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283 results for “Ocean Model”
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>
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model
<p>Model output and observational data and scripts corresponding to the manuscript "Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model"</p> <p><strong>Abstract. </strong></p> <p>This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.</p> <p>Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data.</p> <p>Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.</p> <p>In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.</p>
MOM6-COBALT2 model results for asymmetrical Ocean Carbon Responses to La Niña and El Niño in the Tropical Pacific Ocean
<p>1. MOM6 COBALT2 model results</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc</a></p> <p>(air-sea carbon flux decomposition)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_chl_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_chl_1980_2023.nc</a></p> <p> (sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_no3_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_no3_1980_2023.nc</a></p> <p>(sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1980-2023.nc</a></p> <p> (sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_po4_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_po4_1980_2023.nc</a></p> <p> (sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1980-2023.nc</a></p> <p> (sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface zonal velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_MLD_003_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_MLD_003_1980-2023.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface meridional velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized ocean pCO2 in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/hist_gcb_simA_ocean_static.nc/content" target="_blank" rel="noopener noreferrer">hist_gcb_simA_ocean_static.nc</a></p> <p>(grid of model)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br>pco2_hadv_hdif: horizontal transport term, H_circ;<br>dpco2_vadv_vdif: vertical transport term,V_circ;<br>dpco2_bio: biological term<br>dpco2_rain: surface freshwater term<br>dpco2_sst: thermal term<br>dpco2_dt: pco2 response term<br>dpco2_flux: CO2 flux response term</p> <p>2. dataset</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc</a></p> <p>(CO2 based MLS: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_area.nc</a></p> <div>(the grid of MLS)</div> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc</a></p> <p>(CO2 based SOM-FFN: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc</a></p> <p>(the grid of SOM-FFN)</p> <p><a href="../api/records/13325632/draft/files/precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JRA: detrended and deseasonalized precipitation)</p> <p><a href="../api/records/13325632/draft/files/phosphate_all_woa18_all_p_monthly.nc/content" target="_blank" rel="noopener noreferrer">phosphate_all_woa18_all_p_monthly.nc</a></p> <p>(WOA: sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc</a></p> <p>(AVISO: detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc</a></p> <p>(OISSS: sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc</a></p> <div>(OISSS: detrended and deseasonalized sea surface salinity)</div> <p><a href="../api/records/13325632/draft/files/co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc/content" target="_blank" rel="noopener noreferrer">co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc</a></p> <p>(SOCAT: ocean pCO2)</p> <p><a href="../api/records/13325632/draft/files/mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc/content" target="_blank" rel="noopener noreferrer">mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/nitrate_all_woa18_all_n_monthly.nc/content" target="_blank" rel="noopener noreferrer">nitrate_all_woa18_all_n_monthly.nc</a></p> <p>(WOA: sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc</a></p> <p> (OISST: sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc</a></p> <p>(OISST: detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc/content" target="_blank" rel="noopener noreferrer">chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc</a></p> <p>(GlobColour: sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JAR: detrended and deseasonalized zonal wind velocity)</p> <p><a href="../api/records/13325632/draft/files/TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc/content" target="_blank" rel="noopener noreferrer">TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc</a></p> <p>(TAO: sea surface temperature, sea surface salinity, and ocean pco2)</p> <p>3. Figure</p> <p>Figure1-4, FigureS1-S11 are data analysis files used python</p> <p> </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>
SDUST2024MSS_AO: a mean sea surface model of the Arctic Ocean based on CryoSat-2 SAR altimeter data
<p>This model is a mean sea surface model for ice-covered regions, using CryoSat-2 satellite SAR mode altimeter data from July 2010 to December 2023. The heights are referenced to the WGS-84 ellipsoid, and the grid size is 5 km × 5 km.</p>
Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model
<p>This data is only used for paper submitted to the JPO entitled 'Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model'.</p>
Seismic Model of the Seafloor Sediment and Shallow Oceanic Crust of the Alaska-Aleutian Subduction Zone at the Alaska Peninsula
<p>This dataset is supplementary to</p> <blockquote> <p>Zheng, Mengjie, Sheehan, Anne, Liu, Chuanming, Wu, Mengyu, & Ritzwoller, Michael. (2024). Characterizing Sub-Seafloor Seismic Structure of the Alaska Peninsula Along the Alaska-Aleutian Subduction Zone. <em>Journal of Geophysical Research: Solid Earth</em>, <em>129</em>(11), e2024JB029862. <a href="https://doi.org/10.1029/2024JB029862">https://doi.org/10.1029/2024JB029862</a></p> </blockquote> <p>This dataset contains files of sub-seafloor S-wave velocities and sediment properties.</p>
Ocean model simulations in cold-water coral ecosystems off the coasts of Angola and Namibia in the Southeast Atlantic: Setup, boundary conditions and model results.
<p>The dataset contains all essential data for the setup of high-resolution local area model implementations using the ROMS-AGRIF model version 3.1 in two cold-water coral regions off the coasts of Angola and Namibia in the Southeast Atlantic. The data include computational grids, initialization fields (temperature, salinity), and boundary conditions (temperature, salinity, currents, and sea surface height) for each model area. It also includes model output, which has been used in different studies of the local oceanography of the region .</p> <p>Initialization, forcing and output data for each ROMS-AGRIF model implementation are provided in two compressed archive data files:</p> <ul> <li>Angola Margin: Angola_Model_Setup1.7z</li> <li>Namibia Margin: Namibia_Model_Setup1.7z</li> </ul> <p>The data set description is provided in the file:</p> <ul> <li>DataSet_Description_Angola_Namibia_Model.pdf</li> </ul> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"
<p>This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge.</p> <p>Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations.</p> <p>The dataset used in this research includes:</p> <p>1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID</p> <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> <p>2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting</p> <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Wave Parameters - North Atlantic Ocean - Period 2091-2100 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
FOCI model output used in the study by Ivanciu et al. - Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean
<p>This dataset comprises the output from simulations with the coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020) used in the analysis presented in the study by Ivanciu et al., 2021 “Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean”. Four ensembles of three simulations each were performed: FixODS (II012, II014, II016), FixGHG (II013, II015, II017), INTERACT_O3 (SW128, II010, II011) and PRESC_O3 (JH027, II037, JH039). A detailed description of the simulations can be found in the above-mentioned manuscript.</p>
Model fields supporting the publication "Integrated Assessment of the Risks to Ocean Acidification in the Northern High Latitudes: Regional Comparison of Exposure, Sensitivity and Adaptive Capacity of Pelagic Calcifiers"
<p>These are the model outputs supporting the described manuscript. They include monthly averaged output of aragonite saturation state for each year during the 10-year hindcast. Also included is the particle tracking output, for both the Bering Sea and the Gulf of Alaska, as described in the manuscript.</p>
Code and data archive to accompany "A derivative-free optimisation method for global ocean biogeochemical models", Oliver et. al. 2021
<p>This archive is to accompany the article:</p> <p>A derivative-free optimisation method for global ocean biogeochemical models,<br> Sophy Oliver, Coralia Cartis, Iris Kriest, Simon Tett, and Samar Khatiwala.</p> <p>The optimisation framework used in this study can be found here: https://doi.org/10.5281/zenodo.5517610</p> <p>The original source code of MOPS were from the Supplement of Kriest et al. (2017).<br> The most recent TMM source code is available at https://github.com/samarkhatiwala/tmm.</p> <p>In this archive:</p> <p>Supplement/Configurations/OxfordMOPS_Configs contains:<br> - ReadOnlyFiles (Files and Code specifically used to run the global ocean biogeochemical model MOPS model with<br> the Transport Matrix Method, which have been edited to differ from the versions downloaded from the sources above.)<br> - RunCode (runscripts to run the MOPS model with the TMM)<br> - TWIN_Configs (JSON files required by each optimisation experiment carried out).</p> <p>Supplement/OxfordMOPS_EXP contains data for each iteration of all optimisation experiments carried out.</p> <p>Supplement/OPTCLIMSO_PlottingScripts contains MATLAB plotting scripts used to create results figures of these experiments.</p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10.</li> <li>northernmost latitude = 42.</li> <li>westernmost longitude = -70.</li> <li>easternmost longitude = -5.</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"
<p><strong>Description.</strong></p> <p>The data included in this repository is output of simulations performed with the NEMO-PISCESv2 global ocean-biogeochemical model. Simulations involved forcing the NEMO-PISCESv2 with global warming associated with historical and future emissions, as well as the historical and future trends in atmospheric nitrogen deposition. Future climate change was according to the Representative Concentration Pathway 8.5 scenario (Dufresne et al., 2013; Riahi et al., 2011), which sees rapid warming during the 21<sup>st</sup> century. Historical and future atmospheric nitrogen deposition fields were created via linear interpolation of fields produced by Hauglustaine et al. (2014) at years 1850, 2000, 2030, 2050 and 2100. To represent the amplification of deposition since 1950 (Galloway 2014), 60 % of the increase between 1850 and 2000 occurred from 1950 onwards.</p> <p>In this study, we quantified the effect anthropogenic climate change and anthropogenic increases in atmospheric nitrogen deposition on the marine nitrogen cycle. The response of the marine nitrogen cycle to these combined stressors is highly uncertain, and we therefore employed this complex model with a strong representation of nitrogen cycling in an attempt to constrain the global behaviour of this important cycle. In addition, through the addition of nitrogen isotopes to the ocean-biogeochemical model, we also explored and described how the isotopes responded to these anthropogenic forcings, and if the isotopes uniquely fingerprinted the response for potential monitoring/detection purposes.</p> <p>Our abstract reads:</p> <p>“The open ocean nitrogen cycle is being altered by increases in anthropogenic atmospheric nitrogen deposition and climate change. How the nitrogen cycle responds will determine long-term trends in net primary production (NPP) in the nitrogen-limited low latitude ocean, but is poorly constrained by uncertainty in how the source-sink balance will evolve. Here we show that intensifying nitrogen limitation of phytoplankton, associated with near-term reductions in NPP, causes detectable declines in nitrogen isotopes (δ<sup>15</sup>N) and constitutes the primary perturbation of the 21<sup>st</sup> century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ<sup>15</sup>N by 2060, predominantly due to the effects of climate change that alter ocean circulation, with implications for the nitrogen sources-sink balance. Our results highlight that δ<sup>15</sup>N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21<sup>st</sup> century.”</p> <p> </p> <p><strong>Coordinates</strong></p> <p>Spatial resolution is global (90°S-90°N, 180°W-180°E, surface ocean to 5000 metres depth) and temporal resolution runs from years 1801 to 2100.</p> <p> </p> <p><strong>Citation.</strong></p> <p>Buchanan PJ, Aumont O, Bopp L, Mahaffey C, and Tagliabue A (2021): An isotopic fingerprint of increasingly nitrogen-limited phytoplankton in a changing oceanic nitrogen cycle. Nature Communications.</p> <p> </p> <p><strong>Files provided.</strong></p> <p>The data files provided are those that are required to create the figures for this study and/or perform key analyses (i.e. the time of emergence calculations). In the following, each figure or analysis has an associated python script and we list the data files needed to run that script.</p> <p>Python scripts can be found the lead authors GitHub at <a href="https://github.com/pearseb/PISCESiso_Ncycle_analysis">https://github.com/pearseb/PISCESiso_Ncycle_analysis</a>. </p> <p> </p> <p>Put δ<sup>15</sup>N<sub>NO3</sub> observations on model grid (<em>process-d15Nno3_observations_on_model_grid.py</em>):</p> <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> <p>Model assessment (<em>process-model_assessment.py</em>):</p> <ul> <li>“ETOPO_spinup_d15Nno3.nc”</li> <li>“ETOPO_ORCA2.0_Basins_float.nc”</li> <li>“ETOPO_ORCA2.0.full_grid.nc”</li> <li>“RafterTuerena_watercolumn_d15N_no3_gridded.npz”</li> </ul> <p>Time of emergence calculations (<em>process-compute_toe.py</em>):</p> <ul> <li>“ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_npp.nc”</li> <li>“ETOPO_picontrol_ndep_1y_npp.nc”</li> <li>“ETOPO_future_1y_npp.nc”</li> <li>“ETOPO_future_ndep_1y_npp.nc”</li> <li>“ETOPO_picontrol_1y_nfix.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nfix.nc”</li> <li>“ETOPO_future_1y_nfix.nc”</li> <li>“ETOPO_future_ndep_1y_nfix.nc”</li> </ul> <p>Figure 1 (<em>fig-main1.py</em>):</p> <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> <p>Figure 2 (<em>fig-main2.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Figure 3 (<em>fig-main3.py</em>):</p> <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> <p>Figure 4 (<em>fig-main4.py</em>):</p> <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> <p>Supp Figure 1 (<em>fig-supp1.py</em>):</p> <ul> <li>“figure_d15Nmaps.nc”</li> </ul> <p>Supp Figure 2 (<em>process-model_assessment.py</em>):</p> <ul> <li>Produced by <em>process-model_assessment.py </em>(see data above)</li> </ul> <p>Supp Figure 3 (<em>fig-supp3.py</em>):</p> <ul> <li>“d15nstats.txt”</li> </ul> <p>Supp Figure 4 (<em>fig-supp4.py</em>):</p> <ul> <li>“ndep_Tg_yr.nc”</li> </ul> <p>Supp Figure 5 (<em>fig-supp5.py</em>):y</p> <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> <p>Supp Figure 6 (<em>fig-supp6.py</em>):</p> <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> <p>Supp Figure 7 (<em>fig-supp7.py</em>):</p> <ul> <li>“figure_depthzones.nc”</li> </ul> <p>Supp Figure 8 (<em>fig-supp8.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Supp Figure 9 (<em>fig-supp9.py</em>):</p> <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> <p>Supp Figures 10 and 11 (<em>process-0D_model_phyto_frac.py</em>):</p> <ul> <li>Produced by <em>process-0D_model_phyto_frac.py</em> and no data required.</li> </ul> <p>Supp Figure 12 (<em>process-compute_toe.py</em>):</p> <ul> <li>Produced by <em>process-compute_toe.py </em>(see data above)</li> </ul> <p> </p> <p><strong>References.</strong></p> <p>Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). <em>Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5</em>. <em>Climate Dynamics</em> (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1</p> <p>Galloway, J. N. (2014). The Global Nitrogen Cycle. In <em>Treatise on Geochemistry</em> (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3</p> <p>Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. <em>Atmospheric Chemistry and Physics</em>, <em>14</em>(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014</p> <p>Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., et al. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. <em>Climatic Change</em>, <em>109</em>(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y</p>
Wave Parameters - North Atlantic Ocean - Period 2081-2099 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Dataset for "Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO2: perspectives from model perturbation experiments and a theoretical framework"
<p>Reference: Kewei Lyu, Xuebin Zhang, John A. Church, Quran Wu, Russell Fiedler, and Fabio Boeira Dias (2022), Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO<sub>2</sub>: perspectives from model perturbation experiments and a theoretical framework, <em>Journal of Physical Oceanography</em>, <a href="https://doi.org/10.1175/JPO-D-22-0095.1">https://doi.org/10.1175/JPO-D-22-0095.1</a></p>
Datasets and Model for "Age-Independent Oceanic Plate Thickness and Asthenosphere Melting from SS Precursor Imaging"
<p>The Earth’s asthenosphere is a mechanically weak layer characterized by low seismic velocity and high attenuation. The nature of this layer has been strongly debated. In this study, we process twelve years of seismic data recorded at the global seismological network (GSN) stations to investigate SS waves reflected at the upper and lower boundaries of this layer in global oceanic regions. We observe strong reflections from both the top and the bottom of the asthenosphere, dispersive across all major oceans. The average depths of the two discontinuities are 120 km and 255 km, respectively. The SS waves reflected at the lithosphere and asthenosphere boundary are characterized by anomalously large amplitudes, which require ∼12.5% reduction in seismic velocity across the interface. This large velocity drop can not be explained by a thermal cooling model but indicates 1.5%-2% localized melt in the oceanic asthenosphere. The depths of the two discontinuities show large variations, indicating that the asthenosphere is far from a homogeneous layer but likely associated with strong and heterogeneous small-scale convections in the oceanic mantle. The average depths of the two boundaries are largely constant across different age bands. In contrast to the half space cooling model, this observation supports the existence of a constant-thickness plate in oceanic regions with a complex and heterogeneous origin. This repository contains four datasets and one reference earth model from this study.</p>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
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