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Title: An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

Abstract

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice ofmore » pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.« less

Authors:
 [1];  [1];  [1];  [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Joint Global Change Research Inst.
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Atmospheric Sciences and Global Change Division
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1358489
Alternate Identifier(s):
OSTI ID: 1700523
Report Number(s):
PNNL-SA-125442
Journal ID: ISSN 1866-3516; KP1703030
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Earth System Science Data (Online)
Additional Journal Information:
Journal Name: Earth System Science Data (Online); Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 1866-3516
Publisher:
Copernicus
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION; climate; statistics; data library; Climate, statistics, data library

Citation Formats

Lynch, Cary D., Hartin, Corinne A., Bond-Lamberty, Benjamin, and Kravitz, Benjamin S.. An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology. United States: N. p., 2017. Web. https://doi.org/10.5194/essd-9-281-2017.
Lynch, Cary D., Hartin, Corinne A., Bond-Lamberty, Benjamin, & Kravitz, Benjamin S.. An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology. United States. https://doi.org/10.5194/essd-9-281-2017
Lynch, Cary D., Hartin, Corinne A., Bond-Lamberty, Benjamin, and Kravitz, Benjamin S.. Mon . "An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology". United States. https://doi.org/10.5194/essd-9-281-2017. https://www.osti.gov/servlets/purl/1358489.
@article{osti_1358489,
title = {An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology},
author = {Lynch, Cary D. and Hartin, Corinne A. and Bond-Lamberty, Benjamin and Kravitz, Benjamin S.},
abstractNote = {Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.},
doi = {10.5194/essd-9-281-2017},
journal = {Earth System Science Data (Online)},
number = 1,
volume = 9,
place = {United States},
year = {2017},
month = {5}
}

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