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12,719 results for “Pacific”
Survey of Pacific Northwest public land managers science and values project, 2023
This dataset records survey data about public land managers who work in Oregon and Washington (Forest Service, Bureau of Land Management, Fish and Wildlife Service, National Park Service, Oregon Department of Forestry, Washington Department of Natural Resources). Data was collected in 2023 via the online survey platform Qualtrics. Data collection is complete. The dataset includes measures of managers beliefs about 1) variable density thinning of mature growth forests, 2) salvage logging of burned areas, 3) translocation of plant species from hotter and drier seed zones to adapt to climate change. It includes how managers evaluate the usefulness of scientific evidence and the soundness of action prescriptions for each of the three management issues Respondents were randomly assigned to either receive long-term or short-term studies, and positive or negative results. The dataset includes measures of sense of belonging (how much managers believe they belong at their workplace) and measures of public support/public threat (how much they believe the public understands and supports the actions they take on the landscape). The dataset includes respondent agency.
Long-term growth, mortality and regeneration of trees in permanent vegetation plots in the Pacific Northwest, 1910 to present
A network of more than 130 permanent vegetation plots provides long-term information on patterns and rates of forest succession in most of the major forest zones of the Pacific Northwest. The plot network extends from the coast to the Cascades in western Oregon and Washington and east to ponderosa pine forests in the Oregon Cascades. Most of the permanent plots were established during two intervals: from 1910 to 1948, and from 1970 to 1989. The earlier plots were established by U.S. Forest Service researchers to quantify timber growth in young stands of important commercial species and to help answer other applied forestry questions. The more recent period of plot establishment began under the Coniferous Forest Biome program of the International Biological Program during the 1970s, and continued under the Long-term Ecological Research program. A broader set of objectives motivated plot establishment since 1970, especially quantification of composition, structure, and population and ecosystem dynamics of natural forests. Plots have one of three spatial arrangements: (1) contiguous rectangles subjectively placed within an area of homogeneous forest; (2) circular plots subjectively placed within an area of homogeneous forest; and (3) circular plots systematically located on long transects to sample an entire watershed, ridge, or reserve. Rectangular study areas are mostly 1.0 ha or 0.4 ha (1.0 ac) in size (slope-corrected). Circular plots are 0.1 ha (0.247 ac), not corrected for slope. The tree stratum is the focus of work in closed-forest study areas. All trees larger than a minimum diameter (5 cm for most areas) are permanently tagged. Plots are censused every 5 or 6 years. Attributes measured or assessed at each census include tree diameter, tree vigor, and the condition of the crown and stem. The same attributes are recorded for trees (ingrowth) that have exceeded the minimum diameter since the previous census. In many plots tree locations are surveyed to provide a
Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition (2016-2017)
<p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition, 2016-2017.</p> <p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations measured on seawater samples from the Southern Ocean. Samples were collected with a trace metal clean rosette system to a maximum depth of 1000 m during Legs 1 and 2 of the Antarctic Circumnavigation Expedition (ACE), 2016-2017. Samples were filtered through Akropak Supor filters (0.2 um) in a class 100 clean container, acidified to pH ≤ 2 and stored until analysis (>6 months). Samples from Leg 1 (TMR Casts 3-7) were collected during a transect from Cape Town, South Africa to Hobart, Australia. Samples from Leg 2 (TMR casts 8-20) were collected during a transect from Hobart, Australia to Punta Arenas, Chile. Data cover environments near subantarctic and Antarctic islands (TMR 3, 4, 13-15), in the Mertz Glacier Polynya (TMR 11-12) and near the Antarctic Peninsula (TMR 18), as well as meridional transects to and from the Antarctic continent (TMR 7-12, TMR 18-20).</p>
Tropical Pacific SST and wind anomalies generated by a Nonlinear Inverse Model
<p>Tropical Pacific (40S-40N; 120E-50W) sea surface temperature (SST), zonal wind (U) and meridional wind (V) anomalies generated by the Nonlinear Inverse Model described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5). The data consists in 99 realizations (<a href="../api/records/10411023/draft/files/NLIM_output_085.nc/content" target="_blank" rel="noopener noreferrer">NLIM_output_XXX.nc</a>) of 1,000yrs each emulating SST, U, and V monthly anomalies conditions during 1980-2020 (<a href="../api/records/10411023/draft/files/Monthly_obs_1980_2020.nc/content" target="_blank" rel="noopener noreferrer">Monthly_obs_1980_2020.nc</a>) given in a 2.5deg-2.5deg grid. For observations, we used the NOAA Extended Reconstruction SST v5 reanalysis (SST; Huang et al., 2017) and NCEP-NCAR reanalysis (winds; Kalnay et al., 1996) The observed anomalies are calculated as described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5).</p> <p>Given that the stochastic forcing considered is white in time and space (https://doi.org/10.1038/s41612-024-00675-5; Methods, section "Offline simulation of SSH_{12}, PC2, and spatial patterns fron nonlinear inverse model output"), the spatial patterns and lead-lag relationships are better identified using composites. A modification of the methodology that allows for spatially coherent stochastic forcing will be implemented in a future article.</p> <p>When using the data please cite https://doi.org/10.5281/zenodo.10411023 (the data) and Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5; for the methodology). </p> <p>Any question, please contact Cristian Martinez-Villalobos at his email cristian.martinez.v@uai.cl</p> <p>References</p> <p>Martinez-Villalobos, C., Dewitte, B., Garreaud, R.D. <em>et al.</em> Extreme coastal El Niño events are tightly linked to the development of the Pacific Meridional Modes. <em>npj Clim Atmos Sci</em> <strong>7</strong>, 123 (2024). https://doi.org/10.1038/s41612-024-00675-5</p> <p>Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate 30, 8179–8205 (2017).</p> <p>Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77, 437–471 (1996).</p> <p> </p>
Time-series of shoreline change along the Pacific Rim
<p>This repository contains 40 years of tidally-corrected shoreline change time-series for most sandy coastlines around the Pacific Rim derived from Landsat imagery. <br><br><strong>The time-series were last updated in May 2025. For the latest data always refer to <a href="http://coastsat.space/">http://coastsat.space/</a>.</strong></p> <p>The dataset was used to investigate the impact of ENSO on beach erosion and accretion in:<br>- Vos, K., Harley, M.D., Turner, I.L. <em>et al.</em> Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation. <em>Nat. Geosci.</em> <strong>16</strong>, 140–146 (2023). <a href="https://doi.org/10.1038/s41561-022-01117-8">https://doi.org/10.1038/s41561-022-01117-8</a><em> </em></p> <p><em>CoastSat </em>was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2025. The <em>Coastsat </em>toolbox is publicly available at https://github.com/kvos/CoastSat and described in <em>Vos et al. 2019, </em><a href="https://doi.org/10.1016/j.envsoft.2019.104528">https://doi.org/10.1016/j.envsoft.2019.104528</a>. The time-series of shoreline change were tidally-corrected along cross-shore transects using tide levels from a global tide model (FES2022) and a satellite-derived estimate of the beach slope (as described in <em>Vos et al. 2020, "Beach slopes from satellite-derived shorelines", </em><a href="https://doi.org/10.1029/2020GL088365">https://doi.org/10.1029/2020GL088365</a><em>)</em>.</p> <p>This dataset covers wave-dominated sandy coasts in the Pacific basin where Landsat imagery was available, including a total of 3,000 beaches and more than 100,000 cross-shore transects (100-m alongshore spaced). This includes coastlines in Australia, New Zealand, Japan, Chile , Peru, Mexico and USA (California and Hawaii only).</p> <p>The data is structured as follows:</p> <ul> <li>There is a folder for each country (e.g. Australia)</li> <li> In the country folder, there is a folder for each site (e.g. aus0001, aus0002 etc)</li> <li>In the site folder, there are 4 CSV files: <ul> <li><em>time_series_tidally_corrected.csv</em>: this file contains the tidally-corrected time-series of shoreline change along each transect belonging to the site (e.g. aus0001-0001, aus0001-0002 etc). This is the final product used for coastal change analyses.</li> <li><em>time_series_raw.csv</em>: this file contains the raw time-series of shoreline change, which have not be tidally-corrected. Note that each image is taken at a different stage of the tide.</li> <li><em>tide_levels_fes2022</em>: this file contains the tide levels at the time of image acquisition extracted from FES2022 (global tide model publicly available on AVISO+).</li> <li><em>transect_coordinates_and_beach_slopes.csv</em>: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect, including confidence intervals.</li> </ul> </li> </ul> <p> In addition, there are four geospatial layers (.GEOJSON) which contain important spatial information:</p> <ul> <li> <em>polygons.geojson</em>: this layer contains the polygons that were used to run CoastSat for each beach.</li> <li><em>shorelines.geojson</em>: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat). Each beach has the following attributes: beach length, median orientation, median slope, and mean springs tidal range.</li> <li><em>transects.geojson</em>: this layer contains the cross-shore transects, which are spaced 100 m along each beach. Each transect has the following attributes: orientation, beach slope, linear trend (in m/year), alongshore distance relative to the northern end of the beach (absolute and normalised).</li> <li><em>transects_edit.geojson</em>: this layer is the same as transects.geojson but the transects that are not suitable for shoreline mapping were manually deleted (rocky shores, submerged reef, coastal lagoons and inlets, coastal defences etc...).</li> <li><em>transects_ENSO.geojson</em>: this layer (similar to transects.geojson) contains the transects that were used to analyse ENSO effects on shoreline changes in the Pacific (a total of 83,000).</li> </ul> <p> </p>
Data from: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean
<p><strong>Abstract</strong></p> <p>In the relatively unproductive waters of the tropical ocean, islands can enhance phytoplankton biomass and create hotspots of productivity and biodiversity that sustain upper trophic levels, including fish that are crucial to the survival of islands’ inhabit- ants. This phenomenon, termed the island mass effect 65 years ago, has been widely described. However, most studies focused on individual islands, and very few documented phytoplankton community composition. Consequently, basin-scale impacts on phytoplankton biomass, primary production and biodiversity remain largely unknown. Here we systematically identify enriched waters near islands from satellite chlorophyll concentrations (a proxy for phytoplankton biomass) to analyse the island mass effect for all tropical Pacific islands on a climatological basis. We find enrichments near 99% of islands, impacting 3% of the tropical Pacific Ocean. We quantify local and basin-scale increases in chlorophyll and primary production by contrasting island-enriched waters with nearby waters. We also reveal a significant impact on phytoplankton community structure and biodiversity that is identifiable in anomalies in the ocean colour signal. Our results suggest that, in addition to strong local bio- geochemical impacts, islands may have even stronger and farther-reaching ecological impacts.</p> <p> </p> <p><strong>Data set and method</strong></p> <p>For each island, an algorithm detected the Island Mass Effect (IME) from climatological satellite chlorophyll maps as a contour enclosing the island and surrounding high-chlorophyll waters, termed IME region. A reference (REF) region of the same size was detected alongside each IME region, enclosing nearby non-IME waters. The IME and REF regions were used to build the IME database described in Messié et al. (2022), that includes variables related to satellite chlorophyll, primary production, and PHYSAT phenoclass diversity metrics in IME and REF regions on a climatological basis.</p> <p>This data set includes 4 files:</p> <ul> <li>island_database.csv: information regarding the 664 islands and shallow reefs where the IME detection was applied</li> <li>IME_masks.nc: monthly climatological masks for the IME and REF regions for all islands,</li> <li>IME_database.nc: IME database as a function of island and climatological month (chlorophyll, primary production, and phenoclass-derived variables calculated within the IME and REF masks).</li> <li>PHYSAT_climatology.nc: climatological maps for each PHYSAT phenoclass, used to calculate phenoclass-derived variables in the IME database.</li> </ul> <p>See details regarding data sources and calculations in <a href="https://rdcu.be/cO4qr">Messié et al. (2022)</a>.</p>
Global monthly percentage of vegetation cover (MODIS FCover MODV1A product: America, Pacific)
<p>Monthly Global FCover product generated from MODIS data. Dataset represent monthly gap-filled FCover estimates the period 2000-2015 over Pacific and America. FCover was estimated using linear spectral mixture analysis and interpolated using empirical orthogonal functions algorithm to take advantage of all non-missing available pixels in both the spatial and temporal dimensions to gap-fill missing satellite observations. The global product of vegetation cover (as percentage of cover) based on MODIS images with monthly variation can be used as a critical support for several indicators related to ecologically based modelling.</p>
Seawater dissolved chromium concentration, redox speciation, and stable isotope composition in the North Pacific Ocean
<p>Dissolved seawater chromium concentrations, redox speciation, and stable isotope composition were measured on samples collected in the North Pacific Ocean. Samples were collected over diel cycles (2 for stations 1-5, 1 for station 6) on board the RV Kilo Moana cruise KM1713 from Seward Alaska to Honolulu Hawai’i. Sampling stations spanned the subarctic North Pacific (stations 1 & 2), the dynamic subarctic-subtropical convergence zone (stations 3 and 4) and the subtropical North Pacific (stations 5 and 6). Chromium was enriched from filtered samples by Mg(OH)<sub>2</sub> co-precipitation and analyzed by MC-ICP-MS using either isotope dilution (Cr redox speciation) or double spike methodology.</p>
Dataset for "Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity."
<p>This repository contains the tropical Pacific sea surface temperature and global precipitation data from the CESM1 time slice experiments, which were used for the analysis presented in Karamperidou & DiNezio (2022), Nature Communications (https://www.nature.com/articles/s41467-022-34880-8)</p> <p> </p> <p>From Karamperidou & DiNezio (2022):</p> <p>“To assess the response of ENSO flavors to orbital forcing over the past 12,000 years (12ka), we use a suite of time-slice experiments in 3ka intervals with version 1 of the Community Earth System Model (CESM1). Each experiment is 400-600 years long and was run until the surface climate and oceanic processes controlling tropical climate, such as the depth of the thermocline in the equatorial Pacific or the Atlantic Meridional Overturning Circulation (AMOC), have reached equilibrium. All simulations exhibit minimal drift in global mean surface temperature (less than 0.05<sup>o</sup>C per century), tropical mean surface temperature (less than 0.04<sup>o</sup>C per century), the depth of the equatorial thermocline in the Pacific (less than 0.3m per century), and the strength of the AMOC (less than 0.25 Sv per century) during the periods used in the analyses. With the exception of the 12 ka BP interval which includes ice sheet changes and lower greenhouse gases, the primary forcing in the 0, 3, 6, and 9 ka BP intervals is changes in Earth's precession, and each simulation branched off its preceding one, starting from 0ka sequentially through the Holocene. The maximum TOA energetic imbalance does not exceed 0.45 Wm<sup>-2</sup>, which is much smaller than the imposed radiative forcing.”</p> <p> </p> <p> </p> <p>Karamperidou, C., DiNezio, P.N. Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. <em>Nat Commun</em> <strong>13</strong>, 7244 (2022). https://doi.org/10.1038/s41467-022-34880-8</p>
Resources from: Disparate patterns of genetic divergence in three widespread corals across a pan-pacific environmental gradient highlights species-specific adaptation trajectories
<p>The following files are contained in this repository:</p> <p><br> README.Hume_et_al_2022.zenodov4.txt - This document.</p> <p>scripts.Hume_et_al_2022.zenodov4.pdf - Contains the scripts, or locations of the scripts, used to conduct the data analyses detailed in the associated manuscript.</p> <p>acknowledgements_local_authorities.Hume_et_al_2022.zenodov1.pdf - Acknowledgements of local authorities for the collection of samples used in the associated study.</p> <p>TaraPacific_SST_timeseries_mean_productsV2mai2021.Hume_et_al_2022.zenodov1.csv - The historical temperature data set used for the RDA, Mantel tests and gradient Forest analysis.</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz - The Millepora SNPs referred to as 'raw' in the Methods of the associated manuscript.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Millepora raw SNPs.</p> <p>Millepora_REF_orthologue_genes.Hume_et_al_2022.zenodov2.csv - The Millepora gene list referred to as 'target genes' in the Methods of the associated manuscript.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz - The Millepora de novo assembled transcriptome.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz.md5 - md5 of the Millepora de novo assembled transcriptome.</p> <p> </p> <p>mtORF Phylogeny</p> <p>TP-Johnston_mtORF-Pocillo.fa = all sequences</p> <p>TP-Johnston_mtORF-Pocillo.mafft.fa = mafft alignment</p> <p>TP-Johnston_mtORF-Pocillo.mafft.ML.nwk = ML tree newick</p> <p> </p> <p>Hellberg genotype network Porites</p> <p>TP-Hellberg_MM32-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_MM100-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_ATPaseB.nex = all aligned sequences for this locus with indels encoded,</p> <p>TP-Hellberg_POFAD.nex = POFAD multilocus genotypic distance,</p> <p>TP-Hellberg_Splitstree.nex= Multilocus genotype network in nexus format</p> <p><br> Gradient Forest Analysis</p> <p>Poc_abund.csv - Pocillopora SSH Occurrences per Site er Island</p> <p>Por_abund.csv - Porites SSH Occurrences per Site er Island</p> <p>mean_depth_por.csv - per site per island mean depth among Porites colonies</p> <p>mean_depth_poc.csv - per site per island mean depth among Pocillopora colonies</p>
Journey North - Gray Whale observations by volunteer community scientists across the Eastern Pacific Ocean (1997-2020)
This data package contains Gray Whale migration data consisting of 1,546 total observational reports from 1997 - 2020 across the Eastern Pacific Ocean. These data were collected by 163 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Gray Whale Project is a study of Gray Whale phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked the migration of Gray Whales (Eschrichtius robustus) through the Eastern Pacific Ocean. Observers also provide estimates of the number of whales sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Gray Whale Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Navigation and meteorological data collected during the Tara Pacific Expedition 2016-2019
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems 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. 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. Here we provide the continuous dataset originating from navigation and meteorological instruments acquiring continuously during the full course of the campaign.</p> <p> </p> <p>Variables/ descriptions and units:</p> <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>units</td> </tr> <tr> <td>'dt'</td> <td>date-time stamp</td> <td>iso UTC</td> </tr> <tr> <td>'lat'</td> <td>latitude</td> <td>decimal degree</td> </tr> <tr> <td>'lon'</td> <td>longitude</td> <td>decimal degree</td> </tr> <tr> <td>'flag_origin_latlon'</td> <td>origin of the latitude and longitude</td> </tr> <tr> <td>'cog'</td> <td>course over ground</td> <td>degree</td> </tr> <tr> <td>'sog'</td> <td>speed over ground</td> <td>knots</td> </tr> <tr> <td>'sst_batos'</td> <td>Sea surface temperature measured by the navigation station</td> <td>°C</td> </tr> <tr> <td>'temperature_atm'</td> <td>Atmospheric temperature</td> <td>°C</td> </tr> <tr> <td>'pressure_sealevel'</td> <td>Atmospheric presure</td> <td>hp</td> </tr> <tr> <td>'relative_humidity'</td> <td>relative humidity </td> <td>%</td> </tr> <tr> <td>'apparent_windspeed_bow'</td> <td>apparent wind speed</td> <td>knots</td> </tr> <tr> <td>'apparent_winddir_bow'</td> <td>wind direction from the bow</td> <td>degree</td> </tr> <tr> <td>'apparent_wind_trueN'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'true_wind_speed'</td> <td>knots</td> </tr> <tr> <td>'true_wind_dir'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'sunzenith'</td> <td>sun position relative to zenith</td> <td>radian</td> </tr> <tr> <td>'sunazimuth'</td> <td>sun position relative to north</td> <td>radian</td> </tr> </tbody> </table>
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems 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. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
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>
Northeast Pacific deoxygenation and volcanism during the last deglaciation
<p><strong>File structure:</strong></p> <p><strong>Source data</strong></p> <ul> <li>Contains source data to main text and extended data figures. Data sources are identified within and listed below.</li> </ul> <p> </p> <p><strong>Computer codes</strong></p> <ul> <li><strong>Geochemical inversion</strong> –</li> </ul> <ul> <li>“Data for geochemical inversion.xlsx”: contains Gulf of Alaska sediment and volcanic/terrigenous endmember geochemical data used for data inversion.</li> <li>“geochemical inversion.r”: R script used to perform geochemical inversion.</li> <li>“GOA inversion fraction.csv”: Result of the geochemical inversion, including the volcanic and terrigenous fractions in each Gulf of Alaska sediment sample.</li> <li>“GOA inversion residual.csv”: Result of the geochemical inversion, including the residuals of each element.</li> <li><strong>cluster volcanic geochemical data </strong>– <ul> <li> “Database of volcanic geochemistry.xlsx”: compiled database of the geochemistry of volcanic endmember samples.</li> <li> “cluster.r”: R script used to perform cluster analysis on the volcanic samples.</li> <li> “Clustered volcanic data.csv”: the results of cluster analysis.</li> <li> “Dendroplot.r”: R script used to plot the dendrogram for the cluster analysis.</li> <li> “dendro 15 complete euclidean.pdf”: The dendrogram.</li> <li> “Volcanic endmembers.csv”: final geochemical volcanic endmembers based on the cluster analysis.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Global volcanic eruption compilation –</strong></li> </ul> <ul> <li>“eruption.database.intcal20.xlsx”: This file includes the eruption database compiled by this study, as well as the previous compilation of Huybers and Langmuir 2009 EPSL (referred to as HL09).</li> <li>“eruption.ratio.R” and “volc.freq.R”: These are R scripts that compute the eruption frequency of glaciated and unglaciated volcanoes using the eruption database. “eruption.ratio.R”calls the function inside “volc.freq.R”.</li> <li>“Volcanic eruption summary.xlsx”: This file contains the outputs of the R scripts.</li> </ul> <p> </p> <ul> <li><strong>PISM sensitivity experiment – </strong></li> </ul> <ul> <li>“ciscyc.5km.epica.ts.10a.nc” and other netcdf files: The output of PISM sensitivity experiments, from <em>Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3606536">https://doi.org/10.5281/zenodo.3606536</a></em>. Click the link to see the documentation of these files.</li> <li>“temperature timeseries.xlsx”: the temperature forcing used in the sensitivity experiments.</li> <li>“PISM sensitivity.r”: R script used to analyse the PISM sensitivity experiments, including data binning, lag correlation and regression between ice sheeting response and temperature forcing.</li> <li>“PISM sensitivity.xlsx”: Output of the R script.</li> <li>“GOA.calibration.csv”: SST record from the Gulf of Alaska site 85JC/U1419, calibrated using bayspline.</li> <li>“GOA.Ensemble.csv”: 1000 ensemble output of the bayspline calibration.</li> <li>“predict ice volume SST.r”: R script used to predict the response of CIS ice volume to GOA SST forcing. The script will call the results of PISM sensitivity experiments in “PISM sensitivity.xlsx” and the GOA SST forcing in “GOA.calibration.csv” and “GOA.calibration.csv”.</li> <li>“PISM ice vol GOA SST.csv”: predicted PISM ice vol based on GOA SST forcing and taking into account all sensitivity experiments and the uncertainty in SST reconstruction.</li> <li>“PISM ice vol GOA SST model.csv”: predicted PISM ice vol based on GOA SST forcing based on each sensitivity experiment and the uncertainty in SST reconstruction.</li> </ul> <p> </p> <p><strong>Please cite the following studies when using the data, in addition to citing the present study:</strong></p> <p><strong>GOA age model, IRD and MAR:</strong></p> <p>Walczak, M. H. et al. Phasing of millennial-scale climate variability in the Pacific and Atlantic Oceans. Science 370, 716–720 (2020).</p> <p>Velle, J. H. et al. High resolution inclination records from the Gulf of Alaska, IODP Expedition 341 Sites U1418 and U1419. Geophys. J. Int. 229, 345–358 (2022).</p> <p>Heaton, T. J. et al. Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP). Radiocarbon 62, 779–820 (2020).</p> <p><strong>GOA SST: </strong></p> <p>Praetorius, S. K. et al. North Pacific deglacial hypoxic events linked to abrupt ocean warming. Nature 527, 362–366 (2015).</p> <p>Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022).</p> <p>Tierney, J. E. & Tingley, M. P. BAYSPLINE: A New Calibration for the Alkenone Paleothermometer. Paleoceanogr. Paleoclimatology 33, 281–301 (2018).</p> <p><strong>GOA benthic foraminifera assemblage:</strong></p> <p>Belanger, C. L., Sharon, Du, J., Payne, C. R. & Mix, A. C. North Pacific deep-sea ecosystem responses reflect post-glacial switch to pulsed export productivity, deoxygenation, and destratification. Deep Sea Res. Part Oceanogr. Res. Pap. 164, 103341 (2020).</p> <p>Sharon, Belanger, C., Du, J. & Mix, A. Reconstructing Paleo-oxygenation for the Last 54,000 Years in the Gulf of Alaska Using Cross-validated Benthic Foraminiferal and Geochemical Records. Paleoceanogr. Paleoclimatology 36, e2020PA003986 (2021).</p> <p><strong>GOA productivity:</strong></p> <p>Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022).</p> <p>Addison, J. A. et al. Productivity and sedimentary δ15N variability for the last 17,000 years along the northern Gulf of Alaska continental slope. Paleoceanography 27, PA1206 (2012).</p> <p><strong>GOA bulk sediment neodymium isotopes:</strong></p> <p>Du, J., Haley, B. A., Mix, A. C., Walczak, M. H. & Praetorius, S. K. Flushing of the deep Pacific Ocean and the deglacial rise of atmospheric CO 2 concentrations. Nat. Geosci. 11, 749–755 (2018).</p> <p><strong>GOA volcanic endmember data compilation:</strong></p> <p>Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120.</p> <p>Sarbas, B., Jochum, K. P., Nohl, U. & Hofmann, A. W. GEOROC, the MPI geochemical rock database: a new tool for geochemists. Eos Trans. AGU 80, F1184 (1999).</p> <p><strong>Global and regional volcanic eruption data compilation:</strong></p> <p>Huybers, P. & Langmuir, C. Feedback between deglaciation, volcanism, and atmospheric CO2. Earth Planet. Sci. Lett. 286, 479–491 (2009).</p> <p>Global Volcanism Program, 2013. Volcanoes of the World, v. 4.8.7. 10.5479/si.GVP.VOTW4-2013. (2013).</p> <p>Bryson, R. U., Bryson, R. A. & Ruter, A. A calibrated radiocarbon database of late Quaternary volcanic eruptions. EEarth Discuss 1, 123–134 (2006).</p> <p>Watt, S. F. L., Pyle, D. M. & Mather, T. A. The volcanic response to deglaciation: Evidence from glaciated arcs and a reassessment of global eruption records. Earth-Sci. Rev. 122, 77–102 (2013).</p> <p>Crosweller, H. S. et al. Global database on large magnitude explosive volcanic eruptions (LaMEVE). J. Appl. Volcanol. 1, 4 (2012).</p> <p>Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120.</p> <p>Praetorius, S. et al. Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation. Earth Planet. Sci. Lett. 452, 79–89 (2016).</p> <p>Wilcox, P. S. et al. A new set of basaltic tephras from Southeast Alaska represent key stratigraphic markers for the late Pleistocene. Quat. Res. 92, 246–256 (2019).</p> <p>Davies, L. J., Jensen, B. J. L., Froese, D. G. & Wallace, K. L. Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: Key beds and chronologies over the past 30,000 years. Quat. Sci. Rev. 146, 28–53 (2016).</p> <p><strong>GIA models:</strong></p> <p>Roy, K. & Peltier, W. R. Relative sea level in the Western Mediterranean basin: A regional test of the ICE-7G_NA (VM7) model and a constraint on late Holocene Antarctic deglaciation. Quat. Sci. Rev. 183, 76–87 (2018).</p> <p>Lambeck, K., Purcell, A. & Zhao, S. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quat. Sci. Rev. 158, 172–210 (2017).</p> <p><strong>PISM sensitivity experiments and temperature forcing:</strong></p> <p>Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. & Kleman, J. Numerical simulations of the Cordilleran ice sheet through the last glacial cycle. The Cryosphere 10, 639–664 (2016).</p> <p>Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3606536</p> <p>Dansgaard, W. et al. Evidence for general instability of past climate from a 250-kyr ice-core record. Nature 364, 218–220 (1993).</p> <p>Andersen, K. K. et al. High-resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147–151 (2004).</p> <p>Jouzel, J. et al. Orbital and Millennial Antarctic Climate Variability over the Past 800,000 Years. Science 317, 793–796 (2007).</p> <p>Petit, J. R. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436 (1999).</p> <p>Herbert, T. D. et al. Collapse of the California Current During Glacial Maxima Linked to Climate Change on Land. Science 293, 71–76 (2001).</p> <p><strong>Be10 data compilation:</strong></p> <p>Lesnek, A. J., Briner, J. P., Baichtal, J. F. & Lyles, A. S. New constraints on the last deglaciation of the Cordilleran Ice Sheet in coastal Southeast Alaska. Quat. Res. 96, 140–160 (2020).</p> <p>Haeussler, P. J. et al. Late Quaternary deglaciation of Prince William Sound, Alaska. Quat. Res. 1–20 (2021) doi:10.1017/qua.2021.33.</p> <p>Walcott, C. K., Briner, J. P., Baichtal, J. F., Lesnek, A. J. & Licciardi, J. M. Cosmogenic ages indicate no MIS 2 refugia in the Alexander Archipelago, Alaska. Geochronology 4, 191–211 (2022).</p> <p>Briner, J. P. et al. The last deglaciation of Alaska. Cuad. Investig. Geográfica 43, 429–448 (2017).</p> <p>Tulenko, J. P., Briner, J. P., Young, N. E. & Schaefer, J. M. Beryllium-10 chronology of early and late Wisconsinan moraines in the Revelation Mountains, Alaska: Insights into the forcing of Wisconsinan glaciation in Beringia. Quat. Sci. Rev. 197, 129–141 (2018).</p> <p>Menounos, B. et al. Cordilleran Ice Sheet mass loss preceded climate reversals near the Pleistocene Termination. Science 358, 781–784 (2017).</p> <p>Dulfer, H. E., Margold, M., Engel, Z., Braucher, R. & Team, A. Using 10Be dating to determine when the Cordilleran Ice Sheet stopped flowing over the Canadian Rocky Mountains. Quat. Res. 102, 222–233 (2021).</p> <p>Lesnek, A. J., Briner, J. P., Lindqvist, C., Baichtal, J. F. & Heaton, T. H. Deglaciation of the Pacific coastal corridor directly preceded the human colonization of the Americas. Sci. Adv. 4, eaar5040 (2018).</p> <p>Tulenko, J. P., Briner, J. P., Young, N. E. & Schaefer, J. M. The last deglaciation of Alaska and a new benchmark 10Be moraine chronology from the western Alaska Range. Quat. Sci. Rev. 287, 107549 (2022).</p>
North Pacific Subtropical Gyre RCLV Atlas (Version 2)
<p>This dataset tracks Rotationally Coherent Lagrangian Vortices (RCLVs) at an 8-day resolution in the North Pacific Subtropical Gyre region around Hawai’i. The ‘lat’ and ‘lon’ variables represent the center coordinates of the vortex, or the extremum of integrated relative vorticity. The contour boundaries demark the edge of the RCLV based on 32-day backward-in-time Lagrangian trajectories. In other words, eddies in this dataset represents a fluid mass of substantial size that was coherent for at least 32 days. The fluid masses are tracked through time to assign IDs and RCLV ages. The software used to generate the dataset is publicly available at https://github.com/lexi-jones/RCLVatlas (DOI: <a href="https://doi.org/10.5281/zenodo.7702978" target="_blank" rel="noopener noreferrer">10.5281/ZENODO.7702978</a>).</p> <p>Version 1 of the dataset (https://simonscmap.com/catalog/datasets/RCLV_atlas; DOI: <a href="https://doi.org/10.5281/zenodo.8139149" target="_blank" rel="noopener noreferrer">10.5281/ZENODO.8139149</a>) only includes contours that represent features of age 32 days or older. Version 2 extends the dataset to include eddy genesis by advecting the particle sets backward-in-time to age 24, 16, and 8 days.</p>
Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis
<p>The attached two datasets are the optimized inputs used to analyze predictability limits in the paper Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis. Specifically, the datasets correspond to the inputs used to produce the blue (global) and green (regional) loss curves in Figure S2. They are NetCDF files of dimensions batch (1), time (2), latitude (181), longitude (360), pressure levels (13), and may be run as Graphcast model inputs to initiate a forecast at 00 UTC 20 June 2021. Both datasets have been systematically perturbed to reduce the Graphcast model's loss function, which minimizes forecast eror as described in the manuscript. The global input seeks to reduce the loss over the entire globe, while the regional input seeks only to minimize error within the Pacific Northwest (42N to 60N and 130W to 110W). The optimized inputs result in a reduction of the loss by approximately 85% (global) and 93% (regional) when compared to a control Graphcast forecast without perturbations.</p>
Data and Analysis for Kaplanis, Denny, and Raimondi 2024, "Vertical distribution of rocky intertidal organisms shifts with sea-level variability on the Northeast Pacific Coast".
<p>This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.</p> <p><br>Directory Architecture:</p> <p>Data:<br>Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations. </p> <p>Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.</p> <p>Scripts:<br>All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.</p> <p>Outputs:<br>Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.</p> <p>Figures:<br>All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.</p> <p>Data Processing Workflow - Overview: <br>Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.</p> <p>Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)</p> <p>Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.</p>
Data in support of 'The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo'
<p>Data in support of 'Chandler M, Zilberman NV, Sprintall J. (2024). The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo. <em>Journal of Geophysical Research: Oceans</em>. <a href="https://doi.org/10.1029/2024JC021098" target="_blank" rel="noopener">https://doi.org/10.1029/2024JC021098</a>'</p> <p>There are 4 netCDF files:</p> <ol> <li>swpb_dwbc_deep_argo_profiles_chandler2024.nc</li> <li>swpb_dwbc_deep_argo_trajectories_chandler2024.nc</li> <li>kt_dwbc_deep_argo_time_series_chandler2024.nc</li> <li>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</li> </ol> <p><strong>swpb_dwbc_deep_argo_profiles_chandler2024.nc </strong>contains the delayed-mode profiles of potential temperature and salinity on a 10-dbar pressure grid from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[pressure; latitude; longitude; time; wmo_id; theta; salinity]</em></p> <p><strong>swpb_dwbc_deep_argo_trajectories_chandler2024.nc </strong>contains delayed-mode trajectories from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[latitude; longitude; u; v; pressure; wmo_id; time]</em></p> <p><strong>kt_dwbc_deep_argo_time_series_chandler2024.nc</strong> contains the 2021--2022 monthly time series of dynamic height, salinity, and potential temperature between 2000--4000-dbar computed from the spatially-averaged Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[time; pressure; theta; salinity; dh; region_long; region_lat]</em></p> <p><strong>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</strong> contains seasonal cycles of dynamic height, salinity, and potential temperature (including the decomposition into heave/spice) between 2000--4000-dbar from the Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[pressure; theta; theta_heave; theta_spice; salinity; dh; region_long; region_lat]</em></p> <p>Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (<a href="https://argo.ucsd.edu/" target="_blank" rel="noopener">https://argo.ucsd.edu/</a>). The Argo Program is part of the Global Ocean Observing System. A full list of acknowledgements can be found in the affiliated <a href="https://doi.org/10.1029/2024JC021098">publication</a>.</p> <p><code>Version history:</code><br><code>v1.0 First created (06-March-2024)</code><br><code>v1.1 Updated to include accepted publication reference (15-October-2024)</code></p>
Marine heatwaves statistics for the tropical western and central Pacific Ocean
<p>Processed marine heatwave metrics are provided for the tropical western and central Pacific Ocean region (120°E-140°W, 40°S-15°N). The metrics are computed from daily sea surface temperature (SST) data, from both observations and models. The observed marine heatwave data are calculated from NOAA 0.25° daily Optimum Interpolation Sea Surface Temperature (OISST) over the period 1982-2019. The modelled marine heatwave data are from analysis of 18 model simulations as part of the Coupled Model Intercomparison Project, Phase 6 (CMIP6) over the period 1982-2100, where two future scenarios have been analysed. Marine heatwaves are computed with respect to the 1995-2014 climatology. The marine heatwave data are provided on a grid point basis across the domain. Marine heatwave timeseries metrics are also provided for three case study regions: Fiji, Samoa, and Palau.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.