 1957 to separate both effects and reduce the uncertainty in their estimates.

scholarly journals Late 20th Century Indian Ocean Heat Content Gain Masked by Wind Forcing

2020 ◽  
Vol 47 (22) ◽  
Author(s):  
Caroline C. Ummenhofer ◽  
Svenja Ryan ◽  
Matthew H. England ◽  
Markus Scheinert ◽  
Patrick Wagner ◽  
...  
Keyword(s):  
Indian Ocean ◽  
20Th Century ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Wind Forcing ◽  
Late 20Th Century
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