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234 results for “Global Ocean”
Climate model data from "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"
<p>This Dataset contains the model output described in the study<br> "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"<br> by Pfister and Stocker 2020, published in Journal of Climate.</p> <p>The two zip files contain the model output of the two models Bern3D-LPX and LOVECLIM, in folder structures explained below.</p> <p>Bern3D-LPX:</p> <p>The 3 folders contain model simulations tuned to different ECS values (2, 3 and 6 Kelvin).<br> Each folder contains three subfolders corresponding to three simulations: Control, 2xCO2 and 4xCO2.<br> For each simulation, two netcdf model output files are given: a timeseries file for quick overview of various spatially averaged variables (e.g., global mean temperature), and a full output file for local analyses as done in the study.</p> <p>For the main simulations with an ECS of 3 Kelvin, annual mean output is provided for the first 500 years of each simulation. Thereafter, the full output is available only for selected years, which can be read out from the netcdf time dimension or, e.g., the netcdf variable "baseyear".</p> <p>Simulations with an ECS of 2 and 6 Kelvin are only used for Figure 8 and its discussion, therefore their full output file was written with less yearly outputs than the main simulation with ECS=3 Kelvin to reduce data load.</p> <p><br> LOVECLIM:</p> <p>The 2 folders contain the 2xCO2 and 4xCO2 simulations.<br> No separate Control simulations were made, but the first 1000 years of each simulation are unperturbed and used as a control reference (details in Pfister and Stocker 2020, J.Clim.).</p> <p>The two netcdf files for each simulation correspond to atmospheric variables (atmmmyl_cat.nc) and ocean variables (CLIO3m_cat_CO2_2_regridded.nc). Note that the spatial resolution of the atmosphere and ocean component of LOVECLIM are different. Monthly output is provided for the given variables of the full 2000-year-simulations.</p> <p> </p> <p>For a detailed description how these model outputs were analyzed, please refer to Pfister and Stocker 2020, J. Clim.</p> <p> </p> <p> </p>
Impacts of ocean wave-dependent momentum flux on global ocean climate
<p>This is dataset of ocean climate simulations used in the paper "Impacts of ocean wave-dependent momentum flux on global ocean climate" by Shimura et al. (2020, Geophysical Research Letters).</p> <p>The dataset contains results of four experiments; "expWAVE", "expWAVEctrl", "expWIND", "expWINDctrl". The "expWAVEctrl" and "expWINDctrl" are control experiments of "expWAVE" and "expWIND".</p> <p>The data "expXXX_ocean_month_YYYY_MM.nc.zip" contains monthly mean ocean temperature, u and v components of ocean current.</p> <p>The data "expXXX_ocean_surf_YYYY_MM.nc.zip" contains monthly mean surface heat flux, u and v components of momentum flux.</p>
Global Cenozoic Paleobathymetry with a focus on the Northern Hemisphere Oceanic Gateways
<p>We provide a set of paleobathymetric/topographic reconstructions for the Cenozoic time (66 - 0 Ma). We have re-evaluated the evolution of the Northern hemisphere oceanic gateways (i.e. the Fram Strait, Greenland – Scotland Ridge, the Central American Seaway, and the Tethys Seaway) and embedded their tectonic histories in a new global paleobathymetry and topography model. Our new paleobathymetry model incorporates Northeast Atlantic paleobathymetric variations due to Iceland mantle plume activity, updated regional plate kinematics, and models for the oceanic lithospheric age, sediment thickness, and reconstructed oceanic plateaus and microcontinents. We also provide a global paleotopography model based on new and previously published regional models.</p>
Data from: An analysis of the impacts of Cretaceous Oceanic Anoxic Events on global molluscan diversity dynamics
Oceanic Anoxic Events (OAEs) are contemporaneous with 11 of the 18 largest Phanerozoic extinction events, but the magnitude and selectivity of their paleoecological impact remains disputed. OAEs are associated with abrupt, rapid warming and increased CO2 flux to the atmosphere, thus insights from this study may clarify the impact of current anthropogenic climate change on the biosphere. We investigated the influence of the Late Cretaceous Bonarelli Event (OAE2; Cenomanian – Turonian stage boundary; ~ 94 Ma) on generic- and species-level molluscan diversity, extinction rates, and ecological turnover. Cenomanian – Turonian results were compared with changes across all Cretaceous stage boundaries, some of which are coincident with less severe OAEs. We found increased generic turnover, but not species-level turnover, associated with several Cretaceous OAEs. The absence of a species-level pattern may reflect species occurrence data that are too temporally coarse to robustly detect patterns. Five hypotheses of ecological selectivity relating anoxia to survivorship were tested across stage boundaries with respect to faunality, mobility and diet using generalized linear models. Increasingly benthic taxa were consistently selected against throughout the Cretaceous regardless of the presence or absence of OAEs. These results suggest that: (1) the Cenomanian – Turonian boundary (OAE2) was associated with a decline in mollusk diversity, and increase in extinction rate, that was significantly more severe than Cretaceous background levels; and (2) no differential ecological selectivity was associated with OAE-related diversity declines among the variables tested here.
Data from: Critically endangered island endemic or peripheral population of a widespread species? Conservation genetics of Kikuchi's gecko and the global challenge of protecting peripheral oceanic island endemic vertebrates
Aim: To highlight the significant conservation challenge of evaluating peripheral endemic vertebrates in island archipelago systems and to assess empirically the complexities of approaches to conservation genetic studies across political and biogeographic boundaries. To demonstrate the poignant need for international collaboration and coordination when species delimitation problems with high conservation concern involve island endemics with biogeographically peripheral ranges. Location: Southeast Asia, Lanyu Island, Taiwan, and the Philippines. Methods: Genetic samples were collected and sequenced for one mitochondrial gene and five nuclear loci for species of the Gekko mindorensis-G. kikuchii species complex in Southeast Asia. We used maximum likelihood and Bayesian phylogenetic methods and coalescent-based species delimitation analyses to estimate phylogeographic relationships, construct multilocus haplotype networks and test putative species boundaries. Results: Phylogenetic and population genetic analyses suggest that Kikuchi's Gecko may represent a peripheral population of a widespread species distributed from the northern Philippines to Taiwan. However, we identify a discrepancy between inferences of species boundaries resulting from methods based on allele frequencies versus coalescent-based methods that incorporate evolutionary history. Coalescent-based analyses suggest that G. kikuchii may be a distinct evolutionary lineage. Our study underscores the need for coalescent-based methods in conjunction with population genetic approaches for conservation genetic assessments of widespread species. Main conclusions: This study joins a few recent works suggesting that Philippine-derived anomalies in the fauna of Lanyu (and possibly greater Taiwan) are worthy of careful reconsideration. Determining whether each is the result of recent human-mediated introduction or (possibly more ancient) natural dispersal should be the goal of future studies on this seldom-conceived biogeographic relationship. Isolated species endemic to islands on the outer periphery of biogeographic and political regions represent particular conservation challenges. This is especially true if a species occurs on an isolated island that is allied biogeographically with one nation, but politically administered by another.
Ammonia oxidation and urea oxidation in Chesapeake Bay and in the global ocean
<p>Two datasets are included in this submission. One dataset contains newly meausured ammonia oxidation, urea oxidation, nitrous oxide production from ammonium and urea in Chesapeake Bay, one of the largest estuaries in the world. The other dataset contains compiled observations of ammonia oxidation, urea oxidation, qPCR analysis of amoA and ureC gene abundance in the global ocean from previous studies. </p> <p><span>Tang, W.</span>, <span>C. Hexter</span>, <span>R. Dai</span>, et al. <span>2025</span>. “ <span>Substrate Effect on the Contribution of Ammonium and Urea to Marine Nitrification and Nitrous Oxide Production</span>.” <em>Environmental Microbiology</em> <span>27</span>, no. <span>10</span>: e70187. <a href="https://doi.org/10.1111/1462-2920.70187">https://doi.org/10.1111/1462-2920.70187</a>.</p>
Conserved genetic markers reveal widespread diatom reproduction in the global ocean
<p>Datasets associated with the study "Conserved genetic markers reveal widespread diatom reproduction in the global ocean" (post second revision, V3)</p>
G4D-DOC: A global four-dimensional gridded dataset of ocean dissolved oxygen concentrations retrieval from Argo profiles
<p>Based on temperature and salinity observations from Argo floats, this dataset uses machine-learning methods to reconstruct global ocean dissolved oxygen (DO) concentrations.<br><strong>This version only provides monthly-scale netCDF format data for everyone's use. If you need other time scales, please check previous versions.</strong></p> <h2>Spatiotemporal Characteristics</h2> <ul> <li> <p><strong>Time range:</strong> 2005–2022, <strong>monthly</strong> fields.</p> </li> <li> <p><strong>Geographic range:</strong> Global oceans <strong>excluding the Arctic Ocean</strong>, from 90°S to 84°N and 180°W to 180°E.</p> </li> <li> <p><strong>Horizontal resolution:</strong> 1° × 1° (regular grid).</p> </li> <li> <p><strong>Vertical levels (26):</strong> 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 1995dbar .</p> </li> </ul> <h2>Data Format & Conventions</h2> <ul> <li> <p><strong>Format:</strong> NetCDF4</p> </li> <li> <p><strong>Coordinate conventions:</strong></p> <ul> <li> <p><code>lat</code> (Y axis): 89.5 → −89.5 (descending)</p> </li> <li> <p><code>lon</code> (X axis): converted to <strong>−180 → 180</strong> (1° centers)</p> </li> <li> <p><code>depth</code>: ascending (matching the 26 target levels)</p> </li> </ul> </li> <li> <p><strong>Units:</strong> DO in <strong>μmol/kg</strong> (<code>umol kg-1</code>).</p> </li> </ul> <h2>Variables & Dimensions</h2> <ul> <li> <p><strong>Variables kept:</strong> <code>DO</code>, <code>depth</code>, <code>lat</code>, <code>lon</code> (with a single-valued <code>time</code> coordinate).</p> </li> <li> <p><strong>DO dimensions:</strong> <code>(time, depth, lat, lon)</code>.</p> </li> </ul> <h2>Filenames</h2> <ul> <li> <p><strong>Pattern:</strong> <code>G4D_DOC_YYYY_MM.nc</code><br><em>Example:</em> <code>G4D_DOC_2005_07.nc</code> contains the field for <strong>July 2005</strong>.</p> </li> </ul> <h2>Citation & Disclaimer</h2> <p>Please cite the dataset and relevant literature when using it in publications or products.<br>Recommended citation (example):</p> <blockquote> <p>Xue, C., & Wang, Z. (2025). <em>A global four-dimensional gridded dataset of ocean dissolved oxygen concentrations retrieval from Argo profiles</em> (Monthly NetCDF version). Zenodo. <a target="_new" rel="noopener">https://doi.org/</a>10.5281/zenodo.13920233</p> </blockquote> <p>The data producers are not responsible for any losses arising from data use. Map boundaries or masks do not imply official positions.</p> <h2>Contacts</h2> <ul> <li> <p><strong>Cunjin Xue</strong> — <a rel="noopener">xuecj@aircas.ac.cn</a></p> </li> <li> <p><strong>Zhenguo Wang</strong> — <a rel="noopener">zgwang24@m.fudan.edu.cn</a></p> </li> </ul>
Global increase in tropical cyclone ocean surface waves
<p>The data and codes in this repository can be used to support the main conclusion in the manuscript "Global increase in tropical cyclone ocean surface waves" by Shi et al., submitted to Nature Communications.</p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua and Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean, derived from MODIS-Aqua and Terra XGBoost satellite retrieval products. It covers the time span from 2017 to 2020. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration products derived from MODIS-Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2009 to 2016, derived from MODIS-Terra‘s XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua and Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2021 to 2022 with a 9-km resolution, derived from MODIS-Aqua and Terra XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first *10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration monthly products derived from MODIS-Aqua and Terra
<p>The dataset integrates seamless POC concentration daily products with a 9-km resolution for the global ocean from 2003 to 2022, derived from MODIS-Aqua and Terra XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first *10000 and then rounded using int32.</p>
Two-pool mechanistic CDOM model for the global ocean
<p>This includes all model inputs, outputs, and scripts to run the model for the paper "Quantifying biogeochemical controls of open ocean CDOM from a global mechanistic model." Currently under review at JGR Oceans.</p>
Codes for "Summer westerly wind intensification weakens Southern Ocean seasonal cycle under global warming" - submitted to Geophysical Research Letters
<pre>This repository contains the NCAR Command Language (NCL) codes (*.ncl files) used to produce the related contents of Zhang et al. (2024). For queries about this repository and its contents, please contact me (zhangyiwen2024@gmail.com). Figure 1.ncl: For plotting main Fig.1. Figure 2.ncl: For plotting main Fig.2. Figure 3.ncl: For plotting main Fig.3. Figure 4.ncl: For plotting main Fig.4.<br>Figure 5.ncl: For plotting main Fig.5. References: Zhang et al. Summer westerly wind intensification weakens Southern Ocean seasonal cycle under global warming. Geophysical Research Letters. (2024).</pre>
Strengthening the global environmental assessment seascape in support of ocean sustainability
<p>Ambitious evidence-based policies are urgently needed to redirect mankind’s trajectory towards ocean sustainability. While global environmental assessments (GEAs) synthesizing ocean knowledge are multiplying, it is crucial to ensure that their processes and outputs are conducive to social legitimacy, scientific credibility, and are relevant to decision makers’ needs. Building upon institutional and scientific literature, we consolidate a list of best practices for GEAs to achieve legitimacy, credibility, and salience. We then develop a standardized framework to score the level of implementation of these best practices and to assess the coverage of ocean knowledge in GEAs. Lastly, we apply this framework to review 12 influential reports at the ocean science-policy interface. We found that credibility best practices are well implemented but that all GEAs, and in particular ocean-focused assessments, have significant opportunities to strengthen the implementation of legitimacy and salience best practices. We identify that increasing stakeholder engagement and broadening the diversity of knowledge represented is needed to improve the legitimacy of GEAs. Additionally, we found that GEAs could increase their salience by featuring more actionable knowledge for decision-makers, including futures thinking, multi-scale intervention options, and evaluation of progress towards international targets. Finally, we highlight four recommendations to strengthen the GEA seascape: elevating co-production practices, bridging scales through multi-level approaches, increasing transparency in knowledge choices and gaps, and coordinating assessment processes.</p>
Global warming pattern formation: the role of ocean heat uptake
<p>Datasets for the paper:</p> <p>Hu, S., Xie, S.-P., and Kang, S. M. (2021) Global warming pattern formation: the role of ocean heat uptake. Journal of Climate. Resubmitted after minor revision.</p>
Supporting figures: Seasonality and trend of the global upper-ocean vertical velocity over 1998–2017
<p>These uploaded figures are results of seasonal variations and trend of the global upper-ocean vertical motions, based on BRAN2020, OFES, OMEGA3D and SODA3.3.1. This is to support our revised manuscript entitled '<strong>Seasonality and trend of the global upper-ocean vertical velocity over 1998</strong>–<strong>2017'</strong>, under consideration in <em>Progress in Oceanography</em>. </p>
Dataset of "A hierarchy of global ocean models coupled to CESM1"
<p><strong>Data associated with the following publication:</strong></p> <p>Hsu, T. Y., Primeau, F. W., & Magnusdottir, G. (2022). A Hierarchy of Global Ocean Models Coupled to CESM1.</p> <p><strong>Paper Abstract:</strong></p> <p>We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model with entrainment and detrainment, an Ekman mixed-layer model, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting 3-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation.</p> <p>We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.</p> <p> </p> <p>Methods</p> <p><strong>Description of methods used for generation of data: </strong><br> The data is generated with EMOM, a hierarchy of ocean models that are applied in CESM1. The detailed description of the model is in the paper the dataset is presented in (i.e. A Hierarchy of Global Ocean Models Coupled to CESM1).<br> <br> <strong>Methods for processing the data:</strong></p> <p>This dataset consists of a set of atmospheric and oceanic fields produced by the NCAR CESM1 climate model. The data is in NETCDF format and has been post-processed and formatted using the NCO command language (see http://nco.sourceforge.net/ for more details).</p> <p><strong>Software-specific information needed to interpret the data:</strong><br> The data is in NetCDF format.</p> <p>Usage Notes</p> <p>This README file was generated on 20200416 by Tien-Yiao Hsu</p> <p><strong>Dataset of the paper</strong></p> <p>A Hierarchy of Global Ocean Models Coupled to CESM1</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> Email: tienyiah@uci.edu</p> <p> OrcID: 0000-0002-8121-1525</p> <p> </p> <p> Associate Contact Information</p> <p> Name: Francois Primeau</p> <p> Institution: University of California, Irvine</p> <p> Institutions ROR: [UCI = https://ror.org/04gyf1771]</p> <p> Address: </p> <p> </p> <p> Department of Earth System Science</p> <p> Croul Hall</p> <p> Irvine, CA 92697-3100</p> <p> </p> <p> Email: fprimeau@uci.edu</p> <p> </p> <p> Associate Contact Information</p> <p> Name: Gudrun Magnusdottir</p> <p> Institution: University of California, Irvine</p> <p> Institutions ROR: [UCI = https://ror.org/04gyf1771]</p> <p> Address: </p> <p> </p> <p> Department of Earth System Science</p> <p> Croul Hall</p> <p> Irvine, CA 92697-3100</p> <p> </p> <p> Email: gudrun@uci.edu</p> <p> </p> <p>3. Date of data organized : 20220201</p> <p> </p> <p>4. Information about funding sources that supported the collection of the data:</p> <p> Funder name: Department of Energy</p> <p> Funder uri: https://www.energy.gov/</p> <p> </p> <p>5. Contextual description of the data:</p> <p> </p> <p> The data used to produce the figures in the paper.</p> <p> </p> <p>--------------------------</p> <p>SHARING/ACCESS INFORMATION</p> <p>-------------------------- </p> <p> </p> <p> </p> <p>Licenses/restrictions placed on the data: </p> <p> </p> <p> CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL CC-BY</p> <p> </p> <p>---------------------</p> <p>DATA & FILE OVERVIEW</p> <p>---------------------</p> <p> </p> <p>We separate sets of data in terms of folders. </p> <p> </p> <p>1. AMOC</p> <p> </p> <p> This directory contains the AMOC streamfunction output from </p> <p> simulations OGCM_CTL and OGCM_EXP.</p> <p> </p> <p>2. hierarchy_statistics</p> <p> </p> <p> This directory contains the statistics (mean, variability, ...)</p> <p> and diagnosed quantities (ex: EOF, heat transport) of the hierarchy</p> <p> output.</p> <p> </p> <p> The output of CTL run of year 21 to 120 is in CTL_21-120.</p> <p> The output of EXP run of year 81 to 180 is in EXP_81-180.</p> <p> </p> <p>3. hierarchy_average</p> <p> </p> <p> This directory is similar to is similar to hierarchy_statistics, </p> <p> containing CTL and EXP. The difference is that it is the raw, unprocessed</p> <p> mean data that contains the complete output variables.</p> <p> </p> <p> </p> <p>--------------------------</p> <p>METHODOLOGICAL INFORMATION</p> <p>--------------------------</p> <p> </p> <p> </p> <p>1. Description of methods used for generation of data: </p> <p> </p> <p> The data is generated with EMOM, a hierarchy of ocean models that is</p> <p> applied in CESM1. The detail description of the model is in the paper</p> <p> the dataset is preseted in (i.e. A Hierarchy of Global Ocean Models </p> <p> Coupled to CESM1).</p> <p> </p> <p>2. Methods for processing the data:</p> <p> </p> <p> The output data is mostly the mean and variance of the climate variables.</p> <p> </p> <p>3. Software-specific information needed to interpret the data:</p> <p> </p> <p> The data is in NetCDF format.</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: AMOC</p> <p>---------------------------------------------</p> <p> </p> <p># Filename: MOC_[CTL|EXP].nc</p> <p> </p> <p>Variable list:</p> <p> </p> <p> 1. MOC</p> <p> </p> <p> Unit: Sv</p> <p> </p> <p> The monthly mean value of streamfunction of the meridional overturning</p> <p> circulation in ocean basins.</p> <p> </p> <p> </p> <p># Filename MOC_[CTL|EXP]_timeseries.nc</p> <p> </p> <p>Variable list:</p> <p> </p> <p> 1. AMOC_max</p> <p> </p> <p> Unit: Sv</p> <p> </p> <p> The annual maximum value of the Atlantic Meridional Overturning Circulation.</p> <p> </p> <p> 2. AMOC_max_lat</p> <p> </p> <p> Unit: degree north</p> <p> </p> <p> The latitude of the location where AMOC_max occurs.</p> <p> </p> <p> 3. AMOC_max_z</p> <p> </p> <p> Unit: m</p> <p> </p> <p> The depth of the location where AMOC_max occurs.</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_average</p> <p>---------------------------------------------</p> <p> </p> <p>In this directory, each sub-directory is of the form [MODEL_NAME]_[CTL|EXP]</p> <p>where MODEL_NAME can be SOM, MLM, EMOM or POP2. A sub-directory has three</p> <p>files: atm.nc, ocn.nc and ocn_regrid.nc. </p> <p> </p> <p>atm.nc is the averaged data of atmosphere model output of year 21-121 of each model run on f09 grid.</p> <p>ocn.nc is the averaged data of ocean model output of year 21-121 of each model run on g16 grid.</p> <p>ocn_regrid.nc is the regrided version ocn.nc from grid g16 onto f09.</p> <p> </p> <p>Details of the atm.nc variables can be found in CAM4 documentation</p> <p>https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html</p> <p> </p> <p>Details of the ocn.nc variables can be found in POP2 documentation</p> <p>https://ncar.github.io/POP/doc/build/html/users_guide/model-diagnostics-and-output.html</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_statistics</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins CTL and EXP runs folder where the statistics time</p> <p>is 21-121 for CTL and 81-180 for EXP.</p> <p> </p> <p>Each experiment folder contains sub-directories of the form [MODEL_NAME]_[CTL|EXP]</p> <p>where MODEL_NAME can be SOM, MLM, EMOM or POP2. Each of these directories has</p> <p>the same analysis listed below.</p> <p> </p> <p># Filename: atm_analysis_[AAO|AO|ENSO|NAO|PDO].nc</p> <p> </p> <p> Description: This file contains the derived climate variability patterns (i.e. </p> <p> AAO, AO, ENSO, NAO, PDO). </p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. PCAs(modes, Ny, Nx)</p> <p> </p> <p> Unit: None</p> <p> </p> <p> The normalized PCAs. Different modes of the PCAs are separated according</p> <p> to the first dimension.</p> <p> </p> <p> 2. PCAs_ts(time, modes)</p> <p> </p> <p> Unit: None</p> <p> </p> <p> The timeseries of projected PCAs onto the anomalies (i.e. the inner product of PCAs and anomalous fields).</p> <p> </p> <p># Filename: atm_analysis_mean_anomaly_[VARNAME].nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> VARNAME = [ICEFRAC|TAUX|TAUY|SST]</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. [VARNAME]_[TIMESCALE]M</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = N / m^2</p> <p> TAUY = N / m^2</p> <p> SST = K</p> <p> </p> <p> The mean values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,</p> <p> S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p> </p> <p> 2. [VARNAME]_[TIMESCALE]A</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = N / m^2</p> <p> TAUY = N / m^2</p> <p> SST = K</p> <p> </p> <p> The anomalous values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,</p> <p> S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p> </p> <p> 3. [VARNAME]_[TIMESCALE]ASTD</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = N / m^2</p> <p> TAUY = N / m^2</p> <p> SST = K</p> <p> </p> <p> The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|S|A] </p> <p> where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p> 4. [VARNAME]_[TIMESCALE]ASTD</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = (N / m^2)^2</p> <p> TAUY = (N / m^2)^2</p> <p> SST = K^2</p> <p> </p> <p> The variance of the anomalous values of each grid point. TIMESCALE = [M|S|A] </p> <p> where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p># Filename: atm_analysis_mean_var_[T|U].nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> VARNAME = [T|U]</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. [VARNAME]_[TIMESCALE]M</p> <p> </p> <p> Unit: T = K</p> <p> U = m / s</p> <p> </p> <p> The mean values of each grid point. TIMESCALE = [M|A] where M stands for monthly, A for annnual. </p> <p> </p> <p> 2. [VARNAME]_[TIMESCALE]ASTD</p> <p> </p> <p> Unit: T = K</p> <p> U = m / s</p> <p> </p> <p> The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|A] </p> <p> where M stands for monthly, A for annnual. </p> <p> </p> <p> 3. [VARNAME]_[TIMESCALE]AVAR</p> <p> </p> <p> Unit: T = K</p> <p> U = m / s</p> <p> </p> <p> The variance of the anomalous values of each grid point. TIMESCALE = [M|A] </p> <p> where M stands for monthly, A for annnual. </p> <p> </p> <p># Filename: atm_analysis_SST_CORR.nc</p> <p> </p> <p> Description: This file contains the year-to-year correlation of monthly anomalous SST.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. CORR(months, Ny, Nx)</p> <p> </p> <p> Unit: None</p> <p> </p> <p> The year-to-year correlation of monthly anomalous SST.</p> <p> </p> <p># Filename: ice_analysis_mean_anomaly_[aice|vice].nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> VARNAME = [aice|vice].</p> <p> </p> <p> The variables are exactly of the same structure as described in </p> <p> atm_analysis_mean_anomaly_[VARNAME].nc</p> <p> </p> <p> The unit for aice = None</p> <p> The unit for vice = m</p> <p> </p> <p># Filename: ocn_analysis_mean_anomaly_STRAT.nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> STRAT is the difference of mean ocean temperatures T_top - T_bot.</p> <p> T_top is the mean temperature of the top 50m of the ocean where as</p> <p> T_bot is the mean temperature of the ocean between depth 50m to 503.7m.</p> <p> </p> <p> The variables are exactly of the same structure as described in </p> <p> atm_analysis_mean_anomaly_[VARNAME].nc</p> <p> </p> <p> The unit for STRAT = K</p> <p> </p> <p># Filename: atm_analysis_AHT_OHT.nc</p> <p> </p> <p> Description: This file contains the indirectly derived atmosphere heat transport.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. AHT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly atmospheric heat transport.</p> <p> </p> <p> 2. AHT_AM(year, lat_bnd) </p> <p> </p> <p> Unit: W</p> <p> </p> <p> The annual atmospheric heat transport.</p> <p> </p> <p> 3. AHT_MEAN(lat_bnd) </p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged atmospheric heat transport.</p> <p> </p> <p> 4. AHT_TFLX_CONV(time, lat) </p> <p> </p> <p> Unit: W / m</p> <p> </p> <p> The monthly-zonally-averaged atmospheric heat convergence.</p> <p> </p> <p> 5. AHT_TFLX_CONV_MEAN(lat) </p> <p> </p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-zonally-averaged atmospheric heat convergence.</p> <p> </p> <p># Filename: ocn_analysis_OHT.nc</p> <p> </p> <p> Description: This file contains the derived ocean heat transport.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. ADVT(time, lat)</p> <p> </p> <p> Unit: K / s / m^3</p> <p> </p> <p> The monthly vertically-integrated temperature tendency due to advection and horizontal diffusion.</p> <p> </p> <p> 2. ADVT_MEAN(lat)</p> <p> </p> <p> Unit: K / s / m^3</p> <p> </p> <p> The time-averaged ADVT.</p> <p> </p> <p> 3. OHT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The total monthly ocean heat transport.</p> <p> </p> <p> 4. OHT_MEAN(lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-average of OHT.</p> <p> </p> <p> 5. OHT_ADVT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly ocean heat transport due to advection and horizontal diffusion.</p> <p> </p> <p> 6. OHT_ADVT_MEAN(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged of OHT_ADVT.</p> <p> </p> <p> 7. OHT_ADVT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly ocean heat transport due to advection and horizontal diffusion.</p> <p> </p> <p> 8. OHT_ADVT_MEAN(lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged of OHT_ADVT.</p> <p> </p> <p> </p> <p> 9. OHT_WKRSTT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly ocean heat transport due to weak-restoring.</p> <p> </p> <p> 10. OHT_WKRSTT_MEAN(lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged of OHT_WKRSTT.</p> <p> </p> <p> 11. SHF(time, lat)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly surface heat flux.</p> <p> </p> <p> 12. SHF_MEAN(lat)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-average of SHF.</p> <p> </p> <p> 13. WKRSTT(time, lat)</p> <p> </p> <p> Unit: K / s / m^2</p> <p> </p> <p> The vertically integrated monthly weak-restoring.</p> <p> </p> <p> 14. WKRSTT_MEAN(lat)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-average of WKRSTT.</p> <p> </p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/importance_of_KH</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the average of CAM4 and EMOM output of field during year 21-30.</p> <p> </p> <p>The meaning of the variable can be found in official website</p> <p>https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html</p> <p> </p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_mean_temp</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the annual average of ocean mean temperature of the top 33 layers (507.33m) in</p> <p>the EXP run (sea-ice loss run)</p> <p> </p> <p># Filename: paper2021_[MODEL_NAME]_EXP.ocn_mean_T.nc</p> <p> </p> <p> Description: This file contains the annual average of ocean mean temperature of the top 33 layers (507.33m).</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. TEMP(time, Nz)</p> <p> </p> <p> Unit: degC</p> <p> </p> <p> Ocean temperature.</p> <p> </p> <p> </p> <p> 2. SALT(time, Nz)</p> <p> </p> <p> Unit: kg / m^3 (PSU)</p> <p> </p> <p> Ocean salinity.</p> <p> </p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_heat_content_trend</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the difference of the ocean state between the year 181 and year 81 of the EXP run.</p> <p> </p> <p># Filename: OHC_diff_[MODEL_NAME].nc</p> <p> </p> <p> Description: The difference of the ocean state between the year 181 and year 81 of the EXP run.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. TEMP(time, Nz)</p> <p> </p> <p> Unit: degC</p> <p> </p> <p> Ocean temperature.</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/vice_target_file</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the sea-ice forcing used to derive Q-flux (CTL) and the</p> <p>forcing applied in EXP run. </p> <p> </p> <p># Filename: forcing.vice.[GRID].paper2021_[RUN]_POP2.nc</p> <p> </p> <p> Description: This the sea-ice forcing used in the [RUN] in the grid of [GRID].</p> <p> GRID = [f09|gx1v6]</p> <p> [RUN] = [CTL|EXP]</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. vice_target(time, nlat, nlon)</p> <p> </p> <p> Unit: m^3 / m^2 (volume density)</p> <p> </p> <p> Total ice volume.</p>
Script and data of "Role of Frictional Processes in Mesoscale Eddy Available Potential Energy Budget in the Global Ocean"
<p>% File description:</p> <p>1. Cal_conversions.m: a set of functions calculating the EAPE-EKE and EAPE-EKE conversion terms with CESM output data in B-grid</p> <p>2. smooth2a.m: function of boxcar filtering</p> <p>3. CONV_u100_2d.mat: data of the global distribution of upper 100 m averaged conversion terms used in Figure 2 of the manuscript<br> % Variables inside the file:<br> CONVa_H_u100: MAPE-EAPE conversion driven by frictional process<br> CONVo_H_u100: MAPE-EAPE conversion driven by non-frictional process<br> CONVa_V_u100: EAPE-EKE conversion driven by frictional process<br> CONVo_V_u100: EAPE-EKE conversion driven by non-frictional process</p> <p>4. CONV_profile.mat: data of the vertical profiles of global and regional averaged EAPE-EKE conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_profile: driven by frictional process<br> CONVo_V_GLO_profile: driven by non-frictional process<br> CONVttw_V_GLO_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_profile: driven by frictional process<br> CONVo_V_WBCE_profile: driven by non-frictional process<br> CONVttw_V_WBCE_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_profile: driven by frictional process<br> CONVo_V_STG_profile: driven by non-frictional process<br> CONVttw_V_STG_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_profile: driven by frictional process<br> CONVo_V_SPG_profile: driven by non-frictional process<br> CONVttw_V_SPG_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_profile: driven by frictional process<br> CONVo_V_SO_profile: driven by non-frictional process<br> CONVttw_V_SO_profile: reproduced by TTW balance </p> <p>5. CONV_SeasDiff.mat: data of the seasonal difference (winter minus summer) of global and regional averaged conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of the seasonal difference of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_SeasDiff: driven by frictional process<br> CONVo_V_GLO_SeasDiff: driven by non-frictional process<br> CONVttw_V_GLO_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_SeasDiff: driven by frictional process<br> CONVo_V_WBCE_SeasDiff: driven by non-frictional process<br> CONVttw_V_WBCE_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_SeasDiff: driven by frictional process<br> CONVo_V_STG_SeasDiff: driven by non-frictional process<br> CONVttw_V_STG_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_SeasDiff: driven by frictional process<br> CONVo_V_SPG_SeasDiff: driven by non-frictional process<br> CONVttw_V_SPG_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_SeasDiff: driven by frictional process<br> CONVo_V_SO_SeasDiff: driven by non-frictional process<br> CONVttw_V_SO_SeasDiff: reproduced by TTW balance </p> <p>6. Coord_lon_lat_zw.mat: coordinate information for the variables in "CONV_u100_2d.mat", "CONV_profile.mat"and "CONV_SeasDiff.mat"<br> % Variables inside the file:<br> lon: longitude for the global distributions of the conversion terms<br> lat: latitude for the global distributions of the conversion terms<br> z_w: depth of each vertical level for vertical profiles of conversion terms</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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OpenNeuro
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