CGE Training materials - VULNERABILITY AND ADAPTATION Assessment CHAPTER 8

1. CGE Training materials - VULNERABILITY AND ADAPTATION AssessmentCHAPTER 8 Climate Change Scenarios

2. Objectives and expectations Having read this presentation, in conjunction with the related handbook, the reader should: Be familiar with key terms, concepts and an overview of climate change scenarios; Have a general understanding on the approaches for construction climate scenarios for impact assessment Be familiar with the concept of General Circulation Models (GCM) and Regional Climate Models (RCMs) and their advantages and limitations; Have a general understanding of available methods, tools and data sources necessary for generating climate scenarios.

3. Outline • What are climate change scenarios? • Why we use scenarios? • Climate change overview • Approach to scenario development • Methods, tools and data sources • Future directions in scenario development

4. Why Use Climate Change Scenarios? • We are unsure exactly how regional climate will change • Scenarios are plausible combinations of variables, consistent with what we know about human-induced climate change • Think of them as the prediction of a model, contingent upon the greenhouse gas (GHG) emissions scenarios • Estimates of regional change by models differ substantially, consequently, individual model estimates should be treated more as a scenario • Scenarios help us to understand climate change impacts and determine key vulnerabilities • They can also be used to evaluate and identify adaptation strategies

5. Brief Primer on Regional Climate Change • Temperatures over most land areas are likely to rise: • Other factors, e.g., land-use change, may also be important • Warmer temperatures mean increases in heat waves and evaporation • Global-mean sea level rise: 0.18 to 0.59m by 2100 is based on IPCC 2007 findings: • Modified by local subsidence/uplift • Precipitation will change (increase) globally: • Local changes uncertain: critical uncertainty • Increase in storm intensity in some regions.

6. Evolution of climate models GCM grid

7. Projected Global Surface Warming Multi-model global averages of surface warming relative to 1980-1999 (Source: IPCC, 2007 WG I)

8. Climate and IPCC Socio-economic Scenarios from Special Report on Emission Scenarios (SRES) Climate model Had CM2 2050 Temperature change Precipitation change (Source: Nakicenovic and Swart, 2000.)

9. Projected Patterns of Precipitation Changes Relative changes in precipitation for the period 2090-2099, relative to 1980-1999 based on multi-model averages for A1B SRES scenario (Source: IPCC, 2007 WG I)

10. What are Climate change scenarios? • Climate change scenarios are tools to: • Help envision how regional climates may change with increased greenhouse gas (GHG) concentrations • To understand and evaluate how sensitive systems may be affected by human-induced climate change in the hope for policy-relevant information about expected changes and guidance for appropriate mitigation and adaptation measures • It is critical to keep in mind that climate change scenarios are not a prediction nor forecast of future climate change. • The use of regional climate change scenarios in a V&A assessment means: • They must provide information on the climate variables needed for V&A at a spatial and temporal scale needed for analysis will require daily or even sub-daily spatial data

11. What Are Reasonable Scenarios? • Scenarios should be: • Consistent with our understanding of the anthropogenic effects on climate • Internally consistent: • e.g. Clouds, temperature, precipitation • Scenarios are a communication tool about what is known and not known about climate change: • They should reflect plausible range for key variables.

12. Approach to Climate Change Scenario Development • Evaluate and determine the needs for climate scenario development • Specify the baseline climate • Develop climate change scenarios: • Arbitrary scenarios, analogue scenarios, GCMs, regionalclimatemodels (RCMs), downscaling techniques etc. • There is a range of existing guides to support scenario development process, which are available through UNDP/GEF: Lu (2007) http://www.undp.org/environment/docs/lecrds/applying_climate_information.pdf Puma & Gold (2011) http://content.undp.org/go/cms-service/download/publication/?version=live&id=3259633

13. Approach to Climate Change Scenario Development A range of existing guides to support scenario development process available through UNDP/GEF

14. Evaluation and Determination of Needs for Climate-Scenario Development • Choosing the right method for climate-scenario development can only be done after careful evaluation of the available approaches against the needs (application) and constraints (e.g. financial, computing, workforce, scientific, etc.) that project managers and their teams face • Before embarking on a “fishing expedition” for data, models and tools, it is strongly advisable to allocate time to define clearly the scope of the climate scenario information needed within the framework of the national communication. • Lu, 2006

15. Evaluation and Determination of Needs for Climate Scenario Development (Source: Puma and Gold, 2011 adapted from Lu, 2006)

16. Identification of User Needs Using the UNDP Framework) • The Framework provides the following: • Helps decision makers identify their constraints (e.g. financial, computing, workforce, scientific, etc.) and understand the interplay among them, to better approach climate-scenario development, in particular with respect to resource allocation. • Advices project managers to work together with a team of scientific and technical experts to manage uncertainties, select appropriate scenario methods and build a prospective range of scenarios • A platform that fosters clear and frequent dialogue between team members: • Scientific experts in charge of scenario development, are not fully aware of the managers needs and the non-scientific aspects of projects.

17. Specification of the Baseline Climate • Baseline climate is important to identify key characteristics of the current climate regime. • Baseline climate data helps to identify key characteristics of the current climate regime (such as seasonality, trends and variability, extreme events and local weather phenomena) • There are several questions that need to be answered to define the baseline climate: • Which climate scenarios data are needed? Scale variability • Which baseline period should be selected? WMO 30-year • What data sources are available?

18. Identification of Key data Sources • Baseline climate is important to identify key characteristics of the current climate regime • Key data sources available to define baseline climate include: • National meteorological agencies archives • Weather generators • Climate model outputs • Reanalysis data • Outputs from GCM control simulations.

19. Some Climate Data Sources • IPCC data distribution center http://www.ipcc-data.org/ • International Research Institute for Climate Prediction (IRI) http://iridl.ldeo.columbia.edu/docfind/databrief/cat-atmos.html • Tyndall Centre for Climate Change Research http://www.cru.uea.ac.uk/cru/data/tmc.htm • US National Oceanic and Atmospheric Administration (NOAA) http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html • Program for Climate Model Diagnosis and Intercomparison http://www-pcmdi.llnl.gov

20. IPCC Data Distribution Centre • The IPCC Data Distribution Centre is probably the best site for public-access climate model data • Observed climate data 1901-1990 • Gridded to 0.5 x 0.5° • 10 and 30 year means (Source:http://ipcc-ddc.cru.uea.ac.uk/ http://sedac.ciesin.columbia.edu/ddc/)

21. IPCC Data Distribution Center (continued) • GCM data from • CCC (Canada) • CSIRO (Australia) • ECHAM5 (Germany) • GFDL-R30 (U.S.) • HadCM3 (UK) • NIES (Japan) • IPSL (France) • Can obtain actual (not scaled) GCM output

22. IPCC Data Distribution Centre (continued) • Contains monthly-mean data from GCMs on: • Mean temperature (°C) • Maximum temperature (°C) • Minimum temperature (°C) • Precipitation (mm/day) • Vapour pressure (hPa) • Cloud cover (%) • Wind speed (m/s) • Soil moisture.

23. Observational Record • National meteorological offices • CMORPH (precip, satellite) • Climate research Unit (CRU) of the University of East Anglia • Réanalysis (ERA INT, NCEP) • GPCC (gauge data) • ISCCP (cloudiness, satellite) • TRMM (precip, satellite) • GPCP (precip, satellite and gauge)

24. Development of Climate Change Scenarios • Climate change scenarios need to be at a scale necessary for analysis: • Spatial: • e.g. to watershed or farm level • Temporal: • Monthly • Daily • Sub-daily.

25. Options for Climate Change Scenario Development • Past climates: analogues • Spatial analogues • Arbitrary changes; incremental • Climate models.

26. Past Climates • Options: • Instrumental record • Paleoclimate reconstructions. • Instrumental record: • Pros: • Can provide daily data • Includes past extreme events • Cons: • Range of change in past climate is limited • Data can be limited.

27. Past Climates (continued) • Paleoclimate reconstructions: • From tree rings, boreholes, ice cores, etc. • Can give annual, sometimes seasonal, climate • Can go back hundreds of years • Pro: • Wider range of climates • Cons: • Incomplete data • Uncertainties about values.

28. Past Climates: Reconstruction of N. Hemisphere Temperatures (Source: Mann et al., 1998 )

29. Spatial Analogues Source: NAST, 2000.

30. Spatial Analogues (continued) • Pro: • Communication tool: perhaps easier to understand • Con: • A model result must be used to choose the spatial analogue region • Does not capture changes in variability.

31. Arbitrary/Incremental Scenarios • Assumes uniform annual or seasonal changes across a region for example: • +2°C or +4°C for temperature • +/-10% or 20% change in precipitation • Can also make assumptions about changes in variability and extremes.

32. Arbitrary/Incremental Scenarios (continued) • Pros: • Easy to use • Can simulate a wide range of conditions • Cons: • It assumes a uniform change over the year or across a region and may fail to capture important seasonal or spatial details • The combinations of changes in climate for different variables can be physically implausible.

33. Climate Models • Models are mathematical representations of the climate system. • A model that incorporates the principles of physics, chemistry and biology into a mathematical model of climate, e.g. GCM or RCM (limited area model). • Such a model has to answer what happens to temperature, precipitation, humidity, wind speed and direction, clouds, ice and other variables all around the globe over time • They can be run with different forcings, e.g., higher GHG concentrations. • Models are the only way to capture the complexities of increased GHG concentrations.

34. General Circulation Models • Pros • Can represent the spatial details of future climate conditions for all variables • Can maintain internal consistency • Cons • Relatively low spatial resolution • May not accurately represent climate parameters

35. Example Data from DDC – Temperature

36. Example of GCM Output

37. Example Data – Precipitation

38. Program For Climate Model Diagnosis and Intercomparison (PCMDI) Has GCM Output

39. Downscaling from GCMs • Downscaling is a way to obtain higher spatial resolution output based on GCMs • Options include: • Combine low-resolution monthly GCM output with high-resolution observations • Use statistical downscaling: • Easier to apply • Assumes fixed relationships across spatial scales • Use regional climate models (RCMs): • High resolution • Capture more complexity • Limited applications • Computationally very demanding.

40. Dynamical downscaling…From GCM to RCM GCM are lateral boundary conditions of RCMs or RCMs nudged in GCMs. With GCM acting as boundary conditions for an RCM, is it possible to represent regional climate with good accuracy?

41. Combine Monthly GCM Output with Observations • An approach that has been used in many studies. • Typically, one adds the (low resolution) average monthly change from a GCM to an observed (high resolution) present-day “baseline” climate: • 30 year averages should be used, if possible e.g. 1961-1990 or 1971-2000: • Make sure the baseline from the GCM (i.e., the period from which changes are measured) is consistent with the choice of observational baseline. • This method can provide daily data at the resolution of weather observation stations • Assumes uniform changes within a GCM grid box and over a month: • No spatial or daily/weekly variability.

42. How Many GCM Grid Boxes Should Be Used? • Using the single grid box that includes the area being examined would be ideal, however: • There can be model noise at the scale of single grid boxes • Many scientists do not think single box results are reliable. • Hewitson (2003) recommends using 9 grid boxes: the grid being examined plus the 8 surrounding grid boxes. • Need to consider the total area covered by all those grid boxes. Does it include topography or climates not similar to the area being studied? • Do not use an isolated single location: it is better to do the analysis with group of stations; set of regional small scale indices. • Find out whether the output is not a singular case or influenced by small size or the data sample? • Or is it physically plausible and significant, meaning that you can reasonably develop predictions or real time applications?

43. Statistical Downscaling • Statistical downscaling is a mathematical procedure that relates changes at the large spatial scale that GCMs simulate to a much finer scale: • For example, a statistical relationship can be created between variables simulated by GCMs such as air, sea surface temperature, and precipitation at the GCM scale (predictors) with temperature and precipitation at a particular location (predictands). • There is a direct statistical relationship with sea-surface temperature (SST) indices (or other physically established predictor indices) • Statistical downscaling from numerical model output is widely used in climate change downscaling from daily GCM fields “perfect prognosis” assumption.

44. Downscaling principles • Downscaling as the process of making the link between the state of some variable representing a large space (or “large scale”) and the state of some variable representing a much smaller space (or “small scale”) (Benestad, 2002) • There are two main approaches to downscaling: dynamical and empirical–statistical • Dynamical downscaling makes use of limited area models (RCMs) with progressively higher spatial resolution than the global climate model (GCM). • Statistical downscaling is an extraction of information about statistical relationships between the large-scale climate and the local climate.

45. Downscaling Methods • Do not use isolated single location. • Find out whether the output is a singular case or influenced by small size or the data sample? • Or is it physically plausible and significant, meaning that you can reasonably develop predictions or real time applications? • It is better to do the analysis with: • A group of stations • Or a set of regional small scale indices • Or time series of a grid at high resolution.

46. Statistical Downscaling (continued) • Is most appropriate for: • Subgrid scales (small islands, point processes, etc.) • Complex/heterogeneous environments • Extreme events • Exotic predictands • Transient change/ensembles Is not appropriate for data-poor regions • Where relationships between predictors and predictands may change • Statistical downscaling is much easier to apply than regional climate modelling. • In climate change studies, one important question is what implications a global warming has for the local climate. The local climate can be regarded as the result of a combination of the local geography (physiography) and the large-scale climate (circulation) local climate, y = f(X, l, G) where X = Regional climate; l = local geography; G = Global climate

47. Statistical Downscaling (continued) • Statistical downscaling assumes that the relationship between the predictors and the predictands remains the same. • Those relationships could change. • In such cases, using regional climate models may be more appropriate • Four necessary conditions must be fulfilled in Empirical-Statistical Downscaling (ESD): • Strong relationship • Model representation • Description of change • Stationarity. Spatial structure of precipitation from radar reflection and a typical size of an RCM grid box, showing spatial variations at scales smaller than the model’s spatial resolution.

48. Statistical Downscaling Model (SDSM) • Currently, this is only feasible based on outputs from a few GCMs.

49. Global Data to Use in Downscaling with SDSM • Canadian website with Global Data: • Go to scenarios, then SDSM • Get output for individual grid.

50. Regional Climate Models (RCMs) • These are high resolution models that are “nested” within GCMs: • A common grid resolution is 50 km: • Some are higher resolution • RCMs are run with boundary conditions from GCMs • They give much higher resolution output than GCMs • Hence, much greater sensitivity to smaller scale factors such as mountains, lakes • Good to investigate higher order climate variability.