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Anthropogenic climate change has triggered impacts on natural and human systems world-wide, yet the formal scientific method of detection and attribution has been only insufficiently described. Detection and attribution of impacts of climate change is a fundamentally cross-disciplinary issue, involving concepts, terms, and standards spanning the varied requirements of the various disciplines. Key problems for current assessments include the limited availability of long-term observations, the limited knowledge on processes and mechanisms involved in changing environmental systems, and the widely different concepts applied in the scientific literature. In order to facilitate current and future assessments, this paper describes the current conceptual framework of the field and outlines a number of conceptual challenges. Based on this, it proposes workable cross-disciplinary definitions, concepts, and standards. The paper is specifically intended to serve as a baseline for continued development of a consistent cross-disciplinary framework that will facilitate integrated assessment of the detection and attribution of climate change impacts.

An ever-growing body of evidence suggests that climate change is already impacting human and natural systems around the world. Global environmental assessments assessing this evidence, for example by the Intergovernmental Panel on Climate Change (IPCC) 1 , face increasing challenges to appraise an exponentially growing literature 2 and diverse approaches to climate change attribution. Here we use the language representation model BERT to identify and classify studies on observed climate impacts, producing a machine-learning-assisted evidence map which provides the most comprehensive picture of the literature to date. We identify 100,724 (62,950 − 162,838) publications covering a broad range of impacts in human and natural systems across all continents. By combining our spatially resolved database with human-attributable changes in temperature and precipitation on the grid cell level, we infer that attributable climate change impacts may be occurring in regions encompassing 85% (80%) of the world's population (land area). Our results also reveal a substantial 'attribution gap' as robust evidence for attributable impacts is twice as prevalent in high income compared to low income countries. While substantial gaps remain on con dently establishing attributable climate impacts at the regional and sectoral level, our unique database illustrates the broad extent to which anthropogenic climate change may already be impacting natural systems and societies across the globe. MainThere is overwhelming evidence that the impacts of climate change are already being observed in human and natural systems 3 . These effects are emerging in a range of different systems and at different scales, covering a broad range of research elds from glaciology to agricultural science, and marine biology to migration and con ict research 1 . The evidence base for observed climate impacts is expanding 4 , and the wider climate literature is growing exponentially 5,6 . Systematic reviews and systematic maps offer structured ways to collectively identify and describe this evidence while maintaining transparency, attempting to ensure comprehensiveness and reduce bias 7 . However, their scope is often con ned to very speci c questions covering no more than dozens to hundreds of studies.In the climate science community, evidence-based assessments of observed climate change impacts are performed by the Intergovernmental Panel on Climate Change (IPCC) 1 . Since the rst Assessment Report (AR) of the IPCC in 1990, we estimate that the number of studies relevant to observed climate impacts published per year has increased by more than two orders of magnitude (Fig. 1a). Since the third AR, published in 2001, the number has increased ten-fold. This exponential growth in peer-reviewed scienti c publications on climate change 5,6 is already pushing manual expert assessments to their limits. To address this issue, recent work has investigated ways to handle big literature in sustainability science by scaling systematic review and map methods to large bodies ...

If research on attribution of extreme weather events is to inform emerging climate change policies, it needs to diagnose all of the components of risk.

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