Editing IPCC:AR6/WGI/Chapter-7 (section)

=== 7.2.1 Present-day Energy Budget ===

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Figure 7.2 (upper panel) shows a schematic representation of Earth’s energy budget for the early 21st century, including globally averaged estimates of the individual components ( [[#Wild--2015|Wild et al., 2015]] ). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol–cloud interactions ( [[#7.3|Section 7.3]] ) or through cloud feedbacks ( [[#7.4|Section 7.4]] ) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence of clouds, with otherwise identical atmospheric and surface radiative properties. It has been derived by taking into account information contained in both in situ and satellite radiation measurements taken under cloud-free conditions ( [[#Wild--2019|Wild et al., 2019]] ). A comparison of the upper and lower panels in Figure 7.2 shows that without clouds, 47 W m <sup>–2</sup> less solar radiation is reflected back to space globally (53 ± 2 W m <sup>–2</sup> instead of 100 ± 2 W m <sup>–2</sup> ), while 28 W m <sup>–2</sup> more thermal radiation is emitted to space (267 ± 3 W m <sup>–2</sup> instead of 239 ± 3 W m <sup>–2</sup> ). As a result, there is a 20 W m <sup>–2</sup> radiative imbalance at the TOA in the clear-sky energy budget (Figure 7.2, lower panel), suggesting that the Earth would warm substantially if there were no clouds.

The AR5 ( [[#Church--2013|Church et al., 2013]] ; [[#Hartmann--2013|Hartmann et al., 2013]] ; [[#Myhre--2013b|Myhre et al., 2013b]] ) highlighted the progress that had been made in quantifying the TOA radiation budget following new satellite observations that became available in the early 21st century (Clouds and the Earth’s Radiant Energy System, CERES; Solar Radiation and Climate Experiment, SORCE). Progress in the quantification of changes in incoming solar radiation at the TOA is discussed in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] ( [[IPCC:Wg1:Chapter:Chapter-2#2.2|Section 2.2]] ). Since AR5, the CERES Energy Balance EBAF Ed4.0 product was released, which includes algorithm improvements and consistent input datasets throughout the record ( [[#Loeb--2018b|Loeb et al., 2018b]] ). However, the overall precision of these fluxes (uncertainty in global mean TOA flux of 1.7% (1.7 W m <sup>–2</sup> ) for reflected solar and 1.3% (3.0 W m <sup>–2</sup> ) for outgoing thermal radiation at the 90% confidence level) is not sufficient to quantify the Earth’s energy imbalance in absolute terms. Therefore, the CERES EBAF reflected solar and emitted thermal TOA fluxes were adjusted, within the estimated uncertainties, to ensure that the net TOA flux for July 2005 to June 2015 was consistent with the estimated Earth’s energy imbalance for the same period based on ocean heat content (OHC) measurements and energy uptake estimates for the land, cryosphere and atmosphere ( [[#7.2.2.2|Section 7.2.2.2]] ; [[#Johnson--2016|Johnson et al., 2016]] ; [[#Riser--2016|Riser et al., 2016]] ). ESMs typically show good agreement with global mean TOA fluxes from CERES-EBAF. However, as some ESMs are known to calibrate their TOA fluxes to CERES or similar data ( [[#Hourdin--2017|Hourdin et al., 2017]] ), this is not necessarily an indication of model accuracy, especially as ESMs show significant discrepancies on regional scales, often related to their representation of clouds ( [[#Trenberth--2010|Trenberth and Fasullo, 2010]] ; [[#Donohoe--2012|Donohoe and Battisti, 2012]] ; [[#Hwang--2013|Hwang and Frierson, 2013]] ; J.-L.F. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ; [[#Dolinar--2015|Dolinar et al., 2015]] ; [[#Wild--2015|Wild et al., 2015]] ).

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[[File:80ebc4b33cf03cf7b6c77b27908edece IPCC_AR6_WGI_Figure_7_2.png]]

'''Figure 7.2''' '''|''' '''Schematic representation of the global mean energy budget of the Earth (upper panel), and its equivalent without considerations of cloud effects (lower panel).''' Numbers indicate best estimates for the magnitudes of the globally averaged energy balance components in W m <sup>–2</sup> together with their uncertainty ranges in parentheses (5–95% confidence range), representing climate conditions at the beginning of the 21st century. Note that the cloud-free energy budget shown in the lower panel is not the one that Earth would achieve in equilibrium when no clouds could form. It rather represents the global mean fluxes as determined solely by removing the clouds but otherwise retaining the entire atmospheric structure. This enables the quantification of the effects of clouds on the Earth energy budget and corresponds to the way clear-sky fluxes are calculated in climate models. Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified in the lower panel. Figure adapted from Wild et al. (2015, 2019).

The radiation components of the surface energy budget are associated with substantially larger uncertainties than at the TOA, since they are less directly measured by passive satellite sensors and require retrieval algorithms and ancillary data for their estimation ( [[#Raschke--2016|Raschke et al., 2016]] ; [[#Kato--2018|Kato et al., 2018]] ; [[#Huang--2019|Huang et al., 2019]] ). Confidence in the quantification of the global mean surface radiation components has increased recently, as independent estimates now converge to within a few W m <sup>–2</sup> ( [[#Wild--2017|Wild, 2017]] ). Current best estimates for downward solar and thermal radiation at Earth’s surface are approximately 185 W m <sup>–2</sup> and 342 W m <sup>–2</sup> , respectively (Figure 7.2). These estimates are based on complementary approaches that make use of satellite products from active and passive sensors ( [[#L’Ecuyer--2015|L’Ecuyer et al., 2015]] ; [[#Kato--2018|Kato et al., 2018]] ) and information from surface observations and Earth system models (ESMs; [[#Wild--2015|Wild et al., 2015]] ). Inconsistencies in the quantification of the global mean energy and water budgets discussed in AR5 ( [[#Hartmann--2013|Hartmann et al., 2013]] ) have been reconciled within the (considerable) uncertainty ranges of their individual components ( [[#Wild--2013|Wild et al., 2013]] , 2015; [[#L’Ecuyer--2015|L’Ecuyer et al., 2015]] ). However, on regional scales, the closure of the surface energy budgets remains a challenge with satellite-derived datasets ( [[#Loeb--2014|Loeb et al., 2014]] ; [[#L’Ecuyer--2015|L’Ecuyer et al., 2015]] ; [[#Kato--2016|Kato et al., 2016]] ). Nevertheless, attempts have been made to derive surface energy budgets over land and ocean ( [[#Wild--2015|Wild et al., 2015]] ), over the Arctic ( [[#Christensen--2016b|Christensen et al., 2016b]] ), and over individual continents and ocean basins ( [[#L’Ecuyer--2015|L’Ecuyer et al., 2015]] ; [[#Thomas--2020|Thomas et al., 2020]] ). Since AR5, the quantification of the uncertainties in surface energy flux datasets has improved. Uncertainties in global monthly mean downward solar and thermal fluxes in the CERES-EBAF surface dataset are, respectively, 10 W m <sup>–2</sup> and 8 W m <sup>–2</sup> (converted to 5–95% ranges; [[#Kato--2018|Kato et al., 2018]] ). The uncertainty in the surface fluxes for polar regions is larger than in other regions ( [[#Kato--2018|Kato et al., 2018]] ) due to the limited number of surface sites and larger uncertainty in surface observations ( [[#Previdi--2015|Previdi et al., 2015]] ). The uncertainties in ocean mean latent and sensible heat fluxes are approximately 11 W m <sup>–2</sup> and 5 W m <sup>–2</sup> (converted to 5–95% ranges), respectively ( [[#L’Ecuyer--2015|L’Ecuyer et al., 2015]] ). A recent review of the latent and sensible heat flux accuracies over the period 2000–2007 highlights significant differences between several gridded products over ocean, where root-mean-squared differences between the multi-product ensemble and data at more than 200 moorings reached up to 25 W m <sup>–2</sup> for latent heat and 5 W m <sup>–2</sup> for sensible heat ( [[#Bentamy--2017|Bentamy et al., 2017]] ). This uncertainty stems from the retrieval of flux-relevant meteorological variables, as well as from differences in the flux parametrizations ( [[#Yu--2019|Yu, 2019]] ). Estimating the uncertainty in sensible and latent heat fluxes over land is difficult because of the large temporal and spatial variability. The flux values over land computed with three global datasets vary by 10–20% ( [[#L’Ecuyer--2015|L’Ecuyer et al., 2015]] ).

ESMs also show larger discrepancies in their surface energy fluxes than at the TOA due to weaker observational constraints, with a spread of typically 10–20 W m <sup>–2</sup> in the global average, and an even greater spread at regional scales (J.-L.F. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ; [[#Wild--2013|Wild et al., 2013]] ; [[#Boeke--2016|Boeke and Taylor, 2016]] ; [[#Wild--2017|Wild, 2017]] , 2020; C. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Differences in the land-averaged downward thermal and solar radiation in CMIP5 ESMs amount to more than 30 and 40 W m <sup>–2</sup> , respectively ( [[#Wild--2015|Wild et al., 2015]] ). However, in the global multi-model mean, the magnitudes of the energy budget components of the CMIP6 ESMs generally show better agreement with reference estimates than previous model generations ( [[#Wild--2020|Wild, 2020]] ).

In summary, since AR5, the magnitudes of the global mean energy budget components have been quantified more accurately, not only at the TOA, but also at the Earth’s surface, where independent estimates of the radiative components have converged ( ''high confidence'' ). Considerable uncertainties remain in regional surface energy budget estimates as well as their representation in climate models.

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