Developing Climate Scenarios for V&A Assessments

1. Developing Climate Scenarios for V&A Assessments Consultative Group of Experts on National Communications from Parties not Included in Annex I to the UNFCCC (CGE) Hands-on Training Workshop on Vulnerability and Adaptation for Asia and Pacific Countries 20~24 March 2006 Jakarta, Indonesia Xianfu Lu National Communications Support Programme (NCSP), UNDP-UNEP-GEF Xianfu.lu@undp.org

2. Introduction: what are climate scenarios and why do we need them? Review of climate scenario approaches: how to develop climate scenarios? Treatment of uncertainties: What scenarios tell us and what they do not? Guidance documents, methods, tools and data sources for developing climate scenarios In the next hour or so…

3. A scenario is: “a coherent, internally consistent and plausible description of a possible future state of the world” (Parry and Carter, 1998) Not a forecast or a prediction, but alternative views of what the world could look like in the future Hence, a climate scenario is: “…. a plausible future climate that has been constructed for explicit use in investigating the potential consequences of anthropogenic climate change”. (IPCC, 2001) What are climate scenarios?

4. What are climate scenarios? • Climate scenarios versus climate projections • Climate scenarios versus climate change scenarios

5. Why do we need climate scenarios? • To provide data for impact/adaptation assessment studies; • Any attempt to evaluate future climate change impacts, adaptation and vulnerability requires some assumptions about how climate would change in the future; • However, there are formidable uncertainties associated with the socio-economic drivers and GHG & aerosol emissions, and the responses of global and regional climate to the radiative forcing of GHG & aerosol emissions; • Therefore, precise forecasts of future climate trends are not possible. An alternative approach is to construct Climate Scenarios.

8. Why do we need climate scenarios? • To provide data for impact/adaptation assessment studies; • To act as an awareness-raising device;

12. Decarbonised and adaptive world

13. Why do we need climate scenarios? • To provide data for impact/adaptation assessment studies; • To act as an awareness-raising device; • To aid in strategic planning and/or policy formation; • To structure our knowledge (or ignorance) of the future; • To explore the implications of decisions

14. Review of climate scenario approaches Three general types of climate scenarios representing three approaches to scenario development: • Synthetic/incremental scenarios; • Analogue scenarios; and • Model-based scenarios

15. 4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies Review of climate scenario approaches: Incrementalscenarios Advantages – easy to construct and apply, allows sensitivity of sectors/models to be explored Disadvantages – arbitrary (and unrealistic) changes, not related to wider scenario frameworks

16. 4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies Review of climate scenario approaches: Spatial analoguescenarios Analogue scenarios for UKCIP98 2050s Medium-high scenario [source: David Rogers, Oxford]

17. 4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies Review of climate scenario approaches: Analoguescenarios Palmer Drought Severity Index (PSDI) for the US Corn Belt, 1030~80. [Source: Roserzweig et al., 1993]

18. 4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies Review of climate scenario approaches: Analoguescenarios • Valuable in testing and validating impact models; • But, it is not usually recommended that they be adopted to represent the future climate in quantitative impact assessments.

19. 4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies Review of climate scenario approaches: Model-based scenarios • Simple climate models; • Full General Circulation Models (GCMs); • Regionalization Models/techniques

20. Climate models Simulating the response of the global climate system to increasing greenhouse gas concentrations

21. Simple climate model-derived scenarios An example of Simple Climate Model The MAGICC/SCENGEN Climate Scenario Generator[source: Wigley, Raper, Hulme]

23. Simple climate model-derived scenarios • Advantages • Multiple simulations being conducted rapidly; • Enabling an exploration of the climatic effects of alternative scenarios of radiative forcing, climate sensitivity and other parametrization uncertainties. • Disadvantages • Hardly able to represent the non-linearities of some processes that are captured by more complex models.

24. General Climate Models (GCMs) Conceptual structure of a coupled atmosphere-ocean circulation model [Source: Viner and Hulme, 1997]

25. General Climate Models (GCMs) Advantages – easily accessible, numerous model runs, global in scale, numerous variables Disadvantages – coarse resolution (300km), daily extremes poorly represented

26. Regionalization (downscaling) models/techniques • Statistical Downscaling • Regression-based methods; • Synoptic weather typing; • Stochastic weather generator • Dynamic downscaling • Higher resolution GCMs; • Varying-resolution GCMs; • Regional climate models (RCMs)

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

28. Dynamic downscaling (RCMs) Advantages – higher resolution (50km), local geography well represented, daily weather extremes more realistic Disadvantages – few runs available, can be time-consuming to run, not good for representing uncertainties in risk assessment

29. Why do we have such a hard time in using climate model outputs for scenario development? Ideally, we would like to have … … daily weather, for a place, for now and for a future year

30. Why do we have such a hard time in using climate model outputs for scenario development? However, in reality, the following problems often prevent us from achieving this in most cases... • Climate models are NOT accurate; • Different climate models give different results; • It is expensive to run many (global/regional) climate model experiments for many future emissions; • Climate models give us results at the “wrong” spatial scale

31. Climate models are NOT accurate…

32. Climate models are NOT accurate… … So we cannot use model outputs directly in I, A &V assessment. Rather, we derive the climate change scenarios from climate model simulations and then combine it with observed data to create climate scenarios for a future time horizon.

33. Different models give different results…

34. Different models give different results… … So we have difficulty knowing which model(s) to use, and opted for using a range of model(s).

35. Different models give different results… The range of DJF precipitation changes for 2071~2100 simulated by different GCMs under SRES B2 for the region covering Bhutan (25~30N, 85 ~ 95E)

36. Different models give different results…

37. 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. And, techniques such as pattern-scaling have been developed to expand the range of uncertainties we could explore in climate scenarios.

38. It is expensive to run many (global/regional) climate model experiments for many future emissions… (http://www.aiaccproject.org/resources/GCM/ARTICLES/PATTERN_.PDF)

39. Climate models give us results at the “wrong” scales… … So we have to develop and apply one or more downscaling methods.

40. Uncertainties associated with model-derived climate scenarios Scenario development is largely an exercise of handling uncertainty.

41. Representing uncertainties associated with model-derived climate scenarios • Pattern-scaling; • Defining climate signals; • Scenario annotation; • Probabilistic scenarios

42. Patten-scaling

43. Patten-scaling Two fundamental assumptions: • The defined GCM response patterns adequately depict the climate “signal” under anthropogenic forcing; • These response patterns are representative across a wide range of possible anthropogenic forcings.

44. Defining climate signals If the objective is to specify the impacts of the anthropogenic cliamte signal alone: • To maximise the signal and minimise the noise • using long (30-year or more) averaging periods; • using results from multi-member ensemble simulations; • Comparing the responses of single realisations from experiments completed using different models. • To supply impact assessments with cliamte scenarios contraining both elemetns and also companion descriptions of future cliamte that contain only noise, thus allowing impact assessors to generate their own impact signal-to-noise ratios. • defining noise from observed climate data; • defining noise from model-simulated natural climate variability

45. Scenario annotation To document or explicitly treat the uncertainties in climate scenarios: make a list of caveats, along with some assessment as to their implications for the scenario user.

46. Probabilistic scenarios Cumulative probabilities of temperature and precipitation change for each season (DJF, JJA) for South Asia

47. Guidance documents on climate scenario development • Guidelines on the use of scenario data for climate impact and adaptation assessment(http://ipcc-ddc.cru.uea.ac.uk/guidelines/ggm_no1_v1_12-1999.pdf) • Guidelines for use of scenario data developed from regional climate model experiments (http://ipcc-ddc.cru.uea.ac.uk/guidelines/dgm_no1_v1_10-2003.pdf) • Guidelines for use of scenario data developed from statistical downscaling methods (http://ipcc-ddc.cru.uea.ac.uk/guidelines/dgm_no2_v1_09_2004.pdf) • Using a climate scenario generator for vulnerability and adaptation assessment (http://ncsp.undp.org/site_documents/magicc_scengen_workbook1.pdf) • Using SDSM Version 3.1 — A decision support tool for the assessment of regional climate change impacts (http://www.sdsm.org.uk) • Creating high resolution climate scenarios using PRECIS (http://www.undp.org/cc/pdf/publications%20and%20flyers/RCM_draft.pdf)

48. Models & tools for climate scenario development Simple climate models • MAGICC/SCENGEN 4.1(http://www.cgd.ucar.edu/cas/wigley/magicc/index.html) • COSMIC(can be obtained free of charge by registering with Dr. Larry Williams of the Electric Power Research Institute LJWILLIA@epri.com) Downscaling Tools • Statistical Downscaling Model (SDSM)(http://www.sdsm.org.uk) • LARS-WG (http://www.rothamsted.bbsrc.ac.uk/mas-models/larswg.php) • PRECIS (http://www.metoffice.com/research/hadleycentre/models/PRECIS.html)

49. Data sources for climate scenario development • Climatic Research Unit(http://www.cru.uea.ac.uk/cru/data/) • IPCC Data Distribution Centre (http://ipcc-ddc.cru.uea.ac.uk) • Observed climate • Climate scenarios • GCM archive • Program for Climate Model Diagnosis and Intercomparison (http://www-pcmdi.llnl.gov/) • Future climate in world regions – an intercomparison of model-based projections for the new IPCC emissions scenarios(http://ipcc-ddc.cru.uea.ac.uk/sres/scatter_plots/scatterplots_home.html) • National Centre for Environment Predictions (NCEP) re-analysis data (http://www.cdc.noaa.gov/) • The Canadian Climate Impacts Scenarios Group (http://www.cics.uvic.ca/scenarios/index.cgi?Scenarios)

50. Before embarking on the “shopping expedition” for models/tools and data, spending time to clearly define what climate scenarios (variables, temporal and spatial scales, time horizon etc.) are NEEDED for your V&A study; Pay much attention to the quality of your observed data (for case studies, model evaluation, reference climate to perturb); If you can keep things simple, keep them simple; Whenever time and resource permit, try to use a range of approaches/methods, and model outputs for scenario development; Don’t take on an RCM if you’re not in the ‘game’ already and certainly not unless you have a ‘friend’ Finally, a few points worth remembering…