Remote sensing of multiyear ice in the Antarctic

<p>The changing sea ice cover of polar seas is of key importance for the exchange of heat and moisture between atmosphere and ocean and hence for weather and climate, and in addition, the sea ice and its long-term changes are  an indicator for global change.  In order to properly understand and model the evolution of the sea ice cover and its interaction with the global climate system, we need detailed knowledge about sea ice, i.e., not only its extent, but also, e.g., its thickness and its type.</p> <p>We can broadly distinguish a few different sea ice types that have different dynamic and thermodynamic properties, namely: young ice (YI, thin/smooth new ice), first-year ice (FYI, formed during one cold season), and multiyear ice (MYI, which has survived at least one melt season). The  latter is of particular interest as it is usually thicker than other ice types (thus, takes more time to melt), much less saline, and may accommodate a unique ecosystem. Sea ice types in the Antarctic, until recently, have not been monitored much because of the lack of appropriate remote  sensing methods. While the Antarctic sea ice is greatly dominated by FYI, there are, nevertheless, considerable amounts of MYI, in particular in the Weddell Sea.</p> <p>We have recently adapted an algorithm for the detection of Arctic sea ice types for application in the Antarctic. The algorithm uses data from space-borne microwave radiometers and scatterometers as input. So far we have compiled a time series of daily Antarctic MYI data (and also an estimate of YI and FYI) data at a spatial resolution of 12.5 km, starting in 2013, but excluding the melt seasons when the algorithm does not work. Here give an overview of the data, showing, e.g., the quite large interannual variability of MYI and its evolution in the Weddell Sea, and discuss shortcomings of the algorithm and possible ways forward. The time series of daily Antarctic MYI data can in principle be extended backwards to the year 2000, when the used satellite data first became available, and with planned future satellite missions, it can be continued for years to come.</p>

Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of heat and light, and is of importance for satellite estimates of sea ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.

Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.

Snow microstructure on sea ice: Importance for remote sensing applications

<p>Snow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic.</p><p>The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples.  This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges.</p><p>First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and multiyear ice. Furthermore, liquid water dominates the extreme coarsening of snow grains in the summer months and in winter the temporally large temperature gradients caused strong metamorphism, leading to brine inclusions in the snowpack and large depth hoar structures, all this significantly influences the signal response of remote sensing instruments.</p>

On the 2011 record low Arctic sea ice thickness: a combination of dynamic and thermodynamic anomalies

The sea ice thickness is recognized as an early indicator of climate changes. The mean Arctic sea ice thickness has been declining for the past four decades, and a sea ice thickness record minimum is confirmed occurring in autumn 2011. We used a daily sea ice thickness reanalysis data covering the melting season to investigate the dynamic and thermodynamic processes leading to the minimum thickness. Ice thickness budget analysis demonstrates that the ice thickness loss is associated with an extraordinarily large amount of multiyear ice volume export through the Fram Strait during the season of sea ice advance. Due to the loss of multiyear ice, the Arctic ice thickness becomes more sensitive to atmospheric anomalies. The positive net surface energy flux anomalies melt roughly 0.22 m of ice more than usual from June to August. An analysis of clouds and radiative fluxes from ERA5 reanalysis data reveals that the increased net surface energy absorption supports the enhanced sea ice melt. The enhanced cloudiness led to positive anomalies of net long-wave radiation. Furthermore, the enhanced sea ice melt reduces the surface albedo, triggering an ice–albedo amplifying feedback and contributing to the accelerating loss of multiyear ice. The results demonstrate that the dynamic transport of multiyear ice and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice thickness.

Observations of sea ice melt from Operation IceBridge imagery

The summer albedo of Arctic sea ice is heavily dependent on the fraction and color of melt ponds that form on the ice surface. This work presents a new dataset of sea ice surface fractions along Operation IceBridge (OIB) flight tracks derived from the Digital Mapping System optical imagery set. This dataset was created by deploying version 2 of the Open Source Sea-ice Processing (OSSP) algorithm to NASA's Advanced Supercomputing Pleiades System. These new surface fraction results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. Observations herein show that first-year ice does not ubiquitously have a higher melt pond fraction than multiyear ice under the same forcing conditions, contrary to established knowledge in the sea ice community. We discover and document a larger possible spread of pond fractions on first-year ice leading to both high and low pond coverage, in contrast to the uniform melt evolution that has been previously observed on multiyear ice floes. We also present a selection of optical images that capture both the typical and atypical ice types, as observed from the OIB dataset. The derived OIB data presented here will be key to explore the behavior of melt pond formation Arctic sea ice.

Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

The evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.