3.1. Real-Time Sampling
 To allow for a quantitative comparison between modeled and observed data in this region of high seasonal and interannual variability, we use a daily output of simulated sea ice thickness and sample the model data for the times and positions of all suitable ASPeCt observations. Since each of these model data represents a snapshot corresponding to the specific dates of the individual observations, we will refer to this data set as real-time sampled (RTS) model data.
 To gain insight into the regional distribution of thickness and errors, we split the circumpolar data into five sectors. In agreement with Gloersen et al.  we define the Weddell Sea sector from 60°W to 20°E, the Indian Ocean sector from 20°E to 90°E, the western Pacific Ocean sector from 90°E to 160°E, the Ross Sea sector from 160°E to 130°W, and the Amundsen and Bellingshausen Seas (ABS) sector from 130°W to 60°W. Since previous studies have indicated a systematic underestimation of modeled sea ice volume along parts of the Antarctic Peninsula [Timmermann et al., 2004], we use the 45° meridian to subdivide the Weddell Sea sector into the western and eastern Weddell Sea.
 Scatterplots of the resulting data pairs (Figure 2) indicate that the agreement between the modeled sea ice thicknesses and the corresponding observations is poor. Given that observations from an area with a 1 km radius are compared with data from a model with about 80 km horizontal resolution, this is hardly surprising. The ship observations show the pack ice to be a highly variable and complicated mix of different ice types. Their concentration, thickness, and topography may vary significantly over short spatial scales. However, for about 50% of the data set the difference between observations and real-time sampled model data is smaller than 40 cm.
Figure 2. Scatterplots of real-time sampled (RTS) model sea ice thicknesses versus ASPeCt observations in the interval from 0 to 3 m, which comprises 98.7% of the available ASPeCt data set. Model ice thicknesses have been extracted corresponding to the time and location of each ASPeCt observation. Data have been divided into the western and eastern Weddell Sea sectors, the Indian Ocean sector, the western Pacific Ocean sector, the Ross Sea, and the Amundsen and Bellingshausen Seas (ABS). The solid line is the 1/1 line.
Download figure to PowerPoint
 For an observed ice thickness below 1.2 m the majority of points in the eastern Weddell Sea and the Ross, Amundsen, and Bellingshausen Seas are found well above the 1/1 line, which indicates that the model tends to overestimate the thickness of thin ice. Simulated ice thicknesses, especially in the Ross Sea and the western Pacific sector, seem to gather between 1 and 1.2 m, which is close to the equilibrium thickness that can be achieved by purely thermodynamic sea ice growth [Harder and Lemke, 1994]. On the other hand, observed thicknesses larger than 1.5–1.8 m appear to be underestimated in all sectors except for the Ross, Amundsen, and Bellingshausen Seas. Thus it appears that thermodynamic sea ice growth in the model is too fast, so the equilibrium thickness is reached too soon, while the effect of dynamic thickening appears to be underestimated.
 An outstanding exception to this finding is the western Weddell Sea, where the model consistently and substantially underestimates sea ice thickness. The underestimation of ice thickness in the northwestern Weddell Sea was already found in a comparison of ORCA2-LIM simulated ice thicknesses to data derived from upward looking sonars (ULS) [see Timmermann et al., 2004] and can also be seen in other coupled sea ice–ocean models forced by the NCEP/NCAR reanalysis 2 m temperatures [e.g., Fichefet et al., 2003]. It is mainly attributed to a poor representation of the Antarctic Peninsula in the NCEP/NCAR reanalysis model, especially north of 68°S, which causes a warm bias in the near-surface temperature fields and, even more important, spurious westerlies in this region [Windmüller, 1997]. Using this data set to force a dynamic-thermodynamic sea ice model drives the ice offshore and prevents the formation of the thick, heavily deformed multiyear ice that is actually found in this region. Possible contributions by deficiencies in the model physics are discussed in section 4.
 Another outstanding region is the Amundsen and Bellingshausen Seas sector in which the model appears to overestimate sea ice thickness systematically. Splitting the data into seasons (Figure 3) reveals that this is the only sector in which the majority of observations was made during the austral winter months: 72% of the observations are from July, August, or September. Furthermore, the vast majority of the data in the ABS sector were collected in a narrow region close to the Antarctic Peninsula (near Marguerite Bay; see Figure 1, right), which represents a rather small part of the sector.
Figure 3. Scatterplots of RTS model sea ice thicknesses versus ASPeCt observations for December, January, and February (DJF); March, April, and May (MAM); June, July, and August (JJA); and September, October, and November (SON). Colors refer to the Weddell Sea (blue), Indian Ocean sector (green), western Pacific sector (brown), Ross Sea (red), and Amundsen and Bellingshausen Seas (pink).
Download figure to PowerPoint
 From the scatterplots in Figure 3 (top) it appears that outside the ABS sector the maximum model ice thicknesses are found in austral summer and autumn. However, seasonal splitting of the ASPeCt data set (not shown) reveals that the regions of maximum ice thickness, that is, near the coast of the Antarctic continent, were sampled only during the summer months and in early autumn (for obvious reasons of accessibility), so the RTS model data extraction does not give any results from the thick ice regions during winter and early spring. Finding that the model seems to capture the very thick ice only in December–February thus is merely a sampling bias due to the fact that the regions with the thickest model ice cannot be reached by ship in any other season.
 A striking feature in Figures 2 and 3 is that simulated ice thicknesses appear to aggregate along horizontal lines. The reason for this again is the underestimation of small-scale spatial and temporal variability of sea ice thickness in the model. If the ship changes position only slowly or resamples an area already visited a short time ago (e.g., in the context of station work or supply tasks), it may encounter very different sea ice conditions, while in a few days the model fields change only very little. Therefore these lines do not indicate that the model prefers certain ice thicknesses; they only reflect the scarce sampling in some regions or ice thickness regimes and the low small-scale variability in the model.
3.2. Local and Regional Averaging
 To allow for a comparison of the regional sea ice thickness distribution, real-time sampled (RTS) model ice thicknesses have been gridded on the ORCA2-LIM model grid following the same procedure as for the observed sea ice thickness data (see section 2.2). Maps of the resulting mean model ice thicknesses (Figure 4, left) and of the difference from the mean observed thicknesses (Figure 4, right) indicate that although differences can be quite substantial, most of the qualitative features of the large-scale sea ice distribution agree rather well between the model and the observational data set. Simulated ice thickness is overestimated in most of the Weddell Sea sector, with the pronounced exception of the northwestern Weddell Sea near the Antarctic Peninsula, where ice thickness is underestimated by 1 m and more. The thin ice in the Indian Ocean and western Pacific sectors follows the observations quite realistically; typical differences in this region are smaller than 0.25 m.
Figure 4. Composite means of (left) the real-time sampled (RTS) simulated sea ice thicknesses (m) and (right) the differences from the observed thickness (m).
Download figure to PowerPoint
 Sea ice thickness distribution in the Ross Sea shows a strong spatial variability in both the RTS model ice thickness and the ASPeCt data. In general, the thin ice in the north is captured reasonably well, while toward the south and near the coast, simulated ice thickness appears to be overestimated. With an overestimation of almost 2 m in a couple of near-coastal grid points in the eastern Ross Sea and near Cape Adare, the Ross Sea features the biggest differences of the whole comparison.
 Sea ice thickness in most of the Amundsen and Bellingshausen Seas agrees reasonably well with the observations; most of the grid cells feature an error of less than 0.25 cm. The slight overestimation of thickness along the ice edge in this sector can be attributed to the ice extent in this region being slightly too large [Timmermann et al., 2004].
 As expected from the previous analysis, the model overestimates ice thickness by typically 1 m in the region immediately west of the Antarctic Peninsula. This is consistent with the assumption of a poor representation of the Antarctic Peninsula in the forcing data (see section 3.1). An overestimation of westerly winds in this region is bound to produce an accumulation of sea ice on the western side of the peninsula and an underestimation of sea ice coverage on the eastern side. Whether a bias in the observations due to the local choice of voyage tracks contributes to the discrepancy has to remain as an open question.
 Therefore even after averaging the observations over areas corresponding to a model grid cell, errors can still be substantial. This is not surprising, as the pack is known to diverge and converge on short temporal scales, driven by the highly variable atmospheric forcing and also by tides. Obviously, this strongly affects the ice thickness distribution. Sea ice model forcing consists of daily means for the atmosphere and does not consider tidal movements in the ocean and thus cannot cover these high-frequency variations.
 However, condensing the data further by computing sector-wide mean ice thicknesses (Figure 5) yields a very good agreement for most regions: Except for the western Weddell Sea and the ABS sector, mean modeled and observed ice thicknesses agree very well in all the sectors. The maximum mean sea ice thickness is found in the Ross Sea and the minimum in the Weddell Sea and Indian Ocean sectors, all very close to the climatology derived from ASPeCt data. The western Weddell Sea stands out as a region of significantly underestimated sea ice thickness.
Figure 5. Sector-wide averages of RTS model sea ice thickness and the corresponding ASPeCt observations. Crosses refer to the western Weddell Sea (light blue), the Weddell Sea east of 45°W (dark blue), the Indian Ocean sector (green), the western Pacific sector (brown), the Ross Sea (red), and the Amundsen and Bellingshausen Seas (pink). The pink triangle refers to the average over the ABS seas, excluding the data immediately west of the Antarctic Peninsula. The solid line is the 1/1 line.
Download figure to PowerPoint
 In contrast to the western Weddell Sea, the ABS sector features a significant overestimation of area-mean ice thickness. However, excluding the grid points immediately west of the Antarctic Peninsula from the averaging leads to a very good agreement for the remaining sector (pink triangle in Figure 5).
 Finally, the overall mean (plus or minus standard deviation) Antarctic sea ice thickness is 0.73 ± 0.77 m for the ASPeCt data and 0.63 ± 0.55 m for the real-time sampled model data. This indicates that the model slightly underestimates both the circumpolar mean sea ice thickness and its spatial variability.
3.3. Climatological Mean Ice Thickness
 The goal of this section is to investigate whether a long-term mean model ice thickness distribution can reasonably be evaluated by comparison with the ASPeCt data set. This implies the assessment of possible bias in the observational data.
 As the ASPeCt data naturally include only observations with sea ice actually present, the mean simulated ice thickness is computed considering only grid cells with h > 10−3 m. The 1981–2001 mean distribution of simulated sea ice thickness (Figure 6, left) in the 50-year experiment analyzed here is very similar to the map published by Timmermann et al.  featuring a predominantly zonal distribution with a mean ice thickness between 0.6 and 0.8 m in the central Ross and Weddell Seas. Maximum ice thickness in the Weddell Sea does not exceed 2 m and is found on the southern continental shelf. In the Ross Sea sector, maximum ice thickness between 2 and 3 m is found on the eastern continental shelf while the Ross polynya in the western Ross Sea is the origin of newly formed, relatively thin ice that is exported northward along the 180° meridian. Deformation at the coast near Cape Adare leads to a maximum ice thickness of about 2 m in this region. In the Indian Ocean and the western Pacific sectors, mean model sea ice thickness typically does not exceed 1 m even at the coast; in a large area near Prydz Bay we find a mean simulated sea ice thickness between 0.4 and 0.6 m.
Figure 6. (left) Simulated 1981–2001 mean sea ice thickness (m) on ASPeCt data points and (right) the difference from the RTS model ice thickness. Note the nonlinear color scales.
Download figure to PowerPoint
 Comparing these data with the mean observed sea ice thicknesses (Figure 1) reveals some qualitative agreement but also a number of discrepancies. Similar to the assessment of RTS model data (section 3.2), simulated ice thickness appears to be overestimated in most of the Weddell Sea, with the pronounced exception of the northwestern Weddell Sea near the Antarctic Peninsula. Differences from the ASPeCt climatology can be as large as 1.5 m, which converts to a relative error of more than 100% in some places. However, the qualitative distribution with thick ice in the west and thin ice in the east is captured reasonably well, as is the occurrence of thicker ice near the coast.
 The thin ice in the Indian Ocean and western Pacific sectors again follows the observations quite closely. In the Ross Sea the data sets agree about the thin ice in the north, while toward the south and near the coast the simulated ice thickness is significantly larger than in the ASPeCt climatology. However, the general distribution featuring the thickest ice in the central Ross Sea and thinner ice farther north (toward the Antarctic Circumpolar Current) and south (Ross polynya and surrounding region along the Ross Ice Shelf), and patches of thick ice along the eastern Ross Sea coast, is in good agreement with the mean ASPeCt ice thickness.
 Sea ice thickness in the Amundsen and Bellingshausen Seas again agrees well with the observations. The already known exception is the eastern Bellingshausen Sea along the coast of the Antarctic Peninsula, where the model sea ice is significantly thicker than indicated by the ASPeCt climatology.
 Comparing the long-term mean model ice thickness (Figure 6, left) with the RTS thicknesses (Figure 4, left) reveals a number of significant differences. Using the time mean model data, simulated ice thickness in the southwestern Weddell Sea and on the track along the 30°W meridian appears to be heavily overestimated, while the analysis of RTS model data (section 3.1) features a good agreement. Real-time sampled ice thickness in the southwestern Weddell Sea does not exceed 0.2 m for most of the data points, while the long-term mean model ice thickness at these locations ranges between 0.8 and 1.5 m. Thus the very low observed ice thickness in the southwestern Weddell Sea (Figure 1) can clearly be attributed to a seasonal and/or interannual sampling bias, simply because of the fact that this region is accessible by ship only in years and seasons with a comparatively thin ice cover.
 Therefore, beyond the scope of model evaluation, this paper yields some information about how representative individual observations in the ASPeCt data set are. Regions with a large difference between the long-term mean and the real-time sampled model ice thickness (Figure 6, right) are prone to feature substantial bias in the mean observed ice thickness due to seasonal or interannual variability. Besides the central and southwestern Weddell Sea, this appears to be true in the eastern Ross Sea. In both regions the RTS model ice thicknesses are typically 0.5–1.5 m smaller than the long-term means. Apart from isolated points spread across the model domain, there are only a few locations in the western Pacific sector and in the Amundsen and Bellingshausen Seas where the RTS model ice thickness is higher than the long-term mean. Again, the eastern Bellingshausen Sea coast stands out; it is the only region where the long-term mean model ice thickness is more than 0.75 m smaller than the RTS thickness, which can be easily explained by the fact that most observations in this region are from winter months.
 We conclude that the ASPeCt data underestimate the climatological sea ice thickness in the central and southern Weddell Sea and the eastern Ross Sea by up to 1 m (and even more on single grid points) because of undersampling and a bias toward summer and/or thin ice years when ships could traverse the region. We expect an overestimation of similar magnitude in the Bellingshausen Sea because of a winter bias in the observations. Ice thickness data in most of the Indo-Pacific sectors appear to be representative of the long-term climatology of sea ice thickness. These results are not affected by the choice of the period for which the model climatology is computed: Using only a decadal mean for the period 1991–2001 instead of the 2-decade average for 1981–2001 (shown in Figure 6, left) yields differences of up to 0.3 m in the central Weddell Sea (indicating a negative trend in the ice thickness here), but since the typical bias in this region is between 0.6 and 0.9 m (Figure 6, right), interdecadal variability does not seem to play a crucial role here.