Improved simulation of feedbacks between atmosphere and sea ice over the Arctic Ocean in a coupled regional climate model

Ocean Modelling 29 (2009) 103–114

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Improved simulation of feedbacks between atmosphere and sea ice over the Arctic Ocean in a coupled regional climate model W. Dorn *, K. Dethloff, A. Rinke Alfred Wegener Institute for Polar and Marine Research, Research Unit Potsdam, Telegrafenberg A43, 14473 Potsdam, Germany

a r t i c l e

i n f o

Article history: Received 29 October 2008 Received in revised form 24 February 2009 Accepted 20 March 2009 Available online 3 May 2009 Keywords: Regional climate modeling Arctic climate system Sea ice

a b s t r a c t Modeling sea ice in a realistic manner is still a great challenge, in particular with respect to the minimum ice extent at the end of the summer. Modified descriptions of ice growth, snow and ice albedo, and snow cover on ice have been incorporated into the coupled regional atmosphere–ocean–ice model HIRHAM– NAOSIM, and a series of sensitivity experiments has been performed in order to assess the need for more sophisticated parameterizations of these processes in coupled regional and global models. It is found that the simulation of Arctic summer sea ice responds very sensitively to the parameterization of snow and ice albedo but also to the treatment of ice growth. The parameterization of the snow cover fraction on ice plays an important role in the onset of summertime ice melt. This has crucial impact on summer ice decay when more sophisticated schemes for ice growth and ice albedo are used. It is shown that in case of using a harmonized combination of more sophisticated parameterizations the simulation of the summer minimum in ice extent can be considerably improved due to a more realistic representation of the interactions between atmosphere and sea ice. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction One of the major features of the Arctic climate system is the presence of an ice-covered Arctic Ocean. Owing to its isolating and reflecting properties, the sea-ice cover governs the exchange between atmosphere and ocean and exerts strong influence on atmospheric and oceanic circulations that way. In winter, the heat loss of the Arctic Ocean is substantially damped due to the isolating effect of ice, while in summer, a large amount of the incoming solar radiation is scattered back to the atmosphere over an ice-covered ocean due to the much higher albedo of ice compared to open water. Feedback effects, in which sea ice is involved, like the ice–albedo feedback (e.g., Curry et al., 1995), play an important role in the Arctic climate system and may be regarded as crucial factors in the polar amplification of climate change. A realistic representation of sea ice in coupled climate models is therefore an essential precondition for reliable simulations of the Arctic climate. Intercomparison studies of coupled models have shown that there are still large deviations in the simulation of Arctic sea ice among the models (see e.g., Flato et al., 2004). The representation of the processes at the interface of atmosphere and sea ice is often oversimplified due to poor knowledge of the underlying physics of feedback processes between the climate subsystems. While such feedbacks are completely absent in stand-alone models for the subsystems, their accurate simulation plays a key role in the performance of coupled regional and global models. * Corresponding author. Tel.: +49 331 288 2164; fax: +49 331 288 2178. E-mail address: [email protected] (W. Dorn). 1463-5003/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ocemod.2009.03.010

Coupled climate models usually consist of two independent component models for the atmosphere and the ocean, including sea ice. These models were primarily constructed for stand-alone simulations of the atmospheric and oceanic circulations, respectively. In stand-alone mode, the boundary conditions at the atmosphere–ocean/ice interface are prescribed, for instance by use of climatological or reanalysis data. In such a case, an atmosphere model simulates a near-surface temperature distribution which is largely determined by the prescribed sea-ice distribution, and an ocean–ice model simulates a sea-ice distribution which is largely determined by the prescribed atmospheric temperature distribution. When using the component models within a coupled model system, the prescribed boundary conditions of the one model are simply replaced by corresponding variables computed in the other model. It is quite evident that each inaccuracy and oversimplification in the description of the processes at the interface of the model components may result in large deviations in the simulation of the Arctic climate due to the feedback between the atmospheric temperature and the sea-ice distribution. Dorn et al. (2007) pointed out that the oversimplified thermodynamic ice growth scheme in their ocean–ice model, which merges the contributions of the sea ice and open water fractions to a single heat balance equation of the combined ocean mixed layer–sea ice system, may result in an overestimated magnitude of the ice–albedo feedback effect in the fully coupled model, appearing in terms of too early disappearance of the snow layer and too strong ice decay during the warm season. They suggested that an elaborated subdivision of the incoming atmospheric energy into snow and ice melt from above and mixed layer warming,

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which thus may contribute to lateral and basal ice melt, might be able to overcome this shortcoming. In the present paper, a more sophisticated ice growth scheme for coupled regional and global climate models is presented, which allows for such a subdivision of the atmospheric heat fluxes by means of separate calculations of the heat balances at the various interfaces. The main concept of the scheme is based on the approach by Tremblay and Mysak (1997) with major progress in terms of including a prognostic snow layer. Because there is a considerable number of modifications with regard to both the standard scheme and the thermodynamic ice model by Tremblay and Mysak (1997), a detailed description of the scheme is given in Section 2.1. In addition to the new ice growth scheme, a new snow and ice albedo scheme as well as a new parameterization of the snow cover fraction on ice are presented in Sections 2.2 and 2.3. The new parameterizations have been incorporated and validated in the coupled regional climate model HIRHAM–NAOSIM. In Section 3.1, the impacts of the new schemes, especially on the simulation of sea ice, are analyzed and compared with the standard schemes in a series of year-long sensitivity experiments. In addition, results of a multi-year-long simulation with all new parameterization are presented in Section 3.2 and compared with corresponding results of a simulation with the standard schemes. The motivation for integrating new schemes into coupled models is the attempt to improve the timing of the snow and ice ablating periods in consequence of more realistic representation of the ice– albedo feedback process, which is a process of particular importance for the performance of coupled regional and global models in polar regions. 2. Improved model parameterizations The coupled regional model used in this study is a composite of the regional atmospheric climate model HIRHAM (Christensen et al., 1996; Dethloff et al., 1996) and the high-resolution version of the North Atlantic/Arctic Ocean sea-ice model NAOSIM (Karcher et al., 2003; Kauker et al., 2003). Both model components were adapted for Arctic climate simulations and successfully applied for a wide range of Arctic climate studies. The coupled model system, first introduced by Rinke et al. (2003), was described in detail by Dorn et al. (2007). For further information on the model, it is referred to these publications. Here only details on the parameterizations are given, which have been modified or exchanged by more sophisticated schemes. 2.1. Ice growth parameterization The thermodynamic ice growth scheme is based on the widelyused two-level sea-ice model of Hibler (1979) and includes prognostic equations for snow thickness ðhs Þ, ice thickness ðhi Þ, and ice concentration ðci Þ using zero-layer thermodynamics following the approach by Semtner (1976) (i.e. no prognostic equations for temperatures in snow and ice layers). Since the standard ice growth scheme was described in detail by Dorn et al. (2007), only the new flux-separating ice growth scheme, which calculates the energy balances at the various interfaces separately, instead of calculating the energy balance of the combined ocean mixed layer– sea ice system, is described here. In this new scheme, the continuity equations are given by

ohs lat vi Þ ¼ Stop þ r  ðhs~ s þ Ss þ P s ; ot ohi ow vi Þ ¼ Sice þ r  ðhi~ h þ Sh ; ot oci ow vi Þ ¼ Sice þ r  ðci~ c þ Sc þ Dc ; ot

ð1Þ ð2Þ ð3Þ

lat ice ow ice ow where ~ vi is the ice velocity, Stop s ; Ss ; Sh ; Sh ; Sc , and Sc are the thermodynamic sources and sinks in terms of production rates (see Section 2.1.1 and Table 1), Ps is the snow production rate due to snowfall on sea ice, and Dc is an auxiliary term that accounts for the lead formation due to shear strain (dynamical ridging). Note that hi and hs are here defined as grid cell mean ice thickness and snow thickness, respectively. This means that the ice (snow) mass per unit area is simply the product of ice (snow) density qi ðqs Þ and ice (snow) thickness. (1) and (2) thus reflect the conservation of snow and ice mass, while (3) is an empirical equation for the ice concentration.

2.1.1. Production rates The thermodynamic production rates are derived from surface heat balance equations at the various interfaces (labeled in Fig. 1 with the corresponding number of the equation):

qs Lf

Stop ; ci s q Lf Q si ¼ Q is þ i Stop ; ci h q Lf Q io ¼ Q oi þ i Sbot ; ci h q Lf Q ao ¼ Q oa þ i Sow : 1  ci h Q as ¼ Q sa þ

ð4Þ ð5Þ ð6Þ ð7Þ

The meaning of the symbols are described in Tables 1 and 2. Note that all heat fluxes are here defined positive upward and negative downward. In the applied zero-layer approach, there is no heat storage in the snow and ice layers, i.e. Q sa ¼ Q si and Q is ¼ Q io . Hence, (4) and (5) can be combined to

Q as ¼ Q io þ

qi Lf ci

Stop h þ

qs Lf ci

Stop s :

ð8Þ

The production rates Stop and Stop are pure melting rates (thers h modynamic growth of snow and ice at the interface to the atmosphere is not allowed), while Sow h is a pure freezing rate. The only production rate which allows for both melting and freezing is Sbot h . As long as a snow layer exists, Stop h ¼ 0 takes effect, i.e. melting of sea ice from above is not allowed. Using (8) and the boundary

Table 1 Description of the symbols for heat fluxes, snow and ice production rates, and temperatures used in the text and Fig. 1. Symbol

Description

Q as Q sa Q si Q is Q io Q oi Q ao Q oa

Atmospheric heat flux away from the atmosphere–snow interface Snow heat flux towards the atmosphere–snow interface Snow heat flux away from the snow–ice interface Ice heat flux towards the snow–ice interface Ice heat flux away from the ice–ocean interface Oceanic heat flux towards the ice–ocean interface Atmospheric heat flux away from the atmosphere–ocean interface Oceanic heat flux towards the atmosphere–ocean interface

Stop s Slat s Stop h Sbot h Sice h Sow h Sice c Sow c

Thermodynamic snow production rate at the top of the snow Thermodynamic snow production rate due to lateral ice melt Thermodynamic ice production rate at the top of the ice Thermodynamic ice production rate at the bottom of the ice Thermodynamic ice production rate of the sea-ice fraction Thermodynamic ice production rate of the open water fraction Rate of change in ice concentration due to melting of ice Rate of change in ice concentration due to freezing of open water

T air T ice T ss T ocn T fs

Near-surface air temperature Ice/snow surface temperature Sea surface temperature Upper ocean mixed layer temperature Freezing temperature of sea water (salinity-dependent)

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The decrease in ice concentration is based on the assumptions 0 that the ice thickness is uniformly distributed between 0 and 2hi in the sea-ice covered part of the grid cell, and that all ice melts at the same rate (Hibler, 1979). When freezing occurs over open water areas ðSow h > 0Þ, the ice concentration increases at a rate parameterized by

Sow c ¼

1 maxðSow h ; 0Þ; h0

ð13Þ

where h0 is a reference thickness for lateral freezing (also referred to as lead closing parameter or demarcation thickness between thin and thick ice). Note that Sow h P 0 by definition so that the maximum function in (13) can also be omitted. Instead of using a fixed value for h0 throughout the Arctic, the more sophisticated formula min

max

h0 ¼ maxðh0 ; minðh0 ; hi ÞÞ min h0

Fig. 1. Schematic diagram of the thermodynamic ice growth scheme showing some of the notations used in the text. The temperatures and heat fluxes are accurately 0 0 described in Table 1. Note that hi ¼ hi =ci and hs ¼ hs =ci represent the actual thicknesses of sea ice and snow. The numbers next to the respective interfaces refer to the numbers of the heat balance equations in the text. Table 2 Physical constants used in the thermodynamic equations and their standard value in the coupled model. Symbol

Description

qi qs qw qf

Reference Reference Reference Reference

Lf cpi cpw ki ks

Latent heat of fusion Specific heat capacity of sea ice Specific heat capacity of sea water thermal conductivity of sea ice Thermal conductivity of snow

3.32  105 J kg1 2090 J kg1 K1 4098 J kg1 K1 2.1656 W m1 K1 0.31 W m1 K1

s0

Adaptation time of mixed layer Minimum thickness for lateral freezing Maximum thickness for lateral freezing Freezing temperature of freshwater

3d 0.5 m 1.5 m 273.15 K

min

h0 max h0 Tf 0

Value

density density density density

of of of of

sea ice snow sea water freshwater

910 kg m3 300 kg m3 1025 kg m3 1000 kg m3

condition Stop 6 0, the thermodynamic snow production rate due to s atmospheric forcing finally reads

Stop ¼ s

ci

qs Lf

minðQ as  Q io ; 0Þ:

ð9Þ

The total thermodynamic ice production rate of the sea-ice fractop bot tion ðSice h ¼ Sh þ Sh Þ can be derived from (6) and (8), and is given by

Sice h ¼

ci

qi Lf

ðQ as  Q oi Þ 

qs top S : qi s

ð10Þ

When all snow has disappeared, Q as represents the atmospheric heat flux at the atmosphere–ice interface. Using (7) and the boundary condition Sow h P 0 (melting of sea ice over open water is not allowed), the thermodynamic ice production rate of the open water fraction reads

Sow h ¼

1  ci maxðQ ao  Q oa ; 0Þ: qi Lf

ð11Þ

Thermodynamic ice production is assumed to effect the ice concentration in the following way. When melting of sea ice occurs ðSice h < 0Þ, the ice concentration decreases at a rate given by

Sice c ¼

1 ice 0 minðSh ; 0Þ: 2hi

ð12Þ

ð14Þ

max h0

is used. Here and are thresholds of the actual ice thickness representing nearshore conditions without or with thin ice, where frazil ice is forming, and central Arctic conditions with already thick ice, where newly formed ice is rather laterally freezing on existing min max ice floes. Note that the special case h0 ¼ h0 ¼ 1:2 m is identical to the fixed-value approach by Hibler (1979) used in the standard ice growth scheme (see Dorn et al., 2007). In the standard ice growth scheme, melting of ice does not occur until the whole snow layer has disappeared, whereas the new scheme includes the possibility to melt ice, even if a snow layer is present. Under certain conditions it may thus happen that a decrease in ice concentration would lead to an increase in the actual 0 snow thickness hs ¼ hs =ci . In this case, an additional amount of 0 snow is melted and dumped into the ocean in order to keep hs constant. The respective production rate, which allows for this additional snow melt due to ice concentration changes, is given by

  0 ice top Slat s ¼ min hs Sc  Ss ; 0 :

ð15Þ

This additional snow melt takes only effect if the sea ice melts faster at its bottom side due to a warm mixed layer than the snow from its top side due to atmospheric forcing. It may also happen that the ice cover completely disappears before all snow is melted. In this case, the remaining snow is directly converted into freshwater (Slat s ¼ hs =Dt, for c i ¼ 0). The energy required to melt this additional amount of snow is taken from the ocean mixed layer by means of an additional heat flux

DQ os ¼ qs Lf Slat s :

ð16Þ

Although the snow production rate due to snowfall on sea ice ðP s Þ is not directly contained in the thermodynamic equations, snowfall represents the only source of snow and should therefore be noted briefly. It is assumed that falling precipitation is snow if and only if the air temperature is below the freezing point ðT air < T f 0 Þ. In this case, the atmospheric precipitation rate P r , which is given as freshwater equivalent, is converted into P s by means of

P s ¼ ci

qf P : qs r

ð17Þ

In the other case ðT air P T f 0 Þ, all precipitation is assumed to be liquid and directly dumped into the ocean. Furthermore, evaporation over snow or bare sea ice is assumed to be negligible for the mass balances of snow and sea ice. On the other hand, snow precipitation falling in open water areas is directly converted into liquid water. The heat flux required for this instantaneous melting is considered as component of Q ao in the heat balance equation (7) as specified in Section 2.1.2.

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2.1.2. Heat fluxes In addition to the separate calculation of the heat balances at the various interfaces, which take place in the new ice growth scheme, a few modifications have also been made in terms of the calculation of the heat fluxes. The atmospheric heat fluxes Q as and Q ao are calculated in the atmosphere model HIRHAM from the surface energy balance equations

Q as ¼ Q as þ Q C ;

ð18Þ

Q ao ¼ Q ao þ Q P ;

ð19Þ

with swr Q j ¼ Q sen þ Q lat þ Q lwr j j þ Qj j ;

ð20Þ

where the index j ¼ fas; aog refers either to a snow/ice-covered surdenotes the sensible and Q lat face or open water conditions. Q sen j j the the net short-wave and Q lwr the net longlatent heat flux, and Q swr j j wave radiative flux. The parameterizations of these fluxes were described by Christensen et al. (1996) and have not changed. Q C appearing in (18) represents the heat flux due to changes of the ice/snow surface temperature ðT ice Þ assuming that T ice represents a thin ice (or snow) layer of thickness dh which possesses a heat capacity of C ph ¼ qi cpi dh. The associated heat stored in this layer is then given by

QC ¼

C ph ðT ice  T old ice Þ; Dt

ð21Þ

where Dt is the model time step and T old ice the ice/snow surface temperature at the previous time step. The ice surface temperature, and consequently also the residual heat flux which enters the ocean–ice system, depends substantially on the assumption made for the thickness of the ice/snow layer dh. A small value of dh results in larger amplitudes of T ice in response to temporary fluctuations in the fluxes than a large value. In principle, dh represents the penetration depth of heat into the surface layer within the time step Dt. The penetration rate, which is here simply approximated by dh=Dt, depends on the condition whether the surface layer consists of ice or snow. To allow for the insulating effect of a snow layer as well as the special case of an extremely thin snow or ice cover in total, dh is parameterized in the new ice growth scheme as a function of the actual snow and ice thicknesses:

  q 0 0
0 dh ¼ dh0 min f ðhi Þ; 1  f ðhs Þ þ s f ðhs Þ

qi

ð22Þ

with

  h f ðhÞ ¼ min 1; ; dh0

ð23Þ

where dh0 ¼ 0:04 m is the maximum thickness of the surface layer relating to the currently used time step Dt ¼ 240 s. In the standard ice growth scheme, the thickness of the surface layer is fixed to dh ¼ 0:1 m. This relatively large default value for dh was carried over from the ECHAM4 model (Roeckner et al., 1996) in which a larger penetration depth might be adequate, since a larger time step is used. Finally, the term Q P in (19) represents the heat flux required for melting of snow precipitation falling into open water areas, that is

QP ¼



qf Lf P r ; if T air < T f 0 0;

else:

ð24Þ

Q P takes effect as a correction term for the net heat flux entering the ocean mixed layer and is completely ignored in the standard ice growth scheme. The conductive heat flux ðQ io Þ through the snow/ice layer is assumed to be directly proportional to the temperature gradient

across the layer and inversely proportional to the effective thermo~ Þ of the snow/ice layer: dynamic thickness ðh i

Q io ¼ ki

T fs  T ice ci ðT fs  T ice Þ ¼ ; ~ h i =ki þ hs =ks hi

ð25Þ

with

  ~ ¼ 1 h þ hs k i ; h i i ci ks

ð26Þ

where ki and ks are the thermal conductivities of sea ice and snow. Furthermore, it is assumed that snow melt only takes place when T ice P T f 0 , but note that T ice can actually not rise above T f 0 while a snow/ice layer exists. Therefore, the temperature gradient across the snow/ice layer is assumed to be fixed to ðT fs  T f 0 Þ for melting conditions. In the standard ice growth scheme, the conductive heat flux is ignored in calculating the snow melt rate, since T f 0  T fs for all thermodynamic calculations. This also means that the boundary condition T ice 6 T fs is used (melting starts when the surface temperature reaches T fs ). The latter results in a systematic underestimation of the surface air temperatures during summer as demonstrated later in Section 3.2. The oceanic heat flux towards the ice–ocean interface ðQ oi Þ is given by

Q oi ¼ qw cpw

Dz

s0

ðT ocn  T fs Þ;

ð27Þ

where qw is the densitiy and cpw the specific heat capacity of sea water, Dz the mixed layer depth and s0 a damping time constant for a delayed adaptation of the mixed layer temperature. Dz=s0 can be interpreted as a heat transfer rate (see Dorn et al., 2007). The treatment of the oceanic heat flux towards the atmosphere–ocean interface ðQ oa Þ is special in the sense that the interface temperature T ss is not explicitly calculated. This means that Q oa cannot be specified simply as function of the temperature gradient ðT ocn  T ss Þ. The actual approach is as follows: As a first step, Q ao is calculated in HIRHAM assuming that T ss ¼ T ocn . As long as no freezing occurs at the atmosphere–ocean interface ðSow h ¼ 0Þ, the respective heat fluxes must be balanced according to (7), i.e. Q oa ¼ Q ao . In the other case, the heat fluxes are not balanced ðQ oa < Q ao Þ, and it is assumed that freezing may only occur if T ss ¼ T fs , at least locally. If so, Q oa is parameterized in the same way as Q oi . This means that Q oa is finally prescribed as

Q oa ¼ minðQ oi ; Q ao Þ:

ð28Þ

Note that incoming atmospheric energy over open water ðQ ao < 0Þ is completely used to warm up the mixed layer and not to melt ice (or snow) via instantaneous horizontal mixing. This has the direct consequence that the mixed layer temperature is allowed to rise above the salinity-dependent freezing point even though ice is present in the grid cell. This feature is consistent with measurements by Maykut and Perovich (1987), who showed that the possible elevation of the upper ocean temperature above the freezing point can be up to 5 K for nearshore conditions. Also, partial freezing at the atmosphere–ocean interface may occur before the whole mixed layer reaches the freezing point. The elevation of the mixed layer temperature above the freezing point is controlled by the damping time constant s0 , which determines the ocean heat loss due to melting of ice via Q oi . This heat loss normally prevents the mixed layer temperature ðT ocn Þ to diverge considerably from the freezing temperature ðT fs Þ. Furthermore, T ocn can actually not fall below T fs as long as T fs does not increase drastically. But also in that case, the mixed layer temperature will rapidly adapt to the new freezing point by temporarily increased ice growth.

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W. Dorn et al. / Ocean Modelling 29 (2009) 103–114

2.2. Ice albedo parameterization In nature, the albedo of sea ice depends on several characteristics of the surface (see e.g., Curry et al., 1996; Perovich et al., 2002), in particular on the existence of an overlying snow cover, but also on ice age, ice thickness, brine volume, and whether the ice is completely dry, in a melting state, or already partly covered with melt ponds. On the other hand, the albedo of snow depends on the grain size, which in turn is affected by snow age and whether the snow is wet or dry. Furthermore, the contamination of the snow and ice layer through the deposition of atmospheric aerosol particles (in particular soot) has impact on the albedo as well. In addition to these surface characteristics, the albedo is also affected by the angle of the incident solar radiation and the amount of clouds. In climate models, usually not all of these dependencies are considered, and a couple of simplifications are made in terms of the albedo parameterization of the ice-covered ocean. In the model used here, the surface albedo for ocean grid points is defined as

a ¼ ci asi þ ð1  ci Þaow ;

ð29Þ

where ci is the ice concentration (or ice cover fraction) within the grid cell, and asi and aow are the albedos for sea ice and open water, respectively. While the open water albedo is a fixed constant in HIRHAM (aow ¼ 0:1 in the coupled model version), the sea-ice albedo depends on the surface conditions. 2.2.1. The standard sea-ice albedo scheme In the standard sea-ice albedo scheme, the albedo of the sea-ice surface asi is parameterized as a linear function of the ice/snow surface temperature T ice according to the formula

asi ¼ asi;min þ ðasi;max  asi;min Þf ðT ice Þ;

ð30Þ

with

   T ice  T 0 f ðT ice Þ ¼ min 1; max 0; ; Td  T0

ð31Þ

Snow Cover Fraction

where T 0 ¼ 0  C is the freezing temperature of water and T d a temperature threshold below the freezing point ðT d < T 0 Þ where frozen ice/snow begins to change its albedo. asi;min and asi;max are minimum and maximum values representing melting and dry ice/snow, respectively. The sea-ice surface is assumed to be completely covered with snow, if the snow thickness exceeds a threshold of 0.01 m. In the other case, the surface is bare ice (see ‘threshold approach’ in Fig. 2). The respective minimum and maximum values used in the standard scheme are listed in Table 3. Sensitivity studies were carried out using either T d ¼ 10  C or T d ¼ 2  C, but it turned out that the choice of T d is not the crucial factor for the performance of the albedo scheme. In this paper, simulations are presented in which T d ¼ 2  C has been used.

0.8 0.6 threshold approach primary approach new tanh-approach

0.2 0.0 0

5

10

15

Standard scheme New scheme

Snow on ice

Bare ice

as;min

as;max

ai;min

ai;max

am;min

Melt ponds

am;max

0.75 0.77

0.85 0.84

0.66 0.51

0.75 0.57

None 0.16

None 0.36

2.2.2. The new sea-ice albedo scheme The new sea-ice albedo scheme introduced into the coupled model is based on version 2 suggested by Køltzow (2007) and was derived from measurements during the Surface Heat Budget of the Arctic Ocean (SHEBA) project (Uttal et al., 2002). The seaice surface is here divided into three subtypes: snow covered ice, bare ice, and melt ponds. The overall sea-ice albedo asi is then given as the weighted average of the respective albedos of the three subtypes according to the formula

asi ¼ cs as þ cm am þ ð1  cs  cm Þai ;

ð32Þ

where cs is the fraction of the sea-ice surface covered with snow and cm the corresponding fraction covered with melt ponds. Accordingly, as represents the snow albedo, am the melt pond albedo, and ai the albedo of bare ice. The albedos am ; as , and ai are again temperature-dependent and are determined in an analogous manner as in the standard scheme (cf. (30) and (31)) except that different minimum and maximum values are used (see Table 3) and that the temperature threshold, up to which the sea-ice surface is supposed to be dry, is set to T d ¼ 2  C for calculating am and T d ¼ 0:01  C for as and ai (note that in the latter case T d < T 0 only for numerical reasons; the temperature dependency is here effectively approximated by a step function). Furthermore, a linear transition towards the water albe0 do aow is applied to ai , if the actual ice thickness hi ¼ hi =ci is lower than a threshold ht ¼ 0:25 m. This means that the ice albedo is given by



ai ¼ aow þ ðai  aow Þ min 1;

0 hi ; ht

ð33Þ

where ai denotes the albedo for thick, bare ice derived from (30) and (31). Since the snow cover fraction is not a prognostic variable, an appropriate assumption has to be made on the basis of the prognostic snow thickness ðhs Þ and ice concentration ðci Þ. In a primary approach, the snow cover fraction is derived from the actual snow 0 thickness hs ¼ hs =ci according to the formula 0

cs ¼ ð1  cm Þ

0 hs

hs ; þ h0:5

ð34Þ

where h0:5 ¼ 0:02 m represents the actual snow thickness at which half of the sea ice is covered with snow. For example, a snow thickness of 0.1 m yields cs  83%, a snow thickness of 1 m yields cs  98%, if no melt ponds are present (see ‘primary approach’ in Fig. 2). Note that a snow thickness of 1 m represents a snow water equivalent of approximately 0.3 m. The factor ð1  cm Þ has the meaning to restrict the snow cover fraction to the area of the seaice surface not covered with melt ponds. Due to lack of proper variables to describe melt ponds explicitly, the melt pond fraction cm is simply approximated by the ice surface temperature T ice following the approach by Køltzow (2007):

1.0

0.4

Table 3 Minimum and maximum values used in the standard and the new sea-ice albedo scheme for the albedos of snow, bare ice, and melt ponds.

20

Snow Thickness [cm] Fig. 2. Three different approaches used in the coupled model for the parameterization of the snow cover fraction on sea ice as a function of snow thickness.

cm ¼ cm;max  ð1  f ðT ice ÞÞ;

ð35Þ

where cm;max ¼ 0:22 is the maximum melt pond fraction, and f ðT ice Þ is given by (31) with T d ¼ 2  C again. This crude estimate results in cm ¼ 0, for T ice 6 2  C, and cm ¼ 0:22, for T ice P 0  C, and is in

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W. Dorn et al. / Ocean Modelling 29 (2009) 103–114

rather good agreement with SHEBA observations, except for early summer when the parameterization tends to overestimate the melt pond fraction, leading to an albedo which is too low (Køltzow, 2007). 2.3. The new snow cover parameterization Since in the primary approach a relatively large fraction of the sea-ice surface is not covered with snow, even in case of a very thick snow layer, a new snow cover scheme has been introduced into the coupled model, which enables an almost fully snow covered sea-ice surface. In this secondary approach, the snow cover 0 fraction is derived from the actual snow thickness hs ¼ hs =ci according to the formula

cs ¼ cs;max  tanh



0  hs ; h0:75

ð36Þ

where cs;max ¼ 0:99 is the maximum snow cover fraction, and h0:75 ¼ 0:03 m represents the actual snow thickness at which approximately 75% of the sea ice is covered with snow. This formula is based on a suggestion by Roesch et al. (2001) for the snow cover fraction over land areas; however, modified values are used for the parameters cs;max and h0:75 which are more reasonable for describing snow on sea ice. In this new scheme, the melt pond fraction is subject to the restriction that it is not allowed to exceed the fraction of the seaice surface not covered with snow ð1  cs Þ. This means that cm is restricted to

cm ¼ minð1  cs ; cm Þ;

ð37Þ

cm

where is calculated using (35). This restriction is likely to reduce the shortcoming in early summer when the snow is still very thick in spite of surface temperatures near by the freezing point. Fig. 2

Table 4 Sensitivity experiments conducted for the periods from December 1997 to December 1998 (all experiments) and from January 1980 to October 2000 (only standard and new-ice+alb+snow). Experiment

Description

standard new-alb new-ice new-ice+alb new-ice+alb+snow

Control simulation with the standard parameterizations As standard but with new ice albedo scheme As standard but with new ice growth scheme As new-ice but with new ice albedo scheme As new-ice+alb but with new snow cover parameterization

shows the snow cover fraction on sea ice as a function of snow thickness for the three approaches used in the coupled model. The primary approach as well as the new tanh-approach have been exclusively applied in conjunction with the new sea-ice albedo scheme, while the threshold approach has always been used in conjunction with the standard sea-ice albedo scheme. 3. Model results To analyze the performance of the new parameterizations, a series of sensitivity experiments was conducted with HIRHAM– NAOSIM (see Table 4). These experiments can be divided into five year-long simulations for the period from December 1997 to December 1998 (see Section 3.1) and two multi-year-long simulations for the period from January 1980 to October 2000 (see Section 3.2). The year 1997/98 was chosen to compare the simulations with observational data available from the SHEBA project (Uttal et al., 2002), which included a field campaign on a drifting ice station in the Beaufort Sea from October 1997 to October 1998. In all experiments the ocean and sea-ice fields were initialized from a stand-alone simulation of NAOSIM in the exact same manner as described by Dorn et al. (2007). Since the year-long simulations, however, were made without model spin-up, the ice thickness of the stand-alone run were simply halved in order to come closer to the coupled model’s steady state known from long-term simulations. Otherwise, the impact of the new parameterizations on the sea-ice simulation might be completely superimposed by the spin-up process. The lateral boundary and initial conditions for the atmospheric model were taken from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-40). ERA-40 data were also used as surface boundary conditions outside the overlap area of the model domains of HIRHAM and NAOSIM. For more details, it is referred to the paper by Dorn et al. (2007). 3.1. Year-long sensitivity experiments The left panel of Fig. 3 shows the simulated monthly means of sea-ice extent from December 1997 to December 1998 in comparison to SSM/I satellite derived observational data using the NASA Team algorithm (Cavalieri et al., 1990). Even though the model overestimates the ice extent in winter, the impact of the new parameterizations, especially of the new ice growth scheme, is rather marginal and none of the parameterizations has been able to reduce this bias considerably.

14

24 22

10

SSM/I

9

standard

8

new-alb new-ice

7

new-ice+alb

6

new-ice+alb+snow

3

20

3

Ice Volume [10 km ]

2

11

Ice Extent [10 km ]

12

6

13

18 16 14

standard

12

new-alb new-ice

10

new-ice+alb

8

new-ice+alb+snow

6

5 0

1

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8

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Fig. 3. Simulated monthly means of sea-ice extent (left) and volume (right) within the model domain from December 1997 (Month 0) to December 1998 (Month 12). The seaice extent is defined as the area of all grid cells with at least 15% sea-ice concentration. For comparison, the SSM/I satellite derived sea-ice extent (solid gray line) was calculated for the same domain. The year-long model experiments are described in Table 4.

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On the other hand, the impact of the new parameterizations on sea ice in summer and autumn is substantial. Inclusion of the new albedo scheme into the model has led to a decrease in September ice extent of about 750,000 km2 (experiment new-alb), while inclusion of the new ice growth scheme has led to an increase in September ice extent of the same order approximately (experiment new-ice). One might guess that inclusion of both the new albedo and the new ice growth scheme into the model (experiment newice+alb) would thus result in no dramatic changes in the simulation of September ice extent, but the new albedo scheme dominates the sea-ice retreat during summer also when using the new ice growth scheme. The differences in summer ice extent are therefore rather similar in the experiments new-alb and new-ice+alb. This means that the high sensitivity with respect to the ice albedo parameterization still increases in conjunction with the new ice growth scheme (difference in September ice extent of about 1.5 million km2 between the experiments new-ice and new-ice+alb). Compared to the SSM/I satellite derived data, the inclusion of the new albedo scheme degrades the simulation of summer sea ice, although the scheme was derived from measurements and can actually be classified as more sophisticated. The apparent failure of the new albedo scheme in experiments new-alb and newice+alb is likely to be too early formation of melt ponds due to the simple temperature dependency. The new snow cover parameterization counteracts this early melt pond formation owing to the restriction that at first snow has to disappear before melt ponds may form (see Section 2.3). This leads to a higher albedo in early summer, when the solar irradiation is particularly high, and thus to reduced ice loss during the melting period. Hence, the experiment that additionally includes the new snow cover parameterization (experiment new-ice+alb+snow) results in a better agreement with the observations, even if the summer ice extent is still somewhat lower than in the SSM/I data. The increased sensitivity of the new albedo scheme in conjunction with the new ice growth scheme (experiment new-ice+alb) can solely be attributed to the fictive formation of melt ponds in an early stage of the melting period when snow is still present. Because of the substantially lower albedo of melt ponds, sea ice melts faster than in experiment new-ice and begins earlier to melt than in experiment new-ice+alb+snow where the formation of melt ponds is restricted to the snow-free fraction of the grid cell. In each case, whether faster or earlier melting, the ice–albedo feedback further accelerates the melting process, leading to the strong sea-ice retreat.

In the two experiments with the standard ice growth scheme on the other hand, melting of snow appears already when the ice surface temperature is well below 0 °C, mainly due to contributions from open water areas which always have a low albedo. At this stage, melt ponds does not exist, and the snow albedos in both schemes are not entirely different. For this very reason, the snow cover does not disappear due to the advanced inclusion of melt ponds into the albedo scheme or changed albedo values but due to the oversimplification of a missing separation of the heat fluxes. The effect of the new albedo scheme is therefore damped by the incapability of the standard ice growth scheme to respond reasonably to improvements in the albedo parameterization. The right panel of Fig. 3 shows the simulated monthly means of sea-ice volume from December 1997 to December 1998. The impact of the new parameterizations on the ice volume is qualitatively quite similar as on ice extent, except that the ice volume diverges considerably after one year of simulation, while the ice extent returns to similar winter values. The maximum difference in ice volume between the experiments is of about 4500 km3 after one year. This is approximately 30% of the total ice volume and emphasizes the importance of the parameterizations for the overall simulation of sea ice. In particular the experiments new-alb and new-ice+alb show massive loss of sea ice in summer which can not be compensated in the subsequent winter. The reason for this massive loss of sea ice is not the disqualification of the albedo parameterization per se, but a too early onset of the ice melt period. The validation of model results at single grid points, which represent a relatively large grid cell of several hundreds of square kilometers, with individual local measurements is basically difficult. Huwald et al. (2005) pointed out the high variability at small spatial scales in terms of ice and snow thickness measurements at SHEBA. Individual measurements are therefore not necessarily representative for a larger area. In order to reduce this problem as far as possible, the SHEBA snow and ice thicknesses shown in Fig. 4 together with the model results represent mean values from measurements at the ‘snow mainline’ of the ice camp (Perovich et al., 1999) and an estimate for the ice thickness evolution in the vicinity of the camp based on a model in which several ice thickness and open water observations were assimilated (Lindsay, 2003). The ice thickness data are available via URL http://data.eol.ucar.edu/ cgi-bin/codiac/dss/id=13.122. Compared to the SHEBA ice thickness data, which show the onset of ice decay at around SHEBA day 535, only the model experiments new-ice and new-ice+alb+snow show the onset of heavy ice

Effective Snow Thickness [cm]

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Fig. 4. Mean ice thickness (left) and snow thickness (right) from SHEBA data (see text) and year-long simulations of HIRHAM–NAOSIM (see Table 4). Simulated ice and snow thicknesses were interpolated from the model grid onto the respective position of the SHEBA ice camp. The mean ice thickness includes the open water areas, whereas effective snow thickness refers to the ice-covered part of the grid cell (excluding open water areas). The term ‘SHEBA day’ on the x-axis corresponds to the day from the start of 1997. The time series were smoothed using a 7-day running mean.

W. Dorn et al. / Ocean Modelling 29 (2009) 103–114

3.2. Performance in long-term simulations Two long-term simulations have been carried out for the period 1980–2000, the first one with the standard parameterizations (experiment standard), the second one with all new parameterizations (experiment new-ice+alb+snow). The corresponding year-long sensitivity experiments from Section 3.1 have shown that ice extent and ice volume arrive at quite similar values after one year in spite of a different annual cycle of the two variables (see Fig. 3). The simulated monthly means of sea-ice extent and volume from the long-term simulations are shown in Fig. 5. In agreement with the results of Dorn et al. (2007) the two simulations arrive at a quasi-stationary cyclic state of equilibrium in ice volume after about 8 years. Neglecting this 8-year-long spin-up phase, the experiment with the standard parameterizations shows larger amplitudes in the annual cycles of ice extent and ice volume than the experiment with the new parameterizations. However, the amplitude in the annual cycle of ice extent is in both experiments clearly larger than in SSM/I satellite derived data (see top panel of Fig. 5). A large part of the amplitude bias arises from a systematic overestimation of winter ice extent, almost exclusively due to overestimated ice extent in the Labrador Sea (not shown). In addition, experiment standard shows strong underestimation of summer ice extent compared to the SSM/I data, associated with extremely low ice volume in summer (around 5000 km3). It is highly probable that this is a consequence of an unrealistic representation of the ice–albedo feedback effect in this model version. In comparison with experiment standard, the underestimation of summer ice extent is clearly reduced in experiment new-ice+alb+snow, except for

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decay approximately at the same time, while all other experiments show an earlier onset (SHEBA day 510–520). In these experiments the melting period is longer, leading to stronger decline in ice thickness. The onset of ice decay is mainly controlled by the time of disappearance of the overlying snow cover. This coherence is strict using the standard ice growth scheme, because in this scheme ice melt may occur not until all snow has been melted away, but also in the new ice growth scheme, lateral and basal ice melt of a snow covered ice pack can only occur via mixed layer warming when distinct leads are already existing. An important result of the simulated snow thicknesses shown in Fig. 4 is that there is a shift of the snow melt period of more than 20 days between the experiments standard and new-alb and the experiment new-ice+alb+snow. The simulated date of the disappearance of the snow cover is closest to the SHEBA data in experiment new-ice+alb+snow, even if the simulated decrease in snow thickness is faster than observed. However, this feature appears in all experiments, and also the snow thicknesses in spring and early summer are always higher than measured during the SHEBA campaign. The difference between modeled and observed snow melt rates at the SHEBA ice camp might be a result of two facts: First, as the SHEBA snow thickness data represent mean values from measurements at the ‘snow mainline’ of the ice camp, they do not represent a single measurement but a smoothed average over a number of measurements along a line with different snow conditions at different sites (see e.g., Sturm et al., 2002). Second, simulated snow thickness may differ from measurements due to the assumption of a constant snow density in the model. In reality, there is a variety of different kinds of snow with different densities. In HIRHAM– NAOSIM, a constant snow density of 300 kg m3 is assumed (see Table 2). This assumption might be a suitable average for new snow, but not for melting snow which may be more compact with higher density. Therefore, the different snow melt rates in the model and the SHEBA data should not be overinterpreted.

Ice Volume [10 km ]

110

25

standard new-ice+alb+snow

20 15 10 5 0 0

60

120

180

240

Time [Months] Fig. 5. Simulated monthly means of sea-ice extent (top) and volume (bottom) within the model domain from January 1980 (Month 1) to October 2000 (Month 250). The sea-ice extent is defined as the area of all grid cells with at least 15% seaice concentration. For comparison, the SSM/I satellite derived sea-ice extent (solid gray line) was calculated for the same domain. The multi-year-long model experiments are described in Table 4.

a few years, particularly 1992 (months 145–156), which show similarly low summer minima in spite of greater ice volume. Nevertheless, the predominant reduction of the summer bias indicates that the representation of the ice–albedo feedback effect comes closer to reality using the new parameterizations. In Fig. 6 the results of the two long-term simulations are compared with SHEBA data. Similarly to the year-long experiments, there is a shift of the snow melt period of more than 20 days between the experiments standard and new-ice+alb+snow, and the time of total disappearance of the snow layer agrees better with the SHEBA data in experiment new-ice+alb+snow. Therefore, the onset of ice decay and the shape of the ice thickness curve during the melting period, including a temporal increase in ice thickness between SHEBA days 590 and 600, are in better agreement with SHEBA data in this experiment. Admittedly, at the very end of the SHEBA period (around SHEBA day 620), the experiment new-ice+alb+snow shows almost total loss of ice within the respective grid cells assigned to the position of the SHEBA ice camp. It is supposed that the ice is not melted but drifted away, since simulated and real ice drift might be different. A fundamental problem in the comparison of model and SHEBA data is the fact that the SHEBA ice camp was moving along with the general ice drift, while the modeled ice drift need not necessarily be parallel to the trajectory of the SHEBA ice camp. Accordingly, advection of ice into and out of the specific model grid cell assigned to the position of the SHEBA ice camp might interfere with local ablation and accretion processes. Owing to this large deviation at the end of the time series, the statistical evaluation of this experiment does not reveal better agreement with the SHEBA data than the experiment standard. The correlation between the ice thickness time series is clearly lower, and also the root mean squared error is slightly larger as shown in Table 5. With respect to the effective snow thickness, however, there is a better and quite good correlation between the SHEBA data and experiment new-ice+alb+snow (correlation coef-

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Effective Snow Thickness [cm]

3.5

Ice Thickness [m]

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Fig. 6. As in Fig. 4 but for the multi-year-long model simulations.

Table 5 Correlation coefficient (cor) and root mean squared error (rmse) between SHEBA ice camp data and the corresponding time series of the two multi-year-long simulations. 0 Statistics are presented for mean ice thickness ðhi Þ, effective snow thickness ðhs Þ, net surface short-wave radiative flux ðQ swr Þ, net surface heat flux ðQ atm Þ, 2-m air temperature ðT air Þ, and mean sea level pressure ðps Þ. The unit in the right column refers to the root mean squared error. Variable

hi 0 hs Q swr Q atm T air ps

standard

new-ice+alb+snow

cor

rmse

cor

rmse

0.613 0.869 0.907 0.897 0.904 0.868

0.845 16.45 14.20 14.32 9.474 4.740

0.460 0.929 0.959 0.955 0.929 0.878

0.862 20.80 9.520 9.653 7.993 4.352

Unit

m cm W m2 W m2 °C hPa

ficient of 0.93 compared to 0.46 for ice thickness), even if the root mean squared error is larger as well. The latter is likely to result from overall higher snow thickness in experiment newice+alb+snow. In addition to ice and snow thickness, the SHEBA campaign provided a number of other measurement data. Fig. 7 shows the net surface short-wave radiative flux, the net surface heat flux, the 2m air temperature, and the mean sea level pressure from SHEBA measurements and the two long-term simulations. In experiment standard the summer maximum in net short-wave radiation appears 25 days too early as a result of the too early disappearance of the snow cover (see Fig. 6). Furthermore, the net short-wave radiation is clearly lower during the time of the observed maximum due to the higher albedo of bare ice and the neglect of melt ponds in the standard scheme (see Table 3). In contrast, the experiment new-ice+alb+snow agrees much better with the observations in terms of the time as well as the magnitude of the maximum in net short-wave radiation. Due to the dominant contribution of short-wave radiation to the surface energy balance in summer, the net surface heat flux, which finally enters the ice–ocean system and crucially contributes to the melting of ice, is closer to reality during the melting season using the new parameterizations. Consistent with this finding, the correlation coefficients between SHEBA data and experiment new-ice+alb+snow are higher (about 0.95 compared to 0.90 in experiment standard for both net surface short-wave radiative flux and net surface heat flux) and the root mean squared errors are considerably reduced (about 9.5 W/m2 compared to 14 W/m2 for both fluxes). The 2-m air temperatures of both experiments show large deviations from the SHEBA measurements during the cold season. The model tends to overestimate winter temperatures of partly more

than 10 °C, but in most cases, the deviations are lower in experiment new-ice+alb+snow. The slight improvement in the simulation of the 2-m air temperatures in experiment new-ice+alb+snow might be a consequence of the lower heat capacity C ph in (21), whereby the ice surface temperature responds more quickly to changes in the energy fluxes at the ice surface. This presumption is confirmed by larger variations of the ice surface temperature (and the 2-m air temperature accordingly) in experiment new-ice+alb+snow. The warm bias in simulated winter temperatures might be the main reason for underestimated increase in ice thickness during the cold season as seen in all model simulations (see Fig. 4 and Fig. 6). Dorn et al. (2007) discussed the overestimation of winter temperatures in the coupled model and attributed them to a positive bias in net surface long-wave radiation of about 20 W/m2 compared to European Centre for Medium-Range Weather Forecasts reanalysis data (ERA-40). Such a systematic overestimation of net surface long-wave radiation does not appear in comparison to data from the SHEBA site. Consequently, the observed and simulated net surface heat fluxes agree reasonably well during winter (see upper right panel of Fig. 7). Besides potential uncertainties in the simulation of the radiative and heat fluxes, the temperature bias might also be a consequence of a little larger fraction of open water compared to SSM/I data. As shown, for example, by Lüpkes et al. (2008), small changes in the open water fraction have large impact on wintertime near-surface temperatures over the Arctic Ocean. Dorn et al. (2007) identified the reference thickness for lateral freezing h0 in (13) as a key parameter for the overall ice growth during the cold season. Although the representation of the rate of increase in ice concentration remains almost unchanged in the new ice growth scheme, the treatment of the parameter h0 has changed. Nevertheless, this different treatment only leads to small improvements in ice growth, especially over the marginal seas of the Arctic Ocean. The warm bias in simulated winter temperatures remains an unresolved issue in the model. It is supposed that an overestimation of low-level clouds, accompanied by too much downwelling longwave radiation, as well as an underestimation of low-level temperature inversions, associated with too high turbulent heat exchange in the atmospheric boundary layer, might be jointly responsible for the warm temperature bias in winter. Higher temperatures, in turn, lead to higher long-wave emission and may thus result in relatively small deviations in the net surface heat fluxes seen in Fig. 7. In contrast to the cold season, the temperature simulation during the melting period could be improved. While experiment standard underestimates the 2-m air temperatures by about 2 °C, experiment new-ice+alb+snow shows a quite good agreement with the SHEBA measurements (particularly between the SHEBA days

W. Dorn et al. / Ocean Modelling 29 (2009) 103–114

100 sheba standard new-ice+alb+snow

120 100

Net Surface Heat Flux [W/m 2 ]

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Fig. 7. Net surface short-wave radiative flux (upper left), net surface heat flux (upper right), 2-m air temperature (lower left), and mean sea level pressure (lower right) from measurements at the SHEBA ice camp and the two multi-year-long simulations of HIRHAM–NAOSIM. Simulation data were interpolated from the model grid onto the respective position of the ice camp. The term ‘SHEBA day’ on the x-axis corresponds to the day from the start of 1997. The time series were smoothed using a 7-day running mean.

515 and 605). As pointed out in Section 2.1.2, the underestimation of the near-surface temperatures in experiment standard is a result from the boundary condition T ice 6 T fs  1:8  C in the standard ice growth scheme. Altogether, experiment new-ice+alb+snow shows higher correlation with the SHEBA measurements (correlation coefficient of 0.93 compared to 0.90 in experiment standard) and a lower root mean squared error (8.0 °C compared to 9.5 °C in experiment standard). The variations of the mean sea level pressure are fairly well reproduced in both long-term experiments (correlation coefficients of 0.87 and 0.88, respectively), and the root mean squared errors are in the same order of magnitude. With respect to the mean sea level pressure, the new parameterizations lead only to slight, but not significant improvement in the simulation. Also, no general improvement in the simulation of the mean sea level pressure patterns appears in comparison to ERA-40 data (not shown). On the other hand, the improved simulation of the surface heat flux in summer is consistent with an improved simulation of the summertime sea-ice retreat. Fig. 8 shows the difference in September sea-ice concentration between the two model simulation and SSM/I satellite derived data averaged over the period 1988–2000. Note that the first 8 years (1980–1987) of the simulations have been considered as spin-up time and therefore omitted for the analysis. The simulated ice concentration in September is clearly underestimated over most of the Arctic Ocean in experiment standard. In particular in a strip from the northern Kara Sea to the Fram Strait, this experiment shows ice concentrations approximately

50% lower than observed, indicating substantial difficulties in simulating the correct position of the ice edge. Although the regional occurrence of model differences is quite similar in experiment new-ice+alb+snow, the magnitude of the differences is clearly reduced, except for some narrow straits within the Canadian archipelago and at the Siberian coast. In the strip from the northern Kara Sea to the Fram Strait, the difference is only half the difference found in experiment standard. Over most other regions of the Arctic Ocean the difference to SSM/I data is actually almost zero. It should be noted that the virtual signal of sea ice along the British and Scandinavian coasts originates from the SSM/I data and is a well-known problem of ocean pixels near the coast, which are partly ‘land-contaminated’. This problem might possibly also appear at some of the coasts of the Canadian archipelago. When disregarding the problems in narrow straits, the simulation of the sea-ice concentration is substantially improved if the entire set of improved parameterizations is used. Only in this case, the ice–albedo feedback, which can be regarded as the all-dominant process for summertime ice loss, is represented more realistically in the model. 4. Summary and conclusions More sophisticated schemes for ice growth, ice albedo, and snow cover fraction on ice have been introduced into the coupled regional climate model HIRHAM–NAOSIM, and the performance of these new schemes has been examined and compared with those

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Fig. 8. Mean difference in September sea-ice concentration between model simulation and SSM/I satellite derived data for the period 1988–2000. The model simulations were carried out with the standard parameterizations (left) and improved parameterizations for ice growth, ice albedo, and snow cover on sea ice (right), respectively. Note that around the North Pole no SSM/I data are available, and the ice concentration has been simply set to 100%.

of the standard schemes used before. For this purpose, a series of year-long and multi-year-long sensitivity experiments has been carried out and analyzed, paying particular attention to the model’s skill in reproducing observed summer sea ice extent. While the standard ice growth scheme calculates the energy balance of the combined ocean mixed layer–sea ice system, the new ice growth scheme includes separate calculations of the heat balances at the atmosphere–ocean interface, the atmosphere–ice interface and the ocean–ice interface, though in the applied zerolayer approach, the last two calculations can be combined to one so that effectively only two production rates need to be taken into account. The new ice growth scheme has the advantage that the atmospheric heat fluxes over sea ice and open water are not mixed in their contribution to snow and ice melt, which have led to very early disappearance of the snow layer and to enhanced ice melt in the standard scheme. The new ice albedo scheme is based on version 2 for a new sea ice albedo scheme suggested by Køltzow (2007) and includes a much stronger temperature dependency of the sea-ice albedo than the old scheme due to the given parameterization of melt ponds. In addition, the extended spread between snow and ice albedo possesses the potential to amplify the snow–albedo feedback in the model. This requires a more accurate simulation of both surface temperature and snow precipitation in order to yield realistic surface heat fluxes, which in turn are one of the prerequisites for realistic melting of snow and ice in the model. At least during the melting period, the new ice growth scheme improves the simulation of the near-surface temperatures decisively. This represents a necessary but not sufficient condition for appropriate use of the temperature-dependent albedo scheme. Even using the new ice growth scheme in addition to the new albedo scheme, melting of ice is likely to be overestimated due to overestimated fraction of melt ponds in early summer, whereby the albedo is too low at an early stage when solar radiation is maximum. This shortcoming can be corrected when using a new approach for the parameterization of the snow cover fraction, which does not allow the formation of melt ponds as long as the snow cover is still thick. It has turned out that the parameterization of the snow cover fraction plays a crucial role in the initiation of the snow melt period in early summer. The onset of ice decay and its strength, in turn, depend on the time of disappearance of the snow layer. This clearly indicates the great importance of an exact timing of the snow melt period in coupled model simulations of the Arctic cli-

mate system. If the snow cover fraction is too low, melt ponds can form earlier with the result of amplified melting and further reduction of the snow cover and too early beginning of sea-ice retreat. On the other hand, if melt ponds are completely neglected in the albedo scheme, the albedo in midsummer is too high, leading to underestimated net surface heat fluxes and insufficient loss of ice during the melting period. A multi-year-long simulation using the new parameterizations of ice growth, ice albedo, and snow cover fraction results in a substantial improvement in the simulation of the summer minimum in ice concentration accompanied by higher correlations and lower errors in most variables compared to observations. It is evident that these improvements are a consequence of a more realistic representation of the two-stage snow–albedo/ice–albedo feedback, which is reflected in an improved simulation of the summer maximum in the net surface short-wave radiative flux and, accordingly, in the net surface heat flux. The fact that the simulation with the standard parameterizations does not totally diverge from observed sea-ice conditions is likely to be due to compensating errors in the model. In the standard model version, snow disappears too early, leading to earlier ice melt, but the albedo of a snow-free sea-ice cover is higher in the standard albedo scheme, leading in turn to decreased energy input into the ice–ocean system and thus to slower ice melt. These two shortcomings of the standard parameterizations counteract to each other in their effect on ice decay, but it has been found that the errors in the time of occurrence of the snow melt period have dominant impact on the overall ice loss. This impact increases when the preset spread between snow and ice albedo in the albedo scheme is larger. Despite a couple of improvements in the simulation of Arctic summer sea ice, the coupled regional model has still weaknesses in the simulation of surface temperatures and ice growth during winter. The new parameterizations were developed for the purpose of improving the simulation of the sea-ice retreat in summer. Winter conditions and sea-ice dynamics have been left aside until now and are subject of ongoing research. Acknowledgements This work was in part funded by the European Union project GLIMPSE (EU Grant EVK2-CT-2002-00164). Computational resources for carrying out the model simulations were granted by the North German Supercomputing Center (HLRN) within the

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project hbk00014. SSM/I sea-ice data were provided by the National Snow and Ice Data Center (NSIDC), Boulder, Colorado, SHEBA data by the SHEBA Project Office, University of Washington, and ERA-40 model forcing data by the European Centre for Medium-Range Weather Forecasts (ECMWF). Furthermore, we thank Rüdiger Gerdes, Michael Karcher, and Frank Kauker for providing initial and forcing data for the ice–ocean model and valuable assistance. Finally, thanks are given to two anonymous reviewers for helpful comments on the manuscript. References Cavalieri, D., Gloerson, P., Zwally, J., 1990, updated 2004. DMSP SSM/I monthly polar gridded sea ice concentrations, 1988 to 2003. In: J. Maslanik, J. Stroeve, (Eds.), National Snow and Ice Data Center, Boulder, CO, digital media. Christensen, J.H., Christensen, O.B., Lopez, P., van Meijgaard, E., Botzet, M., 1996. The HIRHAM4 regional atmospheric climate model. DMI Sci. Rep. 96-4, Dan. Meteorol. Inst., Copenhagen, Denmark, 51 pp. Curry, J.A., Rossow, W.B., Randall, D., Schramm, J.L., 1996. Overview of Arctic cloud and radiation characteristics. J. Clim. 9, 1731–1764. Curry, J.A., Schramm, J., Ebert, E.E., 1995. On the sea ice albedo climate feedback mechanism. J. Clim. 8, 240–247. Dethloff, K., Rinke, A., Lehmann, R., Christensen, J.H., Botzet, M., Machenhauer, B., 1996. Regional climate model of the Arctic atmosphere. J. Geophys. Res. 101, 23401–23422. Dorn, W., Dethloff, K., Rinke, A., Frickenhaus, S., Gerdes, R., Karcher, M., Kauker, F., 2007. Sensitivities and uncertainties in a coupled regional atmosphere–ocean– ice model with respect to the simulation of Arctic sea ice. J. Geophys. Res. 112, D10118. doi:10.1029/2006JD007814. Flato, G.M., Participating CMIP Modelling Groups, 2004. Sea-ice and its response to CO2 forcing as simulated by global climate models. Clim. Dyn. 23, 229–241. doi:10.1007/s00382-004-0436-7. Hibler III, W.D., 1979. A dynamic thermodynamic sea ice model. J. Phys. Oceanogr. 9, 815–846. Huwald, H., Tremblay, L.-B., Blatter, H., 2005. Reconciling different observational data sets from Surface Heat Budget of the Arctic Ocean (SHEBA) for model validation purposes. J. Geophys. Res. 110, C05009. doi:10.1029/2003JC002221. Karcher, M.J., Gerdes, R., Kauker, F., Köberle, C., 2003. Arctic warming: evolution and spreading of the 1990s warm event in the Nordic seas and the Arctic Ocean. J. Geophys. Res. 108, 3034. doi:10.1029/2001JC001265.

Kauker, F., Gerdes, R., Karcher, M., Köberle, C., Lieser, J.L., 2003. Variability of Arctic and North Atlantic sea ice: a combined analysis of model results and observations from 1978 to 2001. J. Geophys. Res. 108, 3182. doi:10.1029/ 2002JC001573. Køltzow, M., 2007. The effect of a new snow and sea ice albedo scheme on regional climate model simulations. J. Geophys. Res. 112, D07110. doi:10.1029/ 2006JD007693. Lindsay, R.W., 2003. Changes in the modeled ice thickness distribution near the Surface Heat Budget of the Arctic Ocean (SHEBA) drifting ice camp. J. Geophys. Res. 108, 3194. doi:10.1029/2001JC000805. Lüpkes, C., Vihma, T., Birnbaum, G., Wacker, U., 2008. Influence of leads in sea ice on the temperature of the atmospheric boundary layer during polar night. Geophys. Res. Lett. 35, L03805. doi:10.1029/2007GL032461. Maykut, G.A., Perovich, D.K., 1987. The role of shortwave radiation in the summer decay of a sea ice cover. J. Geophys. Res. 92, 7032–7044. Perovich, D.K., Grenfell, T.C., Light, B., Hobbs, P.V., 2002. Seasonal evolution of the albedo of multiyear Arctic sea ice. J. Geophys. Res. 107, 8044. doi:10.1029/ 2000JC000438. Perovich, D.K., Grenfell, T.C., Light, B., Richter-Menge, J.A., Sturm, M., Tucker III, W.B., Eicken, H., Maykut, G.A., Elder, B., 1999. SHEBA: Snow and Ice Studies CD-ROM. Cold Regions Research and Engineering Laboratory, Hanover, NH, digital media. Rinke, A., Gerdes, R., Dethloff, K., Kandlbinder, T., Karcher, M., Kauker, F., Frickenhaus, S., Köberle, C., Hiller, W., 2003. A case study of the anomalous Arctic sea ice conditions during 1990: insights from coupled and uncoupled regional climate model simulations. J. Geophys. Res. 108, 4275. doi:10.1029/ 2002JD003146. Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dümenil, L., Esch, M., Giorgetta, M., Schlese, U., Schulzweida, U., 1996. The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate. MPI Rep. 218, Max Planck Inst. for Meteorol., Hamburg, Germany, 90 pp. Roesch, A., Wild, M., Gilgen, H., Ohmura, A., 2001. A new snow cover fraction parametrization for the ECHAM4 GCM. Clim. Dyn. 17, 933–946. doi:10.1007/ s003820100153. Semtner Jr., A.J., 1976. A model for the thermodynamic growth of sea ice in numerical investigations of climate. J. Phys. Oceanogr. 6, 379–389. Sturm, M., Holmgren, J., Perovich, D.K., 2002. Winter snow cover on the sea ice of the Arctic Ocean at the Surface Heat Budget of the Arctic Ocean (SHEBA): Temporal evolution and spatial variability. J. Geophys. Res. 107, 8047. doi:10.1029/2000JC000400. Tremblay, L.-B., Mysak, L.A., 1997. Modeling sea ice as a granular material, including the dilatancy effect. J. Phys. Oceanogr. 27, 2342–2360. Uttal, T. et al., 2002. Surface Heat Budget of the Arctic Ocean. Bull. Am. Meteor. Soc. 83, 255–275.