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Climate Change and
Assessment Working Group
Outline
Climate
change and assessment
simulations now available
Merged
CSM and PCM model
(CCSM)
Cooperation
Future
between NSF and DOE
research plans
History
CSM1 and PCM1
 Built
for vector
computers
 Atmosphere: CCM3
 Ocean component:
NCAR ocean model
 Sea ice simplified
dynamics and
thermodynamics
 Built
for parallel
computers
 Atmosphere: CCM3
 Ocean component:
Parallel Ocean
Program (POP)
 Sea ice Model -Naval
Postgraduate School
model:VP,
thermodynamics
Merging of CSM and PCM

Agreement to use the same model components
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CSM and PCM lab staff will develop a merged flux coupler
that can use both sequential and parallel execution mode of
components - ongoing team of NCAR and DOE laboratory
involvement
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Full merger occurs when the new atmospheric model is
available with the new flux coupler
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Merged model - same basic atmosphere,ocean, sea ice,
RTM, and LSM
NSF and DOE efforts may use different resolutions
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Merged model called “CCSM”; PCM, CSM and PCTM will
continue to be analyzed in the meantime
Distributed Involvement
DOE and NSF Supported Project with:
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Los Alamos National Laboratory**
National Center for Atmospheric Research**
Naval Postgraduate School
Oak Ridge National Laboratory**
University of Texas, Austin
Scripps Oceanographic Institute
DOE Program on Climate Diagnostics and Intercomparison
U.S. Army Cold Regions Research and Engineering
Laboratory
National Energy Research Supercomputer Center**
Lawrence Berkeley National Laboratory**
PCM Data Users
(in addition to CSM users)
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Bill Anderson, NCAR
Jeffrey Annis, Scripps
Julie Arblaster, NCAR
Raymond Arritt, Iowa State Univ.
Tim Barnett, Scripps
Cecilia Bitz, U. Washington
Marcia Branstetter, U. Texas
Curtis Covey, LLNL
Ulrich Cubasch, DKRZ
Aiguo Dai, NCAR
Clara Deser, NCAR
Irene Fischer-Burn, DKRZ
John Gregory, IPCC
James Hack, NCAR
Charles Hakkarinen, EPRI
Chick Keller, LANL
Jeff
Kiehl, NCAR
Hans Luthardt, DKRZ
Bob Malone, LANL
Gerald Meehl, NCAR
Sylvia Murphy, NCAR
David Pierce, Scripps
Dennis Shea, NCAR
Scott Smith, LANL
John Taylor, Argonne
Tony Tubbs, Scripps
Warren Washington, NCAR
John Weatherly, CRREL
Michael Wehner, LLNL
Dean Williams, LLNL
Kao J. Chin Yue, LANL
CSM Climate Change Simulations

1% CO2 increase (80 years)
 Historical
1870 to 1999 (GHG)
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Historical 1870 to 1999 (GHG+sulfate)
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Ensemble (4) Historical 1870 to 1999
(GHG+sulfate+solar)
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21st Century Business as Usual (BAU), and
stabilization
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IPCC SRES A1(5), A2, and B2
PCM Historical and Future
Simulations
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CSM greenhouse gas and sulfate aerosol forcing
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1870 control simulation (300 years)
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1995 control simulation (300 years)
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1870 to 1999 GHG+sulfate (ensemble of 10)
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1870 to1999 GHG+sulfate+solar (ensemble of 4)
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1870 to 1999 solar (one)
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“Business as Usual” 2000-2100 (ensemble of 5)
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“stabilization” 2000-2100 (ensemble of 5)
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Business as Usual 2100-2200 (one)
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IPCC SRES A2 and B2 2000-2100 (one each)
PCM 1% CO2 Increase/Year and
Stabilization Experiments
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1995 Control simulation--300 years
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Ensemble of 5 capped at 2X CO2
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One simulation of 100 years with constant 2X
CO2
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One simulation capped at 4X CO2
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One run for 100 years with constant 4XCO2
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One simulation with 0.5% per year capped at
2X CO2
PCM and CSM Presence in the International
Climate Modeling Community
Both prominent in the IPCC Third Assessment Report (2001)
Both represented in the IPCC Data Distribution Centre
(Hamburg)
Both represented in the CLIVAR Coupled Model
Intercomparison Project (CMIP): CMIP1, CMIP2, CMIP2+
Access to CSM: via NCAR (CSM web page)
Access to PCM: runs archived at PCMDI (contact Mike
Wehner: [email protected])
ACPI Demonstration Project
 End
to End test of climate prediction…from
ocean initialization to global prediction of
climate change to regional modeling of
climate change to special impacts models
such as hydrological models of small
regions
 Several
(6) special PCM1 simulations with
6 hour output for regional models for 2000
to 2050
Interim Model - “PCM-CSM
Transitional Model” (PCTM)
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POP with GM and KPP (LANL, NCAR, NPS),
grid modifications (LANL)
R. Smith
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C. Bitz sea ice multi-thickness (5) distribution
thermodynamics and E. Hunke et al. elastic viscous
plastic dynamics (U. of Washington, LANL, NCAR)
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River Transport, Branstetter and Famiglietti
Texas, Austin, NCAR)
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CCM3.2 and later possibly CCM3.6 with liquid water and
sulfate aerosol chemistry - T42
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From July 1999 to June 2001 (2 years), total PCM years
run: 2200; total PCTM years run: 1000; total of 3200
simulated years
(U. Of
Assessing the impacts of human induced surface change
on the global energy balance
Johannes Feddema
University of Kansas
Purpose:
Develop a set of scenarios to simulate the impacts of urbanization and human impacts
on soil structure and land surface properties from 1750 to 2100. Scenarios will be
based on assessments of soil degradation (GLASOD, Oldeman, 1988), human
population density (LandScan; Dobson et al, 2000 and historical land-use data
(Ramankutty and Foley 1999; Klein Goldewijk, 2001).
Specific Goals
Determine the impacts on soil degradation and urbanization on:
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Hydrologic cycle
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Energy balance
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Global temperature signals
In addition:
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Compare projected temperature changes to the existing temperature records
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Overlay GCM simulated impacts with existing temperature stations to assess
the impacts of urbanization on the historical temperature record
Estimated 1998 urban extent in the eastern US
Source: LandScan 1998; Dobson et al, 2000
Future Plans
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Simulations with black carbon distributions in PCM1
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Volcanic+solar ensemble in PCM1
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Ongoing analysis of CSM and PCM simulations
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Higher resolution atmosphere -T85
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Land use change simulations
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Improved archival and cataloging of large data sets EARTHGRID/DOE/
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Simulations related to energy use impacts on the climate system ACPI demonstration project
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Future climate simulation with interactive carbon cycle
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Future climate simulation with statistical solar and volcano data
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Time and space varying SO2 emissions, 20th century
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Simulations with PCTM and CCSM when ready
URBANIZATION: Background
Research question:
Does extensive urbanization have an impact on global climate change?
Urbanization is known to be a significant factor in:
• changing hydrology in local areas and contributes to urban heat islands
(decreased infiltration and reduced water holding capacities).
• obtaining accurate global historical temperature signals.
• changing albedo values.
Future considerations
• Urban areas are expected to increase significantly even in regions of low
population increase due to rural-urban migration and ‘urban sprawl.’ For the
U.S. this is estimated to be a 35 percent increase over the next 25 years.
Estimated 1998 urban extent in western Europe
Source: LandScan 1998; Dobson et al, 2000
URBANIZATION: proposed methodology
Scenario development:
Determine population densities that define urban zones from the Department of
Defense population and landuse databases (LandScan; Dobson et al, 2000).
Create maps of urban extent from 1750 to 2100 based on past and future national
population estimates.
Create a number of urban landuse subclasses that translate to specific infiltration
rates, soil water holding capacities and albedo values.
Model development
Create new urban landuse classes for LSM that will change model parameters related
to:
• Albedo
• Infiltration rates (increase runoff and water loss from environment)
• Soil water holding capacities (reduced moisture availability during dry periods)
SOIL DEGRADATION: background
Research Question
What is the impact of human induced soil loss and soil structure change on climate?
Soil loss and alteration is known to:
• change soil moisture water holding capacities
• increase runoff and reduce moisture availability (dry periods)
• change short-term albedo values, mostly from vegetation change
Soil alteration and desertification have been shown to have a significant impact on
surface energy balances (Williams and Balling, 1996). Comparisons between
degraded and natural ecosystems in the Arizona-Mexico border region suggest that
about 3 days to a week after a rain event there are large changes in the Bowen ratio
(Bryant et al., 1990). Models also suggest that changes in soil water holding
capacities lead to significant changes in hydrology, mostly in wet and dry climate
regions (Feddema, 1999).
Estimated soil degradation severity (1950-1980)
SOIL DEGRADATION: proposed methodology
Scenario Development:
Use of the global soil degradation data (Oldeman, 1988) to manipulate the soil water
holding capacity. Data is based on the 1950-80 period.
Combine the population, slope and soil degradation data to create past and future soil
degradation estimates.
Translate soil degradation estimates to alter water holding capacities and soil depth by
relative percentages.
Model Development
Develop means to manipulate soil depth and water holding capacities (by layer) in
LSM from input data.
Use relative degradation measures to reduce water holding capacity and soil depth
estimates in LSM
Issues
 Need
improved climate change forcing: GHGs
and sulfur cycle; carbon cycle, land-surface
changes (U. Of Kansas); volcanic
 Higher
resolution for atmospheric component
 High
performance is a very high imperative on
DOE machines: must compete for time
 Computational
 Testing
balance of various components
various forcing components
 Initial
state of ocean and sea ice …Levitus,
Barnett
 Ensembles
are an imperative
Examples of Climate
Change Experiments
 Greenhouse
 Sulfate
gases
aerosols (direct effect)
 Stratospheric
 Land
surface changes
 Volcanic
 Solar
ozone
forcing
change forcing
 Biomass
 Various
burning
energy/emissions use strategies
Change of Extremes
Heat
waves, cold snaps
Floods,
droughts
First
freeze dates, hard freeze
frequency
Precipitation
Diurnal
intensity
temperature
DOE Climate Change
Prediction Program (CCPP)
 Develop
climate modeling capability that takes
advantage of new generation parallel architecture
supercomputers
 Build
on the previous DOE CHAMMP modeling
developments
 Develop
model components and coupled models
that can be used for energy policy, IPCC, and the
National Assessments