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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/projections/finding/key-finding-4-6>
   dcterms:identifier "key-finding-4-6";
   gcis:findingNumber "4.6"^^xsd:string;
   gcis:findingStatement "Combining output from global climate models and dynamical and statistical downscaling models using advanced averaging, weighting, and pattern scaling approaches can result in more relevant and robust future projections. For some regions, sectors, and impacts, these techniques are increasing the ability of the scientific community to provide guidance on the use of climate projections for quantifying regional-scale changes and impacts (<em>medium to high confidence</em>)."^^xsd:string;
   gcis:isFindingOf <https://data.globalchange.gov/report/climate-science-special-report/chapter/projections>;
   gcis:isFindingOf <https://data.globalchange.gov/report/climate-science-special-report>;

## Properties of the finding:
   gcis:findingProcess "Scientific understanding of climate projections, downscaling, multimodel ensembles, and weighting has evolved significantly over the last decades to the extent that appropriate methods are now broadly viewed as robust sources for climate projections that can be used as input to regional impact assessments."^^xsd:string;
   
   gcis:descriptionOfEvidenceBase "The contribution of weighting and pattern scaling to improving the robustness of multimodel ensemble projections is described and quantified by a large body of literature as summarized in the text, including Sanderson et al. and Knutti et al. The state of the art of dynamical and statistical downscaling and the scientific community’s ability to provide guidance regarding the application of climate projections to regional impact assessments is summarized in Kotamarthi et al. and supported by Feser et al. and Prein et al."^^xsd:string;
   
   gcis:assessmentOfConfidenceBasedOnEvidence "Advanced weighting techniques have significantly improved over previous Bayesian approaches; confidence in their ability to improve the robustness of multimodel ensembles, while currently rated as <em>medium</em>, is likely to grow in coming years. Downscaling has evolved significantly over the last decade and is now broadly viewed as a robust source for high-resolution climate projections that can be used as input to regional impact assessments."^^xsd:string;
   
   gcis:newInformationAndRemainingUncertainties "Regional climate models are subject to the same structural and parametric uncertainties as global models, as well as the uncertainty due to incorporating boundary conditions. The primary source of error in application of empirical statistical downscaling methods is inappropriate application, followed by stationarity."^^xsd:string;

   a gcis:Finding .

## This finding cites the following entities:


<https://data.globalchange.gov/report/climate-science-special-report/chapter/projections/finding/key-finding-4-6>
   cito:cites <https://data.globalchange.gov/article/10.1002/2014RG000475>;
   biro:references <https://data.globalchange.gov/reference/561b116f-6b89-4efa-a570-fad433524afa>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/projections/finding/key-finding-4-6>
   cito:cites <https://data.globalchange.gov/report/use-climate-information-decision-making-impact-research>;
   biro:references <https://data.globalchange.gov/reference/76fb2a82-f93e-4db9-bed3-cb864bd9c751>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/projections/finding/key-finding-4-6>
   cito:cites <https://data.globalchange.gov/article/10.1002/2016GL072012>;
   biro:references <https://data.globalchange.gov/reference/7e20a75c-7cda-4c6d-8865-544ee639db47>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/projections/finding/key-finding-4-6>
   cito:cites <https://data.globalchange.gov/article/10.1175/2011BAMS3061.1>;
   biro:references <https://data.globalchange.gov/reference/9f2b8aaf-ac22-48bc-bbdd-dd94368767ce>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/projections/finding/key-finding-4-6>
   cito:cites <https://data.globalchange.gov/article/10.1175/JCLI-D-14-00362.1>;
   biro:references <https://data.globalchange.gov/reference/e0e1fdcc-b1e3-4eff-9eea-ff89dcf75c16>.



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