Matthew Janiga ATM552 Climate Change

Outline Basic concepts in sea ice decline. Introduction to sea ice modeling and validation. Abrupt change: Results from the NCAR Community Climate System Model (CCSM3). Future directions.

The Basics The atmospheric and oceanic circulation in the Arctic is anticyclonic associated with the polar high. The trans-polar drift stream transports ice from Siberia to the North Atlantic. Sea ice cover peaks in March and is at a minimum in September. Serreze and Barry (2008)

Effect of the AO DJF Rigor et al. (2002) A positive AO index is associated with more cyclonic flow and increased Fram strait outflow during the winter.

The Sea Ice-Albedo-Feedback α L If slope of α(T) exceeds slope of L(T) absorbed radiation increases faster than emission, runaway melt occurs. Winton(2008)

The Thin Ice Instability Problem Holland et al. (2008)

Thickness / Volume Observations Rothrock. (1999) September Winter

“Rotten” Multi-year Ice Barber et al. (In Press) and “We were traveling in our ship at about 25 km/hr [Amundsen research vessel, southern Beaufort Sea, September 2009] and this is almost the speed the ship would have in open water. This should not have been possible because the satellites were telling us we were in very thick multi-year sea ice.” – D.G. Barber (University of Manitoba) Solid FYI upper layer Thick but “rotten” MYI eaten away by bottom melt, tricks tracking methods.

Sea Ice Modeling The latest modeling is done with coupled land- atmosphere-ocean-ice models. Sea ice is treated as a viscous plastic (e.g. Hibler, 1979). Viscous fluid at low stress, plastic at high stress. The latest models have multiple layers for temperature and heat/radiative fluxes. Ice is treated as a continuous substance (no floes). Latest models simulate ice thickness distributions (ITD) based on ice convergence and melt. The latest models are beginning to include complicated aspects of ice morphology (e.g. brine packets). Bitz (2008) Brine Pocket

Which Models can Simulate the present? Only CCSM3 and HadGEM1 do a “decent” job. Bitz (2008) Holland(2008)

How Good is Mean Thickness? CSIRO-Mk3, MPI-ECHAM5 and GISS-AOM are way to thick initially. CCSM3, BCCR, and HadGEM1 are within the range of uncertainty. Rest, especially GFDL-CM2 are too thin. Thick (thin) models become seasonally ice free late (early). Huge biases in albedo and heat budgets. Thick Thin Thick Holland(2008)

How Good is Mean Extent and the Annual Cycle? Mean sea ice extent and its seasonal cycle is handled better than thickness. Zhang(2006)

How Well is 20 th Century Decline Simulated? Most models overestimate 20 th century extent and underestimate 20 th century losses. HadGEM1 and CCSM3 can simulate 20 th century best overall. Stroeve (2007)

Ensembles from the CCSM3 A1B Holland et al. (2008)

May Thickness Prior to Abrupt Loss Holland et al. (2008) CCSM3 A1B

Response of Atmosphere Today to Ice Extent Dramatic increases in late fall- early winter air temperature (17°C in Nov.) and precipitation over the Arctic Ocean. Large increases in temperature and precipitation over land north of 65°N also. Deser et al. (2009) LW T Precip.

Permafrost Impact Marginal permafrost is especially vulnerable to the rapid warming that can occur during abrupt ice loss events. T i = -0.3°CT i = -1.5°CT i = -5.8°C Lawrence (2008)

Improving the Forecast Satellites and field studies to better understand the current sea ice (thickness, health, radiative properties). Improved parameterizations to capture these processes. Sea ice models that treat ice discretely (individual floes) instead of as a continuous feature. Study of impacts of sea ice loss in a future climate Arctic amplification, weather, Greenland ice sheet. Bitz (2008)

References Barber, D. G., M. Asplin, R. Galley, K. Warner, M. Pucko, M. Gupta, S. Prinsenberg, R. De Abreu, S. Julien. The summer perennial pack ice in the southern Beaufort Sea is not as it appears. Geophys. Res. Lett. In press. Bitz, C., 2008: Numerical modeling of sea ice in the climate system, International Polar Year Sea Ice Summer School, Svalbard.. Deser C., Tomas R., Alexander M., Lawrence D., 2009: The seasonal atmospheric response to projected Arctic sea ice loss In the late 21st century. J. Climate. In Press. Hibler, W., 1979: A dynamic thermodynamic sea ice model. J. Phys. Oceanogr., 9, 815–846. Holland, M, Bitz, C, Tremblay, B, Bailey, D, 2008: The role of natural versus forced change in future rapid summer Arctic ice loss. In Arctic Sea Ice Decline: Observations, Projections, Mechanisms and Implications, Geophysical Monograph Series, eds Deweaver ET, Bitz CM, Tremblay LB (Am Geophysical Union, Washington DC) Vol. 180, pp 111–123. Holland M.M., Serreze M.C., Stroeve J., 2008: The sea ice mass budget of the Arctic and its future change as simulated by coupled climate models. Clim Dyn. doi: /s Lawrence, D. M, Slater, A. G., Tomas, R., Holland, M. M., and Deser, C., 2008: Accelerated Arctic land warming and permafrost degradation during rapid sea ice loss, Geophys. Res. Lett., doi: /2008GL Rigor, I.G., J.M. Wallace, and R.L. Colony, 2002: Response of sea ice to the arctic oscillation. J. Climate, 15, 2648–2663. Rothrock D. A., Y. Yu, and G. A. Maykut, 1999: Thinning of the Arctic sea-ice cover. Geophys. Res. Lett., 26, 3469–3472. Stroeve J., Holland M.M., Meier W., Scambos T., Serreze M.C., 2007: Arctic sea ice decline: Faster than forecast. Geophys.Res Lett 34. doi: /2007GL Winton, M., 2008: Sea ice-albedo feedback and nonlinear arctic climate change. In Arctic Sea Ice Decline: Observations, Projections, Mechanisms and Implications, Geophysical Monograph Series, eds Deweaver ET, Bitz CM, Tremblay LB (Am Geophysical Union, Washington DC) Vol. 180, pp 133–150. Zhang, X., and J.E. Walsh, 2006: Toward a Seasonally Ice-Covered Arctic Ocean: Scenarios from the IPCC AR4 Model Simulations. J. Climate, 19, 1730–1747.

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