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

====== Tropical high-cloud amount feedback ======

Updrafts in convective plumes lead to detrainment of moisture at a level where the buoyancy diminishes, and thus deep convective clouds over high SSTs in the tropics are accompanied by anvil and cirrus clouds in the upper troposphere. These clouds, rather than the convective plumes themselves, play a substantial role in the global TOA radiation budget. In the present climate, the net CRE of these clouds is small due to a cancellation between the SW and LW components ( [[#Hartmann--2001|Hartmann et al., 2001]] ). However, high-clouds with different optical properties could respond to surface warming differently, potentially perturbing this radiative balance and therefore leading to a non-zero feedback.

A thermodynamic mechanism referred to as the ‘stability iris effect’ has been proposed to explain that the anvil cloud amount decreases with surface warming ( [[#Bony--2016|Bony et al., 2016]] ). In this mechanism, a temperature-mediated increase of static stability in the upper troposphere, where convective detrainment occurs, acts to balance a weakened mass outflow from convective clouds, and thereby reduce anvil cloud areal coverage (Figure 7.9). The reduction of anvil cloud amount is accompanied by enhanced convective aggregation that causes a drying of the surrounding air and thereby increases the LW emission to space that acts as a negative feedback ( [[#Bony--2020|Bony et al., 2020]] ). This phenomenon is found in many CRM simulations ( [[#Emanuel--2014|Emanuel et al., 2014]] ; [[#Wing--2014|Wing and Emanuel, 2014]] ; [[#Wing--2020|Wing et al., 2020]] ) and also identified in observed interannual variability ( [[#Stein--2017|Stein et al., 2017]] ; [[#Saint-Lu--2020|Saint-Lu et al., 2020]] ).

Despite the reduction of anvil cloud amount supported by several lines of evidence, estimates of radiative feedback due to high-cloud amount changes is highly uncertain in models. The assessment presented here is guided by combined analyses of TOA radiation and cloud fluctuations at interannual time scale using multiple satellite datasets. The observationally based local cloud amount feedback associated with optically thick high-clouds is negative, leading to its global contribution (by multiplying the mean tropical anvil cloud fraction of about 8%) of –0.24 ± 0.05 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation) for LW ( [[#Vaillant%20de%20Guélis--2018|Vaillant de Guélis et al., 2018]] ). Also, there is a positive feedback due to increase of optically thin cirrus clouds in the tropopause layer, estimated to be 0.09 ± 0.09 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Zhou--2014|Zhou et al., 2014]] ). The negative LW feedback due to reduced amount of thick high-clouds is partly compensated by the positive SW feedback (due to less reflection of solar radiation), so that the tropical high-cloud amount feedback is assessed to be equal to or smaller than their sum. Consistently, the net high-cloud feedback in the tropical convective regime, including a part of the altitude feedback, is estimated to have the global contribution of –0.13 ± 0.06 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Williams--2017|Williams and Pierrehumbert, 2017]] ). The negative cloud LW feedback is considerably biased in CMIP5 GCMs ( [[#Mauritsen--2015|Mauritsen and]] [[#Stevens--2015|Stevens, 2015]] ; [[#Su--2017|Su et al., 2017]] ; [[#Li--2019|Li et al., 2019]] ) and highly uncertain, primarily due to differences in the convective parametrization ( [[#Webb--2015|Webb et al., 2015]] ). Furthermore, high-resolution CRM simulations cannot alone be used to constrain uncertainty because the results depend on parametrized cloud microphysics and turbulence ( [[#Bretherton--2014|Bretherton et al., 2014]] ; [[#Ohno--2019|Ohno et al., 2019]] ). Therefore, the tropical high-cloud amount feedback is assessed as negative but with ''low confidence'' given the lack of modelling evidence. Taking observational estimates altogether and methodological uncertainty into account, the global contribution of the high-cloud amount feedback is assessed to be –0.15 ± 0.2 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation).

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