Relative to the 1979-2000 baseline, September 2012 showed a reduction of Arctic sea ice extent (SIE) of 49%, and a reduction of sea ice volume of 72% (PIOMAS estimate confirmed by CryoSat2 observations, [Overland2013] and [Laxon2012]). Probably due to a weakening of the ice, these trends have been accompanied by an increase of the average sea ice drift of 9(±1.9)% per decade and average deformation of 50(±10)% per decade [Rampal2011].

Figure from [Massonnet2012]. 1979–2010 mean of (x-axis) and trend in (y-axis) September Arctic sea ice extent, as simulated by the CMIP5 models and their members. Members of a same model (if any) are represented by dots (•). Individual models (or the mean of all their members, if any) are represented by crosses (×).

The World Climate Research Programme CMIP5 climate modelling study reveals that the spread of Arctic September sea ice between the individual members is considerable. The September mean SIE (x axis on fig 1) over the satellite era (1979-2010) varies between 2 and 12 km^2 while the SIE trend over the same time period (y axis on fig1) ranges from 0 to -1400 km^2/10years [Massonnet 2012].

This situation calls for a reevaluation of the physical processes involving sea ice in these models. Over the last decade we have used at the Centre for Polar Observation and Modeling (CPOM) a bottom-up approach: including new physics in a stand alone (uncoupled) version of the Los Alamos CICE sea ice model, which in turn can be used in ocean-sea ice coupled models (FAMOS initiative) and fully coupled climate models (Met Office, [Rae2014] and [Keen2013]). We believe that this methodology can contribute to significantly reduce the model response uncertainty in the next round of GCM results, CMIP6

Accounting for melt ponds (and anisotropic rheology)

In a study recently published in Nature Climate Change [Schroeder2014] we showed that by implementing this novel physics of sea ice we can produce the first skillful statistical prediction of September Arctic ice extent. We have implemented two new processes into CICE: a prognostic model for melt-ponds and an elastic anisotropic-plastic (EAP) model that explicitly accounts for the observed sub-continuum anisotropy of the sea-ice cover [Tsamados2013]. The principal concept of our melt-pond model is that the melt water, formed as a result of snow, ice melt and precipitation, runs downhill under the influence of gravity and collects on sea ice starting at the lowest surface height [Flocco2012]. Applying the melt-pond model and the EAP model, we performed a stand-alone sea-ice simulation for the pan-Arctic region (40 km grid resolution) over the period 1979 to September 2013.

We find that the Arctic sea-ice minimum can be accurately forecasted from melt-pond area in spring with a strong correlation between the spring pond fraction and September sea-ice extent. Fig 2 a shows that years with maximum (minimum) sea ice coverage, for example 1996 (2012), correspond to minimum (maximum) pond coverage. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. Hence our model calculations, by showing that the increase of melt-ponds (4%/decade in July, fig2 b) results in the observed decrease in summer albedo (3%/decade in July/August), help explain the acceleration of Arctic sea-ice decrease during the past decades.

Figure from [Scrhoeder2014]. Temporal variability of Arctic melt-pond area. a, Annual cycle of Arctic mean fraction of sea-ice area covered by exposed melt-ponds in our CICE simulation. The grey-shaded area shows the range of pond fraction simulated over the 35-year period. b, Time series of normalized pond fraction (mean over the period from 25 June to 25 July).

A skillful statistical forecast

Our paper demonstrates that inclusion of melt ponds in the sea ice model provides an unprecedented level of skill in predicting observed SIE. For a forecast based on the integrated pond coverage until the 25th of June the skill value is S=0.41 (S=0 for a simple constant trend projection and S=1 for a perfect forecast) and the error is 0.44 Million km^2 (mean September ice extent over the satellite era ~6 Million km^2). For September 2013 we forecast a mean ice extent of 5.55+/-0.44 million km2, which is closer to the observed mean value of 5.35 million km^2 than any of the 23 statistical, model and heuristic predictions presented at the Arctic Sea Ice Outlook webpage in July (median value of 4.0 million km^2).

This study points to the importance of realistic model physics in improving Arctic sea ice forecasts and promises to improve the skill of future forecast and climate models in Arctic regions and beyond. Nevertheless the challenges remaining to produce an operational forecast are immense. For example:

- It is fundamentally impossible to determine the September ice extent from the spring pond fraction perfectly, because the impact of the atmospheric and oceanic conditions in July, August and September are not accounted for (e.g. wind effect).

- We do not think that a statistical approach is ideal to achieve a regional or local forecast. There are forecast systems based on atmosphere-ice-ocean models and including our pond model into those models could provide valuable regional information.

- On seasonal to decadal timescales the predictive skills of models are in addition to model response uncertainty inherently limited by scenario uncertainty as well as internal variability (see for example the APPOSITE project).

- Antarctic sea ice predictions present different challenges and melt ponds are expected to be a less dominant factor in forecasting the sea ice extent.

We conclude that the inclusion of a more realistic sea ice physics will transform future forecast and climate models in the Arctic regions and beyond.

Figure from [Scrhoeder2014]. Verification of predicted September sea-ice extent. Predicted ice extent (anomaly from the trend line in a and b and absolute values in c) is verified by use of SSM/I data for the period 1979–2013. Hindcasts (a) and forecasts (b) are based on three dierent integration periods for pond fraction: 1 May–31 May (pond31), 1 May–25 June (pond56) and 1 May–25 July (pond86) and one for fraction of thin ice: 1 May–25 June (ice56). ferr and perr are the prediction and the forecast error in million km2. The given skill values S are with respect to the variance of the de-trended climatology.


[Overland2013] J. E. Overland and M. Wang, "When will the summer arctic be nearly sea ice free?," Geophys. res. lett., p. n/a--n/a, 2013.
author = {Overland, James E. and Wang, Muyin},
title = {When will the summer Arctic be nearly sea ice free?},
journal = {Geophys. Res. Lett.},
year = {2013},
pages = {n/a--n/a},
month = may,
abstract = {The observed rapid loss of thick multiyear sea ice over the last 7 years
and the September 2012 Arctic sea ice extent reduction of 49% relative
to the 1979–2000 climatology are inconsistent with projections
of a nearly sea ice-free summer Arctic from model estimates of 2070
and beyond made just a few years ago. Three recent approaches to
predictions in the scientific literature are as follows: (1) extrapolation
of sea ice volume data, (2) assuming several more rapid loss events
such as 2007 and 2012, and (3) climate model projections. Time horizons
for a nearly sea ice-free summer for these three approaches are roughly
2020 or earlier, 2030 ± 10 years, and 2040 or later. Loss
estimates from models are based on a subset of the most rapid ensemble
members. It is not possible to clearly choose one approach over another
as this depends on the relative weights given to data versus models.
Observations and citations support the conclusion that most global
climate model results in the CMIP5 archive are too conservative in
their sea ice projections. Recent data and expert opinion should
be considered in addition to model results to advance the very likely
timing for future sea ice loss to the first half of the 21st century,
with a possibility of major loss within a decade or two.},
file = {Overland2013.pdf:Overland2013.pdf:PDF},
issn = {1944-8007},
keywords = {Sea ice, Arctic, Climate change, 0750 Sea ice, 1621 Cryospheric change,
1626 Global climate models, 9315 Arctic region},
owner = {mct},
timestamp = {2013.05.22},
url = {}
[Laxon2012] Unknown bibtex entry with key [Laxon2012]
[Rampal2011] P. Rampal, J. Weiss, C. Dubois, and J. -M. Campin, "Ipcc climate models do not capture arctic sea ice drift acceleration: consequences in terms of projected sea ice thinning and decline," J. geophys. res., vol. 116, p. C00D07--, 2011.
author = {Rampal, P. and Weiss, J. and Dubois, C. and Campin, J.-M.},
title = {IPCC climate models do not capture Arctic sea ice drift acceleration:
Consequences in terms of projected sea ice thinning and decline},
journal = {J. Geophys. Res.},
year = {2011},
volume = {116},
pages = {C00D07--},
month = sep,
abstract = {IPCC climate models underestimate the decrease of the Arctic sea ice
extent. The recent Arctic sea ice decline is also characterized by
a rapid thinning and by an increase of sea ice kinematics (velocities
and deformation rates), with both processes being coupled through
positive feedbacks. In this study we show that IPCC climate models
underestimate the observed thinning trend by a factor of almost 4
on average and fail to capture the associated accelerated motion.
The coupling between the ice state (thickness and concentration)
and ice velocity is unexpectedly weak in most models. In particular,
sea ice drifts faster during the months when it is thick and packed
than when it is thin, contrary to what is observed; also models with
larger long-term thinning trends do not show higher drift acceleration.
This weak coupling behavior (1) suggests that the positive feedbacks
mentioned above are underestimated and (2) can partly explain the
models' underestimation of the recent sea ice area, thickness, and
velocity trends. Due partly to this weak coupling, ice export does
not play an important role in the simulated negative balance of Arctic
sea ice mass between 1950 and 2050. If we assume a positive trend
on ice speeds at straits equivalent to the one observed since 1979
within the Arctic basin, first-order estimations give shrinking and
thinning trends that become significantly closer to the observations.},
file = {Rampal2011.pdf:Rampal2011.pdf:PDF},
issn = {0148-0227},
keywords = {Arctic, IPCC climate models, decline, kinematics, sea-ice, 0750 Cryosphere:
Sea ice (4540), 0762 Cryosphere: Mass balance (1218, 1223), 0798
Cryosphere: Modeling (1952, 4316), 1626 Global Change: Global climate
models (3337, 4928)},
owner = {mct},
publisher = {AGU},
timestamp = {2012.01.29},
url = {}
[Rae2014] J. G. L. Rae, H. T. Hewitt, A. B. Keen, J. K. Ridley, J. M. Edwards, and C. M. Harris, "A sensitivity study of the sea ice simulation in the global coupled climate model, hadgem3," Ocean modelling, vol. 74, pp. 60-76, 2014.
author = {Rae, J.G.L. and Hewitt, H.T. and Keen, A.B. and Ridley, J.K. and
Edwards, J.M. and Harris, C.M.},
title = {A sensitivity study of the sea ice simulation in the global coupled
climate model, HadGEM3},
journal = {Ocean Modelling},
year = {2014},
volume = {74},
pages = {60--76},
number = {0},
month = feb,
file = {Rae2014.pdf:Rae2014.pdf:PDF},
issn = {1463-5003},
keywords = {Sea ice, Modelling, Sensitivity, Coupling, Arctic, Antarctic},
owner = {mct},
timestamp = {2014.03.06},
url = {}
[Keen2013] A. Keen, H. Hewitt, and J. Ridley, "A case study of a modelled episode of low arctic sea ice," , vol. 41, iss. 5-6, p. 1229-1244--, 2013.
author = {Keen, AnnB. and Hewitt, HeleneT. and Ridley, JeffK.},
title = {A case study of a modelled episode of low Arctic sea ice},
year = {2013},
volume = {41},
pages = {1229-1244--},
number = {5-6},
booktitle = {Climate Dynamics},
file = {Keen2013.pdf:Keen2013.pdf:PDF},
issn = {0930-7575},
owner = {mct},
publisher = {Springer Berlin Heidelberg},
timestamp = {2013.12.19},
url = {}
[Schroeder2014] Unknown bibtex entry with key [Schroeder2014]
[Tsamados2013] M. Tsamados, D. L. Feltham, and A. V. Wilchinsky, "Impact of a new anisotropic rheology on simulations of arctic sea ice," J. geophys. res. oceans, p. n/a--n/a, 2013.
author = {Tsamados, M. and Feltham, D. L. and Wilchinsky, A. V.},
title = {Impact of a new anisotropic rheology on simulations of Arctic sea
journal = {J. Geophys. Res. Oceans},
year = {2013},
pages = {n/a--n/a},
month = jan,
abstract = {A new rheology that explicitly accounts for the subcontinuum anisotropy
of the sea ice cover is implemented into the Los Alamos sea ice model.
This is in contrast to all models of sea ice included in global circulation
models that use an isotropic rheology. The model contains one new
prognostic variable, the local structure tensor, which quantifies
the degree of anisotropy of the sea ice, and two parameters that
set the time scale of the evolution of this tensor. The anisotropic
rheology provides a subcontinuum description of the mechanical behavior
of sea ice and accounts for a continuum scale stress with large shear
to compression ratio and tensile stress component. Results over the
Arctic of a stand-alone version of the model are presented and anisotropic
model sensitivity runs are compared with a reference elasto-visco-plastic
simulation. Under realistic forcing sea ice quickly becomes highly
anisotropic over large length scales, as is observed from satellite
imagery. The influence of the new rheology on the state and dynamics
of the sea ice cover is discussed. Our reference anisotropic run
reveals that the new rheology leads to a substantial change of the
spatial distribution of ice thickness and ice drift relative to the
reference standard visco-plastic isotropic run, with ice thickness
regionally increased by more than 1 m, and ice speed reduced by
up to 50%.},
file = {Tsamados2013.pdf:Tsamados2013.pdf:PDF},
issn = {2169-9291},
keywords = {sea ice, stress, anisotropy, arctic, model, rheology, 0750 Sea ice,
0798 Modeling, 0774 Dynamics, 8032 Rheology: general},
owner = {mct},
timestamp = {2013.01.30},
url = {}
[Flocco2012] D. Flocco, D. Schroeder, D. L. Feltham, and E. C. Hunke, "Impact of melt ponds on arctic sea ice simulations from 1990 to 2007," J. geophys. res., vol. 117, iss. C9, p. C09032--, 2012.
author = {Flocco, Daniela and Schroeder, David and Feltham, Daniel L. and Hunke,
Elizabeth C.},
title = {Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007},
journal = {J. Geophys. Res.},
year = {2012},
volume = {117},
pages = {C09032--},
number = {C9},
month = sep,
abstract = {The extent and thickness of the Arctic sea ice cover has decreased
dramatically in the past few decades with minima in sea ice extent
in September 2007 and 2011 and climate models did not predict this
decline. One of the processes poorly represented in sea ice models
is the formation and evolution of melt ponds. Melt ponds form on
Arctic sea ice during the melting season and their presence affects
the heat and mass balances of the ice cover, mainly by decreasing
the value of the surface albedo by up to 20%. We have developed a
melt pond model suitable for forecasting the presence of melt ponds
based on sea ice conditions. This model has been incorporated into
the Los Alamos CICE sea ice model, the sea ice component of several
IPCC climate models. Simulations for the period 1990 to 2007 are
in good agreement with observed ice concentration. In comparison
to simulations without ponds, the September ice volume is nearly
40% lower. Sensitivity studies within the range of uncertainty reveal
that, of the parameters pertinent to the present melt pond parameterization
and for our prescribed atmospheric and oceanic forcing, variations
of optical properties and the amount of snowfall have the strongest
impact on sea ice extent and volume. We conclude that melt ponds
will play an increasingly important role in the melting of the Arctic
ice cover and their incorporation in the sea ice component of Global
Circulation Models is essential for accurate future sea ice forecasts.},
file = {Flocco2012.pdf:Flocco2012.pdf:PDF},
issn = {0148-0227},
keywords = {Albedo, melt ponds, modeling, sea ice, 0748 Cryosphere: Ponds, 0750
Cryosphere: Sea ice (4540), 0762 Cryosphere: Mass balance (1218,
1223), 0798 Cryosphere: Modeling (1952, 4316)},
owner = {mct},
publisher = {AGU},
timestamp = {2012.11.21},
url = {}