1 / 17

Collaborative Research on Sunlight and the Arctic Atmosphere-Ice-Ocean System ( AIOS )

Collaborative Research on Sunlight and the Arctic Atmosphere-Ice-Ocean System ( AIOS ). Hajo Eicken Univ. of Alaska Fairbanks. Bonnie Light Univ. of Washington. Ron Lindsay Univ. of Washington. Rebecca Woodgate Univ. of Washington. Kay Runciman Univ. of Washington. Jeremy Harbeck UAF.

yaholo
Télécharger la présentation

Collaborative Research on Sunlight and the Arctic Atmosphere-Ice-Ocean System ( AIOS )

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

  4. May Temporal and spatial variability June July August September

  5. Key products and parameters • 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) Pan Arctic…from 1987 to present

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

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

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

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

  10. Light transmission • 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 100 W m-2 incident Observations, radiative transfer models,GCM parameterizations

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

  12. Data sources include: • High-Latitude Airborne Annual Expeditions • North Pole Drifting Ice Stations • Coastal Stations: Canada, U.S., Russia • Field Experiments (ARK, SHEBA, etc.) Snow cover climatology • Products: • Monthly means and distribution • Dates of • first snow, • onset of melt, • snow removal • fall freezeup North Pole Stations (1937-1991) 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 m3 • Volume - 0.7 to 1.0 Sv • Freshwater – 800 km3 Ocean heat input is considerable

  15. Large – scale modeling • 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

  16. Outreach • 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 Synthesize and collaborate

  17. End of slideshow

More Related