Publication Date:
2023-12-05
Description:
Clouds are primary modulators of Earth's energy balance. It is thus important to understand the links connecting variabilities in cloudiness to variabilities in other state variables of the climate system, and also describe how these links would change in a changing climate. A conceptual model of global cloudiness can help elucidate these points. In this work we derive simple representations of cloudiness, that can be useful in creating a theory of global cloudiness. These representations illustrate how both spatial and temporal variability of cloudiness can be expressed in terms of basic state variables. Specifically, cloud albedo is captured by a nonlinear combination of pressure velocity and a measure of the low‐level stability, and cloud longwave effect is captured by surface temperature, pressure velocity, and standard deviation of pressure velocity. We conclude with a short discussion on the usefulness of this work in the context of global warming response studies.
Description:
Plain Language Summary:
Clouds are important for Earth's climate, because they affect a large portion of the planet's energy balance, and hence its mean temperature. To better understand how the interplay between cloudiness and energy balance would change in a changing climate, a better theoretical understanding of how clouds are distributed over the planet, and how this connects with the state variables of the climate system such as temperature and wind speed, is required. As theoretical understanding is currently limited, in this work we explore the possibility of very simply representing the spatiotemporal distribution of clouds over the whole planet. We believe that these simple representations advance the field in the direction of a conceptual theory of global cloudiness and its impact on the energy balance. We show that the impact of cloudiness on both solar and terrestrial radiation balance can be captured well globally with only a few predictive fields, like surface temperature or vertical wind speed, combined simply and using only three tunable parameters, and without using any supplementary information such as the particular season or location on the planet.
Description:
Key Points:
Model fits are performed to the spatiotemporal observed cloudiness over all oceans, using a minimal set of predictors and parameters.
Models capture global‐mean, spatial, and most of seasonal variability of cloud radiative effects.
Cloud albedo and longwave effect are captured by pressure velocity and its variance, surface temperature, and lower tropospheric stability.
Description:
CONSTRAIN project EU Horizon 2020
Keywords:
ddc:551.5
;
global cloudiness
;
energy balance
;
cloud controlling factors
Language:
English
Type:
doc-type:article
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