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A model of the three-dimensional evolution of Arctic melt ponds on first-year and multiyear sea ice

01 Dec 2010-Journal of Geophysical Research (American Geophysical Union)-Vol. 115

AbstractDuring winter the ocean surface in polar regions freezes over to form sea ice. In the summer the upper layers of sea ice and snow melts producing meltwater that accumulates in Arctic melt ponds on the surface of sea ice. An accurate estimate of the fraction of the sea ice surface covered in melt ponds is essential for a realistic estimate of the albedo for global climate models. We present a melt-pond-sea-ice model that simulates the three-dimensional evolution of melt ponds on an Arctic sea ice surface. The advancements of this model compared to previous models are the inclusion of snow topography; meltwater transport rates are calculated from hydraulic gradients and ice permeability; and the incorporation of a detailed one-dimensional, thermodynamic radiative balance. Results of model runs simulating first-year and multiyear sea ice are presented. Model results show good agreement with observations, with duration of pond coverage, pond area, and ice ablation comparing well for both the first-year ice and multiyear ice cases. We investigate the sensitivity of the melt pond cover to changes in ice topography, snow topography, and vertical ice permeability. Snow was found to have an important impact mainly at the start of the melt season, whereas initial ice topography strongly controlled pond size and pond fraction throughout the melt season. A reduction in ice permeability allowed surface flooding of relatively flat, first-year ice but had little impact on the pond coverage of rougher, multiyear ice. We discuss our results, including model shortcomings and areas of experimental uncertainty.

Topics: Melt pond (82%), Sea ice (79%), Arctic ice pack (79%), Antarctic sea ice (77%), Sea ice thickness (76%)

Summary (4 min read)

1. Introduction

  • The rate of decline of Arctic summer sea ice extent has increased dramatically in recent years.
  • The inability of GCMs to simulate the rapid reduction in Arctic summer sea ice extent, combined with satellite and field observations demonstrating the importance of sea ice melt, indicate the need for a more realistic representation of sea ice melt processes.
  • Horizontal water transport rates in their model vary from cell to cell depending on the solid fraction in the ice.
  • In section 3 the authors present the results of two model runs that simulate the evolution of melt ponds on first‐year ice and multiyear ice, which are compared with field data and the results of Lüthje et al. [2006].

2. Model Description

  • The automaton grid consists of cells that evolve largely independently of each other, interacting through the transport of water between cells, see Figure 1.
  • One‐dimensional thermodynamic equations following Taylor and Feltham [2004] are solved in the vertical direction in every cell to calculate the heat flux through ice, snow and meltwater (if it exists).
  • Water is driven between adjacent cells by differences in hydraulic head between the cells.
  • A higher spatial (and temporal) resolution calculation was found to have no substantial impact on the results. [19].

2.1. Calculation of Meltwater Transport and Drainage

  • The surface of sea ice is deformed by mechanical processes such as ridging, or thermodynamic processes such as the formation and drainage of melt ponds and the freezing over of partially drained ponds [Fetterer and Untersteiner, 1998] and therefore in places the sea ice surface is likely to have a negative freeboard.
  • The area covered in melt ponds is affected by horizontal and vertical water transport [Eicken et al., 2002].
  • In their model horizontal water 3 of 37 flux is calculated before vertical water flux in a given time step and vertical water flux is calculated if there is any water remaining in the cell. [24].
  • In the model described here vertical flow is limited by the lowest permeability in a vertical column.

2.2. Heat Transport Model

  • The vertical heat transport model is the same as the melt‐pond–sea‐ice model described by Taylor and Feltham [2004].
  • A simple snow model was utilized, following Maykut and Untersteiner [1971].
  • The thermodynamic model of the sea ice component (including the ice lid) was described using the equations describing a mushy layer [Feltham et al., 2006], i.e., the sea ice is assumed to consist of a solid matrix of pure ice surrounded by brine (with no air pockets).
  • The albedo depends (through the upwelling irradiance) on the presence and saturation of snow, the presence and depth of meltwater, the presence and depth of an ice lid on top of the melt pond, the depth of the sea ice beneath the melt pond, and the scattering and absorption coefficients of these media. [29].

2.3. Topography Model and Standard First‐Year and Multiyear Sea Ice Topographies

  • Due to the limited availability of sea ice topography and snow topography data, and in particular combined data, some assumptions have been made in modeling the snow and ice topographies.
  • The mean and variance of snow or ice depth is needed, which can be recovered from field data, and a covariance model is used to determine the correlation between snow or ice thicknesses at separate locations as the distance between locations increases.
  • To create an ice topography two ice topographies were initially generated, one representing ice draft below a putative sea level and one representing freeboard ice height above a putative sea level.
  • A mean ice thickness of 1.70 m was selected for the first‐year ice standard case, this is a mean ice thickness of first‐year ice with a thin snow cover as observed by Perovich et al. [2002b].
  • This is most likely unrealistic and indicates a deficiency in using a simulated rather than measured topography.

3.1. Standard Case Simulation Results

  • Day 140 is several days before the snow begins to melt.
  • Mean pond area reaches its maximum at the same time, however mean pond depth continues to increase due to enhanced melting beneath ponds and water being transported across the surface to the cells with the smallest ice surface height.
  • The percentage decrease in mass over the modeled melt season is 62.2% and the total ice ablation is 1.01 m. [41] (b) Change in mean snow depth (light blue), mean pond depth (red), and mean ice thickness with time for the standard first‐year ice case, with dashed lines representing the corresponding values for the alternative first‐year ice case.
  • In the multiyear ice case the mean surface albedo decreases rapidly as snow melt produces ponds and continues to decrease more slowly until the ponds freeze over.

3.2.1. Comparison of Standard Case Simulations With Observations

  • There is typically substantial variation between observations of melt ponds since observations are made at different points in the melt season and at different locations.
  • The maximum pond fraction observed by Perovich et al. [2002a] was around day 174,early in the season, after this pond fraction decreased, which is a pattern seen in both the first‐year ice and multiyear ice modeled standard cases where there is a peak in pond coverage after snow cover is removed.
  • The mean albedo of the ice surface over the melt season in the first‐year ice case is 0.64 and over the entire domain (which includes open ocean) is 0.56.
  • The authors briefly compare the results of the model presented in this paper with that of Lüthje et al. [2006] to 14 of 37 indicate the impact of the more realistic physics.
  • The total ablation for first‐year ice in the Lüthje et al. [2006] model of 0.75 m, was much less than the total ablation of 1.33 m in the model described here, this is due to the greater mass of water available in their model, due to the separate snow layer and the greater ice mean ice thickness. [55].

4. Sensitivity Studies: Results and Discussion

  • Below the authors present sensitivity studies that examine the impact on pond cover and ice and snow ablation of changes in snow topography, ice topography, and vertical perme- ability for first‐year ice and multiyear ice.
  • Unless otherwise indicated, all parameters are the same as for the appropriate standard case.
  • Tables 2 and 3 summarize the important results for the sensitivity studies (and the standard cases for reference) for first‐year ice and multiyear ice, respectively.
  • FYI denotes first‐year sea ice and MYI denotes multiyear sea ice.

4.1.1. Sensitivity of FYI Pond Coverage to the Snow Cover

  • The following studies examine the sensitivity of pond evolution and ablation to snow depth and roughness.
  • The snow topography used here represents the snow cover that would be expected on hummocky ice [Sturm et al., 2002], the standard deviation is increased from 0.15 m for the standard case to 0.25 m.
  • There was an increase in maximum mean pond area from 219 m2 in the standard case to 315 m2 in the rough snow case and mean pond area exceeds the standard case pond area between days 185 and 195, when pond fraction is at its greatest.
  • The smooth snow case differs from the standard first‐year ice case most obviously at the start of the season as the smaller variability in snow depth results in snow melting at the same rate across the grid causing initial pond fraction (not shown) and mean pond area to be greater than the standard case.
  • The dashed lines represent the corresponding values for the standard first‐year ice case.

4.3. Discussion of Sensitivity Studies

  • Tables 2 and 3 summarize the important results for the standard case runs and the sensitivity studies presented in this paper. [83].
  • The dashed lines represent the corresponding values for the standard multiyear ice case.
  • Such surface flooding was not observed in the multiyear ice case due to the deeper depressions in the ice, limiting the horizontal transport of water. [84].
  • Change in mean snow depth (light blue), mean pond depth (red), and mean ice thickness with time for the smooth ice case.

5. Conclusion and Further Work

  • The authors model uses the cellular automaton concept described by Lüthje et al. [2006], with significant improvements, and the one‐dimensional vertical heat transport model described by Taylor and Feltham [2004].
  • The area‐averaged surface albedo of the summer ice cover is largely determined by the area of the sea ice covered in melt ponds and the open ocean fraction.
  • (top) Variation in the fractional distribution of surface area with time for the low‐permeability case, where mean ice thickness is 2.50 m, standard deviation in ice thickness is 1.10 m, mean snow thickness is 0.30 m, and standard deviation in snow thickness is 0.25 m.
  • The dashed lines represent the corresponding values for the standard multiyear ice case.
  • Since the underlying ice topography plays such a central role in determining the location and extent of pond formation, detailed measurements of topography, in conjunction with observations of the pond evolution and surface forcing, will enable a stricter test of the melt‐pond–sea‐ice model. [92].

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A model of the three-dimensional
evolution of Arctic melt ponds on rst-year
and multiyear sea ice
Article
Published Version
Scott, F. and Feltham, D.L. (2010) A model of the three-
dimensional evolution of Arctic melt ponds on rst-year and
multiyear sea ice. Journal of Geophysical Research, 115
(C12). C12064. ISSN 0148-0227 doi:
https://doi.org/10.1029/2010JC006156 Available at
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A model of the threedimensional evolution of Arctic melt ponds
on firstyear and multiyear sea ice
F. Scott
1
and D. L. Feltham
1,2
Received 27 January 2010; revised 15 July 2010; accepted 20 September 2010; published 28 December 2010.
[1] During winter the ocean surface in polar regions freezes over to form sea ice.
In the summer the upper layers of sea ice and snow melts producing meltwater that
accumulates in Arctic melt ponds on the surface of sea ice. An accurate estimate of the
fraction of the sea ice surface covered in melt ponds is essential for a realistic estimate of
the albedo for global climate models. We present a meltpondseaice model that
simulates the threedimensional evolution of melt ponds on an Arctic sea ice surface. The
advancements of this model compared to previous models are the inclusion of snow
topography; meltwater transport rates are calculated from hydraulic gradients and ice
permeability; and the incorporation of a detailed onedimensional, thermodynamic
radiative balance. Results of model runs simulating firstyear and multiyear sea ice are
presented. Model results show good agreement with observations, with duration of pond
coverage, pond area, and ice ablation comparing well for both the firstyear ice and
multiyear ice cases. We investigate the sensitivity of the melt pond cover to changes in ice
topography, snow topography, and vertical ice permeability. Snow was found to have an
important impact mainly at the start of the melt season, whereas initial ice topography
strongly controlled pond size and pond fraction throughout the melt season. A reduction in
ice permeability allowed surface flooding of relatively flat, firstyear ice but had little
impact on the pond coverage of rougher, multiyear ice. We discuss our results, including
model shortcomings and areas of experimental uncertainty.
Citation: Scott, F., and D. L. Feltham (2010), A model of the threedimensional evolution of Arctic melt ponds on firstyear and
multiyear sea ice, J. Geophys. Res., 115, C12064, doi:10.1029/2010JC006156.
1. Introduction
[2] The rate of decline of Arctic summer sea ice extent has
increased dramatically in recent years. A record minimum of
ice extent was recorded in 2007, beating the previous record
minimum in 2005. The 2007 extent minimum was almost
matched again in 2008. The decrease in sea ice area has
been accompanied by a decrease in sea ice volume. For
instance, Rothrock et al. [1999] observed a 40% reduction in
average ice thickness by analyzing submarine measurements
of sea ice draft from the 1970s and 1990s. Wider area esti-
mates of sea ice thickness, based on satellite altimetry [Laxon
et al., 2003; Giles et al., 2008], also reveal a reduction in ice
thickness.
[
3] Global warming is intensified in polar regions due to
the albedo feedback mechanism [e.g., Ebert et al., 1995]
and, as a result of this, Arctic sea ice is a sensitive indicator
of climate change, as well as being an important climate
component. Climate prediction studies using Global Climate
Models (GCMs), such as the Intergovernmental Panel on
Climate Change AR4 study, are unable to simulate the
observed rapid reduction o f sea ice extent [Solomon et al.,
2007]. The inability of GCMs to simulate the rapid reduc-
tion in Arctic summer sea ice extent, combined with satellite
and field observations demonstrating the importance of sea
ice melt, indicate the need for a more realistic representation
of sea ice melt processes. In particular, GCMs do not model
melt ponds on sea ice. As the melt season progresses, part of
the surface meltwater produced accumulates to form melt
ponds that cover an increasing fraction of the surface,
reaching around 50% at the end of the melt season.
[
4] Melt ponds are a persistent feature of the summertime
sea ice surface in the Arctic [Derksen et al., 1997; Fetterer
and Untersteiner, 1998; Tucker et al., 1999; Yackel et al.,
2000; Tschudi et al., 2001]. Melt ponds have a significant
impact on the both the albedo of sea ice and the amount of
sea ice melt. The albedo of pondcovered ice is variable and
has been measured in field experiments to be between 0.1
and 0.5 [e.g., Perovich et al., 2002b; Eicken et al., 2004].
These albedo values are much lower than bare ice and
snowcovered ice, which are relatively stable at 0.60.65
and 0.840.87 [Perovich, 1996]. Since the ice concentration
in the interior Arctic is greater than 85%, melt ponds con-
tribute significantly to the areaaveraged albedo, with an
1
Centre for Polar Observation and Modelling, Department of Earth
Sciences, University College London, London, UK.
2
British Antarctic Survey, Cambridge, UK.
Copyright 2010 by the American Geophysical Union.
01480227/10/2010JC006156
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, C12064, doi:10.1029/2010JC006156, 2010
C12064 1of37

approximately linear decrease in albedo with increasing
pond fraction [Eicken et al., 2004]. For example, an
uncertainty in pond fraction of 15% over the entire Arctic
Ocean is equivalent to an uncertainty of 10% in the total ice
area in the calculation of mean Arctic Ocean albedo.
[
5] Melt pond parameterizations that can be incorporated
into GCMs are now being developed [Flocco and Feltham,
2007; Pedersen et al., 2009; Flocco et al., 2010], however,
to ensure that parameterizations are realistic we need to
understand the physics that govern melt pond evolution so
that the parameterizations can be physically based.
[
6] Our objective here is to create a model of melt pond
evolution on sea ice, based on the physics believed to
govern pond formation and growth, that can be used to
determine the sensitivity of melt ponds to ice and snow
surface topography and uncertainty in sea ice permeability,
and thus improve our understanding of the evolution of melt
ponds. Our model uses the cellular automaton concept
described by Lüthje et al. [2006], with significant im-
provements described below, and the onedimensional,
vertical heat transport model described by Taylor and
Feltham [2004]. In the model the ice cover is represented
by a horizontal square grid of cells like a checker board and
each cell contains a column of ice, which may have a melt
pond or snow cover, see Figure 1.
[
7] The initial ice and snow topographies have been
generated using standard statistical methods so that first
year and multiyear ice can be modeled using the statistical
properties of necessarily limited observations. Ice surface
and base heights are generated separately leading to a sur-
face topography with some ice surface heights below sea
level init ially. The initial surface topography in the Lüthje
et al. [2006] model is based on ice freeboard measure-
ments and all cells have positive initial freeboard.
[
8] In our model the entire floe is in hydrostatic equilib-
rium, but not necessarily every cell, and sea level with
respect to the floe is recalculated every time step. This al-
lows vertical drainage to be realistically modeled using
Darcys law, rather than take place at a fixed rate as in the
Lüthje et al. [2006] model. Horizontal water transport
rates in our model vary from cell to cell depending on the
solid fraction in the ice. Therefore in the model described
in this paper there is spatial as well as temporal variation
in drainage rate.
[
9] The ice and snow melt rates in our model are calcu-
lated from the detailed thermal and radiative balances
described by Taylor a nd Feltham [2004]. In the [thje et
al., 2006] model bare ice melts at a fixed rate and melting
beneath ponds take place at an enhanced rate using an ad
hoc algorithm motivated by observations. There is no basal
melting in the Lüthje et al. [2006] model and there is no
separate representation of snow cover.
[
10] In section 2 we present the meltpondseaice model
including the model of meltwater transport, an explanation
of how the cellular approach is combined with the one
dimensional thermodynamic model, and a description of the
construction of initial ice and snow topographies. In section
3 we present the results of two model runs that simulate the
evolution of melt ponds on firstyear ice and multiyear ice,
which are compared with field data and the results of Lüthje
et al. [2006]. In section 4 we present sensitivity studies for
both firstyear and multiyear sea ice in which we vary the
initial snow cover (depth and roughness), ice topography
(roughness), and vertical ice permeability, and compare
Figure 1. A schematic diagram of the cellular automaton. Each cell has an individual ice thickness, H,
and has a horizontal surface area of 25 m
2
. Melting decreases the ice thickness in a cell and allows a pond
to form on the surface. Water can drain through a cell or can be transported to adjacent cells.
SCOTT AND FELTHAM: EVOLUTION OF MELT PONDS C12064C12064
2of37

these studies with observations. Finally, in section 5, we
summarize our results and state our main conclusions.
2. Model Description
[11] The automaton grid consists of cells that evolve
largely independently of each other, interacting through the
transport of water between cells, see Figure 1. Each cell
represents a 5 m × 5 m square area of sea ice and, within this
area, ice thickness, meltwater depth and snow cover are
assumed to be uniform. The entire grid represents an 200 m
× 200 m area of a sea ice floe (40 cells per side). The area is
constrained to this size so that it can represent an arbitrary
section of a sea ice floe without the complication of having
to take edge effects into consideration. The boundaries are
periodic so that meltwater transported out of one edge cell is
transported back into the opposite edge cell. A time step of
the model consists of the following five stages:
[
12] 1. Onedimensional thermodynamic equations fol-
lowing Taylor and Feltham [2004] are solved in the vertical
direction in every cell to calculate the heat flux through ice,
snow and meltwater (if it exists). These calculations estab-
lish the albedo, volume of meltwater produced, basal abla-
tion and the saturation of snow on a cell by cell basis.
[
13] 2. Sea level with respect to the floe is established and
used to calculate the hydraulic head in each cell.
[
14] 3. Water is driven between adjacent cells by differ-
ences in hydraulic head between the cells. The volume of
horizontal water transport is calculated using Darcys law
for flow through a porous medium.
[
15] 4. Vertical drainage through the ice in each cell is
calculated using Darcys law and the hydraulic head.
[
16] 5. The volume of water transported into and out of
the cells is updated and one cycle of the automaton model is
complete.
[
17] Note the choice of the order of operation of (2)(4),
which corresponds to the rapidity of the relevant physical
processes, is needed in order to calculate meltwater trans-
port accurately for all practical choices of model time step
(i.e., greater than about a minute). We used a model time
step of 1 h.
[
18] Each cell in the cellular model calls a separate one
dimensional thermodynamic model, as described by Taylor
and Feltham [2004]. The thermodynamic model model is
run at a lower vertical spatial and temporal resolution than
that by Taylor and Feltham [2004] (20 grid points and time
steps of 1 h, compared with 641 grid points and time steps
of 600 seconds in the original model runs), first due to time
constraints, to allow a model run to be completed on a
typical workstation in just over a week, and secondly to
ensure that the cellular automaton and thermodynamic
models are of comparable accuracy. The resolution of the
thermodynamic model was tested in isolation from the cel-
lular model to ensure that the lowerresolution results were
not significantly different from higherresolution results.
The relatively coarse grid length of 5 m was chosen because
this is the average distance water is expected to travel in a
time step length of 1 h. A higher spatial (and temporal)
resolution calculation was found to have no substantial
impact on the results.
[
19] We describe the meltpondseaice model in the
following sections: section 2.1 describes the calculation of
meltwater transport and drainage, section 2.2 briefly de-
scribes the thermodynamic and radiative model used to
calculate melt rates, and section 2.3 describes the generation
of the sea ice and snow topographies.
2.1. Calculation of Meltwater Transport and Drainage
[
20] The surface of sea ice is deformed by mechanical
processes such as ridging, or thermodynamic processes such
as the formation and drainage of melt ponds and the freezing
over of partially drained ponds [Fetterer and Untersteiner,
1998] and therefore in places the sea ice surface is likely
to have a negative freeboard. In this model the entire floe/
computational domain is in hydrostatic equilibrium but not
individual cells. Sea level with respect to the floe is estab-
lished initially using the assumption that the entire floe is in
hydrostatic equilibrium and is then updated as mass is
removed from the surface and base of the ice.
[
21] Mean draft, D, is calculated every time step from
D ¼
P
x
i
þ x
s
þ x
p

A
; ð1Þ
where x is the mass of ice, snow and water in each cell, where
index i represents ice, s represents snow, and p represents
melt pond, r is ocean density, and A is total floe area.
[
22] The area covered in melt ponds is affected by hori-
zontal and vertical water transport [Eicken et al., 2002]. In
this model water can be removed from the grid by vertical
drainage and can be transported between cells, depending on
differences in hydraulic head between cells. We model
vertical and horizontal water transport in each cell using
Darcys law and we assume for simplicity that sea ice is a
saturated porous medium. In the vertical direction the Darcy
velocity, v, reduces to
v ¼
v
g
m
y
H
; ð2Þ
where p
v
is the vertical ice permeability, g is gravitational
acceleration, m is dynamic viscosity, which, for water, is
10
3
kg m
1
s
1
, r
m
is the density of meltwater, which is
initially formed from melted snow and is taken to be
1000 kgm
3
, y is the height of the melt pond surface above
sea level, and H is ice thickness. In the horizontal direction the
Darcy velocity, u, is given by
u ¼
h
g
m
r ; ð3Þ
where p
h
is the ice permeability in the horizontal direction,
and y is the fluid surface height.
[
23] The structure of sea ice is such that the upper surface
and several centimeters below the sea ice surface is often a
highly porous, crusty layer of sea ice [Eicken et al., 2002].
We assume that most horizontal water transport is limited by
flow through this porous crust. The solid fraction in the sea
ice crust is lower than that in the ice below and therefore the
permeability will be greater here than at any other depth in
the sea ice. The permeability at the base of the ice in the
summer melt season is small enough to make horizontal
water flux greater than vertical water flux for the same
pressure gradient, and therefore is the dominant way in
which water is transported. In our model horizontal water
SCOTT AND FELTHAM: EVOLUTION OF MELT PONDS C12064C12064
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Abstract: [1] As part of ice albedo feedback studies during the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment, we measured spectral and wavelength-integrated albedo on multiyear sea ice. Measurements were made every 2.5 m along a 200-m survey line from April through October. Initially, this line was completely snow covered, but as the melt season progressed, it became a mixture of bare ice and melt ponds. Observed changes in albedo were a combination of a gradual evolution due to seasonal transitions and abrupt shifts resulting from synoptic weather events. There were five distinct phases in the evolution of albedo: dry snow, melting snow, pond formation, pond evolution, and fall freeze-up. In April the surface albedo was high (0.8-0.9) and spatially uniform. By the end of July the average albedo along the line was 0.4, and there was significant spatial variability, with values ranging from 0.1 for deep, dark ponds to 0.65 for bare, white ice. There was good agreement between surface-based albedos and measurements made from the University of Washington's Convair-580 research aircraft. A comparison between net solar irradiance computed using observed albedos and a simplified model of seasonal evolution shows good agreement as long as the timing of the transitions is accurately determined.

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161 citations


Journal ArticleDOI
Abstract: . The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007–2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice–ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave–turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice–ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

139 citations


Journal ArticleDOI
TL;DR: The model results indicate that the recent thinning of Arctic sea ice is the main cause of a marked increase in the prevalence of light conditions conducive to sub-ice blooms, and that as little as 20 years ago, the conditions required for sub-ICE blooms may have been uncommon, but their frequency has increased to the point that nearly 30% of the ice-covered Arctic Ocean in July permits sub- ice blooms.
Abstract: In July 2011, the observation of a massive phytoplankton bloom underneath a sea ice–covered region of the Chukchi Sea shifted the scientific consensus that regions of the Arctic Ocean covered by sea ice were inhospitable to photosynthetic life. Although the impact of widespread phytoplankton blooms under sea ice on Arctic Ocean ecology and carbon fixation is potentially marked, the prevalence of these events in the modern Arctic and in the recent past is, to date, unknown. We investigate the timing, frequency, and evolution of these events over the past 30 years. Although sea ice strongly attenuates solar radiation, it has thinned significantly over the past 30 years. The thinner summertime Arctic sea ice is increasingly covered in melt ponds, which permit more light penetration than bare or snow-covered ice. Our model results indicate that the recent thinning of Arctic sea ice is the main cause of a marked increase in the prevalence of light conditions conducive to sub-ice blooms. We find that as little as 20 years ago, the conditions required for sub-ice blooms may have been uncommon, but their frequency has increased to the point that nearly 30% of the ice-covered Arctic Ocean in July permits sub-ice blooms. Recent climate change may have markedly altered the ecology of the Arctic Ocean.

109 citations


Cites methods from "A model of the three-dimensional ev..."

  • ...Although these existing data are too limited to fully validate the simulated melt pond distribution, the general pattern and evolution of time are within the range of field observations and detailed process studies, which were verified with observations (33, 34)....

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References
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01 Jan 2007
Abstract: This report is the first volume of the IPCC's Fourth Assessment Report. It covers several topics including the extensive range of observations now available for the atmosphere and surface, changes in sea level, assesses the paleoclimatic perspective, climate change causes both natural and anthropogenic, and climate models for projections of global climate.

29,120 citations


Journal ArticleDOI
Abstract: A one-dimensional thermodynamic model of sea ice is presented that includes the effects of snow cover, ice salinity, and internal heating due to penetration of solar radiation. The incoming radiative and turbulent fluxes, oceanic heat flux, ice salinity, snow accumulation, and surface albedo are specified as functions of time. The model is applied to the central Arctic.

991 citations


Journal ArticleDOI
Abstract: Comparison of sea-ice draft data acquired on submarine cruises between 1993 and 1997 with similar data acquired between 1958 and 1976 indicates that the mean ice draft at the end of the melt season has decreased by about 1.3 m in most of the deep water portion of the Arctic Ocean, from 3.1 m in 1958–1976 to 1.8 m in the 1990s. The decrease is greater in the central and eastern Arctic than in the Beaufort and Chukchi seas. Preliminary evidence is that the ice cover has continued to become thinner in some regions during the 1990s.

972 citations


Journal ArticleDOI
30 Oct 2003-Nature
TL;DR: An eight-year time-series of Arctic ice thickness is used, derived from satellite altimeter measurements of ice freeboard, to determine the mean thickness field and its variability from 65° N to 81.5° N, which reveals a high-frequency interannual variability in mean ArcticIce thickness that is dominated by changes in the amount of summer melt, rather than byChanges in circulation.
Abstract: Possible future changes in Arctic sea ice cover and thickness, and consequent changes in the ice-albedo feedback, represent one of the largest uncertainties in the prediction of future temperature rise1,2. Knowledge of the natural variability of sea ice thickness is therefore critical for its representation in global climate models3,4. Numerical simulations suggest that Arctic ice thickness varies primarily on decadal timescales3,5,6 owing to changes in wind and ocean stresses on the ice7,8,9,10, but observations have been unable to provide a synoptic view of sea ice thickness, which is required to validate the model results3,6,9. Here we use an eight-year time-series of Arctic ice thickness, derived from satellite altimeter measurements of ice freeboard, to determine the mean thickness field and its variability from 65° N to 81.5° N. Our data reveal a high-frequency interannual variability in mean Arctic ice thickness that is dominated by changes in the amount of summer melt11, rather than by changes in circulation. Our results suggest that a continued increase in melt season length would lead to further thinning of Arctic sea ice.

490 citations


Journal Article
Abstract: [1] As part of ice albedo feedback studies during the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment, we measured spectral and wavelength-integrated albedo on multiyear sea ice. Measurements were made every 2.5 m along a 200-m survey line from April through October. Initially, this line was completely snow covered, but as the melt season progressed, it became a mixture of bare ice and melt ponds. Observed changes in albedo were a combination of a gradual evolution due to seasonal transitions and abrupt shifts resulting from synoptic weather events. There were five distinct phases in the evolution of albedo: dry snow, melting snow, pond formation, pond evolution, and fall freeze-up. In April the surface albedo was high (0.8-0.9) and spatially uniform. By the end of July the average albedo along the line was 0.4, and there was significant spatial variability, with values ranging from 0.1 for deep, dark ponds to 0.65 for bare, white ice. There was good agreement between surface-based albedos and measurements made from the University of Washington's Convair-580 research aircraft. A comparison between net solar irradiance computed using observed albedos and a simplified model of seasonal evolution shows good agreement as long as the timing of the transitions is accurately determined.

422 citations