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Publication Date
12 March 2018

Introduction to CAUSES project: Description of weather and climate models and their near-surface temperature errors near the Southern Great Plains

Many models have a warm screen-level temperature bias over the American Midwest. Biases have large diurnal variations. Diurnal cycle of biases over large regions are highly correlated with biases at SGP site.
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Scientists at the Met Office in the UK, along with colleagues at the Lawrence Livermore National Laboratory, have organized an international multi-model inter-comparison project, called CAUSES (Clouds Above the United States and Errors at the Surface). The project aims to identify the physical processes that lead to the formation of a warm surface air temperature bias present in many weather forecast and climate model simulations over the American Midwest.


As an introduction to a series of papers addressing the physical processes contributing to warm surface air temperature bias in a number of models, this paper describes all the models taking part. This paper also quantifies the warm bias in each model in terms of its magnitude, vertical depth and lateral extent. The biases are also described in terms of their variation throughout the warm season and as a function of lead time and as a function of time of day during the diurnal cycle.


Using observational data collected from the U.S. DOE SGP sites, the models biases are shown to not be confined to the surface, but extended several kilometers into the atmosphere. In most models, the warm bias is shown to vary with time of day. Some models have their largest warm biases during the day, while some have it at night. There is a large degree of correlation between the diurnal cycle of the model biases over large portion of the American Midwest and the diurnal cycle of the bias seen at the SGP site. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale and hence that conclusions drawn from detailed studies using data from SGP will help to address the bias seen over a much wider area.

Point of Contact
Hsi-Yen Ma
Lawrence Livermore National Laboratory (LLNL)
Funding Program Area(s)