气候变化领域集成服务门户(CCPortal): Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations

CCPortal

DOI	10.1175/JCLI-D-19-0855.1
	Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations
	Wills R.C.J.; Battisti D.S.; Armour K.C.; Schneider T.; Deser C.
发表日期	2020
ISSN	0894-8755
起始页码	8693
结束页码	8719
卷号	33 期号:20
英文摘要	Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Ninõ, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Ninõ-like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemblebased estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing. © 2020 American Meteorological Society.
英文关键词	Atmospheric pressure; Climate change; Earth (planet); Greenhouse gases; Pattern recognition; Signal to noise ratio; Volcanoes; Climate model simulations; Earth system model; Ensemble averaging; Internal variability; Linear trend analysis; North Atlantic oscillations; Pattern recognition method; Temperature changes; Climate models; air temperature; climate change; climate modeling; climate variation; detection method; ensemble forecasting; North Atlantic Oscillation; pattern recognition; statistical analysis
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/171163
作者单位	University of Washington, Seattle, WA, United States; California Institute of Technology, Pasadena, CA, United States; National Center for Atmospheric Research, Boulder, CO, United States
推荐引用方式 GB/T 7714	Wills R.C.J.,Battisti D.S.,Armour K.C.,et al. Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations[J],2020,33(20).
APA	Wills R.C.J.,Battisti D.S.,Armour K.C.,Schneider T.,&Deser C..(2020).Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations.Journal of Climate,33(20).
MLA	Wills R.C.J.,et al."Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations".Journal of Climate 33.20(2020).