A globally coherent fingerprint of climate change impacts across natural systems Camille Parmesan* & Gary Yohe† * Integrative Biology, Patterson Laboratories 141, University of Texas, Austin, Texas 78712, USA † John E. Andrus Professor of Economics, Wesleyan University, 238 Public Affairs Center, Middletown, Connecticut 06459, USA ........................................................................................................................................................................................................................... Causal attribution of recent biological trends to climate change is complicated because non-climatic influences dominate local, short-term biological changes. Any underlying signal from climate change is likely to be revealed by analyses that seek systematic trends across diverse species and geographic regions; however, debates within the Intergovernmental Panel on Climate Change (IPCC) reveal several definitions of a ‘systematic trend’. Here, we explore these differences, apply diverse analyses to more than 1,700 species, and show that recent biological trends match climate change predictions. Global meta-analyses documented significant range shifts averaging 6.1 km per decade towards the poles (or metres per decade upward), and significant mean advancement of spring events by 2.3 days per decade. We define a diagnostic fingerprint of temporal and spatial ‘sign-switching’ responses uniquely predicted by twentieth century climate trends. Among appropriate long-term/large-scale/multi-species data sets, this diagnostic fingerprint was found for 279 species. This suite of analyses generates ‘very high confidence’ (as laid down by the IPCC) that climate change is already affecting living systems. The Intergovernmental Panel on Climate Change 1 (IPCC) assessed the extent to which recent observed changes in natural biological systems have been caused by climate change. This was a difficult task despite documented statistical correlations between changes in climate and biological changes 2–5 . With hindsight, the difficulties encountered by the IPCC can be attributed to the differences in approach between biologists and other disciplines, particularly economists. Studies in this area are, of necessity, correlational rather than experimental, and as a result, assignment of causation is inferential. This inference often comes from experimental studies of the effects of temperature and precipitation on the target species or on a related species with similar habitats. Confidence in this inferential process is subjective, and differs among disciplines, thus resulting in the first divergence of opinion within the IPCC. The second impasse came from differences in perspective on what constitutes an ‘important’ factor. Anyone would consider a cur- rently strong driver to be important, but biologists also attach importance to forces that are currently weak but are likely to persist. In contrast, economic approaches tend to discount events that will occur in the future, assigning little weight to weak but persistent forces. Differences of opinion among disciplines can therefore stem naturally from whether the principal motivation is to assess the magnitude of immediate impacts or of long-term trajectories. Most field biologists are convinced that they are already seeing important biological impacts of climate change 1–4,6–9 ; however, they have encountered difficulty in convincing other academic disciplines, policy-makers and the general public. Here, we seek to improve communication, provide common ground for discussion, and give a comprehensive summary of the evidence. How should a ‘climate fingerprint’ be defined? A straightforward view typical of an economist would be to conclude that climate change was important if it were principally responsible for a high proportion of current biotic changes. By this criterion a climate fingerprint appears weak. Most short-term local changes are not caused by climate change but by land-use change and by natural fluctuations in the abundance and distribution of species. This fact has been used by non-biologists to argue that climate change is of little importance to wild systems 10 . This approach, however, effec- tively ignores small, systematic trends that may become important in the longer term. Such underlying trends would be confounded (and often swamped) by strong forces such as habitat loss. Biologists have tended to concentrate on studies that minimize confounding factors, searching for trends in relatively undisturbed systems and then testing for significant associations with climate change. Econ- omists have viewed this as biased (nonrandom exclusion of data) whereas biologists view this as reducing non-climatic noise. Thus, economists focus on total direct evidence and apply heavy time discounting; biologists apply a ‘quality control’ filter to available data, accept indirect (inferential) evidence and don’t apply time discounting. The test for a globally coherent climate fingerprint does not require that any single species show a climate change impact with 100% certitude. Rather, it seeks some defined level of confidence in a climate change signal on a global scale. Adopting the IPCC ‘levels of confidence’ 11 and applying the economists’ view of a fingerprint, we would have “very high confidence” in a fingerprint if we estimated that more than 95% of observed changes were principally caused by climate change, “high confidence” between 95% and 67%, “medium confidence” between 33% and 67%, and “low confidence” below 33%. In contrast, the biologists’ confidence level comes from the statistical probability that global biotic trends would match climate change predictions purely by chance, coupled with support- ing experimental results showing causal relationships between climate and particular biological traits. Here, we present quantitative estimates of the global biological impacts of climate change. We search for a climate fingerprint in the overall patterns, rather than critiquing each study individually. Using the biologists’ approach, we synthesize a suite of correlational studies on diverse taxa over many regions to ask whether natural systems, in general, have responded to recent climate change. Furthermore, we attempt a cross-fertilization by applying an economists’ measure—the estimated proportion of observed changes for which climate trends are the principal drivers—to data sets chosen using biologists’ criteria. We call this a ‘global coherence’ approach to the detection of climate change impacts. First, we explore a biologists’ confidence assessment with two types of analyses of observed change: statistical meta-analyses of effect size in restricted data sets and more comprehensive categori- cal analyses of the full literature. Second, we present a probabilistic model that considers three variables: proportion of observations matching climate change predictions, numbers of competing expla- nations for each of those observations, and confidence in causal articles NATURE | VOL 421 | 2 JANUARY 2003 | www.nature.com/nature 37 © 2003 Nature Publishing Group