%0 Journal Article %1 dunstone2020skilful %A Dunstone, Nick %A Smith, Doug %A Yeager, Stephen %A Danabasoglu, Gokhan %A Monerie, Paul-Arthur %A Hermanson, Leon %A Eade, Rosemary %A Ineson, Sarah %A Robson, Jon I %A Scaife, Adam A. %A Ren, Hong-Li %D 2020 %I IOP Publishing %J Environmental Research Letters %K MyYangtzeWork china colleagues decadal interannual precip skill temperature wpsh %R 10.1088/1748-9326/ab9f7d %T Skilful interannual climate prediction from two large initialised model ensembles %U https://doi.org/10.1088/1748-9326/ab9f7d %X 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.

@article{dunstone2020skilful, 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. }, added-at = {2020-07-20T15:55:46.000+0200}, author = {Dunstone, Nick and Smith, Doug and Yeager, Stephen and Danabasoglu, Gokhan and Monerie, Paul-Arthur and Hermanson, Leon and Eade, Rosemary and Ineson, Sarah and Robson, Jon I and Scaife, Adam A. and Ren, Hong-Li}, biburl = {https://www.bibsonomy.org/bibtex/231c7e086725c12e132f90e0fce281766/pbett}, doi = {10.1088/1748-9326/ab9f7d}, interhash = {626ae397056769ab89bb7b0e841cd655}, intrahash = {31c7e086725c12e132f90e0fce281766}, journal = {Environmental Research Letters}, keywords = {MyYangtzeWork china colleagues decadal interannual precip skill temperature wpsh}, month = jun, publisher = {{IOP} Publishing}, timestamp = {2020-07-20T15:55:46.000+0200}, title = {Skilful interannual climate prediction from two large initialised model ensembles}, url = {https://doi.org/10.1088/1748-9326/ab9f7d}, year = 2020 }