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Jed E. Lenetsky, Bruno Tremblay, Charles Brunette, and Gianluca Meneghello

1. Introduction Skillful seasonal predictions of Arctic sea ice on a regional scale are important for the safe navigation of Arctic waters and for local indigenous communities who use sea ice for hunting, fishing, and recreational activities ( Pearce et al. 2015 ; United States Navy 2014 ). State-of-the-art coupled ocean–ice–atmosphere models have been shown to provide skillful predictions of September pan-Arctic sea ice extent with lead times up to five months, both in seasonal forecast

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Jan Sedlacek, Jean-François Lemieux, Lawrence A. Mysak, L. Bruno Tremblay, and David M. Holland

1. Introduction Sea ice dynamics plays an important role in shaping the sea ice cover in polar regions. In the last few decades, several models have been developed to represent the dynamics of sea ice. Crucial to the model representation of dynamics is the formulation of the rheology, that is, the relationship between applied stresses and the resulting deformations. In these models, sea ice has been modeled either as a continuum (e.g., Coon et al. 1974 ) or as a collection of discrete

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S. Bathiany, D. Notz, T. Mauritsen, G. Raedel, and V. Brovkin

1. Introduction The recent rapid retreat of Arctic summer sea ice has raised the question of whether global warming can bring Arctic sea ice to a so-called tipping point ( Lindsay and Zhang 2005 ; Winton 2006 ; Notz 2009 ). This term implies that at a certain level of warming, sea ice loss would accelerate substantially in contrast to the more gradual change of the forcing. Such rapid loss could have severe consequences for the Arctic climate and ecosystems. In case of large positive

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Louis-Philippe Nadeau, Raffaele Ferrari, and Malte F. Jansen

those time scales (e.g., Broecker 1982 ; Toggweiler 1999 ; Brovkin et al. 2007 ; Adkins 2013 ). Most hypotheses for explaining the changes in deep-ocean stratification and circulation between glacial and interglacial periods have focused on changes in North Atlantic convection and shifts in Southern Hemisphere westerlies (e.g., de Boer et al. 2007 ; Toggweiler 2009 ; Anderson et al. 2009 ). However, increasing attention has been given to the potential role of buoyancy fluxes and sea ice at

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Charles Brunette, Bruno Tremblay, and Robert Newton

1. Introduction Adaptation to a changing Arctic climate relies in part on our capacity to predict sea ice. Since the start of satellite monitoring of the Arctic in the late 1970s, observations have shown a decrease of the average September sea ice extent (SIE) at a rate of 13.3% per decade ( Fetterer et al. 2017 , updated daily). The retreat of the pack ice and the transition to a sea ice–free summer in the Arctic have important implications for marine ecosystems ( Arrigo et al. 2008 ; Frey et

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Megan C. Kirchmeier-Young, Francis W. Zwiers, and Nathan P. Gillett

1. Introduction Sea ice extent (SIE) in the Arctic has decreased throughout the satellite record ( Vaughan et al. 2013a ). Loss of Arctic sea ice has implications in many areas ( IPCC 2014 ; Serreze et al. 2007 ), such as ecosystems, transportation, fisheries/commerce, and Arctic communities. Arctic SIE reached a minimum of 4.28 × 10 6 km 2 in September 2007 ( Stroeve et al. 2008 ), during a period of strong Arctic sea ice decline ( Stroeve et al. 2007 ; Comiso et al. 2008 ; Serreze et al

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James A. Screen

1. Introduction Satellites have routinely measured Arctic sea ice since the late 1970s. Since then, the sea ice cover has significantly reduced in all calendar months, with the largest trend in September—the month of the annual minimum ( Simmonds 2015 ). The September sea ice extent has declined by 40% and its volume by an estimated 65% ( IPCC 2013 ). Paleoclimate records suggest the sea ice cover is now lower than at any time in the previous 1450 yr ( Kinnard et al. 2011 ). This decline in

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Clara Deser, Robert A. Tomas, and Shiling Peng

1998 ), the leading structure of internal wintertime atmospheric variability over the Northern Hemisphere. It remains to be seen whether the adjustment time in a three-level quasigeostrophic model is indicative of that in a more complex atmospheric GCM. Deser et al. (2004 , hereafter D04 ) and the companion study of Magnusdottir et al. (2004 , hereafter M04 ) used an atmospheric GCM to examine the equilibrium circulation response to two types of boundary forcing: sea ice and SST. These boundary

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Paul R. Holland and Noriaki Kimura

1. Introduction Satellites have played a key role in monitoring decadal changes in the sea ice cover, most notably in the passive microwave record of near-daily ice concentration fields since 1978. During this period, Antarctic sea ice has expanded slightly while Arctic sea ice has contracted dramatically ( Parkinson 2014 ). These high-profile changes raise many questions: Are they anthropogenic or natural? What is the role of ice–climate feedbacks? Why are the two poles so different? Are the

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J. J. Day, S. Tietsche, and E. Hawkins

1. Introduction The rapid reduction in Arctic summer sea ice has led to a large increase in demand for forecasts of sea ice conditions at seasonal to interannual time scales ( Eicken 2013 ). This is important information for end users, including those interested in marine accessibility for routing ships (e.g., Stephenson et al. 2011 ). This interest has led to the development of a number of operational seasonal sea ice prediction systems (e.g., Sigmond et al. 2013 ; Chevallier et al. 2013

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