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748 results for “surface temperature”
Sea Surface Temperature Graphs from ERA5
<p>The sea surface temperature graphs were generated from the ERA5 reanalysis product and used in the paper: Graph-Based Deep Learning for Sea Surface Temperature Forecasts, which was accepted at the Tackling Climate Change with Machine Learning Workshop at ICLR 2023.</p>
A long-term (1981-2020) 1-km daily extreme and mean near surface air temperature product over Yellow River Basin of China
<p>The dataset includes the semless 1-km daily extreme and mean near surface air temperature products over Yellow River Basin of China. The fourth version is from 1 January 2011 to 31 Decmber 2020.</p>
Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products
<p>Data and Method described in https://www.mdpi.com/2072-4292/15/2/484, "Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products"</p> <p> </p>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2006–2021)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 2006 to 2021.</li> </ul>
Equilibrium climate sensitivity experiments using EC-Earth3-LR model — Surface Air Temperature data
<p>Three experiments was conducted using a EC-Earth model with the EC-Earth3-LR configuration (REF), which couples atmosphere, land, ocean and sea-ice components. First, we performed a pre-industrial (PI) control simulation (E280) using pre-industrial forcing, holding atmospheric constituents constant at 1850 levels (e.g., CO<sub>2</sub> concentration at 280 ppm). This simulation was initialized by a pre-run steady restart file (from a 500-year pre-industrial control simulation) and ran for 2000 years. We also conducted two sensitivity experiments (E400 and E560) by adjusting the CO<sub>2</sub> concentration to 400 ppm and 560 ppm, respectively, at the start year of the E280 experiment, and continued for over 3000 years (3069 years for E400, and 3013 years for E560). For our statistical analysis, we only considered the integration periods after the spin-up, using the last 2000-year outputs from the three simulations.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2023).</p> <p>Cao, N., Zhang, Q., Wang, Z., Power, K.E., & Liu, C. (2023). The non-negligible impact of internal multi-centennial climate variability on estimating equilibrium climate change. Submitted to <em>Geophysical Research Letters</em>.</p> <p> </p> <p><strong>Model configuration</strong><br> Time periods: 2000-year time slice for all three experiments<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for Surface Air Temperature data.</p>
Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach
<p>Daily gridded lake surface temperature (LST) data (1979-2020) for each Great Lake - Superior (GLS), Michigan (GLM), Huron (GLH), Erie (GLE) and Ontario (GLO) - derived from LSTM detailed in Kayastha et al. (2023) paper: "Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach".</p> <p>Each matfile contains longitude (lon), latitude (lat), as well as the LST for each grid point. The files also contain the variable 'art1' (Area of Node-Base Control volume) required to calculate lake-wide average LST. The depth at each location (dep) is also provided.</p>
China daily near surface average temperature
<p>The data is daily near-surface average temperatures across China from 1979 to 2017. The spatial resolution is 0.5°x0.5°. The original data source is observations from more than 2000 meteorological stations in China.</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2017)
<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 2017. Please <a href="https://zenodo.org/record/7306248"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2016 and <a href="https://zenodo.org/record/8256087"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2018.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2017</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>
The sea around China-surface and bottom temperature data
<p>The temperature data in the sea around China, including sea surface temperature and bottom temperature. </p>
ELITE land surface temperature: seamless 1km LST over China (2002)
<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 ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST.</p> <p>This is the seamless LST dataset in 2002. Please <a href="https://zenodo.org/record/8271722"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2003.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2002</li> <li>Spatial Resolution: 1 KM</li> <li>Temporal Resolution: 2 times per day</li> <li>Data Format: hdf</li> <li>Scale: 0.02</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256</li> <li>Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 </li> <li>Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 </li> </ol> <p> </p> <p>If you have any questions, please contact Prof. Jie Cheng (<a href="mailto:Jie_Cheng@bnu.edu.cn">Jie_Cheng@bnu.edu.cn</a>).</p>
Dataset for "On the sensitivity of aerosol-cloud interactions to changes in sea surface temperature in radiative-convective equilibrium"
<p>Dataset for "On the sensitivity of aerosol-cloud interactions to changes in sea surface temperature in radiative-convective equilibrium".</p> <p>All numbers followed by "K" represent the sea surface temperature, and the number that follows them represents aerosol concentration.</p> <p>Abbreviations:</p> <p>prof - profile</p> <p>Adv - Advective</p> <p>CF245 - Cloud Fraction above 245K level</p> <p> cF - cloud fraction</p> <p>cIce - cloud ice</p> <p>CSRH - Clear-sky Radiative heating rate</p> <p>cWtr - cloud water</p> <p>Lat - latent</p> <p>LW - longwave</p> <p>SW - shortwave</p> <p>TOA - top of atmosphere</p> <p>SFC - surface</p> <p>CS - clear sky</p> <p>Temp - temperature</p> <p>tW - total water</p>
Global LAke Surface water Temperature (GLAST): Global lakes are warming slower than surface air temperature due to accelerated evaporation
<p>This repository houses a dataset, known as the Global LAke Surface water Temperature (GLAST), which provides both temporal and spatial details at high resolution for 92,245 lakes worldwide during the period of 1981-2099, with 36% of them situated in Arctic regions. The dataset was established based on four decades (1982-2020) of Landsat satellite images and a physical model (FLake). For a comprehensive overview of the dataset's production methodology, please refer to the paper titled 'Global lakes are warming slower than surface air temperature due to accelerated evaporation' (Tong et al., 2023, Nature Water). Detailed information regarding each data file can be found in the 'readme.docx' file.</p>
Surface temperature pattern scenarios suggest larger rates of warming than projected [DATA]
<p>Data for global-mean temperature projections and global-mean radiative feedback projections. See PDF for file description.</p>
Data supporting 'The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss' by Neil T Lewis, William J M Seviour, Hannah E Roberts-Straw, and James A Screen.
<p>Data supporting Lewis et al., 2023. The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss. Submitted to Geophysical Research Letters.</p><p>All model output is contained within the folder data/. All data is in NetCDF format.</p><p>The folder data/PAMIP/ contains output from coupled AOGCMs that contributed piArcSIC and futArcSIC timeslice runs to PAMIP. The AOGCMS present are: HadGEM3-GC31-MM, IPSL-CM6A-LR, CESM2-WACCM6, and CESM-WACCM-SC. For each model + run, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Additional output is included in data/PAMIP/ from extended pdSIC-ext and futArcSIC-ext experiments run using CNRM-CM6-1. For each run, a file containing the autocorrelation of surface temperature (as above) is included.</p><p>The folder data/CMIP/ contains output from CMIP6 historical/SSP585 runs using three of the models listed above: HadGEM3-GC31-MM, IPSL-CM6A-LR, and CESM2-WACCM6. For each model, 'pre-industrial' and 'future' output is available. Output in these files was computed from 30-year time-periods, subsampled from the historical/SSP585 runs, selected so that the 30-year average sea-ice area matched that in the corresponding PAMIP runs above. For each model and time period, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Output is also included in data/CMIP/ from CNRM-CM6-1 'present day' and 'future' time periods, selected to match the sea-ice area in the CNRM -ext PAMIP runs. For this model, output files contain the autocorrelation of surface temperaure. </p>
Interacting effects of surface water and temperature on wild and domestic large herbivore aggregations and contact rates
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From performance curves to performance surfaces: Interactive effects of temperature and oxygen availability on aerobic and anaerobic performance in the common wall lizard
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Evidence that stress-induced changes in surface temperature serve a thermoregulatory function
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Global record-breaking recurrence rates indicates more widespread and intense surface air temperature and precipitation extremes
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Data from: The seasonal cycles of tropical sea surface temperature from Earth's axial tilt and orbital eccentricity
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Data for: Connecting hemispheric asymmetries of planetary albedo and surface temperature
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Allen Brain Atlas
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International Brain Laboratory public data
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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.