def land_permafrost_top(run):
"""
Make permafrost metrics.
Code development Eleanor Burke.
Arguments:
run - dictionary containing model run metadata
(see auto_assess/model_run.py for description)
Returns:
metrics - dictionary of metrics names and values
also produces image files in the current working directory
"""
metrics = dict()
# Load the whole monthly soil temperature time-sequence as a single cube.
# STASH m01s08i225
period = "monthly"
soiltemp = load_run_ss(
run, period, 'soil_temperature') # has dims(time, depth, lat, long)
# check soil depths
expected_soil_depths = [0.05, 0.225, 0.675, 2.0]
soil_depths = soiltemp.coord('depth')
if np.array_equal(soil_depths, expected_soil_depths):
msg = ('Soil has changed levels from usual {}, '
'following not supported: {}'.format(expected_soil_depths,
soil_depths))
raise Exception(msg)
# load the whole monthly air temperature (STASH m01s03i236)
airtemp = load_run_ss(run, period, 'air_temperature')
# get the land fraction mask using whole cube
landfrac = get_landfr_mask(run)
# extract northern latitudes
airtemp = airtemp.extract(iris.Constraint(latitude=lambda cell: cell > 0))
soiltemp = soiltemp.extract(
iris.Constraint(latitude=lambda cell: cell > 0))
# calculate the permafrost area and fraction less than zero
# permafrost_area returns a dict, which is added to the main metrics dict
# by metrics.update()
metrics.update(permafrost_area(soiltemp, airtemp, landfrac, run))
# calculate the koven temperature metrics
metrics.update(koven_temp_offsets(soiltemp, airtemp))
metrics.update(koven_temp_atten(soiltemp, airtemp))
return metrics
def mainfunc(run):
"""Main function in stratospheric assessment code."""
metrics = dict()
# Set up to only run for 10 year period (eventually)
year_cons = dict(from_dt=run['from_monthly'], to_dt=run['to_monthly'])
# Read zonal mean U (lbproc=192) and add month number to metadata
ucube = load_run_ss(
run, 'monthly', 'eastward_wind', lbproc=192, **year_cons)
# Although input data is a zonal mean, iris does not recognise it as such
# and just reads it as having a single longitudinal coordinate. This
# removes longitude as a dimension coordinate and makes it a scalar
# coordinate in line with how a zonal mean would be described.
# Is there a better way of doing this?
ucube_cds = [cdt.standard_name for cdt in ucube.coords()]
if 'longitude' in ucube_cds:
ucube = ucube.collapsed('longitude', iris.analysis.MEAN)
if not ucube.coord('latitude').has_bounds():
ucube.coord('latitude').guess_bounds()
# check for month_number
aux_coord_names = [aux_coord.var_name for aux_coord in ucube.aux_coords]
if 'month_number' not in aux_coord_names:
icc.add_month_number(ucube, 'time', name='month_number')
# Read zonal mean T (lbproc=192) and add clim month and season to metadata
tcube = load_run_ss(
run, 'monthly', 'air_temperature', lbproc=192,
**year_cons) # m01s30i204
# Although input data is a zonal mean, iris does not recognise it as such
# and just reads it as having a single longitudinal coordinate. This
# removes longitude as a dimension coordinate and makes it a scalar
# coordinate in line with how a zonal mean would be described.
# Is there a better way of doing this?
tcube_cds = [cdt.standard_name for cdt in tcube.coords()]
if 'longitude' in tcube_cds:
tcube = tcube.collapsed('longitude', iris.analysis.MEAN)
if not tcube.coord('latitude').has_bounds():
tcube.coord('latitude').guess_bounds()
aux_coord_names = [aux_coord.var_name for aux_coord in tcube.aux_coords]
if 'month' not in aux_coord_names:
icc.add_month(tcube, 'time', name='month')
if 'clim_season' not in aux_coord_names:
icc.add_season(tcube, 'time', name='clim_season')
# Read zonal mean q (lbproc=192) and add clim month and season to metadata
qcube = load_run_ss(
run, 'monthly', 'specific_humidity', lbproc=192,
**year_cons) # m01s30i205
# Although input data is a zonal mean, iris does not recognise it as such
# and just reads it as having a single longitudinal coordinate. This
# removes longitude as a dimension coordinate and makes it a scalar
# coordinate in line with how a zonal mean would be described.
# Is there a better way of doing this?
qcube_cds = [cdt.standard_name for cdt in qcube.coords()]
if 'longitude' in qcube_cds:
qcube = qcube.collapsed('longitude', iris.analysis.MEAN)
if not qcube.coord('latitude').has_bounds():
qcube.coord('latitude').guess_bounds()
aux_coord_names = [aux_coord.var_name for aux_coord in qcube.aux_coords]
if 'month' not in aux_coord_names:
icc.add_month(qcube, 'time', name='month')
if 'clim_season' not in aux_coord_names:
icc.add_season(qcube, 'time', name='clim_season')
# Calculate PNJ metrics
pnj_metrics(run, ucube, metrics)
# Calculate QBO metrics
qbo_metrics(run, ucube, metrics)
# Calculate polar temperature metrics
tpole_metrics(run, tcube, metrics)
# Calculate equatorial temperature metrics
teq_metrics(run, tcube, metrics)
# Calculate tropical temperature metrics
t_metrics(run, tcube, metrics)
# Calculate tropical water vapour metric
q_metrics(run, qcube, metrics)
# Summary metric
summary_metric(metrics)
# Make sure all metrics are of type float
# Need at the moment to populate metrics files
for key, value in metrics.items():
metrics[key] = float(value)
return metrics
def age_of_air(run):
"""Calculate the age of air metrics."""
# Create metrics dictionary with MDI incase age of air
# diagnostics not available
metrics = {
'RMS error: tropical Age of Air': -10000.,
'RMS error: NH midlatitude Age of Air': -10000.
}
try:
# Set up to only run for 5 year period
analysis_start_dt, analysis_end_dt = calculate_analysis_years(run)
constraint = dict(
from_dt=analysis_start_dt, to_dt=analysis_end_dt, lbproc=128)
# Calculate age of air metrics if appropriate diagnostic available
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*orography.*', UserWarning)
agecube = load_run_ss(run, 'monthly', 'age_of_stratospheric_air',
**constraint) # m01s34i150
except iris.exceptions.ConstraintMismatchError:
logger.warning('Age of air fields absent. Skipping this diagnostic.')
except ValueError:
logger.warning("Run length < 12 years: Can't assess age of air")
else:
# Create time/zonal means of age data
agecube = agecube.collapsed(['longitude', 'time'], iris.analysis.MEAN)
# Convert units of data from seconds to years
agecube.data /= RSECS_PER_360DAY_YEAR
# Create latitude bounds for area-weighted averaging
agecube.coord('latitude').guess_bounds()
# Convert units of height from metres to km
agecube.coord('level_height').convert_units('km')
# Calculate area-weighted means for tropics
trop_cons = iris.Constraint(latitude=lambda lat: -10 <= lat <= 10)
diag1 = weight_lat_ave(agecube.extract(trop_cons))
diag1.var_name = 'tropical_age_of_air'
# Calculate area-weighted means for mid-latitudes
mlat_cons = iris.Constraint(latitude=lambda lat: 35 <= lat <= 45)
diag2 = weight_lat_ave(agecube.extract(mlat_cons))
diag2.var_name = 'midlat_age_of_air'
# Write age of air data to CWD
outfile = '{0}_age_of_air_{1}.nc'
cubelist = iris.cube.CubeList([diag1, diag2])
iris.save(cubelist, outfile.format(run['runid'], run.period))
# Calculate metrics
diag1sf6 = iai.Linear(diag1, [('level_height', ZSF6_KM)])
diag1co2 = iai.Linear(diag1, [('level_height', ZCO2_KM)])
diag2sf6 = iai.Linear(diag2, [('level_height', Z2_KM)])
diag2co2 = iai.Linear(diag2, [('level_height', Z2_KM2)])
metric1sf6 = np.sqrt(np.mean((diag1sf6.data - AGE_YRS)**2))
metric1co2 = np.sqrt(np.mean((diag1co2.data - AGE_YRS2)**2))
metric2sf6 = np.sqrt(np.mean((diag2sf6.data - AGE2_YRS)**2))
metric2co2 = np.sqrt(np.mean((diag2co2.data - AGE2_YRS2)**2))
trop_age = (metric1sf6 + metric1co2) / 2.
midl_age = (metric2sf6 + metric2co2) / 2.
# Add metrics to dictionary
metrics['RMS error: tropical Age of Air'] = float(trop_age)
metrics['RMS error: NH midlatitude Age of Air'] = float(midl_age)
return metrics