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Introduction

The Arctic is a region of increasing strategic and economic importance. Its influence spans a diverse array of stakeholders across international boundaries, including local populations (e.g., indigenous populations), natural resource industries, fishing communities, commercial shippers, marine tourism operators, national security organizations, regulatory agencies, and the scientific research community (e.g., Arctic Council, 2009). The Arctic also plays a number of roles in moderating global climate by influencing the planetary heat budget and interacting with the oceanic and atmospheric circulation systems as well as the terrestrial environment (Guemas and Salas-Melia, 2008; Lawrence et al., 2008; Deser et al., 2010; AMAP, 2011; Francis and Vavrus, 2012; Jakobsson et al., 2012; Koenigk et al., 2012; Maslowski et al., 2012; Nghiem et al., 2012).

The extent and thickness of Arctic sea ice has recently undergone extraordinary decline (Figure 1.1) that can be linked to climate changes (e.g., IPCC, 2007; Min et al., 2008; Allison et al., 2009; NRC, 2010; Kay et al., 2011; Notz and Marotzke, 2012; ). The last six summers (2007-2012) have experienced the six lowest sea ice extent minima over three decades of satellite record, and the past decade (2003-2012) has exhibited 9 of the 10 lowest minima (updated from Perovich et al., 2011). The reduction of summer sea ice extent has been greatest in the Beaufort and Chukchi seas offshore of Alaska and in the Kara and Laptev Seas north of Russia. These are regions of particular interest to stakeholders concerned with marine access to subsistence activities, shore infrastructure, marine transportation, and natural resource developments. The winter sea ice extent has also shown a downward, though less striking, trend. More notable than winter sea ice extent is the change in composition of the winter sea ice cover, associated with the reduced summer sea ice. The winter sea ice cover now includes a significantly higher percentage of thin, seasonal (i.e., first-year) ice. The fraction of the first-year sea ice in the Arctic Ocean in March increased from 38 percent in the early 1980s to 64 percent in 2010 (Stroeve et al., 2011).

Closely associated with the persistent changes in the characteristics of the ice cover are other observed changes throughout the Arctic system. For instance, in regions of sea ice loss the upper ocean is warmer and fresher (e.g., Jackson et al., 2010; Steele et al., 2011). As a result of increased open water area, biological productivity at the base of



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1 Introduction The Arctic is a region of increasing record, and the past decade (2003-2012) has strategic and economic importance. Its exhibited 9 of the 10 lowest minima influence spans a diverse array of (updated from Perovich et al., 2011). The stakeholders across international reduction of summer sea ice extent has been boundaries, including local populations greatest in the Beaufort and Chukchi seas (e.g., indigenous populations), natural offshore of Alaska and in the Kara and resource industries, fishing communities, Laptev Seas north of Russia. These are commercial shippers, marine tourism regions of particular interest to stakeholders operators, national security organizations, concerned with marine access to subsistence regulatory agencies, and the scientific activities, shore infrastructure, marine research community (e.g., Arctic Council, transportation, and natural resource 2009). The Arctic also plays a number of developments. The winter sea ice extent has roles in moderating global climate by also shown a downward, though less influencing the planetary heat budget and striking, trend. More notable than winter sea interacting with the oceanic and ice extent is the change in composition of atmospheric circulation systems as well as the winter sea ice cover, associated with the the terrestrial environment (Guemas and reduced summer sea ice. The winter sea ice Salas-Melia, 2008; Lawrence et al., 2008; cover now includes a significantly higher Deser et al., 2010; AMAP, 2011; Francis and percentage of thin, seasonal (i.e., first-year) Vavrus, 2012; Jakobsson et al., 2012; ice. The fraction of the first-year sea ice in Koenigk et al., 2012; Maslowski et al., 2012; the Arctic Ocean in March increased from Nghiem et al., 2012). 38 percent in the early 1980s to 64 percent in The extent and thickness of Arctic sea 2010 (Stroeve et al., 2011). ice has recently undergone extraordinary Closely associated with the persistent decline (Figure 1.1) that can be linked to changes in the characteristics of the ice cover climate changes (e.g., IPCC, 2007; Min et al., are other observed changes throughout the 2008; Allison et al., 2009; NRC, 2010; Kay et Arctic system. For instance, in regions of sea al., 2011; Notz and Marotzke, 2012; ). The ice loss the upper ocean is warmer and last six summers (2007-2012) have fresher (e.g., Jackson et al., 2010; Steele et al., experienced the six lowest sea ice extent 2011). As a result of increased open water minima over three decades of satellite area, biological productivity at the base of 5

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6 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies FIGURE 1.1 Arctic sea ice extent has recently undergone extraordinary decline: March 2012 (left) and September 2012 (right). The two periods that define the annual sea ice extent cycle are March, at the end of winter, when the ice is at its maximum extent, and September, at the end of summer, when the ice reaches its annual minimum extent. The purple line indicates the median maximum and minimum ice extents in the given month for the period 1979-2000. Compared with the 1979-2000 average, the September 2012 minimum was 49 percent smaller. SOURCE: Updated from Fetterer et al. (2002), Sea Ice Index, National Snow and Ice Data Center. the marine food chain has increased (e.g., throughout the 21st century (e.g., Meehl et Arrigo and van Dijken, 2011) and sea ice- al., 2007), the declining trends in Arctic sea dependent marine mammals continue to ice extent and multiyear ice composition are lose habitat (e.g., Thomas and Laidre, 2011). expected to continue. Associated changes Increases in the greenness of tundra will likely result in greater marine access to vegetation and permafrost temperatures are the Arctic (e.g., for commercial shipping and linked to warmer land temperatures in offshore natural resource development) and coastal regions, often adjacent to the areas of increased coastal erosion, as well as a range greatest sea ice retreat (e.g., Bhatt et al., of local, regional, and hemispheric changes 2010). in the climate and ecological systems. Given the expectation for a continued The current and projected conditions increase in the global temperature and activity levels in the Arctic call for an

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Introduction 7 improvement in our ability to predict the timescales of interest. At shorter characteristics of the sea ice cover. At the timescales (seasonal to interannual), internal foundation of this challenge lies a set of variability will severely limit sea ice specific questions related to sea ice predictability. For example, summertime prediction common among the broad wind patterns are known to impact summer stakeholder community: (1) Where is the ice and autumn ice extent, yet weather patterns at any given time (extent and are essentially unpredictable beyond about 2 concentration)? (2) What is it like (thickness weeks. Additional atmospheric observations distribution, multiyear ice versus first-year are not going to change this fundamental ice, packed or loose)? (3) What is its prediction barrier. Rather, predictive movement? (4) How and why has it capability for sea ice on these timescales is changed? A major complicating factor in thought to reside primarily in an adequate devising a strategy to more effectively knowledge of the initial ice-ocean state, address these questions is associated with although admittedly little information exists the detailed and differing needs of the on what constitutes an "adequate stakeholders, which shapes temporal and knowledge." At longer (interannual to spatial requirements. For instance, while decadal and beyond) timescales, the role of decadal projections of pan-Arctic ice extent trends in forcing (e.g., rising concentrations and composition may serve the needs of of greenhouse gases, changes in ocean decision makers who determine mixing, increases in river discharge) is likely classification of endangered species or of to provide some predictive potential, as it planners who decide whether to build an accounts for increasingly larger fractions of ice-worthy ship, the long-range predictions the change from present sea ice conditions. will be less useful to marine operators whose A critical gap of uncertainty remains main concern is the position of the ice edge. regarding the timescale between these two Springtime whaling and walrus hunting by regimes, in which the potential predictability coastal communities is dependent on over- is low. Blanchard-Wrigglesworth et al. ice transportation, requiring seasonal (2011) addressed these various prediction projections for the stability of the ice cover, timescales in a numerical modeling study its thickness and roughness, and the and suggested that a time frame of minimal presence of open water close to shore. predictability (for total Arctic Basin ice area) Detailed local information of this kind is not occurs from about 2 to 4r years. Although presently captured by monthly-to-seasonal this study represents a state-of-the-art ice forecasts and is only beginning to be assessment of sea ice predictability, the targeted by experimental sea ice forecasts results are based on one model (Community (e.g., Sea Ice Walrus Outlook 1). Climate System Model [CCSM4]) and are Many of the needs and challenges contingent on the ability of that model to associated with sea ice prediction depend on capture the spectrum of oceanic, atmospheric, and terrestrial variability that 1 http://www.arcus.org/search/siwo affects sea ice. Hence the robustness of this

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8 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies result across climate models is uncertain, simulated late 20th century Arctic ice loss. and the timescales are likely to vary based on Moreover, some simulations for the 21st the sea ice property, simulated atmospheric century revealed decadal-scale periods of and oceanic forcings, and region of interest. gains in ice extent when internal variability Nevertheless, the existence of a minimum in counteracted greenhouse gas-forced trends. predictability at interannual timescales of However, the chaotic internal variability can several years is plausible and likely. For this also reinforce the trend in ice loss, leading to reason, the report focuses on seasonal and instances of very rapid sea ice retreat (e.g., decadal predictability, with the Holland et al., 2006). This large internal understanding that improvements in variability provides an inherent limit to predictions over these two timescales may predictability at these timescales, and as eventually extend into the interannual such, any decadal-scale predictions are timescale of relatively low predictability. necessarily probabilistic in nature. To date, The particular challenges confronting however, little work has been done to assess the prediction of the character and behavior the inherent limits on decadal predictability of sea ice in the seasonal time frame are for different sea ice properties, at different compounded by the increasingly urgent times of year, and in different regions. need for this information by a variety of Some global coupled climate models are stakeholders. As noted above, coastal able to realistically simulate the past regions and the marginal ice zone are an behavior of Arctic sea ice (e.g., Jahn et al., important focus of these needs because sea 2012). Figure 1.2 compares a single ice affects access to shore infrastructure, realization by the Community Climate marine transportation, resource extraction, System Model Version 3 (CCSM3) with and fishing activities. At these timescales observations of the actual ice cover, stakeholders require rapid access to the demonstrating the model's success in information, and errors in that information capturing not only the decadal-scale pace of tend to have immediate and often serious ice loss, but also realistic interannual consequences. Furthermore, the regions and variability. Of particular note is the ability of sea ice properties of interest will likely the modeled ice extent to undergo a decade continue to experience fundamental change of recovery within the inexorable downward accompanying long-term variations in trend. Climate models that were discussed in climate. the 4th Intergovernmental Panel on Climate On decadal timescales, recent work has Change (IPCC) assessment were compiled highlighted the considerable variability of under the Coupled Model Intercomparison Arctic sea ice. Kay et al. (2011), for example, Project, Phase 3 (circa 2006). Simulations found that different ensemble simulations with newer models (circa 2011) that are from a single climate model subjected to the informing the 5th IPCC assessment have same changing external forcing (e.g., just recently become available in CMIP5. As greenhouse gases and solar variability) the models have progressed over this time, exhibited a considerable spread in the

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Introduction 9 FIGURE 1.2 The 20th to 21st century September ice area in the Northern Hemisphere from a single CCSM3 ensemble member (black line) compared with the observed time series of September ice area from Fetterer et al. (2009, red line). It demonstrates the model's success in capturing the decadal-scale pace of ice loss and realistic interannual variability SOURCE: Adapted from Holland et al. (2011). the realism of simulated sea ice area in reach 4.5 Wm-2 (watts/square meter) and response to anthropogenic and natural 8.5 Wm-2 radiative forcing by 2100, forcing has improved in comparisons with respectively. Most simulations capture a satellite observations available since 1979 long-term reduction in ice extent that is (Massonnet et al., 2012; Stroeve et al., 2012). driven fundamentally by external forcing-- Many climate models still simulate an primarily increasing concentrations of Arctic ice pack at odds with observations. greenhouse gases. The spread among the Figure 1.3 displays dozens of model runs runs, however, has not decreased from CMIP5 under two different future appreciably since the IPCC-4 generation, forcing scenarios termed "representative suggesting that basic challenges remain to be concentration pathways" (RCPs; Moss et al., overcome. Because so many variables affect 2010) including RCP4.5 and RCP8.5, which the ice evolution, identification of the causes

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10 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies BOX 1.1 Statement of Taska An ad hoc committee will plan and conduct a public workshop that outlines the current state of Arctic sea ice research, discusses knowledge gaps, and identifies emerging or important new science questions for the coming decade. Through invited presentations and discussion, participants will examine current observations and modeling efforts of sea ice, and identify (but not prioritize) areas of research and technology advances needed to better understand current and future changes. The committee will examine Arctic sea ice prediction, with a particular emphasis on seasonal to decadal timescales. The workshop will be designed to bring together polar scientists and agency representatives to explore whether there are new capabilities and infrastructure available to study sea ice in different ways that might shed new light on emerging research questions. This information may provide the context for future planning and policy development for sea ice research activities. The outcome of this activity will be a consensus report of the committee that builds on workshop presentations and discussions to provide conclusions on the following topics: What key scientific questions remain and how can we improve our understanding of the coupling between oceans, atmosphere, and sea ice (e.g., on what processes should observations be focused)? What systems of monitoring and observations are needed to better understand and predict the connection between changes in Arctic sea ice and its impacts on climate? What aspects of coupled sea ice models do we understand the best and in what ways can models better utilize existing observations and monitoring of sea ice to enhance our understanding of processes and future changes, and improve sea ice prediction? a This report is sponsored by NASA, ONR, and the intelligence community. of discrepancies in a particular model is poorly understood, especially across the full often elusive, and sometimes improvements range of timescales and variables of interest to one process reveal problems in other to stakeholders. Our ability to realize the processes that were originally masked inherent predictability that does exist is because of compensating errors. further hindered by a limited understanding As discussed previously, Arctic sea ice of the coupled and complex interactions prediction has inherent limitations due to between Arctic sea ice, oceans, and the the chaotic nature of the climate system that atmosphere. Advances in understanding and may severely limit the possible predictive seasonal to decadal predictive capabilities power. However, these limitations are require enhancements of our theoretical,

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Introduction 11 FIGURE 1.3 Many climate models still simulate an Arctic ice pack that differs from observations. This figure shows simulations of September sea ice extent from 1900 to 2100 by 23 global climate models participating in the 5th IPCC assessment. Each thin colored line represents one ensemble member. The thicker solid red line depicts observed ice extent, based on the Hadley estimate; except for September 2012 which is from NSIDC. The wide spread among the runs has not decreased appreciably since the 4th IPCC assessment, and because of the complexities associated with sea ice evolution, there are challenges that remain to be overcome. SOURCE: Wang and Overland, 2012. Reproduced/modified by permission of American Geophysical Union. observing, and modeling capabilities. predictability (see Appendix A for a Evidence of the high level of concern about summary of recent efforts). This report seeks these limitations and the challenges involved to build on these efforts, with a specific in addressing them is demonstrated by the emphasis on improved integration between many recent studies that have focused on the diverse community of stakeholders with identifying key questions and a keen interest in and significant recommendations related to Arctic sea ice requirement for improved sea ice

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12 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies BOX 1.2 Terms Couplings--Two-way interaction between different subsystems (e.g., atmosphere, cryosphere, hydrosphere, etc.). Decadal scale (4 to 30 years)--The term "projection" is commonly used when referring to this timescale. Feedbacks--A sequence of interactions that determines the response of a system to an initial disturbance. First-year ice--Floating ice of no more than 1 year's growth developing from young ice; thickness from 0.3 to 2 m (1 to 6.6 ft) where level; ridges of much thicker ice, to 30 m (98 ft), can form where floes are fractured by pressure, and these are rough and sharply angular. Forcings--External data input into models that drive variability and change (e.g., solar radiation, greenhouse gas concentrations, volcanic aerosols). Interannual scale (1-4 years) - The term "prediction" is generally used, although predictions for this timescale presently show little skill relative to climatology or persistence of trend. Internal variability--Interactions within the climate system, as opposed to those forced externally (e.g., by changing greenhouse gas concentrations, solar variability, volcanic aerosols). Examples of internal variability include El Nio, the Arctic Oscillation, and the enhancing or dampening effects of feedbacks. predictions on the seasonal to decadal prediction, and to identify new methods, timescale. observations, and technologies that might This report was developed from insights advance seasonal to decadal sea ice and information gained during a workshop. predictive capabilities through improved The goal of the workshop was to foster a understanding of the Arctic system. The dialogue among stakeholders (e.g., Arctic most prominent theme to emerge from the indigenous residents, polar scientists, agency workshop was the idea of a committed and representatives, and commercial interests) to deliberately integrative approach to Arctic explore current major challenges in sea ice sea ice prediction that would require a

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Introduction 13 Numerical models--Numerical models solve systems of equations describing the fundamental physics, fluid motion, and thermodynamics of an Earth system component. These models can include single Earth system components (i.e., sea ice, ocean, land, or atmosphere) or can include multiple components that are coupled through the exchange of heat, water, and momentum (i.e., ice-ocean models, global climate models). Biogeochemistry, chemistry, and other aspects can also be incorporated through the inclusion of additional coupled equations or parameterizations. Marginal ice zone--A band of pack ice 100 to 200 km (62 to 124 mi.) wide that forms a buffer between open seas and dense interior pack ice; here, waves, swells, and eddies have strong impacts that affect the ice, creating highly variable ice conditions. Multiyear Ice--Ice that has survived at least one melt season; the thickness of multiyear ice floes can range from 2 to 20 m (6.6 to 66 ft) thick. Predictability--The extent to which future states of a system may be predicted based on knowledge of current and past states of the system. Predictability is inherently limited because knowledge of the system's past and current states is imperfect and future variations of the external forcings are not exactly known. Seasonal scale (21 days to 1 year)--The terms "prediction" and "outlook" are commonly used when referring to this timescale. Statistical models--A model based on statistical relationships between different variables in past behavior of the system to be modeled. Weather scale (1 hour to 10 days)--The term "forecasting" is commonly used when referring to this timescale. sustained and coordinated conversation The report addresses Arctic sea ice among the user, modeling, and observation prediction over the seasonal to decadal communities. It was noted that this timescales as a driver of the need for approach needs to go beyond ad hoc improved understanding of sea ice workshops and demands long-term, variability (Box 1.1). The committee's focus continuous, two-way interaction. This was on ice conditions during all seasons theme, which is discussed in more detail in within the whole Arctic marine environment Chapter 3, drove many of the key challenges (i.e., Arctic Ocean and the subpolar seas, and strategies laid out in the report. including the seasonal sea ice zone).

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14 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies Although the Statement of Task does not with a set of overarching challenges that are explicitly mention stakeholders, it was the foundational, including issues related to the committee's view that a report on needs in Arctic environment and its stakeholders. sea ice prediction would be seriously These overarching challenges are deficient if stakeholders were not a followed by challenges and gaps that are prominent part of the underlying discussion. more specific to sea ice predictions, laid out A similar sentiment was also raised in a as a function of timescale from seasonal to recent NRC report: "IPY-related predictive decadal. Chapter 3 presents possible modeling will continue to play a crucial role strategies to significantly advance our in helping commercial enterprises, understanding and predictions of Arctic sea individuals, and governments assess the ice over seasonal to decadal timescales. The regional and global risks associated with organization of Chapter 3 is designed to ongoing melting ice, sea level rise, generally follow the order of key challenges, permafrost degradation, and other effects of though there is not a direct correspondence rising polar temperatures in a warming between the highlighted points made in world" (NRC, 2012a). Chapter 2. Examples of recent and ongoing Further, the committee and workshop activities are provided throughout Chapter 3 participants observed that the motivational to demonstrate successful approaches that questions posed in the Statement of Task have been designed and implemented to were not unique to this activity. Rather, they address related issues. Key challenges and are questions that are often asked of strategies are denoted in gray boxes researchers involved in observing and throughout Chapters 2 and 3. Chapter 4 modeling the Arctic sea ice cover. This concludes with summary comments. realization led the committee to consider Definitions of terms used throughout the additional, more overarching questions in report are provided in Box 1.2. This report the preparation of this report: (1) Given the does not make specific recommendations significant investments and the progress that because of the reliance on the workshop in has been made in observing and modeling developing the ideas put forward in this the Arctic sea ice cover, why are we not report and the relatively short tenure for further advanced in the ability to predict its deliberations and analysis. The report does condition on seasonal to decadal timescales? not include extensive background (2) How can we apply the tools and insights information. The interested reader is we have developed in a systematic way to encouraged to utilize the numerous more effectively address the questions posed references and website links provided in the Statement of Task? throughout the text. After presenting a series of key science questions, Chapter 2 identifies gaps and challenges related to understanding and predicting Arctic sea ice evolution. It begins