Climate Impact Map

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Display as:
Show me under with a probability
      Temperature
      Days
      Mortality costs as share of GDP
      View Methodology 

      Climate Projections

      The climate projection methodology is described in full in Rasmussen et al. (2016). All daily projections from this analysis are freely available online here. The climate projections show on this map are based on Representative Concentration Pathway 2.6, 4.5, and 8.5 (van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise (Taylor et al., 2012). In particular, we used downscaled CMIP5 climate projections prepared by the US Bureau of Reclamation (Brekke et al., 2013). This dataset is bias-corrected and downscaled using the Bias-Correction Spatial Disaggregation (BCSD) method (Thrasher et al., 2012).

      CMIP5 projections do not inherently constitute a probability distribution; rather, they are an ensemble of opportunity, composed of runs conducted by climate modeling teams participating on a voluntary basis and running models that roughly represent ‘best-estimate’ projections of climate behavior. To produce a probabilistic ensemble, we used the Surrogate Model/Mixed Ensemble (SMME) method of Rasmussen et al. (2016). This method weights projections by comparing their global mean surface temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et al., 2016) the MAGICC6 model (Meinshausen et al., 2011). The target global mean temperature distributions for 2080-2099 used were identical to those of Rasmussen et al. (2016). As in that paper, potential temperature outcomes produced the probabilistic simple climate model but not represented within the downscaled CMIP5 dataset were represented by ‘model surrogates’, produced using linear pattern scaling, with residuals added to represent high-frequency variability and non-linearities.

      The gridded projections were aggregated to regional estimates by first transforming the daily min, average, or maximum temperature at the grid scale, then aggregating to regions using a weighted average. Annual and seasonal average temperatures are weighted using the shares of each region’s land area within each grid cell; estimates of days above 95°F/35°C and below 32°F/0°C are weighted using the shares of each region’s population within each grid cell.

      Damage Projections

      Mortality costs

      The methodology for estimating the mortality costs of future climate change is described in full in Carleton et al. (2020). This study uses comprehensive historical mortality records to quantify how death rates across the globe have been affected by observed climate changes.

      Carleton et al. (2020) compile the largest sub-national vital statistics database in the world, detailing 399 million deaths across 41 countries accounting for 55 percent of the global population. By combining these records with decades of detailed daily and local temperature observations, the authors discover that extreme cold and extreme heat have important effects on death rates. These relationships are modified by the climate and income levels of the affected population. Carleton et al. (2020) use these results to model how adaptation affects the sensitivity of a population to extreme temperatures.

      Estimates of the mortality-temperature relationship are used to generate projections of the future impacts of climate change on mortality rates for areas across the globe, dividing the world into 24,378 distinct regions (each containing roughly 300,000 people, about the size of a U.S. county). Using a revealed preference technique to measure the total cost of adaptive behaviors and technologies, these projections capture the full mortality risk of climate change, accounting for both adaptation benefits and costs, in addition to direct mortality impacts.

      These estimates are based upon emissions scenario RCP 4.5 or RCP 8.5, socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database), and are climate model-weighted means over 33 climate models and 1,000 Monte Carlo simulation runs, allowing for an assessment of the uncertainty surrounding any particular projection. The full estimates also reflect statistical uncertainty related to the underlying economic and health data.

      Projected impacts of climate change on mortality rates are then monetized and used to determine the costs of excess mortality risk in a given year. The full mortality risk of climate change mapped here includes the monetized value of both changes in mortality rates and changes in expenditures on adaptation. Damages are valued at an income-scaled value of statistical life (VSL) relying the U.S. EPA’s VSL estimate of $10.95 million (2019 USD). Damages aggregated at a higher geographical level than impact region are totals of the corresponding impact region-level estimates (there are 24,378 impact regions across the globe). Damages are presented as a percent change of projected Gross Domestic Product in each period, based upon socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database).

      References

      Brekke, L., B. L. Thrasher, E. P. Maurer, and T. Pruitt (2013), Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs, 116 pp., U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center, Denver, Colorado.

      Carleton, Tamma and Jina, Amir and Delgado, Michael and Greenstone, Michael and Houser, Trevor and Hsiang, Solomon and Hultgren, Andrew and Kopp, Robert E. and McCusker, Kelly and Nath, Ishan and Rising, James and Rode, Ashwin and Seo, Hee Kwon and and Viaene, Arvid and Yuan, Jiacan and Zhang, Alice Tianbo, Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Aug. 3, 2020). National Bureau of Economics Working Paper No. 27599, Available at NBER: http://www.nber.org/papers/w27599

      Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley, 2011: Emulating coupled atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6—Part 1: Model description and calibration. Atmospheric Chemistry and Physics, 11, 1417–1456, doi:10.5194/acp-11-1417-2011.

      Rasmussen, D. J, Meinshausen M. and Kopp, R.E., 2016: Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis. Journal of Applied Meteorology and Climatology 55, 2301-2322. doi:10.1175/JAMC-D-15-0302.1.

      Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An Overview of CMIP5 and the Experiment Design. Bull. Amer. Meteor. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.

      Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314. doi:10.5194/hess-16-3309-2012.

      van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 5– 31, doi:10.1007/s10584-011-0148-z.

      Download Data

      Climate Impact Map

      Map Filters
      Display as:
      Show Me under with a probability
          Temperature
          Days
          Mortality costs as share of GDP
          View Methodology 

          Climate Projections

          The climate projection methodology is described in full in Rasmussen et al. (2016). All daily projections from this analysis are freely available online here. The climate projections show on this map are based on Representative Concentration Pathway 2.6, 4.5, and 8.5 (van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise (Taylor et al., 2012). In particular, we used downscaled CMIP5 climate projections prepared by the US Bureau of Reclamation (Brekke et al., 2013). This dataset is bias-corrected and downscaled using the Bias-Correction Spatial Disaggregation (BCSD) method (Thrasher et al., 2012).

          CMIP5 projections do not inherently constitute a probability distribution; rather, they are an ensemble of opportunity, composed of runs conducted by climate modeling teams participating on a voluntary basis and running models that roughly represent ‘best-estimate’ projections of climate behavior. To produce a probabilistic ensemble, we used the Surrogate Model/Mixed Ensemble (SMME) method of Rasmussen et al. (2016). This method weights projections by comparing their global mean surface temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et al., 2016) the MAGICC6 model (Meinshausen et al., 2011). The target global mean temperature distributions for 2080-2099 used were identical to those of Rasmussen et al. (2016). As in that paper, potential temperature outcomes produced the probabilistic simple climate model but not represented within the downscaled CMIP5 dataset were represented by ‘model surrogates’, produced using linear pattern scaling, with residuals added to represent high-frequency variability and non-linearities.

          The gridded projections were aggregated to regional estimates by first transforming the daily min, average, or maximum temperature at the grid scale, then aggregating to regions using a weighted average. Annual and seasonal average temperatures are weighted using the shares of each region’s land area within each grid cell; estimates of days above 95°F/35°C and below 32°F/0°C are weighted using the shares of each region’s population within each grid cell.

          Damage Projections

          Mortality costs

          The methodology for estimating the mortality costs of future climate change is described in full in Carleton et al. (2020). This study uses comprehensive historical mortality records to quantify how death rates across the globe have been affected by observed climate changes.

          Carleton et al. (2020) compile the largest sub-national vital statistics database in the world, detailing 399 million deaths across 41 countries accounting for 55 percent of the global population. By combining these records with decades of detailed daily and local temperature observations, the authors discover that extreme cold and extreme heat have important effects on death rates. These relationships are modified by the climate and income levels of the affected population. Carleton et al. (2020) use these results to model how adaptation affects the sensitivity of a population to extreme temperatures.

          Estimates of the mortality-temperature relationship are used to generate projections of the future impacts of climate change on mortality rates for areas across the globe, dividing the world into 24,378 distinct regions (each containing roughly 300,000 people, about the size of a U.S. county). Using a revealed preference technique to measure the total cost of adaptive behaviors and technologies, these projections capture the full mortality risk of climate change, accounting for both adaptation benefits and costs, in addition to direct mortality impacts.

          These estimates are based upon emissions scenario RCP 4.5 or RCP 8.5, socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database), and are climate model-weighted means over 33 climate models and 1,000 Monte Carlo simulation runs, allowing for an assessment of the uncertainty surrounding any particular projection. The full estimates also reflect statistical uncertainty related to the underlying economic and health data.

          Projected impacts of climate change on mortality rates are then monetized and used to determine the costs of excess mortality risk in a given year. The full mortality risk of climate change mapped here includes the monetized value of both changes in mortality rates and changes in expenditures on adaptation. Damages are valued at an income-scaled value of statistical life (VSL) relying the U.S. EPA’s VSL estimate of $10.95 million (2019 USD). Damages aggregated at a higher geographical level than impact region are totals of the corresponding impact region-level estimates (there are 24,378 impact regions across the globe). Damages are presented as a percent change of projected Gross Domestic Product in each period, based upon socioeconomic scenario SSP3 (from the IIASA Shared Socioeconomic Pathways database).

          References

          Brekke, L., B. L. Thrasher, E. P. Maurer, and T. Pruitt (2013), Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs, 116 pp., U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center, Denver, Colorado.

          Carleton, Tamma and Jina, Amir and Delgado, Michael and Greenstone, Michael and Houser, Trevor and Hsiang, Solomon and Hultgren, Andrew and Kopp, Robert E. and McCusker, Kelly and Nath, Ishan and Rising, James and Rode, Ashwin and Seo, Hee Kwon and and Viaene, Arvid and Yuan, Jiacan and Zhang, Alice Tianbo, Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Aug. 3, 2020). National Bureau of Economics Working Paper No. 27599, Available at NBER: http://www.nber.org/papers/w27599

          Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley, 2011: Emulating coupled atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6—Part 1: Model description and calibration. Atmospheric Chemistry and Physics, 11, 1417–1456, doi:10.5194/acp-11-1417-2011.

          Rasmussen, D. J, Meinshausen M. and Kopp, R.E., 2016: Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis. Journal of Applied Meteorology and Climatology 55, 2301-2322. doi:10.1175/JAMC-D-15-0302.1.

          Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An Overview of CMIP5 and the Experiment Design. Bull. Amer. Meteor. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.

          Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314. doi:10.5194/hess-16-3309-2012.

          van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 5– 31, doi:10.1007/s10584-011-0148-z.

          Download Data