Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-16-0366.1 |
Reassessing Model Uncertainty for Regional Projections of Precipitation with an Ensemble of Statistical Downscaling Methods | |
San-Martin, D.1,2; Manzanas, R.1; Brands, S.1; Herrera, S.3; Gutierrez, J. M.1 | |
2017 | |
发表期刊 | JOURNAL OF CLIMATE
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2017 |
卷号 | 30期号:1 |
文章类型 | Article |
语种 | 英语 |
国家 | Spain |
英文摘要 | This is the second in a pair of papers in which the performance of statistical downscaling methods (SDMs) is critically reassessed with respect to their robust applicability in climate change studies. Whereas the companion paper focused on temperatures, the present manuscript deals with precipitation and considers an ensemble of 12 SDMs from the analog, weather typing, and regression families. First, the performance of the methods is cross-validated considering reanalysis predictors, screening different geographical domains and predictor sets. Standard accuracy and distributional similarity scores and a test for extrapolation capability are considered. The results are highly dependent on the predictor sets, with optimum configurations including information from midtropospheric humidity. Second, a reduced ensemble of well-performing SDMs is applied to four GCMs to properly assess the uncertainty of downscaled future climate projections. The results are compared with an ensemble of regional climate models (RCMs) produced in the ENSEMBLES project. Generally, the mean signal is similar with both methodologies (with the exception of summer, which is drier for the RCMs) but the uncertainty (spread) is larger for the SDM ensemble. Finally, the spread contribution of the GCM- and SDM-derived components is assessed using a simple analysis of variance previously applied to the RCMs, obtaining larger interaction terms. Results show that the main contributor to the spread is the choice of the GCM, although the SDM dominates the uncertainty in some cases during autumn and summer due to the diverging projections from different families. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000391855700013 |
WOS关键词 | CLIMATE-CHANGE ; DAILY RAINFALL ; SCENARIOS ; EUROPE ; CIRCULATION ; TEMPERATURE ; VARIABILITY ; ASSESSMENTS ; VALIDATION ; SIMULATION |
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/20657 |
专题 | 气候变化 |
作者单位 | 1.Univ Cantabria, CSIC, Grp Meteorol, Inst Fis Cantabria, Santander, Spain; 2.Predictia Intelligent Data Solut SL, Avda Los Castros S-N,I D S345, Santander 39005, Spain; 3.Univ Cantabria, Grp Meteorol, Deptartamento Matemat Aplicada & Ciencias Computa, Santander, Spain |
推荐引用方式 GB/T 7714 | San-Martin, D.,Manzanas, R.,Brands, S.,et al. Reassessing Model Uncertainty for Regional Projections of Precipitation with an Ensemble of Statistical Downscaling Methods[J]. JOURNAL OF CLIMATE,2017,30(1). |
APA | San-Martin, D.,Manzanas, R.,Brands, S.,Herrera, S.,&Gutierrez, J. M..(2017).Reassessing Model Uncertainty for Regional Projections of Precipitation with an Ensemble of Statistical Downscaling Methods.JOURNAL OF CLIMATE,30(1). |
MLA | San-Martin, D.,et al."Reassessing Model Uncertainty for Regional Projections of Precipitation with an Ensemble of Statistical Downscaling Methods".JOURNAL OF CLIMATE 30.1(2017). |
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