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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.,
