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294 results for “sea surface temperature”
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>
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
Figure 4 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 4. Spatial variability of the climatic rate of the Black Sea level change (cm/yr) for period from 1993 to 2015.
CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> <p>Details for files cesm2.[experiment name].SST.noise.nc</p> <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> <p>For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207</p> <p>For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374</p>
Figure 8 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 8. The relationship between the temperature of the sea surface layer obtained from drifters and SST according to Landsat-5, -7 Level-2 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.
Figure 4 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 4. An example of the absence in the archives of images over the central part of the Caspian Sea (flight track N 166 of the Landsat-7 satellite on 22 July 2008.
Figure 2 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 2. Drifter tracks in the Caspian Sea: (a) from 4 October 2006 to 20 February 2007, and (b) from 19 July 2008 to 10 October 2008.
Figure 10 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 10. Dependence between SST from the drifter and according to data from Landsat-5, -7 sensors having different levels of processing.
Figure 7 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 7. Histogram of temperature determination error values according to Landsat Level-1 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.
Figure 3. A in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 3. A mosaic of Landsat-7 images in the Caspian Sea: (a) from 4 October 2006 to 20 February 2007, and (b) from 19 July 2008 to 10 October 2008. Drifter tracks are superimposed on satellite images.
Figure 9 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 9. Histogram of temperature determination error values according to Landsat Level-2 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.
Figure 6 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 6. The relationship between the temperature of the sea surface layer obtained from drifters and SST according to Landsat-5, -7 Level-1 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.
Figure 5 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 5. Examples of using a cloud mask (satellite image taken on 29 July 2008). At the time of the satellite's flight, all measurement points for the day are blocked by clouds: (a) satellite image in natural colors with missing information along the bands; (b) same image with cloud mask superimposed. Red dots show several locations of one drifter during the day of satellite image acquisition.
North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations
<p>This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x.</p> <p><br>The data are provided as netcdf files.</p> <p>The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245).</p> <p>Each file contains data for the following one dimensional variables:</p> <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul> <p> </p>
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </p>
Data and code to accompany 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record'
<p>Data and analysis code to accompany the manuscript 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record', published in <em>Oceanography and Marine Biology: An Annual Review </em>(<a href="https://doi.org/10.1201/9781003363873-2">https://doi.org/10.1201/9781003363873-2</a>). Data files include records of sea surface temperature (SST) collected in Pacific Grove, California, USA from January 20, 1919 to the end of 2020. The analysis code produces a continuous 100+ year record with adjustments made for time of day the data were collected, and filling gaps in the data set where necessary. </p> <p>This dataset makes use of an earlier 83-year version of the sea surface temperatures produced by Breaker et al. 2005 available at <a href="https://aquadocs.org/handle/1834/20890">https://aquadocs.org/handle/1834/20890</a>, with the data file available at <a href="https://purl.stanford.edu/rc833pc4972">https://purl.stanford.edu/rc833pc4972</a> </p>
Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle
<p>This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC). </p> <p>This dataset includes two .csv files</p> <p><br> 2022-10-14_NORDACC_Turbidity.csv<br> This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to: <br> Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> 2022-10-14_NORDACC_UnderwayData.csv<br> This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to:<br> Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> About NORDACC:</p> <p>The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization.</p> <p>NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1 </p> <p>For more information about NORDACC see the accompanying paper in HardwareX. </p>
GF4ACE -- Data from: Reanalysis-based global radiative response to sea surface temperature patterns: Evaluating the Ai2 climate emulator
Open the record for dataset details and reuse information.
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>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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International Brain Laboratory public data
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OpenNeuro
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