# -*- coding: utf-8 -*-
# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2022 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2022 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2022 UT-Battelle, LLC. All rights reserved.
#
# Additional copyright and license information can be found in the LICENSE file
# distributed with this code, or at
# https://raw.githubusercontent.com/MPAS-Dev/MPAS-Analysis/master/LICENSE
#
import xarray as xr
import numpy as np
import netCDF4
import os
from mpas_tools.ocean.moc import add_moc_southern_boundary_transects
from mpas_tools.io import write_netcdf
from mpas_analysis.shared.constants.constants import m3ps_to_Sv
from mpas_analysis.shared.plot import plot_vertical_section_comparison, \
timeseries_analysis_plot, savefig
from mpas_analysis.shared.io.utility import build_config_full_path, \
make_directories, get_files_year_month, get_region_mask
from mpas_analysis.shared.io import open_mpas_dataset\
from mpas_analysis.shared.timekeeping.utility import days_to_datetime
from mpas_analysis.shared import AnalysisTask
from mpas_analysis.shared.html import write_image_xml
from mpas_analysis.shared.climatology.climatology import \
get_climatology_op_directory
from mpas_analysis.shared.regions import ComputeRegionMasksSubtask
[docs]class StreamfunctionMOC(AnalysisTask):
"""
Computation and plotting of model meridional overturning circulation.
Will eventually support:
* MOC streamfunction, post-processed
* MOC streamfunction, from MOC analysis member
* MOC time series (max value at 24.5N), post-processed
* MOC time series (max value at 24.5N), from MOC analysis member
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip Wolfram, Xylar Asay-Davis
[docs] def __init__(self, config, mpasClimatologyTask, controlConfig=None):
"""
Construct the analysis task.
Parameters
----------
config : mpas_tools.config.MpasConfigParser
Contains configuration options
mpasClimatologyTask : ``MpasClimatologyTask``
The task that produced the climatology to be remapped and plotted
controlconfig : mpas_tools.config.MpasConfigParser, optional
Configuration options for a control run (if any)
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call the constructor from the base class (AnalysisTask)
super(StreamfunctionMOC, self).__init__(
config=config,
taskName='streamfunctionMOC',
componentName='ocean',
tags=['streamfunction', 'moc', 'climatology', 'timeSeries',
'publicObs'])
maskSubtask = ComputeMOCMasksSubtask(self)
self.add_subtask(maskSubtask)
computeClimSubtask = ComputeMOCClimatologySubtask(
self, mpasClimatologyTask, maskSubtask)
plotClimSubtask = PlotMOCClimatologySubtask(self, controlConfig)
plotClimSubtask.run_after(computeClimSubtask)
startYear = config.getint('timeSeries', 'startYear')
endYear = config.get('timeSeries', 'endYear')
if endYear == 'end':
# a valid end year wasn't found, so likely the run was not found,
# perhaps because we're just listing analysis tasks
endYear = startYear
else:
endYear = int(endYear)
years = range(startYear, endYear + 1)
# in the end, we'll combine all the time series into one, but we create
# this task first so it's easier to tell it to run after all the
# compute tasks
combineTimeSeriesSubtask = CombineMOCTimeSeriesSubtask(
self, startYears=years, endYears=years)
# run one subtask per year
for year in years:
computeTimeSeriesSubtask = ComputeMOCTimeSeriesSubtask(
self, startYear=year, endYear=year, maskSubtask=maskSubtask)
combineTimeSeriesSubtask.run_after(computeTimeSeriesSubtask)
plotTimeSeriesSubtask = PlotMOCTimeSeriesSubtask(self, controlConfig)
plotTimeSeriesSubtask.run_after(combineTimeSeriesSubtask)
class ComputeMOCMasksSubtask(ComputeRegionMasksSubtask):
"""
An analysis subtasks for computing cell masks and southern transects for
MOC regions
"""
# Authors
# -------
# Xylar Asay-Davis
def __init__(self, parentTask):
"""
Construct the analysis task and adds it as a subtask of the
``parentTask``.
Parameters
----------
parentTask : ``AnalysisTask``
The parent task, used to get the ``taskName``, ``config`` and
``componentName``
"""
# Authors
# -------
# Xylar Asay-Davis
config = parentTask.config
meshName = config.get('input', 'mpasMeshName')
regionGroup = 'MOC Basins'
subprocessCount = config.getint('execute', 'parallelTaskCount')
# call the constructor from the base class (ComputeRegionMasksSubtask)
super().__init__(
parentTask, regionGroup=regionGroup, meshName=meshName,
subprocessCount=subprocessCount,
useMpasMaskCreator=False)
self.maskAndTransectFileName = None
def setup_and_check(self):
"""
Perform steps to set up the analysis and check for errors in the setup.
Raises
------
IOError :
If a restart file is not available from which to read mesh
information or if no history files are available from which to
compute the climatology in the desired time range.
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call setup_and_check from the parent class
super().setup_and_check()
self.maskAndTransectFileName = get_region_mask(
self.config, '{}_mocBasinsAndTransects{}.nc'.format(
self.meshName, self.date))
def run_task(self):
"""
Compute the requested climatologies
"""
# Authors
# -------
# Xylar Asay-Davis
# call ComputeRegionMasksSubtask.run_task() first
super().run_task()
config = self.config
if os.path.exists(self.maskAndTransectFileName):
return
dsMesh = xr.open_dataset(self.obsFileName)
dsMask = xr.open_dataset(self.maskFileName)
dsMasksAndTransects = add_moc_southern_boundary_transects(
dsMask, dsMesh, logger=self.logger)
write_netcdf(dsMasksAndTransects, self.maskAndTransectFileName,
char_dim_name='StrLen')
# }}}
class ComputeMOCClimatologySubtask(AnalysisTask):
"""
Computation of a climatology of the model meridional overturning
circulation.
Attributes
----------
mpasClimatologyTask : ``MpasClimatologyTask``
The task that produced the climatology to be remapped and plotted
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip Wolfram, Xylar Asay-Davis
def __init__(self, parentTask, mpasClimatologyTask, maskSubtask):
"""
Construct the analysis task.
Parameters
----------
parentTask : ``StreamfunctionMOC``
The main task of which this is a subtask
mpasClimatologyTask : ``MpasClimatologyTask``
The task that produced the climatology to be remapped and plotted
maskSubtask : mpas_analysis.ocean.streamfunction_moc.ComputeMOCMasksSubtask
The subtask for computing MOC region masks that runs before this
subtask
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call the constructor from the base class (AnalysisTask)
super(ComputeMOCClimatologySubtask, self).__init__(
config=parentTask.config,
taskName=parentTask.taskName,
componentName=parentTask.componentName,
tags=parentTask.tags,
subtaskName='computeMOCClimatology')
self.mpasClimatologyTask = mpasClimatologyTask
self.run_after(mpasClimatologyTask)
self.maskSubtask = maskSubtask
self.run_after(maskSubtask)
parentTask.add_subtask(self)
self.includeBolus = None
self.includeSubmesoscale = None
def setup_and_check(self):
"""
Perform steps to set up the analysis and check for errors in the setup.
Raises
------
ValueError
if timeSeriesStatsMonthly is not enabled in the MPAS run
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call setup_and_check from the base class (AnalysisTask),
# which will perform some common setup, including storing:
# self.runDirectory , self.historyDirectory, self.plotsDirectory,
# self.namelist, self.runStreams, self.historyStreams,
# self.calendar
super(ComputeMOCClimatologySubtask, self).setup_and_check()
self.startYear = self.mpasClimatologyTask.startYear
self.startDate = self.mpasClimatologyTask.startDate
self.endYear = self.mpasClimatologyTask.endYear
self.endDate = self.mpasClimatologyTask.endDate
config = self.config
self.mocAnalysisMemberEnabled = self.check_analysis_enabled(
analysisOptionName='config_am_mocstreamfunction_enable',
raiseException=False)
self.sectionName = 'streamfunctionMOC'
self.usePostprocessing = config.getexpression(
self.sectionName, 'usePostprocessingScript')
if not self.usePostprocessing and self.mocAnalysisMemberEnabled:
variableList = \
['timeMonthly_avg_mocStreamvalLatAndDepth',
'timeMonthly_avg_mocStreamvalLatAndDepthRegion']
else:
variableList = ['timeMonthly_avg_normalVelocity',
'timeMonthly_avg_vertVelocityTop',
'timeMonthly_avg_layerThickness']
# Add the bolus velocity if GM is enabled
try:
# the new name
self.includeBolus = self.namelist.getbool('config_use_gm')
except KeyError:
# the old name
self.includeBolus = self.namelist.getbool(
'config_use_standardgm')
try:
self.includeSubmesoscale = \
self.namelist.getbool('config_submesoscale_enable')
except KeyError:
# an old run without submesoscale
self.includeSubmesoscale = False
if self.includeBolus:
variableList.extend(
['timeMonthly_avg_normalGMBolusVelocity',
'timeMonthly_avg_vertGMBolusVelocityTop'])
if self.includeSubmesoscale:
variableList.extend(
['timeMonthly_avg_normalMLEvelocity',
'timeMonthly_avg_vertMLEBolusVelocityTop'])
self.mpasClimatologyTask.add_variables(variableList=variableList,
seasons=['ANN'])
def run_task(self):
"""
Process MOC analysis member data if available, or compute MOC at
post-processing if not.
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip J. Wolfram, Xylar Asay-Davis
self.logger.info("Computing climatology of Meridional Overturning "
"Circulation (MOC)...")
# **** Compute MOC ****
if not self.usePostprocessing and self.mocAnalysisMemberEnabled:
self._compute_moc_climo_analysismember()
else:
self._compute_moc_climo_postprocess()
def _compute_moc_climo_analysismember(self):
"""compute mean MOC streamfunction from analysis member"""
config = self.config
outputDirectory = get_climatology_op_directory(config)
make_directories(outputDirectory)
outputFileName = '{}/mocStreamfunction_years{:04d}-{:04d}.nc'.format(
outputDirectory, self.startYear,
self.endYear)
if os.path.exists(outputFileName):
return
regionNames = config.getexpression(self.sectionName, 'regionNames')
regionNames.append('Global')
# Read in depth and bin latitudes
try:
restartFileName = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least '
'one for MHT calcuation')
with xr.open_dataset(restartFileName) as dsRestart:
refBottomDepth = dsRestart.refBottomDepth.values
nVertLevels = len(refBottomDepth)
refLayerThickness = np.zeros(nVertLevels)
refLayerThickness[0] = refBottomDepth[0]
refLayerThickness[1:nVertLevels] = \
refBottomDepth[1:nVertLevels] - refBottomDepth[0:nVertLevels - 1]
refZMid = refBottomDepth - 0.5 * refLayerThickness
binBoundaryMocStreamfunction = None
# first try timeSeriesStatsMonthly for bin boundaries, then try
# mocStreamfunctionOutput stream as a backup option
for streamName in ['timeSeriesStatsMonthlyOutput',
'mocStreamfunctionOutput']:
try:
inputFileName = self.historyStreams.readpath(streamName)[0]
except ValueError:
raise IOError('At least one file from stream {} is needed '
'to compute MOC'.format(streamName))
with xr.open_dataset(inputFileName) as ds:
if 'binBoundaryMocStreamfunction' in ds.data_vars:
binBoundaryMocStreamfunction = \
ds.binBoundaryMocStreamfunction.values
break
if binBoundaryMocStreamfunction is None:
raise ValueError('Could not find binBoundaryMocStreamfunction in '
'either timeSeriesStatsMonthlyOutput or '
'mocStreamfunctionOutput streams')
binBoundaryMocStreamfunction = np.rad2deg(binBoundaryMocStreamfunction)
# Compute and plot annual climatology of MOC streamfunction
self.logger.info('\n Compute climatology of MOC streamfunction...')
self.logger.info(' Load data...')
climatologyFileName = self.mpasClimatologyTask.get_file_name(
season='ANN')
annualClimatology = xr.open_dataset(climatologyFileName)
if 'Time' in annualClimatology.dims:
annualClimatology = annualClimatology.isel(Time=0)
# rename some variables for convenience
annualClimatology = annualClimatology.rename(
{'timeMonthly_avg_mocStreamvalLatAndDepth':
'avgMocStreamfunGlobal',
'timeMonthly_avg_mocStreamvalLatAndDepthRegion':
'avgMocStreamfunRegional'})
dsMask = xr.open_dataset(self.maskSubtask.maskAndTransectFileName)
regionIndices = {}
for iRegion in range(dsMask.sizes['nRegions']):
regionInFile = str(dsMask.regionNames[iRegion].values.astype('U'))
region = regionInFile.replace('_MOC', '')
regionIndices[region] = iRegion
# Create dictionary for MOC climatology (NB: need this form
# in order to convert it to xarray dataset later in the script)
depth = refZMid
lat = {}
moc = {}
for region in regionNames:
self.logger.info(' Compute {} MOC...'.format(region))
if region == 'Global':
mocTop = annualClimatology.avgMocStreamfunGlobal.values
else:
indRegion = regionIndices[region]
mocVar = annualClimatology.avgMocStreamfunRegional
mocTop = mocVar.isel(nRegions=indRegion).values
# Store computed MOC to dictionary
lat[region] = binBoundaryMocStreamfunction
moc[region] = mocTop
# Save to file
self.logger.info(' Save global and regional MOC to file...')
ncFile = netCDF4.Dataset(outputFileName, mode='w')
# create dimensions
ncFile.createDimension('nz', nVertLevels)
for region in regionNames:
latBins = lat[region]
mocTop = moc[region]
ncFile.createDimension('nx{}'.format(region), len(latBins))
# create variables
x = ncFile.createVariable('lat{}'.format(region), 'f4',
('nx{}'.format(region),))
x.description = 'latitude bins for MOC {}'\
' streamfunction'.format(region)
x.units = 'degrees (-90 to 90)'
y = ncFile.createVariable('moc{}'.format(region), 'f4',
('nz', 'nx{}'.format(region)))
y.description = 'MOC {} streamfunction, annual'\
' climatology'.format(region)
y.units = 'Sv (10^6 m^3/s)'
# save variables
x[:] = latBins
y[:, :] = mocTop
depthVar = ncFile.createVariable('depth', 'f4', ('nz',))
depthVar.description = 'depth'
depthVar.units = 'meters'
depthVar[:] = depth
ncFile.close()
def _compute_moc_climo_postprocess(self):
"""compute mean MOC streamfunction as a post-process"""
config = self.config
outputDirectory = get_climatology_op_directory(config)
make_directories(outputDirectory)
outputFileName = '{}/mocStreamfunction_years{:04d}-{:04d}.nc'.format(
outputDirectory, self.startYear,
self.endYear)
if os.path.exists(outputFileName):
return
dvEdge, areaCell, refBottomDepth, latCell, nVertLevels, \
refTopDepth, refLayerThickness, cellsOnEdge = \
_load_mesh(self.runStreams)
regionNames = config.getexpression(self.sectionName, 'regionNames')
# Load basin region related variables and save them to dictionary
mpasMeshName = config.get('input', 'mpasMeshName')
masksFileName = self.maskSubtask.maskAndTransectFileName
dictRegion = _build_region_mask_dict(
masksFileName, regionNames, mpasMeshName, self.logger)
# Add Global regionCellMask=1 everywhere to make the algorithm
# for the global moc similar to that of the regional moc
dictRegion['Global'] = {
'cellMask': np.ones(np.size(latCell))}
regionNames.append('Global')
# Compute and plot annual climatology of MOC streamfunction
self.logger.info('\n Compute post-processed climatological of MOC '
'streamfunction...')
self.logger.info(' Load data...')
climatologyFileName = self.mpasClimatologyTask.get_file_name(
season='ANN')
annualClimatology = xr.open_dataset(climatologyFileName)
if 'Time' in annualClimatology.dims:
annualClimatology = annualClimatology.isel(Time=0)
# rename some variables for convenience
annualClimatology = annualClimatology.rename(
{'timeMonthly_avg_normalVelocity': 'avgNormalVelocity',
'timeMonthly_avg_vertVelocityTop': 'avgVertVelocityTop',
'timeMonthly_avg_layerThickness': 'layerThickness'})
if self.includeBolus:
annualClimatology['avgNormalVelocity'] = \
annualClimatology['avgNormalVelocity'] + \
annualClimatology['timeMonthly_avg_normalGMBolusVelocity']
annualClimatology['avgVertVelocityTop'] = \
annualClimatology['avgVertVelocityTop'] + \
annualClimatology['timeMonthly_avg_vertGMBolusVelocityTop']
if self.includeSubmesoscale:
annualClimatology['avgNormalVelocity'] = \
annualClimatology['avgNormalVelocity'] + \
annualClimatology['timeMonthly_avg_normalMLEvelocity']
annualClimatology['avgVertVelocityTop'] = \
annualClimatology['avgVertVelocityTop'] + \
annualClimatology['timeMonthly_avg_vertMLEBolusVelocityTop']
# Convert to numpy arrays
# (can result in a memory error for large array size)
horizontalVel = annualClimatology.avgNormalVelocity.values
verticalVel = annualClimatology.avgVertVelocityTop.values
velArea = verticalVel * areaCell[:, np.newaxis]
layerThickness = annualClimatology.layerThickness.values
# Create dictionary for MOC climatology (NB: need this form
# in order to convert it to xarray dataset later in the script)
depth = refTopDepth
lat = {}
moc = {}
for region in regionNames:
self.logger.info(' Compute {} MOC...'.format(region))
self.logger.info(' Compute transport through region '
'southern transect...')
if region == 'Global':
transportZ = np.zeros(nVertLevels)
else:
maxEdgesInTransect = \
dictRegion[region]['maxEdgesInTransect']
transectEdgeGlobalIDs = \
dictRegion[region]['transectEdgeGlobalIDs']
transectEdgeMaskSigns = \
dictRegion[region]['transectEdgeMaskSigns']
transportZ = _compute_transport(maxEdgesInTransect,
transectEdgeGlobalIDs,
transectEdgeMaskSigns,
nVertLevels, dvEdge,
horizontalVel,
layerThickness,
cellsOnEdge)
regionCellMask = dictRegion[region]['cellMask']
latBinSize = \
config.getfloat('streamfunctionMOC{}'.format(region),
'latBinSize')
if region == 'Global':
latBins = np.arange(-90.0, 90.1, latBinSize)
else:
indRegion = dictRegion[region]['indices']
latBins = latCell[indRegion]
latBins = np.arange(np.amin(latBins),
np.amax(latBins) + latBinSize,
latBinSize)
mocTop = _compute_moc(latBins, nVertLevels, latCell,
regionCellMask, transportZ, velArea)
# Store computed MOC to dictionary
lat[region] = latBins
moc[region] = mocTop
# Save to file
self.logger.info(' Save global and regional MOC to file...')
ncFile = netCDF4.Dataset(outputFileName, mode='w')
# create dimensions
ncFile.createDimension('nz', len(refTopDepth))
for region in regionNames:
latBins = lat[region]
mocTop = moc[region]
ncFile.createDimension('nx{}'.format(region), len(latBins))
# create variables
x = ncFile.createVariable('lat{}'.format(region), 'f4',
('nx{}'.format(region),))
x.description = 'latitude bins for MOC {}'\
' streamfunction'.format(region)
x.units = 'degrees (-90 to 90)'
y = ncFile.createVariable('moc{}'.format(region), 'f4',
('nz', 'nx{}'.format(region)))
y.description = 'MOC {} streamfunction, annual'\
' climatology'.format(region)
y.units = 'Sv (10^6 m^3/s)'
# save variables
x[:] = latBins
y[:, :] = mocTop
depthVar = ncFile.createVariable('depth', 'f4', ('nz',))
depthVar.description = 'depth'
depthVar.units = 'meters'
depthVar[:] = depth
ncFile.close()
class PlotMOCClimatologySubtask(AnalysisTask):
"""
Computation of a climatology of the model meridional overturning
circulation.
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip Wolfram, Xylar Asay-Davis
def __init__(self, parentTask, controlConfig):
"""
Construct the analysis task.
Parameters
----------
parentTask : ``StreamfunctionMOC``
The main task of which this is a subtask
controlconfig : mpas_tools.config.MpasConfigParser, optional
Configuration options for a control run (if any)
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call the constructor from the base class (AnalysisTask)
super(PlotMOCClimatologySubtask, self).__init__(
config=parentTask.config,
taskName=parentTask.taskName,
componentName=parentTask.componentName,
tags=parentTask.tags,
subtaskName='plotMOCClimatology')
parentTask.add_subtask(self)
self.controlConfig = controlConfig
def setup_and_check(self):
"""
Perform steps to set up the analysis and check for errors in the setup.
Raises
------
ValueError
if timeSeriesStatsMonthly is not enabled in the MPAS run
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call setup_and_check from the base class (AnalysisTask),
# which will perform some common setup, including storing:
# self.runDirectory , self.historyDirectory, self.plotsDirectory,
# self.namelist, self.runStreams, self.historyStreams,
# self.calendar
super(PlotMOCClimatologySubtask, self).setup_and_check()
config = self.config
self.startYear = config.getint('climatology', 'startYear')
self.endYear = config.getint('climatology', 'endYear')
self.sectionName = 'streamfunctionMOC'
self.xmlFileNames = []
self.filePrefixes = {}
mainRunName = config.get('runs', 'mainRunName')
self.regionNames = ['Global'] + config.getexpression(self.sectionName,
'regionNames')
for region in self.regionNames:
filePrefix = 'moc{}_{}_years{:04d}-{:04d}'.format(
region, mainRunName,
self.startYear, self.endYear)
self.xmlFileNames.append('{}/{}.xml'.format(self.plotsDirectory,
filePrefix))
self.filePrefixes[region] = filePrefix
def run_task(self):
"""
Plot the MOC climatology
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip J. Wolfram, Xylar Asay-Davis
self.logger.info("\nPlotting streamfunction of Meridional Overturning "
"Circulation (MOC)...")
config = self.config
depth, lat, moc = self._load_moc(config)
if self.controlConfig is None:
refTitle = None
diffTitle = None
else:
refDepth, refLat, refMOC = self._load_moc(self.controlConfig)
refTitle = self.controlConfig.get('runs', 'mainRunName')
diffTitle = 'Main - Control'
# **** Plot MOC ****
# Define plotting variables
mainRunName = config.get('runs', 'mainRunName')
movingAveragePointsClimatological = config.getint(
self.sectionName, 'movingAveragePointsClimatological')
colorbarLabel = '[Sv]'
xLabel = 'latitude [deg]'
yLabel = 'depth [m]'
for region in self.regionNames:
self.logger.info(' Plot climatological {} MOC...'.format(region))
title = '{} MOC (ANN, years {:04d}-{:04d})'.format(
region, self.startYear,
self.endYear)
filePrefix = self.filePrefixes[region]
outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix)
x = lat[region]
z = depth
regionMOC = moc[region]
# Subset lat range
minLat = config.getexpression('streamfunctionMOC{}'.format(region),
'latBinMin')
maxLat = config.getexpression('streamfunctionMOC{}'.format(region),
'latBinMax')
indLat = np.logical_and(x >= minLat, x <= maxLat)
x = x.where(indLat, drop=True)
regionMOC = regionMOC.where(indLat, drop=True)
if self.controlConfig is None:
refRegionMOC = None
diff = None
else:
# the coords of the ref MOC won't necessarily match this MOC
# so we need to interpolate
refRegionMOC = _interp_moc(x, z, regionMOC, refLat[region],
refDepth, refMOC[region])
diff = regionMOC - refRegionMOC
plot_vertical_section_comparison(
config, regionMOC, refRegionMOC, diff, xCoords=x, zCoord=z,
colorMapSectionName='streamfunctionMOC{}'.format(region),
colorbarLabel=colorbarLabel,
title=title,
modelTitle=mainRunName,
refTitle=refTitle,
diffTitle=diffTitle,
xlabels=xLabel,
ylabel=yLabel,
movingAveragePoints=movingAveragePointsClimatological,
maxTitleLength=70)
savefig(outFileName, config)
caption = '{} Meridional Overturning Streamfunction'.format(region)
write_image_xml(
config=config,
filePrefix=filePrefix,
componentName='Ocean',
componentSubdirectory='ocean',
galleryGroup='Meridional Overturning Streamfunction',
groupLink='moc',
thumbnailDescription=region,
imageDescription=caption,
imageCaption=caption)
def _load_moc(self, config):
"""compute mean MOC streamfunction from analysis member"""
startYear = config.getint('climatology', 'startYear')
endYear = config.getint('climatology', 'endYear')
outputDirectory = get_climatology_op_directory(config)
make_directories(outputDirectory)
inputFileName = '{}/mocStreamfunction_years{:04d}-{:04d}.nc'.format(
outputDirectory, startYear,
endYear)
# Read from file
ds = xr.open_dataset(inputFileName)
depth = ds['depth']
lat = {}
moc = {}
for region in self.regionNames:
lat[region] = ds['lat{}'.format(region)]
moc[region] = ds['moc{}'.format(region)]
return depth, lat, moc
class ComputeMOCTimeSeriesSubtask(AnalysisTask):
"""
Computation of a time series of max Atlantic MOC at 26.5N.
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip Wolfram, Xylar Asay-Davis
def __init__(self, parentTask, startYear, endYear, maskSubtask):
"""
Construct the analysis task.
Parameters
----------
parentTask : mpas_analysis.ocean.streamfunction_moc.StreamfunctionMOC
The main task of which this is a subtask
startYear : int
The start year of the time series
endYear : int
The end year of the time series
maskSubtask : mpas_analysis.ocean.streamfunction_moc.ComputeMOCMasksSubtask
The subtask for computing MOC region masks that runs before this
subtask
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call the constructor from the base class (AnalysisTask)
super(ComputeMOCTimeSeriesSubtask, self).__init__(
config=parentTask.config,
taskName=parentTask.taskName,
componentName=parentTask.componentName,
tags=parentTask.tags,
subtaskName='computeMOCTimeSeries_{:04d}-{:04d}'.format(
startYear, endYear))
self.maskSubtask = maskSubtask
self.run_after(maskSubtask)
parentTask.add_subtask(self)
self.startYear = startYear
self.endYear = endYear
self.includeBolus = None
self.includeSubmesoscale = None
def setup_and_check(self):
"""
Perform steps to set up the analysis and check for errors in the setup.
Raises
------
ValueError
if timeSeriesStatsMonthly is not enabled in the MPAS run
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call setup_and_check from the base class (AnalysisTask),
# which will perform some common setup, including storing:
# self.runDirectory , self.historyDirectory, self.plotsDirectory,
# self.namelist, self.runStreams, self.historyStreams,
# self.calendar
super(ComputeMOCTimeSeriesSubtask, self).setup_and_check()
config = self.config
self.mocAnalysisMemberEnabled = self.check_analysis_enabled(
analysisOptionName='config_am_mocstreamfunction_enable',
raiseException=False)
self.sectionName = 'streamfunctionMOC'
self.usePostprocessing = config.getexpression(
self.sectionName, 'usePostprocessingScript')
if not self.usePostprocessing and self.mocAnalysisMemberEnabled:
self.variableList = \
['timeMonthly_avg_mocStreamvalLatAndDepth',
'timeMonthly_avg_mocStreamvalLatAndDepthRegion']
else:
self.variableList = ['timeMonthly_avg_normalVelocity',
'timeMonthly_avg_vertVelocityTop',
'timeMonthly_avg_layerThickness']
# Add the bolus velocity if GM is enabled
try:
# the new name
self.includeBolus = self.namelist.getbool('config_use_gm')
except KeyError:
# the old name
self.includeBolus = self.namelist.getbool(
'config_use_standardgm')
try:
self.includeSubmesoscale = \
self.namelist.getbool('config_submesoscale_enable')
except KeyError:
# an old run without submesoscale
self.includeSubmesoscale = False
if self.includeBolus:
self.variableList.extend(
['timeMonthly_avg_normalGMBolusVelocity',
'timeMonthly_avg_vertGMBolusVelocityTop'])
if self.includeSubmesoscale:
self.variableList.extend(
['timeMonthly_avg_normalMLEvelocity',
'timeMonthly_avg_vertMLEBolusVelocityTop'])
def run_task(self):
"""
Process MOC analysis member data if available, or compute MOC at
post-processing if not.
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip J. Wolfram, Xylar Asay-Davis
self.logger.info("\nCompute time series of Meridional Overturning "
"Circulation (MOC)...")
self.startDate = '{:04d}-01-01_00:00:00'.format(self.startYear)
self.endDate = '{:04d}-12-31_23:59:59'.format(self.endYear)
# **** Compute MOC ****
if not self.usePostprocessing and self.mocAnalysisMemberEnabled:
self._compute_moc_time_series_analysismember()
else:
self._compute_moc_time_series_postprocess()
def _compute_moc_time_series_analysismember(self):
"""compute MOC time series from analysis member"""
# Compute and plot time series of Atlantic MOC at 26.5N (RAPID array)
self.logger.info('\n Compute Atlantic MOC time series from analysis '
'member...')
self.logger.info(' Load data...')
outputDirectory = '{}/moc/'.format(
build_config_full_path(self.config, 'output',
'timeseriesSubdirectory'))
try:
os.makedirs(outputDirectory)
except OSError:
pass
outputFileName = '{}/mocTimeSeries_{:04d}-{:04d}.nc'.format(
outputDirectory, self.startYear, self.endYear)
# Get bin latitudes and index of 26.5N
binBoundaryMocStreamfunction = None
# first try timeSeriesStatsMonthly for bin boundaries, then try
# mocStreamfunctionOutput stream as a backup option
for streamName in ['timeSeriesStatsMonthlyOutput',
'mocStreamfunctionOutput']:
try:
inputFileName = self.historyStreams.readpath(streamName)[0]
except ValueError:
raise IOError('At least one file from stream {} is needed '
'to compute MOC'.format(streamName))
with xr.open_dataset(inputFileName) as ds:
if 'binBoundaryMocStreamfunction' in ds.data_vars:
binBoundaryMocStreamfunction = \
ds.binBoundaryMocStreamfunction.values
break
if binBoundaryMocStreamfunction is None:
raise ValueError('Could not find binBoundaryMocStreamfunction in '
'either timeSeriesStatsMonthlyOutput or '
'mocStreamfunctionOutput streams')
binBoundaryMocStreamfunction = np.rad2deg(binBoundaryMocStreamfunction)
dLat = binBoundaryMocStreamfunction - 26.5
indlat26 = np.where(np.abs(dLat) == np.amin(np.abs(dLat)))
inputFiles = sorted(self.historyStreams.readpath(
streamName, startDate=self.startDate,
endDate=self.endDate, calendar=self.calendar))
years, months = get_files_year_month(inputFiles,
self.historyStreams,
'timeSeriesStatsMonthlyOutput')
mocRegion = np.zeros(len(inputFiles))
moc = None
refTopDepth = None
times = np.zeros(len(inputFiles))
computed = np.zeros(len(inputFiles), bool)
continueOutput = os.path.exists(outputFileName)
if continueOutput:
self.logger.info(' Read in previously computed MOC time series')
with open_mpas_dataset(fileName=outputFileName,
calendar=self.calendar,
timeVariableNames=None,
variableList=['mocAtlantic26',
'mocAtlantic'],
startDate=self.startDate,
endDate=self.endDate) as dsMOCIn:
dsMOCIn.load()
if moc is None:
sizes = dsMOCIn.sizes
moc = np.zeros((len(inputFiles), sizes['depth'],
sizes['lat']))
refTopDepth = dsMOCIn.depth.values
# first, copy all computed data
for inIndex in range(dsMOCIn.dims['Time']):
mask = np.logical_and(
dsMOCIn.year[inIndex].values == years,
dsMOCIn.month[inIndex].values == months)
outIndex = np.where(mask)[0][0]
mocRegion[outIndex] = dsMOCIn.mocAtlantic26[inIndex]
moc[outIndex, :, :] = dsMOCIn.mocAtlantic[inIndex, :, :]
times[outIndex] = dsMOCIn.Time[inIndex]
computed[outIndex] = True
if np.all(computed):
# no need to waste time writing out the data set again
return dsMOCIn
for timeIndex, fileName in enumerate(inputFiles):
if computed[timeIndex]:
continue
dsLocal = open_mpas_dataset(
fileName=fileName,
calendar=self.calendar,
variableList=self.variableList,
startDate=self.startDate,
endDate=self.endDate)
dsLocal = dsLocal.isel(Time=0)
time = dsLocal.Time.values
times[timeIndex] = time
date = days_to_datetime(time, calendar=self.calendar)
self.logger.info(' date: {:04d}-{:02d}'.format(date.year,
date.month))
# hard-wire region=0 (Atlantic) for now
indRegion = 0
mocVar = dsLocal.timeMonthly_avg_mocStreamvalLatAndDepthRegion
mocTop = mocVar[indRegion, :, :].values
mocRegion[timeIndex] = np.amax(mocTop[:, indlat26])
if moc is None:
sizes = dsLocal.sizes
moc = np.zeros((len(inputFiles), sizes['nVertLevels']+1,
len(binBoundaryMocStreamfunction)))
try:
restartFile = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at '
'least one restart file for MOC calculation')
with xr.open_dataset(restartFile) as dsRestart:
refBottomDepth = dsRestart.refBottomDepth.values
nVertLevels = len(refBottomDepth)
refTopDepth = np.zeros(nVertLevels + 1)
refTopDepth[1:nVertLevels + 1] = refBottomDepth[0:nVertLevels]
moc[timeIndex, 0:-1, :] = mocTop
description = 'Max MOC Atlantic streamfunction nearest to RAPID ' \
'Array latitude (26.5N)'
descriptionAtl = 'Atlantic MOC streamfunction'
dictionary = {
'dims': ['Time', 'depth', 'lat'],
'coords': {
'Time': {
'dims': ('Time',),
'data': times,
'attrs': {'units': 'days since 0001-01-01'}},
'year': {
'dims': ('Time',),
'data': years,
'attrs': {'units': 'year'}},
'month': {
'dims': ('Time',),
'data': months,
'attrs': {'units': 'month'}},
'lat': {
'dims': ('lat',),
'data': binBoundaryMocStreamfunction,
'attrs': {'units': 'degrees north'}},
'depth': {
'dims': ('depth',),
'data': refTopDepth,
'attrs': {'units': 'meters'}}},
'data_vars': {
'mocAtlantic26': {
'dims': ('Time',),
'data': mocRegion,
'attrs': {'units': 'Sv (10^6 m^3/s)',
'description': description}},
'mocAtlantic': {
'dims': ('Time', 'depth', 'lat'),
'data': moc,
'attrs': {'units': 'Sv (10^6 m^3/s)',
'description': descriptionAtl}}}}
dsMOCTimeSeries = xr.Dataset.from_dict(dictionary)
write_netcdf(dsMOCTimeSeries, outputFileName)
def _compute_moc_time_series_postprocess(self):
"""compute MOC time series as a post-process"""
config = self.config
# Compute and plot time series of Atlantic MOC at 26.5N (RAPID array)
self.logger.info('\n Compute and/or plot post-processed Atlantic MOC '
'time series...')
self.logger.info(' Load data...')
outputDirectory = '{}/moc/'.format(
build_config_full_path(self.config, 'output',
'timeseriesSubdirectory'))
try:
os.makedirs(outputDirectory)
except OSError:
pass
outputFileName = '{}/mocTimeSeries_{:04d}-{:04d}.nc'.format(
outputDirectory, self.startYear, self.endYear)
dvEdge, areaCell, refBottomDepth, latCell, nVertLevels, \
refTopDepth, refLayerThickness, cellsOnEdge = \
_load_mesh(self.runStreams)
mpasMeshName = config.get('input', 'mpasMeshName')
masksFileName = self.maskSubtask.maskAndTransectFileName
dictRegion = _build_region_mask_dict(
masksFileName, ['Atlantic'], mpasMeshName, self.logger)
dictRegion = dictRegion['Atlantic']
latBinSize = config.getfloat('streamfunctionMOCAtlantic',
'latBinSize')
indRegion = dictRegion['indices']
latBins = latCell[indRegion]
latBins = np.arange(np.amin(latBins),
np.amax(latBins) + latBinSize,
latBinSize)
latAtlantic = latBins
dLat = latAtlantic - 26.5
indlat26 = np.where(np.abs(dLat) == np.amin(np.abs(dLat)))
maxEdgesInTransect = dictRegion['maxEdgesInTransect']
transectEdgeGlobalIDs = dictRegion['transectEdgeGlobalIDs']
transectEdgeMaskSigns = dictRegion['transectEdgeMaskSigns']
regionCellMask = dictRegion['cellMask']
streamName = 'timeSeriesStatsMonthlyOutput'
inputFiles = sorted(self.historyStreams.readpath(
streamName, startDate=self.startDate,
endDate=self.endDate, calendar=self.calendar))
years, months = get_files_year_month(inputFiles,
self.historyStreams,
'timeSeriesStatsMonthlyOutput')
mocRegion = np.zeros(len(inputFiles))
moc = np.zeros((len(inputFiles), nVertLevels+1, len(latBins)))
times = np.zeros(len(inputFiles))
computed = np.zeros(len(inputFiles), bool)
continueOutput = os.path.exists(outputFileName)
if continueOutput:
self.logger.info(' Read in previously computed MOC time series')
with open_mpas_dataset(fileName=outputFileName,
calendar=self.calendar,
timeVariableNames=None,
variableList=['mocAtlantic26',
'mocAtlantic'],
startDate=self.startDate,
endDate=self.endDate) as dsMOCIn:
dsMOCIn.load()
# first, copy all computed data
for inIndex in range(dsMOCIn.dims['Time']):
mask = np.logical_and(
dsMOCIn.year[inIndex].values == years,
dsMOCIn.month[inIndex].values == months)
outIndex = np.where(mask)[0][0]
mocRegion[outIndex] = dsMOCIn.mocAtlantic26[inIndex]
moc[outIndex, :, :] = dsMOCIn.mocAtlantic[inIndex, :, :]
times[outIndex] = dsMOCIn.Time[inIndex]
computed[outIndex] = True
if np.all(computed):
# no need to waste time writing out the data set again
return dsMOCIn
for timeIndex, fileName in enumerate(inputFiles):
if computed[timeIndex]:
continue
dsLocal = open_mpas_dataset(
fileName=fileName,
calendar=self.calendar,
variableList=self.variableList,
startDate=self.startDate,
endDate=self.endDate)
dsLocal = dsLocal.isel(Time=0)
time = dsLocal.Time.values
times[timeIndex] = time
date = days_to_datetime(time, calendar=self.calendar)
self.logger.info(' date: {:04d}-{:02d}'.format(date.year,
date.month))
# rename some variables for convenience
dsLocal = dsLocal.rename(
{'timeMonthly_avg_normalVelocity': 'avgNormalVelocity',
'timeMonthly_avg_vertVelocityTop': 'avgVertVelocityTop',
'timeMonthly_avg_layerThickness': 'layerThickness'})
if self.includeBolus:
dsLocal['avgNormalVelocity'] = \
dsLocal['avgNormalVelocity'] + \
dsLocal['timeMonthly_avg_normalGMBolusVelocity']
dsLocal['avgVertVelocityTop'] = \
dsLocal['avgVertVelocityTop'] + \
dsLocal['timeMonthly_avg_vertGMBolusVelocityTop']
if self.includeSubmesoscale:
dsLocal['avgNormalVelocity'] = \
dsLocal['avgNormalVelocity'] + \
dsLocal['timeMonthly_avg_normalMLEvelocity']
dsLocal['avgVertVelocityTop'] = \
dsLocal['avgVertVelocityTop'] + \
dsLocal['timeMonthly_avg_vertMLEBolusVelocityTop']
horizontalVel = dsLocal.avgNormalVelocity.values
verticalVel = dsLocal.avgVertVelocityTop.values
velArea = verticalVel * areaCell[:, np.newaxis]
layerThickness = dsLocal.layerThickness.values
transportZ = _compute_transport(maxEdgesInTransect,
transectEdgeGlobalIDs,
transectEdgeMaskSigns,
nVertLevels, dvEdge,
horizontalVel,
layerThickness,
cellsOnEdge)
mocTop = _compute_moc(latAtlantic, nVertLevels, latCell,
regionCellMask, transportZ, velArea)
moc[timeIndex, :, :] = mocTop
mocRegion[timeIndex] = np.amax(mocTop[:, indlat26])
description = 'Max MOC Atlantic streamfunction nearest to RAPID ' \
'Array latitude (26.5N)'
descriptionAtl = 'Atlantic MOC streamfunction'
dictionary = {
'dims': ['Time', 'depth', 'lat'],
'coords': {
'Time': {
'dims': ('Time',),
'data': times,
'attrs': {'units': 'days since 0001-01-01'}},
'year': {
'dims': ('Time',),
'data': years,
'attrs': {'units': 'year'}},
'month': {
'dims': ('Time',),
'data': months,
'attrs': {'units': 'month'}},
'lat': {
'dims': ('lat',),
'data': latAtlantic,
'attrs': {'units': 'degrees north'}},
'depth': {
'dims': ('depth',),
'data': refTopDepth,
'attrs': {'units': 'meters'}}},
'data_vars': {
'mocAtlantic26': {
'dims': ('Time',),
'data': mocRegion,
'attrs': {'units': 'Sv (10^6 m^3/s)',
'description': description}},
'mocAtlantic': {
'dims': ('Time', 'depth', 'lat'),
'data': moc,
'attrs': {'units': 'Sv (10^6 m^3/s)',
'description': descriptionAtl}}}}
dsMOCTimeSeries = xr.Dataset.from_dict(dictionary)
write_netcdf(dsMOCTimeSeries, outputFileName)
class CombineMOCTimeSeriesSubtask(AnalysisTask):
"""
Combine individual time series of max Atlantic MOC at 26.5N into a single
data set
"""
# Authors
# -------
# Xylar Asay-Davis
def __init__(self, parentTask, startYears, endYears):
"""
Construct the analysis task.
Parameters
----------
parentTask : ``StreamfunctionMOC``
The main task of which this is a subtask
controlconfig : mpas_tools.config.MpasConfigParser, optional
Configuration options for a control run (if any)
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call the constructor from the base class (AnalysisTask)
super(CombineMOCTimeSeriesSubtask, self).__init__(
config=parentTask.config,
taskName=parentTask.taskName,
componentName=parentTask.componentName,
tags=parentTask.tags,
subtaskName='combineMOCTimeSeries')
parentTask.add_subtask(self)
self.startYears = startYears
self.endYears = endYears
def run_task(self):
"""
Plot the MOC time series
"""
# Authors
# -------
# Xylar Asay-Davis
outputDirectory = '{}/moc/'.format(
build_config_full_path(self.config, 'output',
'timeseriesSubdirectory'))
try:
os.makedirs(outputDirectory)
except OSError:
pass
outputFileNames = []
for startYear, endYear in zip(self.startYears, self.endYears):
outputFileName = '{}/mocTimeSeries_{:04d}-{:04d}.nc'.format(
outputDirectory, startYear, endYear)
outputFileNames.append(outputFileName)
outputFileName = '{}/mocTimeSeries_{:04d}-{:04d}.nc'.format(
outputDirectory, self.startYears[0], self.endYears[-1])
if outputFileName in outputFileNames:
# don't try to write to read from and write to the same file
return
ds = xr.open_mfdataset(outputFileNames, concat_dim='Time',
combine='nested', decode_times=False)
ds.load()
write_netcdf(ds, outputFileName)
class PlotMOCTimeSeriesSubtask(AnalysisTask):
"""
Plots a time series of max Atlantic MOC at 26.5N.
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip Wolfram, Xylar Asay-Davis
def __init__(self, parentTask, controlConfig):
"""
Construct the analysis task.
Parameters
----------
parentTask : ``StreamfunctionMOC``
The main task of which this is a subtask
controlconfig : mpas_tools.config.MpasConfigParser, optional
Configuration options for a control run (if any)
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call the constructor from the base class (AnalysisTask)
super(PlotMOCTimeSeriesSubtask, self).__init__(
config=parentTask.config,
taskName=parentTask.taskName,
componentName=parentTask.componentName,
tags=parentTask.tags,
subtaskName='plotMOCTimeSeries')
parentTask.add_subtask(self)
self.controlConfig = controlConfig
def setup_and_check(self):
"""
Perform steps to set up the analysis and check for errors in the setup.
Raises
------
ValueError
if timeSeriesStatsMonthly is not enabled in the MPAS run
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call setup_and_check from the base class (AnalysisTask),
# which will perform some common setup, including storing:
# self.runDirectory , self.historyDirectory, self.plotsDirectory,
# self.namelist, self.runStreams, self.historyStreams,
# self.calendar
super(PlotMOCTimeSeriesSubtask, self).setup_and_check()
config = self.config
self.sectionName = 'streamfunctionMOC'
mainRunName = config.get('runs', 'mainRunName')
filePrefix = 'mocTimeseries_{}'.format(mainRunName)
self.xmlFileNames = ['{}/{}.xml'.format(self.plotsDirectory,
filePrefix)]
self.filePrefix = filePrefix
def run_task(self):
"""
Plot the MOC time series
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip J. Wolfram, Xylar Asay-Davis
self.logger.info("\nPlotting time series of Meridional Overturning "
"Circulation (MOC)...")
config = self.config
dsMOCTimeSeries = self._load_moc(config)
# **** Plot MOC ****
# Define plotting variables
mainRunName = config.get('runs', 'mainRunName')
movingAveragePoints = config.getint(self.sectionName,
'movingAveragePoints')
# Plot time series
self.logger.info(' Plot time series of max Atlantic MOC at 26.5N...')
xLabel = 'Time [years]'
yLabel = '[Sv]'
title = '{}\n{}'.format(r'Max Atlantic MOC at $26.5\degree$N',
mainRunName)
filePrefix = self.filePrefix
outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix)
if config.has_option(self.taskName, 'firstYearXTicks'):
firstYearXTicks = config.getint(self.taskName,
'firstYearXTicks')
else:
firstYearXTicks = None
if config.has_option(self.taskName, 'yearStrideXTicks'):
yearStrideXTicks = config.getint(self.taskName,
'yearStrideXTicks')
else:
yearStrideXTicks = None
fields = [dsMOCTimeSeries.mocAtlantic26]
lineColors = [config.get('timeSeries', 'mainColor')]
lineWidths = [2]
legendText = [mainRunName]
if self.controlConfig is not None:
dsRefMOC = self._load_moc(self.controlConfig)
fields.append(dsRefMOC.mocAtlantic26)
lineColors.append(config.get('timeSeries', 'controlColor'))
lineWidths.append(2)
controlRunName = self.controlConfig.get('runs', 'mainRunName')
legendText.append(controlRunName)
timeseries_analysis_plot(config, fields, calendar=self.calendar,
title=title, xlabel=xLabel, ylabel=yLabel,
movingAveragePoints=movingAveragePoints,
lineColors=lineColors, lineWidths=lineWidths,
legendText=legendText,
firstYearXTicks=firstYearXTicks,
yearStrideXTicks=yearStrideXTicks,
maxTitleLength=90)
savefig(outFileName, config)
caption = u'Time Series of maximum Meridional Overturning ' \
u'Circulation at 26.5°N'
write_image_xml(
config=config,
filePrefix=filePrefix,
componentName='Ocean',
componentSubdirectory='ocean',
galleryGroup='Meridional Overturning Streamfunction',
groupLink='moc',
thumbnailDescription='Time Series',
imageDescription=caption,
imageCaption=caption)
def _load_moc(self, config):
"""compute mean MOC streamfunction from analysis member"""
outputDirectory = build_config_full_path(config, 'output',
'timeseriesSubdirectory')
startYear = config.getint('timeSeries', 'startYear')
endYear = config.getint('timeSeries', 'endYear')
inputFileName = '{}/moc/mocTimeSeries_{:04d}-{:04d}.nc'.format(
outputDirectory, startYear, endYear)
dsMOCTimeSeries = xr.open_dataset(inputFileName, decode_times=False)
return dsMOCTimeSeries
def _load_mesh(runStreams):
# Load mesh related variables
try:
restartFile = runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least one '
'restart file for MOC calculation')
ncFile = netCDF4.Dataset(restartFile, mode='r')
dvEdge = ncFile.variables['dvEdge'][:]
areaCell = ncFile.variables['areaCell'][:]
refBottomDepth = ncFile.variables['refBottomDepth'][:]
latCell = np.rad2deg(ncFile.variables['latCell'][:])
cellsOnEdge = ncFile.variables['cellsOnEdge'][:] - 1
ncFile.close()
nVertLevels = len(refBottomDepth)
refTopDepth = np.zeros(nVertLevels + 1)
refTopDepth[1:nVertLevels + 1] = refBottomDepth[0:nVertLevels]
refLayerThickness = np.zeros(nVertLevels)
refLayerThickness[0] = refBottomDepth[0]
refLayerThickness[1:nVertLevels] = \
(refBottomDepth[1:nVertLevels] -
refBottomDepth[0:nVertLevels - 1])
return dvEdge, areaCell, refBottomDepth, latCell, nVertLevels, \
refTopDepth, refLayerThickness, cellsOnEdge
def _build_region_mask_dict(regionMaskFile, regionNames, mpasMeshName, logger):
if not os.path.exists(regionMaskFile):
raise IOError('Regional masking file {} for MOC calculation '
'does not exist'.format(regionMaskFile))
dsMask = xr.open_dataset(regionMaskFile)
dsMask.load()
regionIndices = {}
for iRegion in range(dsMask.sizes['nRegions']):
regionInFile = str(dsMask.regionNames[iRegion].values.astype('U'))
region = regionInFile.replace('_MOC', '')
regionIndices[region] = iRegion
dictRegion = {}
for region in regionNames:
logger.info('\n Reading region and transect mask for '
'{}...'.format(region))
iRegion = regionIndices[region]
maxEdgesInTransect = dsMask.sizes['maxEdgesInTransect']
transectEdgeMaskSigns = \
dsMask.transectEdgeMaskSigns.isel(nTransects=iRegion).values
transectEdgeGlobalIDs = \
dsMask.transectEdgeGlobalIDs.isel(nTransects=iRegion).values
regionCellMask = \
dsMask.regionCellMasks.isel(nRegions=iRegion).values
indRegion = np.where(regionCellMask == 1)
dictRegion[region] = {
'indices': indRegion,
'cellMask': regionCellMask,
'maxEdgesInTransect': maxEdgesInTransect,
'transectEdgeMaskSigns': transectEdgeMaskSigns,
'transectEdgeGlobalIDs': transectEdgeGlobalIDs}
return dictRegion
def _compute_transport(maxEdgesInTransect, transectEdgeGlobalIDs,
transectEdgeMaskSigns, nz, dvEdge,
horizontalVel, layerThickness, cellsOnEdge):
"""compute mass transport across southern transect of ocean basin"""
transportZEdge = np.zeros([nz, maxEdgesInTransect])
for i in range(maxEdgesInTransect):
if transectEdgeGlobalIDs[i] == 0:
break
# subtract 1 because of python 0-indexing
iEdge = transectEdgeGlobalIDs[i] - 1
coe0 = cellsOnEdge[iEdge, 0]
coe1 = cellsOnEdge[iEdge, 1]
layerThicknessEdge = 0.5*(layerThickness[coe0, :] +
layerThickness[coe1, :])
transportZEdge[:, i] = horizontalVel[iEdge, :] * \
transectEdgeMaskSigns[iEdge, np.newaxis] * \
dvEdge[iEdge, np.newaxis] * \
layerThicknessEdge[np.newaxis, :]
transportZ = transportZEdge.sum(axis=1)
return transportZ
def _compute_moc(latBins, nz, latCell, regionCellMask, transportZ,
velArea):
"""compute meridionally integrated MOC streamfunction"""
mocTop = np.zeros([np.size(latBins), nz + 1])
mocSouthBottomUp = - transportZ[::-1].cumsum()
mocTop[0, 0:nz] = mocSouthBottomUp[::-1]
for iLat in range(1, np.size(latBins)):
indlat = np.logical_and(np.logical_and(
regionCellMask == 1, latCell >= latBins[iLat - 1]),
latCell < latBins[iLat])
mocTop[iLat, :] = mocTop[iLat - 1, :] + \
velArea[indlat, :].sum(axis=0)
# convert m^3/s to Sverdrup
mocTop = mocTop * m3ps_to_Sv
mocTop = mocTop.T
return mocTop
def _interp_moc(x, z, regionMOC, refX, refZ, refMOC):
x = x.values
z = z.values
dims = regionMOC.dims
regionMOC = regionMOC.values
refX = refX.values
refZ = refZ.values
refMOC = refMOC.values
nz, nx = regionMOC.shape
refNz, refNx = refMOC.shape
temp = np.zeros((refNz, nx))
for zIndex in range(refNz):
temp[zIndex, :] = np.interp(
x, refX, refMOC[zIndex, :],
left=np.nan, right=np.nan)
refRegionMOC = np.zeros((nz, nx))
for xIndex in range(nx):
refRegionMOC[:, xIndex] = np.interp(
z, refZ, temp[:, xIndex],
left=np.nan, right=np.nan)
return xr.DataArray(dims=dims, data=refRegionMOC)