Climate Central Temperature Attribution System Methods

The Climate Central Temperature Attribution System (CC-TAS) Version 1.0 operationalizes a multi-method framework to attribute temperature events to anthropogenic climate change. It utilizes two empirical methods (median-scaling and quantile-scaling) and a model-based method using CMIP6 simulations. To improve upon previous frameworks, CC-TAS incorporates high-resolution CFS reanalysis temperatures, ERA5 reanalysis for scale factors, and skew-normal parameterized distributions. The system introduces the 'occurrence ratio' (OR) as a primary metric to assess both warm and cold events, which is then converted into a conservative categorical 'Climate Shift Index' (CSI) for public mapping, primarily focusing on the contiguous United States.

• The CC-TAS employs three distinct methods to estimate the influence of human-caused climate change on temperature: median-scaling, quantile-scaling, and a model-based approach using CMIP6 simulations.
• To enhance accuracy and resolution over previous versions, the system replaced Berkeley Earth data with high-resolution CFS reanalysis temperatures for climatology and uses ECMWF ERA5 reanalysis to compute scale factors.
• The system transitioned from using cumulative probability distribution functions (CDFs) to fitting skew-normal distributions to observations and models to better estimate likelihoods for both extreme and less extreme conditions.
• The 'occurrence ratio' (OR), defined as the ratio of probability distribution functions (PDFs) between the modern and counterfactual climates, is used as the main attribution metric to allow for the analysis of both warming and cooling events.
• The system introduces the Climate Shift Index (CSI), a categorical scale from -5 to 5, designed to be conservative by requiring agreement on the sign of the climate factor across all three methods and rounding values toward zero.
• Initial application of the system to the contiguous U.S. successfully identified the Pacific Northwest heat dome of June 29, 2021, and an unusual winter heat event on December 16, 2021, with CSI levels reaching 5 in some regions.

APA

Pershing, A., Gilford, D. M., & Giguere, J. (2022). Climate Central Temperature Attribution System Methods. Climate Central. https://assets.ctfassets.net/cxgxgstp8r5d/4PyuCIhY6ZT5LEGyfq01rc/9b81e5f911baad59575550e4dd549f57/Climate_Central_Methods_Temperature_Attribution_System_June-2022.pdf

Chicago

Pershing, Andrew, Daniel M. Gilford, and Joseph Giguere. Climate Central Temperature Attribution System Methods. Climate Central, 2022. https://assets.ctfassets.net/cxgxgstp8r5d/4PyuCIhY6ZT5LEGyfq01rc/9b81e5f911baad59575550e4dd549f57/Climate_Central_Methods_Temperature_Attribution_System_June-2022.pdf.

Wikipedia

{{cite report |last1=Pershing |first1=Andrew |last2=Gilford |first2=Daniel M. |last3=Giguere |first3=Joseph |title=Climate Central Temperature Attribution System Methods |publisher=Climate Central |date=June 2022 |url=https://assets.ctfassets.net/cxgxgstp8r5d/4PyuCIhY6ZT5LEGyfq01rc/9b81e5f911baad59575550e4dd549f57/Climate_Central_Methods_Temperature_Attribution_System_June-2022.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{pershing2022climate, author = {Pershing, Andrew and Gilford, Daniel M. and Giguere, Joseph}, title = {{Climate Central Temperature Attribution System Methods}}, institution = {Climate Central}, year = {2022}, month = jun, url = {https://assets.ctfassets.net/cxgxgstp8r5d/4PyuCIhY6ZT5LEGyfq01rc/9b81e5f911baad59575550e4dd549f57/Climate_Central_Methods_Temperature_Attribution_System_June-2022.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }