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

== 7.2 Earth’s Energy Budget and its Changes Through Time ==

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Earth’s energy budget encompasses the major energy flows of relevance for the climate system (Figure 7.2). Virtually all the energy that enters or leaves the climate system does so in the form of radiation at the TOA. The TOA energy budget is determined by the amount of incoming solar (shortwave) radiation and the outgoing radiation that is composed of reflected solar radiation and outgoing thermal (longwave) radiation emitted by the climate system. In a steady-state climate, the outgoing and incoming radiative components are essentially in balance in the long-term global mean, although there are still fluctuations around this balanced state that arise through internal climate variability ( [[#Brown--2014|Brown et al., 2014]] ; [[#Palmer--2014|Palmer and McNeall, 2014]] ). However, anthropogenic forcing has given rise to a persistent imbalance in the global mean TOA radiation budget that is often referred to as Earth’s energy imbalance (e.g., [[#Trenberth--2014|Trenberth et al., 2014]] ; [[#von%20Schuckmann--2016|von Schuckmann et al., 2016]] ), which is a key element of the energy budget framework ( ''N'' ; Box 7.1, Equation 7.1) and an important metric of the rate of global climate change ( [[#Hansen--2005a|Hansen et al., 2005a]] ; [[#von%20Schuckmann--2020|von Schuckmann et al., 2020]] ). In addition to the TOA energy fluxes, Earth’s energy budget al.o includes the internal flows of energy within the climate system, which characterize the climate state. The surface energy budget consists of the net solar and thermal radiation as well as the non-radiative components such as sensible, latent and ground heat fluxes (Figure 7.2, upper panel). It is a key driver of the global water cycle, atmosphere and ocean dynamics, as well as a variety of surface processes.

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=== 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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=== 7.2.2 Changes in Earth’s Energy Budget ===

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==== 7.2.2.1 Changes in Earth’s Top-of-atmosphere Energy Budget ====

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Since 2000, changes in top-of-atmosphere (TOA) energy fluxes can be tracked from space using CERES satellite observations (Figure 7.3). The variations in TOA energy fluxes reflect the influence of internal climate variability, particularly that of El Niño–Southern Oscillation (ENSO), in addition to radiative forcing of the climate system and climate feedbacks ( [[#Allan--2014|Allan et al., 2014]] ; [[#Loeb--2018b|Loeb et al., 2018b]] ). For example, globally, the reduction in both outgoing thermal and reflected solar radiation during La Niña conditions in 2008/2009 led to an energy gain for the climate system, whereas enhanced outgoing thermal and reflected solar radiation caused an energy loss during the El Niños of 2002/2003 and 2009/2010 (Figure 7.3; [[#Loeb--2018b|Loeb et al., 2018b]] ). An ensemble of CMIP6 models is able to track the variability in global mean TOA fluxes observed by CERES, when driven with prescribed sea surface temperatures (SSTs) and sea ice concentrations (Figure 7.3; [[#Loeb--2020|Loeb et al., 2020]] ). Under cloud-free conditions, the CERES record shows a near zero trend in outgoing thermal radiation ( [[#Loeb--2018b|Loeb et al., 2018b]] ), which – combined with an increasing surface upwelling thermal flux – implies an increasing clear-sky greenhouse effect ( [[#Raghuraman--2019|Raghuraman et al., 2019]] ). Conversely, clear-sky solar reflected TOA radiation in the CERES record covering March 2000 to September 2017 shows a decrease due to reductions in aerosol optical depth in the Northern Hemisphere and sea ice fraction ( [[#Loeb--2018a|Loeb et al., 2018a]] ; [[#Paulot--2018|Paulot et al., 2018]] ).

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[[File:875318e9544e9ba9338717b154c7e0fb IPCC_AR6_WGI_Figure_7_3.png]]

'''Figure 7.3''' '''|''' '''Anomalies in global mean all-sky top-of-atmosphere (TOA) fluxes from CERES-EBAF Ed4.0 (solid black lines) and various CMIP6 climate models (coloured lines) in terms of (a) reflected solar, (b) emitted thermal and (c) net TOA fluxes.''' The multi-model means are additionally depicted as solid red lines. Model fluxes stem from simulations driven with prescribed sea surface temperatures (SSTs) and all known anthropogenic and natural forcings. Shown are anomalies of 12-month running means. All flux anomalies are defined as positive downwards, consistent with the sign convention used throughout this chapter. The correlations between the multi-model means (solid red lines) and the CERES records (solid black lines) for 12-month running means are: 0.85 for the global mean reflected solar; 0.73 for outgoing thermal radiation; and 0.81 for net TOA radiation. Figure adapted from [[#Loeb--2020|Loeb et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

An effort to reconstruct variations in net TOA fluxes back to 1985, based on a combination of satellite data, atmospheric reanalysis and high-resolution climate model simulations ( [[#Allan--2014|Allan et al., 2014]] ; [[#Liu--2020|Liu et al., 2020]] ), exhibits strong interannual variability associated with the volcanic eruption of Mount Pinatubo in 1991 and the ENSO events before 2000. The same reconstruction suggests that Earth’s energy imbalance increased by several tenths of a W m <sup>–2</sup> between the periods 1985–1999 and 2000–2016, in agreement with the assessment of changes in the global energy inventory ( [[#7.2.2.2|Section 7.2.2.2]] , and Box 7.2, Figure 1). Comparisons of year-to-year variations in Earth’s energy imbalance estimated from CERES and independent estimates based on ocean heat content change are significantly correlated with similar phase and magnitude ( [[#Johnson--2016|Johnson et al., 2016]] ; [[#Meyssignac--2019|Meyssignac et al., 2019]] ), promoting confidence in both satellite and in situ-based estimates ( [[#7.2.2.2|Section 7.2.2.2]] ).

In summary, variations in the energy exchange between Earth and space can be accurately tracked since the advent of improved observations since the year 2000 ( ''high confidence'' ), while reconstructions indicate that the Earth’s energy imbalance was larger in the 2000s than in the 1985–1999 period ( ''high confidence'' ).

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==== 7.2.2.2 Changes in the Global Energy Inventory ====

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The global energy inventory quantifies the integrated energy gain of the climate system associated with global ocean heat uptake, warming of the atmosphere, warming of the land, and melting of ice. Due to energy conservation, the rate of accumulation of energy in the Earth system ( [[#7.1|Section 7.1]] ) is equivalent to the Earth energy imbalance (Δ ''N'' in Box 7.1, Equation 7.1). On annual and longer time scales, changes in the global energy inventory are dominated by changes in global ocean heat content (OHC; [[#Rhein--2013|Rhein et al., 2013]] ; [[#Palmer--2014|Palmer and McNeall, 2014]] ; [[#Johnson--2016|Johnson et al., 2016]] ). Thus, observational estimates and climate model simulations of OHC change are critical to the understanding of both past and future climate change (Sections 2.3.3.1, 3.5.1.3, 4.5.2.1 and 9.2.2.1).

Since AR5, both modelling and observation-based studies have established Earth’s energy imbalance (characterized by OHC change) as a more robust metric of the rate of global climate change than GSAT on interannual-to-decadal time scales ( [[#Palmer--2014|Palmer and McNeall, 2014]] ; [[#von%20Schuckmann--2016|von Schuckmann et al., 2016]] ; [[#Wijffels--2016|Wijffels et al., 2016]] ; [[#Cheng--2018|Cheng et al., 2018]] ; [[#Allison--2020|Allison et al., 2020]] ). This is because GSAT is influenced by large unforced variations, for example linked to ENSO and Pacific Decadal Variability ( [[#Roberts--2015|Roberts et al., 2015]] ; [[#Yan--2016|Yan et al., 2016]] ; [[#Cheng--2018|Cheng et al., 2018]] ). Measuring OHC change more comprehensively over the full ocean depth results in a higher signal-to-noise ratio and a time series that increases steadily over time (Box 7.2, Figure 1; [[#Allison--2020|Allison et al., 2020]] ). In addition, understanding of the potential effects of historical ocean sampling on estimated global ocean heating rates has improved ( [[#Durack--2014|Durack et al., 2014]] ; [[#Good--2017|Good, 2017]] ; [[#Allison--2019|Allison et al., 2019]] ) and there are now more estimates of OHC change available that aim to mitigate the effect of limited observational sampling in the Southern Hemisphere ( [[#Lyman--2008|Lyman and Johnson, 2008]] ; [[#Cheng--2017|Cheng et al., 2017]] ; [[#Ishii--2017|Ishii et al., 2017]] ).

The assessment of changes in the global energy inventory for the periods 1971–2018, 1993–2018 and 2006–2018 draws upon the latest observational time series and the assessments presented in other chapters of this report. The estimates of OHC change come directly from the assessment presented in ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] ). The assessment of land and atmospheric heating comes from [[#von%20Schuckmann--2020|von Schuckmann et al. (2020)]] , based on the estimates of [[#Cuesta-Valero--2021|Cuesta-Valero et al. (2021)]] and [[#Steiner--2020|Steiner et al. (2020)]] , respectively. Heating of inland waters, including lakes, reservoirs and rivers, is estimated to account for &lt;0.1% of the total energy change, and is therefore omitted from this assessment ( [[#Vanderkelen--2020|Vanderkelen et al., 2020]] ). The cryosphere contribution from the melting of grounded ice is based on the mass-loss assessments presented in Chapter 9, [[IPCC:Wg1:Chapter:Chapter-9#9.4.1|Section 9.4.1]] (Greenland Ice Sheet), [[IPCC:Wg1:Chapter:Chapter-9#9.4.2|Section 9.4.2]] (Antarctic Ice Sheet) and ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1%20|Section 9.5.1]] (glaciers). Following AR5, the estimate of heating associated with loss of Arctic sea ice is based on a reanalysis ( [[#Schweiger--2011|Schweiger et al., 2011]] ), following the methods described by [[#Slater--2021|Slater et al. (2021)]] . [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] [[IPCC:Wg1:Chapter:Chapter-9#9.3.2|Section 9.3.2]] ) finds no significant trend in Antarctic sea ice area over the observational record, so a zero contribution is assumed. Ice melt associated with the calving and thinning of floating ice shelves is based on the decadal rates presented in [[#Slater--2021|Slater et al. (2021)]] . For all cryospheric components, mass loss is converted to heat input using a latent heat of fusion of 3.34 × 10 <sup>5</sup> J Kg <sup>–1</sup> °C <sup>–1</sup> with the second-order contributions from variations associated with ice type and warming of ice from sub-freezing temperatures disregarded, as in AR5. The net change in energy, quantified in Zettajoules (1 ZJ = 10 <sup>21</sup> Joules), is computed for each component as the difference between the first and last year of each period (Table 7.1). The uncertainties in the depth-interval contributions to OHC are summed to get the uncertainty in global OHC change. All other uncertainties are assumed to be independent and added in quadrature.

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'''Table''' '''7.1 |''' '''Contributions of the different components of the global energy inventory for the periods 1971–2018, 1993–2018 and 2006–2018 (Box 7.2 and Cross-Chapter Box 9.1).''' Energy changes are computed as the difference between annual mean values or year mid-points. The total heating rates correspond to Earth’s energy imbalance and are expressed per unit area of Earth’s surface.

{| class="wikitable"
|-
| rowspan="2"| Component

| colspan="2"| 1971–2018

| colspan="2"| 1993–2018

| colspan="2"| 2006–2018

|-
| Energy Gain (ZJ)

| %

| Energy Gain (ZJ)

| %

| Energy Gain (ZJ)

| %

|-
| Ocean

0–700 m

700–2000 m

&gt;2000 m

| 396.0 [285.7 to 506.2]

241.6 [162.7 to 320.5]

123.3 [96.0 to 150.5]

31.0 [15.7 to 46.4]

| 91.0

55.6

28.3

7.1

| 263.0 [194.1 to 331.9]

151.5 [114.1 to 188.9]

82.8 [59.9 to 105.6]

28.7 [14.5 to 43.0]

| 91.0

52.4

28.6

10.0

| 138.8 [86.4 to 191.3]

75.4 [48.7 to 102.0]

49.7 [29.0 to 70.4]

13.8 [7.0 to 20.6]

| 91.1

49.5

32.6

9.0

|-
| Land

| 21.8 [18.6 to 25.0]

| 5.0

| 13.7 [12.4 to 14.9]

| 4.7

| 7.2 [6.6 to 7.8]

| 4.7

|-
| Cryosphere

| 11.5 [9.0 to 14.0]

| 2.7

| 8.8 [7.0 to 10.5]

| 3.0

| 4.7 [3.3 to 6.2]

| 3.1

|-
| Atmosphere

| 5.6 [4.6 to 6.7]

| 1.3

| 3.8 [3.2 to 4.3]

| 1.3

| 1.6 [1.2 to 2.1]

| 1.1

|-
| '''TOTAL'''

| colspan="2"| '''434.9 [324.5 to 545.3] ZJ'''

| colspan="2"| '''289.2 [220.3 to 358.1] ZJ'''

| colspan="2"| '''152.4 [100.0 to 204.9] ZJ'''

|-
| '''Heating Rate'''

| colspan="2"| '''0.57 [0.43 to 0.72] W m''' <sup>–2</sup>

| colspan="2"| '''0.72 [0.55 to 0.89] W m''' <sup>–2</sup>

| colspan="2"| '''0.79 [0.52 to 1.06] W m''' <sup>–2</sup>

|}

For the period 1971–2010, AR5 ( [[#Rhein--2013|Rhein et al., 2013]] ) found an increase in the global energy inventory of 274 [196 to 351] ZJ with a 93% contribution from total OHC change, approximately 3% for both ice melt and land heating, and approximately 1% for warming of the atmosphere. For the same period, this Report finds an upwards revision of OHC change for the upper (&lt;700 m depth) and deep (&gt;700 m depth) ocean of approximately 8% and 20%, respectively, compared to AR5 and a modest increase in the estimated uncertainties associated with the ensemble approach of [[#Palmer--2021|Palmer et al. (2021)]] . The other substantive change compared to AR5 is the updated assessment of land heating, with values approximately double those assessed previously, based on a more comprehensive analysis of the available observations ( [[#von%20Schuckmann--2020|von Schuckmann et al., 2020]] ; [[#Cuesta-Valero--2021|Cuesta-Valero et al., 2021]] ). The result of these changes is an assessed energy gain of 329 [224 to 434] ZJ for the period 1971–2010, which is consistent with AR5 within the estimated uncertainties, despite the systematic increase.

The assessed changes in the global energy inventory (Box 7.2, Figure 1, and Table 7.1) yields an average value for Earth’s energy imbalance ( ''N'' in Box 7.1, Equation 7.1) of 0.57 [0.43 to 0.72] W m <sup>–2</sup> for the period 1971–2018, expressed relative to Earth’s surface area ( ''high confidence'' ). The estimates for the periods 1993–2018 and 2006–2018 yield substantially larger values of 0.72 [0.55 to 0.89] W m <sup>–2</sup> and 0.79 [0.52 to 1.06] W m <sup>–2</sup> , respectively, consistent with the increased radiative forcing from GHGs ( ''high confidence'' ). For the period 1971–2006, the total energy gain was 282 [177 to 387] ZJ, with an equivalent Earth energy imbalance of 0.50 [0.32 to 0.69] W m <sup>–2</sup> . To put these numbers in context, the 2006–2018 average Earth system heating is equivalent to approximately 20 times the annual rate of global energy consumption in 2018. <sup>[[#footnote-001|1]]</sup>

Consistent with AR5 ( [[#Rhein--2013|Rhein et al., 2013]] ), this Report finds that ocean warming dominates the changes in the global energy inventory ( ''high confidence'' ), accounting for 91% of the observed change for all periods considered (Table 7.1). The contributions from the other components across all periods are approximately 5% from land heating, 3% for cryosphere heating and 1% associated with warming of the atmosphere ( ''high confidence'' ). The assessed percentage contributions are similar to the recent study by [[#von%20Schuckmann--2020|von Schuckmann et al. (2020)]] and the total heating rates are consistent within the assessed uncertainties. Cross-validation of heating rates based on satellite and in situ observations ( [[#7.2.2.1|Section 7.2.2.1]] ), and closure of the global sea level budget using consistent datasets (Cross-Chapter Box 9.1 and Table 9.5), strengthen scientific confidence in the assessed changes in the global energy inventory relative to AR5.

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==== 7.2.2.3 Changes in Earth’s Surface Energy Budget ====

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The AR5 ( [[#Hartmann--2013|Hartmann et al., 2013]] ) reported pronounced changes in multi-decadal records of in situ observations of surface solar radiation, including a widespread decline between the 1950s and 1980s, known as ‘global dimming’, and a partial recovery thereafter, termed ‘brightening’ [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ). These changes have interacted with closely related elements of climate change, such as global and regional warming rates (Z. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Wild--2016|Wild, 2016]] ; [[#Du--2017|Du et al., 2017]] ; [[#Zhou--2018a|Zhou et al., 2018a]] ), glacier melt ( [[#Ohmura--2007|Ohmura et al., 2007]] ; [[#Huss--2009|Huss et al., 2009]] ), the intensity of the global water cycle ( [[#Wild--2012|Wild, 2012]] ) and terrestrial carbon uptake ( [[#Mercado--2009|Mercado et al., 2009]] ). These observed changes have also been used as emergent constraints to quantify aerosol effective radiative forcing ( [[#7.3.3.3|Section 7.3.3.3]] ).

Since AR5, additional evidence for dimming and/or subsequent brightening up to several percent per decade, based on direct surface observations, has been documented in previously less-studied areas of the globe, such as Iran, Bahrain, Tenerife, Hawaii, the Taklaman Desert and the Tibetan Plateau ( [[#Elagib--2013|Elagib and Alvi, 2013]] ; [[#You--2013|You et al., 2013]] ; [[#Garcia--2014|Garcia et al., 2014]] ; [[#Longman--2014|Longman et al., 2014]] ; [[#Rahimzadeh--2015|Rahimzadeh et al., 2015]] ). Strong decadal trends in surface solar radiation remain evident after careful data quality assessment and homogenization of long-term records ( [[#Sanchez-Lorenzo--2013|Sanchez-Lorenzo et al., 2013]] , 2015; [[#Manara--2015|Manara et al., 2015]] , 2016; [[#Wang--2015|Wang et al., 2015]] ; Z. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Wang--2016|Wang and]] [[#Wild--2016|Wild, 2016]] ; Y. [[#He--2018|]] [[#He--2018|He et al., 2018]] ; [[#Yang--2018|Yang et al., 2018]] ). Since AR5, new studies on the potential effects of urbanization on solar radiation trends indicate that these effects are generally small, with the exception of some specific sites in Russia and China ( [[#Wang--2014|Wang et al., 2014]] ; [[#Imamovic--2016|Imamovic et al., 2016]] ; [[#Tanaka--2016|Tanaka et al., 2016]] ). Also, surface-based solar radiation observations have been shown to be representative over large spatial domains of up to several degrees latitude/longitude on monthly and longer time scales ( [[#Hakuba--2014|Hakuba et al., 2014]] ; [[#Schwarz--2018|Schwarz et al., 2018]] ). Thus, there is ''high confidence'' that the observed dimming between the 1950s and 1980s and the subsequent brightening are robust and do not arise from measurement artefacts or localized phenomena.

As noted in AR5 ( [[#Hartmann--2013|Hartmann et al., 2013]] ) and supported by recent studies, the trends in surface solar radiation are less spatially coherent since the beginning of the 21st century, with evidence for continued brightening in parts of Europe and the USA, some stabilization in China and India, and dimming in other areas ( [[#Augustine--2013|Augustine and Dutton, 2013]] ; [[#Sanchez-Lorenzo--2015|Sanchez-Lorenzo et al., 2015]] ; [[#Manara--2016|Manara et al., 2016]] ; [[#Soni--2016|Soni et al., 2016]] ; [[#Wang--2016|Wang and]] [[#Wild--2016|Wild, 2016]] ; [[#Jahani--2018|Jahani et al., 2018]] ; [[#Pfeifroth--2018|Pfeifroth et al., 2018]] ; [[#Yang--2018|Yang et al., 2018]] ; [[#Schwarz--2020|Schwarz et al., 2020]] ). The CERES-EBAF satellite-derived dataset of surface solar radiation ( [[#Kato--2018|Kato et al., 2018]] ) does not indicate a globally significant trend over the short period 2001–2012 ( [[#Zhang--2015|Zhang et al., 2015]] ), whereas a statistically significant increase in surface solar radiation of +3.4 W m <sup>−2</sup> per decade over the period 1996–2010 has been found in the Satellite Application Facility on Climate Monitoring (CM SAF) record of the geostationary satellite Meteosat, which views Europe, Africa and adjacent ocean ( [[#Posselt--2014|Posselt et al., 2014]] ).

Since AR5, there is additional evidence that strong decadal changes in surface solar radiation have occurred under cloud-free conditions, as shown for long-term observational records in Europe, USA, China, India and Japan ( [[#Xu--2011|Xu et al., 2011]] ; [[#Gan--2014|Gan et al., 2014]] ; [[#Manara--2016|Manara et al., 2016]] ; [[#Soni--2016|Soni et al., 2016]] ; [[#Tanaka--2016|Tanaka et al., 2016]] ; [[#Kazadzis--2018|Kazadzis et al., 2018]] ; J. [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Yang--2019|Yang et al., 2019]] ; [[#Wild--2021|Wild et al., 2021]] ). This suggests that changes in the composition of the cloud-free atmosphere, primarily in aerosols, contributed to these variations, particularly since the second half of the 20th century ( [[#Wild--2016|Wild, 2016]] ). Water vapour and other radiatively active gases seem to have played a minor role ( [[#Wild--2009|Wild, 2009]] ; [[#Mateos--2013|Mateos et al., 2013]] ; [[#Posselt--2014|Posselt et al., 2014]] ; [[#Yang--2019|Yang et al., 2019]] ). For Europe and East Asia, modelling studies also point to aerosols as an important factor for dimming and brightening by comparing simulations that include or exclude variations in anthropogenic aerosol and aerosol-precursor emissions ( [[#Golaz--2013|Golaz et al., 2013]] ; [[#Nabat--2014|Nabat et al., 2014]] ; [[#Persad--2014|Persad et al., 2014]] ; [[#Folini--2015|Folini and Wild, 2015]] ; [[#Turnock--2015|Turnock et al., 2015]] ; [[#Moseid--2020|Moseid et al., 2020]] ). Moreover, decadal changes in surface solar radiation have often occurred in line with changes in anthropogenic aerosol emissions and associated aerosol optical depth ( [[#Streets--2006|Streets et al., 2006]] ; [[#Wang--2014|Wang and Yang, 2014]] ; [[#Storelvmo--2016|Storelvmo et al., 2016]] ; [[#Wild--2016|Wild, 2016]] ; [[#Kinne--2019|Kinne, 2019]] ). However, further evidence for the influence of changes in cloudiness on dimming and brightening is emphasized in some studies ( [[#Augustine--2013|Augustine and Dutton, 2013]] ; [[#Parding--2014|Parding et al., 2014]] ; [[#Stanhill--2014|Stanhill et al., 2014]] ; [[#Pfeifroth--2018|Pfeifroth et al., 2018]] ; [[#Antuña-Marrero--2019|Antuña-Marrero et al., 2019]] ). Thus, the contribution of aerosol and clouds to dimming and brightening is still debated. The relative influence of cloud-mediated aerosol effects versus direct aerosol radiative effects on dimming and brightening in a specific region may depend on the prevailing pollution levels ( [[#7.3.3|Section 7.3.3]] ; [[#Wild--2016|Wild, 2016]] ).

ESMs and reanalyses often do not reproduce the full extent of observed dimming and brightening ( [[#Wild--2011|Wild and Schmucki, 2011]] ; [[#Allen--2013|Allen et al., 2013]] ; [[#Zhou--2017a|Zhou et al., 2017a]] ; [[#Storelvmo--2018|Storelvmo et al., 2018]] ; [[#Moseid--2020|Moseid et al., 2020]] ; [[#Wohland--2020|Wohland et al., 2020]] ), potentially pointing to inadequacies in the representation of aerosol mediated effects or related emissions data. The inclusion of assimilated aerosol optical depth inferred from satellite retrievals in the MERRA2 reanalysis ( [[#Buchard--2017|Buchard et al., 2017]] ; [[#Randles--2017|Randles et al., 2017]] ) helps to improve the accuracy of the simulated surface solar radiation changes in China ( [[#Feng--2019|Feng and Wang, 2019]] ). However, non-aerosol-related deficiencies in model representations of clouds and circulation, and/or an underestimation of natural variability, could further contribute to the lack of dimming and brightening in ESMs ( [[#Wild--2016|Wild, 2016]] ; [[#Storelvmo--2018|Storelvmo et al., 2018]] ).

The AR5 reported evidence for an increase in surface downward thermal radiation based on different studies covering 1964 to 2008, in line with what would be expected from an increased radiative forcing from GHGs and the warming and moistening of the atmosphere. Updates of the longest observational records from the Baseline Surface Radiation Network continue to show an increase at the majority of sites, in line with an overall increase predicted by ESMs of the order of 2 W m <sup>–2</sup> per decade ( [[#Wild--2016|Wild, 2016]] ). Upward longwave radiation at the surface is rarely measured but is expected to have increased over the same period due to rising surface temperatures.

Turbulent fluxes of latent and sensible heat are also an important part of the surface energy budget (Figure 7.2). Large uncertainties in measurements of surface turbulent fluxes continue to prevent the determination of their decadal changes. Nevertheless, over the ocean, reanalysis-based estimates of linear trends from 1948–2008 indicate high spatial variability and seasonality. Increases in magnitudes of 4 to 7 W m <sup>–2</sup> per decade for latent heat and 2 to 3 W m <sup>–2</sup> per decade for sensible heat in the western boundary current regions are mostly balanced by decreasing trends in other regions ( [[#Gulev--2012|Gulev and Belyaev, 2012]] ). Over land, the terrestrial latent heat flux is estimated to have increased in magnitude by 0.09 W m <sup>–2</sup> per decade from 1989–1997, and subsequently decreased by 0.13 W m <sup>–2</sup> per decade from 1998–2005 due to soil-moisture limitation mainly in the Southern Hemisphere (derived from [[#Mueller--2013|Mueller et al., 2013]] ). These trends are small in comparison to the uncertainty associated with satellite-derived and in situ observations, as well as from land-surface models forced by observations and atmospheric reanalyses. Ongoing advances in remote sensing of evapotranspiration from space ( [[#Mallick--2016|Mallick et al., 2016]] ; [[#Fisher--2017|Fisher et al., 2017]] ; [[#McCabe--2017a|McCabe et al., 2017a]] , b), as well as terrestrial water storage ( [[#Rodell--2018|Rodell et al., 2018]] ) may contribute to future constraints on changes in latent heat flux.

In summary, since AR5, multi-decadal decreasing and increasing trends in surface solar radiation of up to several percent per decade have been detected at many more locations, even in remote areas. There is ''high confidence'' that these trends are widespread, and not localized phenomena or measurement artefacts. The origin of these trends is not fully understood, although there is evidence that anthropogenic aerosols have made a substantial contribution ( ''medium confidence'' ). There is ''medium confidence'' that downward and upward thermal radiation has increased since the 1970s, while there remains ''low confidence'' in the trends in surface sensible and latent heat.

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'''Box 7.2 | The Global Energy Budget'''

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This box assesses the present knowledge of the global energy budget for the period 1971–2018, that is, the balance between radiative forcing, the climate system radiative response and observations of the changes in the global energy inventory (Box 7.2, Figure 1a,d).

The net effective radiative forcing (ERF) of the Earth system since 1971 has been positive ( [[#7.3|Section 7.3]] and Box 7.2, Figure 1b,e), mainly as a result of increases in atmospheric greenhouse gas concentrations (Sections 2.2.8 and 7.3.2). The ERF of these positive forcing agents have been partly offset by that of negative forcing agents, primarily due to anthropogenic aerosols ( [[#7.3.3|Section 7.3.3]] ), which dominate the overall uncertainty. The net energy inflow to the Earth system from ERF for the period 1971–2018 is estimated to be 937 ZJ (1 ZJ = 10 <sup>21</sup> J) with a ''likely'' range of 644 to 1259 ZJ (Box 7.2, Figure 1b).

Box 7.2

The ERF-induced heating of the climate system results in increased thermal radiation to space via the Planck response, but the picture is complicated by a variety of climate feedbacks ( [[#7.4.2|Section 7.4.2]] and Box 7.1) that also influence the climate system radiative response (Box 7.2, Figure 1c). The total radiative response is estimated by multiplying the assessed net feedback parameter, α , from process-based evidence ( [[#7.4.2|Section 7.4.2]] and Table 7.10) with the observed GSAT change for the period (Cross Chapter Box 2.3) and time-integrating (Box 7.2, Figure 1c). The net energy outflow from the Earth system associated with the integrated radiative response for the period 1971–2018 is estimated to be 621 ZJ with a ''likely'' range of 419 to 823 ZJ. Assuming a pattern effect ( [[#7.4.4|Section 7.4.4]] ) on α of –0.5 W m <sup>–2</sup> °C <sup>–1</sup> would lead to a systematically larger energy outflow by about 250 ZJ.

[[File:ebbad856065050657d92f215b2f625b9 IPCC_AR6_WGI_Box_7_2_Figure_1.png]]

'''Box 7.2, Figure'''

'''1 |'''

'''Estimates of the net cumulative energy change (ZJ = 10''' 21 '''Joules) for the period 1971–2018 associated with: (a) observations of changes in the global energy inventory; (b) integrated radiative forcing; and (c) integrated radiative response.''' Black dotted lines indicate the central estimate with ''likely'' and ''very likely'' ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial Earth energy imbalance of 0.2 W m <sup>–2</sup> (a), and an illustration of an assumed pattern effect of –0.5 W m <sup>–2</sup> °C <sup>–1</sup> (c). Background grey lines indicate equivalent heating rates in W m <sup>–2</sup> per unit area of Earth’s surface. Panels '''(d)''' and '''(e)''' show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative forcing, respectively. Panel '''(f)''' shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response. Shading represents the ''very likely'' range for observed energy change relative to pre-industrial levels and ''likely'' range for all other quantities. Forcing and response time series are expressed relative to a baseline period of 1850–1900. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

Combining the ''likely'' range of integrated radiative forcing (Box 7.2, Figure 1b) with the central estimate of integrated radiative response (Box 7.2, Figure 1c) gives a central estimate and ''likely'' range of 340 [47 to 662] ZJ (Box 7.2, Figure 1f). Combining the ''likely'' range of integrated radiative response with the central estimate of integrated radiative forcing gives a ''likely'' range of 340 [147 to 527] ZJ (Box 7.2, Figure 1f). Both calculations yield an implied energy gain in the climate system that is consistent with an independent observation-based assessment of the increase in the global energy inventory expressed relative to the estimated 1850–1900 Earth energy imbalance ( [[#7.5.2|Section 7.5.2]] and Box 7.2, Figure 1a) with a central estimate and ''very likely'' range of 284 [96 to 471] ZJ ( ''high confidence'' ) (Box 7.2, Figure 1d; Table 7.1). Estimating the total uncertainty associated with radiative forcing and radiative response remains a scientific challenge and depends on the degree of correlation between the two (Box 7.2, Figure 1f). However, the central estimate of observed energy change falls well with the estimated ''likely'' range, assuming either correlated or uncorrelated uncertainties. Furthermore, the energy budget assessment would accommodate a substantial pattern effect ( [[#7.4.4.3|Section 7.4.4.3]] ) during 1971–2018 associated with systematically larger values of radiative response (Box 7.2, Figure 1c), and potentially improved closure of the global energy budget. For the period 1970–2011, AR5 reported that the global energy budget was closed within uncertainties ( ''high confidence'' ) and consistent with the ''likely'' range of assessed climate sensitivity ( [[#Church--2013|Church et al., 2013]] ). This Report provides a more robust quantitative assessment based on additional evidence and improved scientific understanding.

In addition to new and extended observations ( [[#7.2.2|Section 7.2.2]] ), confidence in the observed accumulation of energy in the Earth system is strengthened by cross-validation of heating rates based on satellite and in situ observations ( [[#7.2.2.1|Section 7.2.2.1]] ) and closure of the global sea level budget using consistent datasets (Cross-Chapter Box 9.1 and Table 9.5). Overall, there is ''high confidence'' that the global energy budget is closed for 1971–2018 with improved consistency compared to AR5.

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