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DOI | 10.1175/JCLI-D-17-0782.1 |
Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression | |
Haugen, Matz A.1; Stein, Michael L.1; Moyer, Elisabeth J.1; Sriver, Ryan L.2 | |
2018-10-01 | |
发表期刊 | JOURNAL OF CLIMATE
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2018 |
卷号 | 31期号:20页码:8573-8588 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
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 degrees). 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. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000444796000002 |
WOS关键词 | NORTH-AMERICAN CLIMATE ; EXTREME EVENTS ; NATURAL VARIABILITY ; PRECIPITATION ; CONTEXT ; MODELS ; TRENDS ; ATTRIBUTION ; HEMISPHERE ; CALIFORNIA |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20519 |
专题 | 气候变化 |
作者单位 | 1.Univ Chicago, Chicago, IL 60637 USA; 2.Univ Illinois, Urbana, IL USA |
推荐引用方式 GB/T 7714 | Haugen, Matz A.,Stein, Michael L.,Moyer, Elisabeth J.,et al. Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression[J]. JOURNAL OF CLIMATE,2018,31(20):8573-8588. |
APA | Haugen, Matz A.,Stein, Michael L.,Moyer, Elisabeth J.,&Sriver, Ryan L..(2018).Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression.JOURNAL OF CLIMATE,31(20),8573-8588. |
MLA | Haugen, Matz A.,et al."Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression".JOURNAL OF CLIMATE 31.20(2018):8573-8588. |
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