++++++++++++++ ++++++++++++++ probabilistic risk analysis of u.s. climate change impacts...
Post on 20-Dec-2015
214 views
TRANSCRIPT
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Probabilistic Risk Analysis of U.S. Climate Change ImpactsImplications for Climate Policy
Benjamin L. PrestonPew Center on Global Climate ChangeArlington, Virginia USA
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Climate Change & Risk• Policy actions to address global climate change
fundamentally represent risk management strategies– Mitigation – minimize future climate change– Adaptation – minimize damages from climate change
• Effective risk management is aided by robust analysis of risk and its uncertainties
• The risk of adverse impacts from climate change is a function of two factors (Jones, 2001):– The consequences of an adverse event (hazard)– The probability that an adverse event will occur
(exposure)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Climate Change & Risk• Estimating the probability of an adverse
event/effect:1) Identify thresholds for adverse effects
• Examples: O’Neill & Oppenheimer (2002); WBGU (2003)– Examined warming thresholds for impacts of concern
» Sustainability of coral reef ecosystems» Collapse of Atlantic thermohaline circulation» Collapse of the West Antarctic Ice Sheet
2) Assess probability of future climate change• Examples: Wigley & Raper (2001); Webster et al.
(2003)– Developed probability distributions for future global
warming based upon uncertainties in emissions and climate sensitivity
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Risk Analysis Framework
Based on Jones (2001)
IdentifyManagement
Goals
AssessClimate
Uncertainty
Assess Sectoral
SensitivityIdentify Sectoral
Thresholds
Conduct Risk
Analysis
Identify Assessment
Criteria
Stakeholders
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Assess management
optionsMitigation Adaptation
Increase resilience
Constrain future warming
IdentifyManagement
Goals
AssessClimate
Uncertainty
Assess Sectoral
SensitivityIdentify Sectoral
Thresholds
Conduct Risk
Analysis
Identify Assessment
Criteria
Stakeholders
Based on Jones (2001)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sectors/Impacts Considered
• Agriculture (10)
• Aquatic Biodiversity (24)
• Coastal Communities (6)
• Energy (6)
• Extreme Events (7)
• Forestry (8)
• GDP (7)
• Health (13)
•Recreation Welfare (3)•Terrestrial Biodiversity (11)•Thermohaline Circulation (12)•Transportation (8)•Water Resources (9)•WAIS/GIS (10)•Wildfires (5)•Winter Recreation (8)
~(x) indicates number of studies available~multiple thresholds available for some studies
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sectoral Sensitivity Analysis• For each sector, a sensitivity analysis
was conducted– A threshold was calculated for each study
available for a particular sector/impact, using common criteria
– These estimates were integrated to yield a linear or log-linear probability distribution for sectoral sensitivity
– Probability distributions were subsequently used to estimate sectoral thresholds • Threshold = 50th percentile (median) for sectoral
sensitivity
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Assessment Criteria• Market impacts – net decline in GDP, economic
welfare, or increase in dollar damages relative to present or future baselines in the absence of climate change– Estimated by averaging between scenarios or utilizing the
highest scenario that did not yield adverse effects (e.g., forestry and agriculture)
• Biophysical Impacts – 10-20% change in biophysical parameter (e.g., productivity, area burned, water levels, etc.)– Scenarios yielding Impacts of magnitudes beyond this range
were normalized to a 15% effect with a proportional reduction in the corresponding temperature change
• Expert Judgment – reported sectoral thresholds based upon prior analyses, subjective/expert judgment
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Assessment Criteria
Market impacts threshol
d
10%
20%
Biophysical impacts
threshold
10%
20%
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: AgricultureStudy Threshold (oC)
Adams et al. (1999b) 1.9
Adams et al. (1998) 1.9
Adams et al. (1999a) 4.0
Adams et al. (1998) 5.0
Adams et al. (1999b) 5.0
NAST (2000) 5.0
Mendelsohn (2001) 5.0
Mendelsohn and Neumann (1999) 5.0
Jorgenson et al. (2004) 5.3
Adams et al. (2003); Mearns et al (2003)
5.6
Adams et al. (1995) 6.1
Mendelsohn et al. (1996) 8.4+7% precipitation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: Agriculture
Median Sensitivity=5.4oC
Probability distribution for agriculture
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: Aquatic/Marine Biodiversity
Study Threshold (oC) Multiple Studies (range shifts) 0.6
This study (sea-level rise 1mm/year) 0.7
Gregg et al (2003) 0.7
Holbrook et al (1997) 1.0
Sagarin et al (1999) 1.0
Kennedy and Mihursky 1.0
Peterson and Schwing (2003) 1.0
Stemberger et al (1996) 1.0
Petersen and Kitchell 1.0
Multiple Studies (cold-water habitat) 1.2
This study (sea-level rise 2 mm/year) 1.3
Gregg et al (2003) 1.4
Multiple Studies (coral reefs) 1.5
Weinberg et al. (2002) 2.0
Multiple Studies (cool-water habitat) 2.3
Pierce (2004) 2.5
Multiple Studies (warm-water habitat) 3.7
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: Aquatic/Marine Biodiversity
Median Sensitivity=1.4oC
Probability distribution for aquatic biodiversity
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: GDP
Study Threshold (oC)
Jorgenson (2004) 0.0
Nordhaus and Boyer (1999) 1.5
Smith (2004) 3.0
Mendelsohn (2001) 3.8
Mendelsohn and Neumann (1999) 5.0
Jorgenson (2004) 5.3
Darwin et al. (1995) 5.3
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: GDP
Median Sensitivity=3.6oC
Probability distribution for GDP
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: Water ResourcesStudy Threshold
(oC) Joregenson et al. (2004) 0.0
Mendelsohn and Neumann (1999) 0.0
Hurd et al. (1999) 0.0
Leung et al. (2004) 0.4
Fredrick and Schwarz 0.6
Mendelsohn (2001) 1.5
Barnett et al. (2004) 1.5
Leung et al. (2004) 1.8
Leung et al. (2004) 1.8
Mendelsohn and Neumann (1999) 2.0
Chao (1999) 2.2
Joregenson et al. (2004) 2.4
Lettenmaier et al. (1999) 2.4
Mendelsohn (2001) 2.5
Hurd et al. (1999) 3.8
NAST - Water (2000) 4.0
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: Water Resources
Median Sensitivity=2.2oC
Probability distribution for water resources
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: WAIS/GISStudy Threshold (oC)
(Global)Threshold (oC)
(US)Hansen (2004) 1.0 1.2
O‘Neill and Oppenheimer (2002) 2.0 2.4
Feichert et al. (2003) 2.2 2.6
Gregory et al. (2004) 2.7 3.2
Oppenheimer (1998) 3.0 3.6
Huybrecths and De Wolde (1999);
Huybrechts et al. (1991)
3.0 3.6
Greve (2000) 3.0 3.6
IPCC (2001) 3.0 3.6
IPCC (2001) 4.0 4.8
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Example: WAIS/GIS
Median Sensitivity=3.3oC
Probability distribution for WAIS/GIS
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sectoral ThresholdsSector 50th (oC) 5th-95th (oC)
Agriculture 5.4 2.7-8.0
Aquatic Biodiversity 1.4 0.7-2.7
Coastal Communities 0.6 0.1-1.1
Energy 1.3 0.4-4.7
Extreme Events 2.6 1.5-4.5
Forestry 5.2 5.0-5.4
GDP 3.6 0.7-6.5
Health 1.5 1.3-1.8
Recreation Welfare 5.0 5.0
Terrestrial Biodiversity 1.7 0.6-4.5
Thermohaline Circulation 3.5 1.4-5.6
Transportation 2.2 0.8-3.6
WAIS/GIS 3.3 1.8-5.1
Water Resources 2.2 0.2-4.2
Wildfires 3.1 1.3-4.9
Winter Recreation 2.0 0.8-3.1
+
+
+
+
+
+
+
+
+
+
+
+
+
+
“Dangerous Interference”
• Sectoral risks can be analyzed to estimate net U.S. climate sensitivity
• These aggregate estimates may also be utilized as national climate thresholds, for the purposes of comparison or policy guidance
– 50th percentile would provide an average level of protection across U.S. sectors/impacts
– 10th percentile provides protection for the majority of sectors
• But, are such estimates of any utility?
+
+
+
+
+
+
+
+
+
+
+
+
+
+
U.S. Sensitivity & Net Thresholds
Median=2.9o
C
10th=0.9oC
Estimation of net U.S. climate sensitivity
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Climate Uncertainty
• Identifying the likelihood of exceeding sectoral/impact thresholds requires information on the probabilistic uncertainty of future climate change
• Uncertainty in climate change projections originates from two primary sources:1) Climate sensitivity
• Represented by different climate models
2) Future emissions • Represented by different emissions scenarios
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Climate Uncertainty• A probabilistic distribution for future
coterminus U.S. temperature change was developed via ensemble climate modeling
– MAGICC/SCENGEN climate model (v.4.1)
• Simulated 2050 & 2100 temperature change for 17 different climate models
• Simulations driven by 5 different emissions scenarios (IPCC SRES: B1, B2, A1B, A2, A1F1)
– Results were used to model a cumulative probability distribution for future U.S. temperature change
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Climate UncertaintyProbability distributions for 2050 & 2100
(U.S.)
2100 (1.8-6.3oC)Median=3.6oC
2050 (1.7-3.1oC)Median=2.2oC
IPCC 2100 (1.4-5.6oC)
IPCC 2050 (0.8-2.5oC)
USNA 2050 (0.8-2.5oC)
USNA 2100 (1.4-5.6oC)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Risk Analysis
• Sectoral thresholds were subsequently compared with U.S. probability distributions for future temperature change – Pass/Fail Monte Carlo simulation (n=1000)
• Resulted in a sector-specific probability of exceeding thresholds
• Results for individual sectors were subsequently integrated to evaluate net U.S. risk
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Risk AnalysisSector-specific risk estimates (median
thresholds)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Risk AnalysisNet U.S. risk in 2050 & 2100
Net National Risk 2.9oC Threshold
2050=7.5%
2100=72.7%
50th
10th
0.9oC Threshold
2050=100%
2100=100%
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uncertainty Analysis: Thresholds
• Median sectoral sensitivity overlooks variation in the literature for individual sectors
– Sources of variation
• Differential assessment of adaptation
• Assessment methodology
• Geographic/temporal scale
• Climate scenarios/models
• Accounting for this variability increases the uncertainty associated with risk estimates
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uncertainty Analysis: ThresholdsRisk in 2050: 5th-95th percentile sectoral
thresholds
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uncertainty Analysis: ThresholdsRisk in 2100: 5th-95th percentile sectoral thresholds
+
+
+
+
+
+
+
+
+
+
+
+
+
+
U.S. Risk Assessment• The current risk analysis indicates that considerable
variation (e.g., 0-100%) exists in risk to individual sectors by 2050, while estimates of net U.S. risk are dependent upon how thresholds are defined (e.g., 10th vs. 50th percentile)
• By 2100, risk is quite high on both a sectoral and net national basis
• These estimates generally have poor incorporation of adaptation options, which may cause an exaggeration of risk (e.g., health)
• Threshold uncertainty is the primary contributor to overall uncertainty in the risk analysis
• Modifications to assessment criteria, better accounting for adaptation, and reduced threshold uncertainty would have a significant influence on risk for a number of sectors/impacts
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Risk Reduction• CO2 mitigation represents a risk management
strategy to reduce the likelihood of adverse climate change impacts
• Use of WRE stabilization scenarios in MAGICC/SCENGEN results in revised probability distributions for U.S. warming:
Target 2050 Temperature (oC)
(95% CL)
2100 Temperature (oC)
(95% CL)
None 1.7-3.1 1.8-6.3
750 1.7-2.8 1.7-4.2
650 1.7-2.8 1.8-3.9
550 1.7-2.8 1.6-3.4
450 1.6-2.6 1.8-2.7
350 1.6-2.1 1.2-1.9
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Probability Distributions: Stabilization
2050 warming: Stabilization scenarios
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Probability Distributions: Stabilization
2100 warming: Stabilization scenarios
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Risk Reduction from StabilizationEffects of CO2 stabilization on sectoral risk
(2050)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Risk Reduction from StabilizationEffects of CO2 stabilization on sectoral risk
(2100)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Stabilization and U.S. Net Risk
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Policy Implications• CO2 stabilization goals have differential effects
on risk, depending upon sectoral sensitivity– Sensitive sectors are likely to experience adverse
effects regardless of the stabilization target– For less sensitive sectors, risk is reduced
significantly, even for higher stabilization targets
• However, even if stabilization doesn’t reduce risk itself, it can still reduce the magnitude of the adverse effect
• Additional risk reduction strategies are necessary (e.g., adaptation), and may provide additional protection for some sectors at relatively low cost
+
+
+
+
+
+
+
+
+
+
+
+
+
+
“Dangerous Interference” Revisited
Stabilization Target
Median U.S. Warming
(oC)(2100)
No mitigation 3.6
750 ppmv 3.0
650 ppmv 2.7
550 ppmv 2.4
450 ppmv 2.1
350 ppmv 1.4
U.S. Net Sensitivity (Percentile
)
Implied U.S. Temperature Threshold
(oC)50th 2.9
10th 0.9
•Thresholds can be used to identify optimal mitigation targets . . .
?. . .but do they really help?
+
+
+
+
+
+
+
+
+
+
+
+
+
+
“Dangerous Interference” Revisited
• Risk analysis can be used to pursue UNFCCC Article II definitions, and identify associated stabilization targets
• However, aggregate estimates of dangerous climate change either neglect risk to sectors of concern or necessitate stringent stabilization targets that will be difficult to achieve:
– 50th percentile – doesn’t protect more sensitive sectors
– 10th percentile – invariably leads to stringent mitigation targets
• Mitigation and adaptation decisions should be based upon consideration of management goals for individual sectors and regions and the most robust policies (mitigation and adaptation) for achieving those goals
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Conclusions• Unmitigated climate change has a high probability of
inducing adverse effects in a broad range of U.S. sectors
• CO2 stabilization targets can provide significant risk reduction for some sectors, but for others, providing protection necessitates stabilization levels that are likely unrealistic and thus adaptation strategies are critical
• Risk analysis can provide quantitative estimates of relative and absolute risk across sectors and regions, but aggregate estimates for “dangerous” climate change should be considered cautiously
• Substantial progress on impact assessment is required to reduce threshold uncertainty and enhance capacity for risk-based approaches to climate management