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DOI | 10.1175/JCLI-D-18-0882.1 |
Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning | |
Sippel, Sebastian1,2; Meinshausen, Nicolai2; Merrifield, Anna1; Lehner, Flavio3; Pendergrass, Angeline G.3; Fischer, Erich1; Knutti, Reto1 | |
2019-09-01 | |
发表期刊 | JOURNAL OF CLIMATE |
ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2019 |
卷号 | 32期号:17页码:5677-5699 |
文章类型 | Article |
语种 | 英语 |
国家 | Switzerland; USA |
英文摘要 | Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3-5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution. |
英文关键词 | Atmospheric circulation Climate change Climate prediction Regression analysis Statistical techniques Climate variability |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000480264800001 |
WOS关键词 | ATMOSPHERIC CIRCULATION ; NATURAL VARIABILITY ; PRECIPITATION TRENDS ; DYNAMICAL ADJUSTMENT ; NORTH-ATLANTIC ; TEMPERATURE ; UNCERTAINTY ; MODEL ; REGULARIZATION ; PROJECTIONS |
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/186763 |
专题 | 气候变化 |
作者单位 | 1.Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, Zurich, Switzerland; 2.Swiss Fed Inst Technol, Seminar Stat, Zurich, Switzerland; 3.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA |
推荐引用方式 GB/T 7714 | Sippel, Sebastian,Meinshausen, Nicolai,Merrifield, Anna,et al. Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning[J]. JOURNAL OF CLIMATE,2019,32(17):5677-5699. |
APA | Sippel, Sebastian.,Meinshausen, Nicolai.,Merrifield, Anna.,Lehner, Flavio.,Pendergrass, Angeline G..,...&Knutti, Reto.(2019).Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning.JOURNAL OF CLIMATE,32(17),5677-5699. |
MLA | Sippel, Sebastian,et al."Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning".JOURNAL OF CLIMATE 32.17(2019):5677-5699. |
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