Introduction
Arctic sea ice melt season characteristics play an important role in the
radiation balance of the Arctic. Changes in the melt season have important
implications for the Arctic climate system as a whole , and
therefore are crucial for anticipating ecological changes and informing
economic development in the region. In this study, we quantify the impact of
definition choices and internal variability on Arctic sea ice melt season
characteristics (averages and trends of melt onset, freeze onset and melt
season length). This allows us to assess how best to compare observed and
modeled melt season changes and diagnose model biases.
The timing of melt and freeze onset greatly affects regional oceanic heat
budgets . In situ
observations indicate that melt onset is driven primarily by synoptic frontal
systems that produce northward warm air advection . Cloud
formation and light drizzle in the warm air layer then increase downwelling
longwave radiation and initiate melt onset. For each day that melt onset
occurs earlier, 8.7 MJ m-2 is absorbed by the surface
. As summer energy absorption increases with earlier melt
onset dates, positive ocean heat content anomalies in the near-surface
temperature maximum (NSTM) layer increase in magnitude
. At the end of the summer, the heat stored in the NSTM
layer is then mixed toward the surface by shear-driven mixing and
entrainment, delaying freeze onset
. For each day that freeze onset
occurs later, an additional 1.5 MJ m-2 is absorbed by the surface
.
Relationships between sea ice extent and melt season length
, and specifically between sea ice extent and melt onset
date , have been found. Furthermore, because the timing of
melt onset has a large impact on radiation absorption in the Arctic, observed
melt onset dates have been used to predict freeze onset dates in some
regions, such as Baffin Bay and the Laptev and East Siberian
seas . The existence of these relationships raises the
possibility that melt season biases might be contributing to biases in sea
ice extent simulations.
Melt and freeze onset dates also have important ecological and societal
implications in the Arctic. For example, delayed freeze onset has been shown
to decrease snowpack on sea ice, thereby reducing the area that ringed seals
can use for snow caves necessary for birthing . Polar bears
are also dependent on the timing of melt and freeze onset, as they use sea
ice as a platform for seasonal hunting and breeding .
Moreover, prediction of melt onset dates is increasingly important for
operational sea ice forecasts that inform local decision-making in the Arctic
.
Previous efforts to assess melt onset, freeze onset and melt season length in
climate models have used a variety of definitions, as no model definition of melt and
freeze onset directly corresponds to remote sensing definitions
. This
inconsistent definition of melt and freeze onset complicates both comparisons
between models and between models and observations. Furthermore, because of
the chaotic nature of the climate system, there will always be a limit to how
well model projections fit observations, even for trends of more than 35
years . In particular, it has been shown that
the full CMIP5 distribution of 35-year September sea ice extent trends could
be due to internal variability . Recent work suggests that
sea ice trends similar to observations are only found in climate models with too much
global warming . But global warming, known to drive sea
ice extent trends , is also strongly impacted by
internal variability . Furthermore, observational estimates
of the sea ice sensitivity are highly uncertain due to observational
uncertainties in both sea ice extent and global temperature
.
Further complicating the issue, even observational assessments of melt season
characteristics do not use just one definition of melt onset and freeze onset
. Passive
remote sensing techniques utilize brightness temperatures, which are
sensitive to the changes in emissivity that occur when snow and ice change
phase. Algorithms for deriving melt and freeze onset dates from brightness
temperature vary in their methodologies, and differences between algorithms
can arise from inconsistencies in source data, inter-sensor calibration,
masking techniques and other factors .
This study addresses two main questions: what are the impacts of different
definition choices and internal variability on diagnosing and projecting
Arctic sea ice melt season characteristics (melt onset, freeze onset and melt
season length)? How can we use melt season characteristics from satellite
observations for model evaluation, despite those effects? We seek to answer
these questions by using the longest available satellite-derived melt and
freeze onset dataset to compare multiple plausible
definitions of melt and freeze onset in the Community Earth System Model
Large Ensemble (CESM LE) . By using the CESM LE, we are able
to account for the role of internal variability and utilize daily model
variables that are not available from the CMIP5 archive, thereby allowing us
to assess the comparability of different melt and freeze onset definitions.
We also show how melt and freeze onset dates and melt season length are
projected to change by the end of the 21st century under a strong emission
scenario (RCP8.5) and how internal variability and definition differences
impact those projections.
Methods
In this study, we use both model and passive microwave (PMW) satellite data to
assess the timing of continuous sea ice melt and freeze onset in the Arctic,
defined here as north of 66∘ N. A map of the major Arctic seas and
features referred to throughout the text is shown in Fig. S1 in the
Supplement. Pan-Arctic means are taken between 66 and 84.5∘ N.
Pan-Arctic trends in melt season characteristics are calculated as the slope
of the least-squares linear regression of the pan-Arctic mean from 1979 to
2014. Individual grid cell trends in melt season characteristics are
calculated where there are melt onset dates, freeze onset dates or melt
season lengths available for at least 20 years of the 36-year period (as was
done in ). Individual grid cell trends represent the
slope of the least-squares linear regression of each melt season
characteristic from 1979 to 2014.
Community Earth System Model Large Ensemble
To analyze the impact of different model definitions and internal variability
on melt season characteristics, we use the CESM LE . The CESM
LE is a 40-member ensemble of simulations conducted for the period
1920–2100. Each ensemble member starts from slightly different initial
atmospheric conditions and is subject to historical forcing from 1920 to 2005
and RCP8.5 forcing from 2006 to 2100. The CESM LE uses CESM1-CAM5
, and has a nominal resolution of
1∘×1∘. The CESM LE has been used in multiple studies
of Arctic sea ice cover, performing well overall
.
Under RCP8.5 forcing, Arctic sea ice in the CESM LE first reaches September
ice-free conditions by the middle of the 21st century (2032–2053 using
monthly means of ice extent; ). By the end of the 21st
century, ice-free conditions persist for 4–5 months in most years
.
Passive microwave melt and freeze onset data
We utilize the PMW dataset of melt and freeze onset dates from
, updated by , gridded to
25 km × 25 km (data accessed on 16 May 2016; available at the NASA
Cryosphere Science Research Portal). This dataset applies the PMW melt and
freeze onset algorithm to passive microwave brightness temperatures collected
over the period 1979–2014 from the Nimbus 7 Scanning Multichannel Microwave
Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I) and the
Special Sensor Microwave Imager and Sounder (SSMIS). The PMW algorithm uses
brightness temperatures from the 37 V GHz and 19 V GHz (18 V GHz on
SMMR) sensor channels.
Specifically, the PMW melt and freeze onset algorithm describes and utilizes
three parameters (Δ37, GRice, P) based on brightness
temperatures. The parameters are described in detail in .
The parameters are weighted based on their respective normalized expected
ranges, and the sum of the weights is used to determine the dates of melt and
freeze onset at each pixel for each year. In order to minimize the effects of
noise in the data, the validity of the produced melt and freeze onset date is
assessed at each pixel using the neighboring eight pixels. Dates are
considered valid if more than four of the surrounding pixels do not vary by
more than 1 day. In areas of thin ice, ice concentration information
supplements the brightness temperature parameters. If no clear melt signal is
available in thin ice areas, the melt onset date is taken as the day at which
ice concentration drops below 80 % for the last time. Similarly, if no
clear freeze signal is detected, the freeze onset date is taken as the first
day at which ice concentration exceeds 80 %.
As noted earlier, other melt and freeze onset algorithms exist in addition to
the PMW algorithm, such as the advanced horizontal range algorithm (AHRA).
AHRA computes melt onset (but not freeze onset) over both first-year ice and
multiyear ice based on passive microwave temperatures ,
improving upon earlier work that only provided melt onset over multiyear sea
ice . While both the PMW and AHRA algorithms utilize passive
microwave brightness temperatures, they are not equally sensitive to changes
in brightness temperatures. The PMW dataset includes early melt and freeze
onset dates as well as continuous melt and freeze onset dates. The former is
defined as the first day of melt/freeze, while the latter is the day that
melting or freezing conditions begin and persist throughout the rest of the
season. Comparison of the PMW Combined data (which are composed of PMW early
melt onset dates except when early melt is not detected, then the PMW
continuous melt onset date is used) versus the AHRA data shows large mean
differences in early melt onset dates and differences in trends over
1979–2012 . When reproduced with the same inter-sensor
calibration adjustments and masking techniques, trend agreement improves
between PMW Combined and AHRA, but large differences in mean early melt onset
dates remain .
Because the PMW algorithm can be used to derive both melt and freeze onset
dates across the entire Arctic for a 36-year period, the resulting data are
best suited for climate model evaluation. In
this study, we use the continuous melt and freeze onset dates so that we can
determine the continuous melt season length. By using continuous melt season
length, we aim to evaluate changes in season-long characteristics of the melt
season. All further discussion of melt and freeze onset refers to continuous
melt and freeze onset. Note that by using continuous melt and freeze onset
dates, we use an observational melt season length definition that differs
from the definition of melt season length, which
incorporates early melt and freeze onset dates.
Model definitions of melt and freeze onset
Because climate models, including the CESM LE,
do not simulate brightness temperatures, we cannot apply the same methodology
as used in the PMW algorithm to define melt and freeze onset in the model.
However, in contrast to PMW data, we can obtain the actual melt and freeze
onset from physical variables in the model. Here, we define several melt and
freeze onset dates from the existing daily output of the CESM LE that make
physical sense to assess the importance of definition choices and their
suitability for comparisons with the PMW data. Details of the definitions can
be found in Table . In particular, we make use of daily
means of snowmelt, surface temperature, frazil and congelation ice growth,
and thermodynamic ice volume tendency. Of these, only surface temperature and
thermodynamic ice volume tendency are available for all 40 ensemble members.
All others were only saved for two ensemble members (34 and 35). Furthermore,
the surface temperature is from the atmospheric model, while all other
variables are from the sea ice model. To minimize errors associated with
regridding, we generally use the variables on their original grid, which
differs between the sea ice and atmospheric models in CESM. That means that
surface temperature is only regridded onto the sea ice grid when necessary
for melt season length calculations.
CESM LE definitions for melt and freeze onset, showing the model
output variable name used, the threshold used and the number of consecutive
days over which the variable must exceed the threshold for each definition.
Details on how these thresholds and consecutive days were chosen can be found
in the Supplement.
Definition names
Output variable
Threshold
Consecutive
in the CESM
days
Melt onset
Surface temperature
TS
-1 ∘C
3
Thermodynamic ice volume tendency
dvidtt_d
0 cm day-1
3
Snowmelt
melts_d
0.01 cm day-1
5
Freeze onset
Surface temperature
TS
-1.8 ∘C
21
Thermodynamic ice volume tendency
dvidtt_d
0 cm day-1
3
Congelation ice growth
congel_d
0.01 cm day-1
3
Frazil ice growth
frazil_d
0 cm day-1
3
For melt onset in the CESM LE, we create three different definitions, based
on the available output (Table ): one definition using
thermodynamic ice volume tendency (for all 40 members), a second using
surface temperature where ice concentration is greater than zero (for all
40 members) and a third definition using snowmelt (for two members). We
expect that the snowmelt definition matches the PMW definition most closely,
as the brightness temperature melt criteria capture changes in liquid water
content in the snow. The temperature criteria likely also capture snowmelt
onset, but less directly than if melt onset is based on actual snowmelt. In
contrast, the thermodynamic volume tendency captures the onset of surface,
basal and lateral ice melt rather than snowmelt. These different CESM LE
definitions of melt onset provide insight into a range of melt processes.
While not all of them are expected to correspond to satellite observations,
the differences in timing between the model definitions themselves may be
important for certain applications, such as for biophysical processes
and the transport of sediments and contaminants by sea ice
.
As the PMW is based on liquid water content in the snowpack, and the snowmelt
definition is due to snowmelt itself, even the snowmelt melt onset definition
likely does not correspond perfectly to the PMW-based definition.
Furthermore, snowmelt is only saved in two ensemble members, which does not
allow an assessment of the impact of internal variability on this definition.
We will compare all three definitions in order to quantify how the diagnosed
melt onset in the model varies based on the variable used.
For freeze onset in the CESM LE, we create four different definitions
(Table ): one using thermodynamic ice volume tendency
(for all 40 members) and a second using surface temperature where ice
concentration is greater than zero (for all 40 members). It is important for
comparisons with PMW observations that we define freeze onset using both
surface temperature and thermodynamic ice volume tendency, since refreezing
of liquid water in the snow on sea ice is not accounted for as ice growth in
the CESM LE. In the CESM LE, thermodynamic ice volume tendency is a sum of
congelation ice growth along existing sea ice and frazil ice growth in the
water column. Thus, only a surface-temperature-based definition is able to
capture potential freeze onset processes within the snowpack, which are
detected in satellite observations. We also create two additional freeze
onset definitions using frazil ice growth and congelation ice growth, in
order to compare the impact of these two ice growth processes.
CESM LE definitions for melt season length, showing the various melt
and freeze onset definition combinations used to compute melt season length.
For each combination, the melt onset date is subtracted from the freeze onset
date at each grid cell every year.
Melt season length definition name
Melt onset definition
Freeze onset definition
Volume–volume
Thermodynamic ice volume tendency
Thermodynamic ice volume tendency
Temperature–temperature
Surface temperature
Surface temperature
Congelation–snowmelt
Snowmelt
Congelation ice growth
Frazil–snowmelt
Snowmelt
Frazil ice growth
Temperature–snowmelt
Snowmelt
Surface temperature
Melt season length is calculated at each grid cell for each year as the
difference between local freeze onset date and melt onset date. In total, we
create five unique definitions of melt season length, which are detailed in
Table . Two definitions of melt season length keep like
variables together (i.e., use both melt and freeze onset dates from surface
temperature definitions or thermodynamic volume tendency definitions), while the other
three combine variable definitions (e.g., use melt onset dates from the
snowmelt definition and freeze onset dates from the frazil ice growth
definition) in order to span the full range of possible melt season length
definitions in the CESM.
Three key definition decisions were found to impact the melt and freeze onset
definitions in the CESM LE. (1) The period over which one should check for
melt and freeze onset, (2) the threshold each variable must meet for melt and
freeze onset and (3) the number of consecutive days each definition must
pass the threshold for melt and freeze onset to occur. The choices are shown
in Table . Decisions on these three components were
based on what makes physical sense, whether they provide sensible continuous
melt and freeze onset dates and the percent area of the Arctic where melt
and freeze onset conditions are met. Details on the reasons for each of these
choices can be found in the Supplement. We did not use any smoothing
techniques such as running means or medians, which were used in other studies
. We found that smoothing techniques
excessively reduce the number of times that the melt and freeze onset
criteria are met in the CESM LE, at least for some variables. Details can be
found in the Supplement.
Results
CESM LE definitions: average melt season characteristics
Pan-Arctic averages
Using the definitions described in Sect. , we find that
there are large differences in the pan-Arctic averages of melt season
characteristics between CESM LE definitions (Fig. ). To
quantify pan-Arctic definition differences, we define the spread as the
average difference between the earliest and latest melt and freeze onset
definitions over 1979–2014, as well as the difference between the shortest
and longest melt season length definitions over this time period. Here we
discuss only ensemble member 35, as differences in spreads between ensemble
members 34 and 35 are small (Figs. and S2). We find
that the spread in pan-Arctic melt onset definitions in the chosen ensemble
member is 35 days, due largely to the early melt onset dates from the
thermodynamic ice volume tendency definition, which captures ice melt
(including basal melt; see Sect. for a discussion of
the spatial fields, which explains the large spread). This spread of 35 days
in melt definitions is much larger than the 13-day spread found between the
freeze definitions. The large spread in melt onset dates also affects
differences between melt season length definitions, leading to a spread of
43 days in ensemble member 35. Note that spreads in pan-Arctic melt and
freeze onset do not sum to the spread in melt season length, as the melt
season length is calculated at each grid cell and not as a difference in the
pan-Arctic means.
Internal variability introduces additional differences in diagnosed
pan-Arctic melt onset, freeze onset and melt season length
(Fig. ). However, these are much smaller than the
definition spreads, ranging between 4 and 8 days. Average melt onset dates are
less impacted by internal variability than average freeze onset dates, based
on the temperature and thermodynamic ice volume tendency definitions where
all 40 ensemble members are available. Pan-Arctic melt onset dates fall
within a range of 5 days, while pan-Arctic freeze onset dates fall within a
range of 8 days. Average melt season length is affected by internal
variability similarly to average freeze onset dates, with a range of 7 days
in both definitions.
Melt season characteristics averaged over 66 to 84.5∘ N for
PMW satellite observations and each CESM LE definition for (a) melt
onset, (b) freeze onset and (c) melt season length. PMW
satellite observations are shown in red. Other colored lines represent
ensemble member 35, and the gray shading represents the ensemble spread for
the two definitions (surface temperature and thermodynamic ice volume
tendency) that have 40 ensemble members. Plots are reproduced with member 34
in colored lines in Fig. S2.
Histograms of the pan-Arctic average melt season characteristics
over 1979–2014 using the surface temperature definitions (a–c) and
thermodynamic ice volume tendency definitions (d–f) for all 40 CESM
LE ensemble members, showing the impact of internal variability. PMW
observations are denoted by black lines (a–c). Note that the
x-axis limits are different in each panel, but the range is the same
(12 days), to facilitate the assessment of the impact of internal variability
for different processes and definitions.
Spatial averages
Areas in the marginal ice zone have earlier melt onset dates and later freeze
onset dates than those in the central Arctic, but specific spatial
distributions of average melt season characteristics in the CESM LE depend on
the definition. For example, melt onset derived from the snowmelt definition
occurs in mid-June to late June in the central Arctic and parts of the Laptev Sea
(Fig. a). Melt onset dates in the surface temperature
definition are generally later than in the snowmelt definition
(Fig. b), with mid-June to late June melt onsets stretching
from the central Arctic into the East Siberian, Chukchi and Beaufort seas.
The thermodynamic ice volume tendency melt onset definition yields central Arctic melt onset dates about 10 days earlier than the other definitions, as
well as earlier onset dates in the Barents and Chukchi seas
(Fig. c). Average melt onset dates from the
thermodynamic ice volume tendency definition over the satellite era are
earlier in the inflow regions than those derived from surface definitions in
the CESM LE (snowmelt, surface temperature) and PMW observations, since the
thermodynamic ice volume tendency definition reflects basal melt during
spring. Spring basal melt in the CESM LE is largest in the inflow regions,
particularly in the Greenland and Barents seas. Averages of melt season
characteristics over 1979–2014 are similar for ensemble members 34 (shown in
Figs. S3–S5) and 35
(Figs. –), as the impact
of internal variability on the 36-year means of the selected variables is
small.
Average melt onset dates over 1979–2014 for each CESM LE definition
using ensemble member 35: (a) snowmelt definition,
(b) thermodynamic ice volume tendency definition,
(c) surface temperature definition and (d) PMW satellite
observations. The black line denotes the mean March ice edge (15 % ice
concentration) from 1979 to 2014 using (a–c) the CESM LE and
(d) NSIDC Bootstrap sea ice concentrations . Melt
onset dates south of the mean ice edge are less reliable than those north of
the edge. Plots from ensemble member 34 are very similar and shown in
Fig. S3.
Average freeze onset dates for 1979–2014 for each CESM LE
definition using ensemble member 35: (a) congelation ice growth
definition, (b) frazil ice growth definition,
(c) thermodynamic ice volume tendency definition,
(d) surface temperature definition and (e) PMW satellite
observations. Plots from ensemble member 34 are very similar and shown in
Fig. S4.
Average freeze onset dates over the satellite era also vary spatially by
definition (Fig. a–e). In the central Arctic, the
surface temperature definition yields freeze onset dates in early August to
mid-August. Freeze onset definitions based on sea ice variables also show
early August to mid-August freeze onset dates in the region north of the
Canadian Arctic and Greenland, but later freeze onset dates throughout the
rest of the central Arctic. In all definitions, there are strong gradients in
freeze onset in the marginal seas. For example, in the Chukchi Sea, which is
impacted by Pacific water inflow, freeze onset occurs between mid-September
and the end of November. Even stronger gradients exist in the Barents Sea,
which is impacted by Atlantic inflow. Strong gradients in the marginal ice
zones are expected, as these areas show the largest trends in winter ice loss
and are impacted most strongly by sensible and latent heat fluxes
.
Average melt season lengths over 1979–2014, for each CESM LE
definition using ensemble member 35: (a) congelation–snowmelt,
(b) frazil–snowmelt, (c) volume–volume,
(d) temperature–snowmelt, (e) temperature–temperature and
(f) PMW satellite observations. Plots from ensemble member 34 are
very similar and shown in Fig. S5.
As expected, all definitions show the shortest melt seasons in the central Arctic and the longest melt seasons in the marginal seas. Melt seasons along
the Atlantic ice edge and in the Barents Sea are particularly long relative
to the other marginal seas (Fig. ). However, the
previously discussed differences in melt and freeze onset dates between
definitions are noticeable when comparing definitions of melt season length.
For example, thermodynamic ice volume tendency melt onset dates (which occur
earlier than in the other definitions) drive the longer melt season lengths
found along the Atlantic ice edge and in the Barents Sea when using the
volume–volume definition (Fig. c).
Additionally, in the Laptev Sea, surface temperature melt onset dates are
later than those from the other definitions, and this drives shorter melt
season lengths in the temperature–temperature definition than the other
CESM LE definitions by about 25 days (Fig. e).
CESM LE definitions: trends in melt season characteristics
Pan-Arctic trends
Definitions in the CESM LE generally show pan-Arctic melt onset dates
trending earlier and pan-Arctic freeze onset trending later over the period
1979–2014 (Table , Fig. ), in
agreement with previous work ().
But in the CESM LE, internal variability affects the magnitude of these
36-year trends, and in a few cases for melt onset and melt season length even
the sign of the trends. The large effect of internal variability on these
trends is already evident when comparing trends between ensemble members 34
and 35 (Table ). Ensemble member 35 shows larger pan-Arctic
trends than ensemble member 34 over 1979–2014 for almost all model
definitions and melt season characteristics. The only exception is the trend
in melt onset derived from thermodynamic ice volume tendency, which is the
smallest trend in both ensemble members, and shows a negative trend in member
34 but a small positive trend in member 35 (Table ). The
impact of internal variability on the 1979–2014 melt onset trends is even
more pronounced using the full 40-member CESM ensemble, where melt onset
trends fall between -2.4 and 0.8 days decade-1 for the surface temperature and thermodynamic volume tendency
definitions (Fig. ). However, all members show
negative 36-year melt onset trends for the rest of the model simulation if we
shift the trend start year to 1990 for the surface temperature definition and
to 2008 for the volume tendency definition. This shows that forced melt onset
trends over the observed period can be masked by internal variability for
some of the definitions of melt onset in the model.
Pan-Arctic freeze onset trends in the CESM LE are larger than trends in melt
onset in all 40 ensemble members, regardless of definition, and are always
positive over the satellite era (indicating later freeze onset). The 36-year trends in freeze onset are positive throughout the remainder of the
model simulation as well. The surface temperature definition of freeze onset
yields the largest trend over the satellite era in ensemble members 34 and 35
(Table ). The maximum trend of all ensemble members is also
larger in the surface temperature definition than in the thermodynamic volume
tendency definition (Table ). In
Fig. , the pan-Arctic average freeze onset dates are
more affected by internal variability than the averages melt onset dates.
This is true for the pan-Arctic trends as well: there is greater variability
between ensemble members in the freeze onset trends than in the melt onset
trends (Fig. ).
Trends in pan-Arctic melt onset, freeze onset and melt season length
(days decade-1) over 1979–2014 using PMW observations and CESM LE
definitions.
Member 34
Member 35
Ensemble
Ensemble
PMW
trends
trends
minimum
maximum
observations
Melt onset
PMW observations
-2.5
CESM LE surface temperature
-0.9
-1.9
-2.4
0.8
CESM LE therm. volume tendency
-0.5
0.2
-1.5
0.9
CESM LE snowmelt
-0.8
-1.6
Freeze onset
PMW observations
6.9
CESM LE surface temperature
5.1
6.7
1.2
8.6
CESM LE therm. volume tendency
4.1
4.8
1.2
5.7
CESM LE congelation ice growth
4.4
5.1
CESM LE frazil ice growth
3.6
4.1
Melt season length
PMW observations
10.4
CESM LE volume–volume
4.4
4.5
1.1
6.3
CESM LE temperature–temperature
3.9
5.8
-0.1
7.9
CESM LE congelation–snowmelt
4.4
5.7
CESM LE frazil–snowmelt
3.8
4.9
CESM LE temperature–snowmelt
5.6
7.1
Relative to the magnitude of the pan-Arctic trends from 1979 to 2014, the
impact of internal variability is very large. For melt onset in the CESM LE,
the range of ensemble trends due to internal variability is larger than the
magnitude of the melt onset trends. Internal variability even leads to melt
onset trends of both signs, even though trends towards earlier melt onset
dates dominate. Freeze onset trends over the satellite era are all positive,
but the ensemble spread due to internal variability of
7.4 days decade-1 is larger than most of the trends in all ensemble
members except two (7.5 and 8.6 days per decade, both found using the surface
temperature definition).
Histograms of the trends in pan-Arctic melt season characteristics
over 1979–2014 using the surface temperature definitions (a–c) and
thermodynamic volume tendency
definitions (d–f) for all 40 CESM LE
ensemble members. Gray bars represent trends from the other CESM LE
definitions for ensemble members 34 and 35. PMW observations are denoted by
solid black lines. The zero line is denoted by dashed black lines. Given the
magnitude of the trends, the internal variability is very large. Note that
the x-axis limits are different in each panel, but the range is the same
(12 days decade-1), to facilitate the assessment of the impact of
internal variability for different processes and definitions.
Trends in melt onset dates over 1979–2014 for each CESM LE
definition in the two members where they are available (member 34 in
a–c, member 35 in d–f) as well as in the PMW satellite
observations (g). The snowmelt definition is shown in (a)
and (d), the thermodynamic ice volume tendency definition is shown
in (b) and (e) and the surface temperature definition is
shown in (c) and (f). The black line denotes the mean March
ice edge (15 % ice concentration) from 1979 to 2014 using (a–f)
the CESM LE and (g) NSIDC Bootstrap sea ice
concentrations .
Since trends in pan-Arctic freeze onset are consistently larger than melt
onset trends, the majority of the trend in melt season length over 1979–2014
stems from the freeze onset component, in agreement with PMW observations
. For ensemble members 34 and 35, the temperature–snowmelt definition produces the largest trend in melt season length (Table
). Internal variability in melt season length trends is as
large as for the freeze onset trends, with pan-Arctic trends in melt season
length between -0.1 and 7.9 days decade-1 using the surface
temperature and thermodynamic ice volume tendency definitions
(Fig. ). While the majority of ensemble members
show a trend toward a longer pan-Arctic melt season as expected, one member
shows a trend toward a shorter melt season over 1979–2014. This demonstrates
that internal variability can have a large impact on trends, even over
36-year periods. The selection of the trend start date also impacts the trend
distribution. By start year 1981, just 2 years past the beginning of the
satellite period, all ensemble members and definitions have positive 36-year
trends in melt season length for the remainder of the model simulation.
Spatial trends
Spatially, trends in melt onset vary differently than trends in freeze onset.
Melt onset trends are generally negative except along the Atlantic ice edge,
indicating earlier melt onsets across most of the Arctic
(Fig. ). The complex pattern of spatial trends
near the Atlantic ice edge is likely related to the change in location of the
ice edge over 1979–2014. A moving ice edge means that conditions for melt
and freeze onset may be met in grid cells along the edge during some years
but not others. As noted in Sect. 2, trends are only evaluated at grid cells
where there are at least 20 years of valid melt characteristics over the
36-year period.
Because the temperature and snowmelt melt onset definitions capture surface
processes only, we find that the trends in these definitions are more similar
to each other than to the thermodynamic volume tendency definition, which
depends on sea ice melt. In both ensemble members 34 and 35, the snowmelt and
surface temperature definitions of melt onset show negative trends in the
Laptev, East Siberian and Chukchi seas that are not present in the
thermodynamic ice volume tendency definition, indicating that these trends
towards earlier melt represent snowmelt, rather than sea ice melt.
Trends in freeze onset dates over 1979–2014 for each CESM LE
definition in the two members where they are available (member 34 in
a–d, member 35 in e–h) as well as in the PMW satellite
observations (i). The congelation ice growth definition is shown in
(a) and (e), the frazil ice growth definition is shown in
(b) and (f), the thermodynamic ice volume tendency
definition is shown in (c) and (g) and the surface
temperature definition is shown in (d) and (h).
CESM LE definitions of freeze onset produce positive trends throughout almost
all of the Arctic, indicating later freeze-up, with the largest trends
occurring in marginal ice zones (Fig. ). The
marginal ice zones show the greatest ice loss over the satellite era, and
with more open water exposed, trends in sensible and latent heat fluxes have
increased . These fluxes further warm the surface ocean and
delay freeze onset. The magnitudes of the freeze onset trends vary between
definitions, and there are also regional differences between ensemble members
due to internal variability (Fig. ). However,
unlike the trends in melt onset definitions, the regional patterns in freeze
onset trends are largely consistent between definitions. The similarity in
trends between definitions based on surface temperature and sea ice variables
indicates that temperature trends are driving the delayed freeze-up.
Trend in melt season length over 1979–2014 for each CESM LE
definition in the two members where they are available (member 34 in
a–e, member 35 in f–j) as well as in the PMW satellite
observations (k). The congelation–snowmelt definition is shown in
(a) and (f), the frazil–snowmelt definition in
(b) and (g), the volume–volume definition in (c)
and (h), the temperature–snowmelt definition in (d) and
(i) and the temperature–temperature
definition in (e) and (j).
All CESM LE definitions show large positive trends in melt season length in
the Barents Sea and in the Laptev and East Siberian seas, driven by the
freeze onset trends in these regions (Fig. ).
Changes in freeze onset are particularly important to changes in the melt
season in the marginal ice zones, where sea ice has retreated the most over
the satellite period. However, definition differences and internal
variability introduce large variations in the magnitude and even the sign of
the diagnosed melt season lengths. The effect of definition differences is
most pronounced to the north of the Beaufort Sea, where temperature-based
definitions indicate a negative trend in melt season length, while all other
definitions show no or small positive trends in that region
(Fig. e, j). The effect of internal
variability is seen most clearly in the central Arctic, where even the sign
of the trend varies between ensemble members
(Fig. ). Internal variability also affects the
magnitude of the melt season trends in the marginal seas
(Fig. ), as sea ice loss is simulated
differently in ensemble members 34 and 35.
Comparing CESM LE and PMW
Average melt season characteristics
Pan-Arctic average PMW observations fall
within the range of model definitions and internal variability for all melt
season characteristics (Fig. a). Spatially, the
greatest melt onset similarities exist between the CESM LE snowmelt
definition and PMW observations, particularly in the central Arctic Ocean and
Laptev Sea (Fig. ). This agrees with the initial
expectation that PMW data are most closely related to the snowmelt criteria,
as the PMW algorithm is designed to detect surface liquid water. Histograms
of 1979–2014 average melt onset show that the snowmelt definition agrees
best with PMW observations in terms of areal median and the
areal distribution over the satellite era (Fig. ).
However, the snowmelt definition and PMW observations of average melt onset
still do not match exactly. In particular, the snowmelt definition has a
greater areal fraction of melt onset dates before June than the PMW data. As
both ensemble members 34 and 35 show a similar mismatch, this is likely not
due to internal variability, but due to definition differences and/or an
early melt onset model bias in the CESM LE. It is also possible that no model
bias exists and that later melt onset in the PMW data is due to observational
uncertainty. Uncertainty in satellite-derived melt onset dates was assessed
by using two different algorithms, the AHRA and the PMW
Combined algorithm (which, as noted earlier, is composed of PMW early melt
onset dates except when early melt is not detected, then the PMW continuous
melt onset date is used). It was found that the AHRA algorithm shows earlier
melt onset dates than the PMW Combined algorithm in nearly all locations
across the Arctic . The difference between pan-Arctic
average PMW melt onset dates and the melt onset dates found in the CESM LE
using surface-based definitions (snowmelt and surface temperature) is less
than the approximately 20-day melt onset difference found between the two
satellite algorithms in . Therefore the difference between
PMW and CESM LE melt onset dates might be within the observational
uncertainty rather than a model bias. However, compared
early melt onset algorithms, while we assess continuous melt onset. It is
therefore unclear if the observational uncertainty is the same for early and
continuous melt onset.
Average melt season characteristics from 66 to 84.5∘ N for
1979–2014 for PMW satellite observations (filled gray) and each CESM LE
definition (in ensemble member 35): (a) melt onset using the surface
temperature, thermodynamic ice volume tendency and snowmelt definitions,
(b) freeze onset using the surface temperature, thermodynamic ice
volume tendency, frazil ice growth and congelation ice growth definitions and
(c) melt season length using the temperature–temperature,
temperature–snowmelt, volume–volume, frazil–snowmelt and
congelation–snowmelt definitions. Plots from ensemble member 34 are very
similar and are shown in Fig. S6.
For freeze onset, the surface temperature definition agrees best with PMW
observations in terms of median and distribution
(Fig. ). Surface temperature is the only definition
for which freeze onset dates in the central Arctic, Laptev Sea and Kara Sea
are not later than PMW observations over the satellite era
(Fig. ). It is likely that PMW observations agree
well with the CESM's surface temperature definition, since both represent
strictly surface processes. Particularly in the central Arctic, a surface
temperature definition may capture the timing of snow cover or melt pond
refreezing. However, refreezing of ponds or liquid water in the snow on sea
ice is not accounted for in the CESM LE. Therefore this kind of freeze onset
is not captured by the model definitions based on ice growth, explaining the
later freeze onset of those definitions compared to PMW data in the central Arctic.
Comparisons of melt season length emphasize that no one definition fully
captures the PMW observations. All CESM LE definitions show longer melt
seasons in the Barents Sea than shown by the PMW data
(Fig. ). By areal fraction, most definitions show
a longer melt season length in the CESM compared to PMW data
(Fig. ). In terms of pan-Arctic averages, CESM LE melt
season lengths are both shorter and longer than PMW data depending on the
definitions used.
Trends in melt season characteristics
In the PMW observations spanning 1979–2014 ,
pan-Arctic melt onset is occurring 2.5 days earlier per decade and pan-Arctic
freeze onset is occurring 6.9 days later per decade (Table ,
Fig. ). In agreement with PMW data and past studies
, a larger trend in freeze onset than melt onset
is produced by all CESM definitions. The PMW melt onset trend falls just
outside the range of model definition trends (spanning -2.4 to
0.9 days decade-1), while the PMW freeze onset trend is bracketed by
model definition trends (spanning 1.2 to 8.6 days decade-1).
Trends in melt season characteristics versus trends in September sea
ice extent from 1979 to 2014 for PMW observations and all available CESM LE
ensemble members. Each marker represents an ensemble member. Circles
represent ensemble member 34 and triangles represent ensemble member 35. The
red markers represent the PMW melt and freeze onset observations and NSIDC
September sea ice extent . (a) Trends in melt
onset using the surface temperature, thermodynamic ice volume tendency and
snowmelt definitions. (b) Trends in freeze onset using the surface
temperature, thermodynamic ice volume tendency, frazil ice growth and
congelation ice growth definitions. (c) Trends in melt season length
using the temperature–temperature, temperature–snowmelt, volume–volume,
frazil–snowmelt and congelation–snowmelt definitions. Lines represent the
least-squares linear fits.
Trends in melt season characteristics versus trends in September sea
ice sensitivity from 1979 to 2014 for PMW observations and all available CESM
LE ensemble members. Each marker represents an ensemble member. Circles
represent ensemble member 34 and triangles represent ensemble member 35. The
red markers represent the PMW melt and freeze onset observations and sea ice
sensitivity derived from HadCRUT (square), GISTEMP (+) and NCDC (diamond)
global temperature observations and NSIDC September sea ice extent
. (a) Trends in melt onset using the surface
temperature, thermodynamic ice volume tendency and snowmelt definitions.
(b) Trends in freeze onset using the surface temperature,
thermodynamic ice volume tendency, frazil ice growth and congelation ice
growth definitions. (c) Trends in melt season length using the
temperature–temperature, temperature–snowmelt, volume–volume,
frazil–snowmelt and congelation–snowmelt definitions. Lines represent the
least-squares linear fits.
None of the CESM LE definitions yield trends in melt season length (spanning
-0.1 to 7.9 days decade-1) as large as the trends found in the PMW
observations (Table , Fig. ). In
the PMW observations and all but one ensemble member of the CESM LE
definitions, the pan-Arctic melt season is lengthening, and this change is
driven predominately by later freeze onset dates. But PMW observations show
that the average pan-Arctic melt season is lengthening at a rate of 10.4 days
per decade, which is over 30 % larger than any of the melt season trends
found using CESM LE definitions over the satellite era in any ensemble member
(Table , Fig. ). Regionally, we
find that the CESM melt season length trends in the marginal ice zones are
consistently smaller than the PMW melt season length trends, for all
definitions in members 34 and 35 (Fig. ). In
definitions where all 40 ensemble members are available, some members show
trends as large satellite observations in certain regions (such as the
Barents and Chukchi seas), but not across the entire marginal ice zone, like
what is seen in satellite observations. This is driven in particular by
smaller freeze onset trends in the marginal seas compared to PMW data. These
pan-Arctic and regional trend differences suggest that the CESM LE
underestimates the melt season length trend, in particular in the marginal
seas.
Relationship between melt and freeze onset
Earlier melt and later freeze onset dates are related in both CESM LE
definitions and PMW observations (Fig. S7). In previous work, earlier melt
onset has been shown to delay fall freeze onset through increased solar
absorption in the Arctic Ocean . There is moderate
correlation between modeled melt and freeze onset in the CESM LE, but there
is also substantial internal variability and variations between model
definitions. The correlations of melt and freeze onset in the model range
between -0.64 and 0.12, while the PMW correlation is -0.26 (Fig. S7).
However, only about 3.5 % of all available ensemble members and definitions
in the CESM LE show positive correlations, indicating that in general,
earlier melt onset dates are related to later freeze onset dates in the same
year. This forced relationship between melt onset and freeze onset is also
apparent in the ensemble mean, which shows negative correlation coefficients
that bracket the observations (-0.21 using thermodynamic ice volume
tendency and -0.49 using surface temperature).
Pan-Arctic ensemble means of melt season characteristics averaged
over the time periods 1979–1998, 2040–2059 (mid-century) and 2080–2099
(end of century). Surface temperature and thermodynamic ice volume tendency
definitions are averaged over 40 ensembles, and all other definitions are
averaged over the two ensemble members for which they are available
(members 34 and 35).
Definition names
1979–1998
2040–2059
2080–2099
2040–2059 minus
2080–2099 minus
1979–1998
1979–1998
Melt onset
Surface temperature
160
144
127
16
34
Therm. volume tendency
124
114
96
10
29
Snowmelt
155
146
141
9
15
Freeze onset
Surface temperature
257
319
378
62
120
Therm. volume tendency
272
319
368
47
96
Congelation ice growth
273
320
368
48
95
Frazil ice growth
278
321
369
43
91
Melt season length
Temperature–temperature
96
166
245
70
149
Volume–volume
140
196
268
56
128
Congelation–snowmelt
120
174
230
54
111
Frazil–snowmelt
122
173
229
51
107
Temperature–snowmelt
115
170
226
56
112
Melt season characteristics and September sea ice
CESM LE members that have the largest trend in September sea ice extent over
the period 1979–2014 also have the largest melt season length trend
(Fig. ). Correlations between trends in September sea
ice extent and trends in the two CESM LE melt season length definitions with
40 available ensemble members (surface temperature and thermodynamic ice
volume tendency) are both -0.79. In Sect. we showed
that 36-year trends in melt season characteristics are affected strongly by
internal variability. The same is true for September sea ice extent trends,
as shown in previous work . But unlike the observed
trend in melt season length, the observed trend in September sea ice extent
falls within the range of internal variability in the CESM LE
.
While we cannot discern a bias in CESM LE September sea ice extent trends
over the satellite era, a bias may exist for the September sea ice
sensitivity , and an underestimation of melt
season length trends could be a contributing factor. Sea ice sensitivity is
defined as the change in September sea ice extent per degree of global
temperature change. Both models and observations have been shown to have an
approximately linear relationship between Arctic sea ice extent and global
surface temperature . It has also been found that
climate models producing global warming
similar to observations have slower than observed sea ice loss
. However, large observational uncertainty in sea ice
sensitivity complicates model assessment. This agrees
with findings for the CESM LE, where the identification of a September sea
ice sensitivity bias depends on the selected observations and period
. Over the period 1979–2014, September sea ice sensitivity
using the GISTEMP global warming trend falls within the
ensemble spread, but all ensemble members underestimate the sea ice
sensitivity compared to those derived from HadCRUT4 and NCDC
global warming trends (Fig. ). In contrast,
all CESM LE ensemble members and definitions underestimate the pan-Arctic
trend in melt season length from 1979 to 2014 (as shown earlier,
Figs. and ). Hence, if the CESM LE
is indeed underestimating the September sea ice sensitivity, it is possible
that the underestimation of the melt season length trend is a contributing
factor.
Melt season length averaged over the time periods 1979–1998 (top
row), 2040–2059 (middle row) and 2080–2099 (bottom row) using ensemble
member 35. Each column is a different definition: (a) congelation–snowmelt, (b) frazil–snowmelt,
(c) volume–volume, (d) temperature–snowmelt and (e) temperature–temperature.
Histograms of the pan-Arctic trends in melt season characteristics
for 1979–2014 (shaded in gray) and for the end of the 21st century
(2064–2099, shaded in red for melt onset, blue for freeze onset and purple
for melt season length). This shows the change in the trends over the 21st
century as well as the changing impact of internal variability on these
trends. The histograms use the surface temperature definitions
(a–c) and thermodynamic ice volume tendency definitions
(d–f) for all 40 CESM LE ensemble members. Note that the x-axis
range is the same (25 days decade-1) for all panels shown in this
figure, but different from
Fig. .
Pan-Arctic projections under RCP8.5 forcing
All CESM LE definitions project larger changes in freeze onset than in melt
onset by the end of the 21st century, and this pattern is consistent with
modeled and observed trends over the satellite era. Under RCP8.5 forcing,
pan-Arctic melt onset dates are projected to occur 1–2 weeks earlier by the
middle of the 20th century, while freeze onset dates are projected to occur
1–2 months later (Table ). By the end of the 21st century,
pan-Arctic melt onset dates are projected to occur 2 weeks to a month earlier
under RCP8.5. At the same time, pan-Arctic freeze onset dates are projected
to occur in January of the following year, which is 3–4 months later than
modeled and observed freeze onset dates over the satellite era. Later freeze
onset dates are the primary driver of future changes in pan-Arctic melt
season length under RCP8.5, and the melt season is projected to be
5–6 months long by the middle of the 21st century and 7–9 months long by
the end of the 21st century (compared to 3–4 months long over the satellite
era). The largest changes in projected melt season length are seen in the
Chukchi, Beaufort and Barents seas (Fig. ).
Spatial differences between definitions of melt season length decrease over
the 21st century (Fig. ). This is consistent with the
increasing similarity seen in the pan-Arctic means of melt season length
(Fig. ). Variations between definitions decrease as the
sea ice extent, and therefore the areal coverage of melt and freeze onset,
decreases over the simulation, shrinking the region of study towards the
central Arctic (Fig. S8). The only definition that gets less similar to the
others over time is the snowmelt-derived melt onset definition. This is
caused by a more dramatic decrease in areal coverage compared to other melt
definitions (Fig. S8), due to the projected decline of spring snow cover on
sea ice .
Melt season length definitions become more similar in large part due to the
freeze onset component. In particular, the area covered by the surface
temperature freeze onset definition becomes more similar to the area covered
by the thermodynamic ice volume tendency freeze onset definition (Fig. S8).
This is likely due to the ice growth–thickness relationship ,
since thinner ice is less insulating and hence allows freeze onset quickly
after temperatures drop below freezing. A lack of insulation also affects the
increasingly large area of open water , where changes in
surface temperature can quickly trigger frazil ice growth. Thus, as ice
coverage decreases, the dates of freeze onset get more similar between
surface temperature and thermodynamic ice volume tendency definitions.
Additionally, the internal variability of melt season characteristics depends
on definition and is projected to increase through the 21st century.
Figures and show that
surface temperature definitions of melt onset, freeze onset and melt season
length yield greater variations between ensemble members than thermodynamic
ice volume tendency definitions over the satellite era. This is also true
over the period 2064–2099, as seen in Fig. , which shows
the shift in ensemble trends between 1979–2014 and 2064–2099. In all melt
season characteristics and definitions, the range of the pan-Arctic trends
increases between 1979–2014 and 2064–2099, indicating melt onset, freeze
onset and melt season length could be even more affected by internal
variability in the future. Average pan-Arctic melt season characteristics
also yield greater ranges over 2064–2099 (not shown). Changing internal
variability means that future observations will be compared to a wider
possible range of modeled melt season characteristics, making model bias
detection even more challenging.
Conclusions
Melt season length plays an important role in the radiation balance of the
Arctic and the predictability of sea ice cover. Ideally, we could compare
model simulations of melt season characteristics to remote sensing
observations to quantify model biases, but there are three major sources of
uncertainty in this approach. First, internal variability in the climate
system inherently limits how well model projections fit satellite
observations of melt and freeze onset . Second, there are
multiple possible definitions for sea ice melt and freeze onset in climate
models, and none of them exactly correspond to the definitions used by remote
sensing methods , which rely on PMW brightness temperatures
. Third, observational data of melt and freeze onset have uncertainties, for example due to inconsistencies in source data,
inter-sensor calibration and masking techniques . In this
study, we investigate the first two sources of uncertainty, namely the impact
of definition choices and internal variability for diagnosing Arctic sea ice
melt season characteristics (melt onset, freeze onset and melt season
length). We utilize model simulations of the CESM LE with the goal of
assessing how melt season projections are impacted by these factors, and to
determine how satellite observations can be used for model evaluation using
melt season characteristics.
We find that while some similarities exist between PMW observations and CESM
LE definitions, no single definition fully captures the satellite
observations. Definitions of melt season length show impacts of both melt and
freeze onset definitions: a large range between definitions, related
primarily to the melt onset, and a large range between ensemble members,
related primarily to the freeze onset. The average spread between the
shortest and longest pan-Arctic melt season length definitions is over
40 days during the satellite period, primarily because of differences in the
melt onset definitions. In particular, the thermodynamic ice volume tendency
definition (which is affected by surface, lateral and basal melt) produces
melt onset dates much earlier than the surface definitions using snowmelt or
surface temperature, which capture snowmelt rather than ice melt. These
results indicate that the choice of melt onset definition is highly dependent
on the application, and therefore on which processes one is aiming to
capture – sea ice melt or snowmelt. The PMW observations of melt onset, which
capture snowmelt, therefore cannot be used for comparison to model
definitions based on sea ice variables that capture ice melt. Even the
snowmelt melt onset definition is not a perfect fit to PMW satellite
observations. Furthermore, we find that in the late 21st century, the
snowmelt melt onset definition in the model could become less effective for
capturing melt onset over large areas of the Arctic, as spring snow cover on
sea ice is projected to decline under RCP8.5 forcing
. How this decline might impact PMW
brightness temperature-derived satellite observations is unclear.
In contrast to the melt onset definitions, the investigated freeze onset
definitions show greater agreement between each other in terms of averages, spatial patterns and trends over the satellite era. However, they
are still not identical, as the surface temperature definition produces
slightly earlier freeze onset dates than the other three definitions, which
are derived from sea ice variables. The earlier freeze onset dates from the
surface temperature definition indicate that changes in surface temperature
are driving sea ice formation, therefore producing more comparable
definitions for freeze onset than for melt onset (where surface temperature
predominantly affects snowmelt, but not ice melt). The earlier freeze onset
dates found in the surface temperature definition also agree well with PMW
observations, particularly in the central Arctic. As PMW observations likely
capture refreezing of liquid water within the snow on the sea ice in the
central Arctic, rather than the formation of new ice, a better agreement with
the surface temperature definition than the ice-based definitions makes
sense. Furthermore, since refreezing of liquid water in the snow is not
accounted for in the CESM LE, only the surface temperature definition in the
CESM captures surface processes.
Future projections show that the CESM LE definitions of freeze onset become
even more similar to each other over time. This is likely due to thinning
ice, which reduces insulation and allows for faster ice growth once surface
temperatures fall below freezing . The fact that surface
temperature drives ice growth also has important implications for internal
variability. CESM LE freeze onset definitions experience greater internal
variability than melt onset definitions. Similarly, surface temperature
definitions are more variable than those based on thermodynamic ice volume
tendency. This shows that the internal variability of a selected definition
variable impacts the internal variability of the derived melt and freeze
onset.
In both PMW observations and CESM LE definitions, earlier pan-Arctic melt
onset tends to be followed by later pan-Arctic freeze onset over the
satellite era, in agreement with previous work . However,
while the ensemble mean clearly shows this forced response, internal
variability affects this relationship and can reverse this relationship for
individual years in the CESM LE over the satellite era.
The pan-Arctic trend in melt season length is driven mostly by the trend in
freeze onset in the CESM LE, in agreement with previous work for the PMW melt
season length . Yet, despite the use of multiple plausible
definitions and 40 ensemble members, no model definition produces trends in
the pan-Arctic melt season as large as PMW observations. The inability of the
CESM to produce pan-Arctic melt season lengths as large as observations
suggests a model bias. In particular, the marginal ice zones consistently
show smaller trends for all model definitions of freeze onset and melt season
length than PMW observations. This melt season trend bias may have important
implications for September sea ice. High correlations exist between September
sea ice sensitivity and melt season length over the satellite era in the CESM
LE. Observational uncertainty in sea ice sensitivity is substantial
, but the data used here indicate that the CESM LE may
underestimate September sea ice sensitivity. It is therefore possible that an
underestimation of the trend in CESM LE melt season length is one factor
contributing to the potential biases in the simulated sea ice sensitivity in
the CESM.
Under RCP8.5 forcing, the CESM LE projects that the Arctic sea ice melt
season will last 7–9 months by the end of the 21st century, compared to 3–4 months over the satellite era, with later freeze onset dates continuing to be
the dominant driver of these changes. Internal variability in melt season
characteristics is also projected to increase by the end of the 21st century.
This means that definition differences and internal variability will continue
to be factors complicating model–observation comparisons of the Arctic sea
ice melt season, particularly since they are both projected to change over
time.