• Arbetter, T. E., J. A. Curry, M. M. Holland, and J. A. Maslanik, 1997:Response of sea ice models to perturbations in surface heat flux. Ann. Glaciol.,25, 193–197.

  • Battisti, D. S., C. M. Bitz, and R. E. Moritz, 1997: Do general circulation models underestimate the natural variability in the Arctic climate? J. Climate,10, 1909–1920.

  • Bitz, C. M., D. S. Battisti, R. E. Moritz, and J. A. Beesley, 1996: Low-frequency variability in the Arctic atmosphere, sea-ice, and upper-ocean climate system. J. Climate,9, 394–408.

  • Bjork, G., 1992: On the response of the equilibrium thickness distribution of sea ice to ice export, mechanical deformation, and thermal forcing with application to the Arctic Ocean. J. Geophys. Res.,97, 11 287–11 298.

  • Colony, R, 1978: Daily rate of strain of the AIDJEX manned triangle. AIDJEX Bull.,39, 85–110.

  • Curry, J. A., and E. E. Ebert, 1992: Annual cycle of radiation fluxes over the Arctic Ocean: Sensitivity to cloud optical properties. J. Climate,5, 1267–1280.

  • ——, J. L. Schramm, and E. E. Ebert, 1995: Sea ice-albedo climate feedback mechanism. J. Climate,8, 240–247.

  • Ebert, E. E., and J. A. Curry, 1993: An intermediate one-dimensional thermodynamic sea ice model for investigating ice–atmosphere interactions. J. Geophys. Res.,98, 10 085–10 109.

  • Flato, G. M., 1995: Spatial and temporal variability of Arctic ice thickness. Ann. Glaciol.,21, 323–329.

  • ——, and W. D. Hibler III, 1995: Ridging and strength in modeling the thickness distribution of Arctic sea ice. J. Geophys. Res.,100, 18 611–18 626.

  • Gaspar, P., 1988: Modeling the seasonal cycle of the upper ocean. J. Phys. Oceanogr.,18, 161–180.

  • Hakkinen, S., and G. L. Mellor, 1990: One hundred years of Arctic ice cover variations as simulated by a one-dimensional, ice-ocean model. J. Geophys. Res.,95, 15 959–15 969.

  • Hasselmann, K., 1976: Stochastic climate models, Part I. Theory. Tellus,28, 473–485.

  • Hibler, W. D., III, 1980: Modeling a variable thickness sea ice cover. Mon. Wea. Rev.,108, 1943–1973.

  • ——, and J. E. Walsh, 1982: On modeling seasonal and interannual fluctuations of Arctic sea ice. J. Phys. Oceanogr.,12, 1514–1523.

  • ——, W. F. Wecks, A. Kovacs, and S. F. Ackley, 1974: Differential sea-ice drift. I. Spatial and temporal variations in sea-ice deformation. J. Glaciol.,13, 437–455.

  • Holland, M. M., J. A. Curry, and J. L. Schramm, 1997a: Modeling the thermodynamics of a sea ice thickness distribution. 2. Sea ice/ocean interactions. J. Geophys. Res.,102, 23 093–23 107.

  • ——, J. L. Schramm, and J. A. Curry, 1997b: Thermodynamic feedback processes in a single-column sea ice/ocean model. Ann. Glaciol.,25, 327–332.

  • IPCC, 1990: Climate Change: The IPCC Scientific Assessment. Cambridge University Press, 365 pp.

  • Jenkins, G. M., and D. G. Watts, 1968: Spectral Analysis and Its Applications. Holden-Day, 525 pp.

  • Lemke, P., E. W. Trinkl, and K. Hasselmann, 1980: Stochastic dynamic analysis of polar sea ice variability. J. Phys. Oceanogr.,10, 2100–2120.

  • Manabe, S., and R. J. Stouffer, 1994: Multiple-century response of a coupled ocean–atmosphere model to an increase of atmospheric carbon dioxide. J. Climate,7, 5–23.

  • Maykut, G. A., 1982: Large-scale heat exchange and ice production in the central Arctic. J. Geophys. Res.,87, 7971–7984.

  • NSIDC, 1996: Arctic Ocean Snow and Meteorological Observations from Drifting Stations, 1937, 1950–1991, Version 1.0, NSIDC, CD-ROM.

  • Parkinson, C. L., 1992: Spatial patterns of increases and decreases in the length of the sea ice season in the North Polar region, 1979–1986. J. Geophys. Res.,97 (C9), 14 377–14 388.

  • Reynolds, R. W., 1978: Sea-surface temperature anomalies in the North-Pacific Ocean. Tellus,30, 97–103.

  • Rothrock, D. A., 1975: The energetics of the plastic deformation of pack ice by ridging. J. Geophys. Res.,80 (33), 4514–4519.

  • Schramm, J. L., M. M. Holland, and J. A. Curry, 1997a: The effects of snowfall on a snow-ice-thickness distribution. Ann. Glaciol.,25, 287–291.

  • ——, ——, ——, and E. E. Ebert, 1997b: Modeling the thermodynamics of a sea ice thickness distribution. Part 1: Sensitivity to ice thickness resolution. J. Geophys. Res.,102, 23 079–23 091.

  • Slonosky, V. C., L. A. Mysak, and J. Derome, 1997: Linking Arctic sea-ice and atmospheric circulation anomalies on interannual and decadal timescales. Atmos.–Ocean,35, 333–366.

  • Thorndike, A. S., 1974: Strain calculations using AIDJEX 1972 position data. AIDJEX Bull.,24, 107–129.

  • ——, D. A. Rothrock, G. A. Maykut, and R. Colony, 1975: The thickness distribution of sea ice. J. Geophys. Res.,80, 4501–4513.

  • Walsh, J. E., W. D. Hibler III, and B. Ross, 1985: Numerical simulation of Northern Hemisphere sea ice variability. 1951–1980. J. Geophys. Res.,90, 4847–4865.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 183 37 4
PDF Downloads 40 21 1

The Role of Physical Processes in Determining the Interdecadal Variability of Central Arctic Sea Ice

View More View Less
  • 1 Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Boulder, Colorado
Restricted access

Abstract

The importance of the Arctic region for global climate change has recently been highlighted in the results from general circulation model simulations under increasing atmospheric CO2 scenarios. The warming that is predicted by these studies is most pronounced in the polar regions, indicating that it may be the first place in which the effects of global climate change will be detected. However, the natural variability that is present in the Arctic climate system is largely unknown and is likely to obscure the detection of anthropogenically forced changes. Additionally, there is little information on the internal processes of the Arctic ice pack, which are important for determining the variability of the ice cover.

In an effort to address these issues, the variability of the Arctic ice volume is examined using a single column sea ice–ocean mixed layer model. The model contains an ice thickness distribution and the parameterization of export and ridging due to ice divergence and shear. Variability in the ice cover is forced by applying stochastic perturbations to the air temperature and ice divergence forcing fields.

Several sensitivity tests are performed in order to assess the role of different physical processes in determining the variability of the perennial Arctic ice pack. It is found that the surface albedo and ice–ocean feedback mechanisms act to enhance the variability of the ice volume and are particularly important for the simulated response of the sea ice to fluctuations in air temperature, accounting for approximately 62% and 25% of the ice volume variance, respectively. The details of the ice thickness distribution also significantly affect the simulated variability. In particular, the ridging process acts to decrease the simulated variability of the ice pack. It reduces the variance of the ice volume by 50% when air temperature stochastic forcing is applied.

Corresponding author address: Dr. Marika M. Holland, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.

Email: mholland@ucar.edu

Abstract

The importance of the Arctic region for global climate change has recently been highlighted in the results from general circulation model simulations under increasing atmospheric CO2 scenarios. The warming that is predicted by these studies is most pronounced in the polar regions, indicating that it may be the first place in which the effects of global climate change will be detected. However, the natural variability that is present in the Arctic climate system is largely unknown and is likely to obscure the detection of anthropogenically forced changes. Additionally, there is little information on the internal processes of the Arctic ice pack, which are important for determining the variability of the ice cover.

In an effort to address these issues, the variability of the Arctic ice volume is examined using a single column sea ice–ocean mixed layer model. The model contains an ice thickness distribution and the parameterization of export and ridging due to ice divergence and shear. Variability in the ice cover is forced by applying stochastic perturbations to the air temperature and ice divergence forcing fields.

Several sensitivity tests are performed in order to assess the role of different physical processes in determining the variability of the perennial Arctic ice pack. It is found that the surface albedo and ice–ocean feedback mechanisms act to enhance the variability of the ice volume and are particularly important for the simulated response of the sea ice to fluctuations in air temperature, accounting for approximately 62% and 25% of the ice volume variance, respectively. The details of the ice thickness distribution also significantly affect the simulated variability. In particular, the ridging process acts to decrease the simulated variability of the ice pack. It reduces the variance of the ice volume by 50% when air temperature stochastic forcing is applied.

Corresponding author address: Dr. Marika M. Holland, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.

Email: mholland@ucar.edu

Save