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38 Research products

  • European Marine Science
  • 2019-2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bergner, Nora; Heutte, Benjamin; Angot, Hélène; Dada, Lubna; +5 Authors

    This dataset contains CCN concentrations at five supersaturation levels, averaged to 1 min time resolution, measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using the model CCN-100 from Droplet Measurement Technologies (DMT, Boulder, USA). Detailed description of the measurement principle can be found in e.g. Roberts & Nenes (2005). The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al., Submitted; Beck et al., 2022; Dada et al., 2022). The measurements were performed in 1-h cycles, with a 0.5 L/min sample flow and a 2 L/min make up flow, where the supersaturations 0.15, 0.2, 0.3, 0.5 and 1.0 % were measured. The supersaturation of 0.15 % is measured for 20 min, as it takes longer to equilibrate, and the remaining supersaturations were measured for 10 min each. The instrument was calibrated in July 2019 before the campaign, and in March and April 2020 during the campaign. Based on the inter-variability of the calculated supersaturation levels during these calibrations, we can expect values ranging from 0.15-0.20, 0.20-0.25, 0.29-0.33, 0.43-0.5, 0.78-1.0 % for the nominal supersaturations of 0.15, 0.2, 0.3, 0.5 and 1.0 %, respectively. The counting error for the CCNC is associated with the error in the optical counting of particles and is about 10 %. Data were removed during the cooling cycle (i.e., the time when the measurement cycle starts again and the temperature is cooled to set the lowest supersaturation), which corresponds roughly to the first 10 min of each hour (so 50 % of the 0.15 % supersaturation period). Additionally, the first minute of the transition between supersaturations was removed before averaging the data to 1 min time resolution. During some time periods, a difference pattern of mean and standard deviation of the measurements between even and odd hours was observed, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. For correction, the 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements. The dataset contains a pollution mask for local pollution (predominantly exhaust from the Research Vessel Polarstern) with 0 indicating clean, and 1 indicating polluted periods (Beck et al., 2022; Beck et al., 2022).

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    Authors: Beck, Ivo; Moallemi, Alireza; Rolo, Margarida; Quéléver, Lauriane; +3 Authors

    These datasets contain the total particle number concentrations and normalized size distributions (dN/dlogDp) of excited, fluorescent, and hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are split into 20 size bins: 0.5 - 0.6 μm, 0.6 - 0.72 μm, 0.72 - 0.87 μm, 0.87 - 1.05 μm, 1.05 - 1.26 μm, 1.26 - 1.51 μm, 1.51 - 1.82 μm, 1.82 - 2.19 μm, 2.19 - 2.63 μm, 2.63 - 3.16 μm, 3.16 - 3.8 μm, 3.8 - 4.57 μm, 4.57 - 5.5 μm, 5.5 - 6.61 μm, 6.61 - 7.95 μm, 7.95 - 9.56 μm, 9.56 - 11.50 μm, 11.5 - 13.83 μm, 13.83- 16.63 μm and 16.63 - 20 μm. The data were measured by a WIBS-NEO (Wideband Integrated Bioaerosol Sensor, model New Electronics option) by droplet measurement techniques ltd. The data were processed using the IGOR WIBS toolkit V1.36 (DMT) and python version 3.9.7. These datasets have been averaged to 1 hour time resolution. The datasets were cleaned from local pollution sources by applying a pollution flag developed by Beck et al. (2022a,b), which is based on the rate of change in particle number concentration with 1 min time resolution. Data points with more than 10 polluted minutes within an hour were removed from the WIBS datasets. Time periods with zero filter measurements and time periods with unstable flow that affected number concentrations have been removed from the dataset. The WIBS measures the size, asymmetry and fluorescence of particles with an optical diameter of 0.5 – 20 µm. Detected particles are excited by two UV flashlamps at wavelengths of 280 and 370 nm and their emitted fluorescence is measured by two photomultipliers with bandwidths of 310 - 400 nm, and 420 - 650 nm. The WIBS counts excited particles at a maximum frequency of 125 Hz, which corresponds to a maximum concentration of 2.5*104 particles/L with a sample flow of 0.3 L/min. Excited particles were classified as fluorescent if their fluorescent intensity exceeded the background intensity by three standard deviations (3σ) and as hyper-fluorescent if the fluorescent intensity exceeded the background intensity by 9σ. Excited particles with a lower fluorescent intensity were considered to be non-fluorescent. The background fluorescence was determined by measuring the fluorescent signal in the measurement chamber in absence of particles. Background measurements were performed every 26 h. The combination of two excitation wavelengths and two detector wavebands allows the classification of fluorescent particles into seven types: A, B, C, AB, AC, BC, and ABC (Perring et al. (2015); Savage et al. (2017)). For further information about the instrumental setup, refer to Heutte et al. (Submitted).

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  • Authors: Michaelis, Janosch; Hartmann, Jörg; Schmitt, Amelie U; Birnbaum, Gerit; +2 Authors

    The data set consists of high resolution airborne measurements that were obtained mainly over Svalbard and near the sea ice edge north of Svalbard on three days in March 2013 during the campaign "SpringTime Atmospheric Boundary Layer Experiment (STABLE). STABLE was led by the Alfred Wegener Institue (AWI) and by the Finnish Meteorological Institute (FMI). The measurements were performed using the POLAR 5 research aircraft, where all research flights of 5-6 hours duration started and ended at Longyearbyen airport. During STABLE, observations focused on the vertical structure of the lower troposphere as well as boundary layer modifications, e.g. during marine cold-air outbreaks and by convection over leads in sea ice. The data set presented here predominantly consists of measurements that were obtained over the Wijdefjorden, which is a North-South oriented fjord with a length of more than 100km in the northern part of Spitsbergen. The measurements were carried out to study the boundary layer structure in the fjord as well as for analyses of the role of the topography on the atmospheric conditions. In its southern part, the fjord was covered by land-fast sea ice until about 72.5km north of the fjord's head. In its northern part, there was open water. On all three days, the corresponding flight patterns mainly consisted of vertical aircraft profiles between 30m and 1000-1500m height during saw-tooth patterns and of low- and high-level horizontal flight legs from the marginal ice zone towards the fjord and vice versa. Each file consists of measurements from one flight leg, where in each file name we include start and end time (in UTC) and the following abbreviations: • h: low-level horizontal flight leg (below 1000m flight altitude) • H: high-level horizontal flight leg (above 1000m flight altitude) • t: ascent or descent with an altitude difference 1500m (or ascent/descent at high altitudes) The airborne measurements were obtained by instruments installed in and at a turbulence nose-boom. The following variables are included in the data set (see Table 1): air pressure (static & dynamic) and wind obtained from a Rosemount 858 five-hole probe as well as temperature (Pt100 resistance thermometer), all with 100 Hz sampling rate, relative humidity (Vaisala HUMICAP in a Rosemount housing, 1Hz), radar altimeter (1Hz), and surface temperature (KT-19 radiation thermometer, 10Hz). Global Position System (GPS) and Inertial Navigation System (INS) were used to derive the aircraft's height, velocity, and position, and also for the calculation of the three wind components. Besides the GPS-based height, we provide also the more reliable pressure-based altitude. All variables are provided with 100Hz in each file. Air pressure and air temperature data were corrected as described in detail by Michaelis et al. (2022). Relative humidity data were corrected for adiabatic heating, which occurs due to compression of the air entering the Vaisala HUMICAP sensor situated in the Rosemount housing (see Smit et al., 2013). More detailed description on the measurements including the instruments' accuracies and the quality-processing of the measurements is provided by Suomi et al. (2023), for which this data set is a supplement, as well as by Michaelis et al. (2021, 2022) and Tetzlaff et al. (2014, 2015). In the latter four publications, data from the STABLE campaign is used as well but mainly from flight days other than in this data set. The corresponding data are available at Lüpkes et al. (2021a, b). Master tracks for all STABLE research flights can be found at Steinhage (2015). Finally, Hartmann et al. (2018) provide more details on the quality of such airborne measurements in general including instrument calibrations and the determination of related measurement accuracies. Note that for the wind measurements the full accuracy is only achieved and the estimates on uncertainty are only valid for straight level flight sections (Hartmann et al., 2018).

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    Authors: Rabe, Benjamin; Cox, Christopher J; Fang, Ying-Chih; Goessling, Helge; +46 Authors

    This bibliography unites the individual data collected by different types of autonomous platforms deployed during MOSAiC in 2019/2020.

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    Authors: Laiho, Raija; Lampela, Maija; Minkkinen, Kari; Straková, Petra; +5 Authors

    We estimated fine-root biomass (FRB) and production (FRP) and their depth distribution and plant functional type (PFT) composition in four forested boreal peatland site types that varied in soil nutrient and water-table level regimes, ground vegetation and tree stand characteristics. Two were pine-dominated nutrient-poor sites (dwarf-shrub pine bog, tall-sedge pine fen) and two spruce-dominated nutrient-rich sites (Vaccinium myrtillus spruce swamp, herb-rich hardwood-spruce swamp). Measurements were done in two sites per site type: one undrained site and one site that had been drained for forestry. In each of the eight sites, we established three measurement plots. FRB was estimated by separating and visually identifying roots from soil cores extending down to 50-cm depth. The cores were taken in late August, 2016. FRP was estimated using ingrowth cores covering the same depth, and the separated roots were identified using Fourier transform infrared spectroscopy (FTIR). The ingrowth cores were incubated for two years, starting in November 2015 and ending in November 2017. Tree-stand basal area and stem volume per species, and projection cover of ground vegetation per species were determined in summer 2018. We monitored the soil water-table level and soil temperatures in 5 and 30 cm depths with dataloggers. Soil pH, bulk density, and carbon, nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese, boron, zinc, and copper concentrations were measured from peat cores extending down to 50-cm depth and taken simultaneously with the FRB cores. FRB, FRP and peat properties are presented for 10-cm depth segments. FRB, FRP and peat properties are presented for 10-cm depth segments. Peat cores were taken with a box-shaped 65 mm x 37 mm peat corer, except in the wet TP site where a 60 mm x 60 mm corer was used.

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Haapala, Jari; +1 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC11 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 20 November 2019. The thermistor chain was 4.16 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 20 November 2019 and 10 June 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in the first-year ice ridge next to RV Polarstern and remote sensing site RS1. Ice mass balance SIMBA 2020T79 was installed at Met City close to remote sensing site RS1: doi:10.1594/PANGAEA.940712.

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Kolabutin, Nikolai; +3 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC32 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 22 November 2020. The thermistor chain was 5.12 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 22 November 2019 and 15 July 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in deformed second-year ice at Transect North. Radiation station 2020R14 was installed next to the DTC32: doi:10.1594/PANGAEA.948572.

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Haapala, Jari; +1 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC15 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 07 November 2019. The thermistor chain was 4.16 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 05 November 2019 and 07 January 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in the deformed second-year ice ridge next to RV Polarstern and remote sensing site RS1.

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    Authors: Mander, Ülo; Krasnova, Alisa; Escuer-Gatius, Jordi; Espenberg, Mikk; +10 Authors

    1 Study site and set-up The studied hemiboreal riparian forest is a 40-year old Filipendula type grey alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural agricultural land. It is situated in the Agali Village (58o17' N; 27o17' E) in eastern Estonia within the Lake Peipsi Lowland (Varep 1964). The area is characterized by a flat relief with an average elevation of 32m a.s.l., formed from the bottom of former periglacial lake systems, it is slightly inclined (1%) towards a tributary of the Kalli River. The soil is Gleyic Luvisol. The thickness of the humus layer was 15-20 cm. The content of total carbon (TC), total nitrogen (TN), nitrate (NO3- -N), ammonia NH4+-N, Ca and Mg per dry matter in 10cm topsoil was 3.8 and 0.33 %, and 2.42, 2.89, 1487 and 283 mg kg-1, respectively, which was correspondingly 6.3, 8.3, 4.4, 3.6, 2.3, and 2.0 times more than those in 20cm deep zone. The long-term average annual precipitation of the region is 650 mm, and the average temperature is 17.0 °C in July and -6.7 °C in January. The duration of the growing season is typically 175-180 days from mid-April to October (Kupper et al. 2011). The mean height of the forest stand is 17.5 m, the mean stem diameter at breast height 15.6 cm and the growing stock 245 m3 ha−1 (based on Uri et al 2014 and Becker et al 2015). In the forest floor, the following herbs dominate: Filipendula ulmaria (L.) Maxim., Aegopodium podagraria L., Cirsium oleraceum (L.) Scop., Geum rivale L., Crepis paludosa (L.) Moench,), shrubs (Rubus idaeus L., Frangula alnus L., Daphne mezereum L.) and young trees (A. incana, Prunus padus (L.)) dominate. In moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst. 2 Soil flux measurements Soil fluxes were measured using 12 automatic dynamic chambers located close to each studied tree and installed in June 2017. The chambers were made from polymethyl methacrylate (Plexiglas) covered with non-transparent plastic film. Each soil chamber (volume of 0.032 m³) covered a 0.16 m² soil surface. To avoid stratification of gas inside the chamber, air with a constant flow rate of 1.8 L min-1 was circulated within a closed loop between the chamber and gas analyzer unit during the measurements by a diaphragm pump. The air sample was taken from the top of the chamber headspace and pumped back by distributing it to each side of the chamber. For the measurements, the soil chambers were closed automatically for 9 minutes each. Flushing time of the whole system with ambient air between measurement periods was 1 minute. Thus, there were approximately 12 measurements per chamber per day. A Picarro G2508 (Picarro Inc., Santa Clara, CA, USA) gas analyzer using cavity ring-down spectroscopy (CRDS) technology was used to monitor N2O gas concentrations in the frequency of approximately 1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer. Since the 9 minutes of closing each soil chamber for measurements consisted of two minutes for stabilization the trend in the beginning and about two minutes unstable fluctuations at the end, for soil flux calculations, only 5 minutes of the linear trend of N2O concentration change has been used for soil flux calculations. After the quality checking 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period. 3 Stem flux measurements The tree stem fluxes were measured manually with frequency 1-2 times per week from September 2017 until December 2018. Twelve representative mature grey alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements (Machacova et al 2016). Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in following order: at the bottom part of the tree stem (approximately 10 cm above the soil) and at 80 and 170 cm above the ground. The rectangular shape stem chambers were made of transparent plastic containers, including removable airtight lids (Lock & Lock Co Ltd, Seoul, Republic of Korea). For chamber preparation see Schindler et al. (2020). Two chambers per profile were set randomly across 180° and interconnected with tubes into one system (total volume of 0.00119 m³) covering 0.0108 m² of stem surface. A pump (model 1410VD, 12 V; Thomas GmbH, Fürstenfeldbruck, Germany) was used to homogenize the gas concentration prior to sampling. Chamber systems remained open between each sampling campaign. During 60 measurement campaigns, four gas samples (each 25 ml) were collected from each chamber system via septum in a 60 min interval: 0/60/120/180 min sequence (sampling time between 12:00 and 16:00) and stored in pre-evacuated (0.3 bar) 12 ml coated gas-tight vials (LabCo International, Ceregidion, UK). The gas samples were analysed in the laboratory at University of Tartu within a week using gas chromatograph (GC-2014; Shimadzu, Kyoto, Japan) equipped with an electron capture detector for detection of N2O and a flame ionization detector for CH4. The gas samples were injected automatically using Loftfield autosampler (Loftfield Analytics, Göttingen, Germany). For gas-chromatographical settings see Soosaar et al. (2011). 4 Soil and stem flux calculation Fluxes were quantified on a linear approach according to change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston & Hutchison (1995). Stem fluxes were quantified on a linear approach according to change of N2O concentrations in the chamber headspace over time. A data quality control was applied based on R2 values of linear fit for CO2 measurements. When the R2 value for CO2 efflux was above 0.9, the conditions inside the chamber were applicable, and the calculations for N2O gases were also accepted in spite of their R2 values. To compare the contribution of soil and stems, the stem fluxes were upscaled to hectare of ground area based on average stem diameter, tree height, stem surface area, tree density, and stand basal area estimated for each period. A cylindric shape of tree stem was assumed. To estimate average stem emissions per tree, fitted regression curves for different periods were made between the stem emissions and height of the measurements as previously done by Schindler et al. (2020). 5 Eddy covariance instrumentation Eddy-covariance system was installed on a 21 m height scaffolding tower. Fast 3-D sonic anemometer Gill HS-50 (Gill Instruments Ltd., Lymington, Hampshire, UK) was used to obtain 3 wind components. CO2 fluxes were measured using the Li-Cor 7200 analyser (Li-Cor Inc., Lincoln, NE, USA). Air was sampled synchronously with the 30 m teflon inlet tube and analyzed by a quantum cascade laser absorption spectrometer (QCLAS) (Aerodyne Research Inc., Billerica, MA, USA) for N2O concentrations. The Aerodyne QCLAS was installed in the heated and ventilated cottage near the tower base. A high-capacity free scroll vacuum pump (Agilent, Santa Clara, CA, USA) guaranteed air flow rate 15 L min-1 between the tower and gas analyzer during the measurements. Air was filtered for dust and condense water. All measurements were done at 10Hz and the gas-analyzer reported concentrations per dry air (mixing ratios). 6 Eddy-covariance flux calculation and data quality control The fluxes of N2O were calculated using the EddyPro software (v.6.0-7.0, Li-Cor) as a covariance of the gas mixing ratio with the vertical wind component over 30-minute periods. Despiking of the raw data was performed following Mauder (2013). Anemometer tilt was corrected with the double axis rotation. Linear detrending was chosen over block averaging to minimize the influence of a possible fluctuations of a gas analyser. Time lags were detected using covariance maximisation in a given time window (5±2s was chosen based on the tube length and flow rate). While WPL-correction is typically performed for the closed-path systems, we did not apply it as water correction was already performed by the Aerodyne and the software reported mixing ratios. Both low and high frequency spectral corrections were applied using fully analytic corrections (Moncrieff et al. 1997, 2004). Calculated fluxes were filtered out in case they were coming from the half-hour averaging periods with at least one of the following criteria: more than 1000 spikes, half-hourly averaged mixing ratio out of range (300-350 ppb), quality control (QC) flags higher than 7 (Foken et al, 2004). Footprint area was estimated using Kljun et al (2015) implemented in TOVI software (Li-Cor Inc.). Footprint allocation tool was implemented to flag the non-forested areas within the 90% cumulative footprint and fluxes appointed to these areas were removed from the further analysis. Storage fluxes were estimated using point concentration measurements from the eddy system, assuming the uniform change within the air column under the tower during every 30 min period (calculated in EddyPro software). In the absence of a better estimate or profile measurements, these estimates were used to correct for storage change. Total flux values that were higher than eight times the standard deviation were additionally filtered out (following Wang et al., 2013). Overall, the quality control procedures resulted in 61% data coverage. While friction velocity (u*) threshold is used to filter eddy fluxes of CO2 (Papale et al. 2006), visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that 1-9 QC flag system already marks the times when the turbulence is not sufficient. To obtain the continuous time-series and to enable the comparison to chamber estimates over hourly time scales, gap-filling of N2O fluxes was performed using marginal distribution sampling method implemented in ReddyProcWeb online tool (https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb) (described in detail in Wutzler et al 2018). MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis. 7 Ancillary measurements Air temperature and relative humidity were measured within the canopy at 10m height using the HC2A-S3 - Standard Meteo Probe / RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). Based on these data, dew point depression was calculated to characterise chance of fog formation within the canopy. The incoming solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site (Noe et al 201587) using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated based on radiation data. Near-ground air temperature, soil temperature (Campbell Scientific Inc.) and soil water content sensors (ML3 ThetaProbe, Delta-T Devices, Burwell, Cambridge, UK) were installed directly on the ground and 0-10 cm soil depth close to the studied tree spots. During six campaigns from August to November 2017 composite topsoil samples were taken with a soil corer from a depth of 0-10 cm for physical and chemical analysis using standard methods (APHA-AWWA-WEF, 2005).

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Haapala, Jari; +1 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC12 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 05 November 2019. The thermistor chain was 4.16 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 05 November 2019 and 14 May 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in the undeformed second-year ice ridge next to RV Polarstern and remote sensing site RS1.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bergner, Nora; Heutte, Benjamin; Angot, Hélène; Dada, Lubna; +5 Authors

    This dataset contains CCN concentrations at five supersaturation levels, averaged to 1 min time resolution, measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using the model CCN-100 from Droplet Measurement Technologies (DMT, Boulder, USA). Detailed description of the measurement principle can be found in e.g. Roberts & Nenes (2005). The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al., Submitted; Beck et al., 2022; Dada et al., 2022). The measurements were performed in 1-h cycles, with a 0.5 L/min sample flow and a 2 L/min make up flow, where the supersaturations 0.15, 0.2, 0.3, 0.5 and 1.0 % were measured. The supersaturation of 0.15 % is measured for 20 min, as it takes longer to equilibrate, and the remaining supersaturations were measured for 10 min each. The instrument was calibrated in July 2019 before the campaign, and in March and April 2020 during the campaign. Based on the inter-variability of the calculated supersaturation levels during these calibrations, we can expect values ranging from 0.15-0.20, 0.20-0.25, 0.29-0.33, 0.43-0.5, 0.78-1.0 % for the nominal supersaturations of 0.15, 0.2, 0.3, 0.5 and 1.0 %, respectively. The counting error for the CCNC is associated with the error in the optical counting of particles and is about 10 %. Data were removed during the cooling cycle (i.e., the time when the measurement cycle starts again and the temperature is cooled to set the lowest supersaturation), which corresponds roughly to the first 10 min of each hour (so 50 % of the 0.15 % supersaturation period). Additionally, the first minute of the transition between supersaturations was removed before averaging the data to 1 min time resolution. During some time periods, a difference pattern of mean and standard deviation of the measurements between even and odd hours was observed, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. For correction, the 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements. The dataset contains a pollution mask for local pollution (predominantly exhaust from the Research Vessel Polarstern) with 0 indicating clean, and 1 indicating polluted periods (Beck et al., 2022; Beck et al., 2022).

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    Authors: Beck, Ivo; Moallemi, Alireza; Rolo, Margarida; Quéléver, Lauriane; +3 Authors

    These datasets contain the total particle number concentrations and normalized size distributions (dN/dlogDp) of excited, fluorescent, and hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are split into 20 size bins: 0.5 - 0.6 μm, 0.6 - 0.72 μm, 0.72 - 0.87 μm, 0.87 - 1.05 μm, 1.05 - 1.26 μm, 1.26 - 1.51 μm, 1.51 - 1.82 μm, 1.82 - 2.19 μm, 2.19 - 2.63 μm, 2.63 - 3.16 μm, 3.16 - 3.8 μm, 3.8 - 4.57 μm, 4.57 - 5.5 μm, 5.5 - 6.61 μm, 6.61 - 7.95 μm, 7.95 - 9.56 μm, 9.56 - 11.50 μm, 11.5 - 13.83 μm, 13.83- 16.63 μm and 16.63 - 20 μm. The data were measured by a WIBS-NEO (Wideband Integrated Bioaerosol Sensor, model New Electronics option) by droplet measurement techniques ltd. The data were processed using the IGOR WIBS toolkit V1.36 (DMT) and python version 3.9.7. These datasets have been averaged to 1 hour time resolution. The datasets were cleaned from local pollution sources by applying a pollution flag developed by Beck et al. (2022a,b), which is based on the rate of change in particle number concentration with 1 min time resolution. Data points with more than 10 polluted minutes within an hour were removed from the WIBS datasets. Time periods with zero filter measurements and time periods with unstable flow that affected number concentrations have been removed from the dataset. The WIBS measures the size, asymmetry and fluorescence of particles with an optical diameter of 0.5 – 20 µm. Detected particles are excited by two UV flashlamps at wavelengths of 280 and 370 nm and their emitted fluorescence is measured by two photomultipliers with bandwidths of 310 - 400 nm, and 420 - 650 nm. The WIBS counts excited particles at a maximum frequency of 125 Hz, which corresponds to a maximum concentration of 2.5*104 particles/L with a sample flow of 0.3 L/min. Excited particles were classified as fluorescent if their fluorescent intensity exceeded the background intensity by three standard deviations (3σ) and as hyper-fluorescent if the fluorescent intensity exceeded the background intensity by 9σ. Excited particles with a lower fluorescent intensity were considered to be non-fluorescent. The background fluorescence was determined by measuring the fluorescent signal in the measurement chamber in absence of particles. Background measurements were performed every 26 h. The combination of two excitation wavelengths and two detector wavebands allows the classification of fluorescent particles into seven types: A, B, C, AB, AC, BC, and ABC (Perring et al. (2015); Savage et al. (2017)). For further information about the instrumental setup, refer to Heutte et al. (Submitted).

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  • Authors: Michaelis, Janosch; Hartmann, Jörg; Schmitt, Amelie U; Birnbaum, Gerit; +2 Authors

    The data set consists of high resolution airborne measurements that were obtained mainly over Svalbard and near the sea ice edge north of Svalbard on three days in March 2013 during the campaign "SpringTime Atmospheric Boundary Layer Experiment (STABLE). STABLE was led by the Alfred Wegener Institue (AWI) and by the Finnish Meteorological Institute (FMI). The measurements were performed using the POLAR 5 research aircraft, where all research flights of 5-6 hours duration started and ended at Longyearbyen airport. During STABLE, observations focused on the vertical structure of the lower troposphere as well as boundary layer modifications, e.g. during marine cold-air outbreaks and by convection over leads in sea ice. The data set presented here predominantly consists of measurements that were obtained over the Wijdefjorden, which is a North-South oriented fjord with a length of more than 100km in the northern part of Spitsbergen. The measurements were carried out to study the boundary layer structure in the fjord as well as for analyses of the role of the topography on the atmospheric conditions. In its southern part, the fjord was covered by land-fast sea ice until about 72.5km north of the fjord's head. In its northern part, there was open water. On all three days, the corresponding flight patterns mainly consisted of vertical aircraft profiles between 30m and 1000-1500m height during saw-tooth patterns and of low- and high-level horizontal flight legs from the marginal ice zone towards the fjord and vice versa. Each file consists of measurements from one flight leg, where in each file name we include start and end time (in UTC) and the following abbreviations: • h: low-level horizontal flight leg (below 1000m flight altitude) • H: high-level horizontal flight leg (above 1000m flight altitude) • t: ascent or descent with an altitude difference 1500m (or ascent/descent at high altitudes) The airborne measurements were obtained by instruments installed in and at a turbulence nose-boom. The following variables are included in the data set (see Table 1): air pressure (static & dynamic) and wind obtained from a Rosemount 858 five-hole probe as well as temperature (Pt100 resistance thermometer), all with 100 Hz sampling rate, relative humidity (Vaisala HUMICAP in a Rosemount housing, 1Hz), radar altimeter (1Hz), and surface temperature (KT-19 radiation thermometer, 10Hz). Global Position System (GPS) and Inertial Navigation System (INS) were used to derive the aircraft's height, velocity, and position, and also for the calculation of the three wind components. Besides the GPS-based height, we provide also the more reliable pressure-based altitude. All variables are provided with 100Hz in each file. Air pressure and air temperature data were corrected as described in detail by Michaelis et al. (2022). Relative humidity data were corrected for adiabatic heating, which occurs due to compression of the air entering the Vaisala HUMICAP sensor situated in the Rosemount housing (see Smit et al., 2013). More detailed description on the measurements including the instruments' accuracies and the quality-processing of the measurements is provided by Suomi et al. (2023), for which this data set is a supplement, as well as by Michaelis et al. (2021, 2022) and Tetzlaff et al. (2014, 2015). In the latter four publications, data from the STABLE campaign is used as well but mainly from flight days other than in this data set. The corresponding data are available at Lüpkes et al. (2021a, b). Master tracks for all STABLE research flights can be found at Steinhage (2015). Finally, Hartmann et al. (2018) provide more details on the quality of such airborne measurements in general including instrument calibrations and the determination of related measurement accuracies. Note that for the wind measurements the full accuracy is only achieved and the estimates on uncertainty are only valid for straight level flight sections (Hartmann et al., 2018).

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    Authors: Rabe, Benjamin; Cox, Christopher J; Fang, Ying-Chih; Goessling, Helge; +46 Authors

    This bibliography unites the individual data collected by different types of autonomous platforms deployed during MOSAiC in 2019/2020.

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    Authors: Laiho, Raija; Lampela, Maija; Minkkinen, Kari; Straková, Petra; +5 Authors

    We estimated fine-root biomass (FRB) and production (FRP) and their depth distribution and plant functional type (PFT) composition in four forested boreal peatland site types that varied in soil nutrient and water-table level regimes, ground vegetation and tree stand characteristics. Two were pine-dominated nutrient-poor sites (dwarf-shrub pine bog, tall-sedge pine fen) and two spruce-dominated nutrient-rich sites (Vaccinium myrtillus spruce swamp, herb-rich hardwood-spruce swamp). Measurements were done in two sites per site type: one undrained site and one site that had been drained for forestry. In each of the eight sites, we established three measurement plots. FRB was estimated by separating and visually identifying roots from soil cores extending down to 50-cm depth. The cores were taken in late August, 2016. FRP was estimated using ingrowth cores covering the same depth, and the separated roots were identified using Fourier transform infrared spectroscopy (FTIR). The ingrowth cores were incubated for two years, starting in November 2015 and ending in November 2017. Tree-stand basal area and stem volume per species, and projection cover of ground vegetation per species were determined in summer 2018. We monitored the soil water-table level and soil temperatures in 5 and 30 cm depths with dataloggers. Soil pH, bulk density, and carbon, nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese, boron, zinc, and copper concentrations were measured from peat cores extending down to 50-cm depth and taken simultaneously with the FRB cores. FRB, FRP and peat properties are presented for 10-cm depth segments. FRB, FRP and peat properties are presented for 10-cm depth segments. Peat cores were taken with a box-shaped 65 mm x 37 mm peat corer, except in the wet TP site where a 60 mm x 60 mm corer was used.

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Haapala, Jari; +1 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC11 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 20 November 2019. The thermistor chain was 4.16 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 20 November 2019 and 10 June 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in the first-year ice ridge next to RV Polarstern and remote sensing site RS1. Ice mass balance SIMBA 2020T79 was installed at Met City close to remote sensing site RS1: doi:10.1594/PANGAEA.940712.

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Kolabutin, Nikolai; +3 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC32 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 22 November 2020. The thermistor chain was 5.12 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 22 November 2019 and 15 July 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in deformed second-year ice at Transect North. Radiation station 2020R14 was installed next to the DTC32: doi:10.1594/PANGAEA.948572.

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Haapala, Jari; +1 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC15 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 07 November 2019. The thermistor chain was 4.16 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 05 November 2019 and 07 January 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in the deformed second-year ice ridge next to RV Polarstern and remote sensing site RS1.

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    Authors: Mander, Ülo; Krasnova, Alisa; Escuer-Gatius, Jordi; Espenberg, Mikk; +10 Authors

    1 Study site and set-up The studied hemiboreal riparian forest is a 40-year old Filipendula type grey alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural agricultural land. It is situated in the Agali Village (58o17' N; 27o17' E) in eastern Estonia within the Lake Peipsi Lowland (Varep 1964). The area is characterized by a flat relief with an average elevation of 32m a.s.l., formed from the bottom of former periglacial lake systems, it is slightly inclined (1%) towards a tributary of the Kalli River. The soil is Gleyic Luvisol. The thickness of the humus layer was 15-20 cm. The content of total carbon (TC), total nitrogen (TN), nitrate (NO3- -N), ammonia NH4+-N, Ca and Mg per dry matter in 10cm topsoil was 3.8 and 0.33 %, and 2.42, 2.89, 1487 and 283 mg kg-1, respectively, which was correspondingly 6.3, 8.3, 4.4, 3.6, 2.3, and 2.0 times more than those in 20cm deep zone. The long-term average annual precipitation of the region is 650 mm, and the average temperature is 17.0 °C in July and -6.7 °C in January. The duration of the growing season is typically 175-180 days from mid-April to October (Kupper et al. 2011). The mean height of the forest stand is 17.5 m, the mean stem diameter at breast height 15.6 cm and the growing stock 245 m3 ha−1 (based on Uri et al 2014 and Becker et al 2015). In the forest floor, the following herbs dominate: Filipendula ulmaria (L.) Maxim., Aegopodium podagraria L., Cirsium oleraceum (L.) Scop., Geum rivale L., Crepis paludosa (L.) Moench,), shrubs (Rubus idaeus L., Frangula alnus L., Daphne mezereum L.) and young trees (A. incana, Prunus padus (L.)) dominate. In moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst. 2 Soil flux measurements Soil fluxes were measured using 12 automatic dynamic chambers located close to each studied tree and installed in June 2017. The chambers were made from polymethyl methacrylate (Plexiglas) covered with non-transparent plastic film. Each soil chamber (volume of 0.032 m³) covered a 0.16 m² soil surface. To avoid stratification of gas inside the chamber, air with a constant flow rate of 1.8 L min-1 was circulated within a closed loop between the chamber and gas analyzer unit during the measurements by a diaphragm pump. The air sample was taken from the top of the chamber headspace and pumped back by distributing it to each side of the chamber. For the measurements, the soil chambers were closed automatically for 9 minutes each. Flushing time of the whole system with ambient air between measurement periods was 1 minute. Thus, there were approximately 12 measurements per chamber per day. A Picarro G2508 (Picarro Inc., Santa Clara, CA, USA) gas analyzer using cavity ring-down spectroscopy (CRDS) technology was used to monitor N2O gas concentrations in the frequency of approximately 1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer. Since the 9 minutes of closing each soil chamber for measurements consisted of two minutes for stabilization the trend in the beginning and about two minutes unstable fluctuations at the end, for soil flux calculations, only 5 minutes of the linear trend of N2O concentration change has been used for soil flux calculations. After the quality checking 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period. 3 Stem flux measurements The tree stem fluxes were measured manually with frequency 1-2 times per week from September 2017 until December 2018. Twelve representative mature grey alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements (Machacova et al 2016). Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in following order: at the bottom part of the tree stem (approximately 10 cm above the soil) and at 80 and 170 cm above the ground. The rectangular shape stem chambers were made of transparent plastic containers, including removable airtight lids (Lock & Lock Co Ltd, Seoul, Republic of Korea). For chamber preparation see Schindler et al. (2020). Two chambers per profile were set randomly across 180° and interconnected with tubes into one system (total volume of 0.00119 m³) covering 0.0108 m² of stem surface. A pump (model 1410VD, 12 V; Thomas GmbH, Fürstenfeldbruck, Germany) was used to homogenize the gas concentration prior to sampling. Chamber systems remained open between each sampling campaign. During 60 measurement campaigns, four gas samples (each 25 ml) were collected from each chamber system via septum in a 60 min interval: 0/60/120/180 min sequence (sampling time between 12:00 and 16:00) and stored in pre-evacuated (0.3 bar) 12 ml coated gas-tight vials (LabCo International, Ceregidion, UK). The gas samples were analysed in the laboratory at University of Tartu within a week using gas chromatograph (GC-2014; Shimadzu, Kyoto, Japan) equipped with an electron capture detector for detection of N2O and a flame ionization detector for CH4. The gas samples were injected automatically using Loftfield autosampler (Loftfield Analytics, Göttingen, Germany). For gas-chromatographical settings see Soosaar et al. (2011). 4 Soil and stem flux calculation Fluxes were quantified on a linear approach according to change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston & Hutchison (1995). Stem fluxes were quantified on a linear approach according to change of N2O concentrations in the chamber headspace over time. A data quality control was applied based on R2 values of linear fit for CO2 measurements. When the R2 value for CO2 efflux was above 0.9, the conditions inside the chamber were applicable, and the calculations for N2O gases were also accepted in spite of their R2 values. To compare the contribution of soil and stems, the stem fluxes were upscaled to hectare of ground area based on average stem diameter, tree height, stem surface area, tree density, and stand basal area estimated for each period. A cylindric shape of tree stem was assumed. To estimate average stem emissions per tree, fitted regression curves for different periods were made between the stem emissions and height of the measurements as previously done by Schindler et al. (2020). 5 Eddy covariance instrumentation Eddy-covariance system was installed on a 21 m height scaffolding tower. Fast 3-D sonic anemometer Gill HS-50 (Gill Instruments Ltd., Lymington, Hampshire, UK) was used to obtain 3 wind components. CO2 fluxes were measured using the Li-Cor 7200 analyser (Li-Cor Inc., Lincoln, NE, USA). Air was sampled synchronously with the 30 m teflon inlet tube and analyzed by a quantum cascade laser absorption spectrometer (QCLAS) (Aerodyne Research Inc., Billerica, MA, USA) for N2O concentrations. The Aerodyne QCLAS was installed in the heated and ventilated cottage near the tower base. A high-capacity free scroll vacuum pump (Agilent, Santa Clara, CA, USA) guaranteed air flow rate 15 L min-1 between the tower and gas analyzer during the measurements. Air was filtered for dust and condense water. All measurements were done at 10Hz and the gas-analyzer reported concentrations per dry air (mixing ratios). 6 Eddy-covariance flux calculation and data quality control The fluxes of N2O were calculated using the EddyPro software (v.6.0-7.0, Li-Cor) as a covariance of the gas mixing ratio with the vertical wind component over 30-minute periods. Despiking of the raw data was performed following Mauder (2013). Anemometer tilt was corrected with the double axis rotation. Linear detrending was chosen over block averaging to minimize the influence of a possible fluctuations of a gas analyser. Time lags were detected using covariance maximisation in a given time window (5±2s was chosen based on the tube length and flow rate). While WPL-correction is typically performed for the closed-path systems, we did not apply it as water correction was already performed by the Aerodyne and the software reported mixing ratios. Both low and high frequency spectral corrections were applied using fully analytic corrections (Moncrieff et al. 1997, 2004). Calculated fluxes were filtered out in case they were coming from the half-hour averaging periods with at least one of the following criteria: more than 1000 spikes, half-hourly averaged mixing ratio out of range (300-350 ppb), quality control (QC) flags higher than 7 (Foken et al, 2004). Footprint area was estimated using Kljun et al (2015) implemented in TOVI software (Li-Cor Inc.). Footprint allocation tool was implemented to flag the non-forested areas within the 90% cumulative footprint and fluxes appointed to these areas were removed from the further analysis. Storage fluxes were estimated using point concentration measurements from the eddy system, assuming the uniform change within the air column under the tower during every 30 min period (calculated in EddyPro software). In the absence of a better estimate or profile measurements, these estimates were used to correct for storage change. Total flux values that were higher than eight times the standard deviation were additionally filtered out (following Wang et al., 2013). Overall, the quality control procedures resulted in 61% data coverage. While friction velocity (u*) threshold is used to filter eddy fluxes of CO2 (Papale et al. 2006), visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that 1-9 QC flag system already marks the times when the turbulence is not sufficient. To obtain the continuous time-series and to enable the comparison to chamber estimates over hourly time scales, gap-filling of N2O fluxes was performed using marginal distribution sampling method implemented in ReddyProcWeb online tool (https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb) (described in detail in Wutzler et al 2018). MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis. 7 Ancillary measurements Air temperature and relative humidity were measured within the canopy at 10m height using the HC2A-S3 - Standard Meteo Probe / RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). Based on these data, dew point depression was calculated to characterise chance of fog formation within the canopy. The incoming solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site (Noe et al 201587) using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated based on radiation data. Near-ground air temperature, soil temperature (Campbell Scientific Inc.) and soil water content sensors (ML3 ThetaProbe, Delta-T Devices, Burwell, Cambridge, UK) were installed directly on the ground and 0-10 cm soil depth close to the studied tree spots. During six campaigns from August to November 2017 composite topsoil samples were taken with a soil corer from a depth of 0-10 cm for physical and chemical analysis using standard methods (APHA-AWWA-WEF, 2005).

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    Authors: Salganik, Evgenii; Hoppmann, Mario; Scholz, Daniel; Haapala, Jari; +1 Authors

    Temperature and heating-induced temperature were measured along a chain of thermistors. Digital Thermistor Chain DTC12 is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the MOSAiC expedition on 05 November 2019. The thermistor chain was 4.16 m long and included sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature during the heating cycle of 20 s and after the heating cycle during the following 40 s as a function of geographic position (GPS), depth, and time between 05 November 2019 and 14 May 2020 in sample intervals of 6 hours. It also contains manually estimated positions of air-snow, snow-ice, and ice-water interfaces. The DTC was installed in the undeformed second-year ice ridge next to RV Polarstern and remote sensing site RS1.

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