E' S' Takle1, B' Rockel2, W' J' Gutowski, Jr'1, J' Roads3,

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Toward Improving Predictability through
Transferability Experiments

• E. S. Takle1, B. Rockel2, W. J. Gutowski, Jr.1,
  J. Roads3,
• R. W. Arritt1, I. Meinke2, and C. Jones4
• 1Iowa State University, Ames, IA
• 2GKSS Research Centre, Geesthacht , Germany
• 3Scripps Institution of Oceanography,
  UCSD,LaJolla, CA
• 4Université du Québec à Montréal
• gstakle_at_iastate.edu

Indo-US Workshop on High-Performance Computing
for Regional Weather and Climate 30 June - 2 July
2005, NCAR, Boulder, CO
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Transferability is proposed as the next step
beyond model intercomparison projects (MIPs)
for advancing our understanding of the global
energy balance and the global water cycle by use
of models
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Regional Model Intercomparison Projects
ARCMIP
GLIMPSE
BALTIMOS
GKSS/ICTS
PRUDENCE
RMIP
SGMIP
QUIRCS
PIRCS
AMMA
IRI/ARC
LA PLATA
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Lessons Learned from MIPs

• No single model stands out as being best at
  simulating all variables
• Most MIPs have helped individual modelers
  identify specific shortcomings of their models
• Regional models run in climate mode simulate real
  sequences of events if such events are strongly
  coupled to the large-scale flow (stratiform
  precip vs. convective precip)

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Project to Intercompare Regional Climate
Simulations (PIRCS) Experiment PIRCS 1a
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Lessons Learned

• MIP ensemble means frequently are closer to
  observations than any individual model
• MIP ensembles recognize extreme events but fail
  to capture the magnitude of such extremes
• Models generally capture well the diurnal and
  seasonal cycles of temperature, although with
  larger error under extreme cold and stably
  stratified surface conditions (ARCMIP)

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Project to Intercompare Regional Climate
Simuations (PIRCS) Experiment PIRCS 1b
Precipitation (mm/day)
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Lessons Learned

• MIP ensemble means frequently are closer to
  observations than any individual model
• MIP ensembles recognize extreme events but fail
  to capture the magnitude of such extremes
• Models generally capture well the diurnal and
  seasonal cycles of temperature, although with
  larger error under extreme cold and stably
  stratified surface conditions (ARCMIP)

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Lessons Learned

• Most models produce too many high-level clouds,
  too few mid-level clouds, and too many low clouds
• There is wide disagreement on partitioning
  between convective and stratiform precipitation
  even when precipitation totals agree
• Some but not all models can capture the diurnal
  cycle of precipitation even with nocturnal
  convection
• All models tend to produce to many light rain
  events and not enough high-intensity rain events

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Lessons Learned

• All models (with 50 km resolution) fail to
  capture the timing between maximum and minimum
  3-hourly precipitation accumulation from MCSs
• Seasonal cycles of precipitation are captured
  over a wide range of climate regimes
• Precipitation generally is underestimated in both
  extreme events and very moist climates

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Transferability Objective

• Regional climate model transferability
  experiments are designed to advance the science
  of high-resolution climate modeling by taking
  advantage of continental-scale observations and
  analyses.

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Objective

• Regional climate model transferability
  experiments are designed to advance the science
  of high-resolution climate modeling by taking
  advantage of continental-scale observations and
  analyses.
• MIPs have helped modelers eliminate major model
  deficiencies. Coordinated studies with current
  models can advance scientific understanding of
  global water and energy cycles.

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Use of Regional Models to Study Climate

• How portable are our models?

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Use of Regional Models to Study Climate

• How portable are our models?
• How much does tuning limit the general
  applicability to a range of climatic regions?

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Use of Regional Models to Study Climate

• How portable are our models?
• How much does tuning limit the general
  applicability to a range of climatic regions?
• Can we recover some of the generality of
  first-principles models by examining their
  behavior on a wide range of climates?

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Slide source J Roads
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Transferability Working Group
Slide source J Roads
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Transferability Working Group (TWG) Overall
Objective

• To understand physical processes underpinning the
  global energy budget, the global water cycle, and
  their predictability through systematic
  intercomparisons of regional climate simulations
  on several continents and through comparison of
  these simulated climates with coordinated
  continental-scale observations and analyses

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Types of Experiments

• Multiple models on multiple domains (MM/MD)
• Hold model choices constant for all domains

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Types of Experiments

• Multiple models on multiple domains (MM/MD)
• Hold model choices constant for all domains
• Not
• Single models on single domains
• Single models on multiple domains
• Multiple models on single domains

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TRANSFERABILITY EXPERIMENTS FOR ADDRESSING
CHALLENGES TO UNDERSTANDING GLOBAL WATER CYCLE
AND ENERGY BUDGET
ARCMIP
GLIMPSE
BALTEX
BALTIMOS
BALTEX
GKSS/ICTS
PRUDENCE
RMIP
MAGS
SGMIP
QUIRCS
PIRCS
CAMP
GAPP
GAPP
GAME
GAME
AMMA
LBA
LBA
IRI/ARC
CATCH
MDB
LA PLATA
MDB
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Specific Objectives of TWG

• Provide a framework for systematic evaluation of
  simulations of dynamical and climate processes
  arising in different climatic regions

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Specific Objectives of TWG

• Provide a framework for systematic evaluation of
  simulations of dynamical and climate processes
  arising in different climatic regions
• Evaluate transferability, that is, quality of
  model simulations in non-native regions

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Specific Objectives of TWG

• Provide a framework for systematic evaluation of
  simulations of dynamical and climate processes
  arising in different climatic regions
• Evaluate transferability, that is, quality of
  model simulations in non-native regions
• Meta-comparison among models and among domains

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We recognize that

• The water cycle introduces exponential, binary,
  and other non-linear processes into the climate
  system

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We recognize that

• The water cycle introduces exponential, binary,
  and other non-linear processes into the climate
  system
• Water cycle processes occur on a wide range of
  scales, many being far too small to simulate in
  global or regional models

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We recognize that

• The water cycle introduces exponential, binary,
  and other non-linear processes into the climate
  system
• Water cycle processes occur on a wide range of
  scales, many being far too small to simulate in
  global or regional models
• The water cycle creates spatial heterogeneities
  that feed back strongly on the energy budget and
  also the circulation system

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Strategy

• Identify key processes relating to the water
  cycle and energy budget that express themselves
  to different degrees in different climatic
  regions

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Strategy

• Identify key processes relating to the water
  cycle and energy budget that express themselves
  to different degrees in different climatic
  regions
• Create hypotheses that can be tested by use of
  MM/MD experiments.

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Locations of hotspots having high
land-atmosphere coupling strength as identified
by Koster et al. (2004) with GEWEX Continental
Scale Experiments overlain.
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Considerations for Developing Hypotheses

• Exploit the availability of CEOP data
• Vertical profiles at isolated points
• Components of energy budget and hydrological
  cycle
• Sub-daily data
• High-resolution observations of events
• Recognize the limitations of reanalyses in
  data-sparse regions

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Slide source B. Rockel
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Candidate Issues Highly Relevant to Hypotheses on
the Water and Energy Cycles

• Static stability (CAPE)
• Diurnal timing
• Seasonal patterns
• Spatial patterns
• Monsoon characteristics
• Diurnal timing of precip
• Onset timing
• Precip spatial patterns

• Snow processes
• Rain-snow partitioning
• Snow-water equivalent
• Snowmelt
• Snow-elevation effects
• Soil moisture
• Frozen soils
• Cloud formation

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Expected Outcomes

• Improved understanding of the water cycle and its
  feedbacks on the energy budget and circulation
  system

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Expected Outcomes

• Improved understanding of the water cycle and its
  feedbacks on the energy budget and circulation
  system
• Improved capability to model climate processes at
  regional scales

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Expected Outcomes

• Improved understanding of the water cycle and its
  feedbacks on the energy budget and circulation
  system
• Improved capability to model climate processes at
  regional scales
• Improved applicability to impacts models

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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
FCAFuture, region A
Variable or Process 2
Variable or Process 1
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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
FCAFuture, region A
CCACurrent, region A
Variable or Process 2
CCA
Variable or Process 1
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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
FCAFuture, region A
CCACurrent, region A
Variable or Process 2
Model Simulations
CCA, model 1 (on its home domain)
CCA
Variable or Process 1
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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
FCAFuture, region A
CCACurrent, region A
Variable or Process 2
Model Simulations
CCA, model 1
CCA, model 2
CCA
Variable or Process 1
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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
CCB
FCAFuture, region A
CCACurrent, region A
CCBCurrent, region B
Variable or Process 2
Model Simulations
CCA, model 1
CCA, model 2
CCA
Variable or Process 1
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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
CCB
FCAFuture, region A
CCACurrent, region A
CCBCurrent, region B
Variable or Process 2
Model Simulations
CCA, model 1
CCA, model 2
CCB, model 2 (on its home domain)
CCA
Variable or Process 1
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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
CCB
FCAFuture, region A
CCACurrent, region A
CCBCurrent, region B
Variable or Process 2
Model Simulations
CCA, model 1
CCA, model 2
CCB, model 2
CCB, model 1
CCA
Variable or Process 1
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Simulating Future Climates with Models Trained
on Current Climates
FCA
Climates
CCB
FCAFuture, region A
CCACurrent, region A
CCBCurrent, region B
Variable or Process 2
Model Simulations
CCA, model 1
CCA, model 2
CCB, model 2
CCB, model 1
CCA
Fully spanning FCA requires More models More
domains
Variable or Process 1
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Plan of Work

• Phase 0 Write an article for BAMS summarizing
  lessons learned from various MIPs and describe
  how transferability experiments will provide new
  insight on the global climate system,
  particularly the water cycle and energy budget,
  report preliminary results

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Plan of Work

• Phase 0 Write an article for BAMS summarizing
  lessons learned from various MIPs and describe
  how transferability experiments will provide new
  insight on the global climate system,
  particularly the water cycle and energy budget,
  report preliminary results
• Phase 1 Conduct pilot studies

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Transferability Domains and CSE Reference Sites
Slide source B. Rockel
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Plan of Work

• Phase 0 Write an article for BAMS summarizing
  lessons learned from various MIPs and describe
  how transferability experiments will provide new
  insight on the global climate system,
  particularly the water cycle and energy budget,
  report preliminary results
• Phase 1 Conduct pilot studies
• Phase 2 Perform sensitivity studies on key
  processes relating to the water cycle. Create and
  test hypotheses by MM/MD

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Plan of Work

• Phase 0 Write an article for BAMS summarizing
  lessons learned from various MIPs and describe
  how transferability experiments will provide new
  insight on the global climate system,
  particularly the water cycle and energy budget,
  report preliminary results
• Phase 1 Conduct pilot studies
• Phase 2 Perform sensitivity studies on key
  processes relating to the water cycle. Create and
  test hypotheses by MM/MD
• Phase 3 Prediction, global change, new
  parameterizations

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Transferability Consolidates Lessons Learned from
Modeling and Observations

• Models Use experience gained from simulating
  home domains

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Transferability Consolidates Lessons Learned from
Modeling and Observations

• Models Use experience gained from simulating
  home domains
• CEOPS Use dominant features of the water cycle
  and energy budget of each CSE to generate
  testable hypotheses
• Review what has been learned
• Identify unique climate features

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Transferability Experiments Meet GEWEX Phase II
Objectives

• Produce consistent descriptions of the Earths
  energy budget and water cycle

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Transferability Experiments Meet GEWEX Phase II
Objectives

• Produce consistent descriptions of the Earths
  energy budget and water cycle
• Enhance the understanding of how the energy and
  water cycle processes contribute to climate
  feedback

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Transferability Experiments Meet GEWEX Phase II
Objectives

• Produce consistent descriptions of the Earths
  energy budget and water cycle
• Enhance the understanding of how the energy and
  water cycle processes contribute to climate
  feedback
• Develop parameterizations encapsulating these
  processes and feedbacks for atmospheric
  circulation models

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Current Status

• Three models (RSM/Scripps, Lokalmodell/GKSS,
  RegCM3/ISU) simulating seven domains
• Additional groups have expressed interest (R.
  Leung, MM5, WRF C. Jones, Canadian Regional
  Model Y. Wang, self-developed model)
• More collaborating modeling groups are being
  sought
• Contact E. S. Takle (gstakle_at_iastate.edu)

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Examples of Analyses
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Slide source B. Rockel
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Slide source B. Rockel
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Slide source B. Rockel
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Slide source B. Rockel
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Summary

• Transferability experiments will allow new
  insight on global water and energy cycles that
  will advance climate and weather modeling on all
  time and spatial scales
• Modeling groups are invited to participate and
  simulate periods defined by the CEOP on the
  transferability domains
• Countries are encouraged to consider establishing
  Continental Scale Experiments to attract
  international interest in studying water and
  energy cycles in different climates
• Collaborations with the operational and research
  communities of India are invited to further
  advance this GEWEX activity

http//rcmlab.agron.iastate.edu/twg gstakle_at_iastat
e.edu