1. A case study of avoiding the heat- related mortality impacts of climate change under mitigation scenarios Simon N. Gosling 1 and Jason A. Lowe 2 1 Walker Institute for Climate System Research, University of Reading 2 Met Office Hadley Centre

2. Outline Methods –The health models. –Climate change scenarios. Business as usual impacts. Avoided impacts. Limitations and conclusions.

3. Methods

4. The health models Six city-specific empirical-statistical models for: –Boston, Budapest, Dallas, Lisbon, London, Sydney. Described and validated in Gosling et al. (2007) and previously applied in Gosling et al. (2009). Assume no demographic changes with CC. Assumes no acclimatisation/adaptation –Therefore impacts are indicative of a requirement for adaptation, rather than definitive values. Gosling SN, McGregor GR, Páldy A (2007) Climate change and heat-related mortality in six cities Part 1: model construction and validation. International Journal of Biometeorology 51: 525-540. Gosling SN, McGregor GR, Lowe JA (2009) Climate change and heat-related mortality in six cities Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change. International Journal of Biometeorology 53: 31-51.

5. Climate change scenarios (1) ScenarioPathway to peak Date of peakRate of decline in emissions Emissions floor A1B-2016-2-HA1B20162% per yearHigh A1B-2016-4-LA1B20164% per yearLow A1B-2016-5-LA1B20165% per yearLow A1B-2030-2-HA1B20302% per yearHigh A1B-2030-5-LA1B20305% per yearLow

6. Climate change scenarios (2) ClimGen uses patterns for a given GCM by fitting a regression, for each month, variable and GCM grid cell, between climate variable and global-mean temperature. –Gives estimated change in climate per degree change in global-mean temperature ΔT, for a given GCM. –Patterns obtained for 21 GCMs from IPCC AR4. Global-mean temperature change from MAGICC for each emissions scenario applied to ClimGen. ClimGen downscales to 0.5x0.5 degree resolution for each GCM pattern. Monthly means downscaled to daily temperature data using Mac- PDM.09 (Gosling and Arnell, 2010). Applied delta method (mean future – mean present) + observations. Gosling SN, Arnell NW (2010) Simulating current global river runoff with a global hydrological model: model revisions, validation and sensitivity analysis. Hydrological Processes, in press.

7. Business as usual impacts

8. A1B impact & inter-study comparisons Mortality Attributable to CC McMichael et al. (2003) Sydney 2050s = 149% inc. (ECHAM4 Hi scenario);125 - 240%. Dessai (2003) Lisbon 2080s = 59.5 - 173.1 (2xCO2 with 1 GCM & 1 RCM, 30% inc. in pop); 17-56. Donaldson et al. (2001) UK 2080s = 350% inc. (Med-Hi scenario); 280 - 350%. Impacts broadly agree with previous assessments.

9. Avoided impacts

10. Absolute avoided impacts (2016-5-L) GCM uncertainty Time Magnitude of avoided impact varies considerably with GCM. –GCM uncertainty can be greater than difference between Δtime

11. Relative avoided impacts (2016-5-L) GCMs in agreement upon magnitude of relative avoided impacts. Magnitude of avoided impact increases with time. Mitigation reduces, but does eliminate impacts of climate change.

12. Comparing policies (ensemble mean) Benefits in 2030s are minor. Emissions- peaking year is a greater driver of avoided impact than emissions reduction rate. Up to 70% of A1B impacts could be avoided by 2080s.

13. Comparing policies (2080s distributions) Width of distribution is lower with 2016- peaking policies than 2030- peaking policies. Little differences across same emissions- reductions policies.

14. Comparing policies (policy vs. A1B) Width of distribution is lower with time into the future. Approximately, policy delays 2050 BaU impacts by 30 years.

15. Caveats, limitations and conclusions

16. Caveats and limitations Limitations of health models. –No adaptation. –No demographic changes. Pattern-scaling assumes that the relationship between global temperature change and local climate response is linear and invariant. Pattern-scaling not validated for emissions reductions. All 21 GCMs considered equally credible. Dont consider relative probabilities of achieving policy scenarios. Delta method means frequency of extremes (e.g. heat waves) is same under CC as in present.

17. Conclusions Impacts under A1B are consistent with previous studies. Only one other study has considered potential benefits of mitigation policy for temperature-related mortality (Hayashi et al. 2010). Magnitude of benefits increase towards end of century. GCM uncertainty means absolute avoided impacts are considerably different across GCMs. Year of peak emissions is a greater driver of avoided impact than rate of emission reduction. Up to ~70% of impacts could be avoided, but not 100%. Hayashi A et al. (2010) Evaluation of global warming impacts for different levels of stabilisation as a step toward determination of the long-term stabilisation target. Climatic Change 98: 87-112.

18. Thank you for your time