Rapid systematic assessment of the detection and attribution of regional anthropogenic climate chang...

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" Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change "
Stone, DA; Hansen, G

Document Type	:	AL
Record Number	:	909529
Doc. No	:	LA65s3v7jx
Title & Author	:	Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change [Article]\ Stone, DA; Hansen, G
Date	:	2016
Title of Periodical	:	Lawrence Berkeley National Laboratory
Abstract	:	© 2015, The Author(s). Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies in considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.