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52 results for “Ocean circulation”
Data from: Satellite-based Lagrangian model reveals how upwelling and oceanic circulation shape krill hotspots in the California Current System [updated]
<p><strong>Abstract</strong></p> <p>In the California Current System, wind-driven nutrient supply and primary production, computed from satellite data, provide a synoptic view of how phytoplankton production is coupled to upwelling. In contrast, linking upwelling to zooplankton populations is difficult due to relatively scarce observations and the inherent patchiness of zooplankton. While phytoplankton respond quickly to environmental forcing, zooplankton grow slower and tend to aggregate into mesoscale “hotspot” regions spatially decoupled from upwelling centers. To better understand mechanisms controlling the formation of zooplankton hotspots, we use a satellite-based Lagrangian method where variables from a plankton model, forced by wind-driven nutrient supply, are advected by near-surface currents following upwelling events. Modeled zooplankton distribution reproduces published accounts of euphausiid (krill) hotspots, including the location of major hotspots and their interannual variability. This satellite-based modeling tool is used to analyze the variability and drivers of krill hotspots in the California Current System, and to investigate how water masses of different origin and history converge to form predictable biological hotspots. The Lagrangian framework suggests that two conditions are necessary for a hotspot to form: a convergence of coastal water masses, and above average nutrient supply where these water masses originated from. The results highlight the role of upwelling, oceanic circulation, and plankton temporal dynamics in shaping krill mesoscale distribution, seasonal northward propagation, and interannual variability.</p> <p><strong>Data set description</strong></p> <p>This data set includes 2 files:</p> <ul> <li>a satellite-based 1993-2023 monthly retrospective of krill concentrations (Zbig) modeled using the growth-advection method in the California Current upwelling system. Inputs include the nitrate supply product described below and GlobCurrent 15 m oceanic currents. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/krill-hotspots-in-the-california-current/.</li> <li>a satellite-based 1993-2023 monthly retrospective of wind-driven nitrate supply estimated in a 150 km coastal band at 0.125° latitudinal resolution. Nitrate supply was calculated based primarily on CCMP v3.1 winds, AVISO geostrophic currents, and a climatology of in situ nitrate at 60m. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/nitrate-supply-estimates-in-upwelling-systems/.</li> </ul> <p>See details regarding data sources and calculations in <a href="https://doi.org/10.3389/fmars.2022.835813">Messié et al. (2022)</a>.</p> <p>[IMPORTANT NOTE:] There is an error in the Ekman pumping fields (trans_pump, Nsupply_pump, Nsupply_total) that will be corrected soon (those fields are not used in publications where only coastal transport was considered). Please contact me if you need Ekman pumping fields before this is fixed.</p>
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Effect of changing ocean circulation on deep ocean temperature in the last millennium: simulation output data
<ul> <li>This dataset contains the output of model simulations used in the paper:<br> Scheen, Jeemijn and Stocker, Thomas F., "Effect of changing ocean circulation on deep ocean temperature in the last millennium", Earth System Dynamics Discussions, https://doi.org/10.5194/esd-11-925-2020, 2020 </li> <li>All figures can be reproduced when combining this dataset with the published analysis code. <br> </li> <li>In addition this dataset contains the data behind Fig. 2 of the paper:<br> Gebbie, G. and Huybers, P. : "The Little Ice Age and 20th-century deep Pacific cooling", Science, 363, 70-74, https://doi.org/10.1126/science.aar8413, 2019<br> </li> <li>Download either the small (unzipped 5 Gb) or large (unzipped 22 Gb) version of the dataset. <strong>Warning: this needs to be loaded into memory when running the notebook.</strong> You only need the small version to run the github notebook and reproduce the figures, but you are free to explore additional variables in the large version.</li> </ul> <p>Overview of doi's:</p> <ul> <li>paper: <a href="https://doi.org/10.5194/esd-11-925-2020">https://doi.org/10.5194/esd-11-925-2020</a></li> <li>code (analysis and figures): <a href="https://doi.org/10.5281/zenodo.4022947">https://doi.org/10.5281/zenodo.4022947</a></li> <li>data (simulation output): <a href="https://doi.org/10.5281/zenodo.4022927">https://doi.org/10.5281/zenodo.4022927</a></li> </ul>
Particle tracking dataset for: Exceptional 20th century ocean circulation in the Northeast Atlantic
<p>Particle tracking data for: "Exceptional 20th century ocean circulation in the Northeast Atlantic" Peter T. Spooner, David J. R. Thornalley, Delia W. Oppo, Alan Fox, Svetlana Radionovskaya, Neil L. Rose, Robbie Mallett, Emma Cooper, J. Murray Roberts</p> <p>VIKING20 (is a 1/20th degree ocean model, forced by a hindcast simulation of the atmosphere: CORE2 (Griffies et al., 2009). The reverse tracks of 113200 particles per year for 50 years, (1959-2009) were simulated with the ARIANE software (Döös, 1995) modified to include independent vertical motion of particles. Particles were seeded at the seabed in 10 km x 10 km boxes centered on MC16-A/17-5P and RAPID-21-3K (representing the settling location). The reverse tracks 'rose' (sinking) at 100 m/day (Takahashi & Be, 1984) and were then allowed to drift freely within the upper 100 m of the water column for six months (i.e. spanning the reasonable lifespan for many species of planktic foraminifera).</p> <p>Track data for the full 50 years are stored in a single netcdf file (output of ncdump -h <filename> given below). The 3D particle positions are in variables traj_lon, traj_lat and traj_depth with the Viking20 model along-track temperature, salinity and density in traj_temp, temp_sal and traj_dens, respectively. The main complication is the obscure storage of time (see also ARIANE software documentation). Variable init_t gives particle start time, counting in 5-day periods from 12:00 pm on 29 December 1957. Viking20 uses a fixed 365 day year so the year can be found for track 'traj' according to:</p> <p> year = 1958 + ( (init_t(traj)-1) \ 73 ) where '\' represents integer division, discarding the remainder.</p> <p>All particle tracks 'begin' (actually the end of the track in time as these are tracked backwards) at the start of July (12:00 pm July 1 in model). Particles are ordered by release time, so trajectories 1-113200 are 1959; 113201-226400 are 1960; etc. Positions are stored every 5 days, counting backwards.</p> <p>Further details are available from the authors.</p> <p> </p> <p>References</p> <p>Döös, K. (1995). Interocean exchange of water masses. Journal of Geophysical Research, 100(C7), 13499. <a href="https://doi.org/10.1029/95JC00337">https://doi.org/10.1029/95JC00337</a></p> <p>Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G., Chassignet, E. P., et al. (2009). Coordinated Ocean-ice Reference Experiments (COREs). Ocean Modelling, 26(1–2), 1–46. <a href="https://doi.org/10.1016/J.OCEMOD.2008.08.007">https://doi.org/10.1016/J.OCEMOD.2008.08.007</a></p> <p>Takahashi, K., & Be, A. W. H. (1984). Planktonic foraminifera: factors controlling sinking speeds. Deep Sea Research Part A. Oceanographic Research Papers, 31(12), 1477–1500. <a href="https://doi.org/10.1016/0198-0149(84)90083-9">https://doi.org/10.1016/0198-0149(84)90083-9</a></p> <p> </p> <p>$ ncdump -h ariane_trajectories_qualitative.nc</p> <p>netcdf ariane_trajectories_qualitative {</p> <p>dimensions:</p> <p>ntraj = 5660000 ;</p> <p>nb_output = UNLIMITED ; // (74 currently)</p> <p>variables:</p> <p><strong>double init_x(ntraj) ;</strong></p> <p>init_x:title = "What is init_x ?" ;</p> <p>init_x:longname = "Initial position in i" ;</p> <p>init_x:units = "No dimension" ;</p> <p>init_x:missing_value = 1.e+20 ;</p> <p><strong>double init_y(ntraj) ;</strong></p> <p>init_y:title = "What is init_y ?" ;</p> <p>init_y:longname = "Initial position in j" ;</p> <p>init_y:units = "No dimension" ;</p> <p>init_y:missing_value = 1.e+20 ;</p> <p><strong>double init_z(ntraj) ;</strong></p> <p>init_z:title = "What is init_z ?" ;</p> <p>init_z:longname = "Initial position in k" ;</p> <p>init_z:units = "No dimension" ;</p> <p>init_z:missing_value = 1.e+20 ;</p> <p><strong>double init_t(ntraj) ;</strong></p> <p>init_t:title = "What is init_t ?" ;</p> <p>init_t:longname = "Initial position in l (time)" ;</p> <p>init_t:units = "See global attributes..." ;</p> <p>init_t:missing_value = 1.e+20 ;</p> <p><strong>double init_age(ntraj) ;</strong></p> <p>init_age:title = "What is init_age ?" ;</p> <p>init_age:longname = "Initial age (time)" ;</p> <p>init_age:units = "seconds" ;</p> <p>init_age:missing_value = 1.e+20 ;</p> <p><strong>double init_transp(ntraj) ;</strong></p> <p>init_transp:title = "What is init_transp ?" ;</p> <p>init_transp:longname = "Initial transport" ;</p> <p>init_transp:units = "m3/s" ;</p> <p>init_transp:missing_value = 1.e+20 ;</p> <p><strong>double l_matureage(ntraj) ;</strong></p> <p>l_matureage:title = "What is l_matureage ?" ;</p> <p>l_matureage:longname = "Larval age of maturity" ;</p> <p>l_matureage:units = "days" ;</p> <p>l_matureage:missing_value = 1.e+20 ;</p> <p><strong>double l_descendage(ntraj) ;</strong></p> <p>l_descendage:title = "What is l_descendage ?" ;</p> <p>l_descendage:longname = "Larval age of competency" ;</p> <p>l_descendage:units = "days" ;</p> <p>l_descendage:missing_value = 1.e+20 ;</p> <p><strong>double l_maxspeedup(ntraj) ;</strong></p> <p>l_maxspeedup:title = "What is l_maxspeedup ?" ;</p> <p>l_maxspeedup:longname = "Max upward larval swim speed" ;</p> <p>l_maxspeedup:units = "mm s-1" ;</p> <p>l_maxspeedup:missing_value = 1.e+20 ;</p> <p><strong>double l_maxspeeddown(ntraj) ;</strong></p> <p>l_maxspeeddown:title = "What is l_maxspeeddown ?" ;</p> <p>l_maxspeeddown:longname = "Max downward larval swim speed" ;</p> <p>l_maxspeeddown:units = "mm s-1" ;</p> <p>l_maxspeeddown:missing_value = 1.e+20 ;</p> <p><strong>int l_targetdepth(ntraj) ;</strong></p> <p>l_targetdepth:title = "What is l_targetdepth ?" ;</p> <p>l_targetdepth:longname = "Target shallow depth" ;</p> <p>l_targetdepth:units = "No dimension" ;</p> <p>l_targetdepth:missing_value = -1. ;</p> <p><strong>double final_x(ntraj) ;</strong></p> <p>final_x:title = "What is final_x ?" ;</p> <p>final_x:longname = "Final position in x (or i)" ;</p> <p>final_x:units = "No dimension" ;</p> <p>final_x:missing_value = 1.e+20 ;</p> <p><strong>double final_y(ntraj) ;</strong></p> <p>final_y:title = "What is final_y ?" ;</p> <p>final_y:longname = "Final position in y (or j)" ;</p> <p>final_y:units = "No dimension" ;</p> <p>final_y:missing_value = 1.e+20 ;</p> <p><strong>double final_z(ntraj) </strong>;</p> <p>final_z:title = "What is final_z ?" ;</p> <p>final_z:longname = "Final position in z (or k)" ;</p> <p>final_z:units = "No dimension" ;</p> <p>final_z:missing_value = 1.e+20 ;</p> <p><strong>double final_t(ntraj) ;</strong></p> <p>final_t:title = "What is final_t ?" ;</p> <p>final_t:longname = "Final position in t (time)" ;</p> <p>final_t:units = "See global attributes..." ;</p> <p>final_t:missing_value = 1.e+20 ;</p> <p><strong>double final_age(ntraj) ;</strong></p> <p>final_age:title = "What is fial_age ?" ;</p> <p>final_age:longname = "Final Age." ;</p> <p>final_age:units = "seconds" ;</p> <p>final_age:missing_value = 1.e+20 ;</p> <p><strong>double final_transp(ntraj) ;</strong></p> <p>final_transp:title = "What is final_transp ?" ;</p> <p>final_transp:longname = "Final transport" ;</p> <p>final_transp:units = "m3/s" ;</p> <p>final_transp:missing_value = 1.e+20 ;</p> <p><strong>float traj_lon(nb_output, ntraj) ;</strong></p> <p>traj_lon:title = "What is traj_lon ?" ;</p> <p>traj_lon:longname = "Trajectory: x positions" ;</p> <p>traj_lon:units = "No dimension" ;</p> <p>traj_lon:missing_value = 1.e+20 ;</p> <p><strong>float traj_lat(nb_output, ntraj) ;</strong></p> <p>traj_lat:title = "What is traj_lat ?" ;</p> <p>traj_lat:longname = "Trajectory: y positions" ;</p> <p>traj_lat:units = "No dimension" ;</p> <p>traj_lat:missing_value = 1.e+20 ;</p> <p><strong>float traj_depth(nb_output, ntraj) ;</strong></p> <p>traj_depth:title = "What is traj_depth ?" ;</p> <p>traj_depth:longname = "Trajectory: z positions" ;</p> <p>traj_depth:units = "No dimension" ;</p> <p>traj_depth:missing_value = 1.e+20 ;</p> <p><strong>float traj_time(nb_output, ntraj) ;</strong></p> <p>traj_time:title = "What is traj_time ?" ;</p> <p>traj_time:longname = "Trajectory: time positions" ;</p> <p>traj_time:units = "See global attributes" ;</p> <p>traj_time:missing_value = 1.e+20 ;</p> <p><strong>float traj_iU(nb_output, ntraj) ;</strong></p> <p>traj_iU:title = "ind i on grid U" ;</p> <p>traj_iU:longname = "Trajectory: i on grid U" ;</p> <p>traj_iU:units = "No dimension" ;</p> <p>traj_iU:missing_value = 1.e+20 ;</p> <p><strong>float traj_jV(nb_output, ntraj) ;</strong></p> <p>traj_jV:title = "ind j on grid V" ;</p> <p>traj_jV:longname = "Trajectory: j on grid V" ;</p> <p>traj_jV:units = "No dimension" ;</p> <p>traj_jV:missing_value = 1.e+20 ;</p> <p><strong>float traj_kW(nb_output, ntraj) ;</strong></p> <p>traj_kW:title = "ind k on grid W" ;</p> <p>traj_kW:longname = "Trajectory: k on grid W" ;</p> <p>traj_kW:units = "No dimension" ;</p> <p>traj_kW:missing_value = 1.e+20 ;</p> <p><strong>float traj_temp(nb_output, ntraj) ;</strong></p> <p>traj_temp:title = "What is traj_temp ?" ;</p> <p>traj_temp:longname = "Trajectory: temperatures" ;</p> <p>traj_temp:units = "degres" ;</p> <p>traj_temp:missing_value = 1.e+20 ;</p> <p><strong>float traj_salt(nb_output, ntraj) ;</strong></p> <p>traj_salt:title = "What is traj_salt ?" ;</p> <p>traj_salt:longname = "Trajectory: salinities" ;</p> <p>traj_salt:units = "psu" ;</p> <p>traj_salt:missing_value = 1.e+20 ;</p> <p><strong>float traj_dens(nb_output, ntraj) ;</strong></p> <p>traj_dens:title = "What is traj_dens ?" ;</p> <p>traj_dens:longname = "Trajectory: densities" ;</p> <p>traj_dens:units = "..." ;</p> <p>traj_dens:missing_value = 1.e+20 ;</p> <p> </p> <p>// global attributes:</p> <p>:key_roms = ".FALSE." ;</p> <p>:key_symphonie = ".FALSE." ;</p> <p>:key_B2C_grid = ".FALSE." ;</p> <p>:key_sequential = ".TRUE." ;</p> <p>:key_alltracers = ".TRUE." ;</p> <p>:key_ascii_outputs = ".FALSE." ;</p> <p>:key_iU_jV_kW = ".TRUE." ;</p> <p>:key_read_age = ".FALSE." ;</p> <p>:mode = "qualitative" ;</p> <p>:forback = "backward" ;</p> <p>:bin = "nobin" ;</p> <p>:init_final = "NONE" ;</p> <p>:nmax = 10000000 ;</p> <p>:tunit = 86400. ;</p> <p>:ntfic = 5 ;</p> <p>:tcyc = 1639872000. ;</p> <p>:key_approximatesigma = ".FALSE." ;</p> <p>:key_computesigma = ".TRUE." ;</p> <p>:zsigma = 1000. ;</p> <p>:memory_log = ".TRUE." ;</p> <p>:output_netcdf_large_file = ".FALSE." ;</p> <p>:key_interp_temporal = ".TRUE." ;</p> <p>:maxcycles = 50 ;</p> <p>:delta_t = 86400. ;</p> <p>:frequency = 5 ;</p> <p>:nb_output = 73 ;</p> <p>:mask = ".TRUE." ;</p> <p>:key_region = ".FALSE." ;</p> <p>:key_larvae = ".TRUE." ;</p> <p>:imt = 1784 ;</p> <p>:jmt = 1719 ;</p> <p>:kmt = 46 ;</p> <p>:lmt = 3796 ;</p> <p>:key_computew = ".TRUE." ;</p> <p>:w_surf_option = "" ;</p> <p>:key_partialsteps = ".TRUE." ;</p> <p>:key_jfold = ".FALSE." ;</p> <p>:pivot = "T" ;</p> <p>:key_periodic = ".FALSE." ;</p> <p>:dir_mesh = "./GRID" ;</p> <p>:fn_mesh = "1_mesh_mask.nc" ;</p> <p>:nc_var_xx_tt = "glamt" ;</p> <p>:nc_var_xx_uu = "glamu" ;</p> <p>:nc_var_zz_ww = "gdepw_0" ;</p> <p>:nc_var_e2u = "e2u" ;</p> <p>:nc_var_e1v = "e1v" ;</p> <p>:nc_var_e1t = "e1t" ;</p> <p>:nc_var_e2t = "e2t" ;</p> <p>:nc_var_e3t = "e3t" ;</p> <p>:nc_var_tmask = "tmask" ;</p> <p>:nc_mask_val = 0. ;</p> <p>:c_dir_zo = "./DATA" ;</p> <p>:c_prefix_zo = "V20_nest_5d_" ;</p> <p>:ind0_zo = 1958 ;</p> <p>:indn_zo = 2009 ;</p> <p>:maxsize_zo = 4 ;</p> <p>:c_suffix_zo = "_U.nc" ;</p> <p>:nc_var_zo = "vozocrtx" ;</p> <p>:nc_var_eivu = "NONE" ;</p> <p>:nc_att_mask_zo = "missing_value" ;</p> <p>:c_dir_me = "./DATA" ;</p> <p>:c_prefix_me = "V20_nest_5d_" ;</p> <p>:ind0_me = 1958 ;</p> <p>:indn_me = 2009 ;</p> <p>:maxsize_me = 4 ;</p> <p>:c_suffix_me = "_V.nc" ;</p> <p>:nc_var_me = "vomecrty" ;</p> <p>:nc_var_eivv = "NONE" ;</p> <p>:nc_att_mask_me = "missing_value" ;</p> <p>:c_dir_te = "./DATA" ;</p> <p>:c_prefix_te = "V20_nest_5d_" ;</p> <p>:ind0_te = 1958 ;</p> <p>:indn_te = 2009 ;</p> <p>:maxsize_te = 4 ;</p> <p>:c_suffix_te = "_T.nc" ;</p> <p>:nc_var_te = "votemper" ;</p> <p>:nc_att_mask_te = "missing_value" ;</p> <p>:c_dir_sa = "./DATA" ;</p> <p>:c_prefix_sa = "V20_nest_5d_" ;</p> <p>:ind0_sa = 1958 ;</p> <p>:indn_sa = 2009 ;</p> <p>:maxsize_sa = 4 ;</p> <p>:c_suffix_sa = "_T.nc" ;</p> <p>:nc_var_sa = "vosaline" ;</p> <p>:nc_att_mask_sa = "missing_value" ;</p> <p>}</p> <p> </p>
Phanerozoic global climatic fields simulated using the FOAM ocean-atmosphere general circulation model
<p>These files contain the output of Phanerozoic global climate simulations conducted using the coupled ocean-atmosphere FOAM general circulation model. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. All simulations have been conducted using identical boundary conditions; pCO2: 2240 ppm, solar luminosity: 1368 W m-2, vegetation: rocky desert, orbital configuration: null eccentricity and minimum obliquity. Only the continental configuration was varied from one time slice to the other (sensitivity test to the continental configuration), using the reconstructions of Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/).</p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All file names use the following pattern: "[age]rd_1368W_EccN_[model_component]_2240ppm.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos', 'ocean' or 'coupl' (atmospheric and oceanic components, plus coupler).</p>
Evolution of ocean circulation in the North Atlantic Ocean during the Miocene: impact of the Greenland Ice Sheet and the Eastern Tethys Seaway
<p>This dataset contains atmosphere and ocean outputs (NetCDF files) from modeling experiments with realistic early Miocene paleogeography as well as sensitivity to Greenland Ice Sheet and Eastern Tethys Seaway. The set of simulation targets the evolution of the North Atlantic Deep Water during the Miocene. The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, GMD). It includes 3 ocean-atmosphere simulations. Data are monthly averages over the last 100 years of the simulations. </p>
CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> <p>Decoding file names:</p> <p>Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend. </p> <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> <p>Variables:</p> <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul> <p> </p> <p> </p> <p> </p>
CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> <p>Details for files cesm2.[experiment name].SST.noise.nc</p> <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> <p>For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207</p> <p>For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374</p>
North Atlantic circulation: a perspective from ocean reanalyses
<p>We provide data here from our paper "North Atlantic circulation: a perspective from ocean reanalyses". All files are netcdf or text files (or a tar of files).</p> <p>clim_MLD.tar.gz : time mean mixed layer depths (Fig 1)</p> <p>clim_AMOC.tar.gz : time mean AMOC streamfunctions (Fig 2)</p> <p>clim_gyre.tar.gz : time mean barotropic streamfunctions (Fig 3)</p> <p>clim_box_transports.csv : volume transports for the subtropical and subpolar regions split into upper, lower, boundary and interior (Fig 4)</p> <p>clim_transports.tar : heat and freshwater transports by latitude (Fig 5)</p> <p>clim_scatter_fig6-7.tar : time mean labrador sea densities, AMOC and subpolar gyre strengths, heat and freshwater transports (Fig 6+7)</p> <p> timeseries_TS.tar : timeseries of temperature and salinity anomalies in the upper 500m over region 25-45N and 45-65N. Also volumes of water> 10 degrees or 35.3PSU (Fig 8)</p> <p>timeseries_fig9.tar : timeseries of labrador sea density and mixed layer depth (Fig 9)</p> <p>dens_control_data.nc : the relationship between salinity and temperature control on density timeseries (Fig 10)</p> <p>timeseries_amoc.tar : timeseries of AMOC at 26.5N and 50N (Fig 11)</p> <p>rapid_corr.csv : correlations and standard deviations of components of AMOC at 26.5N compared to observations (Fig 12)</p> <p>timeseries_osnap.nc : timeseries of overturning across the OSNAP section (Fig 13)</p> <p>timeseries_gyres.tar : timeseries of subpolar and subtropical gyre strengths (Fig 14)</p> <p>Fig 15 uses data from Fig 9, 11,14</p> <p>timeseries_transports.tar : timeseries of heat and freshwater transports at 26.5N and 50N (Fig 16, 17)</p> <p>Fig 18 uses data from Fig 11, 16,17<br> </p>
Ocean circulation during the last nine interglacials inferred from carbon 13 isotopes - model outputs
<p>This dataset contains the model output corresponding to the paper entitled "Ocean circulation during the last nine interglacials inferred from carbon 13 isotopes" submitted to Paleoceanography. For the description of the model and simulations we refer to this article.</p> <p>Model outputs:</p> <p>The outputs of two series of simulations can be found in the files.</p> <p>1) MIS experiments:</p> <p>- Atmospheric CO<sub>2</sub> values (ppm) for each interglacial are in: iloveclim_CO2_MIS.txt</p> <p>- Oceanic ð<sup>13</sup>C values (permil) are in:</p> <p>iloveclim_PI_CC.nc for the pre-industrial</p> <p>iloveclim_MISXX.nc for MISXX (XX being 1,5,7,9,11,13,17 or 19)</p> <p> </p> <p>2) Sensitivity experiments</p> <p>- Atmospheric CO<sub>2</sub> values (ppm) are in: iloveclim_CO2_sensitivity_expe.txt</p> <p>- Streamfunction values (Sv) are in:</p> <p>iloveclim_PI-CC_stream.nc for the pre-industrial</p> <p>iloveclim_MIS17_CC_stream.nc for the standard MIS17 simulation</p> <p>iloveclim_MIS17-CC-hosing0.2Sv_stream.nc for the hosing simulation with 0.2sv</p> <p>iloveclim_MIS17-CC-hosing-0.2Sv_stream.nc for the hosing simulations with -0.2Sv</p> <p>iloveclim_MIS17-CC-brines0.4_stream.nc for the simulation with the sinking of brines</p> <p>- Oceanic ð<sup>13</sup>C values (permil) are in:</p> <p>iloveclim_MIS17-CC.nc for the standard MIS17 simulation</p> <p>iloveclim_MIS17-CC-hosing0.2Sv.nc for the hosing simulation with 0.2sv</p> <p>iloveclim_MIS17-CC-hosing-0.2Sv.nc for the hosing simulations with -0.2Sv</p> <p>iloveclim_MIS17-CC-brines0.4.nc for the simulation with the sinking of brines</p>
Assets (code, scripts and datasets) for the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks"
<p>This dataset contains all relevant software and data related to the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks", submitted to AGU journals.</p>
Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation
<p>These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data</p>
Data related to the paper: Surface Heating Steers Planetary-Scale Ocean Circulation
<p>Preprocessed outputs to reproduce figures in the paper: <a href="https://doi.org/10.1175/JPO-D-23-0016.1">https://doi.org/10.1175/JPO-D-23-0016.1</a>.</p>
Supporting model data for Paleogeographic controls on the evolution of Late Cretaceous ocean circulation by Ladant, J.-B., et al. in Climate of the Past, doi:10.5194/cp-2019-157.
<p>The dataset is comprised of CCSM4 model variables required to reproduce the figures shown in the following manuscript:</p> <p>Ladant, J.-B., C. J. Poulsen, F. Fluteau, C. R. Tabor, K. G. MacLeod, E. E. Martin, S. J. Haynes and M. A. Rostami, Paleogeographic controls on the evolution of Late Cretaceous ocean circulation, Climate of the Past, doi:10.5194/cp-2019-157.</p>
Data: Precession-induced Tipping of the Atlantic Meridional Ocean Circulation
Open the record for dataset details and reuse information.
Supplementary material for paper "Contribution of the wind and Loop Current Eddies to the circulation in the southern Gulf of Mexico" submitted to journal of Ocean Dynamics
<p>Movie including the time evolution of the daily SSH and surface velocity vector fields in the BoC, the meridional velocity in the CG zonal section, meridional velocity in the 22°N zonal section, the vq sections at 22°N, the time series of PVF2 and CPFV2 and the time series of daily transport through the CG western arm.</p>
MITgcm model setup and output for "Modeling ocean circulation in the Bellingshausen Sea"
<p>MITgcm model setup and output for "Modeling ocean circulation in the Bellingshausen Sea".</p> <p>Here, it contains the results of the Amundsen and the Bellingshausen Sea from 1992 to 2020. </p> <p><Changes from run260 to this model><br> This is improved version of run260 with 70 vertical layers. <br> To adjust melt rate of the George VI ices shelf, we change the values of heat transfer coefficient γT similar to run260.</p> <p>This experiment was conducted in two separate sessions due to changes in timestep (150 -> 120).<br> We can refer to input/data.diagnostics for the details of the model output name.<br> Outputs are the monthly mean.</p> <p><br> <strong>(Contents)</strong></p> <p>ABSmodel_1992_2009_code.tar.gz (code to run this simulation between 1992 and 2009)</p> <p>ABSmodel_2010_2020_code.tar.gz (code to run this simulation between 2010 and 2020)</p> <p>ABSmodel_1992_2009_input.tar.gz (input file required for this simulation between 1992 and 2009)</p> <p>ABSmodel_2010_2020_input.tar.gz (input file required for this simulation between 1992 and 2009)</p> <p>ABSmodel_1992_2009_results.tar.gz</p> <p>ABSmodel_2010_2020_results.tar.gz</p> <p>(due to size limit of 50GB, please check https://ecco.jpl.nasa.gov/drive/files/ECCO2/LLC1080_REG_AMS/Hyogo_et_al_2022 for complete model output. Complete datasets can also be obtained by rerunning the simulation.)</p> <p><strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..</p> <p>mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /forcing/era_xx/ .<br> cp ../build/mitgcmuv .<br> qsub run_omp_high_t1.pbs</p>
Glacial-Interglacial Controls on Ocean Circulation and Temperature during the Permo-Carboniferous
<p>Monthly climatologies of the late Pennsylvanian-early Permian (~300 Ma) glacial and interglacial simulations using the Community Earth System Model v1.2. The NetCDF files include all atmosphere and ocean variables used to make the figures in the study (see README.txt file for more information).</p>
MDM data for "Wind driven ocean circulation changes can amplify future cooling of the North Atlantic warming hole" - submitted to Journal of Climate
<p>Data files for MDM simulation used in Journal of Climate submission, "Wind driven ocean circulation changes can amplify future cooling of the North Atlantic warming hole"</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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