Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1038/s41467-020-14342-9 |
Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections | |
Ehud Strobach; Golan Bel | |
2020-01-23 | |
发表期刊 | Nature |
出版年 | 2020 |
英文摘要 | Climate predictions are only meaningful if the associated uncertainty is reliably estimated. A standard practice is to use an ensemble of climate model projections. The main drawbacks of this approach are the fact that there is no guarantee that the ensemble projections adequately sample the possible future climate conditions. Here, we suggest using simulations and measurements of past conditions in order to study both the performance of the ensemble members and the relation between the ensemble spread and the uncertainties associated with their predictions. Using an ensemble of CMIP5 long-term climate projections that was weighted according to a sequential learning algorithm and whose spread was linked to the range of past measurements, we find considerably reduced uncertainty ranges for the projected global mean surface temperature. The results suggest that by employing advanced ensemble methods and using past information, it is possible to provide more reliable and accurate climate projections. |
领域 | 资源环境 |
URL | 查看原文 |
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文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/249901 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Ehud Strobach,Golan Bel. Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections[J]. Nature,2020. |
APA | Ehud Strobach,&Golan Bel.(2020).Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections.Nature. |
MLA | Ehud Strobach,et al."Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections".Nature (2020). |
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