On the Arctic Seasonal Cycle Jonas Mortin

On the Arctic Seasonal Cycle
Jonas Mortin
Cover image: An aerial view of the overlapping nila breaking off from the smooth snowcovered sea ice.
Photo by: Alice O’Connor, 2003.
File freely available from: http://nsidc.org/gallery/coppermine/displayimage.php?pos=-391.
c
Jonas
Mortin, Stockholm 2014
ISBN 978-91-7447-846-4
Printed in Sweden by US-AB, Stockholm 2014
Distributor: Department of Meteorology, Stockholm University
Abstract
The seasonal cycle of snow and sea ice is a fundamental feature of the Arctic climate system. In the Northern Hemisphere, about 55 million km2 of sea
ice and snow undergo complete melt and freeze processes every year. Because
snow and sea ice are much brighter (higher albedo) than the underlying surface,
their presence reduces absorption of incoming solar energy at high latitudes.
Therefore, changes of the sea-ice and snow cover have a large impact on the
Arctic climate and possibly at lower latitudes. One of the most important determining factors of the seasonal snow and sea-ice cover is the timing of the
seasonal melt-freeze transitions. Hence, in order to better understand Arctic
climate variability, it is key to continuously monitor these transitions.
This thesis presents an algorithm for obtaining melt-freeze transitions using scatterometers over both the land and sea-ice domains. These satelliteborne instruments emit radiation at microwave wavelengths and measure the
returned signal. Several scatterometers are employed: QuikSCAT (1999–2009),
ASCAT (2009–present), and OSCAT (2009–present). QuikSCAT and OSCAT
operate at Ku -band (λ = 2.2 cm) and ASCAT at C-band (λ = 5.7 cm), resulting
in slightly different surface interactions. This thesis discusses these dissimilarities over the Arctic sea-ice domain, and juxtaposes the time series of seasonal
melt-freeze transitions from the three scatterometers and compares them with
other, independent datasets.
The interactions of snow and sea ice with other components of the Arctic
climate system are complex. Models are commonly employed to disentangle
these interactions. But this hinges upon robust and well-formulated models,
reached by perpetual testing against observations. This thesis also presents
an evaluation of how well eleven state-of-the-art global climate models reproduce the Arctic sea-ice cover and the summer length—given by the melt-freeze
transitions—using surface observations of air temperature.
Sammanfattning
Säsongscykeln av snö och havsis är en grundläggande del av det Arktiska klimatsystemet. På den norra hemisfären smälter och fryser årligen cirka 55 miljoner km2 snö och havsis. Eftersom snö och havsis är mycket vitare än den
underliggande ytan, d.v.s. har ett högre albedo, reducerar de på höga latituder absorptionen av infallande solstrålning. Av den anledningen har förändringar av snö- och havsistäcket stor påverkan på det Arktiska klimatsystemet,
samt möjligen på klimtatet på lägre latituder. Tidpunkten för säsongsskiften—
smält- och frysövergångar—är en avgörande faktor för säsongscykeln av snö
och havsis. För att bättre förstå variabiliteten av det Arktiska klimatsystemet
är det därför viktigt att kontinuerligt observera dessa säsongsskiften.
Denna avhandling presenterar en algoritm för att erhålla tidpunkter för
dessa säsongsskiften över både land och havsis. Metoden appliceras på retursignalen från satellitburna mikrovågsinstrument. Tre av dessa instrument har
använts: QuikSCAT (1999–2009), ASCAT (2009–nutid) och OSCAT (2009–
nutid). QuikSCAT och OSCAT avger strålning med en våglängd av 2.2 cm
(Ku -band) och ASCAT med en våglängd av 5.7 cm (C-band). De olika våglängderna resulterar i något olika samspel med land- och havsisytan, vilket
denna avhandling diskuterar. Vidare sammanställs tidsserier av säsongsskiften
från dessa tre instrument över havsis samt jämförs med andra oberoende data.
Det Arktiska klimatsystemet är komplext för vilket snö och havsis är viktiga komponenter. För att studera sambanden används ofta modeller och det
är avgörande för dess trovärdighet att de är utvärderade med observationer.
Denna avhandling presenterar även en utvärdering av elva av den senaste generationen klimatmodeller med avseende på hur väl de återger det Arktiska
havsistäcket samt dess sommarlängd, vilket ges av tiden mellan säsongsskiften.
List of Papers
In addition to an introduction, the following papers, referred to in the text by
their Roman numerals, are included in this thesis.
I: Mortin, J., R. G. Graversen, and G. Svensson (2013). Evaluation of panArctic melt-freeze onset in CMIP5 climate models and reanalyses using surface observations, Climate Dynamics, doi: 10.1007/s00382-0131811-z.
II: Mortin, J., T. M. Schrøder, A. W. Hansen, B. Holt, and K. C. McDonald (2012). Mapping of seasonal freeze-thaw transitions across the panArctic land and sea ice domains with satellite radar, Journal of Geophysical Research–Oceans, 117, C08004, doi: 10.1029/2012JC008001.
III: Mortin, J., S. E. L. Howell, L. Wang, C. Derksen, G. Svensson, R. G.
Graversen, and T. M. Schrøder (2014). Extending the QuikSCAT record
of seasonal melt-freeze transitions over Arctic sea ice using ASCAT, Remote Sensing of Environment, 141, 214–230, doi: 10.1016/j.rse.2013.11.004.
IV: Mortin, J., S. E. L. Howell, C. Derksen, L. Wang, G. Svensson, and R.
G. Graversen (2014). OSCAT as a successor to QuikSCAT: a comparison
over Arctic sea ice with emphasis on the seasonal melt-freeze transitions,
Submitted to Annals of Glaciology.
For Papers I–IV, the analysis and writing was undertaken by me; the coauthors contributed with valuable comments and adjustments. The initiative
for Paper I was mine, but the study developed significantly in close collaboration with G. Svensson and R. Graversen. To my knowledge, the original
idea for Paper II came from T. Schrøder and his supervisors at Jet Propulsion
Laboratory, B. Holt and K. McDonald—work that I inherited and improved.
The idea for Paper III came from discussion between S. Howell, C. Derksen,
and myself at the IPY conference in Montreal 2012, and the work was refined by means of a close collaboration between Stockholm University and
Environment Canada, including two research visits. Paper IV was a natural
continuation of Papers II and III, as new data became available.
All reprints were made with permission from the publishers.
The following paper is not included in the thesis:
Brown, L. C., S. E. L. Howell, J. Mortin, and C. Derksen (2014) Evaluation of the Interactive Multisensor Snow and Ice Mapping System (IMS) for
monitoring sea ice phenology, Submitted to Remote Sensing of Environment.
Contents
Abstract
v
Sammanfattning
vii
List of Papers
ix
1
Introduction
13
2
The Arctic seasonal cycle
2.1 Sea ice and snow in the Arctic . .
2.2 Transition from winter to summer
2.3 Transition from summer to winter
2.4 Recent changes . . . . . . . . . .
2.5 Future changes . . . . . . . . . .
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Microwave remote sensing
3.1 Surface observations and satellite measurements . . . . . . . .
3.2 Properties of satellite measurements . . . . . . . . . . . . . .
3.3 Microwave measurements in the Arctic . . . . . . . . . . . .
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Conclusions
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5
Outlook
37
Acknowledgments
References
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xxxix
xli
1. Introduction
The Arctic region is an area of extremes. During winter, air temperatures over
the sea-ice covered Arctic Ocean and the snow-covered landmass commonly
fall below −40◦ C as a result of the 24-hour darkness that persists in winter for
days or months, depending on latitude. During summer, on the other hand, the
region experiences 24-hour daylight, and air temperatures commonly exceed
25◦ C over the land areas. The large difference in solar heat input, or insolation,
over the year yields a very strong seasonal cycle in temperature as well as in
the extent of snow and sea ice. Snow covers an area of about 45 million km2
on the Northern Hemisphere at its maximum in February, which is reduced to
about 2 million km2 in August, most of which covers the Greenland ice sheet
(Figure 1.1; Barry et al., 2007). Similarly, sea ice extends well beyond the
Arctic Ocean during winter, covering roughly 15 million km2 at its maximum
in March, decreasing to less than 7 million km2 at its minimum in September
(Figure 1.1; NSIDC, 2014). Hence, 55 million km2 of sea ice and snow—20%
of the Northern Hemisphere—undergo complete melt and freeze transitions
every year.
Snow and sea ice are both important climate variables for several reasons.
Most notably, they have a higher albedo (reflectivity of visible light) than the
underlying surface, and they have strong insulating properties, thereby disconnecting the underlying surface from the atmosphere. Moreover, they can be
seen as storages of freshwater (sea ice) and precipitation (snow), which are
released during spring and summer. For the Arctic climate, the high albedo
is most likely the most important property: by means of the high albedo, the
areal extent of snow and sea ice limits the amount of insolation absorbed during
summer. For example, snow-covered sea ice has an albedo of about 0.8, meaning that it reflects 80% of the insolation, compared with the albedo of open
water of less than 0.1. Areas that are covered with sea ice throughout summer
absorb about 30% less insolation than areas devoid of sea ice (Perovich and
Polashenski, 2012). For the same reason, the timing of the sea-ice and snow
melt, whereby they expose the low-albedo surface, is important for the amount
of absorbed insolation during summer. Similarly, the date when the ocean and
land surfaces become covered with sea ice and snow in the fall is important.
For every earlier day that the sea-ice melt begins, heat sufficient to melt 3 cm
of ice is absorbed in the upper ocean (Perovich et al., 2007). Equivalently in
13
(a) February
(b) August
Sea ice minimum 2012 (September)
Figure 1.1: Mean extent of snow cover (dark blue) and sea ice (light blue) during
1966–2006 for (a) February and (b) August. The sea-ice extent of September
2012 is outlined in (b). The figure is designed by Hugo Ahlenius (UNEP/GRIDArendal, 2007), based on data by Armstrong and Brodzik (2005), and is adopted
from the UNEP report Global Outlook for Ice and Snow (UNEP, 2007).
the fall, for every later day that sea ice is formed, heat that can melt 0.5 cm of
sea ice is added. This heat warms the upper ocean, where it is stored until the
fall, delaying ice formation and warming the lower atmosphere. The effect is
similar over land, because snow has a much higher albedo than all other land
surfaces (Bauer and Dutton, 1962). In other words, through the property of
high albedo, the annual evolution of the snow and sea-ice cover, as well as
the area over which this evolution occurs, is important for the Arctic climate
system, particularly during summer.
The Arctic region is experiencing rapid changes, driven primarily by increasing air temperatures (IPCC, 2007). This is clearly indicated by earlier
melt transitions, later freeze transitions, as well as decreasing volumes of snow
and sea ice. In recent decades, the temperatures in the Arctic region have increased roughly twice as fast as in the Northern Hemisphere (e.g. Bekryaev
et al., 2010), and are expected to continue to do so (e.g. Abe et al., 2011;
Knutti and Sedláček, 2012). Coincidently, melt is occurring earlier and freezeup later over both the sea-ice and land domains (Kim et al., 2012; Markus
et al., 2009; Wang et al., 2013). The sea-ice extent is decreasing during all
seasons, but summers experience the largest decline (NSIDC, 2014). Since
1979, when the satellite record started, the annual minimum sea-ice extent in
September has decreased from about 7 million km2 to around 5 million km2
(Figure 1.2), corresponding to a decline of circa 14% per decade. In 2012, the
lowest ever observed extent of 3.4 million km2 was reached (Figures 1.1b and
14
15
8
13
7
11
6
9
5
7
4
Snow
Sea ice
5
Sea−ice extent [million km2]
Snow extent [million km2]
1.2). Concurrently, the sea ice is becoming thinner, making it more susceptible
to variations in wind patterns and atmospheric and oceanic heat fluxes (e.g.
Schweiger et al., 2011; Zhang et al., 2012). A thinner ice cover also increases
transmittance and absorption of insolation (Nicolaus et al., 2012). The terrestrial, seasonal snow cover has experienced a similar retreat in spring, with a
decrease in extent of about 20% per decade since 1979, which has accelerated
in recent years (Figure 1.2; Derksen and Brown, 2012). In addition, the total
snow mass has decreased by about 7% per decade since 1982 (Takala et al.,
2011).
3
1967
1972
1977
1982
1987
1992
Year
1997
2002
2007
2012
Figure 1.2: Annual minima of sea-ice extent during the complete satellite record,
1979–2013 and June snow-cover extent during 1966–2013. Sea-ice and snow
data are provided by the National Snow and Ice Data Center (Cavalieri et al.,
1996) and Rutger University Global Snow Lab (see Brown and Robinson, 2011),
respectively.
Both the decline in areal extent of snow and sea ice and the longer summers—
the period between the melt and freeze transitions—act to further increase the
heat input into the system during summer. In effect, it is likely that these
changes have implications well beyond the Arctic region, as many recent studies suggest. For example, the sea-ice retreat has been suggested to cause
increases in air temperature and humidity (e.g. Kurtz et al., 2011; Serreze
et al., 2009), alterations in the large-scale atmospheric flow (e.g. Hopsch et al.,
2012), in turn altering winter conditions (e.g. Honda et al., 2009; Park et al.,
2013) and summer weather at mid-latitudes (e.g. Screen, 2013; Tang et al.,
2013). Also, decreases in the oceanic CO2 uptake (Else et al., 2013) and substantial ecological implications (e.g. Wassmann et al., 2011) have been suggested. For the same reason, the snow-cover retreat has been suggested to
15
increase air temperatures (e.g. Chapin III et al., 2005), affect the stability and
distribution of permafrost (e.g. Lawrence and Slater, 2009) and the vegetation
growing season (e.g. Thum et al., 2009), in addition to biodiversity, and human
activities and infrastructure (Callaghan et al., 2012).
Because of their climatic implications, it is key to continuously monitor
both the areal extent of snow and sea ice as well as the timing and variability
of the seasonal melt-freeze transitions. While the current network of surface
observations provides high-quality meteorological data, it is spatially limited,
particularly across the sea-ice domain. Satellite-borne remote sensing instruments, however, offer a nearly complete daily coverage of the Arctic region.
Microwave instruments are particularly suitable for monitoring of the variability of snow and sea ice, because they are able to measure the surface in the Arctic region all-day and all-year, in contrast to instruments operating at visual or
infrared wavelengths. Furthermore, they are sensitive to the liquid-water content of the surface. This property can be exploited to obtain information about,
for example, the areal fraction of sea ice. Moreover, because the amount of
liquid water at the surface varies when it undergoes melt and freeze processes,
time series of microwave measurements provide information about the timing
of these transitional processes, which thereby can be mapped.
Satellite-borne microwave instruments have been operating at least since
1978, currently yielding a record of 36 years, which is rather short for climate
studies. Models are aimed to replicate the complex climate system and can
help extend this record. Furthermore, models can be modified to study individual processes, to disentangle the complex land-ocean-ice-atmosphere interactions, and to provide projections of the future climate. For example, the climate system can be forced by changing the concentration of greenhouse gases
in the atmosphere in order to investigate the response on temperatures. Also,
important mechanisms in the climate system can be studied, such as those contributing to an early or late melt onset. But the models must first be thoroughly
evaluated and compared with observations. The air temperature is commonly
utilized to assess the variability and evolution of the climate. Because surface
observations of air temperature are acquired in a dense worldwide network,
this variable is useful for evaluating models. During the Arctic summer, however, air temperatures remain close to 0◦ C over snow and ice surfaces, making
an evaluation using this parameter problematic. Instead, the period between
the melt and freeze transitions—referred to as the summer length or the melt
season length—can be used. As it is related to the heat uptake during summer,
can be obtained using air temperatures, and is a sensitive climate indicator, it
provides a powerful tool to investigate how well the state-of-the-art climate
models capture the Arctic seasonal cycle, and ultimately, the Arctic climate
system.
16
This thesis addresses some of the issues pertaining to the pan-Arctic seasonal cycle with emphasis on the seasonal melt-freeze transitions. Paper I provides an evaluation of how well eleven state-of-the-art global climate models
represent the Arctic seasonal cycle, quantified using the summer length over
both the land and sea-ice domains. Surface observations and reanalyses are
used to assess both the sea-ice cover and the summer length development in
these models, from 1950 through 2100. In Paper II, a different approach is
used to estimate the timing of the seasonal melt-freeze transitions, utilizing
a satellite-borne microwave instrument. Paper II includes a novel methodology to detect the transitions over both the land and sea-ice domains from
QuikSCAT, which was operational during 1999–2009. The results are compared with surface observations of snow depth and air temperatures. To extend
the transition time series beyond 2009, Paper III investigates the applicability
of another, currently operational, microwave instrument, ASCAT. QuikSCAT
and ASCAT measure at different microwave wavelengths, of which the interaction with sea ice is discussed. In addition, Paper III documents improvements
to the methodology and compares transitions obtained from these instruments
and several other data sources. Paper IV relates microwave measurements from
QuikSCAT and the very similar, and currently operating, instrument OSCAT.
The seasonal melt-freeze transitions are obtained from both instruments and
the differences are discussed, relating also to other datasets.
17
18
2. The Arctic seasonal cycle
2.1
Sea ice and snow in the Arctic
Snow and sea ice share nearly the same constituents—ice, air, and water—
although at different ratios. Snow is composed by small and layered ice crystals surrounded by air. In fact, by volume, snow consists mostly of air. Sea
ice, on the other hand, consists primarily of ice with inclusions of air, liquid
water, as well as brine, which is a solution of salt in water. The relative amount
of these constituents in both media varies with season, sea-ice type, and snow
compression. Particularly the liquid-water content varies greatly over the year
in snowpacks, while remaining relatively constant in sea ice.
Sea ice is commonly categorized as either first-year ice (FYI) or multiyear
ice (MYI), where the latter has survived a full summer’s melt, which results
in dissimilar physical characteristics between the two ice categories. Due to
its older age, MYI is thicker than FYI, usually 3–6 m compared with less than
2 m (e.g. Laxon et al., 2013). MYI contains less brine and more and larger
air bubbles than does FYI, because during the melt season, brine is rejected
from the ice, leaving small pockets of air. MYI is also rougher, and it typically
has an undulating surface, as a result of melt-refreeze cycles as well as ridging
and rafting from collisions with surrounding ice. In general, MYI is covered
by a thicker snow cover because it has had more time to accumulate. Recent
estimates indicate that the snow depth in spring varies between 10–30 cm over
FYI (Brucker and Markus, 2013) and between 20–60 cm over MYI (Kwok
et al., 2011).
Sea ice influences the local climate mainly through two characteristics, as
discussed in the introduction. First, sea ice has a much higher albedo than the
underlying ocean, 0.5–0.7 compared with less than 0.1, thereby reflecting more
insolation. If the sea ice is covered with fresh snow, however, the albedo can
exceed 0.9, and as melt ponds form on the ice during summer, it can decrease
to below 0.5. Second, sea ice limits the exchange of energy between the atmosphere and the ocean surface. Particularly during winter, when it shields the
warmer ocean water from the much colder atmosphere (commonly differing by
30–40◦ C). Moreover, the snow cover on top has a high thermal insulation (i.e.
poor heat transfer) because it is largely constituted by immobile air, whereby it
19
efficiently disconnects the underlying surface from the atmosphere. Since the
sea ice is typically more than a meter thick and snow-covered in winter, the
ice-snow column can sustain a strong temperature gradient.
(a)
(b)
Figure 2.1: (a) Surface currents in the Arctic region and (b) mean sea-ice motion
during May 1979–2012. Sea-ice vectors in (b) are retrieved using microwave
satellite instruments, drift buoys, and reanalysis data, and are provided by the
National Snow and Ice Data Center (Fowler et al., 2013). The surface-current
figure (a) is adopted from the Arctic Monitoring and Assessment Programme
Assessment Report: Arctic Pollution Issues (AMAP, 1998).
Sea ice is continually in motion, driven by winds and ocean currents. Although the direction and speed vary, the motion is organized on monthly and
longer time scales and follows a pattern with two main components: the Beaufort Gyre and the Transpolar Drift (Figure 2.1). As a result of wind patterns
from an average high-pressure system, the Beaufort Gyre transports sea ice in
an anti-cyclonic (clockwise) motion. Sea ice can rotate in the Beaufort Gyre
for several years, allowing it to grow thick and to deform. The wind-driven
Transpolar Drift transports sea ice from the Eurasian side across the Arctic
Ocean and to the Atlantic Ocean, pushing sea ice against Greenland and the
Canadian Arctic Archipelago, thereby compressing and deforming the ice. As
a result, the thickest sea ice in the Arctic is found just north of Greenland. Sea
ice following the Transpolar Drift is usually exported out of the Arctic Ocean
within two years. The main sea-ice export occurs East of Greenland through
Fram Strait: about 20% of the total ice area passes Fram Strait annually, of
which 80% is MYI (Serreze and Barry, 2009). Moreover, the largest sea-ice
velocities—up to 20–30 cm per second—are found in this area (Figure 2.1b;
Kwok, 2009). The movement of sea ice gives rise to large, linear cracks in
the ice pack called leads that can be several hundred meters wide (depicted
20
on the cover). Leads can be used for navigation and are much darker (lower
albedo) than the surrounding ice, thus absorbing more insolation. Over leads
that are formed during the cold season, large amounts of heat and moisture
are released to the atmosphere because of the large ocean-atmosphere temperature difference. Landfast ice—areas of sea ice frozen to the shoreline or
to the seafloor—however, is largely unaffected by winds and ocean currents,
and is usually found along the Eurasian coastline and in the Canadian Arctic
Archipelago.
The Arctic land domain spans a variety of climate conditions—from polar desert (very cold and very dry) to maritime temperate climate (wet and
warmer). Moreover, the topography and the vegetation density of the Arctic region are highly variable (Figure 2.2). Similarly, the spatial variability of
snow depth over the Arctic land domain is large, usually ranging from 30–80
cm at maximum (Serreze and Barry, 2009), although these numbers are uncertain as surface observations are sparse and snow depth varies substantially
over small scales. Snow remains for more than 30 weeks over large land areas
and all year over the Greenland ice sheet. As noted earlier, snow has a much
higher albedo than the underlying surface: for fresh snow it exceeds 0.8, while
for land surfaces it is usually lower than 0.2. The strong thermal insulation
of the snow cover plays a large role, for example, for the distribution and the
stability of permafrost (i.e. frozen ground) which underlays about 20% of the
Northern Hemisphere land area (Zhang et al., 2000).
2.2
Transition from winter to summer
During winter, air temperatures commonly fall below −40◦ C over the sea-ice
covered Arctic Ocean. Over land, winter air temperatures vary substantially
depending on region: with its maritime temperate climate, parts of Scandinavia commonly experience winter temperatures close to 0◦ C, while the inner
continent of Eastern Russia experiences daily-mean air temperatures below
−40◦ C. Snow is accumulating during all cold months, and most of the Arctic
region is covered with snow and sea ice in winter. As noted earlier, sea ice covers 15 million km2 of the Northern Hemisphere in winter, which decreases to
about 5 million km2 after the full transition to summer (Figure 1.1). Similarly,
snow covers 45 million km2 in winter and 2 million km2 in summer.
The melting temperature of snow is around 0◦ C, while sea ice begins to
melt at lower temperatures, around −1.8◦ C (e.g. Andreas and Ackley, 1982).
In spring, when air temperatures reach the melting point of snow, the liquidwater content of the snowpack increases. At first, the water is held within the
snow grains, but as melt progresses, water fills the air pockets and can begin to
drain. Over land, the draining meltwater can move into the underlying surface
21
Figure 2.2: (a) Topography, bathymetry and (b) vegetation density of the Arctic
region. Topography data in (a) are provided by the Scatterometer Climate Record
Pathfinder (scp.byu.edu) as part of its high-resolution scatterometer products, and
the tree fraction in (b) is acquired from the Moderate-resolution Imaging Spectroradiometer (MODIS; Hansen et al., 2006).
or form ponds if the surface is frozen or saturated. Eventually, a large amount
of this meltwater gathers in large rivers that run into the Arctic Ocean (e.g.
Yang et al., 2007). Over sea ice, the meltwater forms melt ponds that typically
follow the ice topography and the snow-depth contours (Petrich et al., 2012).
Melt ponds can become more than a meter deep, and eventually, the meltwater
can escape the ice through cracks and holes in the ice, or over the side of the
ice floe. During the melt season, sea ice continues to disintegrate, also from
the bottom (Eicken, 1994). Until the snow or sea ice has completely melted,
the surface air temperature will remain close to the melting point, as all available energy goes into melt rather than warming. When melt begins, the albedo
decreases locally in the melting snowpack, absorbing more insolation that enhances further melt, which further decreases the albedo. As melt progresses,
the albedo continues to decrease when melt ponds are formed and the underlying surface (bare ground or ocean water) is exposed, which helps to further
accelerate melt of surrounding snow and sea ice by absorbing more insolation.
Due to this snow-ice-albedo feedback, melt usually progresses rapidly once it
is initiated. During the melt process, diurnal melt-refreeze cycles are common.
In studies related to snow or sea-ice melt, this multistep process is commonly reduced to one date. Depending on the data source and the definition of
melt, one particular stage of the melt process is hence studied. For example,
22
40
Earlier melt
30
20
10
0
Later melt
−10
−20
2m−temp.
Skin temp.
Soil temp.
2.0 cm
1.0 cm
Snow depth
Snow depth
−30
−40
Melt timing wrt ASCAT [days]
the timing of melt onset can be estimated using an air-temperature threshold
(e.g. Paper I; Persson, 2012; Rigor et al., 2000). In comparison, when the
melt timing is estimated from microwave instruments, it is usually related to
the increase of liquid-water content in the snowpack, discussed further in Section 3.3 and Papers II–IV. Because it takes time for the liquid-water content to
increase in the snowpack, the melt-timing estimate based on air temperatures
usually occurs earlier than the estimate based on microwave measurements:
from a few days (Paper II) to about a week (Paper III). This makes melt-timing
estimates from microwave instruments difficult to validate with surface observations. Other geophysical variables can be used to derive information about
the later stages of the melt process. For example, snow depth or sea-ice concentration falling below a given threshold provides information that the air
temperature cannot provide, since air temperatures remain close to the melting
point once the snow and sea ice begin to melt. To examine the whole melt
process, several parameters must hence be combined, such as air temperature,
changes of the snowpack liquid-water content (microwave measurements), soil
temperature, or snow depth (Figure 2.3). It is notable, however, that Markus
et al. (2009) obtain both an early and a late stage of the melt and freeze processes over Arctic sea ice using passive microwave radiometers (Section 3.3),
thereby attaining more information about the processes than with single estimates.
Figure 2.3: Timing of the melt transition obtained from multiple datasets over
land, shown with respect to the melt timing estimated using the microwave instrument ASCAT. Two different snow-depth thresholds are shown. Box-andwhiskers show 20- and 80-percentiles (whiskers), first to third quartiles (boxes),
and averages (dots). Temperature data are obtained from ERA-Interim (ECMWF,
2014) and snow-depth data from the National Snow and Ice Data Center (Brown
and Brasnett, 2010).
23
Timings of the melt transition derived from microwave instruments in Papers II–IV indicate that melt generally begins in Scandinavia and western Russia in March, where weather systems from the Atlantic Ocean commonly cause
mild winters. Over the remaining Arctic land domain, a distinct north-south
melt-timing gradient is evident—April at lower latitudes and May/June near
the Arctic Ocean. This is expected, as lower latitudes receive more insolation
in spring and experience more transport of warm (and humid) air masses from
the south. At high elevation, however, melt occurs later due to colder temperatures. Over the Arctic Ocean, melt typically begins in early May in the
marginal seas (i.e. the outskirts of the Arctic Ocean). However, it is generally in early June that most of the sea-ice domain begins to melt. By July, the
melt process is well underway over the whole region, although some terrestrial
snow remains over northern land areas aside from glaciers and the Greenland
ice sheet.
2.3
Transition from summer to winter
As mentioned earlier, summer temperatures vary greatly within the Arctic region: while air temperatures over the sea-ice domain remain close to 0◦ C,
daily-mean air temperatures well above 20◦ C are common over land. The sea
ice that remains is scattered with melt ponds and consists largely of ice floes of
varying size that move around relatively freely. When air temperatures over the
central Arctic drop below the freezing point as the incoming energy from insolation decreases, the relatively shallow melt ponds, consisting largely of fresh
water, freeze. Snow that falls can thereafter remain. Each of these processes
increases the albedo, triggering the snow-ice-albedo feedback, although the
freeze process over sea ice is typically not as rapid as the melt process because
the insolation is weaker (Persson, 2012). Over open water, the whole, typically
well-mixed upper water column must be cooled to the freezing point before ice
can form. The freezing point is typically −1.8◦ C in the Arctic Ocean due to
its saline water. Therefore, open-water areas freeze up to a month later than
do the shallow melt ponds on the remaining sea ice (Papers I–II). Ice formation usually starts beneath the surface, where small ice crystals form, float to
the surface, and bond together into ice sheets. The sheets continue to thicken
throughout the winter, although snow accumulation can slow this process due
to its insulating properties, whereby it shields the ice from the cold atmosphere
(e.g. Hezel et al., 2012). Over land, non-frozen ground is cooled by air temperatures close to the freezing point and snowfall. Once the ground has frozen,
snow that falls, remains and accumulates. Early in the freeze process, the surface can freeze and re-melt as a result of diurnal variations in temperatures and
insolation, particularly at lower Arctic latitudes.
24
Earlier freeze
40
30
20
10
Later freeze
0
−10
−20
2m−temp.
Skin temp.
Soil temp.
1.0 cm
2.0 cm
Snow depth
Snow depth
−30
−40
Freeze timing wrt ASCAT [days]
As for the melt process, the freeze-up process occurs in several stages and
the transition date can be defined differently depending on what parameters
that are used. Over land, for example, air and soil temperatures can be used to
obtain the timing of an early stage of the freeze process, and snow depth a later
stage (Figure 2.4). As discussed in Paper II, some microwave instruments are
sensitive to the snow accumulation, while others are sensitive to the soil state
and therefore represent an earlier stage of the freeze-up (e.g. Naeimi et al.,
2012). This makes validation of transition obtained from microwave instruments using surface observations difficult. Over sea ice, similarly to at melt,
air temperature and sea-ice concentration can provide information complementary to microwave measurements, which typically are related to changes in the
liquid-water content of the surface (Papers II–IV). But again, to obtain the
whole freeze-up process, several parameters must be combined.
Figure 2.4: As Figure 2.3, but for the freeze process.
Based on estimates from satellite-borne microwave instruments (Papers
II–IV), freeze-up begins in the central Arctic in mid-August, when the sea-ice
cover is still shrinking due to the relatively warm ocean water. As noted earlier,
open water freezes about a month later than the sea-ice cover due to the larger
water column that needs to be cooled. Starting in mid-September, the ice cover
grows slowly, typically reaching the outskirts of the Arctic Ocean in December. Across the northern North American and eastern Eurasian continents, the
ground freezes in September and snow begins to accumulate. Freeze-up then
progresses westward. The ground across the whole land domain has usually
frozen by November. The exception is western Russia and east Scandinavia,
where freeze-up might occur in the beginning of the subsequent year, and in
these areas, melt-freeze cycles are common also during winter.
25
2.4
Recent changes
Estimates of the timing of the melt and freeze processes constitute a useful
quantification of the Arctic seasonal cycle. Trends of an earlier melt transition
and a later freeze transition have been observed over both the land and sea-ice
domain. For example, Markus et al. (2009) found that during 1979–2007 over
the sea-ice domain, melt and freeze transitions occurred 0.5–6 days earlier
and 1–8 days later per decade, respectively, depending on region. Using a
slightly different approach over Arctic sea ice, Belchansky et al. (2004) found
an earlier melt of 2–5 days per decade and a later freeze-up by 1–3 days per
decade during 1979–2001. Over land, Wang et al. (2013) reported an earlier
melt by about 1–3 days per decade. Although Papers II–IV estimate the timing
of the seasonal melt-freeze transitions, the covered period (1999–2013) is too
short for a trend analysis.
As discussed earlier, the timing of the sea-ice and snow melt influences the
cumulative heat input during summer, since a snow and ice free surface has a
much lower albedo. Similarly in fall, the timing of sea-ice formation and snow
accumulation is important, as these media increase the albedo substantially.
Perovich et al. (2007) found that for every day earlier melt is initiated, 8.7
MJ m−2 is absorbed in the upper ocean—heat sufficient to melt 3 cm of ice.
Similarly, for every day later that freeze-up occurs, 1.5 MJ m−2 is absorbed,
which is enough to melt 0.5 cm of ice. Perovich et al. (2007) further suggested
that variations in the timing of melt onset are more important than variations
in the insolation for the year-to-year variability of the absorbed heat during
summer. The trends of an earlier melt and a later freeze-up thereby have the
same effect as the retreat of sea ice and snow, whereby larger low-albedo areas
are exposed during summer. For example, Perovich and Polashenski (2012)
estimated that 30% more heat is absorbed in the upper water column over open
water areas than over sea-ice covered areas (despite the relatively low albedo
of melt ponds). The additional absorbed heat due to the processes of an earlier
melt, a later freeze-up, and less sea ice and snow, can delay ice formation and
warm the lower atmosphere in the fall. Because the recent trends of all these
processes favor more heat input, the Arctic climate is under significant change,
most likely with implications also for the mid-latitude climate (e.g. Hopsch
et al., 2012; Screen, 2013; Tang et al., 2013). The effect over land is equivalent,
but has different implications, such as permafrost stability, vegetation growing
cycle, and animal habitats (e.g. Brown et al., 2007; IPCC, 2013; Stone et al.,
2002).
26
2.5
Future changes
It is important to monitor the processes related to the ocean heat input during
summer, particularly in light of this recent development. The retreat of snow
and sea ice is expected to continue as air temperatures will rise further (IPCC,
2013). The rate of the sea-ice retreat depends on our future emission trajectory
of greenhouse gases, but also differs between climate models. The climate
models that best represent the observed trend and extent of sea ice project
that the Arctic will become almost ice-free in summer by the middle of this
century (Liu et al., 2013) or perhaps even sooner (Wang and Overland, 2012).
Similarly, the snow cover will continue to decline rapidly. In fact, the retreat of
terrestrial snow is occurring faster than what is indicated by the climate models
(Derksen and Brown, 2012).
Pertaining to the future change of the seasonal melt-freeze transitions, Paper I uses air temperatures from climate models given the RCP8.5 scenario,
which is the most extreme and most commonly analyzed scenario. This analysis indicates that by year 2100, melt onset will occur about 20 days earlier
than at date over both the land and sea-ice domains, and that freeze-up is projected to occur about 30 days later over the land domain and over sea ice at low
Arctic latitudes (∼60–70◦ N). Over sea ice at high latitudes (> 70◦ N), freezeup is projected to occur about 90 days later, largely attributed to a complete
sea-ice retreat in summer, whereafter, as discussed earlier, a larger water column must be cooled before ice can form. This projected change of the melt
and freeze onset—roughly 2 and 3 days per decade, respectively—over land
and low-latitude sea ice is consistent with the aforementioned, recent trends
from satellite data, although at the lower end of the estimates. The trends
are sensitive to the size and location of the area, as well as to the period for
which they are obtained. Particularly because there is a strong spatial and
year-to-year variability of the timing of the seasonal melt-freeze transitions as
well as of the snow and sea-ice extent (e.g. Papers I and III). However, as
noted earlier, many of the climate models are known to inaccurately reproduce
the recent Arctic sea-ice and snow cover variability, and to underestimate the
trends (e.g. Derksen and Brown, 2012; Liu et al., 2013; Stroeve et al., 2012).
Consequently, the estimated changes in summer length in Paper I should be
considered conservative, despite the most extreme scenario being used.
27
28
3. Microwave remote sensing
3.1
Surface observations and satellite measurements
The Arctic sea-ice domain is inaccessible. Here, systematic observations are
limited largely to drift buoys that measure a few meteorological and oceanographic variables, such as surface pressure, air temperature, and position (ice
movement). At any given year, up to 35 drift buoys under the International
Arctic Buoy Programme are active in the Arctic Ocean (Figure 3.1; IABP,
2014). However, a more extensive acquisition of geophysical variables, such as
surface energy fluxes or precipitation, is restricted to spatiotemporally limited
field campaigns. For example, during the Surface Heat Budget of the Arctic
Ocean (SHEBA) and the Arctic Summer Cloud Ocean Study (ASCOS) campaigns, a wide range of surface and atmospheric variables were observed during a full year in the Beaufort and Chukchi Seas 1997–1998, and in the Central
Arctic during a summer month in 2008, respectively. An example of another
type of campaign is IceBridge, which by means of aircraft-mounted multiinstrument measurements bridges the gap of snow-depth and ice-thickness retrieval between the ICESat and ICESat-2 satellites, during 2009–2016 (Studinger
et al., 2010). In addition to being spatiotemporally limited, these field campaigns are inconsistent in terms of what type of observations that is being acquired.
Over the Arctic land domain, the network of systematic surface observations is geographically denser. It is primarily constituted by weather stations
that report common meteorological variables, such as 2-meter temperature,
snow depth, and cloudiness. The number of reporting stations depends on the
variable: currently, about 1000 and 300 weather stations report the 2-meter
temperature and the snow depth, respectively (Figure 3.1b). The network of
stations is denser in populated areas, like Scandinavia, leaving large areas unmonitored (Figure 3.1a). This hampers the large-scale mapping of these variables, particularly those that vary substantially on a small geographical scale,
such as snow depth. In addition to the weather stations, during field campaigns, a wider range of geophysical variables are observed that are difficult
to systematically measure, such as soil temperature and surface energy fluxes.
But, again, these campaigns are limited in space and time.
29
1000
(b)
900
800
2−meter temperature
Snow depth
Buoy
700
600
500
400
300
200
100
Number of stations/buoys
(a)
0
Temperature
Snow depth
Both
1930
1940
1950
1960
1970
Year
1980
1990
2000
2010
Figure 3.1: (a) Geographical distribution of stations that reported at least nine
months of observations during 2012, as well as cumulative trajectories of buoys
during 1999–2009. (b) Number of stations and buoys that each year reported
observations during at least nine months, where only the period 1999–2009 is
represented for the buoys. In (a), red circles conceal blue and yellow circles in
Scandinavia and Alaska. Station and buoy data are provided by the National Climatic Data Center (NCDC, 2014) and the International Arctic Buoy Programme
(IABP, 2014), respectively.
Due to their spatiotemporal coverage, satellite-borne instruments are indispensable for consistent monitoring of geophysical variables, particularly in inaccessible areas or where surface observations are few. However, these instruments measure geophysical variables indirectly, i.e. without physical contact
with the measured object. As an example, satellite-borne scatterometers—
active microwave instruments—can be used to obtain the near-surface wind
speed over open-water areas because of their sensitivity to the surface roughness, which is a function of the wind speed (e.g. Draper and Long, 2002).
These measurements are neither as precise nor as accurate as those acquired
by, for instance, anemometers, which more directly measure wind speed. Instead, the strength of satellite measurements is broad spatiotemporal coverage
and consistency of measurements.
The accuracy of geophysical variables obtained from satellite instruments
depends on the physical relationship between the measured quantity and the
sought after geophysical variable, and on the algorithm that relates the two.
Therefore, in algorithm development, it is essential to compare the remotely
sensed geophysical variables with surface observations to ensure their accuracy
and consistency. Another approach is to combine surface observations with
satellite measurements, in order to utilize the strength of each measurement
technique. For example, to map the global snow-water equivalent—the height
of a melted snow column—the GlobSnow algorithm combines terrestrial surface observations of snow depth with measurements from passive microwave
30
radiometers, thereby increasing both the spatial coverage of the surface observations and the accuracy and precision of the satellite measurements (e.g.
Takala et al., 2011).
3.2
Properties of satellite measurements
Currently, there are at least 250 operational satellite instruments (EO Handbook, 2014) operating in a wide range of wavelengths (λ ) on the electromagnetic spectrum: from ultraviolet (λ ≈ 115 nm) to microwave L-band (λ ≈ 3
dm) wavelengths. These instruments measure a plethora of quantities from
which geophysical variables related to all components of the Earth’s climate
system can be obtained: radiation balance, atmospheric humidity and temperature profiles, clouds and aerosols, vegetation indices, ocean temperature
and color, and the state of the cryosphere, to name a few. Other applications
include cartography and land mapping, fire detection, volcanic eruption monitoring, and protection of ecosystems. In essence, satellite instruments measure
electromagnetic radiation at a given frequency and are either active or passive;
the former refers to when the instrument emits a signal and records the returned
signal, and the latter refers to when the instrument merely records information.
The many applications for which satellite remote sensing is utilized originate
in the interaction of electromagnetic energy with different components of the
climate system. The most intuitive example is probably for visible light: solar radiation interacts with the surface by scattering and absorption, returning
electromagnetic radiation with varying intensity at different frequencies (i.e.
colors). Similarly, although not perceivable by the human eye, all natural media emit microwave radiation that can be recorded by a passive satellite-borne
sensor and reconstructed as an image (Figure 3.2a). Several geophysical variables can be retrieved from this information, such as sea-ice extent, because
of the physical relationship between microwave emission and the liquid-water
content of the surface: ocean water emits less efficiently than does sea ice. An
algorithm taking this into account can thereby translate microwave emission
(at given wavelengths) to sea-ice concentration (Figure 3.2b).
Satellite remote sensing is constrained not only by the physical relationship of the observed quantity and the sought after geophysical variable, but
also by spatiotemporal resolution and coverage. In brief, the spatial resolution
(or footprint) is the size of the smallest detail an instrument can resolve, and
is inherently related to the spatial coverage—with a higher spatial resolution,
a smaller area can be observed. The spatial resolution of satellite instruments
designed for scientific applications ranges from less than 1 m to more than 50
km; these instruments have swath widths (coverage) of less than 10 km and
more than 1800 km, respectively (EO Handbook, 2014). The temporal reso31
Brightness Temperature [K]
300
290
280
270
260
250
240
230
220
210
200
190
180
170
(a)
(b)
Figure 3.2: (a) Brightness temperatures and (b) the retrieved sea-ice extent for
17 September, 2007. Sea-ice extent is given by sea-ice concentrations larger
than 15%. The brightness temperatures are acquired by the Special Sensor Microwave/Imager at 19 GHz, vertical polarization. Data are provided by the National Snow and Ice Data Center (Cavalieri et al., 1996; Maslanik and Stroeve,
2004).
lution is the frequency with which the satellite observes a given location and
is also related to the spatial coverage. For example, polar-orbiting satellites,
which are the most common type, pass close to the Poles on each revolution.
Consequently, they measure a location at northern latitudes more frequently
than a location at the Equator.
3.3
Microwave measurements in the Arctic
For geophysical variables related to the Arctic cryosphere, microwave measurements are most commonly employed. This is primarily attributed to two
characteristics that enable measurements of the surface to be acquired all-day
and all-year: the atmosphere and clouds are nearly transparent at microwave
wavelengths, unlike at visual and infrared wavelengths, and measurements can
be acquired independently of solar illumination. Moreover, they are indirectly
sensitive to the liquid-water content of the surface, because liquid water is essentially opaque at microwave wavelengths, while sea ice and snow are not.
The sensitivity of microwave measurements to liquid water can be exploited for a wide range of applications. For example, as previously mentioned, because the ocean water emits radiation at microwave wavelengths less
efficiently than does sea ice, the sea-ice concentration can be estimated (Figure 3.2). Because the liquid-water content varies when the surface undergoes
melt and freeze processes, time series of microwave measurements provide in32
−8
0
−12
−10
−16
−20
−20
−30
−24
−40
01
02
03
04
05
06
07
Month 2011
08
09
10
11
2−meter temp. [°C]
Backscatter [dB]
formation on the timing of these processes. In fact, surface melt and freeze
processes generally yield the largest variations in an annual time series, over
both the land and sea-ice domains (Papers II–IV), for a given wavelength and
incidence angle. As an example, Figure 3.3 shows measurements from an active microwave instrument (scatterometer) over a location covered with MYI.
In brief, winter values are high as the signal penetrates the MYI and scatters on the air bubbles, back to the sensor. In spring, the MYI is masked as
microwave energy cannot penetrate the wet snowpack, and the signal thus decreases markedly. In fall, when the melt ponds freeze, the signal increases
as it can again interact with the bubbles in the MYI. Over land and FYI, the
microwave measurements vary similarly with the melt and freeze processes,
although the physical processes are somewhat different (Papers II–III). The algorithm that is used to detect these changes is presented and evaluated in Paper
II. Papers III–IV present improvements to the algorithm implementation and
apply the algorithm to other scatterometers.
12
Figure 3.3: Annual time series of measurements from the scatterometer OSCAT
(black) and 2-meter temperature from ERA-Interim (red) over a MYI location
during 2011. The melt and freeze timings, as estimated by the algorithm used in
Papers II–IV, are shown as a dot and a star, respectively.
Several studies have previously used passive microwave instruments (radiometers) to estimate the timing of the seasonal melt-freeze transitions (e.g.
Markus et al., 2009; Wang et al., 2013). However, passive microwave radiometers measure at a coarse spatial resolution (25–50 km), and thus inadequately
resolve small-scale features such as detailed topography or coastlines. Synthetic aperture radar (SAR) instruments have also been used (e.g. Winebrenner
et al., 1996), but their high spatial resolution (≤100 m) is achieved at the cost
of a low spatial coverage. Scatterometers cover almost the entirety of the Arctic region at a spatial resolution of about 5 km (when resolution-enhanced),
thus providing a balance between resolution and coverage. Being active instruments, scatterometers measure the surface by emitting a microwave pulse
33
and measuring the returned signal after it has interacted with the surface. Three
scatterometers are utilized in this thesis: QuikSCAT, which operated at a wavelength of about 2 cm (Ku -band) during 1999–2009; ASCAT, operating at a 5cm wavelength (C-band) since 2009; and OSCAT, operating at a wavelength of
about 2 cm (Ku -band) since 2009. Papers II–IV focus on time series from these
scatterometers to estimate the timing of the seasonal melt-freeze transitions:
Paper II uses data from QuikSCAT, and to extend the time series of seasonal
transitions to the present, Papers III–IV use data from ASCAT and OSCAT.
The resulting estimates are evaluated and compared, also with other independent sources of melt-freeze transitions obtained from passive microwave radiometer, snow depth, as well as 2-meter and skin temperature. Scatterometers
have previously been used to retrieve the seasonal melt-freeze transitions (e.g.
Howell et al., 2005; Wang et al., 2011), although these studies have typically
been limited to a smaller domain than in Papers II–IV, or to only one transition.
A wide range of other geophysical variables related to the Arctic sea ice
and snow can be obtained using microwave instruments. For example, MYI
and FYI can be distinguished because of a stronger scatterometer signal from
MYI than from FYI at dry-snow conditions, as discussed in detail in Paper
III. This is useful for monitoring the evolution and status of the sea-ice cover.
The aforementioned sea-ice concentration can be combined with drift-buoy
movement and wind patterns to estimate the movement and the age of sea ice
(e.g. Fowler et al., 2013; Maslanik et al., 2011). Using high-resolution SAR
imagery, the type, thickness, and movement of individual sea-ice floes can be
obtained with high confidence (e.g. Dierking, 2013). Additionally, microwave
instruments can help estimate the snow-water equivalent over land (e.g. Takala
et al., 2011), snow depth on sea ice (Brucker and Markus, 2013), terrestrial
snow-cover extent (e.g. Helfrich et al., 2007), characterization of melt ponds
(e.g. Scharien and Yackel, 2005), sea-ice thickness (e.g. Laxon et al., 2013),
and other cryosphere-related parameters. The curious reader is referred to the
National Snow and Ice Data Center (nsidc.org), which provides a wide range
of datasets related to the cryosphere.
34
4. Conclusions
The duration of the seasonal sea-ice and snow cover modifies the energy input
to the Arctic climate system by means of a high albedo. During recent decades,
a substantial retreat of the snow and sea-ice extent has been observed, thereby
exposing increasing low-albedo areas in summer. Moreover, because the melt
transitions are occurring earlier and the freeze transitions later, the low-albedo
areas are exposed during longer periods, thereby absorbing more insolation
(e.g. Markus et al., 2009; Wang et al., 2013). The resulting increase in heat
input postpones ice formation and warms the lower atmosphere with potential
implications well beyond the Arctic region (e.g. Parmentier et al., 2013; Tang
et al., 2013). It is therefore key to continuously monitor the seasonal meltfreeze transitions in order to understand the implications of the current climate
change.
Because of their nearly complete spatial coverage, measurements acquired
by satellite-borne instruments complement the sparse distribution of surface
observations in the Arctic. Microwave instruments are well suited for monitoring the melt and freeze transitions, because of their continuous acquisition
of measurements and their sensitivity to the liquid-water content of the surface. Alterations of the amount of liquid water generally generate the largest
signals in time series of microwave measurements, though the interaction of
microwave radiation with the snow and sea-ice surface is complex and can
consequently be challenging to interpret (Papers II–IV). By detecting these
signals, the timing of the seasonal melt-freeze transitions can be estimated.
Paper II presents a novel algorithm for retrieving the timing of the transitions
over both land and sea ice from measurements acquired by QuikSCAT (1999–
2009; λ = 2.2 cm). Paper III presents an improved retrieval in the marginal
seas and shows that over sea ice, ASCAT (2009–present; λ = 5.7 cm) can
be used to extend the time series of transitions from QuikSCAT, despite the
two instruments operating at different wavelengths. It is shown, however, that
QuikSCAT’s measurements respond slightly stronger to the early melt of FYI,
making it less sensitive to sea-ice dynamics. Paper IV shows, by applying an
improved version of the algorithm, that OSCAT (2009–present; λ = 2.2 cm)
also extends the transition time series of QuikSCAT over sea ice, calibration
inconsistencies of OSCAT notwithstanding.
To evaluate the transitions retrieved from scatterometers, Papers II–III re35
late these estimates to transition-timing indicators based on other data sources—
such as snow depth, air temperatures, and passive microwave radiometers.
Hereby, more information on the melt and freeze processes is provided than
from a single estimate. For example, over sea ice at melt, air temperatures pass
the melting point about a week earlier than the melt date obtained from scatterometers, which is mainly based on an increase of the liquid-water content
(Papers II–III). At freeze-up, air temperatures reach the freezing point 2–10
days later over remaining sea ice, and 20–30 days earlier over open water, as
compared with the freeze indicators from scatterometers. However, these discrepancies are related to the different freezing points of the melt ponds (0◦ C)
and the ocean water (−1.8◦ C). When adjusting the temperature thresholds accordingly, the difference of estimates from scatterometer and air temperature is
very small (Paper III). This shows that careful considerations have to be made
when defining the melt and freeze indicators. Over land, the melt process typically occurs rapidly: melt indicators from air temperature, snow depth, and
scatterometer differ by a few days on average. At freeze-up, however, the air
temperatures pass the freezing point on average 20 days before snow starts to
accumulate, where the latter process is that detected by QuikSCAT (Paper II).
Climate models that aim to replicate the Arctic climate system must be
tested against surface observations. Paper I provides an evaluation of the latest
generation global climate models of the seasonal cycle based on air temperatures. Using summer length as a metric of the seasonal cycle, there are large
model-model and model-observation disparities of up to four months, despite
the monthly averages of the air temperature being relatively well-represented
by the models. However, the average of all investigated models represents
the summer length fairly well. Nevertheless, the large disparities indicate that
the summer length is a sensitive parameter, responding to small changes in air
temperatures during the transitional processes. The models capture the summer length better in the central Arctic than over areas that are ice-free during
summer. This is likely attributed to the difficulty of modeling the sensitive dynamics of sea-ice formation in open water. During the 21st century, the climate
models project that summers will become about 1 month longer over the landmass, and 1–3 months longer over the sea-ice domain, increasing with latitude.
This difference is mainly caused by the sea-ice retreat, since open water areas
freeze about 30–40 days later than do ice-covered areas (Papers I–II). Consequently, the freeze onset is projected to be initiated within roughly 10 days
across the whole Arctic Ocean by 2100, compared with 80 days presently.
Considering the large additional heat input associated with such an increase
of the summer length, the Arctic and mid-latitude climate we know today will
most likely undergo significant changes.
36
5. Outlook
There are many ways to estimate the timing of the seasonal melt-freeze transitions over the sea-ice domain. As argued in Sections 2.2–2.3, multiple data
sources must be combined to fully capture the melt-freeze processes. For
example, transitions retrieved from scatterometers and passive microwave radiometers represent slightly different stages of the melt and freeze processes
(Paper III). Therefore, scatterometers and radiometers could be utilized in conjunction to improve, and reduce the uncertainty of, the retrieval of the meltfreeze processes from both instruments. Such an analysis could furthermore
help delineate the relationship between the measurements from these instruments for a wide range of atmospheric and surface conditions. Other data
sources could also be included, from which different stages of the transitional
processes can be retrieved, such as air-temperature data. To gain a full picture
of the thermodynamic state of the sea ice, auxiliary parameters could prove
useful, such as sea-ice thickness or sea-ice motion. Thereby, a more integrated
understanding of the sea-ice state could be achieved, which, in turn, could help
improve for example sea-ice related forecasts. Perhaps this approach can be
applied to investigate also the thermodynamic state of Antarctic sea ice.
The work presented in this thesis could be used to study the causes of the
variability of the seasonal transitions. The timing of the melt onset has been
shown to strongly impact the Arctic climate system (e.g. Perovich et al., 2007),
but the mechanisms causing an anomalously early or late melt initiation on a
large scale are not well-understood. Persson (2012) used the extensive dataset
acquired during the SHEBA campaign to delineate the atmospheric and surface
processes during the seasonal transitions. He found that the synoptic weather
systems played an integral role for the melt-freeze processes at the SHEBA
location during 1997–1998. His analysis could be applied on a larger scale,
both in space and time, to assess the generality of his conclusions. Advection
of warm and humid air by weather systems most likely play an essential role
for melt-freeze variability also outside of the SHEBA location. But how early
in the spring can these systems initiate surface melt? When does melt occur
in absence of such weather systems? Does it apply similarly to all parts of
the Arctic Ocean? These are some questions that could be targeted on a large
spatiotemporal scale. Papers II–IV provide a starting point in doing so by
estimating when the transitions occur.
37
Papers II–IV provide 15-year-long transition time series, which is short
in a climatological sense and too short for trend analyses. However, the time
series will most likely extend beyond the currently operational instruments,
since the C-band instrument ASCAT has been approved for another satellite
platform, and other Ku -band scatterometers have been proposed (Vogelzang
and Stoffelen, 2012). Additionally, resolution-enhanced data from the C-band
scatterometers onboard the ERS-1 and ERS-2 platforms are available, together
covering the period 1992–2000, thus overlapping with QuikSCAT. But the data
have a lower temporal resolution than the newer scatterometers, potentially
requiring alterations of the algorithm employed in Papers II–IV.
The algorithm in Papers II–IV could be improved to provide more information about the melt-freeze processes from scatterometer data. For example,
it could also assess the uncertainty, or robustness, of the retrieved transitions.
One indication of the uncertainty is the strength of the backscatter change that
the algorithm interprets as the transition. Another indication is the agreement
of the different temporal scales that are examined when analyzing microwave
measurements, described in Paper II: a larger conformity of the time scales
should give a more robust transition estimate. Additionally, using this approach, several plausible transition dates could be obtained, possibly accompanied by a probability estimate. Such an uncertainty assessment would be
very useful when combining many data sources to investigate the thermodynamic state of sea ice.
38
Acknowledgments
I would like to thank everyone at, and outside of, MISU who made my PhD
studies a pleasurable and fruitful experience. First and foremost, I would like
to extend my deepest gratitude to my supervisors, Gunilla Svensson and Rune
Graversen. Gunilla, your deep knowledge in a large variety of topics is impressive, and your passion for the climate sciences is enviable. Rune, your ability
to consistently provide fresh ideas, no matter the topic, is admirable. You see
opportunities that others don’t. Thank you both for always making time for
small and large questions, and for motivational and practical problems. You
have consistently given excellent scientific advice, even though a majority of
my work lies in the outskirts of your area of expertise. I have truly enjoyed
working with you both.
A special thanks to Stephen Howell, Chris Derksen, and Libo Wang at
Environment Canada for a very enjoyable and productive collaboration, and for
all warming encouragement. Thank you for being so open and friendly when
we met at the IPY conference in Montreal, which sparked the collaboration
that enriched my studies immensely. Thank you also for the hospitality when
I came over on a research visit. Steve, I am deeply grateful your mentorship,
adeptly helping me to navigate in the world that is microwave remote sensing
of sea ice.
I would also like to take this opportunity to thank Thomas Schrøder, for
investing a lot of time and effort in me, both during my Master’s thesis and my
PhD studies. This thesis largely builds on your work that you let me inherit,
for which I am very thankful. You are truly indefatigable. Thanks also to Ben
Holt, for happily and proficiently providing insights in discussions over email,
and Kyle McDonald.
Jenny Lindvall and Cecilia Wesslén, my office mates, thank you for the
harmonic, friendly, and productive atmosphere you helped create. But more
importantly, thank you for the abundant off-topic conversations and support.
To the same-generation PhD students, together with whom I went on the great
journey that is being a PhD student, thank you for being awesome in general.
Most notably Markus Löfvenström, Anna Lewinschal, Kristoffer Hultgren,
Saeed Falahat, Raza Ranjha, Leon Chafik, Joakim Kjellsson, Marie Kapsch,
and John Hanley. With you, it was always possible to ventilate various aspects
of being a PhD student, for which I am grateful. My thanks also go to the older
generation PhD students, for your help with technical questions, informal PhD
mentorship, and overall friendliness: Johan Liakka, Sebastian Mårtensson, Johannes Lindvall, Joe Sedlar, and Anders Engström. Recently, it was great seeing a new, cheery, and undestroyed generation PhD students come to MISU:
Henrik Carlson, Eva Nygren, Sara Broomé, Malin Ödalen, Koen Hendrickx,
Filippa Fransner, Wing Leung, Cian Woods et al. Also thanks to some of the
more accomplished researchers, most notably Louis-Phillippe Caron, Heiner
Körnich, Michael Tjernström, Bodil Karlsson, Frida Bender, Caroline Leck,
Farahnaz Khosrawi, and Linda Megner.
Susanne Ericson, Pat Johansson, Eva Tiberg, and Cecilia Törnqvist deserve recognition for their essential help with administrative tasks. Especially
Susanne for solving all my burdensome requests with dash and much humor.
Thanks also to Anna-Karin Bergström and Åsa Öman.
For financial assistance, I would like to thank the Bolin Centre for Climate Research, APECS, the Canadian Government, the Knut and Alice Wallenberg foundation, and IGS. Thanks also to David Long at Brigham Young
University for your patient support and provision of scatterometer data, the National Snow and Ice Data Center and the European Centre for Medium-Range
Weather Forecasts for excellent scientific data, as well as NSC and PDC for
supercomputer access.
Last, but by no means least, I would like to express deep gratefulness to my
family. Sofie, let me make myself clear. This thesis wouldn’t exist if it weren’t
for your endless support. But more importantly, your exemplary emotional
generosity and kindness, limitless patience, and fabulous humor greatly enrich
my life. Malte. Long-awaited and longed-for, you make me see new things in
life. More important things. I truly look forward to seeing you grow up, and I
am incredibly happy that it was you, and not someone else, that came. I would
also like to thank my parents and my brother for your support, in whatever I
undertake.
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