sensitivities and uncertainties in a coupled regional atmosphere-ocean-ice model with respect to the...

Sensitivities and uncertainties in a coupled regional

atmosphere-ocean-ice model with respect to the

simulation of Arctic sea ice

W. Dorn,1 K. Dethloff,1 A. Rinke,1 S. Frickenhaus,2 R. Gerdes,2 M. Karcher,2

and F. Kauker2

Received 21 July 2006; revised 17 January 2007; accepted 5 February 2007; published 24 May 2007.

[1] A series of sensitivity experiments using a coupled regional atmosphere-ocean-icemodel of the Arctic has been conducted in order to identify the requirements needed toreproduce observed sea-ice conditions and to address uncertainties in the description ofArctic processes. The ability of the coupled model to reproduce observed summer ice retreatdepends largely on a quasi-realistic ice volume at the beginning of the melting period,determined by the relationship between winter growth and summer decay of ice. Whilesummer ice decay is strongly affected by the parameterization of the sea-ice albedo, winterice growth depends significantly on the parameterization of lateral freezing. Reciprocalmodel biases due to uncertainties in the atmospheric energy fluxes can be compensated to acertain extent. However, potential underlying weaknesses of the model cannot beeliminated that way. Since lateral freezing also determines the ice concentration duringwinter, and thus the heat loss of the ocean and the near-surface air temperature, the modeltuning possibilities are limited. A large uncertainty in the model relates to the simulation oflong-wave radiation most likely as a result of overestimated cloud cover. The resultssuggest that uncertainties in the descriptions for Arctic clouds, snow, and sea-ice albedo,and lateral freezing and melting of sea ice, including the treatment of snow, are responsiblefor large deviations in the simulation of Arctic sea ice in coupled models. Improveddescriptions of these processes are needed to reduce model biases and to enhance thecredibility of future climate change projections.

Citation: Dorn, W., K. Dethloff, A. Rinke, S. Frickenhaus, R. Gerdes, M. Karcher, and F. Kauker (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.

1. Introduction

[2] Sea ice plays a prominent role in the Arctic climatesystem because the presence of sea ice modifies the exchangeof heat, moisture, and momentum between atmosphere andocean, and therefore the atmospheric and oceanic processesand circulations which in turn have an impact on the existenceand spatial distribution of sea ice. Furthermore, the sea-ice-albedo feedback effect is an important factor in the amplifi-cation of climate change in the Arctic [e.g., Curry et al.,1995], so that the changes in Arctic sea ice have the potentialto impact Arctic and global climate significantly [Dethloff etal., 2006]. Hence a realistic simulation of Arctic sea ice is oneof the major challenges in coupled climate modeling.[3] Recent coupled-model intercomparison studies have

shown that different global atmosphere-ocean-ice models(hereafter referred to as AOI models) produce quite different

sea-ice thickness and extent already in their present-dayclimate [Walsh and Timlin, 2003; Holland and Bitz, 2003;Flato and Participating CMIP Modeling Groups, 2004].Therefore it is not surprising that projections of the21st-century ice extent by these models differ considerablyfrom each other and are strongly dependent on the models’simulations of present-day ice extent [Walsh and Timlin,2003]. The outcome of this is a wide range in the projectedpolar amplification of climate change and thus in themagnitude and regional pattern of high-latitude warmingand its potential consequences. The uncertainty in modelingpresent-day Arctic sea-ice conditions and in its implicationsfor climate projections indicates the need for improveddescriptions of physical processes involved in atmosphere-ice-ocean feedbacks.[4] This paper addresses the ability of a pan-Arctic coupled

regional AOI model to reproduce present-day Arctic sea-iceconditions. The regional model approach allows for realisticlarge-scale atmospheric conditions at the model’s lateralboundaries as well as the simulation of atmosphere-ice-oceaninteractions with high resolution. Previous studies with pan-Arctic coupled and uncoupled regional models by Maslaniket al. [2000] and Rinke et al. [2003] have highlighted the

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, D10118, doi:10.1029/2006JD007814, 2007

1Alfred Wegener Institute for Polar and Marine Research, Potsdam,Germany.

2Alfred Wegener Institute for Polar and Marine Research, Bremerhaven,Germany.

Copyright 2007 by the American Geophysical Union.0148-0227/07/2006JD007814

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importance of the atmospheric circulation in driving anom-alous sea-ice retreat during summer 1990. However, the fullycoupled versions of their models were not able to simulateboth sea-ice anomaly and atmospheric circulation in a satis-factory manner. A potential shortcoming of the two studiescould be that the associated coupled-model simulations werecarried out only for a single year using initial ice thicknessesfrom stand-alone simulations.[5] In the following sections, we first describe the model

components in sections 2.1, 2.2, and 2.3, the couplingprocedure in section 2.4, and the boundary conditions insection 2.5. An overview about the model experiments isgiven in section 3, while the results of these experiments arepresented in section 4. There, we comment on the need foran adequate spin-up time in modeling Arctic sea ice with acoupled regional model. We further address sensitivities anduncertainties in the model, and we discuss some necessaryimprovements of Arctic process descriptions required tosimulate Arctic sea ice in a more realistic fashion. Theprimary focus is set on the simulation of the Arctic sea-iceanomaly during summer 1998 when unusually strongreductions in ice cover occurred in the Beaufort andChukchi seas and along the Canadian coast [e.g., Maslaniket al., 1999], but some of the results are transferable to otheryears as well. The year 1998 was chosen because a numberof observations are available from the Surface Heat Budgetof the Arctic Ocean (SHEBA) project, which included ayearlong field campaign on a drifting ice station in theBeaufort Sea from October 1997 through October 1998.

2. Model Description

[6] The coupled regional AOI model used in this study isa composite of the two stand-alone models HIRHAM andNAOSIM. Each of them has been applied for a wide rangeof Arctic climate studies [e.g., Dethloff et al., 2002; Rinkeet al., 2004; Karcher et al., 2003; Kauker et al., 2003], andalso an earlier version of the coupled HIRHAM-NAOSIMhas already been used by Rinke et al. [2003] in a case studyfor anomalous Arctic sea-ice conditions. In accordancewith the stand-alone models, domain choice and configura-tion of the coupled model’s components have been retainedunchanged.

2.1. Atmosphere Model

[7] The atmospheric component HIRHAM [Christensenet al., 1996] was set up on an integration domain that coversthe whole Arctic north of about 60�N (see Figure 1) athorizontal resolution of 0.5� (�50 km) on a rotated latitude-longitude grid with the North Pole on the geographicalequator at 0�E [Dethloff et al., 1996]. In the vertical, themodel has 19 unevenly spaced levels in hybrid sigma-pressure coordinates from the surface up to 10 hPa withthe highest resolution in the lower troposphere. HIRHAMincludes prognostic equations for horizontal wind compo-nents, temperature, specific humidity, cloud water, andsurface pressure and diagnostic equations for vertical wind,geopotential, cloud cover, and mixed clouds. The equationsare solved using a time step of 240 s.[8] The physical parameterizations for subgrid-scale pro-

cesses are adapted from the general circulation model

ECHAM4 [Roeckner et al., 1996] and include compre-hensive descriptions for radiative transfer, atmosphericboundary layer processes, gravity wave drag, cumulus con-vection, and large-scale condensation. In addition, balanceequations for energy and hydrology at the land surface and aheat conductivity equation for the temperatures in five soillayers between the surface and 10 m depth are solved for allland grid cells. Over ocean grid cells, which are at leastpartly covered with sea ice, a heat balance equation is solvedfor the uppermost snow/ice layer to obtain the snow/icesurface temperature and the residual heat flux (Qai) availablefor freezing or melting of sea ice or melting of snow.

2.2. Ocean Model

[9] The ocean-ice component is the high-resolution regionalversion of NAOSIM [Karcher et al., 2003; Kauker et al.,2003]. It uses a horizontal resolution of 0.25� (�25 km) on arotated spherical grid, where the model equator corresponds tothe geographical 30�W/150�E meridian, and 30 unevenlyspaced z-coordinate levels in the vertical. NAOSIM’s oceancomponent is based on the Geophysical Fluid DynamicsLaboratory modular ocean model MOM-2 [Pacanowski,1996] and includes prognostic equations for horizontal velo-city components, potential temperature, and salinity anddiagnostic equations for vertical velocity, density, and pres-sure. The model domain encloses the northern North Atlantic,the Nordic seas, and the Arctic Ocean (see Figure 1). Thesouthern model boundary of NAOSIM is approximatelylocated at 50�N in the Atlantic. Here an open-boundarycondition has been implemented following thework of Stevens[1991], while all other boundaries, including the Bering Strait,

Figure 1. Geographical position of the model domains ofthe coupled model’s atmosphere component HIRHAM andits ocean-ice component NAOSIM. The domain of coupling(given by the overlap area) covers the whole Arctic Ocean,including all marginal seas, the Nordic seas, and parts of thenorthern North Atlantic.

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are treated as closed walls. The model computations for bothocean and sea ice are carried out with a time step of 900 s. Formore details on the model configuration, see the paper ofKarcher et al. [2003] or Kauker et al. [2003].

2.3. Sea-Ice Model

[10] The NAOSIM version used here incorporates adynamic-thermodynamic sea-ice model with elastic-viscous-plastic (EVP) dynamics according to the study of Hunke andDukowicz [1997] and zero-layer thermodynamics followingthe work of Semtner [1976] with prognostic equations for icethickness and ice concentration based on the widely used two-level sea-ice model by Hibler [1979] with two ice thicknesscategories (thick ice and thin ice/open water). In addition,NAOSIM allows for a prognostic snow layer on sea ice. Thecontinuity equations for mean snow thickness (hs), mean icethickness (hi), and ice concentration (ci) are given by

@hs@t

þr � hs~við Þ ¼ Ss þ Ps; ð1Þ

@hi@t

þr � hi~við Þ ¼ Sh; ð2Þ

@ci@t

þr � ci~við Þ ¼ Sc þ Dc; ð3Þ

where ~vi is the ice velocity; Sh, Ss, and Sc are thethermodynamic sources and sinks in terms of productionrates; Ps is the snow production rate due to snowfall on seaice; and Dc is the lead formation due to shear strain(dynamical ridging). Note that hi and hs are defined here asgrid-cell mean ice thickness and snow thickness, respec-tively. This means that the ice (snow) mass per unit area issimply the product of ice (snow) density ri (rs) and ice(snow) thickness. Equations (1) and (2) thus reflect theconservation of snow and ice mass, while equation (3) is anempirical equation for the ice concentration. The thermo-dynamic production rates take the form

Ss ¼1

rsLfmin Qa; 0ð Þ; ð4Þ

Sh ¼1

riLfQa � Qoð Þ � rs

riSs; ð5Þ

Sc ¼ci

2himin Sh; 0ð Þ þ 1� ci

riLfh0max Qao; 0ð Þ; ð6Þ

where Lf is the latent heat of fusion, Qa and Qo are the netatmospheric and oceanic heat fluxes, respectively, and h0 isa fixed reference thickness for lateral freezing, also referredto as lead closing parameter or demarcation thicknessbetween thin and thick ice. h0 is a purely empiricalparameter and will be discussed in section 3. The netatmospheric heat flux is given by the weighted average

Qa ¼ ciQai þ 1� cið ÞQao; ð7Þ

where Qai and Qao are the net atmospheric heat fluxes overthe sea-ice covered and the open-water fraction of the gridcell, respectively, which are calculated in the atmospheremodel. Note that all fluxes are here defined positive upwardand negative downward.[11] The oceanic heat flux toward the ice-ocean interface is

assumed to be directly proportional to the difference betweenthe upper-ocean mixed-layer temperature (To) and the tempe-rature at the ice-ocean interface, which in turn is assumed tobe identical with the freezing temperature of seawater (Tfs):

Qo ¼ rwcpwDz

t0To � Tfsð Þ: ð8Þ

Here rw is the density and cpw is the specific heat capacityof seawater, Dz is the mixed layer depth, and t0 is a fixeddamping time constant for a delayed adaptation of themixed-layer temperature, which is taken to be 3 days.Alternative formulations for the ratio Dz/t0, which can beinterpreted as a heat transfer rate, have been discussed byMcPhee [1992] and Omstedt and Wettlaufer [1992] whorelate the heat transfer rate to kinematic variables.[12] The freezing temperature of seawater depends on the

salinity and is calculated according to the formula

Tfs ¼ Tf0 þ bfSo; ð9Þ

where Tf0 = 0�C is the freezing temperature of freshwater, Sois the salinity at the ocean surface, which is assumed to beequal to the upper-ocean mixed-layer salinity, and bf =�0.0544K/psu is a constant conversion coefficient. Note thatthe salinity in NAOSIM is actually given as deviation fromthe reference salinity Sref = 35 psu according to S0o = So� Sref.

2.4. Model Coupling

[13] The coupling between HIRHAM and NAOSIM iscarried out every hour, while the exchange of variablesbetween ocean and ice model takes place at each time step.A full list of the exchange variables is given in Table 1.[14] Since HIRHAM and NAOSIM use grids with dif-

ferent orientation and resolution, the variables must beinterpolated from one grid to the other. For this purpose,an auxiliary grid is constructed in advance by extending thecoarser grid, here the atmosphere grid, in such a way that itencloses the whole area of the finer grid. Each grid cell ofthe finer grid is then subdivided into 3 3 subgrid cells,and each of these subgrid cells is then assigned to a grid cellof the auxiliary grid.[15] The interpolation between the grids is carried out in

three steps. First, all land grid cells are filled up withrespective values of the surrounding ocean grid cells bysuccessive extrapolation. This is necessary to avoid inclu-sion of improper values into the interpolation due todifferent land-sea masks. The next step represents the actualinterpolation, which is performed by averaging the values ofall subgrid cells that have been assigned to the current gridcell of the auxiliary grid (ocean/ice to atmosphere) or byaveraging the respective nine values of the auxiliary gridcells that have been assigned to the nine subgrid cells of thecurrent ocean grid cell (atmosphere to ocean/ice). The windstress vectors are transformed into the coordinate system ofthe ocean model by a simple rotation before their compo-


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nents undergo the interpolation procedure. As a last step, allocean grid cells that lie outside of the overlap area of thetwo model domains are marked as uncoupled domain andafterward treated as in uncoupled mode, i.e., the models’surface forcing in those regions is taken from observationaldata as described in the following section.

2.5. Boundary Conditions

[16] As themodels are regional, forcing data are required atHIRHAM’s lateral boundaries and also at HIRHAM’s lower-and NAOSIM’s upper-boundary points that lie outside of theoverlap area of the two model domains. These data are takenfrom the most recent European Centre for Medium-RangeWeather Forecasts (ECMWF) reanalyses (ERA-40) and areupdated every 6 hours at the HIRHAM’s lateral boundariesand every 24 hours at the surface boundaries outside of theoverlap area.[17] NAOSIM’s lateral boundaries, which are given as

open boundaries (see section 2.2), are treated in a differentway because reanalysis data are not available for the ocean.The model determines outflow and inflow points and allowsfor the outflow of tracers and the radiation of waves. Atinflow points, temperature and salinity are restored with atime constant of 180 days toward a yearly mean climatology[Levitus and Boyer, 1994; Levitus et al., 1994]. The bar-oclinic part of the horizontal velocity is calculated from asimplified momentum balance, while the barotropic veloci-ties normal to the boundary are taken from a lower-resolutionversion of the model that covers the whole Atlantic Ocean.[18] At NAOSIM’s upper-boundary points outside of the

overlap area, the input data comprise daily means of 2-m airand dew-point temperature, cloud cover, precipitation, windspeed, and wind stress. The atmospheric fluxes are calcu-lated using standard bulk formulas, which are also used inthe stand-alone version of NAOSIM. Since runoff from theland is not explicitly included, a salinity-restoring flux withan adjustment timescale of 180 days is added to the surfacefreshwater flux.[19] Although there are only few ocean grid cells within

the HIRHAM domain, which are not covered by NAOSIM(primarily over the northernmost part of the Pacific), a simpleextrapolation from NAOSIM values onto these grid cells is

not reasonable because they are too far away from theNAOSIM domain that similar sea surface conditions can beexpected. For this reason, sea surface temperature and sea-icecover fraction are taken from ERA-40, whereas ice thickness,snow thickness, and sea surface salinity are prescribed bydefault values (hi = 1 m, hs = 0 m, So = 35 psu).

3. Experimental Design

[20] To isolate the importance of uncertain processdescriptions and initial conditions with respect to thethermodynamic evolution of sea ice in the model, a seriesof sensitivity experiments was conducted with HIRHAM-NAOSIM (Table 2). Each of these experiments covers theperiod from May 1989 to December 1999, and in each case,the atmosphere was initialized with ERA-40 data, whileinitial ocean and sea-ice fields were taken from a stand-alone run of NAOSIM described by Karcher et al. [2003]except for an initial ice experiment (h1.2-uni) where icethickness and ice concentration were prescribed in thefollowing way: If the stand-alone run yielded an iceconcentration greater than 0.5, the initial ice concentrationwas set to 1.0 and the ice thickness was initialized with1.0 m. In the other case, initial ice concentration andthickness were set to zero.[21] In addition, a coupled model experiment without an

adequate own spin-up time was carried out (nospinup). This

Table 1. List of the Exchange Variables Between Atmosphere (A), Ocean (O), and Ice (I) Components of

HIRHAM-NAOSIM

Variable Statusa From To Required for Computing of

Wind stress r A O + I Ocean current, ice driftHeat fluxes a A O + I Sea surface temperature, ice growthMoisture fluxes a A O Sea surface salinityPrecipitation rate a A O + I Sea surface salinity, snow accumulationAir temperature a A I Differentiation of snow/rainSea surface temperature i O A + I Heat and moisture fluxes, ice growthSea surface salinity i O A + I Freezing point of seawaterOcean current i O I Ice driftIce concentration i I A Grid cell averaged fluxesIce thickness i I A Ice surface temperatureSnow thickness i I A Ice surface temperatureMomentum flux i I O Ocean currentHeat flux i I O Sea surface temperatureFreshwater flux i I O Sea surface salinity

aStatus of ‘‘i’’ means instantaneous, ‘‘a’’ means averaged over coupling interval, while ‘‘r’’ means running mean over12 coupling intervals. The running mean is applied to avoid sudden changes in the forcing due to strongly varying wind fields.

Table 2. HIRHAM-NAOSIM Sensitivity Experiments With

Respect to Initial Ice Thickness, Lead Closing Parameter (h0),

and Ice Albedo Scheme

Experiment Description

h1.2-std Control run with h0 = 1.2 m, standardalbedo scheme, and standard ice initialization

h1.2-uni As h1.2-std but with uniform initial icethicknesses of 1.0 m

h0.5-std As h1.2-std but with h0 = 0.5 mh1.2-alb As h1.2-std but with new snow and ice

albedo schemeh2.0-alb As h1.2-alb but with h0 = 2.0 mnospinup Short-term run for the year 1998 without

model spin-up


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experiment was started on 1 December 1997 using initialfields from the stand-alone run of NAOSIM mentionedabove and was exceptionally running only for the year1998. In this short-term run, h0 was set to 1.0 m since thesame value was also used in the stand-alone run (seecomments in the paragraph after next).[22] The thermodynamic evolution of sea ice depends

largely on the atmospheric heat fluxes. During the coldseason, the atmospheric heat flux over open water isgenerally much larger than over an insulating ice layer(i.e., Qao � Qai > 0). The area of open water is thereforeimportant for the total heat loss of the ocean-ice system anddetermines the rate of ice growth. In equation (6), theempirical parameter h0 directly controls the rate of increasein ice concentration under freezing conditions (Qao > 0).This indirectly affects Sh by modifying the weighting of therespective contributions to Qa in equation (5). Consequently,a faster/slower increase in ice concentration leads to adecelerated/accelerated ice thickness growth. In this manner,h0 determines the relationship between basal and lateral sea-ice growth during the cold season and might indirectlyinfluence summer sea-ice and atmospheric processes.[23] As a result of the empirically based conservation

equation for ice concentration, it is impossible to deduce adefault value for h0 from physical principles. Hibler [1979]suggested a value of h0 = 0.5 m, derived from a simplethermodynamic experiment; on the other hand, Bjornsson etal. [2001] argued that a value of h0 = 1.0 m is suitable whenmodeling the large-scale Arctic sea-ice cover, while a valueof h0 = 0.3 m leads to more reasonable ice concentrationswithin polynyas. Here two experiments were performedusing h0 = 0.5 m (h0.5-std) and h0 = 1.2 m (h1.2-std),respectively.[24] In contrast to winter conditions, the atmospheric heat

fluxes during the melting period are dominated by thecontribution of solar radiation. Because of the importanceof the ice-albedo feedback for summer ice retreat [e.g.,Lynch et al., 2001], a new snow and ice albedo schemefollowing suggestion 2 of Køltzow et al. [2003] has beentested in the coupled model. This scheme was derived frommeasurements during the SHEBA project and differs fromthe standard scheme in many respects. It includes, forinstance, a quite different temperature dependency of theice albedo due to an explicit temperature-dependent param-eterization of melt ponds (Figure 2) and allows for arealfractions of snow, melt ponds, and bare ice. The majordifference is that the new scheme decreases the ice albedo inmost instances, particularly for melting conditions when theoverlying snow cover has already disappeared.

4. Simulation Results

4.1. Ice Volume and Extent

[25] The top panel of Figure 3 shows the simulated icevolume within the NAOSIM domain (see Figure 1) from theexperiments h0.5-std, h1.2-std, and h1.2-uni. Although atleast the experiments h0.5-std and h1.2-std were initializedwith sea-ice fields from a stable run of the stand-aloneocean-ice model, the modeled ice volume is far from asteady state at the beginning in all coupled model experi-ments. This is in contrast to the results of the stand-alonerun in which the ice volume nearly persists at values like in

the early stages of the coupled simulations (see Figure 4).However, for the coupled model, the initial ice volumeappears to be too high in h0.5-std and h1.2-std, while it isobviously much too low in h1.2-uni where the model startedfrom uniformly 1-m-thick ice.[26] Nevertheless, all coupled simulations arrive at a

quasi-stationary cyclic state of equilibrium after about 6–10 years, and this equilibrium is only little affected by theinitial sea-ice state even though the initial ice volume differsby a factor of 4. On the other hand, the coupled model’sstate of equilibrium depends significantly on the rate ofincrease in ice concentration given by h0. The simulationwith h0 = 0.5 m results in a mean ice volume that is justabout half as large as using h0 = 1.2 m. This dependency onh0 is in qualitative agreement with the findings of Hollandet al. [1993] who found an increase in mean ice thickness ofabout 1 m when decreasing the growth rate of ice concen-tration to 50% (equivalent to a doubling of h0).[27] The corresponding sea-ice extent of the two h0

experiments is shown in the bottom panel of Figure 3 incomparison with SSM/I satellite-derived data using theNASA Team algorithm [Cavalieri et al., 1990, updated2004]. The model generally overestimates the sea-ice extentduring winter, and none of the experiments has been able toreduce this shortcoming substantially. In contrast, the simu-lation with h0 = 1.2 m agrees quite well with the observedsummer ice extent after some years, while the simulationwith h0 = 0.5 m tends to underestimate the summer iceextent considerably.[28] A common result of the experiments is that summer

ice extent is significantly correlated with the ice volume atthe beginning of the melting period (ensemble correlationcoefficient of 0.92 between ice volume in April and iceextent in September). However, the modeled ice volume isgenerally affected by ice growth during winter as well as icedecay during summer, and thus not only by h0 but also bythe parameterizations relating to Qai and Qao in the atmo-sphere model.[29] The experiments conducted using the new snow and

ice albedo scheme show that due to a decrease of theice albedo (h1.2-alb versus h1.2-std; see annotation insection 3), the energy input into the ocean-ice systemincreases, leading to quicker decay of sea ice duringsummer and accordingly to reduced ice volume at the endof the summer (Figure 4). However, the impact on ice

Figure 2. Temperature dependency of the sea-ice albedoin the standard scheme (dark gray lines) and the new sea-icealbedo scheme (light gray lines). The solid/dashed linesrepresent lower/upper limits of the albedo arising in case ofno/complete snow cover.


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volume is not as strong as due to the change of h0, and theresulting ice volume of experiment h1.2-alb lies finallybetween that of h1.2-std and h0.5-std. The same is truefor the summer ice extent.[30] As the new albedo scheme may be considered as

more sophisticated than the standard scheme, leading inprinciple to a more realistic simulation of the ice-albedofeedback and the associated ice decay during summer, onemay conclude that underestimated ice growth during winteris the major reason for the apparent underestimation of the icevolume in experiment h1.2-alb. This conclusion suggests thatsimulations with the new albedo scheme may yield morereasonable summer ice extent and concentration when usinga higher value of h0 at the same time. Then, quicker ice lossduring summer due to lower ice albedo is balanced byincreased ice growth during winter, and the model’s state ofequilibrium in ice volume is situated higher than before. Thispresumption is supported by the experiment h2.0-alb wherethe new albedo scheme is applied in conjunction with h0 =2.0 m. The summer ice volume is here almost identical to

that of h1.2-std, and also the summer ice extent agrees heremuch better with the SSM/I data than in experiment h1.2-alb.The impact on winter ice extent is marginal again.[31] A rough comparison with available ice thickness

observations, for instance with the climatologies of Bourkeand Garrett [1987] and Laxon et al. [2003], shows that thesimulated ice thicknesses in h0.5-std and h1.2-alb are defi-nitely too thin in the model’s steady state (roughly frommonth 70 onward), while they are much closer to, but stillsomewhat thinner than, the observations in h1.2-std andh2.0-alb. On the other hand, the initial ice thicknesses (forMay 1989) are clearly too thick in these four experiments. Thelatter holds for the whole stand-alone run and consequently forthe run without spin-up as well. However, a detailed validationof the modeled ice volume is currently not possible since allavailable observational data include regional gaps.[32] A comparison with ice thickness estimates derived

from the extensive observations during SHEBA [Lindsay,2003] is presented in Figure 5. The ice thickness data areavailable via URL http://www.joss.ucar.edu/cgi-bin/codiac/

Figure 3. Simulated monthly means of sea-ice volume (top) and sea-ice extent (bottom) within themodel domain from May 1989 (month 5) to December 1999 (month 132). The sea-ice extent is heredefined as the area of all grid cells with at least 15% sea-ice concentration. For comparison, the SSM/Isatellite-derived sea-ice extent (solid line) was calculated for the same domain. The model simulationswere carried out with ho = 0.5 m (h0.5-std, dotted lines), with ho = 1.2 m and standard ice initialization(h1.2-std, dashed lines), and with ho = 1.2 m and initialization with uniform 1-m ice thickness (h1.2-uni,dot and dash line).


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dss/id=13.122. Although this data set represents only region-al ice conditions in the vicinity of the SHEBA ice camp, itindicates that the modeled ice thicknesses in h1.2-std andh2.0-alb can be considered as more realistic than in h0.5-stdand h1.2-alb. This conclusion can also be drawn by com-paring the modeled ice thicknesses with the ice thicknesscurve at the SHEBA Pittsburgh site produced by Huwald etal. [2005]. However, Huwald et al. [2005] have also shownthat there is high spatial variability of ice thickness gaugemeasurements at small spatial scales, indicating the diffi-culties in validating modeled ice thicknesses with individualmeasurements.

4.2. Winter Ice Concentration

[33] Figure 6 shows the satellite-derived and modeled sea-ice concentration inMarch 1998. At first view, there is a rathergood agreement between the simulations and the observationwith respect to the ice edge, except for the Labrador Sea,where the model clearly overestimates the formation andpersistence of ice. This model bias appears in all long-termexperiments of the coupled model and all winters after thefirst melting season and is responsible for the overestimationof the total ice extent. In contrast, the uncoupled NAOSIMsimulation and the run without spin-up agree better with the

Figure 4. As Figure 3 but for the uncoupled NAOSIM simulation (dashed line) and coupled modelsimulations with the new snow and ice albedo scheme using h0 = 1.2 m (h1.2-alb, dotted lines) and h0 = 2.0 m(h2.0-alb, dot and dash lines), respectively.

Figure 5. Mean ice thickness estimated from observationsnear the SHEBA drifting ice camp in the Beaufort Sea during1997–1998 [Lindsay, 2003] and from simulations ofHIRHAM-NAOSIM. Simulated ice thicknesses were inter-polated from the model grid onto the respective position ofthe ice camp. The mean ice thickness includes the open-waterareas. The term ‘‘SHEBA day’’ on the x axis corresponds tothe day from the start of 1997. The time series weresmoothed using a 7-day running mean.


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Figure 6. Sea-ice concentration in March 1998 from SSM/I satellite-derived data (top left), anuncoupled NAOSIM run (top right), and the HIRHAM-NAOSIM experiments described in Table 2.


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satellite data over the Labrador Sea, but on the other hand,they overestimate sea ice over the Greenland Sea more thanthe coupled model, so that the total ice extent is quite similar.[34] At closer inspection, it is also visible that there is a

well-defined correlation between h0 and the modeled open-water fraction within the ice covered area: With increasingh0, the open-water fraction increases too. In particular, inexperiment h2.0-alb, there are large areas where the iceconcentration is well below 95%. This is contrary to theobservation. As a more quantitative measure, the root meansquare error (RMSE) of sea-ice concentrations greater than50% has been calculated and confirms that, with respect towinter ice concentration, h2.0-alb (RMSE = 8.4%) can beregarded as the most unrealistic and h0.5-std (RMSE =6.5%) as the most realistic long-term experiment. TheRMSE of the other experiments lies in-between (h1.2-std:7.0%; h1.2-uni: 7.2%; h1.2-alb: 6.9%).[35] The impact of the ice concentration on 2-m air

temperature during winter is shown in Figure 7. HIRHAM-NAOSIM generally tends to overestimate the winter temper-atures over the ice-covered ocean. The magnitude of thismodel bias depends on the ice thickness (compare nospinupand h0.5-std, which show similar ice concentrations but quitedifferent ice thicknesses) but particularly on the open-waterfraction due to enhanced heat transfer from the ocean to theatmosphere in the absence of an insulating ice cover. Thisfinding agrees with the analyses of uncoupled HIRHAMsimulations by Rinke et al. [2006]. The temperature overes-timation increases considerably if the open-water fractionincreases too. While the temperatures are about 8–12 K toowarm in h0.5-std, they are more than 12 K too warm inh2.0-alb over most of the Arctic Ocean. Even if a value ofh0 >1.0 m yields more reasonable ice thicknesses in themodel, it prevents total freezing of the Arctic Ocean inwinter and results not only in unrealistic sea-ice concen-trations but also in too-warm temperatures.

4.3. Summer Ice Concentration

[36] The satellite-derived and modeled sea-ice concentra-tion in September 1998 is presented in Figure 8. Theexperiments demonstrate the effects of an unrealistic icevolume on summer ice extent and concentration: If the seaice is too thin at the beginning of the melting period (asmost notably in h0.5-std), the ice cover is quicker to openwith the result of stronger ice retreat and underestimation ofsea-ice concentration throughout the Arctic. In contrast, too-thick sea ice (as in the short-term experiment without spin-up) results in effects exactly the opposite to the above. Theexperiments in which the ice thicknesses are likely to beclosest to reality (h1.2-std and h2.0-alb) also show the bestagreement in ice extent and concentration.[37] This finding does not hold for the uncoupled model.

Here a rather good agreement with the satellite data hasbeen obtained with an ice thickness distribution which issimilarly too thick as in the coupled model experiment‘‘nospinup’’. At least the observed opening of the BeaufortSea is present in the uncoupled model, while it is com-pletely missing in ‘‘nospinup’’ where the ocean-ice modelis coupled to an atmosphere model. The different behaviorof the coupled and the uncoupled model appears also in theice concentration itself. The coupled model tends to over-

estimate the open-water areas within the summer sea-icecover.[38] Although experiment h1.2-alb shows quasi-realistic

sea-ice retreat in the Beaufort Sea and also in the Barentsand Kara Seas, there are considerably larger areas of openwater in the Laptev and East Siberian seas. This underes-timation of sea ice is associated with differences in theatmospheric circulation during the previous summer months(Figure 9). In contrast to observations and all other experi-ments, this experiment shows a pronounced cyclonic flowover the Laptev Sea which provides an atmospheric windstress for drifting ice away from the East Siberian Seatoward the central Arctic Ocean and Kara Sea. The redis-tribution of ice mass within the Arctic leads to a situation inwhich thermodynamic loss of ice is regionally either inten-sified by dynamic ice loss or partly compensated byincreased influx of ice.[39] As the effect of the atmospheric circulation on the sea-

ice distribution is quite evident, the atmospheric response toincorrect sea-ice concentrations or thicknesses is not thatdefinite. In particular, there is no linear relationship betweenover- or underestimated ice concentrations or thicknesses anddeviations in the atmospheric circulation. The underlyingnonlinear feedback process is not yet understood. However, itis remarkable that significant pressure deviations occurpredominantly over the Laptev and Kara seas, a region wherealso strong deviations in the simulated ice edge appear. Suchdifferences in the positions of the ice edge are able to triggermodel deviation in mean sea-level pressure [Rinke et al.,2003, 2006]. On the other hand, the position of the ice edge inthe Beaufort Sea, which varies considerably among thesimulations, has obviously no or only marginal impact onthe simulation of the atmospheric circulation. Differences inthe simulated ice drift can therefore not be regarded as themain reason for the model variations in the strength of thesimulated ice retreat in the Beaufort Sea.

4.4. Atmospheric Heat Fluxes

[40] Some potential shortcomings of the coupled modelbecome apparent when looking at the mean seasonal cycleof atmospheric heat and radiative fluxes averaged over allsea areas north of 70�N (Figure 10). As noted before, ahigher value of h0 leads to lower ice concentrations (sea-icecover) during winter and to an increased heat transfer fromthe ocean to the atmosphere. This effect is visible in boththe sensible and latent heat fluxes. However, the differencesamong the model simulations as well as between simulationand ERA-40 data are on average clearly lower than 10W/m2.In accordance with the largest deviations in ice concentration,h2.0-alb also shows the largest deviations in the heat fluxes.[41] On the other hand, all simulations show an overesti-

mation of net long-wave radiation inwinter of about 20W/m2

compared to ERA-40. This means that the net surface heatflux during winter is approximately 10–20W/m2 too high inthe coupled model. The outcomes of this are too-warm icesurface temperatures (and consequently too-warm 2-m airtemperatures) in winter and an underestimated conductiveheat flux through the ice with the result of too-low ice growth.The long-wave radiation bias is partly compensated by anopposed bias in sensible and latent heat flux, most notably inh2.0-alb, but with the consequence of the largest temperaturebias.


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Figure 7. Difference of 2-m air temperature in winter 1997/1998 (December to March) between thesame simulations of HIRHAM-NAOSIM as in Figure 6 and ERA-40 reanalysis data.


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Figure 8. As in Figure 6 but for September 1998.


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[42] The overestimation of net long-wave radiation inwinter is associated with an overestimation of cloud coverof about 10% compared to ERA-40 (Figure 10) but more than20% compared to some satellite products [e.g., Schweiger et

al., 1999]. Despite general difficulties in validating modeledwintertime Arctic cloud cover as pointed out by Wyser andJones [2005], the overestimation of low-level clouds over theArctic Ocean during winter is a well-known problem in

Figure 9. Difference of mean sea-level pressure in summer 1998 (June to September) between the samesimulations of HIRHAM-NAOSIM as in Figure 8 and ERA-40 reanalysis data.


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HIRHAM and was already discussed by Rinke et al. [1997].In the coupled model system, this bias reduces the thermo-dynamic growth of sea ice and leads to an increased wintertemperature bias as a result of a positive feedback. While onemight expect that the bias in cloud cover could also increasedue to increased moisture flux from the surface in case oflarger open-water areas, no such positive feedback is appar-ent in the simulations.[43] During summer, the differences between the model

simulations are rather low with respect to the atmosphericfluxes. Even the two simulations with the new albedo schemeshow only minor deviations from the standard simulations. Inparticular, all simulations show a similar underestimation ofnet short-wave radiation of about 20 W/m2 and also anunderestimation of 2-m air temperatures of 2–3 K duringsummer.[44] While the reason for the underestimation of net

short-wave radiation is still unclear (lower sea-ice coverand lower cloud cover in summer should actually beaccompanied by an overestimation of net short-wave radia-tion), the underestimation of the 2-m air temperature can beexplained by the fact that the model limits the ice surfacetemperature to values below the salinity-dependent freezingpoint of seawater as long as ice is present in the grid cell.

This is indeed a rather rough assumption. On the one hand,the snow and ice surface temperature, at least, may beexpected to rise up to the freezing point of freshwater,which is up to 2 K higher, since the ice crystal structureaccommodates only negligibly small concentrations of salt.On the other hand, incoming atmospheric energy over openwater can be used either to melt ice laterally or to warm upthe mixed layer, even above the salinity-dependent freezingtemperature, assuming the matter of fact that horizontalmixing does not occur instantaneously.[45] Maykut and Perovich [1987] have argued that the

possible elevation of the upper-ocean temperature above thefreezing point depends on the lead width and can be up to 5 Kfor nearshore conditions. They have further noted that thelateral melt rate depends on lead width and ice thickness in avery complex manner. For central Arctic conditions, justabout 20% of the total melt rate can be attributed to lateralmelt. In the current setup of the coupled model, all atmo-spheric energy over open water is used to melt snow and ice,in fact independent from the present lead width and icethickness. This oversimplification may lead to an overesti-mated ice and, in particular, snow melt in the model with theresult of an overestimated magnitude of the ice-albedofeedback effect.

Figure 10. Mean seasonal cycle (1996–1999) of selected variables averaged over all sea areas north of70�N from ERA-40 reanalysis data and simulations of HIRHAM-NAOSIM. Observed mean sea-icecover is based on SSM/I data instead of ERA-40. Fluxes are positive toward the ocean-ice surface.


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4.5. Snow Thickness

[46] Validation of the modeled snow thickness is prettydifficult because area-wide snow thickness measurements arenot available yet. Anyway, the SHEBA project has alsoprovided snow measurements [Perovich et al., 1999] whichcan be used for a rough validation of the modeled snowthickness. However, measurements of snow depth at SHEBAshow high variability at small spatial scales as pointed out byHuwald et al. [2005] and are not necessarily representativefor a larger area. Compared to themeasurements at the ‘‘snowmainline’’ (Figure 11), all model simulations overestimatesnow thickness in winter and spring, but the onset of snowmelt coincides quite well with the observation (around themiddle of May, corresponding to SHEBA days 490–500).While the observed snow melt continued till early July, themodel clearly overestimates snow melt and does not showany snow already in the middle of June (approximatelySHEBA day 530) when just half of the observed snow haddisappeared.[47] The model simulations differ about 2 weeks at most

in the beginning and the end of the snow-melt period, butthe length is almost equal and there is no clear indicationthat the new albedo scheme has significant impact on thesnow-melt rate. However, the end of the snow-melt periodcoincides with the beginning of the ice-melt period (seeFigure 5). The total amount of thermodynamic ice lossdepends mainly on the date of disappearance of the snowcover. Too-early disappearance of the snow cover due tooverestimated snow melt leads to increased decay of sea iceand vice versa. It may be supposed that the underestimationof ice concentration over the central Arctic Ocean duringsummer originates from an overestimated snow-melt rate in

the coupled model and, consequently, from the simplicityof the thermodynamic ice scheme, which is reflected inequations (4) and (5).

5. Summary and Conclusions

[48] A pan-Arctic coupled regional AOI model wasapplied to gain insight into uncertain and sensitive Arcticprocess descriptions in a coupled model, which need to beimproved in order to reproduce observed sea-ice conditionsin a more realistic fashion. Particular attention was paid tothe ability of the model to reproduce the Arctic sea-iceanomaly during summer 1998 and the associated atmo-spheric conditions.[49] Because the boundary conditions at the interface of

atmosphere and ocean-ice model are normally not perfect ina coupled model system, the ice volume arising in asimulation of the stand-alone ocean-ice model may differconsiderably from that of a corresponding coupled modelsimulation. It has turned out that a spin-up time of about 6–10 years is needed to reach a quasi-stationary cyclic state ofequilibrium in the coupled model if the initial ice conditionsare far from this state. The coupled model’s steady statelevel is rather independent from the initial ice conditions butdepends significantly on uncertain process descriptionsaffecting thermodynamic growth and decay of ice in themodel.[50] A quasi-realistic ice thickness distribution at the

beginning of the melting period has been found to be adecisive precondition for the ability of the coupled model toreproduce observed summer sea-ice extent. In contrast tothe stand-alone ocean-ice model, where the atmosphericconditions are prescribed, the coupled model has to calcu-late the atmospheric fluxes in response to the given iceconditions and vice versa. Owing to positive feedbacksarising from this interaction, the coupled model may beexpected to be more sensitive to the ice thickness distribu-tion than the stand-alone ocean-ice model.[51] Some indications exist that those experiments of the

coupled model, which show the best agreement in simulatedice extent with observations, also offer the most realistic icethickness distribution. Nevertheless, it is not well definedwhat a really realistic ice thickness distribution is becauseice thickness observations are only sparsely available. Thisuncertainty remains a fundamental issue in Arctic climatemodeling in general and sea-ice modeling in particular andwill make the development of improved parameterizationsdifficult.[52] It has turned out that the widely used, but rather

intuitively reasoned parameterization of lateral ice growth,which is especially reflected in an arbitrary referencethickness for lateral freezing (h0), provides an opportunityto tune a coupled model toward a quasi-realistic state ofequilibrium in terms of the ice volume. Even thoughsummer sea-ice retreat develops more realistically in thiscase, the simulation of winter ice concentrations and near-surface temperatures may be getting worse. The right choiceof h0 is likely to depend on the model used, particularly onthe parameterizations of atmospheric processes that deter-mine the surface fluxes. Because of the prime importance ofthe areas of open water for the oceanic heat loss during thecold season, the parameterization of lateral freezing can be

Figure 11. Mean snow thickness from measurements at the‘‘snow mainline’’ of the SHEBA drifting ice camp in theBeaufort Sea during 1997–1998 [Perovich et al., 1999] andfrom simulations of HIRHAM-NAOSIM. Simulated snowthicknesses were interpolated from the model grid onto therespective position of the ice camp. Effective snow thicknessrefers to the ice covered part of the grid cell (excluding open-water areas). The term ‘‘SHEBA day’’ on the x axis corres-ponds to the day from the start of 1997. The time series weresmoothed using a 7-day running mean.


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regarded as a key process for the ice growth at large. Thisconclusion might be universally valid even if using other icemodels that include empirical parameters with differentinterpretations [e.g., Mellor and Kantha, 1989; Hakkinenand Mellor, 1992].[53] In the particular model used in this study, a higher

value of h0 has to be used in order to give reasonable icegrowth in winter. The associated higher heat loss of theocean due to larger open-water areas has to compensate fora systematic overestimation of net surface long-wave radia-tion of about 20 W/m2. The reason for this model bias mightbe an overestimation of low-level clouds over the ArcticOcean. Uncertainties in the simulation of Arctic clouds areamong the major problems in most regional [e.g., Maslaniket al., 2000; Mikolajewicz et al., 2005] and global models[Chen et al., 1995]. It has become apparent that they cannotonly account for unrealistic summer ice decay but particu-larly also for unrealistic winter ice growth in a coupled AOImodel.[54] A change in a surface flux-related parameterization is

able to affect both atmospheric circulation and sea-iceconditions, as demonstrated by the integration of a moresophisticated snow and ice albedo scheme into the coupledmodel. The ice albedo has direct impact on the ice-albedofeedback, which definitely represents one of the mostdominant processes for thermodynamic loss of ice duringsummer. Even small changes in a coupled model’s icealbedo scheme may lead to significant changes in thesimulation of summer sea ice due to this positive feedback.Hence any basic change of the ice albedo scheme requiresreadjusting the relationship between growth and decay ofsea ice.[55] Furthermore, there are indications that the magnitude

of the ice-albedo feedback effect is overestimated in thecoupled model associated with too-early disappearance ofthe snow cover. It is supposed that an elaborated subdivisionof the incoming atmospheric energy into snow and ice meltfrom above, lateral ice melt, and mixed layer warming willbe able to overcome this shortcoming. Such schemes havealready been partly realized in ice models [e.g., Tremblayand Mysak, 1997] with specific intent to allow for morerealistic sea-ice retreat during summer, but they are ratherseldom in fully coupled dynamical AOI models.[56] In order to achieve a realistic regional distribution of

sea ice in late summer, it also requires that the coupledmodel reproduces the observed atmospheric circulationduring the preceding summer months. Nevertheless, incontrast to the clear response of the sea-ice cover to theatmospheric circulation, the atmospheric response to incor-rect sea-ice cover is not that definite. Unrealistic sea-icecover, as a result of incorrect thermodynamic ice loss, mayfavor model deviations in atmospheric circulation, but thesedeviations can clearly differ in their strength, probably inconsequence of regional feedbacks. Owing to the variety ofprocesses involved in such regional feedbacks, it is hard todistinguish between cause and effect of model deviations ina coupled model system without systematic sensitivityexperiments. Some of such experiments have been pre-sented in this paper, but several further experiments, espe-cially with respect to the cloud scheme and the treatment ofsnow and ice melt, are required to assess the importance of

individual processes for the simulation of Arctic sea ice andto develop improved parameterizations for these processes.[57] The results of this paper suggest that uncertain

process descriptions for Arctic clouds, snow, and sea-icealbedo, and lateral freezing and melting of sea ice, includingthe treatment of snow, might also be the reason for the largedeviations in the simulation of Arctic sea ice with globalAOI models. A coupled AOI model responds definitelymore sensitively to such uncertainties than a pure atmo-sphere or ocean-ice model due to the feedbacks arisingbetween the components of the Arctic climate system.Given that the magnitude of the long-wave radiation biasover the Arctic Ocean even exceeds the estimated change inthe radiative forcing up to the year 2100 from all emissionscenarios [Cubasch et al., 2001], it becomes apparent thatmodel biases in polar regions must be further reduced inorder to enhance the credibility of future climate changeprojections.

[58] Acknowledgments. This work was funded by the German FederalMinistry for Education and Research (BMBF) project ACSYS II (BMBF grant03PL034C) and the European Union project GLIMPSE (EU grant EVK2-CT-2002-00164). The model simulations were carried out on the parallel IBM-p690 computer system at the North German Supercomputing Center (HLRN)under project ID hbk00014. SSM/I sea-ice concentrations were obtained fromthe National Snow and Ice Data Center (NSIDC), Boulder, CO, SHEBA datawere provided by the SHEBA Project Office, University of Washington, andERA-40 data were provided by the European Centre for Medium-RangeWeather Forecasts (ECMWF). Finally, we would like to thank Jan Sedlacekand two anonymous reviewers for their helpful comments to improve themanuscript.

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