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Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC)3 project was established in 2016 (www.ac3-tr.de/). It comprises modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, shipborne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data. For example, a distinct atmospheric moistening, an increase of regional storm activities, an amplified winter warming in the Svalbard and North Pole regions, and a decrease of sea ice thickness in the Fram Strait and of snow depth on sea ice have been identified. A positive trend of tropospheric bromine monoxide (BrO) column densities during polar spring was verified. Local marine/biogenic sources for cloud condensation nuclei and ice nucleating particles were found. Atmospheric–ocean and radiative transfer models were advanced by applying new parameterizations of surface albedo, cloud droplet activation, convective plumes and related processes over leads, and turbulent transfer coefficients for stable surface layers. Four modes of the surface radiative energy budget were explored and reproduced by simulations. To advance the future synthesis of the results, cross-cutting activities are being developed aiming to answer key questions in four focus areas: lapse rate feedback, surface processes, Arctic mixed-phase clouds, and airmass transport and transformation.

Der Austausch von Wissen und Information zwischen verschiedenen gesellschaftlichen Gruppen ist oft nicht trivial. Vertreter aus der Öffentlichkeit, verschiedenen Fachkreisen und Behörden oder aus der Wissenschaft generieren sehr unterschiedliches Wissen unter Einbeziehung von unterschiedlichen Graden der Problemorientierung und in ihrer jeweiligen Sprache. Zur Überwindung dieser Barrieren stehen verschiedene Instrumente zur Verfügung. In diesem Artikel werden drei weitverbreitete Formen des Wissenstransfers diskutiert: (1) Assessments mit ihren verschiedenen Formen z. B. auf unterschiedlichen räumlichen Skalen, (2) Indikatoren mit möglichen Rahmenkonzepten, Indikatorensätze und Formen der Evaluierung und (3) web-basierte Plattformen als einfache Möglichkeit der Verbreitung von aktuellen Informationen. Dabei werde zwei Beispiele ausführlich dargestellt, nämlich das am Klimabüro für Polargebiete und Meeresspielgel konzipierte Meereisportal und der am Mitteldeutschen Klimabüro entwickelte Deutsche Dürremonitor.

A new algorithmto retrieve characteristics (albedo and melt pond fraction) of summer ice in the Arctic fromoptical satellite data is described. In contrast to other algorithms this algorithm does not use a priori values of the spectral albedo of the sea-ice constituents (such asmelt ponds,white ice etc.). Instead, it is based on an analytical solution for the reflection from sea ice surface. The algorithm includes the correction of the sought-for ice and ponds characteristics with the iterative procedure based on the Newton–Raphson method. Also, it accounts for the bi-directional reflection from the ice/snow surface, which is particularly important for Arctic regions where the sun is low. The algorithm includes an original procedure for the atmospheric correction, as well. This algorithm is implemented as computer code called Melt Pond Detector (MPD). The input to the current version of the MPD algorithm is the MERIS Level 1B data, including the radiance coefficients at ten wavelengths and the solar and observation angles (zenith and azimuth). Also, specific parameters describing surface and atmospheric state can be set in a configuration input file. The software output is the map of the melt ponds area fraction and the spectral albedo of sea-ice in HDF5 format. The numerical verification shows that theMPD algorithm provides more accurate results for the light ponds than for the dark ones. The spectral albedo is retrieved with high accuracy for any type of ice and ponds.

The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

Sea ice thickness information is important for sea ice modelling and ship operations. Here a method to detect the thickness of sea ice up to 50 cm during the freeze-up season based on high incidence angle observations of the Soil Moisture and Ocean Salinity (SMOS) satellite working at 1.4 GHz is suggested. By comparison of thermodynamic ice growth data with SMOS brightness temperatures, a high correlation to intensity and an anticorrelation to the difference between vertically and horizontally polarised brightness temperatures at incidence angles between 40 and 50° are found and used to develop an empirical retrieval algorithm sensitive to thin sea ice up to 50 cm thickness. The algorithm shows high correlation with ice thickness data from airborne measurements and reasonable ice thickness patterns for the Arctic freeze-up period.

[1] Measurements of sea ice concentration from the Special Sensor Microwave Imager (SSM/I) using seven different algorithms are compared to ship observations, sea ice divergence estimates from the Radarsat Geophysical Processor System, and ice and water surface type classification of 59 wide-swath synthetic aperture radar (SAR) scenes. The analysis is confined to the high-concentration Arctic sea ice, where the ice cover is near 100%. During winter the results indicate that the variability of the SSM/I concentration estimates is larger than the true variability of ice concentration. Results from a trusted subset of the SAR scenes across the central Arctic allow the separation of the ice concentration uncertainty due to emissivity variations and sensor noise from other error sources during the winter of 2003-2004. Depending on the algorithm, error standard deviations from 2.5 to 5.0% are found with sensor noise between 1.3 and 1.8%. This is in accord with variability estimated from analysis of SSM/I time series. Algorithms, which primarily use 85 GHz information, consistently give the best agreement with both SAR ice concentrations and ship observations. Although the 85 GHz information is more sensitive to atmospheric influences, it was found that the atmospheric contribution is secondary to the influence of the surface emissivity variability. Analysis of the entire SSM/I time series shows that there are significant differences in trend between sea ice extent and area, using different algorithms. This indicates that long-term trends in surface and atmospheric properties, unrelated to sea ice concentration, influence the computed trends.

Two algorithms have been used in a hybrid scheme in order to obtain sea iceconcentration maps at 12 km resolution from 19, 37, and 85 GHz SSM/I data.The first one is an algorithm based on the polarization difference near 90GHz and the second one is the NASA Team algorithm which uses the 19 and 37GHz SSM/I channels. Ice concentrations are calculated using the 85 GHzchannels. In addition, the lower frequency channels are used to decidewhether the data points belong to the ice-free ocean or to the ice-coveredarea. This combination of high and low frequency channels eliminatesincorrect high ice concentrations caused by weather effects over the in factice-free ocean using the rather weather independent low frequencies whileretaining high resolution over ice with the high frequency. The estimationof proper tie points for the 85 GHz algorithm was a major task. Astatistical linear regression method for reference brightness temperatureestimation was applied in order to avoid misarranged guesses of the tiepoints. This method requires independent ice concentration reference datawhich were derived from aircraft dual-polarized passive microwavemeasurements at 19 and 37 GHz and optical line scanner images. ERS-2 SARimages were used to analyze the capability of the SSM/I to resolve featuressuch as the evolution of the marginal ice zone in the Fram Strait and theStorfjorden Polynya. Two different numerical atmospheric models were used toanalyze the effect of an increased resolution of ice data from 50 to 12 kmon the model results. It was found that the representation of the ice edgezone significantly influences the modelled atmospheric boundary-layertemperatures. The temperatures obtained with the high resolution ice dataagree significantly better with aircraft observed data.

A radiative transfer model to compute brightness temperatures in the microwave frequency range for polar regions including sea ice, open ocean, and atmosphere has been developed and applied to sensitivity studies and retrieval algorithm development. The radiative transfer within sea ice is incorporated according to the “many layer strong fluctuation theory” of Stogryn [1986, 1987] and T. Grenfell [Winebrenner et al., 1992]. The reflectivity of the open water is computed with the three-scale model of Schrader [1995]. Both surface models supply the bistatic scattering coefficients, which define the lower boundary for the atmospheric model. The atmospheric model computes the gaseous absorption by the Liebe et al. [1993] model. Scattering by hydrometeors is determined by Mie or Rayleigh theory. Simulated brightness temperatures have been compared with special sensor microwave imager (SSM/I) observations. The comparison exhibits shortcomings of the ice model for 37 GHz. Applying a simple ad hoc correction at this frequency gives consistent comparison results within the range of observational accuracy. The simulated brightness temperatures show the strong influence of clouds and variations of wind speed over the open ocean, which will affect the sea ice retrieval even for an ice-covered ocean. Simulated brightness temperatures have been used to train a neural network algorithm for the total sea ice concentration, which accounts for these effects. Sea ice concentrations sensed from the SSM/I data using the network and the NASA sea ice algorithm show systematic differences in dependence on cloudiness.