Downscaling for Mountain
Hydrology
Ethan Gutmann
Martyn Clark, Roy Rasmussen,
Idar Barstad, Jeff Arnold, Levi Brekke
24th October 2015
Importance of Mountains to Water Resources
Colorado River : Climate and Water
Ficklin et al (2013)
Revealing and reducing uncertainties
Emissions
Scenario(s)
Combined uncertainty
scenarios
projections
Global Climate
Model(s)
models
calibration
GCM initial conditions
ens.
members
methods
Downscaling
method (s)
models
Hydrologic
Model
Parameter(s)
Hydrologic
Model
Structure(s)
4
4
Revealing and reducing uncertainties
Emissions
Scenario(s)
Combined uncertainty
scenarios
projections
Global Climate
Model(s)
models
calibration
GCM initial conditions
ens.
members
methods
Downscaling
method (s)
structure
Hydrologic
Model
Structure(s)
Hydrologic
Model
Parameter(s)
Internal Variability
• 30 Member CESM Ensemble
• Random Perturbation to the Initial conditions • P+=1e‐10… (a butterfly flapped it’s wings)
Dynamic Downscaling
• High‐resolution Regional Climate Model
• Simulations based on atmospheric physics
• Computationally expensive
• Detailed Physics
– Provides greater confidence in climate change scenario
October 2001
PRISM
WRF
November 2001
PRISM
WRF
December 2001
PRISM
WRF
January 2002
PRISM
WRF
February 2002
PRISM
WRF
March 2002
PRISM
WRF
Models vs “Observations”…
x
• Relies on stationary statistical relationships
• Computationally cheap
Observations
Statistical Downscaling
GCM
A continuum of downscaling options
A dichotomy of downscaling options
^
• Statistical downscaling based on rescaling GCM outputs
increasing physical representation
– BCSD, BCCA, AR
• Statistical downscaling based on GCM dynamics (water vapor, wind, convective potential, etc.)
– Regression‐based methods
– Analog methods
• Sophisticated circulation methods to relate the space‐
time variability of downscaled fields to synoptic scale atmospheric predictors (self‐organized maps, etc.), possibly enhanced stochastically
• Dynamical downscaling using simple weather models
• Dynamical downscaling using state‐of‐the‐art RCMs
Intermediate Complexity Quasi‐dynamical Downscaling
Atmospheric Research model (ICAR)
Identify the key physics and develop a simple model
GOAL: >90% of the information for <1% of the cost
ICAR
Linear
Mountain
Wave Theory
High-res DEM
High-res 3D
grid
High-res
Advection,
Microphysics,
LSM, PBL, radiation,
convection
Bias correction
GCM
low-res
3D data
Bias correction
Model Physics
ICAR Dynamics
ICAR Vertical Winds
SWM
Topography
WRF Vertical Winds
ICAR water vapor simulation
Ideal ICAR Evaluation
Ideal ICAR Evaluation
U = 5 m/s
RH=0.9
RH=0.95
RH=0.99
U = 10 m/s
U = 15 m/s
ICAR Precipitation
Real Simulation
WRF and ICAR have very similar precipitation distributions. ICAR requires 1‐0.1% of the computational effort of WRF. This enables a pseudo‐
dynamical downscaling for a wide variety of GCM / scenario combinations
ICAR
Monthly Precipitation
Preci
p
[mm]
What about Climate Change?
ICAR Climate Change
February Precipitation Change (8‐year mean)
•
Preliminary results
– ICAR without convection or linear winds
•
1yr not 8yr run – (ran out of disk space)
•
No postprocessing/calibration
Importance of coupled simulations
Temperature change in March
•
Land surface feedbacks influence climate change signal locally
•
Mountain snowpack and lakes dampen temperature change
•
Important for ET simulations, not simulated by statistical methods
•
ICAR can simulate the basic feedbacks
ICAR Mar CCSM (CMIP5)
Revealing and reducing uncertainties
Emissions
Scenario(s)
Combined uncertainty
scenarios
projections
Global Climate
Model(s)
models
calibration
GCM initial conditions
ens.
members
methods
Downscaling
method (s)
structure
Hydrologic
Model
Structure(s)
Hydrologic
Model
Parameter(s)
Investigating Uncertainty
Thompson Microphysics
Wind Parameter
• Precipitation change sensitive to many model parameters
• ICAR allows us to explore this sensitivity
• More confidence in climate change signal in some areas than others
•
e.g. San Juans vs. Front Range
Simple Microphysics
Summary
• Many statistical downscaling methods results are inconsistent with dynamical downscaling • ICAR provides a pseudo‐
dynamical option more consistent with fully dynamical downscaling
• ICAR provides the ability to explore uncertainty in climate change (or weather forecasts)
This is the End.