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Cameron Bertossa
Peter Hitchcock
Arthur DeGaetano
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Riwal Plougonven


A previous study has shown that a large portion of subseasonal-to-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for 2-m temperature exhibit properties of univariate bimodality, in some locations occurring in over 30% of forecasts. This study introduces a novel methodology to identify “bimodal events,” meteorological events that trigger the development of spatially and temporally correlated bimodality in forecasts. Understanding such events not only provides insight into the dynamics of the meteorological phenomena causing bimodal events, but also indicates when Gaussian interpretations of forecasts are detrimental. The methodology that is developed allows one to systematically characterize the spatial and temporal scales of the derived bimodal events, and thus uncover the flow states that lead to them. Three distinct regions that exhibit high occurrence rates of bimodality are studied: one in South America, one in the Southern Ocean, and one in the North Atlantic. It is found that bimodal events in each region appear to be triggered by synoptic processes interacting with geographically specific processes: in South America, bimodality is often related to Andes blocking events; in the Southern Ocean, bimodality is often related to an atmospheric Rossby wave interacting with sea ice; and in the North Atlantic, bimodality is often connected to the displacement of a persistent subtropical high. This common pattern of large-scale circulation anomalies interacting with local boundary conditions suggests that any deeper dynamical understanding of these events should incorporate such interactions.

Significance Statement

Repeatedly running weather forecasts with slightly different initial conditions provides some information on the confidence of a forecast. Occasionally, these sets of forecasts spread into two distinct groups or modes, making the “typical” interpretation of confidence inappropriate. What leads to such a behavior has yet to be fully understood. This study contributes to our understanding of this process by presenting a methodology that identifies coherent bimodal events in forecasts of near-surface air temperature. Applying this methodology to a database of such forecasts reveals several key dynamical features that can lead to bimodal events. Exploring and understanding these features is crucial for saving forecasters’ resources, creating more skillful forecasts for the public, and improving our understanding of the weather.

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