EGU2020-14932
https://doi.org/10.5194/egusphere-egu2020-14932
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Contribution of clouds radiative forcing to the local surface temperature variability

Oscar Rojas, Marjolaine Chiriaco, Sophie Bastin, and Justine Ringard
Oscar Rojas et al.
  • LATMOS/IPSL, UVSQ Université Paris-Saclay, Sorbonne Université, CNRS, 11 bd d’Alembert, Guyancourt, France

The local contribution of clouds to the surface energy balance and temperature variability is an important topic in order to apprehend how this intake affects local climate variability and extreme events, how this contribution varies from one place to another, and how it evolves in a warming climate. The scope of this study is to understand how clouds impact temperature variability, to quantify their contribution, and to compare their effects to other surface processes. To do so, we develop a method to estimate the different terms that control temperature variability at the surface (∂T2m /∂t) by using this equation: ∂T2m /∂t=R+HA+HG+Adv where R is the radiation that is separated into the cloud term (Rcloud) and the clear sky one (RCS), HA the atmospheric heat exchange, HG the ground heat exchange, and Adv the advection. These terms are estimated hourly, almost only using direct measurements from SIRTA-ReOBS dataset (an hourly long-term multi-variables dataset retrieved from SIRTA, an observatory located in a semi-urban area 20-km South-West of Paris; Chiriaco et al., 2019) for a five-years period. The method gives good results for the hourly temperature variability, with a 0.8 correlation coefficient and a weak residual term between left part (directly measured) and right part of the equation.

A bagged decision trees analysis of this equation shows that RCS dominates temperature variability during daytime and is mainly modulated by cloud radiative effect (Rcloud). During nighttime, the bagged decision trees analysis determines that Rcloud is the term controlling temperature changes. When a diurnal cycle analysis (split into seasons) is performed for each term, HA becomes an important negative modulator in the late afternoon, chiefly in spring and summer, when evaporation and thermal conduction are increased. In contrast, HG and Adv terms do not play an essential role on temperature variability at this temporal scale and their contribution is barely considerable in the one-hour variability, but still they remain necessary in order to obtain the best coefficient estimator between the directly measured observations and the method estimated. All terms except advection have a marked monthly-hourly cycle.

Next steps consist in characterize the types of clouds and study their physical properties corresponding to the cases where Rcloud is significant, using the Lidar profiles also available in the SIRTA-ReOBS dataset.

How to cite: Rojas, O., Chiriaco, M., Bastin, S., and Ringard, J.: Contribution of clouds radiative forcing to the local surface temperature variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14932, https://doi.org/10.5194/egusphere-egu2020-14932, 2020

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