Multiple‐Instance Superparameterization: 1. Concept, and Predictability of Precipitation

Todd R Jones,David A Randall,Mark D Branson

doi:10.1029/2019ms001610

Abstract

AbstractWe have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model (SP‐CAM). The new configuration, called the multiple‐instance SP‐CAM, or MP‐CAM, uses the average heating and drying rates from 10 independent two‐dimensional cloud‐permitting models (CPMs) in each grid column of the global model, instead of a single CPM. The 10 CPMs start from slightly different initial conditions and simulate alternative realizations of the convective cloud systems. By analyzing the ensemble of possible realizations, we can study the predictability of the cloud systems and identify the weather regimes and physical mechanisms associated with chaotic convection. We explore alternative methods for quantifying the predictability of precipitation. Our results show that unpredictable precipitation occurs when the simulated atmospheric state is close to critical points as defined by Peters and Neelin (2006, https://doi.org/10.1038/nphys314). The predictability of precipitation is also influenced by the convective available potential energy and the degree of mesoscale organization. It is strongly controlled by the large‐scale circulation. A companion paper compares the global atmospheric circulations simulated by SP‐CAM and MP‐CAM.

Highlights

Since the 1960s, low‐ and medium‐resolution atmospheric models, including global circulation models (GCMs), have used cumulus parameterizations to represent the effects of unresolved convective clouds (e.g., Arakawa & Schubert, 1974; Kuo, 1974; Manabe et al, 1965)
We have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model (SP‐CAM)
The cloud‐permitting models (CPMs) used as a superparameterization in SP‐CAM is a stochastic parameterization because the solutions that it produces are sensitively dependent on their initial conditions

Summary

Introduction

Since the 1960s, low‐ and medium‐resolution atmospheric models, including global circulation models (GCMs), have used cumulus parameterizations to represent the effects of unresolved convective clouds (e.g., Arakawa & Schubert, 1974; Kuo, 1974; Manabe et al, 1965). Deterministic parameterizations are intended to give the “expected values” of the convective heating and drying rates These can be interpreted as ensemble averages over the many possible realizations that are consistent with a given large‐scale weather state (e.g., Arakawa, 2004). Spatial averaging plays an explicit and key role in the derivations of the equations used in a deterministic parameterization (e.g., Arakawa & Schubert, 1974), ensemble averages are never explicitly introduced. For this reason, the idea that today's deterministic parameterizations represent ensemble means appears to be based on a hopeful interpretation rather than a demonstrated fact

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