CAUSES: Clouds Above the United States and Errors at the Surface

Description

We present an outline of a new collaboration project aiming to involve people from both the GEWEX Global Atmospheric System Studies (GASS) and Atmospheric System Research (ASR) / Atmospheric Radiation Measurement (ARM) communities. The project aims to evaluate and improve the representation of clouds and radiation in several weather and climate models. We welcome expressions of interest from modelling groups wishing to participate in a multi-model comparison and from instrument and retrieval scientists with expertise in relevant observational data-sets. The project has been named CAUSES, which stands for "Clouds Above the United States and Errors at the Surface". The work will focus on the spring and summer warm surface-temperature bias that exists over the central United States in several weather and climate models. Previous work has shown that the surface temperature bias, which is seen in long-term climate simulations, can be reproduced within only a few days of simulation by running the climate models in weather-forecasting mode. This means that short NWP-style simulations can be used to understand the cause of the bias. We hypothesize that the warm surface temperature bias is due to a combination of errors involving the land and atmosphere. One part of the CAUSES project will focus on the simulation of precipitation and the surface energy balance. The other part, presented here, will focus on errors in the clouds and radiation and their interaction and we aim to determine which cloud regimes contribute the most to the surface radiation errors. In order to have a wealth of observational data, with which to evaluate the models, we focus our efforts around the Southern Great Plains (located in the region of warm temperature bias that extends over the central U.S.). The period of investigation has been chosen to be the spring and early summer of 2011, as the ARM campaign called MC3E (Mid-latitude Continental Convective Clouds Experiment) collected lots of additional observations at this time. We will describe the methods used to set up and run the models and describe some of the processing of model and observational data done to ensure a fair comparison. Some preliminary results will also be presented, showing an analysis of cloud regimes in the Met Office global model alongside a similar analysis from the observations.