Downscaling precipitation and temperature under climate change over semi-arid regions of southwestern United States of America.

Persistent Link:
http://hdl.handle.net/10150/187394
Title:
Downscaling precipitation and temperature under climate change over semi-arid regions of southwestern United States of America.
Author:
Shrestha, Bijaya Prakash.
Issue Date:
1995
Publisher:
The University of Arizona.
Rights:
Copyright © 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.
Abstract:
Two different space-time models are developed to estimate downscaled precIpItation and temperature under the 2 x CO₂ scenario of climate change over semi-arid regions of southwestern USA. represented by Arizona and New-Mexico (upper RioGrande river basin). Local precipitation and temperature are assumed to be dependent upon two effects: the first one, a global effect, is captured by atmospheric circulation pattern (CP) types and the other, a local effect, is reflected by spatially averaged daily pressure heights of the 500 hPa pressure field (h) within the region. CP classification is performed for the 500 hPa pressure fields of observed data and that obtained from the output of the Max Plank Institute (MPI) general circulation (GCM) model T21 for the 1 x CO₂ and 2 x CO₂ scenarios. The evolution of CP types for different scenarios are modeled by a Markov process. Daily precipitation and temperature conditioned on a CP type are modeled by multivariate autoregressive processes. The daily precipitation probability is linked to h through a parametric regression and daily precipitation amount is modeled by a gamma distribution. The daily temperature is modeled by a two sided normal distribution whose parameters are estimated conditioned on fitted values of h. Models are validated using split sampling. Simulations are performed to generate a series of daily rainfall and temperature (maximum and minimum) both in Arizona and New Mexico stations. Statistical properties of model outputs and statistical significance tests are carried out for current conditions and under climate change using 2 x CO₂ scenarios. The results show that precipitation and temperature are increasing significantly with the increase in CO₂ content. Increases in temperature are more prominent in spring and fall. However the actual amounts of increase in precipitation and temperature depend both on the season and station location.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Systems and Industrial Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Duckstein, Lucien

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleDownscaling precipitation and temperature under climate change over semi-arid regions of southwestern United States of America.en_US
dc.creatorShrestha, Bijaya Prakash.en_US
dc.contributor.authorShrestha, Bijaya Prakash.en_US
dc.date.issued1995en_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.description.abstractTwo different space-time models are developed to estimate downscaled precIpItation and temperature under the 2 x CO₂ scenario of climate change over semi-arid regions of southwestern USA. represented by Arizona and New-Mexico (upper RioGrande river basin). Local precipitation and temperature are assumed to be dependent upon two effects: the first one, a global effect, is captured by atmospheric circulation pattern (CP) types and the other, a local effect, is reflected by spatially averaged daily pressure heights of the 500 hPa pressure field (h) within the region. CP classification is performed for the 500 hPa pressure fields of observed data and that obtained from the output of the Max Plank Institute (MPI) general circulation (GCM) model T21 for the 1 x CO₂ and 2 x CO₂ scenarios. The evolution of CP types for different scenarios are modeled by a Markov process. Daily precipitation and temperature conditioned on a CP type are modeled by multivariate autoregressive processes. The daily precipitation probability is linked to h through a parametric regression and daily precipitation amount is modeled by a gamma distribution. The daily temperature is modeled by a two sided normal distribution whose parameters are estimated conditioned on fitted values of h. Models are validated using split sampling. Simulations are performed to generate a series of daily rainfall and temperature (maximum and minimum) both in Arizona and New Mexico stations. Statistical properties of model outputs and statistical significance tests are carried out for current conditions and under climate change using 2 x CO₂ scenarios. The results show that precipitation and temperature are increasing significantly with the increase in CO₂ content. Increases in temperature are more prominent in spring and fall. However the actual amounts of increase in precipitation and temperature depend both on the season and station location.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairDuckstein, Lucienen_US
dc.contributor.committeememberSzidarovszky, Ferencen_US
dc.contributor.committeememberSorooshian, Sorooshen_US
dc.contributor.committeememberBuras, Nathanen_US
dc.contributor.committeememberMaddock, Thomas IIIen_US
dc.identifier.proquest9622970en_US
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