气候变化领域集成服务门户(CCPortal): Predictable patterns of wintertime surface air temperature in Northern Hemisphere and their predictability sources in the SEAS5

CCPortal

DOI	10.1175/JCLI-D-20-0542.1
	Predictable patterns of wintertime surface air temperature in Northern Hemisphere and their predictability sources in the SEAS5
	Fan H.; Wang L.; Zhang Y.; Tang Y.; Duan W.; Wang L.
发表日期	2020
ISSN	0894-8755
起始页码	10743
结束页码	10754
卷号	33 期号:24
英文摘要	Based on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m pattern over the Pacific–North America region, which is rooted in the tropical forcing of the eastern Pacific-type El Niño–Southern Oscillation (ENSO). The MSN EOF3 shows a wavelike T2m pattern over the Pacific–North America region, which has an approximately 908 phase difference from that associated with MSN EOF2, and a loading center over midlatitude Eurasia. Its sources of predictability include the central Pacific-type ENSO and Eurasian snow cover. The MSN EOF4 reflects T2m variability surrounding the Tibetan Plateau, which is plausibly linked to the remote forcing of the Arctic sea ice. The information on the leading predictable patterns and their sources of predictability is further used to develop a calibration scheme to improve the prediction skill of T2m. The calibrated prediction skill in terms of the anomaly correlation coefficient improves significantly over midlatitude Eurasia in a leave-one-out cross-validation, implying a possible way to improve the wintertime T2m prediction in the SEAS5. Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
英文关键词	Atmospheric pressure; Atmospheric temperature; Climatology; Orthogonal functions; Sea ice; Signal to noise ratio; Snow; Statistical methods; American meteorological societies; Anomaly correlations; Calibrated prediction; Copyright informations; Empirical orthogonal function analysis; European centre for medium-range weather forecasts; Leave-one-out cross validations; Surface air temperatures; Weather forecasting; air temperature; climate prediction; El Nino-Southern Oscillation; empirical orthogonal function analysis; hindcasting; Northern Hemisphere; sea ice; snow cover; surface temperature; weather forecasting; winter
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/171027
作者单位	Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China; School of Atmospheric Sciences, Nanjing University, Nanjing, China; Environmental Science and Engineering, University of Northern British Columbia, Prince George, BC, Canada; State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Fan H.,Wang L.,Zhang Y.,et al. Predictable patterns of wintertime surface air temperature in Northern Hemisphere and their predictability sources in the SEAS5[J],2020,33(24).
APA	Fan H.,Wang L.,Zhang Y.,Tang Y.,Duan W.,&Wang L..(2020).Predictable patterns of wintertime surface air temperature in Northern Hemisphere and their predictability sources in the SEAS5.Journal of Climate,33(24).
MLA	Fan H.,et al."Predictable patterns of wintertime surface air temperature in Northern Hemisphere and their predictability sources in the SEAS5".Journal of Climate 33.24(2020).