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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/detection-attribution/finding/key-finding-3-2>
   dcterms:identifier "key-finding-3-2";
   gcis:findingNumber "3.2"^^xsd:string;
   gcis:findingStatement "The science of event attribution is rapidly advancing through improved understanding of the mechanisms that produce extreme events and the marked progress in development of methods that are used for event attribution (<em>high confidence</em>)."^^xsd:string;
   gcis:isFindingOf <https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution>;
   gcis:isFindingOf <https://data.globalchange.gov/report/climate-science-special-report>;

## Properties of the finding:
   gcis:findingProcess "Owing to the improved physical understanding of extreme weather and climate events as the science in these fields progress, and owing to the high promise of newly developed methods for exploring the roles of different influences on occurrence of extreme events, there is <em>high confidence</em> that the science of event attribution is rapidly advancing.\r\n"^^xsd:string;
   
   gcis:descriptionOfEvidenceBase "This Key Finding paraphrases a conclusion of the National Academy of Sciences report on attribution of extreme weather events in the context of climate change. That report discusses advancements in event attribution in more detail than possible here due to space limitations. Weather and climate science in general continue to seek improved physical understanding of extreme weather events. One aspect of improved understanding is the ability to more realistically simulate extreme weather events in models, as the models embody current physical understanding in a simulation framework that can be tested on sample cases. NAS provides references to studies that evaluate weather and climate models used to simulated extreme events in a climate context. Such models can include coupled climate models (e.g., Taylor et al. 2012; Flato et al. 2013), atmospheric models with specified sea surface temperatures, regional models for dynamical downscaling, weather forecasting models, or statistical downscaling models. Appendix C includes a brief description of the evolving set of methods used for event attribution, discussed in more detail in references such as NAS, Hulme, Trenberth et al., Shepherd, Horton et al., Hannart, and Hannart et al. Most of this methodology as applied to extreme weather and climate event attribution, has evolved since the European heat wave study of Stott et al."^^xsd:string;
   
   gcis:assessmentOfConfidenceBasedOnEvidence "There is <em>very high confidence</em> that weather and climate science are advancing in their understanding of the physical mechanisms that produce extreme events. For example, hurricane track forecasts have improved in part due to improved models. There is <em>high confidence</em> that new methods being developed will help lead to further advances in the science of event attribution. <br><p> Improving science of event attribution has a high likelihood of impact, as it is one means by which scientists can better understand the relationship between occurrence of extreme events and long-term climate change. A further impact will be the improved ability to communicate this information to the public and to policymakers for various uses, including improved adaptation planning.\r\n</p></br>\r\n"^^xsd:string;
   
   gcis:newInformationAndRemainingUncertainties "While the science of event attribution is rapidly advancing, studies of individual events will typically contain caveats. In some cases, attribution statements are made without a clear detection of an anthropogenic influence on observed occurrences of events similar to the one in question, so that there is reliance on models to assess probabilities of occurrence. In such cases there will typically be uncertainties in the model-based estimations of the anthropogenic influence, in the estimation of the influence of natural variability on the event’s occurrence, and even in the observational records related to the event (e.g., long-term records of hurricane occurrence). Despite these uncertainties in individual attribution studies, the science of event attribution is advancing through increased physical understanding and development of new methods of attribution and evaluation of models."^^xsd:string;

   a gcis:Finding .

## This finding cites the following entities:


<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1007/s40641-016-0033-y>;
   biro:references <https://data.globalchange.gov/reference/0c07ed76-7acf-4e78-840b-df6c232acb27>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/report/nas-attribution-extreme-weather-2016>;
   biro:references <https://data.globalchange.gov/reference/2180df56-f5ec-49e5-9733-d61778bf49d1>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1175/BAMS-D-11-00094.1>;
   biro:references <https://data.globalchange.gov/reference/29dec54f-92a8-4543-93f1-941da4f4d750>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1007/s40641-016-0042-x>;
   biro:references <https://data.globalchange.gov/reference/4d446ea9-dfd2-45e6-aca4-56e6f85ffa58>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1007/s10584-016-1595-3>;
   biro:references <https://data.globalchange.gov/reference/81ca1b2d-040c-4fd0-aa50-8290a8e25893>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1038/nature03089>;
   biro:references <https://data.globalchange.gov/reference/95994754-90b8-4fac-b8a8-0c135f7a2e02>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/report/ipcc-ar5-wg1/chapter/wg1-ar5-chapter09-final>;
   biro:references <https://data.globalchange.gov/reference/a46eaad1-5c17-46f7-bba6-d3fee718a092>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1177/0309133314538644>;
   biro:references <https://data.globalchange.gov/reference/c94faf72-2d6d-46dc-a432-1d34a24b4d5f>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1175/bams-d-14-00034.1>;
   biro:references <https://data.globalchange.gov/reference/ce628e6b-5e45-4ab9-b288-ac5d93256c26>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1038/nclimate2657>;
   biro:references <https://data.globalchange.gov/reference/cedf32db-4b9f-401a-b69e-2b8eb5457389>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   cito:cites <https://data.globalchange.gov/article/10.1175/jcli-d-14-00124.1>;
   biro:references <https://data.globalchange.gov/reference/dc720443-bc58-4fd7-bb36-19f8f5743293>.



<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-2>
   prov:wasDerivedFrom <https://data.globalchange.gov/report/climate-science-special-report/chapter/front-matter/figure/confidence---likelihood>.