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2 Gaps in Our Understanding In this chapter, the committee identifies the Arctic system, but are also influenced by it. a number of key challenges that currently limit our ability to understand and predict sea ice evolution. These key challenges were The Arctic sea ice cover is part of the motivated by several key science questions larger Arctic system, which in turn is part of that the committee considers crucial to the larger global system, with interactions answer in order to improve our across all components. Arctic sea ice is a key understanding and ability to predict sea ice element of these systems, influencing and on seasonal to decadal scales (Box 2.1). The influenced by change across a wide range of key science questions are intended to serve temporal and spatial scales (Figure 2.1). A as a distillation of the more detailed myriad of feedback mechanisms link the discussions that follow. atmosphere, sea ice, ocean, seafloor, and land, many of which are not yet fully understood (Maslowski et. al., 2012). For OVERARCHING CHALLENGES example, winds and ocean currents can alter the distribution of sea ice. These changes in Many of the overarching challenges are the sea ice cover can then affect large-scale not necessarily unique to the topic of sea ice circulation patterns in the atmosphere (e.g., prediction. However, their acknowledgment Deser et al., 2010; Francis and Vavrus, 2012) is important because they need to be and ocean (e.g., Guemas and Salas-Melia, considered in formulating strategies to 2008), which in turn may impact weather, advance predictive capabilities. fisheries, and the global climate system. Other system components affecting and Key Challenge: Treating Sea Ice as Part of the Global System affected by sea ice cover include biological and chemical processes (e.g., Simpson et al., A key to advancing our understanding and 2007; Kelly et al., 2010b; Deming and predictive capabilities is the treatment of the sea ice cover as an integral part of the Fortier, 2011; ). In addition, bathymetry complex Arctic system, which in turn is an plays a role in processes such as sea ice integral element of the global system. Adding to this complexity is the broad range formation and evolution (Jakobsson et al., of human activities that not only influence 2012; Nghiem et al., 2012). 15
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16 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies BOX 2.1 KEY SCIENCE QUESTIONS · What are the implications of the recent dramatic shifts in the Arctic from predominantly multiyear ice to first-year ice, and how will the associated complexities of this regime shift affect sea ice variability and predictability? · In a rapidly changing Arctic regime, how will forcings and couplings between the various components of the ocean, atmosphere, cryosphere, and seafloor and land systems modify or influence the processes governing the characteristics of the sea ice cover? · What are the impacts of extreme events and feedback mechanisms on Arctic sea ice evolution and our ability to predict it? · How will the changing Arctic sea ice characteristics and dynamics affect stakeholders on a variety of timescales, including prediction requirements? Adding to the complexity of the natural Understanding how the recent regime shift system is a range of human interactions. in the Arctic sea ice cover, resulting in a Humans not only influence the Arctic (e.g., significant reduction in the amount of multiyear ice compared with first-year ice, impacts of oil spills, ship discharges, and affects the processes governing the land and sea greenhouse gas emissions), they atmosphere-sea ice-ocean system and the are also influenced by it (e.g., decisions to models and observations used to study and pursue commercial shipping routes and predict Arctic sea ice dynamics. offshore developments in response to a changing ice cover, long-term strategies for The recent decline in the extent of Arctic Arctic security, and development of future summer sea ice has resulted in a dramatic Arctic monitoring systems). The Arctic sea shift in its composition (Figure 2.2). First- ice cover, therefore, cannot be viewed in year ice is becoming more prevalent within isolation, as it is constantly responding to a the Arctic Basin, reducing the size of the host of regional and global forces, and these multiyear ice pack (e.g., Maslanik et al., are in turn directly influencing the ice 2011). Furthermore, the multiyear ice that cover's seasonal and decadal presence or does remain is younger and thinner (Haas et absence. Treating the Arctic as an integrated al., 2008; Comiso, 2012). This rapid change whole and a vital component of the global to a new state is likely to have important system is necessary to significantly advance implications for sea ice variability (Goosse et our understanding of the sea ice cover as al., 2009; Hutchings and Rigor, 2012) and, part of a complex system. ultimately, predictability (Holland et al., 2011). For instance, recent research suggests Key Challenge: Impacts of the Regime that first-year ice is not only more Shift of Arctic Sea Ice
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Gaps in Our Understanding 17 FIGURE 2.1 The Arctic system is made up of various components including a complex network of process interactions, interdependent feedbacks, and thresholds. There are many interconnections among system components and important changes in one component may influence numerous other parts of the system, including sea ice. SOURCE: Adapted from the Study of Environmental Arctic Change. susceptible to summer melt (Perovich and Arctic coast (Mahoney et al., 2007). Polashenski, 2012) but it is also likely to be Some of the greatest changes to the more easily ridged, ruptured, and Arctic Ocean are occurring in the Chukchi transported by winds (Rampal et al., and Beaufort seas, where the increased 2009).The delay in the formation of shore- summer ice retreat has created a fast ice as well as its reduced stability and substantially enlarged marginal ice zone overall duration are also significant changes, (Holland and Stroeve, 2011). As a seasonal with implications for stakeholders along the feature contained within the Arctic Basin,
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18 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies this region of transition between the ice- current models. These complex interactions covered and open ocean is expected to have need to be simulated with sufficient accuracy different governing processes than the to robustly project changes in the system. marginal ice zones in the Greenland Sea or A number of smaller-scale processes also Southern Ocean. There are also indications need to be parameterized differently in first- that the extent of the North Atlantic year and multiyear ice because of inherent marginal ice zone has increased over the variations in their morphologies. For past several decades (Strong, 2012). instance, recent work by Perovich and It is important to understand how the Polashenski (2012) conducted near Barrow, transition from a multiyear to first-year ice Alaska, found that once surface melt begins, regime affects (a) the realism of current seasonal ice albedos are consistently less numerical models and their than those for multiyear ice. This finding parameterizations, (b) the power of suggests that the shift from a multiyear to statistical prediction methods, (c) the seasonal ice cover has significant processes governing the atmosphere-sea ice- implications for the heat and mass budget of ocean system, and (d) instrument design the ice through a strong positive feedback on and observational strategies. Currently, sea melting, and for primary productivity ice models' treatment of ice dynamics and through an increase in the amount of thermodynamics employs parameterizations sunlight penetrating into the upper ocean that were often developed using insights (Arrigo et al., 2012). gained from observations taken in a A shift from predominantly multiyear to primarily multiyear ice regime. For example, mostly first-year ice will also affect the skill many sea ice models use ice dynamic and ice of statistical prediction methods. These thickness distribution treatments based on methods use statistical relationships the Arctic Ice Dynamics Joint Experiment determined from past system behavior to that took place during the 1970s (e.g., predict the future state of the ice cover. An Thorndike et al., 1975; Hibler, 1979). Model underlying assumption is that these parameterizations based on the Arctic relationships are stationary. However, given system's behavior in the past may not apply the transition to a thinner and increasing in the new state. Moreover, it is likely that if, first-year ice pack, statistical relationships as expected, the substantial ice retreat that have provided predictive skill for the continues and the remaining ice transforms past may no longer be valid. to a largely seasonal character, the oceanic The fundamental shift in the ice regime and atmospheric circulation and is also likely to have significant implications thermodynamic structure will respond to regarding the design of observation studies the changes in the surface state, affecting and networks, including the large-scale patterns. The regime shift may instrumentation. As is the case with sea ice also cause changes in physical and models, most Arctic observational system biochemical processes that are not or have designs and instruments were "developed" not been adequately accounted for in in the era of a predominantly multiyear sea
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Gaps in Our Understanding 19 FIGURE 2.2 Sea ice is shifting from a composition of predominantly multiyear ice to include increasing amounts of first-year ice. Sea ice age distribution in March 1988 (upper left) compared with 2012 (lower right) illustrates the extensive loss in recent years of older ice types. Ice age is determined using satellite observations to track ice parcels. Older multiyear ice tends to be thicker and thus more resistant to forcing than younger first-year ice. SOURCE: James Maslanik, Charles Fowler, and Mark Tschudi. ice cover and thus may need retooling to maximize their longevity. There are recent address thinner, more mobile ice conditions. examples of work dedicated to transitioning For example, many buoys deployed in the design of ice-based instruments for coordination with the International Arctic deployment in the seasonal ice zone, where Buoy Programme were largely designed for they tend to be more vulnerable (e.g., placement on multiyear sea ice floes to Polashenski et al., 2011). New sustained
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20 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies measurement strategies can also take are important parameters for an early advantage of innovative technologies such as snowmelt, which may prematurely destroy unmanned aerial systems and autonomous subnivean lairs subjecting ringed seal pups underwater vehicles to answer specific to adverse weather and increased predation questions without the risks of deployment (Kelly et al., 2009; Hezel et al., 2012; see on seasonal sea ice or in open water. Figure 2.3). Understanding stakeholders' needs for Key Challenge: Identifying Diverse and seasonal to decadal sea ice prediction is Emerging Stakeholder Requirements crucial to guiding the future directions of modeling, observations, and overall As the Arctic is being transformed by research. Effectively quantifying and globalization and climate change, new stakeholders with additional and more communicating the inherent limitations in sophisticated requirements are emerging. sea ice predictability is also needed to Clearly defining these diverse needs as they establish reasonable stakeholder relate to seasonal to decadal sea ice expectations. Significant issues that will need prediction is crucial to inform the future to be overcome include: (1) how to directions of modeling, observations, and aggregate stakeholder input; (2) how to overall research. prioritize among stakeholder interests and needs; (3) how to communicate the The term stakeholder covers a wide limitations of modeling and forecasting to range of communities with interests in the users and policy makers; (4) how to development and application of Arctic sea maximize the utility of sea ice predictions ice prediction across seasonal to decadal containing uncertainties; and (5) how to timescales. The term includes indigenous assess opportunities for overlapping residents on the Arctic coast, scientific interests and efficiency of efforts in meeting researchers, commercial users (e.g., fishing, stakeholder needs. shipping, natural resource development, and A realistic expectation for the future marine tourism.), and naval and coast guard state of Arctic sea ice is needed to manage planners. Not surprisingly, this diverse risks, deploy limited resources, and plan group of stakeholders has a broad and long-term infrastructure. Plausible future evolving set of needs and, hence, sea ice sea ice scenarios projected by fully coupled, prediction requirements. In addition to ice large-scale global climate models (GCMS) type (e.g., multiyear/perennial vs. first- can be useful for strategic planning. For year/seasonal ice), extent, thickness, and instance, they are used by federal and private motion, there is growing interest in the entities and commercial shipping companies ability to predict snow cover depth, melt looking to understand future navigation onset, freeze-up/ice season length, and the seasons on potential Arctic shipping routes. size and shape of ice floes (e.g., Ray et al., GCMs are also used by federal agencies to 2010). For example, earlier snowmelt onset study the plausible futures of endangered date and thinner snow cover during winter
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Gaps in Our Understanding 21 FIGURE 2.3 Where sufficient snow accumulates on the sea ice cover, ringed seals may excavate snow caves (lairs) above the breathing holes to protect the pup from adverse weather and predation. Early snowmelt onset causes premature lair abandonment exposing pups to hyperthermia and predation. Images courtesy of Brendan Kelly (left) and Juha Taskinen (right). species and the possible merits of regulatory needs--by either nested grids or regional strategies (e.g., NOAA, 2010). However, high-resolution models--but they face better characterizing the uncertainties challenges related to the extreme through continued intercomparisons of computational demands, lack of suitable model outputs as well as validation with sea initialization conditions, inadequate ice observations is necessary to enhance projections of atmospheric and oceanic confidence in using this information for forcing, and insufficient software and decision making. resources to manipulate and store the Although projections from GCM enormous datasets produced. simulations fulfill some requirements for Parameterizations that have been developed planning, regional forecasts and seasonal for relatively coarse resolutions, such as the predictions at shorter timescales and finer viscous-plastic sea ice rheology (Hibler, spatial resolutions are more useful to 1979), may not be applicable at very fine offshore drilling operations, marine spatial scales. This may necessitate transportation systems, and subsistence additional model developments such as the hunters. Many of these requirements call for incorporation of more detailed timely predictions that resolve the highly representations of sea ice dynamic varying ice characteristics that occur near components (e.g. Hopkins and Thorndike, the coastline, demanding accuracy of the 2006; Sulsky et al., 2007) into large-scale prediction within a few hundred kilometers climate models. (or tens of kilometers) of shore and within the marginal ice zone. Modelers are developing methods to address these
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22 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies CHALLENGES IN ADVANCING characteristics, and resulting shifts in PREDICTIVE CAPABILITY atmospheric patterns all affect surface- atmosphere exchanges. Additionally, Key Challenge: Competing Approaches to important feedbacks between the Seasonal Sea Ice Prediction atmosphere and ice that influence the sea ice evolution are not accounted for by using Although limitations in the various historical atmospheric conditions. In approaches used to generate seasonal forecasts are generally acknowledged, there coupled atmosphere-ocean-ice model is a lack of quantitative information about predictions, atmospheric forcing is free to their relative strengths and weaknesses. evolve consistent with the underlying distributions of sea ice and ocean Three approaches are used to provide temperatures, but the simulations of sea ice seasonal ice forecasts, including (1) evolution suffer from systematic biases. This statistical algorithms (e.g., Drobot et al., causes the models to drift to their preferred 2006); (2) coupled ice-ocean models driven (and biased) climate state once constraints by prescribed atmospheric forcing (e.g., by observations are removed. Methods to Zhang et al., 2008b); and (3) more recently, bias correct (or "de-drift") forecasts with coupled atmosphere-ocean-ice models. All coupled models have been explored (e.g., three approaches have contributed to the Yeager et al., 2012, for ocean conditions). SEARCH (Study of Environmental Arctic Alternatively, "anomaly-initialization" in Change 1) Sea Ice Outlook. 2 Each of the which observed anomalies are added to the methods has acknowledged limitations, but model''s preferred climate state has been the relative accuracies, strengths, and used in a number of decadal prediction weaknesses are poorly known. For example, studies (e.g., Pohlmann et al., 2009). statistical relationships based on past The relative benefits of these various observations may not be valid in a future approaches have not been assessed for sea Arctic dominated by thinner ice (Holland ice prediction. Determinations of their and Stroeve, 2011). prediction accuracy are challenged further In coupled ice-ocean prediction systems, by observation-based uncertainties in the sea atmospheric forcing is usually based on an ice variables that are to be predicted. This is ensemble of past years' winds and especially the case when the predictions temperature fields derived from atmospheric extend to new variables (e.g., sea ice reanalyses. However, the statistics of past thickness, morphology, and stability) that forcing fields may not be applicable to the are of particular and growing interest to new Arctic state, because the preponderance stakeholders. of thin ice, large-scale changes in surface Key Challenge: Observational Requirements for Seasonal Sea Ice 1 http://www.arcus.org/search/index.php. Prediction 2 http://www.arcus.org/search/seaiceoutlook/index. php.
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Gaps in Our Understanding 23 Seasonal sea ice prediction capability The accuracy requirements for bathymetry, depends on adequate knowledge of initial which constrains ocean currents and ice-ocean conditions, even though the controls the distribution of warm and cold specific requirements associated with "adequate knowledge" have yet to be water masses, are largely unknown established. This challenge is compounded (Jakobsson et al., 2012). by the need for a fast turnaround in There is little information on what acquiring and accessing observational data. observational quality, spatial density, location, and accuracy are required for There has been a dearth of experiments different variables to realize useful predictive performed to systematically evaluate the power. Compounding the problem is the sensitivities of sea ice predictions to the type, fact that observations of the atmosphere, sea quality, density, and frequency of ice, ocean, and seafloor are made from vastly observations of the Arctic atmosphere, different sensors, including in situ, airborne, ocean, and sea ice. These sensitivities arise and satellite instruments, along with a from the specification of the initial state of variety of methods. Each approach brings the sea ice and its drivers in numerical with it a specific set of characteristics, which models. often go undocumented. During the forecast period, atmospheric There has been little if any effort to variations, which are largely unpredictable establish and broadly apply measurement beyond a week or two, are expected to have a protocols and standard definitions. For significant influence on sea ice and will limit instance, even for one of the most its seasonal predictability. Instead, sea ice fundamental parameters such as air predictability on these timescales resides in temperature, there is no existing national or the initial ice and ocean state. Factors international protocol for predeployment believed to be among the most important for calibration, maintenance of measurement predicting sea ice behavior on the seasonal stability during deployment, post- scale include accurate knowledge of (1) sea deployment calibration, and cross ice conditions at the start of the season calibration among different sensors and (particularly the ice thickness distribution different algorithms (e.g., different and the partitioning between seasonal and algorithms to measure ice surface multiyear ice) and (2) upper ocean temperature from different satellite sensors). conditions, such as ocean mixed-layer heat Adequately acquiring and making content.. However, the relationship between observations available within several days of prediction skill and the uncertainty in each the beginning date of the model simulation of these factors is poorly known. The are necessary if the observations are to be importance of initial values of other useful for initializing operational seasonal variables--such as snow on sea ice, ocean model predictions. Ice extent is available temperature and salinity profiles below the within a day of satellite data acquisitions ice, and ocean current distributions--is from multiple satellite sensors (passive poorly understood, but may be considerable. microwave, active microwave, and
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24 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies multispectral sensors). Recent efforts variables and interactions that affect the ice undertaken by the National Aeronautics and cover, it is a tremendous achievement that Space Administration (NASA) IceBridge the models simulate the observed ice project (Kurtz et al., 2012) and the Sea Ice behavior as well as they do. Any errors in the Outlook, 3 for example, have demonstrated variables, processes, or feedbacks involved in the ability to deliver observations of sea ice these calculations, however, will be thickness and snow depth within a period of propagated and integrated by the ice over weeks from airborne platforms. This time and result in an unrealistic achievement was the result of a coordinated representation. A key challenge is simulating effort backed by dedicated resources (i.e., realistic atmospheric and oceanic measurement platform, instruments, conditions, which in turn depend on personnel, and funding resources). assumptions about future trends in carbon However, near-real-time access remains dioxide emissions, aerosol loading, changing problematic for many other types of data, surface characteristics, etc. Related to this such as ocean temperatures, salinities, and challenge is the difficulty in determining ice draft collected from seafloor moorings. which processes in a particular model are Moreover, ocean currents, the distribution responsible for unrealistic aspects of sea ice of warm and cold waters, and ocean mixing simulations, especially systematic biases are controlled, guided, or restricted by such as those responsible for the large model seafloor characteristics. About 89 percent of spread in Figure 1.3. the Arctic Ocean still needs to be surveyed to determine where interpolation or Key Challenge: Projecting Realistic extrapolation may result in unrealistic Forcings and Feedbacks for Decadal Sea bathymetric features (e.g., false seamounts) Ice Predictions or where important structures (e.g., A key challenge in coupled climate models is canyons) have been missed (Jakobsson et al., the capability to realistically simulate 2012). In general, prioritizing data atmospheric and oceanic conditions of acquisition with sufficient temporal and relevance to sea ice variability, including spatial coverage and timely accessibility to the identification of model processes that observations is critical if they are to play an contribute to unrealistic forcings and feedbacks. effective role in seasonal, operational sea ice prediction. Coupled climate models calculate their Phenomena such as El Niņo-Southern own atmospheric and oceanic variables from Oscillation (ENSO), the Arctic Oscillation the basic laws of physics, providing the (AO), the Pacific Decadal Oscillation, and thermodynamic and dynamic drivers of sea the Arctic Dipole are known to affect the ice ice evolution. Considering the number of cover through their influence on ice transport, storm tracks, and heat transport 3 (e.g., Rigor et al., 2002; Wang et al., 2009; http://www.arcus.org/search/seaiceoutlook/ice- thickness-data. Ogi et al., 2010). What drives these
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Gaps in Our Understanding 25 atmospheric patterns into their positive and affect wind patterns (e.g., Liu et al., 2012), negative phases in any given year or water vapor content, and cloud amount sequence of years, however, is not well (e.g., Winton, 2006; Kay et al., 2012)--can understood. Although models generally then feed back onto the ice cover (Overland simulate these major modes of variability, et al., 2012;Wu et al., 2012) and steer ocean studies reveal potentially important currents. Capturing these feedbacks in discrepancies in the statistics of their coupled models is critical if decadal variations (e.g., Stenchikov et al., 2006). predictions are to be successful. Recent studies have suggested that phasing As evidenced in summer 2007, extreme of the AO may be related to stratospheric events (e.g., anomalous winds as one of influences in some situations (Black, 2002; several key factors) may combine with Cohen and Jones, 2011) and to surface preconditioning and ice-albedo feedback to forcing in others (Overland and Wang, result in abrupt change (e.g., a large decrease 2010). Thus models need to include a of sea ice in a short time) (Haas et al., 2008; realistic representation of the stratosphere Perovich et al., 2008; Zhang et al., 2008a; and its interaction with the tropospheric Lindsay et al., 2009; Ogi and Wallace, 2012) circulation. that can have decadal impacts. For example, The present capability to predict, in drastic loss of perennial sea ice owing to detail, the large-scale modes of atmospheric persistent wind patterns in 2005 and 2007 variability is limited, and in some cases there (AMAP, 2011) may influence the long-term may be little deterministic predictability sea ice trends. Models do simulate extreme beyond a few weeks. Experiments addressing events of this type (e.g., Holland et al., 2006) such changes in large-scale atmospheric but the realism of how simulated extreme modes are challenged by natural variability, events modify key parameters needs to be because many ensemble members can be further assessed. required to detect significant changes in the Another characteristic that highlights the circulation (Bhatt et al., 2008; Deser et al., interconnectedness of the Arctic system is 2012). Despite these challenges, models need the influence of the Atlantic and Pacific to realistically simulate the statistics of these oceans on the Arctic Ocean. Relatively warm atmospheric variations, including their water masses from the Atlantic and Pacific spatial patterns, frequency of occurrence, enter the Arctic Ocean, and because they are and response to varying forcings, if they are saltier than the surface waters, reside below to accurately simulate sea ice variability on the mixed layer. The Arctic Ocean's present decadal scales. Whether fluctuations in stratification, resulting primarily from the spatial extent, intensity, and frequency of vertical salinity profile, largely limits heat these large-scale oscillations will change as transfer to the ice cover from the deeper greenhouse gases continue to accumulate is layers. These deeper layers contain vast a key open question. Evidence does suggest quantities of heat that could melt all of the that high-latitude surface changes--which sea ice relatively quickly (e.g., Alexeev et al., include changes in sea ice cover that may 2011). As the Arctic transitions to a state
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26 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies dominated by first-year ice and as 2007). Owing in part to insufficient vertical atmospheric circulation patterns potentially resolution in modeled oceans, processes change in response to sea ice loss and other related to the mixed layer and stratification factors, the stratification of the upper-ocean are not well captured by models. layers could also change, modifying the Additionally, lateral oceanic transports amount of Atlantic and Pacific layer heat affecting, for example, the net heat flux from that affects the ice cover. Moreover, the North Atlantic into the Arctic Basin, are additional solar energy absorbed in areas also often poorly simulated in current where ice has retreated will also affect climate models. mixed-layer characteristics (Perovich et al.,