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High Cloud Feedback
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The high cloud feedback is defined as the change in high cloud properties (e.g. height, opacity, coverage) due to global warming, which amplifies or weakens the warming.[1] It is one part of the total cloud feedback which is an important variable in the climate system[1]. This feedback is the reason for a large part of the uncertainty of todays climate models and has a larger intermodel spread than any other radiative feedback.[2]
The cloud feedback, and therefore also the high cloud feedback, has a positive longwave and a negative shortwave part which are summed up to get the total feedback.[3] The longwave part is describing the interaction of the clouds with the longwave radiation coming from the earths surface. It is dominated by the altitude and temperature of the cloud top, resulting in a positive feedback.[1] [4]The shortwave feedback on the other hand describes the interaction of the clouds with the shortwave radiation coming directly from the sun. It is dominated by the optical thickness resulting in a negative shortwave feedback.[1]
For high clouds the feedback balances itself with longwave and shortwave to a large part which doesn't mean that these processes are neglectable since they can change independent of one another.[2] It is together with the middle high cloud feedback a larger contributor to the total cloud feedback than low clouds.[2]
The calculation and modeling of high cloud feedback states a challenge and is an active field of research.[1]
Physical Background
[edit]The high cloud feedback describes the effects of high clouds (cloud top pressure ≤ 440 hPa) interacting with radiation.[1] A negative feedback reduces the effect of a forcing back towards an equilibrium state. The shortwave part of the high cloud feedback is negative.[1] This is e.g. due to solar radiation being reflected by high clouds.[1] A positive feedback amplifies the effect of a forcing. The longwave part of the high cloud feedback is positive.[1] This is due to the reduction of outgoing longwave radiation by high clouds absorbing or reflecting the terrestrial radiation.[1] The total high cloud feedback is the sum of the longwave and shortwave feedback.[3]
The high cloud properties which mainly influence the high cloud feedback are the cloud area fraction, the cloud top height and the optical depth.[1] These cloud attributes, and therefore also the cloud feedback, are not spatially homogeneous.[1] Hence the cloud feedback is mainly expressed as a global mean.[1]
The cloud feedback is quantified by measuring the difference of the radiative flux between all-sky (with clouds) and clear-sky (without clouds).[1] It provides a challenge to model the various radiative interactions and their effects on clouds without introducing biases or unwanted dependencies.[2] To gain insight to the connections between a feedback parameter and a cloud property, a model has to decompose the cloud feedback completely.[2] This is not feasible or possible in every climate model and is therefore often parameterized, which introduces a large dependency on the cloud parameterizations.[2]
Longwave Feedback
[edit]The total longwave (LW) part of the high cloud feedback is positive.[2]
It is dominated by the positive cloud altitude feedback[4] which is mainly found in the tropics with the mechanisms being identical in the extra tropics.[1] The LW radiation emitted by the high cloud tops is proportional to the temperature at the cloud top which is again depending on the altitude.[1] The altitude of the high clouds changes with rising temperatures, due to the following mechanisms:[1] Higher temperatures on the surface force the moisture to rise, which is fundamentally described by the Clausius Clapeyron equation.[1][4] The altitude at which the radiative cooling is still effective if closely tied to the relative humidity and rises equally.[1][4] The altitude, at which the radiative cooling becomes inefficient due to a lack of moisture, then determines the detrainment height of deep convection due to the mass conservation.[1][4] The could top height is therefore determined by the surface temperature.[1] There are three theories on how the temperature changes at the cloud top with the change in altitude.[1] The FAT (Fixed Anvil Temperature) hypothesis argues, that the isotherms shift upwards with global warming and the temperature at the cloud top stays therefore constant.[5] This results in a positive feedback, since no more radiation is emitted while the surface temperature is rising.[5] According to the FAT hypothesis this leads to a feedback of 0,27 W m K[4]. The second hypothesis PHAT (Proportionally Higher Anvil Temperature) claims a smaller cloud feedback of 0.20 W m K[4], due to a slight warming of the cloud tops which has been measured.[4] The static stability increases with higher surface temperatures in the upper troposphere and lets the clouds shift slightly to warmer temperatures.[1] The third hypothesis is FAP (Fixed Anvil Pressure) which assumes a constant top cloud pressure with a warming climate, as if the cloud profile did not move upwards.[4] This results in a negative LW feedback, which does not agree with observations.[4] It can be used to calculate the impact of the cloud height change on the LW feedback.[4] Most models agree with the PHAT hypothesis which also agrees the most with observations.[4]
The optical depth feedback is determined by the increasing optical depth of the high clouds with rising temperatures.[6] The optical depth determines the LW emission of the cloud, so that it increases with the optical depth.[6] A larger emission therefore reduces warming and leads to a negative optical depth feedback.[6] It is only a small part of the LW feedback of high clouds, but plays a larger role in the feedback of low clouds.[1]
In which direction the area fraction of high clouds impacts the high cloud feedback is a topic of ongoing research and discussion.[1] Two mechanisms can lead to a decrease in the area fraction and a therefore a negative feedback.[1] The warming at the surface increases latent heating which is brought up by convection.[1] More latent heat leads to less radiative cooling which then decreases the convective mass flux which again decreases the cloud area fraction.[1] Another argument for a smaller area fraction is that the self-aggregation of clouds increases at higher temperatures.[1] This would lead to small convective areas and large dry areas which increase the radiative longwave cooling, resulting in a negative feedback.[1] Since the area fraction of high clouds is extremely sensitive to cloud micro physics in models[1], there are also models which predict an increase in high cloud area fraction[4] which would lead to a positive feedback.
Shortwave Feedback
[edit]The total shortwave (SW) part of the high cloud feedback is negative.
The change of cloud area fraction with warming is a topic of discussion, similar to the LW feedback.[2] The SW high cloud feedback depends on the cloud area fraction due to the reflection. With a larger cloud area fraction more solar radiation can be reflected.[4] A decreasing cloud fraction would lead to a positive SW feedback.[2] It was found that the high cloud SW feedback is anticorrelated to the laps rate feedback which influences the cloud coverage.[4] Therefore the high cloud SW feedback cloud be computed together with the laps rate feedback to simplify the calculations in climate models. It is important to note, that this is a topic of ongoing discussion.[4]
The impact of the cloud height and optical thickness on the SW feedback is neglectable.[1]
Challenges
[edit]It is difficult to detect the reason for a change in the SW and LW radiation due to cloud feedback, because there are a lot of cloud responses which could be the cause for a specific radiation feedback.[2] Furthermore is it difficult to not count in clear sky effects[2]. There are techniques to decompose the cloud feedbacks in models and their triggers in detail by showing the cloud fraction as a function of cloud-top pressure and the optical depth of the cloud. This technique is not applicable to a conventional Global Circulation Model (GCM) output but needs a special simulation from the International Satellite Cloud Climatology Project (ISCCP)[2]
Another challenge when dealing with (high) cloud feedbacks, is that the LW and SW part often cancel each other out, so that only a small total feedback is left.[2] The positive and negative feedback parts are not neglectable, since they can change independent of one another with rising temperature.[2] They are all parts of a feedback have to be looked at in order to fully understand the mechanisms and implement them into models.[2]
The high cloud feedback is not fully understood and will continue to be a topic of active research.[1]
References
[edit]- ^ a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag Ceppi, Paulo; Brient, Florent; Zelinka, Mark D.; Hartmann, Dennis L. (2017). "Cloud feedback mechanisms and their representation in global climate models". WIREs Climate Change. 8 (4). doi:10.1002/wcc.465. ISSN 1757-7780.
- ^ a b c d e f g h i j k l m n o Zelinka, Mark D.; Klein, Stephen A.; Hartmann, Dennis L. (2012-06-01). "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels". Journal of Climate. 25 (11): 3715–3735. doi:10.1175/JCLI-D-11-00248.1. ISSN 0894-8755.
- ^ a b Colman, R. A. (2015-04-27). "Climate radiative feedbacks and adjustments at the Earth's surface". Journal of Geophysical Research: Atmospheres. 120 (8): 3173–3182. doi:10.1002/2014JD022896. ISSN 2169-897X.
- ^ a b c d e f g h i j k l m n o p Zelinka, Mark D.; Hartmann, Dennis L. (2010-08-27). "Why is longwave cloud feedback positive?". Journal of Geophysical Research: Atmospheres. 115 (D16). doi:10.1029/2010JD013817. ISSN 0148-0227.
- ^ a b Hartmann, Dennis L.; Larson, Kristin (2002). "An important constraint on tropical cloud ‐ climate feedback". Geophysical Research Letters. 29 (20). doi:10.1029/2002GL015835. ISSN 0094-8276.
- ^ a b c Stephens, G. L. (1978-11-01). "Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes". Journal of the Atmospheric Sciences. 35 (11): 2123–2132. doi:10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2. ISSN 0022-4928.