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16 results for “heat content”
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </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>
Global Ocean Heat Content Anomalies based on Argo data
<p><strong>NOTE for users: please use the latest version of the product at https://zenodo.org/doi/10.5281/zenodo.10182972. </strong>Ocean Heat Content Anomalies (OHCA) are calculated (during 2005-2022) subtracting the mean over the period 2005-2021 from the monthly time series. Yearly OHCA time series are then calculated. OHC fields are mapped using locally stationary Gaussian processes with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In the present version, mapping is done in latitude and longitude with monthly subsets of data (a future version will add time to the mapping). Mapping is done separately for different vertical sections. Different vertical sections are combined to estimate: 1. Global OHC timeseries (e.g., for level 0-2000m: GCOS_0000_2000_OHCA_J_m2_oc, for OHC in J/m2; GCOS_0000_2000_OHCA_ZJ, for OHC in ZJ; the attribute “GCOS_area” is included for both variable types in the netcdf file and it tells the corresponding surface area); 2. Volume averaged temperature anomaly (global) timeseries (e.g., for level 0-2000m: GCOS_0000_2000_vol_ave_temp_anom, in degC; the attribute “GCOS_volume'' is included for this variable type in the netcdf file and it tells the corresponding volume). Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included.</p>
CIGAR-CS Global Ocean Reanalysis 1961-2022 Ocean Heat Content
<p>Dataset describing the <strong>CIGAR-CS (v1) </strong>reanalysis Ocean Heat Content.</p> <p><strong>CIGAR</strong>: The CNR ISMAR Global Historical Reanalysis (<a href="http://cigar.ismar.cnr.it">http://cigar.ismar.cnr.it</a>)</p> <p><strong>CS</strong>: Contemporary Stream</p> <p>CIGAR-CS is an ensemble ocean reanalysis with 32 members, covering the period from 1959 to real-time, and based on the NEMO4 model, a variational data assimilation scheme with variational quality control of in-situ profiles and time-varying background-error covariances, a surface correction scheme of air-sea fluxes, a deep-ocean bias correction scheme, and an advanced ensemble generation scheme with stochastic physics and perturbation of input datasets.</p> <p>It includes yearly mean files for each of the <strong>32 ensemble members</strong> from 1961-2022 for these selected variables:<br> - Ocean heat content (full column)<br> - Temperature analysis increments<br> - Surface net air-sea heat fluxes</p> <p>Heat fluxes and analysis increments are provided for potential use in ocean warming attribution studies.</p> <p>To ease the use of the OHC data, all fields are remapped from the irregular ORCA1 tripolar grid (1/3deg to 1deg of spatial resolution) to a regular 0.5degx0.5deg grid through bilinear interpolation.</p> <p>(Note: all diagnostics in the reference paper were computed on the native irregular grid; possible differences, therefore, may exist and are due to the errors introduced by the interpolation)</p>
Seasonal forecasts of ocean heat content in ECMWF-SEAS5 and CMCC-SPS3
<p>Seasonal forecasts of ocean heat content in the upper 300m from two Copernicus Climate Change Service Systems: ECMWF SEAS5 and CMCC SPS3. Data was used for the following study:</p> <p>McAdam, R., Masina, S., Balmaseda, M. <em>et al.</em> Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems. <em>Clim Dyn</em> 58, 3335–3350 (2022). https://doi.org/10.1007/s00382-021-06101-3</p>
Ocean Heat Content Anomalies in the North Atlantic based on mapping Argo data using local Gaussian processes defined over space
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2005-2022, in the North Atlantic, north of 20N) subtracting the time mean over the period 2005-2021 from the monthly time series of OHC. OHC fields are mapped using a locally stationary Gaussian process (defined over space) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In this product, mapping is done in latitude and longitude with monthly subsets of data. Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA for 0-2000 dbar. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. </p>
Dataset for: Identification of genomic regions of wheat associated with grain Fe and Zn content under drought and heat stress using genome-wide association study
<p>The study material in the GWAS panel with 282 advanced breeding lines of bread wheat genotypes from IARI stress breeding program was selected to map the genomic regions responsible for grain iron and Zinc content under drought and heat stress treatments.</p> <p>Phenotypic data:</p> <p>The GWAS panel was evaluated at IARI, New Delhi - DL (28.6550° N, 77.1888° E, MSL 228.61 m) under Irrigated (IR), Restricted Irrigated (RI) and Late sown (LS) treatment conditions with augmented RCBD design. Data was collected on Grain Iron and Grain zinc content along with thousand-grain weight. Around 20 g of grain sample from each of 282 genotypes from the GWAS panel under all three conditions were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000; Oxford Instruments plc, Abingdon, United Kingdom) calibrated with glass beads-based values. To record TGW, manual counting of grains was followed and the weight of the grains was recorded in grams with an electronic balance.</p> <p>Genotypic data:</p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 10546 high-quality SNPs was used in GWAS analysis.</p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>
Dataset for: Identification of genomic regions of wheat associated with grain Fe and Zn content under drought and heat stress using genome-wide association study
Open the record for dataset details and reuse information.
Ocean Heat Content Reconstructions based on Anthropogenic Carbon
<p>Data from "<strong>Heat and carbon coupling reveals ocean warming due to circulation changes</strong>" by Bronselaer & Zanna, 2020, Nature, https://doi.org/10.1038/s41586-020-2573-5</p> <p>The files contain Linear Change in Ocean Heat Content (top 2000 m) relative to 1951 for observations and CMIP5 data, and the decomposition into added and redistributed (due to changes in ocean circulation). </p>
Numerical model output data for publication "Control of the oceanic heat content of the Getz-Dotson Trough, Antarctica, by the Amundsen Sea Low"
<p>Contains MITgcm model output data to reproduce the analyses of the paper "Control of the oceanic heat content of the Getz-Dotson Trough, Antarctica, by the Amundsen Sea Low", by Dotto and co-authors, published in Journal of Geophysical Research-Oceans (doi: 10.1029/2020JC016113). The model simulation is presented and described in Kimura et al. (2017; doi: 10.1002/2017JC012926) and in Dotto et al. (2019; doi: 10.1175/JPO-D-19-0064.s1). See the 'ReadMe.txt' file for a description of the data. </p> <p> </p>
Body Heat Content and Dissipation in Obese and Normal Weight Adults
ClinicalTrials.gov study NCT00266500. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Effect of Alveolar Minute Ventilation on Respiratory Gas Heat Content
ClinicalTrials.gov study NCT01943227. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Heat Shock Transcription Factor 4 Promotes Malignant Biological Behavior in Colorectal Cancer Through Stiffness Rather Than Collagen Content.
GEO Series GSE273846. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
HOMAGE Monthly Time series of global average steric height anomalies and ocean heat content estimates from gridded in-situ ocean observations version 01
The [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO); Institute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09. The data are averaged over the quasi-global ocean domain (i.e., where valid values are defined; note that gaps exist, in particular towards polar latitudes), at monthly intervals. The input profiling data (i.e, temperature and salinity profiles at depth levels), editing, quality flags and processing schemes vary across the different gridded products, please refer to the documentation for each institution’s data product for details. Since 2005, the profiling data are dominated by the observations from the global Argo network (e.g., https://argo.ucsd.edu/), which comprises nearly 4000 active floats (as of 08/2022). Before 2005, non-Argo data such as XBT profilers were used, and the global ocean coverage was significantly more sparse. Data sets from SIO and BOA are Argo-only, while the others also include other observations, such as expendable bathythermographs (XBTs) and Conductivity-Temperature-Depth (CTD) observations. The data are active forward stream data files and will be frequently updated as new observations are acquired by Argo, and processed by the data centers.
Effect of the Steep Trendelenburg Position on Respiratory Gas Heat Content
ClinicalTrials.gov study NCT02153164. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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