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ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2018)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2018. Please <a href="https://zenodo.org/record/8266456"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2017 and <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2019)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2019. Please <a href="https://zenodo.org/record/8256087"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2018 and <a href="https://zenodo.org/record/8264798"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
Iberian Summer Surface Temperature and Fluxes for Energy Balance
<p>This dataset holds selected postprocessed files for surface temperature and fluxes involved in the surface energy balance.</p><p>Four WRF experiments nested in ERA-Interim were prepared. The first one (N) was configured as in standard numerical downscaling experiments using the Noah LSM. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 hours. The third and the fourth ones (S and C) are similar to N and D but use a diffusive soil scheme instead of NOAH LSM. The experiments covered the period 2010-2014 after a year of spin-up (2019). </p><p>The following 3-hourly files are included:</p><ul><li>Tsoil: soil temperature for the first 2 top levels of the surface.</li><li>T2: 2 metre temperature. </li><li>Latent: Latent heat flux.</li><li>Sensible: Sensible heat flux.</li><li>NetSW: net short-wave radiation flux at the surface.</li><li>NetLW: net long-wave radiation flux at the surface.</li><li>GRDFLX: ground flux toward lower layers of the soil.</li></ul><p>The <i>N, D, C or S </i>characters in the file names indicate whether the files come from the WRF N, D, C or S experiments. The files include a table including the 3-hourly data for each grid point over the Iberian Peninsula: year | month | day | hour | V1 | ... | V2058 </p><p>The 2058 grid points included in each file are listed in the same order as in the file WRFmask_points_withoutUrban_withLandType.dat </p><p> </p><p> </p>
Rising surface temperatures lead to more frequent and longer burrow retreats in males of the fiddler crab, Minuca pugnax
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Biodiversity facets, canopy structure and surface temperature of grassland communities
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GF4ACE -- Data from: Reanalysis-based global radiative response to sea surface temperature patterns: Evaluating the Ai2 climate emulator
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Surface measurement data of polished LTCC: Characterization of pores in polished low temperature co-fired glass-ceramic composites for optimization of their micromachining
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Drivers and projections of global surface temperature anomalies at the local scale
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Talus Subsurface and Surface Temperature Data from Southwestern Montana, USA, 2010-2020
Between 2010 and 2020, temperature data loggers (HOBO, Onset Computer Corporation) were deployed in rocky ‘talus’ within territories of the American pika (Ochotona princeps) near active pika haypiles (food caches) at five sites within Gallatin Canyon and one site in the Crazy Mountains in the Custer-Gallatin National Forest near Bozeman, Montana. Data loggers were placed between 20 and 40 cm beneath the surface of the talus to measure subsurface temperatures. Four additional data loggers were paired with four of the subsurface data loggers at the Gallatin Canyon sites to collect ambient temperatures. Ambient temperature data loggers were placed near the subsurface data loggers, approximately two meters above the ground, on the trunk of a tree and in a plastic case to shade the data logger from direct sunlight. Data from ambient temperature loggers derive from files containing ‘AMB’ in the file name.
Talus Surface & Subsurface Temperature Data from Oregon & Colorado, USA, 2011-2019
Between 2011 and 2019, temperature data loggers were buried in rocky talus patches (hereafter “sites”) potentially occupied by American pikas (Ochotona princeps). Data collection spanned three ecoregions: Grand Mesa, Colorado (GRME), Mt. Hood, Oregon (MTHO), and the Columbia River Gorge, Oregon (CRGO). Sensors were placed either near the surface of the talus (shaded from sunlight) or in the interstices (75-80 cm deep) and programmed to record the temperature every 2-hours. At some sites when noted, surface sensors were placed directly above a paired interstitial sensor at the same GPS coordinate, and sensors were replaced each year. MTHO sites were located near or within the burn scar from the 2011 Dollar Lake Fire and spanned a range of burn severities (Varner et al. 2015, dx.doi.org/10.1071/WF15050). All CRGO sites except Mosier were within the range of pikas in this ecoregion and complement or extend previous analyses of talus microclimates in this ecoregion (Varner & Dearing 2014, dx.doi.org/10.1371/journal.pone.0104648; those data are available at https://collections.lib.utah.edu/ark:/87278/s63b984d).
GLASS Land Surface Temperature product (1981-2000): instantaneous LST
<ul> <li>GLASS Land Surface Temperature product (1981-2000): instantaneous LST have been produced from historical NOAA AVHRR data. The LST data were generated by integrating several Split-Window Algorithms with the Random Forest method (RF-SWA). The individual SWAs and the RF-SWA were trained and tested on simulation datasets obtained from globally representative atmospheric profiles. The validation against <em>in-situ</em> LST shows that the GLASS LST product has a precision of 1.18 K.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
Data and GrADS scripts for "Effect of atmospheric circulation on surface air temperature trends in years 1979-2018" (forthcoming in Climate Dynamics)
<p>Data and GrADS scripts associated with "Effect of atmospheric circulation on surface air temperature trends in years 1979-2018", forthcoming in Climate Dynamics.</p> <p>The README file, the scripts and the GrADS data descriptor files are in the file "circulation.zip". Unpacking this with "unzip circulation.zip" creates the directory "circulation" together with the individual files.</p> <p>The three netcdf data files (T_anomalies_ERA5_1979-2018.nc, T_anomalies_circ_1979-2018.nc and T_trends_CMIP5_42mod_1979-2018.nc) must be downloaded to the same "circulation" directory for the GrADS scripts to work.</p> <p>See the README file within "circulation.zip" for further information.</p> <p> </p>
An All-sky 1 km Daily Surface Air Temperature Product over Mainland China
<ul> <li>An all-sky daily mean surface air temperature (T<sub>a</sub>) product at 1 km spatial resolution over mainland China for 2003–2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three T<sub>a</sub> estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The validation results showed that R<sup>2</sup> and root mean square error (RMSE) values of the three models ranged from 0.984 to 0.986 and 1.342 K to 1.440 K, respectively, indicating that this high-resolution product has satisfactory accuracy.</li> <li>The format of the data is tif and a sample data is provided in example.zip. The dataset is stored by year, with 6 *.zip files per year.</li> <li>There is also an all-sky 0.01° daily surface air temperature product over Beijing for 2003–2019 (http://doi.org/10.5281/zenodo.4405123), which is a sub-dataset generated from this dataset for easy and convenient understanding of this dataset. And the sub-dataset has a data volume of only 264MB after compressed.</li> </ul>
Evidence that stress-induced changes in surface temperature serve a thermoregulatory function
<p>Changes in body temperature following exposure to stressors have been documented for nearly two millennia, however, the functional value of this phenomenon is poorly understood. We tested two competing hypotheses to explain stress-induced changes in temperature, with respect to surface tissues. Under the first hypothesis, changes in surface temperature are a consequence of vasoconstriction that occurs to attenuate blood-loss in the event of injury and serves no functional purpose <em>per se</em>; defined as the Haemoprotective Hypothesis. Under the second hypothesis, changes in surface temperature reduce thermoregulatory burdens experienced during activation of a stress response, and thus hold a direct functional value; here, the Thermoprotective Hypothesis. To understand whether stress-induced changes in surface temperature have functional consequences, we tested predictions of the Haemoprotective and Thermoprotective hypotheses by exposing Black-capped Chickadees (n=20) to rotating stressors across an ecologically relevant ambient temperature gradient, while non-invasively monitoring surface temperature (eye region temperature) using infrared thermography. Our results show that individuals exposed to rotating stressors reduce surface temperature and dry heat loss at low ambient temperature and increase surface temperature and dry heat loss at high ambient temperature, when compared to controls. These results support the Thermoprotective Hypothesis and suggest that changes in surface temperature following stress exposure have functional consequences and are consistent with an adaptation. Such findings emphasize the importance of the thermal environment in shaping physiological responses to stressors in vertebrates, and in doing so, raise questions about their suitability within the context of a changing climate.</p>
High-resolution air temperature observations near the surface using fiber-optic distributed temperature sensing
<p>Time-lapse animation of air temperature observations near the surface, highlighting wave-like motion in opposite direction of the mean wind. </p> <p> </p>
Data supplementing the article: Schultz, N.M., Lawrence P.J, Lee X., Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research - Biogeosciences
<p>These data supplement the article: Schultz, N.M., Lawrence P.J, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation, under review at the Journal of Geophysical Research - Biogeosciences.</p> <p>contact: Natalie M. Schultz, natalie.schultz@yale.edu</p> <p>Below are descriptions of the data files included here:</p> <p><br> (1) Global LST data: globalLST_Forest_Open.YYYY.nc [2003-2013]</p> <p>- DayLST1/NightLST1 and DayLST2/NightLST2 are the final (after the DEM correction) LST values for forest, and open land cover classes, respectively.<br> - Count variables show the number of pixels of each land cover class in each 0.5 degree grid<br> - The average elevation of each class is given by the DEM1 and DEM2 vars<br> - The DEM correction is the dLSTdDEM vars</p> <p>(2) Global fluxes data: globalFluxes_Forest_Open.YYYY.nc [2003-2013]<br> - Again, forest class = var1, open class = var2<br> - SWRABS is absorbed solar radiation<br> - LE is the latent heat flux<br> - HP is the heating potential term, as defined in the manuscript<br> - As described for the LST data, class pixel counts and DEM data are included</p> <p>(3) climzones3.nc<br> - The delineation of the three climate zones defined in this paper</p> <p>(4) MERRA inversion data: MERRA_11yr_TS_T10M.mat [2003-2013]<br> - 11 years of daily 1am local data averaged over 8-day intervals for 2003-2013<br> - TS = surface temperature<br> - T10M = 10M air temperature (above d)</p> <p> </p> <p> </p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2024
<p><strong>Changelog:</strong></p> <p>v2: Updates the recalculation process to using daily ESA CCI-SSTv3 and updates the temperature sensivity to those in Humphreys (2024). OISST version of the recalculated SOCAT data are no longer produced.</p> <p>v1.1: Corrects an error in the ESA CCI-SSTv3 tsv file having all NaN's for the reanalysis temperature and fCO2(sw). No other files were affected.</p> <p>v1: Initial Release</p> <p> </p> <p><strong>Description</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2024 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/9wpn-th28">https://doi.org/10.25921/648f-fv35</a>) is a quality-controlled dataset containing 38.6 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; (Dong et al., 2022, 2024; Ford et al., 2024; 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 recalculation 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 recalculation methodology is described in detail in Goddijn-Murphy et al. (2015). The recalculation 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 recalculation of the fugacity of CO₂ (fCO₂<sub> (sw)</sub>) from the SOCAT version 2024 dataset to a consistent sub-skin temperature field. The recalculation 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). The recalculated SOCAT dataset was produced with a climate quality and depth consistent (0.2 m) temperature dataset, the European Space Agency (ESA) Climate Change Initiative sea surface temperature (CCI-SST) product V3 (Embury et al., 2024; Good & Embury, 2024)</p> <p>Following the recommendations in Dong et al (2022), the CCI-SST was bias corrected with respect to SST drifters (~0.2m) to remove a cool bias (0.04K) identified in the CCI-SST validation report (Embury, 2023) and a climate data record intercomparison (Atkinson et al., 2023). This bias correction was applied as a globally fixed value. The daily CCI-SST data (0.05 º; ~5km at the equator) were linearly interpolated to the SOCAT observations, providing both an SST and SST uncertainty values representative for each individual SOCAT observation. The SOCAT fCO<sub>2 (sw)</sub> was then recalculated to the CCI-SST temperature using the updated temperature sensitivities described in Humphreys (2024).</p> <p>We have applied the recalculation process to both the main SOCAT dataset (data flags A,B,C,D).</p> <p><strong> </strong></p> <p><strong>Data records</strong></p> <p>The resulting reanalysed data are provided as a tab-separated value file (individual cruise 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 2024 dataset.</p> <p>The original SOCAT version 2024 data are included in full, with five additional columns containing the reanalysed data:</p> <p>* T_subskin - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* T_subskin_uncertainty - The uncertainty (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_subskin.</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_subskin.</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). As a consistency check to confirm the gridding method and precision, values within the gridded dataset are cross-checked against the original SOCAT gridded dataset. The unweighted SOCAT fCO2 (sw) showed a mean absolute difference of 0.02 μatm and for the cruise weighted fCO2 (sw) a difference of 0.21 μatm (N = 370920). Within the unweighted data, ~1200 monthly 1 degree regions (~0.3 %) have a difference greater than ±1 μatm, which occur in locations where SOCAT fCO<sub>2 (sw)</sub> observations could not be matched to the satellite reference data and therefore were not included in the gridding. The cruise-weighted data has ~10,000 of the monthly 1 degree regions (~2 %) with a difference greater than ±1 μatm, which occur in the same regions as the unweighted data.</p> <p>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). In addition the full satellite SST fields at monthly 1 degree resolution are included within the netCDF file (‘sst_subskin_full’), so that the SST data can be used in any further processing (e.g. used with fCO<sub>2 (sw)</sub><sup> </sup>interpolation approaches). Full meta data are included within the file.</p> <p><strong> </strong></p> <p><strong>Quick-start Guide</strong></p> <p><strong>Individual cruise data (.tsv files)</strong></p> <p>The individual cruise data files include the original SOCAT data as well as recalculated fCO<sub>2 (sw)</sub> (‘fCO2_reanalysed [uatm]’ column) with their paired temperatures (‘T_subskin [C]’ column). The original SOCAT data columns for fCO<sub>2 (sw)</sub> (‘fCO2_rec [uatm]’) and SST (‘SST [deg C]’) can be replaced with the recalculated columns as a quick start.</p> <p> </p> <p><strong>Gridded cruise data (monthly 1 degree; .nc files)</strong></p> <p>The gridded cruise data files are the original SOCAT netcdf files (unweighted and cruise weighted), that have been appended with the recalculated values.</p> <p>If users use the unweighted SOCAT fCO<sub>2</sub><sub> </sub><sub>(sw)</sub> (‘fco2_ave_unwtd’) with its paired temperature (‘sst_ave_unwtd’). These variables can be replaced with the recalculated fCO<sub>2 (sw)</sub> (‘fco2_reanalysed_ave_unwtd’) and the paired temperature (‘sst_subskin_unweighted’).</p> <p>If users use the cruise-weighted SOCAT fCO<sub>2</sub><sub> </sub><sub>(sw)</sub> (‘fco2_ave_weighted’) with its paired temperature (‘sst_ave_weighted’). These variables can be replaced with the recalculated fCO<sub>2 (sw)</sub> (‘fco2_reanalysed_ave_weighted’) and the paired temperature (‘sst_subskin_weighted’).</p> <p><strong> </strong></p> <p><strong>Additional information</strong></p> <p>1. Due to the temporal range of the ESA CCI-SST the recalculated values are only available from 1980 onwards.</p> <p>2. This submission contains two files contained within a single zip file: Fordetal_SOCATv2024_ESACCIv3_biascorrected_Humpherys_daily_v2.nc</p> <p>Fordetal_SOCATv2024_ESACCIv3_biascorrected_Humpherys_daily_unc_withheader_v2.tsv</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p> </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> </strong></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>v2023: <a href="https://doi.org/10.5281/zenodo.8229316">https://doi.org/10.5281/zenodo.8229316</a></p> <p> </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>), the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>) and the European Space Agency Ocean Carbon 4 Climate project (OC4C; 3-18399/24/I-NB). This work was funded by the European Union under grant agreement no. 101083922 (OceanICU) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p> </p> <p><strong>References</strong></p> <p>Atkinson, C., Rayner, N., Kennedy, J., Sikorski, T., Bonino, G., Quilestino-Olario, R., et al. (2023, November 30). ESA CCI Phase 3 Sea Surface Temperature (SST): Climate Assessment Report D5.1 v1.1. Retrieved July 18, 2024, from https://climate.esa.int/documents/2370/SST_CCI_D5.1_CAR_v1.1-signed.pdf</p> <p>Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O’Brien, K. M., Olsen, A., et al. (2016). A multi-decade record of high-quality fCO<sub>2</sub> data in version 3 of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT). <em>Earth System Science Data</em>, <em>8</em>(2), 383–413. https://doi.org/10.5194/essd-8-383-2016</p> <p>Dong, Y., Bakker, D. C. E., Bell, T. G., Huang, B., Landschützer, P., Liss, P. S., & Yang, M. (2022). Update on the Temperature Corrections of Global Air‐Sea CO<sub>2</sub> Flux Estimates. <em>Global Biogeochemical Cycles</em>, <em>36</em>(9). https://doi.org/10.1029/2022GB007360</p> <p>Dong, Y., Bakker, D. C. E., Bell, T. G., Yang, M., Landschützer, P., Hauck, J., et al. (2024). Direct observational evidence of strong CO<sub>2</sub> uptake in the Southern Ocean. <em>Science Advances</em>, <em>10</em>(30), eadn5781. https://doi.org/10.1126/sciadv.adn5781</p> <p>Embury, O. (2023). SST CCI Product Validation and Intercomparison Report. https://climate.esa.int/documents/2369/SST_CCI_D4.1_PVIR_v2.1-signed.pdf</p> <p>Embury, O., Merchant, C. J., Good, S. A., Rayner, N. A., Høyer, J. L., Atkinson, C., et al. (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. <em>Scientific Data</em>, <em>11</em>(1), 326. https://doi.org/10.1038/s41597-024-03147-w</p> <p>Ford, D. J., Shutler, J. D., Blanco-Sacristán, J., Corrigan, S., Bell, T. G., Yang, M., et al. (2024). Enhanced ocean CO<sub>2</sub> uptake due to near-surface temperature gradients. <em>Nature Geoscience</em>. https://doi.org/10.1038/s41561-024-01570-7</p> <p>Goddijn-Murphy, L. M., Woolf, D. K., Land, P. E., Shutler, J. D., & Donlon, C. (2015). The OceanFlux Greenhouse Gases methodology for deriving a sea surface climatology of CO<sub>2</sub> fugacity in support of air-sea gas flux studies. <em>Ocean Science</em>, <em>11</em>(4), 519–541. https://doi.org/10.5194/os-11-519-2015</p> <p>Good, S. A., & Embury, O. (2024). ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis product, version 3.0 [Application/xml]. NERC EDS Centre for Environmental Data Analysis. https://doi.org/10.5285/4A9654136A7148E39B7FEB56F8BB02D2</p> <p>Holding, T., Ashton, I. G., Shutler, J. D., Land, P. E., Nightingale, P. D., Rees, A. P., et al. (2019). The FluxEngine air–sea gas flux toolbox: simplified interface and extensions for in situ analyses and multiple sparingly soluble gases. <em>Ocean Science</em>, <em>15</em>(6), 1707–1728. https://doi.org/10.5194/os-15-1707-2019</p> <p>Humphreys, M. P. (2024). Temperature effect on seawater <em>f</em> CO<sub>2</sub> revisited: theoretical basis, uncertainty analysis and implications for parameterising carbonic acid equilibrium constants. <em>Ocean Science</em>, <em>20</em>(5), 1325–1350. https://doi.org/10.5194/os-20-1325-2024</p> <p>Sabine, C. L., Hankin, S., Koyuk, H., Bakker, D. C. E., Pfeil, B., Olsen, A., et al. (2013). Surface Ocean CO<sub>2</sub> Atlas (SOCAT) gridded data products. <em>Earth System Science Data</em>, <em>5</em>(1), 145–153. https://doi.org/10.5194/essd-5-145-2013</p> <p>Shutler, J. D., Land, P. E., Piolle, J. F., Woolf, D. K., Goddijn-Murphy, L., Paul, F., et al. (2016). FluxEngine: A flexible processing system for calculating atmosphere-ocean carbon dioxide gas fluxes and climatologies. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>33</em>(4), 741–756. https://doi.org/10.1175/JTECH-D-14-00204.1</p> <p>Watson, A. J., Schuster, U., Shutler, J. D., Holding, T., Ashton, I. G. C., Landschützer, P., et al. (2020). Revised estimates of ocean-atmosphere CO<sub>2</sub> flux are consistent with ocean carbon inventory. <em>Nature Communications</em>, <em>11</em>(1), 1–6. https://doi.org/10.1038/s41467-020-18203-3</p> <p>Woolf, D. K., Land, P. E., Shutler, J. D., Goddijn-Murphy, L. M., & Donlon, C. J. (2016). On the calculation of air-sea fluxes of CO<sub>2</sub> in the presence of temperature and salinity gradients. <em>Journal of Geophysical Research: Oceans</em>, <em>121</em>(2), 1229–1248. https://doi.org/10.1002/2015JC011427</p> <p> </p>
Merged Hadley-OI sea surface temperature and sea ice concentration data set
<p>The merged Hadley-OI sea surface temperature (SST) and sea ice concentration (SIC) data sets were specifically developed as surface forcing data sets for AMIP style uncoupled simulations of the Community Atmosphere Model (CAM). The Hadley Centre's SST/SIC version 1.1 (HADISST1), which is derived gridded, bias-adjusted in situ observations, were merged with the NOAA-Optimal Interpolation (version 2; OI.v2) analyses. The HADISST1 spanned 1870 onward but the OI.v2, which started in November 1981, better resolved features such as the Gulf Stream and Kuroshio Current which are important components of the climate system. Since the two data sets used different development methods, anomalies from a base period were used to create a more homogeneous record. Also, additional adjustments were made to the SIC data set.</p>
Data from: Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity
<p>The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR) – key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO<sub>2</sub> forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades, and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.</p>
Response to Sea Surface Temperature and Primary Productivity to change in Earth's Orbit Eccentricity - Simulations
<p>This dataset contains ocean and ocean biogeochemistry outputs from modeling experiments with present-day geography and various Earth's orbit confiurations. The set of simulation targets the role of Eccentricity on the tropical ocean sea surface temperature and primary productivity (Beaufort & Sarr, 2024) . The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed formulti-millennial climate simulations, GMD) and offline version of PISCESv2 model (Aumont et al., 2015 - PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, GMD). It includes 4 simulations. Data are monthly averages over the last 100 years of the simulations.</p> <p>Complementary outputs (4 simulations) can be found at https://www.seanoe.org/data/00728/84031/ (Beaufort et al., 2022)</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
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International Brain Laboratory public data
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OpenNeuro
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