ABSTRACT. Surface melt in the ablation zone is dominated by atmospheric temperature and surface albedo. We developed a surface mass-balance model with a dynamic component of glacier ice albedo which includes surface properties, clouds and the angle of the sun. The ice albedo reduction is mainly caused by impurity accumulation of non-biological origin such as dust and black carbon (BC), which is currently not included in other surface mass-balance models. Simulations show that dust from meltout is the main source of impurity mass at the melting glacier ice surface, and current rates of atmospheric deposition of dust play only a minor role. However, for BC the atmospheric deposition is the main source where ice melt rates are below 1 m, and atmospheric deposition is most likely from intercontinental transport due to the scarce population and lack of forests in Greenland.

Albedo is the dominant factor governing surface melt variability in the ablation area of ice sheets and glaciers. Aerosols such as mineral dust and black carbon (soot) accumulate on the ice surface and cause a darker surface and therefore a lower albedo. The darkening effect on the ice surface is currently not included in sea level projections, and the effect is unknown. We present a model framework which includes ice dynamics, aerosol transport, aerosol accumulation and the darkening effect on ice albedo and its consequences for surface melt. The model is applied to a simplified geometry resembling the conditions of the Greenland ice sheet, and it is forced by several temperature scenarios to quantify the darkening effect of aerosols on future mass loss. The effect of aerosols depends non-linearly on the temperature rise due to the feedback between aerosol accumulation and surface melt. According to our conceptual model, accounting for black carbon and dust in future projections of ice sheet changes until the year 3000 could induce an additional volume loss of 7 %. Since we have ignored some feedback processes, the impact might be even larger

Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in automotive perception sensors. This is a crucial prerequisite, since the quality of an automated driving function strongly depends on the reliability of the perception data, especially under adverse conditions. This publication presents a systematic review on faults and suitable detection and recovery methods for automotive perception sensors and suggests a corresponding classification schema. A systematic literature analysis has been performed with focus on lidar in order to review the state-of-the-art and identify promising research opportunities. Faults related to adverse weather conditions have been studied the most, but often without providing suitable recovery methods. Issues related to sensor attachment and mechanical damage of the sensor cover were studied very little and provide opportunities for future research. Algorithms, which use the data stream of a single sensor, proofed to be a viable solution for both fault detection and recovery.

Abstract. Ice loss due to surface melt of the Greenland ice sheet has increased in recent years. Surface melt in the ablation zone is controlled by atmospheric temperature and surface albedo. Impurities such as mineral dust and black carbon darken the snow and ice surfaces and therefore reduce the surface albedo which leads to more absorbed solar energy and ultimately amplifying melt. These impurities accumulate on the ice surface both from atmospheric fallout and by melt-out of material which was enclosed in the snowpack or the ice compound. A general impurity accumulation model is developed and applied to calculate the surface albedo evolution at two locations in western Greenland. The model is forced either by regional climate model output or by a parameterisation for temperature and precipitation. Simulations identify mineral dust as the main contributor to impurity mass on ice where the dominating part originates from melt out of englacial dust. Daily reduction of impurities is in the range of one per-mille which leads to a residence time of decades on the ice surface. Therefore the impurities have a prolonged effect on surface melt once they are located on the ice surface. The currently englacially stored mineral dust and black carbon will effect future melt and sea level rise and can be studied with the presented model.

Abstract. Modelling the evolution of the Earth system on long timescales requires the knowledge and understanding of driving mechanisms, such as the hydrological cycle. This is dominant in all components of the Earth's system, such as atmosphere, ocean, land surfaces/vegetation and the cryosphere. Observations and measurements of stable water isotopes in climate archives can help to decipher and reconstruct climate change and its regional variations. For the cryosphere, the δ18O cycle in the current generation of Earth system models is missing and an efficient and accurate tracer transport scheme is required. We describe ISOPOLIS 1.0, a modular semi-Lagrangian transport scheme of second-order accuracy, which is coupled to the polythermal and thermomechanical ice sheet model SICOPOLIS (version 2.9). Model skill is demonstrated by experiments with a simplified ice sheet geometry and by comparisons of simulated ice cores with data from Greenland (GRIP) and Antarctica (Vostok). The presented method is a valuable tool to investigate the transport of any kind of passive tracer inside the cold ice part of a polythermal ice sheet and is an important step to model the whole δ18O cycle.