Climate Change and Assessment Working Group

OutlineOutline Climate change and assessment

simulations now available

Merged CSM and PCM model (CCSM)

Cooperation between NSF and DOE

Future research plans

HistoryHistoryCSM1 and PCM1 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 PCMMerging of CSM and PCM Agreement to use the same model components

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

Full merger occurs when the new atmospheric model is available with the new flux coupler

Merged model - same basic atmosphere,ocean, sea ice, RTM, and LSM

NSF and DOE efforts may use different resolutions Merged model called “CCSM”; PCM, CSM and PCTM will

continue to be analyzed in the meantime

Distributed InvolvementDistributed InvolvementDOE and NSF Supported Project withDOE and NSF Supported Project with::

Los Alamos National Laboratory**Los Alamos National Laboratory** National Center for Atmospheric Research**National Center for Atmospheric Research** Naval Postgraduate SchoolNaval Postgraduate School Oak Ridge National Laboratory**Oak Ridge National Laboratory** University of Texas, AustinUniversity of Texas, Austin Scripps Oceanographic InstituteScripps Oceanographic Institute DOE Program on Climate Diagnostics and IntercomparisonDOE Program on Climate Diagnostics and Intercomparison U.S. Army Cold Regions Research and Engineering LaboratoryU.S. Army Cold Regions Research and Engineering Laboratory National Energy Research Supercomputer Center**National Energy Research Supercomputer Center** Lawrence Berkeley National Laboratory**Lawrence Berkeley National Laboratory**

PCM Data UsersPCM Data Users (in addition to CSM users)(in addition to CSM users)

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, NCARHans Luthardt, DKRZBob Malone, LANLGerald Meehl, NCARSylvia Murphy, NCARDavid Pierce, ScrippsDennis Shea, NCARScott Smith, LANLJohn Taylor, ArgonneTony Tubbs, ScrippsWarren Washington, NCARJohn Weatherly, CRRELMichael Wehner, LLNLDean Williams, LLNLKao J. Chin Yue, LANL

CSM Climate Change SimulationsCSM Climate Change Simulations

1% CO2 increase (80 years)

Historical 1870 to 1999 (GHG)

Historical 1870 to 1999 (GHG+sulfate)

Ensemble (4) Historical 1870 to 1999 (GHG+sulfate+solar)

21st Century Business as Usual (BAU), and stabilization

IPCC SRES A1(5), A2, and B2

PCM Historical and Future PCM Historical and Future SimulationsSimulations

CSM greenhouse gas and sulfate aerosol forcing

1870 control simulation (300 years)

1995 control simulation (300 years)

1870 to 1999 GHG+sulfate (ensemble of 10)

1870 to1999 GHG+sulfate+solar (ensemble of 4)

1870 to 1999 solar (one)

“Business as Usual” 2000-2100 (ensemble of 5)

“stabilization” 2000-2100 (ensemble of 5)

Business as Usual 2100-2200 (one)

IPCC SRES A2 and B2 2000-2100 (one each)

PCM 1% CO2 Increase/Year and PCM 1% CO2 Increase/Year and Stabilization ExperimentsStabilization Experiments

1995 Control simulation--300 years

Ensemble of 5 capped at 2X CO2

One simulation of 100 years with constant 2X CO2

One simulation capped at 4X CO2

One run for 100 years with constant 4XCO2

One simulation with 0.5% per year capped at 2X CO2

PCM and CSM Presence in the International PCM and CSM Presence in the International Climate Modeling CommunityClimate Modeling Community

Both prominent in the IPCC Third Assessment Report (2001)Both prominent in the IPCC Third Assessment Report (2001)

Both represented in the IPCC Data Distribution Centre Both represented in the IPCC Data Distribution Centre (Hamburg)(Hamburg)

Both represented in the CLIVAR Coupled Model Both represented in the CLIVAR Coupled Model Intercomparison Project (CMIP): CMIP1, CMIP2, CMIP2+Intercomparison Project (CMIP): CMIP1, CMIP2, CMIP2+

Access to CSM: via NCAR (CSM web page)Access to CSM: via NCAR (CSM web page)

Access to PCM: runs archived at PCMDI (contact Mike Access to PCM: runs archived at PCMDI (contact Mike Wehner: mwehner@pcmdi.gov)Wehner: mwehner@pcmdi.gov)

ACPI Demonstration ProjectACPI 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)

POP with GM and KPP (LANL, NCAR, NPS), R. Smith grid modifications (LANL)

C. Bitz sea ice multi-thickness (5) distribution thermodynamics and E. Hunke et al. elastic viscous plastic dynamics (U. of Washington, LANL, NCAR)

River Transport, Branstetter and Famiglietti (U. Of Texas, Austin, NCAR)

CCM3.2 and later possibly CCM3.6 with liquid water and sulfate aerosol chemistry - T42

From July 1999 to June 2001 (2 years), total PCM years run: 2200; total PCTM years run: 1000; total of 3200 simulated years

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 GoalsDetermine the impacts on soil degradation and urbanization on:• Hydrologic cycle• Energy balance• Global temperature signalsIn addition:• Compare projected temperature changes to the existing temperature records• Overlay GCM simulated impacts with existing temperature stations to assess

the impacts of urbanization on the historical temperature record

Assessing the impacts of human induced surface change on the global energy balance

Johannes FeddemaUniversity of Kansas

Estimated 1998 urban extent in the eastern US

Source: LandScan 1998; Dobson et al, 2000

Future PlansFuture Plans

Simulations with black carbon distributions in PCM1

Volcanic+solar ensemble in PCM1

Ongoing analysis of CSM and PCM simulations

Higher resolution atmosphere -T85

Land use change simulations

Improved archival and cataloging of large data sets - EARTHGRID/DOE/

Simulations related to energy use impacts on the climate system - ACPI demonstration project

Future climate simulation with interactive carbon cycle

Future climate simulation with statistical solar and volcano data

Time and space varying SO2 emissions, 20th century

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.

Source: LandScan 1998; Dobson et al, 2000

Estimated 1998 urban extent in western Europe

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

IssuesIssues 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 balance of various components

Testing various forcing components

Initial state of ocean and sea ice …Levitus, Barnett

Ensembles are an imperative

Examples of Climate Examples of Climate Change ExperimentsChange Experiments

Greenhouse gases Sulfate aerosols (direct effect) Stratospheric ozone Land surface changes Volcanic forcing Solar change forcing Biomass burning Various energy/emissions use strategies

Change of Extremes

Heat waves, cold snaps

Floods, droughts

First freeze dates, hard freeze frequency

Precipitation intensity

Diurnal temperature

DOE Climate Change DOE Climate Change Prediction Program (CCPP)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