Editing Cloud feedback (section)

=== Physical Background ===
The high cloud feedback describes the change of radiation at the top of the atmosphere that is due to a change of high cloud properties.<ref name="Ceppi-2017" /> 

A negative feedback reduces the effect of a forcing back towards an equilibrium state. The shortwave part of the high cloud feedback is negative, but very close to zero.<ref name="Ceppi-2017" /> It can be influenced e.g. by changes in the reflection of [[Solar irradiance|solar radiation]] by the high cloud tops and their amount.<ref name="Ceppi-2017" /> A positive feedback amplifies the effect of a forcing. The longwave part of the high cloud feedback is positive.<ref name="Ceppi-2017" /> This is due to the increased reduction of [[outgoing longwave radiation]] with rising temperatures, triggered by the changing amount of high clouds that absorb and reflect the terrestrial radiation.<ref name="Ceppi-2017" /> The total high cloud feedback is the sum of the longwave and shortwave feedback and is positive.<ref name="Colman-2015">{{Cite journal |last=Colman |first=R. A. |date=2015-04-27 |title=Climate radiative feedbacks and adjustments at the Earth's surface |url=https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014JD022896 |journal=Journal of Geophysical Research: Atmospheres |language=en |volume=120 |issue=8 |pages=3173–3182 |doi=10.1002/2014JD022896 |bibcode=2015JGRD..120.3173C |issn=2169-897X}}</ref>

The high cloud properties which mainly influence the high cloud feedback are the cloud area fraction, the cloud top height and the [[optical depth]].<ref name="Ceppi-2017" /> These cloud attributes, and therefore also the cloud feedback, are not spatially homogeneous.<ref name="Ceppi-2017" /> Hence the cloud feedback is mostly expressed as a global mean.<ref name="Ceppi-2017" />

The cloud feedback is quantified by measuring the difference of the [[radiative flux]] between all-sky (with clouds) and clear-sky (without clouds).<ref name="Ceppi-2017" /> It remains a challenge to model the various radiative interactions and their effects on clouds without introducing [[Bias|biases]] or unwanted dependencies.<ref name="Zelinka-2012" /> To gain insight to the connections between a feedback parameter and a cloud property, the model would have to realistically represent all the physical processes influencing the clouds.<ref name="Zelinka-2012" /> Because of the coarse resolution of most climate models, they need to rely on cloud parameterizations, which brings about large uncertainties.<ref name="Zelinka-2012" />