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dc.contributor.authorPeyser, Cheryl
dc.contributor.authorYin, Jianjun
dc.date.accessioned2017-07-27T19:31:41Z
dc.date.available2017-07-27T19:31:41Z
dc.date.issued2017-06-05
dc.identifier.citationInterannual and Decadal Variability in Tropical Pacific Sea Level 2017, 9 (6):402 Wateren
dc.identifier.issn2073-4441
dc.identifier.doi10.3390/w9060402
dc.identifier.urihttp://hdl.handle.net/10150/624972
dc.description.abstractA notable feature in the first 20-year satellite altimetry records is an anomalously fast sea level rise (SLR) in the western Pacific impacting island nations in this region. This observed trend is due to a combination of internal variability and external forcing. The dominant mode of dynamic sea level (DSL) variability in the tropical Pacific presents as an east-west see-saw pattern. To assess model skill in simulating this variability mode, we compare 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) models with 23-year satellite data, 55-year reanalysis products, and 60-year sea level reconstruction. We find that models underestimate variance in the Pacific sea level see-saw, especially at decadal, and longer, time scales. The interannual underestimation is likely due to a relatively low variability in the tropical zonal wind stress. Decadal sea level variability may be influenced by additional factors, such as wind stress at higher latitudes, subtropical gyre position and strength, and eddy heat transport. The interannual variability of the Nino 3.4 index is better represented in CMIP5 models despite low tropical Pacific wind stress variability. However, as with sea level, variability in the Nino 3.4 index is underestimated on decadal time scales. Our results show that DSL should be considered, in addition to sea surface temperature (SST), when evaluating model performance in capturing Pacific variability, as it is directly related to heat content in the ocean column.
dc.description.sponsorshipNSF [1513411]en
dc.language.isoenen
dc.publisherMDPI AGen
dc.relation.urlhttp://www.mdpi.com/2073-4441/9/6/402en
dc.rights© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en
dc.subjectsea level riseen
dc.subjectclimate variabilityen
dc.subjectPacific Oceanen
dc.subjectclimate modellingen
dc.subjectwind stressen
dc.titleInterannual and Decadal Variability in Tropical Pacific Sea Levelen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Geoscien
dc.identifier.journalWateren
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-06-27T10:14:52Z
html.description.abstractA notable feature in the first 20-year satellite altimetry records is an anomalously fast sea level rise (SLR) in the western Pacific impacting island nations in this region. This observed trend is due to a combination of internal variability and external forcing. The dominant mode of dynamic sea level (DSL) variability in the tropical Pacific presents as an east-west see-saw pattern. To assess model skill in simulating this variability mode, we compare 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) models with 23-year satellite data, 55-year reanalysis products, and 60-year sea level reconstruction. We find that models underestimate variance in the Pacific sea level see-saw, especially at decadal, and longer, time scales. The interannual underestimation is likely due to a relatively low variability in the tropical zonal wind stress. Decadal sea level variability may be influenced by additional factors, such as wind stress at higher latitudes, subtropical gyre position and strength, and eddy heat transport. The interannual variability of the Nino 3.4 index is better represented in CMIP5 models despite low tropical Pacific wind stress variability. However, as with sea level, variability in the Nino 3.4 index is underestimated on decadal time scales. Our results show that DSL should be considered, in addition to sea surface temperature (SST), when evaluating model performance in capturing Pacific variability, as it is directly related to heat content in the ocean column.


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