The dataset includes the sediment and biota sampling point in Svalbard Archipelago, along Krossfjorden and Kongsfjorden. Sediment was sampled with the grab from the MS-Teisten (Kings Bay) while amphipods were sampled from the coast with nets (Polar Circle - NPI).

Aerosol scattering coefficient at 1 wavelength (530 nm) measured using a nephelometer M903 and absorption coefficient at 3 wavelengths (467, 530, 660 nm) measured using a Particle Soot Absorption Photometer (PSAP), both manufactured by Radiance Research.

The main goal of the UV-ICARE project was to establish a network of UV-monitoring stations on Svalbard, which is optimally coordinated and homogenised. This implies harmonization of operational routines, data analysis and storage, including a thorough instrument inter-comparison. The latter was a central element of activities. In the inter-comparison campaign that took place in Ny-Ålesund from 17 to 23 April, 2018 participated (from left to right in the photo): 1.Narrow-band filter radiometer UV-RAD, managed by the Institute of Polar Science at the National Research Council, Italy (CNR-ISP). The instrument measures the erythemally weighted solar UV irradiance (UVE) and ozone column at Ny-Ålesund. 2.Filter GUV radiometer, produced by Biospherical Instr., operating under responsibility of the Norwegian Institute for Air Research (NILU). The radiometer provides solar UVE irradiance and ozone column at Ny-Ålesund. 3.Kipp & Zonen UVS-AE-T radiometer that measures UVE irradiance and operates at Hornsund station under responsibility of the Institute of Geophysics at the Polish Academy of Sciences (IGF-PAS). 4.Kipp & Zonen UVS-E-T radiometer that also provides solar UVE irradiance and works at Longyearbyen station, under management of Masaryk University (MU), Brno and University of South Bohemia (USB), Czech Republic. 5.Brewer #050 spectroradiometer (shown in the second picture) that provided UVE irradiance and ozone column as a reference instrument. The devise operates at Ny-Ålesund under responsibility of CNR-ISP. The inter-comparison resulted in very good agreement (±5 %) of the instruments involved, over most of solar zenith angles and weather conditions sampled, despite different technical specifications. On the basis of this campaign, it can be stated that all measurements in Ny-Ålesund, Hornsund and Longyearbyen are directly comparable at solar zenith angles < 80°.

Equivalent black carbon from aerosol absorption coefficient at 660nm measured using a Particle Soot Absorption Photometer (PSAP), manufactured by Radiance Research. MAC equal to 10 m^2/g.

Aerosol scattering coefficient at 1 wavelength (530 nm) measured using a nephelometer M903 manufactured by Radiance Research and absorption coefficient at 7 wavelengths (370, 470, 520, 590, 660, 880, 950 nm) measured using an AETHALOMETER AE33 from Aerosol Magee Scientific.

The dataset includes fourteen seawater surface sampling points taken with Niskin bottles on board the MS-Teisten (Kings Bay)

The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere. Turbulent parameters are measured at the Amundsen-Nobile Climate Change Tower (CCT) by means of a Gill R3 sonic anemometer installed at 7.5 m from the ground since 2010. It measures the three components of the wind (u, v and w) and the sonic temperature at a rate of 20 Hz. These micro-meteorological measurements are complemented by standard meteorological ones at 4 levels: 2, 5, 10 and 33 m (acquisition time step equal to 1 minute). From these measurements, sensible heat flux, friction velocity and roughness length are calculated. Wind components and sonic temperature measurements were used to estimate friction velocity and kinematic heat flux. Before computing the micrometeorological parameters, a preliminary analysis is applied in order to assess the data quality and to remove low quality records. After the quality analysis application, mean values of the turbulence statistics were computed following two coordinate rotations to ensure the mean lateral and vertical velocities were zero (McMillen, 1988). Half-hour turbulent statistics (heat fluxes and friction velocity) were derived using two time-scales: a standard averaging time of 30 min and a reduced one (2 min) necessary for filtering out submeso motions contributions that can greatly alter the estimation of turbulent fluxes in a strong and long-lived stable BL. The short averaging time scale was evaluated on the basis of spectral analysis of data in order to include all turbulent scales, but excluding submeso motions (larger than turbulence). The turbulent statistics evaluated over the short subsets and then re-averaged over 30 min following Vickers and Mahrt (2006). Turbulent parameter relative to unfavorable wind direction ([150÷270] degrees) for which the tower was upwind of the sonic anemometer were not discarded but are flagged (flagdir=1) in the final dataset. More, the percentage of NaNs relative to each run is indicated. The wind speed vertical profile measured by slow response standard meteorological anemometers at 2, 5, 10 and 33 m was used for estimating the roughness length assuming a typical log wind profile under statically neutral conditions. Mahrt, L., 1998. Flux Sampling Errors for aircraft and towers. J. Atmos. Ocean. Technol. 15, 416-429. Mc Millen, R.T., 1988. An Eddy correlation technique with extended applicability to non-simple terrain. Boundary-Layer Meteorol. 43, 231-245. Vickers D, Mahrt L. 2006. A solution for flux contamination by mesoscale motions with very weak turbulence. Boundary-Layer Meteorol. 118: 431–447. https://doi.org/10.1007/s10546-005-9003-y. Zahn, E., Chor, T.L., Dias, N. L., 2016. A Simple Methodology for Quality Control of Micrometeorological Datasets. American Journal of Environmental Engineering 6(4A): 135-142 DOI: 10.5923/s.ajee.201601.20.