MODELING THE IMPACTS OF CLIMATE CHANGE ON TROPICAL CYCLONE FORMATIONS IN THE WESTERN NORTH PACIFIC

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Authors
Carter, Robert B.
Subjects
tropical cyclone
climate change
statistical modelling
large scale environmental factors
LSEF
Western Pacific
Representative Concentration Pathway
RCP
climate system predictors
hindcast
tropical cyclone formation areas
Climate Forecast System Reanalysis
Climate Forecast System version 2
Coupled Model Intercomparison Project Phase 5
CMIP5
Community Climate System Model version 4
Advisors
Murphree, Tom
Meyer, David, Statistical Solutions LLC
Date of Issue
2019-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
A dynamical-statistical modeling approach was used to investigate climate change impacts on tropical cyclone (TC) formations in the western North Pacific (WNP). Reanalysis data and climate model projections were analyzed for 1981–2050 to identify both past and future impacts. The projections came from the Community Climate System Model version 4 (CCSM4). The primary analysis tool was a statistical model that uses information about large-scale environmental factors (LSEFs) to predict the probability of TC formation. This model was built using data on the LSEFs and TC formations during the last several decades. We forced the statistical model with the reanalysis LSEFs and with the climate model LSEFs outputs from the climate model to produce formation probability analyses for 1981–2018. The two methods for calculating the probabilities both showed increasing trends during this period for the central WNP region in which most WNP formations occur. These trends are consistent with the observed increase in TC formations in this region during this period. The climate model probabilities based on long-term mean, El Niño, and La Niña LSEFs for 1981–2018 were also consistent with observed TC formations. For future decades, the climate model projections showed multi-decadal increases in both TC formation probabilities and the WNP area in which TC formation is most probable. We recommend continued use of dynamical-statistical methods as observational data and climate models improve.
Type
Thesis
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Department
Meteorology (MR)
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Distribution Statement
Approved for public release; distribution is unlimited.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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