SimCloud version 1.0: a simple diagnostic cloud scheme for idealized climate models

Abstract

A simple diagnostic cloud scheme (SimCloud) for general circulation models (GCMs), which has a modest level of complexity and is transparent in describing its dependence on tunable parameters, is proposed in this study. The large-scale clouds, which form the core of the scheme, are diagnosed from relative humidity. In addition, the marine low stratus clouds, typically found off the west coast of continents over subtropical oceans, are determined largely as a function of inversion strength. A “freeze-dry” adjustment based on a simple function of specific humidity is also available to reduce an excessive cloud bias in polar regions. Other cloud properties, such as the effective radius of cloud droplet and cloud liquid water content, are specified as simple functions of temperature. All of these features are user-configurable. The cloud scheme is implemented in Isca, a modeling framework designed to enable the construction of GCMs at varying levels of complexity, but could readily be adapted to other GCMs. Simulations using the scheme with realistic continents generally capture the observed structure of cloud fraction and cloud radiative effect (CRE), as well as its seasonal variation. Specifically, the explicit low-cloud scheme improves the simulation of shortwave CREs over the eastern subtropical oceans by increasing the cloud fraction and cloud water path. The freeze-dry adjustment alleviates the longwave CRE biases in polar regions, especially in winter. However, the longwave CRE in tropical regions and shortwave CRE over the extratropics are both still too strong compared to observations. Nevertheless, this simple cloud scheme provides a suitable basis for examining the impacts of clouds on climate in idealized modeling frameworks.