The recent Arctic sea ice reduction comes with an increase in the ice-free season duration, with comparable contributions of earlier ice retreat and later advance. CMIP5 models all project that the trend towards later advance should progressively exceed and ultimately double the trend towards earlier retreat, causing the ice-free season to shift into autumn. We show that such a shift is a basic feature of the thermodynamic response of seasonal ice to warming. The detailed analysis of an idealised thermodynamic ice–ocean model stresses the role of two seasonal amplifying feedbacks. The summer feedback generates a 1.6-day-later advance in response to a 1-day-earlier retreat. The underlying physics are the property of the upper ocean to absorb solar radiation more efficiently than it can release heat right before ice advance. The winter feedback is comparatively weak, prompting a 0.3-day-earlier retreat in response to a 1-day shift towards later advance. This is because a shorter growth season implies thinner ice, which subsequently melts away faster. However, the winter feedback is dampened by the relatively long ice growth period and by the inverse relationship between ice growth rate and thickness. At inter-annual timescales, the thermodynamic response of ice seasonality to warming is obscured by inter-annual variability. Nevertheless, in the long term, because all feedback mechanisms relate to basic and stable elements of the Arctic climate system, there is little inter-model uncertainty on the projected long-term shift into autumn of the ice-free season.
Arctic sea ice has strikingly declined in coverage (Cavalieri and
Parkinson, 2012), thickness (Kwok and Rothrock, 2009; Renner et al., 2014;
Lindsay and Schweiger, 2015) and age (Maslanik et al., 2011) over the last
4 decades. CMIP5 global climate and Earth system models simulate and
project this decline to continue over the 21st century (Massonnet et al., 2012; Stroeve et al., 2012)
due to anthropogenic
Less Arctic sea ice also implies changes in ice seasonality, which are important to investigate because of socio-economic (e.g. on shipping; Smith and Stephenson, 2013) and ecosystem implications. Indeed, the length of the Arctic sea ice season exerts a first-order control on the light reaching phytoplankton (Arrigo and van Dijken, 2011; Wassmann and Reigstad, 2011; Assmy et al., 2017) and is crucial to some marine mammals, such as walruses (Laidre et al., 2015) and polar bears (Stern and Laidre, 2016), who use sea ice as a living platform.
Various seasonality diagnostics are discussed in the sea ice literature and definitions as well as approaches vary among authors. The open-water season duration can be diagnosed from satellite ice concentration fields, either as the number of ice-free days (Parkinson, 2014) or as the time elapsed between ice retreat and advance dates, corresponding to the day of the year when ice concentration exceeds or falls under a given threshold (Stammerjohn et al., 2012; Stroeve et al., 2016). The different definitions of the length of the open-water season can differ in subtleties of the computations (notably filtering) and may not always entirely be consistent and comparable. In addition, the melt season duration, distinct from the open-water season duration, has also been analysed from changes in passive microwave emission signals due to the transition from a dry to a wet surface during melting (Markus et al., 2009; Stroeve et al., 2014).
As for changes in the Arctic open-water season duration, satellite-based
studies indicate an increase by
The observed increase in the ice-free season duration should continue over the next century, as projected by the CESM Large Ensemble (Barnhart et al., 2016), but this signal is obscured by important levels of internal variability. Other CMIP5 ESMs likely project a longer ice-free season as well, and this is true in the Alaskan Arctic where they have been analysed (Wang and Overland, 2015). In both these studies, the simulated future increases in the ice-free season duration are dominated by the later ice advance. Such behaviour remains unexplained and should be investigated with a larger set of models and regions.
In the present study, we aim at better quantifying the potential changes in Arctic sea ice seasonality and understanding the associated mechanisms. We first revisit the ongoing changes in Arctic sea ice retreat and advance dates using satellite passive microwave records, at both inter-annual and multi-decadal timescales. We also analyse, for the first time over the entire Arctic, all CMIP5 historical and RCP8.5 simulations covering 1900–2300 and study mechanisms at play using a one-dimensional ice–ocean model.
We analyse the recent past and future of sea ice seasonality by computing a series of diagnostics based on satellite observations, Earth system models and a simple ice–ocean model.
Passive microwave sea ice concentration (SIC) retrievals, namely the GSFC
Bootstrap SMMR-SSM/I quasi-daily time series product, over 1980–2015
(Comiso, 2000, updated 2015), are used as an observational basis. We also
use the CMIP5 Earth system model historical simulations and future projections
of SIC. Because of high inter-annual variability in ice advance and retreat
dates and because some models lose multi-year ice only late into the
21st century, we retain the nine ESM simulations that pursue RCP8.5 until
2300 (first ensemble member, Table 1). Analysis focuses on 1900–2200,
combining historical (1900–2005) and RCP8.5 (2005–2200) simulations. The year 2200
corresponds to the typical date of year-round Arctic sea ice disappearance
(Hezel et al., 2014). We also extracted the daily SST output from
IPSL-CM5A-LR. All model outputs were interpolated on a 1
Linear trends in ice retreat and advance dates over 2000–2200 (200 years),
and long-term ice advance amplification ratios for the individual
and mean CMIP5 models and for the 1-D model. Trends and ratios are given as
median
To investigate how mean state biases may affect ESM simulations, we also
included in our analysis a 1958–2015 forced-atmosphere ISPL-CM simulation,
i.e. an ice–ocean simulation that was performed with the NEMO-LIM 3.6 model
(Rousset et al., 2015), driven by the DFS5 atmospheric forcing (Dussin et
al., 2016). NEMO-LIM 3.6 is similar to the ice–ocean component of
IPSL-CM5A-LR, except that (i) horizontal resolution is twice as high
(1
We use slightly updated computation methods for ice retreat (
The same seasonality diagnostics are computed from model outputs. Yet, since
the long-term ESM simulations used here only have monthly SIC outputs, we
compute the ice seasonality diagnostics based on monthly SIC fields linearly
interpolated daily. Such operation drastically reduces error dispersion but
introduces a small systematic bias on
Evolution of the ice seasonality diagnostics (ice retreat date,
blue; ice advance date, orange):
The ice seasonality diagnostics and their spatial distribution are reasonably well captured by the mean of selected CMIP5 models over the recent past (Fig. 2). The spatial distribution of ice seasonality diagnostics varies among models, reflecting a possible dependence on the mean state or differences in the treatment of ice dynamics. Larger errors in some individual models (Fig. S3) are associated with an inaccurate position of the ice edge. Overall, ESMs tend to have a shorter open-water season than observed (Figs. 2a–c and S3), which is visible in the North Atlantic and North Pacific regions and can be related to the systematic bias due to the use of interpolated monthly data, but also to the tendency of our model subset to overestimate sea ice. Such an interpretation is supported by (i) the visibly better consistency of the simulated ice seasonality diagnostics with observations in the forced-atmosphere ISPL-CM simulation than in IPSL-CM5A-LR and (ii) by the fact that models with simulated ice extent rather close to observations over the recent past (CESM, CNRM or MPI; Massonnet et al., 2012) are more in line with observed seasonality diagnostics than the other models (Figs. 2 and S3).
Maps and frequency histograms of
Trends in ice retreat and advance dates were calculated for each satellite
or model pixel, from the slope of a least-square fit over a given period,
using years when both
To describe the relative contribution of ice advance and retreat dates to
changes in open-water season duration, we introduce a first diagnostic,
termed the long-term ice advance vs. retreat amplification coefficient
(
In order to study the shorter-term association between retreat and ice
advance, we introduce a second diagnostic, termed the short-term ice advance
vs. retreat amplification coefficient (
For computations of
All trends and ice advance vs. retreat amplification coefficients given in
the rest of the text are the median (
We use the Semtner (1976) zero-layer approach for ice growth and melt above an upper oceanic layer taking up heat, whereas snow is neglected. The model simplifies reality by assuming constant mixed-layer depth, no horizontal advection in ice and ocean, no heat exchange with the interior ocean, and no sensible heat storage in the snow–ice system. The ice–ocean seasonal energetic cycle is computed over 300 years, using climatological solar, latent, and sensible heat fluxes and increasing downwelling long-wave radiation, to represent the greenhouse effect. Ice retreat and advance dates are diagnosed from model outputs (see Appendix A for details). We argue that the Semtner (1976) zero-layer approach is appropriate to study the response of CMIP5 models to warming, as the CMIP5 models with more complicated thermodynamics cannot be distinguished from those using the Semtner zero-layer approach (Massonnet et al., 2018). The zero-layer approach is known to alter the sea ice seasonal cycle (Semtner, 1984), but should not fundamentally affect the processes discussed here.
Over 1980–2015, the ice-free season duration has increased by
Maps and frequency histograms of linear trends (for hatched zones
only) in
Trends simulated by the mean of selected CMIP5 models are comparable with
observations, in terms of ice retreat date (
In terms of mean state and contemporary trends, models seem realistic enough
for an analysis of changes at pan-Arctic scales but might be less meaningful
at regional scales. We first study the contemporary link between earlier
retreat and ice advance by looking at the sign of
Based on observations (Fig. 4), we find positive values of
Long-term ice advance vs. retreat amplification coefficient from
passive microwave ice concentration retrievals (SMMR; over 1980–2015), and
for all individual models over 1980–2015, 2015–2050 and 2050–2085. We use a
75 % (
CMIP5 models are consistent with the robust link between earlier ice retreat
and later advance dates found in observations (Stammerjohn et al.,
2012; Stroeve et al., 2016). More generally, we find a robust link between
earlier retreat and later advance in all cases: both
Schematics of the mechanisms shaping the thermodynamic response of sea ice seasonality to a radiative forcing perturbation. The numbers give annual averages simulated by the 1-D model. Changes in ice retreat and advance dates are split between reference (ref) and feedback (fb) responses. See Appendix A for details of the computations.
We now focus on the respective contribution of changes in retreat and ice
advance dates to the increasingly long open-water season by analysing the
magnitude of
As far as future changes are concerned, all models show a qualitatively
similar evolution (Figs. 1 and S5). Projected changes in ice retreat and ice
advance dates start by approximately 2000 and continue at a nearly constant
pace from 2040 until 2200. By 2040, the trend in ice advance date typically
becomes larger than the trend in ice retreat date, as indicated by the
corresponding mean
To further understand these contrasting trends between ice retreat and ice
advance dates, we mapped
This finding expands the recent analyses of the CESM Large Ensemble
project (Barnhart et al., 2016) and of Alaskan Arctic sea ice in
CMIP5 models, finding faster ice coverage decrease in autumn than in spring
(Wang and Overland, 2015). Both studies propose that the extra heat uptake
in the surface ocean due to an increased open-water season as a potential
explanation. As suggested earlier, this indeed explains why
The reason why
To come to this statement, we would need diagnostics unavailable in CMIP5,
in particular a daily description of the surface energy budget. This is why
we used a 1-D thermodynamic model of sea ice growth and melt in relation with
the upper-ocean energy budget (Semtner, 1976) to study the idealised
thermodynamic response of seasonal ice to a radiative forcing perturbation.
Without any particular tuning, the 1-D model simulations feature an evolution
that is similar to the long-term behaviour of CMIP5 models (Fig. 1b), with
trends in ice advance date (8.2 days decade
As explained above, the seasonal relationships between ice advance and
retreat dates are underpinned by atmosphere–ice–ocean feedbacks. The
non-radiative feedback framework of Goosse et al. (2018; see Appendix A for
details) clarifies the study of these relationships. Changes in dates of ice
retreat (
According to this analysis, feedbacks between the dates of retreat and
advance dominate the thermodynamic response of ice seasonality (Fig. 5): the
reference response to the applied perturbation of 0.1 W m
Ice growth and melt processes generate a relatively weak winter amplifying
feedback of ice advance date onto ice retreat date: a shorter growth season
implies thinner ice, which subsequently melts away faster. The winter
feedback factor is (see Appendix A for derivation)
Energetics of the summer ice-free ocean generate a summer amplifying
feedback of ice retreat date onto ice advance date, much stronger than the
winter feedback. The summer feedback factor is (see Appendix A for
derivation)
Short-term ice advance vs. retreat amplification coefficient from passive microwave ice concentration retrievals (SMMR; over 1980–2015), and for all individual models over 1980–2015, 2015–2050 and 2050–2085.
In practise, keeping only the dominant term,
The CMIP5 response of ice seasonality differs from the idealised
thermodynamic response in two notable ways. First,
As to why the 1-D response would emerge in the course of this century, there
are a series of potential reasons that we cannot disentangle with the
limited available CMIP5 outputs. (i) The contribution of the subsurface
ocean to the surface energy budget, neglected in the 1-D approach, is likely
larger today than in the future Arctic. Over the 21st century, the Arctic
stratification increases in CMIP5 models (Vancoppenolle et al., 2013;
Steiner et al., 2014), whereas the oceanic heat flux convergence should
decrease (Bitz et al., 2005). (ii) The solar contribution to the upper-ocean
energy budget is smaller today than in the future, as the date of retreat
falls closer to the summer solstice. (iii) The surface energy budget is less
spatially coherent today than in the future, when the seasonal ice zone
moves northwards. The solar radiation maximum drastically changes over 45 to
65
The aforementioned processes, ignored in the 1-D model may explain why
The analysis presented in this paper, focused on changes in sea ice
seasonality and the associated driving mechanisms, raised the following new
findings.
All CMIP5 models consistently project that the trend towards later advance
progressively exceeds and ultimately doubles the trend towards earlier
retreat over this century, causing the ice-free season to shift into autumn. The long-term shift into autumn of the ice-free season is a basic feature of
the thermodynamic response of seasonal ice to warming. The thermodynamic shift into autumn of the ice-free season is caused by the
combination of relatively strong summer and relatively weak winter feedback
processes. Thermodynamic processes only explain the long-term response of ice
seasonality, not the inter-annual variations, nor the delayed emergence of
the long-term response, which are both consistently simulated features among
CMIP5 models.
A central contribution of this paper is the detailed study of the mechanisms
shaping the thermodynamic response of sea ice seasonality to radiative
forcing in the Semtner (1976) ice–ocean thermodynamic model, using the
non-radiative feedback framework of Goosse et al. (2018). The low seawater
albedo as compared with ice and the enhanced solar radiation uptake by the
ocean had previously been put forward to explain the increase in the length
of the open-water season (Stammerjohn et al., 2012). Our analysis completes
this view. Extra solar heat reaching the ocean due to earlier ice retreat is
absorbed at a higher rate than it can be released until ice advance. This
provides a powerful feedback at the source of the shift into autumn of the
open-water season. In addition, the link between later advance and earlier
retreat the next spring is weak because of the damping effects of the long
ice growth period and of the inverse relationship between growth rate and
ice thickness. All of these processes are simple enough to be captured by
most of the climate models, which likely explains why the different models
are so consistent in terms of future ice seasonality.
The link between earlier ice retreat and later advance is found in both satellite retrievals and climate projections, regardless of the considered period and timescale, expanding findings from previous works (Stammerjohn et al., 2012; Serreze et al., 2016; Stern and Laidre, 2016; Stroeve et al., 2016) and further stressing the important control of thermodynamic processes on sea ice seasonality. Yet, two notable features are in contradiction with the thermodynamic response of seasonal ice to warming. First, the long-term response of ice seasonality to warming only appears by mid-century in CMIP5 simulations, when changes in the ice-free season emerge out of variability (Barnhart et al., 2016). Second, changes in ice retreat date are larger than changes in ice advance date at inter-annual timescales. Transport or coupling processes (involving the atmosphere, sea ice, ocean) are the most likely drivers but their effect could not be formally identified because of the lack of appropriate diagnostics in CMIP5. Such a set-up, with a long-term control by thermodynamic processes, has other analogues in climate change studies (Bony et al., 2004; Kröner et al., 2017; Shepherd, 2014).
As the Arctic sea ice seasonality is a basic trait of the Arctic Ocean, a shift of the Arctic sea-ice free season would also have direct ecosystem and socio-economic impacts. The shift in the sea ice seasonal cycle will progressively break the close association between the ice-free season and the seasonal photoperiod in Arctic waters, a relation that is fundamental to photosynthetic marine organisms existing in the present climate (Arrigo and van Dijken, 2011). Indeed, because the ice advance date is projected to overtake the onset of polar night (Fig. 1), typically by 2050, changes in the photoperiod are at this point solely determined by the ice retreat date, and no more by the advance date. The duration of the sea ice season also restricts the shipping season (Smith and Stephenson, 2013; Melia et al., 2017). The second clear implication of the foreseen shift of the Arctic open-water season is that the Arctic navigability would expand to autumn, well beyond the onset of polar night, supporting the lengthening of the shipping season mostly by later closing dates (Melia et al., 2017).
Better projecting future changes in sea ice and its seasonality is fundamental to our understanding of the future Arctic Ocean. Detailed studies of the drivers of sea ice seasonality, in particular the upper-ocean energy budget, the role of winter and summer feedbacks, and the respective contribution of thermodynamic and dynamic processes, are possible tracks towards reduced uncertainties. Further knowledge can be acquired from observations (e.g. Steele and Dickinson, 2016) and Earth system model analyses, for which the expanded set of ice–ocean diagnostics expected in CMIP6, including daily ice concentration fields (Notz et al., 2016), will prove instrumental.
Scripts available from Marion Lebrun (marion.lebrun@locean-ipsl.upmc.fr) upon request.
To characterise the purely thermodynamic response of seasonal ice to a
radiative forcing perturbation, we use the Semtner (1976) zero-layer
approach for ice growth and melt above an upper oceanic layer taking up
heat. Snow is neglected. The ice model equations for surface temperature
(
Schematic representation of the analysis framework applied to the
1-D model outputs, illustrating the mechanisms of change in ice seasonality
between a reference year (solid line) and a subsequent year
(dashed line). Ice appears at the ice advance date (
The atmospheric solar (
The following diagnostics of the ice–ocean seasonality (see Fig. A1) are
derived from 1-D model outputs:
Two other markers of the ice–ocean seasonality prove useful and were also
diagnosed:
The simulated trend towards later ice advance is on average 1.9 times the
trend towards earlier retreat, a value consistent with the CMIP5 value. An
advantage of the 1-D model is that the required diagnostics to investigate
the ice seasonality drivers are easily available.
Nevertheless, the response of ice seasonality is not straightforward because there are feedbacks between ice retreat and advance dates. First, later advance delays ice growth, reduces the winter maximum thickness and, in turn, implies earlier retreat. Second, earlier retreat adds extra solar heat to the upper ocean, delaying ice advance. To understand the changes in ice seasonality and attributing their causes, we apply the non-radiative feedback framework introduced by Goosse et al. (2018).
Thermodynamic response of sea ice seasonality to warming in the
1-D model.
We split the changes in ice retreat (
To formulate what determines the changes in ice retreat date, we focus on
the ice season (Fig. A1) and use the maximum ice thickness to connect
We now combine growth and melt seasons and eliminate
The proposed decomposition (Eq. A10) is supported by analysis: the sum of calculated reference and feedback responses (black dashed line in Fig. A2a) matches the total change in ice retreat date as diagnosed from model output (yellow line in Fig. A2a).
The link between ice advance date and the previous ice retreat date stems
from the conservation of energy in the ice-free upper ocean. Once ice
disappears on
Analysis supports the proposed decomposition: the sum of calculated feedback and reference responses (black dashed curve in Fig. A2a) is equal to the total response diagnosed from model outputs (yellow curve in Fig. A2a).
Forced and feedback responses clarify the drivers of the shift into autumn
that characterises the thermodynamic response of ice seasonality to the
perturbation of the radiative forcing. The response of the system is
dominated by changes in ice advance date, which are by far dominated by the
feedback response (0.8 d yr
The response of ice retreat date, following winter processes, is
characterised by roughly equal contributions of reference (
Now considering the ice advance vs. retreat amplification coefficient, it
can be expressed as a function of feedback and reference responses:
Let us finally note that both feedback factors are determined by fundamental
physical features of ice–ocean interactions, likely going beyond climate
uncertainties. The winter feedback is determined by the shape of the
seasonal cycle and the non-linear dependence of ice growth rate, which are
likely invariant across models. As for the summer feedback, the scaling
detailed in Appendix B indicates that the related feedback factor is
constrained by celestial mechanics, ubiquitous clouds and near-freezing
temperatures. This likely contributes to the low level of uncertainty in
The 1-D model results show a direct link between, on the one hand, the ratio of
long-term trends in ice advance and retreat date
(
Assuming that non-solar components cancel each other, the mean heat gain is
mostly solar:
The mean heat loss is mostly non-solar:
Taken together, these elements give an estimated
The supplement related to this article is available online at:
All authors conceived the study and co-wrote the paper. ML and MV performed analyses.
The authors declare that they have no conflict of interest.
We thank Sebastien Denvil for technical support and Roland Seferian, Jean-Baptiste Sallée, Olivier Aumont and Laurent Bopp for scientific discussions. We also thank the anonymous reviewers for their constructive comments that helped to improve the paper. Edited by: Dirk Notz Reviewed by: two anonymous referees