Skilful interannual climate prediction from two large initialised model ensembles. (28th August 2020)

• Record Type: Journal Article
• Authors: Dunstone, Nick; Smith, Doug; Yeager, Steve; Danabasoglu, Gokhan; Monerie, Paul-Arthur; Hermanson, Leon; Eade, Rosie; Ineson, Sarah; Robson, Jon; Scaife, Adam; Ren, Hong-Li
• Is Part Of: Environmental research letters. Volume 15:Number 9(2020:Sep.)
• Publication Date: 2020-08-28
• Subjects: interannual -- climate prediction -- monsoon prediction -- global temperature -- large ensemble; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ab9f7d
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: Climate prediction skill on the interannual timescale, which sits between that of seasonal and decadal, is investigated using large ensembles from the Met Office and CESM initialised coupled prediction systems. A key goal is to determine what can be skillfully predicted about the coming year when combining these two ensembles together. Annual surface temperature predictions show good skill at both global and regional scales, but skill diminishes when the trend associated with global warming is removed. Skill for the extended boreal summer (months 7–11) and winter (months 12–16) seasons are examined, focusing on circulation and rainfall predictions. Skill in predicting rainfall in tropical monsoon regions is found to be significant for the majority of regions examined. Skill increases for all regions when active ENSO seasons are forecast. There is some regional skill for predicting extratropical circulation, but predictive signals appear to be spuriously weak.