Title Meeting Name/Presenter Date. OVERARCHING GOAL To develop a useful downscaled regional climate...

Title

Meeting Name/PresenterDate

OVERARCHING GOAL

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

CHALLENGES TO ADDRESS

•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?

ADDRESSING THE CHALLENGE OF EXPENSE

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 MODEL UNCERTAINTY

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?

Knutti et al. (2013), GRL, doi:10.1002/grl.50256

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.

GCMs that best simulate present-day rainfall and temperature.

OUR MODELING APPROACH, PART 1

MODEL DOMAIN

•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)


MODLEING STRATEGY

•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)


BIAS-CORRECTION METHOD

•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.

R

CCSM CCSM CCSM

ERAINT ERAINT ERAINT

CCSM ERAINT CCSM

=ERA-Interim

CCSM4

+

EARLY RESULTS

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.

WRF TEST SIMULATIONS: TROPICAL CYCLONE GONU, JUNE 1-7, 2007

OVERVIEW

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

MODIS Image courtesy NASA Earth Observatory


WIND TRAJECTORIES ANIMATION

THIS MOVIE•Shows wind Trajectories for TC Gonu•Colors = Wind Speed (blue = slower, red = faster)


RADAR REFLECTIVITY ANIMATION

THIS MOVIE•Shows radar reflectivity estimate for TC Gonu•Colors = Rainfall Intensity (blues = less; reds = more)


CLOUD TOP TEMPERATURE ANIMATION

THIS MOVIE•Shows cloud top temperature estimate for TC Gonu•Whiter colors = higher, colder clouds•Greyer colors = lower, warmer clouds


WIND SPEED ANIMATION, 1000 M ASL

THIS MOVIE•Shows wind speeds for TC Gonu•Colors: blues = weaker winds; reds = stronger winds


OBSERVED SATELLITE INFRARED CHANNEL VERSUS WRF ‘PSEUDO’-INFRARED CHANNEL

WRF TEST SIMULATIONS: SHAMAL WIND EVENT, FEBRUARY 2008

WIND VECTORS ANIMATION, 250 M ASL

MODIS Image courtesy of NASA Visible Earth

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: SUMMERTIME CONVECTION, JULY 1995

IMAGE OF WRF RAIN WATER MIXING RATIO

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 1995

IMAGE OF MONTHLY TOTAL RAINFALL: SIMULATED (COLOR FILL) VERSUS OBSERVED (DOTS)

THIS IMAGE•Overall WRF can simulate the patterns of rainfall in the Oman Mountains, albeit with some biases due to displacement.

CONCLUSIONS

•DY ??

[email protected]

AGEDI.ae

Title Meeting Name/Presenter Date. OVERARCHING GOAL To develop a useful downscaled regional climate...

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