Axel J. Schweiger
University of Washington
Applied Physics Laboratory
Radiation:
Sea ice thickness and extent are extremely sensitive to radiative forcings:
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Sensitivity of ice thickness for change in radiative forcing. |
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H change for 10 Wm-2 in downwelling SWRadiative forcing fields, including those from major numerical weather prediction centers, differ by as much as 100 Wm-2 in monthly and 50 Wm-2 in annual means. This is largely due to the poor representation of clouds in those models. Radiative forcing fields computed from NOGAPS are likely to have similar problems.
Surface albedo is not just a function of the ice state but because of the spectral properties of ice/snow depends also on atmospheric conditions, mainly cloudiness. Cloudy sky albedoes can be significantly higher (0.9) than clear sky albedoes (0.8).
Recommendation:- Thoroughly evaluate candidate radiative forcings fields. Consider integrating cloud retrievals from satellite (AVHRR, TOVS) using methodologies developed by POLES, ISCCP and currently used by polar pathfinder projects. If requirements make a direct use of satellite retrievals impossible, use satellite retrievals, recent climatologies and measurements from field experiments (e.g. SHEBA) to determine errors and biases in NOGAPS fields.
- Validate surface albedos and surface temperatures using forthcoming AVHRR polar pathfinder project data.
- Implement a surface albedo scheme which accounts for cloudiness. This can be relatively easily accomplished if atmospheric forcings fields provide spectral information on downwelling shortwave radiation. Otherwise it can be parameterized in terms of cloudiness itself.
Snow is an important variable controlling, surface albedo, heat conduction and freshwater budget of the ocean and thereby ice thickness. Precipitation forecasts over sea ice in numerical weather forecast models (NCEP, ECMWF) appear to be flawed. [Don't know no about NOGAPS accuracy].
P-E estimates using satellite retrievals (TOVS) show promise.
Sea ice models usually compute wind stress from the geostrophic wind using a fixed drag coefficient for air-ice. Comparison of modeled and buoy velocities have shown that a time and space varying drag coefficient can significantly improve ice velocity forecasts.
- If not already done, compare NOGAPS wind fields to NCEP, ECMWF and buoy data.
- Consider using a time, space varying drag coefficient based on either climatology (Ip, Thomas, 1998) or computed from boundary layer stratification estimates provide by satellite (TOVS). This could be a simple parameterization (e.g. Overland and Colony, 1994) or a more sophisticated boundary layer model, e.g. (Lindsay, et al. 1998)
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M.C. Serreze, J.A. Maslanik, 1997. Arctic precipitation as presented in the NCEP/NCAR reanalysis. Annals of Glaciology, Vol 25. 429-433.
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R. Kwok, A. Schweiger, D. A. Rothrock, S. Pang and C. Kottmeier (1998). Assessment of Sea Ice Motion from Sequential Passive Microwave Observations with ERS and Buoy Ice Motions. Journal of Geophysical Research, Vol. 103, C4, 8191-8214
Overland and Colony, J. E. and R. L. Colony. 1994. Geostrophic drag coefficients for the central Arctic derived from Soviet Drifting station data. Tellus, 46A, 75-85, 1994.
Lindsay, R.W. , J.A. Francis, P.O.G. Person, D.A. Rothrock and A.J. Schweiger. 1997. Surface Turbulent Fluxes over Pack Ice Inferred from TOVS observations. Annals of Glaciology. Vol. 25, 393-399.
Thomas, D. The quality of sea ice velocity estimates, J. Geophys. Res. in press
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