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Whitepaper on Improvements to
Climate Models to Provide Useful
Information to Water Managers
Presented to:
Workshop on Advanced Climate Modeling and
Decision Making
Aspen CO
Presented by:
Joe Barsugli, University of Colorado
Chris Anderson, Iowa State University
Joel Smith, Stratus Consulting
Jason Vogel, Stratus Consulting
September 21, 2009
STRATUS CONSULTING

Briefing Outline
Introduction: What does the water
community want?
Overview of key science points
Options for Improving the Science to Assist
Decision Making
– GCMs
– Downscaling
Closing: what can science provide the water
community?
STRATUS CONSULTING

Main Purpose of the Study
“This white paper is intended to provide WUCA
with a detailed but non-technical overview of the
current state of climate modeling, how modeling
activities are evolving, and where the best
investments in these predictive tools should be
made in the future to assist water utilities in
their long-range planning efforts.”
STRATUS CONSULTING

We Identified Four Objectives Water
Utilities Seem to be Asking For
Help focus the analysis on specific outcomes useful to
the water community
1. How do we get model agreement on change in
key parameters (e.g., increase in seasonal or
annual precipitation)?
2. How do we get a narrower range of model output?
3. How do we get reliable (usable?) climate model
resolution at a spatial and temporal scale that
matches our current system models?
4. How do we get reliable projections within our
planning horizons?
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Spatial/Temporal Needs and Current
Uses of Climate Information
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Spatial and Temporal Requirements
for Climate Model Outputs
Variables typically used:
– Temperature
– Precipitation
– Streamflow
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Key Points about Utility System
Models
Utilities have multiple models w/ different
functions
Most models use streamflow as input
– Need hydrology models to translate
climate change projections to runoff/WQ
etc.
– Hydrologic modeling is another source of
uncertainty
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Key Points about Utility System
Models
Seattle has and NYCDEP developing model
w/ integrated hydrology that can use climate
model output
Others have spreadsheet correlations
between climate and hydrology
– Could revise spreadsheet to use climate
change projections and hydrology
models
Water quality is challenging to model
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Options for Improving GCMs
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GCMs -- Insights from the science
• Climate model simulations have generally improved
• The average across all models (“multi-model
average”) simulates current climatological averages
better than any individual model, but the multi-model
average will understate potential changes in
extremes.
• The range of global-mean climate projections as
assessed by the IPCC has not appreciably
narrowed although confidence has increased
• The uncertainty of global and regional climate
change is larger than the range simulated by the
current generation of models
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GCMs -- Insights from the science
• Better to use a range of individual models, rather
than a single model or the multi-model average
• There is no “best” model
• The relationship between model performance and
reliability of projections on global and regional scales
is not well understood
• On a regional scale, culling models based on
performance in simulating current climate does not
necessarily yield a narrower range of projections
STRATUS CONSULTING

GCM-1. Develop and enhance GCM ensembles
• Perform large (10–30 member) ensembles of
projections for each GCM out to at least the year 2060
at multiple modeling centers at a resolution of 50–100
km in the atmosphere component, and with high
resolution in the tropical oceans.
• Archive sufficient GCM output data over the North
American domain for use as input to dynamically
downscaled runs.
• Archive and disseminate a subset of GCM output
variables for water resources applications over the
North American domain at appropriate spatial and
temporal scales
• Coordinate with modeling centers worldwide to meet
the above goals for as many models as possible in the
CMIP5 archive.
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GCM-1. Develop and enhance GCM ensembles
Discussion:
• Discriminate the regional climate change signal from
variability.
• Richer set of model scenarios for use in adaptation studies.
• Facilitate a coordinated effort to produce ensembles of
regional climate change projections through statistical and
dynamical downscaling.
• Until there is clear evidence that higher resolution leads
to a convergence among different models, we
recommend a better sampling of the range of uncertainty
through running ensembles
• Data storage and dissemination requirements may be a
limiting factor if higher-resolution GCMs are used.
• Cost: Addl. Computing + Data archive: $3-30M over 5
years.
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GCM-2. Improve use of observations to constrain
climate models’ projections
• Extend statistical detection and attribution studies
that combine observed data and climate model output
to improve probabilistic estimates of the magnitude of
the observed anthropogenic climate change signal on
both global and regional scales.
• Developing advanced statistical methods to combine
observations, GCMs, and RCMs to improve
probabilistic projections of regional climate change out
to the mid-21st century (See DS-1).
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GCM-2. Improve use of observations to constrain
climate models’ projections
Discussion
• This effort builds on work on Bayesian methods (Tebaldi and
Lobell, 2008), fingerprinting, and numerous other attribution
and signal estimation studies.
• There will be better observations over time of how global and
regional climates are changing.
• Data assimilation is technologically complex and
computationally intensive.
• Data assimilation for climate models requires better models
and better observations, particularly of the oceans. Waiting for
improvements in these areas may delay results. Estimation of
ocean heat uptake is critical.
• These methods may not significantly narrow the range of
projections in a few years.
• Costs: Grant program $2M; DA: $10 M/year
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GCM-3. Improve modeling of the tropical Pacific
Focused research program combining modeling,
observations, and theory with the goals of:
• reducing climate model bias in the tropical Pacific
• seeking convergence among the models in projections
for this region on all time scales, but with an emphasis
on the climate change signal.
• using the observed data to constrain the ocean and
atmosphere models. An innovative method would be to
integrate coupled ocean/atmosphere data assimilation
into the model development cycle.
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GCM-3. Improve modeling of the tropical Pacific
Discussion
• Can narrow the range of precipitation projections over North
America.
• Convergence of models here would have payoffs for prediction
on multiple time scales
• Builds on work with coupled models and data assimilation used
for seasonal (ENSO) and decadal prediction
The tropical Pacific Ocean is comparatively well observed on
decadal scales; ARGO data (since ~2000) will allow
unprecedented accuracy in data assimilation.
• This option has a low certainty of success (challenging
problem).
• Natural multi-decadal variations in ENSO amplitude may prove
too large for useful predictions of change in ENSO characteristics
to be reliable.
• Costs: model development, computation, research:1-10M/yr
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GCM-4. Evaluate decadal prediction efforts for water
utilities’ planning
This effort would fund:
• Tracking progress of decadal prediction efforts worldwide
and evaluate the potential for using decadal predictions in
water utility planning.
• Research to integrate predictions of climate variability on
annual-to-decadal scales with projections of climate change.
• Downscaling efforts only if the potential skill of these
prediction can be demonstrated through “hindcast”
experiments
CMIP5 will include the first intercomparison of decadal
predictions. Decadal prediction runs are planned to extend to
2035 using GCMs with 50–100-km resolution in the atmosphere.
STRATUS CONSULTING

GCM-4. Evaluate decadal prediction efforts for water
utilities’ planning
Discussion
• May provide improved regional projections because oceans
start closer to observed conditions than in free-running GCMs.
• Has potential for skill in predicting Atlantic and Pacific decadal
oscillations, potentially improving predictions over most of North
America.
• A component of CMIP5.
• Will enhance the understanding of decadal variability and the
ability to integrate decadal variability with climate change.
• Predictive skill may be too low to be useful.
• Many methodological issues need to be worked out regarding
how to initialize the models to the observed ocean conditions.
• This option has a low probability of success (but high potential
reward).
• Costs:

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Options for Improving Downscaling

Insights on Downscaling from
Literature
GCM projections are unlikely to produce output at scale
of water utility planning tools within the next decade.
Neither dynamical nor statistical downscaling has the
capability to correct large-scale errors.
Careful consideration must be given to RCM domain size:
Big ones drift from large-scale climate conditions; small
ones prevent development of regional circulation.
Warm season precipitation RCM disparities: convective
parameterization, land surface parameterization.
Cold season precipitation RCM disparities: microphysics
parameterization
Cold season temperature RCM disparities: land surface
parameterization
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Insights on Downscaling from
Literature
RCM resolution: Many regional circulations in the United
States are better simulated with 10-20 km than with
40-60 km grid spacing.
RCMs with 2-10 km spacing are largely untested.
Statistical downscaling lacks wide application of rigorous
tests to examine sensitivity to stationarity assumptions.
Because statistical and dynamical techniques have
complementary strengths, they have been applied in
tandem at least once and results were promising.
Both statistical and dynamical downscaling communities
have engaged climate data user communities
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Downscaling Option 1:
Develop Regional Climate Change
Ensembles
Develop a very large ensemble (100-200 members) of
local projections out to 2050 through the combined use of
RCMs with 10-20 km grid spacing and statistical
downscaling.
Provide a community archive containing three tiers of
accessibility with a core set of variables within the most
accessible tier and full RCM output in the least accessible
tier.
The ensemble will enable refinement of quantification of
uncertainty and advancement of understanding of
sources of variability in projections so that scientists will
be able to be better informed when expressing
confidence levels in projections.
Only works if GCM-1 is pursued.
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Downscaling Option 1:
Develop Regional Climate Change
Ensembles
The ensemble output would be suitable for either riskbased
or scenario-based decision tools.
Water utility planners may have a voice in design of
regional scenarios.
Nearly all components needed for this option already
exist. Coordination between CMIP5 and COREDEX is
ongoing.
The challenge is that this scale of focused community
coordination (GCM, RCM, SD, and users) has not been
attempted in the United States.
$10M for computing; $5M-10M/yr infrastructure;
$5M-10M/yr for 5-10 years of research grants
STRATUS CONSULTING

Downscaling Option 2:
Develop RCM Components
Reduce the disparity among RCM simulations given
accurate large-scale climate conditions.
– Test, evaluate, and reformulate RCM component
models that are known to create disparity.
• Warm season priorities should be convective
parameterization and land models.
• Cold season priorities should be snow formation
parameterization and snow pack evolution
models.
• Use multiple RCMs in systematic studies of
domain size, boundary condition specification, and
nudging.
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Downscaling Option 2:
Develop RCM Components
Substantial insights into regional processes will result in
understanding so that scientists are better informed when
making statements about confidence in regional
projections.
May build directly upon infrastructure of existing
programs, like NARCCAP.
Does not necessarily ensure development of probabilistic
projections only the tools that may be used to make
them, and model development may be hindered by lack
of appropriate observations.
$5M-10M/yr in research grants needed; $1M-$5M in
computing infrastructure.
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Downscaling Option 3:
Develop Statistical Downscaling
Techniques
Develop a wider array (20 or more) of techniques for
downscaling daily data and extremes and producing
probabilistic local projections.
– A wide array of tools is preferred in order to
understand the uncertainty in local projections due to
downscaling methodological design.
– The developmental methodology should ensure
rigorous assessment of the error resulting from
stationarity assumptions.
– Probabilistic techniques should focus on estimating
climate PDFs and identifying sources of uncertainty,
such as the contributions to uncertainty from intermodel
variability and internal climate variability.
– Best applied in tandem with GCM-1.
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Downscaling Option 3:
Develop Statistical Downscaling
Techniques
Large ensemble of local projections can be produced if
large number GCM simulations are available.
Local projections will be suitable for both risk assessment
tools and scenario based tools.
Community archive facility and program infrastructure
may build upon programs already in existence.
Absent from these local projections will be the
contribution from changes in regional climate dynamics.
$1M for community archive; $5M-$10M/yr for 5-10 years
for research grants
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Option
GCM-1
Ensembles
GCM-2
Constraints
GCM-3
Tr. Pacific
GCM-4
Decadal
DS-1
Ensembles
Objective
DS-2
RCM Component
DS-3
Stat. PDF/extreme
Agreement
Range
(narrow/
understand)
Time and
Space Scale
Time
Horizons
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Concluding Thoughts

Prospects for Improving the Science to be
More Useful for Water Resources
Management
1. Model agreement on key parameters
– Need more agreement on change in
circulation patterns, storm tracks, etc.
– May take years for improvement
– In meantime, we can better understand
sources of uncertainty
• Use available projections
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Prospects for Improving the Science to be
More Useful for Water Resources
Management
2. Narrow Range of Model Output
– Only modest progress expected
– Two key sources of uncertainty unlikely
to be significantly narrowed:
• GHG emission projections
• 2xCO 2 climate sensitivity
– Enhanced use of observations may
constrain projections
– We can better understand the distribution
within the range and confidence in the
range
STRATUS CONSULTING

Prospects for Improving the Science to be
More Useful for Water Resources
Management
3. Increase GCM resolution
– Desired to be at same scale as utility
models
– GCM resolution likely to increase from
100-400km to 50-200km
• More coarse than most utility models
– Higher resolution does not guarantee
improved accuracy
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Prospects for Improving the Science to be
More Useful for Water Resources
Management
4. Improved Projections Within Utility Planning
Horizons
– Planning horizons often 30 years or less
– Natural variability can have larger effect on
climate w/in a few decades than GHGs
– Need improved simulations of climate
variability drivers, such as Tropical Pacific
Ocean
• May take years to realize such
improvements
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Concluding Thoughts
Are substantial
opportunities to
improve understanding
of regional climate
change
– Should take
advantage of
CMIP5
– Analyze outputs of
GCMs and
downscaling
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Concluding Thoughts
The GCMs will improve
– Science will
improve
– As will model
resolution
But…
– Improving
resolution is
complicated
Noticeable
improvements can take
a decade or more
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Concluding Thoughts
Decision making
– Choices have been
framed as
• Continue to rely
on the past
• Wait and see
– Need to work with
what we know
• Improved
decision
analysis
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The “Uncertainty Prayer”
Grant us…
The ability to reduce the uncertainties we can;
The willingness to work with the uncertainties
we cannot;
And the scientific knowledge to know the
difference.
Joe Barsugli
STRATUS CONSULTING