globalchange  > 全球变化的国际研究计划
DOI: 10.1002/joc.5462
WOS记录号: WOS:000474001900006
论文题名:
An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment
作者: 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
通讯作者: Gutierrez, J. M.
刊名: INTERNATIONAL JOURNAL OF CLIMATOLOGY
ISSN: 0899-8418
EISSN: 1097-0088
出版年: 2019
卷: 39, 期:9, 页码:3750-3785
语种: 英语
英文关键词: bias adjustment ; CORDEX ; downscaling ; model output statistics ; perfect prognosis ; reproducibility ; validation ; weather generators
WOS关键词: CLIMATE-CHANGE PROJECTIONS ; BIAS CORRECTION ; DAILY PRECIPITATION ; FUTURE CLIMATE ; DAILY TEMPERATURE ; WEATHER GENERATORS ; MODEL OUTPUT ; CORDEX ; SCENARIOS ; FRAMEWORK
WOS学科分类: Meteorology & Atmospheric Sciences
WOS研究方向: Meteorology & Atmospheric Sciences
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: .


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/142943
Appears in Collections:全球变化的国际研究计划

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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

Recommended Citation:
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,2019-01-01,39(9):3750-3785
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