The atmospheric contribution to Arctic sea-ice variability Marie-Luise Kapsch

The atmospheric contribution
to Arctic sea-ice variability
Marie-Luise Kapsch
Cover Image: A view of the Arctic Ocean, taken aboard the Russian research vessel Akademik
Fedorov at approximately 78◦ N and 152◦ E. September 2013.
Photo by: Marie-Luise Kapsch
c
Marie-Luise
Kapsch, Stockholm 2015
ISBN 978-91-7649-228-4
Printer: Holmbergs, Malmö 2015
Distributor: Department of Meteorology, Stockholm University
Abstract
The Arctic sea-ice cover plays an important role for the global climate system. Sea ice and the overlying snow cover reflect up to eight times more of
the solar radiation than the underlying ocean. Hence, they are important for
the global energy budget, and changes in the sea-ice cover can have a large
impact on the Arctic climate and beyond. In the past 36 years the ice cover
reduced significantly. The largest decline is observed in September, with a rate
of more than 12% per decade. The negative trend is accompanied by large
inter-annual sea-ice variability: in September the sea-ice extent varies by up
to 27% between years. The processes controlling the large variability are not
well understood. In this thesis the atmospheric contribution to the inter-annual
sea-ice variability is explored. The focus is specifically on the thermodynamical effects: processes that are associated with a temperature change of the ice
cover and sea-ice melt. Atmospheric reanalysis data are used to identify key
processes, while experiments with a state-of-the-art climate model are conducted to understand their relevance throughout different seasons. It is found
that in years with a very low September sea-ice extent more heat and moisture
are transported in spring into the area that shows the largest ice variability. The
increased transport is often associated with similar atmospheric circulation patterns. Increased heat and moisture over the Arctic result in positive anomalies
of water vapor and clouds. These alter the amount of downward radiation at
the surface: positive cloud anomalies allow for more longwave radiation and
less shortwave radiation. In spring, when the solar inclination is small, positive
cloud anomalies result in an increased surface warming and an earlier seasonal
melt onset. This reduces the ice cover early in the season and allows for an
increased absorption of solar radiation by the surface during summer, which
further accelerates the ice melt. The modeling experiments indicate that cloud
anomalies of similar magnitude during other seasons than spring would likely
not result in below-average September sea ice. Based on these results a simple
statistical sea-ice prediction model is designed, that only takes into account the
downward longwave radiation anomalies or variables associated with it. Predictive skills are similar to those of more complex models, emphasizing the
importance of the spring atmosphere for the annual sea-ice evolution.
Zusammenfassung
Das arktische Meereis spielt eine entscheidende Rolle im globalen Klimasystem. Meereis und die aufliegende Schneedecke reflektieren bis zu acht mal
mehr Sonnenstrahlung als offene Wasserflächen. Daher ist das Meereis bedeutend für den globalen Energiehaushalt und Veränderungen des Eises haben
große Auswirkungen auf das Klima, innerhalb und ausserhalb der Arktis. Im
Laufe der vergangenen 36 Jahre hat sich das Meereis räumlich stark zurückgezogen. Der größte Rückgang, mehr als 12% pro Jahrzehnt, ist im September zu beobachten, wenn die Ausdehnung des Eises ihr saisonales Minimum
erreicht. Neben dem langjährigen negativen Trend zeigt die Meereisbedeckung auch große jährliche Variabilität: die räumliche Meereisausdehnung im
September kann von Jahr zu Jahr bis zu 27% variieren. Die Prozesse, die
zu der starken Variabilität beitragen sind nicht ausreichend verstanden. In der
vorliegenden Doktorarbeit wird der Einfluss der Atmosphäre auf die Meereisschwankungen untersucht. Der Fokus liegt dabei auf den thermodynamischen Prozessen: Prozesse die mit einer Temperaturveränderung des Eises und
Schmelzprozessen verbunden sind. Atmosphärische Reanalysedaten werden
verwendet, um entscheidende Prozesse zu identifizieren. Außerdem werden
Experimente mit einem Klimamodell durchgeführt, um die Auswirkungen
dieser Prozesse in verschiedenen Jahreszeiten zu verstehen. In Jahren, in denen die Meereisausdehnung im September sehr gering ist, wird im Frühjahr
mehr Wärme und Feuchtigkeit in die Teile der Arktis transportiert, in denen die
Meereisschwankungen am größten sind. Dieser verstärkte Transport ist oft mit
ähnlichen atmosphärischen Zirkulationsmustern verbunden. Mehr Wärme und
Feuchtigkeit in der Arktis führen zu mehr Wasserdampf und Wolken, welche
wiederum die zum Boden gerichtete Strahlung beeinflussen: Positive Wolkenanomalien führen zu mehr langwelliger Strahlung und weniger kurzwelliger
Sonnenstrahlung am Boden. Im Frühjahr, wenn die Sonneneinstrahlung in
der Arktis gering ist, haben positive Wolkenanomalien eine verstärkte Bodenerwärmung zur Folge. Dies löst eine frühere Schmelze aus und führt somit
zu einer früheren Reduktion des Meereises. Dadurch kann im Verlauf des
Sommers mehr Sonnenstrahlung von der Oberfläche absorbiert werden, was
die Eisschmelze beschleunigt. Experimente mit einem Klimamodell zeigen,
dass ähnliche Wolkenanomalien in anderen Jahreszeiten einen unwesentlichen
Einfluss auf die Meereisbedeckung im September haben. Basierend auf
diesen Ergebnissen wird ein einfaches statistisches Vorhersagemodell entwickelt, welches ausschliesslich Anomalien der langwelligen Strahlung im Frühjahr, oder Variablen, die damit in Verbindung stehen, für die Vorhersage der
Eisausdehnung im September berücksichtigt. Die Fähigkeit des Modells die
Meereisausdehnung vorherzusagen ist vergleichbar mit anderen, komplizierteren Modellen. Dies zeigt, dass die atmosphärischen Prozesse im Frühjahr
wichtig für die jährliche Meereisentwicklung sind.
Sammanfattning
Havsisen i Arktis spelar en avgörande roll för det globala klimatsystemet. Isen
och snötäcket ovanpå reflekterar upp till åtta gånger mer solstrålning än det
öppna havet. Därför är havsisen viktig för den globala energibalansen, och
förändringarna i denna kan ha stora inverkningar på klimatet både i och utanför Arktis. Under de senaste 36 åren har havsisen minskat avsevärt. Den
största minskningen, mer än 12% per årtionde, observeras i september, när
isutbredingen är som minst varje år. Utöver den negativa trenden uppvisar
havsisen även stora årliga variationer; isens utbredning i september kan variera med upp till 27% mellan enskilda år. De processer som styr dessa variationer är inte väl förstådda. I denna avhandling behandlas det atmosfäriska
bidraget till de årliga variationerna i havsisen med fokus framförallt på termodynamiska effekter: processer som innebär en förändring av temperaturen
och därmed smältning av is. Data från atmosfäriska reanalyser har använts
för att identifiera viktiga processer, medan experiment med en kopplad klimatmodell har använts för att förstå inverkan av dessa under olika årstider. Då
havsisens utbredning varit exceptionellt låg i september föregås detta av en vår
då mer värme och fukt än normalt transporterats till de områden som uppvisar
de största variationerna i havsisen. Den ökade transporten hänger ofta samman med vissa distinkta typer av atmosfärscirkulation. Ökad värme och fukt
i Arktis ger i sin tur mer vattenånga och moln än vanligt och detta ändrar den
inkommande strålningen från atmosfären till ytan; positiva anomalier i moln
leder till mer långvågig värmestrålning och mindre kortvågig solstrålning. På
våren, då solen står lågt, resulterar positiva anomalier i molnen till en ökning
av uppvärmningen vid ytan vilket tidigarelägger starten av den årliga smältningen. Detta leder i sin tur till att isutbredningen minskar tidigare på säsongen
vilket möjliggör en ökning av absorptionen av solinstrålning under sommaren,
som i sin tur accelererar isavsmältningen. Experiment med en kopplad klimatmodell visar att liknande anomalierna i moln under andra årstider än våren inte
signifikant påverkar havsisutbredningen i september. Baserat på dessa resultat
har en enkel statistisk prognosmodell utvecklats som tar hänsyn till anomalier
i den inkommande långvågiga strålningen, och variabler relaterade till detta,
under våren. Dess förmåga att förutse havsisen i september är fullt jämförbar
med andra betydligt mer komplicerade modeller, vilket visar hur viktig atmosfären under våren är för den årliga utvecklingen av havsisen.
This
thesis
is
dedicated
to
my
parents.
List of Papers
An introduction and the following papers, referred to in the text by their Roman
numerals, are included in this thesis.
PAPER I Kapsch, M.-L., R. G. Graversen and M. Tjernström (2013),
Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent, Nature Clim. Change, 3 (8): 744–748,
doi: 10.1038/nclimate1884.
PAPER II Kapsch, M.-L., R. G. Graversen, T. Economou and M. Tjernström (2014), The importance of spring atmospheric conditions
for predictions of the Arctic summer sea-ice extent, Geophys.
Res. Lett., 41: 5288–5296, doi: 10.1002/2014GL060826.
PAPER III Kapsch, M.-L., R. G. Graversen, M. Tjernström and R. Bintanja
(2015), The effect of downwelling longwave and shortwave radiation on Arctic summer sea ice. Under revision in J. Climate.
PAPER IV Kapsch, M.-L., R. G. Graversen and M. Tjernström (2015), Atmospheric transport of heat and moisture in spring of years with
low September sea-ice extent. Manuscript.
Reprints of Paper I and II were made with permission from the publishers.
Author’s contribution
The data analysis, the model experiments and most of the writing for Paper
I-IV was conducted by myself. R. G. Graversen and M. Tjernström added
valuable comments, suggestions and ideas to all the papers presented in this
thesis. The idea for Paper I originated from R. G. Graversen and was further
developed by R. G. Graversen, M. Tjernström and me. R. G. Graversen
contributed significantly to the writing of Paper I. The initial idea for Paper II
was mine but developed through discussions with all co-authors. T. Economou
provided statistical advice for Paper II. Most of the writing for Paper II was
done by myself, with support from R. G. Graversen and feedback by all
co-authors. The idea for Paper III evolved during a discussion between R.
Bintanja and myself. It was further developed in close collaboration with R.
Bintanja and R. G. Graversen. I conducted all model experiments, the data
analysis and the writing for Paper III; R. G. Graversen supported the writing
and all co-authors provided feedback. The idea for Paper IV arose from
discussions between R. G. Graversen and me. The data analysis, interpretation
and writing for Paper IV was carried out by myself. R. G. Graversen and M.
Tjernström provided significant feedback.
The following papers are not included in the thesis:
Kapsch, M.-L., M. Kunz, R. Vitolo, and T. Economou (2012), Long-term
trends of hail-related weather types in an ensemble of regional climate models
using a Bayesian approach, J. Geophys. Res., 117, D15107, doi:10.1029/2011J
D017185.
Mortin, J., G. Svensson, R.G. Graversen, M.-L. Kapsch, J.C. Stroeve, and
L.N. Boisvert (2015), Melt onset over Arctic sea ice controlled by atmospheric
moisture transport, submitted to Geophys. Res. Lett.
Contents
Abstract
iii
Zusammenfassung
v
Sammanfattning
vii
List of Papers
ix
Author’s contribution
xi
1
Introduction
1
2
Sea ice as part of the global climate system
2.1 The global climate system . . . . . . . . . . . . . . . . . . .
2.2 Sea-ice characteristics . . . . . . . . . . . . . . . . . . . . . .
3
3
5
3
Methodology
3.1 Atmospheric reanalyses . . . . . . . . . . . . . . . . . . . . .
3.2 Global climate models . . . . . . . . . . . . . . . . . . . . .
9
9
10
4
Changes of the sea-ice cover
4.1 Long-term changes of the sea-ice cover . . . . . . . . . . . .
4.2 Inter-annual sea-ice variability . . . . . . . . . . . . . . . . .
13
13
18
5
Conclusions and Outlook
25
Acknowledgements
References
xxxi
xxxiii
1. Introduction
The perennial sea-ice cover is one of the most unique and important features
of the Arctic climate system. During winter, sea ice extends to about 15.6 million km2 , thereby covering the entire Arctic Ocean and parts of the adjacent
seas (Fig. 1). In summer the area covered by sea ice is reduced by a size almost as large as Europe due to a strong seasonal cycle; the sea-ice minimum
is reached in September, when the sea-ice extent (see definition in box) has
reduced to about 6.0 million km2 . Sea ice represents an important boundary
between the atmosphere and the underlying ocean, as it controls the energy exchange between the two; for example, in winter sea ice isolates the relatively
warm ocean from cold Arctic atmospheric temperatures, which occasionally
drop below -40◦ C. Hence, changes and variations of the sea-ice cover of any
form, e.g. thickness or extent, can have a variety of impacts on the local climate and the climate beyond the Arctic boundaries (ACIA, 2005; Francis et al.,
2009; IPCC, 2013; Maslowski et al., 2012).
Figure 1: Sea-ice concentration during the seasonal maxima (left) and minima (right) of the sea-ice extent between 1979–2014. The concentration maps
are averages of the ice concentration over all years during the day of the seaice extent maximum (typically in February or March) or minimum of each year
(September). Sea-ice data are provided by the National Snow and Ice Data Center
(NSIDC).
1
Ice concentration: Relative amount of an area/grid box covered by ice.
Ice extent: Total area in million km2 covered by at least 15% of ice.
From a socio-economic perspective sea ice serves as an important platform for subsistence hunting for many Arctic native communities; for example,
coastal communities build trails across shortfast sea ice, ice that is grounded to
the underlying surface, to reach migrating whales (Druckenmiller et al., 2013).
It also plays a substantial role for Arctic maritime activities, such as e.g. oil
drilling and shipping. Hence, it is essential to identify and track changes of the
ice cover in order to allow for safe access and an adaptation of native communities (Eicken, 2013).
The physical as well the socio-economic implications require a more thorough understanding of critical processes governing the Arctic sea ice. This
thesis will give an overview of some of the important processes that impact the
sea-ice cover, with specific focus on the observed year-to-year variability of
sea ice. It will address the question: What role do atmospheric processes play
for the development of the sea-ice extent throughout the year? To introduce
the subject an overview of the role of sea ice in the global climate system is
given in Chapter 2, together with an introduction of important sea-ice characteristics. This is followed in Chapter 3 by a brief summary and evaluation
of the data and methods used in the papers included in this thesis. Chapter 4
summarizes processes related to the long-term changes of the ice cover; further, the year-to-year variability of the sea-ice extent is discussed in respect to
the results obtained in Papers I, II and IV. Note, that we will focus primarily on
the processes contributing to changes and variability of the summer ice extent.
Lastly, Chapter 5 gives a summary and shows how key results of this thesis are
used to design and implement a relatively simple sea-ice prediction system as
presented in Paper II.
2
2. Sea ice as part of the global
climate system
2.1
The global climate system
The Arctic plays a key role within the global climate system, specifically for
the energy exchange throughout the Northern Hemisphere. Therefore, a short
summary of the processes that govern the global energy exchange is given in
the following.
The global climate is largely determined by the amount of energy that is
absorbed by land surfaces, the oceans and the atmosphere. The global heat
budget is controlled by the fluxes through the top of the atmosphere (TOA;
Fig. 2). All the energy that enters the Earth’s climate system through the TOA
originates from the sun. Globally about 47% of the solar radiation that enters
the TOA is absorbed by the surface, while the rest either prevails within the
atmosphere due to absorption (23%) or is reflected back to space by clouds
(22%) and the surface (7%; Wild et al., 2013). The energy surplus by the
sun is compensated for by longwave radiation. Longwave radiation is emitted
by the Earth’s surface, clouds and atmospheric constituents such as water vapor and gases. The atmosphere receives longwave radiation from the Earth’s
surface. Most of this radiation is absorbed by atmospheric constituents and
clouds, except for a small fraction that is transmitted in the so-called ’atmospheric window’. The absorbed radiation is re-emitted by the atmosphere back
to the surface or out to space.
As the Earth is a sphere the equatorial regions receive more solar radiation
than the polar regions (Fig. 2). This leads to an uneven heating between those
regions, which is not fully compensated for by the loss of energy through emission of longwave radiation; the latter is only a function of temperature. This
results in a meridional temperature gradient that generates an atmospheric and
oceanic net poleward transport of energy, which counteracts the temperature
imbalance. The meridional oceanic heat transport is largest around 20◦ N and
much smaller at higher latitudes, as the ocean looses much of its energy to the
atmosphere already at low latitudes and the ocean fraction of the northward
energy transport becomes smaller (Wunsch, 2005). The atmospheric transport
is largest in the mid- and higher latitudes; at 50◦ N it is about 4 × 1015 W, which
3
Figure 2: Annual mean top-of-the-atmosphere incoming shortwave radiation and outgoing longwave radiation as function of latitude. Values are based
on ERA-Interim reanalysis from 1979 to 2014 (see Section 3.1).
is almost one order of magnitude larger than for the ocean. Note that the atmospheric transport is strongly affected by the rotation of the Earth (Coriolis
effect). Hence, the atmospheric transport is not due to a direct meridional thermal cell throughout the hemispheres: in the mid- and higher latitudes the Coriolis force balances the pressure-gradient force resulting from the temperature
differences. This leads to an eastward mean flow of energy at these latitudes.
Most of the energy is transported in form of moist-static energy, thus, moisture
and heat, by synoptic and quasi-stationary planetary waves (Fig. 3; Trenberth
and Stepaniak, 2003). Synoptic waves are cyclones and anticyclones (transient
eddies) with a typical spatial scale in the order of 1000 km. Quasi stationary
planetary waves, also called Rossby waves, have a horizontal scale of about
10,000 km. While Northern Hemispheric cyclones transport most of the energy along the two major storm tracks, located in the western North Atlantic
and North Pacific, the energy transport by Rossby waves is less confined to a
specific region (Fig. 3).
The interplay between the amount of absorbed and emitted radiation and
the atmospheric circulation is of major importance for the global and regional
climate. Alterations in either of them affect the other. On the one hand, a
stronger temperature gradient between the equator and the poles alters the energy transport: for example, mid- and high-latitude storms are more intense
in winter than in summer, mainly due to the absence of solar radiation in the
winter Arctic and the stronger meridional temperature gradient. On the other
hand, changes in heat and moisture transport can alter the incoming shortwave
and outgoing longwave radiation: for example, an increased advection of heat
impacts the amount of emitted and absorbed longwave radiation directly. Advection of moisture has the potential to alter the amount of clouds, which affect
both shortwave and longwave radiation; clouds reflect solar radiation (cloud
4
albedo; see definition in box) and absorb and emit longwave radiation. This
emphasizes the delicate balance between the loss (gain) of radiative energy at
the TOA and the gain (loss) of energy by meridional transport.
Albedo: Ratio between reflected and incoming solar radiation.
The Arctic plays a key role for the global climate system, as it acts as
a heat sink. Sea ice is an important contributor to the energy deficit at high
latitudes due to its high surface albedo; hence, it significantly contributes to
the temperature gradient between mid-latitudes and the Arctic. Variations in
the sea-ice extent therefore have a significant impact on the heat budget of
the atmosphere and oceans and the meridional energy transport. Hence, it is
important to understand recent changes and variability of the sea-ice cover.
2.2
Sea-ice characteristics
To understand changes in the sea-ice cover it is important to familiarize with
the properties of sea ice and their variability throughout the Arctic. A short
summary of the most significant properties in the scope of this thesis is given
in the following.
Sea-ice growth and melt depend largely on the heat exchange between the
atmosphere, sea-ice and the underlying ocean. Clouds, water vapor and atmospheric constituents determine the downward shortwave and longwave radiation that reach the surface (Section 2.1). Part of this energy is absorbed by
the surface: practically all longwave radiation is absorbed by the uppermost
surface layer, while the amount of absorbed shortwave radiation depends on
the surface albedo and the fraction of shortwave radiation that penetrates the
ice. Additionally, turbulent fluxes contribute to part of the surface energy exchange: exchange of sensible heat leads directly to temperature changes; latent
heat exchange is a temperature change due to the phase change of water, such
as evaporation and sublimation. The amount of energy provided by the ocean
depends on the heat flux from the ocean towards the ice bottom. The ocean in
the immediate vicinity of the ice bottom is close to the freezing point. However, the underlying ocean is usually relatively warm: remnants of warm water
that was heated by solar radiation throughout summer, when the ice extent
is low (Fig. 1), prevail throughout subsequent seasons (Maykut and McPhee,
1995). Further, a current of relatively warm Atlantic water extends throughout the Arctic (Section 4.1). This potentially results in an upward heat flux.
The processes that impact the sea ice through alterations in the heat fluxes are
referred to as thermodynamic processes.
5
Figure 3: Annual mean energy transport for 1979–2001. The transport is
shown as vectors and the divergence in Wm−2 . Shown are the atmospheric energy transports for the (top) total, (middle) quasi-stationary, and (bottom) transient components. Adopted from Trenberth and Stepaniak (2003).
6
It should be noted that sea ice consists not only of solid ice but includes
a significant amount of brine and gas. The amount of brine within sea ice
depends largely on the ice age, growth state and ice thickness. In turn, the
amount of brine determines the optical properties of sea ice, for example its
albedo or transmittance for radiation (Perovich et al., 1998). Snow, which often
accumulates on top of sea ice, additionally alters the optical properties of the
sea-ice cover: fresh snow has an even higher albedo than ice, while melt ponds,
which are accumulations of water that form on top of the ice due to snow melt,
have a relatively low albedo compared to ice. Therefore, the albedo of the seaice cover ranges from as little as 0.1 to more than 0.8 (Perovich et al., 2002).
In contrast the albedo of open water is typically less than 0.1. This implies that
sea ice reflects up to eight times more of the incoming solar radiation than an
equivalent area of open ocean.
Thermodynamic: Processes involving changes in the internal energy.
Dynamic: Processes associated with wind forcing.
Sea ice is continuously in motion. Winds and ocean currents move the ice
around, which leads to a deformation of the ice cover through rafting and ridging; this has a significant impact on the ice thickness. About 30 to 80% of the
total ice volume can be attributed to ice deformation (Thomas and Dieckmann,
2003). The forcing by winds and currents on the ice results in a redistribution
of ice, the opening of leads and an export of ice from the Arctic into the adjacent seas. For example, the average ice volume flux through the Fram Strait,
between Greenland and Svalbard, is estimated to be 2200 km3 or 700,000 km2
each year (Kwok, 2009; Kwok et al., 2004). Most of this transport can be
attributed to the most dominant atmospheric circulation pattern that prevails
in the Arctic: a high pressure system over the Beaufort Sea and low pressure along the Eurasian coast (Fig. 4a). The high pressure results in a clockwise drift of the sea ice, referred to as Beaufort Gyre. The gyre transports sea
ice from the central Arctic towards the Canadian Archipelago and Greenland,
where it favors the accumulation of relatively thick and old ice (Fig. 4b). Similarly, it leads to ice drift along the Alaskan coast to the East Siberian Sea.
The low pressure along the Eurasian site transports ice from the East Siberian,
Laptev and Kara Seas towards the Atlantic side of the Arctic, where it eventually exits the Arctic. The transport and redistribution of ice strongly affect ice
extent and thickness in different areas of the Arctic region. Redistribution of
ice potentially results in the opening of leads; areas of open water that result
from fractures in the ice. In winter new ice forms almost instantaneously on
open leads, due to the exposure of water to the cold winter atmosphere. The
7
ice in leads grows quickly, hence, open leads contribute significantly to the
formation of new ice in the Arctic. Thereby they counteract the ice loss of sea
ice by ice export. Further, ice transport contributes to the freshwater budget of
the Arctic Ocean. Sea ice rejects part of the brine during its growth process
and older ice becomes fresher. The export of sea ice therefore corresponds
to export of freshwater: it was shown that about 70% of the freshwater export
through the Fram Strait can be attributed to sea-ice export (Königk, 2005). The
freshwater budget within the Arctic in turn has significant impact on the ocean
stratification (see Section 4.1). The processes that lead to ice changes through
redistribution and ice export are in the following referred to as dynamical processes. Note that the dynamical systems that move the ice around also advect
energy into the Arctic, primarily in form of moisture and heat (Section 2.1). As
such energy transport potentially alters the downward radiation to the surface
it can impact the ice surface also through thermodynamic processes.
Figure 4: Sea-ice transport (left) and age distribution (right). Left: Atmospheric sea-level pressure (black lines) and ice drift (black arrows). Highlighted
are the Beaufort Gyre and the Transpolar Drift Stream. Adopted from Kwok and
Untersteiner (2011). Right: Mean sea-ice age for the time period 2005 to 2010.
Adopted from Eicken and Mahoney (2015).
8
3. Methodology
The results of this thesis are mostly based on atmospheric reanalyses and simulations with a coupled atmosphere-ice-ocean model. A description and brief
evaluation of the data and model used in this thesis is given below.
3.1
Atmospheric reanalyses
A reanalysis, as the one used in Paper I, II and IV, is a comprehensive set
of atmospheric data that is created by combining global observations with a
numerical weather forecast model. Model forecasts are dependent on the initial state of the surface and atmosphere. For reanalyses, this initial state is
taken from global observations. Further, during the numerical forecasting process the model is permanently ’adjusted’ towards observed variables. Such
procedure is called data assimilation and is very complex in itself. The observations used in atmospheric reanalyses comprise data from satellites, radiosondes, buoys, in-situ surface weather stations and many other systems.
Currently about 7 to 9 million observations are assimilated into reanalyses
(https://climatedataguide.ucar.edu; Fig. 5). The data assimilation scheme accounts for changes in observational systems, uncertainties of the observational
data and in the numerical model. Further, as model versions are updated over
time reanalyses are released regularly for the entire time-period they cover.
This makes reanalysis to a powerful and one of the most consistent available
data sets.
However, especially in regions where observations are sparse reanalyses
are highly dependent on the model physics and satellite observations; the Arctic is one of these regions (Fig. 5). Yet, satellite observations and models
show large uncertainties due to their retrieval algorithms and model physics
(Section 3.2), respectively. Further, many model variables of interest are not
observed directly; they are model products that rely on variables that are observed and assimilated. Example of such variables are clouds, radiation and
surface fluxes. The results in Paper I, II and IV of this thesis are based on the
ERA-Interim reanalysis from the European Center for Medium Range Weather
Forecast (ECMWF; Dee, 2011). ERA-Interim has been evaluated against data
from surface observations in the Arctic, primarily from land stations, and sug9
gested to be one of the best available reanalysis products, although it contains
errors as do all other reanalyses (for a throughout discussion see the Supplementary Information of Paper I; e.g. Cox et al., 2014, 2012). It is worth to
mention that, although ERA-Interim is able to capture climate variables in the
Arctic reasonably well, several processes that are important for local processes
in the Arctic are not well described in the model (Section 2.2). For example,
the sea-ice albedo is prescribed by climatological values and does not depend
on surface properties such as snow and the ice thickness, which is set constant throughout the year (Karlsson and Svensson, 2013). This impacts the
surface fluxes and the interaction between ocean, sea ice and atmosphere. Atmospheric reanalyses from different research centers use different models and
observations as well as different data assimilation schemes. Thus, results obtained from different reanalysis products might differ, especially for variables
that are not directly included in the data assimilation. A comparison of three
different reanalysis products in Paper I of this thesis revealed that the major
conclusions drawn from the results presented in this thesis are independent on
the chosen reanalysis.
3.2
Global climate models
Global climate models are an important tool, not only for future climate projections but also to increase the understanding of processes within the climate
system. For example, it is possible to separate processes to study their individual climate contribution. In Paper III the Community Earth System Model
Version 1 (CESM1; Hurrell et al., 2013) is used to investigate processes that
were found to be important contributors to the year-to-year sea-ice variability
(see Section 4.2). CESM1 is a modeling system that couples models for the
atmosphere, land, glaciers, rivers, oceans and sea ice. Note, that a slab-ocean
model is used in this thesis; the ocean is represented by an ocean-mixed layer,
with a typically depth of about 50 m deep and fixed horizontal energy fluxes.
Each component comprises a large number of physical laws and equations that
describe climate-related processes. Solving these equations can be computationally expensive. Therefore, specifically equations of small-scale processes
are simplified; so-called parameterized. Usually, the finer the horizontal and
vertical model resolution the more of the small-scale processes are resolved.
Depending on the aim of the study, one might prefer lower model resolution
over small-scale processes or vice versa.
Many studies evaluated the performance of CESM1 and the different components therein. Jahn et al. (2011) showed that, although there are biases in
e.g. the ice motion fields and the position of the Beaufort Gyre, sea-ice characteristics are represented well. That comprises the trend in the Arctic sea-ice
10
Figure 5: Available surface observations.
Top: Surface observations over land areas from global weather stations. Map obtained from
https://catalog.data.gov/dataset/integrated-surface-global-hourly-data. Bottom:
Surface observations over ocean from buoys, ships, argo floats and gliders
as well as shore and bottom stations during July 2015. Map available from
http://osmc.noaa.gov/Monitor/OSMC/OSMC.html.
extent as well as the distribution of the multi-year sea ice (Section 2.2). Neale
et al. (2013) found that the atmospheric component of CESM1 captures climate processes and dynamics reasonably well. Nevertheless, CESM1 exhibits
biases in the atmospheric fluxes discussed in Section 2.1. Part of these biases
can likely be attributed to the simulation of the cloud cover: clouds are spatially confined and their development and occurrence depends on a variety of
processes, which makes their simulation challenging (Neale et al., 2013). Due
11
to these limitations output from climate models should not be taken as truth,
rather as an approximation of reality.
Note that model simulations are dependent on the initial conditions, thus,
climate variations (Section 3.1). In climate related studies ensemble techniques are often used to account for such dependences (see definition box).
In Paper III we performed ensemble simulations in order to account for the
uncertainty that is associated with climate variability. As CESM1 shows large
deviations between individual ensemble members (Jahn et al., 2011) a rather
large ensemble is used to achieve robust results.
Ensemble (as used here): Set of model simulations based on different initial
conditions.
12
4. Changes of the sea-ice cover
4.1
Long-term changes of the sea-ice cover
The sea ice has undergone dramatic changes throughout the last decades. Since
1979, the beginning of continuous satellite observations, the annual average
sea-ice extent has decreased with a rate of about 4% per decade (Meier et al.,
2007; Stroeve et al., 2012). Further, sea-ice has thinned substantially. A comprehensive set of observations indicates that the mean Arctic ice thickness has
decreased by about 60% between 1975 to 2012; from 3.59 m to 1.25 m (Lindsay and Schweiger, 2015). The latter is accompanied by a spatial increase of
relatively thin first-year sea ice, ice that survives only one winter, and a loss of
relatively thick and old ice that survived throughout one or more melt seasons
(multi-year ice). The largest trends in ice extent and thickness are evident during summer, when sea-ice extent reaches its seasonal minimum (Fig. 1 and 6).
These changes have large impact on the climate; particularly on the global energy budget, as they are accompanied by a large increase in the solar radiation
that is absorbed by the ocean and atmosphere (Fig. 7; Section 2.1).
Figure 6: September sea-ice extent from 1979 to 2014. Shown are the seaice extent and the corresponding linear trend throughout the period. Data are
obtained from National Snow and Ice Data Center.
13
Figure 7: Change in the sea-ice concentration (top) and top-of-theatmosphere absorbed solar radiation (bottom) during June, July, August
from 2000 to 2014. Figures are obtained from NASA’s Goddard Space Flight
Center Scientific Visualization Studio.
Atmospheric contribution to the sea-ice decline
One of the main contributors to the sea-ice decline is the increase in surface
temperatures (Kay et al., 2011). Global mean surface temperatures increased
at a rate of about 0.2◦ C per decade between 1979 and 2012, due to the rise of
anthropogenic greenhouse gases (Crowley, 2000; IPCC, 2013). Greenhouse
gases absorb longwave radiation, whereby the amount of longwave radiation
that is radiated out to space is reduced (Section 2.1). In the Arctic surface
temperatures have risen about twice as much as the global average, which
is referred to as Arctic amplification (Screen and Simmonds, 2010; Serreze
and Barry, 2011; Serreze and Francis, 2006). One of the main contributors
14
to the Arctic amplification and the associated sea-ice retreat is the so-called
ice-albedo feedback (e.g. Curry et al., 1995). Sea-ice melt results in the exposure of open water. Due to the low ocean albedo more shortwave radiation
is absorbed by the surface when the ice has melted, resulting in enhanced solar heating of the upper ocean layers (Section 2.2; Perovich et al., 2007). The
stored heat is released specifically in the fall season, when atmospheric temperatures drop in response to the seasonal cycle of the solar insolation, and
significantly impacts the timing of the fall freeze-up and the ice growth. Steele
et al. (2008) calculated that an increase in the ocean heat storage as is observed
since the late 1960s results in a delay of the fall freeze-up by 13 to 71 days or
a decrease of the winter ice growths of up to 56 to 75 cm. This emphasizes
the direct link between atmospheric and oceanic conditions. Note that the icealbedo feedback can also act on relatively thick ice, where it leads to a decrease
of the albedo due to increases in the melt-pond fraction, reductions of the snow
cover and sea-ice thickness (Curry et al., 1995).
Several other feedback mechanisms likely also contributed to the sea-ice
decline. Modeling studies suggest that the water-vapor feedback is one of the
most important feedbacks besides the ice-albedo feedback (Pithan and Mauritsen, 2014). The reduction of the sea-ice extent allows for an increased oceanto-atmosphere moisture flux. This could lead to an increase in atmospheric
moisture: Boisvert and Stroeve (2015) show that an increase in water vapor
can be observed in satellite observations. Since water vapor is one of the most
efficient greenhouse gases this would potentially contribute to an amplified
surface warming and the associated sea-ice melt. Similarly, the cloud feedback is suggested to have contributed to the surface warming (Graversen and
Wang, 2009; Pithan and Mauritsen, 2014); note however that models still have
problems in realistically representing clouds (see Section 3.2).
Changes and variability of the atmospheric circulation are also suggested
to have contributed to the observed sea-ice decline (for a summary see Döscher
et al., 2014). The atmospheric circulation affects the ice cover through the
thermodynamic and dynamic processes discussed in Section 2.2. However,
in contrast to the temperature increase over the last decades and the associated feedback mechanisms, the atmospheric circulation does not show a single
pattern that would explain the continuous decline of the ice cover; it has undergone major shifts throughout the last decades (e.g. Maslanik et al., 2007;
Serreze et al., 2007). One example of such shifts is the change of the Arctic Oscillation, an atmospheric circulation pattern that is characterized by low
(high) sea-level pressure over the Arctic and high (low) pressure over the the
mid-latitudes, which is referred to as positive (negative) phase of the Arctic
Oscillation (Rigor et al., 2002). In the late 1980s the Arctic Oscillation pattern
changed from a negative to a positive phase. This change was accompanied by
15
regional shifts of the Beaufort Gyre and transpolar driftstream, which favored
export of thick ice from the Central Arctic through the Fram Strait, especially
during summer (Fig. 4; Kwok et al., 2013). Since the mid-1990s the Arctic Oscillation varies significantly without showing a dominant phase, hence, likely
contributed little to the continued decline. However, a pattern called Arctic
Dipole Anomaly has been observed more frequently during the summers of
the recent decade (Kwok et al., 2013). It is characterized by high pressure
over the Canadian side and low pressure over the Eurasian side of the Arctic,
leading to a cross Arctic flow from the Pacific to the Atlantic. A strengthening
of the Beaufort Gyre and the transpolar drift stream are associated with this
pattern, favoring ice export. Besides the dynamic impact on the ice, the Dipole
Anomaly is affecting the sea ice through thermodynamic processes. Anomalous atmospheric and oceanic heat and moisture transport from the Pacific into
the Arctic are associated with the pattern (Paper IV; Graversen et al., 2011;
Wang et al., 2009). The sequence of the positive phase of the Arctic Oscillation and the persistence of the Dipole Anomaly are, due to their significant
interaction with the sea ice, likely important contributors to the sea-ice decline
(Overland and Wang, 2005).
Further, an increase in the poleward energy transport throughout the last
decades is suggested to have influenced the Arctic temperature amplification
and the associated sea-ice decline (Section 2.1; Alexeev et al., 2005; Bengtsson et al., 2012; Graversen and Wang, 2009). Based on atmospheric reanalysis data, Graversen (2006) showed that the variability of the poleward energy
transport has a significant impact on the Arctic climate. He found an overall
positive trend in the transport and suggested that about 7% of the total temperature increase between 1979 and 2001 can be attributed to the increased
transport. Several model studies investigated the specific importance of such
increase for the observed temperature change in the Arctic. Alexeev et al.
(2005) conducted simplified model experiments, where a forcing is induced at
the low latitudes only. In fact, an Arctic amplification of the surface temperature still occurred even though the high latitudes were not directly exposed to
a forcing. This let to the conclusion that the amplification in the Arctic must
be due to an increase in the northward energy transport. Modeling studies by
Bengtsson et al. (2012) indicate that the atmospheric poleward transport of
water vapor is more important than the transport of heat; transport of moisture
shows a positive trend, while the heat transport exhibits a small negative trend.
They argue that the enhanced moisture transport led to an increase in the downward longwave radiation within the Arctic, as it alters the atmospheric opacity.
Hence, it likely contributed significantly to the surface warming and sea-ice
decline in the Arctic, in concert with other feedback mechanisms.
16
Oceanic contribution to the sea-ice decline
The oceanic impact on recent sea-ice changes is less explored than the atmospheric contribution due to a lack of comprehensive and continuous data
(Döscher et al., 2014). However, the inflow of warm waters from the Pacific
and Atlantic are suggested to have significantly contributed to the recent loss
of sea ice.
Pacific water enters the Arctic Ocean through the Bering Strait. As the
Pacific water is relatively fresh and has a low density it stays close to the surface. It eventually follows the Beaufort Gyre and exits the Arctic through the
Canadian Archipelago and the Fram Strait (Fig. 4 and 8). Since the late 1990s
an increased inflow of warm water from the Pacific through the Bering Strait is
observed (Polyakov et al., 2012). As the Pacific water stays close to the surface
it has a direct impact on the sea ice (Shimada et al., 2006). Hence, it is likely a
significant contributor to the thinning and reduction of the sea ice cover in the
Chukchi and Beaufort Seas (Steele et al., 2010).
Figure 8: Pathways of Atlantic and Pacific waters throughout the Arctic.
Water from the Pacific Ocean enters the Arctic Ocean through the Bering Strait
(blue). Atlantic water penetrates the Arctic Ocean through the Barents Sea and
Fram Strait (red). Illustration by Jack Cook, Woods Hole Oceanographic Institution.
17
Water from the North Atlantic enters the Arctic Ocean through the Barents Sea and the Fram Strait (Fig. 8). As Atlantic water is relatively warm,
salty and dense it remains close to the surface only in the relatively shallow
Barents and Kara Seas, but cools and deepens as it penetrates further into the
Arctic along the continental shelves. It eventually separates from the surface
and a relatively cold and fresh layer forms on top of the 200 to 300 m deep
warm core of the Atlantic water (Polyakov et al., 2010). This freshwater layer
is called halocline and is characterized by a very stable stratification that suppresses vertical mixing (see definition in box). This layer is formed through
river run-off, precipitation and sea-ice melt processes. A warming of the Atlantic water is observed since the late 1970s. Due to the vertical variations
of the Atlantic water such warming likely affected sea-ice changes predominantly in the Barents and Kara Seas, where it is in close proximity to sea ice
(Koenigk and Brodeau, 2014). For example, Polyakov et al. (2010) show that
increases in Atlantic water temperatures precede a sea-ice decline in these regions, although, with a five year lag. How a warming of the Atlantic water
might impact sea ice beyond the Barents and Kara Seas is relatively uncertain, as the fresh and cool halocline hampers vertical heat transport. However,
the Atlantic water becomes fresher and colder along its Arctic pathway, which
suggests some form of vertical heat exchange (Dmitrenko et al., 2014).
Halocline: Layer of water with strong vertical salinity gradient.
Lastly, river discharge into the Arctic Ocean increased by about 7% from
1936 to 1999 (Peterson et al., 2002). Increased river discharge impacts ocean
stratification through a freshening of the water: the fresher the surface water is
the stronger the stratification, which would hypothetically counteract a sea-ice
decline. However, Ogi et al. (2001) showed that river discharge is associated
with an increased heat flux into the Arctic Ocean, leading to increases of seasurface temperatures and a delay of the fall freeze up, although, the impact of
rivers on sea ice is confined to the areas in the vicinity of the river deltas (Frey
et al., 2003).
4.2
Inter-annual sea-ice variability
Besides the long-term trend Arctic sea ice shows large year-to-year variability
(Fig. 6). The largest variability is evident in September (Serreze et al., 2007).
For example, in September of 2013 and 2014 the sea-ice extent was about
1.7 million km2 larger than in 2012. This corresponds to an area larger than
the size of France, Sweden, Germany and the UK together. Very similar inter18
annual variability occurs throughout the entire record of the satellite observed
ice extent. The three largest negative deviations from the trend line are evident
in the years 1990, 2007 and 2012. The sea-ice extent is lowest towards the
end of the record due to the underlying long-term decline of the ice (Fig. 6;
Section 4.1). The sea-ice variability is not spatially uniform: ice anomalies are
largest in the Kara, Laptev, East Siberian, Chukchi and Beaufort Seas (Fig. 9;
Paper IV).
Anomaly: Deviation of a specific value from a climatological mean.
Figure 9: Sea ice conditions during years with an anomalous low September
sea-ice concentration (SIC). Ice concentrations are shown for the years 1990,
1995, 2003, 2007 and 2012 (color) together with the ice-edge (SIC>15%) averaged over the ±2 adjacent years (black line) are shown. This figure is taken from
Paper IV.
Atmospheric contribution to sea-ice variability
The atmospheric processes that are important for the inter-annual variability
of the sea-ice cover are not much different from the processes that contribute
to the long-term trend discussed in Section 4.1. Alterations of atmospheric
water vapor and clouds impact sea ice thermodynamically, while atmospheric
circulation alters the atmospheric and oceanic transport of heat and moisture
into the Arctic and forces sea ice dynamically. It is rather a matter of time
19
scale: atmospheric processes and feedbacks persistent over years are responsible for the long-term trend in the sea-ice extent, while atmospheric events on
time scales of days to weeks can have a significant impact on the seasonal seaice evolution and, hence, on the magnitude of the September sea-ice minimum
(Fig. 1 and 9). The studies presented in this section investigated primarily processes during years of significantly negative sea-ice anomalies or years when
the ice recovered from such an event. In this section we put the results of
these studies in a general perspective and discuss the importance of processes
specifically in regard to their timing during the year.
The probably to-date most discussed year, in terms of the processes that
contributed to anomalous ice conditions, is 2007. In September of 2007 the
sea-ice extent dropped to 4.3 million km−2 , the lowest extent observed until
that year; to-date only 2012 shows an even lower sea-ice extent (Fig. 6). The
ice extent was also 1.6 million km−2 and 0.4 million km−2 below the summer extent of 2006 and 2008, respectively. This suggests that the unusual ice
conditions were not only subject to the long-term trend in ice extent and thickness (Section 4.1; Lindsay et al., 2009) but also part of the atmospheric and
oceanic variability. Most studies investigated the atmospheric conditions over
the area that experienced the largest ice retreat, which comprises large parts
of the East Siberian and Beaufort Seas and the Chukchi Sea (Fig. 9). The
majority of the studies pointed towards the specific importance of the summer atmospheric conditions. Summer of 2007 was dominated by a positive
Dipole Anomaly (Section 4.1), which resulted in an anomalously large transport of heat and moisture from the Pacific into the Arctic (Graversen et al.,
2011; Wang et al., 2009). The transport led to an increased cloud cover, therefore, increased fluxes of downward longwave radiation and turbulent fluxes at
the surface (Dong et al., 2014; Schweiger et al., 2008; Sedlar and Devasthale,
2012). Graversen et al. (2011) argued that the energy surplus at the surface due
to the increased downward fluxes of longwave radiation led to a larger-thanaverage surface melt and allowed for the melt of about one meter of ice. The
associated reduction of the ice and appearance of open water throughout the
summer resulted in more shortwave absorption by the ocean: according to Perovich et al. (2008) five times more shortwave radiation was absorbed during
summer of 2007 over the Arctic as compared to other years. The latter effect
represents a larger than average ice-albedo feedback (Section 4.1). Wang et al.
(2009) further showed that the prevalent Dipole Anomaly led to a significant
transport of warm Pacific water into the Arctic, which likely accelerated the
sea-ice melt. The dynamical impact of the anomalous atmospheric circulation
pattern, due to ice redistribution and export, was also suggested to be a contributing factor to the anomalous ice conditions in 2007 (Hunke, 2014; Zhang
et al., 2008).
20
These and other studies indicate the importance of atmospheric circulation
anomalies during summer for the sea-ice evolution. In fact, an almost linear
relationship between changes of the sea-ice extent throughout the season and
summer wind anomalies was suggested (Ogi and Wallace, 2012). Other studies also pointed towards the importance of atmospheric processes earlier in the
season. For example, Eastman and Warren (2010) found a significant correlation between the September sea-ice extent and the cloud amount during March
to May, leading to positive downward longwave flux anomalies prior to melt
onset. Indeed, observational studies showed that a significant surface warming
can occur up to two months before melt onset associated with increases in the
downward longwave fluxes (Persson, 2012).
In Paper I, we underline the specific importance of the spring downwelling
longwave radiation for the ice evolution in the succeeding months. In years
with a below-average September sea-ice extent large part of the Arctic, including the Beaufort, Chukchi, East Siberian and Laptev Seas as well as large part
of the Arctic Ocean (see e.g. Fig. 8), exhibits positive downward longwave
radiation anomalies at the surface in April and May (Fig. 10). These radiation anomalies are associated with an anomalous transport of moisture into
and positive anomalies of clouds and water vapor over the area. The increased
longwave radiation leads to a warming of the surface and enhanced ice melt
in spring, which results in below-average sea ice from May onwards. This
allows for a larger than average ice-albedo feedback and an accelerated surface melt throughout summer. In fact, we estimate that the energy surplus due
to the spring longwave anomaly and the associated summer ice-albedo feedback have the potential to cause a negative September ice anomaly of 13–26%;
note, that this estimate is confined to a smaller area comprising parts of the
East Siberian and Beaufort Seas and the Arctic Ocean (black box in Fig. 10).
In contrast, positive downward longwave radiation anomalies are not evident
in spring of years with an above-average September sea-ice extent; instead a
negative anomaly in downward longwave radiation was evident.
The latter study does not only indicate a strong relationship between spring
longwave radiation and September sea ice, but also suggests that radiation
anomalies in spring are remotely controlled; by advection of moisture into the
area (Fig. 10). In Paper IV, we therefore explored the atmospheric circulation
patterns associated with positive anomalies of net longwave radiation during
years with well below-average September sea ice. Note, that net longwave radiation gives an estimate of the energy that is absorbed by the surface, thus,
the energy that potentially goes into ice melt. The regional focus of this study
is on the Beaufort, East-Siberian and Laptev Seas, which exhibit the largest
sea-ice variability as compared to other regions (see Fig. 9 and 10). The study
reveals that net longwave anomalies during spring occur in form of three to
21
Figure 10: Surface radiation anomalies and energy convergence during
April and May for years with a low September sea-ice extent. Anomalies
of net- (a) and downwelling longwave radiation (b) and heat (c) and moisture
convergence (d) averaged over April and May. Gray lines encapsulate values that
are significantly different from zero on the 95% level. The black box marks one
of the study area. Figures adopted from supplementary information of Paper I.
six episodes, with a typical lengths of on average 53 days. Those episodes are
mostly associated with circulation patterns similar to the Arctic Oscillation and
Dipole Anomaly discussed in Section 4.1, which lead to anomalous advection
of heat and/or moisture into the Arctic (Fig. 11). The additional energy leads,
in most years, to an earlier-than-usual melt onset in spring, thus, an accelerated
sea-ice decline throughout the rest of the year. It should be noted that years
with positive September sea-ice anomalies are also associated with episodes
of enhanced net longwave radiation. However, the episodes in those years are
shorter and they occur less frequently; further, they also more frequently alternate with periods of negative net longwave anomalies. This suggests that
the processes that alter the sea-ice concentration different years are likely not
substantially different, instead it is the frequency, length and strength of the
same conditions that constitute the difference between years with positive and
negative sea-ice anomalies.
22
Figure 11: Circulation patterns (CP) associated with episodes of anomalous
net longwave radiation (LWN) during spring of years with low September
sea ice. Downwelling longwave radiation anomalies (LWD; color) and 850-hPa
geopotential heights (contour). CP1, CP2 and CP3 are averaged over four, five
and five LWN episodes, respectively. In total 22 LWN episodes were analyzed.
Figure adopted from Paper IV.
Many of the here presented processes are still subject to discussion; for
example, the effect of clouds and water vapor on the sea ice (Graversen et al.,
2011; Kay et al., 2008; Schweiger et al., 2008). One reason for this is the lack
of surface observations in the Arctic that would allow for a thorough study of
such processes: to-date only one surface based observational data set exists
over sea ice that covers a whole annual cycle (Uttal et al., 2002). Further,
studies that weight the importance of atmospheric processes during different
seasons are largely absent.
Experiments with a coupled atmosphere-ocean model in Paper III provide insight on effects of downward radiation anomalies, associated with of
clouds and water vapor, during different seasons on the sea-ice evolution (Section 3.2). In Paper III, downward longwave and shortwave radiation anomalies
are implemented in the model during different seasons, separately and in combination with each other. An implementation of spring downward longwave
radiation anomalies, of the same size as identified in years with below-average
sea-ice extent in Paper I, into the model during April and May revealed that
CESM1 is able to realistically simulate the sea-ice response to enhanced spring
longwave radiation. However, the sea-ice response is only statistically significant for longwave anomaly of double the size. Hence, anomalies of 12 Wm−2
were applied to investigate the effect of seasonal varying downward radiation
anomalies. The study revealed that positive longwave radiation anomalies during spring and summer have the largest impact on the sea-ice evolution, while
positive shortwave anomalies affect sea ice mainly in summer. The high albedo
of the ice cover in spring leads to a reflection of most of the shortwave energy,
thus, positive shortwave anomalies in spring do not result in an accelerated
sea-ice melt. When positive longwave anomalies are applied in combination
23
with negative shortwave anomalies, to mimic cloudy conditions, a significant
impact on the sea ice is still evident in spring. During summer the longwave
warming effect and shortwave cooling effect of clouds cancel each other (see
Section 2.1), thus, summer clouds appear to have little effect on the sea-ice
evolution. In winter shortwave radiation is largely absent, hence, cloud and
water vapor anomalies only result in increased downward longwave radiation
at the surface. The model experiments reveal that cloud anomalies play only a
small role for the ice evolution during winter, as compared to the spring season. Anomalies in winter mainly lead to a reduction of the ice growth (Liu
and Key, 2014). The conclusions drawn from Paper III, therefore, emphasize
the importance of the spring atmospheric processes for the September sea-ice
concentration.
It should be noted that most of the studies discussed in this section focused
on the contribution to the ice variability over the Pacific side of the Arctic. The
Atlantic side of the Arctic, comprising the East Greenland, Barents and Kara
Seas, appears to be dominated by oceanic processes (Section 4.1; Smedsrud
et al., 2013).
24
5. Conclusions and Outlook
In this thesis, a thorough investigation of atmospheric conditions during years
that show similar summer sea-ice conditions is conducted on basis of atmospheric reanalysis data (Paper I and IV; Chapter 3). Identified key processes
are explored in experiments with a coupled atmosphere-ocean model, in order
to understand their relevance for the sea-ice evolution during different seasons
of the year (Paper III). By focusing on several years the processes important
for the sea-ice evolution are put in a general perspective, as compared to many
previous studies that focused on single years (e.g. Graversen et al., 2011; Kay
et al., 2008; Persson, 2012; Schweiger et al., 2008).
The methods applied in the present study allowed for an identification of
processes that are generally important during a number of years in the data
record; specifically, we found that spring anomalies of downward longwave
radiation are important for the ice evolution. In Paper II, we used this key finding to design a simple statistical prediction model for the September sea-ice
extent: spring atmospheric anomalies related to downward longwave radiation, e.g. water vapor and clouds, were used as predictor variables. In fact,
the statistical model takes exclusively the spring atmospheric conditions into
account. Relatively high correlations between observed and predicted sea-ice
extent indicate that a realistic representation of those anomalies is important
for seasonal predictions of the ice cover. However, the results also reveal that
not all of the September sea-ice variability can be explained by the spring atmospheric conditions: anomalies far away from the trend-line are not captured
by the prediction model. One possible reason for this is that anomalous conditions during summer, as discussed in Section 4.2, affect the sea-ice evolution
significantly.
The model was further applied in connection to the Sea Ice Outlook (SIO),
an international effort to summarize, coordinate and evaluate seasonal sea-ice
predictions for the scientific community, stakeholders and the public (Stroeve
et al., 2014). In June, July and August of each year the Sea-Ice Prediction Network calls for predictions of the September sea-ice extent from international
research groups or individuals. In 2014 and 2015 predictions conducted with
the statistical model were submitted; the predictions were based on spring atmospheric water vapor, taken from ERA-Interim and ECMWF’s operational
forecasts. The sea-ice prediction in 2014 is similar to contributions from cou25
pled, much more complex model systems (Fig. 12). Compared to the observed
sea-ice extent in 2014 the prediction turned out too low. However, predicted
and observed ice extent are in close proximity, which emphasizes that spring
atmospheric conditions likely play a key role for predictions of the summer
ice extent. The prediction for 2015 is significantly lower than predictions from
most of the other research groups; yet, the outcome and therefore the interpretation lies in the future (Fig. 13).
Figure 12: June Sea-Ice Outlook contributions 2014. Distribution of individual Pan-Arctic Outlook values for September 2014 sea ice extent. The observed sea-ice extent in September of 2014 was 5.3 million km2 , based on National Snow and Ice Data Center (NSIDC) estimates. Figure downloaded from
http://www.arcus.org/sipn/sea-ice-outlook/2014/june.
26
Figure 13: June Sea-Ice Outlook contributions 2015. Similar to Fig. 12
but for 2015. Figure downloaded from http://www.arcus.org/sipn/sea-iceoutlook/2015/june.
Open science questions
The findings of this thesis point towards the specific importance of spring atmospheric conditions for the sea-ice evolution. One logical next step is to
analyze how variables connected to these processes are represented in climate
models. Do atmospheric circulation patterns, as identified in Paper IV, occur
with the same frequency and strength in the models? And, do models that
feature realistic circulation patterns provide a better depiction of the summer
sea-ice evolution? The wide range of sea-ice predictions in the SIO contributions indicate that there is large uncertainty in the seasonal prediction of
the inter-annual sea-ice variability (Fig. 12 and 13). Therefore, such analysis
could help to identify possible shortcomings in the prediction systems. To understand the key processes might help to improve seasonal predictions. Better
predictions may allow for improved planning and risk analysis for maritime
operations.
In this thesis thermodynamic processes were regarded separately from dynamical processes. However, anomalous atmospheric circulation patterns that
27
advect warm and moist air also act on the sea ice through dynamical wind forcing. For example, Maslanik et al. (2007) found that summer sea-ice anomalies
are significantly correlated with sea-ice transport. In fact, patterns similar to
the patterns that advect heat and moisture during the net longwave radiation
episodes (Paper IV) were identified to control the sea-ice transport. As observations are sparse model simulations could be conducted to weight the importance of the thermodynamic impact on sea ice against the dynamical impact.
One approach could be to use a coupled sea-ice and ocean model and prescribe
atmospheric anomalies such as wind, heat and moisture anomalies as boundary conditions. An analysis of the ice velocity fields and the ice thickness and
concentration fields could help to identify the specific contribution of the two
processes to ice variability.
The results presented in this thesis also indicate that a large range of processes determine the seasonal ice evolution: in some years anomalous conditions during spring and associated feedback processes dominate the ice evolution (e.g. 1990), in other years atmospheric processes during summer might be
more important (e.g. 2007). Hence, future research is required on the importance of the processes that contribute to the ice variability. In a further study
we investigated the remote effect of temperature variability on the Arctic sea
ice. A correlation analysis between detrended sea-surface temperatures and
sea-ice extent reveals that positive temperature anomalies in the eastern North
Pacific and negative temperature anomalies in the central North Pacific during
spring and early summer are associated with positive September sea-ice extent
anomalies (Fig. 14). The correlation pattern that emerges in the North Pacific
is similar to the so-called Pacific Decadal Oscillation, a leading pattern that is
characterized by high (low) sea-surface temperatures in the central North Pacific and low (high) temperatures in the eastern North Pacific (Mantua et al.,
1997). A more thorough investigation of this relationship is necessary to determine whether the correlations reveal a physical relationship. The Pacific
Decadal Oscillation is a climate oscillation that usually shifts from positive to
negative phase every 20-30 years; hence, it has not been linked to inter-annual
ice variability.
28
Figure 14: Correlations between September Arctic sea-ice extent and seasurface temperature (SST) fields. Correlations are shown for SST fields from
January to August. All fields were linearly detrended prior correlation analysis to
account for trends in the time series. Bright colors show statistically significant
correlations (student’s t-test; α=0.05).
29
30
Acknowledgements
First and foremost I would like to thank my supervisors Rune Graversen and
Michael Tjernström for giving me the opportunity to study at MISU. I owe
you gratitude for sharing your great expertise with me through many stimulating discussions. Thank you Rune for your excellent ideas, your patience and
the support and guidance you have given me throughout the years. Michael,
I am grateful for many fruitful discussions and your unconditional moral as
well as financial support. Without the latter I would have missed out on many
interesting and stimulating conferences and summer schools.
Special thank to my co-authors Theodorus Economou and Richard Bintanja for excellent discussions and valuable suggestions. Both of you added
significantly to the quality of our papers and I am glad for your engagement in
our collaborations. Theo, you are the best in explaining things the easy way
and your positive and cheerful attitude always pushed my motivation level.
Many thanks also to Vladimir Alexeev and Frank Selten, who shared their immense expertise while visiting MISU. Valodya, I’ll never forget the five weeks
on the Akademik Fedorov that have taught me that work and fun can be one
and the same. I would also like to thank Hajo Eicken and Matt Druckenmiller
for inspiring and encouraging me to work ’on’ the Arctic. Without your support and the opportunities you have given me I would most definitely have
chosen another path.
Furthermore I would like to thank all the people at MISU that made the
daily work much more enjoyable. Special thanks to Jonas for the stimulating and motivating discussions about work and everything else. Let’s see if
you ever make it back to science. I owe gratitude to Caroline and Rodrigo for
several inspiring discussions and their engagement in my research committee.
Thanks to Peggy for the candy supply during the first years of my PhD and for
introducing me to the MISU life. I thank Friederike for many nice adventures
during and after work. I am particularly thankful to Peter, who enlightened
me with many interesting stories during lunch or his legendary Oyster parties.
Thanks to Johan for many nice chats and for revealing his secret mushroom
spots around Stockholm. Special thanks to Cecilia for all the beautiful cupcakes through all the years, nice discussions and your help with the translation
of the abstract. Cecilia, Henrik, Eva, Jonas, Joakim and Peggy: all of you made
conferences and summer schools much more fun. Thanks to Abubakr, Marcus,
Wing, Iulia, Sebastian, Fabienne, John, Leon, Georgia, Anna, Louis-Philippe,
Anders, Cian, Koen, Heiner and many others for a pleasant time at MISU. Special thanks also to Susanne, Cecilia and Eva for all the administrative support
and Janne for taking care of the office environment.
I owe my deepest gratitude to my parents. Danke für eure bedingungslose
Unterstützung und Motivation während all den Jahren. Ohne euch wäre das
alles nicht möglich gewesen. My love and thank go to Julia, Thomas, Magdalena, Karlotta, Spille and Eva for always being there for me and for making
the life next to the PhD so enjoyable. Special thanks to Julien for encouraging me if things did not go my way and many beautiful distractions. Finally,
thanks to my friends who stuck to me through all the years.
Last but not least I would like to express my thanks to a variety of institutions for their financial support. I am grateful to the Swedish Research
Council Formas, who funded most of my PhD studies through the ADSIMNOR project. Many thanks goes to the Bolin Center for Climate Research for
supporting my travels to four conferences and summer schools. I am grateful
to the National Science Foundation (NSF), US, for funding my attendance at
the summer school ’Climate Change in the Arctic Ocean’ and giving me the
opportunity to study sea ice at close range. Lastly, I would like to thank the
Association of Early Career Scientists for supporting my participation in the
International Polar Year Conference in Montreal.
References
ACIA, 2005: Impacts of a Warming Arctic: Arctic Climate Impacts Assessment. 1042 pp, Cambridge University Press, Cambridge. 1
Alexeev, V., P. Langen, and J. Bates, 2005: Polar amplification of surface warming on an aquaplanet in
“ghost forcing” experiments without sea ice feedbacks. Clim. Dyn., 24 (7-8), 655–666, doi:10.1007/
s00382-005-0018-3. 16
Bengtsson, L., K. I. Hodges, S. Koumoutsaris, M. Zahn, and P. Berrisford, 2012: The changing energy balance of the polar regions in a warmer climate. J. Climate, 26 (10), 3112–3129, doi:10.1175/
JCLI-D-12-00233.1. 16
Boisvert, L. N. and J. C. Stroeve, 2015: The arctic is becoming warmer and wetter as revealed by the
atmospheric infrared sounder. Geophys. Res. Lett., 42 (11), 4439–4446, doi:10.1002/2015GL063775.
15
Cox, C. J., V. P. Walden, G. P. Compo, P. M. Rowe, M. D. Shupe, and K. Steffen, 2014: Downwelling longwave flux over Summit, Greenland, 2010–2012: Analysis of surface-based observations and evaluation
of era-interim using wavelets. J. Geophys. Res., 119 (21), 12,317–12,337, doi:10.1002/2014JD021975.
10
Cox, C. J., V. P. Walden, and P. M. Rowe, 2012: A comparison of the atmospheric conditions at Eureka,
Canada, and Barrow, Alaska (2006–2008). J. Geophys. Res., 117 (D12), doi:10.1029/2011JD017164.
10
Crowley, T. J., 2000: Causes of climate change over the past 1000 years. Science, 289 (5477), 270–277. 14
Curry, J. A., J. L. Schramm, and E. E. Ebert, 1995: Sea ice-albedo climate feedback mechanism. J. Climate,
8 (2), 240–247, doi:10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2. 15
Dee, D. P., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation
system. Q.J.R. Meteorol. Soc., 137, 553–597. 9
Dmitrenko, I. A., et al., 2014: Heat loss from the atlantic water layer in the northern Kara Sea: causes and
consequences. Ocean Science, 10 (4), 719–730, doi:10.5194/os-10-719-2014. 18
Dong, X., B. Zib, B. Xi, R. Stanfield, Y. Deng, X. Zhang, B. Lin, and C. Long, 2014: Critical mechanisms
for the formation of extreme Arctic sea-ice extent in the summers of 2007 and 1996. Clim. Dyn., 43 (12), 53–70, doi:10.1007/s00382-013-1920-8. 20
Döscher, R., T. Vihma, and E. Maksimovich, 2014: Recent advances in understanding the Arctic climate
system state and change from a sea ice perspective: a review. Atmos. Chem. Phys., 14 (24), 13 571–
13 600, doi:10.5194/acp-14-13571-2014. 15, 17
Druckenmiller, M. L., H. Eicken, J. C. George, and L. Brower, 2013: Trails to the whale: reflections of
change and choice on an Iñupiat icescape at Barrow, Alaska. Polar Geography, 36 (1-2), 5–29, doi:
10.1080/1088937X.2012.724459. 2
Eastman, R. and S. G. Warren, 2010: Interannual variations of arctic cloud types in relation to sea ice. J.
Climate, 23 (15), 4216–4232, doi:10.1175/2010JCLI3492.1. 21
Eicken, H., 2013: Ocean science: Arctic sea ice needs better forecasts. Nature, 497 (7450), 431–433. 2
Eicken, H. and A. R. Mahoney, 2015: Sea ice: Hazards, risks, and implications for disasters. In Coastal
and Marine Hazards, Risks, and Disasters, J. T. Ellis, D. J. Sherman, and J. F. Shroder, Eds., Elsevier,
Boston, doi:10.1016/B978-0-12-396483-0.00013-3. 8
Francis, J. A., W. Chan, D. J. Leathers, J. R. Miller, and D. E. Veron, 2009: Winter Northern Hemisphere
weather patterns remember summer Arctic sea-ice extent. Geophys. Res. Lett., 36 (7), L07 503, 1944–
8007, doi:10.1029/2009GL037274. 1
Frey, K. E., L. C. Smith, and D. E. Alsdorf, 2003: Controls on Eurasian coastal sea ice formation, melt
onset and decay from ERS scatterometry: regional contrasts and effects of river influx. Int. J. Remote
Sens., 24 (24), 5283–5315, doi:10.1080/0143116031000101684#.Vdw9a0tlB94. 18
Graversen, R., T. Mauritsen, S. Drijfhout, M. Tjernström, and S. Mårtensson, 2011: Warm winds from
the Pacific caused extensive Arctic sea-ice melt in summer 2007. Clim. Dyn., 36 (11-12), 2103–2112,
doi:10.1007/s00382-010-0809-z. 16, 20, 23, 25
Graversen, R. and M. Wang, 2009: Polar amplification in a coupled climate model with locked albedo.
Clim. Dyn., 33 (5), 629–643, doi:10.1007/s00382-009-0535-6. 15, 16
Graversen, R. G., 2006: Do changes in the midlatitude circulation have any impact on the Arctic surface air
temperature trend? J. Climate, 19 (20), 5422–5438, doi:10.1175/JCLI3906.1. 16
Hunke, E. C., 2014: Weighing the importance of surface forcing on sea ice: a September 2007 modelling
study. Q. J. R. Meteorolog. Soc., doi:10.1002/qj.2353. 20
Hurrell, J. W., et al., 2013: The Community Earth System Model: A framework for collaborative research.
Bull. Amer. Meteor. Soc., 94, 1339–1360, doi:10.1175/BAMS-D-12-00121.1. 10
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA. 1, 14
Jahn, A., et al., 2011: Late-twentieth-century simulation of Arctic sea ice and ocean properties in the
CCSM4. J. Climate, 25 (5), 1431–1452, doi:10.1175/JCLI-D-11-00201.1. 10, 12
Karlsson, J. and G. Svensson, 2013: Consequences of poor representation of Arctic sea-ice albedo and
cloud-radiation interactions in the CMIP5 model ensemble. Geophys. Res. Lett., 40 (16), 4374–4379,
doi:10.1002/grl.50768. 10
Kay, J. E., M. M. Holland, and A. Jahn, 2011: Interannual to multidecadal Arctic sea ice extent trends in a
warming world. Geophys. Res. Lett., 38, L15 708, doi:10.1029/2011GL048008. 14
Kay, J. E., T. L’Ecuyer, A. Gettelman, G. Stephens, and C. O’Dell, 2008: The contribution of cloud and
radiation anomalies to the 2007 Arctic sea ice extent minimum. Geophys. Res. Lett., 35 (8), doi:10.1029/
2008GL033451, l08503. 23, 25
Koenigk, T. and L. Brodeau, 2014: Ocean heat transport into the Arctic in the twentieth and twenty-first
century in EC-Earth. Clim. Dyn., 42 (11-12), 3101–3120, doi:10.1007/s00382-013-1821-x. 18
Königk, T., 2005: Sea-ice export through Fram Strait: variability and interactions with climate. Ph.D. thesis,
Max Planck Insitute for Meteorology. 8
Kwok, R., 2009: Outflow of Arctic Ocean sea ice into the Greenland and Barents Seas: 1979–2007. J.
Climate, 22 (9), 2438–2457, doi:10.1175/2008JCLI2819.1. 7
Kwok, R., G. Cunningham, and S. Pang, 2004: Fram strait sea ice outflow. J. Geophys. Res. Oceans,
109 (C1). 7
Kwok, R., G. Spreen, and S. Pang, 2013: Arctic sea ice circulation and drift speed: Decadal trends and
ocean currents. J. Geophys. Res. Oceans, 118 (5), 2408–2425, doi:10.1002/jgrc.20191. 16
Kwok, R. and N. Untersteiner, 2011: The thinning of Arctic sea ice. Phys. Today, 64 (4), 36–41, doi:
10.1063/1.3580491. 8
Lindsay, R. and A. Schweiger, 2015: Arctic sea ice thickness loss determined using subsurface, aircraft,
and satellite observations. The Cryosphere, 9 (1), 269–283, doi:10.5194/tc-9-269-2015. 13
Lindsay, R. W., J. Zhang, A. Schweiger, M. Steele, and H. Stern, 2009: Arctic sea ice retreat in 2007 follows
thinning trend. J. Climate, 22 (1), 165–176, doi:10.1175/2008JCLI2521.1. 20
Liu, Y. and J. R. Key, 2014: Less winter cloud aids summer 2013 Arctic sea ice return from 2012 minimum.
Environ. Res. Lett., 9 (4), 044 002, doi:10.1088/1748-9326/9/4/044002. 24
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate
oscillation with impacts on salmon production. Bull. Am. Meteorol. Soc., 78 (6), 1069–1079. 28
Maslanik, J., S. Drobot, C. Fowler, W. Emery, and R. Barry, 2007: On the Arctic climate paradox and the
continuing role of atmospheric circulation in affecting sea ice conditions. Geophys. Res. Lett., 34 (3),
doi:10.1029/2006GL028269, l03711. 15, 28
Maslowski, W., J. C. Kinney, M. Higgins, and A. Roberts, 2012: The future of Arctic sea ice. Annu. Rev.
Earth Planet. Sci., 40 (1), 625–654, doi:10.1146/annurev-earth-042711-105345. 1
Maykut, G. A. and M. G. McPhee, 1995: Solar heating of the Arctic mixed layer. J. Geophys. Res. Oceans,
100 (C12), 24 691–24 703, doi:10.1029/95JC02554. 5
Meier, W. M., J. Stroeve, and F. Fetterer, 2007: Whither Arctic sea ice? A clear signal of decline regionally, seasonally and extending beyond the satellite record. Ann. Glaciol., 46 (1), 428–434, doi:
10.3189/172756407782871170. 13
Neale, R. B., J. Richter, S. Park, P. H. Lauritzen, S. J. Vavrus, P. J. Rasch, and M. Zhang, 2013: The mean
climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J.
Climate, 26 (14), 5150–5168, doi:10.1175/JCLI-D-12-00236.1. 11
Ogi, M., Y. Tachibana, F. Nishio, and M. A. Danchenkov, 2001: Does the fresh water supply from the Amur
River flowing into the Sea of Okhotsk affect sea ice formation? J. Meteor. Soc. Japan, 79 (1), 123–124,
doi:10.2151/jmsj.79.123. 18
Ogi, M. and J. M. Wallace, 2012: The role of summer surface wind anomalies in the summer Arctic sea ice
extent in 2010 and 2011. Geophys. Res. Lett., 39 (9), doi:10.1029/2012GL051330, l09704. 21
Overland, J. E. and M. Wang, 2005: The third Arctic climate pattern: 1930s and early 2000s. Geophys. Res.
Lett., 32 (23), L23 808, doi:10.1029/2005GL024254. 16
Perovich, D., T. Grenfell, B. Light, and P. Hobbs, 2002: Seasonal evolution of the albedo of multiyear
Arctic sea ice. J. Geophys. Res. Oceans, 107 (C10). 7
Perovich, D. K., B. Light, H. Eicken, K. F. Jones, K. Runciman, and S. V. Nghiem, 2007: Increasing
solar heating of the Arctic Ocean and adjacent seas, 1979–2005: Attribution and role in the ice-albedo
feedback. Geophys. Res. Lett., 34 (19), doi:10.1029/2007GL031480. 15
Perovich, D. K., J. A. Richter-Menge, K. F. Jones, and B. Light, 2008: Sunlight, water, and ice: Extreme Arctic sea ice melt during the summer of 2007. Geophys. Res. Lett., 35 (11), doi:10.1029/
2008GL034007, l11501. 20
Perovich, D. K., C. S. Roesler, and W. S. Pegau, 1998: Variability in Arctic sea ice optical properties. J.
Geophys. Res. Oceans, 103 (C1), 1193–1208. 7
Persson, P., 2012: Onset and end of the summer melt season over sea ice: thermal structure and surface
energy perspective from SHEBA. Clim. Dyn., 39 (6), 1349–1371, doi:10.1007/s00382-011-1196-9. 21,
25
Peterson, B. J., R. M. Holmes, J. W. McClelland, C. J. Vörösmarty, R. B. Lammers, A. I. Shiklomanov,
I. A. Shiklomanov, and S. Rahmstorf, 2002: Increasing river discharge to the Arctic Ocean. Science,
298, 2171–2173, doi:10.1126/science.1077445. 18
Pithan, F. and T. Mauritsen, 2014: Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci., 7 (3), 181–184. 15
Polyakov, I. V., A. V. Pnyushkov, and L. A. Timokhov, 2012: Warming of the intermediate Atlantic Water
of the Arctic Ocean in the 2000s. J. Climate, 25 (23), 8362–8370, doi:10.1175/JCLI-D-12-00266.1. 17
Polyakov, I. V., et al., 2010: Arctic Ocean warming contributes to reduced polar ice cap. J. Phys. Oceanogr.,
40 (12), 2743–2756, doi:10.1175/2010JPO4339.1. 18
Rigor, I. G., J. M. Wallace, and R. L. Colony, 2002: Response of sea ice to the Arctic Oscillation. J. Climate,
15 (18), 2648–2663, doi:10.1175/1520-0442(2002)015<2648:ROSITT>2.0.CO;2. 15
Schweiger, A. J., J. Zhang, R. W. Lindsay, and M. Steele, 2008: Did unusually sunny skies help drive the
record sea ice minimum of 2007? Geophys. Res. Lett., 35 (10), doi:10.1029/2008GL033463. 20, 23, 25
Screen, J. A. and I. Simmonds, 2010: The central role of diminishing sea ice in recent Arctic temperature
amplification. Nature, 464 (7293), 1334–1337, doi:10.1038/nature09051. 14
Sedlar, J. and A. Devasthale, 2012: Clear-sky thermodynamic and radiative anomalies over a sea ice sensitive region of the Arctic. J. Geophys. Res., 117, D19 111, doi:10.1029/2012JD017754. 20
Serreze, M. and R. G. Barry, 2011: Processes and impacts of Arctic amplification: a research synthesis.
Glob. Planet. Change, 77 (1-2), 85–96, doi:10.1016/j.gloplacha.2011.03.004. 14
Serreze, M. and J. Francis, 2006: The Arctic Amplification Debate. Climatic Change, 76 (3-4), 241–264,
doi:10.1007/s10584-005-9017-y. 14
Serreze, M. C., M. M. Holland, and J. Stroeve, 2007: Perspectives on the Arctic’s shrinking sea-ice cover.
Science, 315 (5818), 1533–1536, doi:10.1126/science.1139426. 15, 18
Shimada, K., T. Kamoshida, M. Itoh, S. Nishino, E. Carmack, F. McLaughlin, S. Zimmermann, and
A. Proshutinsky, 2006: Pacific ocean inflow: influence on catastrophic reduction of sea ice cover in
the Arctic Ocean. Geophys. Res. Lett., 33 (8), L08 605, doi:10.1029/2005GL025624. 17
Smedsrud, L. H., et al., 2013: The role of the Barents Sea in the Arctic climate system. Rev. Geophys.,
51 (3), 415–449, doi:10.1002/rog.20017. 24
Steele, M., W. Ermold, and J. Zhang, 2008: Arctic Ocean surface warming trends over the past 100 years.
Geophys. Res. Lett., 35 (2), doi:10.1029/2007GL031651. 15
Steele, M., J. Zhang, and W. Ermold, 2010: Mechanisms of summertime upper Arctic Ocean warming and
the effect on sea ice melt. J. Geophys. Res. Oceans, 115 (C11), doi:10.1029/2009JC005849. 17
Stroeve, J., L. C. Hamilton, C. M. Bitz, and E. Blanchard-Wrigglesworth, 2014: Predicting September sea
ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008–2013. Geophys. Res. Lett., 41, 2411–2418,
doi:10.1002/2014GL059388. 25
Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W. N. Meier, 2012: Trends in
Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett., 39 (16), doi:10.1029/
2012GL052676. 13
Thomas, D. N. and G. S. Dieckmann, (Eds.) , 2003: Sea Ice: An Introduction to its Physics, Chemistry,
Biology and Geology. Blackwell, Oxford, U. K. 7
Trenberth, K. E. and D. P. Stepaniak, 2003: Seamless poleward atmospheric energy transport and implications for the Hadley Circulation. J. Climate, 16 (22), 3706–3722, doi:10.1175/1520-0442(2003)
016<3706:SPAETA>2.0.CO;2. 4, 6
Uttal, T., et al., 2002: Surface Heat Budget of the Arctic Ocean. Bull. Am. Meteorol. Soc., 83 (2), 255–275,
doi:10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2. 23
Wang, J., J. Zhang, E. Watanabe, M. Ikeda, K. Mizobata, J. E. Walsh, X. Bai, and B. Wu, 2009: Is the
dipole anomaly a major driver to record lows in arctic summer sea ice extent? Geophys. Res. Lett.,
36 (5), doi:10.1029/2008GL036706, l05706. 16, 20
Wild, M., D. Folini, C. Schär, N. Loeb, E. Dutton, and G. König-Langlo, 2013: The global energy balance
from a surface perspective. Clim. Dyn., 40 (11-12), 3107–3134, doi:10.1007/s00382-012-1569-8. 3
Wunsch, C., 2005: The total meridional heat flux and its oceanic and atmospheric partition. J. Climate,
18 (21), 4374–4380, doi:10.1175/JCLI3539.1. 3
Zhang, J., R. Lindsay, M. Steele, and A. Schweiger, 2008: What drove the dramatic retreat of Arctic sea ice
during summer 2007? Geophys. Res. Lett., 35 (11), doi:10.1029/2008GL034005, l11505. 20