Simulating Mountain Climates:
Challenges and Approaches
Philip B. Duffy
Chief Scientist
Climate Central, Inc.
MtnClim Conference
June 8, 2010
“Drink Responsibly”
Outline
Challenges
Approaches
Parting Thoughts
Challenges
Few observing stations
Strong local feedbacks to warming
loss of snow cover, etc.
Topographic variability
influences every aspect of climate.
Sparse observing network
• Impedes understanding of
changes/trends
• Compromises accuracy of
empirical downscaling
methods
• Impairs evaluation of
dynamical models.
Ca. 1930
Local Feedbacks
Result in non-uniform responses to greenhouse warming
Snow/ice-albedo feedback
(One of the infamous Himalayan glaciers)
Local Feedbacks…
Vegetation response
Fine spatial resolution:
• Not the solution to every problem,
• But necessary for good results in
mountainous regions
High-resolution
global climate models
T42
300 km
grid spacing
T239
50 km
grid spacing
A fine-resolution global atmospheric model
is a great downscaling tool
Refining resolution improves
the large-scale solution
(as well as adding detail)
300 km model vs. 50 km model:
Comparison on 300 km grid
old
Fine-resolution global model
The Good news

Compared to coarseresolution GCMs, can have
• better simulation of
large-scale climate
• improved representation
of extremes
Provides a globally
consistent solution.
Can work beautifully to drive
a nested model.
The Bad news

The most
computationally
demanding of any
option.
Produces a lot of
output
Not likely to get below
25 km anytime soon,
and even so very few
simulations
Nested dynamical models
Dynamical downscaling:
Usually uses a nested, limited-domain climate model
that is based on physical laws
Driver variables
(temperature, wind,
moisture, etc.)
passed to nested
model at lateral
boundaries every3
or 6 model hours
Nested dynamical models
The Good news

Based on physical laws,
so should correctly
represent changes
mesoscale circulation and
local feedbacks in response
to increasing GHG.
Produces a full suite of
output variables.
The Bad news

Computationally very
demanding.
Generally preserves
biases (errors) from the
driving GCM. (“GIGO”)
Representations of land
surface and hydrology
can be rudimentary
Most GCM simulations
don’t save output
needed for dynamical
downscaling.
Limitation:
Nested models tend to preserve biases of driving model
Seasonal cycle of precipitation in CA
Nested model
Driving model
Observations
A Thing of Beauty: high-resolution global model drives
a high-resolution nested model
“Observations” (PRISM)
Nested hi-res climate
models
“Regional climateprediction.net”:
a huge ensemble of regional climate projections
•
Richard Jones,
Phil Mote, et al.
• Vary parameter values
in both nested and driving
models.
• Plan to perform thousands
of simulations using citizen
volunteers
• Very limited output
See: Climateprediction.net/content/regional-model
Statistical/Empirical downscaling
Adds detail based on observations
Statistical downscaling…
The Good news

The Bad news

Computationally not very
demanding
Produces results for only a few
variables
Does not require special
output from the GCM
Resolution and domain limited by
availability of gridded observations
(a version of GIGO)
Can be applied to large
ensembles of GCM
simulations
Can include correction of
GCM biases
Key assumptions are questionable:
1. relationships derived from
observations will apply in the future – not
true where local feedbacks important
2. bias correction derived in historical
period will apply in the future.
Global
Climate
Model
(GCM)
~2º
Gridded
Observations
0.125º
GCM
Regridded
to 1º
Observations
Regridded to
1º
BiasCorrected
GCM
(1º)
“BCSD” downscaling
method
Bias-Corrected
and downscaled
GCM
(0.125º)
Statistically downscaled WCRP CMIP 3 climate projections
o 112 simulations of 1950 - 2100 downscaled to 12 km grid
o monthly temperature and precipitation only
o Results available now:
http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/
o Major expansion in progress
Planned library of statistically downscaled
projections
• Will comprehensively downscale new (CMIP5) GCM
simulations.
• Successor to: http://gdodcp.unllnlorg/downscaled_cmip3_projections/dcpInterface.html#
Welcome
• Will include derived indices of societal-impacts (e.g. drought)
• Results will be distributed through LLNL and though IPCC Data
Distribution Centers
• Multiple institutions involved: Climate Central, Santa Clara U.,
US Bureau of Reclamation, IPCC WGII, Army Corps of
Engineers, Livermore Lab
Statistical/Empirical downscaling:
Limitations
Ensemble mean temperature 2080-2099
Temperatures show detail
based upon observations
Temperature changes by 2080-2099
Temperature changes
are coarse (GCM scale)
Precipitation responses can differ from GCM response
% Change in Precip 1970-1999 vs 2030-2059
GCMs
RCMs
Terrain
Source: Salathe et al. Climate Dynamics, 2010
Source: Rocky Mtn.
Climate Organization
Time
I’m almost done…..
Surface energy/moisture balance models
 Produce a self-consistent suite of important
variables
Given all these limitations,
what’s the best approach?
Multiple downscaling
methods can be
combined
Nested dynamical
model(s)
Ensemble of
Global models
Surface
energy/moisture
balance model
Bias
correction/statistic
al downscaling
Parting Thoughts
 The challenges in simulating mountain
climates are mostly the same as those
encountered elsewhere, in extreme form.
 All downscaling methods have strengths and
limitations.
 Combining multiple methods seems like a
promising approach, but involves a lot of
work.
Science Marches On!