caa a22 4500 605472254 CHVBK 20210128100337.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00382-014-2335-x doi (NATIONALLICENCE)springer-10.1007/s00382-014-2335-x Potential predictability of Northern America surface temperature in AGCMs and CGCMs [Elektronische Daten] [Youmin Tang, Dake Chen, Xiaoqin Yan] In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier prediction by coupled models (T1), and a two-tier prediction by atmospheric models only (T2). Furthermore, the potential predictability was optimally decomposed into different modes for both T1 and T2, by extracting the most predictable structures. Emphasis was placed on the comparison of the predictability between T1 and T2. It was found that the potential predictability of the NA SAT is seasonal and spatially dependent in both T1 and T2. Higher predictability occurs in spring and winter and over the southeastern US and northwestern Canada. There is no significant difference of potential predictability between T1 and T2 for most areas of NA, although T1 has higher potential predictability than T2 in the southeastern US. Both T1 and T2 display similar most predictable components (PrCs) for the NA SAT, characterized by the inter-annual variability mode and the long-term trend mode. The first one is inherent to the tropical Pacific sea surface temperature forcing, such as the El Nino-Southern Oscillation, whereas the second one is closely associated with global warming. In general, the PrC modes can better characterize the predictability in T1 than in T2, in particular for the inter-annual variability mode in the fall. The prediction skill against observations is better measured by the PrC analysis than by principal component analysis for all seasons, indicating the stronger capability of PrCA in extractingprediction targets. Springer-Verlag Berlin Heidelberg, 2014 Seasonal climate prediction nationallicence Potential predictability nationallicence Ensemble predictions nationallicence Tang Youmin Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, V2N 4Z9, Prince George, BC, Canada aut Chen Dake State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China aut Yan Xiaoqin Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, V2N 4Z9, Prince George, BC, Canada aut Climate Dynamics Springer Berlin Heidelberg 45/1-2(2015-07-01), 353-374 0930-7575 45:1-2<353 2015 45 382 https://doi.org/10.1007/s00382-014-2335-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00382-014-2335-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tang Youmin Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, V2N 4Z9, Prince George, BC, Canada aut NATIONALLICENCE 700 1- Chen Dake State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China aut NATIONALLICENCE 700 1- Yan Xiaoqin Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, V2N 4Z9, Prince George, BC, Canada aut NATIONALLICENCE 773 0- Climate Dynamics Springer Berlin Heidelberg 45/1-2(2015-07-01), 353-374 0930-7575 45:1-2<353 2015 45 382