Large‐eddy simulation of the transient and near‐equilibrium behavior of precipitating shallow convection

By A. Seifert, T. Heus, Robert Pincus, Bjorn Stevens. 2015

149 citations recorded by the source corpus.

Large‐eddy simulation is used to study the sensitivity of trade wind cumulus clouds to perturbations in cloud droplet number concentrations. We find that the trade wind cumulus system approaches a radiative‐convective equilibrium state, modified by net warming and drying from imposed large‐scale advective forcing. The system requires several days to reach equilibrium when cooling rates are specified but much less time, and with less sensitivity to cloud droplet number density, when radiation depends realistically on the vertical distribution of water vapor. The transient behavior and the properties of the near‐equilibrium cloud field depend on the microphysical state and therefore on the cloud droplet number density, here taken as a proxy for the ambient aerosol. The primary response of the cloud field to changes in the cloud droplet number density is deepening of the cloud layer. This deepening leads to a decrease in relative humidity and a faster evaporation of small clouds and cloud remnants constituting a negative lifetime effect. In the near‐equilibrium regime, the decrease in cloud cover compensates much of the Twomey effect, i.e., the brightening of the clouds, and the overall aerosol effect on the albedo of the organized precipitating cumulus cloud field is small.

Subfields: Atmospheric Science, Earth-Surface Processes, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e502cc60741f729cc95dc98608344bf42d165cbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. L. Harris
2. Shian‐Jiann Lin
3. Andrew Gettelman
4. I. Held
5. Andrew T. Wittenberg
6. V. Ramaswamy
7. J. Golaz
8. Yi Ming

In Earth-Surface Processes

1. J. Golaz
2. J. Mülmenstädt
3. E. Gryspeerdt
4. Johannes Quaas
5. Yi Ming
6. Leo Donner
7. O. Sourdeval
8. N. Bellouin

In Global and Planetary Change

1. Shian‐Jiann Lin
2. L. Harris
3. Andrew Gettelman
4. J. Golaz
5. P. Phillipps
6. Yi Ming
7. M. Winton
8. E. Gryspeerdt

Citations in the corpus

• The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies (2018, background): “761 with a larger droplet concentration (e.g., Seifert et al., 2015; Stevens, 2017).”
• Constraining the aerosol influence on cloud fraction (2016, background): “Shallow cumulus clouds found at low LTS have a very weak relationship between AOD and CF, which is due to an apparent inability of CDNC to influence CF in this regime (similar to model-based results [Seifert et al., 2015]).”
• Bounding Global Aerosol Radiative Forcing of Climate Change (2020, background): “Some high-resolution simula-1286 –26– tions find a small change to C as a function of aerosol (Seifert et al., 2015), producing1287 an upper bound of a 0% enhancement of the RFaci.”
• Constraining the aerosol influence on cloud liquid water path (2018, result): “Existing studies show a mixed picture; while some model (Quaas et al., 2009; Seifert et al., 2015; Grosvenor et al., 2017; Neubauer et al., 2017) and observational studies (Gryspeerdt et al.”