CR6.1

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.

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.

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.

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.

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.