The ISPOL campaign took place in the Weddell Sea from 27 November 2004 until 2 January 2005. The main aim of the campaign was to gather data on physical and biogeochemical interactions between the atmosphere, ice, and ocean during the transition from austral winter to summer. The ISPOL experiment was mainly conducted on a single ice floe in the Weddell Sea, where the research vessel (RV) Polarstern was moored. The ice floe was located around 68° S, 55° W, drifting 290 km during the experiment with a 98 km south–north displacement Fig. ,. The entire ISPOL floe comprised 2 m thick second-year ice (SYI) covered by 0.8 m of snow, along with locally formed and advected first-year ice (FYI) featuring modal thicknesses of 0.9 and 1.8 m, respectively, and topped with 0.3 m of snow . The Alfred Wegener Institute (AWI) measurement station was operated over the FYI, and the Finnish Institute of Marine Research (FIMR) station obtained readings from the SYI. Broadband albedo was measured from a pair of Kipp & Zonen CM5 (FIMR station) and Kipp & Zonen CM22 (AWI station) pyranometers, installed at about 1.5 m above the surface. After the FIMR radiation station was relocated on 17 and 27 December due to the ice flow break-up, thick snow sites were chosen to maintain consistency in the radiation time series. Therefore, we refer to the measurements from the AWI and FIMR radiation sites as FYI and SYI respectively.

The surface albedo was calculated as the ratio of the reflected and incoming shortwave radiation. The accuracy is approximately 3 % for the shortwave radiation measurements . The measured ice floe was surrounded by leads and fragmented ice. The weather was warm, with air temperature mostly above −5 °C and even around zero degrees during the first week of December. The meteorological observations of and observed properties of snow and sea ice from , , and are used to assess the performance of the regional atmospheric and oceanic models described in Sect. .

A field campaign on the land-fast sea ice in the McMurdo Sound was carried out in November 2022 to assess the physical properties and spatial variability of snow on the land-fast sea ice. The campaign was part of the Aotearoa / New Zealand Marsden Fund project “Can Snow Change the Fate of Antarctic Sea Ice?” (; ). Figure  shows the locations of the camp site (CS), Ridge Site (RS), Transition Site (TS), New Ice Site (NIS), and Turtle Rock Site (TRS) of the Marsden field campaign in McMurdo Sound, Antarctica.

The albedo observations used in this study were collected over two areas of different sea ice thickness (∼2.3 m CS and 1.2 m NIS) located at about 2 km distance from each other. Figure b also shows Landsat surface temperatures, illustrating potential sources of different sea ice characteristics in the region. The thickest sea ice formed earlier in the winter and was closer to the edge of the Ross Ice Shelf. Broadband albedo was measured on both locations using a pair of Kipp & Zonen CM4 pyranometers installed on a drone, while a continuous time series of broadband albedo was measured with a pair of Kipp & Zonen CMP22 pyranometers installed at about 1.2 m above the surface in a fixed station at the CS over the thicker ice. The drone's pyranometers are embedded into gimbals that ensure their horizontal alignment. However, continuous adjustment of the alignment generates vibrations that, when summed to the instrumental error, cause a total uncertainty in the measured albedo of the order of ±3 %. The uncertainty of albedo measured with the fixed CMP22 pyranometers is around ±1 %. Snow depth was measured in snow pits and along horizontal transects using a magnetostrictive device (magnaprobe), while ice thickness was measured in ice holes and along transects with an electromagnetic induction device (EM-31) carried on a sledge. The campaign was characterised by a steady temperature increase from -25 to -3 °C with a snowstorm in the last week of the campaign (27 and 28 November).

Data from two multispectral satellites, Landsat 9 and Sentinel 2, are used to estimate the high-resolution broadband surface albedo over the Marsden field campaign site. The instruments on board Landsat 9 and Sentinel 2 provide images at different spatial resolutions, depending on the wavelength of the spectral band, but the broadband albedo products can be calculated at 30 and 20 m resolutions respectively. The images were acquired in different channels in visible–shortwave infrared wavelengths (452–2294 and 458–2280 nm respectively) during the period of the Marsden expedition. To obtain the broadband albedo values, the algorithm of narrowband to broadband albedo conversion was applied, which uses the blue, red, near-infrared, and shortwave infrared bands to estimate the broadband albedo. For the Sentinel 2 image, the L2A reflectance product with already calibrated bands is used. However, the Landsat 9 L1 image went through further processing, using the methodology proposed by for high latitudes. This involved several correction steps (radiometric calibration and zenith angle, atmospheric and topographic corrections) to estimate the surface albedo. Correction for the anisotropy was not applied, since existing correction models are not able to suitably account for albedo variations at high solar zenith angles . The uncertainty of the Landsat 9 albedo product, based on the applied methodology described in Sect. , is ±0.02 in polar regions . For Sentinel 2 albedo imagery, an uncertainty of ± 0.05 was estimated by and .

Furthermore, the CLARA-A3 blue-sky albedo product dates mentioned in the text refer to the central date of the monthly averaging period, which has a coarser resolution of 25 km, is used to evaluate the albedo variability over the whole Weddell and Ross seas. have evaluated the mean bias of the data against a selection of high-quality in situ surface albedo measurements to be generally 10 %–15 %.

Two high-resolution satellite images from the Marsden campaign site and two lower-resolution, broader-scale satellite images – one from the ISPOL site and one from the Marsden campaign site – are utilised. The commonly used albedo products from the Moderate Resolution Imaging Spectroradiometer MODIS, e.g. do not cover the open ocean; thus we are not able to use them.

MetROMS-UHel is a coupled ocean–sea ice model. It consists of the terrain-following regional ocean model ROMS Regional Ocean Modelling System, and the dynamic–thermodynamic sea ice model CICE . The model uses the development version 3.7 of ROMS code and version 6.3.1 of CICE for sea ice. The model is run on a 1/4° grid with the northern border at 30° S and a relocated south pole. This gives a Cartesian resolution of 8–12 km on the continental shelves around Antarctica. ERA5 reanalysis is used for atmospheric forcing. A detailed description of the model setup can be found in .

The sea ice has five thickness categories and seven vertical ice layers, each having one snow layer. The lower bounds for the ice thickness categories are 0.0, 0.64, 1.39, 2.47, and 4.57 m. The model uses elastic–viscous–plastic formulation for dynamics with an incremental remapping scheme for advection. For thermodynamics, a mushy layer formulation is used , with a level-pond parameterisation with refreezing based on Stefan's law. Albedo is calculated with a Delta-Eddington multiple scattering radiative transfer model . The shortwave radiation forcing given to the Delta-Eddington model is calculated using total cloud cover, humidity, and solar zenith angle.

In the model, apparent optical properties (AOPs, e.g. albedo and transmittance) of ice and snow are calculated by the Delta-Eddington model using ice and snow microscopic inherent optical properties (IOPs, i.e. extinction coefficient, asymmetry parameter, single scattering albedo) that depend on snow depth, ice thickness, snow optical grain size, and ice absorption–scattering coefficients . It calculates the AOPs for three different surface types: snow-covered ice, bare ice, and ponded ice for all thickness categories. The horizontal fraction of surface type is used to get the weighted average albedo of the grid cell. The IOPs for bare and ponded ice are taken from semi-empirical modelling based on SHEBA observations over the Arctic , and the IOPs for snow use the equivalent ice sphere approximation for specific snow grain size from .

The ocean model NEMO (Nucleus for European Modelling of the Ocean, ) is coupled with the dynamic–thermodynamic sea ice model SI3 (Sea Ice Modelling Integrated Initiative, ). The model runs at 1/4° resolution, with a northern border at 55° S, resulting in 10 km Cartesian resolution at the ISPOL location. Oceanic forcings at the border are from the reference ORCA025 simulations , while the atmospheric forcings are from the JRA55 reanalysis (coherent with the forcings of ORCA025). The grid has 121 unevenly spaced vertical layers with a resolution of 1 m at the surface and up to 200 at 5000 m depth.

NEMO's SI3 has five categories of varying thickness to represent the ice pack, with upper bounds for the categories at 0.5, 1.1, 2.1, and 3.7 m. We note that the sea ice categories differ from those of MetROMS-UHel, as model setups were done separately, and respective sea ice model default categories were used. The last category has no upper bound and can represent arbitrary thick ice. The sea ice of any thickness category is described by two ice layers and one snow layer. The maximum sea ice concentration per pixel is 95 %, to account for unresolved polynyas and leads. The albedo of the snow/sea ice is calculated through empirical methods, taking into account factors such as ice thickness, snow depth, and surface temperature. The albedo parameterisations are based on but revised following and . The impact of melt ponds on the albedo has also been included . Melt pond properties as given by the physical level-ice scheme are characterised by the volume and level area of meltwater per unit grid cell area. They can refreeze, in which case a lid can appear, masking the effect of ponds on surface albedo.

SI3 assumes that snow, bare ice and ponded ice can coexist. However, if snow is present over sea ice, the total albedo of the grid cell is equivalent to only the snow albedo, with a deep snow asymptotic value of 0.85 for dry snow and 0.75 for melting snow . If no snow is present on the ice, the total albedo is computed as a linear combination, weighted by the surface type fraction, of the bare ice albedo (which depends on ice thickness, with a maximum asymptotic value of 0.6) and the pond albedo (ranging from 0.5 to 0.27 for deep ponds), following . NEMO model runs are only available for the years 1980 to 2018, hence for the ISPOL case and not for the Marsden field campaign.

The regional atmospheric model HARMONIE Climate cycle 43 (HCLIM, ) using the non-hydrostatic HARMONIE-AROME physics package is set up for the two Antarctic domains with 2.5 km grid spacing. ERA5 reanalysis is used for atmospheric forcing. The HCLIM simulations use the Simple Sea-ice Scheme SICE,. SICE is a one-dimensional thermodynamic sea ice parameterisation scheme. The external ice concentration field, in this case from ERA5, is required to define ice-covered grid cells.

SICE allows for simple bare sea ice albedo parameterisations that are dependent on surface air temperature. In this case, cold bare ice has an albedo of 0.71 and melting bare ice has an albedo of 0.61. When ice is covered by snow a different albedo scheme is used. Because snow upon the ice is an insulating layer with higher albedo and lower thermal conductivity than the underlying ice, HCLIM couples SICE, the surface modelling platform SURFEX,, and the Interactions Surface Biosphere Atmosphere Explicit Snow 12-layer model ISBA-ES, to represent snow on the ice. This is the same snow scheme used by HCLIM on land. The topmost snow layer is always less than or equal to 0.05 m thick. The scheme describes snow thermal conductivity, radiation flux, snow accumulation, melt, and snowpack compaction in the 12 snow layers. However, there are no parameterisations of specific snow-over-ice processes, such as snow–ice formation or melt ponds. Snow albedo is calculated using the method of CROCUS , in three ([0.3–0.8], [0.8–1.5], and [1.5–2.8] µm) spectral bands, which then are combined to the total broadband albedo. The default snow albedo depends on snow density, snow optical grain size, and ageing.

The snow grain size used in the snow albedo calculation, which is only a function of snow density, determines the reflectivity of snow to shortwave radiation. In the default HCLIM setting, it is an empirical relation from measured at NOAA-ARS snow research station in Danville, Vermont, USA. It is modified further by an extra ageing term for the visual spectral band, which represents the darkening effects of snow grain metamorphism and impurities' accumulation in the snow . This term was designed for the Alps (Col de Porte) and is not representative of the polar regions. Therefore we updated the snow on sea ice and snow on land terms by setting the age-dependent term to zero.

The snow optical grain size and density relation of is not appropriate for the polar regions, where snow grain sizes are known to be smaller . Snow grain sizes that are too large lead to snow albedo values that are too low. This affects surface meteorology, surface energy, and mass balance. For example, the first test runs conducted for this study showed a surface temperature warm bias as large as 5 °C over sea ice compared to the ISPOL campaign measurements.

For this study, the snow grain size and density relation is updated using the output from the regional climate model RACMO version 2.3p3 . RACMO2.3p3 has updated its spectral snow albedo and radiative transfer scheme and the fresh snow grain sizes, which better correspond to Antarctic observations . Figure a shows the updated snow grain size to snow density relation together with the HCLIM default relation. In the default HCLIM setting, only large grain sizes are allowed, which leads to maximum snow albedo values of 0.75. With the improved grain sizes, the fresh snow albedo can reach a more realistic 0.85. The resulting differences in the monthly averaged broadband albedo are shown in Fig. b and c and are as large as 0.3.

The Modèle Atmosphérique Régional (MAR) is a hydrostatic regional atmospheric model specifically developed for the polar areas . For this study, a pan-Antarctic domain with a 25 km resolution (305×272 grid points) and 24 vertical levels, ranging from 2 m to 17 km above the surface, is used. ERA5 reanalysis is used as a lateral atmospheric forcing and sea surface temperature/sea ice cover. The sea ice thickness is initialised from 1 m. Snow can accumulate at the surface of the sea ice afterwards. Sea ice can also melt to a 0.1 m minimal value.

The snow–ice module includes submodules for surface albedo, meltwater refreezing, and snow metamorphism, based on the snow model CROCUS . Thirty levels were used to represent the snowpack over Antarctica and the surrounding sea ice. Snow albedo in the CROCUS model is calculated in three separate spectral bands as a function of the snow properties in the top 3 cm of the snowpack. The snow albedo depends on the impurities in the snow (ageing term) and the optical diameter of snow grains. Unlike in HCLIM, the snow optical diameter also takes into account snow metamorphism, based on empirical laws describing the evolution rate of the type (dendric/non-dendric) and size of the snow grains . As described above for the HCLIM model, the CROCUS model was not initially made for polar regions, so the same correction was applied by setting the snow ageing term to 0. Hence, the snow albedo of MAR has the same three-band scheme as HCLIM, but MAR prognostically simulates the snow optical grain size, while HCLIM estimates this grain size from the snow density. If no snow is present over the sea ice, then the albedo of the bare ice varies exponentially between 0.55 and 0.5 as a function of accumulated meltwater . Finally, if the snow depth is thinner than 0.1 m, the total albedo transitions linearly between snow albedo to bare ice albedo.

When evaluating MAR albedo during the Marsden campaign, the values from the closest sea ice grid point, centred at 77.79° S, 166.71° W, are used.

The polar version of the Regional Atmospheric Climate Model RACMOp2.4 consists of two major parts; the dynamical core of HIRLAM that models large-scale motions in the atmosphere and the physical parameterisations of the IFS , which represents physical processes on the sub-grid scale. This includes, for example, a cloud, turbulence and precipitation scheme. Additional parameterisations have been added in RACMO for a dedicated glaciated land surface tile, such as a multi-layer snow scheme, firn-densification modules, grain size evolution, and melt and refreezing parameterisations.

The RACMO version used in this study, version 2.4p1, has been developed recently and includes changes in several key aspects with respect to previous model versions . The IFS physics parameterisations have been updated from cycle 33r1 to cycle 47r1 . This includes changes in all major schemes in particular in the new cloud scheme, and a new lake model is introduced. Other changes include a new multi-layer snow scheme for seasonal snow on non-glaciated surfaces and a new land-ice mask with fractional ice cover, resulting in a better representation of partly glaciated regions. A detailed description can be found in . For experiments used in this study, RACMO is run on an 11 km horizontal resolution grid and is forced by ERA5 at the lateral boundaries.

As RACMO is not coupled to an ocean model, the sea ice extent is prescribed by ERA5. RACMO uses the ECMWF IFS model for sea ice, which employs a four-layer sea ice slab with a fixed maximum thickness of 1.5 m. There is no snow modelled on top of sea ice. The albedo is based on and thus does not depend on snow conditions. Instead, monthly sea ice albedo values for the Arctic Ocean, valid on the 15th of each month, are linearly interpolated to the forecast time. For the Antarctic, the seasonal cycle is shifted by 6 months. The bare sea ice albedo represents summer months, while the dry snow albedo is applied during winter months, with both direct and diffuse components. The albedo is derived using separate values for four spectral bands in the near-infrared and visible light, and the broadband albedo is the sum of the spectral albedos weighted by the relative solar flux in each wavelength region and further weighted by the direct and diffuse fractions. While this parameterisation scheme intends to capture key surface, cloud, and atmosphere effects, the albedo values are climatological means and do not depend on the state of the surface and atmosphere within the RACMO model. For example, the effect of fresh snowfall on the albedo on sea ice during summer is therefore often not captured properly, as the albedo model does not take the actual conditions into account.

Given the findings of , , and that highlight the limitations of ERA5 sea ice albedo over the Arctic, ERA5 is included in this analysis over Antarctica. In ERA5, the sea ice concentration is given by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) (409a) dataset (from 1979 to August 2007), and the OSI SAF open dataset (September 2007 onward). Like RACMO, ERA5 also uses the ECMWF IFS model for sea ice. ERA5 considers the Marsden CS to be on land, not on sea ice. When evaluating ERA5 albedo during the Marsden campaign, the values from the closest sea ice grid point, centred at 77.85° S, 166.54° W, are used. Since HCLIM, MAR, and RACMO use sea ice concentrations from ERA5, it can be assumed that these models extrapolate the sea ice concentrations from adjacent ERA5 ocean grid points to areas classified as land in ERA5.

The model's ability to accurately reproduce top-layer snow and ice conditions is influenced by atmospheric modelling (as in HCLIM, RACMO, and MAR) or by the atmospheric forcing (as in MetROMS-UHel with ERA5 or NEMO with JRA55), particularly during the melt season. The comparison between modelled and observed 2 m temperature is presented in Fig. , while additional atmospheric variables such as air pressure, relative humidity, wind direction, and wind velocity are provided in Figs.  and .

HCLIM reproduces the surface temperature observed during the ISPOL campaign best (with a mean difference of 0.2 °C), as well as the surface pressure and wind speed and direction. RACMO and ERA5 have, on average, a 1.4 and 1.6 °C warm bias, respectively, indicative of the low sea ice albedo, as discussed later. ERA5, with a resolution of 0.25° (approximately 28 km), exhibits a temperature pattern comparable to the 11 km resolution RACMO. In contrast, MAR, which has the coarsest spatial resolution of the models in the set at 25 km, displays significant diurnal variability and is, on average, 2.3 °C colder than observations, with deviations reaching up to 13 °C.

The conditions during the Marsden campaign were notably different – the air temperatures were well below freezing (mostly below −10 °C). The Marsden field campaign took place in a challenging region for models, located a few kilometres north of Ross Island (see Fig. ). Notably, in both ERA5 and MAR, the Marsden field campaign site is inaccurately represented as land rather than sea ice. Even so, HCLIM, RACMO, and ERA5 differ by only 0.8, 1.2, and 0.8 °C from the observations. Figure  shows that on average, MAR exhibits a 2.8 °C warm bias and pronounced diurnal temperature fluctuations. Figure  reveals the problems that atmospheric models have in simulating the correct wind direction over the Marsden campaign site: instead of the observed predominantly southerly winds, the models predicted more easterly winds. The reason for this discrepancy is the resolution differences and the proximity of Ross Island and the Ross Ice Shelf.

We have included total cloud coverage of the models in Fig. . There are considerable differences in cloud cover over the ISPOL and Marsden campaign sites between the models, which can only partly be explained by differences in resolution. The snow and ice albedo can be, on average, 4 %–6 % higher under cloudy conditions compared to clear skies . However, only MetROMS-UHel, MAR, and NEMO account for the effect of clouds in their sea ice albedo parameterisations, whereas HCLIM, RACMO, and ERA5 do not include this effect.

In situ measurements provide a detailed understanding of the temporal variability of sea ice albedo, along with general snow and ice characteristics. However, these measurements fall within the sub-grid scale of the models used in this study, with HCLIM offering the highest resolution at 2.5 km. Additionally, both campaign locations present challenges for model reproduction – McMurdo Sound is characterised by complex topography, and the ISPOL campaign took place close to the edge of the modelled December sea ice extent. Figure  shows the month-long ice and/or snow albedo time series. Table  presents the mean albedo values with standard deviation. Since both campaigns measured on either an ice floe or land-fast ice, we compare these measurements to the modelled sea ice albedo rather than the modelled total albedo, which would include open-water albedo if present in a model grid cell.

On the ice floe, to which RV Polarstern was moored during the ISPOL campaign, snow depth decreased on average from 32 to 14 cm on FYI and from 95 to 83 on SYI during December 2004 . On the FYI, the thinner snowpack had a mean albedo of 0.78 (SD = 0.06), while on SYI the thicker snow cover had a mean albedo of 0.82 (SD = 0.05). The standard deviation of albedo primarily reflects the diurnal variability observed during the ISPOL campaign, driven by near-melt snow conditions during the day, refreezing at night, and changes in the solar zenith angle. Additional changes in observed albedo were influenced by snowfall, drifting snow, and the relocation of the equipment .

The snow on top of the 1 to 4 m thick land-fast ice in the McMurdo Sound had a relatively thin (∼0.21 m, CS) or patches of thin (∼0.02 m, NIS) snow on top. Snow thickness variability in the observed range between 0 and 0.2–0.3 m has a big impact on the albedo. For densely packed, fine-grained snow, the snow layer becomes semi-infinite – meaning that further increases in depth no longer affect snow reflectance – at a depth of approximately 0.10 m .

Overall, a mean surface albedo of 0.79 (SD = 0.03) was observed. A snowstorm during the last week of the campaign led to an increase in albedo which reached up to 0.9. The observed albedo was primarily influenced by variations in drifting snow accumulation patterns within the field of view of the downward-looking pyranometer. The snow was often reported as optically translucent, allowing the bare ice beneath to be visible. In nature, bare ice albedo varies between 0.1 and 0.7 depending on the ice thickness and the presence of cracks, melt, brine pockets, and air bubbles . Bare ice albedo values above 0.56, however, have been observed only during melting, when the surface of the ice decomposes into white ice or “surface scattering layer”. White ice is typical of summer Arctic sea ice but has never been observed over Antarctic sea ice . However, recently reported the presence of this “surface scattering layer”, which they referred to as the weathering crust, over blue ice areas of ice shelves of the Northern Victoria Land, Antarctica.

Considering that the measurements are from a single point location, while model output represents grid cell averages, the models reproduce high albedo values and diurnal variability at the ISPOL location but struggle to accurately capture the albedo variability at the Marsden site. MetROMS-UHel successfully captures both the overall level of albedo and the strong diurnal variability observed during the ISPOL case, with a mean modelled albedo of 0.77 (SD = 0.09). In the Marsden case, MetROMS-UHel suggests a constant albedo of 0.85. NEMO output was only available for the ISPOL case study, in which case it has a monthly mean albedo of the sea ice of 0.81 (SD = 0.02). As only daily NEMO albedo fields were saved, any modelled diurnal variability is lost for this analysis.

HCLIM simulates a snow albedo of 0.78 (SD = 0.03) for the ISPOL case, close to the observed albedo over SYI. The HCLIM snow albedo in the Marsden campaign case is 0.84 (SD = 0.00). The HCLIM bare ice albedo for the Marsden case is 0.71 (SD = 0.00), coming from the simplistic two-value (0.61 or 0.71) parameterisations of bare ice albedo in the model. HCLIM bare ice albedo is too high, as bare ice albedo values above 0.56 are unlikely in this context.

MAR simulates a monthly mean albedo of 0.83 (SD = 0.01) and 0.74 (SD = 0.13) for the ISPOL and Marsden field campaigns respectively. A small diurnal variation is present in the model output at the ISPOL location but not to the extent of the observations or of the MetROMS-UHel simulations. A significant increase in MAR's surface albedo, from 0.55 to 0.85, occurred between 10 and 13 November when a thin layer of snow fell within the previously bare ice grid cell in the model, resulting in an immediate rise in the modelled albedo.

RACMO and ERA5 both use time-interpolated monthly albedo values based on the albedo scheme developed by for the Arctic sea ice. Differences between the two models come from the resolution: RACMO was run at 11 km resolution, but ERA5 has a 0.25° (∼28 km) resolution. At the ISPOL location, both RACMO and ERA5 had lower surface albedo values than expected from observations, with RACMO at 0.69 (SD = 0.06) and ERA5 at 0.63 (SD = 0.04). However, over the Marsden campaign site, RACMO more accurately represents the observed conditions compared to all other models. In this instance, the assigned monthly average sea ice albedo values of align with the albedos observed in this case study. ERA5 differs in this case, as the values from the nearest sea ice grid point are used because the campaign site is inaccurately represented as land rather than sea ice.

Comparisons with the Marsden CS measurements come with a caveat: to analyse the sea ice properties, instead of land, the EAR5 and MAR model values from the closest sea ice grid point were used. In both cases, the nearest sea ice model grid point is not located at the CS site but towards NIS or TRS sites or farther. The proximity of Ross Island and the Ross Ice Shelf results in rapidly changing sea ice properties over short distances, as described in Sect.  and . While observations taken farther from land – such as during the ISPOL campaign – offer a cleaner comparison, we work with the data available.

Overall, when compared to measurements from the ISPOL and Marsden campaign sites, the models reproduced the conditions observed during the ISPOL campaign but did not accurately simulate the conditions during the Marsden campaign. For the ISPOL case, all modelled albedos, except RACMO and ERA5, fell within the observed ranges. The MetROMS-UHel model accurately captured both the magnitude and diurnal variability of snow albedo for the ISPOL case. In contrast, RACMO's prescribed albedo was consistently about 0.1 too low throughout the ISPOL campaign season. The snow and ice conditions in the Marsden campaign area were very different. All models, except RACMO, simulated sea ice albedo as being dominated by cold, snowy conditions and were unable to account for the patchiness and drifting snow patterns observed in the region. RACMO accurately predicted the observations from the Marsden campaign, despite relying on monthly averages of sea ice albedo for its predictions.

A certain optical thickness of snow is required to obscure the underlying ice, and similarly, bare ice must reach a specific thickness to mask the darker ocean beneath. Therefore, to interpret the comparison between modelled and observed albedo, it is valuable to analyse the modelled snow and ice thickness in comparison to the observed values. During both campaigns, ice and snow thicknesses were measured at multiple locations across the campaign sites, leading to broad distributions, especially of snow thicknesses across both sea ice campaign regions. Only NEMO and MetROMS-UHel have sub-grid ice categories, which are used to analyse such unresolved spatial variability of sea ice thickness in Sect. . Here, modelled grid-box-averaged sea ice thickness and snow thickness are examined and compared with measurements taken closest to the location of the albedo measurements. The models are not regridded to a common low-resolution grid in order to preserve high-resolution information.

Figure  and Table  compare the measured snow thickness in the ISPOL and Marsden campaigns with grid-averaged model outputs. Generally, the thicker the sea ice, the greater the ice freeboard and the amount of snow it can support.

The ISPOL location measurements were done over the FYI and SYI. MetROMS-UHel snow thickness follows the level and trend of the ISPOL FYI measurement site: 0.35 m thickness at the beginning of the month and only a few cm thick at the end. The grid-box-averaged bare sea ice thickness underneath is 1.15 m. HCLIM predicts thicker snow, 0.8 m at the beginning of the month, dropping to 0.4 m at the end of the month, with a 1.85 m sea ice underneath the snow. NEMO also predicts a thicker, 0.71 m, snow cover over a 3.5 m bare sea ice. While HCLIM shows a decrease in snow thickness over the month, NEMO snow thickness is variable but not decreasing. The snow cover modelled by NEMO and HCLIM is thicker than measured at the ISPOL SYI and FYI locations. However, there were sites at the ISPOL ice floe where snow thicknesses were in that range . MAR only provides the combined sea ice and snow depth output, which amounted to 1.96 m. RACMO has a fixed sea ice thickness of 1.5 m and no explicitly modelled snow on top (Table ).

During the Marsden campaign, snow thickness measurements were done in multiple locations (averages given in Table ). Reportedly, the thicker ice at CS was covered by a thin (0.21 m) but heterogeneous snow layer with small spots (1–2 m²) of bare ice. The thinner ice (at NIS and TRS) was mostly bare with small patches (1–2 m²) of thin (approximately 1–2 cm) snow . In Fig. , the model output is exclusively compared to the measurements at CS, closest to the radiation time-series measurements. The models predict a thin (0.09 m (MetROMS-UHel) or 0.15 m (HCLIM)), uniform snow cover. The thickness of bare sea ice is modelled to be 1.73 and 1.97 m for MetROMS-UHel and HCLIM respectively. Because MAR considers the Marsden CS to be on land, and the closest sea ice grid point is further away, MAR's snow and sea ice thickness are not fully representative. MAR's combined sea ice and snow depth is 0.50 m at the start of the period and increases to 0.53 m throughout 10–12 November, causing the increase of albedo seen in Fig.  due to snowfall. ERA5 and RACMO sea ice have a constant and uniform thickness of 1.5 m and no modelled snow.

Even within a small sea ice region, multiple types of sea ice can be present. NEMO and MetROMS-UHel have sub-grid ice categories, which can be used to analyse the unresolved spatial variability of sea ice thickness within each model grid cell. However, NEMO output for the Ross Sea in November 2022 was unavailable, as the model data only extend up to 2018. Therefore, we compared MetROMS-UHel with the Marsden campaign measurements from November 2022, and with NEMO for November 2004. The model's intrinsic bare ice characteristics per category remain consistent year to year, while the presence of ice types, the fractional snow cover, and the cloud coverage change.

Figure a–b show the probability distributions of the albedo measured from a drone flying a single vertical profile at an altitude between 30 and 50 m over thick ice and another profile between 30 to 70 m altitude over thin ice with patchy snow cover. The flight lasted ∼10 min, without significant changes in solar zenith angle and cloud conditions. At these heights, the footprint of the drone's downward-facing pyranometer, calculated as the area contributing to 90 % of the received radiation, has a radius ranging from 90 m (at 30 m height) to 210 m (at 70 m height). Hence, these probability distributions represent the albedo averaged over footprints of ∼0.02–0.04 km² and, therefore, are better suited for validating satellite albedo products and model simulations than the fixed-station measurements.

Over the thick ice, the downward-facing pyranometer of the fixed station mostly receives the radiation reflected from the surface type located just underneath it (∼ a few m²), having a mean albedo of 0.80. In contrast, the pyranometer on the drone captures radiation from all surface types within its footprint, weighted by their respective fractions. This resulted in a higher mean albedo of 0.86. Since the fraction of bare ice was significantly smaller than that of snow-covered ice, the drone measured more radiation reflected from the patchy snow cover compared to the bare ice.

Starting from the altitude of 30 m and above, the measured albedo did not change with altitude, meaning that the spatial distribution of surface albedo variability included in the footprint of the downward facing pyranometer did not change with increasing footprint radius. Hence, the small variations in the drone-based albedo observed from 30 m above the surface and at higher altitudes were associated with measurement errors (caused for instance by small vibrations of the drone and small deviations from the horizontal alignment of the pyranometers when compensating for changes in wind speed) rather than with spatial variability. The spread of the drone-based albedo probability distributions, which represent the measurement uncertainty during ∼ 10 min flight, is much narrower than the spread of the probability distribution of the albedo measured from the fixed station close to the surface during a 1-month-long observation period (Fig. b).

The temporal variability of the fixed-station albedo is caused by the change in solar zenith angle; the changes in cloud cover; the occurrence of precipitation and snowdrift; and, to a lesser extent, snow metamorphism (which was weak because the surface temperature was well below freezing for the whole period). During the studied period, the daily minimum solar zenith angle decreased from 60.5 to 56.2°, and cloud cover ranged from 0 to 8 oktas. The continuous snow drift, snow erosion, and formation of snow patches and dunes changed the snow thickness in the footprint area of the fixed downward-looking pyranometer in a similar way to that in the surrounding area. Hence, we can argue that the probability distribution of the fixed-station albedo illustrates the effects of both temporal and spatial albedo variability, assuming that the snow thickness variability that occurred right below the pyranometer is a good proxy for the larger-scale spatial variability.

Drone-based albedo measurements over the thin ice showed a mean albedo of 0.66, as the ice surface was dominated by bare ice with snow patches. The thinner and younger ice was formed in August 2022, which is why it had less snow than the thicker ice, which was formed in March 2022.

The modelled MetROMS-UHel albedos for the 1.4 and 2.4 m ice thickness categories are included in Fig. c–d. The selected model thickness categories are the closest to the observed sea ice thicknesses. The modelled NEMO albedos for 1.1 m and 2.1 m ice categories are compared to the modelled MetROMS-UHel albedos for the 1.4 and 2.4 m ice thickness categories for November 2004 of the Marsden field campaign site location in Fig. a–b.

The discrepancy between the observations and the models over the thinner ice is large: Δmean=0.2 for MetROMS-UHel. In this case, the MetROMS-UHel modelled albedo is snow-dominated, as both ice categories are covered with a layer of snow. The same situation occurs in 2004, where the MetROMS-UHel albedo is also dominated by snow, but NEMO had snow-free conditions. NEMO's bare ice albedo depends mainly on ice thickness, with a maximum albedo value of 0.5 for 1 m thick sea ice and approaching an albedo value of 0.6 for 1.5 m and thicker ice, with additional adjustments based on cloud fraction.

The drone-based albedo measurements over the thicker ice are reproduced by MetROMS-UHel, with differences between the average values of the two distributions being negligible (Δ=0.004). MetROMS-UHel albedo has a narrower distribution (SD = 0.00) than the observed distribution (0.02). NEMO's albedo is 0.59, which is close to the maximum value assigned by the model to bare ice albedo but is far too low compared to the observations.

These model-to-observation comparisons demonstrate that direct comparisons with surface-based point measurements are not meaningful when there is metre-scale spatial heterogeneity in surface albedo. MetROMS-UHel and NEMO have information on sub-grid ice categories, but the snow on the ice, if any, covers it uniformly. The comparisons also show that the correct estimate of the snow cover depth is essential to correctly model the sea ice albedo.

Expanding the analysis to cover larger areas that align with the model resolution is a fairer evaluation of the models. This section compares the model output to high-resolution (30 m Landsat 9 and 20 m Sentinel 2) satellite images over a limited area (260×260 km² Landsat 9 and 110×110 km² Sentinel 2) in McMurdo Sound, which includes the Marsden campaign site. In Sect. , the comparison is expanded to lower-resolution satellite observations over larger areas, covering the entire Ross and Weddell seas.

Landsat 9 albedo observations on 1 November in Fig. , and Sentinel 2 observations on 14 November 2022 in Fig. , are compared to the modelled total albedo of sea ice (combination of ice, snow and ocean). The satellite images show a variety of albedo values over the region: open ocean with an albedo of around 0.1, thicker bare land-fast ice with an albedo of 0.7 and higher depending on the snow cover, and everything in between. The image also shows bare and snow-covered land.

Overall, the albedo in the region increased between the two dates. This is also evident at the Marsden campaign site, shown in Fig. , where the Landsat 9 sea ice albedo is lower than the in situ observations, while the Sentinel-2 albedo is higher. Except over the land-fast sea ice, where concentrations remain unchanged, sea ice concentrations in McMurdo Sound increased locally, which explains the observed albedo increase over the sea ice. The higher albedo on the latter date could have various causes, such as solar zenith angle (SZA) differences due to satellite fly-by times, using different satellite sensors and data processing variations. The uncertainty of the Landsat 9 albedo product is ± 0.02 in polar regions, while that of the Sentinel-2 albedo imagery is ± 0.05. We can estimate the effect of solar zenith angle on snow and ice albedo between the two dates using parameterisation from . Assuming pure, dry fresh snow with an albedo of 0.9 and a solar zenith angle of 0°, the albedo reduction due to the solar zenith angle at the Landsat 9 overpass (SZA=70.8°) is 0.038. For the Sentinel 2 overpass (SZA=67.3°), the corresponding albedo effect is 0.036. For ice and snow with lower albedo values, the effect is more pronounced – for example, sea ice with an albedo of 0.5 experiences albedo effects of 0.1, for both SZA=70.8 and 67.3°. However, the difference in albedo between the two observation dates remains small, on the order of 0.01 or less.

Because of the small area of the sea ice in the satellite images, more quantitative comparisons are difficult. Figure  shows the probability distributions for the sea ice albedo on the Landsat 9 image (Fig. ), highlighting model deficiencies over the limited sample area. The surface albedo over the ocean/sea ice part of the Landsat 9 image has a wide distribution between 0 (open ocean) and 0.9 (snow-covered sea ice) and a clear maximum at 0.4. The regional climate models, particularly MAR with its low spatial resolution, lack detailed representation of the observed surface albedo. RACMO, ERA5, and HCLIM have a narrow distribution of sea ice albedo values, between the 0.4–0.75 range. Although HCLIM operates at a high, 2.5 km resolution, its sea surface temperature and sea-ice-concentration fields are derived from the 0.25° (∼28 km) resolution ERA5 reanalysis. As a result, any high-resolution sea ice variables output by HCLIM are influenced by the lower-resolution ERA5 forcing. The same is true for RACMO and MAR.

Among the tested models, MetROMS-UHel displays the largest variation in sea ice albedo over the same area covered by the satellite albedo products. The distribution of the modelled sea ice albedo is bimodal. Over this small sea ice area, the sea ice concentration in the model remains generally high (above 90 %). The fractional snow cover on top of the sea ice is also a key factor in determining sea ice albedo. The combination of high sea ice concentration (>95 %) and snow fraction (>90 %) in McMurdo Sound resembles the features of land-fast sea ice observed in the satellite images. This results in the high albedo peak centred at 0.8 in the MetROMS-UHel albedo distribution. However, unlike the Landsat 9 image, the MetROMS-UHel output fails to capture the distinct characteristics of free-floating sea ice both spatially and in its probability distribution. The MetROMS-UHel sea ice albedo distribution exhibits a lower albedo peak centred at 0.4, but albedos below 0.3 are not represented.

Model comparisons with such high-resolution, albeit limited-area, satellite products are valuable and should be further explored, ideally using larger satellite image datasets. These comparisons reveal the complexities of observed sea ice, highlighting model deficiencies while guiding advancements in model development.

The largest area of comparison is the Weddell (2 799 169 km²) and Ross seas (3 200 661 km²), defined by the size of the HCLIM respective domains. The maps for the satellite images, the model, and ERA5 over the Weddell Sea and Ross Sea are shown in Figs.  and . The distributions of the surface albedo over these areas are shown in Figs.  and . Furthermore, similar distributions but for sea ice and snow only (with fractional ocean albedo removed) are shown in Figs.  and in Appendix , illustrating the snow and ice albedo parameterisation distribution, without sea-ice-concentration effects.

The surface albedo in the sea ice zone is always strongly influenced by sea ice concentration. HCLIM, MAR, and RACMO use the sea-ice-concentration fields from ERA5, which itself is satellite-derived. The spatial surface albedo patterns in Fig.  are therefore the same in these models. However, RACMO and ERA5 have lower sea ice albedo compared to CLARA-A3 and other models. This is better revealed in Fig. . The sea ice zone albedo in ERA5 has the first mode at 0.60, although the maximum observed sea ice zone albedo is as high as 0.85. The secondary mode of 0.05 is from points with no sea ice cover. RACMO has the first mode at 0.65. Figure  shows that sea-ice-concentration-independent snow and ice parameterisation over the whole region is about 0.65 for both ERA5 and RACMO. This value is consistent with Fig. 6, with Antarctic values shifted by 6 months, which shows sea ice albedo over Antarctica in mid-December to be around 0.6–0.7.

The regional oceanic models differ in the sea-ice-concentration patterns. We note that NEMO predicts the sea ice margin to be close to the ISPOL field campaign site. NEMO also has the first mode lower than expected from the observations, at 0.70, and does not describe 0.80 and higher albedo values due to overall lower sea ice concentrations. Figure  shows that the first mode for snow and ice albedo is at 0.80. Therefore, the main difference in the surface albedo comes from sea ice concentrations in NEMO and MetROMS-UHel, not from their albedo parameterisations. MetROMS-UHel model shows the best agreement with the spatial distribution and density distribution of the CLARA-A3 satellite albedo.

Over the Ross Sea domain, shown in Fig. , the albedo spatial pattern is a combination of sea ice concentration and snow fraction. The sea ice concentrations in the defined domain are mostly high, and we see only open water at the Ross Sea polynya. The CLARA-A3 image shows more small-scale variability, but the distribution shows a clear mode at 0.75–0.80. The models predict it correctly but do not describe the variety of lower, 0.2–0.7 albedo values. Exceptions are the ERA5, with a lower-than-observed strong mode at 0.7, and MetROMS-UHel, which matches the distribution of observed albedo values well. Notably, MetROMS-UHel reproduces the coastal polynya in front of the Ross Ice Shelf that can be seen from CLARA-A3 in Fig. .

RACMO does better over the Ross Sea in November 2004 than over the Weddell Sea in December 2004. When interpolating between monthly climatological averages in the sea ice albedo parameterisation of , the predictions are occasionally accurate. The regional climate models MAR, HCLIM, and RACMO have similar behaviour over the Ross Sea, although RACMO has slightly lower first-mode snow albedo. The small differences come from parameterisations only, as the sea-ice-concentration fields are all the same ERA5 input.