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Clouds Above the United States and Errors at the Surface (CAUSES)

 
Poster PDF

Authors

Cyril Julien Morcrette — Met Office - UK
Kwinten Van Weverberg — Met Office - UK
Hsi-Yen Ma — Lawrence Livermore National Laboratory
Maike Ahlgrimm — Deutscher Wetterdienst
Eric Bazile — Meteo France
Larry Berg — Pacific Northwest National Laboratory
Anning Cheng — National Oceanic and Atmospheric Administration (NOAA)
Frederique Cheruy — Laboratory of Dynamic Meteorology
Jason N. S. Cole — Canadian Centre for Climate Modelling and Analysis
Richard M Forbes — European Centre for Medium-Range Weather Forecasts
William I. Gustafson — Pacific Northwest National Laboratory
Maoyi Huang — National Oceanic and Atmospheric Administration (NOAA)
Woo-Sung Lee — Canadian Centre for Climate Modelling and Analysis
Ying Liu — Pacific Northwest National Laboratory
Yun Qian — Pacific Northwest National Laboratory
Romain Roehrig — National Center for Meteorological Research
Yi-Chi Wang — Research Center for Environmental Change Academia Sinica T
Shaocheng Xie — Lawrence Livermore National Laboratory
Stephen Klein — Lawrence Livermore National Laboratory
Jon Petch — UK Meteorological Office

Category

General topics – Clouds

Description

Many GCMs have a warm bias in their two-meter temperature (T2M) predictions over the American midwest in summer. As part of the CAUSES project, 11 GCMs have produced simulations over this area and have provided detailed diagnostic output to allow the models to be evaluated against observations from the SGP site. A first step in analysing the CAUSES simulations is to focus on the ARM SGP site. In many models, the diurnal range in the bias is comparable to the mean value of the bias, so a daily-mean value of the error hides much of the details of the model behaviour. The evolution of the bias over 5 days of lead-time is also studied and it is shown that many models are showing a significant growth of their bias. The T2M bias in each model is also evaluated over the whole of the US and regions of statistically significant 1) bias and 2) bias change are identified. The co-location of biases in the simulations and in the initial fields are also identified. Regions with no bias in the initial state, but a significant warming leading to an overall bias, are identified in a number of the models. The time when the warm bias is at its largest is also found, both at SGP and over the US as a whole. We show that models fall into two main categories, those that have their largest bias shortly after local noon, and those that have it shortly before sunrise. In the former cases, this is likely to be due to insufficient cloud and too much insolation, while in the latter, it suggests that models are not cooling quickly enough at night. We show that the magnitude and diurnal phase of the bias seen in each model at SGP is also seen in each model over a much wider portion of the midwest. The speed with which each model creates its own warm bias and how it evolves from its initial characteristics to a quasi-equilibrium is also quantified in each model. This quantitative evaluation of the magnitude of the biases in several models, as well as their depth and spatial extent, forms the basis for further analysis of the CAUSES simulations using the observational data collected at SGP and its surroundings.