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414 results for “sea temperature”
Circulation dynamics: currents, waves, temperature measurements from moorings in lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
Starting August 2018, five moorings deployed on the seafloor of multiple lagoons in the Beaufort Sea will record currents, waves, temperature, and pressure. Moorings are retrieved and re-deployed each August. This data is being collected to better understand the multi-seasonal circulation dynamics between the Beaufort Sea and coastal lagoons. Two moorings are deployed in Elson Lagoon, one in Stefansson Sound, one in Jago Lagoon, and one in Kaktovik Lagoon. Each mooring contains two data loggers: RBRduo3 T.D wave loggers and Lowell Instruments LLC TCM-1 tilt current meters. The RBR instruments measure temperature, pressure, and derived wave energy, average wave period, average wave height, maximum wave period, maximum wave height, 1/10 wave period, 1/10 wave height, significant wave period, significant wave height, tidal slope, depth, and sea pressure. The Lowell LLC TML-1 tilt current meters measure water velocity, heading, and temperature.
Daily sea surface temperature in Santa Barbara channel between 1982 and 2023
This data package contains sea surface temperature (SST) data in the Santa Barbara Channel area. Data was obtained from the NOAA National Centers for Environmental Information (NCEI) at 0.25° resolution for the time between 1982 and 2023. This Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis (Version 2.1) derived its data from satellite (Advanced Very High Resolution Radiometer (AVHRR)) and in situ platforms (i.e., ships and buoys) and yielded 18 gird points within the Santa Barbara Channel.
SBC LTER: Reference: Sea-surface water temperature, Santa Barbara Harbor, Santa Barbara, CA, USA, 1955 to present, ongoing
The SBC-LTER has access to data on seawater temperature collected at Santa Barbara Harbor, Santa Barbara, CA, USA through the Scripps Institution of Oceanography Manual Shore Stations program. The SIO Manual Shore Stations program provides data and information about this shore station. For further information, please visit the SIO Manual Shore Stations website at https://library.ucsd.edu/dc/object/bb07606686. Please note: manual shore station data is updated periodically, not continuously. Funding for the Shore Stations Program provided by the California Department of Parks and Recreation, Natural Resources Division, Award# C22820005. Contact shorestation@ucsd.edu if you have questions
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Portobello Marine Laboratory sea surface temperature time series
<p>This table contains the daily sea surface temperature observations taken at the Portobello Marine Laboratory wharf (LAT: -45.8160, LON: 170.6500). The first column is time in MATLAB datenum format. The second column is daily sea surface temperature recorded at 9am local time. Measurements are recorded to an accuracy of <span class="math-tex">\(\pm\)</span>0.1°C. Missing observations have been assigned the value -999. Additional station details and sampling information can be found in <a href="https://environment.govt.nz/publications/new-zealand-coastal-sea-surface-temperature/">Chiswell and Grant (2018)</a>.</p> <p>We acknowledge the foresight and dedication of the founders of this <em>in situ</em> dataset in the 1950s. We are grateful for all the people involved in the data collection. Notably these include</p> <ul> <li>Doug Mackie (data acquisition and record maintenance)</li> <li>Elizabeth (Betty) Batham who championed the long term climate sampling</li> <li>All the researchers who have assisted with sampling</li> </ul>
Moored current and temperature measurements in the Southern Adriatic Sea at mooring site BB and FF, March 2012-June 2020
<p>This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea). </p>
Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]
The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable. The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.
Temperature logger deployment methods and irradiance-biased temperature data, King Abdullah University of Science and Technology, Red Sea, 2023.
Solar irradiance can offset the temperature recorded by underwater sensing instruments (aka "loggers"). We collected temperature and PAR (photosynthetic active radiation) data during two short-term in situ deployments on a shallow fringing reef adjacent to the King Abdullah University of Science and Technology (KAUST) in the Red Sea. The first deployment quantified the measurement bias due to solar heating over five days in February 2023 while the second compared the effect of different shading methods on logger performance over 24 hours in June 2023. We also recorded temperature in a controlled calibration bath in the lab with ten of the most widely used loggers to further assess their accuracy, response time, and intra-logger variation. Finally, to understand current practices of measuring temperature on coral reefs, we summarized logger deployment method details from a literature review of coral reef studies published from 2013 to 2022. Such details included how often loggers recorded the temperature, the depth where loggers were deployed, and whether the authors reported shading or protecting their loggers. This data package is complete and part of a larger project that aims to develop an instrument deployment framework for restoration-based reef monitoring, which includes instrument recommendations and deployment guidelines.
Count data of air-breathing fauna from visual transect surveys including water temperature, time, sea and weather conditions in Shark Bay Marine Park, Western Australia from February 2008 to July 2014
This dataset provides information on the relative abundances of air breathing fauna (dugongs, dolphins, sea snakes, marine birds, and sea turtles) in the study area of the Eastern Gulf of Shark Bay, Western Australia. The dataset comprises transects that quantify animal abundances in three microhabitats (shallow seagrass banks, seagrass bank edges, and deep sandy channels). These microhabitats vary in their food supply as well as their potential to facilitate or inhibit detection and escape from predators, mainly the tiger shark (Galeocerdo cuvier). As a result these data have been used to examine risk-specific habitat use behaviors of these fauna, in addition to general abundance estimates.
Effects of paternal temperature on larval performance in purple sea urchin
The data is used to support the manuscript “Leach, T. S., Hofmann, G. E. (2023). Marine heatwave temperatures enhance larval performance but are meditated by paternal thermal history and inter-individual differences in the purple sea urchin, Strongylocentrotus purpuratus. Frontiers in Physiology. DOI: 10.3389/fphys.2023.1230590”
Brazilian Earth System Model: CMIP5 Sea ice concentration and Air Temperature data
<p>The Brazilian Earth System Model, Version 2.5 (BESM-OAV2.5) used here is a global climate coupled ocean-atmosphere-sea ice model, and is part of CMIP5 project. The atmospheric component of BESM-OAV2.5 is BAM (Brazilian Atmospheric Model) and was described in detail by Figueroa et al., (2016). BAM, developed at Center for Weather Forecasting and Climate Studies of the National Institute for Space Research CPTEC-INPE has been constantly reformulated over the last years (Figueroa et al., 2016; Nobre et al., 2013). The lastest version, used here and described by Veiga et al., (2019), has spectral horizontal representation truncated at triangular wave number 62, grid resolution of approximately 1.875∘×1.875∘, and 28 sigma levels in the vertical, with unequal increments between the vertical levels (i.e., a T62L28). The oceanic component of BESM-OAV2.5 is the Modular Ocean Model, Version 4p1, from National Oceanic and Atmospheric Administration-Geophysical Fluid Dynamics Laboratory (MOM4p1/NOAA-GFDL), described in detail by Griffies, (2009). The MOM4p1 includes a Sea Ice Simulator (SIS) built-in ice model (Winton 2000). The SIS has five ice thickness categories and three vertical layers (one snow and two ice). To calculate ice internal stresses are used the elastic-viscous-plastic technique described by Hunke and Dukowicz, (1997). The thermodynamics is given by a modified Semtner’s three-layer scheme (Semtner, 1976). SIS is able to calculate sea ice concentration, snow cover, thickness, brine content and temperature. Furthermore, SIS calculates ice-ocean fluxes and transmits fluxes between atmosphere and ocean. The horizontal grid resolution of MOM4p1 in the longitudinal direction is a set to 1˚. The latitudinal direction varies uniformly, in both hemispheres, from 1∕4<sup>o </sup>between 10<sup>o</sup> S and 10<sup>o </sup>N to 1<sup>o </sup>of resolution at 45<sup>o</sup> and to 2<sup>o</sup> of resolution at 90<sup>o</sup>. The vertical axis has 50 levels (upper 220m, has 10 m resolution, increasing to about 360 at deeper levels. The MOM4p1 and BAM models were coupled using FMS coupler. FMS coupled was developed by NOAA-GFDL. The BAM model receives SST and ocean albedo from MOM4p1 and SIS (hour by hour). The MOM4p1 receives momentum fluxes, specific humidity, pressure, heat fluxes, vertical diffusion of velocity components and freshwater. </p> <p>This study used two numerical experiments from CMIP5: (i) piControl: it runs for 700 years, forced by invariant pre-industrial atmospheric CO<sub>2</sub> concentration level (280ppmv) and (ii) Abrupt 4xCO<sub>2</sub>: it runs for 460 years, comprising an abrupt instantaneous quadrupling of atmospheric CO<sub>2 </sub>level concentration from the piControl simulation. The design of both experiments follows the CMIP5 protocol (Taylor et al., 2012).</p> <p> </p>
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
What Controls the Mean East–West Sea Surface Temperature Gradient in the Equatorial Pacific: The Role of Cloud Albedo
<p>Climatologies for the climate model simulations performed by Burls and Fedorov 2014, Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-13-00255.1">https://doi.org/10.1175/JCLI-D-13-00255.1</a>. This table shows how the names of the simulation files provided in this dataset relate to the experiment names provided in Table 1 of Burls and Fedorov (2014, JOC).</p> <table> <thead> <tr> <th scope="col">Experiment # in Article (Table 1)</th> <th scope="col">Name of Files</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>PreInd_T31_gx3v7*.nc</td> </tr> <tr> <td>2</td> <td>80p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>3</td> <td>60p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>4</td> <td>40p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>5</td> <td>20p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>6</td> <td>20p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>7</td> <td>40p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>8</td> <td>60p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>9</td> <td>80p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>10</td> <td>20p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>11</td> <td>40p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>12</td> <td>60p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>13</td> <td>80p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>14</td> <td>20p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>15</td> <td>40p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>16</td> <td>60p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>17</td> <td>80p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>18</td> <td>20p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>19</td> <td>40p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>20</td> <td>60p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>21</td> <td>80p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>22</td> <td>PreInd_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>23</td> <td>40p_LWP_1590deg_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>24</td> <td>60p_LWP_1590deg_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>25</td> <td>40p_ILWP_1590deg_tropx2_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>26</td> <td>60p_ILWP_1590deg_tropx2_0.9x1.25_gx1v6*.nc</td> </tr> </tbody> </table> <p>Article abstract:</p> <p>The mean east–west sea surface temperature gradient along the equator is a key feature of tropical climate. Tightly coupled to the atmospheric Walker circulation and the oceanic east–west thermocline tilt, it effectively defines tropical climate conditions. In the Pacific, its presence permits the El Niño–Southern Oscillation phenomenon. What determines this temperature gradient within the fully coupled ocean–atmosphere system is therefore a central question in climate dynamics, critical for understanding past and future climates. Using a comprehensive coupled model [Community Earth System Model (CESM)], the authors demonstrate how the meridional gradient in cloud albedo between the tropics and midlatitudes (Δα) sets the mean east–west sea surface temperature gradient in the equatorial Pacific. To change Δα in the numerical experiments, the authors change the optical properties of clouds by modifying the atmospheric water path, but only in the shortwave radiation scheme of the model. When Δα is varied from approximately −0.15 to 0.1, the east–west SST contrast in the equatorial Pacific reduces from 7.5°C to less than 1°C and the Walker circulation nearly collapses. These experiments reveal a near-linear dependence between Δα and the zonal temperature gradient, which generally agrees with results from the Coupled Model Intercomparison Project phase 5 (CMIP5) preindustrial control simulations. The authors explain the close relation between the two variables using an energy balance model incorporating the essential dynamics of the warm pool, cold tongue, and Walker circulation complex.</p>
Planktonic Mg/Ca-derived IPWP upper ocean temperature, heat content and sea water δ18O over the last 360 ka
<p>This dataset contains planktonic foraminifera Mg/Ca-derived temperature estimates, age control points and sea water δ18O (δ18Osw) of cores ODP807, KX21-2, MD10-3340, SO18480-3 and MD98-2162 from the Indo-Pacific Warm Pool (IPWP) over the last 360 ka. It also includes reconstructed IPWP stacks of SST, TWT, upper OHC and δ18Osw, and numerical simulated upper OHC, δ18Osw (sea water) and δ18Op (rainfall) from the CESM model and GISS-ModelE2-R model.</p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Winter Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Spring Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) spring cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Data For Calculating 1880 to 1975 sea surface temperature
<p>The data used in the programs 8075DegRise_CO2NMDPSIRR_V5.bas (DOI 10.5281/zenodo.1418561) and 8075DegRise_NoNMDP_V1.bas (DOI 10.5281/zenodo.1419629).</p> <p>All data is entered one item per line.The order of the entries is:</p> <p>First entry – The total number of sea surface temperature entries.</p> <p>Second entry – The number of entries for all other data.</p> <p>Third entry – The offset from the first entry of a set of data to the year 1880. This is the same for all sets of data except sea surface temperature which is based at 1880 and never changes.</p> <p>The nonzeroed non-normalized sea surface temperature anomalies, the number of which is specified by the First entry</p> <p>The nonzeroed non-normalized North Magnetic Dip Pole kilometers moved from the previous year, the number of entries is specified in the Second entry.</p> <p>The nonzeroed non-normalized solar irradiation average for this year, the number of entries is specified in the Second entry.</p> <p>The nonzeroed non-normalized CO2 ppm average for this year, the number of entries is specified in the Second entry.</p>
Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE
<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, <em>Climate of the Past,</em> 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul> <p> </p> <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul> <p> </p> <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul> <p> </p> <ul> <li><strong>Methodology:</strong> The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
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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)
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DANDI Archive for NWB datasets
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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.