Earth's energy imbalance (EEI) represents the rate of global energy accumulation in response to radiative forcings and feedbacks. Ocean heat uptake (OHU) poses a vital constraint on EEI and its uncertainty. Considering recent geodetic observations, geophysical corrections, and new estimates of the ocean's expansion efficiency of heat, we translate steric sea‐level change, the difference of total sea‐level and ocean‐mass change, into an OHU of 0.86 [0.62, 1.10, 5%–95%] Wm ⁻² for the period 2005–2019. Adding components of non‐oceanic heat uptake, we obtain an EEI of 0.94 [0.70, 1.19] Wm ⁻² , which is at the upper end of previous assessments, but agrees within uncertainty. Interannual geodetic OHU variability exhibits a higher correlation with top‐of‐the‐atmosphere net radiative flux than hydrographic‐only data, but has a three times larger standard deviation. The radiation fluxes and the geodetic approach suggest an increase in heat uptake since 2005, most markedly in recent years.

Global warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned.

Earth’s energy imbalance (EEI) is a fundamental metric of global Earth system change, quantifying the cumulative impact of natural and anthropogenic radiative forcings and feedback. To date, the most precise measurements of EEI change are obtained through radiometric observations at the top of the atmosphere (TOA), while the quantification of EEI absolute magnitude is facilitated through heat inventory analysis, where ~ 90% of heat uptake manifests as an increase in ocean heat content (OHC). Various international groups provide OHC datasets derived from in situ and satellite observations, as well as from reanalyses ingesting many available observations. The WCRP formed the GEWEX-EEI Assessment Working Group to better understand discrepancies, uncertainties and reconcile current knowledge of EEI magnitude, variability and trends. Here, 21 OHC datasets and ocean heat uptake (OHU) rates are intercompared, providing OHU estimates ranging between 0.40 ± 0.12 and 0.96 ± 0.08 W m⁻² (2005–2019), a spread that is slightly reduced when unequal ocean sampling is accounted for, and that is largely attributable to differing source data, mapping methods and quality control procedures. The rate of increase in OHU varies substantially between − 0.03 ± 0.13 (reanalysis product) and 1.1 ± 0.6 W m⁻² dec⁻¹ (satellite product). Products that either more regularly observe (satellites) or fill in situ data-sparse regions based on additional physical knowledge (some reanalysis and hybrid products) tend to track radiometric EEI variability better than purely in situ-based OHC products. This paper also examines zonal trends in TOA radiative fluxes and the impact of data gaps on trend estimates. The GEWEX-EEI community aims to refine their assessment studies, to forge a path toward best practices, e.g., in uncertainty quantification, and to formulate recommendations for future activities.

Observing the Earth radiation budget (ERB) from satellites is crucial for monitoring and understanding Earth’s climate. One of the major challenges for ERB observations, particularly for reflected shortwave radiation, is the conversion of the measured radiance to the more energetically relevant quantity of radiative flux, or irradiance. This conversion depends on the solar-viewing geometry and the scene composition associated with each instantaneous observation. We first outline the theoretical basis for algorithms to convert shortwave radiance to irradiance, most commonly known as empirical angular distribution models (ADMs). We then review the progression from early ERB satellite observations that applied relatively simple ADMs, to current ERB satellite observations that apply highly sophisticated ADMs. A notable development is the dramatic increase in the number of scene types, made possible by both the extended observational record and the enhanced scene information now available from collocated imager information. Compared with their predecessors, current shortwave ADMs result in a more consistent average albedo as a function of viewing zenith angle and lead to more accurate instantaneous and mean regional irradiance estimates. One implication of the increased complexity is that the algorithms may not be directly applicable to observations with insufficient accompanying imager information, or for existing or new satellite instruments where detailed scene information is not available. Recent advances that complement and build on the base of current approaches, including machine learning applications and semi-physical calculations, are highlighted.

This study provides observational evidence for feedbacks that amplify the short term hydrological response associated with the warm phase of the El Niño‐Southern Oscillation. Our analyses make use of a comprehensive set of independent satellite observations collected over decades to show that much larger local changes to cloud (~50%K^‐1) and precipitation (~60%K^‐1) occur than would be expected from the guidance of Clausius‐Clapyeron (C‐C) theory (~7%K^‐1). This amplification comes from atmospheric feedbacks involving shifts in the patterns of latent and radiative heating that mutually act on the dynamics enhancing changes to the hydrological cycle. We also confirm the existence of an opposing negative flux feedback at the ocean surface, driven largely by solar radiation changes, that opposes the surface warming. Estimates of the strength of this and other feedback factors associated with warming in the Niño3 region are provided from observations. These observations are also used to examine comparative processes and feedbacks in CMIP5 AMIP model experiments.