Presentations: Wed, 25 May | Room 1.15/16
Snow cover is a primary control on Antarctic sea ice mass balance as it controls basal ice growth and snow ice formation. It is also a primary control on the surface energy budget, partitioning of solar radiation, and sea ice biological communities. Finally, knowledge of its distribution is critical for accurate estimation of sea ice thickness from satellite altimeters. The floe-scale distribution of snow is highly variable, driven by wind redistribution over complex sea ice surface topography. Yet, our understanding of the seasonal evolution of snow depth distribution is poor and its representation in models is simple or non-existent.
We present observations of the three-dimensional distribution of snow depth, ice thickness, and surface topography from a suite of cruises in the Weddell, Bellingshausen, Ross, and East Antarctic Seas that span the full growth season – from autumn, through winter, to late spring. The distribution of snow depth changes from a right-skewed distribution in autumn as snow initially accumulates around ridges to a gaussian by spring as snow deepens and ice surface topography roughens. While the distribution is spatially complex, the spectral distribution of snow features is similar across seasons. Using these data we construct a simple statistical model for the seasonal evolution of floe-scale snow depth distribution. We also compare our results to prior observations from drilling transects and larger-scale airborne observations from NASA’s Operation IceBridge. For the latter we use a convolutional neural network to demonstrate that the surface topography can be used as a reliable predictor of the snow depth distribution at regional scales.
How to cite: Maksym, T., Mei, M. J., Wever, N., Trujillo, E., Leonard, K., Ackley, S., Weissling, B., Williams, G., and Singh, H.: Spatiotemporal evolution of snow depth distribution on Antarctic sea ice, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13506, https://doi.org/10.5194/egusphere-egu22-13506, 2022.
A wide variety of surface types are present in the Arctic: Ocean, ice, snow and melt ponds cover the surface featuring a strong heterogeneity. Due to the differences in their albedo the composition of these surface types strongly impacts the radiative feedback and hence the energy budget which is crucial in climate models. During the summer period the variability is particularly high because the increased temperatures lead to melt pond formation. The seasonal development of melt ponds features fast and local changes in fraction of surface types and thus in albedo. To study the ice-albedo feedback and its impact on the Arctic climate, large scale and regular information on these characteristics are necessary. This can be facilitated by the use of satellite remote sensing.
In 2016, the Sentinel-3 mission was launched providing full coverage of the Arctic on a daily basis aside from cloud coverage limitations. The devices these satellites carry include the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). Together these two instruments measure 30 spectral bands at wavelengths between 400 nm and 12 μm. We present the available melt pond fraction and surface albedo products retrieved from the optical Setinel-3 satellite data with the Melt Pond Detector (MPD) algorithm developed by Zege and others. However, these measurements cannot resolve surface type heterogeneity beyond the spatial resolution of 1.2 km and require additional information to enable spectral unmixing of these surface types at a sub-pixel scale. To investigate the performance and enable improvements of the established retrieval, higher resolution satellite imagery is used. The Sentinel-2 twin satellites were launched in 2015 and 2017 and provide spectral measurements in the optical and near-infrared range at a resolution of 10 m whereas the temporal and spatial coverage is limited. A classification algorithm developed by Wang et al. is applied to obtain melt pond fractions of this increased accuracy for the years 2018 to 2021. Here, we present the melt pond fraction for selected Sentinel-2 scenes and their correspondence with the allocated MPD results. These show good agreement for landfast ice areas with distinct melt ponds, while in general drift and resolution issues are likely to be responsible for discrepancies. For the period of June and July 2020, the available and cloud free scenes along the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift track are evaluated. This observation indicates a melt onset on the MOSAiC floe mid of June, roughly one week prior to the vicinity.
How to cite: Niehaus, H., Istomina, L., Malinka, A., Zege, E., Sperzel, T., and Spreen, G.: Satellite Remote Sensingof Melt Ponds and Albedoin the Arctic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4719, https://doi.org/10.5194/egusphere-egu22-4719, 2022.
Sea ice albedo is a key climate variable that affects the Earth’s radiation budget. Spatio-temporal variation of sea ice albedo can be retrieved from pre existing satellite observation processing chains such as the CLARA2-SAL product. However, currently there is only one albedo product which is derived from instantaneous multi-angle measurements and that is from MISR [1]. The accuracy of surface albedo products is usually affected by error accumulation from atmospheric corrections to the top-of-atmosphere bi-directional reflectance factor (BRF) and the modelling of bottom of the atmosphere BRF and subsequent modelling to bi-directional reflectance distribution function (BRDF) using these BRFs. Sea ice surfaces being both anisotropic and dynamic have satellite product accuracies that also depend on the length of deployed time window, thus requiring sufficient numbers of observations over a short period of time. In this study, we present a data fusion method using the high accuracy near simultaneous sampling of the Multiangle Imaging SpectroRadiometer (MISR) generated at the Langley Research Center applying a Rayleigh atmospheric correction, with the MOD35 cloud mask which is part of the MOD29 Surface Temperature and Ice Extent product derived from the Moderate Imaging Spetroradiometer (MODIS), both onboard the Terra satellite.
We assume that the MISR bi-hemispherical reflectance (BHR) albedo is independent of solar angle, a crucial condition for instantaneous albedo products. As the accuracy of MOD29 cloud mask is assessed at >90% [1], this synergistic method can retrieve an improved BHR of the Arctic sea ice between April and September of each year from 2000 to 2019, and of the Antarctic sea ice between September and March of each year from 2000 to 2019. This study is a follow-on from Kharbouche and Muller (2018), that developed this method and focused on the Arctic region for the time span between March and September from 2000 to 2016.
For both polar regions, we create four daily sea ice products consisting of different averaging time window (±1 day, ±3 days, ±7 days and ±15 days), each containing the number of samples, mean and standard deviation. For all four MISR cloud-free daily sea ice products, we derive 1km, 5km and 25km spatial resolutions. We perform an assessment of the day-of-year trend of sea ice BHR between 2000 and 2019 for the Arctic, and between 2000 and 2019 for Antarctic, confirming a continuing decline of sea ice shortwave albedo in the Arctic depending on the day of year and length of observed time window, and providing a novel sea ice shortwave albedo product analysis for Antarctica.
Acknowledgements. This work was supported by the QA4ECV project www.QA4ECV.eu, of the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement number 607405. We thank our colleagues at JPL and NASA LaRC for processing the MISR data, especially Sebastian Val and Steve Protack and Jeff Walter, respectively and Richard Frey and Steve Ackerman at CIMMS, SSEC, University of Madison, WI for the analysis of the MOD35 cloud mask using CALIPSO shown in [1].
[1] https://doi.org/10.3390/rs11010009
How to cite: Aguilar, L., Muller, J.-P., Kharbouche, S., Johnson, T., and Tsamados, M.: MISR Arctic and Antarctic Sea Ice Albedo 2000-2019 Product Creation and Trend Analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12055, https://doi.org/10.5194/egusphere-egu22-12055, 2022.
Investigating the temporal and spatial changes in Arctic sea ice freeboard, thickness and volume is crucial for climate and environmental research. In this study, the freeboard, thickness and volume of the Arctic sea ice between 2011 and 2020 were acquired from CryoSat-2 data and it was compared with NASA Operation IceBridge and Alfred Wegener Institue AWI datasets. The effect of wind field and temperature and itscontribution on Arctic sea ice were also investigated. The main steps of our research are as follows. 1) Selecting data points above 66 degrees north latitude and filtering out sea ice by flag value and mask; Using OSI SAF sea ice concentration (SIC) data to further constrict the area; Interpolating the latest mean sea surface data DTU21 into CryoSat-2 data; Calculating the sea ice freeboard via spatial altimetry relationship. 2) Combining with snow density, snow depth, multi-year ice density and first-year ice density to estimate the sea ice thickness respectively from freeboard according to the assumption of hydrostatic equilibrium. 3) Using the area and extent provided by NSIDC to interpret the volume. 4) Utilizing Seasonal and Trend decomposition using Loess(STL) to analyse the seasonal and interannual variations of sea ice. 5) Dividing the Arctic Ocean by its marginal sea, coupled with the HY-2B microwave scatterometer data and NCEP/NCAR reanalysis data, the impact of the Beaufort Sea, the Chukchi Sea, the East Siberian Sea, the Laptev Sea, the Kara Sea, the Barents Sea, the Greenland Sea, and the Baffin Bay wind field on the sea ice were studied. 6) Exploring the correlation between the sea surface temperature and the sea ice freeboard, thickness and volume. The result indicated that (1) the freeboard and thickness was decreasing about 9.748% and 8.80% during 2011-2020,respectively; (2) there are interlunar variations in sea ice freeboard and thickness, the freeboard and thickness of sea ice reach the minimum in August to September each year and the maximum appears in March to April; (3) Arctic sea ice is affected by both thermodynamics and dynamics, the reduction of Arctic sea ice was largely due to the sea surface wind field, one of the dynamic factors is the wind field on the sea surface. The sea ice changes in the various sea areas of the Arctic Ocean are related to the wind field to varying degrees. The changes in the sea surface wind field in recent years have further promoted the reduction of sea ice, making it possible for the Arctic to open to navigation in the summer. Fully understanding the evolution of sea ice change in the Arctic Ocean is helpful for humans to better protect the earth.
How to cite: Zhang, Y.: Spatio-temporal Variablity of Arctic Sea Ice Freeboard, Thickness and Volume from CryoSat-2 and Its Possible Drivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7540, https://doi.org/10.5194/egusphere-egu22-7540, 2022.
Sea-ice floating upon the Arctic ocean is a constantly moving, growing and melting surface. The seasonal cycle of sea ice volume has an average change of 10 000 Km$^3$ or 9 billion tonnes of sea ice. The role of dynamic redistribution of sea ice, the process by which it flows and deforms when blown by winds and floating upon ocean currents, has been observable during winter growth by the incorporation of satellite remote sensing of ice thickness and drift. CMIP6 models contain dynamic sea-ice components that simulate the drift and mass balance of Arctic sea ice.
We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide maps of ice growth, melt and dynamic redistribution. Winter growth and summer melt seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. We reveal key circulation patterns that contribute to summer melt and minimum sea ice volume and extent. Specifically, we show the importance of ice drift to the interannual variability in Arctic sea-ice volume, and the regional distribution of sea ice growth and melt rates. When comparing these observations to CMIP6 models long term trends are revealed. We show how the divergence and mechanical redistribution of sea ice is a key component in the resilience of central Arctic ice volume to anthropogenic climate change.
How to cite: Heorton, H. and Tsamados, M.: Arctic sea-ice volume budget from satellite observations and CMIP6 models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12506, https://doi.org/10.5194/egusphere-egu22-12506, 2022.
In this work we present results from a new sea ice reanalysis over the satellite era. We use a newly created sea ice data assimilation system CICE-PDAF, combining the Los Alamos Sea Ice Model (CICE) and the Parallelized Data Assimilation Framework (PDAF), to take advantage of the new observations of the sea ice cover produced in the last decade by Cryosat-2. Sea ice thickness and sea ice thickness distribution observations from Cryosat-2, alongside sea ice concentration observations, are assimilated to explore their effects on our current estimates of the Arctic sea ice cover. In particular we look at its effects on the sea ice thickness distribution. The true state of the Arctic sub-grid scale thickness distribution system is not well known, and yet it plays a key role in the dynamic and thermodynamic processes present in the model to produce a good estimate of the Arctic sea ice state. Thus by combining knowledge from state-of-the-art sea ice models with knowledge from newly developed observations we hope to produce a clearer picture of the Arctic sea ice and its thickness distribution.
How to cite: Williams, N., Byrne, N., Feltham, D., Van Leeuwen, P. J., Schroeder, D., Bannister, R., and Shepherd, A.: Utilising Cryosat-2 observations of the Arctic sea ice cover to produce a new Arctic sea ice reanalysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3764, https://doi.org/10.5194/egusphere-egu22-3764, 2022.
We will introduce the Sea Ice Evaluation Tool (SITool) developed to evaluate the skill of Arctic and Antarctic model reconstructions of sea ice concentration, extent, edge location, drift, thickness, and snow depth. It is a Python-based software and consists of well-documented functions used to derive various sea ice metrics and diagnostics. The SITool version 1.0 is used to evaluate the performance of global sea ice reconstructions from nine models that provided sea ice output under CMIP6 Ocean Model Intercomparison Project with two different atmospheric forcing datasets: the Coordinated Ocean-ice Reference Experiments version 2 (CORE-II) and the updated Japanese 55-year atmospheric reanalysis (JRA55-do). The improved Arctic and Antarctic sea ice areal properties and ice drift simulation have been recognized in OMIP models forced by JRA55-do. The processes contributing to these improvements are assessed and discussed. It is found that improvements in the simulation of summer ice concentration in the interior region are linked, in both hemispheres, to improvements in the downward surface net shortwave radiation flux in JRA55-do. The austral winter ice concentration simulation is improved in the ice edge region relating to the dynamic process dominated by surface wind stress. The obvious improvement of the ice drift magnitude simulation is in the Arctic ice edge region from November to April dominated by the decreased surface wind stress forced by JRA55-do, while the improvement in the Antarctic is much smaller. This study provides clues to improve the atmospheric reanalysis product for a better sea ice simulation in ocean-sea ice models and more attention can be paid to the radiation flux and wind fields.
How to cite: Lin, X., Massonnet, F., Fichefet, T., and Vancoppenolle, M.: Sea ice evaluation tool: application to CMIP6 OMIP and the sensitivity of sea ice simulation to atmospheric forcing uncertainties, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1181, https://doi.org/10.5194/egusphere-egu22-1181, 2022.
The Weddell Sea Polynya is a large opening within the sea ice cover of the Weddell sea sector, typically found sitting over the Maud Rise in its largest occurrences. It has been a rare event in the satellite period, appearing throughout the 1970s and again in 2016/17. Many mechanisms have been suggested to cause the onset of the Weddell Sea Polynya, from deep convection of the ocean and upwelling at the Maud Rise, in addition to increased cyclone activity and the influence of atmospheric rivers. It is thought that with increasing atmospheric greenhouse gasses, the Weddell Sea Polynya will be even less frequent, due to an intensification of the haline stratification within the polynya region. The opening of the polynya creates an ocean to air heat flux in the cooler months, with the potential to influence atmospheric dynamics. The atmospheric response to the polynya and regional ice loss may be observed locally within the low-pressure region of the Weddell Sea or further afield climate. Here, we use high and low resolution AGCM experiments with the HadGEM3 UK Met Office model, alongside PRIMAVERA high-resolution analysis of the polynya, to evaluate the atmospheric response to the polynya and associated features, in addition to the role of model resolution in resolving the polynya and its associated features.
How to cite: Ayres, H. and Ferreira, D.: The atmospheric response to the Weddell Sea Polynya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1209, https://doi.org/10.5194/egusphere-egu22-1209, 2022.
Sightings have long reported the presence of unconsolidated ice crystals spanning up to several meters in thickness under Antarctic landfast sea ice. This so-called sub-ice platelet layer (SIPL) was until recently considered as exotic and out of the scope of standard sea ice models.
Here we show that a realistic, highly porous and isothermal SIPL emerges in one-dimensional mushy-layer sea ice model simulations, provided appropriate thermal forcing. The model SIPL develops once conductive heat fluxes are insufficient to cause internal freezing of the new, highly porous ice. Sufficiently high snow and ice thicknesses are key to the onset of the SIPL development, whereas high liquid content and isothermal character stabilize the SIPL.
Two model features are necessary to the emergence of the SIPL: an advective formulation of salt dynamics, and a high value for the liquid fraction of new ice. We surmise that large-scale ice-ocean models should capture a SIPL at physically sensible locations and times if the aforementioned issues are properly considered.
How to cite: Vancoppenolle, M., Wongpan, P., and Langhorne, P.: Emergence of a sub-ice platelet layer in mushy-layer sea ice model simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11971, https://doi.org/10.5194/egusphere-egu22-11971, 2022.
Overwash is an important aspect of the dynamics in the marginal ice zone where sea ice and ocean waves interact. Overwash dissipates wave energy, and the presence of water on top of sea ice can drive growth or melting, depending on the local thermodynamic conditions. The presence of water on floes is also important for biologic and chemical processes. While overwash has been observed and investigated under experimental conditions, it has not yet been studied in the marginal ice zone. One reason for this lack of in-situ measurements and observations is due to the marginal ice zone being highly dynamic, and the onset of overwash only occurring under specific and sensitive conditions. To facilitate future observations we have produced a model of the extent of overwash into fields of sea ice by combining a new model of the onset of overwash and a standard attenuation model. This model of overwash extent is validated against experimental observations and is used to provide the extent of overwash for realistic ice and wave field conditions observed during the July 2017 voyage of the South African icebreaker S.A. Agulhas II.
How to cite: Pitt, J. and Bennetts, L.: Model predictions of overwash extent into the marginal ice zone. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2294, https://doi.org/10.5194/egusphere-egu22-2294, 2022.
Fragmentation of the sea ice cover by wind-generated waves is an
important mechanism impacting ice evolution.
Fractured ice is more sensitive to melt, leading to a local reduction in
concentration, facilitating wave propagation, hence introducing a
positive feedback loop accelerating sea ice retreat.
Although this process and the concept of floe size distribution (FSD)
have been incorporated in several sea ice components of global climate
models (GCM), the physics governing ice breakup under wave action
remains poorly understood, and its parametrisation highly simplified.
We propose a numerical model of wave-induced sea ice breakup to estimate
the FSD resulting from repeated fracture events.
This model, based on linear water wave theory and viscoelastic sea ice
rheology, solves for the scattering of an incoming wave spectrum by the
ice cover and derives the corresponding strain field. Fracture occurs
when the undergone strain exceeds a prescribed threshold.
We find that under realistic wave forcing, lognormal FSDs appear
consistently in a large variety of model configurations.
This result contrasts with the power-law FSD behaviour often assumed by
modellers.
We discuss the properties of these modelled distributions, and
investigate the stochastic processes affecting their emergence.
How to cite: Mokus, N. and Montiel, F.: Modelling the wave-induced fragmentation of the sea ice cover, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13263, https://doi.org/10.5194/egusphere-egu22-13263, 2022.
Sea ice is not homogenous and is instead made up of individual pieces of ice that are called floes. Observations show that these floes range in size from just metres to tens of kilometres. Sea ice and climate models have historically assumed a fixed floe size, if there is an explicit representation of floe size at all. There have been several recent efforts to include a treatment of variable floe size within sea ice models. These models have included several processes thought to be important in floe size evolution including break-up of sea ice by waves, lateral melt and growth, welding together of floes, and brittle fracture processes. Floe size can have a direct impact on sea ice evolution via several mechanisms including lateral melt rate, momentum exchange between the sea ice, ocean, and atmosphere, and the ice rheology. Floe size distribution (FSD) models have so far been used within sea ice models to primarily explore the direct impact of floe size on the sea ice cover, and there has been little exploration of the possible resulting feedback processes.
In this study we consider a prognostic approach to modelling the FSD within the CICE sea ice model where the shape of the FSD is an emergent characteristic. We consider results from both standalone sea ice simulations and fully coupled climate simulations. These results are used to explore whether an improved representation of sea ice-ocean and sea ice-atmosphere feedbacks modifies the impact of floe size on the sea ice concentration and thickness over both pan-Arctic and localised scales. We will focus on feedbacks that result from changes to the lateral melt rate, considering in particular whether there is a significant impact from the ice-ocean albedo feedback mechanism. Finally, we will discuss the necessary conditions for there to be significant feedbacks resulting from the inclusion of floe size distribution models in sea ice and climate models.
How to cite: Bateson, A., Feltham, D., Schröder, D., Ridley, J., and Frew, R.: Feedbacks emerging from variable floe size in the Arctic sea ice cover, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8338, https://doi.org/10.5194/egusphere-egu22-8338, 2022.
Brine channel networks and brine inclusions are dominating features of the microstructure of saline ice that determine the physical properties and influence thermodynamic and convective processes within the ice. Similar to sea ice, salinity and the spatial distribution of brine are important properties of sea spray ice. However, their manifestation in the microstructure and their relation to the growth conditions are scarcely investigated for spray ice. Towards a physical understanding of brine inclusion and brine drainage processes in sea spray ice, we characterize microstructures of samples from the field and from systematic experiments in the cold lab under varying growth conditions (temperatures, spray rate, salinity). By means of 3D micro-computed tomography images, we examine main characteristics of the brine features. We will present first results of the microstructure characterization and discuss their relation to the growth conditions.
How to cite: Willibald, C.: Brine channel network and brine inclusions – characterization of the three-phase 3D microstructure of saline spray ice, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12706, https://doi.org/10.5194/egusphere-egu22-12706, 2022.
