Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021GL095382 |
Machine-Learning based Reconstructions of Past Regional Sea Level Variability from Proxy Data | |
Cristina Radin; Veronica Nieves | |
2021-11-16 | |
发表期刊 | Geophysical Research Letters
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出版年 | 2021 |
英文摘要 | The analysis of past regional climate-related sea level variations has important implications for diagnosing changes in future sea level driven by climate fluctuations. As the climate changes, there is a need for new explanatory variables of within-region climate factors and for more complex methods able to identify nonlinear relationships, such as machine learning algorithms. This study demonstrates the application of a new machine learning-based methodology to reconstruct historical sea level tide gauge records from proxy data (i.e., upper-ocean temperature estimates in open ocean regions), which provide a reasonably good dynamical representation of coastal sea level variations linked to slow and persistent natural processes like internal climate variability. The learning performance of our method was evaluated against observations of multiple stations and across a variety of model reconstructions, as shown and evidenced by the results. |
领域 | 气候变化 |
URL | 查看原文 |
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/342077 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Cristina Radin,Veronica Nieves. Machine-Learning based Reconstructions of Past Regional Sea Level Variability from Proxy Data[J]. Geophysical Research Letters,2021. |
APA | Cristina Radin,&Veronica Nieves.(2021).Machine-Learning based Reconstructions of Past Regional Sea Level Variability from Proxy Data.Geophysical Research Letters. |
MLA | Cristina Radin,et al."Machine-Learning based Reconstructions of Past Regional Sea Level Variability from Proxy Data".Geophysical Research Letters (2021). |
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