Ice Mass Balance Buoys Research Articles

Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics

Abstract. Icepack (v1.1.0) – the column thermodynamics model of the Community Ice CodE (CICE) version 6 – is used to assess how changing the thermodynamics from the Bitz and Lipscomb (1999) physics (hereafter BL99) to the mushy-layer physics impacts the model performance in reproducing in situ landfast ice observations from two ice mass balance (IMB) buoys co-deployed in the landfast ice close to Nain (Labrador) in February 2017. To this end, a new automated surface retrieval algorithm is used to determine the in situ ice thickness, snow depth, basal ice congelation and snow-ice formation from the measured vertical temperature profiles. Icepack simulations are run to reproduce these observations using each thermodynamics scheme, with a particular interest in how the different physics influence the representation of snow-ice formation and ice congelation. Results show that the BL99 parameterization represents well the ice congelation but underrepresents the snow-ice contribution to the ice mass balance. In particular, defining snow-ice formation based on the hydrostatic balance alone does not reproduce the negative freeboards observed for several days in the IMB observations, resulting in an earlier snow-flooding onset, a positive ice thickness bias and reduced snow depth variations. We find that the mushy-layer thermodynamics with default parameters significantly degrades the model performance, overestimating both the congelation growth and snow-ice formation. The simulated thermodynamics response to flooding, however, better represents the observations, and the best results are obtained when allowing for negative freeboards in the mushy-layer physics. We find that the mushy-layer thermodynamics produces a larger variability in congelation rates at the ice bottom interface, alternating between periods of exceedingly fast growth and periods of unrealistic basal melt. This pattern is related to persistent brine dilution in the lowest ice layer by the congelation and brine drainage parameterizations. We also show that the mushy-layer congelation parameterization produces significant frazil formation, which is not expected in a landfast ice context. This behavior is attributed to the congelation parameterization not fully accounting for the conductive heat flux imbalance at the ice–ocean boundary. We propose a modification of the mushy-layer congelation scheme that largely reduces the frazil formation and allows for better tuning of the congelation rates to match the observations. Our results demonstrate that the mushy-layer physics and its parameters can be tuned to closely match the in situ observations, although more observations are needed to better constrain them.

Sea ice cover in the Copernicus Arctic Regional Reanalysis

Abstract. The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part of the European Arctic including substantial amounts of ice-covered areas. Sea ice in CARRA is modelled by means of a one-dimensional thermodynamic sea ice parameterisation scheme, which also explicitly resolves the evolution of the snow layer over sea ice. In the present study, we assess the representation of sea ice cover in CARRA and validate it against a wide set of satellite products and observations from ice mass balance buoys. We show that CARRA adequately represents general interannual trends towards thinner and warmer ice in the Arctic. Compared to ERA5, sea ice in CARRA shows a reduced warm bias in the ice surface temperature. The strongest improvement was observed for winter months over the central Arctic and the Greenland and Barents seas where a 4.91 °C median ice surface temperature error in ERA5 is reduced to 1.88 °C in CARRA on average. Over Baffin Bay, intercomparisons suggest the presence of a cold winter-time ice surface temperature bias in CARRA. No improvement over ERA5 was found in the ice surface albedo with spring-time errors in CARRA being up to 0.08 higher on average than those in ERA5 when computed against the CLARA-A2 satellite retrieval product. Summer-time ice surface albedos are comparable in CARRA and ERA5. Sea ice thickness and snow depth in CARRA adequately resolve the annual cycle of sea ice cover in the Arctic and bring added value compared to ERA5. However, limitations of CARRA indicate potential benefits of utilising more advanced approaches for representing sea ice cover in next-generation reanalyses.

Snow thermal conductivity and conductive flux in the Central Arctic: Estimates from observations and implications for models

During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m−1 K−1 to 0.47 W m−1 K−1 with a median from all data of 0.39 W m−1 K−1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m−2, but the average residual during winter is −6 W m−2, which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

Sea ice heat and mass balance measurements from four autonomous buoys during the MOSAiC drift campaign

As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), four autonomous seasonal ice mass balance buoys were deployed in first- and second-year ice. These buoys measured position, barometric pressure, snow depth, ice thickness, ice growth, surface melt, bottom melt, and vertical profiles of temperature from the air, through the snow and ice, and into the upper ocean. Observed air temperatures were similar at all four sites; however, snow–ice interface temperatures varied by as much as 10°C, primarily due to differences in snow depth. Observed winter ice growth rates (November to May) were <1 cm day−1, with summer melt rates (June to July) as large as 5 cm day−1. Air temperatures changed as much as 2°C hour−1 but were dampened to <0.3°C hour−1 at the snow–ice interface. Initial October ice thicknesses ranged from 0.3 m in first-year ice to 1.2 m in second-year ice. By February, this range was only 1.20–1.46 m, due in part to differences in the onset of basal freezing. In second-year ice, this delay was due to large brine-filled voids in the ice; propagating the cold front through this ice required freezing the brine. Mass balance results were similar to those measured by autonomous buoys deployed at the North Pole from 2000 to 2013. Winter average estimates of the ocean heat flux ranged from 0 to 3 W m−2, with a large increase in June 2020 as the floe moved into warmer water. Estimates of average snow thermal conductivity measured at two buoys during periods of linear temperature profiles were 0.41 and 0.42 W m−1 °C−1, higher than previously published estimates. Results from these ice mass balance buoys can contribute to efforts to close the MOSAiC heat budget.

Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates

Abstract. In this study, a new method to assimilate freeboard (FB) derived from satellite radar altimetry is presented with the goal of improving the initial state of sea ice thickness predictions in the Arctic. In order to quantify the improvement in sea ice thickness gained by assimilating FB, we compare three different model runs: one reference run (refRun), one that assimilates only sea ice concentration (SIC) (sicRun), and one that assimilates both SIC and FB (fbRun). It is shown that estimates for both SIC and FB can be improved by assimilation, but only fbRun improved the FB. The resulting sea ice thickness is evaluated by comparing sea ice draft measurements from the Beaufort Gyre Exploration Project (BGEP) and sea ice thickness measurements from 19 ice mass balance (IMB) buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The sea ice thickness of fbRun compares better than refRun and sicRun to the longer BGEP observations more poorly to the shorter MOSAiC observations. Further, the three model runs are compared to the Alfred Wegener Institute (AWI) weekly CryoSat-2 sea ice thickness, which is based on the same FB observations as those that were assimilated in this study. It is shown that the FB and sea ice thickness from fbRun are closer to the AWI CryoSat-2 values than the ones from refRun or sicRun. Finally, comparisons of the abovementioned observations and both the fbRun sea ice thickness and the AWI weekly CryoSat-2 sea ice thickness were performed. At the BGEP locations, both fbRun and the AWI CryoSat-2 sea ice thickness perform equally. The total root-mean-square error (RMSE) at the BGEP locations equals 30 cm for both sea ice thickness products. At the MOSAiC locations, fbRun's sea ice thickness performs significantly better, with a total 11 cm lower RMSE.

Ice bottom evolution derived from thermistor string-based ice mass balance buoy observations

ABSTRACT Digital information on sea ice extent, thickness, volume, and distribution is crucial for understanding Earth's climate system. The Snow and Ice Mass Balance Apparatus (SIMBA) is used to determine snow and ice temperatures in Arctic, Antarctic, ice-covered seas, and boreal lakes. Snow depth and ice thickness are derived from SIMBA temperature regimes (SIMBA_ET and SIMBA_HT). In warm conditions, SIMBA_ET temperature-based ice thickness may have errors due to the isothermal vertical profile. SIMBA_HT provides a visible ice-bottom interface for manual quantification. We propose an unmanned approach, combining neural networks, wavelet analysis, and Kalman filtering (NWK), to mathematically establish NWK and retrieve ice bottoms from various SIMBA_HT datasets. In the Arctic, NWK-derived total thickness showed a bias range of −5.64 cm to 4.01 cm and a correlation coefficient of 95%−99%. For Baltic Sea ice, values ranged from 1.31 cm to 2.41 cm (88%−98% correlation), and for boreal lake ice, −0.7 cm to 2.6 cm (75%−83% correlation). During ice growth, thermal equilibrium, and melting, the bias varied from −3.93 cm to 2.37 cm, −1.92 cm to 0.04 cm, and −4.90 cm to 3.96 cm, with correlation coefficients of 76%−99%. These results demonstrate NWK's robustness in retrieving ice bottom evolution in different water environments.

Different mechanisms of Arctic first-year sea-ice ridge consolidation observed during the MOSAiC expedition

Sea-ice ridges constitute a large fraction of the ice volume in the Arctic Ocean, yet we know little about the evolution of these ice masses. Here we examine the thermal and morphological evolution of an Arctic first-year sea-ice ridge, from its formation to advanced melt. Initially the mean keel depth was 5.6 m and mean sail height was 0.7 m. The initial rubble macroporosity (fraction of seawater filled voids) was estimated at 29% from ice drilling and 43%–46% from buoy temperature. From January until mid-April, the ridge consolidated slowly by heat loss to the atmosphere and the total consolidated layer growth during this phase was 0.7 m. From mid-April to mid-June, there was a sudden increase of ridge consolidation rate despite no increase in conductive heat flux. We surmise this change was related to decreased macroporosity due to transport of snow-slush to the ridge keel rubble via adjacent open leads. In this period, the mean thickness of the consolidated layer increased by 2.1 m. At the peak of melt in June–July we suggest that the consolidation was related to the refreezing of surface snow and ice meltwater and of ridge keel meltwater (the latter only about 15% of total consolidation). We used the morphology parameters of the ridge to calculate its hydrostatic equilibrium and obtained a more accurate estimate of the actual consolidation of the keel, correcting from 2.2 m to 2.8 m for average keel consolidation. This approach also allowed us to estimate that the average keel melt of 0.3 m, in June–July, was accompanied by a decrease in ridge draft of 0.9 m. An ice mass balance buoy in the ridge indicated total consolidation of 2.8 m, of which 2.1 m was related to the rapid mode of consolidation from April to June. By mid-June, consolidation resulted in a drastic decrease of the macroporosity of the interior of keel while the flanks had little or no change in macroporosity. These results are important to understanding the role of ridge keels as meltwater sources and sinks and as sanctuary for ice-associated organisms in Arctic pack ice.

Seasonal and interannual variability of the landfast ice mass balance between 2009 and 2018 in Prydz Bay, East Antarctica

Abstract. Landfast ice (LFI) plays a crucial role for both the climate and the ecosystem of the Antarctic coastal regions. We investigate the snow and LFI mass balance in Prydz Bay using observations from 11 sea ice mass balance buoys (IMBs). The buoys were deployed offshore from the Chinese Zhongshan Station (ZS) and Australian Davis Station (DS), with the measurements covering the ice seasons of 2009–2010, 2013–2016, and 2018. The observed annual maximum ice thickness and snow depth were 1.59 ± 0.17 and 0.11–0.76 m off ZS and 1.64 ± 0.08 and 0.11–0.38 m off DS, respectively. Early in the ice growth season (May–September), the LFI basal growth rate near DS (0.6 ± 0.2 cm d−1) was larger than that around ZS (0.5 ± 0.2 cm d−1). This is attributed to cooler air temperature (AT) and lower oceanic heat flux at that time in the DS region. Air temperature anomalies were more important in regulating the LFI growth rate at that time because of thinner sea ice having a weaker thermal inertia relative to thick ice in later seasons. Interannual and local spatial variabilities for the seasonality of LFI mass balance identified at ZS are larger than at DS due to local differences in topography and katabatic wind regime. Snow ice contributed up to 27 % of the LFI total ice thickness at the offshore site close to ground icebergs off ZS because of the substantial snow accumulation. Offshore from ZS, the supercooled water was observed at the sites close to the Dålk Glacier from July to October, which reduced the oceanic heat flux and promoted the LFI growth. During late austral spring and summer, the increased oceanic heat flux led to a reduction of LFI growth at all investigated sites. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades. Based on the results derived from this study, we argue that an increased understanding of snow (on LFI) processes, local atmospheric and oceanic conditions, as well as coastal morphology and bathymetry, are required to improve the Antarctic LFI modeling.

Database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.

Changes in the annual sea ice freeze–thaw cycle in the Arctic Ocean from 2001 to 2018

Abstract. The annual sea ice freeze–thaw cycle plays a crucial role in the Arctic atmosphere—ice–ocean system, regulating the seasonal energy balance of sea ice and the underlying upper-ocean. Previous studies of the sea ice freeze–thaw cycle were often based on limited accessible in situ or easily available remotely sensed observations of the surface. To better understand the responses of the sea ice to climate change and its coupling to the upper ocean, we combine measurements of the ice surface and bottom using multisource data to investigate the temporal and spatial variations in the freeze–thaw cycle of Arctic sea ice. Observations by 69 sea ice mass balance buoys (IMBs) collected from 2001 to 2018 revealed that the average ice basal melt onset in the Beaufort Gyre occurred on 23 May (±6 d), approximately 17 d earlier than the surface melt onset. The average ice basal melt onset in the central Arctic Ocean occurred on 17 June (±9 d), which was comparable with the surface melt onset. This difference was mainly attributed to the distinct seasonal variations of oceanic heat available to sea ice melt between the two regions. The overall average onset of basal ice growth of the pan Arctic Ocean occurred on 14 November (±21 d), lagging approximately 3 months behind the surface freeze onset. This temporal delay was caused by a combination of cooling the sea ice, the ocean mixed layer, and the ocean subsurface layer, as well as the thermal buffering of snow atop the ice. In the Beaufort Gyre region, both (Lagrangian) IMB observations (2001–2018) and (Eulerian) moored upward-looking sonar (ULS) observations (2003–2018) revealed a trend towards earlier basal melt onset, mainly linked to the earlier warming of the surface ocean. A trend towards earlier onset of basal ice growth was also identified from the IMB observations (multiyear ice), which we attributed to the overall reduction of ice thickness. In contrast, a trend towards delayed onset of basal ice growth was identified from the ULS observations, which was explained by the fact that the ice cover melted almost entirely by the end of summer in recent years.

A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval

Abstract. As changes to Earth's polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is clear. By linking an algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's law, we have developed a retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan's Law Integrated Conducted Energy (SLICE). With an initial condition at the beginning of the sea ice growth season, the method can model basin-wide absolute sea ice thickness by combining the one-dimensional SLICE retrieval with an ice motion dataset. The advantages of the SLICE retrieval method include daily basin-wide coverage, lack of atmospheric reanalysis product input requirement, and a potential for use beginning in 1987. Validation of the retrieval against measurements from 10 ice mass balance buoys shows a mean correlation of 0.89 and a mean bias of 0.06 m over the course of an entire sea ice growth season. Despite its simplifications and assumptions relative to models like the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS), basin-wide SLICE performs nearly as well as PIOMAS when compared against CryoSat-2 and Operation IceBridge using a linear correlation between collocated points.

Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic transpolar drift during 2019–2020

Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond.

Ice Mass Balance Buoys

Ice Mass Balance Buoys

Observation and modelling of snow and sea ice mass balance and its sensitivity to atmospheric forcing during spring and summer 2007 in the Central

Snow depth and sea ice thickness were observed applying an ice mass balance buoy (IMB) in the drifting ice station Tara during the International Polar Year in 2007. Detailed in situ observations on meteorological variables and surface fluxes were taken during May to August. For this study, the operational analyses and short-term forecasts from two numerical weather prediction (NWP) models (ECMWF and HIRLAM) were extracted for the Tara drift trajectory. We compared the IMB, meteorological and surface flux observations against the NWP products, also applying a one-dimensional thermodynamic sea ice model (HIGHTSI) to calculate the snow and ice mass balance and its sensitivity to atmospheric forcing. The modelled snow depth time series, controlled by NWP-based precipitation, was in line with the observed one. HIGHTSI reproduced well the snowmelt onset, the progress of the melt, and the first date of snow-free conditions. HIGHTSI performed well also in the late August freezing season. Challenges remain to model the “false bottom” observed during the melting season. The evolution of the vertical temperature profiles in snow and ice was better simulated when the model was forced by in situ observations instead of NWP results. During the melting period, the nonlinear ice temperature profile was successfully modelled with both forcing options. During spring and the melting season, total sea ice mass balance was most sensitive to uncertainties in NWP results for the downward longwave radiation, followed by the downward shortwave radiation, air temperature, and wind speed. Citation : Cheng B, Vihma T, Palo T, et al. Observation and modelling of snow and sea ice mass balance and its sensitivity to atmospheric forcing during spring and summer 2007 in the Central Arctic. Adv Polar Sci, 2021, 32(4): 309-323, doi: 10.13679/j.advps.2021.0047

A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements

Snowpack on sea ice can adjust changes in sea ice conditions and plays a vital role in the Earth's climate system. Snow depth, an important parameter of snowpack, is a necessary variable for retrieving sea ice thicknesses based on satellite altimeter data. Here, regression analysis (RA) is used to determine the best gradient ratio (GR) combination of brightness temperatures for estimating snow depths, and the RA model is proposed. Based on the RA model, one additional deep learning model is built, namely, the 5-variable long short-term memory (5VLSTM) model (or the RA-5VLSTM model). Meanwhile, an additional neural network model is built for comparisons, namely, the 5-variable genetic wavelet neural network (5VGWNN) model (or the RA-5VGWNN model). Using Operation IceBridge (OIB) and ice mass balance buoy (IMB) data, these three models, plus three existing algorithms, are compared to assess their performances in estimating snow depth. The results show that the RA-5VLSTM model performed pretty well among the six algorithms, with an RMSE of 7.16 cm. The RA-5VLSTM model, a robust approach, was less influenced by the uncertainty in the input data. From January to April during 2012–2020, the average monthly snow depth in the Beaufort Sea and the Chukchi Sea mainly showed a downward trend, while an upward trend was observed in the Central Arctic in most months. Variations in snow depth in the Central Arctic were mainly affected by the autumn 2-m air temperature (T2m) and the sea surface temperature (SST). Variations in snow depth in the Chukchi Sea were mainly affected by the autumn sea ice velocity.

Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements

Abstract. Although snow depth on sea ice is a key parameter for sea ice thickness (SIT) retrieval, there currently does not exist reliable estimations. In the Arctic, nearly all SIT products use a snow depth climatology (the modified Warren-99 climatology, W99m) constructed from in situ data obtained prior to the first significant impacts of climate change. In the Antarctic, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the altimetric snow depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the Ka-band frequency SARAL/Altika and the Ku-band frequency CryoSat-2. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modelling and Assimilation System (PIOMAS) or its equivalent in the Antarctic the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model, and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in the Arctic)), the Advanced Microwave Scanning Radiometer-2 (AMSR2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka–Ku product (only in the Arctic). The ASD product is further validated in the Arctic against the ice mass balance (IMB) buoys, the CryoSat Validation Experiment (CryoVEx) and Operation Ice Bridge's (OIB) airborne measurements. These comparisons demonstrate that ASD is a relevant snow depth solution, with spatiotemporal patterns consistent with those of the alternative Ka–Ku DuST product but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data: comparisons with OIB's airborne snow radar in the Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in the Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction in the average SIT of about 30 cm.

Spatially distributed simulations of the effect of snow on mass balance and flooding of Antarctic sea ice

AbstractSouthern Ocean sea ice can exhibit widespread flooding and subsequent snow-ice formation, due to relatively thick snow covers compared to the total ice thickness. Considerable subkilometer scale variability in snow and ice thickness causes poorly constrained uncertainties in determining the amount of flooding that occurs. Using datasets of snow depth and ice thickness acquired in the Weddell Sea during austral winter 2013 (AWECS campaign) from three floes, we demonstrate large spatial variability of a factor 10 and 5 for snow and combined snow and ice thickness, respectively. The temporal evolution after the floe visit was recorded by automatic weather station and ice mass balance buoys. Using a physics-based, multi-layer snow/sea ice model in a one-dimensional and distributed mode to simulate the thermodynamic processes, we show that the distributed simulations, modeling flooding across the entire heterogeneous floe, produced vastly different amounts of flooding than one-dimensional single point simulations. Three times the flooding is produced in the one-dimensional simulation for the buoy location than distributed (floe-averaged) simulations. The latter is in close agreement with buoy observations. The results suggest that using point observations or one-dimensional simulations to extrapolate processes on the floe-scale can overestimate the amount of flooding and snow-ice formation.

Inter-annual variation in lake ice composition in the European Arctic: observations based on high-resolution thermistor strings

Abstract. Climate change and global warming strongly impact the cryosphere. The rise of air temperature and change of precipitation patterns lead to dramatic responses of snow and ice heat and mass balance. Sustainable field observations on lake air–snow–ice–water temperature regime have been carried out in Lake Orajärvi in the vicinity of the Finnish Space Centre, a Flagship Supersite in Sodankylä in Finnish Lapland since 2009. A thermistor-string-based snow and ice mass balance buoy called “Snow and ice mass balance apparatus (SIMBA)” was deployed in the lake at the beginning of each ice season. In this paper, we describe snow and ice temperature regimes, snow depth, ice thickness, and ice compositions retrieved from SIMBA observations as well as meteorological variables based on high-quality observations at the Finnish Space Centre. Ice thickness in Lake Orajärvi showed an increasing trend. During the decade of data collection (1) the November–May mean air temperature had an increasing trend of 0.16 ∘C per year, and the interannual variations were highly correlated (r = 0.93) with the total seasonal accumulated precipitation; (2) the maximum granular ice thickness ranged from 15 % to 80 % of the maximum total ice thickness; and (3) the snow depth on lake ice was not correlated (r = 0.21) with the total precipitation. The data set can be applied to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and to improve the parameterization of snow to ice transformation in snow and ice models. The data are archived at https://doi.org/10.5281/zenodo.4559368 (Cheng et al., 2021).

A Preliminary Investigation of Arctic Sea Ice Negative Freeboard from in-situ Observations and Radar Altimetry

The negative freeboard of sea ice (i.e., the height of ice surface below sea level) with subsequent flooding is widespread in the Southern Ocean, as opposed to the Arctic, due to the relatively thicker ice and thinner snow. In this study, we used the observations of snow and ice thickness from 103 ice mass balance buoys (IMBs) and NASA Operation IceBridge Aircraft Missions to investigate the spatial distribution of negative freeboard of Arctic sea ice. The Result showed that seven IMBs recorded negative freeboards, which were sporadically located in the seas around Northeast Greenland, the Central Arctic Ocean, and the marginal areas of the Chukchi-Beaufort Sea. The observed maximum values of negative freeboard could reach −0.12 m in the seas around Northeast Greenland. The observations from IceBridge campaigns also revealed negative freeboard comparable to those of IMBs in the seas around North Greenland and the Beaufort Sea. We further investigated the large-scale distribution of negative freeboard using NASA CryoSat-2 radar altimeter data, and the result indicates that except for the negative freeboard areas observed by IMBs and IceBridge, there are negative freeboards in other marginal seas of the Arctic Ocean. However, the comparison of the satellite data with the IMB data and IceBridge data shows that the Cryosat-2 data generally overestimate the extent and magnitude of the negative freeboard in the Arctic.