Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

ACCESS-AM2 Southern Ocean cloud and radiation data and code for SHAP analysis

The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) and SHAP analysis code and data used for the study described in Fiddes et al. (2024) 'A machine learning approach for evaluating Southern Ocean cloud-radiative biases over the Southern Ocean in a global atmosphere model' accepted in Geoscientific Model Development Included files:  - code.zip, inc:            - pre-process_modis.ipynb: process the modis data, described in Fiddes et al. 2022 (https://doi.org/10.5194/acp-22-14603-2022)           - pre-process.ipynb: organises model and modis data for analysis. Produces the files: COSP_vars_MODIS_2015-2019.nc, COSP_vars_cg207_2015-2019.nc and COSP_vars_bx400_2015-2019.nc           - run_XGBoost+SHAP_control.ipynb: run the XGBoost model and SHAP analysis for the control run (bx400). Produces the files: SHAP_values_SWCRE_2015-2019_bx4002.nc, XGBoost_predicted_SWCRE_2015-2019_bx4002.nc, SHAP_interactions_bx400.nc           - run_XGBoost+SHAP_ice.ipynb: run the XGBoost model and SHAP analysis for the ice experiment run (cg207). Produces the files: SHAP_values_SWCRE_2015-2019_cg2072.nc, XGBoost_predicted_SWCRE_2015-2019_cg2072.nc           - analysis+plots_ML.ipynb: plots and stats presented in paper  - COSP_vars_MODIS_2015-2019.nc - COSP_vars_cg207_2015-2019.nc - COSP_vars_bx400_2015-2019.nc - SHAP_values_SWCRE_2015-2019_bx4002.nc - SHAP_values_SWCRE_2015-2019_cg2072.nc - XGBoost_predicted_SWCRE_2015-2019_cg2072.nc - XGBoost_predicted_SWCRE_2015-2019_bx4002.nc - SHAP_interaction_bx400.nc The cloud types used in this work can be found at https://doi.org/10.5281/zenodo.6004061

opencc-by-4.0Oct 2024View details →

Adjusted ERA5, COREv2 and JRA-55 products constrained by ocean observations

This dataset contains ERA5, JRA-55 and COREv2 air-sea flux fields that have been adjusted to match ocean heat and salt content change in EN4 and IAP ocean observations. It also contains estimes of meridional heat and freshwater transports, globally and in the Atlantic and Indo-Pacific, based on these adjusted air-sea surface flux fields. Please consult the README for more information on the dataset.  The net heat flux and net freshwater flux into the ocean have been adjusted using the "Optimal Transformation Method" (OTM), a watermass-based inverse method that uses physics-based constraints to close the observed ocean heat and salt budgets. The formulation of OTM and a model validation is provided at Zika & Sohail (2024). The process of producing these adjusted air-sea fluxes is described in Sohail & Zika (2025).

opencc-by-4.0Nov 2024View details →

Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products

v2 update: <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> The v2 data is used for the final published version of the Global Carbon Budget 2024. ----------------- Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products). There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs.   Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly): fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude (3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual. Fair data use statement: The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720). Acknowledgement: Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output." Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication. Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional 3D output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html

opencc-by-4.0Aug 2024View details →

Monthly global ocean carbonyl sulfide and carbon disulfide flux data (2000–2019)

This data product reports simulated monthly global ocean–atmosphere fluxes of carbonyl sulfide (OCS) and carbon disulfide (CS2) at 0.5° × 0.5° resolution (equivalent to 55 km × 55 km at the equator) between January 2000 and December 2019. Data are contained in two NetCDF files: <ul> <li>ocs-flux-monthly-2000-to-2019.nc: Monthly global ocean OCS fluxes, 2000–2019</li> <li>cs2-flux-monthly-2000-to-2019.nc: Monthly global ocean CS2 fluxes, 2000–2019</li> </ul> Data characteristics <ul> <li>Version: 1.0.1 (2025-04-07)</li> <li>Spatial coverage: global</li> <li>Spatial resolution: 0.5° longitude × 0.5° latitude</li> <li>Temporal coverage: 2000-01-15 thru 2019-12-15 (nominal timestamps fall on the 15th day of each month)</li> <li>Temporal resolution: monthly</li> </ul> Related manuscript Sun, W., Merder, J., Zhao, G., Lennartz, S. T., & Michalak, A. M. (2025). Tropical sources dominate the ocean carbonyl sulfide budget. Under consideration in Global Biogeochemical Cycles.

opencc-by-4.0Oct 2021View details →

profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean

The dataset includes  profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020   interpolated on regular 2 m grid for the World Ocean  Data was collected from open archive (Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)) <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a> Global array of Bio-Argo floats equipped with Chl (mg m−3) and PAR(μmol photons m-2 s-1) sensors at -60°S..60°N was used in this study. Data for 2013-2020 was downloaded from the IFREMER data archive (ftp://ftp.ifremer.fr/, <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a>). It includes 464 floats measuring Chl (~ 70000 profiles), and 167 floats measuring both PAR (~26000 profiles) and Chl. Before the analysis, the measurements of each Bio-Argo buoy were visually checked to filter the outliers in Chl or PAR data. After visual analysis about 1600 profiles of PAR and 2800 profiles of Chl were excluded from the dataset. Chl (mg m−3) was retrieved from a Chl fluorometer (excitation at 470 nm; emission at 695 nm) sensors of three types (FLBB, ECO-Triplet, or MCOMS). We use the raw fluorescence-based estimates of Chl (product “non-adjusted Chl”) derived directly from the measurements of fluorescence with factory calibration coefficients without the corrections on non-photochemical quenching, CDOM fluorescence, and other effects (see (<a href="http://www.argodatamgt.org/Documentation">http://www.argodatamgt.org/Documentation</a>)). A multispectral ocean color radiometer (OCR-504, SATLANTIC Inc.) was used to measure PAR. Only instantaneous PAR measurements made within ± 1.5 hours from noon (10:30-13:30 hours) were used. Then the data from all buoys were interpolated on regular 2-m grid and included in  one dataset.

opencc-by-4.0Nov 2021View details →

Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."

Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument." The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea.  This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour.  We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle.  This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.

opencc-by-4.0Nov 2021View details →

Exploring the Pocillopora cryptic diversity: a new genetic lineage in the western Indian Ocean or remnants from an ancient one?

Cryptic species and lineages have been widely reported during the last decades, particularly in the marine realm. Misidentifications and ignoring species complexes imply many consequences, notably biasing biodiversity and connectivity assessments, which in turn mislead our understanding of ecosystems and impact the effective design and management of conservation plans. Focusing on the Indo-Pacific coral genus Pocillopora, playing key roles in reef ecosystems as one of the main bio-constructors, we report the first Pocillopora PSH16 (ORF53; sensu Gélin et al. 2017, Mol Phylogenet Evol 109:430–446) colonies (N = 19) in the western Indian Ocean (Nosy Tanikely, Madagascar), 6,000 km further from its current distribution. Colonies were identified according to their mitochondrial Open Reading Frame (ORF) haplotype and Bayesian assignment tests based on 13-microsatellite genotypes. Additionally, we performed genetic structure and diversity analyses with sympatric colonies from other Pocillopora species and Pocillopora PSH16 colonies from the tropical southwestern Pacific, revealing (1) a weak clonal richness, (2) a weak genetic diversity and (3) a relative isolation for the newly reported PSH16 colonies. These colonies thus represent either a new, distinct and uncommon, genetic lineage, or isolated remnants of a wider one. In any case, unless specific management measures are implemented, their long-term maintenance seems compromised due to restricted gene flow within a restricted pool of genes.   This dataset contains the microsatellite genotypes analysed (98 Pocillopora colonies × 13 loci + ORF).  Missing data are encoded as "?". The sampling marine province and the population are indicated for each individual.

opencc-by-4.0Oct 2020View details →

Fifty-year changes of the world ocean's surface layer in response to climate change

This object includes two files. One (GlobalML_Trend_1970_2018.mat) contains the 1970-2018 trends of mixed-layer depth, 0-200 stratification, and pycnocline stratification, as described in: Sallée, J.B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Naveira Garabato, A., Sutherland, P., Kuusela, M., 2020, Fifty-year changes of the world ocean’s surface layer in response to climate change, 591, 592–598, https://doi.org/10.1038/s41586-021-03303-x. The second one (<a href="https://zenodo.org/api/files/15c80d5f-a6f9-4b7b-bde1-12de43732195/GlobalML_Climato_1970_2018.mat">GlobalML_Climato_1970_2018.mat</a>) contains a climatology of mixed-layer depth based on the same methodology as in Sallée et al., 2021 (nature; doi:https://doi.org/10.1038/s41586-021-03303-x) but without regressing a trend. The climatological field is therefore different than in the paper; more robust in region where trends are unphysical (e.g.  winter high latitude)

CCE LTER P1908 Upwelling Filament Ocean Acidification Phytoplankton Iron Incubation Experiments

Metatranscriptome assembly, read counts, and annotations from four sets of trace metal clean ocean acidification experimeints in the California Current Ecosystem. Experiments were conducted during August 2019 as part of the CCE LTER program. Samples were collected at the initial time point (T0), and three pCO2 treatments (400, 800, and 1200 ppm) with two time points for each experiment.  Poly-A selected mRNA was sequenced on an Illumina NovaSeq 6000 and then assembled with Trinity for each separate experiment. Proteins from the assembly were then predicted with Genemark S-T. Taxonomic annotation was performed with DIAMOND BLASTP searches against PhyloDB v1.076 and based on the Lineage Proability Index from the top hits. Functional annotation was similarly performed using KEGG with KEGG Orthology annotation based on KofamKOALA results. Read quantification was conducted with Bowtie2.  Predicted proteins from each assembly is provided in fasta format. The read counts and annotations for each experiment are in tab-delimited files.

OSSE dataset for assessing the sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain

The data stored in this repository are part of the manuscript entitled "The sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach" submitted in consideration for publication for in European Geosciences Union: Biogeosciences.   The netcdf file includes oceanographic (physical + biogeochemical) data, namely the partial pressure of carbon dioxide (pCO2) data at the surface ocean from the high-resolution (±10km) forced NEMO-PISCES coupled ocean model (BIOPERIANT12) and from the semi-idealized observing system simulation experiments (OSSEs) we performed.

Seawater isoprene loss and production rates across contrasting oceanic regions

Measured biological variables and isoprene process rate constants across contrasting regions of the global ocean. SST: sea surface temperature; SSS: sea surface salinity; ZML: mixed layer depth; U10 24h: wind speed at 10 m above sea surface, averaged over 24 hours; chla: chlorophyll-a concentration; BA: bacterial abundance; kloss: rate constant of isoprene loss in incubations (microbial degradation + chemical oxidation); kvent: rate constant of isoprene ventilation to the atmosphere; kmix: rate constant of isoprene vertical mixing by turbulent diffusion at the bottom of the mixed layer (negative means import into the surface mixed layer); total τ: turnover time due to all sinks; kprod: rate constant of isoprene production, assuming 24-h steady state for the isoprene concentration; sp. prod. rate: chla-normalized daily rate of isoprene production. Results of the coastal seawater dark incubations for isoprene loss kinetics. SST: lab-incubation temperature (within ±0.5ºC of the in-situ temperature); chla: chlorophyll-a concentration; isoprene concentration; std err: standard error of duplicate isoprene concentration measurements. The experiments were conducted with water from the Blanes Bay Microbial Observatory (coastal NW Mediterranean) and the coral reef lagoon of Moorea (French Polynesia). Results of the coastal seawater dark incubations for isoprene oxidation assays. SST: in-situ and incubation temperature; chla] chlorophyll-a concentration; isoprene concentration.

Radiocarbon in the land and ocean components of the Community Earth System Model: data to prepare figures

The files contain the data to plot the graphics displayed in the publication by Frischknecht, T., Ekici, A., Joos, F. Radiocarbon in the land and ocean components of the Community Earth System Model, Global Biogeochemical Cycles, 2022, in press.

opencc-by-4.0Dec 2019View details →

Antarctic Circumnavigation Expedition sample log: samples collected in the Southern Ocean during the austral summer of 2016/17.

Dataset abstract The Antarctic Circumnavigation Expedition (ACE) spent 90 days circumnavigating Antarctica on the R/V Akademik Tryoshnikov during the austral summer of 2016/17. This dataset provides a record of the samples that were collected during the expedition. Dataset contents <ul> <li>ace_sample_log.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> </ul> Dataset license This sample log is made available under a Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

Influence of the tropical Indian Ocean tripole on summertime cold extremes over central Siberia

These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, zonal and meridional winds.

Ensemble statistics for modelled Eddy Kinetic Energy in the Southern Ocean

This dataset contains surface eddy kinetic energy over the Southern Ocean region, sourced from a 50-member ensemble of 0.25° ocean model simulations. It is used in the paper "Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics" published in Journal of Geophysical Research - Oceans. This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (<a href="https://www.nemo-ocean.eu/doc">Madec, 2008</a>). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in <a href="https://doi.org/10.1016/j.ocemod.2013.02.004">Brankart, 2013</a>) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. Data is for the period 1979-2015. The sea level anomaly is found according to <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al (2020)</a> and converted into surface geostrophic velocity anomaly using the geostrophic relation. This velocity field is then used to calculate the eddy kinetic energy (EKE). Data is averaged over calendar month, and restricted to the latitude range 40°-60°S. A full description of this process is included in the companion paper. The dataset includes EKE files (eke_0??.nc), with monthy EKE saved for the period 1979-2015 for each ensemble member, and a single file (tau.nc) for the monthly-averaged wind stress over the same period.

Dataset associated with paper "Topographic hotspots of Southern Ocean eddy upwelling"

Data repository for Yung, Morrison and Hogg (2022) Topographic hotspots of Southern Ocean eddy upwelling, submitted to Frontiers in Marine Science   This repository contains processed data, created using scripts available in the github repository <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code</a>.   The data comes from the repeat year atmospheric forcing version of the ACCESS-OM2 modelling suite 0.1 degree model (see Kiss et al. (2020), http://www.cosima.org.au, model available at <a href="https://github.com/COSIMA/access-om2">https://github.com/COSIMA/access-om2</a>). The model was spun up for 270 years, and the next 10 years of output were used.   Daily resolution data was used in the calculation of quantities, which results in a large amount of raw data (~4TB for Southern Ocean latitudes (35-70S), 10 years). Therefore, we only provide relevant processed data. Details of these calculations are available in the above github repository.   Any quantities calculated along sea surface height contours are labelled with a letter. These are referred to in the following table.   | Letter |  SSH  | | :---:  | :---: | |   A    | -0.1m | |   B    | -0.2m | |   C    | -0.3m | |   D    | -0.4m | |   E    | -0.5m | |   F    | -0.6m | |   G    | -0.7m | |   H    | -0.8m | |   I    | -0.9m | |   J    | -1.0m | |   K    | -1.1m | |   L    | -1.2m | |   M    | -1.3m | |   N    | -1.4m | |   O    | -1.5m | |   P    | -0.15m| |   Q    | -0.25m| |   R    | -0.35m| |   S    | -0.45m| |   T    | -0.55m| |   U    | -0.65m| |   V    | -0.75m| |   W    | -0.85m| |   X    | -0.95m| |   Y    | -1.05m| |   Z    | -1.15m| |   Z1   | -1.25m| |   Z2   | -1.35m| |   Z3   | -1.45m|   There are two versions of the along-contour coordinates for each contour. The latlon named files are more useful for analysis, the other is used while computing transport across contours. These are made using <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb</a>.   Distance along contour files contain the cumulative distance along the contour in 10^3 km from 80E (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb</a>). Dimensions are contour index, counting from 80E. There are also segment length files of each part of the contour.   vh_eddy files contain the eddy transport across the contours, averaged over 10 years. These are calculated by taking the time mean of the residual transport (\(\overline{vh}\)), e.g. SO_L_vol_trans_across_contour_binned.nc, and subtracting the mean transport \(\overline{v}\overline{h}\), calculated from the time mean isopycnal thicknesses along contours (e.g. SO_L_dzu_across_contour_binned) and the time mean velocity, \(v = vh/h\) (calculated in <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb</a>). The full files are provided for the contour L (SSH=-1.2 m) as is provided in the paper manuscript Fig. 6. These eddy transports have dimensions of sigma1 and contour index (the two extra SO_L files have time too).   vh_eddy_interp files contain the interpolated eddy transports at hotspots, for the density range 1032.2kg/m^3 <= sigma_1 <= 1032.5kg/m^3. Calculation method provided at <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb</a>.   We also provide files that summarise the transport in density and SSH space for hotspots and the circumpolar total. See <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb</a> for calculation details. The exact names and specifications are provided in the README.   We provide 10 year mean files of the energy conversion and energy terms over the Southern Ocean latitude range. These are made by binning daily transports and layer thicknesses into sigma 1 bins over the Southern Ocean (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb</a>) and then calculating energy terms (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb</a>, <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb</a>)     The files named with contour_energies contain the EKE, Form stress and Reynolds stress averaged over 10 years but extracted along the same contours as eddy transport. <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb</a>   We also provide 10 year averaged density binned transport, layer thickness and densities.   Please refer to the README file for additional details.

CE-MS data for Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS)

These folders contain the original data used to develop the ACME software [1]. The Golden and Silver dataset come from simulations. They underrepresent the complexity in the CE-MS observations but provide additional data with known peak locations and peak properties. For more information see [1] The Dev-, Train-, and Test-set contain CE-MS [2] observations of Mix25 (a standard set of 25 organic compounds relevant to astrobiology) and labels for peak locations from subject matter experts.  The ACME software is available at:  https://github.com/JPLMLIA/OWLS-Autonomy    When using the data please cite this dataset [3] and the two papers below.  For further questions please reach out to: Steffen Mauceri,  Steffen.Mauceri@jpl.nasa.gov   References: [1] Mauceri, S., Lee, J., Wronkiewicz, M., et.al. (2022). Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS). (submitted) Earth and Space Science [2] Mora et al., F.(2021). Detection of biosignatures by capillary electrophoresis and mass spectrometry in the presence of salts relevant to missions to ocean worlds (submitted). Astrobiology. [3] 10.5281/zenodo.5849873 © 2022. California Institute of Technology. Government sponsorship acknowledged

Moana Ocean Hindcast

The Moana Ocean Hindcast model is a 25+ years 3D hydrodynamic hindcast for New Zealand waters. While this repository contains sample files, the full dataset can be downloaded from the project webpage at https://www.moanaproject.org/ Model results are available as hourly and daily average values for temperature, salinity, velocity (u, v, w), and sea surface elevation.  A full description of the simulation is provided by Souza et al. (in preparation for submission to Geoscientific Model Development).

opencc-by-4.0Feb 2022View details →

Evolution of Indian Ocean Paleoceanography and South-East Asian Climate during the Miocene in response to change in regional topography

This directory contain outputs of 9 paleo-climate simulations performed with the IPSL-CM5A2 and PISCES-v2 models. The simulations have used in a paper to be published in Nature Geoscience (2022) entitled "Divergent South Asian Monsoon Rainfall and Wind Histories due to topography effects" (Sarr et al.) that investigates the co-evolution of Arabian Sea upwelling and South Asian Monsoon rainfall and winds over the Miocene.  It includes simulations with both early Miocene and late Miocene paleogeography. SimulationsOutputs.tar directory contains NetCDF files with ocean, ocean biogeochemistry and atmosphere variables. Data are monthly average over the last 100 years of each simulation. TopoMiocene.tar contains the paleogeographies used for the simulations.  More informations on output contents can be find in README_detailsOutput.md document as well as within the Methods section of the publication. PISCES_update.tar contains updated routines for the PISCES-offline model (Aumont et al., 2015) that have been used for the publication. It contains a REAME.md file that explain how to include those updates within the reference code.