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Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2023
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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).</p> <p>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.</p> <p>The original SOCAT version 2023 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>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.</p> <p><strong>Comments</strong></p> <p>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.</p> <p>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.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>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).</p> <p><strong>Previous versions</strong></p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p>v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a></p> <p><strong>Acknowledgements</strong></p> <p>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.</p> <p>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].</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2022
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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).</p> <p>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.</p> <p>The original SOCAT version 2022 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>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.</p> <p><strong>Comments</strong></p> <p>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.</p> <p>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.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>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).</p> <p>Previous versions:</p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p><strong>Acknowledgements</strong></p> <p>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.</p> <p>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].</p>
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.
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.
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.
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.
A scrubbed subset of near-surface, soil, and air temperature data acquired across multiple locations on Teakettle Experimental Forest, 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 at the Teakettle Experimental Forest (Lat 36.967, Long -119.017, elevation 2000-2800 m, www.fs.fed.us/psw/ef/teakettle/). Temperature sensors were located at 44 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 select sites, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens (see garden schematic for details). An additional 33 sites were located across the site by way of a stratified sampling scheme which targeted low, medium, and high elevation areas, low, medium, and high radiation areas, and cold air pooling areas. In June 2012, in order to concentrate sensors in a smaller study area (ease of access and to make this more similar to other sites, 22 sites were "retired," and 7 new sites were installed, for a total of 18 during the remainder of the study. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor. using HOBO (Onset, www.onsetcomp.com) devices.
A scrubbed subset of 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.
A scrubbed subset of 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.
Summer high frequency measurements of dissolved O2 and CO2 concentrations and water temperature at the surface of 11 northern lakes
This dataset includes high frequency paired measurements of dissolved O2 and CO2 concentrations at the surface (0.5 to 2 m depth) of 11 lakes in the Northern Hemisphere. Measurements were taken every 2 hours in summer (July and August) of various years depending on lakes (between 2011 and 2014). The dataset is used to test a conceptual framework on the controls of coupling and decoupling of these two gases. In all lakes dissolved CO2 was measured with infrared analyzer coupled with a diffusion membrane and dissolved O2 with optodes. All gas measurements are paired with water temperature provided by one of the gas probe (usually from the O2 probe).
Nyack Floodplain RiverNet surface water and groundwater dissolved oxygen, conductivity, water level, and temperature Northwest Montana, USA, 2012-2020
Water dissolved oxygen, conductivity, temperature, and level from ten locations in the Nyack Floodplain of the Middle Fork of the Flathead River in Northwest Montana, USA. Measurements are made hourly for the period of 2012 to 2020. Six sensor are placed in groundwater wells and four are placed in surface water. Data up to June 26h, 2019 have been cleaned to remove bad data and flag potentially anomalous observations.
Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona
This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.
High frequency sediment surface temperature measurements at GCE-LTER study hammock PC_i_29 from 15-Aug-2008 to 24-Sep-2012
Sediment surface temperature was recorded at 5 minute intervals adjacent to 7 groundwater wells installed at the GCE-LTER study hammock PC_i_29 on Sapelo Island, Georgia, from 15-Aug-2008 to 24-Sep-2012. Temperature was measured using Onset Hobo Tidbit temperature loggers that were placed onto the surface of the sediment in shallow PVC enclosures to protect against solar radiation. Data were downloaded from the logger and processed using the manufacturer's software, then imported into MATLAB for post-processing, quality control and documention. Individual data tables were created for each location to minimize file size for analysis, but complete location and deployment information are included in each table to facilitate integration. These data were collected as part of the Georgia Coastal Ecosystems LTER Intensive Hammock Characterization project to illucidate the timing of marsh inundation, and will be used for groundwater modeling studies at two marsh hammocks on Sapelo Island, Georgia.
Soil surface temperature measurements from the GCE-LTER Seawater Addition Long-Term Experiment (SALTEx) Project
SALTEx (Seawater Addition Long-Term Experiment) is a field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. The SALTEx experiment was initiated in 2012 and consists of 31 field plots, each 2.5 m on a side. There are three treatments (Press, Pulse, and Fresh) and two types of controls (with and without sides), each consisting of six replicates. The Press treatment plots receive regular (4 times each week) additions of a mixture of seawater and fresh river water. Pulse plots receive the same mixture of seawater and river water during September and October, which is historically a time of low flow in the river when natural saltwater intrusion occurs. The Fresh treatment plots receive regular additions of fresh river water. Treatment water is added during low tide to facilitate its infiltration into the soil, and all plots are inundated by astronomical tides at high tide. We are measuring soil surface temperature in the plots as one of the response variables for the SALTEx project.
Surface temperature mapped from thermal infrared survey from UAV campaign at Niwot Ridge, 2017.
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment. Surface temperature of Niwot Ridge saddle was mapped from thermal infrared survey on June 21, July 11, 18, 25, and August 14, 2017.
Sediment temperature at three depths below sediment surface in intertidal mudflats in Virginia, 2013-2014
We logged the sediment temperature at three intertidal mudflat sites between September, 2013 and September, 2014. At each site, three submersible temperature loggers (HOBO Pendant UA-002; Onset Computer Corporation) were buried at 3 cm, 10 cm and 20 cm below the sediment surface. Temperature was recorded in ten minute intervals. Each site was unvegetated with muddy sediment. For sites 1 and 2, data was recorded in two time series. Time series 1 from 09/02/2013 - 12/16/2013 and time series 2 from 12/19/2013 - 09/18/2014. At site 2, 20 cm depth, no data was recorded after 12/16/2013. At site 3, data was recorded at all depths continuously from 09/2/2013 to 08/28/2014. Date and time was recorded as GMT offset by -5 hours (Eastern Standard Time).
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018
<p>This map shows the maximum land surface temperature for the wet season of the Greater Paramaribo Region, Suriname, 2016-2018. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite and were obtained from the United States Geological Survey (USGS). A detailed description is provided in the metadata document.</p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696837,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></p>
Dry season standardised maximum land surface temperature of the Greater Paramaribo Region 2015-2019
<p>This map shows the maximum land surface temperature for the dry season of the Greater Paramaribo Region 2015-2019. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite. See details in the metadata document.</p><p>This map was made for the Tropenbos Suriname and the University of Twente-Faculty Geo-information Science and Earth Observation (ITC) project "Naar een groen en leefbaarder Paramaribo" and must be accredited as follows: </p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Dry season standardised maximum land surface temperature of the Greater Paramaribo Region 2015-2019, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696767,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></p><p> </p>
ScienceDex guides
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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.