Multimodel detection and attribution of extreme temperature changes
| dc.contributor.author | Min, Seung-Ki | |
| dc.contributor.author | Zhang, Xuebin | |
| dc.contributor.author | Zwiers, Francis W. | |
| dc.contributor.author | Shiogama, Hideo | |
| dc.contributor.author | Tung, Yu-Shiang | |
| dc.contributor.author | Wehner, Michael | |
| dc.date.accessioned | 2025-04-10T20:27:29Z | |
| dc.date.available | 2025-04-10T20:27:29Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | Recent studies have detected anthropogenic influences due to increases in greenhouse gases on extreme temperature changes during the latter half of the twentieth century at global and regional scales. Most of the studies, however, were based on a limited number of climate models and also separation of anthropogenic influence from natural factors due to changes in solar and volcanic activities remains challenging at regional scales. Here, the authors conduct optimal fingerprinting analyses using 12 climate models integrated under anthropogenic-only forcing or natural plus anthropogenic forcing. The authors compare observed and simulated changes in annual extreme temperature indices of coldest night and day (TNn and TXn) and warmest night and day (TNx and TXx) from 1951 to 2000. Spatial domains from global mean to continental and subcontinental regions are considered and standardization of indices is employed for better intercomparisons between regions and indices. The anthropogenic signal is detected in global and northern continental means of all four indices, albeit less robustly for TXx, which is consistent with previous findings. The detected anthropogenic signals are also found to be separable from natural forcing influence at the global scale and to a lesser extent at continental and subcontinental scales. Detection occurs more frequently in TNx and TNn than in other indices, particularly at smaller scales, supporting previous studies based on different methods. A combined detection analysis of daytime and nighttime temperature extremes suggests potential applicability to a multivariable assessment. | |
| dc.description.reviewstatus | Reviewed | |
| dc.description.scholarlevel | Faculty | |
| dc.identifier.citation | Min, S.-K., Zhang, X., Zwiers, F., Shiogama, H., Tung, Y.-S., & Wehner, M. (2013). Multimodel detection and attribution of extreme temperature changes. Journal of Climate, 26(19), 7430–7451. https://doi.org/10.1175/JCLI-D-12-00551.1 | |
| dc.identifier.uri | https://doi.org/10.1175/JCLI-D-12-00551.1 | |
| dc.identifier.uri | https://hdl.handle.net/1828/21826 | |
| dc.language.iso | en | |
| dc.publisher | Journal of Climate | |
| dc.subject | UN SDG 13: Climate Action | |
| dc.subject | #journal article | |
| dc.subject | Pacific Climate Impacts Consortium (PCIC) | |
| dc.title | Multimodel detection and attribution of extreme temperature changes | |
| dc.type | Article |
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