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Erica Rosenblum and Ian Eisenman

several CMIP phases as comprehensive climate models have continued to be developed. The two most recent phases have been phase 3 (CMIP3; Meehl et al. 2007 ) and phase 5 (CMIP5; Taylor et al. 2012 ), which were used to project future climate change in the Intergovernmental Panel on Climate Change (IPCC) Fourth and Fifth Assessment Reports (AR4 and AR5), respectively. The historical simulations have shown substantial bias in reproducing Arctic sea ice changes during the satellite record, with the

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Michael Sigmond and John C. Fyfe

1. Introduction Antarctic sea ice extent (SIE) has increased by about 1% decade −1 since the introduction of reliable (satellite based) measurements in 1979 (e.g., Turner et al. 2013 ) and reached its highest observed value in September 2013 ( Fetterer et al. 2009 ). The question of why Antarctic sea ice has increased in a warming world represents one of the most fundamental unsolved mysteries in polar climate science. Previous studies have suggested a number of possible explanations

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Michael E. Kelleher, Blanca Ayarzagüena, and James A. Screen

, the pattern of influence has an AO-like structure with an anomalous center at high latitudes. The AO has also been linked to Arctic sea ice cover changes due to its influence on high-latitude temperature and wind. Rigor et al. (2002) found the winds associated with the positive AO phase cause a wind-driven movement of sea ice, which map onto one of the main Arctic sea ice circulation regimes presented in Proshutinsky and Johnson (1997) . The AO induces anomalous sea ice motion and therefore

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K. Andrea Scott, Mark Buehner, Alain Caya, and Tom Carrieres

1. Introduction An accurate estimate of the sea ice state is critical for providing information to ensure safe ship navigation in ice-infested waters, for improved numerical weather prediction (NWP) near ice-covered regions, and for climate studies. If the Arctic continues to warm as projected, there will be an increased need for accurate sea ice information because of an increase in ship traffic for transport and natural resource extraction in ice-covered regions. The recent results of the

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Matthieu Chevallier, David Salas y Mélia, Aurore Voldoire, Michel Déqué, and Gilles Garric

1. Introduction Within the last few years, the shrinking summer Arctic sea ice cover has awakened interest in obtaining seasonal outlooks of the sea ice cover. Such outlooks are intended to give valuable information, for example, on marine accessibility of maritime routes or on the duration of the ice-free season in the marginal ice zones. Only a few institutions produce sea ice predictions using a coupled atmosphere–ocean general circulation model (AOGCM), although such models are becoming the

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Mitchell Bushuk, Xiaosong Yang, Michael Winton, Rym Msadek, Matthew Harrison, Anthony Rosati, and Rich Gudgel

seasonal sea ice predictions in the Barents Sea. The sea ice cover in the Barents Sea is a dominant contributor to winter Arctic sea ice variability and trends ( Cavalieri and Parkinson 2012 ) and influences local economic activity such as fisheries, shipping, and natural resource industries ( Jung et al. 2016 ). These factors have motivated a need for accurate seasonal sea ice predictions in this region. A number of recent studies, using both statistical methods ( Schlichtholz 2011 ; Onarheim et al

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Gary Grunseich and Bin Wang

1. Introduction Changes in sea ice coverage have widespread influences on global ocean ( Holland et al. 2001 ) and atmospheric circulation from seasonal to decadal time scales. Influences of spring Arctic sea ice on the East Asian summer monsoon (EASM; Guo et al. 2014 ), and the autumn sea ice in different regions of the Arctic on the strength of the winter Asian monsoon ( Chen et al. 2014 ; Mori et al. 2014 ) have been identified. The decline of sea ice extent starting in the late twentieth

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Jun Inoue, Judith A. Curry, and James A. Maslanik

estimating the ice concentration during the summer melt season, understanding the melt-pond evolution during summer is vital to understanding the sea ice–albedo feedback. Curry et al. (1995 , 2001 ) argue that explicit treatment of melt ponds in climate models is needed for correct simulation of the sea ice albedo feedback. Evolution of melt ponds also modifies the amount of shortwave radiation into the open water through multiple reflections ( Inoue et al. 2005a ). Based on observations obtained

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Benjamin I. Barton, Yueng-Djern Lenn, and Camille Lique

1. Introduction The Arctic has been predicted to be free of sea ice in summer by the middle of the twenty-first century ( Wang and Overland 2012 ; Snape 2013 ; Notz and Stroeve 2016 ). This follows an Arctic-wide decline in sea ice extent over recent decades ( Screen and Simmonds 2010 ). The Barents Sea alone has seen a 50% reduction in annual sea ice area between 1998 and 2008 ( Årthun et al. 2012 ), associated with a strong sea ice decline in all seasons including winter ( Onarheim and

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Achim Stössel

1. Introduction It is a major challenge for sea ice–ocean general circulation models (GCMs) to arrive at a reasonable simulation of Southern Ocean sea ice simultaneously with long-term global deep-ocean properties and circulation. This applies to coupled atmosphere–ice–ocean GCMs (e.g., Holland and Raphael 2006 ; Bitz et al. 2005 ; Ogura et al. 2004 ; Jungclaus et al. 2005 ) as much as to ice–ocean GCMs that are forced by atmospheric variables (e.g., Goosse and Fichefet 1999 ; Timmermann

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