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Lynch, Cary; Hartin, Corinne; Bond-Lamberty, Ben; Kravitz, Ben (2017)
Languages: English
Types: Article
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. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. 5 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. This paper presents 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. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Regardless of epoch chosen, local temperature sensitivity to global mean temperature change is similar with differences of ≤ 0.2 °C. 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. The dataset and netCDF data generation code are available at doi:10.5281/zenodo.235905.
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