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Intercomparison of Convective-Aggregation States with two Cloud Resolving Models
  • Paolina Bongioannini Cerlini,
  • Miriam Saraceni,
  • Lorenzo Silvestri
Paolina Bongioannini Cerlini
University of Perugia
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Miriam Saraceni
University of Perugia

Corresponding Author:[email protected]

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Lorenzo Silvestri
University of Perugia
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Abstract

The Radiative-Convective Equilibrium (RCE) of two models exhibiting convective aggregation has been compared. The goal of the work, following the suggestion from the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP), is to identify key parameters controlling self-aggregation in RCE for both models and discuss the processes controlled by these parameters in order to find the simulations similarities and to test their differences. The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), have different physical and numerical formulations. This allowed us to compare the sensitivity to processes related to self-aggregation. When self-aggregation occurs, the two models present similar statistics for what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction (leading to an “Iris effect’), within the spread of the RCEMIP values. On the other hand, they differ both in the degree of organization and the organization feedback: SAM is strongly organized (is on the highest quartile of the RCEMIP for the Iorg Index) and the convective organization is achieved by cloud-radiative feedback; ARPS is weakly organized (on the multi-model average of the RCEMIP for the Iorg Index) and the moisture-convection feedback is leading to the convective organization. The prevalence of one mechanism over the other has been found in the interaction between the microphysics and the sub-cloud layer properties. This comparison suggests that, in order to have a robust measure of climate sensitivity, climate models should include both types of convective organization mechanisms as shown by the two models.