Climate Scenarios in Vulnerability, Impact and Adaptation Assessments: an overview AIACC Scenarios Training Course Norw

1. Climate Scenarios in Vulnerability, Impact and Adaptation Assessments: an overview AIACC Scenarios Training Course Norwich, 16-25 April 2002 Dr Mike Hulme

2. Norwich What we might ideally like …. … daily weather, for a place, for now and for a future year

3. Why does creating climate scenarios give us so many problems? Problem 1. Models are not accurate …. … so we ‘cannot’ use data from climate models directly in environmental or social simulation models

4. Why does creating climate scenarios give us so many problems? Problem 2. Different climate models give different results … … so we have difficulty knowing which climate model(s) to use

5. Why does creating climate scenarios give us so many problems? Problem 3. It is expensive to run many (global/regional) climate model experiments for many future emissions …. .… so we often have to make choices about which emissions scenarios from which we build our climate scenarios

6. Why does creating climate scenarios give us so many problems? Problem 4. Climate models give us results at the ‘wrong’ spatial scale … … so we have to develop and apply one or more downscaling methods.

7. Our problems would be much easier if …. Climate models were fully accurate Different climate models gave the same results One could run a GCM experiment over 200 simulated years in one day on a PC Climate models had a resolution of 1km But they don’t!

8. Be clear about what you need ….. • · How many scenarios do you want? Which uncertainties are you going to explore? • · What non-climate information do you need in your scenario(s)? • · Do you need local data for case studies/sites, or national/regional coverage? • · What spatial resolution do you really need – 300k, 100k, 50k, 10k, 1k? Can you justify this choice? • · Do you need changes in average climate, or in variability? • · Do you need changes in daily weather, or just monthly totals? • · What climate variables are essential for your study?

9. A framework for conducting integrated assessment of climate change for policy applications NB. this has a UK interpretation

10. Historical climate data ….… necessary as a baseline and also to explore historical/current vulnerability

11. The four IPCCSRES storylines … a major international effort to construct an overarching framework for thinking about the future with regard to emissions of greenhouse gases ….. global and continental rather than national and local.

12. Downscale SRES socio-economic scenarios

13. … or create socio-economic scenarios bottom-up for local communities or regions

14. Advantages – easy to construct and apply, allows sensitivity of sectors/models to be explored Incremental Scenarios for Sensitivity Analysis Disadvantages – arbitrary (and unrealistic) changes, not related to wider scenario frameworks

15. Scenarios from Global Climate Model Experiments Advantages – easily accessible, numerous model runs, global in scale, numerous variables Disadvantages – coarse resolution (300km), daily extremes poorly represented

16. WINTER PRECIPITATION FROM 9 CLIMATE MODELS %

17. Overcoming problem 2 (model differences) - vintage - validation - credibility - resolution - accessibility - politics!

18. Problem 4: Spatial scale – downscaling options

19. Advantages – higher resolution (50km), local geography well represented, daily weather extremes more realistic Scenarios from Regional Climate Model Experiments Disadvantages – few runs available, can be time-consuming to run, not good for representing uncertainties in risk assessment

20. Spatial scale may still be a ‘problem’ (problem 4)

21. UKCIP 1998: GLOBAL MODEL UKCIP 2002: REGIONAL MODEL 50km grid 300km grid

22. The model will still not be ‘accurate’ (problem 1)

23. WINTER PRECIPITATION OVER BRITAIN

24. Overcoming problem 1 (accuracy) … • Use raw GCM/RCM outputs (rarely done) • Add model-derived changes to an observed baseline • mean changes (common) • inter-annual changes (less common) • Calibrate and perturb a weather generator – Rob Wilby • Applying an empirical downscaling method – Bruce Hewitson

25. Overcoming problem 4 (scale) (may have to be tackled whether using GCMs or RCMs) … - Simple interpolation of model changes (300k, 50k, 14k) onto an observed climatology (1k, timeseries) - Weather generators – to places or catchments or grids - Statistical downscaling

26. Simple interpolation – combining observed data with modelled changes

27. Advantages – site or locality specific scenarios, long and multiple daily weather sequences produced Scenarios from Weather Generators Disadvantages – requires a lot of historic data to calibrate, based on empirical relationships which may change, climate model data not always available

28. Representing uncertainties (especially emissions uncertainties – problem 3) remains an issue

29. Advantages – easily to explore uncertainties, multiple & integrated scenarios, accessible Scenarios from Climate Scenario Generators Disadvantages – coarse resolution (300km), no daily data, not readily updated

30. MAGICC/SCENGEN framework

31. Advantages – makes (some) uncertainties explicit, good for risk assessment, can be applied at different scales Probabilistic Scenarios for Risk Assessment Disadvantages – not yet a well developed methodology, requires a lot of model data to develop, expert assumptions still needed

32. UKCIP02 scenario strategy

33. How will you link climate and non-climate scenarios? Decided to link SRES futures with UK climate scenarios We chose A1FI, B2, A2 and A1 1-to-1 mapping of climate and non-climate scenarios

34. What non-climate information is needed in your project? Non-climatic – socio-economic – indicators for the UK have been produced by the UK Climate Impacts Programme for each of the four SRES storylines

35. What types of uncertainties are critical to your project? Both emissions and modelling uncertainties are important Our strategy was to explicitly quantify the emissions uncertainty (4 different emissions spanning the IPCC range), but only to provide general guidance about the relative importance of modelling uncertainty

36. What climatic variables are required for I, A & V assessments in your project? A wide range - T, P, SL, CO2, RH, snow, cloud, etc., surface rather than upper air, however We aimed to produce generic climate scenarios for many different applications (UKCIP, government policy, academic research, public awareness, etc.)

37. At what spatial and temporal scales are these variables required? We decided that we must have information at 50km resolution We needed to analyse both monthly and daily data

38. What baseline climate data are you planning to use? A 5km gridded, dataset for UK 26 surface climate variables Monthly series for 1961-2000

39. Which project(s) in your region you envisage you will be able to collaborate with to develop climate scenarios? We worked with the government (Ministry of Environment), climate modellers (Hadley Centre) and users (through the UK Climate Impacts Programme)

43. Designing climate scenarios is largely an exercise in handling uncertainties … Source: Hadley Centre

44. Purpose(s) of (climate) scenarios ….. To make predictions of the future – [wrong] To provide data for impact/adaptation/assessment studies To act as an awareness-raising device To aid strategic planning and/or policy formation To scope the range of plausible futures To structure our knowledge (or ignorance) of the future To explore the implications of decisions To function as learning-machines, bridging analyses and participation

45. Temperature and precipitation: effects of natural variability