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DOI | 10.1007/s10584-018-2167-5 |
Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool | |
Casanueva, A.1,2; Bedia, J.3; Herrera, S.1; Fernandez, J.1; Gutierrez, J. M.4 | |
2018-04-01 | |
发表期刊 | CLIMATIC CHANGE |
ISSN | 0165-0009 |
EISSN | 1573-1480 |
出版年 | 2018 |
卷号 | 147页码:411-425 |
文章类型 | Article |
语种 | 英语 |
国家 | Spain; Switzerland |
英文摘要 | The use and development of bias correction (BC) methods has grown fast in recent years, due to the increased demand of unbiased projections by many sectoral climate change impact applications. Case studies are frequently based on multi-variate climate indices (CIs) combining two or more essential climate variables that are frequently individually corrected prior to CI calculation. This poses the question of whether the BC method modifies the inter-variable dependencies and eventually the climate change signal. The direct bias correction of the multi-variate CI stands as a usual alternative, since it preserves the physical and temporal coherence among the primary variables as represented in the dynamical model output, at the expense of incorporating the individual biases on the CI with an effect difficult to foresee, particularly in the case of complex CIs bearing in their formulation non-linear relationships between components. Such is the case of the Fire Weather Index (FWI), a meteorological fire danger indicator frequently used in forest fire prevention and research. In the present work, we test the suitability of the direct BC approach on FWI as a representative multi-variate CI, assessing its performance in present climate conditions and its effect on the climate change signal when applied to future projections. Moreover, the results are compared with the common approach of correcting the input variables separately. To this aim, we apply the widely used empirical quantile mapping method (QM), adjusting the 99 empirical percentiles. The analysis of the percentile adjustment function (PAF) provides insight into the effect of the QM on the climate change signal. Although both approaches present similar results in the present climate, the direct correction introduces a greater modification of the original change signal. These results warn against the blind use of QM, even in the case of essential climate variables or uni-variate CIs. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000428427200004 |
WOS关键词 | FIRE DANGER ; STATISTICAL CORRECTION ; PRECIPITATION ; SYSTEM ; IMPACT ; SIMULATIONS ; MODELS ; RISK |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; 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/29822 |
专题 | 气候变化 |
作者单位 | 1.Univ Cantabria, Meteorol Grp, Dept Appl Math & Comp Sci, E-39005 Santander, Spain; 2.MeteoSwiss, Fed Off Meteorol & Climatol, CH-8058 Zurich, Switzerland; 3.Predictia Intelligent Data Solut, Santander 39005, Spain; 4.Univ Cantabria, CSIC, Meteorol Grp, Inst Phys Cantabria, E-39005 Santander, Spain |
推荐引用方式 GB/T 7714 | Casanueva, A.,Bedia, J.,Herrera, S.,et al. Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool[J]. CLIMATIC CHANGE,2018,147:411-425. |
APA | Casanueva, A.,Bedia, J.,Herrera, S.,Fernandez, J.,&Gutierrez, J. M..(2018).Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool.CLIMATIC CHANGE,147,411-425. |
MLA | Casanueva, A.,et al."Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool".CLIMATIC CHANGE 147(2018):411-425. |
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