Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods

Abstract Cold season (December – February) 2‐m temperatures have a far‐reaching impact on critical infrastructure; medium or long‐term predictability would thus be an invaluable asset. However, a better understanding of the most prevalent underlying temperature patterns is needed before improvements...

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Published in:International Journal of Climatology
Main Author: Mercer, Andrew E.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2020
Subjects:
Online Access:http://dx.doi.org/10.1002/joc.6965
https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6965
https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.6965
https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6965
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spelling crwiley:10.1002/joc.6965 2024-09-15T18:24:19+00:00 Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods Mercer, Andrew E. 2020 http://dx.doi.org/10.1002/joc.6965 https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6965 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.6965 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6965 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor International Journal of Climatology volume 41, issue 4, page 2383-2396 ISSN 0899-8418 1097-0088 journal-article 2020 crwiley https://doi.org/10.1002/joc.6965 2024-08-13T04:16:52Z Abstract Cold season (December – February) 2‐m temperatures have a far‐reaching impact on critical infrastructure; medium or long‐term predictability would thus be an invaluable asset. However, a better understanding of the most prevalent underlying temperature patterns is needed before improvements in predictability are possible. Previous studies have shown that cluster analysis preprocessed with principal component analysis (PCA) can help identify the most prevalent spatial patterns within different atmospheric datasets. Additionally, nonlinear variability can be identified when coupling cluster analysis with PCA preprocessing based on the kernel matrix from support vector machines (known as KPCA). In this study, these methods were employed to create an updated 2‐m cold season temperature climatology.Temperatures were quantified using NCEP/NCAR global reanalysis data spanning 1948–2017. Kernel and rotated PCA combined with k ‐means cluster analysis was tested against cluster analysis with no PCA preprocessing to identify the fields that best characterize the underlying climatological structures within the 2‐m temperature data. Four patterns emerged from the analysis. Two patterns were primarily constrained to years early in the study period that had a strong relationship with phasing in the North Atlantic Oscillation and the Arctic Oscillation. A third pattern revealed the anomalous warming along the West Coast that has been increasingly observed in recent years, while a fourth showed a broad anomalous warm region centered on the Midwest that expanded across most of the eastern two‐thirds of the nation. These final two patterns were weakly related to phasing within the Pacific North American teleconnection and phasing of the El Niño Southern Oscillation. Most years in the final two clusters occurred post 1990, demonstrating suggesting these anomalous warming patterns are becoming more prevalent in recent decades. These patterns can be used in future work to inform medium‐ and long‐term forecasting applications. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Wiley Online Library International Journal of Climatology 41 4 2383 2396
institution Open Polar
collection Wiley Online Library
op_collection_id crwiley
language English
description Abstract Cold season (December – February) 2‐m temperatures have a far‐reaching impact on critical infrastructure; medium or long‐term predictability would thus be an invaluable asset. However, a better understanding of the most prevalent underlying temperature patterns is needed before improvements in predictability are possible. Previous studies have shown that cluster analysis preprocessed with principal component analysis (PCA) can help identify the most prevalent spatial patterns within different atmospheric datasets. Additionally, nonlinear variability can be identified when coupling cluster analysis with PCA preprocessing based on the kernel matrix from support vector machines (known as KPCA). In this study, these methods were employed to create an updated 2‐m cold season temperature climatology.Temperatures were quantified using NCEP/NCAR global reanalysis data spanning 1948–2017. Kernel and rotated PCA combined with k ‐means cluster analysis was tested against cluster analysis with no PCA preprocessing to identify the fields that best characterize the underlying climatological structures within the 2‐m temperature data. Four patterns emerged from the analysis. Two patterns were primarily constrained to years early in the study period that had a strong relationship with phasing in the North Atlantic Oscillation and the Arctic Oscillation. A third pattern revealed the anomalous warming along the West Coast that has been increasingly observed in recent years, while a fourth showed a broad anomalous warm region centered on the Midwest that expanded across most of the eastern two‐thirds of the nation. These final two patterns were weakly related to phasing within the Pacific North American teleconnection and phasing of the El Niño Southern Oscillation. Most years in the final two clusters occurred post 1990, demonstrating suggesting these anomalous warming patterns are becoming more prevalent in recent decades. These patterns can be used in future work to inform medium‐ and long‐term forecasting applications.
format Article in Journal/Newspaper
author Mercer, Andrew E.
spellingShingle Mercer, Andrew E.
Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods
author_facet Mercer, Andrew E.
author_sort Mercer, Andrew E.
title Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods
title_short Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods
title_full Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods
title_fullStr Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods
title_full_unstemmed Dominant United States Cold‐Season near surface temperature anomaly patterns derived from kernel methods
title_sort dominant united states cold‐season near surface temperature anomaly patterns derived from kernel methods
publisher Wiley
publishDate 2020
url http://dx.doi.org/10.1002/joc.6965
https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6965
https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.6965
https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6965
genre North Atlantic
North Atlantic oscillation
genre_facet North Atlantic
North Atlantic oscillation
op_source International Journal of Climatology
volume 41, issue 4, page 2383-2396
ISSN 0899-8418 1097-0088
op_rights http://onlinelibrary.wiley.com/termsAndConditions#vor
op_doi https://doi.org/10.1002/joc.6965
container_title International Journal of Climatology
container_volume 41
container_issue 4
container_start_page 2383
op_container_end_page 2396
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