Arctic sea ice - use of observational data

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Arctic sea ice: use of observational
data and model hindcasts to refine
future projections of ice extent
Tracy S. Rogers
a
, John E. Walsh
a
, Matthew Leonawicz
b
& Michael
Lindgren
b
a
International Arctic Research Center, University of Alaska,
Fairbanks, AK, USA
b
Scenarios Network for Alaska and Arctic Planning, University of
Alaska, Fairbanks, AK, USA
Published online: 13 Jan 2015.
To cite this article: Tracy S. Rogers, John E. Walsh, Matthew Leonawicz & Michael Lindgren (2015):
Arctic sea ice: use of observational data and model hindcasts to refine future projections of ice
extent, Polar Geography, DOI: 10.1080/1088937X.2014.987849
To link to this article: http://dx.doi.org/10.1080/1088937X.2014.987849
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Downloadedby[TracyRogers]at01:5224January2015

Arctic sea ice: use of observational data and model hindcasts to refine
future projections of ice extent
TRACY S. ROGERSa
*, JOHN E. WALSHa
, MATTHEW LEONAWICZb
and
MICHAEL LINDGRENb
a
International Arctic Research Center, University of Alaska, Fairbanks, AK, USA;
b
Scenarios Network for Alaska and Arctic Planning, University of Alaska,
Fairbanks, AK, USA
(Received 18 December 2013; accepted 11 November 2014)
This manuscript presents an evaluation of global climate models to guide future
projections of Arctic sea ice extent (SIE). Thirty-five model simulations from
Coupled Model Intercomparison Project, Phase 5 were examined to select model
subsets using comparison to observational data (1979–2013). The study extends
previous work by highlighting the seasonality of sea ice trends, utilizing a multi-
step selection process to demonstrate how the timing of an ice-free Arctic varies
with the hindcast performance of the models, and extending the analysis to
include sudden ice loss events (SILE). Although the models’ trends for the
historical period are generally smaller than observed, the models’ projected
trends show a similar seasonality, largest in September and smallest in March to
April. A multi-step evaluation process is applied to obtain progressively smaller
subsets of the best-performing models. As the number of models retained
becomes smaller, the simulated historical trend becomes larger and the median
date of a projected ice-free Arctic becomes earlier. An examination of SILE
through the historical period and model projections from 2014 through 2099
shows that SILE can account for between half and all of the future net loss of
SIE. We created an application for exploring sea ice data: http://spark.rstudio.
com/uafsnap/sea_ice_coverage/.
1. Introduction
There is growing literature on the recent decline of pan-Arctic sea ice coverage (e.g.
(Meier et al. 2007, Parkinson and Cavalieri 2008, Overland and Wang 2013, Stroeve
et al. 2012a). Several assessment studies have examined potential consequences of an
ice-diminished or seasonally ice-free Arctic (ACIA 2005, AMSA 2009, SWIPA 2011),
pointing to the need for information about the timing of changes in marine access to
key areas of the Arctic.
Global climate models are the most powerful tools for assessing the future
trajectory of Arctic sea ice cover, including the timing and seasonality of the
disappearance of summer sea ice. However, climate models contain uncertainties and
must be used cautiously in predictive applications; hindcasts (also known as historical
simulations) by global climate models generally underestimate the rate of ice loss
*Corresponding author. Email: tsrogers@alaska.edu
Polar Geography, 2015
http://dx.doi.org/10.1080/1088937X.2014.987849
# 2015 Taylor & Francis

relative to the observed rate of the past few decades (Stroeve et al. 2012a). Other
evaluations of global climate model simulations of Arctic sea ice extent (SIE),
including both hindcasts for recent decades and projections for the remainder of the
twenty-first century have been reported by Arzel et al. (2006), Zhang and Walsh
(2006), Massonnet et al. (2012), Rogers et al. (2013), Liu et al. (2013), Overland and
Wang (2013), among others.
The latest generation of models, from the Coupled Model Intercomparison
Project, Phase 5 (CMIP5), has been found to show marginal improvement over the
previous generation in the ability to capture the overall loss of Arctic sea ice
during the past few decades (Overland and Wang 2013, Stroeve et al. 2012b). In
this study, we extend previous work with (1) the addition of two contrasting years
of summer sea ice changes (2012 and 2013), (2) highlighting the significance of the
seasonality of sea ice trends, (3) utilizing a two-step model selection process to
identify best-performing models, and (4) extending the analysis to include sudden
ice loss events (SILE). The objective of this study is two-fold: (1) an evaluation of
climate models using observed data and hindcasts and (2) investigating the timing
and seasonality of the loss of Arctic sea ice over the remainder of the twenty-first
century.
2. Observed SIE, 1979–2013
Observed SIE data from 1979 to 2011 were obtained from the National Snow and
Ice Data Center’s passive microwave bootstrap data-set (Comiso 1995, 1999,
Comiso and Kwok 1996). 2012 and 2013 are from NSIDC’s sea ice index (Fetterer
et al. 2002, Meier et al. 2008), which uses a different algorithm for calculating sea
ice. The sea ice data were interpolated from their original resolution (∼12.5 km) to a
0.4° by 0.4° resolution grid by averaging nearby cells for the purpose of comparison
to global climate model output in the next part of this study. While a coarser
resolution has decreased accuracy, this loss is rarely significant and a compromise
was necessary for comparison with global model output in Section 3. Each pixel
with sea ice presence (15% or more sea ice) was converted into an area using the
cosine of the latitude multiplied by 12,347 km2
(area inside a 1° latitude by 1°
longitude pixel at the Equator). We performed linear regression analyses to estimate
sea ice trends.
Our evaluation of sea ice trends identified significant negative pan-Arctic
trends in summer months (Table 1). Especially with the inclusion of 2012 and
2013, Arctic sea ice has continued to decline rapidly in summer months and
more slowly in winter months. We tested each trend against the null hypothesis
that its trend was zero. We used confidence intervals at 95% to determine
the necessary sample size to reach significance, and then calculated the effective
sample size (which accounts for the effect of autocorrelation on years and
significance) to determine whether that trend was significant, using the
following equation:
eff ¼ n Â
1 À ar
1 þ ar
where eff is the effective sample size, n is the actual sample size, and ar is the
autoregression (of the not detrended time series) at a lag of 1 year. When analyzed
2 T. S. Rogers et al.

by calendar month and season, only the months June through October showed
trends that were statistically significant (Table 1). When the calendar months are
grouped into annual, summer (June through September), and winter (December
through March) seasons, only the summer trend is statistically significant (While
July through September is typically considered summer for sea ice, we included
June as part of the summer period, primarily because it is also part of the
meteorological Arctic summer).
3. Climate model evaluation and projections
In the CMIP5 set of simulations, climate models continued to undersimulate the
rate at which SIE was diminishing in the Arctic (Overland and Wang 2013, Stroeve
et al. 2012b). Because climate models vary greatly in their ability to reproduce
observed trends, eliminating and evaluating models is a necessary step when
selecting a model or a group of models for future simulations. Stroeve et al. (2012b)
eliminated models that were outside the range of observed values in September
1953–1995 before investigating the range of remaining climate models. Overland
et al. (2011) suggested using an ensemble of models based on their ability to
reproduce trends and warned that comparing models to the mean state has several
disadvantages. They argue that trends or a seasonal cycle may provide more robust
metrics for capturing model performance.
Climate models are designed to capture trends due to external forcing as well
as extreme maxima/minima associated with internal variability (and also any
effects of external forcing on extremes), but they are not intended to capture the
Table 1. Arctic SIE, 1979–2013.
Time
period
1979–2013
(mean
106
km2
)
SD
(105
km2
)
Trend (104
km2
yr−1
)
Decadal trend
(% decade−1
)
Effective
sample
size
95%
significance
January 14.6 4.8 −2.3 −1.5 6.0 No
February 15.4 4.4 −2.1 −1.3 6.6 No
March 15.6 4.0 −1.6 −1.0 13.5 No
April 14.8 3.5 −1.4 −0.9 17.5 No
May 13.3 3.5 −0.7 −0.5 25.7 No
June 11.8 3.7 −2.1 −1.7 19.4 Yes
July 9.7 7.5 −5.5 −5.0 16.3 Yes
August 7.1 8.2 −5.6 −6.8 18.8 Yes
September 6.3 10.4 −7.1 −9.2 17.8 Yes
October 8.6 8.0 −4.3 −4.4 10.7 Yes
November 10.9 6.2 −3.4 −2.9 10.8 No
December 13.1 4.8 −2.0 −1.5 9.7 No
Summer 8.7 7.1 −5.1 −1.0 15.7 Yes
Winter 15.2 4.3 −2.0 −3.0 7.2 No
Annual 11.8 5.0 −3.2 −2.1 6.9 No
Note: Average values from 1979 to 2013, providing SD, trend, and decadal trend for each
month and following periods: annual, summer (JJAS), and winter (DJFM). Additionally, we
used confidence intervals to calculate the sample size for the trend to be statistically different
from 0 at 95%, and the trends effective sample size.
Arctic Sea Ice 3

timing of extreme events, e.g. SILE. This kind of internal variability adds
uncertainty to trends and means for shorter periods. Overland et al. (2011) noted
that comparing trends in periods as long as 20–50 years may be problematic due
to internal variability. Nevertheless, Kay et al. (2011) found that, while a 10-year
trend has the same sign as externally forced trends approximately 66% of the
time, a 20-year time period is sufficient for a model (CCSM3) to capture the sign
of an externally forced trend with 95% confidence. On the basis of their findings,
the 35-year period used in this evaluation is likely to capture an underlying
trend.
To initiate this study, we examine trends in output from 1979 to 2013 in 35
CMIP5 models from 17 climate modeling centers (see the full list of models in
Appendix 1). For the model evaluation process, one goal was to examine the
effect that model selection had on hindcasts and, subsequently, on simulations
through 2099. Some papers have used a ranking system for climate models to
determine an even smaller selection of models for more targeted projection and
scenarios work (Rogers et al. 2013, Walsh et al. 2008). We test whether hindcasts
are improved by two stages of filtering: from 35 to 16 models and from 16 to 5
models. Our first elimination step is to remove models based on deviation from
Table 2. Climate models.
Model
acronym Research organization, model name
ACCESS 1.0 Australian Community Climate and Earth System Simulator, Model 1.1
ACCESS 1.3 Australian Community Climate and Earth System Simulator, Model 1.3
CCSM 4 National Center for Atmospheric Research, Community Climate System
Model 4
CESM CAM5 National Center for Atmospheric Research, Community Earth System
Model CAM 5
CESM
WACCM
National Center for Atmospheric Research, Community Earth System
Model WACCM
CMCC CMS Centro Euro-Mediterraneo per I Cambiamenti Climatici, Climate Model S
CNRM CM5 Centre National de Recherches Meteorologiques, Model 5
FIO ESM The First Institute of Oceanography, Earth System Model
GFDL CM3 NOAA Geophysical Fluid Dynamics Laboratory, Climate Model 3
GFDL
ESM2M
NOAA Geophysical Fluid Dynamics Laboratory, Earth Systems Model 2M
HADGEM2
AO
Met Office Hadley Centre, Gem 2 Atmosphere-Ocean Model
HADGEM2
ES
Met Office Hadley Centre, Gem 2 Earth System Model
IPSL
CM5ALR
Institut Pierre-Simon Laplace, Climate Model 5 A Low-Resolution
IPSL
CM5BLR
Institut Pierre-Simon Laplace, Climate Model 5 B Low-Resolution
MIROC 5 Japan Agency for Marine-Earth Science and Technology, Model for
Interdisciplinary Research on Climate 5
MPI ESMMR Max Planck Institut fur Meteorologie, Earth Systems Model, Medium
Resolution
Note: These are the models that remained after elimination, with the model acronym, full
organization name, and model name.

the summer and winter means, followed by a ranking based on their summer
trend and annual SIE.
3.1. Elimination and evaluation
The simulations used here include hindcasts for the time period 1860–2005, using
historic emissions, while model projections for 2006–2099 were based on the
Representative Concentration Pathways (RCP) 8.5 forcing. We used the first
ensemble member from each available model. Since model resolutions ranged
from 0.4° by 0.4° to 2.5° by 2.5°, we interpolated all of the models into 0.4° by 0.4°
resolution grid, so they shared a common grid with each other and our
interpolated observed data.
Our first step in the elimination process was to remove models that fell too far
outside the expected range for sea ice, and was based in part on the methods of
Stroeve et al. (2012b). We chose the summer time period instead of September
because the range of the observations’ mean September SIE during our evaluation
period (1979–2013; 3.6–8.1 million km2
) was much greater than the range in Stroeve
et al.’s evaluation period (1953–1995; 6.1–8.4 million km2
). Based on our September
range, we would have nearly kept every model, despite some modeled values below
5 million km2
before the year 2000. We removed models that were outside the
observed range of summer values (6.7–9.1 million km2
) for at least 10 years (out of
35 years). We added an additional metric to eliminate models that did not follow
the seasonal cycle of sea ice – we calculated the difference between the observed
Table 3. Arctic sea ice model performance.
Model
Summer trend
deviation
(km2
)
Absolute
error (km2
)
Standardized mean
and rank
ACCESS 1.0* 1.21 (−0.96) 0.73 (−1.40) −1.18 (1)
ACCESS 1.3 1.14 (−1.02) 1.03 (−0.78) −0.90 (2)
HADGEM2
AO*
1.45 (−0.76) 1.08 (−0.67) −0.72 (3)
IPSL
CM5BLR*
1.36 (−0.84) 1.21 (−0.38) −0.61 (4)
HADGEM2 ES 1.47 (−0.74) 1.29 (−0.22) −0.48 (5)
CESM CAM5* 1.56 (−0.67) 1.45 (0.10) −0.29 (6)
CNRM CM5* 1.99 (−0.32) 1.39 (−0.01) −0.16 (7)
CESM
WACCM
3.90 (1.24) 0.84 (−1.16) 0.04 (8)
GFDL CM3 0.75 (−1.33) 2.08 (1.42) 0.04 (9)
MIROC 5 3.92 (1.26) 0.87 (−1.11) 0.07 (10)
CCSM 4 2.67 (0.24) 1.50 (0.20) 0.22 (11)
FIO ESM 3.40 (0.83) 1.23 (−0.36) 0.24 (12)
IPSL CM5ALR 2.90 (0.42) 1.73 (0.69) 0.55 (13)
CMCC CMS 3.42 (0.85) 1.93 (1.09) 0.97 (14)
MPI ESMMR 2.12 (−0.22) 2.51 (2.30) 1.04 (15)
GFDL ESM2M 4.86 (2.02) 1.54 (0.29) 1.16 (16)
Note: The summer trend and absolute error values are shown, with standardized ratings in
parentheses. The models we selected for our subset of 5 have an asterisk. The third column
shows the mean of each model’s standardized ratings, with the overall rank in parentheses.
Arctic Sea Ice 5

mean summer and winter (DJFM) values (7.5 million km2
), and eliminated models
that deviated by at least 2.0 million km2
from the observed annual range. This
evaluation criterion is a measure of the model’s sensitivity to forcing by the seasonal
cycle of solar radiation. The first metric eliminated nine models, while the second
metric eliminated 10 models, leaving 16 for comparison (Table 2). Our choice of
evaluation criteria admittedly has some subjectivity. However, these criteria served
the purpose of retaining approximately half the models while eliminating others on
the basis of deficiencies that are transparent to most users who target sea ice
applications of global climate model output.
In order to address the sensitivity to the seasonal definition, we also ran these
elimination steps for the more traditional months of July through September as
summer, and January through March as winter, which resulted in 15 models. Of
these, 13 were among the 16 models from above, indicating that the choice to
include June and December did not have a large effect on model selection.
Our second step was to create a smaller set of models by ranking the remaining
16 models on the basis of (1) their difference from the observed data’s summer
Figure 1. Thirty-ﬁve summer model hindcasts, 1979–2013. The thick blue line is the observed
data, the thick black line is mean of the 35 models, and each gray line represents a model.

trend and (2) the mean of the absolute value of 12 monthly differences from the
observed data-set:
1
12
X12
i¼1
model mean monthi À observed mean monthij j;
where model mean month represents the model’s mean value for 1979–2013 of the
given calendar month (e.g. January), and the observed mean month is the satellite
observations’ mean for that same month. We then standardized the results from
both metrics to put them on the same scale:
ðxi À xÞ
SD
;
where xi is each model’s monthly mean, xis the mean of all models, and SD is the
standard deviation of xi À x. We averaged these two standardized values to obtain
our ranking for the models, and used the top five models as a smaller subset of
models. However, to maximize model diversity within such a narrow selection, we
limited models within the top five to one per model center. The resulting top five
Figure 2. Sixteen summer model hindcasts, 1979–2013. The thick blue line is the observed
data, the thick black line is mean of the 16 models, and each gray line represents a model.
Arctic Sea Ice 7

models were ACCESS 1.0, HADGEM2 AO, IPSL CM5BLR, CESM CAM5, and
CNRM CM5 (Table 3).
When compared with CMIP3 projections in Rogers et al. (2013), more CMIP5
models are effectively capturing the September decline of Arctic sea ice. Stroeve
et al. (2012b) analyzed the difference betwseen CMIP3 and CMIP5 models, and
came to the conclusion that CMIP5 models were more consistent with historical
observations although they noted that some of the improved agreement over the
post-1979 period was the result of a smaller bias in 1979.
September hindcasts from all 35 models are shown in Figure 1, while the hindcasts
from the selected 16 models are shown in Figure 2. It is apparent from Figure 1 that
the models, almost without exception, capture the sign of the underlying trend.
However, the simulated trends are generally smaller than observed, a fact noted by
Stroeve et al. (2012b) and others. A comparison of Figures 1 and 2 shows the observed
trends are reproduced more realistically by the models that survived the initial
Figure 3. Seasonal cycle: 1979–2013. The range of SIE for 35, 16, and 5 models and the
observed mean values. The 35 models are dark golden, the 16 models are red, and the 5 are
blue. For summer, winter, and annual time periods, triangles represent upper and lower
ranges.

screening process described above. In particular, many of the 35 models significantly
underestimated the SIE trend during the time period (Table 3).
When comparing the sets of 35, 16, and 5 models, we found some differences.
All three sets of models had similar mean values, but much larger differences in
range (Figure 3). In both significance tests and model trends, the 5 and 16 model
sets performed better than the 35 model set (Table 4). However, the 5 model set
only improved on the 16 model set in a few areas, such as September and summer
trends. Based on the limited improvements, the 16 model set may be better for
projection analysis since more information can be gained from a broader range of
scenarios.
3.2. Projections
Projections from these models indicated a range of possible futures (Figures 4–9).
Hindcasts in all models showed relatively stable September sea ice until the late
1900s, with a rapidly increasing decline in the late 1990s and 2000s. The loss of sea
ice is generally referenced to a nominal threshold of 1 million km2
(Overland and
Wang 2013). While this extent represents a somewhat arbitrary criterion for an ‘ice-
free’ Arctic, it is consistent with marine access to most of the Arctic Ocean by non-
icebreaking ships. The top five models indicated that the Arctic becomes ice-free in
September between 2027 and 2081, with a median year of 2034 and mean year of
2043 (Figure 4). The set of 16 models indicated a range of 2027–2081, a median of
2052, and a mean of 2054 (Figure 5). In their analysis of 36 CMIP5 models
(essentially the same as our full set of 35 models), Overland and Wang (2013)
indicated a range starting at 2007, with some models not reaching 1 million km2
by
2100, a median year of 2055, and mean year of 2100.
Table 4. 1979–2013 Model trends and significance tests.
Trends (104
km2
yr−1
) Significance
Month 35 Models 16 Models 5 Models 35 Models 16 Models 5 Models
January −3.0 −2.5 −2.6 0.60 0.44 0.40
February −2.9 −2.5 −2.9 0.51 0.56 0.40
March −2.9 −2.6 −3.1 0.49 0.56 0.40
April −2.8 −2.4 −3.2 0.51 0.56 0.40
May −2.5 −2.4 −2.8 0.49 0.50 0.40
June −2.5 −2.5 −2.3 0.51 0.63 0.60
July −3.2 −3.5 −3.7 0.63 0.75 0.80
August −4.5 −4.6 −6.2 0.71 0.75 1.00
September −5.0 −5.0 −6.7 0.74 0.81 1.00
October −4.6 −4.4 −5.1 0.80 0.88 0.80
November −3.3 −3.1 −3.0 0.74 0.81 0.80
December −3.3 −2.9 −2.9 0.74 0.69 0.80
Summer −3.8 −3.9 −4.7 0.69 0.75 1.00
Winter −2.9 −2.5 −2.9 0.51 0.56 0.40
Annual −3.4 −3.2 −3.7 0.6 0.63 0.60
Note: The first three columns are model trends for 35, 16, and 5 models subsets, while the
next three columns are significance tests for those subsets. These columns have the
proportion of models from each set that reached 95% significance. We used effective sample
sizes and confidence intervals, outlined in (2), to calculate the significance.
Arctic Sea Ice 9

The 16 model mean indicated that sea ice loss will continue through 2099 in all
months. Specifically, March SIE reaches 9 million km2
by 2099 (Figure 6), June SIE
reaches 5 million km2
(Figure 7), and December SIE reaches 4 million km2
by 2099
(Figure 7). The fastest decline in the models occurs in summer months, particularly
in September. However, annual SIE declines in all model projections, with a mean
of nearly 4 million km2
by 2099 (Figure 8).
We have created a companion application to allow users to explore these data for
five models: http://spark.rstudio.com/uafsnap/sea_ice_coverage/. This application
permits user-specified plots of SIE for the five models from 1860 to 2099 and can
display observed data from 1979 to 2011. Regression analyses and concentration
maps are available with this tool.
3.3. Sudden ice loss events
The accelerated loss of Arctic sea ice over the past decade has prompted interest in
abrupt changes and the possibility of ‘tipping points’ or irreversibility. Whether or
not they represent tipping points, SILE such as occurred in 2007 and 2012 increase
Figure 4. September model projections: 1860–2099. Simulations from 16 climate models.
The thick blue line is the observed data, the thick black line is the mean of the 16 models, and
each gray line represents a model.

the likelihood that future years will have less SIE than previous years, primarily due
to the loss of multi-year ice and the increased absorption of solar heat in the newly
open water. Holland et al. (2008) examined rapid ice loss events, which were defined
as periods when the loss of September SIE over a 5-year period exceeded a rate of
0.5 million km2
yr−1
. Our goal was to examine years in which large amounts of ice
disappeared and to determine the extent to which these SILEs account for net ice loss in
model projections through 2099 using our set of 16 models. Our hypothesis is that single-
year SILEs such as 2007 account for most of the ice loss over periods longer than a few
decades, and these events will continue into the future. We test this hypothesis using the
output from the models’ simulations through 2099.
In the 1979–2013 observed record, several years stand out as possible SILEs
(with SIE in million km2
): 1985 (6.3), 1990 (6.3), 1995 (6.0), 2005 (5.5), 2007 (4.1),
and 2012 (3.6). In each of these cases, the previous several years had significantly
higher SIE. For the purposes of this study, SILEs were defined as years in which
(1) the preceding 2 years had a mean SIE at least 750,000 km2
higher than that
year, and (2) ice extent for that year was 100,000 km2
lower than the previous
Figure 5. Simulations from five models, 1860–2099. The September SIE from five models,
the five model mean in solid black, and observed is in thick blue (1979–2013).
Arctic Sea Ice 11

record minimum. Within the observed record, 1995, 2005, 2007, and 2012 satisfied
both criteria.
Applying these criteria to climate models, we investigated SILEs using
projections and tested models for SILEs between 2013 and 2099 for 16 models.
While these models had a mean of 4.8 SILEs, some models only have SILEs until
2050; after that, ice reaches near zero values (Table 5). The seven models that
project the last SILE to occur prior to 2050 have a mean of 3.1 SILEs. The nine
models that project the last SILE after 2050 have a mean 6.0 SILEs. The final
SILE projected by a model ranges from 2021 (HADGEM2 AO) to 2089
(CMCC CMS).
We used two methods to calculate the amount of loss related to SILEs from 2014
through 2099, by (1) calculating the amount of ice loss as the difference between the
SIE of the SILE year and the mean of the previous 5 years (mean method), and (2)
calculating the net loss between each SILE and the previous minimum (min
method). These two methods created a range of possible loss that can be attributed
Figure 6. March model projections: 1860–2099. Simulations from 16 climate models. The
thick blue line is the observed data, the thick black line is the mean of all 16 models, and each
gray line represents a model.

to SILEs although we can expect the mean method to result in an exaggerated
estimate, as we already know that a SILE represents a sudden decrease in sea ice
from at least the previous 2 years.
The mean method, for the most part, resulted in higher SIE loss than the total
2013 SIE for that model, while the min method resulted in approximately half that
much loss (Table 5; Figure 10). When applied to the observed record, the mean
method resulted in 3.8 million km2
sea ice loss from 1979 to 2013, while the min
method resulted in 2.2 million km2
. For comparison, the September trend from
1979 to 2013 (−71,000 km2
) accounts for a loss of 2.5 million km2
.
A few models were anomalous relative to the others, in particular CCSM4,
CESM CAM5, and HADGEM2 ES. The first two had much higher mean-
method ice loss than others. In these models, the mean-method ice loss was
approximately double the 2013 mean extent. The high loss in CCSM4 can
potentially be explained by high variability (calculated using the difference
from a 10-year running mean), but CESM CAM5 had near average variability
Figure 7. June model projections: 1860–2099. Simulations from 16 climate models. The
thick blue line is the observed data, the thick black line is the mean of all 16 models, and each
Arctic Sea Ice 13

(Table 5). CCSM4 and CESM CAM5 had above average numbers of SILEs, 8
and 7, respectively. There were no SILEs after 2013 in HADGEM2 ES, likely
due to the model’s low variability, its steady decline in the years following 2013,
and the loss of sea ice by 2035. A comparison of SIE between these three models
is shown in Figure 11.
The min method is likely a more meaningful measure than the mean method
when comparing climate models. It is not possible for more sea ice loss to have
occurred due to SILEs than was lost during the time frame, but, in a majority of the
models, the loss attributed to SILEs by the mean method was as large as or larger
than 2013 SIE.
4. Conclusions
The main findings of this study can be summarized as follows:
Figure 8. December model projections: 1860–2099. Simulations from 16 climate models.
The thick blue line is the observed data, the thick black line is the mean of all 16 models, and
each gray line represents a model.

The observed trend of Arctic sea ice over 1979–2013 is significantly negative in
the calendar months of June through October. Confirming earlier studies, the trend
is largest in September.
Global climate models run with observed greenhouse gas forcing capture the
negative trends of September Arctic sea ice over the same time period, although,
as found in previous studies, the simulated trends are generally smaller than
observed.
Selection of a subset of models based on the fidelity of their hindcast generally
results in a larger negative trend on the simulations of the historical period.
Of the five models that rank most highly when compared with historical
observational data, four project an ice-free Arctic by 2050 and all five project an
ice-free Arctic by 2099. Additionally, the 35, 16, and 5 models sets projected median
ice-free dates of 2055, 2052, and 2034. Thus, the models with more realistic hindcast
simulations project an earlier loss of summer sea ice. However, extrapolation of the
recent observed trend would lead to an ice-free Arctic even sooner than projected
by the models with the earliest ice loss.
Figure 9. Annual model projections: 1860–2099. Simulations from 16 climate models. The
thick blue line is the observed data, the thick black line is mean of all 16 models, and each
Arctic Sea Ice 15

The models’ projected trends are largest in September and smallest in March to
April, the approximate time of the seasonal sea ice maximum. This seasonality of
the projected trends is consistent with the observed trends of the post-1979 period,
reconfirming earlier studies.
SILEs account for somewhere between half and all of the ice loss in the RCP 8.5
scenario simulations of these climate models.
Because model simulations are the basis for projections of future changes in sea
ice, credibility is essential for the output to be used by planners and policy-makers.
The fact that the models’ hindcast simulations capture the seasonality of the
observed trends of Arctic sea ice gives credence to the model simulations. The
models’ simulations of SILEs also add confidence in the ability of models to
capture sea ice variability and trends.
While all models project a reduction of sea ice in the remainder of the twenty-first
century, the rate of loss does vary considerably among models. The reduction of the
number of models from 16 to 5 advanced the ice-free date by a greater amount
(2055 to 2034) than the reduction from 35 to 16 (2055 to 2052). The multi-stage
model selection process in this study provides a way to narrow the uncertainty and
arguably enhance the credibility of the model-derived information provided to
users. While users have different requirements for confidence or certainty in the
model output, the method utilized here illustrates an approach that can be tailored
to the demands of particular users in need of information on the rate of future ice
loss in the Arctic and on weighing the uncertainty (spread in model projections).
An issue that has not been addressed in the present study is the reason(s) why
some models are better than others in capturing the Arctic sea ice of the past several
Table 5. Sudden ice loss events.
Number
of
SILES
2013 SIE
(106
km2
)
SILE
(mean
106
km2
)
SILE
(Min
106
km2
)
First
SILE
Last
SILE
Variation
(105
km2
)
ACCESS 1.0 4 4.6 6.5 3.4 2020 2027 4.2
ACCESS 1.3 5 4.6 5.9 2.3 2021 2044 3.3
CCSM 4 8 6.7 12.1 3.9 2015 2062 5.3
CESM CAM5 6 4.4 8.0 3.0 2020 2038 3.6
CESM
WACCM
8 7.5 7.5 3.8 2015 2081 5.4
CMCC CMS 3 7.1 3.5 1.6 2072 2089 4.5
CNRM CM5 2 3.5 3 1.7 2031 2037 2.9
FIO ESM 6 4.7 5.9 2.0 2050 2083 5.1
GFDL CM3 3 3.4 3.4 1.5 2016 2030 2.4
GFDL ESM2M 6 5.7 7.9 3.1 2023 2070 5.0
HADGEM2 AO 2 3.2 2.5 1.1 2020 2021 3.2
HADGEM2 ES 0 5.4 0.0 0.0 NA NA 2.1
IPSL CM5ALR 6 5.3 7.7 3.4 2024 2059 4.2
IPSL CM5BLR 5 7.9 5.9 3.2 2034 2081 5.7
MIROC 5 6 6.3 8.2 3.7 2020 2066 4.4
MPI ESMMR 6 5.3 7.7 3.4 2024 2059 4.2
Mean 4.8 5.4 6.0 2.6 2027 2056 4.1
Note: The models, the number of SILEs, the 2013 SIE, the mean SILE loss, the min SILE
loss, the first and last SILE, and the model variation, calculated using a 10-year running
mean.

decades. While this issue will likely require controlled experiments with various
models, it represents a fundamental challenge of sea ice research in the context of
predictive applications.
Acknowledgments
We thank NSIDC for providing Arctic sea ice data. This work was supported by the
NOAA Climate Program Office through Grant NA110AR4310172, by NSF through award
number 1023131, and by the Alaska Climate Science Center through Cooperative
Agreement Number G10AC00588 from the United States Geological Survey. The contents
are solely the responsibility of the authors and do not necessarily represent the official views
of NOAA or the USGS. All statistical analyses were performed using the R language and
environment for statistical computing and graphics. For more information, see http://www.
r-project.org/.
Figure 10. Sudden ice loss events. Each model has three bars: the blue bar represents the net
loss from modeled SILEs using the ‘SILE mean method’; the red bar is the net loss calculated
using the ‘SILE min method’; and the gray bar is 2013 SIE.
Arctic Sea Ice 17

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Arctic Sea Ice 19

Appendix 1. Full list of models
Model acronym Model center
ACCESS1.0 Australian Community Climate and Earth System Simulator
ACCESS1.3 Australian Community Climate and Earth System Simulator
BCC.CSM11 Beijing Climate Center, China Meteorological Administration
BCC.CSM11M Beijing Climate Center, China Meteorological Administration
CAN.ESM2 Canadian Centre for Climate Modelling and Analysis
CCSM.4 National Center for Atmospheric Research, USA
CESM.CAM5 National Center for Atmospheric Research, USA
CESM.WACCM National Center for Atmospheric Research, USA
CMCC.CM Centro Euro-Mediterraneo per I Cambiamenti Climatici, Italy
CMCC.CMS Centro Euro-Mediterraneo per I Cambiamenti Climatici, Italy
CNRM.CM5 Centre National de Recherches Meteorologique, France
CSIRO.MK3 Australian Commonwealth Scientific and Industrial Research
Organization
FIO.ESM The First Institute of Oceanography, China
GFDL.CM2 NOAA Geophysical Fluid Dynamics Laboratory, USA
GFDL.CM3 NOAA Geophysical Fluid Dynamics Laboratory, USA
GFDL.ESM2G NOAA Geophysical Fluid Dynamics Laboratory, USA
GFDL.ESM2M NOAA Geophysical Fluid Dynamics Laboratory, USA
GISS.E2H NASA Goddard Institute for Space Studies, USA
GISS.E2R NASA Goddard Institute for Space Studies, USA
HADGEM2.AO Met Office Hadley Centre, UK
HADGEM2.CC Met Office Hadley Centre, UK
HADGEM2.ES Met Office Hadley Centre, UK
INM.CM4 Institute for Numerical Mathematics, Russia
IPSL.CM5ALR Institut Pierre-Simon Laplace, France
IPSL.CM5AMR Institut Pierre-Simon Laplace, France
IPSL.CM5BLR Institut Pierre-Simon Laplace, France
MIROC.4H Japan Agency for Marine-Earth Science and Technology
MIROC.5 Japan Agency for Marine-Earth Science and Technology
MIROC.ESM Japan Agency for Marine-Earth Science and Technology
MIROC.
ESMCHEM
Japan Agency for Marine-Earth Science and Technology
MPI.ESMLR Max Planck Institut fur Meteorologie, Germany
MPI.ESMMR Max Planck Institut fur Meteorologie, Germany
MRI.CGCM3 Meteorological Research Institute, Japan
NOR.ESM1M Bjerkness Centre for Climate Research, Norway
NOR.ESM1ME Bjerkness Centre for Climate Research, Norway

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