Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-16-0271.1 |
On the Robustness of Bayesian Fingerprinting Estimates of Global Sea Level Change | |
Hay, Carling C.1,2,3; Morrow, Eric D.1,2; Kopp, Robert E.1,2,4; Mitrovica, Jerry X.3 | |
2017-04-01 | |
发表期刊 | JOURNAL OF CLIMATE |
ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2017 |
卷号 | 30期号:8 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Global mean sea level (GMSL) over the twentieth century has been estimated using techniques that include regional averaging of sparse tide gauge observations, combining satellite altimetry observations with tide gauge records in empirical orthogonal function (EOF) analyses, and most recently the Bayesian approaches of Kalman smoothing (KS) and Gaussian process regression (GPR). Estimated trends in GMSL over 1901-90 obtained using the Bayesian techniques are 1.1-1.2 mm yr(-1), approximately 20% lower than previous estimates. It has been suggested that the adoption of a less restrictive subset of records biased the Bayesian-derived estimates. In this study, different subsets of records are used to demonstrate that GMSL estimates based on the Bayesian methodologies are robust to tide gauge selection. A method for determining the resolvability of individual sea level components estimated in a Bayesian framework is also presented and applied. It is found that the incomplete tide gauge observations result in posterior correlations between individual sea level contributions, making robust separation of the individual components impossible. However, various weighted sums of these components, as well as the total sum (i.e., GMSL), are resolvable. Finally, the KS and GPR methodologies allow for the simultaneous estimation of sea level at sites with and without observations. The first KS and GPR global maps of sea level change over the twentieth century are presented. These maps provide new estimates of twentieth-century sea level in data-sparse regions. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000399679900017 |
WOS关键词 | RISE ; TRENDS ; MODEL |
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/19382 |
专题 | 气候变化 |
作者单位 | 1.Rutgers State Univ, Dept Earth & Planetary Sci, Piscataway, NJ 08854 USA; 2.Rutgers State Univ, Inst Earth Ocean & Atmospher Sci, Piscataway, NJ 08854 USA; 3.Harvard Univ, Dept Earth & Planetary Sci, 20 Oxford St, Cambridge, MA 02138 USA; 4.Rutgers State Univ, Rutgers Energy Inst, New Brunswick, NJ USA |
推荐引用方式 GB/T 7714 | Hay, Carling C.,Morrow, Eric D.,Kopp, Robert E.,et al. On the Robustness of Bayesian Fingerprinting Estimates of Global Sea Level Change[J]. JOURNAL OF CLIMATE,2017,30(8). |
APA | Hay, Carling C.,Morrow, Eric D.,Kopp, Robert E.,&Mitrovica, Jerry X..(2017).On the Robustness of Bayesian Fingerprinting Estimates of Global Sea Level Change.JOURNAL OF CLIMATE,30(8). |
MLA | Hay, Carling C.,et al."On the Robustness of Bayesian Fingerprinting Estimates of Global Sea Level Change".JOURNAL OF CLIMATE 30.8(2017). |
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