Marcus Huntemann

Environmental information for a marine ecosystem research approach for the northern Antarctic Peninsula (RV Polarstern expedition PS81, ANT-XXIX/3)

During the austral summer expedition PS81, ANT-XXIX/3 with the German research ice breaker Polarstern in 2013, research was carried out to investigate the role of environmental factors on the distribution of benthic communities and marine mammal and krill densities around the northern tip of the Antarctic Peninsula. For these studies collated in this special issue and studies in this area, we present a collection of environmental parameters with probable influence on the marine ecosystems around the Antarctic Peninsula.

Response of passive microwave sea ice concentration algorithms to thin ice

The influence of sea ice thickness brightness temperatures and ice concentrations retrieved from passive microwave observations is quantified, using horizontally homogeneous sea ice thickness retrievals from ESAs SMOS sensor observations at high incidence angles. Brightness temperatures are influenced by thickness below 18 cm (89GHz) and 50 cm (1.4 GHz). Ice concentration retrievals reduced by ice thickness below 0.17 m and 0.33 m, with higher frequency algorithms being less influenced.

Discrete dipole approximation simulations on GPUs using OpenCL—Application on cloud ice particles

Abstract The global distribution and climatology of ice clouds are among the main uncertainties in climate modeling and prediction. In order to retrieve ice cloud properties from remote sensing measurements, the scattering properties of all cloud ice particle types must be known. The discrete dipole approximation (DDA) simulates scattering of radiation by arbitrarily shaped particles and is thus suitable for cloud ice crystals. The DDA models the particle as a collection of equal dipoles on a lattice, and is computationally much more expensive than approximations restricted to more regularly shaped particles. On a single computer the calculation for an ice particle of a specific size, for a given scattering plane at one specific wavelength can take several days. We have ported the core routines of the scattering suite “ADDA” to the open computing language (OpenCL), a framework for programming parallel devices like PC graphics cards (graphics processing units, GPUs) or multi-core CPUs. In a typical case we can achieve a speed-up on a GPU as compared to a CPU by a factor of 5 in double precision and a factor of 15 in single precision. Spreading the work load over multiple GPUs will allow calculating the scattering properties even of large cloud ice particles.

Living on the edge of a shrinking habitat: the ivory gull, Pagophila eburnea, an endangered sea-ice specialist

The ongoing decline of sea ice threatens many Arctic taxa, including the ivory gull. Understanding how ice-edges and ice concentrations influence the distribution of the endangered ivory gulls is a prerequisite to the implementation of adequate conservation strategies. From 2007 to 2013, we used satellite transmitters to monitor the movements of 104 ivory gulls originating from Canada, Greenland, Svalbard-Norway and Russia. Although half of the positions were within 41 km of the ice-edge (75% within 100 km), approximately 80% were on relatively highly concentrated sea ice. Ivory gulls used more concentrated sea ice in summer, when close to their high-Arctic breeding ground, than in winter. The best model to explain the distance of the birds from the ice-edge included the ice concentration within approximately 10 km, the month and the distance to the colony. Given the strong links between ivory gull, ice-edge and ice concentration, its conservation status is unlikely to improve in the current context of sea-ice decline which, in turn, will allow anthropogenic activities to develop in regions that are particularly important for the species.

Retrieval of sea ice thickness during melt season from in situ, airborne and satellite imagery

Currently available sea ice thickness retrieval algorithms are compromised in summer in the presence of melt. This study presents a new approach to estimate sea ice thickness in summer in the presence of melt ponds. Analysis of field data obtained during RV “Polarstern” cruise ARK27/3 (August - October 2012) has shown a clear connection of ice thickness under melt ponds to their measured spectral albedo and to saturation value in the HSL colour space from field photographs. An empirical function has been derived from the data, which gives a possibility to access sea ice thickness information stored in the historic dataset of in situ imagery. A similar function has been applied to aerial imagery. The presented approach can be used (1) as fast, non-intrusive method to estimate sea ice thickness in situ; (2) for general circulation model input, validation, ice mass balance estimate; (3) for assessing summer sea ice thickness dynamics from historic data to be used in the context of Arctic change.

Combined SMAP/SMOS Thin Sea Ice Thickness Retrieval

The spaceborne passive microwave sensors Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) provide brightness temperature data in the L band (1.4 GHz). At this low frequency the atmosphere is close to transparent and in polar regions the thickness of thin sea ice can be derived. SMOS measurements cover a large incidence angle range, whereas SMAP observes at a fixed 40 incidence angle. By using brightness temperatures at a fixed incidence angle obtained directly (SMAP), or through interpolation (SMOS), thin sea ice thickness retrieval is more consistent as the incidence angle effects do not have to be taken into account. Here we transfer a retrieval algorithm for the thickness of thin sea ice (up to 50 cm) from SMOS data at 40 to 50 incidence angle to the fixed incidence angle of SMAP. The SMOS brightness temperatures (TBs) at a given incidence angle are estimated using empirical fit functions. SMAP TBs are calibrated to SMOS to provide a merged SMOS–SMAP sea ice thickness product. The new merged SMOS–SMAP thin ice thickness product was improved upon in several ways compared to previous thin ice thickness retrievals. (i) The combined product provides a better temporal and spatial coverage of the polar regions due to the usage of two sensors. (ii) The radio frequency interference (RFI) filtering method was improved, which results in higher data availability over both ocean and sea ice areas. (iii) For the intercalibration between SMOS and SMAP brightness temperatures the root mean square difference (RMSD) was reduced by 30 % relative to a prior attempt. (iv) The algorithm presented here allows also for separate retrieval from any of the two sensors, which makes the ice thickness dataset more resistant against failure of one of the sensors. A new way to estimate the uncertainty of ice thickness retrieval was implemented, which is based on the brightness temperature sensitivities.

Method for detection of leads from Sentinel-1 SAR images

ABSTRACT The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. Here, an algorithm providing an automatic lead detection based on synthetic aperture radar images is described that can be applied to a wide range of Sentinel-1 scenes. By using both the HH and the HV channels instead of single co-polarised observations the algorithm is able to classify more leads correctly. The lead classification algorithm is based on polarimetric features and textural features derived from the grey-level co-occurrence matrix. The Random Forest classifier is used to investigate the importance of the individual features for lead detection. The precision–recall curve representing the quality of the classification is used to define threshold for a binary lead/sea ice classification. The algorithm is able to produce a lead classification with more that 90% precision with 60% of all leads classified. The precision can be increased by the cost of the amount of leads detected. Results are evaluated based on comparisons with Sentinel-2 optical satellite data.

Thickness of thin sea ice retrieved from SMOS and SMAP

Sea ice is an important quantity for the radiation budget of the Earths climate system. In this paper we present a comparison of SMAP and SMOS measured brightness temperatures and translate a validated retrieval algorithm for the thickness of thin sea ice from SMOS to SMAP radiometer observations. For October to December all observations of SMAP in forward and backward direction are compared as daily averages and found with low RMSD of 2.25K and 2.42K in TB,v and TB,h, respectively. The mean of forward and backward observations from SMAP are mapped to SMOS equivalent brightness temperatures using a linear regression. SMAP was found to yield lower brightness temperatures compared to SMOS by about 5K in both polarizations while difference decreases with increasing brightness temperatures. An existing ice thickness retrieval for SMOS using averaged brightness temperatures in the incidence angles range of 40° to 50° is transferred to SMAP using another linear regression. Ice thicknesses retrieved from both sensors show good agreement with a correlation of r = 0.969 and RMSD of 3.31 cm for ice thicknesses from 1 cm to 50 cm.

A New Method to Filter Out Radio-Frequency Interference (RFI) from SMOS Level 1C Data for Sea Ice Applications

The Soil Moisture and Ocean Salinity (SMOS) satellite carries a passive microwave radiometer working at 1.4 GHz (L-Band). A unique synthetic aperture antenna consisting of several small antennas allows SMOS to observe a single geographic location under various incidence angles within single overflights. Here we present a preprocessing method starting from SMOS Level 1C data for sea ice applications which reduces the instrumental noise and filters radio frequency interference while preserving valuable data better than previously suggested methods in cryospheric applications. The filter employs binning on incidence angles, so that the filtered data can be used for comparison with surface emissivity models or may serve as input to retrieval procedures.