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mark-petersenmark-petersen
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Add FOV_E3SM directory and python scripts only.
On June 20 2024 a large number of data files were added to the QuickViz repo by RenevanWesten. I removed those with an interactive rebase, and am now adding the python scripts back in. The total size of the data files were 1.6GB.
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‎ocean/FOV_E3SM/E3SM_Antarctic/Program/Ocean/ACC_transport.py‎

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#Program determines the ACC strength
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from pylab import *
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import numpy
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import datetime
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import time
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import glob, os
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import math
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import netCDF4 as netcdf
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#Making pathway to folder with all data
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directory = '../../Data/'
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def ReadinData(filename, depth_min_index, depth_max_index):
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fh = netcdf.Dataset(filename, 'r')
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#First get the u-grid
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lat = fh.variables['lat'][:] #Longitude
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depth = fh.variables['depth'][depth_min_index:depth_max_index] #Depth (m)
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layer = fh.variables['layer'][depth_min_index:depth_max_index] #Layer thickness (m)
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grid_y = fh.variables['DY'][:] #Meridional grid cell length (m)
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u_vel = fh.variables['UVEL'][depth_min_index:depth_max_index] #Zonal velocity (m/s)
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fh.close()
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return lat, depth, layer, grid_y, u_vel
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#-----------------------------------------------------------------------------------------
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#--------------------------------MAIN SCRIPT STARTS HERE----------------------------------
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#-----------------------------------------------------------------------------------------
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depth_min = 0
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depth_max = 6000
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#-----------------------------------------------------------------------------------------
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files = glob.glob(directory+'Data/Drake_Passage/E3SM_data_year_*.nc')
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files.sort()
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#-----------------------------------------------------------------------------------------
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#Define empty array's
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time = np.zeros(len(files))
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for year_i in range(len(files)):
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date = files[year_i][-7:-3]
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year = int(date[0:4])
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time[year_i] = year
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#-----------------------------------------------------------------------------------------
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#Get all the relevant indices to determine the mass transport
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fh = netcdf.Dataset(files[0], 'r')
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depth = fh.variables['depth'][:] #Depth (m)
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fh.close()
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#Get the dimensions of depth and latitude
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depth_min_index = (fabs(depth_min - depth)).argmin()
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depth_max_index = (fabs(depth_max - depth)).argmin() + 1
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#-----------------------------------------------------------------------------------------
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#Determine the section length per depth layer
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lat, depth, layer_field, grid_y, u_vel = ReadinData(files[0], depth_min_index, depth_max_index)
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for lat_i in range(len(lat)):
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#Get all the layers which have a maximum depth below given range
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if np.sum(layer_field[:, lat_i]) > depth_max:
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#Adjust the last layer
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layer_field[-1, lat_i] -= (np.sum(layer_field[:, lat_i]) - depth_max)
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#-----------------------------------------------------------------------------------------
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#Define empty array's
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transport_all = ma.masked_all(len(time))
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for time_i in range(len(time)):
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#Now determine for each month
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print(time_i)
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lat, depth, layer_field_old, grid_y, u_vel = ReadinData(files[time_i], depth_min_index, depth_max_index)
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#Determine the meridional transport
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transport = u_vel * layer_field * grid_y
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#Determine the transport per depth layer (in Sv) and take sum to determine total transport
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transport_all[time_i] = np.sum(transport) / 1000000.0
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#-----------------------------------------------------------------------------------------
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print('Data is written to file')
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fh = netcdf.Dataset(directory+'Ocean/ACC_transport_depth_'+str(depth_min)+'-'+str(depth_max)+'_m.nc', 'w')
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fh.createDimension('time', len(time))
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fh.createVariable('time', float, ('time'), zlib=True)
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fh.createVariable('Transport', float, ('time'), zlib=True)
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fh.variables['Transport'].long_name = 'Volume transport'
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fh.variables['time'].units = 'Year'
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fh.variables['Transport'].units = 'Sv'
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#Writing data to correct variable
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fh.variables['time'][:] = time
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fh.variables['Transport'][:] = transport_all
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fh.close()

‎ocean/FOV_E3SM/E3SM_Antarctic/Program/Ocean/ACC_transport_plot.py‎

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#Plot the ACC strength
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from pylab import *
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import numpy
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import time
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import glob, os
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import math
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import netCDF4 as netcdf
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#Making pathway to folder with all data
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directory = '../../Data/'
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#-----------------------------------------------------------------------------------------
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#--------------------------------MAIN SCRIPT STARTS HERE----------------------------------
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#-----------------------------------------------------------------------------------------
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depth_min = 0
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depth_max = 6000
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#-----------------------------------------------------------------------------------------
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fh = netcdf.Dataset(directory+'Ocean/ACC_transport_depth_'+str(depth_min)+'-'+str(depth_max)+'_m.nc', 'r')
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time = fh.variables['time'][:]
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transport = fh.variables['Transport'][:]
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fh.close()
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fig, ax = subplots()
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ax.plot(time, transport, '-k', linewidth = 2.0)
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ax.set_xlim(500, 600)
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ax.set_ylim(90, 210)
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ax.set_xlabel('Model year')
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ax.set_ylabel('Volume transport (sv)')
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ax.set_xticks([500, 520, 540, 560, 580, 600])
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ax.grid()
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ax.set_title('ACC strength, E3SM Antarctic')
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show()

‎ocean/FOV_E3SM/E3SM_Antarctic/Program/Ocean/AMOC_transport.py‎

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#Program determines the AMOC strength
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from pylab import *
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import numpy
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import datetime
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import time
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import glob, os
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import math
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import netCDF4 as netcdf
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#Making pathway to folder with all data
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directory = '../../Data/'
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def ReadinData(filename, depth_min_index, depth_max_index):
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fh = netcdf.Dataset(filename, 'r')
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#First get the u-grid
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lon = fh.variables['lon'][:] #Longitude
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depth = fh.variables['depth'][depth_min_index:depth_max_index] #Depth (m)
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layer = fh.variables['layer'][depth_min_index:depth_max_index] #Layer thickness (m)
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grid_x = fh.variables['DX'][:] #Zonal grid cell length (m)
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v_vel = fh.variables['VVEL'][depth_min_index:depth_max_index] #Meridional velocity (m/s)
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salt = fh.variables['SALT'][depth_min_index:depth_max_index] #Salinity (g / kg)
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fh.close()
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return lon, depth, layer, grid_x, v_vel
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#-----------------------------------------------------------------------------------------
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#--------------------------------MAIN SCRIPT STARTS HERE----------------------------------
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#-----------------------------------------------------------------------------------------
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depth_min = 0
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depth_max = 1000
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lat_FOV = 26
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section_name = 'FOV_section_26N'
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#-----------------------------------------------------------------------------------------
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files = glob.glob(directory+'Data/'+section_name+'/E3SM_data_year_*.nc')
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files.sort()
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#-----------------------------------------------------------------------------------------
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#Define empty array's
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time = np.zeros(len(files))
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for year_i in range(len(files)):
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date = files[year_i][-7:-3]
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year = int(date[0:4])
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time[year_i] = year
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#-----------------------------------------------------------------------------------------
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#Get all the relevant indices to determine the mass transport
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fh = netcdf.Dataset(files[0], 'r')
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depth = fh.variables['depth'][:] #Depth (m)
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fh.close()
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#Get the dimensions of depth and latitude
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depth_min_index = (fabs(depth_min - depth)).argmin()
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depth_max_index = (fabs(depth_max - depth)).argmin() + 1
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#-----------------------------------------------------------------------------------------
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#Determine the section length per depth layer
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lon, depth, layer_field, grid_x, v_vel = ReadinData(files[0], depth_min_index, depth_max_index)
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for lon_i in range(len(lon)):
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#Get all the layers which have a maximum depth below given range
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if np.sum(layer_field[:, lon_i]) > depth_max:
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#Adjust the last layer
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layer_field[-1, lon_i] -= (np.sum(layer_field[:, lon_i]) - depth_max)
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#-----------------------------------------------------------------------------------------
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#Define empty array's
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transport_all = ma.masked_all(len(time))
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for time_i in range(len(time)):
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#Now determine for each month
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print(time_i)
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lon, depth, layer_field_old, grid_x, v_vel = ReadinData(files[time_i], depth_min_index, depth_max_index)
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#Determine the meridional transport
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transport = v_vel * layer_field * grid_x
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#Determine the transport per depth layer (in Sv) and take sum to determine total transport
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transport_all[time_i] = np.sum(transport) / 1000000.0
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#-----------------------------------------------------------------------------------------
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print('Data is written to file')
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fh = netcdf.Dataset(directory+'Ocean/AMOC_transport_depth_'+str(depth_min)+'-'+str(depth_max)+'_m.nc', 'w')
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fh.createDimension('time', len(time))
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fh.createVariable('time', float, ('time'), zlib=True)
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fh.createVariable('Transport', float, ('time'), zlib=True)
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fh.variables['Transport'].long_name = 'Volume transport'
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fh.variables['time'].units = 'Year'
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fh.variables['Transport'].units = 'Sv'
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#Writing data to correct variable
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fh.variables['time'][:] = time
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fh.variables['Transport'][:] = transport_all
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fh.close()

‎ocean/FOV_E3SM/E3SM_Antarctic/Program/Ocean/AMOC_transport_plot.py‎

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#Program plots the AMOC strength
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from pylab import *
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import numpy
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import datetime
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import time
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import glob, os
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import math
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import netCDF4 as netcdf
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#Making pathway to folder with all data
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directory = '../../Data/'
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#-----------------------------------------------------------------------------------------
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#--------------------------------MAIN SCRIPT STARTS HERE----------------------------------
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#-----------------------------------------------------------------------------------------
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depth_min = 0
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depth_max = 1000
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#-----------------------------------------------------------------------------------------
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fh = netcdf.Dataset(directory+'Ocean/AMOC_transport_depth_'+str(depth_min)+'-'+str(depth_max)+'_m.nc', 'r')
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time = fh.variables['time'][:]
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transport = fh.variables['Transport'][:]
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fh.close()
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fig, ax = subplots()
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ax.fill_between([-100, 2500], 16, 19, alpha=0.25, edgecolor='orange', facecolor='orange')
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ax.plot(time, transport, '-k', linewidth = 2.0)
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ax.set_xlim(500, 600)
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ax.set_ylim(-2, 22)
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ax.set_xlabel('Model year')
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ax.set_ylabel('Volume transport (sv)')
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ax.set_xticks([500, 520, 540, 560, 580, 600])
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ax.grid()
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ax.set_title('AMOC strength, E3SM Antarctic')
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show()

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