1 / 21

Mark New Ana Lopez, Fai Fung, Milena Cuellar Funded by Tyndall and Environment Agency

From Climate Data to Adaptation Large-ensemble GCM Information and an Operational Policy-Support Model. Mark New Ana Lopez, Fai Fung, Milena Cuellar Funded by Tyndall and Environment Agency. Adaptation Challenges. Uncertainty in climate information Interactions with other uncertain changes

alexa
Download Presentation

Mark New Ana Lopez, Fai Fung, Milena Cuellar Funded by Tyndall and Environment Agency

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. From Climate Data to Adaptation Large-ensemble GCM Information and an Operational Policy-Support Model Mark New Ana Lopez, Fai Fung, Milena Cuellar Funded by Tyndall and Environment Agency

  2. Adaptation Challenges • Uncertainty in climate information • Interactions with other uncertain changes • Integrated assessment

  3. Wimbleball Water Resource Zone

  4. Route Map • Large ensemble climate data • River flow ensemble • Water resource system modelling

  5. Large GCM Ensemble: CPdN • Explore model uncertainty by varying settings of poorly constrained model parameters • HADCM3L model: standard atmosphere & low resolution ocean. • 26 perturbed parameters (radiation, large scale cloud formation, ocean circulation, sulphate cycle, sea ice formation and energy convection) • Initial condition ensembles. • Transient runs: • 1920-2000 forced with historical CO2, solar and volcanic forcing. • 2000-2080 forced with different possible scenarios

  6. IPCC 4AR models CPDN model runs First 246 Completed Simulations Global Mean Temperature: SRES A2 Anomaly from 1961-1990

  7. Data Available • 10-year seasonal mean fields • Monthly mean (time series): • Large regions (Giorgi) • Selected grid-boxes (including UK) • Variables include • Total precipitation rate • Convective cloud amount • Surface air temperature (1.5m) • Relative humidity (1.5m)

  8. Modelling Set-up • Downscale climate in space and time • SW England -> River Exe • Monthly -> Daily • Generate ensemble of daily river flows • CATCHMOD rainfall-runoff model • Run flow-ensemble through water resource model

  9. August 1930-1985 August 2020-2060 Observed Observed Model Model Frequency Frequency Monthly Precip (mm/d) Monthly Precip (mm/d) Downscaling: Precipitation • Gamma transform method • Remove GCM monthly biases • Select daily values from observations

  10. Downscaling: Precipitation

  11. Downscaling: PET • Calculate GCM PET from • Temperature, RH & cloud-cover (radiation) • Adjust for climatological bias • No daily downscaling

  12. Downscaling: PET

  13. River Flows

  14. River Flows Mean Flow Change: 2020-2039 from 1961-1990 % Change Month

  15. Wimbleball Water Resource Model • Supplies: • Somerset & Devon (Exeter, Tiverton) • River & reservoir dominated • 50 ML/d Groundwater • Lancmod WR model

  16. Wimbleball Reservoir: Historic Monthly Storage, 1930-2005 Storage (Ml x 104) Month

  17. Wimbleball Reservoir: 2040 Ensemble Monthly Storage, 2040 Storage (Ml x 104) Month

  18. Wimbleball Reservoir: Changing Risk September Storage Storage (Ml x 104) Year

  19. Failure to Meet Demand Devon Demand Failure No. Simulations Year

  20. Failure to Meet Demand Devon Demand Failure No. Simulations Ave. Shortfall Year

  21. Outstanding Issues / Future Work • Biases in runoff simulations • Simplistic downscaling • Higher multiple year failures in simulations • Scenarios / ensembles of changing demand • Incorporating adaptation options • Staged methodology • Relative likelihoods • Comparison with UKCIP08 / ENSEMBLES

More Related