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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-3-1>
   dcterms:identifier "key-finding-3-1";
   gcis:findingNumber "3.1"^^xsd:string;
   gcis:findingStatement "The <em>likely</em> range of the human contribution to the global mean temperature increase over the period 1951–2010 is 1.1° to 1.4°F (0.6° to 0.8°C), and the central estimate of the observed warming of 1.2°F (0.65°C) lies within this range (<em>high confidence</em>). This translates to a <em>likely</em> human contribution of 93%–123% of the observed 1951-2010 change. It is <em>extremely likely</em> that more than half of the global mean temperature increase since 1951 was caused by human influence on climate (<em>high confidence</em>). The <em>likely</em> contributions of natural forcing and internal variability to global temperature change over that period are minor (<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 "Detection and attribution studies, climate models, observations, paleoclimate data, and physical understanding lead to <em>high confidence</em> (<em>extremely likely</em>) that more than half of the observed global mean warming since 1951 was caused by humans, and <em>high confidence</em> that internal climate variability played only a minor role (and possibly even a negative contribution) in the observed warming since 1951. The key message and supporting text summarizes extensive evidence documented in the peer-reviewed detection and attribution literature, including in the IPCC AR5."^^xsd:string;
   
   gcis:descriptionOfEvidenceBase "This Key Finding summarizes key detection and attribution evidence documented in the climate science literature and in the IPCC AR5, and references therein. The Key Finding is essentially the same as the summary assessment of IPCC AR5. <br><br> According to Bindoff et al., the <em>likely</em> range of the anthropogenic contribution to global mean temperature increases over 1951–2010 was 1.1° to 1.4°F (0.6° to 0.8°C, compared with the observed warming 5th to 95th percentile range of 1.1° to 1.3°F (0.59° to 0.71°C). The estimated likely contribution ranges for natural forcing and internal variability were both much smaller (−0.2° to 0.2°F, or −0.1° to 0.1°C) than the observed warming. The confidence intervals that encompass the <em>extremely likely</em> range for the anthropogenic contribution are wider than the <em>likely</em> range, but nonetheless allow for the conclusion that it is <em>extremely likely</em> that more than half of the global mean temperature increase since 1951 was caused by human influence on climate (<em>high confidence</em>)<em>.</em> <br><br> The attribution of temperature increases since 1951 is based largely on the detection and attribution analyses of Gillett et al., Jones et al., and consideration of Ribes and Terray, Huber and Knutti, Wigley and Santer, and IPCC AR4. The IPCC finding receives further support from alternative approaches, such as multiple linear regression/energy balance modeling and a new methodological approach to detection and attribution that uses additive decomposition and hypothesis testing, which infer similar attributable warming results. Individual study results used to derive the IPCC finding are summarized in Figure 10.4 of Bindoff et al., which also assesses model dependence by comparing results obtained from several individual CMIP5 models. The estimated potential influence of internal variability is based on Knutson et al. and Huber and Knutti, with consideration of the above references. Moreover, simulated global temperature multidecadal variability is assessed to be adequate, with <em>high confidence</em> that models reproduce global and Northern Hemisphere temperature variability across a range of timescales. Further support for these assessments comes from assessments of paleoclimate data and increased confidence in physical understanding and models of the climate system. A more detailed traceable account is contained in Bindoff et al. Post-IPCC AR5 supporting evidence includes additional analyses showing the unusual nature of observed global warming since the late 1800s compared to simulated internal climate variability, and the recent occurrence of new record high global mean temperatures are consistent with model projections of continued warming on multidecadal scales (for example, Figure 3.1)."^^xsd:string;
   
   gcis:assessmentOfConfidenceBasedOnEvidence "There is <em>very high confidence</em> that global temperature has been increasing and that anthropogenic forcings have played a major role in the increase observed over the past 60 years, with strong evidence from several studies using well-established detection and attribution techniques. There is <em>high confidence</em> that the role of internal variability is minor, as the CMIP5 climate models as a group simulate only a minor role for internal variability over the past 60 years, and the models have been assessed by IPCC AR5 as adequate for the purpose of estimating the potential role of internal variability. <br><p>The amount of historical warming attributable to anthropogenic forcing has a very high likelihood of consequence, as it is related to the amount of future warming to be expected under various emission scenarios, and the impacts of global warming are generally larger for higher warming rates and higher warming amounts.</p></br>"^^xsd:string;
   
   gcis:newInformationAndRemainingUncertainties "As discussed in the main text, estimation of the transient climate response (TCR), defined as the global mean surface temperature change at the time of CO<sub>2</sub> doubling in a 1% per year CO<sub>2</sub> transient increase experiment, continues to be an active area of research with considerable remaining uncertainties. Some detection attribution methods use model-based methods together with observations to attempt to infer scaling magnitudes of the forced responses based on regression methods (that is, they do not use the models’ climate sensitivities directly). However, if climate models are significantly more sensitive to CO<sub>2</sub> increases than the real world, as suggested by the studies of Otto et al. and Lewis and Curry (though see differing conclusions from other studies in the main text), this could lead to an overestimate of attributable warming estimates, at least as obtained using some detection and attribution methods. In any case, it is important to better constrain the TCR to have higher confidence in general in attributable warming estimates obtained using various methods. <br><br> The global temperature change since 1951 attributable to anthropogenic forcings other than greenhouse gases has a wide estimated <em>likely</em> range (−1.1° to +0.2°F in Fig. 3.1). This wide range is largely due to the considerable uncertainty of estimated total radiative forcing due to aerosols (i.e., the direct effect combined with the effects of aerosols on clouds). Although more of the relevant physical processes are being included in models, confidence in these model representations remains low. In detection/attribution studies there are substantial technical challenges in quantifying the separate attributable contributions to temperature change from greenhouse gases and aerosols. Finally, there is a range of estimates of the potential contributions of internal climate variability, and some sources of uncertainty around modeled estimates (e.g., Laepple and Huybers 2014). However, current CMIP5 multimodel estimates (<em>likely</em> range of ±0.2°F, or 0.1°C, over 60 years) would have to increase by a factor of about three for even half of the observed 60-year trend to lie within a revised <em>likely</em> range of potential internal variability (e.g., Knutson et al. 2013; Huber and Knutti 2012). Recently, Knutson et al. examined a 5,000-year integration of the CMIP5 model having the strongest internal multidecadal variability among 25 CMIP5 models they examined. While the internal variability within this strongly varying model can on rare occasions produce 60-year warmings approaching that observed from 1951–2010, even this most extreme model did not produce any examples of centennial-scale internal variability warming that could match the observed global warming since the late 1800s, even in a 5,000-year integration."^^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-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1007/s00382-012-1585-8>;
   biro:references <https://data.globalchange.gov/reference/012e025d-f023-40bd-b3e9-4820e5e323f1>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1038/ngeo1836>;
   biro:references <https://data.globalchange.gov/reference/0800c47a-0630-4dae-ab87-342a53eda4f4>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1038/nclimate3224>;
   biro:references <https://data.globalchange.gov/reference/0c399138-4b5b-42cb-9cab-d17063612610>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/report/ipcc-ar4-wg1/chapter/ar4-wg1-chapter9>;
   biro:references <https://data.globalchange.gov/reference/0ed56ad5-5f94-49f0-bc21-d9d480654774>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1038/ncomms13676>;
   biro:references <https://data.globalchange.gov/reference/30db4465-656b-433c-9d33-63eee28aa49a>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1175/JCLI-D-12-00567.1>;
   biro:references <https://data.globalchange.gov/reference/570a5677-e743-4fb2-a031-e64752586f7c>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/report/ipcc-ar5-wg1/chapter/wg1-ar5-chapter10-final>;
   biro:references <https://data.globalchange.gov/reference/57fea764-f852-4539-ab1e-8010701383c7>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/report/ipcc-ar5-wg1/chapter/wg1-ar5-chapter08-final>;
   biro:references <https://data.globalchange.gov/reference/6c7c285c-8606-41fe-bf93-100d80f1d17a>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/report/ipcc-ar5-wg1/chapter/wg1-ar5-chapter05-final>;
   biro:references <https://data.globalchange.gov/reference/6f4c1264-ab24-4802-9171-ea967deecc6c>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1007/s00382-016-3079-6>;
   biro:references <https://data.globalchange.gov/reference/885b86a4-36fa-4d56-aea0-9d7f728eb0b3>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1002/grl.50500>;
   biro:references <https://data.globalchange.gov/reference/8c65d87d-1fe3-45e8-8d26-8c3617d25ae8>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1007/s00382-014-2342-y>;
   biro:references <https://data.globalchange.gov/reference/9a061d96-379f-4f6a-8f83-15ac9b1f8a74>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/report/ipcc-ar5-wg1/chapter/wg1-ar5-chapter07-final>;
   biro:references <https://data.globalchange.gov/reference/9e2542c2-865e-4863-98d1-242b11016592>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   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-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1038/ngeo1327>;
   biro:references <https://data.globalchange.gov/reference/b7b21ea4-8c3c-407a-a885-4ecacd3b2f00>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1073/pnas.1412077111>;
   biro:references <https://data.globalchange.gov/reference/c3d91980-51fe-4764-89fc-8ab52a38afa9>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.5194/acp-13-3997-2013>;
   biro:references <https://data.globalchange.gov/reference/d809f9d4-37d8-4d2d-a769-177883545997>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1007/s00382-013-1736-6>;
   biro:references <https://data.globalchange.gov/reference/e27f1a95-f93b-412d-8a33-42bd83809d75>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/article/10.1002/jgrd.50239>;
   biro:references <https://data.globalchange.gov/reference/ee56b7fa-1961-49cc-aeea-823510341d5f>.

<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   cito:cites <https://data.globalchange.gov/report/ipcc-ar5-wg1>;
   biro:references <https://data.globalchange.gov/reference/f03117be-ccfe-4f88-b70a-ffd4351b8190>.



<https://data.globalchange.gov/report/climate-science-special-report/chapter/detection-attribution/finding/key-finding-3-1>
   prov:wasDerivedFrom <https://data.globalchange.gov/report/climate-science-special-report/chapter/front-matter/figure/confidence---likelihood>.