气候变化领域集成服务门户(CCPortal): Observation-based simulations of humidity and temperature using quantile regression

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DOI	10.1175/JCLI-D-20-0403.1
	Observation-based simulations of humidity and temperature using quantile regression
	Poppick A.; McKinnon K.A.
发表日期	2020
ISSN	0894-8755
起始页码	10691
结束页码	10706
卷号	33 期号:24
英文摘要	The human impacts of changes in heat events depend on changes in the joint behavior of temperature and humidity. Little is currently known about these complex joint changes, either in observations or projections from general circulation models (GCMs). Further, GCMs do not fully reproduce the observed joint distribution, implying a need for simulation methods that combine information from GCMs with observations for use in impact studies. We present an observation-based, conditional quantile mapping approach for the simulation of future temperature and humidity. A temperature simulation is first produced by transforming historical temperature observations to include projected changes in the mean and temporal covariance structure from a GCM. Next, a humidity simulation is produced by transforming humidity observations to account for projected changes in the conditional humidity distribution given temperature, using a quantile regression model. We use the Community Earth System Model Large Ensemble (CESM1-LE) to estimate future changes in summertime (June–August) temperature and humidity over the continental United States (CONUS), and then use the proposed method to create future simulations of temperature and humidity at stations in the Global Summary of the Day dataset. We find that CESM1-LE projects decreases in summertime humidity across CONUS for a given deviation in temperature from the forced trend, but increases in the risk of high dewpoint on historically hot days. In comparison with raw CESM1-LE output, our observation-based simulation largely projects smaller changes in the future risk of either high or low humidity on days with historically warm temperatures. 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	Regression analysis; American meteorological societies; Copyright informations; General circulation model; Humidity and temperatures; Observation based simulation; Temperature and humidities; Temperature observations; Temperature simulations; Large dataset; climate change; data set; ensemble forecasting; general circulation model; humidity; regression analysis; statistical analysis; surface temperature; weather forecasting; United States
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/171031
作者单位	Carleton College, Northfield, MN, United States; University of California, Los Angeles, Los Angeles, CA, United States
推荐引用方式 GB/T 7714	Poppick A.,McKinnon K.A.. Observation-based simulations of humidity and temperature using quantile regression[J],2020,33(24).
APA	Poppick A.,&McKinnon K.A..(2020).Observation-based simulations of humidity and temperature using quantile regression.Journal of Climate,33(24).
MLA	Poppick A.,et al."Observation-based simulations of humidity and temperature using quantile regression".Journal of Climate 33.24(2020).