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DOI | 10.1002/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 |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
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 |
推荐引用方式 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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