# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2020 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2020 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2020 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
from __future__ import absolute_import, division, print_function, \
unicode_literals
import xarray
import numpy
from mpas_analysis.shared import AnalysisTask
from mpas_analysis.shared.climatology import RemapMpasClimatologySubtask
from mpas_analysis.ocean.plot_climatology_map_subtask import \
PlotClimatologyMapSubtask
from mpas_analysis.ocean.utility import compute_zmid
[docs]class ClimatologyMapOHCAnomaly(AnalysisTask): # {{{
"""
An analysis task for comparison of the anomaly from a reference year
(typically the start of the simulation) of ocean heat content (OHC)
Attributes
----------
mpasClimatologyTask : ``MpasClimatologyTask``
The task that produced the climatology to be remapped and plotted
refYearClimatolgyTask : ``RefYearMpasClimatologyTask``
The task that produced the climatology from the first year to be
remapped and then subtracted from the main climatology
"""
# Authors
# -------
# Xylar Asay-Davis
[docs] def __init__(self, config, mpasClimatologyTask, refYearClimatolgyTask,
controlConfig=None): # {{{
"""
Construct the analysis task.
Parameters
----------
config : ``MpasAnalysisConfigParser``
Configuration options
mpasClimatologyTask : ``MpasClimatologyTask``
The task that produced the climatology to be remapped and plotted
refYearClimatolgyTask : ``RefYearMpasClimatologyTask``
The task that produced the climatology from the first year to be
remapped and then subtracted from the main climatology
controlConfig : ``MpasAnalysisConfigParser``, optional
Configuration options for a control run (if any)
"""
# Authors
# -------
# Xylar Asay-Davis
fieldName = 'deltaOHC'
# call the constructor from the base class (AnalysisTask)
super(ClimatologyMapOHCAnomaly, self).__init__(
config=config, taskName='climatologyMapOHCAnomaly',
componentName='ocean',
tags=['climatology', 'horizontalMap', fieldName, 'publicObs'])
sectionName = self.taskName
mpasFieldName = 'deltaOHC'
# read in what seasons we want to plot
seasons = config.getExpression(sectionName, 'seasons')
if len(seasons) == 0:
raise ValueError('config section {} does not contain valid list '
'of seasons'.format(sectionName))
comparisonGridNames = config.getExpression(sectionName,
'comparisonGrids')
if len(comparisonGridNames) == 0:
raise ValueError('config section {} does not contain valid list '
'of comparison grids'.format(sectionName))
variableList = ['timeMonthly_avg_activeTracers_temperature',
'timeMonthly_avg_layerThickness']
depthRanges = config.getExpression('climatologyMapOHCAnomaly',
'depthRanges',
usenumpyfunc=True)
self.mpasClimatologyTask = mpasClimatologyTask
self.refYearClimatolgyTask = refYearClimatolgyTask
for minDepth, maxDepth in depthRanges:
depthRangeString = '{:g}-{:g}m'.format(numpy.abs(minDepth),
numpy.abs(maxDepth))
remapClimatologySubtask = RemapMpasOHCClimatology(
mpasClimatologyTask=mpasClimatologyTask,
refYearClimatolgyTask=refYearClimatolgyTask,
parentTask=self,
climatologyName='{}_{}'.format(fieldName, depthRangeString),
variableList=variableList,
comparisonGridNames=comparisonGridNames,
seasons=seasons,
minDepth=minDepth,
maxDepth=maxDepth)
self.add_subtask(remapClimatologySubtask)
outFileLabel = 'deltaOHC_{}'.format(depthRangeString)
remapObservationsSubtask = None
if controlConfig is None:
refTitleLabel = None
refFieldName = None
diffTitleLabel = 'Model - Observations'
else:
controlRunName = controlConfig.get('runs', 'mainRunName')
refTitleLabel = 'Control: {}'.format(controlRunName)
refFieldName = mpasFieldName
diffTitleLabel = 'Main - Control'
for comparisonGridName in comparisonGridNames:
for season in seasons:
# make a new subtask for this season and comparison grid
subtaskName = 'plot{}_{}_{}'.format(
season, comparisonGridName, depthRangeString)
subtask = PlotClimatologyMapSubtask(
self, season, comparisonGridName,
remapClimatologySubtask, remapObservationsSubtask,
controlConfig=controlConfig, subtaskName=subtaskName)
subtask.set_plot_info(
outFileLabel=outFileLabel,
fieldNameInTitle=r'$\Delta$OHC over {}'.format(
depthRangeString),
mpasFieldName=mpasFieldName,
refFieldName=refFieldName,
refTitleLabel=refTitleLabel,
diffTitleLabel=diffTitleLabel,
unitsLabel=r'GJ m$^{-2}$',
imageCaption='Anomaly in Ocean Heat Content over '
'{}'.format(depthRangeString),
galleryGroup='OHC Anomaly',
groupSubtitle=None,
groupLink='ohc_anom',
galleryName=None)
self.add_subtask(subtask)
# }}}
def setup_and_check(self): # {{{
'''
Checks whether analysis is being performed only on the reference year,
in which case the analysis will not be meaningful.
Raises
------
ValueError: if attempting to analyze only the reference year
'''
# 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(ClimatologyMapOHCAnomaly, self).setup_and_check()
startYear, endYear = self.mpasClimatologyTask.get_start_and_end()
refStartYear, refEndYear = \
self.refYearClimatolgyTask.get_start_and_end()
if (startYear == refStartYear) and (endYear == refEndYear):
raise ValueError('OHC Anomaly is not meaningful and will not work '
'when climatology and ref year are the same.')
# }}}
# }}}
class RemapMpasOHCClimatology(RemapMpasClimatologySubtask): # {{{
"""
A subtask for computing climatologies of ocean heat content from
climatologies of temperature
Attributes
----------
minDepth, maxDepth : float
The minimum and maximum depths for integration
"""
# Authors
# -------
# Xylar Asay-Davis
def __init__(self, mpasClimatologyTask, refYearClimatolgyTask, parentTask,
climatologyName, variableList, seasons, comparisonGridNames,
minDepth, maxDepth):
# {{{
'''
Construct the analysis task and adds it as a subtask of the
``parentTask``.
Parameters
----------
mpasClimatologyTask : ``MpasClimatologyTask``
The task that produced the climatology to be remapped
refYearClimatolgyTask : ``RefYearMpasClimatologyTask``
The task that produced the climatology from the first year to be
remapped and then subtracted from the main climatology
parentTask : ``AnalysisTask``
The parent task, used to get the ``taskName``, ``config`` and
``componentName``
climatologyName : str
A name that describes the climatology (e.g. a short version of
the important field(s) in the climatology) used to name the
subdirectories for each stage of the climatology
variableList : list of str
A list of variable names in ``timeSeriesStatsMonthly`` to be
included in the climatologies
seasons : list of str, optional
A list of seasons (keys in ``shared.constants.monthDictionary``)
to be computed or ['none'] (not ``None``) if only monthly
climatologies are needed.
comparisonGridNames : list of {'latlon', 'antarctic'}
The name(s) of the comparison grid to use for remapping.
minDepth, maxDepth : float
The minimum and maximum depths for integration
'''
# Authors
# -------
# Xylar Asay-Davis
subtaskName = 'remapMpasClimatology_{:g}-{:g}m'.format(
numpy.abs(minDepth), numpy.abs(maxDepth))
# call the constructor from the base class
# (RemapMpasClimatologySubtask)
super(RemapMpasOHCClimatology, self).__init__(
mpasClimatologyTask, parentTask, climatologyName,
variableList, seasons, comparisonGridNames,
subtaskName=subtaskName)
self.refYearClimatolgyTask = refYearClimatolgyTask
self.run_after(refYearClimatolgyTask)
self.minDepth = minDepth
self.maxDepth = maxDepth
# }}}
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 base class
# (RemapMpasClimatologySubtask), which will set up remappers and add
# variables to mpasClimatologyTask
super(RemapMpasOHCClimatology, self).setup_and_check()
# don't add the variables and seasons to mpasClimatologyTask until
# we're sure this subtask is supposed to run
self.refYearClimatolgyTask.add_variables(self.variableList,
self.seasons)
# }}}
def customize_masked_climatology(self, climatology, season): # {{{
"""
Mask the melt rates using ``landIceMask`` and rescale it to m/yr
Parameters
----------
climatology : ``xarray.Dataset`` object
the climatology data set
season : str
The name of the season to be masked
Returns
-------
climatology : ``xarray.Dataset`` object
the modified climatology data set
"""
# Authors
# -------
# Xylar Asay-Davis
ohc = self._compute_ohc(climatology)
refFileName = self.refYearClimatolgyTask.get_file_name(season)
refYearClimo = xarray.open_dataset(refFileName)
if 'Time' in refYearClimo.dims:
refYearClimo = refYearClimo.isel(Time=0)
refOHC = self._compute_ohc(refYearClimo)
climatology['deltaOHC'] = ohc - refOHC
climatology.deltaOHC.attrs['units'] = 'GJ m$^{-2}$'
climatology.deltaOHC.attrs['description'] = \
'Anomaly from year {} in ocean heat content'.format(
self.refYearClimatolgyTask.startYear)
climatology = climatology.drop(self.variableList)
return climatology # }}}
def _compute_ohc(self, climatology): # {{{
"""
Compute the OHC from the temperature and layer thicknesses in a given
climatology data sets.
"""
dsRestart = xarray.open_dataset(self.restartFileName)
dsRestart = dsRestart.isel(Time=0)
# specific heat [J/(kg*degC)]
cp = self.namelist.getfloat('config_specific_heat_sea_water')
# [kg/m3]
rho = self.namelist.getfloat('config_density0')
unitsScalefactor = 1e-9
nVertLevels = dsRestart.sizes['nVertLevels']
zMid = compute_zmid(dsRestart.bottomDepth, dsRestart.maxLevelCell,
dsRestart.layerThickness)
vertIndex = xarray.DataArray.from_dict(
{'dims': ('nVertLevels',), 'data': numpy.arange(nVertLevels)})
temperature = climatology['timeMonthly_avg_activeTracers_temperature']
layerThickness = climatology['timeMonthly_avg_layerThickness']
masks = [vertIndex < dsRestart.maxLevelCell,
zMid <= self.minDepth,
zMid >= self.maxDepth]
for mask in masks:
temperature = temperature.where(mask)
layerThickness = layerThickness.where(mask)
ohc = unitsScalefactor * rho * cp * layerThickness * temperature
ohc = ohc.sum(dim='nVertLevels')
return ohc # }}}
# }}}
# vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python