TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-10-2999-2016 Quantification of ice production in Laptev Sea polynyas and its sensitivity to thin-ice parameterizations in a regional climate modelGutjahrOlivergutjahr@uni-trier.dehttps://orcid.org/0000-0002-3116-8071HeinemannGüntherhttps://orcid.org/0000-0002-4831-9016PreußerAndreashttps://orcid.org/0000-0003-0134-6890WillmesSaschahttps://orcid.org/0000-0002-2710-0699DrüeClemenshttps://orcid.org/0000-0002-8240-9031Department of Environmental Meteorology, University of Trier, Behringstraße 21, 54296 Trier, GermanyOliver Gutjahr (gutjahr@uni-trier.de)9December2016106299930196April201625May201619October20169November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/10/2999/2016/tc-10-2999-2016.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/10/2999/2016/tc-10-2999-2016.pdf
The quantification of sea-ice production in the Laptev Sea polynyas is
important for the Arctic sea-ice budget and the heat loss to the atmosphere.
We estimated the ice production for the winter season 2007/2008
(November–April) based on simulations with the regional climate model
COSMO-CLM at a horizontal resolution of 5 km and compared it to remote
sensing estimates. A reference and five sensitivity simulations were
performed with different assumptions on grid-scale and subgrid-scale ice
thickness considered within polynyas, using a tile approach for fractional
sea ice. In addition, the impact of heat loss on the atmospheric boundary
layer was investigated.
About 29.1 km3 of total winter ice production was estimated for the
reference simulation, which varies by up to +124 % depending on the
thin-ice assumptions. For the most realistic assumptions based on remote
sensing of ice thickness the ice production increases by +39 %. The use
of the tile approach enlarges the area and enhances the magnitude of the heat
loss from polynyas up to +110 % if subgrid-scale open water is assumed
and by +20 % for realistic assumptions. This enhanced heat loss causes in
turn higher ice production rates and stronger impact on the atmospheric
boundary layer structure over the polynyas. The study shows that ice
production is highly sensitive to the thin-ice parameterizations for
fractional sea-ice cover. In summary, realistic ice production estimates
could be retrieved from our simulations. Neglecting subgrid-scale energy
fluxes might considerably underestimate the ice production in coastal
polynyas, such as in the Laptev Sea, with possible consequences on the Arctic
sea-ice budget.
Introduction
The rate of sea-ice growth strongly depends on the energy fluxes at the ice
or ocean surface. If the total atmospheric heat flux is negative, the ocean
loses heat either directly to the atmosphere or via conduction through an
existing sea-ice cover. In the former case frazil ice forms, which aggregates
subsequently to a new thin-ice layer under calm conditions. In the latter
case basal freezing occurs to balance this heat loss. Most of the heat loss
from the ocean occurs over open water or thin-ice areas, such as leads and
polynyas, within an otherwise compact sea-ice cover
. Although the fraction of such areas in
polar oceans is relatively small during winter, they are of major importance
for the heat budget of the atmospheric boundary layer (ABL)
and the ocean circulation, such as the Arctic
circumpolar boundary current e.g..
The Laptev Sea (Siberia) is a very shallow shelf sea with water depths
between 15 and 200 m and comprises an area of about
500 × 103 km2. It is one of
the most significant regions where a considerable amount of the total Arctic
sea ice is produced .
The newly formed sea ice is subsequently transported by the Transpolar Drift
Stream and accounts for about 20 % of the total ice export through Fram
Strait . The Laptev Sea thus plays a key role for future
Arctic sea-ice development .
Quasi-stationary latent-heat or flaw polynyas reoccur frequently along the
Siberian coast and along the fast-ice edge
due to offshore wind stress .
These polynyas are narrow, long bands of open water and/or thin ice, which
separate landfast ice from seaward drifting ice on the Siberian continental
shelves during winter , predominantly from October until
June . In general, the ice cover in the Laptev Sea can be
divided into three regimes: fast ice, pack ice, and flaw polynyas in between
.
Particularly in winter, sea water at the freezing point is directly exposed
to a cold atmosphere, resulting in intense ice formation
. Due to this strong surface heat loss within coastal
polynyas frazil ice forms, which is subsequently transported toward the
downwind edge of the polynyas .
This process creates a spatial gradient of
thin-ice thickness (TIT) increasing from open-water conditions at the
windward polynya edge to thicker ice at the downwind side. Here, at the
polynya edge, the advected frazil ice accumulates to a thin layer
, which thickens and consolidates before it
drifts further offshore. During the ice formation, salt is excluded from the
ice matrix and is drained as brine from the sea ice . This
salt input induces haline convection and erodes the density stratification of
the underlying water column and, if penetrative,
dense bottom water forms . The long-term mean
probability for convective mixing down to the seafloor is only about 20 %
in the western and about 70 % in the eastern Laptev Sea
, which is owed to the general preservation
of the stratification throughout the winter caused by freshwater input from
the Lena River during summer . The cold,
dense bottom water contributes to the Arctic halocline and
thermohaline structure in the Eurasian Basin .
The horizontal resolution of regional climate models is generally too coarse
to represent leads and small polynyas explicitly. Therefore, they have to be
treated as inhomogeneities of momentum and energy fluxes on a subgrid scale.
investigated three approaches to account for such
subgrid-scale inhomogeneities within a model grid box: the (i) aggregation,
(ii) mosaic, and (iii) tile approach (TA). In the aggregation approach the
parameters for the fluxes (such as roughness length or albedo) are
weight-averaged over different surface types within a grid box and then the
fluxes are calculated from these grid-scale means. In contrast, in the mosaic
approach the fluxes are explicitly calculated on a sub-scale grid and
averaged afterwards. In the standard version (v5.0_clm1) of the regional
climate model COnsortium for Small-scale MOdel – Climate Limited area Mode
(COSMO-CLM or CCLM; ), which is the climate version of the
numerical weather prediction model COSMO of the German Meteorological Service
, a model grid box is assumed to be either completely
covered with sea ice or completely ice free. However, if neglecting
subgrid-scale energy fluxes or heat loss from open-water or thin-ice areas,
the energy transfer is underestimated and subsequently also the sea-ice
production (IP). This underestimation affects the sea-ice budget and associated
processes connected to the ocean, such as salt release and deep water
formation . Although the ocean
processes are not represented in CCLM, the quantification of IP can be seen as a proxy for dense water formation.
In other regional climate models, such as the Polar Weather
Research and Forecasting (Polar-WRF) model, fractional sea ice is already a default option
with the assumption of subgrid-scale open water and with fixed sea-ice
concentrations (SIC) and ocean temperatures during a 48 h simulation
. However, assumptions have to be made for the
subgrid-scale thin-ice thickness, since particularly in winter leads and
polynyas are rarely ice free .
In the following we quantify the sea-ice production in the Laptev Sea
polynyas (Siberia) and investigate its sensitivity on the assumptions of
thin-ice thickness associated with the tile approach. The sea-ice module of
was already successfully applied in the Laptev Sea by
, who could show that polynyas significantly affect the
ABL. More recently, calculated
sea-ice production rates for this region based on COSMO simulations with an
assumed thin-ice thickness of 10 cm (B10) or open water (B00) within
polynyas. Their model results showed that the presence of grid-scale thin ice
affects the IP considerably.
The implementation of a TA for subgrid-scale energy fluxes constitutes, from
a physical point of view, an improvement to the representation of polynyas in regional
climate models. However, it is unclear how sensitive the energy fluxes, the
resulting IP, and the ABL are to the choice of grid-scale and subgrid-scale
ice thickness. By varying the thin-ice thickness in a sensitivity experiment,
we aim to quantify these uncertainties. As a benchmark for our study we use
the IP estimations of . We further comprise model results
of and derived IP from Moderate Resolution Imaging
Spectroradiometer (MODIS) data.
This paper is structured as follows: in Sect. 2, a short overview of the
model configuration and the study region is given; in Sect. 3 the basics of
the sea-ice module are described (see details in Appendices
and ). The model is validated with in situ data in Sect. 4
and the effects on the atmospheric boundary layer and on ice production rates
are presented in Sects. 5 and 6 and discussed with respect to remote sensing
estimates in Sect. 7. Finally, we conclude in Sect. 8.
Model domains of COSMO-CLM at a horizontal resolution of 15 km
(C15, whole Arctic). The study domain of the Laptev Sea polynyas (LSP) with
a resolution of 5 km (C05, blue box) is shown in detail in
Fig. . The sea-ice extent (white shaded) is from
4 January 2008.
CCLM configuration and model domains
The domain of CCLM (Fig. ) covers the whole Arctic at a
horizontal resolution of 15 km (C15). CCLM was run on
450 × 350 grid boxes and with 42 vertical layers, of which 16 are below
2 km height. Nested within, we performed simulations for the Laptev
Sea (Fig. and Table ) at 5 km resolution (C05)
with 260 × 260 grid boxes and 60 vertical levels, of which 24 levels are
below 2 km height. We subdivided this domain into four polynya
regions, which have been already used in previous studies, e.g. by
: the north-eastern Taimyr polynya (NET), the Taimyr
polynya (T), the Anabar–Lena polynya (AL), and the western New Siberian
polynya (WNS). The total area of the masks is 26.19 × 104 km2.
The C15 model is forced by ERA-Interim data with updates to
the lateral boundaries every 6 h. The C05 models are then forced by the
output of C15 with an update frequency of 1 h. The models were run
in a forecasting procedure for the winter period November 2007 to April 2008
(182 days in total). They were restarted every simulation day at 18:00 UTC and
simulated the following 30 h. Thereby the initial sea-ice conditions
(see Sect. 3.2) were prescribed to the sea-ice concentration and thickness
of the following day. The first 6 h were cut off as spin-up. The
simulation output (00:00–23:00 UTC) was stored at a temporal resolution of 1 h.
Model domain at 5 km resolution (C05) over the Laptev Sea
(approximately 1500 km × 1500 km) with the sea-ice concentration
from AMSR-E showing open polynyas (≤ 70 %) on 4 January 2008. Four
polynya regions are superimposed as orange polygons: north-eastern Taimyr
polynya (NET), the Taimyr polynya (T), the Anabar–Lena polynya (AL), and the
western New Siberian polynya (WNS). A → B denotes the 214 km
long cross section (magenta) used in Sect. . The locations of the
four AWS stations are marked with green triangles.
Overview of the performed simulations with COSMO-CLM for the
winter period 2007/2008. The grid-scale thin-ice thickness (TIT) within
polynyas (ice concentration: 0 < SIC ≤ 70 %) is shown in centimetres and
the assumed subgrid-scale TIT is shown in parentheses. The latter is only
required if the tile approach (TA) is used.
Model runΔxRegionTIT [cm]TAInformationC1515 kmArctic10 (–)noforced by ERA-InterimC05-ref5 kmLaptev Sea10 (–)noreference runC05-10/05 kmLaptev Sea10 (0)yessubgrid-scale open waterC05-10/15 kmLaptev Sea10 (1)yesC05-10/105 kmLaptev Sea10 (10)yesC05-50/55 kmLaptev Sea50 (5)yesC05-50/15 kmLaptev Sea50 (1)yesrealistic assumptions
Surface fluxes were calculated by a bulk transfer scheme with a stability
dependence (see Appendix ). The vertical
diffusion was parameterized by a level-2.5 closure scheme
based on a prognostic equation for turbulent kinetic energy (TKE). Radiation
processes were calculated hourly using the scheme extended
for ice clouds. We applied a Runge–Kutta scheme of third order
. Additionally, a fast-wave solver for sound and gravity
waves was used . All simulations were run without spectral nudging.
To improve the simulation of heat loss over fractional sea ice in CCLM, we
modified the standard version with regard to the following points: (i) we
implemented the thermodynamic two-layer sea-ice module of
, (ii) we used daily sea-ice thickness (SIT) fields from
the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) data set
as initial data, (iii) we implemented a new albedo scheme
for sea ice based on , and (iv) we implemented a
tile approach for the energy balance over fractional sea ice. The TA is a
simplification of the mosaic approach, considering only the percentage of
different surface types but not their exact location. According to
the TA provides similarly good results as the mosaic
approach, but with distinctly less computation time. Thus, we decided to
implement this variant. First steps in the direction of a tile approach in
CCLM were made by . However, their adjustments were
limited to area-weighted albedo values and to surface roughness values within
a grid box that is covered with fractional sea ice. Although points (ii)–(iii)
represent new modifications to CCLM as well, we accept them as the
default option for our simulations.
The C15 simulation was performed without a TA in order to introduce effects
from the TA only through the 5 km simulations. In case of C05, we performed
a reference simulation in the Laptev Sea area without a TA (C05-ref) and five
sensitivity simulations with the TA. The configuration of the reference
simulation is similar (as far as possible) to the configuration of the
previous studies of , where no TA was available. However,
they used an older model version, which causes differences. C05-ref is thus
comparable to the simulations of and further the reference
without TA for our sensitivity simulations.
Thin-ice thickness distribution (≤ 20 cm) in the Laptev Sea
derived from MODIS data for the winter periods (November–March)
2002/2003–2014/2015. The bars indicate the relative distribution of each
thickness class from the total number of TIT ≤ 0.2 m appearances
between the winter seasons 2002/2003 and 2014/2015. Contributions of each
month with respect to the whole winter season for each thickness class are
indicated by the blueish colours (see the legend). The mean thickness
(±1 standard deviation) in this period is 13.5 ± 0.5 cm
(8.7 cm for ≤ 10 cm). In the winter period 2007/2008, the mean is
14.0 ± 2 cm (7.7 cm for ≤ 10 cm).
While the sea-ice thickness outside the polynyas was specified as explained
in Sect. , the ice thickness within the polynya areas has to
be prescribed. For C15 and C05-ref the ice thickness in polynyas areas is
generally 10 cm, but we assume 1 cm thin ice at polynya grid boxes in
C05-ref, where the sea-ice concentration is 0 %. Such areas
particularly produce new ice and consist of a mixture of open water, grease
and frazil ice, and thin solid ice. However, in winter open-water areas occur
mostly at the windward side of polynyas, which is only a small fraction of
the entire polynya area.
For three of the five sensitivity simulations we assumed also a grid-scale
ice thickness of 10 cm for polynyas and assumed either
subgrid-scale open water (C05-10/0) or a subgrid-scale TIT of 1 cm
(C05-10/1) and 10 cm (C05-10/10). The fourth and
fifth sensitivity simulations were configured with a grid-scale ice thickness
of 50 cm and a subgrid-scale TIT of 5 cm (C05-50/5) or
1 cm (C05-50/1). See Table for an overview of the
simulations. The assumption of 10 cm TIT originates from the fact that the
mean TIT below 20 cm, derived from MODIS data, is of the order of
≈ 10 cm. derived a mean thin-ice thickness of 11.6 cm
for November to April (1979–2008), while from the MODIS TIT histogram in
Fig. a mean thin-ice thickness of 13.5 cm for November to
March in the period 2002/2003–2014/2015 and of 14 cm for the winter 2007/2008
results, respectively. In a previous study by 10 cm was
thus assumed to be a realistic value for the thin-ice thickness within Laptev
Sea polynyas. For better comparisons we assumed 10 cm as well in our
reference simulation (and in two sensitivity runs).
Scheme of the modified two-layer thermodynamic sea-ice module of
, extended with a tile approach for fractional sea ice.
Sea ice is distinguished as bare ice or as snow-covered ice (with
hs= 0.1 m snow depth if sea-ice thickness
hi> 0.2 m). The subgrid-scale open ocean fraction is either
ice free (C05-10/0) or assumed to be covered with 1 cm (C05-10/1, C05-50/1),
5 cm (C05-50/5), or 10 cm thin ice (C05-10/10). In the reference simulation
(C05-ref), grid boxes with 0 % sea-ice concentration are covered with 1 cm
grid-scale thin ice. If the index k denotes either sea ice (i) or
ocean (o), then QA,k is the total atmospheric heat flux,
Kk* is the net shortwave, and Lk* the net longwave radiation.
Hk and Ek are the sensible and latent heat fluxes. Tk is the
surface temperature, hi the ice thickness, Toi the ice–ocean
interface temperature, and Tsi the snow–ice interface temperature.
QI denotes the conductive heat flux through the ice and
QW the turbulent heat flux from the oceanic mixed layer into the
ice.
The first three sensitivity simulations investigate the effect of the TA, if
even thinner subgrid-scale ice is assumed. The C05-50/5 and C05-50/1 runs
are motivated by the fact that the sea-ice cover in the marginal ice zone
consists of thicker ice floes (> 20 cm) (detected by microwave
satellite sensors) and thin ice (here the assumed 5 or
1 cm), which is not detected by microwave sensors. Thus we denote
C05-50/1 as the simulation with “realistic” assumptions based on MODIS TIT.
Although this is a crude simplification to the real sea-ice thickness, it is
suited for our purpose to investigate the impact on the magnitude of ice
production and on the modification of the ABL. We intended to consider sea
ice in a computational cheap approach that still incorporates realistic
thermodynamical processes. For a more sophisticated approach, a full
dynamic–thermodynamic sea-ice model needs to be coupled to CCLM.
The two-layer thermodynamic sea-ice moduleBasic module
In this section the sea-ice module (Fig. ) is briefly
described. The module considers a snow and sea-ice layer and was described
and originally implemented in the COSMO model by . It is
based on the module of . For this study it is
reimplemented within the version 5.0_clm1 of CCLM extended with the
Køltzow sea-ice albedo scheme (see Appendix ). More important
for this study is the implementation of a tile approach for the surface
energy balance over fractional sea ice (see Appendix ). The module
and hence sea-ice growth calculation is only applied to grid boxes with an
initial sea-ice cover. Formation of grease ice in open water is not
parameterized in CCLM, which is a difficult task even for stand-alone sea-ice
ocean models. Nevertheless, a more sophisticated parameterization has been
recently developed by . For this reason we calculated
sea-ice production in a post-processing step (see Sect. ).
The module assumes a constant ocean/ice interface temperature of
Toi=-1.7 ∘C; i.e. Toi is not dependent on salinity.
A temperature of -1.7 ∘C assumes approximately a salinity of
31.1 psu. The module ignores turbulent heat fluxes from the ocean
at the lower boundary. Heat conductivity parameters are
2.3 W m-1 K-1 for sea ice and
0.76 W m-1 K-1 for snow. The module assumes a snow cover of
hs= 0.1 m if the ice thickness exceeds a threshold hi>hc
with hc= 0.2 m.
Sea-ice concentration and thickness for initial conditions
SIC is prescribed from Advanced Microwave
Scanning Radiometer – Earth Observing System (AMSR-E) data ,
provided by the University of Bremen. The original data sets are available on
a daily basis at a horizontal resolution of 6.25 km. In order to use them
for CCLM, we interpolated the SIC fields onto the C15 and C05 grid,
respectively, by a bilinear approach for every simulation day. All grid boxes
with SIC ≤ 70 % are treated as polynyas
. Realistic polynya areas are
retrieved by using this threshold, as shown by in
comparison to a polynya signature simulation method .
SIT is taken from the PIOMAS data set . The PIOMAS data are
available on a daily basis with a mean grid spacing of about 25 km
. These daily fields were masked with the daily SIC fields
to obtain consistent sea-ice extents. Thereby sea ice outside the AMSR-E mask
was removed and grid boxes which were ice free in the daily PIOMAS fields but
covered with ice in the mask were assigned with an interpolated SIT from a
nearest neighbour method.
state that PIOMAS seems to overestimate thin-ice
thickness and underestimates thicker ice. Nevertheless, the overestimation
should not be problematic in our application, since we have to set TIT for
daily fields according to AMSR-E data. Underestimations of thicker ice is of
minor concern to our study due to the focus on areas with thin ice. Using
this setup, the sea-ice thickness fields are much more realistic than in
previous studies, where a constant thickness of 1 m was assumed
outside polynyas .
Polynya area
In Fig. daily polynya areas for the winter period 2007/2008
are shown. According to the AMSR-E data set, which has been used to prescribe
the SIC in CCLM and in the COSMO simulations of , large
polynya events (> 104 km2) occurred at the end of November, in
January, and in March/April. The polynya areas of are
approximately of the same order as those used for CCLM, whereas the retrieved
polynya areas from MODIS2km are considerably larger. This discrepancy is
caused by different threshold definitions for polynyas and by different
horizontal resolutions. For the MODIS2km data, polynyas were defined as areas
with thin ice, ≤ 20 cm, as in . Given that
and the higher horizontal resolution it is likely that also leads within the
polynya masks, not resolved by the microwave satellite data, are contributing
to the total thin-ice area and hence larger areas result. If areas of
open water outside polynyas are considered as well, then the potential area
for ice production increases in CCLM up to the area derived from MODIS2km
data, except in the period of late November to the middle of January, which
remains lower. Another difference between the polynya area derived from CCLM
and in particular the area from is that the latter nearly
never drops to zero during this winter.
Total daily polynya area interpolated from AMSR-E (using a 70 %
threshold) onto the CCLM 5 km grid in the Laptev Sea for the winter period
2007/2008 aggregated for the four polynya masks. In addition, the polynya
area plus open-water area (OWA = 1 - SIC) for the polynya masks is
shown. Based on remote sensing the polynya areas estimated from
and MODIS2km data are shown. The total area of the
polynya masks is 26.19 × 104 km2.
Estimation of sea-ice production
In accordance to previous model or satellite-based studies, IP was calculated in a post-processing step using the energy
balance . This approach assumes that
if the water within a polynya is at the freezing point, all energy loss to
the atmosphere through the ocean surface is compensated by freezing. Hence
sea-ice growth only occurs if the total atmospheric energy flux over ice
(index k=i) or ocean (index k=o) QA,k=Kk*+Lk*+Hk+Ek is
negative, i.e. the ocean loses heat:
∂hi∂t=-QA,kρi⋅Lf,
with hi the sea-ice thickness, ρi= 910 kg m-3 the density
of sea ice, and Lf= 0.334 × 106 J kg-1 the latent heat of
fusion. We restricted this estimation to the four polynya areas in the Laptev
Sea (see Fig. ), which are identical to those of
. Hence, direct comparisons of our results with
estimations from remote sensing were possible.
We further calculated the IP using the MOD/MYD29 sea-ice surface temperature
product derived from MODIS Terra and Aqua data. In
combination with ERA-Interim data (2 m temperature, 2 m dew point
temperature, 10 m horizontal wind components, and pressure at mean sea
level), an energy balance model e.g. was applied to derive thin-ice thicknesses up
to 0.2 m at a horizontal resolution of about 2 km. We
refer to this estimation as MODIS2km. The turbulent fluxes of sensible and
latent heat were calculated by an iterative bulk approach
based on the Monin–Obukhov similarity theory. Thereby,
the turbulent exchange coefficient CH is a function of stability and of
the roughness length for momentum and for heat
. Shortwave radiation is not considered as the method is
restricted to night-time conditions during winter. This method is only
applicable to clear-sky conditions, as clouds and fog impede an estimation of
sea-ice surface temperature . Therefore the number of useful
swaths per day is variable. For instance, in the Laptev Sea there are about
10 to 14 swaths per day (2002/2003 to 2014/2015 – November–March).
Cloud-induced gaps in our daily sea-ice surface temperature and thin-ice
thickness composites were filled by a spatial feature reconstruction
procedure . This method interpolates
information of previous and subsequent days to fill gaps caused by
cloud cover. Based on these corrected composites and using the method
described in , ice production rates were calculated for
each pixel with an ice thickness ≤ 0.2 m, i.e. for polynya areas.
In a sensitivity analysis of this method (without the spatial feature
reconstruction), stated an uncertainty for the
ice-thickness retrieval of ±1.0, ±2.1, and
±5.3 cm for thin-ice classes of 0–5,
5–10, and 10–20 cm, respectively. Therefore, we
constrained our analysis to ice thicknesses ≤ 0.2 m, as this
range is regarded as sufficient to get reliable results for ice production
.
Furthermore, we compared our results to the estimations of
. In their study they used a constant transfer coefficient
for heat CH= 3 × 10-3 to calculate H and E from AMSR-E data and
using MODIS thin-ice distributions and National Centers for Environmental
Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis
data (2.5∘× 2.5 ∘) as atmospheric
forcing for an energy balance model. However, we omitted the most western
polynya mask of their study and compared the IP only to the four remaining
masks shown in Figs. and .
We also compared our IP estimations to model-based estimations of
. conducted two COSMO simulations at 5 km
horizontal resolution (without a tile approach) for the same winter 2007/2008
in the Laptev Sea. One simulation assumed a grid-scale thin-ice thickness of
10 cm within polynyas (B10) and one simulation assumed open water (B00).
Both simulations further assumed a sea-ice thickness of 1 m
outside polynyas. Both simulations were forced by a 15 km COSMO
simulation, which was nested within the output of the global GME model.
Evaluation with in situ data
The model output of the reference and the five sensitivity simulations were
first evaluated with in situ data. During the Transdrift XIII-2 expedition
from 11 to 29 April 2008 four automatic weather stations (AWS,
Table ) were deployed on the fast ice of WNS (see Fig. ) . The AWS
measured wind speed and direction at 3 m height with an accuracy of
2 % in speed and 3∘ in direction, air temperature and
relative humidity at 2 m height with and accuracy of 0.5 K and
4 %, and pressure with an accuracy of 1 hPa.
Furthermore, net radiation was measured by a net radiometer with an accuracy
of 5 W m-2.
Here, we compared hourly C05 data with the AWS data. In order to judge
whether the simulations deviate significantly from the AWS data two-sided
t tests were performed (α= 95 %). The statistical comparisons
were only performed for data pairs without missing values and only for days
when the SIC was > 95 %. This limitation is necessary because the
time series of CCLM represent spatial averages of a grid box, whereas the AWS
time series are point data on a solid ice cover. If the SIC of CCLM is
< 100 % then the grid average automatically differs from the
station time series, which always represent conditions at 100 % SIC.
Overview of the four automatic weather stations (AWS) with hourly
measurements which were deployed during the Transdrift XIII-2 expedition from
11 to 29 April 2008 . See the location of the AWS
in Fig. .
Statistical comparison of 2 m temperature, 10 m wind speed (3 m
in case of the AWS), and net radiation (K*+L*) of the four AWS
and the C05 simulations: C05-ref (reference), C05-10/0 (open water), and
C05-50/1 (realistic). Hourly means are denoted by x‾ and standard
deviations are denoted by σ. The Pearson correlation coefficient (r)
was calculated with the AWS and C05 time series. The critical correlation
coefficient (α= 5 %), which depend on the sample size of
the AWS time series, is between 0.1 and 0.2. In addition, the resulting
p values (p) of two-sided t tests (α= 5 %) are shown.
Significant differences are marked with *. Data pairs with missing
values in the AWS data or where the sea-ice concentration is < 95 % were
removed prior to the analyses.
Data2 m temperature (∘C) 10 m wind speed (m s-1) Net radiation (W m-2) x‾σrpx‾σrpx‾σrpAWS1-20.443.24––3.381.55––-17.8731.70––C05-ref-20.491.950.800.833.411.570.750.83-30.2126.010.79< 0.01*C05-10/0-20.521.920.790.733.481.580.750.48-30.3626.220.81< 0.01*C05-50/1-20.531.920.790.683.461.590.750.56-29.9026.350.80< 0.01*AWS2-19.503.50––2.631.33––-10.3737.23––C05-ref-20.402.230.80< 0.01*3.331.310.69< 0.01*-28.6124.320.73< 0.01*C05-10/0-20.472.130.78< 0.01*3.361.360.68< 0.01*-28.7025.330.75< 0.01*C05-50/1-20.492.130.78< 0.01*3.361.350.69< 0.01*-28.5425.130.75< 0.01*AWS3-18.915.57––2.751.58––-11.7630.28––C05-ref-19.383.070.850.203.171.480.70< 0.01*-24.2027.340.67< 0.01*C05-10/0-19.443.100.870.153.171.460.69< 0.01*-23.3928.950.69< 0.01*C05-50/1-19.483.080.860.123.171.460.70< 0.01*-23.4628.930.70< 0.01*AWS4-13.362.29––4.171.99––-13.8026.17––C05-ref-16.173.290.67< 0.01*4.672.360.910.12-35.5527.630.70< 0.01*C05-10/0-16.223.250.66< 0.01*4.752.400.910.07-34.2628.510.74< 0.01*C05-50/1-16.243.280.65< 0.01*4.652.290.920.13-34.2528.510.73< 0.01*
Table shows the results for the reference simulation (C05-ref) and
two sensitivity simulations: C05-10/0 (subgrid-scale open water) and C05-50/1
(realistic assumptions). In the remainder of the paper, we concentrate
on the comparison of these three simulations. Since the comparison was made
only over a solid sea-ice cover, the other simulations showed very similar
results (not shown).
In general, the inter-model differences are minor for all variables. The
comparison for the 2 m temperature shows that the C05 simulations are
generally able to reproduce the observed temperatures during the measurement
period. The temporal correlation is about r= 0.8, except for the comparison
with AWS4. The bias is about -1 ∘C for AWS2–3, less for AWS1,
and about -3 ∘C for AWS4. Except for the latter, C05
underestimates the variability of the 2 m temperature. This might be caused
by the assumptions made on snow properties in C05. Although the t tests are
significant for differences of about 1 ∘C, this difference
is sufficient for our analysis keeping in mind that grid-box averages were
compared with point data.
In case of wind speed there is a good agreement (r≥ 0.7), although we
compared 10 m wind speed of CCLM with measurements at 3 m
height. The mean and standard variance are both in accordance with the AWS
data, although significant for differences ≥ 0.4 m s-1.
Inversely, this agreement implies that CCLM underestimates wind speeds at
10 m, although we do not have reference data at 10 m for
a evaluation. Significant differences were found for the comparison of the
net radiation. Although the temporal correlation is high (r≥ 0.7), the
mean of C05 is about 13–22 W m-2 lower than observed, meaning a
slightly too-high heat flux through the sea-ice cover. This difference might
be caused by the assumption on the sea-ice properties (e.g. a constant
temperature at the ice–ocean interface) or by the slight cold bias of the ABL
above the sea ice.
Albeit some deviations CCLM is able to reproduce the basic conditions of the
near-surface variables during this period with our chosen configurations.
However, the reasons for these deviations need further investigation with
longer time series.
Surface temperature and 10 m wind field on 4 January 2008 at
15:00 UTC in (a) C05-ref (reference), (b) C05-50/1
(realistic), and (c) C05-10/0 (open water). In
(d) anomalies (C05-10/0 minus C05-ref) over the AL polynya are shown
for wind vectors (note different scale) and wind magnitude (colour shaded) at
10 m height and for pressure at mean sea level (green contour lines). The
white line in (a) to (c) (magenta in d) marks the
cross section A → B used for
Fig. .
Effects of the tile approach on the atmospheric boundary layerCase study on 4 January 2008
The effects of the TA are exemplified for a case study on 4 January 2008.
On this day a low was located over the Taimyr peninsula in the western Laptev
Sea. The large pressure gradient generated strong, prevailing off-shore
winds, which caused a large opening of polynyas at the fast-ice edge in the
Laptev Sea (Fig. ). The 10 m wind speed reached
10 to 15 m s-1 and was blowing offshore over the AL. The associated sea-ice concentrations for that day are shown in
Fig. . Within polynyas, the SIC is between 0 and 70 %.
Surface temperature
The surface temperatures (Tsfc) of the C05 simulations at 15:00 UTC
(Fig. ) show a clear signal of the polynyas. Within the AL
polynya the surface temperatures are -22 to
-24 ∘C in C05-ref (Fig. a), which is +6 to
+16 ∘C warmer than the surrounding fast and pack ice.
Furthermore, Tsfc is about +2 ∘C warmer at the downwind
side than at the windward side. This is, however, not realistic compared to
nature. One would expect higher temperatures at the windward side due to the
spatial gradient of the thin ice with the thinnest or even open water at the
windward side. Since in C05-ref a homogeneous thin-ice thickness of 10 cm is
assumed within the polynya, this effect is not represented in the simulation.
Much higher surface temperatures were simulated by all sensitivity
simulations. As an upper limit, C05-10/0 simulates > 10 ∘C
higher surface temperatures. This increase of surface temperature is in
accordance with results of , who found an increase of
14 ∘C for sea-ice concentrations of about 60 % in
winter. In the more realistic configuration of C05-50/1 temperatures are
about ≤ 10 ∘C warmer. The warmest areas within the
polynya tend to be at the windward side now, which is owed to the TA and the
thereby considered spatial thin-ice gradient. Another effect, which becomes
visible, is that the marginal area of the polynyas with warmer Tsfc
increases with the use of TA. This effect is most obvious for the NET
polynyas. This increases the area where heat is transferred from the ocean
into the atmosphere and causes smooth transitions from the fast or pack ice
to the polynyas. An increased heat loss has to be balanced by an increase in
frazil ice production.
10 m wind speed
The wind speed is the main driver for mixing in the ABL above polynyas and
mainly controls the sensitive heat flux until the warming of the ABL reduces
the vertical temperature gradient and thus the sensitive heat loss and
subsequently the ice production. Figure shows the
10 m wind speed and direction on 4 January 2008 at 15:00 UTC. In
the reference run (C05-ref) the wind speed above the AL polynya is about
14 to 18 m s-1. The wind speed slightly increases
(Fig. d) in the sensitivity simulations (+2 to
+5 m s-1). This increase is caused by increased pressure
gradients in the vicinity of the AL polynya which develop as the convection
above the polynya lowers the local surface pressure by up to -0.5 hPa.
Furthermore, the pressure anomaly is orientated alongside the polynya,
resulting in slightly more anticyclonic wind directions. This increase of
near-surface wind speed is in agreement with results from idealized studies
conducted by (see Fig. 5c therein).
concluded that the increase in wind speed results in an increased net ice
production, despite an increased boundary layer warming. The increase in
near-surface wind speed causes a larger momentum flux (not shown, but see
Sect. for the TKE) and
higher energy loss from the ocean. Furthermore, although not represented in
the present CCLM model, higher wind speeds increase the sea-ice drift within
polynyas, so that newly formed ice is likely to drift faster and a
strong heat loss is maintained. Both processes are expected to increase the
IP. However, the latter issue has to be investigated by coupled
atmosphere–sea-ice–ocean model simulations.
Vertical cross sections
Figure shows vertical cross sections of the potential
temperature Θ, the horizontal and vertical wind speed, the cloud area
fraction, and the TKE over the AL polynya. In
C05-ref (Fig. a) Θ is about -29 ∘C
at 10 m height over the polynya, about -30 ∘C
about the pack ice, and colder than -31 ∘C over the fast
ice. The boundary layer is stably stratified over the pack and the fast ice
but over the polynya a well-mixed convective boundary layer (CBL) has
developed, capped by an inversion at approximately 300 to 500 m height.
Comparing the cross sections for the three shown model configurations, it
becomes obvious that the assumptions on the thin ice within a polynya have
considerable effects on the ABL.
As mentioned earlier, the onset of the CBL at the windward polynya edge is
displaced to the polynya interior in the reference simulation (C05-ref)
compared to the sensitivity simulations. This is due to the too thick ice in
this area, preventing a large enough heat transfer and hence vertical mixing.
This is also reflected in the low values of TKE compared to twice as high
values for the sensitivity runs. In the open-water configuration (C05-10/0)
the CBL is thus about 3 ∘C warmer than in the reference run.
The warm air spreads as a plume downstream the polynya and can be tracked
several hundred kilometres over the pack ice (not shown).
Vertical cross sections of the potential temperature Θ,
horizontal wind speed (black contour lines), turbulent kinetic energy (TKE in
m2 s-2, magenta contour lines), and cloud fraction (white contour
lines and orange labels) on 4 January 2008 at 15:00 UTC for
(a) C05-ref (reference), (b) C05-50/1 (realistic), and
(c) C05-10/0 (open water). The horizontal distance is about 240 km
and the location of the cross section A (pack ice) → B (fast
ice) is shown in Fig. .
Total atmospheric energy flux on 4 January 2008 at 15:00 UTC in
(a) C05-ref (reference), (b) C05-50/1 (realistic), and
(c) C05-10/0 (open water). Negative fluxes are directed upwards. The
white line marks the cross section A → B used for
Fig. .
One might suspect that the warmer CBL of the sensitivity simulations might
lead to a reduced vertical temperature gradient over the polynya and thus to
a negative feedback for the surface sensible heat flux and thus ice
production. This is not the case, since the surface temperature in areas with
fractional ice cover is also warmer by about 6 to
16 ∘C, so that the vertical gradient remains or even
increases. Secondly, the wind speed in the sensitivity runs is increased over
the polynya due to increased local pressure gradients and thus enhances the
turbulent fluxes. The increased sensible heat flux causes also TKE production by buoyancy.
Almost all sensitivity simulations show considerably less cloud formation
above and downstream the polynya compared to the reference simulation.
Although the amount and location of clouds vary, the clouds almost vanish.
The reason for this is that the maximum value of the specific humidity is
about 0.4 × 10-3 kg kg-1 over the AL polynya in all
simulations, and if the CBL warms the condensation of water vapour is
inhibited. As a result, nearly no clouds form above the polynya.
Total atmospheric energy flux
The above-mentioned findings result in an increased heat loss from the ocean,
which is confirmed and shown in Fig. . In the reference
simulation the total atmospheric heat flux (QA) is, almost homogeneously,
about -500 W m-2 over the AL polynya (note that the negative
sign denotes upward fluxes). By assuming subgrid-scale open water the heat
loss considerably increases, exceeding -1000 W m-2 at the
windward edge and in the centre of the polynya. Higher and more structured
values of QA resulted also from C05-50/1. The smooth transition at the
polynya margins is also visible from this figure. Since we based the
estimation of ice production on this quantity it is clear that there will be
considerable differences as well.
Energy balance components for the winter period 2007/2008
In this section we analyse how the assumptions on subgrid-scale thin ice
within the tile approach affects the energy balance at the surface over the
whole winter season 2007/2008. We compared daily means of the components of
total atmospheric heat fluxes, which were spatially averaged over polynyas
(Fig. ). Thereby, at least nine grid boxes within the polynya
masks have to had a SIC ≤ 70 % in order to be considered in the analysis.
In principle, the processes presented in Sect. come into
effect whenever a polynya is present. Thus, if the tile approach is used,
QA is always more negative due to the consideration of subgrid-scale
energy fluxes (Fig. ) and more energy or heat is lost from
the ocean. In the reference simulation (C05-ref) about
-253 W m-2 is lost on average within polynyas. If the
subgrid-scale thin ice is reduced or replaced by open water then QA is
much higher, reaching about -529 W m-2 in the latter, which is
about +110 %. In the simulation with a realistic configuration (C05-50/1)
the increase is about +20 %, reaching about -303 W m-2 on average.
Temporal means of the energy balance QA and its components
(H is sensible heat flux, E is latent heat flux, L* is net longwave, and
K* net shortwave radiation) averaged over polynya grid boxes for the
winter period 2007/2008. For the averaging at least nine grid boxes in the model
domain had to be polynyas to include them in the
calculation.
Total sea-ice production (IP) (km3) in the winter
period 2007/2008, aggregated over polynyas within the four polynya masks
(Fig. ). The daily mean (x‾) and standard
deviation (σ) are given in km3 day-1. The Pearson
correlation coefficient (r) was calculated with the IP time series and the
estimates of . The 95 % confidence interval (CI) of r
was calculated based on the Fisher z transformation. The critical value is
rc,α=0.05,n=182= 0.15 and r is significant if
r>rc. Two-sided t tests (α= 5 %) were
performed and the resulting p values (p) are given. Significant
differences are marked with *. The results from
assumed an ice thickness of 10 cm (B10) or open water (B00) within polynyas,
both without a tile approach.
a Only for November–March; b comparisons
with were made only for November–March.
Daily sea-ice production within the Laptev Sea polynyas in the
winter period 2007/2008, aggregated within the four polynya masks (only
considering polynyas > 277 km2 in the C05 and ,
simulations – B10, B00). The total sea-ice production is given in
parentheses in the legend (see also Table ).
The largest contribution to QA constitutes the sensible heat flux H
(Fig. ). About 66 % of the heat is lost via H in the
reference simulation, which slightly decreases to 57 to 65.6 % if
the tile approach is used. The strongest impact on the sensible heat flux
shows C05-10/0. Here, H doubles from 167 W m-2 in C05-ref to
-325 W m-2 due to the absence of the isolating sea-ice cover.
The increase is less in the other sensitivity simulations, e.g. +7.1 % in the case of C05-50/1.
The other components contribute much less to the heat loss. However, the
Bowen ratio , which is the ratio H / E, reduces from about
4.0 (C05-ref) to 2.5 (C05-50/1) and 2.3 (C05-10/0). The latent heat flux (E)
nearly doubles for C05-50/1 compared to C05-ref, which is caused on one
hand by the increase of the vertical gradient of specific humidity and on the
other hand by the increase of the near-surface wind speed and TKE, enhancing
the turbulence above the polynyas. Shortwave radiation K* only becomes
important in the time from March until April, when the melting season begins.
Therefore, K* is small compared to the other terms.
Total sea-ice production (m) within the Laptev Sea polynyas in the
winter period 2007/2008 simulated by (a) C05-ref (reference),
(b) C05-50/1 (realistic), and (c) C05-10/0
(open water).
Effects on sea-ice production in the winter period 2007/2008
The daily sea-ice production rates were calculated for the individual
polynyas as described in Sect. . Here we compared the
ice production of the C05 simulations to the remote sensing estimations of
, to estimates based on MODIS2km
(Sect. ), and to model results of .
The time series of daily IP (km3 day-1) are shown in
Fig. and the total ice production for the whole winter
are shown in Table . The total IP in the winter 2007/2008 is about
29.1 km3 in the reference simulation (C05-ref), which is not
significantly different from the 33.0 km3 estimated by
. The temporal correlation is r= 0.65, which is
sufficiently high, but some differences are visible
(Fig. ). This result agrees well with the remote sensing
estimates, although used a constant CH for calculating
the heat fluxes and much coarser atmospheric data.
The strongest, significant increase (p< 0.01) in IP was estimated from
C05-10/0 with 65.2 km3 (+125 %), where subgrid-scale
open water was assumed. For realistic assumptions (C05-50/1) the IP increases
to 38.3 km3 (+39 %), which is not significant at the
95 % level. The increase of IP is caused by the higher heat fluxes from the
ocean into the overlying atmosphere as presented above. Compared to the
results based on MODIS2km, where we estimated about 49.1 km3 due
to the large polynya area, most of the simulations produced much less ice.
Exceptions are C05-10/0 and C05-10/1, which reach or even exceed this
estimate. These results show that there are large differences not only
between the models but also between remote sensing approaches. For instance,
the time of polynya openings and IP also differ (r= 0.45). Although such
high IP could be reproduced in the Laptev Sea, the question remains of which
remote sensing data set should be used for calibrating the models.
The IP based on an older reference COSMO simulation by with
10 cm ice within polynyas (B10) is slightly less compared to our reference
run (C05-ref), but significantly lower compared to .
Although the open-water sensitivity run of (B00) produces
higher IP, it is considerably less compared to our C05-10/0 run. B10 and B00
show a slightly higher correlation with the IP time series of
. The differences of B10/B00 with respect to our C05
simulations can be explained by differences in the model version,
configuration, and nesting chain (GME vs. ERA-Interim, different model
domains). However, we cannot quantify the individual contributions to the
deviations. All C05 sensitivity simulations show a higher daily standard
deviation compared to the data from , which increases with
decreasing subgrid-scale thin ice. This is logical, because if a polynya opens
then more heat is released compared to the reference simulation and thus the IP is higher.
Spatial maps of the total IP within polynyas in the winter 2007/2008 are shown
in Fig. . In all simulations the highest IP occurs in the
NET polynyas, with rates > 2 m/winter in C05-ref and
> 5 m/winter in C05-10/0. Further, with considering the
subgrid-scale thin ice or open water the spatial gradient of ice production
is much better represented, with higher rates at the windward edge of the
polynyas. Overall, we think that the assumptions made in C05-50/1 are
realistic. Although we are aware that open-water areas may occur at the
windward site of polynyas, the area of open water is much smaller compared to
the entire polynya area (see Fig. ). Thus, the heat flux
and IP would be overestimated if open water were assumed in every grid box with
fractional sea ice.
Discussion
The simulation results of our study showed that there is a high sensitivity
of the ice production to the assumptions on subgrid-scale thin-ice
distribution, with considerable effects on the atmospheric boundary layer.
The ABL receives more heat the thinner the ice is and warmed by up to
+3 ∘C in our subgrid-scale open-water configuration.
However, the warmer ABL did not prevent further heat release from the ocean
due to a weakened vertical temperature gradient. The simulations showed that
the vertical temperature gradients remain or even exceed the gradients of the
reference simulations. Two effects are responsible for this: (i) as the ice
becomes thinner the surface temperature increases stronger than the heating
of the ABL, which increases the vertical gradient; and (ii) the near-surface
wind speed is enhanced due to increased local pressure gradients, which
increases the wind-shear and thus the sensible heat fluxes. Further, the warm
plumes over the polynyas are efficiently advected over the pack ice. Thus
heat is removed from above the polynyas and a strong temperature gradient is
maintained, which enhances the ice production.
Constraining the assumptions on subgrid-scale thin ice was found to be
difficult. The comparison of model-based IP with remote sensing estimates
revealed large discrepancies. Further, large differences were found between
the two remote sensing approaches. The usage of higher resolved MODIS data
and ERA-Interim, compared to the approach of in which NCEP
and AMSR-E data were used, nearly produced +50 % more ice. The
configuration of C05-50/1, in which we assumed 50 cm thick grid-scale and 1 cm
subgrid-scale thin ice, seems to be a realistic assumption in the marginal
ice zone of the polynyas.
The resulting IP is close to the estimates of , which we
defined as a baseline for our sensitivity experiment. However, if for
instance the results of MODIS2km were defined as a baseline, then even
thinner ice might be considered. We are aware that our sea-ice module is
simplified compared to more sophisticated sea-ice models, where the thin-ice
thickness might be a prognostic variable and not a constant. However, despite
our simple assumption, the results are promising and satisfactory in order to
represent sea ice in a regional climate model in a computationally cheap
approach. We further argue, based on our results, that assuming subgrid-scale
open water within fractional sea ice, such as in Polar-WRF
, leads to too-high heat fluxes from the ocean into the atmosphere.
Probability density functions of the turbulent transfer coefficients
for heat (CH) within the Laptev Sea polynyas in the winter period
(November–April) 2007/2008, aggregated within the four polynya masks. The
grey bars show the histogram of CH from C05-ref. Only values below
6 × 10-3 have been used for the construction of this figure.
The mean values of the C05 simulations are
≈ 2.5 × 10-3 and the standard deviations are
≈ 0.28 × 10-3, except for C05-50/5 and C05-50/1 where
the mean values are ≈ 2.27 × 10-3 and
≈ 2.31 × 10-3, and the standard deviations are
≈ 0.18 × 10-3, respectively. The constant value of
CH= 3.0 × 10-3 of is marked
with an arrow. The mean CH value derived from MODIS2km
(November–March) is CH= 2.3 ± 0.3 × 10-3.
However, even if more sophisticated sea-ice models were used to estimate the
IP, the issues remain of how to constrain parameters and to which data set to
compare. It is not our intention to disentangle all factors
controlling the estimation of sea-ice production based on different
approaches, data sets, or models, but several issues are important in a
general sense:
Polynya area is affected by the definition of polynyas (e.g. SIC ≤ 70 %
or hi< 0.2 m) and the horizontal resolution of
the model and the satellite products.
Heat loss is affected by the vertical temperature gradient, wind speed,
parameterization of the energy balance components (turbulent fluxes), sea-ice
thickness and properties, and the parameterization of the heat flux
through the ice. Particularly important is the horizontal resolution of the
atmospheric data set and the assumptions on the turbulent exchange
coefficient for heat (CH). assumed a constant value of
CH= 3 × 10-3. However, the mean values from C05 over polynyas
(winter 2007/2008) are about (2.5 ± 0.28) × 10-3 (Fig. ),
except for C05-50/5 and C05-50/1, which simulated slightly lower values of
(2.27 ± 0.18) × 10-3 and (2.31 ± 0.18) × 10-3.
Since the warm surface temperatures of polynyas and the resulting vertical
temperature gradients are not well represented in ERA-Interim or NCEP, the
usage of a high value of CH seems to partly compensate for this issue. The
CH values based on MODIS data and ERA-Interim are lower than simulated by
CCLM with a mean of CH= (2.3 ± 0.3) × 10-3. A similar probability
distribution function was derived by , who combined MODIS
and NCEP. Because of the horizontal resolution of MODIS, polynyas are
represented as anomalies in the surface temperature field, causing larger
vertical temperature gradients and hence CH values, which are comparable to CCLM.
Surface temperatures in remote sensing approaches also depend
on the number of swaths per day, e.g. clear-sky conditions, and their
distribution over the day. If not equally distributed, the surface
temperature and the ice production may be biased.
These influence factors together control differences between model and remote
sensing sea-ice production estimates within polynyas. The polynya area is an
obvious factor with the simple relationship: the larger the polynya area, the
larger the sea-ice production. Note that sea-ice grows also outside
polynyas, which was simulated by our model and contributed to the total IP.
However, compared to the contribution of the polynya areas to the total, the
IP of such areas is of minor importance. The explanations for differences in
the heat or energy loss within polynyas is manifold. In our opinion, the most
relevant factors, besides polynya area, are the thin-ice thickness and the
parameterizations of the turbulent heat fluxes, in particular the differences
in CH.
The complexity of these factors make a comparison of model and remote sensing
studies difficult. It further indicates that some assumptions in the remote
sensing approaches, such as a constant value for CH, might be
oversimplified. Furthermore, a problematic issue is the usage of coarse
atmospheric data sets, such as NCEP or ERA-Interim, for remote sensing
approaches, if not combined with high-resolution satellite products. The
horizontal resolution of such atmospheric reanalysis data sets is not
sufficient to represent polynyas adequately. Thus subsequent errors, such as
wrong simulations of the atmospheric boundary layer over polynyas, are the
consequence. These errors are then transferred to the remote sensing approach
and might result in wrong sea-ice production estimates. From a modelling
point of view the question arises of what reference for IP estimates should be
used. This question is not easily answered and is still an open issue. A
strategy might be a simultaneous application of both modelling and remote
sensing approaches in order to compensate for weaknesses. This issue
directly impedes the decision of an optimal model configuration.
According to our study, the approach of constitutes the
closest reference because of the same satellite data that were used to derive
polynya area at a comparable horizontal resolution. Although the definitions
of polynyas are different, the assumption of 0.1 m thin ice in
areas of SIC ≤ 70 % is similar to the definition of
≤ 0.2 m as in . Larger differences evolve from
the assumptions made on CH (Fig. ) and the horizontal
resolution of the atmospheric data. Given these deviations, the IP based on
C05-ref and C05-50/1 is still close to the results of .
Although the use of MODIS, i.e. higher resolved satellite products, results
in higher IP estimates, the reason for this is the higher horizontal
resolution that causes larger polynya areas and not the representation of
subgrid-scale energy fluxes within polynyas in ERA-Interim, which is still
too coarse. For thicker ice the CH values converge to
≤ 1.5 × 10-3, a value also reported by .
Given these issues, the decision of which TIT should be used with the TA is
another degree of freedom and cannot sufficiently be answered from our study.
A justified assumption is to rely on MODIS TIT (Fig. ). The
mean derived TIT for the winter periods (November–March) 2002/2003–2014/2015 is
13.5 ± 0.5 cm, which is slightly thicker than our assumed TIT in
CCLM. Unfortunately, the MODIS TIT distribution for the polynya areas shows
no maximum at a specific ice thickness, which gives no preference for the
choice of the subgrid TIT for the tile approach.
Conclusions
In this study we quantified the ice production in the Laptev Sea polynyas for
the winter 2007/2008 based on simulations with a regional atmospheric model (CCLM)
and remote sensing data. A new tile approach for fractional sea ice,
considering subgrid-scale thin ice, was implemented into CCLM. Besides a
reference run, five sensitivity simulations with different assumptions on
grid-scale and subgrid-scale ice within polynyas were performed. We further
investigated the impact on the atmospheric boundary layer above polynyas.
The results show that the ice production is highly sensitive to the
assumptions made on the ice thickness within polynyas. Compared to the
estimated total winter ice production of 29.1 km3 of the reference
simulation, the ice production more than doubled when subgrid-scale open water
was assumed and increased by about (+39 %) for the most realistic
assumptions based on remote sensing of ice thickness. The increase of the ice
production is caused by a larger heat loss from the ocean, whose magnitude is
proportional to the thin-ice thickness. Although the atmospheric boundary
layer is heated by up to +3 ∘C in the open-water
configuration, strong vertical temperature gradients and associated high
sensible heat fluxes at the surface were maintained. On one hand, the
tile approach improves the physical representation of polynyas in CCLM
because fractional sea ice is considered; on the other hand, a new degree of
freedom is introduced to constrain the thin-ice thickness. The derivation of
an optimal configuration for CCLM or other regional climate models remains
difficult because of sparse observed ice thickness distributions within
polynyas. We used remote sensing data as a baseline to constrain our
configuration, but several issues were found which hamper such comparisons.
At this point coupled atmosphere–ice–ocean models are needed to fully
simulate feedback processes.
In summary, realistic ice production estimates could be retrieved from our
simulations. Neglecting subgrid-scale energy fluxes might considerably
underestimate the ice production in coastal polynyas, such as in the Laptev
Sea, with possible consequence on the Arctic sea-ice budget.
Data availability
The data used in this study are available on request from the authors
(gutjahr@uni-trier.de or heinemann@uni-trier.de).
Sea-ice albedo scheme
We implemented a modified Køltzow scheme
(Fig. ) to replace the default treatment of sea-ice
albedo, which was previously set to αi= 0.75 for ice
thickness > 0.1 m and αi= 0.2 for ice thickness ≤ 0.1 m
. Furthermore, the Køltzow scheme includes a
parameterization of melt ponds (see , for details), yet
they are of no importance for our study. The scheme is based on measurements
retrieved during the Surface heat Budget of the Arctic Ocean (SHEBA) project
. It is forced by the surface temperature Tsfc, which
may be either the ice (Ti) or the snow surface temperature (Ts)
(Fig. ). If no snow cover is present the albedo only
depends on the ice thickness. If the ice thickness exceeds the threshold
value of hc= 0.2 m, a snow cover on sea ice is assumed in
accordance to the sea-ice module. Sea ice thicker than hc is treated as
thick ice and the albedo is estimated by
αi=0.84ifTsfc≤-2∘C0.84-0.1452+Tsfcif0∘C>Tsfc>-2∘C0.51ifTsfc>0∘C. sets the albedo for cold sea ice to a high value of 0.84,
which is supposed to include the effects of snow on sea ice in winter and
spring. In the original scheme set the threshold for thin
ice to hc= 0.25 m, but since the values above are only valid for
snow-covered sea ice, we set hc= 0.2 m to be consistent with the
sea-ice module.
Sea-ice albedo resulting from the modified Køltzow scheme
depending on the ice surface temperature and
thickness. Thereby the threshold thickness above which a snow cover of 10 cm
is assumed is hc= 0.2 m (bold black line). In addition the
melt pond fraction is shown as a function of the ice temperature (bold green
line) and the resulting modification (dashed green line) of the sea-ice
albedo (dashed black line). For bare sea-ice (thin black lines) a constant
albedo value is assumed, which is linearly decreasing from 0.57
at 20 cm ice thickness (bold grey line) to 0.07 (ocean
albedo; ), but constant over all surface temperatures,
as shown in Eq. (). The vertical blue lines mark the
transition range from cold to melting conditions.
For thin ice, we implemented a linear decrease towards the ocean albedo
(αo= 0.07):
αi=αo+hi/hc⋅αc-αo.
As a starting value we use αc= 0.57, the albedo of thick
bare sea ice from .
Figure shows a summary of both cases. If the ice thickness
is at least 0.2 m (bold black line) then the albedo is constant
(αi= 0.84) for cold, snow-covered sea ice. It decreases with
increasing surface temperature if -2 ∘C are exceeded. This
temperature denotes a threshold where melting begins and sea ice is changing
its albedo characteristics. In addition, if melt ponds occur (black solid
line), the albedo is somewhat lower during the melting season. The fraction
of melt ponds increases with Tsfc>-2 ∘C to a maximum of
22 % (bold green line), an upper limit set by ,
and the albedo of melt ponds converges to the albedo of sea water (dashed
green line). Furthermore, in Fig. the thin-ice albedo is
exemplified for four ice thicknesses which are not covered with snow and for
which a constant albedo is assumed (thin black lines).
If the tile approach is used, subgrid-scale open water reduces the
grid-average albedo accordingly, compared to a complete coverage with sea
ice. A comparable, though less pronounced, reduction of albedo occurs if
1 cm thin-ice coverage is assumed for subgrid-scale open water.
Implementation of the tile approach in CCLM
In order to simulate the subgrid-scale energy fluxes over fractional sea ice,
it is necessary to differentiate the energy balance and its components over
water and ice. Over sea ice (index k=i) or ocean (index k=o) the total
atmospheric heat flux (see Fig. ) is
QA,k=Kk*+Lk*+Hk+Ek,
with Kk* the net shortwave radiation, Lk* the net longwave radiation,
Hk the turbulent flux of sensible heat, and Ek the turbulent flux of
latent heat.
All routines of CCLM, except the sea-ice and the turbulence module, calculate
with grid-box averaged coefficients or fluxes (flux averaging approach;
), which is best suited if the sea-ice module only requires
the fluxes over ice . The procedure is described in Sect. .
As initial data the module requires the sea surface temperature, the
sea-ice fraction (A) and extent, SIT, the surface
temperature of sea ice (Ti), specific humidity at the ice surface, the
wind-speed on the lowest model level, and incoming longwave and shortwave
radiation (see , for more details).
The calculation of the components of the energy balance equations are shown
in the next subsections.
Shortwave radiation
The grid-box average of the albedo αm (index “m” for “mixed”) is
calculated as
αm=A⋅αi+(1-A)⋅αo,
with A the sea-ice fraction, αo= 0.07 the albedo of the ocean, and
αi=f(Ti, h) the albedo of sea ice as a function of sea-ice
temperature (Ti) and thickness (h) (see Sect. ). Based on
this mixed albedo the upward shortwave radiation is calculated as
K↑m=αm⋅K↓,
with K↓ the incoming shortwave radiation. The grid-box averaged net
shortwave radiation is calculated as
Km*=K↓-K↑m=1-αm⋅K↓.
This grid-box averaged net shortwave radiation is the input for the sea-ice
module where the upward shortwave radiation over ice K↑i is
calculated as
K↑i=αi⋅K↓.
The final net shortwave radiation over sea ice or ocean becomes
Kk*=1-αk⋅K↓=1-αk1-αm⋅Km*,
where the index k refers either to i (sea ice) or o (ocean).
Longwave radiation
The subgrid-scale ocean surface temperature (To) is assumed to be at the
freezing point (-1.7 ∘C) if open water is assumed, or to be
a prognostic variable if a thin-ice cover is assumed. The ice surface
temperature (Ti) is also a prognostic variable in the sea-ice module.
To account for subgrid-scale longwave radiation, we calculate the upward
longwave radiation over sea ice and ocean as
L↑k=ϵσTk4-(1-ϵ)L↓,
with σ the Stefan–Boltzmann constant, L↓ the incoming
longwave radiation, ϵ the surface emissivities of sea water and ice,
which are assumed to be equal (ϵ= 0.996), and Tk the surface
temperature of ice or ocean.
Then the net longwave radiation balance over sea ice or ocean becomes
Lk*=L↓-L↑k.
Turbulent fluxes of sensible and latent heat
We modified the parameterization of the turbulent fluxes of sensible (H)
and latent heat (E) within a grid box, in contrast to the standard version
of CCLM and the sea-ice module of . Over sea ice or
ocean the roughness length z0 and the turbulent coefficients of heat and
moisture CH were previously calculated from the predominant surface type
of a grid box: ice or sea water. We modified this procedure by a
tile approach; now the fluxes are calculated both for sea ice and ocean
within a grid box with different z0 and CH. Afterwards they are
averaged in a “flux-averaging approach” and an average CH is calculated
for other modules. The calculation of the momentum flux is not modified and
for the details of the calculation we refer the reader to .
In CCLM a stability- and roughness-length-dependent surface flux formulation
is used, which is based on flux calculations after . The
fluxes are calculated with a bulk approach:
H=-ρcpCH|vh|Θsfc-Θ,E=-ρLfCH|vh|qsfc-q,
with ρ the air density, cp the heat capacity of air, and Θ and
Θsfc the potential temperature at the lowest model layer and at the
surface (ice or ocean). q and qsfc are the specific humidity at the
lowest model layer and at the surface (ice or ocean), Lf the latent heat
of fusion (and sublimation in case of sea ice), |vh|=u2+v2 the
absolute wind speed, and CH the turbulent transfer coefficient for heat
and moisture.
To calculate the turbulent transfer coefficients it is first necessary to
calculate the roughness length of sea-water (z0,o) and sea ice (z0,i).
In case of sea ice we set z0,i= 0.001 m as in
. Over open water a modified Charnock formula is used
see. In case of H and E, we assume the additional
roughness length for heat zh to be equal to z0 over
subgrid-scale open ocean within the sea-ice cover.
The transfer coefficients are calculated over sea ice (CH,i) and
ocean (CH,o). The turbulent fluxes over sea ice (Hi, Ei)
and ocean (Ho, Eo) can be retrieved by inserting these coefficients
into Eqs. ()–(). Then all terms of Eq. () are
known to solve the energy balance over both surface types.
The fluxes of sensible and latent heat, the turbulent transfer coefficient
for heat, and the surface temperature are averaged according to the sea-ice
concentration A:
Hm=A⋅Hi+(1-A)⋅Ho,Em=A⋅Ei+(1-A)⋅Eo,CHm=A⋅CH,i+(1-A)⋅CH,o,Tsfc=A⋅Ti+(1-A)⋅To.
The grid-averaged temperature fields are used for the comparisons in Sect. .
Oliver Gutjahr implemented the tile approach and other
modifications to the CCLM source code, conducted the CCLM simulations,
designed the study and wrote the paper. Günther Heinemann assisted in
designing the experiments and the structure of the paper, as well as the
writing process. Andreas Preußer wrote the section on MODIS and
calculated the sea-ice production from MODIS data. Sascha Willmes calculated
the ice production rates based on AMSR-E and NCEP and assisted in the
discussion on the comparison of remote sensing and modelling results.
Clemens Drüe contributed to the translation of the equations into source
code.
Acknowledgements
This work was funded by the German Federal Ministry of Education and Research (BMBF) under
grant 03G0833D, and is part of the German–Russian Transdrift project. We
thank the CLM community and the German Meteorological Service for providing
the basic COSMO-CLM model. The AMSR-E data were provided by the University of
Bremen, the MODIS data were provided by the US National Snow and Ice Data
Center, ERA-Interim by the ECMWF, and the PIOMAS data set by the Polar
Science Center (University of Washington). Finally, we thank the DKRZ for
providing computational time and two anonymous reviewers for critical
comments.
Edited by: D. Notz
Reviewed by: two anonymous referees
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