GSTDTAP  > 气候变化
DOI10.1002/joc.5462
An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment
Gutierrez, J. M.1; Maraun, D.2; Widmann, M.3; Huth, R.4,14; Hertig, E.5; Benestad, R.6; Roessler, O.7; Wibig, J.8; Wilcke, R.9; Kotlarski, S.10; San Martin, D.1,11; Herrera, S.12; Bedia, J.1; Casanueva, A.12; Manzanas, R.1; Iturbide, M.1; Vrac, M.13; Dubrovsky, M.14,22; Ribalaygua, J.15; Portoles, J.15; Raty, O.16; Raisanen, J.16; Hingray, B.17; Raynaud, D.17; Casado, M. J.18; Ramos, P.18; Zerenner, T.19; Turco, M.20; Bosshard, T.21; Stepanek, P.22; Bartholy, J.23; Pongracz, R.23; Keller, D. E.10,24; Fischer, A. M.10; Cardoso, R. M.25; Soares, P. M. M.25; Czernecki, B.26; Page, C.27
2019-07-01
发表期刊INTERNATIONAL JOURNAL OF CLIMATOLOGY
ISSN0899-8418
EISSN1097-0088
出版年2019
卷号39期号:9页码:3750-3785
文章类型Article
语种英语
国家Spain; Austria; England; Czech Republic; Germany; Norway; Switzerland; Poland; Sweden; France; Finland; Hungary; Portugal
英文摘要

VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process-based, etc.). Here we describe the participating methods and first results from the first experiment, using "perfect" reanalysis (and reanalysis-driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics-including bias correction-and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method-to-method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor-predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO-CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at and data and validation results are available from the VALUE validation portal for further investigation: .


英文关键词bias adjustment CORDEX downscaling model output statistics perfect prognosis reproducibility validation weather generators
领域气候变化
收录类别SCI-E
WOS记录号WOS:000474001900006
WOS关键词CLIMATE-CHANGE PROJECTIONS ; BIAS CORRECTION ; DAILY PRECIPITATION ; FUTURE CLIMATE ; DAILY TEMPERATURE ; WEATHER GENERATORS ; MODEL OUTPUT ; CORDEX ; SCENARIOS ; FRAMEWORK
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/184661
专题气候变化
作者单位1.Univ Cantabria, CSIC, Inst Fis Cantabria, Meteorol Grp, Santander, Spain;
2.Karl Franzens Univ Graz, Wegener Ctr Climate & Global Change, Graz, Austria;
3.Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, W Midlands, England;
4.Charles Univ Prague, Fac Sci, Dept Phys Geog & Geoecol, Prague, Czech Republic;
5.Univ Augsburg, Inst Geog, Augsburg, Germany;
6.Norwegian Meteorol Inst, Osla, Norway;
7.Univ Bern, Oeschger Ctr Climate Change Res, Dept Geog, Bern, Switzerland;
8.Univ Lodz, Dept Meteorol & Climatol, Lodz, Poland;
9.Swedish Meteorol & Hydrol Inst, Rossby Ctr, Norrkoping, Sweden;
10.Fed Off Meteorol & Climatol MeteoSwiss, Zurich, Switzerland;
11.SME, Predictia Intelligent Data Solut, Madrid, Spain;
12.Univ Cantabria, Meteorol Grp, Dept Matemat Aplicada & Comp, Santander, Spain;
13.CNRS, IPSL, LSCE, Paris, France;
14.Czech Acad Sci, Inst Atmospher Phys, Prague, Czech Republic;
15.FIC, Madrid, Spain;
16.Univ Helsinki UHEL, Helsinki, Finland;
17.Univ Grenoble Alpes, CNRS, IRD, Grenoble INP,IGE, Grenoble, France;
18.Agencia Estatal Meteorol AEMET, Madrid, Spain;
19.Univ Bonn, Meteorol Inst, Bonn, Germany;
20.Univ Barcelona, Dept Appl Phys, Barcelona, Spain;
21.SMHI, Norrkoping, Sweden;
22.Czech Acad Sci, Global Change Res Inst, Brno, Czech Republic;
23.ELU, Budapest, Hungary;
24.Swiss Fed Inst Technol, C2SM, Zurich, Switzerland;
25.Univ Lisboa IDL, Fac Ciencias, Inst Dom Luiz, Lisbon, Portugal;
26.Adam Mickiewicz Univ, Poznan, Poland;
27.Univ Toulouse, CNRS, CERFACS, CECI, Toulouse, France
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GB/T 7714
Gutierrez, J. M.,Maraun, D.,Widmann, M.,et al. An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2019,39(9):3750-3785.
APA Gutierrez, J. M..,Maraun, D..,Widmann, M..,Huth, R..,Hertig, E..,...&Page, C..(2019).An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment.INTERNATIONAL JOURNAL OF CLIMATOLOGY,39(9),3750-3785.
MLA Gutierrez, J. M.,et al."An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment".INTERNATIONAL JOURNAL OF CLIMATOLOGY 39.9(2019):3750-3785.
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