Preprints
https://doi.org/10.5194/wcd-2021-33
https://doi.org/10.5194/wcd-2021-33

  18 Jun 2021

18 Jun 2021

Review status: a revised version of this preprint was accepted for the journal WCD and is expected to appear here in due course.

Bimodality in Ensemble Forecasts of 2-Meter Temperature: Identification

Cameron Drew Bertossa1, Peter Hitchcock1, Arthur DeGaetano1, and Riwal Plougonven2 Cameron Drew Bertossa et al.
  • 1Dept. Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
  • 2Laboratoire de Météorologie Dynamique, CNRS-UMR8539, Institut Pierre Simon Laplace, École Normale Supérieure, École polytechnique, Université Pierre et Marie Curie, Paris, France

Abstract. Bimodality and other types of non-Gaussianity arise in ensemble forecasts of the atmosphere as a result of non-linear spread across ensemble members. In this paper, bimodality in 50-member ECMWF ENS-extended ensemble forecasts is identified and characterized. Forecasts of 2-meter 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 four 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 post-processing: several commonly used skill scoring methods and ensemble dressing methods are found to perform poorly in the presence of bimodality.

Cameron Drew Bertossa et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2021-33', Anonymous Referee #1, 20 Jul 2021
  • RC2: 'Comment on wcd-2021-33', Anonymous Referee #2, 25 Jul 2021
  • AC1: 'Comment on wcd-2021-33', Cameron Bertossa, 27 Aug 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2021-33', Anonymous Referee #1, 20 Jul 2021
  • RC2: 'Comment on wcd-2021-33', Anonymous Referee #2, 25 Jul 2021
  • AC1: 'Comment on wcd-2021-33', Cameron Bertossa, 27 Aug 2021

Cameron Drew Bertossa et al.

Cameron Drew Bertossa et al.

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Short summary
While the assumption of Gaussianity leads to many simplifications, ensemble forecasts often exhibit non-Gaussian distributions. This work has systematically identified the presence of a specific case of non-Gaussianity, bimodality. It was found that bimodality occurs in a large portion of global 2-meter temperature forecasts. This has drastic implications on forecast skill as the minimum probability in a bimodal distribution often lays at the maximum probability of a Gaussian distribution.