DOI: 10.1175/JCLI-D-17-0782.1
Scopus记录号: 2-s2.0-85054050361
论文题名: Estimating changes in temperature distributions in a large ensemble of climate simulations using quantile regression
作者: Haugen M.A. ; Stein M.L. ; Moyer E.J. ; Sriver R.L.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期: 20 起始页码: 8573
结束页码: 8588
语种: 英语
英文关键词: Climate variability
; Ensembles
; General circulation models
; Intraseasonal variability
; Parameterization
; Seasonal variability
Scopus关键词: Atmospheric radiation
; Climate change
; Parameterization
; Temperature distribution
; Climate variability
; Ensembles
; General circulation model
; Intraseasonal variability
; Seasonal variability
; Climate models
英文摘要: Understanding future changes in extreme temperature events in a transient climate is inherently challenging. A single model simulation is generally insufficient to characterize the statistical properties of the evolving climate, but ensembles of repeated simulations with different initial conditions greatly expand the amount of data available. We present here a new approach for using ensembles to characterize changes in temperature distributions based on quantile regression that more flexibly characterizes seasonal changes. Specifically, our approach uses a continuous representation of seasonality rather than breaking the dataset into seasonal blocks; that is, we assume that temperature distributions evolve smoothly both day to day over an annual cycle and year to year over longer secular trends. To demonstrate our method's utility, we analyze an ensemble of 50 simulations of the Community Earth System Model (CESM) under a scenario of increasing radiative forcing to 2100, focusing on North America. As previous studies have found, we see that daily temperature bulk variability generally decreases in wintertime in the continental mid- and high latitudes (>40°). A more subtle result that our approach uncovers is that differences in two low quantiles of wintertime temperatures do not shrink as much as the rest of the temperature distribution, producing a more negative skew in the overall distribution. Although the examples above concern temperature only, the technique is sufficiently general that it can be used to generate precise estimates of distributional changes in a broad range of climate variables by exploiting the power of ensembles. © 2018 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/110636
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: University of Chicago, Chicago, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States
Recommended Citation:
Haugen M.A.,Stein M.L.,Moyer E.J.,et al. Estimating changes in temperature distributions in a large ensemble of climate simulations using quantile regression[J]. Journal of Climate,2018-01-01,31(20)