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748 results for “surface temperature”
Datasets and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China"
<p>We have provided the data and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China" for reference and further reading. These data can be used to replicate the analyses presented in the paper. If you wish to use the data for other purposes, please contact the authors for permission. Thank you.</p>
Using RTE method to invert the surface temperature of Xi'an in four seasons
Open the record for dataset details and reuse information.
Widespread changes in surface temperature persistence under climate change
<p>Datasets of all data used to create each individual figure file in our manuscript.</p>
Data for "Joint Dependence of Longwave Feedback on Surface Temperature and Relative Humidity"
<p>The datasets provided here are for the paper, "Joint Dependence of Longwave Feedback on Surface Temperature and Relative Humidity", by Brett A. McKim, Nadir Jeevanjee, and Geoffrey K. Vallis</p> <p>The datasets provided here includes the longwave clear-sky feedback and OLR used in Figures 1, 3, 4, 5. The datasets also includes the transmission to space as a function of wave number used in Figure 2.</p> <p>These datasets are the output of calculations from PyRads, a python-based column model with line-by-line radiative transfer (https://github.com/danielkoll/PyRADS).</p>
Figure 11. Sea surface temperatures for 15 March 2011 in Delineating the fishes of the Clinus superciliosus species complex in southern African waters (Blennioidei: Clinidae: Clinini), with the validation of Clinus arborescens Gilchrist & Thompson, 1908 and Clinus ornatus Gilchrist & Thompson, 1908, and with descriptions of two new species
Figure 11. Sea surface temperatures for 15 March 2011, showing usual summer temperature gradient and upwelling areas on the west coast. (The MODIS Sea Surface Temperature data were downloaded from the Marine Sensing Unit website http://www.afro-sea.org.za).
Changes in body surface temperature play an under-appreciated role in the avian immune response
<p>Fever and hypothermia are well characterized components of systemic inflammation. However, our knowledge of the mechanisms underlying such changes in body temperature is largely limited to rodent models and other mammalian species. In mammals, high dosages of an inflammatory agent (e.g., lipopolysaccharide, LPS) typically leads to hypothermia (decrease in body temperature below normothermic levels), which is largely driven by a reduction in thermogenesis, and not changes in peripheral vasomotion (i.e., changes in blood vessel tone). In birds, however, hypothermia occurs frequently, even at lower dosages, but the thermoeffector mechanisms associated with the response remain unknown. We immune-challenged zebra finches (<i>Taeniopygia guttata</i>) with LPS and monitored changes in subcutaneous temperature and energy balance (i.e., body mass, food intake), and assessed surface temperatures of, and heat loss across, the eye region, bill, and legs. We hypothesized that if birds employ similar thermoregulatory mechanisms to similarly-sized mammals, LPS-injected individuals would reduce subcutaneous body temperature and maintain constant surface temperatures when compared with saline-injected individuals. Instead, LPS-injected individuals showed a slight elevation in body temperature, and this response coincided with a reduction in peripheral heat loss, particularly across the legs, as opposed to changes in energy balance. However, we note that our interpretations should be taken with caution due to small sample sizes within each treatment. We suggest that peripheral vasomotion, allowing for heat retention, is an underappreciated component of the sickness-induced thermoregulatory response of small birds.</p>
Wood Brook catchment: A coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics
<p>This folder contains data, model input files, model executable and source code required to reproduce results in the paper:"Evaluating a coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics in a mixed-use farmland catchment" by Han Qiu, Phillip Blaen, Sophie Comer-Warner, David M. Hannah, Stefan Krause and Mantha S. Phanikumar (Water Resources Research).</p> <p>The catchment (referred to herein as Wood Brook, Mill Brook, Mill Haft, BIFOR) is a mixed-use farmland catchment in central England. The "Data" folder contains information needed to create model input files. The "Figures" folder contains MS excel files with observed data (streamflows, groundwater heads, stream, streambed and groundwater temperatures etc) and simulation results to recreate the figures in the WRR paper. The other three folders contain model inputs, outputs and source code.</p>
Global lake surface water temperature layers
<p>In modeling species distributions and population dynamics, spatially-interpolated climatic data are often used as proxies for real, on-the-ground measurements. In shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections -- particularly among pleustonic and epilimnetic organisms. I used a global database of satellite-derived lake surface water temperatures (LSWT) to assess and correct for the statistical correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high-resolution global maps of air-LSWT offsets, corresponding uncertainty measures, and derived LSWT-based bioclimatic layers for use by the scientific community.</p>
The warm-season ground surface temperature - surface air temperature over China mainland
<p>This is a dataset for describing warm-season ground surface temperature - surface air temperature over China mainland.</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2016.1-2016.4)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2016.01-2016.04</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2016.01-2016.04</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
Data from: Role of horizontal temperature advection in Arctic surface warming in early spring
<p><span>Extreme Arctic warming scenario is studied with an idealized numerical experiment of polar warming. We employ the Community Earth System Model version 1.0 (CESM1.0) in this study. The control starts from the rest of the data with standard configurations (using a CO<sub>2</sub> concentration of 285 ppm). Overall, the model climate reaches a quasi-equilibrium state after 1,000 years of integration (Yang et al., 2015). A surface albedo perturbation experiment (0.1A) is carried out to achieve global warming and AA. During years 1501-2000, 0.1A is "parallel" to control, with the same initial conditions at the end of year 1500, and it reaches quasi-equilibrium after the 500-year integration. In the numerical result study, we focus on the equilibrium responses using the monthly averaged fields over the last 200 years of integration. Here we offered SAT anomaly, 500 hPa geopotential height, surface wind anomaly, and surface horizontal heat advection anomaly.</span></p>
Supplemental Data Set for "Constraining Changes in Surface Dust Thickness on Mars Using Diurnal Surface Temperature Observations from EMIRS"
<p>Supporting data set for manuscript "Constraining Changes in Surface Dust Thickness on Mars Using Diurnal Surface Temperature Observations from EMIRS". Provides the binned and averaged EMIRS surface temperatures acquired before and after the January 2022 dust storm for each region of interest to derive changes in surface dust thickness.</p>
Red Sea surface temperature
<p>Daily sea-surface temperature observations from the years 1985 to 2015 (inclusive), for 16703 regularly-spaced locations across the Red Sea; see Donlon et al. (2012) for details.</p><p> </p><p>These data have been analysed by Hazra and Huser (2021), Simpson and Wadsworth (2021), Simpson et al. (2023), and Sainsbury-Dale et al. (2023), among others.</p><p> </p><p>## References</p><p>- Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W. (2012). The operational sea surface temperature and sea ice analysis (OSTIA) system. *Remote Sensing of Environment*, 116:140–158.</p><p>- Hazra, A. and Huser, R. (2021). Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model. *Annals of Applied Statistics*, 15:572–596.</p><p>- Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2023) Likelihood-free parameter estimation with neural Bayes estimators. doi:10.1080/00031305.2023.2249522.</p><p>- Simpson, E. S., Opitz, T., and Wadsworth, J. L. (2023). High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields. *Extremes*, to appear.</p><p>- Simpson, E. S. and Wadsworth, J. L. (2021). Conditional modelling of spatio-temporal extremes for Red Sea surface temperatures. *Spatial Statistics*, 41(100482).</p>
Datasets for origins of Southern Ocean warm sea surface temperature bias in CMIP6 models
<p>Datasets for origins of Southern Ocean warm sea surface temperature bias in CMIP6 models</p>
ELITE land surface temperature: Himawari-8/AHI hourly clear-sky 0.02° LST (2020)
<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 clear-sky LST dataset derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8/AHI thermal infrared data, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The temporal resolution and spatial resolution of this dataset are one hour and 0.02°, respectively.</p> <p>This is the ELITE Himawari-8/AHI clear-sky LST product in 2020. Please <a href="https://zenodo.org/record/7316873"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019 and <a href="https://zenodo.org/record/7281765"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W)</li> <li>Temporal Coverage: 2020</li> <li>Spatial Resolution: 0.02°</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <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 (eliteqrs@126.com).</p>
Supplementary Materials for "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850"
<p><strong>Supplementary Materials for</strong>:</p> <p>R. Connolly, W. Soon, M. Connolly, S. Baliunas, J. Berglund, C.J. Butler, R.G. Cionco, A.G. Elias, V. Fedorov, H. Harde, G.W. Henry, D.V. Hoyt, O. Humlum, D.R. Legates, N. Scafetta, J.-E. Solheim, L. Szarka, V.M. Velasco Herrera, H. Yan and W.J. Zhang (<strong>2023</strong>). "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850". <em>Research in Astronomy and Astrophysics</em>. <a href="https://doi.org/10.1088/1674-4527/acf18e">https://doi.org/10.1088/1674-4527/acf18e</a></p> <p><strong>Description:</strong></p> <p>A dataset for the detection and attribution of Northern Hemisphere surface temperature trends since 1850.</p> <table> <caption>Contents of dataset</caption> <thead> <tr> <th scope="col">Tab</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>NH temperatures</td> <td>5 different Northern Hemisphere (NH) annual surface temperature (ST) estimates in temperature anomalies (°C) relative to 1901-2000 averages.</td> </tr> <tr> <td>TSI 1850-2018</td> <td>27 different Total Solar Irradiance (TSI) reconstructions. At 1 Astronomical Unit (W/m2) relative to 1901-2000 average.</td> </tr> <tr> <td>IPCC AR6 forcings</td> <td>IPCC AR6 radiative forcings (RF) time series (for the 1850-2018 period) in units of W/m2</td> </tr> <tr> <td>Fitting statistics</td> <td>Fitting statistics and results from all fits</td> </tr> <tr> <td>Key results</td> <td>Summary statistics and results</td> </tr> <tr> <td>Fits - Rural-only</td> <td>Multi-linear regression fitting results for Rural-only ST</td> </tr> <tr> <td>Fits - Urban & rural</td> <td>Multi-linear regression fitting results for Urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - SST</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for Sea surface temperature (SST)</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Tree-rings</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for tree-ring proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Glaciers</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for glacier-length proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Rural-only</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to Rural-only ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Urban & rural</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - SST</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to SST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Tree-rings</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to tree-ring proxy-based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Glaciers</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to glacier-length proxy-based ST</td> </tr> </tbody> </table> <p> </p>
Raw data for publication titled " GaN buffer growth temperature and efficiency of InGaN/GaN quantum wells: The critical role of nitrogen vacancies at the GaN surface"
<p>Raw data (Time-resolved photoluminescence and Secondary Ion Mass Spectrometry) used for the publication: <a href="https://doi.org/10.1063/5.0040326">https://doi.org/10.1063/5.0040326</a></p> <p>Layer sequence of each sample could be found in the excel sheet named SampleLibrary</p> <p> </p>
ELITE land surface temperature: FY-4A/AGRI hourly 4km clear-sky LST (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 4km hourly clear-sky LST dataset derived by the iTES algorithm (Liu et al. 2022) from the FY-4A/AGRI thermal infrared data, covering the AGRI 4km nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W). The in-situ validation results show that the bias and RMSE of the retrieved AGRI LST are 0.58 and 2.93 K in the daytime, and −0.30 and 2.18 K in the nighttime, respectively. The temporal resolution and spatial resolution of this dataset are one hour and 4km, respectively.</p> <p>This is the ELITE FY-4A/AGRI clear-sky LST product in 2018. Please <strong><em>click here</em></strong> to download the ELITE LST product in 2019.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W)</li> <li>Temporal Coverage: 2018 (starting from Apr.)</li> <li>Spatial Resolution: 4 km (subsatellite point)</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Liu, W., Shi, J., Liang, S., Zhou, S., & Cheng, J. (2022). Simultaneous retrieval of land surface temperature and emissivity from the FengYun-4A advanced geosynchronous radiation imager. International Journal of Digital Earth, 15, 198-225</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
Keeping cool with poop: Urohidrosis lowers leg surface temperature by up to 6ºC in breeding White storks
<p><span>Storks (<em>Ciconiidae</em>) are renowned for defecating on their legs when exposed to high temperatures, a phenomenon known as 'urohidrosis'. Previous work suggested that this behaviour can </span><span>cool down the blood supply to the legs and thus prevent hyperthermia in captive storks when overheated. However, no study has quantified the magnitude or duration of its evaporative cooling effect in free-ranging birds. Here, we combine urohidrosis data with thermal imaging and microclimate data to investigate the thermoregulatory significance of urohidrosis in White storks <em>Ciconia</em> <em>ciconia</em> during the breeding season. We show that urohidrosis can reduce leg surface temperature by up to 6.7 ºC (4.40 ± 1.04 ºC). Yet its cooling effect was of short duration (lasting no more than 2.5 min) and decreased with time since defecation. Thus, for urohidrosis to significantly contribute to heat dissipation, storks must perform it repeatedly when overheated. Indeed, individuals can perform up to 11 urohidrosis events per hour; our estimates indicate that repeated urohidrosis could represent a significant amount of heat loss during short-time spans — equivalent to 4% of daily field metabolic rate for an adult stork. Our results points to an absence of differences in the cooling efficiency of urohidrosis between adults and nestlings, probably because all nestlings were recorded during the last phase of the ontogeny of thermoregulation. Besides, during the hottest days adult storks delivered water to their nestlings, which might allow them to replenish body water reserves to sustain urohidrosis and other heat dissipation behaviours such as panting or gular fluttering. Our results indicate that urohidrosis might buffer the impact of high temperatures in breeding storks, preventing overheating and lethal hyperthermia. Gaining knowledge about behavioural thermoregulation in the heat is therefore crucial to better predict the future persistence and vulnerability of species under different climate warming scenarios.</span></p>
Precipitation isotope changes over the East Asian monsoon region driven by sea surface temperature during the Last Glacial Maximum
<p>This dataset includes speleothem and ice core proxies used for paper [Lan et al., (2023): Precipitation isotope changes over the East Asian monsoon region driven by sea surface temperature during the Last Glacial Maximum].</p>
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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.