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Full text release has been delayed at the author's request until August 03, 2026

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Refining Climate Model Projections: Spatial Statistical Downscaling and Bayesian Model Averaging for Climate Model Integration

Katugoda Gedara, Ayesha Kumari Ekanayaka
http://orcid.org/0000-0002-3170-7549
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721145605871185

Abstract Details

2024, PhD, University of Cincinnati, Arts and Sciences: Statistics.
In this dissertation, two innovative statistical methodologies are developed to enhance the accuracy of climate model projections. Climate models simulate future global climate conditions but are constrained by coarse resolutions due to computational limitations. Consequently, these projections must be refined to finer resolutions before they can be effectively utilized in regional studies. The first methodology introduces a novel spatial statistical model for downscaling climate model projections. This approach significantly enhances precision by incorporating spatial dependencies and stands out by providing meaningful uncertainty estimates, a feature often missing in many previous downscaling approaches. Additionally, the method achieves computational efficiency through a basis representation, making it adept at managing large datasets effectively. Furthermore, climate models originate from various research groups, each based on different understandings and assumptions about the Earth's climate. This leads to significant uncertainty in the choice of models for subsequent analysis. Since there is no definitive way to select the best model or a few reliable ones, climate scientists often seek methods to combine projections from multiple models to mitigate this uncertainty. The second method in this dissertation introduces a comprehensive approach to integrate projections from multiple climate models using Bayesian Model Averaging (BMA). The proposed method effectively tackles the challenge of implementing BMA for climate model integration in a full Bayesian framework by employing Polya-Gamma augmentation and yields combined climate projections with improved accuracy and reliable uncertainty estimates.
Emily Kang, Ph.D. (Committee Chair)
Bledar Konomi, Ph.D. (Committee Member)
Won Chang, Ph.D. (Committee Member)
117 p.
Statistics
Statistical Downscaling; Spatial dependence; Uncertainty quantification; Massive data; Climate model integration; Bayesian Model Averaging

Recommended Citations

Citations

  • Katugoda Gedara, A. K. E. (2024). Refining Climate Model Projections: Spatial Statistical Downscaling and Bayesian Model Averaging for Climate Model Integration [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721145605871185

    APA Style (7th edition)

  • Katugoda Gedara, Ayesha Kumari Ekanayaka. Refining Climate Model Projections: Spatial Statistical Downscaling and Bayesian Model Averaging for Climate Model Integration. 2024. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721145605871185.

    MLA Style (8th edition)

  • Katugoda Gedara, Ayesha Kumari Ekanayaka. "Refining Climate Model Projections: Spatial Statistical Downscaling and Bayesian Model Averaging for Climate Model Integration." Doctoral dissertation, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721145605871185

    Chicago Manual of Style (17th edition)

ucin1721145605871185
© 2024, some rights reserved.Creative Commons License Refining Climate Model Projections: Spatial Statistical Downscaling and Bayesian Model Averaging for Climate Model Integration by Ayesha Kumari Ekanayaka Katugoda Gedara is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.