Title of Poster / Presentation
Presentation Type
Oral Presentation
Start Date
5-4-2022 9:00 AM
End Date
5-4-2022 11:00 AM
Subjects
Climatology -- Observations -- Pacific Northwest, Atmospheric circulation, Precipitation probabilities
Advisor
Paul Loikith
Student Level
Doctoral
Abstract
The ability of the latest state-of-the-art suite of climate models to simulate observed large-scale atmospheric circulation patterns over the Pacific Northwest of North America is evaluated. Twelve representative patterns of atmospheric circulation are identified using machine learning applied to observational data. Climate model data from simulations of the historical period are mapped to each observational-derived pattern, and the resulting differences between patterns, as well as differences in frequency of pattern occurrence, are quantified. In general, models are able to simulate the range of circulation patterns with reasonable accuracy, although model skill varies to some degree across the 25-member climate model ensemble. Surface temperature and precipitation associated with each circulation pattern are found to also be reasonably simulated by the models, with some biases noted. This boosts confidence that the models are simulating temperature and precipitation patterns for the correct physical reasons. The models exhibit a range of skill at simulating pattern occurrence frequency, with more agreement in fall through spring compared with summer. Results indicate that the climate models are appropriate for assessing future projections of key atmospheric circulation patterns and their impacts on temperature and precipitation over the region.
Rights
© Copyright the author(s)
IN COPYRIGHT:
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).
DISCLAIMER:
The purpose of this statement is to help the public understand how this Item may be used. When there is a (non-standard) License or contract that governs re-use of the associated Item, this statement only summarizes the effects of some of its terms. It is not a License, and should not be used to license your Work. To license your own Work, use a License offered at https://creativecommons.org/
Persistent Identifier
https://archives.pdx.edu/ds/psu/37460
Included in
Climate Model Skill at Simulating Large-Scale Atmospheric Circulation Patterns and Associated Temperature and Precipitation over the Pacific Northwest
The ability of the latest state-of-the-art suite of climate models to simulate observed large-scale atmospheric circulation patterns over the Pacific Northwest of North America is evaluated. Twelve representative patterns of atmospheric circulation are identified using machine learning applied to observational data. Climate model data from simulations of the historical period are mapped to each observational-derived pattern, and the resulting differences between patterns, as well as differences in frequency of pattern occurrence, are quantified. In general, models are able to simulate the range of circulation patterns with reasonable accuracy, although model skill varies to some degree across the 25-member climate model ensemble. Surface temperature and precipitation associated with each circulation pattern are found to also be reasonably simulated by the models, with some biases noted. This boosts confidence that the models are simulating temperature and precipitation patterns for the correct physical reasons. The models exhibit a range of skill at simulating pattern occurrence frequency, with more agreement in fall through spring compared with summer. Results indicate that the climate models are appropriate for assessing future projections of key atmospheric circulation patterns and their impacts on temperature and precipitation over the region.