Status of the Sea Ice Model Testing of CICE4.0 in the coupled model context is underway Includes numerous SE improvements, improved ridging formulation, improvements to the vertical thermodynamics The influence that these modifications have on the simulated climate is being assessed Additional modifications have been introduced and are being tested within CICE4.0 Melt pond parameterization and improved shortwave radiation treatment By early 2007, we anticipate that the new sea ice model will include all of these modifications

Short-Term Simulations Using the Community Climate System Model Background Discussed at several past CAB meetings, especially by Eric Sundquist and Tom Crowley at CAB meeting last February. The issue came up again at the SSC/CAB/ WG Cochairs meeting in Breckenridge. There was extensive discussion of short -term climate simulations at the Aspen WGCM/AIMES Workshop held in August. CGD discussion last Monday, Nov 6th.

Short-Term Simulations: Proposed Form Start in about 1980, then run in pure simulation or simulation/assimilation mode until The short-term simulation would be from 2006 to Need an ensemble size of >10 to address extremes. Does it make an important difference if the CCSM is initialized to the actual climate of 2005? This requires data assimilation into the ocean, and possibly sea ice extent. Do we need to initialize the tropical Pacific for ENSO and N Atlantic for MOC?

Blue: T85,  1 Red: T42,  1 Black: T31,  3 CCSM3: Present Day Control Runs Maximum MOC in North Atlantic

Increases in Global Ocean Temps (Results from CCSM3 Ensemble) Gent et al, J Climate 11, CCSM3 Special Issue, 2006 L = Levitus et al (2005) Ensemble Members Relative Model Error < 25%

Advantages of Short-Term Simulations Because the runs are short, the atmosphere model can be run at higher resolution: produces relevant regional information for the relatively near-term. Most of the climate change is already committed, so much less dependent on future GHG scenarios. The much smaller range in transient climate response makes the multi-model ensemble more interesting because the range in model sensitivity is no longer the primary cause for the differences in the model short-term simulations.

Projections for Arctic Land Temp

Challenges of Short-Term Simulations No experience so far with assimilating data into the CCSM ocean and sea ice components, or with coupled model assimilation a la Hadley Centre. Should run chemistry in prognostic mode or with time slices? Should carbon cycle be included? This increases the CCSM project workload as these S-T simulations would be in addition to the more familiar, long future scenario runs planned for CCSM4 that includes a form of carbon cycle.

Scientific Opportunities I Runs without and with data assimilation between could be used to address the decadal timescale predictability of the climate system. Stimulus for multi-scale modeling activities, eg very high resolution one-way downscaling over the U.S.A. What is impact on SST biases in the upwelling regions? Changes in extremes, eg heat waves, floods, droughts. Atmospheric chemistry component added; simulations of air pollution (aerosols, ozone) in major urban areas.

Scientific Opportunities II Assessment of aerosol forcing compared with ongoing observations may allow a better understanding of aerosol climate forcing, and hence climate sensitivity. Better representation of soil, topographic, vegetation controls of carbon cycle; different permafrost classes. S-t runs emphasize human-induced land use changes, which requires use of the dynamic vegetation module. Mineral aerosols might help sort out the importance of human versus natural controls of dust emissions.

Nino3 SST Power Spectrum Gent and Kiehl, 2004; Collins et al, 2006

B Same FV as 400 yr control POP2 ocean with several improvements Land with new hydrology Standard CSIM

HadiSST data set Less power than, but longer than, Reynolds data set

B Changes to FV deep convection scheme and momentum transport due to convection