Find research datasets worth reusing

GRACE/GRACE-FO Level-4 Monthly Global Ocean Mass Anomaly version 01 from NASA MEaSUREs HOMaGE project

This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist). This file contains the goma time series based on the spherical harmonic gravity fields provided by the G/GFO SDS centers: JPL, CSR, GFZ. The data are frequently updated as new monthly observations are acquired by the GFO mission. The processing of the spherical harmonics gravity field coefficients is as follows: (1) GAD + GSM: the monthly de-aliasing product GAD is added back to the GSM L2 gravity fields; (2) [GSM + GAD] coefficients are averaged over the global ocean with a coastal buffer of 300 km (to avoid land-ocean leakage); (3) the spatial mean of atmospheric loading of the entire global ocean domain is removed (via the GAA L2 data product). A GIA correction using the ICE-6GD model (Peltier et al., 2018) is applied.

NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global daily VR2017 (NOBM_DAY) at GES DISC

This is the assimilated daily data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth > 200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).

ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans

The River Carbon Flux data set represents estimates for the riverine export of carbon and of sediments. This data set includes the amounts of carbon and of sediments that are discharged to the oceans by rivers for each coastal grid point which receives river inputs. This data set contains three compressed (*.zip) files: the original data at 2.5 x 2.0 degrees, and global maps at spatial resolutions of 0.5 and 1.0 degree which the ISLSCP II staff has created from the original data.

restrictednotspecifiedJun 2025View details →

MODIS/Terra Ocean Reflectance Daily L2G-Lite Global 1km SIN Grid V006

The MODOCGA Version 6 data product was decommissioned on July 31, 2023.The MODOCGA Version 6 Level 2 Gridded Lite (L2G-lite) Ocean Reflectance product provides an estimate of the surface spectral reflectance data from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) bands 8 through 16. Data have been corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. MODOCGA is a daily land product with a pixel size of 1 kilometer (km). The product is referred to as ocean reflectance because bands 8 through 16 are used primarily to produce ocean products. The MODOCGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either a full or compact format layer within the Hierarchical Data Format (HDF) file.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).Improvements/Changes from Previous Versions* New product for MODIS Version 6.

NRT AMSR2 Unified L2B Global Swath Ocean Products V1

The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The GCOM-W1 NRT AMSR2 Unified L2B Global Swath Ocean Products is a swath product containing global sea surface temperature over ocean, wind speed over ocean, water vapor over ocean and cloud liquid water over ocean, using resampled NRT Level-1R data provided by JAXA. This is the same algorithm that generates the corresponding standard science products in the AMSR SIPS. The NRT products are generated in HDF-EOS-5 augmented with netCDF-4/CF metadata and are available via HTTPS from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/ocean/. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. The AMSR SIPS produces AMSR2 standard science quality data products, and they are available at the NSIDC DAAC.

restrictednotspecifiedJun 2025View details →

MODIS/Aqua Ocean Reflectance Daily L2G-Lite Global 1km SIN Grid V006

The MYDOCGA Version 6 data product was decommissioned on July 31, 2023.The MYDOCGA Version 6 Level 2 Gridded Lite (L2G-lite) Ocean Reflectance product provides an estimate of the surface spectral reflectance data from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands 8 through 16. Data have been corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. MYDOCGA is a daily land product with a pixel size of 1 kilometer (km). The product is referred to as ocean reflectance because bands 8 through 16 are used primarily to produce ocean products. The MYDOCGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either a full or compact format layer within the Hierarchical Data Format (HDF) file.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).Improvements/Changes from Previous Versions* New product for MODIS Version 6.

Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6 Version 5.2

This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.2. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 5.1 and version 5.2 releases are described in detail in the user handbook.

SEASAT SCATTEROMETER DERIVED GLOBAL GRIDDED MONTHLY OCEAN WIND STRESS (Chelton)

Contains monthly averaged ocean surface wind stress derived from Seasat-A Scatterometer (SASS) wind retrievals, from July 1978 until October 1978, gridded on a 2.5-degree by 2.5 degree global grid. The vector average wind stress is stored in units of dynes per centimeter squared (dyn/cm^2). Data is provided in formatted ASCII text. The primary data set used to construct these wind stress fields consists of 96 days of SASS vector winds supplied by Robert Atlas at GSFC. The directional ambiguities in the raw SASS data had been objectively removed using the GSFC Laboratory for Atmospheric Sciences atmospheric general circulation model.

NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global monthly VR2017 (NOBM_MON) at GES DISC

This is the assimilated monthly data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth >200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).

opencc-by-4.0Jan 2020View details →

Dynamic Ligand Impacts on Dissolved Iron Distributions in a Global Ocean Biogeochemical Model

Annual mean CESM ocean output files on the coarse resolution gx3v7 grid. Archived here in support of the paper submitted to JAMES by Sherman et al. "Dynamic Ligand Impacts on Dissolved Iron Distributions in a Global Ocean Biogeochemical Model".   gdev.450 = dynamic ligand gdev.457 = constant ligand = 1 nM gdev.460 = implicit ligand gdev.458 = dynamic ligand + Sol Fe Depo X3 gdev.459 = constant ligand = 1 nM + Sol Fe Depo X3 gdev.461 = implicit ligand + Sol Fe Depo X3

opencc-by-4.0Jan 2020View details →

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Surface ocean DMS concentrations and sea-to-air flux estimated using an Artificial Neural Network model.  <a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/DMS_Concentration_monthly_mean.csv?versionId=4dabc517-22c9-4829-84e3-5df97ef8aa74">DMS_Concentration_monthly_mean.csv</a> and <a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/DMS_Flux_Wang2020.mat?versionId=a5e8de9f-5962-4940-974b-e14731a0c466">DMS_Flux_Wang2020.mat</a> are previous versions based on BGD paper. <a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/DMS_clim_Wang20_v1.mat?versionId=02ca48ed-306e-44da-a406-3ac48f75e811">DMS_clim_Wang20_v1</a>.mat and <a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/Sea2Air_Flux_Wang20_v1.mat?versionId=6244f1bc-26d1-4f5e-b6ea-93be132bf6d3">Sea2Air_Flux_Wang20_v1.mat</a> are newer versions based on BGD paper revision. PMEL_NAAMES_* data are raw DMS data along with environmental parameters, so interested user can play with the models. The ANN models can be found in the the following repository: <a href="https://github.com/weileiw/ANN-DMS-code">https://github.com/weileiw/ANN-DMS-code</a>

opencc-by-4.0Jun 2020View details →

Global pattern formation of net ocean surface heat flux response to greenhouse warming

Datasets used for figures in the following paper. Hu, S., Xie, S. P., and Liu, W. (2020) Global pattern formation of net ocean surface heat flux changes in a warming climate. J. Clim., doi: https://doi.org/10.1175/JCLI-D-19-0642.1.

opencc-by-4.0Nov 2024View details →

Supporting Data for Hahn et al. J. Climate: Contribution of AMOC Decline to Uncertainty in Global Warming via Ocean Heat Uptake and Climate Feedbacks

This dataset includes CESM2 model output for the dehose4x experiment in Hahn et al.: “Contribution of AMOC Decline to Uncertainty in Global Warming via Ocean Heat Uptake and Climate Feedbacks” submitted to Journal of Climate. The piControl and abrupt-4xCO2 experiments for CESM2 and other CMIP6 models can be found in the Earth System Grid Federation (ESGF) repository at <a href="https://esgf-node.llnl.gov/projects/esgf-llnl/" target="_blank" rel="noopener">https://esgf-node.llnl.gov/projects/esgf-llnl/</a>.

opencc-by-4.0Jul 2024View details →

Global ocean carbon uptake enhanced by rainfall : CO2 flux datasets

1) NETCDF files containing the annual mean maps of the CO2 flux diagnostics considering the different effects of rain over the period 2008-2018 (Parc et al. 2024) <ul> <li>map_statflux_REF.nc : Diagnostic reference flux taking into the ocean skin effect and formation of diurnal warm layers (Bellenger et al. 2017)</li> </ul> - Diagnostics based on the ERA5 reanalysis rain dataset (Hersbach et al. 2020) :  <ul> <li>map_statflux_KR_rERA5.nc : Diagnostic flux integrating the impact of rain-induced turbulence (Harrison et al. 2012) to the reference flux</li> <li>map_statflux_DIL_DS1_rERA5.nc : Diagnostic flux integrating the impact of rain-induced dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_DIL_DS2_rERA5.nc : Diagnostic flux integrating the impact of rain-induced dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_INT_DS1_rERA5.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_INT_DS2_rERA5.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_WD_rERA5.nc : Additional diagnostic CO2 flux due to wet deposition (Komori et al. 2007)</li> </ul> - Diagnostics based on the IMERG satellite-based rain dataset (Huffman et al. 2023) :  <ul> <li>map_statflux_KR_rIMERG.nc : Diagnostic flux integrating the impact of rain-induced turbulence (Harrison et al. 2012) to the reference flux</li> <li>map_statflux_DIL_DS1_rIMERG.nc : Diagnostic flux integrating the impact of rain-induced dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_DIL_DS2_rIMERG.nc : Diagnostic flux integrating the impact of rain-induced dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_INT_DS1_rIMERG.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_INT_DS2_rIMERG.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_WD_rIMERG.nc : Additional diagnostic CO2 flux due to wet deposition (Komori et al. 2007)</li> </ul> All these files contain three variables :  <ul> <li>MFLUX : Annual mean of diagnostic flux (gC/m2/yr)</li> <li>SFLUX : Standard deviation of diagnostic flux</li> <li>WEIGHT : Number of data time steps used for the statistics</li> </ul> 2) Excel file containing the monthly means of the global ocean CO2 sink (PgC/y) corresponding to all the different diagnostics previously described (Parc et al. 2024) : GlobalOceanSink_2008-2018_rain_monthly_diagnostics.xlsx References :  <ul> <li>Bellenger, H. et al. Extension of the prognostic model of sea surface temperature to rain‐induced cool and fresh lenses. J. Geophys. Res. Oceans 122, 484–507 (2017).</li> <li>Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).</li> <li>Harrison, E. L. et al. Nonlinear interaction between rain- and wind-induced air-water gas exchange. J. Geophys. Res. Oceans 117, (2012).</li> <li>Supply, A., Boutin, J., Reverdin, G., Vergely, J.-L. & Bellenger, H. Variability of Satellite Sea Surface Salinity Under Rainfall. in Satellite Precipitation Measurement (eds. Levizzani, V. et al.) vol. 69 1155–1176 (Springer International Publishing, Cham, 2020).</li> <li>Komori, S., Takagaki, N., Saiki, R., Suzuki, N. & Tanno, K. The Effect of Raindrops on Interfacial Turbulence and Air-Water Gas Transfer. in Transport at the Air-Sea Interface (eds. Garbe, C. S., Handler, R. A. & Jähne, B.) 169–179 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2007). doi:10.1007/978-3-540-36906-6_12.</li> <li>Huffman, G., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. & Tan, J. GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07. NASA Goddard Earth Sciences Data and Information Services Center https://doi.org/10.5067/GPM/IMERG/3B-HH/07 (2023).</li> </ul>

restrictedcc-by-4.0Oct 2024View details →

Codes for "Intensifying Inverse KE Cascade Over Energetic Oceans Under Global Warming" By Geng et al. Submitted to Nature Climate Change

This repository contains the necessary codes for the study of "Intensification of Oceanic Inverse Energy Cascade Under Global Warming" . Specifically, this repository contains the following items: (1) The codes for computing the global kinetic energy cascade, the four metrics of inverse KE cascade and their trends. (2) The function codes needed for coarse-graining filtering and trend analysis. (3) Necessary data for running the programs at MATLAB.

openJul 2024View details →

Data from: Global distribution of siphonophores across horizontal and vertical oceanic gradients

This study gives a worldwide view of where siphonophores live in the ocean, using DNA data from samples collected during a global expedition. We looked at 77 samples from different depths and oceans. We found about a quarter of all known siphonophore species, some in places they hadn’t been seen before. In total, we identified 42 species. Some species were found to have wider distributions than previously thought. The study also looked at variations within species. Siphonophores with a certain feature (pneumatophores) were less common in shallower waters but more common in deeper waters. This is the first time that this kind of DNA data has been used to study these creatures, showing it’s a useful method for studying organisms that are often damaged when collected with nets. This dataset supports the original research published under the same title: Global distribution of siphonophores across horizontal and vertical oceanic gradients.