| 项目编号: | 1353740
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| 项目名称: | Natural Variations and Forced Changes in Historical and Future Precipitation and Drought |
| 作者: | Aiguo Dai
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| 承担单位: | SUNY at Albany
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| 批准年: | 2013
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| 开始日期: | 2014-06-01
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| 结束日期: | 2018-05-31
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| 资助金额: | USD499886
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Atmospheric and Geospace Sciences
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| 英文关键词: | climate change
; forced change
; natural variability
; drought
; change
; self-calibrated palmer drought severity index
; potential change
; ensemble
; secular change
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| 英文摘要: | Abstract
This project is focused on the detection and attribution of climate change, meaning the task of distinguishing forced climate change from the naturally occurring internal variability of the climate system. The method for making this separation is to take a large ensemble of climate change simulations for the 20th and 21st centuries and use its ensemble mean as an estimate of forced climate change. Assuming that the ensemble mean gives a sufficiently accurate representation of the forced change, this estimate can be factored out of the observed record to reveal the natural variability. The detection and attribution methodology is applied to variables including surface air temperature, precipitation, and the self-calibrated Palmer Drought Severity Index (scPDSI), on both global and regional spatial scales. The ensemble of climate model integrations to be used for the study comes from version 5 of the Coupled Model Intercomparison Project (CMIP5).
The forced climate change signal in the ensemble mean is determined using principle component analysis, which unlike trend analysis does not assume that climate change proceeds linearly in time, and can capture features such as changes in the strength of the annual cycle due to the seasonality of forced climate change. The forced signal from the model ensemble would be factored out of the observed record using methods including simple subtraction, regression, and maximum covariance analysis. Regional patterns of forced change and natural variability (drought in the Southwest US, for one) would be examined for relationships with large-scale modes of climate variability including the Interdecadal Pacific Oscillation (IPO), the Atlantic Multidecadal Oscillation (AMO), and El Nino/Southern Oscillation (ENSO) events. Further work would attempt to establish mechanisms relating sea surface temperature (SST) patterns to changes over land (drought, in particular) through anomalous atmospheric circulations. Finally, once the natural variability is separated from the forced climate change, an assessment would be made of the potential value of the natural variability modes for statistical climate prediction on decadal timescales.
The work seeks to distinguish the relative contributions of natural variability and forced change in the observed climate record, a distinction which is of great societal value. A separation of forced change and internal variability would be quite helpful for planning purposes, as the natural variability component is likely to reverse at some point, while the forced secular changes will likely be permanent. The project also seeks to identify the predictive skill associated with natural modes of climate variability, and any such skill would be useful for a variety of stakeholders affected by variations in climate. In addition, the PI has conducted a variety of outreach efforts to audiences including the media, K-12 students and teachers, and farmers, regarding potential changes in climate and drought, and will continue to do so under this award. The award will support and train two graduate students, thereby providing for the future workforce in this research area. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/96835
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Aiguo Dai. Natural Variations and Forced Changes in Historical and Future Precipitation and Drought. 2013-01-01.
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