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dc.contributor.advisorHirschboeck, Katherine K.en_US
dc.contributor.authorNi, Fenbiao
dc.creatorNi, Fenbiaoen_US
dc.date.accessioned2013-04-25T09:56:00Z
dc.date.available2013-04-25T09:56:00Z
dc.date.issued2000en_US
dc.identifier.urihttp://hdl.handle.net/10150/284104
dc.description.abstractTree rings can be reliable recorders of past weather and climate variations. Tree rings from mountain regions can be linked to upper air atmospheric sounding observations and large-scale atmospheric circulation patterns. A "synoptic dendroclimatology" approach is used to define the relationship between tree rings and a specific upper air anomaly feature that affects climate in the western US. I have also reconstructed this anomaly feature using both regression and fuzzy logic approaches. Correlation analysis between 500 mb geopotential heights and tree rings at a site near Eagle, Colorado reveals an important anomaly centered over the western US. This center can be viewed as a circulation anomaly center index (CACI) that can quantitatively represent the relationship between atmospheric circulation and tree growth variations. To reconstruct this index from tree rings, I used both a multiple linear regression (MLR) and a fuzzy-rule-based (FRB) model. The fuzzy-rule-based model provides a simple structural approach to capture nonlinear relationships between tree rings and circulation. The reconstructing capability of both models is validated directly from an independent data set. Results show that the fuzzy-rule-based model performs better in terms of calibration and verification statistics than the multiple linear regression model. The reconstructed anomaly index can provide a long-term temporal context for evaluation of circulation variability and how it is linked to both climate and tree rings.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectPhysical Geography.en_US
dc.subjectStatistics.en_US
dc.subjectPhysics, Atmospheric Science.en_US
dc.titleAnalysis and reconstruction of the relationship between a circulation anomaly feature and tree rings: Linear and nonlinear approachesen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest9965886en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineAtmospheric Sciencesen_US
thesis.degree.namePh.D.en_US
dc.identifier.bibrecord.b40480720en_US
refterms.dateFOA2018-09-06T00:54:28Z
html.description.abstractTree rings can be reliable recorders of past weather and climate variations. Tree rings from mountain regions can be linked to upper air atmospheric sounding observations and large-scale atmospheric circulation patterns. A "synoptic dendroclimatology" approach is used to define the relationship between tree rings and a specific upper air anomaly feature that affects climate in the western US. I have also reconstructed this anomaly feature using both regression and fuzzy logic approaches. Correlation analysis between 500 mb geopotential heights and tree rings at a site near Eagle, Colorado reveals an important anomaly centered over the western US. This center can be viewed as a circulation anomaly center index (CACI) that can quantitatively represent the relationship between atmospheric circulation and tree growth variations. To reconstruct this index from tree rings, I used both a multiple linear regression (MLR) and a fuzzy-rule-based (FRB) model. The fuzzy-rule-based model provides a simple structural approach to capture nonlinear relationships between tree rings and circulation. The reconstructing capability of both models is validated directly from an independent data set. Results show that the fuzzy-rule-based model performs better in terms of calibration and verification statistics than the multiple linear regression model. The reconstructed anomaly index can provide a long-term temporal context for evaluation of circulation variability and how it is linked to both climate and tree rings.


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