Some of the ongoing research using Mosaic data presented at this week's
International Symposium on Sea Ice across Spatial and Temporal Scales hosted by the
International Glacial SocietyPart 1.
Apologies if I missed anyNew insights on Arctic sea-ice ridges from the MOSAiC expedition – an overviewMats A. Granskog, Evgenii Salganik, Benjamin Lange, Dmitry Divine, Morven Muilwijk, Yusuke Kawaguchi, Marcel Nicolaus, Polona Itkin
Ridges compose a large fraction of the Arctic sea-ice volume, but are still the least studied and understood part of the Arctic ice pack, in part due the logistical challenges studying these ice masses. During MOSAiC focused ridge studies were conducted from winter to advanced melt in summer with a diverse set of methods, from manual drilling and sampling through electromagnetic mapping to automated observations and remotely operated vehicle (ROV) mapping of the ice underside. Both physical and biological sampling were conducted. Despite challenging conditions, e.g.with loss of instruments to ridging events, novel data sets of the temporal evolution of ridges were collected. Here we highlight some of the new findings. New insights into the consolidation, i.e. refreezing of water filled voids in the ridge keels, include evidence for either snow–slush or snow meltwater to significantly contribute to rapid consolidation of ridge keels. The exact mechanisms require further study. Rare observations over time during advanced melt also indicate complex and spatially varying melting of ridge keels, but overall more rapid melt of keels than adjacent level ice was observed. Thus ridge keels provide a significant but often overlooked contribution to the summer meltwater balance (both through melting but also through refreezing of meltwater in the ridge keel). Ridge keels also affect the lateral extent of meltwater layers below the ice, and thus also exert some indirect control of exchange between the ice and ocean. Furthermore, ridge keels can impact ocean mixing and atmosphere–ocean momentum transfer. Acoustic Doppler current profilers deployed upstream and downstream a large ridge reveal increased turbulent kinetic energy near the ridge keels, compared to under-level ice on the same floe. Surprisingly, however, at this particular ridge there were no significant differences in horizontal currents or turbulence between the fore and lee sides of the ridge. The negligible difference in turbulence can be accounted for by evanescent internal waves in the deep and well-mixed boundary layer, maintained by brine rejection due to the sea-ice growth during the winter. Given the large fraction of deformed ice, it’s probably time to pay closer attention to how well models capture ridge-related processes and whether these subgrid processes need to be better represented in sea-ice models. Do any of these processes matter on a climate-scale?
Solar heat partitioning at the MOSAiC Central ObservatoryDon Perovich, Madison Smith, Melinda Webster, Bonnie Light, David Clemens-Sewall, Chris Polashenski, Marika Holland, Felix Linhardt, Amy MacFarlane, Chris Cox, Matthew Shupe
The partitioning of incident solar irradiance between reflection to the atmosphere, absorption in the ice and transmission to the ocean impacts the surface heat budget, the upper ocean heating, and the magnitude of the surface, internal, bottom and lateral ice melt. Solar partitioning at the MOSAiC Central Observatory was estimated by assimilating observations with a two stream radiative transfer model. Data sources include observations of incident solar irradiance, albedo, surface state, snow depth, ice thickness and pond depth. The temporal evolution of solar partitioning at specific sites and the spatial variability along transect lines were determined. There was a slow increase in absorption during spring due to increasing incident solar irradiance and a steady albedo. The largest amount of absorbed solar heat was in summer due to increasing incident solar irradiance and decreasing albedo due in large part to melt pond formation. There was a rapid decrease in absorbed solar heat during late summer as incident irradiance decreased and albedo increased from freezeup and snowfall. Ponds absorbed more than twice as much solar heat as bare ice. On 25 July, ponds covered about 18% of the area and contributed roughly 50% of the absorbed solar heat.
Progress towards a single-column model (icepack) case study for the MOSAiC expeditionDavid Clemens-Sewall, Marika Holland, Angela Bliss, Christopher Cox, Michael Gallagher, Jennifer Hutchings, Bonnie Light, Donald Perovich, Chris Polashenski, Kirstin Schulz, Madison Smith, Melinda Webster
To improve the representation of sea ice thermodynamics in Earth system models (ESMs), we seek to compare model simulations with observations. However, direct comparison between models and in‐situ observations is challenging because the sea ice components of ESMs typically simulate vastly larger spatial scales (e.g. 100×100 km) than the footprint of in‐situ observations (e.g. 1×1 km). Additionally, standalone sea ice simulations are typically forced with reanalysis data, which have considerable biases and uncertainties. To address these challenges, we are developing a MOSAiC‐based forcing package to conduct a case study of the Icepack model. We simulate the evolution of snow and sea ice on a Lagrangian, drifting parcel following the Central Observatory from October to July. The model is initialized from ice conditions observed in autumn and forced with observed fluxes from the atmosphere and ocean. We present progress towards this case study, including the compilation of the initial conditions and forcing, and preliminary comparisons of the simulated snow and ice thicknesses and albedo evolution with observations. We discuss the challenges introduced by ice dynamics, lateral boundary conditions, and measurement gaps. Anticipated applications of this case study include improved parameterizations of melt ponds, snow and albedo processes.
Two decades (2000–23) of pan Arctic meltpond fraction dataNiklas Neckel, Anja Rösel, Lars Kaleschke, Gerit Birnbaum, Christian Haas
Melt ponds are influencing the Arctic energy budget as they strongly reduce the surface albedo of sea ice. It is therefore highly important to monitor their temporal and spatial evolution. Here we build on the work of Rösel and Kaleschke (2012) to extend their time series of moderate resolution image spectroradiometer (MODIS) melt pond estimates to the present. To do so we make use of a spectral unmixing algorithm implemented via a neural network to reduce computational costs. The results will be compared to classification results of helicopter-borne camera data acquired during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Furthermore, we will apply classification results from the modular aerial camera system (MACS) newly employed on AWI’s research aircrafts to validate the MODIS results on a larger scale. The derived time series of 23 years meltpond data will be carefully analyzed for any trends, both in the temporal and spatial domain, and might be of interest to better parametrize sea ice models.
Atmospheric drivers of temporal variability in melt pond coverage and albedo: a model-observation synthesisMelinda Webster, Marika Holland, Chris Polashenski, Hannah Chapman-Dutton
Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing ice melt. The spatio-temporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. In this work, we combine climate modeling, Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) observations and satellite products to investigate key impacts and atmospheric drivers of the temporal variability in melt pond coverage and associated albedo change. The analysis begins with an inter-comparison between two configurations of Version 2 of the Community Earth System Model (CESM2): one with and one without tuned parameterizations of snow albedo and melt onset temperature. The tuned version was optimized for improved realism of the mean sea-ice state. We investigate how the sensitivity of the sea ice surface response to summer snowfall events and cold air outbreaks differs between model configurations, and assess potential model biases using local scale MOSAiC observations and pan-Arctic scale satellite observations. The scaling, synthesis and intercomparison of model and observational results are used to pinpoint atmosphere–ice processes that warrant improved representation, which, in turn, can aid accurate simulations of albedo feedbacks in a warming climate.
Insights to seasonal sea-ice surface roughness evolution and variability using MOSAiC airborne laser scanningArttu Jutila, Nils Hutter, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Gerit Birnbaum, Christian Haas
Between September 2019 and September 2020, we conducted a total of 35 floe grid and 29 transect flights over the MOSAiC Central Observatories and surrounding sea ice with the airborne laser scanner to map changes of the sea-ice surface during the full annual cycle at high spatial resolution and coverage. In this work, we take advantage of the large and unique data set to take a look at the evolution and variability of sea-ice surface roughness in the Central Observatory and within the Distributed Network. Sea-ice surface roughness has been identified as an important influencing factor for e.g. melt ponds, remote sensing applications, numerous processes acting at the ocean–ice–atmosphere interface, and maritime operations. Here, we calculate sea-ice surface roughness from the point cloud data as the standard deviation of across-swath surface elevation on a per scan-line basis. First results indicate sea-ice surface roughness distributions that are similar both in the Central Observatory and within the surrounding Distributed Network during the analysed first part of the MOSAiC drift from October 2019 to July 2020.
MOSAiC airborne laser scanning of the sea-ice surface: a year round data product of high-resolution digital elevation modelsNils Hutter, Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyl, Gerit Birnbaum, Christian Haas
During the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition an airborne laser scanner was used to map the sea-ice surface at sub-meter resolution. We conducted 64 flights over the Arctic sea ice between September 2019 and September 2020 to measure sea-ice surface elevation during the full annual cycle at high spatial resolution and coverage. The flights ranged from repeated, local-scale 5×5 km2 floe grid surveys to regional-scale transects more than 100 km long. In this presentation, we give an overview of the first version of the released data with illustrative examples. The data products include point cloud segments, gridded segments, and gridded merged maps of elevation and freeboard with a spatial resolution of 0.5 m. The latter product is corrected for atmospheric backscatter, sea-ice drift, and offset in elevation due to degraded INS/GPS solutions >85° N. For floe grid surveys, all data are combined to merged two-dimensional elevation maps. We present a comprehensive validation of the data quality achieved with the corrections and highlight both potentials and resulting limits of the data for different use cases. The presented data offer a unique possibility to study the temporal evolution, spatial distribution, and variability of the snow and sea-ice surface and their properties in addition to validating satellite products, of which we will highlight first applications.
Modeling the sea ice and snow heat conduction through the lens of the MOSAiC datasetLorenzo Zampieri, Nils Hutter, Marika Holland
The parameterization of the heat conduction through sea ice and snow remains simple in state-of-the-art models. Specifically, it relies on prescribed conductivity parameters constant in time and space, therefore neglecting the substantial heterogeneity of these mediums down to the unresolved subgrid scale. This assumption clashes with robust observational evidence, which indicates that snow and ice conductivities can vary greatly depending on the environmental conditions and the history of the sea ice. The winter observations collected during the MOSAiC expedition are unique tools for advancing the quantitative understanding of heat conduction in sea ice and improving the realism of the thermodynamic parameterizations in models. Our investigation utilizes gridded helicopter-borne thermal infrared imaging, laser scanner elevation observations, and meteorological measurements to assess the model bias and diagnose the importance of unresolved processes and topographic heterogeneity on heat conduction. We evidence different heat conduction regimes depending on the ice thickness, type (i.e. ridged or level ice), and snow patchiness. In the light of these results, we discuss strategies for an effective parametrization of these unresolved processes in sea ice models, and their harmonization with the preexisting model infrastructure. Furthermore, we comment on the potential of emerging data-driven analysis techniques and machine learning in facilitating the formulation of parameterization at different stages of the development process.
Linking the evolution of floe-scale ice characteristics to its deformation history using satellite observationsNils Hutter, Cecilia Bitz, Luisa von Albedyl
Arctic sea ice is a mosaic of ice floes whose distribution and thicknesses greatly impact the interaction of sea ice with the atmosphere and the ocean. However, we are still lacking knowledge of the physics to describe the complex interplay of ice floes that are a key characteristic of sea ice. In our contribution, we outline a framework to characterize sea-ice deformation at the floe-scale from observational data by studying the mechanical interaction of multiple identifiable floes. We use Sentinel SAR imagery and ICESat-2 data acquired during the MOSAiC expedition to map ice floes and their thickness in the larger area around Polarstern. This combination of data products allows us to describe the floe-size distribution of floe diameters from hundreds of kilometers down to tens of meters. With the repeated coverage of SAR imagery, ice motion is tracked and deformation estimates are derived. By combining both floe-size estimates and deformation rates we provide insights into how the floe composition changes in regions that were exposed to deformation. Finally, we present a parameterization of this relationship between floe sizes and mechanical redistribution for large-scale continuum sea-ice models.
Snow and ice thickness derived from sea ice mass balance buoys in the transpolar drift systemAndreas Preußer, Thomas Krumpen, Marcel Nicolaus
Sea ice controls and is influenced by the exchange of energy, moisture and momentum between the underlying ocean and the lower atmospheric boundary layer. The physical properties of sea ice play a critical role in modulating these interactions. Of particular importance is the temporal evolution of the thickness of the ice and snow layers, both of which are a result of seasonally and spatially highly variable growth and decay processes. To investigate whether large-scale changes in the Arctic sea ice cover such as a general thinning and increased drift speeds are also imprinted on long term data sets from autonomous drifting platforms, we present an analysis of sea ice properties derived from sea ice mass balance buoys deployed in the transpolar drift system between 2012 and 2023, thus including the period of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) experiment in 2019/20. In particular, we aim to assess whether the observed variations in sea ice mass balance by ice growth and melt in recent years are significantly different from previous years, or whether they remain consistent on an interannual time scale. To achieve this, a uniform processing scheme is developed and applied to large set of buoys with the aim to minimize methodological ambiguities in the derivation of snow–ice–ocean interfaces. We also present comparisons with external factors (both thermodynamic and dynamical) derived from satellite data and atmospheric reanalysis that influence the local sea ice mass balance observed by the buoys during their drift towards Fram Strait and adjacent seas.
Novel techniques for estimation of snow depth over sea ice using the KuKa surface-based, dual-frequency, polarimetric radarRosemary Willatt, Vishnu Nandan, Julienne Stroeve, Robbie Mallett, Thomas Newman, Stefan Hendricks, Robert Ricker, James Mead, Polona Itkin, Rasmus Tonboe, David Wagner, Gunnar Spreen, Glen Liston, Martin Schneebeli, Daniela Krampe, Michel Tsamados, Oguz Demir
Sea ice thickness is a WMO-recognized essential climate variable, necessitating retrievals over the Arctic Ocean on spatiotemporal scales only feasible via satellite observations. Snow cover plays key roles in the growth, melt and evolution of sea ice, e.g. via insulation, albedo and drag properties. Snow is also a major source of uncertainty in satellite retrievals of sea ice thickness from satellite altimetry. Effective remote sensing of snow can therefore provide a step-change in the accuracy of sea ice thickness observations. Spatially and temporally variable snow properties such as density, layering and microstructure make development of snow depth products a challenge, and limited availability of in situ datasets drive reliance upon other remotely sensed datasets such as from airborne instruments. Investigations into how electromagnetic (EM) radiation interacts with sea ice and its snow cover are therefore central to progress. We present novel dual-polarization techniques for snow depth retrieval using data from deployment of the ‘KuKa’ surface-based Ku- and Ka-band radar during MOSAiC. Our snow depth estimations are accurate to 1 cm and with r2 up to 0.78 when compared with independent MagnaProbe snow depth measurements. We discuss the potential for application of the technique on airborne and satellite scales, using data from existing satellite instruments to examine feasibility of upscaling. We also find that the waveform shape techniques can provide r2 up to 0.73, indicating that satellite radar altimeters aboard missions such as CryoSat-2, Sentinels 3 and 6, and CRISTAL may provide information on snow depth over Arctic sea ice even using a single-frequency approach. Lastly we discuss dual-frequency snow depth retrievals using KuKa data and compare to results from satellite instruments. We also outline insights from other types of satellite instruments to contextualize our results.