Transcript Future water scarcity with climate change

A case study of avoiding the heatrelated mortality impacts of climate
change under mitigation scenarios
Simon N. Gosling1 and Jason A. Lowe2
1
Walker Institute for Climate System Research, University of Reading
2 Met Office Hadley Centre
Outline
• Methods
– The health models.
– Climate change scenarios.
• Business as usual impacts.
• Avoided impacts.
• Limitations and conclusions.
Methods
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.
Climate change scenarios (1)
Scenario
Pathway
to peak
Date of peak
Rate of decline in
emissions
Emissions
floor
A1B-2016-2-H
A1B
2016
2% per year
High
A1B-2016-4-L
A1B
2016
4% per year
Low
A1B-2016-5-L
A1B
2016
5% per year
Low
A1B-2030-2-H
A1B
2030
2% per year
High
A1B-2030-5-L
A1B
2030
5% per year
Low
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 MacPDM.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.
Business as usual impacts
A1B impact & inter-study comparisons
Mortality
Attributable
to CC
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%.
McMichael et al. (2003) Sydney 2050s
= 149% inc. (ECHAM4 Hi
scenario);125 - 240%.
• Impacts broadly agree with previous assessments.
Avoided impacts
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
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.
Comparing policies (ensemble mean)
• Benefits in 2030s
are minor.
• Emissionspeaking year is a
greater driver of
avoided impact
than emissions
reduction rate.
• Up to 70% of A1B
impacts could be
avoided by
2080s.
Comparing policies (2080s
distributions)
• Width of
distribution is
lower with 2016peaking policies
than 2030peaking
policies.
• Little differences
across same
emissionsreductions
policies.
Comparing policies (policy vs. A1B)
• Width of
distribution is
lower with time
into the future.
• Approximately,
policy delays
2050 BaU
impacts by 30
years.
Caveats, limitations and
conclusions
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.
• Don’t consider relative probabilities of achieving policy
scenarios.
• Delta method means frequency of extremes (e.g. heat
waves) is same under CC as in present.
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.
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