1. Collaborative Research on Sunlight and the Arctic Atmosphere-Ice-Ocean System (AIOS) Hajo Eicken Univ. of Alaska Fairbanks Ron Lindsay Univ. of Washington Bonnie Light Univ. of Washington Rebecca Woodgate Univ. of Washington Don Perovich CRREL John Weatherly CRREL Kay Runciman Univ. of Washington The AIOS sunlight team Jeremy Harbeck UAF

2. Sunlight and the Arctic AIOS Goals 1.Enhance our understanding of the present role that solar radiation plays in the Arctic AIOS 2.Improve our ability to predict its future role. Objectives 1.Determine the spatial and temporal variability of the partitioning of solar energy. 2.Understand how changes in sea ice, snow cover, and cloudiness will affect the solar partitioning and the resulting impacts. 3.Assess the impact of seasonal, interannual, and decadal variability on absorbed sunlight. 4.Establish if the variability of solar heating corresponds to the dominant modes of large-scale variability (e.g., Arctic Oscillation). 5.Evaluate and improve the treatment in models of solar energy in the Arctic AIOS. Where does all the sunlight go?

3. Solar partitioning Determine solar energy Incident on surface Reflected to atmosphere Absorbed in snow and ice Transmitted to ocean Reflected Absorbed Transmitted Atmosphere Ice Ocean

4. Temporal and spatial variability May June July AugustSeptember

5. Key products and parameters Pan Arctic…from 1987 to present Description of solar partitioning Monthly values from 1987 to 2007 of Incident solar energy Reflected solar energy Absorbed in snow and ice, Transmitted to ocean Spectral, integrated, PAR Pan – Arctic over ocean All on 25 x 25 km EASE grid (Equal Area Scalable Earth)

6. Approach Integrative and synthetic Combine observations and models Iterative process Observations Satellite data Field results Laboratory studies Models Process (ponds, clouds, bio…) Radiative transfer Ice – ocean Global climate

7. Incident solar radiation Field observations, satellite data, reanalysis, RT models Courtesy of A. Schweiger

8. Albedo – large-scale Large-scale from satellite 1982

9. Albedo – evolution 1. Dry snow: albedo of 0.85. 2. Melting snow: linearly decreasing albedo (0.80 to 0.71). 3. Pond formation: linearly decreasing albedo (0.67 to 0.50). 4. Pond evolution: remainder of melt season - linearly decreasing albedo (minimum of 0.20). 5. Fall freezeup: albedo linearly increases to 0.85. Dry snowFall freezeupPond evolution Melting snow Pond formation Details from field observations and models

10. Optical observations from SHEBA SHEBA data indicate larger transmittance to ocean some near infrared transmittance Plane parallel radiative transfer model Includes vertical variation Inherent optical properties Absorption coefficient Scattering coefficient Phase function Compare to CCSM3 parameterization Light transmission Observations, radiative transfer models,GCM parameterizations 100 W m -2 incident

11. Ice extent, type and concentration Satellite data No data Land Ocean Melt First-year Mixed-ice Multi-year

12. Snow cover climatology Data sources include: High-Latitude Airborne Annual Expeditions North Pole Drifting Ice Stations Coastal Stations: Canada, U.S., Russia Field Experiments (ARK, SHEBA, etc.) North Pole Stations (1937-1991) Products: Monthly means and distribution Dates of first snow, onset of melt, snow removal fall freezeup Field observations, reanalysis, satellite

13. Melt ponds Pond evolution is a key to solar partitioning Albedo is smaller and changing “Skylight” to ocean Pond physical evolution controlled by Surface meltwater production Ice topography Ice permeability Couple pond hydrology model with Surface topography statistics Melt rate data Optical observations Radiative transfer model Significant uncertainty in pond evolution

14. Influx of upper ocean heat Recent results indicate Bering Strait fluxes increased from 2001 to 2004 Heat – melt 640,000 m 3 Volume - 0.7 to 1.0 Sv Freshwater – 800 km 3 Ocean heat input is considerable

15. Ice – ocean Drive Arctic stand-alone CCSM ice-ocean model with collected solar data Investigate impact of ice-albedo feedback on ice-mass balance. Assess decadal variability in solar heating of the Arctic A-I-O Global climate model Assess variability in solar heating – control climate versus IPCC scenarios. Compare model changes in net sunlight and mass balance to A-I-O model Large – scale modeling

16. Outreach Synthesize and collaborate Scientific community Archived datasets Web site Map based One click to data Integrated datasets will benefit Sea ice mass balance Oceanography Atmospheric chemistry Large-scale modeling Future field planning Public General interest article on ice-albedo feedback K-5 synthesis puzzles

17. End of slideshow