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This dataset contains all data used in the article: Observational characterization of atmospheric disturbances generating meteotsunamis in the Balearic Islands Joan Villalonga*(1,2), Sebastià Monserrat (1), Damià Gomis (1,3), Gabriel Jordà*(2) (1) Departament de Física (UIB), Palma, Spain. (2) Centre Oceanogràfic de Balears, CN-Instituto Español de Oceanografía (IEO-CSIC), Palma, Spain. (3) Institut Mediterrani d’Estudis Avançats (UIB-CSIC), Esporles, Spain. Corresponding email: joan.villalonga@uib.cat There are 7 data files: Atm_pres_all: containing the atmospheric pressure time series measured in the different meteorological stations used in the work. Each station contain its name and position in coordinates. All the time series have a temporal resolution of 1 min. The data have been obtained from BalearsMeteo (http://balearsmeteo.com/) and from SOCIB (https://www.socib.es/). ciutadella_SL_AtmPres: containing the sea level and atmospheric pressure records in Ciutadella from 2018 to 2021. All the time series have a temporal resolution of 1 min. The data have been provided by PortIB (https://www.portsib.es/ca/paginas/inici). ciutadella_SL_long: containing the sea level records in Ciutadella from 2014 to 2021. All the time series have a temporal resolution of 1 min. The data have been provided by PortIB (https://www.portsib.es/ca/paginas/inici). ciutadella_spectral_data: containing the sea level and atmospheric pressure power wavelet spectra in Ciutadella from 2018 to 2021. Computed from the data in ciutadella_SL_AtmPres. corr_rissagues_1min_allfreq_12h: containing the maximum lagged correlation matrices between the atmospheric pressure time series measured at the 12h surrounding each meteotsunami event in 2021. They have been computed from the data in Atm_pres_all. sepic_index_vars: containing the five ERA5 1-hour time series of the variables used to compute the meteotsunami index as described in Sepic, et al,. 2016. wind_ciutadella: containing the time series of the wind speed and direction provided by ERA5 reanalysis over Ciutadella during the period of study. For more details, please consult the manuscript of the article

During the RU-Land_2021_Yakutia summer field campaign in August and September 2021 in the Verkhoyansk Mountain Range in Eastern Yakutia and in the Central Yakutian Lowland, multispectral drone-based images were acquired over 53 selected lakes to analyse the vegetation and shallow lake waters along shores and to record the current lake shorelines. The images were taken in the course of further investigations of the lakes during that summer expedition. Baisheva et al. (2022) gives an overview of the lakes studied and the corresponding hydrochemistry. In addition, we published datasets including water isotope data of the lake (Stieg et al. 2022) and vegetation surveys of the lakeshores (Stieg et al. 2022). The dataset with the corresponding processed lake images, the so-called orthomosaics, can be found here: https://doi.pangaea.de/10.1594/PANGAEA.956223. Here we provide the event list, which gives an overview of the relevant lake information. Due to the varying lake sizes, only sections of the shore were recorded for some lakes (see information on orthomosaic quality). Some orthomosaics contain several lakes because the lakes are small and are located close to each other. This is especially the case for the thermokarst lakes in the Central Yakutian lowland. Occasionally, there are multiple orthomosaics (indicated with _1 and _2) because either different sections of the shore have been recorded or they were acquired on different days. The lake sizes were calculated from the processed orthomosaics. For fragmented orthomosaics, additionally, Sentinel-2 satellite data was used to calculate the lake area provided in the metadata. All data were collected and processed by scientists from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Germany, the University of Potsdam, Germany, Technische Universität Berlin (TUB), Germany and the North-Eastern Federal University of Yakutsk (NEFU), Russia.

Additional file 7. List of transcripts showing significant differential splicing (adjusted p value

This dataset contains methane and nitrous oxide dissolved gas concentration, dissolved methane carbon isotope, and ancillary hydrographic data from research cruises in the North American Arctic Ocean between 2015-2018. Ocean samples for methane and nitrous oxide analysis were collected from Niskin bottles mounted on a CTD rosette. Water was collected into glass serum bottles and allowed to overflow three times before preserving with mercuric chloride and sealing with with butyl rubber stoppers and aluminum crimp seals. Gas concentrations were determined using a purge and trap system coupled to a gas chromatograph/mass spectrometer, following the method of Capelle et al. (2015). Equilibrium dry atmospheric concentrations were 328.25, 329.14, 330.11, and 330.96 ppb for N2O and 1919.64, 1933.67, 1934.92, and 1933.50 ppb for CH4 in 2015, 2016, 2017, and 2018, respectively. Equilibrium dissolved concentrations were calculated from the measured temperature and salinity following Wiesenburg and Guinasso (1979) for CH4 and Weiss and Price (1980) for N2O. Equilibrium concentrations were calculated based on sample temperature and salinity and the atmospheric N2O or CH4 concentrations measured at Barrow, Alaska by the NOAA Earth System Research Laboratory Global Monitoring Division (Dlugokencky et al., 2020a,b), with corrections to local sea level pressure and 100% humidity. Oxygen concentration was determined using an oxygen sensor mounted on the Niskin rosette, calibrated with discrete samples analyzed by Winkler titration. The mixed layer depth was defined based on a potential density difference criterion of 0.125 kg/m³ relative to the density at 5 m depth, using CTD profiles binned to 1 m. The mixed layer depth was set to 5 m as a minimum. The instantaneous gas transfer velocities and fluxes are based on the instantaneous wind speed at the time of sampling. The 30-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using up to the prior 30 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). The 60-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using the prior 60 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). Atmospheric sea level pressure was obtained from the NCEP/NCAR reanalysis product, which is provided by the NOAA-ESRL Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded). Fractional ice cover was obtained from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (https://osi-saf.eumetsat.int). Sea ice concentration product AMSR-2 (identifier OSI-408) was used in 2017–2018 and SSMIS (identifier OSI-401-b) was used in 2015–2016.

The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2019T63 (a.k.a. PRIC_0902) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Polarstern PS122 (MOSAiC) in 2019/20) as part of the project PRIC. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2019-10-07T05:00:16 and 2020-05-04T06:30:17. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.

Conductivity-temperature-depth profiles were measured using a Seabird SBE 911plus CTD during RV HEINCKE cruise HE618. The CTD was equipped with duplicate sensors for temperature (SBE3plus), conductivity (SBE4) and oxygen (SBE43). Additional sensors such as a WET Labs C-Star transmissometer, a WET Labs ECO-AFL fluorometer and an altimeter (PSA-916 Teledyne (Benthos)) were mounted to the CTD. Temperature, conductivity and oxygen sensors are calibrated by the manufacturer once a year before being mounted in January. They are used throughout the year and no post-cruise or in-situ calibration is applied. All other sensors are calibrated irregularly. Data were connected to the station book of the specific cruise as available in the DSHIP database. Processing of the data including removal of obvious outliers followed the procedures described in CTD Processing Logbook of RV HEINCKE (hdl:10013/epic.47427). The processing report for this dataset is linked below.

The data sets includes pollen records from 16 sediment cores taken at the margins of Lake Tiefer See / north-eastern Germany. The sediment cores were primarily studied to produce a lake level reconstruction for Tiefer See. Pollen analysis was applied for pollen stratigraphic dating of the sediments in comparison with a high resolution pollen record from the centre of Lake Tiefer See. Moreover, pollen analysis was also used to infer past water levels trough time at the core locations. The sediment cores for pollen analysis were taken and analysed between 2012 and 2017, either from the ice or a raft. For coring either a piston corer or a chamber corer were used. Only three of the sediment records (TS-2, TS-3, TS-17) cover the Holocene largely continuously, the others mainly only cover early- or late Holocene sections.

Details on fuzzy clustering. This file includes the data for Fig. 5. It shows the classification of TFs of A. filiformis and S. purpuratus into the four modes of expression. (XLSX 69 kb)

Additional file 1.