Comparison of Arctic sea ice thickness variability in IPCC Climate of the 20th Century experiments and in ocean sea ice hindcasts

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi: /2006jc003616, 2007 Comparison of Arctic sea ice thickness variability in IPCC Climate of the 20th Century experiments and in ocean sea ice hindcasts Rüdiger Gerdes 1 and Cornelia Köberle 1 Received 30 March 2006; revised 18 October 2006; accepted 19 December 2006; published 10 April [1] Arctic sea ice is an important climate component and often regarded as an early indicator of anthropogenic global change. For an assessment of coupled climate models, their performance with respect to the development of Arctic sea ice thickness during the 20th century is examined. Their behavior is compared with results from an ocean sea ice model using the Arctic Ocean Model Intercomparison Project (AOMIP) atmospheric forcing for the period In lieu of actually observed sea ice thickness, this model result under realistic atmospheric forcing serves as a benchmark for the coupled climate models. The hindcast exhibits virtually no trend in Arctic ice volume over its integration period Most of the coupled climate models show a negative trend over the 20th century that accelerates towards the end of that century. Citation: Gerdes, R., and C. Köberle (2007), Comparison of Arctic sea ice thickness variability in IPCC Climate of the 20th Century experiments and in ocean sea ice hindcasts, J. Geophys. Res., 112,, doi: /2006jc Introduction [2] Sea ice is an important climate component as it controls the ocean-atmosphere heat flux and affects the short wave radiation balance at the surface through its highly variable albedo. Theoretical reasons and an actually observed shrinking Arctic sea ice cover have heightened the status of Arctic sea ice as an early indicator of anthropogenic global change. Coupled climate models predict a dramatic decrease of Arctic sea ice, including a complete loss of multiyear ice in the course of the current century. Whether this development has already started is an open question. A limited amount of thickness data from upward looking sonar on board of British and U.S. submarines is available from the National Snow and Ice Data Center, University of Colorado at Boulder. While those data do not completely cover the Arctic Ocean in space and time, they provide information about long-term changes along repeated sections. Rothrock et al. [1999] estimated a substantial decrease in Arctic ice volume from the 1960s to the 1990s. Wadhams and Davis [2000], who compared data from two cruises in the Eurasian Basin that were taken in 1976 and 1996, could confirm the decrease of ice volume there. On the other hand, Winsor [2001] found no thinning of the ice in the Beaufort Sea, the North Pole area, and along several submarine tracks connecting the two regions during the 1990s. Hindcast experiments with ocean sea ice models indicate a pronounced temporal and spatial variability of sea ice. Holloway and Sou [2002] point out that the decrease estimated by Rothrock et al. could be biased high because the available observations do not capture a redistribution of sea ice within in the Arctic Ocean. Köberle and Gerdes [2003] 1 Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany. Copyright 2007 by the American Geophysical Union /07/2006JC (hereafter KG03) show a decline of the Arctic sea ice volume after maximum ice volume in the mid-1960s. However, conditions in the 1990s only returned to those that already existed in the 1950s according to that simulation. Rothrock and Zhang (2005) found very similar results in an ocean sea ice simulation for the period The minimum in Arctic ice volume at the end of the simulation is 37% less than the maximum that was reached in However, this minimum is only 17% lower than the sea ice volume in the 1950s after the initial adjustment phase of the model. In the KG03 and the Rothrock and Zhang simulations, the ice volume decreased from the mid-1960s because of increasing air temperatures. The ice export to lower latitudes had no corresponding trend. [3] Here, we examine the variability of Arctic sea ice volume in a number of global coupled climate models for which sea ice thickness data were available for the Climate of the 20th Century experiment (20CM3) of the IPCC suite of experiments. We are especially interested in the trends in Arctic ice volume occurring in those experiments. We compare the variability characteristics with that of an ocean sea ice hindcast integration. Since direct observations cannot provide sea ice thickness with the necessary temporal resolution and spatial coverage, we take these model results that were gained with realistic (reanalysis) atmospheric forcing data as benchmark for the coupled model results. [4] The paper starts with a short discussion of the hindcast results in section 2. Section 3 provides a short description of the IPCC 20C3M experiment and of the participating climate models. Section 4 presents characteristics of sea ice volume variability in the hindcast and the IPCC experiments. Discussion and conclusions are presented in section AOMIP Hindcast Results [5] For this investigation, we had results for the period from six AOMIP hindcast simulations 1of11

2 Figure 1. Mean sea ice thickness for the AOMIP hindcasts Model names are given atop each panel. (Goddard Space Flight Center, GSFC; Institute of Ocean Science, IOS, Sydney, British Columbia; Alfred Wegener Institute, AWI; Naval Postgraduate School, NPS; University of Washington, UW, Seattle) available. The companion papers in this issue by Proshutinsky et al. [2007], Martin and Gerdes [2007], and Johnson et al. [2007] provide details of the models and the experimental procedure. The atmospheric forcing is taken from the NCAR/NCEP reanalysis [Kalnay et al., 1996], and the models were integrated starting from rest and climatological temperature and salinity distributions in The AWI undertook two AOMIP simulations with different models from the NAOSIM (North Atlantic Arctic Ocean Sea Ice Models) hierarchy. For the lower resolution version of the AWI 2of11

3 GERDES AND KO BERLE: ARCTIC SEA ICE VARIABILITY Figure 2. Standard deviation of annual mean sea ice thickness for the AOMIP hindcasts and the integration period Model names are given atop each panel. model (hereafter AWI1, 1 horizontal resolution, 19 levels) we also had sea ice thickness data for the whole integration period available. [6] Annual mean sea ice thickness averaged over is shown for the AOMIP models in Figure 1. Consistently, the thickest ice is situated north of Greenland and the Canadian Archipelago. The AOMIP results differ somewhat in maximum thickness north of Greenland with GSFC showing less than 4 m thick ice there. Most results feature a pattern of decreasing ice thickness following the anticyclonic sea ice motion [see Martin and Gerdes, 2007] in the central Arctic Ocean. Very thick ice is again present in the eastern parts of the Chukchi and East Siberian seas in the AWI1, AWI2, NPS, and IOS results. UW and GSFC differ from the rest of the models as they simulate ice thickness decreasing rather uniformly from the maximum off Greenland. [7] Sea ice variability in the AOMIP simulations is illustrated by the standard deviation of monthly mean sea ice thickness at each location in Figure 2 and by the time series of sea ice volume in Figure 3. The standard deviation for the AWI1, AWI2, NPS, and IOS model results indicate high variability in a band around the periphery of the western basin of the Arctic, from north of Greenland through the Beaufort, Chukchi, and East Siberian seas, and into the interior Arctic following the transpolar drift. High variability is also present in the East Greenland Current and around the islands of the eastern Arctic. The UW model shows a similar pattern with much reduced amplitude. The variability in the GSFC model is extremely low. [8] The total Arctic ice volume between 1979 and 2001 (Figure 3) varies among AOMIP simulations from around Figure 3. Time series of annual mean Arctic sea ice volume (in km3) for the AOMIP hindcast simulations. 3 of 11

4 15,000 km 3 to above 20,000 km 3. The temporal variability is similar with a minimum in ice volume in the early 1980s, a maximum in the second half of the 1980s and a rather monotonic decline afterwards. The rate of decrease is higher in the AWI2 and UW models. [9] In summary, we find that AOMIP sea ice simulations behave similarly between 1979 and The range of results is relatively small although the UW and GSFC models have less variability and produce a mean sea ice thickness distribution that is rather smooth and lacks the dynamic structure of the majority of AOMIP results. Ice volume differs in magnitude which is a result of the sensitivity of thermodynamic growth on internal model parameters that have not been fixed in the AOMIP protocol. The similarity of results indicates that sea ice thickness variability is determined by the external forcing. Because variability in ocean heat flux is only strong where the sea ice is thin, the variability in Arctic ice volume in the models is mainly given by the prescribed atmospheric forcing. This property makes the AOMIP hindcasts a more reliable reconstruction of actual temporal developments in Arctic sea ice over recent decades than the results of coupled climate models that are much less constraint by observations. The development in a coupled model can deviate from the actual development for the same model deficits as in the AOMIP hindcasts. Furthermore, the developments are subject to internal variability of the coupled system that by its nature differs from the one realization of climate variability in the real atmosphere. Furthermore, Arctic sea ice in a coupled model is affected by the representation of high latitude atmospheric variability that might not be well captured in coupled climate models. [10] From the AOMIP runs discussed above, we have available only a limited period, the common analysis period of the project. For the estimation of long-term trends in the presence of decadal and multidecadal variability of the atmospheric forcing, we desire records that are as long as possible. This consideration and the general similarity of sea ice variability in AOMIP results prompted the use of the AWI1 model results for the period for further analysis. [11] The AWI1 hindcast simulation was performed with a coupled ocean sea ice model that was forced by prescribed surface wind stresses and by surface heat and salt fluxes that are calculated using given atmospheric fields and predicted SST, sea ice thickness and concentration. The experiment follows the AOMIP specifications except that the fresh water flux at the ocean surface contains a flux correction that is necessary to make model, prescribed forcing data sets and climatological surface salinity compatible with each other. The procedure is described in detail by Köberle and Gerdes [2007]. It consists of a spin-up of the model using a sequence of four times the forcing data The surface restoring term of salinity is then averaged over the forcing period and is applied as a constant salinity flux in a repetition of the fourth cycle of the forcing period. This eliminates the undesirable negative feed back of the restoring. At the same time, it prevents the drift of the model that occurs when surface restoring of salinity is switched off (see examples in Häkkinen et al. [2007]). The sea ice model is not directly affected by the ocean surface flux correction. Apart from small differences in the forcing, the hindcast experiment is identical to that of KG03. The sea ice results are very similar to those described in that paper. There, a validation using available sea ice data has been made that can be used for the current results as well. The KG03 as well as the current hindcast experiments show an increase in Arctic Ocean ice volume from the early 1950s towards the mid-1960s. The ice accumulation phase was terminated by an ice export event in the winter 1967/68 that removed a considerable fraction of the total ice volume from the Arctic Ocean. The ice volume further declined until the mid-1990s. Smaller maxima in ice volume on a decadal timescale are mostly due to wind related fluctuations of the export rate towards the Nordic Seas. The long term decline from the mid-1960s to the mid-1990s is forced by the increase in surface air temperature over that period (KG03). Noting the several decades long decline in Arctic ice volume in the hindcast that is consistent with observations, it should also be observed that the KG03 hindcast shows virtually no trend in ice volume over the whole period for which atmospheric reanalysis data were available. 3. IPCC Model Data [12] IPCC model results that enter this comparison are listed in Table 1. These are the models for which sea ice thickness data for the Climate of the 20th Century experiment (20C3M) were available from the PCMDI data server as of January 1, Detailed information about the climate models and the 20C3M experiment can be obtained at ipcc_model_documentation.php. [13] Different numbers of realizations were made for the different models although most groups submitted just one realization of the 20C3M experiment (Table 1). Except for the calculation of trends in sea ice volume time series, we use only the results from the realization labeled run 1 and do not form ensemble averages. It should be understood that ensemble means contain less model-generated intrinsic variability than individual realizations. On the other hand, the relative contribution of externally forced natural variability and the effect of increasing greenhouse gas concentrations is enhanced in ensemble means. Thus, we use single realizations to assess the internal dynamics of the models, and we use ensemble means when estimating trends in ice volume. We discuss the differences between individual realizations regarding long term trends using the MRI results (5 realizations) as an example. [14] The 20C3M experiment includes historical sulfate aerosol and greenhouse gas concentrations. In some models, volcanic aerosol optical depth and solar irradiation are specified according to the observed development (Table 1). Thus, part of the variability generated in the models is externally forced. This might be especially true for trends and at the end of the 20th century, when effects of increasing greenhouse gases in the atmosphere may become apparent. We cannot expect that the climate models reproduce the history of Arctic sea ice volume as it developed in the real climate system or as it is generated in the hindcast experiment that is forced with the actual state of the atmosphere as it is represented in the reanalysis data. The internally generated variability of the ocean-atmosphere-sea ice system in the coupled model will prevent any phase 4of11

5 Table 1. Characteristics of Coupled Climate Models Entering the Comparison a Model ID Realizations Initial Time Sea Ice Physics Sea Ice Resolution Additional Natural Forcing BCCR Bjerknes Centre VP layer no CCSM3 NCAR Thickness distribution EVP About 1 4 layers yes CGCM3.1 (T47)CCCMA Cavitating fluid layer no CGCM3.1 (T63) CCCMA Cavitating fluid layer no CNRM-CM3 Météo-France Thickness distribution EVP layer no CSIROMk3.0 CSIRO Cavitation fluid layers no ECHAM5/MPI-OM Max Planck Institute for Meteorology VP 1.5, conformal mapping grid with grid poles over Greenland and Antarctica 0 layer FGOALSg1.0 LASG Thickness distribution EVP layers no GISS-AOM NASA GISS Cavitation fluid mass layers 4 thermal layers no GISS-ER US NASA GISS VP layers yes INM-CM3.0 Institute for Thickness distribution No dynamics layer yes Numerical Mathematics IPSL-CM4 Institut Pierre Simon Laplace VP 2 2 cos(lat) 2 layers no MIROC3.2 (hires) JAMSTEC EVP layer yes MIROC3.2(medres) JAMSTEC EVP layer yes MRI-CGCM Meteorological Research Institute Drift with ocean currents layer yes PCM NCAR EVP 27 km 4 layers yes UKMOHadCM3 Hadley Center Drift with ocean currents layer no UKMOHadGEM1 Hadley Center Thickness distribution EVP layer yes a The model identifier is that used by PCMDI and that we adhere to in the paper. The originating group is given below the model identifier. VP stands for viscous-plastic rheology [Hibler, 1979], while EVP denotes the elastic-viscous-plastic variant of that rheology [Hunke and Dukowicz, 1997]. Forcing always includes greenhouse gas concentrations and sulfate aerosols. A yes in the column additional natural forcing refers to solar radiation and volcanic aerosols, which vary according to observations. If a no is listed, these forcing functions are kept constant. Details are available at model_documentation/ipcc_model_documentation.php. This table is based on the compilation of Zhang and Walsh [2006]. no 5of11

6 Table 2. Summary of Sea Ice Results From NAOSIM Hindcast and IPCC 20C3M Experiments a Model Ice Volume Standard Dev Trend Ice Volume Standard Dev Trend Ice Volume Standard Dev Trend Hindcast , ,4 BCCR-BCM , , ,5 CCSM , , ,6 CGCM3.1 (T63) , , CGCM3.1 (T47) , , ,1 CNRM-CM , ,6 CSIRO-Mk , ,9 ECHAM5/MPI-OM , FGOALS-g , ,7 GISS-AOM , , ,9 GISS-ER , , ,1 INM-CM , , ,6 IPSL-CM , , ,5 MIROC3.2 (hires) , ,4 MIROC3.2 (medres) , , ,5 MRI-CGCM , , ,8 PCM , , ,1 UKMO-HadCM , , ,3 UKMO-HadGEM , , ,8 a Arctic sea ice volume, standard deviation of the annual means, and the trend in sea ice volume are given separately for each of the periods , , and The trend is given as km 3 /yr. It is calculated from the ensemble mean of all available realizations of the 20C3M experiment from each IPCC model. The standard deviations are means over the standard deviations calculated for each realization. relationship between that part of the model variability and its counterpart in the climate system. However, the externally driven variability should ideally be present in the climate model results as it was observed. The distinction between internal variability and externally forced variability is an important task in the analysis of the IPCC experiments. 4. Results [15] To characterize Arctic sea ice properties in the different models, we have compiled Arctic sea ice volume, the standard deviation of the annual means, and trends for three subperiods ( , , and ) in Table 2. The Arctic Ocean is for our purposes defined as the area north of 65.5 N except the Nordic Seas between 60 W and 20 E, 65.5 N and 80.5 N. The averaged ice thickness distribution for the hindcast is shown in Figure 4 in comparison with corresponding fields for selected IPCC models. The results shown in Figure 4 were selected such that the range of variability in the IPCC results is well represented. The annual cycle is shown exemplary in Figure 5. Time series of Arctic sea ice volume for most IPCC models and the AWI1 hindcast are shown in Figure 6 and the standard deviation in annual mean sea ice thickness for these models is collected in Figure 7. As explained above, these results are based on analysis of a single realization. Although the annual and monthly means are not stationary, means derived from ensemble averages and single realizations in Figures 4 and 5 produce virtually identical patterns. [16] Averaged ice volume varies between less than 10,000 km 3 (MIROC3.2 hires) at the end of the experiment ( ) and almost 100,000 km 3 (FGOALS-g1.0). However, most of the models are in a range between 15,000 and 30,000 km 3 that seems acceptable regarding the results of the AOMIP hindcast simulations (Figure 3). Ice volume in the hindcast is sensitive to various uncertain parameters like the albedo of different materials (ice, snow, melting ice and snow) and the lead closing parameter h o. These uncertainties also affect the coupled climate models, possibly explaining part of the spread of average ice volumes. [17] Although the models shown in Figure 4 all fall in the acceptable range of sea ice volume, the spatial distributions are rather diverse. Many models tend to accumulate ice near the coast, in front of protrusions, and in embayments. One typical example is BCCR-BCM2.0 where largest ice thickness is found near Bering Strait, in front of a land connection between Siberia and the New Siberian Islands, in the Laptev Sea, north of Greenland, and the Canadian Archipelago. BCCR also shows very thin ice in the Beaufort Sea. This pattern is suggestive of an intensive anticyclonic ice drift that extends very close to the coast and leads to accumulation of sea ice on the upwind side while sea ice is depleted on the downwind side of obstacles. Another recurring pattern (see CSIRO, INM, and to some extent IPSL and MIROC medres) has thickest ice in the interior of the Arctic Ocean, away from all coasts. This is common for thermodynamic-only sea ice models like INM. IPSL and MIROC, however, use viscous-plastic and EVP rheologies and the physical reason for their behavior is not obvious. The most realistic patterns are present in the CCSM and UKMO-HadGEM1 results. There, thickest ice occurs north of Greenland and ice thickness decreases as it moves further in the large-scale anticyclonic drift. CCSM shows thin ice in the Beaufort Sea and very thick ice in the East Siberian Sea. These features are also present in the IPSL and MIROC (medres) results. Sea ice redistribution between the Canadian and Siberian sides of the Arctic is a common feature of sea ice variability in the Arctic Ocean [Zhang et al., 2000; Holloway and Sou, 2002; KG03] and periods with large ice thicknesses in the East Siberian Sea occur in the hindcast as well. The presence of thick ice on the Siberian side of the Arctic Ocean in the above climate models could thus be due to a higher frequency of occurrence of a certain anomaly pattern. 6of11

7 GERDES AND KO BERLE: ARCTIC SEA ICE VARIABILITY Figure 4. Sea ice thickness averaged over the period for the AOMIP hindcast (upper left) and selected climate model results (upper row from left to right: BCCR, CSIRO; middle row from left: GISS-AOM, INM, IPSL; lower row from left: MIROC medres, CCSM, and UKMO-HadGEM1). The hindcast, BCCR, and IPSL use viscous-plastic (VP) sea ice rheology, INM has a thermodynamic sea ice model, CSIRO and GISS-AOM use a cavitating fluid sea ice rheology [Flato and Hibler, 1995] while the remaining models employ the EVP rheology. [18] The problematic spatial structure in sea ice thickness is reflected in the extremes of the annual cycle, i.e. the April (end of winter) and September (end of summer) distributions (Figure 5). The hindcast shows a pronounced spatially asymmetric difference between summer and winter with the Barents, Kara, and Laptev seas and large areas north of these shelf seas either ice free or covered with very thin ice during summer. In the hindcast, the coastal areas in the Chukchi Sea are also free of ice in summer. IPCC model results, selected to include a representative of each basic sea ice rheology used in IPCC models, are combined in Figure 5. Several IPCC models exhibit a rather uniform reduction in ice thickness without the asymmetry between eastern and western Arctic (ECHAM5/MPI-OM, INM, UKMO-HadCM3). Several models with a pole-centred ice thickness pattern show a more or less concentric shrinkage and expansion during the annual cycle. This is especially apparent in the thermodynamic sea ice model of INM. Only a few models show a retreat of ice from the Siberian shelf seas during summer although the ice there thins considerably. In most models, the summer ice cover in the Barents, Kara, and Laptev seas is too extensive (ECHAM5/MPI-OM, MRI, PCM). In case of the MRI model, a peculiar redistribution of sea ice between the western Arctic (April) and eastern Arctic (September) occurs during the annual cycle. Here, sea ice thickness actually increases in the Eurasian basin towards summer. Because overall similar performances occur in rather different types of sea ice models, we reckon that a large part of the shortcomings are due to biases in the atmospheric forcing that the sea ice models experience. [19] From the standard deviation listed in Table 2, the variability of sea ice volume in CGCM3.1 (T63), CSIRO, GISS-ER, MIROC3.2 (medres), PCM, and UKMOHadGEM1 come closest to that of the hindcast simulation. This variability is to a large degree a statement about the variability in atmospheric forcing of the sea ice component. A few model results exceed the variability in the hindcast but most models show relatively weak sea ice volume variability. In FGOALS the high variability is associated with a strong trend at the beginning of the 20th century and 7 of 11

8 Figure 5. Mean ( ) April (upper panels) and September (lower panels) ice thickness distribution for the AWI1 AOMIP hindcast simulation (upper row, left) and selected IPCC model results. overall too large ice volume. The sea ice reaches far south in the Nordic Seas and is thus probably subject to much higher atmospheric variability. [20] The hindcast contains one period of extremely high ice volume in the 1960s (Figure 6). Similar events are very seldom in the climate models. CCSM3 has a similarly outstanding ice volume maximum around 1920, coincident with large ice volume in CSIRO. IPSL has a very high variability and at least two ice accumulation periods during the 20th century. [21] The trend (based on ensemble means) in Arctic sea ice volume (Table 2) is negative for the period in all climate models considered here. All climate model simulations exceed the trend of the hindcast over this period. Over the whole 20th century, almost all models indicate a negative trend in Arctic sea ice volume. Exceptions are the ECHAM5/MPI-OM and FGOALS results. The latter cannot be considered realistic as the ice volume is far too high and the average ice thickness in the Arctic Ocean is almost 10 m. [22] An anthropogenic trend in sea ice volume is expected to be especially large near the end of the 20th century. We have thus also calculated trends for the period (Table 2). However, even the ensemble mean trend is not 8of11

9 Figure 6. Time series of annual mean Arctic sea ice volume for the IPCC models of Table 1 and the AOMIP hindcast simulation. The range in all plots is 22,000 km 3, but the axis extremes have been adjusted to accommodate the individual model results. The blue horizontal lines represent the temporal mean over the 20th century, and the red horizontal lines are at one standard deviation above and below the mean. robust for this short period. We find a much larger scatter with positive and negative trends more equally distributed than for the trend over the last 50 years. [23] The MRI 20C3M experiment has relatively realistic multidecadal sea ice volume variability (Figure 6) and several realizations are available (Table 1). For these realizations (Table 3), we find that the trend over the whole 20th century is always negative and it is of similar magnitude among the realizations. The trend over the last 50 years of the 20th century varies between 212 km 3 /yr and +5 km 3 /yr. For the years the trend in individual realizations varies between 563 km 3 /yr and 162 km 3 /yr. Observation periods of several decades are necessary to distinguish a trend from natural variability from a single realization. [24] The standard deviation of total ice volume in Table 2 does not reflect variability in sea ice that is related to redistribution of ice mass within the Arctic Ocean. The spatial pattern of variability is illustrated by the standard deviation of sea ice thickness at each individual grid point in Figure 7. For the hindcast, we find highest variability in the regions of very thick mean ice north of Greenland and the Canadian Archipelago. High variability is also present in the East Siberian Sea. Relatively high variability is furthermore found in the transpolar drift and the Beaufort Gyre. There is a peculiar band of low variability separating the regions of high variability north of the American continent and in the Canadian Basin. The climate models exhibit very diverse patterns of variability (Figure 7). Most of the selected models show lower standard deviation than the hindcast. This is in contrast to the variability in the Arcticwide integrated ice volume (Table 2) that is generally closer to the hindcast result. This indicates that the hindcast features more internal sea ice redistribution in the Arctic Ocean without changes in overall sea ice volume. The climate models, on the other hand, have more ice thickness variability that is homogeneous in space, presumably due to rather uniform melting and freezing. In CSIRO the variability is restricted to the seasonally ice free regions of the Nordic and Barents seas as well as the Bering Sea and north of the Canadian Archipelago. This is in stark contrast to the pattern in the hindcast simulation that shows little sea ice thickness variance in the Eurasian Arctic. Similar to CSIRO, BCCR, GISS-AOM, and MIROC (medres) have variability that is restricted to the near coastal areas and are probably governed by interannual changes in the seasonal cycle. [25] Contrary to these results, we find in the INM model a sea ice thickness variability that is restricted to the region around the North Pole. IPSL shows a pattern that agrees in many respects with the hindcast result. However, it misses the high variance north of Greenland and Canada. Here, the variance is mainly found over the East Siberian Sea. Contrary to the results discussed above, the variance is probably too large to be explained by interannual variability 9of11

10 GERDES AND KO BERLE: ARCTIC SEA ICE VARIABILITY Figure 7. Standard deviation of annual mean sea ice thickness for the period for the AOMIP hindcast (upper left) and selected climate model results (upper row from left to right: BCCR, CSIRO; middle row from left: GISS-AOM, INM, IPSL; lower row from left: MIROC medres, CCSM, and UKMO-HadGEM1). in seasonal ice cover. The same is true for the CCSM result and to some degree for the UKMO-HadGEM1 result. Both models show enhanced variability in regions that are also high in variability in the hindcast simulation. Especially CCSM shows very high standard deviation north of Canada and Greenland, over the East Siberian Sea and along the transpolar drift. 5. Discussion and Conclusions [26] We have compared available sea ice thickness results for the IPCC Climate of the 20th Century experiment with corresponding results of AOMIP hindcast simulations. No direct comparison with observed sea ice thickness was done because sea ice thickness data that cover the Arctic Ocean spatially and temporarily with the necessary resolution are not available. The AOMIP hindcast results are similar among each other in many respects. Especially, the variability of sea ice thickness distribution is apparently mostly determined by the identical, prescribed atmospheric forcing. Since longer time series were available from the AWI1 AOMIP simulation, those results were used for detailed comparisons of trends and multidecadal variability. The AWI1 hindcast results have been validated earlier with the available direct sea ice draft observations. That comparison and the strong relationship between atmospheric forcing and simulated sea ice thickness variability justify our use of AOMIP hindcast results as a benchmark for the IPCC model results. It should be clear, however, that the AOMIP results are model results and that there exists a certain range of results among the AOMIP hindcasts that indicate the degree of uncertainty in those calculations. Table 3. Linear Trends in Arctic Sea Ice Volume for the Periods , , and for Individual Realizations of the 20C3M Experiment With the MRI Modela a 10 of 11 Units are in km3/yr

11 [27] From the comparison we find considerable deficiencies in sea ice properties in many IPCC results. Most of the models have problems to reproduce the spatial sea ice thickness distribution of the hindcast, especially its asymmetric summer ice thickness distribution. These deficiencies are related to simple sea ice rheologies in some of the IPCC model experiments. Thermodynamic sea ice models and those where the sea ice drifts with the ocean currents tend to pile up sea ice in the center of the Arctic Ocean, away from the coasts. In other cases, where more sophisticated sea ice rheologies are incorporated, other reasons must be considered. Since the sea ice is governed by the wind and surface air temperature, biases in the atmospheric forcing will have a strong impact on the sea ice. [28] Biases in the sea ice thickness distribution may have important consequences for the atmospheric circulation [Gerdes, 2006]. Too large ice cover and ice thickness in the European sector of the Arctic could be significant in ocean-atmosphere interactions and long term variability involving Atlantic Water. This feature is not an artefact of a too small averaging period (50 years) as patterns with extreme summer ice cover in the Eurasian Arctic never occur in the hindcast. This problem is probably related to deficiencies in the preferential oceanic heat transport into the Barents Sea [Kauker et al., 2003] and atmospheric heat transport into the shelf sea areas further east [Goose et al., 2004]. Other problems with the sea ice distribution like excessive accumulation over the Siberian shelf seas could be important for the formation of the Arctic halocline, the formation of dense waters on the shelves and the transformation of Atlantic Water in the Arctic. Especially the later would affect the overflows of dense water from the Nordic Seas to the North Atlantic. [29] The IPCC results agree in a negative trend in Arctic sea ice volume over the 20th century. This trend accelerates in the second half of that century. The robust negative trend from 1950 to 2000 is in contrast to the hindcast simulation that indicates no trend over that period. This suggests that the internal multidecadal variability of the real climate system is underestimated in IPCC models. Whether the negative trend in the IPCC models ice volume accelerates further towards the end of the 20th century could not be established because the results for different IPCC models varied too much. Apparently, an analysis period of 15 years is too short even with the relatively weak internal variability of the IPCC results. [30] The hindcast is dominated by an accumulation of sea ice in the mid-1960s and a return to values before that event in the last decade of the 20th century. The hindcast is limited in length by the availability of atmospheric forcing data. The strong multidecadal variability prevents detection of an anthropogenic trend within this limited period. In a five-member ensemble of one IPCC model (MRI) with rather strong multidecadal variability in Arctic ice volume, four realizations have a clear negative trend while one realization shows practically no trend over the last 50 years of the 20th century. We conclude that observations of sea ice thickness over several decades would be necessary to clearly establish the existence of a long-term trend. As an alternative to waiting several decades until sufficiently long observational time series have been collected, we suggest to reconstruct forcing data for hindcast simulations that cover at least the whole 20th century [Kauker and Meier, 2003; F. Kauker et al., manuscript in preparation, 2007]. This is planned for the next phase of AOMIP. [31] Acknowledgments. We acknowledge the support of the European Union through the INTAS Nordic Seas project, and the ESA through the GLOBICE project. We acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, U.S. Department of Energy. This research is supported by the National Science Foundation Office of Polar Programs under cooperative agreements OPP and OPP with the International Arctic Research Center, University of Alaska Fairbanks. References Flato, G. M., and W. D. 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