Potential sea ice predictability and the role of stochastic sea ice strength perturbations

Geophysical Research Letters

Volumn 41, Issue 23, 2014, Pages 8396-8403

  Juricke, Stephan ^a,b   Goessling, Helge F ^a   Jung, Thomas ^a  

^a ALFRED WEGENER INSTITUTE FOR POLAR AND MARINE RESEARCH   (Germany)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

coupled climate model; potential predictability; sea ice; stochastic parametrizations

Indexed keywords

CLIMATE MODELS; PARAMETERIZATION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TIME MEASUREMENT; UNCERTAINTY ANALYSIS;

COUPLED CLIMATE MODEL; DATA ASSIMILATION; INITIAL CONDITION UNCERTAINTIES; INITIAL PERTURBATION; MODEL UNCERTAINTIES; PARAMETRIZATIONS; POTENTIAL PREDICTABILITY; SEA-ICE THICKNESS;

SEA ICE;

CLIMATE MODELING; DATA ASSIMILATION; ICE THICKNESS; REGIONAL PATTERN; SEA ICE; TIMESCALE; UNCERTAINTY ANALYSIS;

ARCTIC OCEAN;

EID: 84921540559     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2014GL062081     Document Type: Article

References (32)

1. Blanchard-Wrigglesworth, E., C. M. Bitz, and, M. M. Holland, (2011), Influence of initial conditions and climate forcing on predicting Arctic sea ice, Geophys. Res. Lett., 38, L18503, doi: 10.1029/2011GL048807.
2. Brankart, J. -M., (2013), Impact of uncertainties in the horizontal density gradient upon low resolution global ocean modelling, Ocean Model., 66, 64-76, doi: 10.1016/j.ocemod.2013.02.004.
3. Bright, D. R., and, S. L. Mullen, (2002), Short-range ensemble forecasts of precipitation during the Southwest Monsoon, Weather Forecasting, 17, 1080-1100, doi: 10.1175/1520-0434(2002)017<1080:SREFOP>2.0.CO;2.
4. Buizza, R., M. Miller, and, T. N. Palmer, (1999), Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. R. Meteorol. Soc., 125, 2887-2908, doi: 10.1002/qj.49712556006.
5. Chevallier, M., D. S. y Mélia, A. Voldoire, M. Déqué, and, G. Garric, (2014), Seasonal forecasts of the pan-Arctic sea ice extent using a GCM-based seasonal prediction system, J. Clim., 26, 6092-6104, doi: 10.1175/JCLI-D-12-00612.1.
6. Danilov, S., G. Kivman, and, J. Schröter, (2004), A finite-element ocean model: Principles and evaluation, Ocean Model., 6, 125-150, doi: 10.1016/S1463-5003(02)00063-X.
7. Day, J. J., S. Tietsche, and, E. Hawkins, (2014), Pan-Arctic and regional sea ice predictability: Initialization month dependence, J. Clim., 27, 4371-4390, doi: 10.1175/JCLI-D-13-00614.1.
8. Holland, M. M., D. Bailey, and, S. Vavrus, (2011), Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3, Clim. Dyn., 36, 1239-1253, doi: 10.1007/s00382-010-0792-4.
9. Holland, M. M., E. Blanchard-Wrigglesworth, J. Kay, and, S. Vavrus, (2013), Initial-value predictability of Antarctic sea ice in the Community Climate System Model 3, Geophys. Res. Lett., 40, 2121-2124, doi: 10.1002/grl.50410.
10. Jung, T., T. N. Palmer, and, G. J. Shutts, (2005), Influence of a stochastic parameterization on the frequency of occurrence of North Pacific weather regimes in the ECMWF model, Geophys. Res. Lett., 32, L23811, doi: 10.1029/2005GL024248.
11. Juricke, S., and, T. Jung, (2014), Influence of stochastic sea ice parameterization on climate and the role of atmosphere-sea ice-ocean interaction, Philos. Trans. R. Soc. A, 372 (2018), 20,130,283, doi: 10.1098/rsta.2013.0283.
12. Juricke, S., P. Lemke, R. Timmermann, and, T. Rackow, (2013), Effects of stochastic ice strength perturbation on Arctic finite element sea ice modeling, J. Clim., 26, 3785-3802, doi: 10.1175/JCLI-D-12-00388.1.
13. Koenigk, T., and, U. Mikolajewicz, (2009), Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model, Clim. Dyn., 32, 783-798, doi: 10.1007/s00382-008-0419-1.
14. Li, X., M. Charron, L. Spacek, and, C. Guillem, (2008), A regional ensemble prediction system based on moist targeted singular vectors and stochastic parameter perturbations, Mon. Weather Rev., 136, 443-462, doi: 10.1175/2007MWR2109.1.
15. Lin, J. W. -B., and, J. D. Neelin, (2002), Considerations for stochastic convective parameterization, J. Atmos. Sci., 59, 959-975, doi: 10.1175/1520-0469(2002)059<0959:CFSCP>2.0.CO;2.
16. Lorenz, E. N., (1963), Deterministic nonperiodic flow, J. Atmos. Sci., 20, 130-141, doi: 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.
17. Lott, F., L. Guez, and, P. Maury, (2012), A stochastic parameterization of non-orographic gravity waves: Formalism and impact on the equatorial stratosphere, Geophys. Res. Lett., 39, L06807, doi: 10.1029/2012GL051001.
18. Magnusson, L., J. Nycander, and, E. Källén, (2009), Flow-dependent versus flow-independent initial perturbations for ensemble prediction, Tellus A, 61, 194-209, doi: 10.1111/j.1600-0870.2008.00385.x.
19. Palmer, T. N., (2012), Towards the probabilistic Earth-system simulator: A vision for the future of climate and weather prediction, Q. J. R. Meteorol. Soc., 138, 841-861, doi: 10.1002/qj.1923.
20. Plant, R. S., and, G. C. Craig, (2008), A stochastic parameterization for deep convection based on equilibrium statistics, J. Atmos. Sci., 65, 87-105, doi: 10.1175/2007JAS2263.1.
21. Shutts, G. J., (2005), A kinetic energy backscatter algorithm for use in ensemble prediction systems, Q. J. R. Meteorol. Soc., 131, 3079-3102, doi: 10.1256/qj.04.106.
22. Sidorenko, D., Q. Wang, S. Danilov, and, J. Schröter, (2011), FESOM under coordinated ocean-ice reference experiment forcing, Ocean Dyn., 61, 881-890, doi: 10.1007/s10236-011-0406-7.
23. Sidorenko, D., et al. (2014), Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part I: Model formulation and mean climate, Clim. Dyn., doi: 10.1007/s00382-014-2290-6.
24. Sigmond, M., J. Fyfe, G. M. Flato, V. V. Kharin, and, W. J. Merryfield, (2013), Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system, Geophys. Res. Lett., 40, 529-534, doi: 10.1002/grl.50129.
25. Stevens, B., et al. (2013), Atmospheric component of the MPI-M Earth System Model: ECHAM6, J. Adv. Model. Earth Syst., 5, 146-172, doi: 10.1002/jame.20015.
26. Tietsche, S., J. J. Day, V. Guemas, W. J. Hurlin, S. P. E. Keeley, D. Matei, R. Msadek, M. Collins, and, E. Hawkins, (2014), Seasonal to interannual Arctic sea ice predictability in current global climate models, Geophys. Res. Lett., 41, 1035-1043, doi: 10.1002/2013GL058755.
27. Timmermann, R., S. Danilov, J. Schröter, C. Böning, D. Sidorenko, and, K. Rollenhagen, (2009), Ocean circulation and sea ice distribution in a finite element global sea ice-ocean model, Ocean Model., 27, 114-129, doi: 10.1016/j.ocemod.2008.10.009.
28. Valcke, B., (2013), The OASIS3 coupler: A European climate modelling community software, Geosci. Model Dev., 6, 373-388, doi: 10.5194/gmdd-5-2139-2012.
29. Wang, Q., S. Danilov, and, J. Schröter, (2008), Finite element ocean circulation model based on triangular prismatic elements, with application in studying the effect of topography representation, J. Geophys. Res., 113, C05015, doi: 10.1029/2007JC004482.
30. Wang, Q., S. Danilov, D. Sidorenko, R. Timmermann, C. Wekerle, X. Wang, T. Jung, and, J. Schröter, (2013), The Finite Element Sea ice-Ocean Model (FESOM): Formulation of an unstructured-mesh ocean general circulation model, Geosci. Model Dev. Discuss., 6, 3893-3976, doi: 10.5194/gmdd-6-3893-2013.
31. Weisheimer, A., T. N. Palmer, and, F. J. Doblas-Reyes, (2011), Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles, Geophys. Res. Lett., 38, L16703, doi: 10.1029/2011GL048123.
32. Williams, P. D., (2012), Climatic impacts of stochastic fluctuations in air-sea fluxes, Geophys. Res. Lett., 39, L10705, doi: 10.1029/2012GL051813.