PNNL

Abstract

The Clouds Above the United States and Errors at the Surface (CAUSES) project is aimed at gaining a better understanding of the physical processes that are leading to the creation of warm screen-temperature biases over the American Midwest, which are seen in many numerical models. Here in Part 1, a series of 5-day hindcasts, each initialised from re-analyses and performed by 11 different models, are evaluated against screen-temperature observations. All the models have a warm bias over parts of the Midwest. Several ways of quantifying the impact of the initial conditions on the evolution of the simulations are presented, showing that within a day or so all models have produced a warm bias that is representative of their bias after 5 days, and not closely tied to the conditions at the initial time. Although the surface temperature biases sometimes coincide with locations where the re-analyses themselves have a bias, there are many regions in each of the models where biases grow over the course of 5 days or are larger than the biases present in the reanalyses. At the Southern Great Plains site, the model biases are shown to not be confined to the surface, but extend several kilometres into the atmosphere. In most of the models, there is a strong diurnal cycle in the screen-temperature bias and in some models the biases are largest around midday, while in the others it is largest during the night. While the different physical processes that are contributing to a given model having a screen-temperature error will be discussed in more detail in the companion papers (Parts 2 and 3) the fact that there is a spatial coherence in the phase of the diurnal cycle of the error across wide regions and that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP suggest that the detailed evaluations of the role of different processes in contributing to errors at SGP will be representative of errors that are prevalent over a much larger spatial scale.