Abstract: The H2020 project Integrated Arctic Observation System (INTAROS) aspires to increase the temporal and geographic coverage of in situ observations and add new key geophysical and biogeochemical variables in selected regions of the Arctic. By using a combination of mature and new instruments and sensors in integration with existing observatories, INTAROS aims to fill selected gaps in the present-day system and build additional capacity of the Arctic monitoring networks for ocean and sea ice. Three reference sites have been selected as key locations for monitoring ongoing Arctic changes: Costal Greenland, paramount for freshwater output from the Greenland ice sheet; North of Svalbard (covering the region from shelf to deep basin) – the hot-spot for ocean-air-sea ice interactions, and heat and biological energy input to the European Arctic; and Fram Strait – the critical gateway for exchanges between the Arctic and the World oceans. The existing observatories in the reference sites have been extended with new moorings and novel autonomous instrumentation, in particular for biogeochemical measurements and sea ice observations. Bottom-mounted instruments have been also implemented for seismic observations. A distributed observatory for ocean and sea ice in the Arctic Ocean and sub-Arctic seas includes non-stationary components such as ice-tethered observing platforms, float, gliders, and ships of opportunities, collecting multidisciplinary observations, still missing from the Arctic regions. New sensors, integrated platforms and experimental set-ups are currently under implementation during a two-year long deployment phase (2018-2020) with an aim to evaluate their sustained use in a future iAOS. New observations will be used for integration of new data products, demonstration studies and stakeholder consultations, contributing also to ongoing and future long-term initiatives (e.g. SAON).

Abstract: This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS) and standard deviation of the background (SD) to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System) Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100?m height in eastern France (OPE), a high-mountain observatory in the south-west of France (PDM), a regional marine background site in Crete (FKL) and a marine clean-air background site in the Southern Hemisphere on Amsterdam Island (AMS). This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers. More than 96?% of the spikes manually identified by station managers were successfully detected both in the SD and the REBS methods after the best adjustment of parameter values. At PDM, measurements made by two analyzers located 200?m from each other allow us to confirm that the CH4 spikes identified in one of the time series but not in the other correspond to a local source from a sewage treatment facility in one of the observatory buildings. From this experiment, we also found that the REBS method underestimates the number of positive anomalies in the CH4 data caused by local sewage emissions. As a conclusion, we recommend the use of the SD method, which also appears to be the easiest one to implement in automatic data processing, used for the operational filtering of spikes in greenhouse gases time series at global and regional monitoring stations of networks like that of the ICOS atmosphere network.

Abstract: Due to poor weather conditions including common heavy cloud cover at polar latitudes, daily satellite imaging is not always accessible. Nevertheless, fast events including heavy rainfall inducing floods appear as significant in the ice and snow budget while being ignored by satellite based studies since the slower sampling rate is unable to observe such short phenomena. We complement satellite imagery with a set of ground based autonomous automated high resolution digital cameras. The recorded oblique views, acquired at a rate of 3 images per day, are processed for comparison with the spaceborne imagery. Delaunay triangulation based mapping using a dense set of reference points provides the means for an accurate projection by applying a rubber sheeting algorithm. The measurement strategy of identifying binary information of ice and snow cover is illustrated through the example of a particular flood event. We observe a snow cover evolution from 100% to 44.5% and back to 100% over a period of 2 weeks.