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Dataset . 2023
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Dataset . 2023
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Dataset . 2022
License: CC BY
Data sources: Datacite
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Dataset . 2022
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Dataset . 2022
License: CC BY
Data sources: Datacite
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Dataset . 2022
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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https://doi.org/10.5281/zenodo...
Dataset . 2023
License: CC BY
Data sources: Sygma
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NAHosMIP data

Authors: Jackson, Laura; Alastue de Asenjo, Eduardo; Bellomo, Katinka; Danabasoglu, Gokhan; Hu, Aixue; Jungclaus, Johann; Lee, Warren; +4 Authors
Abstract

This package is of data and code from the publication Jackson et al, (2022), Understanding AMOC stability: the North Atlantic Hosing Model Intercomparison Project. GMD [Full reference to come] This is the initial paper of the North Atlantic Hosing Model Intercomparison Project (NAHosMIP). See https://www.tipes.dk/na-hosing-mip/ Data included is from several experiments: Priority u03-hos Uniform hosing of 0.3Sv in the North Atlantic Priority u03-r20 Initialised 20yrs into u03-hos with no additional hosing Priority u03-r50 Initialised 50yrs into u03-hos with no additional hosing u03-r70 Initialised 70yrs into u03-hos with no additional hosing u03-r100 Initialised 100yrs into u03-hos with no additional hosing Priority g01-hos Hosing around Greenland of 0.1Sv Priority g01-r50 Initialised 50yrs into g01-hos with no additional hosing g01-r100 Initialised 100yrs into g01-hos with no additional hosing The models that have run this protocol are: CESM2, CanESM5, EC-Earth3, HadGEM3-GC3-1LL, HadGEM3-GC3-1MM, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR. Not all of these experiments are conducted with all models. Also MPI-ESM1-2-LR has the same experiments repeated with 0.5Sv uniform hosing (u05) and 0.3Sv hosing around Greenland (g03). Experiments are compared with piControl experiments (available from https://pcmdi.llnl.gov/CMIP6/Guide/dataUsers.html ). All hosing experiments were started from the same start year as the piControl experiments, apart from CESM2 which started the hosing experiments after 500 years of the control. Annual mean data for the AMOC streamfunction in depth and latitude space in all experiments is available in the files MODEL_msftmz.tar and MODEL msftyz.tar. MLD.tar contain March mixed layer depth from u03-hos for all models. SST.tar and SSS.tar contain annual mean sea surface temperature and salinity, and SICE.tar contains march sea ice concentration from u03-hos for all models. FOV.tar contains data for Fov from the controls (mostly as netcdf, but in a some cases as a python pickle file). For some models this is output as a standard diagnostic, but for other models it has been calculated offline using monthly mean meridional velocity and salinity files. The script for calculating Fov is listed below as calc_fov.py Con_MLD.tar shows March mixed layer depth from the controls for all models and Con_BSF.tar shows the barotropic streamfunction from the controls for all models. Both are regridded to a regular grid. Hos_regrid.tar contains files from HadGEM3-GC3-1MM showing the hosing applied regridded to a regular grid for plotting. calc_FW_GC31LL.tar contains several files. It contains the script to calculate freshwater transports calc_fw_transp.py (see below). It also contains files required to run the script for HadGEM3-GC3-1LL as an example. These are grid and mask files, a .ini file (containing details of variable and file names etc) and one file of monthly meridional velocity and salinity data. Data used to calculate freshwater transports used in the manuscript are available from ESGF. ---------------------------------- Scripts attached are listed below. Those used to make figures from the paper have filenames of the forms plt_F*.py. Others for doing processing are of the form calc*.py. These are all included in scripts.tar plt_F1_hos.py - uses data from Hos_regrid.py to plot hosing fields plt_F2_strmf.py - uses AMOC data from msftyz and msftmz files calc_mts.py - processes AMOC data to make timeseries calc_mts_4xco2.py - processes AMOC data from abrupt 4xCO2 experiments available from ESGF plt_F3_mts.py - uses AMOC timeseries plt_F4_compare.py - uses AMOC timeseries plt_F5_mts_GRL.py - uses AMOC timeseries plt_F6_recov.py - uses AMOC timeseries calc_mld_dmv.py - calculated maximum mixed layer depth and volume of deep mixed layer from data in MLD.tar plt_F7_mld_amoc.py - uses AMOC and MLD timeseries calc_SSS.py - calculates max and min SSS fields from data in SSS.tar calc_sice.py - calculates max and min sea ice fields from data in SICE.tar plt_F8_SST.py - calculates max and min SST fields from data in SST.tar. Plots these along with SSS and sea ice processed data calc_fw_transp.py - script for calculating freshwater transport by the overturning (Fov) offline in those models where it is not output. This is contained in calc_FW_GC31LL.tar (see below) plt_F9_Fov.py - uses data from FOV.tar plt_F10_mld.py - uses data from Con_MLD.tar to plot control March mixed layer depths plt_F11_bsf.py - uses data from Con_BSF.tar to plot control barotropic streamfunctions

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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