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opencc-by-4.0May 2023View details →

HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)

Hyperspectral Water Leaving Reflectance spectra (2988) measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the HYPSTAR® (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in Goyens et al.- Remote Sens. 2022 - 14(21)- 5607; https://doi.org/10.3390/rs14215607. <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e., <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated from the HYPSTAR® reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: Suspended particulate matter estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>

opencc-by-4.0Jun 2023View details →

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the IFEVA site in Argentina

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the  HYPERNETS site at IFEVA in Buenos Aires  Argentina (IFAR). It is a subset of the complete data record which consists of the best quality IFAR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as: HDRF = π L / E where L is the directional upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The IFAR site is a temporary test site located in the Agronomy Faculty campus in Buenos Aires city, Argentina (34.592322°S, 58.479017°W). The venue is managed by the IFEVA (Agricultural Physiology and Ecology Research Institute) and characterized by natural pastures with different treatments distributed in 16 patches of 7mx7m. The HYPSTAR®-XR sensor has been deployed in June 2021 at the top of a 2.4 m high tripod that is pointing to one of the patches where the vegetation has no specific treatment (natural) and is cut regularly every year in February. Data is collected every 30 min between 14:00 and 18:00 hs UTC (11:00 to 15:00 local time). The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full IFAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to remove outliers and only supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely.

opencc-by-4.0Jun 2023View details →

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Wytham Woods site in the United Kingdom

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the Wytham Woods HYPERNETS site in the United Kingdom (WWUK). It is a subset of the complete data record which consists of the best quality WWUK measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as: HDRF = π L / E where L is the directional upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The WWUK site is a deciduous broadleaf forest comprised primarily of Oak, Hazel, Ash, Sycamore and Beech. It is located approximately 5 km North-West of Oxford, UK and has an extensive history of scientific research. The site follows the typical seasonal dynamics of a temperate forest with distinctive periods of leaf-off, green up and senescence across the growing season. The HYPERNETS site itself (51.777206 degrees N, 1.338494 W), is located at a height of 28 m upon a flux tower in the centre of the forest. The HYPSTAR®-XR sensor was installed in October 2021. Data are collected e very 30 minutes between 9am and 6pm local time between viewing zenith angles of 0 and 30 degrees. The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full WWUK data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, two additional screening procedures are developed to remove outliers and only supply the best quality data suitable for satellite validation. For Wytham wood, sequences are only supplied that match a typical vegetation spectrum. As such, data is only provided between April and October during the leaf-on period. Reflectances are then tested against three parameters to check that they are vegetation spectrum. Firstly, that there is a peak in the green portion of the visible wavebands (560 nm). Secondly, that a red edge is detected. Finally, the Normalized Difference Vegetation Index (NDVI) is calculated. Spectra with an NDVI of less than 0.42 are removed from the final data set. After the vegetation quality flag are applied, a sigma-clipping method is used to remove outliers. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely.

Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2023

Note: The authors recommend the use of the ESA CCI-SST version of this dataset. The Surface Ocean CO₂ Atlas (SOCAT) version 2023 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/r7xa-bt92">https://doi.org/10.25921/r7xa-bt92</a>) is a quality-controlled dataset containing 35.6 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models. Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system. Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes. This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2023 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007). For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2023 dataset. The original SOCAT version 2023 data are included in full, with four additional columns containing the reanalysed data: * T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location. * fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. * pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature. * pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file. Comments 1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns. 2. This submission contains four files contained within a single zip file: SOCATv2023with_header.tsv, SOCATv2023.nc, SOCATv2023with_header_ESACCI.tsv and SOCATv2023_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets. 3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset. How to cite these data Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013). Previous versions v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a> v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a> v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a> v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a> Acknowledgements The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].

Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2022

Note: The authors recommend the use of the ESA CCI-SST version of this dataset. The Surface Ocean CO₂ Atlas (SOCAT) version 2022 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/1h9f-nb73">https://doi.org/10.25921/1h9f-nb73</a>) is a quality-controlled dataset containing 33.7 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models. Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system. Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes. This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2022 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007). For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2022 dataset. The original SOCAT version 2022 data are included in full, with four additional columns containing the reanalysed data: * T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location. * fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. * pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature. * pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file. Comments 1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns. 2. This submission contains four files contained within a single zip file: SOCATv2022with_header.tsv, SOCATv2022.nc, SOCATv2022with_header_ESACCI.tsv and SOCATv2022_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets. 3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset. How to cite these data Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013). Previous versions: v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a> v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a> v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a> Acknowledgements The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].

opencc-by-4.0Jun 2023View details →

SeaFlux v2023: harmonised sea-air CO2 fluxes from surface pCO2 data products using a standardised approach

BE SURE TO DOWNLOAD 2023.02 See the additional notes for updates on the products.  Fluxes calculated using the standardized approach:  \(F\text{CO}_2=K_0 \cdot K_w \cdot (p\text{CO}_2^\text{sea} - p\text{CO}_2^\text{atm})\ \cdot (1 - [ice])\). We provide each of the components to this equation to reduce the potential for errors in fluxes due to methodological differences. The netCDF files contain the following data (note that only bold names have been updated in v2023):  <ul> <li>fgco2_all_winds_products: the sea-air CO2 flux for all spCO2 products (6) and kw from all wind products (5). </li> <li>fgco2_global: the globally integrated sea-air CO2 fluxes for all spCO2 products (6) and kw from all wind products (6)</li> <li>sol:  \(K_0\)  is calculated using the Weiss (1974) parameterization with EN4 salinity and OISST temperatures </li> <li>kw: \(k_w\)  is calculated for winds with each being scaled independently to a 14-C bomb flux estimate of 16.5 cm/hr using the quadratic formulation by Wanninkhof (1992). <ul> <li>CCMPv2</li> <li>ERA5</li> <li>JRA55</li> <li>NCEP1</li> <li>NCEP2</li> </ul> </li> <li>spco2_SOCOM_unfilled: \(p\text{CO}_2^\text{sea}\) downloaded from various sources contains the following products: <ul> <li>CMEMS_FFNN</li> <li>CSIR_ML6</li> <li>JENA_MLS</li> <li>JMA_MLR</li> <li>MPI_SOMFFN</li> <li>NIES_FNN</li> </ul> </li> <li>spco2_filler: scaled version of the Landschützer et al. (2020) climatology used to fill missing regions of spco2_SOCOM_unfilled</li> <li>fco2atm: \(p\text{CO}_2^\text{atm}\) is calculated from NOAA's marine boundary layer product with ERA5 mean sea level pressure corrected for pH2O. The virial coefficient is then applied to pCO2atm</li> <li>ice: \([ice]\)  is the ice fraction from the OISST product</li> <li>area_ocean: the surface area of the ocean including the fractional area of the coastal regions</li> <li>seafrac: the fraction of a pixel that is ocean</li> </ul> Units are listed in the metadata of each of the netCDF variables.

Molecular Maquettes of the Pyrite Surface Sites

The dataset provides an introductory level representation of the n(FeS2)(p) nanoparticles with n=4, n=8, n=18, and n=32 Fe ions. In addition to the paramagnetic and coupled peripheral Fe ions, the latter two compositions involve one and six, low spin, bulk Fe2+ sites as in the pyrite crystal structure. Calculations were carried out at the MN15/def2SVP level along with all models being embedded into a polarizable continuum model (SMD). The zip files contain representative files that support the XYZ Cartesian Coordinate files. They include spin density contour plots, formatted checkpoint files, the corresponding cube and the output files. Initial model structures were created using the high resolution, low temperature crystal structure An extension is being curated in order to disseminate the structure and electronic properties/features of these nano-scale models of reactive pyrite surfaces. Please stay tuned for further updates ...

Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction

This Zip file contains the cartesian coordinates of optimized stationary points of the OH(2Π) + C2H4 potential energy surface published in our article “OH(2Π) + C2H4 Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (Phys. Chem. A 2023, 127, 21, 4609–4623), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c08662">https://doi.org/10.1021/acs.jpca.2c08662</a>. All calculations have been performed with  Gaussian 09, Revision D.01. All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.

Optimized structures of the stationary points on the potential energy surface of the O(3P, 1D) + HCCCN(X1Σ+) reaction

This Zip file contains the cartesian coordinates of optimized stationary points of the O(3P, 1D) + HCCCN(X1Σ+) potential energy surface published in our article “Reactions O(3P, 1D) + HCCCN(X1Σ+) (Cyanoacetylene): Crossed-Beam and Theoretical Studies and Implications for the Chemistry of Extraterrestrial Environments” (J. Phys. Chem. A 2023, 127, 3, 685–703), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c07708">https://doi.org/10.1021/acs.jpca.2c07708</a>. All calculations have been performed with  Gaussian 09, Revision D.01. All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.

Optimized structures of the stationary points on the potential energy surface of the dissociation of the CH3OH˙+ cation

This Zip file contains the optimized stationary points structures of the potential energy surface (PES) for the dissociation of the  CH3OH˙+ cation. The PES has been published in our paper “Fragmentation of interstellar methanol by collisions with He˙+: an experimental and computational study” (Phys. Chem. Chem. Phys., 2022, 24, 22437-22452), that can be found in https://doi.org/10.1039/D2CP02458F . All calculations have been performed with Gaussian 09, Revision D.01 and the structures were optimized at ωB97X-D/aug-cc-pVTZ level of theory.

opencc-by-4.0Mar 2023View details →

Source Data for "Transport properties and doping evolution of the Fermi surface in cuprates"

Source data for the publication "Transport properties and doping evolution of the Fermi surface in cuprates", in Scientific Reports (https://doi.org/10.1038/s41598-023-39813-z) and on arxiv (https://doi.org/10.48550/arXiv.2303.05254). This dataset is organized in the following way: For every figure of the manuscript there is a separate folder, which includes the figure itself, as well as one or more additional folders for the individual panels. In those, there are one or more .csv files with the data. Some of the .csv files have two header lines, for example when the temperature and \(n_{\mathrm{H}}\) are recorded for multiple doping levels. Additional comments: <ul> <li>Figure 1 <ul> <li>The generic phase boundaries are not included.</li> <li>The precision of values of \(n_{\mathrm{loc}}\)is increased for presentation purposes</li> </ul> </li> <li>experimental doping values are typically rounded to 2 decimal points, doping errors to 3 decimal points</li> <li>estimated \(n_{\mathrm{eff}}\) are rounded to 5 decimal points</li> <li>experimental \(n_{\mathrm{H}}\) from the literature are rounded to 3 decimal points</li> <li>otherwise, if it exists, experimental values are typically rounded to the error</li> <li>temperature is always given in Kelvin</li> <li>\(C_2\)is given in \([\mathrm{TK}^{-2}]\) (i.e. Tesla Kelvin^-2)</li> <li>the unit for \(n_{\mathrm{eff}}\), \(n_{\mathrm{loc}}\), \(n_{\mathrm{H}}\) is [per CuO2 unit cell]</li> <li>the unit for the resistivity in figure 4 is described in the methods section of the article</li> </ul>

opencc-by-4.0Jul 2023View details →

Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface

Experimental Record of a Longwave-Evaporation experiment. The record to be referenced in a forthcoming scientific paper.

opencc-by-4.0Oct 2023View details →

Output data for "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023)

Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023).Output data is included for 50 nm particles containing sodium myristate (c14na) and myristic acid (myristica), mixed with NaCl (nacl) in different surfactant mass fractions. Data about the critical points is also included for particles containing sodium myristate for particle size range 50-200 nm.Plotters have been provided for the following:<ul><li>Part2_plotter_50_200_nm: Plots the critical supersaturations, diameters, and the relative change in cloud droplet concentrations for dry particles with 50-200 nm diameters containing c14na</li><li>Part2_plotter_50nm: Plots the Köhler curves, surface tension and partitioning factors for 50 nm particles containing c14na</li><li>Part2_plotter_50nm_myristica: Plots the Köhler curves and surface tensions for 50 nm particles containing myristica and also plots c14na for comparison (separate output files for the compounds and c14na data here is different than for the Part2_plotter_50nm plotter)</li></ul>Each plotter needs the user to set the location where the output files are stored. In addition, a function is included:<ul><li>relative_change_in_cloud_droplet_number_conc: This function is called in "Part2_plotter_50_200_nm" and calculates the relative change in cloud droplet number concentration from the critical supersaturations.</li></ul>

openCC (other)Aug 2022View details →

Marcell Experimental Forest chemistry of surface water draining the S6 catchment, 1986 - ongoing

This data set is a record since 1986 of chemistry for surface water draining the S6 catchment at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. Unfiltered water is usually collected every one or two weeks as part of the long-term monitoring program of the S6 catchment. Some samples were collected more often for various other studies and are included in this data set. Samples are routinely measured for pH, specific conductivity, anions (chloride, sulfate), cations (calcium, magnesium, potassium, sodium, aluminum, iron, manganese, strontium), silicon, nutrients (ammonium, nitrate+nitrite, soluble reactive phosphorus, total nitrogen, total phosphorus), and total organic carbon. Occasionally, stable water isotopes as well as concentrations of dissolved organic carbon (DOC), bacterial respiration of dissolved organic matter, biodegradable DOC (BDOC), ferrous and ferric iron, total mercury (filtered or unfiltered), and methylmercury (filtered or unfiltered) were measured. Ultraviolet (UV) absorbance, a measure of water color or dissolved organic matter optical properties, was also measured for some samples. More solutes and values will be added as additional metadata are documented (pre-1986 to 1992), water samples are collected and analyzed (concentrations and isotopes), or archived water samples are analyzed for stable water isotopes. The MEF is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC0Mar 2024View details →

GOES-R Land Surface Products at AmeriFlux and NEON Eddy Covariance Tower Locations

The terrestrial carbon cycle varies dynamically over short periods that can be difficult to observe. Geostationary (“weather”) satellites like the Geostationary Operational Environmental Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence, and its Advanced Baseline Imager (ABI) measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature, and downwelling shortwave radiation (DSR) at 318 ABI fixed grid pixels containing eddy covariance towers for years 2020-2022. We demonstrate how to best combine satellite and in-situ datasets, and show how ABI attributes useful for carbon cycle science vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.

openCC (other)Feb 2018View details →

Near-surface, soil, and air temperature data acquired across multiple locations in the foothills of the Tehachapi mountains at Tejon Ranch, California, 2011-2017

These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies in the foothills of the Tehachapi mountains at Tejon Ranch (Lat 34.983, Long -118.716, elevation 750-930 m, www.tejonranch.com). Temperature sensors were located at 23 sites across the Tehachapi foothills. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges and valleys. To characterize surface temperature variation within a site, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens. An additional 18 sensors were placed along three transects over the landscape running N-S. They were placed strategically to sample topographic inflection points (hill tops and valley bottoms) as well as north and south facing slopes. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor. using HOBO (Onset, www.onsetcomp.com) devices.

openCC (other)Feb 2018View details →

Near-surface, soil, and air temperature data acquired across multiple locations in the Tehachapi mountains at Tejon Ranch, California, 2011-2017

These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies in the Tehachapi mountains at Tejon Ranch (Lat 34.967, Long -118.583, elevation 1600-1700 m, www.tejonranch.com). Temperature sensors were located at 23 sites across the Tehachapi foothills. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges and valleys. To characterize surface temperature variation within a site, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens. An additional 18 sensors were placed along three transects over the landscape running N-S. They were placed strategically to sample topographic inflection points (hill tops and valley bottoms) as well as north and south facing slopes. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor. using HOBO (Onset, www.onsetcomp.com) devices.

openCC (other)Apr 2018View details →

A scrubbed subset of near-surface, soil, and air temperature data acquired across multiple locations on the San Joaquin Experimental Range, California, 2012-2017

These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies at the San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Temperature sensors were located at 23 sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. To characterize surface temperature variation within a site, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens. An additional 18 sensors were placed along three transects over the landscape running E-W. They were placed strategically to sample topographic inflection points (hill tops and valley bottoms) as well as north and south facing slopes. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor, using HOBO (Onset, www.onsetcomp.com) devices.

openCC (other)Oct 2023View details →

Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]

The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable. The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.