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ESTIMATING RISK PROBABILITIES CENTRE FOR STRATEGIC ECONOMIC STUDIES BUSINESS AND LAW Roger Jones February 26 2009 WWW.VU.EDU.AU 1 Structure of talk • • • • The problem with prediction Estimating risk probabilities Hedging adaptation and mitigation How much climate change do we need to adapt to by when? 2 Scales of approach Top-down approach Global World development Global greenhouse gases Global climate models Regionalisation Impacts Vulnerability (social) Climate adaptation policy Vulnerability (physical) Local Adaptive capacity Indicators based on: Economic resources Technology Infrastructure Information & skills Institutions Equity Bottom-up approach Past Dessai and Hulme 2005 Present Future 3 Characterising uncertainty in AR4 5 Probability (%) 4 3 2 Likely 1 Very Likely Virtually Certain 0 -1 0 1 2 3 4 5 6 7 8 9 10 Global Mean Temperature Change (°C) 4 The likelihood of prediction 5 Virtually Certain Highly Likely Likely 4 Probability (%) As Likely As Not Unlikely Highly Unlikely 3 Exceptionally unlikely 2 1 0 0 1 2 3 4 5 6 7 8 9 10 Global Mean Temperature Change (°C) 5 The likelihood of risk . 6 Global mean warming (°C) 5 Remote chance 4 Highly unlikely Unlikely 3 As likely as not Likely Highly likely 2 Virtually certain 1 0 1990 2010 2030 2050 2070 2090 Year 6 Framing climate change risk Low probability, extreme outcomes Least likely Moderately likely Considerable damage to most systems Increased damage to many systems, fewer benefits Highly likely Almost certain Damage to the most sensitive, many benefits Happening now Vulnerable to current climate Probability Consequence Core benefits of adaptation and mitigation Probability – the likelihood of reaching or exceeding a given level of global warming Consequence – the effect of reaching or exceeding a given level of global warming Risk = Probability × Consequence 7 100 100 75 cm 50 cm 25 cm 80 75 cm Sea Level Rise (cm) Sea Level Rise (cm) 80 60 50 cm 40 25 cm 20 75 cm 60 50 cm 40 0 0 0 100 0 Probability (%) 75 cm 60 50 cm 40 25 cm 20 0 80 75 cm 60 50 cm 40 25 cm 20 0 0 5 10 Probability (%) 100 80 75 cm 60 50 cm 40 25 cm 20 0 0 100 Probability (%) Sea Level Rise (cm) 80 100 Probability (%) 100 Sea Level Rise (cm) 100 Sea Level Rise (cm) Sea Level Rise (cm) 100 25 cm 20 80 75 cm 60 50 cm 40 25 cm 20 0 0 5 10 Probability (%) 0 100 Probability (%) 8 The new global growth path • Global growth has accelerated in the past decade, driven by the developing countries, especially China and India • This growth is energy and coal intensive, and likely to continue • Realistic projections of energy use and CO2 emissions to 2030 are above the SRES marker scenarios, including A1FI 9 Implications for GHG emissions and atmospheric concentrations • The implications this new growth path are explored by: • developing a reference case projections of CO2 emissions from fuel combustion to 2030 • assuming that other emissions grow in a similar manner • developing policy emissions paths (minimum emissions paths) • explore CO2-e concentrations and temperatures in a simple climate model • Minimum emissions paths (MEPs) from 2010 to 2030 were explored in Sheehan et al. GEC 2007 • The 2030 MEP resembles the SRES A1B “on steroids” • Current growth to 2100 under reference conditions resembles SRES A1FI “on steroids” 10 Minimum emissions paths 2010–2030 CO2 emissions (Gt/yr C) a) 25 20 15 10 5 0 1990 2010 2030 2050 2070 2090 Year MEP2030 MEP 2025 MEP 2015 MEP 2010 MEP 2020 11 Likelihood of exceedance – range of reference and policy scenarios Global mean warming (°C) . 7 6 5 4 Remote chance Highly unlikely Unlikely 3 As likely as not Likely Highly likely 2 Virtually certain IPCC Low 1 IPCC High 0 1990 2010 2030 2050 2070 2090 Year 12 Framing adaptation • Goal setting • Where do we want to go? (aspirational goals) • How do we want to get there? • What are the risks? • What are the barriers? (e.g., lack of adaptive capacity) 13 100 90 80 70 60 50 40 30 20 10 0 en era tio Fo na res le qu ts ity uc ce ss Br ion idg ed es ign life Int erg An Elenual cti cro on ps cy cle W s/p ho rof le it & Pla farm los pla nt s bre n nin To g uri edin Tr sm g ee de cyc c les Ge rop vel o ne s pm rat en ion Ne ts al w su irri cc ga e tio n p ssio Tr an n roj sp e cts ort inf ras tru Ma ctu jor re urb Pr ote an i nf cte d a rast rea ruc tur s e La rge da ms Planning horizons 14 90 70 50 40 60 Weak initial response Strong late adjustment Strong initial response Some ongoing adjustment 30 20 10 0 Up-front response 80 Operational pathways As pir ati on al g oa l Operational pathways and aspirational goals 15 How much climate change needs to be adapted to by when Types of climate information required: • Climate variability (daily to decadal) • Ongoing rate of change • Past and near term commitments to climate change • Climate sensitivity • Regional climate change projections • Greenhouse gas emission policies (Mitigation) 16 Reference and policy scenarios for hedging adaptation and mitigation Mean Global Warming (°C) 5 Emission scenarios 4 3 2 Climate sensitivity Past and nearterm commitments Warming rate 1 0 1990 2010 2030 2050 2070 2090 Year Reference Policy Observations 17 Hedging adaptation and mitigation – reference and policy scenarios Mean Global Warming (°C) 5 Mitigation benefits 4 MIT-AD 3 AD-MIT Adaptation benefits 2 1 0 1990 2010 2030 2050 2070 2090 Year Reference Policy Observations 18 Hedging strategies between reference and policy scenarios with high policy uncertainty Mean Global Warming (°C) 7 6 5 4 3 2 1 0 1990 2010 2030 2050 2070 2090 Year Adaptation Ad-Mit Mit-Ad Mitigation A1FI aug–MEP2030 MEP2010–2030 19 Whole of climate approach • Links current climate and adaptive responses with future possibilities • Ongoing variability and extremes are the main drivers of current adaptation to climate, links between variability and longer-term change give these experiences a future dimension. • Long-term fluctuations in natural climate variability may be affecting some regions • Not all change is anthropogenic 20 Whole of climate approach An understanding of the dynamics of climate variability is needed to: • Diagnose fluctuations, shifts or trends as temporary, persistent or permanent. • If the dynamics of the change are not understood, statistical or other methods can be used to explore “what if” questions based on understandings of climate model and historical behaviour. 21 Regional example of climate changes – Melbourne, Australia The Melbourne Region has experienced many step changes rather than trends For a 1 by 1 degree area over greater Melbourne: Annual rainfall :statistically significant downward shift in 1996 in rainfall from just over 900 mm to 750 mm, -17%. Max temp: Statistically significant upward step change 1998 of 0.6°C. About half of this can be explained by the decrease in rainfall (due to a decrease in cloud cover). About half (0.3°C) is added warming Analysis of annual frequency of days >35°C and >40°C not significant All days under 30°C have become significantly warmer During summer (DJF) almost 1°C warmer 22 Regional example of impacts – southeastern Australia Streamflow: 60% up to 80% across western Victoria, 25–60% in eastern Victoria. Extreme fire weather index (temperature, lower humidity and higher winds): 100 on Black Friday in 1939, 115 on Ash Wednesday in 1983 150–200 on Black Saturday, February 2000 Viticulture: harvest 4–6 weeks earlier, crop losses, smoke damage Horticulture, dairy: under stress in irrigation regions Snow: reduced snow cover Human health (heat stress): hundreds(?) dead from heat stress, 220+ from fires, event trauma, drought stress in rural regions Environment: woodland birds decline, tree die-back accelerated, tree planting failures, icon wetlands critical, frequent hot fires 23 Exploring decision analysis Post 1997 rain short term CV + climate change Post 1997 rain long term CV + climate change Post 1997 rain – climate change p1 p2 p3 Recovery expected soon, long-term gradual reduction? Partial recovery expected in decades Serious long-term deficits Benefit if correct Penalty if incorrect Benefit if correct Penalty if incorrect Benefit if correct Penalty if incorrect 24 Choosing climate information • Understand risk and risk management options – how is climate information used in decision-making for specific risks? • What is my planning horizon and operational pathway? E.g., up front, incremental, wait and see • What’s my climate baseline? • Choose global scenarios based on sensitivity, risk tolerance and hedging strategies – choose scenarios that are 50% likely to be exceeded to • Determine local scaling and down-scaling needs for key climate variables • Undertake assessment e.g., modelling, expert analysis 25 Thresholds and key vulnerabilities • Determine critical limits. E.g., sea level rise, storm severity or surge protection, flooding, public health limits, water quality and supply • Diagnose specific climate conditions leading to critical limits • Establish plausible combinations of change in mean and variability, natural and anthropogenic, leading to critical thresholds • Determine likelihood that such conditions may be exceeded within planning horizons. For cities, many of these horizons will be long-term 26 Caveats and working principles • All probabilities are subjective – test different plausible assumptions to test whether outcomes (decisions on risk management) are sensitive to assumptions • What information is required to make a specific decision? The less important climate is compared to other risk factors, the less precision will be required • A 1°C warming in 2030 (from 1990) is as likely as not. From 2040+, considerable hedging between adaptation and mitigation is required. Without solid emissions policy, hedging for >3°C warming by 2100 needs to be contemplated. • Sea level rise estimates need to consider outcomes not quantified in the AR4, including Greenland and perhaps West Antarctica 27 CONTACT DETAILS ROGER N JONES BUSINESS AND LAW CENTRE FOR STRATEGIC ECONOMIC STUDIES PHONE +61 3 9919 1992 FAX +61 3 9919 1350 EMAIL [email protected] WWW.VU.EDU.AU 28