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Title. Meeting Name/Presenter Date. To develop a useful downscaled regional climate dataset to enable a variety of sectors to assess the impacts of climate variability and change. WHY DOWNSCALE?. Before After. OVERARCHING GOAL. Expense

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  1. Title Meeting Name/Presenter Date

  2. To develop a useful downscaled regional climate dataset to enable a variety of sectors to assess the impacts of climate variability and change. • WHY DOWNSCALE? • Before • After OVERARCHING GOAL

  3. Expense • Downscaling properly can be expensive. How should we perform the downscaling? • Model Uncertainty • There are many global climate models (GCMs). Which climate model should we downscale? • GCMs are imperfect. How do we address deficiencies in a given GCM? CHALLENGES TO ADDRESS

  4. DOWNSCALING APPROCHES • Statistical (uses empirical approach: cheap but cannot resolve processes well) • Delta Method (add GCM mean change signal to present-day observations) • Bias Corrected Spatial Disaggregation (BCSD) • Dynamical (uses weather model: expensive but resolves processes well) • Classic (directly downscale uncorrected GCM) • Bias-corrected (correct the GCM with an observationally-based ‘reanalysis’ prior to dynamical downscaling) • Hybrid • Dynamically downscale lower-resolution outer domains continuously, and the higher-resolution (more expensive) domains intermittently. Then, use statistical downscaling to fill in the gaps where high resolution simulations were not dynamically downscaled. • We will employ the Hybrid approach by dynamically downscaling bias-corrected GCM output and employing BCSD to fill in the gaps. Addressing the challenge of expense

  5. There are approximately ~20 different GCM ‘families’ that support the newly released 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Which one do we downscale? • GCMs that best simulate present-day rainfall and temperature. Knutti et al. (2013), GRL, doi:10.1002/grl.50256 Addressing the challenge of MODEL Uncertainty • To reduce uncertainty, we will downscale the NCAR CESM/CCSM4 model because it more accurately simulates present-day climate than other global climate models. Additionally, we will bias-correct CESM/CCSM4 in order to address uncertainty due to model deficiencies.

  6. Employ the Weather Research and Forecasting model (WRF) for dynamical downscaling • Nested Domains: 36-km outer domain (D1); 12-km middle domain (D2); 4-km inner domain (D3) • MODEL DOMAIN OUR MODELING APPROACH, PART 1

  7. 25-year Historical ‘Truth’ simulation from 1980-2005. WRF driven with ERA-Interim • 120-year Historical+Future simulation with CCSM4 RCP8.5 simulation (‘business as usual’ emissions scenario) • 50-year Future “branch” simulation with CCSM4 RCP4.5 simulation (moderate emissions scenario) • MODLEING STRATEGY OUR MODELING APPROACH, PART 2

  8. Employ a methodology that retains the more accurate ‘mean’ state from ERA-Interim, but retains the ‘eddy’ state from CCSM4. • The ‘mean’ state is *always* a 25-year base period from 1980-2005, which ensures that the climate change signal is included in the perturbation for CCSM4. • BIAS-CORRECTION METHOD • = • ERA-Interim OUR MODELING APPROACH, PART 3 • + • CCSM4

  9. In the following slides we show results from several case studies • Tropical Cyclone Gonu, February 1-7 2007. • A wintertime “Shamal” wind event, February 2008. • A summertime month with a significant convective storm that brought rainfall, July 1995. EARLY Results

  10. BACKGROUND • Strongest Tropical Cyclone Ever Recorded in Arabian Sea • Extensive damage to Oman, UAE, Iran and Pakistan • Imperative that we can simulate extremes such as GONU WRF Test Simulations: Tropical cyclone gonu, June 1-7, 2007Overview MODIS Image courtesy NASA Earth Observatory

  11. THIS MOVIE • Shows wind Trajectories for TC Gonu • Colors = Wind Speed (blue = slower, red = faster) WRF Test Simulations: Tropical cyclone gonu, June 1-7, 2007Wind trajectories animation

  12. THIS MOVIE • Shows radar reflectivity estimate for TC Gonu • Colors = Rainfall Intensity (blues = less; reds = more) WRF Test Simulations: Tropical cyclone gonu, June 1-7, 2007radar reflectivity animation

  13. THIS MOVIE • Shows cloud top temperature estimate for TC Gonu • Whiter colors = higher, colder clouds • Greyer colors = lower, warmer clouds WRF Test Simulations: Tropical cyclone gonu, June 1-7, 2007cloud top temperature animation

  14. THIS MOVIE • Shows wind speeds for TC Gonu • Colors: blues = weaker winds; reds = stronger winds WRF Test Simulations: Tropical cyclone gonu, June 1-7, 2007Wind speed animation, 1000 m asl

  15. WRF Test Simulations: Tropical cyclone gonu, June 1-7, 2007observed satellite infrared channel versus wrf ‘pseudo’-infrared channel

  16. THIS MOVIE • Shows vectors for Shamal wind event that occurred during the Dubai Desert Classic golf tournament • Colors: blues = weaker winds; reds = stronger winds • Note the onset of the event from the northwest WRF Test Simulations: Shamal wind event, February 2008Wind vectors animation, 250 m asl MODIS Image courtesy of NASA Visible Earth

  17. THIS IMAGE • Shows the rainwater mixing ratio in a WRF simulation of the only convective rainfall event in July 1995. • This can be thought of as a 3-d depiction of rainfall • Demonstrates that WRF can simulate these rare but hydrologically important summer rainfall events. WRF Test Simulations: Summertime convection, july 1995image of wrf rain water mixing ratio

  18. THIS IMAGE • Overall WRF can simulate the patterns of rainfall in the Oman Mountains, albeit with some biases due to displacement. WRF Test Simulations: Summertime convection, july 1995image OF MONTHLY TOTAL RAINFALL: SIMULATED (color fill) versus observed (DOTS)

  19. DY ?? conclusions

  20. LNRClimateChange@ead.ae AGEDI.ae

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