Posts Tagged «Modeling»

The Arctic Sea Ice Volume Anomaly time series is calculated using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) developed at APL/PSC.  Updates will be generated at approximately monthly intervals.

Briegleb, B. P. and B. Light, “A Delta-Eddington Multiple Scattering Parameterization for Solar Radiation in the Sea Ice Component of the Community Climate System Model“, NCAR/TN-472+STR, 100pp, 2007.

Häkkinen, S., F. Dupont, M. Karcher, F. Kauker, D. Worthen, and J. Zhang,’ Model simulation of Greenland Sea upper-ocean variability’, J. Geophys. Res., 112, C06S90, doi:10.1029/2006JC003687, 2007.

Holloway G., F. Dupont, E. Golubeva, S. Hakkinen, E. Hunke, M. Jin, M. Karcher, F. Kauker, M. Maltrud, M. A. Morales Maqueda, W. Maslowski, G. Platov, D. Stark, M. Steele, T. Suzuki, J. Wang, and J. Zhang,’ Water properties and circulation in Arctic Ocean models’, J. Geophys Res., 112, C04S03, doi:10.1029/2006JC003642, 2007.

Light, B., G.A. Maykut, and T.C. Grenfell,’ A two-dimensional Monte Carlo model of radiative transfer in sea ice’, J. Geophys. Res., 108, 10.1029/2002JC001513, 2003.

Light, B., G.A. Maykut, and T.C. Grenfell,’ A temperature-dependent, structural-optical model of first-year sea ice’, J. Geophys. Res., 109, 10.1029/2003JC002164, 2004.

Lindsay, R. W. and J. Zhang, “Assimilation of ice concentration in an ice-ocean model”, J. Atmos. Ocean. Tech., 23, 742-749, 2006.

Lindsay, R.W., J. Zhang, A. Schweiger and M.A. Steele, “Seasonal predictions of ice extent in the Arctic Ocean”, J. Geophys. Res., 113(C2), 2008.

Miller, R.L. and others including J. Zhang, CMIP5 historical simulations (1850-2012) with GISS ModelE2, J. Adv. Model. Earth Syst., 6, no. 2, 441-477, doi:10.1002/2013MS000266, 2014.

The overarching goal of the MIZMAS project is to enhance our understanding of MIZ processes and interactions, and to strengthen our prediction capability of future climate change, particularly the changes in both the ITD and the FSD, in the CBS. We propose numerical investigations of the historical and contemporary changes in the sea ice and upper ocean of the CBSMIZ. We also plan to investigate future changes of the CBSMIZ under global warming scenarios. These investigations involve new and potentially transformative theoretical and numerical work to develop, implement, and validate a new coupled ice–ocean Marginal Ice Zone Modeling and Assimilation System (MIZMAS) that will enhance the representation of the unique MIZ processes by incorporating a FSD and corresponding model improvements.

Payne, A. J., P. R. Holland, A. P. Shepherd, I. C. Rutt, A. Jenkins, and I. Joughin,’ Numerical modeling of ocean-ice interactions under Pine Island Bay’s ice shelf’, J. Geophys. Res.-Oceans, 112(C10), 14., 2007

Significant changes in arctic climate have been detected in recent years. One of the most striking changes is the decline of sea ice concurrent with changes in atmospheric circulation and increased surface air temperature.

Proshutinsky, A. et al. including M. Steele and J. Zhang, “Arctic Ocean study: Synthesis of model results and observations”, EOS, 40(4), 2005.

Proshutinsky, A., I. Ashik, S. Hakkinen, E. Hunke, R. Krishfield, M. Maltrud, W. Maslowski, and J. Zhang,’ Sea level variability in the Arctic Ocean from AOMIP models’, J. Geophys Res., 112, C04S08, doi:10.1029/2006JC003916, 2007.

Schmidt, G.A. and others including J. Zhang, Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive, J. Adv. Model. Earth Syst., 6, no. 2, 141-184, doi:10.1002/2013MS000265, 2014.

Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, Uncertainty in modeled arctic sea ice volume, J. Geophys. Res., doi:10.1029/2011JC007084, 2011

Project investigators aim to improve upon the existing seasonal ensemble forecasting system and use the system to predict sea ice conditions in the arctic and subarctic seas with lead times ranging from two weeks to three seasons.

This project is motivated by recent findings showing the sensitivity of Arctic Ocean circulation to background deep-ocean diapycnal mixing. Mixing in the stratified ocean is related to internal wave energy, which tends to be low under the Arctic Ocean ice cover. Consequently, as ice cover declines background mixing may increase and, among other changes, bring more Atlantic Water heat to the surface to melt ice, a potentially important positive climate feedback. To understand the influence of background mixing and to improve models of the changing Arctic Ocean, we are taking advantage of the latest analysis techniques to examine existing internal…

Steele, M., J. Zhang, D.A. Rothrock, and H. Stern: The force balance of sea ice in a numerical model of the Arctic Ocean, J. Geophys. Res., 102, 21061-21079, 1997.

The AOMIP science goals are to validate and improve Arctic Ocean models in a coordinated fashion and investigate variability of the Arctic Ocean and sea ice at seasonal to decadal time scales, and identify mechanisms responsible for the observed changes.

Uotila, P., D.M. Holland, M.A. Morales Maqueda, S. Hakkinen, G. Holloway, M. Karcher, M. Steele, N. Yakovlev, J. Zhang, A. Proshutinsky, “An energy-diagnostics intercomparison of coupled ice-ocean Arctic models“, Ocean Modeling, 11, 1–27, 2005.

This project will investigate, through modeling and data assimilation, the historical evolution of the Antarctic sea ice–ocean system from 1979 to the present to enhance our understanding of the large-scale changes that have occurred in the sea ice and the upper ocean in response to changes in atmospheric circulation.

Vavrus, S., D. Waliser, A. Schweiger, and J. Francis, “Simulations of 20th and 21st century Arctic cloud amount in the global climate models assessed in the IPCC AR4”, Clim Dynam, 33, 1099-1115, 2009.

Zhang, J.L. and D.A. Rothrock, “Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates“, Mon. Weather Rev., 131, 845-861, 2003.