2008: A global view of non-Gaussian SST variability
The skewness and kurtosis of daily sea surface temperature (SST) variations are found to be strongly linked at most locations around the globe in a new high-resolution observational dataset, and are analyzed in terms of a simple stochastically forced mixed layer ocean model. The predictions of the a...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.331.5045 2023-05-15T17:32:11+02:00 2008: A global view of non-Gaussian SST variability Philip Sura Prashant D. Sardeshmukh The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.5045 http://gemini.met.fsu.edu/publications/Sura-Sardeshmukh-JPO2008.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.5045 http://gemini.met.fsu.edu/publications/Sura-Sardeshmukh-JPO2008.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://gemini.met.fsu.edu/publications/Sura-Sardeshmukh-JPO2008.pdf text ftciteseerx 2016-09-11T00:02:16Z The skewness and kurtosis of daily sea surface temperature (SST) variations are found to be strongly linked at most locations around the globe in a new high-resolution observational dataset, and are analyzed in terms of a simple stochastically forced mixed layer ocean model. The predictions of the analytic theory are in remarkably good agreement with observations, strongly suggesting that a univariate linear model of daily SST variations with a mixture of SST-independent (additive) and SST-dependent (multiplicative) noise forcing is sufficient to account for the skewness–kurtosis link. Such a model of non-Gaussian SST dynamics should be useful in predicting the likelihood of extreme events in climate, as many important weather and climate phenomena, such as hurricanes, ENSO, and the North Atlantic Oscillation (NAO), depend on a detailed knowledge of the underlying local SSTs. 1. Text North Atlantic North Atlantic oscillation Unknown |
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ftciteseerx |
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English |
description |
The skewness and kurtosis of daily sea surface temperature (SST) variations are found to be strongly linked at most locations around the globe in a new high-resolution observational dataset, and are analyzed in terms of a simple stochastically forced mixed layer ocean model. The predictions of the analytic theory are in remarkably good agreement with observations, strongly suggesting that a univariate linear model of daily SST variations with a mixture of SST-independent (additive) and SST-dependent (multiplicative) noise forcing is sufficient to account for the skewness–kurtosis link. Such a model of non-Gaussian SST dynamics should be useful in predicting the likelihood of extreme events in climate, as many important weather and climate phenomena, such as hurricanes, ENSO, and the North Atlantic Oscillation (NAO), depend on a detailed knowledge of the underlying local SSTs. 1. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Philip Sura Prashant D. Sardeshmukh |
spellingShingle |
Philip Sura Prashant D. Sardeshmukh 2008: A global view of non-Gaussian SST variability |
author_facet |
Philip Sura Prashant D. Sardeshmukh |
author_sort |
Philip Sura |
title |
2008: A global view of non-Gaussian SST variability |
title_short |
2008: A global view of non-Gaussian SST variability |
title_full |
2008: A global view of non-Gaussian SST variability |
title_fullStr |
2008: A global view of non-Gaussian SST variability |
title_full_unstemmed |
2008: A global view of non-Gaussian SST variability |
title_sort |
2008: a global view of non-gaussian sst variability |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.5045 http://gemini.met.fsu.edu/publications/Sura-Sardeshmukh-JPO2008.pdf |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
http://gemini.met.fsu.edu/publications/Sura-Sardeshmukh-JPO2008.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.5045 http://gemini.met.fsu.edu/publications/Sura-Sardeshmukh-JPO2008.pdf |
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1766130178471231488 |