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Cloud feedback
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<h2 id="overview">Overview</h2> <p>Clouds have two major effects on the <a href="/facts/Earth%2527s_energy_budget/PHytQRdx">Earth's energy budget</a>: they reflect shortwave radiation from sunlight back to space due to their high <a href="/facts/Albedo/TJDRdKEB">albedo</a>, but the water vapor contained inside them also absorbs and re-emits the longwave radiation sent out by the Earth's surface as it is heated by sunlight, preventing its escape into space and retaining this heat energy for longer.<a class="footnote-ref" id="fnref:20" href="#fn:20"><sup>20</sup></a>: 1022 </p><p>In <a href="/facts/Meteorology/xblFr28q">meteorology</a>, the difference in the <a href="/facts/Radiation_budget/PHytQRdx">radiation budget</a> caused by clouds, relative to cloud-free conditions, is described as the cloud radiative effect (CRE).<a class="footnote-ref" id="fnref:21" href="#fn:21"><sup>21</sup></a> This is also sometimes referred to as cloud <a href="/facts/Radiative_forcing/VSLpzv66">radiative forcing</a> (CRF).<a class="footnote-ref" id="fnref:22" href="#fn:22"><sup>22</sup></a> However, since cloud changes are not normally considered an external forcing of climate, CRE is the most commonly used term. </p><p>At the top of the atmosphere, it can be described by the following equation<a class="footnote-ref" id="fnref:23" href="#fn:23"><sup>23</sup></a> </p> Δ R T O A = R A v e r a g e − R C l e a r {\displaystyle \Delta R_{TOA}=R_{Average}-R_{Clear}} <p>The net cloud radiative effect can be decomposed into its longwave and shortwave components. This is because net radiation is absorbed solar minus the outgoing longwave radiation shown by the following equations </p> Δ R T O A = Δ Q a b s − Δ O L R {\displaystyle \Delta R_{TOA}=\Delta Q_{abs}-\Delta OLR} <p>The first term on the right is the shortwave cloud effect (<i>Q</i>abs ) and the second is the longwave effect (OLR). </p><p>The shortwave cloud effect is calculated by the following equation </p> Δ Q a b s = ( S o / 4 ) ⋅ ( 1 − α c l o u d y ) − ( S o / 4 ) ⋅ ( 1 − α c l e a r ) {\displaystyle \Delta Q_{abs}=(S_{o}/4)\cdot (1-\alpha _{cloudy})-(S_{o}/4)\cdot (1-\alpha _{clear})} <p>Where <i>S</i>o is the <a href="/facts/Solar_constant/boJt09Uf">solar constant</a>, <i>∝</i>cloudy is the <a href="/facts/Albedo/TJDRdKEB">albedo</a> with clouds and <i>∝</i>clear is the albedo on a clear day. </p><p>The longwave effect is calculated by the next following equation </p> Δ O L R = σ T z 4 − F c l e a r u p {\displaystyle \Delta OLR=\sigma T_{z}^{4}-F_{clear}^{up}} <p>Where σ is the <a href="/facts/Stefan%25E2%2580%2593Boltzmann_constant/A0FvPHIh">Stefan–Boltzmann constant</a>, T is the temperature at the given height, and F is the upward flux in clear conditions. </p><p>Putting all of these pieces together, the final equation becomes </p> Δ R T O A = ( S o / 4 ) ⋅ ( ( 1 − α c l o u d y ) − ( 1 − α c l e a r ) ) − σ T z 4 + F c l e a r u p {\displaystyle \Delta R_{TOA}=(S_{o}/4)\cdot ((1-\alpha _{cloudy})-(1-\alpha _{clear}))-\sigma T_{z}^{4}+F_{clear}^{up}} <p>Under dry, cloud-free conditions, water vapor in atmosphere contributes 67% of the <a href="/facts/Greenhouse_effect/LGdNYYj3">greenhouse effect</a> on Earth. When there is enough moisture to form typical cloud cover, the greenhouse effect from "free" water vapor goes down to 50%, but water vapor which is now inside the clouds amounts to 25%, and the net greenhouse effect is at 75%.<a class="footnote-ref" id="fnref:24" href="#fn:24"><sup>24</sup></a> According to 1990 estimates, the presence of clouds reduces the <a href="/facts/Outgoing_longwave_radiation/anrmbs9N">outgoing longwave radiation</a> by about 31 W/m2. However, it also increases the global <a href="/facts/Albedo/TJDRdKEB">albedo</a> from 15% to 30%, and this reduces the amount of <a href="/facts/Solar_radiation/SI26I7aD">solar radiation</a> absorbed by the Earth by about 44 W/m2. Thus, there is a net <i>cooling</i> of about 13 W/m2.<a class="footnote-ref" id="fnref:25" href="#fn:25"><sup>25</sup></a> If the clouds were removed with all else remaining the same, the <a href="/facts/Earth/HLLmDfj0">Earth</a> would lose this much cooling and the global temperatures would increase.<a class="footnote-ref" id="fnref:26" href="#fn:26"><sup>26</sup></a>: 1022 </p><p><a href="/facts/Climate_change/rEN4BDE7">Climate change</a> increases the amount of water vapor in the atmosphere due to the <a href="/facts/Clausius%25E2%2580%2593Clapeyron_relation/Lo9SfNqM">Clausius–Clapeyron relation</a>, in what is known as the water-vapor feedback.<a class="footnote-ref" id="fnref:27" href="#fn:27"><sup>27</sup></a> It also affects a range of cloud properties, such as their height, the typical distribution throughout the atmosphere, and <a href="/facts/Cloud_physics/TuhjzA5B">cloud microphysics</a>, such as the amount of water droplets held, all of which then affect clouds' radiative forcing.<a class="footnote-ref" id="fnref:28" href="#fn:28"><sup>28</sup></a>: 1023 Differences in those properties change the role of clouds in the Earth's energy budget. The name <i>cloud feedback</i> refers to this relationship between climate change, cloud properties, and clouds' radiative forcing.<a class="footnote-ref" id="fnref:29" href="#fn:29"><sup>29</sup></a>: 2224 Clouds also affect the magnitude of internally generated <a href="/facts/Climate_variability_and_change/D3X6Ulbu">climate variability.</a><a class="footnote-ref" id="fnref:30" href="#fn:30"><sup>30</sup></a><a class="footnote-ref" id="fnref:31" href="#fn:31"><sup>31</sup></a> </p> <h2 id="representation-in-climate-models">Representation in climate models</h2> <p><a href="/facts/Climate_model/5xQBYLcl">Climate models</a> have represented clouds and cloud processes for a very long time. Cloud feedback was already a standard feature in climate models designed in the 1980s.<a class="footnote-ref" id="fnref:32" href="#fn:32"><sup>32</sup></a><a class="footnote-ref" id="fnref:33" href="#fn:33"><sup>33</sup></a><a class="footnote-ref" id="fnref:34" href="#fn:34"><sup>34</sup></a> However, the physics of clouds are very complex, so models often represent various types of clouds in different ways, and even small variations between models can lead to significant changes in temperature and <a href="/facts/Precipitation/uq3nGQc8">precipitation</a> response.<a class="footnote-ref" id="fnref:35" href="#fn:35"><sup>35</sup></a> <a href="/facts/Climate_scientist/GBHhtmrv">Climate scientists</a> devote a lot of effort to resolving this issue. This includes the Cloud Feedback Model Intercomparison Project (CFMIP), where models simulate cloud processes under different conditions and their output is compared with the observational data. (AR6 WG1, Ch1, 223) When the <a href="/facts/Intergovernmental_Panel_on_Climate_Change/sh76du1u">Intergovernmental Panel on Climate Change</a> had published its Sixth Assessment Report (<a href="/facts/AR6/Dc5drLUK">AR6</a>) in 2021, the <a href="/facts/Error_bar/lQTsGFxR">uncertainty range</a> regarding cloud feedback strength became 50% smaller since the time of the <a href="/facts/IPCC_Fifth_Assessment_Report/aYz6LE8H">AR5</a> in 2014.<a class="footnote-ref" id="fnref:36" href="#fn:36"><sup>36</sup></a>: 95 </p> Remaining uncertainty about cloud feedbacks in <a href="/facts/IPCC_Sixth_Assessment_Report/Dc5drLUK">IPCC Sixth Assessment Report</a><a class="footnote-ref" id="fnref:37" href="#fn:37"><sup>37</sup></a>: 975 <table><tbody><tr><th>Feedback</th><th>Direction</th><th>Confidence</th></tr><tr><td>High-cloud altitude feedback</td><td>Positive</td><td>High</td></tr><tr><td>Tropical high-cloud amount feedback</td><td>Negative</td><td>Low</td></tr><tr><td>Subtropical marine low-cloud feedback</td><td>Positive</td><td>High</td></tr><tr><td>Land cloud feedback</td><td>Positive</td><td>Low</td></tr><tr><td>Mid-latitude cloud amount feedback</td><td>Positive</td><td>Medium</td></tr><tr><td>Extratropical cloud optical depth feedback</td><td>Small negative</td><td>Medium</td></tr><tr><td>Arctic cloud feedback</td><td>Small positive</td><td>Low</td></tr><tr><td>Net cloud feedback</td><td>Positive</td><td>High</td></tr></tbody></table> <p>This happened because of major improvements in the understanding of cloud behaviour over the subtropical oceans. As the result, there was <i>high confidence</i> that the overall cloud feedback is positive (contributes to warming).<a class="footnote-ref" id="fnref:38" href="#fn:38"><sup>38</sup></a>: 95 The AR6 value for cloud feedback is +0.42 [–0.10 to 0.94] W m–2 per every 1 °C (1.8 °F) in warming. This estimate is derived from multiple lines of evidence, including both models and observations.<a class="footnote-ref" id="fnref:39" href="#fn:39"><sup>39</sup></a>: 95 The tropical high-cloud amount feedback is the main remaining area for improvement. The only way total cloud feedback may still be slightly negative is if either this feedback, or the optical depth feedback in the <a href="/facts/Southern_Ocean/F15W9ADT">Southern Ocean</a> clouds is suddenly found to be "extremely large"; the probability of that is considered to be below 10%.<a class="footnote-ref" id="fnref:40" href="#fn:40"><sup>40</sup></a>: 975 As of 2024, most recent observations from the <a href="/facts/CALIPSO/VLqkoF0j">CALIPSO</a> satellite instead indicate that the tropical cloud feedback is very weak.<a class="footnote-ref" id="fnref:41" href="#fn:41"><sup>41</sup></a><a class="footnote-ref" id="fnref:42" href="#fn:42"><sup>42</sup></a> </p><p>In spite of these improvements, clouds remain the least well-understood climate feedback, and they are the main reason why models estimate differing values for equilibrium <a href="/facts/Climate_sensitivity/toPBU6Qe">climate sensitivity</a> (ECS). ECS is an estimate of long-term (multi-century) warming in response to a <i>doubling</i> in CO2-equivalent greenhouse gas concentrations: if the future emissions are not low, it also becomes the most important factor for determining 21st century temperatures.<a class="footnote-ref" id="fnref:43" href="#fn:43"><sup>43</sup></a>: 95 In general, the current generation of gold-standard climate models, <a href="/facts/CMIP6/rImqdYS3">CMIP6</a>, operates with larger climate sensitivity than the previous generation, and this is largely because cloud feedback is about 20% more positive than it was in CMIP5.<a class="footnote-ref" id="fnref:44" href="#fn:44"><sup>44</sup></a>: 93 <a class="footnote-ref" id="fnref:45" href="#fn:45"><sup>45</sup></a> </p><p>However, the <i>median</i> cloud feedback is only slightly larger in CMIP6 than it was in CMIP5;<a class="footnote-ref" id="fnref:46" href="#fn:46"><sup>46</sup></a>: 95 the average is so much higher only because several <a href="/facts/Hot_model/toPBU6Qe">"hot" models</a> have much stronger cloud feedback and higher sensitivity than the rest.<a class="footnote-ref" id="fnref:47" href="#fn:47"><sup>47</sup></a>: 93 <a class="footnote-ref" id="fnref:48" href="#fn:48"><sup>48</sup></a> Those models have a sensitivity of 5 °C (41 °F) and their presence had increased the median model sensitivity from 3.2 °C (37.8 °F) in CMIP5 to 3.7 °C (38.7 °F) in CMIP6.<a class="footnote-ref" id="fnref:49" href="#fn:49"><sup>49</sup></a> These model results had attracted considerable attention when they were first published in 2019, as they would have meant faster and more severe warming if they were accurate.<a class="footnote-ref" id="fnref:50" href="#fn:50"><sup>50</sup></a><a class="footnote-ref" id="fnref:51" href="#fn:51"><sup>51</sup></a> It was soon found that the output of those "hot" models is inconsistent with both observations and <a href="/facts/Paleoclimate/p3x22QaC">paleoclimate</a> evidence, so the consensus AR6 value for cloud feedback is smaller than the mean model output alone. The best estimate of climate sensitivity in AR6 is at 3 °C (37 °F), as this is in a better agreement with observations and paleoclimate findings.<a class="footnote-ref" id="fnref:52" href="#fn:52"><sup>52</sup></a>: 93 <a class="footnote-ref" id="fnref:53" href="#fn:53"><sup>53</sup></a><a class="footnote-ref" id="fnref:54" href="#fn:54"><sup>54</sup></a> </p> <h2 id="role-of-aerosols">Role of aerosols</h2> <p>Atmospheric <a href="/facts/Aerosol/X1jXFLp2">aerosols</a>—fine partices suspended in the air—affect cloud formation and properties, which also alters their impact on climate. While some aerosols, such as <a href="/facts/Black_carbon/yBb6hpLy">black carbon</a> particles, make the clouds darker and thus contribute to warming,<a class="footnote-ref" id="fnref:55" href="#fn:55"><sup>55</sup></a> by far the strongest effect is from <a href="/facts/Sulfate/yuWVxsUf">sulfates</a>, which increase the number of cloud droplets, making the clouds more reflective, and helping them cool the climate more. That is known as a <i>direct</i> aerosol effect; however, aerosols also have an <i>indirect</i> effect on <a href="/facts/Liquid_water_path/5UVglIJy">liquid water path</a>, and determining it involves computationally heavy continuous calculations of evaporation and condensation within clouds. Climate models generally assume that aerosols increase liquid water path, which makes the clouds even more reflective.<a class="footnote-ref" id="fnref:56" href="#fn:56"><sup>56</sup></a> However, satellite observations taken in 2010s suggested that aerosols decreased liquid water path instead, and in 2018, this was reproduced in a model which integrated more complex cloud microphysics.<a class="footnote-ref" id="fnref:57" href="#fn:57"><sup>57</sup></a> Yet, 2019 research found that earlier satellite observations were biased by failing to account for the thickest, most water-heavy clouds naturally raining more and shedding more particulates: very strong aerosol cooling was seen when comparing clouds of the same thickness.<a class="footnote-ref" id="fnref:58" href="#fn:58"><sup>58</sup></a> </p><p>Moreover, large-scale observations can be confounded by changes in other atmospheric factors, like humidity: i.e. it was found that while post-1980 improvements in air quality would have reduced the number of clouds over the <a href="/facts/East_Coast_of_the_United_States/vrKUc0Wl">East Coast of the United States</a> by around 20%, this was offset by the increase in relative humidity caused by atmospheric response to <a href="/facts/AMOC/R8dgsesx">AMOC</a> slowdown.<a class="footnote-ref" id="fnref:59" href="#fn:59"><sup>59</sup></a> Similarly, while the initial research looking at sulfates from the <a href="/facts/2014%25E2%2580%25932015_eruption_of_B%25C3%25A1r%25C3%25B0arbunga/cG9sr3CW">2014–2015 eruption of Bárðarbunga</a> found that they caused no change in liquid water path,<a class="footnote-ref" id="fnref:60" href="#fn:60"><sup>60</sup></a> it was later suggested that this finding was confounded by counteracting changes in humidity.<a class="footnote-ref" id="fnref:61" href="#fn:61"><sup>61</sup></a> </p> <p>To avoid confounders, many observations of aerosol effects focus on <a href="/facts/Ship_tracks/UVK9WIdX">ship tracks</a>, but post-2020 research found that visible ship tracks are a poor proxy for other clouds, and estimates derived from them overestimate aerosol cooling by as much as 200%.<a class="footnote-ref" id="fnref:62" href="#fn:62"><sup>62</sup></a> At the same time, other research found that the majority of ship tracks are "invisible" to satellites, meaning that the earlier research had underestimated aerosol cooling by overlooking them.<a class="footnote-ref" id="fnref:63" href="#fn:63"><sup>63</sup></a> Finally, 2023 research indicates that all climate models have underestimated sulfur emissions from volcanoes which occur in the background, outside of major eruptions, and so had consequently overestimated the cooling provided by anthropogenic aerosols, especially in the Arctic climate.<a class="footnote-ref" id="fnref:64" href="#fn:64"><sup>64</sup></a> </p> <p>Estimates of how much aerosols affect cloud cooling are very important, because the amount of sulfate aerosols in the air had undergone dramatic changes in the recent decades. First, it had increased greatly from 1950s to 1980s, largely due to the widespread burning of <a href="/facts/Sulfur/IK0amTfM">sulfur</a>-heavy coal, which caused an observable reduction in visible sunlight that had been described as <a href="/facts/Global_dimming/JBMzvfYk">global dimming</a>.<a class="footnote-ref" id="fnref:65" href="#fn:65"><sup>65</sup></a><a class="footnote-ref" id="fnref:66" href="#fn:66"><sup>66</sup></a> Then, it started to decline substantially from the 1990s onwards and is expected to continue to decline in the future, due to the measures to combat <a href="/facts/Acid_rain/f769GyNz">acid rain</a> and other impacts of <a href="/facts/Air_pollution/zmk9Dy7S">air pollution</a>.<a class="footnote-ref" id="fnref:67" href="#fn:67"><sup>67</sup></a> Consequently, the aerosols provided a considerable cooling effect which counteracted or "masked" some of the <a href="/facts/Greenhouse_effect/LGdNYYj3">greenhouse effect</a> from human emissions, and this effect had been declining as well, which contributed to acceleration of <a href="/facts/Climate_change/rEN4BDE7">climate change</a>.<a class="footnote-ref" id="fnref:68" href="#fn:68"><sup>68</sup></a> </p><p>Climate models do account for the presence of aerosols and their recent and future decline in their projections, and typically estimate that the cooling they provide in 2020s is similar to the warming from human-added <a href="/facts/Atmospheric_methane/oH20tvwB">atmospheric methane</a>, meaning that simultaneous reductions in both would effectively cancel each other out.<a class="footnote-ref" id="fnref:69" href="#fn:69"><sup>69</sup></a> However, the existing uncertainty about aerosol-cloud interactions likewise introduces uncertainty into models, particularly when concerning predictions of changes in weather events over the regions with a poorer historical record of atmospheric observations.<a class="footnote-ref" id="fnref:70" href="#fn:70"><sup>70</sup></a><a class="footnote-ref" id="fnref:71" href="#fn:71"><sup>71</sup></a><a class="footnote-ref" id="fnref:72" href="#fn:72"><sup>72</sup></a><a class="footnote-ref" id="fnref:73" href="#fn:73"><sup>73</sup></a> </p> <h2 id="possible-break-up-of-equatorial-stratocumulus-clouds">Possible break-up of equatorial stratocumulus clouds</h2> <p class="note">See also: <a href="/facts/Tipping_points_in_the_climate_system/fpPR0g5I">Tipping points in the climate system</a></p> <p>In 2019, a study employed a <a href="/facts/Large_eddy_simulation/ULBNg1SY">large eddy simulation</a> model to estimate that equatorial <a href="/facts/Stratocumulus_cloud/xMpHbmbj">stratocumulus clouds</a> could break up and scatter when <a href="/facts/Carbon_dioxide/xGzpppEp">CO2</a> levels rise above 1,200 <a href="/facts/Parts_per_million/PozRuoLM">ppm</a> (almost three times higher than the current levels, and over 4 times greater than the preindustrial levels). The study estimated that this would cause a surface warming of about 8 °C (14 °F) globally and 10 °C (18 °F) in the subtropics, which would be in addition to at least 4 °C (7.2 °F) already caused by such CO2 concentrations. In addition, stratocumulus clouds would not reform until the CO2 concentrations drop to a much lower level.<a class="footnote-ref" id="fnref:74" href="#fn:74"><sup>74</sup></a> It was suggested that this finding could help explain past episodes of unusually rapid warming such as <a href="/facts/Paleocene-Eocene_Thermal_Maximum/QnPVzRR7">Paleocene-Eocene Thermal Maximum</a>.<a class="footnote-ref" id="fnref:75" href="#fn:75"><sup>75</sup></a> In 2020, further work from the same authors revealed that in their large eddy simulation, this <a href="/facts/Tipping_points_in_the_climate_system/fpPR0g5I">tipping point</a> cannot be stopped with <a href="/facts/Solar_radiation_modification/Ho5UblE7">solar radiation modification</a>: in a hypothetical scenario where very high CO2 emissions continue for a long time but are offset with extensive solar radiation modification, the break-up of stratocumulus clouds is simply delayed until CO2 concentrations hit 1,700 ppm, at which point it would still cause around 5 °C (9.0 °F) of unavoidable warming.<a class="footnote-ref" id="fnref:76" href="#fn:76"><sup>76</sup></a> </p><p>However, because large eddy simulation models are simpler and smaller-scale than the <a href="/facts/General_circulation_model/6QUar9RF">general circulation models</a> used for climate projections, with limited representation of atmospheric processes like <a href="/facts/Subsidence_(atmosphere)/tfn3NPJK">subsidence</a>, this finding is currently considered speculative.<a class="footnote-ref" id="fnref:77" href="#fn:77"><sup>77</sup></a> Other scientists say that the model used in that study unrealistically extrapolates the behavior of small cloud areas onto all cloud decks, and that it is incapable of simulating anything other than a rapid transition, with some comparing it to "a knob with two settings".<a class="footnote-ref" id="fnref:78" href="#fn:78"><sup>78</sup></a> Additionally, CO2 concentrations would only reach 1,200 ppm if the world follows <a href="/facts/Representative_Concentration_Pathway/zoNyvobf">Representative Concentration Pathway</a> 8.5, which represents the highest possible greenhouse gas emission scenario and involves a massive expansion of <a href="/facts/Coal/83kV3qxg">coal</a> infrastructure. In that case, 1,200 ppm would be passed shortly after 2100.<a class="footnote-ref" id="fnref:79" href="#fn:79"><sup>79</sup></a> </p> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Cloud_formation/KDgo0Wqu">Cloud formation</a></li> <li><a href="/facts/Earth%2527s_energy_budget/PHytQRdx">Earth's energy budget</a></li> <li><a href="/facts/Fixed_anvil_temperature_hypothesis/eXHBrxkG">Fixed anvil temperature hypothesis</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>IPCC, 2021: Annex VII: Glossary [Matthews, J.B.R., V. Möller, R. van Diemen, J.S. Fuglestvedt, V. Masson-Delmotte, C. Méndez, S. Semenov, A. Reisinger (eds.)]. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2215–2256, doi:10.1017/9781009157896.022. <a href="https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVII.pdf" target="_blank">https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVII.pdf</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Stephens, Graeme L. (2005-01-01). "Cloud Feedbacks in the Climate System: A Critical Review". Journal of Climate. 18 (2): 237–273. Bibcode:2005JCli...18..237S. CiteSeerX 10.1.1.130.1415. doi:10.1175/JCLI-3243.1. ISSN 0894-8755. S2CID 16122908. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Forster, P.; Storelvmo, T.; Armour, K.; Collins, W.; Dufresne, J.-L.; Frame, D.; Lunt, D.J.; Mauritsen, T.; Watanabe, M.; Wild, M.; Zhang, H. 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