# 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
from mpas_analysis.shared import AnalysisTask
from mpas_analysis.shared.climatology import RemapMpasClimatologySubtask
from mpas_analysis.shared.plot 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
----------
mpas_climatology_task : mpas_analysis.shared.climatology.MpasClimatologyTask
The task that produced the climatology to be remapped and plotted
ref_year_climatology_task : mpas_analysis.shared.climatology.RefYearMpasClimatologyTask
The task that produced the climatology from the first year to be
remapped and then subtracted from the main climatology
"""
[docs] def __init__(self, config, mpas_climatology_task,
ref_year_climatology_task, control_config=None):
"""
Construct the analysis task.
Parameters
----------
config : mpas_tools.config.MpasConfigParser
Configuration options
mpas_climatology_task : mpas_analysis.shared.climatology.MpasClimatologyTask
The task that produced the climatology to be remapped and plotted
ref_year_climatology_task : mpas_analysis.shared.climatology.RefYearMpasClimatologyTask
The task that produced the climatology from the first year to be
remapped and then subtracted from the main climatology
control_config : mpas_tools.config.MpasConfigParser, optional
Configuration options for a control run (if any)
"""
field_name = 'deltaOHC'
# call the constructor from the base class (AnalysisTask)
super().__init__(config=config, taskName='climatologyMapOHCAnomaly',
componentName='ocean',
tags=['climatology', 'horizontalMap', field_name,
'publicObs', 'anomaly'])
self.mpas_climatology_task = mpas_climatology_task
self.ref_year_climatology_task = ref_year_climatology_task
section_name = self.taskName
# read in what seasons we want to plot
seasons = config.getexpression(section_name, 'seasons')
if len(seasons) == 0:
raise ValueError(f'config section {section_name} does not contain '
f'valid list of seasons')
comparison_grid_names = config.getexpression(section_name,
'comparisonGrids')
if len(comparison_grid_names) == 0:
raise ValueError(f'config section {section_name} does not contain '
f'valid list of comparison grids')
depth_ranges = config.getexpression('climatologyMapOHCAnomaly',
'depthRanges',
use_numpyfunc=True)
mpas_field_name = 'deltaOHC'
variable_list = ['timeMonthly_avg_activeTracers_temperature',
'timeMonthly_avg_layerThickness']
for min_depth, max_depth in depth_ranges:
depth_range_string = \
f'{np.abs(min_depth):g}-{np.abs(max_depth):g}m'
remap_climatology_subtask = RemapMpasOHCClimatology(
mpas_climatology_task=mpas_climatology_task,
ref_year_climatology_task=ref_year_climatology_task,
parent_task=self,
climatology_name=f'{field_name}_{depth_range_string}',
variable_list=variable_list,
comparison_grid_names=comparison_grid_names,
seasons=seasons,
min_depth=min_depth,
max_depth=max_depth)
self.add_subtask(remap_climatology_subtask)
out_file_label = f'deltaOHC_{depth_range_string}'
remap_observations_subtask = None
if control_config is None:
ref_title_label = None
ref_field_name = None
diff_title_label = 'Model - Observations'
else:
control_run_name = control_config.get('runs', 'mainRunName')
ref_title_label = f'Control: {control_run_name}'
ref_field_name = mpas_field_name
diff_title_label = 'Main - Control'
for comparison_grid_name in comparison_grid_names:
for season in seasons:
# make a new subtask for this season and comparison grid
subtask_name = f'plot{season}_{comparison_grid_name}_{depth_range_string}'
subtask = PlotClimatologyMapSubtask(
self, season, comparison_grid_name,
remap_climatology_subtask, remap_observations_subtask,
controlConfig=control_config, subtaskName=subtask_name)
subtask.set_plot_info(
outFileLabel=out_file_label,
fieldNameInTitle=f'$\\Delta$OHC over {depth_range_string}',
mpasFieldName=mpas_field_name,
refFieldName=ref_field_name,
refTitleLabel=ref_title_label,
diffTitleLabel=diff_title_label,
unitsLabel=r'GJ m$^{-2}$',
imageCaption=f'Anomaly in Ocean Heat Content over {depth_range_string}',
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
"""
# 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().setup_and_check()
start_year, end_year = self.mpas_climatology_task.get_start_and_end()
ref_start_year, ref_end_year = \
self.ref_year_climatology_task.get_start_and_end()
if (start_year == ref_start_year) and (end_year == ref_end_year):
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
----------
ref_year_climatology_task : mpas_analysis.shared.climatology.RefYearMpasClimatologyTask
The task that produced the climatology from the first year to be
remapped and then subtracted from the main climatology
min_depth, max_depth : float
The minimum and maximum depths for integration
"""
def __init__(self, mpas_climatology_task, ref_year_climatology_task,
parent_task, climatology_name, variable_list, seasons,
comparison_grid_names, min_depth, max_depth):
"""
Construct the analysis task and adds it as a subtask of the
``parent_task``.
Parameters
----------
mpas_climatology_task : mpas_analysis.shared.climatology.MpasClimatologyTask
The task that produced the climatology to be remapped
ref_year_climatology_task : mpas_analysis.shared.climatology.RefYearMpasClimatologyTask
The task that produced the climatology from the first year to be
remapped and then subtracted from the main climatology
parent_task : mpas_analysis.shared.AnalysisTask
The parent task, used to get the ``taskName``, ``config`` and
``componentName``
climatology_name : 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
variable_list : 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.
comparison_grid_names : list of {'latlon', 'antarctic'}
The name(s) of the comparison grid to use for remapping.
min_depth, max_depth : float
The minimum and maximum depths for integration
"""
depth_range_string = f'{np.abs(min_depth):g}-{np.abs(max_depth):g}m'
subtask_name = f'remapMpasClimatology_{depth_range_string}'
# call the constructor from the base class
# (RemapMpasClimatologySubtask)
super().__init__(
mpas_climatology_task, parent_task, climatology_name,
variable_list, seasons, comparison_grid_names,
subtaskName=subtask_name)
self.ref_year_climatology_task = ref_year_climatology_task
self.run_after(ref_year_climatology_task)
self.min_depth = min_depth
self.max_depth = max_depth
def setup_and_check(self):
"""
Perform steps to set up the analysis and check for errors in the setup.
"""
# first, call setup_and_check from the base class
# (RemapMpasClimatologySubtask), which will set up remappers and add
# variables to mpas_climatology_task
super().setup_and_check()
# don't add the variables and seasons to mpas_climatology_task until
# we're sure this subtask is supposed to run
self.ref_year_climatology_task.add_variables(self.variableList,
self.seasons)
def customize_masked_climatology(self, climatology, season):
"""
Compute the ocean heat content (OHC) anomaly from the temperature
and layer thickness fields.
Parameters
----------
climatology : xarray.Dataset
the climatology data set
season : str
The name of the season to be masked
Returns
-------
climatology : xarray.Dataset
the modified climatology data set
"""
ohc = self._compute_ohc(climatology)
ref_file_name = self.ref_year_climatology_task.get_file_name(season)
ref_year_climo = xr.open_dataset(ref_file_name)
if 'Time' in ref_year_climo.dims:
ref_year_climo = ref_year_climo.isel(Time=0)
ref_ohc = self._compute_ohc(ref_year_climo)
climatology['deltaOHC'] = ohc - ref_ohc
climatology.deltaOHC.attrs['units'] = 'GJ m^-2'
start_year = self.ref_year_climatology_task.startYear
climatology.deltaOHC.attrs['description'] = \
f'Anomaly from year {start_year} in ocean heat content'
climatology = climatology.drop_vars(self.variableList)
return climatology
def _compute_ohc(self, climatology):
"""
Compute the OHC from the temperature and layer thicknesses in a given
climatology data sets.
"""
ds_restart = xr.open_dataset(self.restartFileName)
ds_restart = ds_restart.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')
units_scale_factor = 1e-9
n_vert_levels = ds_restart.sizes['nVertLevels']
z_mid = compute_zmid(ds_restart.bottomDepth, ds_restart.maxLevelCell-1,
ds_restart.layerThickness)
vert_index = xr.DataArray.from_dict(
{'dims': ('nVertLevels',), 'data': np.arange(n_vert_levels)})
temperature = climatology['timeMonthly_avg_activeTracers_temperature']
layer_thickness = climatology['timeMonthly_avg_layerThickness']
masks = [vert_index < ds_restart.maxLevelCell,
z_mid <= self.min_depth,
z_mid >= self.max_depth]
for mask in masks:
temperature = temperature.where(mask)
layer_thickness = layer_thickness.where(mask)
ohc = units_scale_factor * rho * cp * layer_thickness * temperature
ohc = ohc.sum(dim='nVertLevels')
return ohc