Dáithí Stone
National Institute of Water and Atmospheric Research, Wellington, New Zealand.

Over the past couple of decades a large number of studies have diagnosed the contribution of emissions from human activities to observed climate trends, by confronting process-based expectations with long-term monitoring. Over the same period a much larger number of studies have diagnosed the contribution of observed climate changes to trends in various natural, managed, and human systems. For convenience and following IPCC terminology, we will refer to both types of studies as detection and attribution (D&A).

What has been almost universally lacking is a connection between the impact and climate D&A studies at the scale of individual impacts, for instance how human emissions have contributed, via climate change, to an observed range shift of a specific species.

Reframing impact D&A studies such that they are aligned with climate D&A studies would result in loss of relevance. On the other hand, performing bespoke comprehensive climate D&A analyses, as performed in the last IPCC assessment report, is impractical for the myriad impacts. Here Dáithí discusses a way around this latter challenge, by developing an automated algorithm that rapidly performs bespoke D&A assessments. This algorithm is perhaps best described as a predictor of what a comprehensive expert assessment would conclude. In this talk Dáithí will introduce the algorithm, test its performance against the collection of IPCC AR5 assessments, use it to develop a general understanding of regional climate D&A worldwide, and finally apply it to the ultimate purpose of bridging the climate-impact D&A gap.