First Advisor

Paul C. Loikith

Term of Graduation

Spring 2021

Date of Publication

7-21-2021

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Geography

Department

Geography

Language

English

Physical Description

1 online resource (vii, 41 pages)

Abstract

Atmospheric rivers (ARs)--long corridors of intense atmospheric water vapor transport--significantly influence the hydrologic cycle and regional hydrometeorological extremes across the contiguous United States (CONUS). Ongoing and future climate change may alter AR characteristics and impacts, making confident climate model projections of future change, especially at regional scales, of critical importance. In order to better constrain uncertainty in such projections of future change, we perform a comprehensive climate model evaluation of AR climatology over the CONUS. Using an established AR detection algorithm, we evaluate the representation of ARs in historical simulations (1984-2013) from a suite of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Models are evaluated against the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Model performance for individual models and the multi-model mean is presented for AR frequency, intensity, area, and linked extreme precipitation in order to highlight systematic biases. Results are summarized over seven US National Climate Assessment regions. Positive AR frequency biases are present in the Western CONUS for all seasons except summer, with positive biases for the Southeast in summer/spring as well. The Midwest and Eastern CONUS show negative biases in spring and fall, respectively. AR area is systematically overestimated across models, with all regions and seasons showing significant positive biases. AR IVT biases are low for all seasons and regions except the Southwest during winter. ARs in models make up a larger percentage (positive bias) of extreme precipitation just east of the Sierras in winter/spring than in observations, with negative biases predominating in other seasons/regions. Conversely, ARs are more likely to lead to extreme precipitation in simulations, with the exception of parts of the Midwest and Northern Great Plains in summer. Some positive AR frequency biases may be explained by the large positive AR area biases. Overall, there is reasonable qualitative pattern agreement between MERRA-2 and models in the examined variables, particularly AR frequency and AR IVT.

Rights

© 2021 Ilan González-Hirshfeld

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

https://archives.pdx.edu/ds/psu/36355

Available for download on Thursday, July 21, 2022

Share

COinS