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ESMValTool ozone diagnostics #473

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f1e8588
first draft of esmvaltool ozone diagnostics
axel-lauer Dec 3, 2025
cb81694
add ESACCI-OZONE data (variable o3) for ESMValTool
axel-lauer Dec 10, 2025
b16785d
update ozone-zonal diagnostic
axel-lauer Dec 10, 2025
7402cb7
cleaned up ozone.py
axel-lauer Dec 10, 2025
5501551
automatic formatting fixes
axel-lauer Dec 15, 2025
91df83f
added 473 to change log
axel-lauer Dec 15, 2025
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1 change: 1 addition & 0 deletions changelog/473.feature.md
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Added ESMValTool ozone diagnostics.
1 change: 1 addition & 0 deletions packages/climate-ref-esmvaltool/src/climate_ref_esmvaltool/dataset_registry/data.txt
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Expand Up @@ -80,6 +80,7 @@ ESMValTool/OBS/Tier2/ESACCI-CLOUD/OBS_ESACCI-CLOUD_sat_AVHRR-AMPM-fv3.0_Amon_rlu
ESMValTool/OBS/Tier2/ESACCI-CLOUD/OBS_ESACCI-CLOUD_sat_AVHRR-AMPM-fv3.0_Amon_rlutcs_198201-201612.nc 21f096ecafff659e5c7e3338060425f7194e5d1b39c9510865496e04ecac3d75
ESMValTool/OBS/Tier2/ESACCI-CLOUD/OBS_ESACCI-CLOUD_sat_AVHRR-AMPM-fv3.0_Amon_rsut_198201-201612.nc f2c3f3afcdc2e730df7985c210a3de89b0d4f83b150e0c3846f7ac3c5fa9c54a
ESMValTool/OBS/Tier2/ESACCI-CLOUD/OBS_ESACCI-CLOUD_sat_AVHRR-AMPM-fv3.0_Amon_rsutcs_198201-201612.nc d180d3140d4c1f6b9bb1960e07b45f192643f047e7c272c8c8c7070296ca3ab7
ESMValTool/OBS/Tier2/ESACCI-OZONE/OBS6_ESACCI-OZONE_sat_L3_AERmon_o3_198410-202212.nc 7ed9446b66e3b8382df4b6ce3074499a9ad00b7173a784c95df289fc9971ce69
ESMValTool/OBS/Tier2/HadCRUT5/OBS_HadCRUT5_ground_5.0.1.0-analysis_Amon_tas_185001-202112.nc edc3ee50b942dfbeccfd58b574df3393555379c2de3418c9717ac11dbafc12fc
ESMValTool/OBS/Tier2/ISCCP-FH/OBS_ISCCP-FH_sat_v0_Amon_rlut_198401-201612.nc 650b347df432f6e5f3f693310aad695a7502f2905ac545753c7d4ccb0592adbe
ESMValTool/OBS/Tier2/ISCCP-FH/OBS_ISCCP-FH_sat_v0_Amon_rlutcs_198401-201612.nc a90d9e035447f8778a2f64362411c079536d9dea559f6d53d032710b2c9b00e3
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12 changes: 12 additions & 0 deletions packages/climate-ref-esmvaltool/src/climate_ref_esmvaltool/diagnostics/__init__.py
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Expand Up @@ -13,6 +13,13 @@
from climate_ref_esmvaltool.diagnostics.ecs import EquilibriumClimateSensitivity
from climate_ref_esmvaltool.diagnostics.enso import ENSOBasicClimatology, ENSOCharacteristics
from climate_ref_esmvaltool.diagnostics.example import GlobalMeanTimeseries
from climate_ref_esmvaltool.diagnostics.ozone import (
O3LatMonthMapplot,
O3LatTimeMapplot,
O3PolarCapTimeseriesNH,
O3PolarCapTimeseriesSH,
O3ZonalMeanProfiles,
)
from climate_ref_esmvaltool.diagnostics.regional_historical_changes import (
RegionalHistoricalAnnualCycle,
RegionalHistoricalTimeSeries,
Expand All @@ -37,6 +44,11 @@
"ENSOCharacteristics",
"EquilibriumClimateSensitivity",
"GlobalMeanTimeseries",
"O3LatMonthMapplot",
"O3LatTimeMapplot",
"O3PolarCapTimeseriesNH",
"O3PolarCapTimeseriesSH",
"O3ZonalMeanProfiles",
"RegionalHistoricalAnnualCycle",
"RegionalHistoricalTimeSeries",
"RegionalHistoricalTrend",
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355 changes: 355 additions & 0 deletions packages/climate-ref-esmvaltool/src/climate_ref_esmvaltool/diagnostics/ozone.py
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import pandas

from climate_ref_core.constraints import (
PartialDateTime,
RequireContiguousTimerange,
RequireTimerange,
)
from climate_ref_core.datasets import FacetFilter, SourceDatasetType
from climate_ref_core.diagnostics import DataRequirement

# from climate_ref_core.metric_values.typing import SeriesDefinition
from climate_ref_esmvaltool.diagnostics.base import ESMValToolDiagnostic
from climate_ref_esmvaltool.recipe import dataframe_to_recipe
from climate_ref_esmvaltool.types import Recipe


class O3LatTimeMapplot(ESMValToolDiagnostic):
"""
Calculate the ozone diagnostics - zonal mean total column ozone vs. time.
"""

name = "Ozone Diagnostics"
slug = "ozone-lat-time"
base_recipe = "ref/recipe_ref_ozone.yml"

data_requirements = (
DataRequirement(
source_type=SourceDatasetType.CMIP6,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"experiment_id": "historical",
"table_id": "AERmon",
},
),
),
group_by=("source_id", "member_id", "grid_label"),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(1996, 1),
end=PartialDateTime(2014, 12),
),
RequireContiguousTimerange(group_by=("instance_id",)),
),
),
DataRequirement(
source_type=SourceDatasetType.obs4MIPs,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"source_id": "C3S-GTO-ECV-9-0",
"frequency": "mon",
},
),
),
group_by=("source_id",),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(1996, 1),
end=PartialDateTime(2014, 12),
),
),
),
)
facets = ()

@staticmethod
def update_recipe(
recipe: Recipe,
input_files: dict[SourceDatasetType, pandas.DataFrame],
) -> None:
"""Update the recipe."""
recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
dataset = recipe_variables["toz"]["additional_datasets"][0]
# set time range of model (CMIP6) dataset (should match observational period)
dataset["timerange"] = "1996/2014"
recipe["datasets"] = [dataset]
diagnostic = "lat_time_mapplot"
recipe["diagnostics"] = {diagnostic: recipe["diagnostics"][diagnostic]}
recipe["diagnostics"][diagnostic]["variables"]["toz"]["timerange"] = "1996/2014"


class O3PolarCapTimeseriesSH(ESMValToolDiagnostic):
"""
Calculate the ozone diagnostics - October SH polar mean (60S-85S) time series.
"""

name = "Ozone Diagnostics"
slug = "ozone-sh-oct"
base_recipe = "ref/recipe_ref_ozone.yml"

data_requirements = (
DataRequirement(
source_type=SourceDatasetType.CMIP6,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"experiment_id": "historical",
"table_id": "AERmon",
},
),
),
group_by=("source_id", "member_id", "grid_label"),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(1950, 1),
end=PartialDateTime(2014, 12),
),
RequireContiguousTimerange(group_by=("instance_id",)),
),
),
DataRequirement(
source_type=SourceDatasetType.obs4MIPs,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"source_id": "C3S-GTO-ECV-9-0",
"frequency": "mon",
},
),
),
group_by=("source_id",),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(1996, 1),
end=PartialDateTime(2021, 12),
),
),
),
)
facets = ()

@staticmethod
def update_recipe(
recipe: Recipe,
input_files: dict[SourceDatasetType, pandas.DataFrame],
) -> None:
"""Update the recipe."""
recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
dataset = recipe_variables["toz"]["additional_datasets"][0]
# set model (CMIP6) time range to 1950...2014
dataset["timerange"] = "1950/2014"
recipe["datasets"] = [dataset]
diagnostic = "polar_cap_time_series_SH"
recipe["diagnostics"] = {diagnostic: recipe["diagnostics"][diagnostic]}


class O3PolarCapTimeseriesNH(ESMValToolDiagnostic):
"""
Calculate the ozone diagnostics - March NH polar mean (60N-80N) time series.
"""

name = "Ozone Diagnostics"
slug = "ozone-nh-mar"
base_recipe = "ref/recipe_ref_ozone.yml"

data_requirements = (
DataRequirement(
source_type=SourceDatasetType.CMIP6,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"experiment_id": "historical",
"table_id": "AERmon",
},
),
),
group_by=("source_id", "member_id", "grid_label"),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(1950, 1),
end=PartialDateTime(2014, 12),
),
RequireContiguousTimerange(group_by=("instance_id",)),
),
),
DataRequirement(
source_type=SourceDatasetType.obs4MIPs,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"source_id": "C3S-GTO-ECV-9-0",
"frequency": "mon",
},
),
),
group_by=("source_id",),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(1996, 1),
end=PartialDateTime(2021, 12),
),
),
),
)
facets = ()

@staticmethod
def update_recipe(
recipe: Recipe,
input_files: dict[SourceDatasetType, pandas.DataFrame],
) -> None:
"""Update the recipe."""
# Make sure only grid cells south of 80N are considered as there are no
# measurements north of 80N in March. Specifying 85N as northern boundary
# in the orignal 'recipe_ref_ozone.yml' is a bug!
recipe["preprocessors"]["create_time_series_NH"]["extract_region"]["end_latitude"] = 80
recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
dataset = recipe_variables["toz"]["additional_datasets"][0]
# set model (CMIP6) time range to 1950...2014
dataset["timerange"] = "1950/2014"
recipe["datasets"] = [dataset]
diagnostic = "polar_cap_time_series_NH"
# adjust plot title to reflect bug fix regarding northern boundary (see above)
recipe["diagnostics"][diagnostic]["scripts"]["plot"]["plots"]["timeseries"]["pyplot_kwargs"][
"title"
] = "Total Column Ozone, 60-80N, March"
recipe["diagnostics"] = {diagnostic: recipe["diagnostics"][diagnostic]}


class O3ZonalMeanProfiles(ESMValToolDiagnostic):
"""
Calculate the ozone diagnostics - stratospheric zonal mean profiles.
"""

name = "Ozone Diagnostics"
slug = "ozone-zonal"
base_recipe = "ref/recipe_ref_ozone.yml"

data_requirements = (
DataRequirement(
source_type=SourceDatasetType.CMIP6,
filters=(
FacetFilter(
facets={
"variable_id": "o3",
"experiment_id": "historical",
"table_id": "Amon",
},
),
),
group_by=("source_id", "member_id", "grid_label"),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(2005, 1),
end=PartialDateTime(2014, 12),
),
RequireContiguousTimerange(group_by=("instance_id",)),
),
),
# TODO: Use ESACCI-OZONE (SAGE-OMPS, variable o3) from obs4MIPs once available.
)
facets = ()

@staticmethod
def update_recipe(
recipe: Recipe,
input_files: dict[SourceDatasetType, pandas.DataFrame],
) -> None:
"""Update the recipe."""
recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
dataset = recipe_variables["o3"]["additional_datasets"][0]
# set model (CMIP6) time range to 2005...2014
dataset["timerange"] = "2005/2014"
recipe["datasets"] = [dataset]
diagnostic = "zonal_mean_profiles"
# adjust plot title to actual time range
recipe["diagnostics"][diagnostic]["scripts"]["plot"]["plots"]["zonal_mean_profile"]["pyplot_kwargs"][
"suptitle"
] = "{long_name} (2005-2014 mean)"
recipe["diagnostics"] = {diagnostic: recipe["diagnostics"][diagnostic]}
recipe["diagnostics"][diagnostic]["variables"]["o3"]["timerange"] = "2005/2014"


class O3LatMonthMapplot(ESMValToolDiagnostic):
"""
Calculate the ozone diagnostics - zonal mean total column ozone vs. annual cycle plot.
"""

name = "Ozone Diagnostics"
slug = "ozone-annual-cycle"
base_recipe = "ref/recipe_ref_ozone.yml"

data_requirements = (
DataRequirement(
source_type=SourceDatasetType.CMIP6,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"experiment_id": "historical",
"table_id": "AERmon",
},
),
),
group_by=("source_id", "member_id", "grid_label"),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(2005, 1),
end=PartialDateTime(2014, 12),
),
RequireContiguousTimerange(group_by=("instance_id",)),
),
),
DataRequirement(
source_type=SourceDatasetType.obs4MIPs,
filters=(
FacetFilter(
facets={
"variable_id": "toz",
"source_id": "C3S-GTO-ECV-9-0",
"frequency": "mon",
},
),
),
group_by=("source_id",),
constraints=(
RequireTimerange(
group_by=("instance_id",),
start=PartialDateTime(2005, 1),
end=PartialDateTime(2014, 12),
),
),
),
)
facets = ()

@staticmethod
def update_recipe(
recipe: Recipe,
input_files: dict[SourceDatasetType, pandas.DataFrame],
) -> None:
"""Update the recipe."""
recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
dataset = recipe_variables["toz"]["additional_datasets"][0]
# set model (CMIP6) time range to 2005...2014
dataset["timerange"] = "2005/2014"
recipe["datasets"] = [dataset]
diagnostic = "lat_month_mapplot"
recipe["diagnostics"] = {diagnostic: recipe["diagnostics"][diagnostic]}
recipe["diagnostics"][diagnostic]["variables"]["toz"]["timerange"] = "2005/2014"
1 change: 1 addition & 0 deletions packages/climate-ref-esmvaltool/src/climate_ref_esmvaltool/recipes.txt
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Expand Up @@ -10,6 +10,7 @@ ref/recipe_enso_characteristics.yml 34c2518b138068ac96d212910b979d54a8fc
ref/recipe_ref_annual_cycle_region.yml 64ebc687789dad6c45a2361b45218cb5a0ad0e38c516840c65fc7e8bf7b5ace7
ref/recipe_ref_cre.yml 4375f262479c3b3e1b348b71080a6d758e195bda76516a591182045a3a29aa32
ref/recipe_ref_fire.yml 2ad82effaca4e742d8abe6a0aa07bb46e1e92ef0d2d240760f7623b0ba045926
ref/recipe_ref_ozone.yml 388aaf01721c0856ab7143b47b9d2f1efeaaae4ef57e11572c1e133b49aad0a6
ref/recipe_ref_sea_ice_area_basic.yml 7d01a8527880663ca28284772f83a8356d9972fb4f022a4000e50a56ce044b09
ref/recipe_ref_scatterplot.yml b99d1736e16256d161847b025811d7088ad9f892d4887fb009fa99c4079135a0
ref/recipe_ref_timeseries_region.yml 86f36e442021caba201601d8cf4624f8ce6715ce421670a467c792db2910db22
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