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Double benefit of limiting global warming for tropical cyclone exposure


Tropical cyclone (TC) impacts are expected to worsen under continued global warming and socio-economic development. Here we combine TC simulations with an impact model to quantify country-level population exposure to TC winds for different magnitudes of global mean surface temperature increase and future population distributions. We estimate an annual global TC exposure increase of 26% (33 million people) for a 1 °C increase in global mean surface temperature, assuming present-day population. The timing of warming matters when additionally accounting for population change, with global population projected to peak around mid-century and decline thereafter. A middle-of-the-road socio-economic scenario combined with 2 °C of warming around 2050 increases exposure by 41% (52 million). A stronger mitigation scenario reaching 2 °C around 2100 limits this increase to 20% (25 million). Rapid climate action therefore avoids interference with peak global population timing and limits climate-change-driven exposure. Cumulatively, over 1.8 billion people could be saved by 2100.

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Fig. 1: Increase in annual population exposure with GMST.
Fig. 2: Population exposure for different base years of socio-economic development and 2 °C of warming.
Fig. 3: Future population exposure for different warming scenarios.

Data availability

The historical48 and future49 population data are freely available online. The historical TC exposure data are available from the Tropical Cyclone Exposure Database archive50. The TC track simulations are available for scientific purposes only and upon request from WindRiskTech ( Requests regarding the CLIMADA impact model should be addressed to D.N.B. ( The data to display country outlines and coastlines are based on the Generic Mapping Tools51. All remaining data that support the findings of this study are available from the corresponding author upon request.

Code availability

The simulations to quantify the TC impact were conducted using the Python tool CLIMADA, available at In particular, the tool to generate the artificial TC exposure time series is freely available at The model to generate the TC tracks, the model to construct the temperature scenarios (MAGICC 6.8 and the climate module of the Potsdam Real-Time Integrated Model for the probabilistic assessment of emission paths (PRIMAP)) and the PRIMAP emissions module used for the creation and extension of the NDC pathways are proprietary and cannot be shared publicly. All remaining code that was used to analyse the data and produce the figures is available from the corresponding author upon request.


  1. Geiger, T., Frieler, K. & Bresch, D. N. A global historical data set of tropical cyclone exposure (TCE-DAT). Earth Syst. Sci. Data 10, 185–194 (2018).

    Article  Google Scholar 

  2. Berlemann, M. & Wenzel, D. Hurricanes, economic growth and transmission channels: empirical evidence for countries on differing levels of development. World Dev. 105, 231–247 (2018).

    Article  Google Scholar 

  3. Peduzzi, P. et al. Global trends in tropical cyclone risk. Nat. Clim. Change 2, 289–294 (2012).

    Article  Google Scholar 

  4. Estrada, F., Botzen, W. J. W. & Tol, R. S. J. Economic losses from US hurricanes consistent with an influence from climate change. Nat. Geosci. 8, 880–884 (2015).

    Article  CAS  Google Scholar 

  5. Bakkensen, L. A. & Mendelsohn, R. O. Risk and adaptation: evidence from global hurricane damages and fatalities. J. Assoc. Environ. Resour. Econ. 3, 555–587 (2016).

    Google Scholar 

  6. Geiger, T., Frieler, K. & Levermann, A. High-income does not protect against hurricane losses. Environ. Res. Lett. (2016).

  7. Vecchi, G. A. et al. Tropical cyclone sensitivities to CO2 doubling: roles of atmospheric resolution, synoptic variability and background climate changes. Clim. Dyn. 53, 5999–6033 (2019).

    Article  Google Scholar 

  8. Noy, I. The socio-economics of cyclones. Nat. Clim. Change 6, 343–345 (2016).

    Article  Google Scholar 

  9. Kummu, M. et al. Over the hills and further away from coast: global geospatial patterns of human and environment over the 20th–21st centuries. Environ. Res. Lett. (2016).

  10. Emanuel, K. Assessing the present and future probability of Hurricane Harvey’s rainfall. Proc. Natl Acad. Sci. USA 114, 12681–12684 (2017).

    Article  CAS  Google Scholar 

  11. Kossin, J. P. A global slowdown of tropical-cyclone translation speed. Nature 558, 104–107 (2018).

    Article  CAS  Google Scholar 

  12. Woodruff, J. D., Irish, J. L. & Camargo, S. J. Coastal flooding by tropical cyclones and sea-level rise. Nature 504, 44–52 (2013).

    Article  CAS  Google Scholar 

  13. Kossin, J. P., Emanuel, K. A. & Vecchi, G. A. The poleward migration of the location of tropical cyclone maximum intensity. Nature 509, 349–352 (2014).

    Article  CAS  Google Scholar 

  14. Emanuel, K. Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl Acad. Sci. USA 110, 12219–12224 (2013).

    Article  CAS  Google Scholar 

  15. Bakkensen, L. A., Shi, X. & Zurita, B. D. The impact of disaster data on estimating damage determinants and climate costs. Econ. Disasters Clim. Change 2, 49–71 (2017).

    Article  Google Scholar 

  16. Gettelman, A., Bresch, D. N., Chen, C. C., Truesdale, J. E. & Bacmeister, J. T. Projections of future tropical cyclone damage with a high-resolution global climate model. Climatic Change 146, 575–585 (2017).

    Article  Google Scholar 

  17. Mendelsohn, R., Emanuel, K., Chonabayashi, S. & Bakkensen, L. The impact of climate change on global tropical cyclone damage. Nat. Clim. Change 2, 205–209 (2012).

    Article  Google Scholar 

  18. Strobl, E. The economic growth impact of natural disasters in developing countries: evidence from hurricane strikes in the Central American and Caribbean regions. J. Dev. Econ. 97, 130–141 (2012).

    Article  Google Scholar 

  19. Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10, 4321–4345 (2017).

    Article  Google Scholar 

  20. Aznar-Siguan, G. & Bresch, D. N. CLIMADA v1: a global weather and climate risk assessment platform. Geosci. Model Dev. 12, 3085–3097 (2019).

    Article  Google Scholar 

  21. The Emissions Gap Report 2018 (UNEP, 2018).

  22. Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene—HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).

    Article  Google Scholar 

  23. Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. (2016).

  24. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Article  Google Scholar 

  25. Murakami, H., Vecchi, G. A. & Underwood, S. Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nat. Clim. Change 7, 885–889 (2017).

    Article  Google Scholar 

  26. Meinshausen, M. et al. Greenhouse-gas emission targets for limiting global warming to 2 °C. Nature 458, 1158–1162 (2009).

    Article  CAS  Google Scholar 

  27. Rocha, M. et al. Paris Agreement in Force, but No Increase in Climate Action Tech. Rep. (Climate Action Tracker, 2016).

  28. Gütschow, J., Jeffery, M. L., Schaeffer, M. & Hare, B. Extending near-term emissions scenarios to assess warming implications of Paris Agreement NDCs. Earth’s Future 6, 1242–1259 (2018).

    Article  Google Scholar 

  29. Czajkowski, J. & Done, J. As the wind blows? Understanding hurricane damages at the local level through a case study analysis. Weather Clim. Soc. 6, 202–217 (2014).

    Article  Google Scholar 

  30. Zhai, A. R. & Jiang, J. H. Dependence of US hurricane economic loss on maximum wind speed and storm size. Environ. Res. Lett. (2014).

  31. Done, J. M., Simmons, K. M. & Czajkowski, J. Relationship between residential losses and hurricane winds: role of the Florida building code. ASCE ASME J. Risk Uncertain. Eng. Syst. A (2018).

  32. Rigaud, K. K. et al. Groundswell: Preparing for Internal Climate Migration (World Bank, 2018).

  33. Fussell, E. et al. Weather-related hazards and population change. Ann. Am. Acad. Polit. Soc. Sci. 669, 146–167 (2017).

    Article  Google Scholar 

  34. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  35. Equatorial Southern Oscillation Index (ESOI) (NOAA, 2019);

  36. Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J. & Neumann, C. J. The International Best Track Archive for Climate Stewardship (IBTrACS) unifying tropical cyclone data. Bull. Am. Meteorol. Soc. 91, 363–376 (2010).

    Article  Google Scholar 

  37. Emanuel, K., Sundararajan, R. & Williams, J. Hurricanes and global warming: results from downscaling IPCC AR4 simulations. Bull. Am. Meteorol. Soc. 89, 347–367 (2008).

    Article  Google Scholar 

  38. Holland, G. A revised hurricane pressure–wind model. Mon. Weather Rev. 136, 3432–3445 (2008).

    Article  Google Scholar 

  39. Dobson, A. J. An Introduction to Generalized Linear Models (Chapman & Hall/CRC, 2002).

  40. Meinshausen, M. & Alexander, R. NDC and INDC Factsheets. November 2016 version (Australian-German Climate and Energy College, The University of Melbourne, 2016).

  41. IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) (Cambridge Univ. Press, 2014).

  42. Boden, T. A., Andres, R. J. & Marland, G. Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2014) (V. 2017) (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, 1999);

  43. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge University Press, 2014).

  44. Gütschow, J., Jeffery, M. L., Gieseke, R. & Gebel, R. The PRIMAP-hist National Historical Emissions Time Series (1850–2015) v.1.2 (GFZ Data Services, 2018).

  45. Gütschow, J. et al. The PRIMAP-hist national historical emissions time series. Earth Syst. Sci. Data 8, 571–603 (2016).

    Article  Google Scholar 

  46. Jeffery, M. L., Gütschow, J., Rocha, M. R. & Gieseke, R. Measuring success: improving assessments of aggregate greenhouse gas emissions reduction goals. Earth’s Future 6, 1260–1274 (2018).

    Article  Google Scholar 

  47. Rogelj, J., Meinshausen, M. & Knutti, R. Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat. Clim. Change 2, 248–253 (2012).

    Article  Google Scholar 

  48. Klein Goldewijk, C. G. M. Anthropogenic Land-Use Estimates for the Holocene: HYDE 3.2 (DANS, 2017);

  49. Jones, B. & O’Neill, B. C. Global Population Projection Grids Based on Shared Socioeconomic Pathways (SSPs), 2010–2100 (NASA Socioeconomic Data and Applications Center, 2017);

  50. Geiger, T., Frieler, K. & Bresch, D. N. A Data Collection of Tropical Cyclone Exposure Data Sets (TCE-DAT) (GFZ Data Services, 2017);

  51. Wessel, P. et al. The Generic Mapping Tools version 6. Geochem. Geophys. Geosyst. (2019).

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We thank T. Vogt for his valuable contribution to reworking the emulator code and making it publicly available. We further thank the ISIMIP team for their support. T.G. acknowledges funding through the framework of the Leibniz Competition SAW-2016-PIK-1 (ENGAGE) and funding by the German Federal Ministry of Education and Research (BMBF) project FKZ:01LA1829A (SLICE). K.F. acknowledges funding for ISIMIP through the BMBF for research projects 01LS1201A2 (ISIMIP2b) and 01LS1711A (ISIpedia). J.G. acknowledges support from the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (16\_II\_148\_Global\_A\_IMPACT) and from the BMBF (01LS1711A). The content and views presented in this paper are solely those of the authors and do not necessarily represent the views of Deutscher Wetterdienst (DWD).

Author information

Authors and Affiliations



T.G. and K.F. conceived and designed the research. K.E. generated the TC simulations. D.N.B. conducted the impact simulations. J.G. provided the future warming scenarios. T.G. developed the software tool and analysed and interpreted the data with help from all authors. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Tobias Geiger.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Cindy Bruyère, Ilan Noy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Normalized distribution of global annually exposed population from tropical cyclones.

The distributions are shown for two GMST levels (1 °C and 2 °C) and two fixed population patterns (2015 (observed) and 2050 (SSP2)) for modeled exposure (colored lines) based on RCP6.0 simulations compared to 1980–2015 observed exposure (black line). The logarithm of annual exposed population is binned for the observations (36 years, 12 bins) and for the simulations for all years falling in a 0.5 °C temperature interval around the desired GMST value (50 bins).

Extended Data Fig. 2 Annual population exposure with global mean surface temperature (GMST) for constant tropical cyclone frequency.

Same as Fig. 1 but with fixed TC frequency at 1 °C of warming compared to the pre-industrial level.

Supplementary information

Supplementary Information

Supplementary Fig. 1, Tables 1 and 2, and References.

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Geiger, T., Gütschow, J., Bresch, D.N. et al. Double benefit of limiting global warming for tropical cyclone exposure. Nat. Clim. Chang. 11, 861–866 (2021).

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