Abstract

AbstractConvection is usually parameterized in global climate models, and there are often large discrepancies between results obtained with different convection schemes. Conventional methods of comparing convection schemes using observational cases or directly in three‐dimensional (3D) models do not always clearly identify parameterization strengths and weaknesses. In this paper we evaluate the response of parameterizations to various perturbations rather than their behavior under particular strong forcing. We use the linear response function method proposed by Kuang (2010) to compare 12 physical packages in five atmospheric models using single‐column model (SCM) simulations under idealized radiative‐convective equilibrium conditions. The models are forced with anomalous temperature and moisture tendencies. The temperature and moisture departures from equilibrium are compared with published results from a cloud‐resolving model (CRM). Results show that the procedure is capable of isolating the behavior of a convection scheme from other physics schemes. We identify areas of agreement but also substantial differences between convection schemes, some of which can be related to scheme design. Some aspects of the model linear responses are related to their RCE profiles (the relative humidity profile in particular), while others constitute independent diagnostics. All the SCMs show irregularities or discontinuities in behavior that are likely related to threshold‐related mechanisms used in the convection schemes, and which do not appear in the CRM. Our results highlight potential flaws in convection schemes and suggest possible new directions to explore for parameterization evaluation.

Highlights

  • Atmospheric deep convection is an important process that is still imperfectly understood

  • The overall goal of this paper is to advance our understanding of what can be learned about model physics from single-column models (SCMs) run in radiative-convective equilibrium (RCE) configurations

  • Even between the SCMs that use similar convection schemes, the difference in their RCE profiles is non-trivial: The two Zhang-McFarlane cases (WRF-ZM and SCAM) show similar shapes in their relative humidity (RH) profiles but Weather Research and Forecasting (WRF)-ZM is consistently somewhat drier than SCAM and the temperatures vary by several K at some levels, while the RH profiles of the two Betts-Miller cases (Betts-Miller-Janjic in WRF and Simplified Betts-Miller in Unified Model (UM)) differ in both shape and magnitude

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Summary

Introduction

Atmospheric deep convection is an important process that is still imperfectly understood. It generates most of the observed precipitation and is the main source of heating to balance radiative cooling. Global climate models (GCMs) usually have a horizontal resolution that is much bigger than individual convective clouds. This makes the representation of convection in GCMs challenging as it cannot be explicitly resolved. The collective effect of subgrid-scale convection on the resolved flow is expressed through parameterizations, which are approximate equations to capture the essence of unresolved processes in a realistic HWONG ET AL. There are attempts based on machine learning (e.g., Gentine et al, 2018; O’Gorman & Dwyer, 2018)

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