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Karen L. Smith, Lorenzo M. Polvani, and L. Bruno Tremblay

1. Introduction Since 2007, a year of, what was at the time, unprecedented Arctic sea ice loss, there has been a substantial and organized effort to improve seasonal Arctic sea ice prediction in order to better inform industries and communities that depend on reliable sea ice information ( Stroeve et al. 2015 ; Jung et al. 2016 ). To that end, recent studies have focused on identifying key sources of Arctic sea ice predictability to improve both statistical and dynamical sea ice forecasts

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Hans W. Chen, Fuqing Zhang, and Richard B. Alley

1. Introduction Arctic sea ice is melting at an accelerating rate ( Comiso et al. 2008 ) that is possibly unprecedented over at least the past few millennia ( Kinnard et al. 2011 ; Polyak et al. 2010 ). Sea ice plays a central role in the local climate system through its influence on surface albedo, heat and moisture fluxes between the atmosphere and ocean, surface friction, and ocean circulation. Decreased sea ice coverage is one of the main drivers of Arctic amplification ( Screen and

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Ruth E. Petrie, Len C. Shaffrey, and Rowan T. Sutton

1. Introduction Arctic sea ice has been in decline since approximately the start of the satellite era (circa 1979) when observations of the sea ice extent became more widely available ( Comiso 2012 ). The rate of decline of sea ice extent has increased from ~4% decade −1 between 1978 and 2010 to ~8.3% decade −1 between 1996 and 2010 ( Comiso 2012 ). Sea ice is a physical barrier between the ocean and atmosphere that modulates the ocean–atmosphere fluxes of heat, moisture, and momentum. The

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Jessica Liptak and Courtenay Strong

1. Introduction Arctic sea ice cover impacts and responds to the local atmospheric features and large-scale variability associated with the Arctic Oscillation (AO; Thompson and Wallace 1998 ) and North Atlantic Oscillation (NAO; e.g., Hurrell et al. 2003 ). Changes in surface turbulent heat fluxes and wind stress forcing resulting from sea ice–atmosphere interaction produce immediate and long-term effects on the Arctic climate system that extend into subsequent seasons. Wu and Zhang (2010

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Igor Podgorny, Dan Lubin, and Donald K. Perovich

1. Introduction The remoteness and harsh environment of polar regions have recently motivated innovative and effective use of unmanned aerial vehicles (UAVs) for studies of atmospheric science, sea ice, and climate change. Inoue et al. (2008) used Aerosonde UAVs to map melt pond fraction on Arctic sea ice, and Tschudi et al. (2008) used these measurements to test satellite retrievals of melt pond coverage. Similarly, Aerosondes have been successfully used in wintertime boundary layer energy

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Flavio Lehner, Andreas Born, Christoph C. Raible, and Thomas F. Stocker

), but the robustness of this NAO reconstruction remains questionable ( Lehner et al. 2012a ; Pinto and Raible 2012 ). The apparent difficulties of relating the MCA–LIA transition to fundamental changes in the leading mode of atmospheric winter variability opens the opportunity for alternative mechanisms that also employ other components of the climate system, namely, the ocean or sea ice. Zhong et al. (2011) forced a climate model with a series of decadally paced volcanic eruptions, while leaving

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Shuyu Zhang, Thian Yew Gan, and Andrew B. G. Bush

1. Introduction Under the impact of global warming, Arctic sea ice (ASI) has been decreasing significantly in recent decades. With a warmer atmosphere, the melting season has lengthened ( Stroeve et al. 2017 ) and the ice cover has become younger and thinner ( Kwok 2018 ; Lindsay and Schweiger 2015 ; Stroeve et al. 2014 ). Warming over the Arctic region is twice the global mean, through Arctic amplification. Arctic amplification occurs because the warmer atmosphere induces ice loss ( Serreze

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J. N. Stroh, Gleb Panteleev, Max Yaremchuk, Oceana Francis, and Richard Allard

1. Introduction Sea ice models are an important component of any ice–ocean data assimilation (DA) system in the Arctic Ocean (AO) and the Southern Ocean. Currently, there are several DA systems that are widely applied to reconstruct Arctic ice conditions in reanalysis or quasi-operational mode. For example, there are systems based on the MITgcm ( Menemenlis et al. 2008 ; Heimbach 2008 ; Forget et al. 2015 ; Fenty et al. 2017 ), ROMS ( Wang et al. 2013 ), HYCOM ( Lisæter et al. 2007 ; Sakov

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Mark England, Alexandra Jahn, and Lorenzo Polvani

1. Introduction The rapid loss of Arctic sea ice over the last 50 years has been one of the most alarming signals of a changing climate. September sea ice extent has decreased by roughly 50% since 1979 ( Comiso et al. 2017 ; Stroeve and Notz 2018 ). Current model projections show that a summer ice-free Arctic before 2100 is very likely unless future warming is limited to 1.5°C or less ( Jahn 2018 ; Niederdrenk and Notz 2018 ; Sigmond et al. 2018 ) and is likely to occur by the middle of this

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Amélie Simon, Guillaume Gastineau, Claude Frankignoul, Clément Rousset, and Francis Codron

1. Introduction The Arctic is a region of pronounced climate change. Since the mid-twentieth century, the Arctic has warmed more than twice as fast as the rest of the planet (e.g., Blunden and Arndt 2012 ), a phenomenon referred to as Arctic amplification. The Intergovernmental Panel on Climate Change (IPCC) Special Report on the Ocean and Cryosphere in a Changing Climate ( Meredith et al. 2019 ) concluded that over the 1979–2018 period the Arctic sea ice extent has shrunk in all months of the

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