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Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).</p> <p><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Dataset for "Regional Uncertainty Analysis in the Air-Sea CO2 Flux"
<p>This repository contains processed and output data used in the "Regional Uncertainty Analysis in the Air-Sea CO2 Flux" project. </p> <ul> <li><strong>fractional-uncertanties-1x1-1993-2022.nc </strong>: fractional uncertanies calculated with FluxError</li> </ul> <p>The following is the processed data used to calculate fractional uncertanties.</p> <p><strong>Individual Datasets</strong></p> <p>Sea Surface Temperature (SST)</p> <ul> <li><strong>oisst-1x1-1993-2022.nc : </strong>NOAA SST</li> <li><strong>cobe2-1x1-1993-2022.nc :</strong> COBE2 SST </li> <li><strong>esa-1x1-1993-2022.nc : </strong>ESA SST</li> <li><strong>ostia-1x1-1993-2022.nc : </strong>OSTIA SST</li> </ul> <p>10m Wind Speed</p> <ul> <li><strong>ccmp-1x1-1993-2022.nc : </strong>CCMP 10m wind speed</li> <li><strong>jra3q-wind-1x1-1993-2022.nc : </strong>JRA wind speed</li> <li><strong>era5-wind-1x1-1993-2022.nc : </strong>ERA5 wind speed</li> </ul> <p>Sea Surface Salinity (SSS)</p> <ul> <li><strong>en4-1x1-1993-2022.nc : </strong>EN4 salinity </li> <li><strong> glorys-1x1-1993-2022.nc :</strong> GLORYS salinity <strong> </strong></li> <li><strong>oras5-1x1-1993-2022.nc :</strong> ORAS5 salinity </li> </ul> <p>Atmospheric xCO2</p> <ul> <li><strong>noaa-mbl_197901-202301_1x1.nc : </strong>atmospheric xCO2</li> </ul> <p>Ocean pCO2</p> <ul> <li><strong>pco2-1x1-1993-2022.nc : </strong>Global Carbon Budget ocean model and data product output, converted to pCO2</li> </ul> <p>Sea Level Pressure </p> <ul> <li><strong>era5-slp-1x1-1993-2022.nc : </strong>ERA5 sea level pressure</li> </ul> <p>1 Degree Ocean Mask</p> <ul> <li><strong>ocean-mask_invariant_1x1.nc : </strong>Ocean mask </li> </ul> <p><strong>Merged datasets: </strong>these datasets are larger and contain the ensemble of datasets above merged into single files</p> <ul> <li><strong>salinity-1x1-1993-2022.nc : </strong>merged salinity datasets</li> <li><strong>sst-1x1-1993-2022.nc : </strong>merged SST datasets</li> <li><strong>wind-1x1-1993-2022.nc : </strong>merged wind speed datasets</li> </ul>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Concentrating solar power (CSP) plants AI-training dataset for flux density measurements.
<p>In this dataset, the tools required for the training of a neural net in the context of flux density measurements in concentrating solar power (CSP) plants are included. An Excel file with 931 meteorological conditions and the positions of the power plant and the receiver is included, as well as 15928 pairs of images resulting from ray-tracing in Solarturm Juelich (STJ) each of these conditions with 17 different combinations of heliostats. <br> <br>This dataset is part of the WP1 of TOPCSP european project (funded by HORIZON MSCA Doctoral Network, Project number 101072537).</p>
Monthly global ocean carbonyl sulfide and carbon disulfide flux data (2000–2019)
<p>This data product reports simulated monthly global ocean–atmosphere fluxes of carbonyl sulfide (OCS) and carbon disulfide (CS2) at 0.5° × 0.5° resolution (equivalent to 55 km × 55 km at the equator) between January 2000 and December 2019.</p> <p>Data are contained in two NetCDF files:</p> <ul> <li>ocs-flux-monthly-2000-to-2019.nc: Monthly global ocean OCS fluxes, 2000–2019</li> <li>cs2-flux-monthly-2000-to-2019.nc: Monthly global ocean CS2 fluxes, 2000–2019</li> </ul> <p>Data characteristics</p> <ul> <li>Version: 1.0.1 (2025-04-07)</li> <li>Spatial coverage: global</li> <li>Spatial resolution: 0.5° longitude × 0.5° latitude</li> <li>Temporal coverage: 2000-01-15 thru 2019-12-15 (nominal timestamps fall on the 15th day of each month)</li> <li>Temporal resolution: monthly</li> </ul> <p>Related manuscript</p> <p>Sun, W., Merder, J., Zhao, G., Lennartz, S. T., & Michalak, A. M. (2025). Tropical sources dominate the ocean carbonyl sulfide budget. Under consideration in <em>Global Biogeochemical Cycles.</em></p>
MIROC4-ACTM CO2 Inversion flux (2001-2020; case s050_ux4_gvjf)
<p>Details in :</p> <p>Chandra, N., Patra, P. K., Niwa, Y., Ito, A., Iida, Y., Goto, D., Morimoto, S., Kondo, M., Takigawa, M., Hajima, T., and Watanabe, M.: Estimated regional CO<sub>2</sub> flux and uncertainty based on an ensemble of atmospheric CO<sub>2</sub> inversions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1039, in review, 2021.</p>
PIANO (Penetration and Interruption of Alpine Foehn) – flux station data set
<p>ABSTRACT</p> <p>This resource comprises meteorological and turbulence data from four flux stations operated during the PIANO (Penetration and Interruption of Alpine Foehn) field campaign. The campaign took place in and around Innsbruck, Austria, during autumn and early winter 2017. The goal of the PIANO campaign was to study south foehn events, in particular the interaction between cold air pools and foehn, the mechanisms by which foehn can break through to reach the valley floor and the processes affecting the subsequent breakdown of foehn. This dataset provides near-surface turbulence observations (including surface fluxes obtained using the eddy covariance technique), along with radiation and soil measurements, as well as meteorological information.</p> <p>DATA SET DESCRIPTION</p> <p>1. Spatial coverage and locations</p> <p>Three eddy covariance (EC) stations were operated at grassland sites during the PIANO campaign. One station (‘EC_South’) was installed in the Wipp Valley near to the village of Patsch, south of the city of Innsbruck. Two stations were installed in the Inn Valley, one to the east of Innsbruck in the region of Thaur (‘EC_East’) and one to the west of Innsbruck at Innsbruck Airport (‘EC_West’). Data from a fourth EC station at the Innsbruck Atmospheric Observatory (IAO, Karl et al. (2020)) in the centre of Innsbruck (‘EC_Centre’) was also used. Precise station co-ordinates are provided in the data files.</p> <p>Three of the stations were located on grassland surrounded by mixed agricultural fields: the two stations in the Inn Valley (EC_East, EC_West) were installed on the fairly flat valley floor, while the site in the Wipp Valley (EC_South) gently sloped downwards to the west. During the campaign the vegetation was generally short at 5-10 cm. As far as possible, sites were selected to have a clear fetch for at least a few hundred metres. All three grassland sites experienced snow cover during winter. The urban station (EC_Centre) is a long-term site installed above roof level and representative of the surrounding neighbourhood close to the city centre of Innsbruck.</p> <p>2. Temporal coverage</p> <p>The temporal coverage of the datasets for the PIANO campaign are as follows:</p> <p>• EC_West: 15 Sep 2017 - 31 Dec 2017<br> • EC_South: 08 Sep 2017 – 15 Dec 2017<br> • EC_East: 13 Oct 2017 – 15 Dec 2017<br> • EC_Centre: 1 Sep 2017 – 31 Dec 2017</p> <p>The timeseries for EC_East begins later than the other sites because electrical interference thought to be from a nearby transmitter meant there was no useable flux data for the first month. The site was relocated on 13 October 2017 (no data is included before this date). Repeated theft of the batteries at EC_East resulted in gaps for the last few days of the dataset in December 2017. Due to issues with remote data collection, data availability at EC_West is low in September 2017. The PIANO campaign took place during autumn and early winter 2017 but the EC_West station was operated for longer (until 22 May 2018 after which use of the site was no longer permitted) as it provided a useful rural comparison station for the urban measurements (Karl et al., 2020; Ward et al., submitted). Data for 1 January – 22 May 2018 are available from the first author on request. Data collection at the long-term EC_Centre/IAO site began in spring 2017 and is ongoing.</p> <p>3. Instrument details</p> <p>At EC_West a closed-path eddy covariance system (CPEC200, Campbell Scientific) provided fast response measurements of the three wind components, temperature, water vapour mixing ratio and carbon dioxide mixing ratio. At EC_East and EC_South a sonic anemometer (CSAT3B, Campbell Scientific) and krypton hygrometer (KH20, Campbell Scientific) provided fast response measurements of the three wind components, temperature and water vapour. These fast data were logged at 20 Hz (CR6, Campbell Scientific). All three stations were equipped with a four-component radiometer (CNR4, Kipp and Zonen) to provide incoming and outgoing shortwave and longwave radiation. Meteorological measurements included air temperature and humidity (Rotronic HC2A-S3, mounted in an actively ventilated radiation shield Rotronic RS12T), atmospheric pressure (Campbell CS100, mounted inside the logger box) and precipitation (ARG100 tipping bucket gauge, Campbell Scientific). Soil instruments comprised two soil heat flux plates at 0.05 m depth (HFP01, Hukseflux), two soil temperature sensors (107, Campbell Scientific) at 0.02 and 0.04 m depth and a soil probe (ACC-SEN-SDI, Acclima) providing soil moisture and soil temperature at 0.05 m depth. At each site, the fast-response anemometer and gas analyser were mounted on a tripod at around 2.5 m above ground, while the radiometer and temperature-humidity probe were slightly lower, at around 2.0 m (exact sensor heights are provided in the data files).</p> <p>At EC_Centre a closed-path eddy covariance system (CPEC200, Campbell Scientific) provided fast response measurements of the three wind components, temperature, water vapour mixing ratio and carbon dioxide mixing ratio at 10 Hz (CR3000, Campbell Scientific) measured at 42.8 m above ground level on a lattice mast installed on top of a university building. A four-component radiometer (CNR4, Kipp and Zonen) provided incoming and outgoing shortwave and longwave radiation and air temperature and humidity are also measured (Rotronic HC2A-S3, mounted in a ventilated radiation shield). Atmospheric pressure is measured by a pressure sensor mounted inside one of the electronics boxes supplied as part of the CPEC200 (EC100, Campbell Scientific). No soil or precipitation measurements were made at the urban station.</p> <p>4. Data processing</p> <p>The fast-response eddy covariance data were processed to 30-min statistics following standard procedures using EddyPro version 7.0.7 (LI-COR Biosciences, 2021). These include despiking of raw data, time-lag compensation using maximum covariance, double coordinate rotation (meaning the 30-min mean vertical wind speed is forced to zero), simple block averaging (i.e. no filtering was applied), humidity correction of sonic temperature (Schotanus et al., 1983), and spectral corrections at low frequencies (Moncrieff et al., 2004) and high frequencies (after Fratini et al. (2012) for the closed-path CPEC200 data and Moncrieff et al. (1997) for the krypton hygrometer data). Oxygen (Tanner et al., 1993; van Dijk et al., 2003) and density (Webb et al., 1980) corrections were also applied at the sites with krypton hygrometers. Automated calibration (zero and span for carbon dioxide and zero for water vapour) was performed for the CPEC instruments once per day at EC_West and twice per day at EC_Centre.</p> <p>In addition to the standard processing described above, gust speeds were calculated from the sonic data. First the instantaneous horizontal wind speed was calculated (neglecting any vertical component). A 3-s running mean of the horizontal wind speed was then obtained, and the gust speed taken as the maximum of this 3-s running mean over a 1-min averaging interval.</p> <p>The dissipation rate of turbulent kinetic energy was obtained from the fast-response measurements of the three wind components (u, v, w) as follows. First, spectra were calculated for u, v and w using evenly spaced logarithmic frequency bins. The inertial subrange was identified as the region around 1 Hz where a local linear fit to the spectral slope was within ±20% of the expected -5/3 slope. The dissipation rate was calculated for each frequency bin in the identified inertial subrange according to Kolmogorov theory (e.g. Kaimal and Finnigan, 1994), using a value of 0.55 for u and 0.73 for v and w for the Kolmogorov inertial subrange constants, and the mean value over the frequency bins was used to provide the dissipation rate for u, v, and w for each 30-min period. Further discussion can be found in Ward et al. (in prep.).</p> <p>Quality control removed data during times of power outage and instrument malfunction and data adversely affected by rainfall (all KH20 data during rainfall were removed). To exclude any potential effects of turbulence distortion, data were removed when the wind direction was within ±10° of the mounting structure. Data falling outside physically reasonable thresholds were removed, including times when the rotation angle exceeded 45°. Stationarity tests following Foken and Wichura (1996) were applied with a threshold of 100 (i.e. data were excluded when the difference between 5-min and 30-min statistics exceeded 100%).</p> <p>For the meteorological, radiation and soil data, quality control removed data during times of power outage and instrument malfunction (including when dew on the radiometer adversely affected readings).</p> <p>5. Data file structure</p> <p>Two files in netCDF format are provided containing processed and quality-controlled data:</p> <p>• PIANO_EC_MetData_QC_1min_v1-00.nc containing the meteorological, radiation and soil data for each site at 1-min resolution. This file also contains horizontal wind speed (before co-ordinate rotation), wind direction and gust speed for each site at 1-min resolution.</p> <p>• PIANO_EC_FluxData_QC_30min_v1-00.nc containing processed statistics and fluxes for each site at 30-min resolution.</p> <p>There are also quicklook plots (provided in PNG format, monthly and for the whole period) showing the data contained in these files.</p> <p>Four sets of files in ASCII format are provided containing the fast (10/20 Hz) eddy covariance data for each site for every 30-minute period. These files are timestamped with the time corresponding to the end of the period and are named:</p> <p>• PIANO_EC_FastData_SITENAME_yyyymmdd_HHMM.csv.</p> <p>These sets of files are provided as a single .zip folder for each site which is named according to the site.</p> <p>All timestamps are given in UTC (in seconds since 00:00 UTC 01 January 1970) and denote the end of the averaging period.</p> <p>The following variables can be found in the MetData file: air temperature (ta), relative humidity (rh), atmospheric pressure (pa), precipitation (prec), soil temperature (ts1, ts2, ts3), soil volumetric water content (vwc), soil heat flux from each heat flux plate (shf1, shf2), incoming shortwave radiation (swin), outgoing shortwave radiation (swout), incoming longwave radiation (lwin), outgoing longwave radiation (lwout), wind speed (wspeed, i.e. vector average horizontal wind speed before double rotation), wind direction (wdir) and gust speed (gust).</p> <p>The following variables can be found in the FluxData file: friction velocity (ustar), sensible heat flux (h), latent heat flux (le), carbon dioxide flux (fco2), stability parameter (zeta), turbulent kinetic energy (tke), wind speed (wspeed, i.e. vector average wind speed after double rotation), wind direction (wdir), unrotated vertical wind velocity (wunrot, i.e. before double rotation), the standard deviation of the wind components and temperature (sigu, sigv, sigw, sigt), and dissipation rate of turbulent kinetic energy calculated from u, v and w spectra (epu, epv, epw).</p> <p>The following variables can be found in the RawData files: unrotated lateral, longitudinal and vertical wind components (in m s-1), temperature (in degree C), water vapour concentration (supplied for EC_West and EC_Centre as the mixing ratio (in mmol m-1) and supplied for EC_South and EC_East as the absolute humidity (g m-3) and carbon dioxide mixing ratio (in μmol mol-1) for EC_West and EC_Centre. Note that the absolute value of the water vapour concentration from the krypton hygrometers should not be used. These lateral, longitudinal and vertical wind components are as measured in the co-ordinate system of the sonic anemometers and the angle of installation of the sonic needed to convert to north-south east-west co-ordinates is given in the FluxData file.</p> <p>6. Publications</p> <p>Data from these flux stations have been included in multiple publications as part of the PIANO project (Haid et al., 2020; Haid et al., 2021; Muschinski et al., 2021; Umek et al., 2021; Umek et al., submitted) as well as publications as part of a related study on turbulent exchange in complex environments (Ward et al., in prep.; Ward et al., submitted).</p> <p>7. Contact</p> <p>Contact helen.ward(at)uibk.ac.at for any questions regarding the data set.</p> <p>8. Acknowledgements</p> <p>The PIANO campaign was supported by the Austrian Science Fund (FWF) and the Weiss Science Foundation under Grant P29746-N32. Collection of this dataset was also supported by an FWF Lise Meitner project (M2244-N32) and a research stipend from Innsbruck University. Measurements at IAO are supported by the Bundesministerium für Wissenschaft, Forschung und Wirtschaft (Hochschulraum-Strukturmittel grant), the European Commission for funding ALP-AIR within FP7-PEOPLE and the FWF (P30600_NBL, P33701-N). The PIANO campaign was also supported by KIT IMK-IFU, Austro Control GmbH, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), the Hydrographic Service of Tyrol, Innsbrucker Kommunalbetriebe AG (IKB), Bergisel Betriebsgesellschaft m.b.H., Innsbrucker Nordkettenbahnen Betriebs GmbH, T-Mobile Austria GmbH, Unser Lagerhaus Warenhandelsgesellschaft, PEMA Immobilien GmbH, HTL Anichstraße, Hilton Innsbruck, TINETZ-Tiroler Netze GmbH, Land Tirol, and the communities Patsch and Völs.</p> <p>9. References</p> <p>Foken T, Wichura B (1996) Tools for quality assessment of surface-based flux measurements. Agric. For. Meteorol. 78: 83-105 doi: 10.1016/0168-1923(95)02248-1</p> <p>Fratini G, Ibrom A, Arriga N, Burba G, Papale D (2012) Relative humidity effects on water vapour fluxes measured with closed-path eddy-covariance systems with short sampling lines. Agric. For. Meteorol. 165: 53-63 doi: 10.1016/j.agrformet.2012.05.018</p> <p>Haid M, Gohm A, Umek L, Ward HC, Muschinski T, Lehner L, Rotach MW (2020) Foehn–cold pool interactions in the Inn Valley during PIANO IOP2. Q. J. R. Meteorol. Soc. 146: 1232-1263 doi: 10.1002/qj.3735</p> <p>Haid M, Gohm A, Umek L, Ward HC, Rotach MW (2021) Cold-air pool processes in the Inn Valley during foehn: A comparison of four cases during PIANO. Boundary Layer Meteorology doi: 10.1007/s10546-021-00663-9</p> <p>Kaimal JC, Finnigan JJ (1994) Atmospheric Boundary Layer Flows: Their structure and management. Oxford University Press, 289 pp.</p> <p>Karl T et al. (2020) Studying urban climate and air quality in the Alps - The Innsbruck Atmospheric Observatory. Bull. Amer. Meteorol. Soc. doi: 10.1175/BAMS-D-19-0270.1</p> <p>LI-COR Biosciences (2021) Eddy Covariance Processing Software - version 7.0.7, Available at www.licor.com/EddyPro.</p> <p>Moncrieff JB, Clement R, Finnigan JJ, Meyers T (2004) Averaging, detrending and filtering of eddy covariance time series. In: X Lee,</p> <p>Massman WJ and Law BE (Editors), Handbook of Micrometeorology: a guide for surface flux measurements.</p> <p>Moncrieff JB et al. (1997) A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide. Journal of Hydrology 188-199: 589-611</p> <p>Muschinski T, Gohm A, Haid M, Umek L, Ward HC (2021) Spatial heterogeneity of the Inn Valley Cold Air Pool during south foehn: Observations from an array of temperature. Meteorol. Z. 30: 153-168 doi: 10.1127/metz/2020/1043</p> <p>Schotanus P, Nieuwstadt FTM, Bruin HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Bound.-Layer Meteor. 26: 81-93 doi: 10.1007/bf00164332</p> <p>Tanner B, Swiatek E, Greene J (1993) Density fluctuations and use of the krypton hygrometer in surface flux measurements. Management of irrigation and drainage systems: integrated perspectives. American Society of Civil Engineers, New York, NY: 945-952</p> <p>Umek L, Gohm A, Haid M, Ward HC, Rotach MW (2021) Large eddy simulation of foehn-cold pool interactions in the Inn Valley during PIANO IOP2. Quart J Roy Meteorol Soc 147: 944-982 doi: 10.1002/qj.3954</p> <p>Umek L, Gohm A, Haid M, Ward HC, Rotach MW (submitted) Influence of grid resolution of large-eddy simulations on foehn-cold pool interaction. Quart J Roy Meteorol Soc</p> <p>van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen Sensitivity of Krypton and Lyman-α Hygrometers. J. Atmos. Ocean. Technol. 20: 143-151 doi: 10.1175/1520-0426(2003)020<0143:osokal>2.0.co;2</p> <p>Ward HC, Rotach MW, Gohm A, Graus M, Karl T, Haid M, Umek L, Muschinski T (submitted) Energy and mass exchange at an urban site in mountainous terrain – the Alpine city of Innsbruck. Atmos. Chem. Phys.</p> <p>Ward HC, Rotach MW, Graus M, Karl T, Gohm A, Umek L, Haid M (in prep.) Turbulence characteristics at an urban site in highly complex terrain.</p> <p>Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water-vapor transfer. Q. J. R. Meteorol. Soc. 106: 85-100</p> <p></p> <p></p>
Reactive nitrogen fluxes over peatland (Bourtanger Moor) and forest (Bavarian Forest National Park) using micrometeorological measurement techniques
<p>Within the framework of the research projects NITROSPHERE and FORESTFLUX, field campaigns were carried out to investigate the biosphere-atmosphere exchange of reactive nitrogen compounds. We applied novel fast-response instruments in eddy-covariance setups for continuous determination of surface ammonia (NH<sub>3</sub>) and total reactive nitrogen (<span class="math-tex">\(\Sigma\)</span>N<sub>r</sub>) fluxes using two different analytical devices. While high-frequency measurements of ammonia were measured with a quantum cascade laser absorption spectrometer (QCL), a custom-built converter called TRANC coupled to a chemiluminescence detector was used for the determination of total reactive nitrogen. High-resolution data of surface-atmosphere fluxes of reactive compounds are still scarce, but highly desired for testing and validating local inferential and larger scale models. We provide access to campaign data including concentrations, fluxes and ancillary measurements of meteorological data. Campaigns were conducted in natural (forest) and semi-natural (peatland) ecosystem types. The published datasets stress the importance of recent advancements in laser spectrometry and help improve our understanding of the temporal variability of surface-atmosphere exchange in different ecosystems, thereby providing validation opportunities for inferential models simulating the exchange of reactive nitrogen.</p>
Morpho-sedimentary outlines displayed in Figures 1, S1, and S6-S15 of the article "Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert"
<p>Morpho-sedimentary outlines of the aeolian landforms in the Lut Desert.</p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Estimating historical air-sea CO2 fluxes: Incorporating physical knowledge within a data-only approach
<p>Reconstructed surface ocean pCO2 and air-sea CO2 fluxes for 1990-2019 using the pCO2-Residual Approach (JAMES 2021MS002960, in review)</p> <p>Surface ocean pCO2 (spo2) and resulting estimates of the air-sea CO2 flux (fCO2) are included in the netcdf file at monthly temporal resolution and for 1x1 grid cell spatial resolution. SeaFlux (https://zenodo.org/record/5482547#.YlT72y-B0_U) variables are used to calculate the fluxes from surface ocean pCO2.</p>
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </p>
Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project
<p>Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript:</p> <blockquote> <p>"Harmonized, gap-filled dataset from 20 urban flux tower sites" </p> <p><a href="https://doi.org/10.5194/essd-14-5157-2022">https://doi.org/10.5194/essd-14-5157-2022</a></p> </blockquote> <p>Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata. </p> <p><strong>Data can be downloaded from the bottom of this page. </strong></p> <table> <tbody> <tr> <td> <p><strong>Sitename</strong></p> </td> <td> <p><strong>City</strong></p> </td> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>Observed period</strong></p> </td> <td> <p><strong>References</strong></p> </td> </tr> <tr> <td> <p>AU-Preston</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Aug 2003 – Nov 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>AU-SurreyHills</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Feb 2004 – Jul 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>CA-Sunset</p> </td> <td> <p>Vancouver</p> </td> <td> <p>Canada</p> </td> <td> <p>Jan 2012 – Dec 2016</p> </td> <td> <p>(Christen et al., 2011; Crawford and Christen, 2015)</p> </td> </tr> <tr> <td> <p>FI-Kumpula</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Karsisto et al., 2016)</p> </td> </tr> <tr> <td> <p>FI-Torni</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Järvi et al., 2018; Nordbo et al., 2013)</p> </td> </tr> <tr> <td> <p>FR-Capitole</p> </td> <td> <p>Toulouse</p> </td> <td> <p>France</p> </td> <td> <p>Feb 2004 – Mar 2005</p> </td> <td> <p>(Masson et al., 2008; Goret et al., 2019)</p> </td> </tr> <tr> <td> <p>GR-HECKOR</p> </td> <td> <p>Heraklion</p> </td> <td> <p>Greece</p> </td> <td> <p>Jun 2019 – Jun 2020</p> </td> <td> <p>(Stagakis et al., 2019)</p> </td> </tr> <tr> <td> <p>JP-Yoyogi</p> </td> <td> <p>Tokyo</p> </td> <td> <p>Japan</p> </td> <td> <p>Mar 2016 – Mar 2020</p> </td> <td> <p>(Hirano et al., 2015; Ishidoya et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Jungnang</p> </td> <td> <p>Seoul</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jan 2017 – Apr 2019</p> </td> <td> <p>(Jo et al., n.d.; Hong et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Ochang</p> </td> <td> <p>Ochang</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jun 2015 – Jul 2017</p> </td> <td> <p>(Hong et al., 2019, 2020)</p> </td> </tr> <tr> <td> <p>MX-Escandon</p> </td> <td> <p>Mexico City</p> </td> <td> <p>Mexico</p> </td> <td> <p>Jun 2011 – Sep 2012</p> </td> <td> <p>(Velasco et al., 2011, 2014)</p> </td> </tr> <tr> <td> <p>NL-Amsterdam</p> </td> <td> <p>Amsterdam</p> </td> <td> <p>Netherlands</p> </td> <td> <p>Jan 2019 – Oct 2020</p> </td> <td> <p>(Steeneveld et al., 2020)</p> </td> </tr> <tr> <td> <p>PL-Lipowa</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013; Pawlak et al., 2011)</p> </td> </tr> <tr> <td> <p>PL-Narutowicza</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013, 2006)</p> </td> </tr> <tr> <td> <p>SG-TelokKurau</p> </td> <td> <p>Singapore</p> </td> <td> <p>Singapore</p> </td> <td> <p>Feb 2015 – Feb 2016</p> </td> <td> <p>(Roth et al., 2017)</p> </td> </tr> <tr> <td> <p>UK-KingsCollege</p> </td> <td> <p>London</p> </td> <td> <p>UK</p> </td> <td> <p>Apr 2012 – Jan 2014</p> </td> <td> <p>(Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b)</p> </td> </tr> <tr> <td> <p>UK-Swindon</p> </td> <td> <p>Swindon</p> </td> <td> <p>UK</p> </td> <td> <p>May 2011 – Apr 2013</p> </td> <td> <p>(Ward et al., 2013)</p> </td> </tr> <tr> <td> <p>US-Baltimore</p> </td> <td> <p>Baltimore</p> </td> <td> <p>USA</p> </td> <td> <p>Jan 2002 – Jan 2007</p> </td> <td> <p>(Crawford et al., 2011)</p> </td> </tr> <tr> <td> <p>US-Minneapolis</p> </td> <td> <p>Minneapolis</p> </td> <td> <p>USA</p> </td> <td> <p>Jun 2006 – May 2009</p> </td> <td> <p>(Peters et al., 2011; Menzer and McFadden, 2017)</p> </td> </tr> <tr> <td> <p>US-WestPhoenix</p> </td> <td> <p>Phoenix</p> </td> <td> <p>USA</p> </td> <td> <p>Dec 2011 – Jan 2013</p> </td> <td> <p>(Chow, 2017; Chow et al., 2014)</p> </td> </tr> </tbody> </table> <p>For further site information and timeseries plots see <a href="https://urban-plumber.github.io/sites">https://urban-plumber.github.io/sites</a>.</p> <p>For processing code see <a href="https://github.com/matlipson/urban-plumber_pipeline">https://github.com/matlipson/urban-plumber_pipeline</a>.</p> <p><strong>Data</strong></p> <p>Two data archives are available on this page.</p> <ul> <li>The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form.</li> <li>The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file.</li> </ul> <p><strong>Full collection</strong></p> <p>The full archive includes site folders with:</p> <ul> <li><code>index.html</code>: A summary page with site characteristics and timeseries plots.</li> <li><code>SITENAME_sitedata_v1.csv</code>: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc.</li> <li><code>timeseries/</code> (following files are available as netCDF and txt) <ul> <li><code>SITENAME_raw_observations_v1</code>: site observed timeseries before project-wide quality control.</li> <li><code>SITENAME_clean_observations_v1</code>: site observed timeseries after project-wide quality control.</li> <li><code>SITENAME_metforcing_v1</code>: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation.</li> <li><code>SITENAME_era5_corrected_v1</code>: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.</li> </ul> </li> </ul> <p><strong>"Obs Only"</strong></p> <p>This archive contains duplicate data from the full collection (observations after QC):</p> <ul> <li><code>UP_all_clean_observations_UTC_v1.nc</code>: in coordinated universal time (UTC)</li> <li><code>UP_all_clean_observations_localstandardtime_v1.nc</code>: in local standard time</li> </ul> <p><strong>Site references</strong></p> <p>Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015.</p> <p>Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017.</p> <p>Chow, W. T. L., Volo, T. J., Vivoni, E. R., Jenerette, G. D., and Ruddell, B. L.: Seasonal dynamics of a suburban energy balance in Phoenix, Arizona, International Journal of Climatology, 34, 3863–3880, https://doi.org/10.1002/joc.3947, 2014.</p> <p>Christen, A., Coops, N. C., Crawford, B. R., Kellett, R., Liss, K. N., Olchovski, I., Tooke, T. R., van der Laan, M., and Voogt, J. A.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmospheric Environment, 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing the variability of urban CO2 fluxes in Melbourne, Australia, Atmospheric Environment, 41, 51–62, https://doi.org/10.1016/j.atmosenv.2006.08.030, 2007a.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia, J. Appl. Meteor. Climatol., 46, 477–493, https://doi.org/10.1175/JAM2462.1, 2007b.</p> <p>Crawford, B. and Christen, A.: Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor Appl Climatol, 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015.</p> <p>Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon dioxide fluxes measurements in a highly vegetated suburban area, Atmospheric Environment, 45, 896–905, https://doi.org/10.1016/j.atmosenv.2010.11.017, 2011.</p> <p>Fortuniak, K., Kłysik, K., and Siedlecki, M.: New measurements of the energy balance components in Łódź, in: Preprints, sixth International Conference on Urban Climate: 12-16 June, 2006, Göteborg, Sweden, Sixth International Conference On Urban Climate, Göteborg, Sweden, 64–67, 2006.</p> <p>Fortuniak, K., Pawlak, W., and Siedlecki, M.: Integral Turbulence Statistics Over a Central European City Centre, Boundary Layer Meteorology; Dordrecht, 146, 257–276, https://doi.org/10.1007/s10546-012-9762-1, 2013.</p> <p>Goret, M., Masson, V., Schoetter, R., and Moine, M.-P.: Inclusion of CO2 flux modelling in an urban canopy layer model and an evaluation over an old European city centre, Atmospheric Environment: X, 3, 100042, https://doi.org/10.1016/j.aeaoa.2019.100042, 2019.</p> <p>Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal Variation of CO2 Flux in an Urban Area of Tokyo, Sola, 11, 100–103, https://doi.org/10.2151/sola.2015-024, 2015.</p> <p>Hong, J., Lee, K., and Hong, J.-W.: Observational data of Ochang and Jungnang in Korea, 2020.</p> <p>Hong, J.-W., Hong, J., Chun, J., Lee, Y. H., Chang, L.-S., Lee, J.-B., Yi, K., Park, Y.-S., Byun, Y.-H., and Joo, S.: Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements, Carbon Balance and Management, 14, 13, https://doi.org/10.1186/s13021-019-0128-6, 2019.</p> <p>Ishidoya, S., Sugawara, H., Terao, Y., Kaneyasu, N., Aoki, N., Tsuboi, K., and Kondo, H.: O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions, Atmospheric Chemistry and Physics, 20, 5293–5308, https://doi.org/10.5194/acp-20-5293-2020, 2020.</p> <p>Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmospheric Measurement Techniques, 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018.</p> <p>Jo, S., Hong, J.-W., and Hong, J.: The observational flux measurement data of suburban and low-residential areas in Korea (in preparation), n.d.</p> <p>Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., W., O. K., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land‐surface models in the high‐latitude city Helsinki, Q.J.R. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016.</p> <p>Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Climate, 10, Part 2, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a.</p> <p>Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Climate, 10, Part 2, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b.</p> <p>Masson, V., Gomes, L., Pigeon, G., Liousse, C., Pont, V., Lagouarde, J.-P., Voogt, J., Salmond, J., Oke, T. R., Hidalgo, J., Legain, D., Garrouste, O., Lac, C., Connan, O., Briottet, X., Lachérade, S., and Tulet, P.: The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment, Meteorol Atmos Phys, 102, 135, https://doi.org/10.1007/s00703-008-0289-4, 2008.</p> <p>Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components, Atmospheric Environment, 170, 319–333, https://doi.org/10.1016/j.atmosenv.2017.09.049, 2017.</p> <p>Nordbo, A., Järvi, L., Haapanala, S., Moilanen, J., and Vesala, T.: Intra-City Variation in Urban Morphology and Turbulence Structure in Helsinki, Finland, Boundary-Layer Meteorol, 146, 469–496, https://doi.org/10.1007/s10546-012-9773-y, 2013.</p> <p>Pawlak, W., Fortuniak, K., and Siedlecki, M.: Carbon dioxide flux in the centre of Łódź, Poland—analysis of a 2-year eddy covariance measurement data set, International Journal of Climatology, 31, 232–243, https://doi.org/10.1002/joc.2247, 2011.</p> <p>Peters, E. B., Hiller, R. V., and McFadden, J. P.: Seasonal contributions of vegetation types to suburban evapotranspiration, Journal of Geophysical Research: Biogeosciences, 116, https://doi.org/10.1029/2010JG001463, 2011.</p> <p>Roth, M., Jansson, C., and Velasco, E.: Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city, Int. J. Climatol., 37, 2679–2698, https://doi.org/10.1002/joc.4873, 2017.</p> <p>Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt, R.: Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece, Atmospheric Environment, 201, 278–292, https://doi.org/10.1016/j.atmosenv.2019.01.009, 2019.</p> <p>Steeneveld, G.-J., Horst, S. van der, and Heusinkveld, B.: Observing the surface radiation and energy balance, carbon dioxide and methane fluxes over the city centre of Amsterdam, Copernicus Meetings, https://doi.org/10.5194/egusphere-egu2020-1547, 2020.</p> <p>Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Molina, L. T., and Lamb, B.: Energy balance in urban Mexico City: observation and parameterization during the MILAGRO/MCMA-2006 field campaign, Theor Appl Climatol, 103, 501–517, https://doi.org/10.1007/s00704-010-0314-7, 2011.</p> <p>Velasco, E., Roth, M., Tan, S. H., Quak, M., Nabarro, S. D. A., and Norford, L.: The role of vegetation in the CO2 flux from a tropical urban neighbourhood, Atmospheric Chemistry and Physics, 13, 10185–10202, https://doi.org/10.5194/acp-13-10185-2013, 2013.</p> <p>Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A., and Molina, L. T.: Sources and sinks of carbon dioxide in a neighborhood of Mexico City, Atmospheric Environment, 97, 226–238, https://doi.org/10.1016/j.atmosenv.2014.08.018, 2014.</p> <p>Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmospheric Chemistry and Physics, 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013.</p>
Dataset: Intercomparison of flux, gradient, and variance-based optical turbulence ($C_n^2$) parameterizations
<p>This repository contains the dataset for the manuscript</p> <p>Pierzyna, M, et al. "Intercomparison of flux, gradient, and variance-based optical turbulence (Cn2) parameterizations." <em>Applied Optics</em>, 2024. <a href="https://doi.org/10.1364/AO.519942">https://doi.org/10.1364/AO.519942</a></p> <p>The data is organized in the following structure:</p> <ul> <li>`met_cn2_*_10m.nc`: netCDF files containing Cn2 estimated from meteorological data obtained at the CESAR site<br> using the flux-based and gradient-based methods at the 10 m level.</li> <li>`wrf_cn2_*.nc`: netCDF files containing Cn2 estimated from WRF model output using the variance-based method (80m)<br> and flux, gradient, and variance-based methods (10m).</li> <li>`wrf_meteo_*.nc`: netCDF files containing a cross-section of CESAR site extracted from WRF model output. This data<br> serves as input for `wrf_cn2_*.nc` files.</li> </ul>
Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia
This repository contains the code for the publication 'Climate-based prediction of carbon fluxes from deadwood in Australia'.
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018
<p>The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements.</p> <p><strong>If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details.</strong></p> <p>The following data sets are included:</p> <p><strong>TM5-4DVar_ACOS_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together.</p> <p><strong>TM5-4DVar_RT_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_SAT</strong>: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT.</p> <p><strong>TM5-4DVar_IS_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements.</p> <p><strong>TM5-4DVar_prior_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_arideast</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_aridwest</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_humid</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region.</p> <p>All data sets have the following <strong>variables</strong>:</p> <p>MonthDate: date (YYYY-MM-DD) of the middle of the individual month</p> <p>Month: MM</p> <p>Year: YYYY</p> <p>NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month.</p> <p>NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.</p> <p> </p> <p>Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013. </p> <p>Metz, E.-M., Vardag, S.N., Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V. K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K., Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S., Deutscher, N.M., Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.</p>
Calving flux estimated from tsunami waves
<p>The following geophysical field data in July 2015 and July 2016 at Bowdoin Glacier in northwest Greenland is provided in this dataset: </p> <p>(1) Tsunami wave data: Low-pass filtered tsunami waves record in July 2015 and July 2016. The unit is meter. Local time.</p> <p>(2) UAV ortho-images and DEMs: Tiff format ortho-images and DEMs. Coordinates are in UTM19 north / WGS84.</p> <p>(3) Ice surface speed observed by GPS station near the glacier front. Coordinates are WGS 84. UTC time.</p> <p> </p>
Dataset for "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements"
<p>Data of measurements and model output of the publication "Gross primary productivity of four European ecosystems constrained by joint CO<sub>2</sub> and COS flux measurements".</p> <p>Data consists of micrometeorological data, COS and CO<sub>2</sub> flux measurements for 4 sites including filters for the fluxes.</p> <p>The sites include: a managed temperate mountain grassland in Austria (18.06.-21.08.2015), a Mediterranean savanna ecosystem in Spain(29.04.-24.05.2016)), a Temperate beach forest in Denmark(07.06.-03.07.2016) and an agricultural soy bean field in Italy(03.07.-01.08.2017).</p> <p>Version 2: param2950** are now correct (were filled with the same values for all field sites) </p> <p>For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.