We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the errorThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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The regional atmosphere (RA) configuration of the Met Office Unified Model currently requires different cloud fraction parametrizations (CFPs) for tropical and midlatitude simulations. To explore the scope for unification of these two RA configurations, this article presents a detailed evaluation of simulations over tropical, midlatitude, and arctic domains, with two different diagnostic CFPs: a prognostic CFP, and no CFP at all. Furthermore, a novel, hybrid approach was used that treats liquid cloud diagnostically and ice cloud prognostically. Using observations from three US Department of Energy Atmospheric Radiation Measurement supersites, it is shown that none of these CFPs stands out as superior over all domains. Over the frequently overcast Arctic, the all-or-nothing approach best captures the cloud radiative properties. Conversely, CFPs are of benefit in regions with frequent partial cloudiness, such as the midlatitudes and the Tropics. However, their improved cloud radiative properties often hide an error compensation. All models underestimate overcast, low-base cloud with small water paths in convective environments. In addition, midlatitude overcast, low-base, optically thick clouds in the morning, possibly associated with overnight convection, are frequently too broken. Diagnostic schemes compensate for these errors by producing spurious, scattered afternoon cloud, which could be due to a correct cloud response to too eager convective initiation. Winter clouds over the midlatitudes are improved when liquid cloud is represented diagnostically with a bimodal saturation-departure probability density function, without error compensation. Although it is difficult to unify the RA across the globe around a single CFP scheme, the newly proposed hybrid scheme performs reasonably well for cloud cover across all regions. It also exhibits short-wave biases that are smaller than most other configurations and is less affected by excessive liquid water paths and compensating errors than fully diagnostic schemes are. Surface precipitation is fairly insensitive to the CFP in the simulations shown here.

<p>Hindcasts from the United Kingdom Met Office weather model are used as inputs to an in-flight icing index from the literature. This index uses information about model-predicted temperature, relative humidity, vertical velocity and cloud liquid water content. Parts of the icing index formulation are then modified slightly, in the light of comparisons between hindcast model data and ground-based remote sensing observations. Firstly, the link to relative humidity is replaced with a link to model-predicted cloud cover. Secondly, although super-cooled liquid water icing is due to cloud condensate in the liquid phase, the model may not always correctly partition its condensate into the correct phase. So the second modification is to consider all condensate irrespective of phase when calculating the icing index. The skill of the original and new index are then assessed quantitatively against satellite-derived icing potential. We show that the new indices have substantially better reliability than the operational index used up until recently. Finally, we present a case study, when icing was reported, and discuss ways of presenting the likelihood and severity information.</p>