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748 results for “surface temperature”
NAHosMIP - monthly surface air temperature and precipitation v3
<p>This dataset is for data generated from the North Atlantic Hosing Model Intercomparison Project (NAHosMIP), which is documented in <a href="https://gmd.copernicus.org/articles/16/1975/2023/gmd-16-1975-2023.html" target="_blank" rel="noopener">Jackson et al, 2023</a>. </p> <p>Data used in that paper (including AMOC streamfunctions) can be found <a href="https://zenodo.org/records/7643437">here</a> </p> <p><strong>Experiments</strong></p> <ul> <li>picon - preindustrial control which was run as part of CMIP6 (<a href="https://gmd.copernicus.org/articles/9/1937/2016/">Eyring et al., 2016</a>),</li> <li>u03-hos - constant uniform hosing of 0.3 Sv. </li> <li>u03-r50 - experiment with no hosing initialised 50 years into u03-hos</li> <li>u03-r100 - experiment with no hosing initialised 100 years into u03-hos</li> </ul> <p><strong>Models</strong></p> <p>Eight CMIP6 models took part: CanESM5, CESM2, EC-Earth3, HadGEM3-GC3-1LL, HadGEM3-GC3-1MM, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR </p> <p><strong>Variables</strong></p> <ul> <li>tas - surface air temperature (monthly resolution)</li> <li>pr - precipitation (monthly resolution)</li> <li>evspsbl - surface evaporation (including sublimation and transpiration)</li> <li>psl - sea level pressure</li> </ul> <p><strong>File name convention</strong></p> <p>We use the CMIP file naming convention, so for example:</p> <p>tas_Amon_HadGEM3-GC31-LL_u03-r100_r1i1p1f1_gn_215001-215912.nc</p> <pre><code>$variable_$timeresolution_$model_$experiment_$version_$grid_$date.nc</code></pre> <p>Files with the same variable and model are combined in a tar file:</p> <pre><code>$variable_$timeresolution_$model_$version_$grid.tar</code><br><br><strong>AMOC timeseries<br></strong><br>These are included in the file M26.tar. This is the maximum streamfunction at 26.5N<strong><br><br>Additional precip and wind files<br><br></strong>Also included are files used by <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003959">Ben-Yami et al, 2024</a> who examined the impacts of an AMOC <br>collapse on monsoons. The files contain monthly mean (mmean) or annual mean (ymean) of <br>precipitation (prcp) and surface winds (ua and va) for the experiments <br>u03-r50 (for HadGEM3-GC31-MM, CanESM5, CESM2) or u03-r100 (for IPSL-CM6A-LR)<br><br>Files are named<br><br></pre> <pre><code>BY24_$model_$exp.tar</code></pre> <pre><br><br></pre>
Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100)
<p>A long-term (1850-2100) monthly air temperature (tas) product with a spatial resolution of 0.5 degree. This is a merged product from 31 CMIP6 models using the Deep-learning model which reduce bias, spatial downscaling and data merge at the same time,. To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain one historical data (1850-2014) , four future scenarios data during 2015-2100 (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and four future scenarios data in Australia . The dataset is stored in NetCDF format, containing the variable tas, representing air temperature, produced in centigrade (℃) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.75E to 179.75E, the latitude from -89.75N to 89.75N. </p>
Dataset containing DTS-data used in Karttunen et al. "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland"
<p>This record contains DTS-data used in the following study:</p> <p>Karttunen et al. (2021): Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland, submitted to AMTD</p> <p> </p> <p>DTS_highfreq_SMEARIII_Karttunen_et_al.zip contains continuous high frequency potential temperature profiles measured along the SMEAR III 31-metre tall mast. See more information on the data in the netCDF-file attributes and on the measurement setup in the related manuscript.</p> <p>DTS_statistics_SMEARIII_Karttunen_et_al.nc contains profiles for the turbulence temperature statistics calculated from the continuous DTS potential temperature profiles.See more information in the netCDF-file attributes and the related manuscript.</p> <p> </p>
Soil Moisture and Sea Surface Temperature Data for Wikle et al. (2022)
<p>Raw data (.nc) in NetCDF4 format, and formatted and rearranged data (.csv) in CSV format. With R Markdown document detailing the steps taken. All data obtained originally from NOAA's NCEP and NCDC data store systems. </p> <p>Data used in developing and demonstrating explainable AI models for <em>An Overview of Model Agnostic Explainability Methods for Machine Learning Applied to Environmental Data</em>, Wikle et al. (2022), for the <em>Special Issue on Environmental Data Science</em> for <em>Environmetrics. </em>See https://zenodo.org/record/6353636 for the corresponding model codebase. </p>
Plymouth sound and surroundings surface temperature and salinity from FVCOM model
<p>Output of surface temperature and salinity from a high-resolution model of the Plymouth sound and surrounding areas, monthly files covering from May - October 2016. The model setup is as for the operational model (https://www.plymouthmarineforecasts.org/) except with observed river flows and boundary forcing was bias corrected against observations from the L4 buoy timeseries (https://www.westernchannelobservatory.org.uk/). This dataset was used in calculations of coastal CO2 flux in:</p> <p>bg-2021-166<br> Title: Tidal mixing of estuarine and coastal waters in the Western English Channel is a control on spatial and temporal variability in seawater CO2<br> Author(s): Richard Peter Sims et al.</p> <p>The code and model mesh data required to create plots therein are also included in plot_data.zip</p>
Global 1-km monthly mean land surface temperature product (2003-2020)
<p>The monthly mean land surface temperature (MMLST) reflects stable intra- and inter-annual temperature variations, and has a wide range of applications in climatological and meteorological studies. We used a combination method (including a weighted average model derived from 253 flux sites and considering the influence of the count of valid observations) and MODIS instantaneous LST products (MOD11A1 and MYD11A1) to generate a global 1-km MMLST dataset for the years 2003–2020. The validation with SURFRAD stations showed a root mean square error of 1.5 K and a Bias of 0.5 K. Compared with existing MMLST product, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature, and had a better ability to retrieve spatial details of temperature variations.</p> <p> </p>
An integrated dataset of daily lake surface water temperature over Tibetan Plateau
<p>A dataset for daily surface temperature of 160 lakes over Tibetan Plateau for period from 1978 to 2017. The new dataset was developed based on combination of remote sensing (MODIS) and model (slightly modified <em>air2water </em>model).</p>
Multiproxy Reconstruction of Pliocene North Atlantic Sea Surface Temperatures and Implications for Rainfall in North Africa
<p>This dataset accompanies the publication by Wycech et al. "Multiproxy Reconstruction of Pliocene North Atlantic Sea Surface Temperatures and Implications for Rainfall in North Africa" in <em>Paleoceanography and Paleoclimatology</em>. The dataset is comprised of the raw paleo-proxy (Mg/Ca ratios and U<sup>k’</sup><sub>37</sub>) data and reconstructed sea surface temperatures (SSTs) from the early Pliocene (5 Ma) to modern. The provided data were input into the accompanying R codes, which executed principal component analysis and generated the results described in Wycech et al.</p>
Tightly linked zonal and meridional sea surface temperature gradients over the past five million years
<p>Climatologies for the climate model simulations performed by Fedorov et al., Nature Geoscience, <a href="https://www.nature.com/articles/ngeo2577">https://www.nature.com/articles/ngeo2577</a>. This table shows how the names of the simulation files provided in this dataset relate to the experiment names provided in Table S2 of Fedorov et al., (2015, Nature Geoscience). Note that experiments 1-26 are from Burls and Fedorov (2014) and published in <a href="https://doi.org/10.5281/zenodo.6762450">https://doi.org/10.5281/zenodo.6762450</a></p> <table> <tbody> <tr> <td><strong>Experiment # in Article (Table S2)</strong></td> <td><strong>Name of Files</strong></td> </tr> <tr> <td>27</td> <td> <p>abrupt2xCO2_T31_gx3v7*.nc</p> </td> </tr> <tr> <td>28</td> <td> <p>abrupt4xCO2_T31_gx3v7*.nc</p> </td> </tr> <tr> <td>29</td> <td> <p>abrupt8xCO2_T31_gx3v7*.nc</p> </td> </tr> <tr> <td>30</td> <td> <p>abrupt16xCO2_T31_gx3v7*.nc</p> </td> </tr> <tr> <td>Extended Exp 11</td> <td>40p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>Extended Exp 16</td> <td>60p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> </tbody> </table> <p>Article abstract:</p> <p>The climate of the tropics and surrounding regions is defined by pronounced zonal (east–west) and meridional (equator to mid-latitudes) gradients in sea surface temperature. These gradients control zonal and meridional atmospheric circulations, and thus the Earth’s climate. Global cooling over the past five million years, since the early Pliocene epoch, was accompanied by the gradual strengthening of these temperature gradients. Here we use records from the Atlantic and Pacific oceans, including a new alkenone palaeotemperature record from the South Pacific, to reconstruct changes in zonal and meridional sea surface temperature gradients since the Pliocene, and assess their connection using a comprehensive climate model. We find that the reconstructed zonal and meridional temperature gradients vary coherently over this time frame, showing a one-to-one relationship between their changes. In our model simulations, we systematically reduce the meridional sea surface temperature gradient by modifying the latitudinal distribution of cloud albedo or atmospheric CO<sub>2</sub> concentration. The simulated zonal temperature gradient in the equatorial Pacific adjusts proportionally. These experiments and idealized modelling indicate that the meridional temperature gradient controls upper-ocean stratification in the tropics, which in turn controls the zonal gradient along the equator, as well as heat export from the tropical oceans. We conclude that this tight linkage between the two sea surface temperature gradients posits a fundamental constraint on both past and future climates.</p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Data from: Experimental Investigation of Efficiency and Deposit Process Temperature during Multi-Layer Friction Surfacing
<p>This dataset contains the data for the publication "Experimental Investigation of Efficiency and Deposit Process Temperature during Multi-Layer Friction Surfacing"</p>
The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature [data set]
<p>This collection contains the dataset and the code which were used to find the thermal parameters’ lateral variations of the Volgo–Uralian subcraton through the Bayesian Markov Chain Monte Carlo (MCMC) statistical approach. The code originally was given in the analogous study of Antarctica's geothermal structure by Lösing et al. (2020) and it can be found in https://github.com/MareenLoesing/GHF-Antarctica-Bayesian. The main changes to the code of Lösing et al. (2020) are listed in the section 2 of the readme file.</p> <p>For an official use of the Bayesian inversion code please also cite: Lösing, M., Ebbing, J. & Szwillus, W. (2020) Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion. Front. Earth Sci., 8, 105. doi:10.3389/feart.2020.00105</p> <p>The lateral variations of the thermal parameters for the single-layer and multi-layer crust are saved in “GHF_Volgo-Uralia_Single-layer.csv” and “GHF_Volgo-Uralia_Multi-layer.csv” respectively.</p>
Dataset for Smaller_Sensitivity_of_Precipitation_to_Surface_Temperature_under_Massive_Atmosphere
<p>This file is the dataset for "Smaller Sensitivity of Precipitation to Surface Temperature under Massive Atmosphere".</p> <p>Uploaded as 5 groups (1-D radiative transfer model, GCM fixsst simulations, GCM aqua planet simulations, GCM present continent simulations, and cloud-resolving simulations), The data is time average of balanced state.</p> <p>For our article, GCM fixsst simulations are designed for group A, H and sensitivity test 1; GCM aqua planet simulations are designed for group B, C, D, and sensitivity test 2; GCM present continent simulations for group E, F, G; and RCE simulations for sensitivity test 4.</p>
Near-surface temperatures in three blockfields in Norway and Svalbard
<p>near-surface temperature measurements in three blockfields in Norway and Svalbard</p> <p>raw data to the manuscript 'Near-surface temperatures and potential for frost weathering in blockfields in Norway and Svalbard', submitted to Earth Surface Processes and Landforms</p>
CICE6 standalone sea ice surface temperature output
<p>CICE6 standalone sea ice concentration and sea ice surface temperature data for July-August and October-November 2019. JRA55-do 1.4.0 (2010-2018) and JRA55-do 1.5.0 (2019) was used for atmospheric forcing, ocean forcing was taken from an ACCESS-OM2 (1deg_jra55_iaf_omip2_cycle6) output. A wave propagation and attenuation model (Meylan et al., 2014) was implemented into CICE6 to enable wave forcing from WaveWatch III (CAWCR). </p>
The Meltwater Pulse1A Triggered an Extreme Cooling Event: Evidence From Southern China. Meltwater Pulse Cooling Event (MCE). Winter temperature data during the last deglacial of Huguangyan Maar lake, Surface water temperature and seasonal diatom assemblage data of Huguangyan and Yunlong Lake.
<p>Here we present results of The lake averaged monthly mean surface water temperature over the period from September 2013 to August 2015 from Yunlong Tianchi Lake(YL)(25°52.2′N, 99°16.8′E, altitude: 2551 m a.s.l), southwestern China. The dataset include sediment trap main diatom percentages over the period from September 2013 to August 2015 from YL. Lake water temperature profiles at different depths (1, 3, 6, 9, 11, 13, 16 m) from November 2008 to May 2009 in Huguang Maar Lake (HML)(21°9′N, 110°17′E), Southern China. AMS radiocarbon dates of plant remains and bulk sediment samples for Huguangyan Maar Lake over the last ~17 cal ka BP. The main diatom assemblage percentages (%) from 17 to 10 cal ka BP at Huguangyan Maar Lake. Diatom-based reconstruction of winter temperature (WT) from 17 to 10 cal ka BP at Huguangyan Maar Lake.</p>
Supplementary files for the article "Reconstruction of the surface temperature employing models with different complexity levels: a study for the mid-Holocene."
<p>Dear members of the scientific community,</p> <p>This dataset contains all data and scripts used to prepare the manuscript "Reconstruction of the Surface Temperature Employing Models with Different Complexity Levels: A Study for the Mid-Holocene." </p> <p>Notice that the compressed file contains folders for the mid-Holocene and pre-industrial scenarios and the climatologies of the scenarios' differences. To sum up, we provide each model output adopted in this study and their ensembles: high-complexity models (HCM), Reduced-complexity models (RCM), and All-complexity models (ACM). The statistics and plots can be generated by running the R scripts within the "R_script" folder.</p> <p><br>Best regards,</p> <p>Emerson D. Oliveira</p>
Fig. 2 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery
Fig. 2 — The four sections of the sampling site Table 1 — Landsat images features (29) Product Type Pixel size (collected) Pixel size (resampled) Thermal band Landsat 4-5 TM L1 120-meters 30-meters Band 6 Landsat 7 ETM+ L1 60-meters 30-meters Band 6 Landsat 8 OLI/TIRS L1 100-meters 30-meters Band 10/ Band11
Fig. 3 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery
Fig. 3 — SST anomalies: a) T1 Cross-section, b) T2 Cross-section, c) T3 Cross-section, and d) T4 Cross-section
Physical connection between the tropical Indian Ocean tripole and western Tibetan Plateau surface air temperature during boreal summer
<p><em>These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, omega, geopotential height, zonal and meridional winds.</em></p>
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International Brain Laboratory public data
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OpenNeuro
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