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Combating climate change requires unified action across all sectors of society. However, this collective action is precluded by the ‘consensus gap’ between scientific knowledge and public opinion. Here, we test the extent to which the iconic cities around the world are likely to shift in response to climate change. By analyzing city pairs for 520 major cities of the world, we test if their climate in 2050 will resemble more closely to their own current climate conditions or to the current conditions of other cities in different bioclimatic regions. Even under an optimistic climate scenario (RCP 4.5), we found that 77% of future cities are very likely to experience a climate that is closer to that of another existing city than to its own current climate. In addition, 22% of cities will experience climate conditions that are not currently experienced by any existing major cities. As a general trend, we found that all the cities tend to shift towards the sub-tropics, with cities from the Northern hemisphere shifting to warmer conditions, on average ~1000 km south (velocity ~20 km.year-1), and cities from the tropics shifting to drier conditions. We notably predict that Madrid’s climate in 2050 will resemble Marrakech’s climate today, Stockholm will resemble Budapest, London to Barcelona, Moscow to Sofia, Seattle to San Francisco, Tokyo to Changsha. Our approach illustrates how complex climate data can be packaged to provide tangible information. The global assessment of city analogues can facilitate the understanding of climate change at a global level but also help land managers and city planners to visualize the climate futures of their respective cities, which can facilitate effective decision-making in response to on-going climate change., PLoS ONE, 14 (7), ISSN:1932-6203

1. Some ecosystems show nonlinear responses to gradual changes in environmental conditions, once a threshold in conditions-or critical point-is passed. This can lead to wide shifts in ecosystem states, possibly with dramatic ecological and economic consequences. Such behaviours have been reported in drylands, savannas, coral reefs or shallow lakes for example. Important research effort of the last decade has been devoted to identifying indicators that would help anticipate such ecosystem shifts and avoid their negative consequences. 2. Theoretical and empirical research has shown that, as an ecosystem approaches a critical point, specific signatures arise in its temporal and spatial dynamics; these changes can be quantified using relatively simple statistical metrics that have been referred to as ``early warning signals'' (EWS) in the literature. Although tests of those EWS on experiments are promising, empirical evidence from out-of-laboratory datasets is still scarce, in particular for spatial EWS. The recent proliferation of remote-sensing data provides an opportunity to improve this situation and evaluate the reliability of spatial EWS in many ecological systems. 3. Here, we present a step-by-step workflow along with code to compute spatial EWS from raster data such as aerial images, test their significance compared to permutation-based null models, and display their trends, either at different time steps or along environmental gradients. We created the R-package spatial-warnings (MIT license) to help achieve all these steps in a reliable and reproducible way, and thereby promote the application of spatial EWS to empirical data. 4. This software package and associated documentation provides an easy entry point for researchers and managers into spatial EWS-based analyses. By facilitating a broader application, it will leverage the evaluation of spatial EWS on real data, and eventually contribute to providing tools to map ecosystems' fragility to perturbations and inform management decisions.

Combating climate change requires unified action across all sectors of society. However, this collective action is precluded by the ‘consensus gap’ between scientific knowledge and public opinion. Here, we test the extent to which the iconic cities around the world are likely to shift in response to climate change. By analyzing city pairs for 520 major cities of the world, we test if their climate in 2050 will resemble more closely to their own current climate conditions or to the current conditions of other cities in different bioclimatic regions. Even under an optimistic climate scenario (RCP 4.5), we found that 77% of future cities are very likely to experience a climate that is closer to that of another existing city than to its own current climate. In addition, 22% of cities will experience climate conditions that are not currently experienced by any existing major cities. As a general trend, we found that all the cities tend to shift towards the sub-tropics, with cities from the Northern hemisphere shifting to warmer conditions, on average ~1000 km south (velocity ~20 km.year-1), and cities from the tropics shifting to drier conditions. We notably predict that Madrid’s climate in 2050 will resemble Marrakech’s climate today, Stockholm will resemble Budapest, London to Barcelona, Moscow to Sofia, Seattle to San Francisco, Tokyo to Changsha. Our approach illustrates how complex climate data can be packaged to provide tangible information. The global assessment of city analogues can facilitate the understanding of climate change at a global level but also help land managers and city planners to visualize the climate futures of their respective cities, which can facilitate effective decision-making in response to on-going climate change.