Since the industrial era, anthropogenic emissions of Greenhouse gases (GHG) in the atmosphere have lowered the total amount of infrared energy radiated by the Earth towards space. Now the Earth is emitting less energy towards space than it receives radiative energy from the sun. As a consequence there is an Earth Energy Imbalance (EEI) at the top of the atmosphere. Because of this EEI, the climate system stores energy, essentially in the form of heat. This excess of energy perturbs the global water-energy cycle and generates the so-called “climate changes”. The excess of energy warms the ocean, leading to sea level rise and sea ice melt. It melts land ice, leading to sea level rise. It makes land surface temperature rise, changing the hydrological cycle and generating droughts and floods. It is essential to estimate and analyse the EEI if we want to understand the Earth’s changing climate.
Measuring the EEI is challenging because it is a globally integrated variable whose variations are small (smaller than 1 W.m-2) compared to the amount of energy entering and leaving the climate system (~340 W.m-2). Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. An accuracy of <0.1 W.m-2 at decadal time scales is desirable if we want to monitor future changes in EEI associated with anthropogenic forcing, which shall be a noncontroversial science based information used by the GHG mitigation policies.
To date, the most accurate approach to estimate EEI consists of making the inventory of the energy stored in different climate system reservoirs (atmosphere, land, cryosphere and ocean) and estimating their changes with time. At large scale, variations in internal and latent heat energy dominate largely over the variations in other forms of energy (potential energy and kinetic energy). The ocean concentrates the vast majority of the excess of energy (~93%) associated with EEI. For this reason the global Ocean Heat Content (OHC) places a strong constraint on the EEI estimate. Thus it is crucial to characterise the uncertainty in EEI and OHC to strengthen the robustness of this estimation.
Four methods exist to estimate the OHC:
To date, the best results are given by the first method mainly based on Argo network. However, one of the limitations of the method is the poor sampling of the deep ocean (>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 due to the too-short spin up simulations and inaccurate initial conditions of the reanalysis, mask a significant part of the OHC signal related to EEI. The method based on estimation of ocean net heat fluxes from space is not appropriate for OHC calculation due to a too strong uncertainty (±15 W.m-2) for the science objective on EEI. The last option based on the “Altimetry-GRACE” approach is promising because it provides consistent spatial and temporal sampling of the ocean, it samples nearly the entire global oceans, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. To date the uncertainty in OHC from this method is ±0.47 W.m-2, which is greater than what is needed (<0.3 W.m-2) to pin down the global mean value of EEI.
This activity focuses on the “Altimetry-GRACE” approach to estimate the EEI. The objectives are twofold:
This innovative study will be performed in coordination with initiatives focused on climate change studies and EEI as the Global Water and Energy Exchanges project (GEWEX) and the Climate and Ocean Variability, Predictability and Change project (CLIVAR) of WCRP.
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 has been performed based on the global ARGO network.
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-2 over the whole period within an uncertainty of ±0.12 W.m-2 (68% confidence level – 0.20 W.m-2 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.
The MOHeaCAN product from “altimetry-gravimetry” is now available, documented and can be downloaded at https://doi.org/10.24400/527896/a01-2020.003. Users will be mainly interested in ocean heat content time series at regional (grids) and global scales, and Earth energy imbalance time series. Feedback from interested users on this product are welcome.