“Applying probabilistic climate change information to strategic resource assessment and planning”

1. “Applying probabilistic climate change information to strategic resource assessment and planning” Funded by ENVIRONMENT AGENCY TYNDALL CENTRE OUCE Oxford University Centre for the Environment

2. Overall Objective To develop a risk-based framework for handling probabilistic climate change information and for estimating uncertainties inherent to impact assessments performed by the Agency for strategic planning (water resources and biodiversity in the first instance). OUCE Oxford University Centre for the Environment

3. Specific Objectives • To develop and compare methods for generating regional/local scale climate change probabilities from coarse resolution CP.net data. • To trial the application of probabilistic climate change information to Agency-relevant case studies (initially for water resources and biodiversity management). • To explore the added-value of probabilistic scenarios for strategic planning and practical lessons learnt from the case studies. • To share the techniques and experience gained from the exemplar projects with a wider community of partner organisations and stakeholders. OUCE Oxford University Centre for the Environment

4. climateprediction.net aims to… • Sample uncertainty in climate models across • Physics • Initial conditions • Climate forcing • Provide better understanding of plausible future climate changes that can be forecast with one GCM species OUCE Oxford University Centre for the Environment

5. Experimental Strategy • Distributed public computing – port HadCM3 to windows/linux/mac • Each participant runs a specific experiment • Different model physics, initial conditions, forcing • Currently 17 million model years OUCE Oxford University Centre for the Environment

6. Phase 1 • 2 x CO2 equilibrium experiments • 15 years calibration at 1 x CO2 • 15 years control at 1 x CO2 • 15 years at 2 x CO2 OUCE Oxford University Centre for the Environment

7. ClimatePrediction.net OUCE Oxford University Centre for the Environment

8. Data Available • Global mean time series • Eight year seasonal climatologies • Surface air temperature • Precipitation • Cloudiness • Surface heat budget OUCE Oxford University Centre for the Environment

9. Phase 2 • Transient simulations with HadCM3 • 1920-2000 “hindcast” • 2001-2080 forecast • Launched with BBC in February OUCE Oxford University Centre for the Environment

10. Data Available in Phase 2 • More variables • Global mean monthly time series • Regional monthly time series (Giorgi; NAO; MOC) • UK grid-box monthly series • Ten-year seasonal climatologies (1920-2080) OUCE Oxford University Centre for the Environment

11. First Results • Use of CP.Net probabilistic climate change data for water resource assessment in the Thames basin • CATCHMOD: water balance model of River Thames basin • CP.net data available from Experiment 1 • Results and discussion OUCE Oxford University Centre for the Environment

12. CATCHMOD: water balance model of River Thames basin. OUCE Oxford University Centre for the Environment

13. River Thames Basin upstream of Kingston gauge and GCM grid-boxes OUCE Oxford University Centre for the Environment

14. CATCHMOD: parameters • Six key parameters controlling • Direct runoff • Soil WC at which evaporation is reduced • Drying curve gradient • Storage constant for unsaturated zone • Storage constant for saturated zone Wilby and Harris (2005) OUCE Oxford University Centre for the Environment

15. CATCHMOD • Inputs: daily time series of precipitation (PPT) and potential evaporation (PET) • Output: daily time series of river flow • Parameters :chosen as the ones that best reproduce observed flows for the period 1960-1991 OUCE Oxford University Centre for the Environment

16. CP.net Data • Grand ensemble of 2578 simulations of the HadAM3 GCM • Explores 7 parameter perturbations and perturbed initial conditions • 450 IC ensembles (model versions) OUCE Oxford University Centre for the Environment

17. CP.net variables and CATCHMOD Inputs • 8-year seasonal means for: • total cloud amount in LW radiation • surface (1.5m) air temperature • total precipitation rate • Use these to calculate change factors for PPT and PET over Thames • Change factors used to perturb CATCHMOD daily time series of PPT & PET OUCE Oxford University Centre for the Environment

18. Temperature at 2xCO2 PPT (%CF) PET (%CF) PPT vs PET Results: Change Factors OUCE Oxford University Centre for the Environment

19. Results: Standard CATCHMOD + unperturbed HadAM3 * present day OUCE Oxford University Centre for the Environment

20. Results: CP.net and CATCHMOD Q50 Q50 OUCE Oxford University Centre for the Environment

21. Results: CP.net and CATCHMOD Q95 Q95 OUCE Oxford University Centre for the Environment

22. Factors not Considered • Full set of CP.net perturbations • Emissions uncertainty • Downscaling uncertainty • Alternative model structures (GCM and Hydrological) • Coupled transient climate response OUCE Oxford University Centre for the Environment

23. Are Probabilistic Approaches Useful? • CP.net provides useful climate information – particularly joint probabilities of key variables • Enable more informed decision making • Issues for Water Utility stakeholders • Understanding the information • Having time and resources to use information • Regulatory constraints • In many cases other (non-climate) factors are more uncertain OUCE Oxford University Centre for the Environment

24. CP.net parameters OUCE Oxford University Centre for the Environment

25. Potential Evaporation Penman PET is a function of mean air T, mean vapour pressure (vp), sunshine and wind speed Present : calculate monthly Penman PET using observed climate variables for London (monthly long term means 1961-1990, UK national grid) 2xCO2 : calculate monthly Penman PET assuming: wind speed = constant relative humidity = constant thus relative change in vp=relative change in svp relative change in sunshine = - relative change in cloud amount T at 2xCO2= observed T + deltaT vp at 2xCO2= observed vp x (1+CF(svp)) sunshine at 2xCO2 = observed sunshine x (1-CF(cloud)) CF calculated using control and 2xCO2 phases for all the variables. OUCE Oxford University Centre for the Environment

26. Smoothed frequency distributions and CDFs: Q50 • Uncertainties: • Climate model parameterization • Hydrological model parameterization • No downscaling • No hydrological model structure OUCE Oxford University Centre for the Environment

27. Smoothed frequency distributions and CDFs: Q95 • Uncertainties: • Climate model parameterization • Hydrological model parameterization • No downscaling • No hydrological model structure OUCE Oxford University Centre for the Environment

28. Smoothed frequency distributions and CDFs: Q95 • Uncertainties: • Climate model parameterization • Hydrological model parameterization • No downscaling • No hydrological model structure OUCE Oxford University Centre for the Environment

29. Frequency distribution of flows: annual statistics • Uncertainties: • CP.net parameter dependence • No hydrological model • No downscaling • No hydrological model structure OUCE Oxford University Centre for the Environment

30. Frequency distribution of flows: annual statistics • Uncertainties: • CP.net parameter dependence • No hydrological model • No downscaling • No hydrological model structure OUCE Oxford University Centre for the Environment

31. Frequency distribution of flows: annual statistics • Uncertainties: • CP.net parameter dependence • No hydrological model • No downscaling • No hydrological model structure OUCE Oxford University Centre for the Environment