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

Hajo EickenUniv. of Alaska

Fairbanks

Ron LindsayUniv. of Washington

Bonnie LightUniv. of Washington

Rebecca WoodgateUniv. of Washington

Don PerovichCRREL

John WeatherlyCRREL

Kay RuncimanUniv. of Washington

The AIOS sunlight team

Jeremy HarbeckUAF

Sunlight and the Arctic AIOS

Goals1. Enhance our understanding of the present role that solar radiation plays in the Arctic AIOS2. Improve our ability to predict its future role.

Objectives1. 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?

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

Temporal and spatial variabilityMay

June

July August September

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)

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

Incident solar radiation

Field observations, satellite data, reanalysis, RT models

Courtesy of A. Schweiger

Albedo – large-scale

Large-scale from satellite

1982

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 snow Fall freezeupPond evolutionM

elti

ng

sn

ow

Po

nd

fo

rma

tion

Details from field observations and models

• 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

Ice extent, type and concentration

Satellite data

No data Land Ocean Melt First-year Mixed-ice Multi-year

Snow cover climatologyData 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

Vertical meltwaterpercolation

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

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

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

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

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