Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble

Michael K Tippett; Meghana Ranganathan; Michelle L'Heureux; Anthony G Barnston; Timothy DelSole

doi:10.1007/s00382-017-3721-y

Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble

Clim Dyn. 2019;53(12):7497-7518. doi: 10.1007/s00382-017-3721-y. Epub 2017 May 13.

Authors

Michael K Tippett^{1

2}, Meghana Ranganathan³, Michelle L'Heureux⁴, Anthony G Barnston⁵, Timothy DelSole^{6

7}

Affiliations

¹ 1Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY USA.
² 2Department of Meteorology, Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia.
³ 3Swarthmore College, Swarthmore, PA USA.
⁴ National Oceanic and Atmospheric Administration/National Weather Service/National Centers for Environmental Prediction, Climate Prediction Center, College Park, MD USA.
⁵ 5International Research Institute for Climate and Society, The Earth Institute of Columbia University, Palisades, New York, NY USA.
⁶ 6George Mason University, Fairfax, VA USA.
⁷ Center for Ocean-Land-Atmosphere Studies, Calverton, MD USA.

Abstract

Here we examine the skill of three, five, and seven-category monthly ENSO probability forecasts (1982-2015) from single and multi-model ensemble integrations of the North American Multimodel Ensemble (NMME) project. Three-category forecasts are typical and provide probabilities for the ENSO phase (El Niño, La Niña or neutral). Additional forecast categories indicate the likelihood of ENSO conditions being weak, moderate or strong. The level of skill observed for differing numbers of forecast categories can help to determine the appropriate degree of forecast precision. However, the dependence of the skill score itself on the number of forecast categories must be taken into account. For reliable forecasts with same quality, the ranked probability skill score (RPSS) is fairly insensitive to the number of categories, while the logarithmic skill score (LSS) is an information measure and increases as categories are added. The ignorance skill score decreases to zero as forecast categories are added, regardless of skill level. For all models, forecast formats and skill scores, the northern spring predictability barrier explains much of the dependence of skill on target month and forecast lead. RPSS values for monthly ENSO forecasts show little dependence on the number of categories. However, the LSS of multimodel ensemble forecasts with five and seven categories show statistically significant advantages over the three-category forecasts for the targets and leads that are least affected by the spring predictability barrier. These findings indicate that current prediction systems are capable of providing more detailed probabilistic forecasts of ENSO phase and amplitude than are typically provided.

Keywords: ENSO; Ensemble forecasting; Probabilistic verification.