Abstract
Abstract. Bimodality and other types of non-Gaussianity arise in ensemble forecasts of the atmosphere as a result of nonlinear spread across ensemble members. In this paper, bimodality in 50-member ECMWF ENS-extended ensemble forecasts is identified and characterized. Forecasts of 2 m temperature are found to exhibit widespread bimodality well over a derived false-positive rate. In some regions bimodality occurs in excess of 30 % of forecasts, with the largest rates occurring during lead times of 2 to 3 weeks. Bimodality occurs more frequently in the winter hemisphere with indications of baroclinicity being a factor to its development. Additionally, bimodality is more common over the ocean, especially the polar oceans, which may indicate development caused by boundary conditions (such as sea ice). Near the equatorial region, bimodality remains common during either season and follows similar patterns to the Intertropical Convergence Zone (ITCZ), suggesting convection as a possible source for its development. Over some continental regions the modes of the forecasts are separated by up to 15 ∘C. The probability density for the modes can be up to 4 times greater than at the minimum between the modes, which lies near the ensemble mean. The widespread presence of such bimodality has potentially important implications for decision makers acting on these forecasts. Bimodality also has implications for assessing forecast skill and for statistical postprocessing: several commonly used skill-scoring methods and ensemble dressing methods are found to perform poorly in the presence of bimodality, suggesting the need for improvements in how non-Gaussian ensemble forecasts are evaluated.
Highlights
The atmosphere is a highly chaotic system that is not predicted
The bimodality of the forecast suggests instead that there are two potential scenarios that may occur and that the spread about these two modes is considerably less than the standard deviation of the ensemble as a whole might suggest. Those lead times whose validation observations would have been predicted with a higher probability with a kernel density estimate (KDE) probability density function (PDF) compared to a Gaussian PDF occur in quite a few of the lead times
The continuous ranked probability score (CRPS) is based on the cumulative density function (CDF) of the ensemble: the CRPS is defined as the squared difference in area above and below the CDF curve according to where the validation point lies
Summary
The atmosphere is a highly chaotic system that is not predicted. An important development has been the use of ensemble forecasts in order to develop a probabilistic viewpoint of the future state of the atmosphere. The bimodality of the forecast suggests instead that there are two potential scenarios that may occur and that the spread about these two modes is considerably less than the standard deviation of the ensemble as a whole might suggest Those lead times whose validation observations would have been predicted with a higher probability with a KDE PDF compared to a Gaussian PDF occur in quite a few of the lead times (blue dashes). These lead times commonly align with bimodality in the distribution (red dashes). This work shows that such bimodality in forecasts of 2 m temperature is reasonably common
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