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@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix gcis: <http://data.globalchange.gov/gcis.owl#> .
@prefix cito: <http://purl.org/spar/cito/> .
@prefix biro: <http://purl.org/spar/biro/> .

<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   dcterms:identifier "key-finding-6-4";
   gcis:findingNumber "6.4"^^xsd:string;
   gcis:findingStatement "Extreme temperatures in the contiguous United States are projected to increase even more than average temperatures. The temperatures of extremely cold days and extremely warm days are both expected to increase. Cold waves are projected to become less intense while heat waves will become more intense. The number of days below freezing is projected to decline while the number above 90°F will rise. (<em>Very high confidence</em>)"^^xsd:string;
   gcis:isFindingOf <https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change>;
   gcis:isFindingOf <https://data.globalchange.gov/report/climate-science-special-report>;

## Properties of the finding:
   gcis:findingProcess "There is <em>very high confidence</em> in projected changes in temperature extremes over the United States based upon the convergence of evidence from multiple model simulations, analyses, and assessments."^^xsd:string;
   
   gcis:descriptionOfEvidenceBase "The key finding and supporting text summarize extensive evidence documented in the climate science literature (e.g., Fischer et al. 2013; Sillmann et al. 2013; Wuebbles et al. 2014; Sun et al. 2015). Similar statements about changes have also been made in other national assessments (such as NCA3) and in reports by the Climate Change Science Program (such as SAP 3.3: Weather and Climate Extremes in a Changing Climate). <br><br> Projections are based on global model results and associated downscaled products from CMIP5 for RCP4.5 (lower scenario) and RCP8.5 (higher scenario). Model weighting is employed to refine projections for each RCP. Weighting parameters are based on model independence and skill over North America for seasonal temperature and annual extremes. The multimodel mean is based on 32 model projections that were statistically downscaled using the Localized Constructed Analogs technique. Downscaling improves on the coarse model output, establishing a more geographically accurate baseline for changes in extremes and the number of days per year over key thresholds. The upper bound for projected changes is the average of the three warmest models. All increases are significant (i.e., more than 50% of the models show a statistically significant change, and more than 67% agree on the sign of the change)."^^xsd:string;
   
   gcis:assessmentOfConfidenceBasedOnEvidence "<em>Very high</em> \r\n<p><br><p> <strong>Likelihood of Impact: </strong> </p>  <p> <em>Extremely likely</em> </p></br></p>"^^xsd:string;
   
   gcis:newInformationAndRemainingUncertainties "Global climate models are subject to structural and parametric uncertainty, resulting in a range of estimates of future changes in temperature extremes. This is partially mitigated through the use of model weighting and pattern scaling. Furthermore, virtually every ensemble member of every model projection contains an increase in temperature by mid- and late-century. Empirical downscaling introduces additional uncertainty (e.g., with respect to stationarity)."^^xsd:string;

   a gcis:Finding .

## This finding cites the following entities:


<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   cito:cites <https://data.globalchange.gov/report/ccsp-sap-3_3-2008>;
   biro:references <https://data.globalchange.gov/reference/12d42a98-494b-4cec-bf08-060021c85ec2>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   cito:cites <https://data.globalchange.gov/article/10.1175/jhm-d-14-0082.1>;
   biro:references <https://data.globalchange.gov/reference/62c66ef3-cddb-4797-ba0e-5672fbcc27b3>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   cito:cites <https://data.globalchange.gov/article/10.1002/jgrd.50188>;
   biro:references <https://data.globalchange.gov/reference/68c9d1ed-fd78-4967-b47d-2504ac27b649>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   cito:cites <https://data.globalchange.gov/report/noaa-techreport-nesdis-144>;
   biro:references <https://data.globalchange.gov/reference/b63c9720-f770-4718-89cc-53b3616e2bec>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   cito:cites <https://data.globalchange.gov/article/10.1175/BAMS-D-12-00172.1>;
   biro:references <https://data.globalchange.gov/reference/b91893b4-24a8-46ba-b09a-013d462caf1b>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   cito:cites <https://data.globalchange.gov/article/10.1038/nclimate2051>;
   biro:references <https://data.globalchange.gov/reference/fd46b5d5-da35-4c38-80cc-8d9bd688f116>.



<https://data.globalchange.gov/report/climate-science-special-report/chapter/temperature-change/finding/key-finding-6-4>
   prov:wasDerivedFrom <https://data.globalchange.gov/report/climate-science-special-report/chapter/front-matter/figure/confidence---likelihood>.