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748 results for “surface temperature”
Data for "The Geologic Impact of 16 Psyche's Surface Temperatures"
<p>This repository contains the data used to create the figures in "The Geologic Impact of 16 Psyche's Surface Temperatures"</p>
The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study
<p>This repository contains the code and the data used to produce the results of "The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study" a discussion paper by G. Bonino, D. Iovino, L. Brodeau, S. Masina submitted to Geoscientific Model Development.</p> <p>- DATA.tar contains the 5 days model outputs to produce the figures in the manuscript.</p> <p>- CODE.tar contains the code and the namelists to run the experiments. The namelists and the modified code for run each experiments are available in the subfolder CODE/cfgs/. </p>
Direct and indirect effects of anthropogenic forcing on lake surface water temperature
<p>Here are the data from this paper (Direct and indirect effects of anthropogenic forcing on lake surface water temperature)</p>
Regridded subset of MODIS chlorophyll, OC-CCI chlorophyll, MODIS sea surface temperature; basin bathymetric depth
<p>The North Atlantic phytoplankton bloom depends on a confluence of environmental factors that drive transient periods of exponential phytoplankton growth and interannual variability in bloom magnitude. I analyze interannual bloom variability in the North Atlantic via extreme value theory where the Generalized Extreme Value Distribution (GEVD) is fitted spatially to annual maxima of satellite-measured surface chlorophyll. I find excellent agreement between the observed distribution of interannual bloom maxima and those predicted from the GEVD. The spatial distribution of fitted GEVD parameters closely follows basin bathymetry where the largest extremes and heaviest distribution tails are found on the continental shelves and slopes. Trend analyses suggest weak evidence for changes in GEVD parameters, despite regional trends in mean chlorophyll levels and sea surface temperature. These results provide a framework to quantify interannual bloom variability and call for further work examining how extreme blooms propagate through food webs and contribute to carbon export.</p>
Dataset for "Investigating sources of surface ozone in central Europe during the hot summer in 2018: High temperatures, but not so high ozone"
<p>Data for figures in the manuscript "Investigating sources of surface ozone in central Europe during the hot summer in 2018: High temperatures, but not so high ozone".</p> <p><em>Zohdirad, H., Jiang, J., Aksoyoglu, S., Namin, M. M., Ashrafi, K., Prévôt, A. S. H. Investigating sources of surface ozone in central Europe during the hot summer in 2018: High temperatures, but not so high ozone. Atmos Environ, 2022.</em></p>
Record low Arctic sea ice extent in 2012 linked to two-year La Niña-driven sea surface temperature pattern
<p class="Abstract"><span>Arctic summer sea ice decline accelerated from the mid-2000s to 2012, with the 2012 record low remaining unbroken. While frequent La Niña events during this period have been suggested as a driver of this trend acceleration, no convincing evidence has been presented. Here, using a climate model nudged to observed pan-tropical sea surface temperatures (SST), we show that the back-to-back La Niña events during 2010–2011, followed by a North Pacific cooling and a marginal El Niño, were a key contribution to the 2012 record low. Specifically, the La Niña events in 2010–2011 warmed the Arctic Pacific sector, whereas tropical SST anomalies in 2012 strengthened the Greenland high pressure, leading to an Arctic dipole-like pressure pattern and strengthening of transpolar ice drift. These Arctic temperature and circulation anomalies led to the record low sea ice extent in 2012, highlighting the strong influence of tropical SSTs on Arctic climate.</span></p>
Sea-surface temperature anomalies mediate changes in fish richness and abundance in Western North Atlantic and Gulf of Mexico estuaries
<p class="MsoNormal"><strong><span>Aim: </span></strong><span>Anthropogenic-driven warming of marine systems has resulted in a series of biological and physiological responses that are fundamentally altering ecosystem structure. Because estuaries exist at the land-ocean interface, they are particularly vulnerable to the effects of ocean warming as they can undergo rapid biogeochemical and hydrological shifts due to climate and land-use change. We explored how fish diversity structures—turnover, richness and abundance—have changed in the western North Atlantic and Gulf of Mexico estuaries through space and time and the drivers of change. </span></p> <p class="MsoNormal"><span><strong>Location:</strong> North Atlantic and Northern Gulf of Mexico</span></p> <p class="MsoNormal"><strong><span>Taxa</span></strong><span>: Fish</span></p> <p class="MsoNormal"><strong><span>Results:</span></strong><span> We found that species richness and abundance, turnover have increased in North Atlantic and northern Gulf of Mexico estuaries in the last 3 decades. These changes were mediated largely by sea-surface temperature anomalies, especially in more northern estuaries where warming has been relatively pronounced. </span><span>There is also an indication that urbanization, perhaps through habitat fragmentation and/or fisheries activities may be contributing to the increase in fish richness in many of these estuaries. </span></p> <p class="MsoNormal"><strong><span>Main Conclusion: </span></strong><span>The increasing trajectory of turnover in many of the estuaries suggests that the fish communities have changed fundamentally from the baselines. A fundamental change in community composition can lead to an irreversible trophic imbalance or alternative stable states among other outcomes. Thus, predicting how shifting community structures might influence food webs, ecosystem stability and human resource use remains a pertinent task.</span></p>
Data used in "Marine heatwaves make more contribution to changing air–water exchange of semi-volatile organic compounds than mean sea surface temperature raising"
<p>Data used in "Marine heatwaves make more contribution to changing air–water exchange of semi-volatile organic compounds than mean sea surface temperature raising"</p>
Spatial impact of urban expansion on lake surface water temperature based on the perspective of watershed scale
<p>This is the original data from the article "Spatial impact of urban expansion on lake surface water temperature based on the perspective of watershed-scale"</p>
Data to plot figures in Flux Adjustment on Seasonal-Scale Sea Surface Temperature Drift written by the Authors
<p>These are data for plotting the figures shown in Flux Adjustment on Seasonal-Scale Sea Surface Temperature Drift written by Ryusuke Masunaga et al.</p> <p>The directory names correspond to the figure numbers in the paper. Please contact the Authors if you have any questions.</p> <p>In the grd files, data are stored as single precision float.</p>
The relative influence of sea surface temperature anomalies on the benthic composition of an Indo-Pacific and Caribbean coral reef over the last decade
<p>Rising ocean temperatures are the primary driver of coral reef declines throughout the tropics. Such declines include reductions in coral cover that facilitate the monopolisation of the benthos by other taxa such as macroalgae, resulting in reduced habitat complexity and biodiversity. Long term monitoring projects present rare opportunities to assess how sea surface temperature anomalies (SSTAs) influence changes in the benthic composition of coral reefs across distinct locations. Here, using extensively monitored coral reef sites from Honduras (in the Caribbean Sea), and from the Wakatobi National Park located in the centre of the coral triangle of Indonesia, we assess the impact of global warming on coral reef benthic compositions over the period 2012-2019. Bayesian Generalised Linear Mixed effect Models revealed increases in sponge, and hard coral coverage through time, while rubble coverage decreased at the Indonesia location. Conversely, the effect of sea surface temperature anomalies (SSTA) did not predict any changes in benthic coverage. At the Honduras location, algae and soft coral coverage increased through time, while hard coral and rock coverage were decreasing. The effects of SSTA at the Honduras location included increased rock coverage, but reduced sponge coverage, indicating disparate responses between both systems under SSTAs. However, redundancy analyses showed intra-location site variability explained the majority of variance in benthic composition over the course of the study period. Our findings show that SSTAs have differentially influenced the benthic composition between the Honduras and the Indonesia coral reefs surveyed in this study. However, large intra-location variance which explains the benthic composition at both locations indicates that localised processes have a predominant role for explaining benthic composition over the last decade. The sustained monitoring effort is critical for understanding how these reefs will change in their composition as global temperatures continue to rise through the Anthropocene.</p>
An Observational and Modeling Study of Inverse-Temperature Layer and Water Surface Heat Flux
<p>The data are used for an observational and modeling analysis of water temperature distribution and water surface energy budget. </p>
Monthly MODIS LST data related to the article: A new fully gap-free time series of land surface temperature from MODIS LST data
<p>Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps.</p> <p>[1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333</p> <p>LICENSE: Open Data Commons Open Database License (ODbL) http://opendatacommons.org/licenses/odbl/</p> <p>Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006.</p> <p><strong>The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min</strong> (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), <strong>aggregated to monthly data</strong>. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL:<br> <br> GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid",<br> DATUM["Not specified (based on Clarke 1866 spheroid)",<br> SPHEROID["Clarke 1866",6378206.4,294.9786982138982,<br> AUTHORITY["EPSG","7008"]]],<br> PRIMEM["Greenwich",0],<br> UNIT["degree",0.0174532925199433]]<br> </p> <p><strong>File name</strong> abbreviations:</p> <ul> <li>avg = average of daily averages</li> <li>min = minimum of daily minima</li> <li>max = maximum of daily maxima</li> </ul> <p>Meaning of <strong>pixel values</strong>:</p> <ul> <li>The <strong>pixel values</strong> are coded in <strong>degree Celsius * 100</strong> (hence, to obtain °C divide the pixel values by 100.0).</li> </ul> <p>Version <strong>changelog</strong>:</p> <ul> <li>V1.1.0: GeoTIFF metadata updated.</li> <li>V1.0.0: original upload</li> </ul>
Long Simulation of Global Sea Surface Temperature using Linear Inverse Model
<p>The content of this dataset includes (a) observed product, which is the averaged product of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST), the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), and the Centennial in situ Observation-Based Estimates (COBE). This is called "global_sst_1958to2017.mat", i.e., 60 yrs of monthly SST over 1958-2017; it also contains the trend, the seasonal climatology, the anomaly field after subtracting trend and seasonal climatology, longitude, latitude, time, and land-sea mask. (b) 20 realizations of long SST simulation generated by a Linear Inverse Model (LIM). Each realization contains 6000 months (or 500 yrs) of SST. The file is named "stochastic_simulation_lim_sst_mem*.mat". </p> <p>To cite dataset if plot or extracted used in any publication, use the reference below:</p> <p>Xu, T., et al. (2022). "An increase in marine heatwaves without significant changes in surface ocean temperature variability." <span>Nature Communications</span> <strong>13</strong>(1): 7396.</p>
Globigerinoides ruber Mg/Ca-based sea surface and Globorotalia hirsuta Mg/Ca-based subsurface water temperature records during the onset of the Late Pliocene from IODP Site U1313
<p><strong>Data related to: Pang, X., Voelker, A. H. L., Lu, S., and Ding, X.: Distinct seasonal changes and precession forcing of surface and subsurface temperatures in the mid-latitudinal North Atlantic during the onset of the Late Pliocene, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-603, 2024.</strong></p>
Nonstationarity of the Atlantic Meridional Overturning Circulation's fingerprint on sea surface temperature
<p>Associated data for "Nonstationarity of the Atlantic Meridional Overturning Circulation’s fingerprint on sea surface temperature". V2 updated to include datasets for the supplemental information.</p>
40-year monthly mean AVHRR GAC Land Surface Temperature data for the Pan-Arctic region (Pan-Arctic AVHRR LST)
<p><em>This data collection contains 40 years of monthly mean daytime AVHRR Global Area Coverage (GAC) land surface temperature (LST) data. This dataset covers the 1981-2020 perdiod and covers the whole globe above 50° latitude. The spatial extent of the dataset is the following : (-180°, 50°N) ; (180°, 90°N)</em></p> <p><strong>Dataset description:</strong></p> <p>The LST monthly mean composites are computed from daily daytime LST files, that were generated from the EUMETSAT AVHRR PyGAC FDR (https://navigator.eumetsat.int/product/EO:EUM:DAT:0862) as described in Dupuis et al. (2024). These daily LST files contain only cloud-free pixels and pixels with sufficient quality regarding satellite zenith angle and error margin from the radiative transfer modelling. The probabilistic cloud mask from the CLARA-A3 (https://navigator.eumetsat.int/product/EO:EUM:DAT:0874) dataset has been used. The LST monthly means do not contain any water masks, as potential users might have different requirements regarding water masks. The dataset has been validated against in situ data from the SURFRAD (https://gml.noaa.gov/grad/surfrad/overview.html), ARM (https://arm.gov/capabilities/observatories/nsa) and KIT (https://www.imk-asf.kit.edu/english/skl_stations.php) networks.</p> <p><strong>Data & File Overview:</strong></p> <p>Short description: AVHRR GAC LST daytime monthly mean composites: daily land surface temperature data are averaged to monthly composites for every afternoon and mid-day satellite (10 different satellites).</p> <ul> <li>File List: This dataset contains monthly daytime land surface temperature (LST) data for the AVHRRs onboard NOAA and MetOp satellites. </li> <li>Filename: Pan_Arctic_LST_avhrr_XXXXX_YYYYMM_DAY__***.nc, where XXXXX represents the satellite identifier, YYYYMM the monthly timestamp (YYYY=year, MM=month) and *** the timestamp of the file generation.</li> <li>Relationship between files: Each file covers a one-month period and is recorded by a different satellite.</li> </ul> <p>Satellite identifiers:<br><em>AVN07 : NOAA 7</em><br><em>AVN09 : NOAA 9</em><br><em>AVN11 : NOAA 11</em><br><em>AVN14 : NOAA 14</em><br><em>AVN16 : NOAA 16</em><br><em>AVN18 : NOAA 18</em><br><em>AVN19 : NOAA 19</em><br><em>AVMEA : MetOp-A</em><br><em>AVMEB : MetOp-B</em><br><em>AVMEC : MetOp-C</em></p> <p><strong>Data specific information:</strong></p> <p>The LST files are available as a gridded product in the WGS84 coordinate reference system and are distributed as NetCDF files. The dataset covers the pan-Arctic region (-180°, 90°, 180°, 50°) at a spatial resolution of 0.05°x0.05° pixel size.<br>Each *.nc file contains one variable (LST) with three dimensions (time, lat, lon) and five coordinates (time, lat, lon, band and spatial_ref).</p> <p>- spatial_ref (): stores the spatial information, such as the coordinate reference system (CRS) and WKT string.<br>- time (time): stores the timestamp, here the month and the year of the monthly mean. The timestamp is the same for all pixels belonging to the same composite.<br>- lat (lat): stores the latitude of each pixel<br>- lon (lon): stores the longitude of each pixel<br>- band (): empty inherited layer </p> <p> </p> <p><strong>Credit:</strong></p> <p>To use this data please cite this dataset and the respective journal publication:</p> <p>Dupuis, S., Göttsche, F.-M., & Wunderle, S. (2024). Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region. <em>The Cryosphere, 18</em>(12), 6027-6059. <a href="https://doi.org/10.5194/tc-18-6027-2024" target="_blank" rel="nofollow noopener">https://doi.org/10.5194/tc-18-6027-2024</a></p> <p> </p> <div> <div><span>@Article</span><span>{</span><span>tc-18-6027-2024</span><span>,</span></div> <div><span>AUTHOR</span><span> = </span><span>{</span><span>Dupuis, S. and G\"ottsche, F.-M. and Wunderle, S.</span><span>}</span><span>,</span></div> <div><span>TITLE</span><span> = </span><span>{</span><span>Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region</span><span>}</span><span>,</span></div> <div><span>JOURNAL</span><span> = </span><span>{</span><span>The Cryosphere</span><span>}</span><span>,</span></div> <div><span>VOLUME</span><span> = </span><span>{</span><span>18</span><span>}</span><span>,</span></div> <div><span>YEAR</span><span> = </span><span>{</span><span>2024</span><span>}</span><span>,</span></div> <div><span>NUMBER</span><span> = </span><span>{</span><span>12</span><span>}</span><span>,</span></div> <div><span>PAGES</span><span> = </span><span>{</span><span>6027--6059</span><span>}</span><span>,</span></div> <div><span>URL</span><span> = </span><span>{</span><span>https://tc.copernicus.org/articles/18/6027/2024/</span><span>}</span><span>,</span></div> <div><span>DOI</span><span> = </span><span>{</span><span>10.5194/tc-18-6027-2024</span><span>}</span></div> <div><span>}</span></div> </div> <p> </p> <p><strong>Information about funding sources that supported the collection of the data:</strong><br>Dr. Alfred Bretscher Fund (University of Bern)</p> <p> </p>
Monthly RACMO2.4p1 data for Greenland (11 km) and Antarctica (27 km) for SMB, SEB, near-surface temperature and wind speed (2006-2015)
<p>Version 2: Updated missing months in the Antarctic data set.</p> <p>Monthly-accumulated (named monthlyS) and monthly-averaged (named monthlyA) data for RACMO2.4p1 for Greenland (GRN) and Antarctica (ANT) on a 11 km and 27 km horizontal resolution grid, respectively, are presented in this data set and are available for 2006 until 2015. The data include the surface mass balance (SMB), snow melt (mltgl), refreezing (rfrzgl), precipitation (pr), runoff (totrunoff), drifting snow erosion (sndiv), sublimation (sublgl) and sublimation due to blowing snow (sublsd), all in kg m-2 mo-1. For the surface energy balance (SEB): the downward shortwave radiation (rsds), shortwave upward radiation (rsus), downward longwave radiation (rlds), upward longwave radiation (rlus), sensible heat flux (hfss) and latent heat flux (hfls) are available. The SEB components are in J m-2. To convert to W m-2, divide by the amount of seconds in a month. In addition, the near-surface temperature (tas), in K, and near-surface wind speed (sfcwind), in m s-1, are included.</p> <p><br>This data set does not represent new surface mass balance and climate products for Greenland and Antarctica. This will follow in later publications, where RACMO2.4 simulations are presented covering the full historical time period of ERA5 with higher horizontal resolution. </p>
Figure data for "Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds"
<p>Data supporting figures in the paper "Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds" published at <em>Communications Earth & Environment. </em></p>
Temperature data collected in the surface turf aggregations and the water masses below, in the Varildsfjorden in the Oslofjord, Norway
<p>Temperature data collected in the surface turf aggregations and the water masses just below the turf, in the Varildsfjorden in the Oslofjord, Norway</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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