MOSAiC news

[A-Team]

DKP probably never looked at the autonomous data.

Didn't DKP more or less oversee the SIMB3 autonomous buoy invention? The team at Cryosphere Innovation were grad students at Dartmout/CRREL.

Don't have to crawl around the data warehouse, the article is out! And very thorough. However the experimental situation was very challenging.

Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift
https://doi.org/10.1525/elementa.2021.000103  January 2022 free

B Light, MM Smith,Perovich, Donald K et al

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the MOSAiC expedition.

The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice.

The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition.

The spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing.

In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice.

While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin melting ice are simulated.

"Leg 3 was carried out on the original floe (CO1) which was composed of second-year ice, some of it containing areas of patchy sediment likely entrained when the ice grew on the Siberian shelf. Areas of open water proximal to CO1 grew new ice during the winter, providing access to first-year ice. Operations were suspended on May 12, just after a storm broke up the CO and the surrounding ice into numerous smaller floes. This dynamic event defined the end of Leg 3.

When R/V Polarstern returned to begin Leg 4 on 17 June, it returned to the same collection of floes as CO1, although repositioning the observatory to a more stable area within the CO1 was necessary due to strong ice dynamics. This new floe was designated CO2. Leg 4 was terminated on July 31 by advanced melt and proximity to the ice edge. In contrast to the drift continuity of CO1 and CO2, CO3 was established after an August gap on a first-year floe near the North Pole, beginning a new drift.

The optical measurement program began in April on CO1 during Leg 3. At this time, the snow cover was dry and cold. The transition to surface melt occurred during the May logistics gap. An autonomous station (L2, in the MOSAiC distributed network  left behind on a floe proximal to CO1 recorded hourly temperature and daily surface photographs.

Measured air and snow surface temperatures first exceeded 0°C on May 26. The L2 surface image also indicated liquid droplets on the camera lens on this date, indicative of rain. Surface melt ponds appeared in the imagery beginning May 28, in accord with widespread ponding in satellite imagery.

Although snowfall and freezing conditions followed for a few days, more widespread ponding at L2 resumed on June 9, and remained approximately continuous for the remainder of the melt season. Optical measurements were carried out without interruption during the entire CO2 phase (Leg 4).

After the August gap, optical measurements resumed at CO3 on August 21 to complete the annual cycle by capturing the return to freezing conditions. Optical measurements were curtailed on September 19."

[Jim Hunt]

Didn't DKP more or less oversee the SIMB3 autonomous buoy invention?

At a high level perhaps?

Bruce Elder was always the one that answered my questions in the early days.

[uniquorn]

The radiation buoys were AWI though. Crawling around the data warehouse can be quite rewarding.

Visual panoramic photographs of the surface conditions during the MOSAiC campaign 2019/20.
Nicolaus, Marcel; Arndt, Stefanie; Birnbaum, Gerit; Katlein, Christian (2021)
https://doi.org/10.1594/PANGAEA.938534

Abstract:
Panoramic photographs of the surface conditions around the icebreaker RV Polarstern were recorded during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between 20 October 2019 and 12 October 2020. The photographs were taken with a digital rotating scanning camera (Seitz, Switzerland) based on a Canon 3-linear RGB sensor and using an operational software system by Panomax (Switzerland). The camera covers a full round view (360°) and was mounted above the crow's nest in approx. 33m above sea level. The camera worked without interruptions over the entire expedition. Each photo consists of 15680x2048 pixels. The recording of a single panorama took, depending on light conditions, up to 18 minutes. The recording interval was 20 minutes (72 photos per day). All times are given in UTC and camera times are corrected to the GPS time stamp.

very small animation of a few frames entering the MIZ
small example.

[uniquorn]

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 Society
Part 1.
Apologies if I missed any

New insights on Arctic sea-ice ridges from the MOSAiC expedition – an overview
Mats 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 Observatory
Don 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 expedition
David 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 data
Niklas 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 synthesis
Melinda 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 scanning
Arttu 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 models
Nils 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 dataset
Lorenzo 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 observations
Nils 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 system
Andreas 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 radar
Rosemary 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.

[uniquorn]

Part2


Approaches to determine the surface roughness of Arctic sea ice using a laser scanner onboard the helicopter-borne measurement system HELiPOD
Sven Bollmann, Dominik Hanke, Falk Pätzold, Lutz Bretschneider, Konrad Bärfuss, Jesper Sandgaard, Ulf Bestmann, Astrid Lampert

During the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in 2019/20 the helicopter-borne measurement system HELiPOD was deployed in the Central Arctic. The system was equipped with around 60 sensors for studying the atmospheric boundary layer, radiation and surface properties. As surface roughness and open water fractions in sea ice are key properties related to the exchange processes between surface and the atmosphere, a laser scanner was used to provide a three-dimensional point cloud representation of the surrounding environment. The acquired point cloud was transformed into the body-fixed coordinate system of the HELiPOD. After fusion with highly resolved position and attitude data from an integrated GNSS/INS navigation system, a digital elevation model of the overflown sea ice area could be calculated. Images from an onboard fish eye camera were used to validate the elevation model. The surface roughness can eventually be derived from the elevation data. Based on a previous examination of the statistical properties of the sea ice surface, several methods are currently being applied to the elevation data and their suitability for the data set is being discussed.



Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinéad L. Farrell, Nathan T. Kurtz, Renée Mie Fredensborg Hansen

Information about the sea ice surface topography and deformation is crucial for studies of sea ice mass balance, sea ice modeling and ship navigation through the ice pack. NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) has been on-orbit for over 4 years, sensing the sea ice surface topography with six laser beams capable of capturing individual features such as pressure ridges. To assess the capabilities and uncertainties of ICESat-2 products, coincident high-resolution measurements of the sea ice surface topography are required. During the year-long Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in the Arctic Ocean, we successfully carried out a coincident underflight of ICESat-2 with a helicopter-based airborne laser scanner (ALS) achieving an overlap of more than 100 km. Despite the comparably short data set, the high-resolution ALS measurements on centimetre scales demonstrate their vales for evaluating the performance of ICESat-2 products. Here we present the results of our study, which investigated how sea ice surface roughness and topography are represented in different ICESat-2 products, and how sensitive ICESat-2 measurements are to leads and small cracks in the ice cover. We compared the ALS measurements with ICESat-2’s primary sea ice height product, ATL07, and the high-fidelity surface elevation product developed by the University of Maryland (UMD). We developed a ridge-detection algorithm and found that 16% (4%) of the number of obstacles in the ALS data set are found using the strong (weak) center beam in ATL07. Significantly higher detection rates of 42% (30%) are achieved when using the UMD product. We also analyzed the presence of leads in the ICESat-2 data. While only one lead is indicated in ATL07 for the underflight, the ALS reveals many small and only partly open cracks that appear to be overlooked by ATL07. Eventually, this study links the MOSAiC ALS measurements with ICESat-2 measurements from space, to allow studying the evolution of surface topography and deformation of the sea ice in the vicinity of the MOSAiC camp in the context of large-scale changes captured by ICESat-2.



Differential summer melt rates of ridge keels and level ice in the central Arctic Ocean during the MOSAiC expedition
Evgenii Salganik, Benjamin A. Lange, Philipp Anhaus, Christian Katlein, Ilkka Matero, Julia Regnery, Knut V. Høyland, Mats A. Granskog

During the melt season, sea ice melts from the surface and bottom. The melt rates substantially vary for sea ice ridges and undeformed first- and second-year ice. Ridges generally melt faster than undeformed ice, while the melt of ridge keels is often accompanied by further summer growth of their consolidated layer, which increases their survivability. We examine the spatial variability of ice melt for different types of ice from in situ drilling, coring and multibeam sonar scans of remotely operated underwater vehicle. Six sonar scans performed between 24 June and 21 July were analyzed and validated using seven ice drilling transects. The area investigated by the sonar (0.4 km×0.2 km) consisted of several ice ridges, surrounded by first- and second-year ice. We show a substantial difference in melt rates for sea ice with a different draft. We also show how ridge keels decay depending on the keel draft, width, steepness and location relative to the surrounding ridge keel edges. We also use temperature buoy data to distinguish snow, ice surface and bottom melt rates for both ridges and level ice. These results are important for quantifying ocean heat fluxes for different types of ice during the advanced melt, and for estimation of the ridge contribution to the total ice mass and summer meltwater balances of the Arctic Ocean.



High resolution analysis of sea ice deformation during MOSAiC
Matias Uusinoka, Arttu Polojärvi, Jari Haapala, Mikko Lensu

Past observations on sea ice deformation have usually relied on satellite imagery resulting in low spatial and temporal resolutions even if a lower bound of scale invariance in ice deformation is likely at the scale of ice thickness. In response to the lack of high resolution observational data, ship radar imagery gathered during MOSAiC between November 2019 and May 2020 was used for statistical analysis of seasonal evolution in sea ice deformation and spatio-temporal scaling characteristics with scales down to tens of meters and 1-minute temporal interval. To account for possible sources of error, different definitions of strain were considered. Deformation rate components derived from infinitesimal strain were found to follow the established spatio-temporal power law scaling in the domain of 20 km×20 km. With a 1-minute temporal resolution, the spatial scaling exponent β was observed to approach 0.9 during strong deformation events signifying a high level of localization observable in the domain. Strain-rate statistics were supported by an analysis of relative surface area change to better describe the seasonal evolution of the ice cover and help in distinguishing deformation events


Seasonality of spectral radiative fluxes and optical properties of Arctic sea ice
Marcel Nicolaus, Christian Katlein, Philipp Anhaus, Mario Hoppmann, Gunnar Spreen, Hannah Niehaus, Evelyn Jäkel, Manfred Wendisch, Christian Haas, Ran Tao

The solar partitioning of sea ice is important for physical and biological processes in the ice-covered Arctic ocean and atmosphere. Here, we analyse data from autonomous drifting stations to investigate the seasonal evolution of the spectral albedo, transmittance and absorptance for different sea ice, snow and surface conditions as measured during the MOSAiC expedition in 2020. We find that the spatial variability of these quantities was small during spring, and that it strongly increased after melt onset on 26 May, when the liquid water presence on the surface increased. The enhanced variability was then mostly determined by the formation of melt ponds, which increased the total energy absorption of the sea ice by 50% compared to adjacent bare ice. The temporal evolution of surface albedo and sea ice transmittance was mostly event-driven and thus neither continuous nor linear. However, absorptivity and transmittance showed strong variability, which depended on internal sea ice optical properties and under-ice biological processes, not only on surface conditions. The heterogeneity of sea ice conditions strongly impacted the partitioning of the solar short-wave radiation. Thus, this study shows that the evolution of melt ponds determines the total (summer) heat deposition and sea ice melt much more than the melt onset date. The small-scale heterogeneity and the timing and duration of ponding events have to be considered when comparing (local) in-situ observations with large-scale data sets, as well as for improvements in numerical models.


Modeling the seasonal evolution of the ice thickness distribution along the MOSAiC drift
Florent Birrien, Frank Kauker, Luisa von Albedyll, Valentin Ludwig, Kirstin Schulz, Helge Goessling, Michael Karcher

The single-column sea ice model ICEPACK (CICE consortium) was adapted to simulate the evolution of the ice thickness distribution (ITD) along the MOSAiC drift. This Lagrangian approach allows full exploitation of the extensive observational data on sea ice and snow collected during the MOSAiC expedition. The model is initialized with observed ITDs (derived from airborne electromagnetic thickness measurements) with 10 cm thick bins, which provide an accurate and representative initial state of the sea ice. The model is then driven by atmospheric (re)analyses (NCEP/CFSv2 or ERA5) and with prescribed snow cover (derived from SIMBA buoys), observed ocean conditions and deformation fields derived from buoy arrays of the distributed network to investigate the thermodynamic (growth/melting) and dynamic (bulging) response of sea ice along the track. Here we will present the Lagrangian framework and a comparison between simulated and observed ITDs, and evaluate the performance of the model in describing sea ice evolution during the MOSAiC winter period. Special attention will be given to ocean forcing, which proved to be conceptually difficult to implement. Vertical heat fluxes at the interface of the mixed layer as well as the temperature and salinity of the mixed layer calculated or collected during MOSAiC are used to determine the lower boundary layer of ICEPACK. Different implementations are described and their implications for sea ice development are discussed briefly.


Light availability and variability over and under the Arctic sea ice
Ran Tao, Marcel Nicolaus, Christian Katlein, Philipp Anhaus, Maddie Smith, Bonnie Light, Niels Fuchs, Niels Fuchs, Niklas Neckel, Christian Haas

The availability of sunlight on and under the Arctic sea ice controls many physical and biological processes. Solar energy contributes to sea ice thermodynamic melt, and shapes habitat conditions. Due to the harsh climate and logistical limitations, up to now it has been difficult to compose a long-term dataset describing the seasonality of collocated surface albedo and under-ice transmittance over an integrated area. Here, we show the temporal evolution and spatial distribution of light over and under sea ice during the MOSAiC expedition from May–September 2020. Albedo was estimated from surface images collected by helicopter and drone flights, and under-ice transmittance was mapped with a remotely operated vehicle, covering in total an approximately 150 m grid area. We compare the estimated albedo to in-situ observations, apply it to classified surface types, and then investigate its spatial variability and impact on the surface net influx of solar irradiance. We present measurements of light transmittance before melt onset, during the melt season, and during freeze-up in the Central Arctic. By combining both albedo and transmittance, we are able to determine the solar partitioning of various ice and surface types, quantifying the amount of energy being absorbed by the sea ice. This study provides a comprehensive view of light availability in and under sea ice, and highlights the importance of spatial heterogeneity for the large-scale energy budget.

Combining observational data with numerical models for high-resolution snow and ice mass balance studies
Polona Itkin, Glen Liston

Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) observations span the entire seasonal cycle of Arctic snow and sea ice cover. However, the measurements of atmospheric and ocean forcing, as well as distributed measurements of snow and ice properties, were occasionally interrupted for logistic reasons. The most prolonged interruption happened during the onset of the summer melt. Here were present a novel modeling tool that can assimilate the relevant observational data to provide continuous high temporal resolution time series of snow and sea ice parameters over the entire annual cycle. We use this tool to analyze differences between the three ice types found in the MOSAiC Central Observatory: relatively deformed second year ice, second year ice with extensive smooth refrozen melt pond surfaces, and first year ice. We demonstrate how, despite different initial conditions, the snow and ice mass balances are similar for all ice types at the end of the accumulation period and throughout the melt period. Finally, we quantify the role of individual synoptic events on controlling local snow sources and sinks, including snow erosion from level ice, and accumulation around pressure ridges and in leads with open water and thin ice.


Daily drift-aware sea ice freeboard and thickness maps from satellite altimetry
Robert Ricker, Thomas Lavergne, Stefan Hendricks, Mari Anne Killie

The polar regions are a hot spot of climate change, and large-scale satellite observations to monitor sea ice decline are important. One of the essential climate variables is sea ice thickness, controlling the heat exchange between ocean and atmosphere. Within the European Space Agency Climate Change Initiative project, consistent sea ice thickness time series across different satellite altimetry missions are generated to observe long-term trends. To provide monthly maps of ice freeboard and thickness, daily trajectories are averaged on a 25 km grid, while each trajectory only represents the ice thickness in the moment of the satellite overflight. However, sea ice can drift significantly within 1 month, especially in areas with typically high drift rates, such as in the Beaufort Gyre or Fram Strait. Moreover, in the context of climate change, studies suggest that sea ice will become more mobile in the future. Neglecting sea ice drift when generating monthly sea ice thickness maps from satellite altimetry will cause blurring of the spatial distribution of ice thickness. We therefore suggest synergizing sea ice freeboard and thickness information from satellite altimetry with sea ice drift estimates from passive microwave satellite sensors. With our approach, we successively advect individual parcels of satellite altimeter measurements daily over a time span of 1 month to obtain drift-aware sea ice freeboard and thickness maps. Because of the drift correction, we can also determine sea ice that was overflown by the satellite multiple times. This allows us to estimate growth rates and changes in the sea ice thickness distribution due to deformation and thermodynamic ice growth between satellite overflights. With the estimation of sea ice growth, measurements can be corrected for the time offset between the acquisition day and the target day, the day to which all measurements within a month are projected. Here we present the first new daily drift-aware sea ice freeboard and thickness maps, using CryoSat-2 and ICEsat-2 data, covering the entire Arctic sea ice domain. Moreover, we will show first validation results, using MOSAiC data of year-long sea ice thickness observations as well as airborne data sets.




DMS(O/P) distribution and conversion processes in sympagic and pelagic ecosystems: results from the MOSAiC expedition
Jacqueline Stefels, Maria van Leeuwe, Deborah Bozzato, Alison Webb, Ellen Damm

This presentation is a contribution to the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The MOSAiC field campaign took place on board RV Polarstern, drifting with the Arctic sea ice, from October 2019 to October 2020. As partner of the MOSAiC team, our project contributed to the production of a time series of sulphur compounds in Arctic sea ice and underlying seawater. The aim of our project was to address how seasonality, sea ice dynamics and water characteristics in the Arctic Ocean affect the cycling of organic sulfur compounds. The sampling of sea ice and surface water was part of the concerted actions of the BGC, ICE and ECO teams during MOSAiC. A crucial compound for organisms to survive the cold and saline environment of sea ice is the organic sulfur compound dimethylsulfoniopropionate (DMSP), which is mainly synthesized by algae. Between 1% and 10% of total primary production is invested in DMSP, making it a key compound in the lower – and potentially also higher – trophic levels. DMSP is also the precursor of the climate active semi-volatile compound dimethylsulfide (DMS). Our work combines measurements of concentrations of DMSP, DMS and the (photo-)oxidation product of DMS, dimethyl sulfoxide (DMSO), transformation rates of these compounds using stable isotope addition experiments and identification of the microorganisms driving these processes. We show persistent features of DMS(O/P) distribution in vertical profiles of the MOSAiC floe, and surface-water distribution of DMS(O/P) in relation to water mass, link these profiles to algal community structure, and discuss the connection between ice and surface water DMS(O/P) concentrations. We will present a conceptual model of how the growth of sea ice in the central Arctic Ocean results in specific DMS(P) distribution patterns.

[uniquorn]

Part3

From regional to Arctic-wide melt pond fraction estimates
Hannah Niehaus, Larysa Istomina, Tim Sperzel, Evelyn Jäkel, Ran Tao, Marcel Nicolaus, Gunnar Spreen

A wide variety of surface types are present in the Arctic: open ocean, sea ice, snow and melt ponds cover the surface, which is thus strongly heterogeneous. Due to the large differences in their albedo, the areal composition of these surface types strongly impacts the radiative feedback and hence the surface energy budget, which is crucial for climate models and future simulations. However, climate models struggle to realistically represent melt ponds. This is caused by the complexity and variability of melt pond formation and evolution and the mismatch between true and observational scales. We have derived a dataset of melt pond fractions from high resolution (10 m) Sentinel-2 satellite imagery, to bridge between in-situ and medium-resolution (300 m–1 km) satellite observations. In-situ measurements are locally and temporally limited but provide high resolution. Medium-resolution satellite observations can cover the complete cloud-free Arctic but can not resolve individual melt ponds. The developed Sentinel-2 product is evaluated by comparison with higher resolution (0.5 m) helicopter-borne and satellite observations measured in June and July 2020 during the MOSAiC campaign. In a next step, the Sentinel-2 derived melt pond fraction product is used to improve Arctic-wide melt pond faction products from medium resolution Sentinel-3 observations. We retrieve melt pond and sea ice fractions from medium-resolution (1.2 km) Sentinel-3 satellite imagery with the MPD algorithm developed by Zege et al. Comparing the Sentinel-3 and Sentinel-2 products indicates an overestimation of melt pond fraction at medium resolution. This may be related to the limitation of this algorithm to two surface types: sea ice and melt ponds. We present a further developed MPD algorithm, including open water as a third surface type. For this purpose we use the temperature history along the drift track as prior knowledge to constrain the algorithm. This leads to a significant reduction in overestimation of Sentinel-3 melt pond fraction, paving the way for improved Arctic-wide melt pond fraction datasets and an enhanced representation of melt ponds in climate models.



Anatomy of the MOSAiC sea ice deformation events
Jari Haapala, Luisa von Albedyll, Jenny Hutchings, Polona Itkin, Thomas Krumpen, Ola Persson, Chris Polashenski, Gunnar Spreen, Matias Uusinoka

Formation of sea ice fractures, cracks, leads and linear kinematic features, have a large impact on exchanges of energy and matter between the ocean and atmosphere, surface heterogeneity, and sea ice mass balance. Fracturing also weakens the dynamic strength of pack ice. Based on ice radar, satellite images, and ice drift and stress buoy data we have identified the most significant local ice dynamics events at the Central Observatory. In this presentation, we will provide an anatomy of events that occurred 15–25 November 2019 and 20 March–4 April 2021. The analysis shows where and when large-scale shearing, lead opening and compression occur, with ridging often under shear. In a single spot, these modes of deformation are occurring consecutively but within a few km2 areas, all these modes occur simultaneously. Furthermore, old shear zones, leads, and ridges are reactivated throughout the winter.




Microwave emission of snow and sea ice during the MOSAiC expedition
Gunnar Spreen, Marcus Huntemann, Lars Kaleschke, Philip Rostosky, Julienne Stroeve, Rasmus T. Tonboe

For 50 years, satellite microwave radiometer observations have provided one of the longest time series about the state of Arctic. Without them the strong decrease in Arctic sea ice cover during recent decades could not have been monitored daily, Arctic-wide, and independent of cloud and illumination conditions. For a better physical interpretation of the satellite microwave signal a combination of in-situ microwave radiometers observing the sea ice and measurements of the snow and sea ice physical properties are needed. The MOSAiC drift expedition from October 2019 to September 2020 offered the opportunity to perform such combined measurements for a full seasonal cycle. Microwave radiometers measuring between 0.5 and 89 GHz were deployed on the ice and on the ship. While ice dynamics, logistics and technical failures reduced the available data during some periods, at six frequencies observations are available from all seasons. Strongest brightness temperature variability is observed during atmospheric events such as warm air intrusion and rain on snow. However, longer-term brightness temperature changes differ for different frequencies and can only partly be linked with temperature changes. Residuals, not explainable by temperature changes directly, are mostly associated with the natural evolution of snow and ice conditions over the course of the year during the expedition. This includes effects such as sea ice growth and melt, snow accumulation, melt and metamorphism, among others. We will provide a first joined analysis of this combined microwave radiometer dataset. Measurements will be compared with results from microwave emission models forced by snow, ice and environmental conditions measured on the ice floe at the same time. While not representative for a wider region the in-situ observations will be contrasted with the temporal evolution of microwave brightness temperatures measured by space-borne radiometers. Such analysis will support method development and contribute to upcoming satellite missions like CIMR and AMSR3.


Snow refreeze as one of the mechanisms of Artic sea ice ridge consolidation
Evgenii Salganik, Benjamin A. Lange, Dmitry Divine, Polona Itkin, Christian Katlein, Marcel Nicolaus, Mario Hoppmann, Knut V. Høyland, Mats A. Granskog

During the freezing period, the consolidated part of sea ice ridges is usually up to 1.6–1.8 times thicker than surrounding level ice. Meanwhile, during the melt season, ridges are often observed fully consolidated, but this process is not fully understood. We present the evolution of the morphology and temperature of a first-year ice ridge studied during MOSAiC from its formation to advanced melt. From October to May the draft of first-year ice at the MOSAiC coring site increased from 0.3 m to 1.5 m, while from January to July the ridge consolidated layer thickness reached 3.9 m. We observed several types of ridge consolidation. From the beginning of January until mid-April, the ridge consolidated slowly by heat loss to the atmosphere with a total consolidated layer growth of 0.7 m. From mid-April to mid-June, there was a rapid increase in ridge consolidation rates despite conductive heat fluxes did not increase. In this period, the mean thickness of the consolidated layer increased by 2.2 m. Our observations suggest that this sudden change was related to the transport of snow-slush inside the ridge keel via adjacent open leads that decreased ridge macroporosity which could result in more rapid consolidation. Such observations are important for the mass balance of deformed sea ice and snow.


Colors of the Arctic – mapping the evolution of sea ice texture and stratigraphy from first-year to second-year ice
Marc Oggier, Bonnie Light, Kristin Timm, Niels Fuchs, Cody C. Owen, Madison M. Smith, Amy Lauren, MOSAiC Sea Ice Coring Consortium

During the MOSAiC Expedition drift, we follow the evolution of both first-year (FYI) and second-year (SYI) ice. We tracked a suite of physical and ecological properties derived from discrete samples obtained by coring. The cores collected in parallel for stratigraphy analysis allow the creation of a continuous record of the evolution of the sea ice microstructure and will support the analysis of the collocated properties. While sea ice structural properties occur over a wide range of scales, their foundation lies in the arrangement of ice crystals and inclusions of brine, gas, and entrained impurities. We are using a combination of vertical and horizontal, thick (~5 mm thick) and thin (~<0.5 mm thick) sections to quantify the size and orientation distribution of crystal and both brine and gas inclusions, map the ice texture (granular, columnar, lamellar, platelet), and characterize the transition zone between textural domains or strong micro-structural gradient. Following a month-long laboratory work, we used the ColorIce algorithm for the segmentation and classification of thin sections. Here, we present preliminary results for early and late growth, late growth and melt season for both SYI and FYI.

[oren]

Very interesting, though quite overwhelming for the severely-time-constrained hobbyist. Are the presentations themselves available anywhere?
I managed to find this link but can't see anything beyond the abstracts.

https://www.igsoc.org/wp-content/uploads/2023/06/procabstracts_80.html

[John_the_Younger]

One takeaway I was interested in relates to the several studies of ridges (in first year ice).  I had been dubious of comments by another ASIFer (elsewhere) that ridges only formed in multiyear ice.

[binntho]

One takeaway I was interested in relates to the several studies of ridges (in first year ice).  I had been dubious of comments by another ASIFer (elsewhere) that ridges only formed in multiyear ice.

Answer moved to here.

[seaice.de]

Very interesting, though quite overwhelming for the severely-time-constrained hobbyist. Are the presentations themselves available anywhere?
I managed to find this link but can't see anything beyond the abstracts.

https://www.igsoc.org/wp-content/uploads/2023/06/procabstracts_80.html

No, unfortunately the presentations are not available. Therefore, the decision between the three parallel slots was not easy and I missed a lot.

[uniquorn]

Visual panoramic photographs of the surface conditions during the MOSAiC campaign 2019/20.
Nicolaus, Marcel; Arndt, Stefanie; Birnbaum, Gerit; Katlein, Christian (2021)
https://doi.org/10.1594/PANGAEA.938534

1/4 size crop of apr1 2020 00h-18h

[Alexander555]

Is that what they mean with the word " ridging" ?

[binntho]

It certainly looks like a very good example of ridging. However, it is mostly a recently-frozen-over lead that is being squashed - thin ice that is unable to resist the older, thicker and mostly flat floes on each side. There is also what looks like some snow on top that is being pushed up into the narrow ridges.

And these ridges seem inconsequential -but they might collect some snow and cause thickening that way.

[binntho]

Is that what they mean with the word " ridging" ?

I don't know if you are experienced with lake or river ice and how it can become compressed against an immobile obstacle. The ice breaks up into floes that can stack vertically like books or horizontally, one on top of each other.

For something similar to happen with FYI during the Arctic Winter, with ice thickness of 2-3 meters, requires immense pressures. But it is possible, particularly where a large field of extremely thick (and therefore massive) MYI squezes from one side against an immobile continent or continental shelf.

The ice in the Arctic has traditionally been thought to move with underlying ocean currents and prevailing wind, the two major currents being the Beaufort Gyre and the Transatlantic Drift. Both of these seem to have weakened with more dispersion, with the ice becoming much more susceptible to wind forcing than ocean currents.



The Beufort Gyre and, to the right, the Trans Arctic drift (not labelled), from Gyre Exploration Project

In a regime of very thick MYI ice and a slow but steady gyre, new ice will be crushed between floes of thicker ice, and ice will be crushed against Alaska and particularly Siberia, which was traditionally said to contain the thickest ice (with keel marks still visible on the sea floor at depths of several tens of meters). The forces involved would have been huge, enough to crumble and even stack 2-3 meter thick ice both vertically and horizontally.

[uniquorn]

Very fortunate to having a well-appointed research vessel enter the baffling area of ice decay between Greenland and the North Pole.

It's not at all clear what the final destination is though the publicist writing 'Follow' suggests it is out of satellite range which for Sentinel-1AB is 87.5ºN (WorldView satellites don't have a pole hole but clouds often preclude imaging). The PS is moving rapidly, a full degree of latitude north in the last 12 hours (111 km by 20-08-15 19:00 ) plus 2º of longitude west.

They're fixing the helicopters but may or may not share those photos (or much else en route). The bow radar is no longer reporting at 6 hrs frequency, having stopped on Aug 9th. If restarted upon mooring, it would benefit from synchronous overhead views and shipboard commentary.

https://data.meereisportal.de/maps/animations/Iceradar/?C=M;O=D

Let's hope the PS focuses on the current melt season rather than searching for another meaningless ice jumble. (N-ICE2015 already followed four of them to oblivion.)

The PS reached the rarely studied Wandel Sea a couple years back after a strong narrow cyclone blew up the Fram, pushing the ice well north of the flux gate. The captain toyed with the safety of continuing through the Lincoln Sea and around Greenland via the Nares. The PI wrote me that the ship is equipped to do CTD tests on its own and did so (but the data could not yet be shared).

Fram export winds ceased in mid-May, not displaying a consistent pattern since. Despite the lack of a dramatic storm in the Straits this season, the 'cumulative impact' of moderate but generally non-northerly winds could possibly explain both the lift-off from the CAA, churning of Lincoln Sea ice, and the remarkable deterioration towards the Pole.

Since the ice has not been notably pushed, southerly winds would have needed to bring some mix of sunshine, warm air and overturned warm water acting on dodgier-than-realized ice.  Morris Jesup is not favorably located for katabatic winds off the Greenland summit ridge. No one here has systematically examined online daily records of the automatic weather station there.

This same 'Follow' publicist wrote in a double falsehood yesterday that the ship had passed through the offshore wind-created Northeast Water polynya. The facts and location concerning  that water opening have been established from 90 years of arduous on-site field work, beginning with Lauge Koch in 1933.

Read about it here:

http://www.issibern.ch/teams/Polynya/

https://sci-hub.se/10.1007/bf00240265

https://www.tandfonline.com/doi/abs/10.1080/00167223.2010.10669503?journalCode=rdgs20

https://www.researchgate.net/publication/226224526_The_Northeast_Water_polynya_Greenland_Sea

The Sentinel-1B below was colored by embedding a small fiducial black square, setting the colorpicker after some trial and error to radius 5, and clicking to find black elsewhere in the image. The selection was then filled with 'open water blue' though parts may have small floes or be half-slush.

Polarstern arrived at the pole on Aug19 2020.




Here are the panoramic photos from aug13-19 at 15% size, somewhat compressed at crf31.  9.35MB

[HapHazard]

wow thanks

[gerontocrat]

I downloaded the mp4 and opened it with a bog standard media player.

I have quite a big screen and it filled it from left to right. Spectacular!

I noticed that when there was a sort of 50-50 ice to open water sea fog tended to appear. A paper I found recently said that those are the conditions most likely to cause sea fog.

[uniquorn]

Here is a list of url's for the panoramic files for aug2020 if anyone wants to download full size images. There are 2197 files around 2.4MB each. A few don't exist.

[oren]

Thanks, uniquorn. The ice really looked bad in Aug 2020, in what is supposed to be a safe haven for thick ice.

[Niall Dollard]

Polarstern is making steady progress across the CAB on the Russian side. Circa 83 N 130 E

Surprised there hasnt been more updates on the ASIF (unless I missed it in some of the other threads).

Link to current location (and photo gallery) :

https://follow-polarstern.awi.de/expedition/arktis-2023/?lang=en

The scientists are lifting out blocks of ice at various stations along their route. The attached image shows that the thickness of the block was 1.05m. The image does not give a date stamp but it looks to me this was at the fourth ice station (location indicated on the second attached image).

[uniquorn]

Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition
Janna E. Rückert,Philip Rostosky,Marcus Huntemann,David Clemens-Sewall,Kerstin Ebell,Lars Kaleschke,Juha Lemmetyinen,Amy R. Macfarlane,Reza Naderpour,Julienne Stroeve,Andreas Walbröl,Gunnar Spreen
https://doi.org/10.1525/elementa.2023.00039

Warm air intrusions over Arctic sea ice can change the snow and ice surface conditions rapidly and can alter sea ice concentration (SIC) estimates derived from satellite-based microwave radiometry without altering the true SIC. Here we focus on two warm moist air intrusions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition that reached the research vessel Polarstern in mid-April 2020. After the events, SIC deviations between different satellite products, including climate data records, were observed to increase. Especially, an underestimation of SIC for algorithms based on polarization difference was found. To examine the causes of this underestimation, we used the extensive MOSAiC snow and ice measurements to model computationally the brightness temperatures of the surface on a local scale. We further investigated the brightness temperatures observed by ground-based radiometers at frequencies 6.9 GHz, 19 GHz, and 89 GHz. We show that the drop in the retrieved SIC of some satellite products can be attributed to large-scale surface glazing, that is, the formation of a thin ice crust at the top of the snowpack, caused by the warming events. Another mechanism affecting satellite products, which are mainly based on gradient ratios of brightness temperatures, is the interplay of the changed temperature gradient in the snow with snow metamorphism. From the two analyzed climate data record products, we found that one was less affected by the warming events. The low frequency channels at 6.9 GHz were less sensitive to these snow surface changes, which could be exploited in future to obtain more accurate retrievals of sea ice concentration. Strong warm air intrusions are expected to become more frequent in future and thus their influence on SIC algorithms will increase. In order to provide consistent SIC datasets, their sensitivity to warm air intrusions needs to be addressed.

extract:

Of particular interest here is the large-scale surface glazing, observed at the MOSAiC CO, which can affect the microwave emissions as described above. Before, during, and after the warm air intrusions, the actual SIC in the vicinity of MOSAiC was high (>95%). Single leads opened during the events but nothing major in comparison to the periods before and after as confirmed by optical (MODIS) and radar (Sentinel-1) satellite data, by observations from the expedition participants, and by helicopter-borne thermal infrared imagery (Thielke et al., 2022). The latter gives a value for lead fraction, that is, fraction of open water and thin (<30 cm) young ice, which was on the order of 1.5% over the CO on April 23, about three days after the intrusions. Still the warm air intrusion events affected satellite products of SIC based on microwave radiometry. In conjunction with the warming events, and lasting for several days after them, most satellite products showed a (wrong) decrease in SIC and inter-product variability increased.

[uniquorn]

Buoys, buoys – everywhere you look, buoys! Data from instruments moored in the ice characterise the sea ice, atmosphere and ocean during the MOSAiC expedition
05-13-2024
https://www.meereisportal.de/en/news-overview/news-detail-view/buoys-buoys-everywhere-you-look-buoys-data-from-instruments-moored-in-the-ice-characterise-the-sea-ice-atmosphere-and-ocean-during-the-mosaic-expedition

extract:

Figure 3 shows a representative outcome of the winter case study. The sites were located approximately 50 km from one another, and these relative distances, with changes of between 1 and 2 km, remained fairly stable. During the 30-day case study, the buoy network covered a spatial gradient of absolute salinity in the upper mixed layer below the ocean’s surface, with generally higher values in the southwest and lower values in the northeast section of the area in question. The observed gradient is embedded in the large-scale gradient of near-surface salinity and freshwater concentration between the Eurasian and Amerasian Basins. Within the network, ocean salinity varied considerably, both weekly and even daily. At the same time, the spatial variation in surface temperature was less pronounced than the temporal variation (no more than 5° C). The median ice drift varied to nearly the same extent as the windspeed, though the drift wasn’t free, being affected by various forces, e.g. ice-internal forces. A semidiurnal fluctuation in the sea-ice drift was observed, which is indicative of forcing by tides and / or inertial motion in the upper layers of the ocean. These fluctuations demonstrate the coupling of the ocean and ice. During the case study, the atmosphere did not manifest these semidiurnal fluctuations. The observed variability in snow depth at different measuring points can be attributed to the redistribution of snow and to local effects.

The chief advantages of autonomous instruments are the spatial distribution of observations and their ability to close chronological gaps in the manual observations made at the Central Observatory. When said observations had to be suspended from 16 May to 18 June 2020 due to the Polarstern’s departure for a personnel rotation, thanks to the buoy network it was still possible to gather data from 83.4° N to roughly 82.4° N. Figure 4 shows a representative outcome of the summer case study, based on data gathered by mass-balance buoys. The figure reflects the data from three buoys (from the network’s main node). From 26 May, the surface temperature rose above the freezing point, which also led to a significant warming of the snow (above the dashed line) and the sea ice below. As can be seen in Figure 4a–c, the frigid core of the sea ice gradually warmed from May to June 2020 – initially in response to intensive warming from above, and gradually also from below. This also implies that the percentage of brine in the ice by volume slowly rose. Two of the three buoys already showed surface melting at this time (reduced snow-cover depth), while the ice thickness on the underside barely changed. Here, too, we can recognise the varying influence of the atmosphere and ocean on the sea ice.

The full, very thorough, documentation:

Research Article| May 10 2024
The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms
https://doi.org/10.1525/elementa.2023.00103