How to run the CMIP7 ScenarioMIP workflow¶

Here we demonstrate how to run the workflow that was used in CMIP7's ScenarioMIP.

Note: this is not yet complete, we will add further steps in future.

Imports¶

In [1]:

import multiprocessing
import os
import platform
from functools import partial
from pathlib import Path

import openscm_units
import pandas_indexing as pix
import pandas_openscm
import pint
import seaborn as sns
from pandas_openscm.io import load_timeseries_csv

import gcages.cmip7_scenariomip.pre_processing.reaggregation.basic
from gcages.cmip7_scenariomip.harmonisation import (
    create_cmip7_scenariomip_global_harmoniser,
)
from gcages.cmip7_scenariomip.infilling import CMIP7ScenarioMIPInfiller
from gcages.cmip7_scenariomip.post_processing import CMIP7ScenarioMIPPostProcessor
from gcages.cmip7_scenariomip.pre_processing import CMIP7ScenarioMIPPreProcessor
from gcages.cmip7_scenariomip.scm_running import CMIP7ScenarioMIPSCMRunner
from gcages.index_manipulation import split_sectors

import multiprocessing import os import platform from functools import partial from pathlib import Path import openscm_units import pandas_indexing as pix import pandas_openscm import pint import seaborn as sns from pandas_openscm.io import load_timeseries_csv import gcages.cmip7_scenariomip.pre_processing.reaggregation.basic from gcages.cmip7_scenariomip.harmonisation import ( create_cmip7_scenariomip_global_harmoniser, ) from gcages.cmip7_scenariomip.infilling import CMIP7ScenarioMIPInfiller from gcages.cmip7_scenariomip.post_processing import CMIP7ScenarioMIPPostProcessor from gcages.cmip7_scenariomip.pre_processing import CMIP7ScenarioMIPPreProcessor from gcages.cmip7_scenariomip.scm_running import CMIP7ScenarioMIPSCMRunner from gcages.index_manipulation import split_sectors

In [2]:

# Setup pint
pint.set_application_registry(openscm_units.unit_registry)

# Setup pint pint.set_application_registry(openscm_units.unit_registry)

In [3]:

pandas_openscm.register_pandas_accessors()

pandas_openscm.register_pandas_accessors()

Starting point¶

The starting point is an emissions scenario submission. This must be in a format like the below, i.e. a pandas DataFrame with a MultiIndex with levels: ["model", "scenario", "region", "variable", "unit"] and years as the columns.

Naming conventions and reporting¶

The naming convention and reporting rules are specific to CMIP7's ScenarioMIP. These are quite complex and not a solid target. As a result, there aren't super clear docs. Our best advice is to use an existing submission as an example, or simply try using one of the different reaggregators (see below) and let its error messages guide you through what else is needed.

Here we use the 'basic' naming convention. An example of the data required when using this convention is provided below (it is based on some example data we use for testing, because it requires reporting thousands of timeseries so writing it by hand would be hard work). The example data does not contain all possible timeseries that can be reported, rather just a selection useful for this example (again, a discussion of the complete reporting rules is out of scope here, docs specifically on this to come we hope).

In [4]:

EXAMPLE_INPUT_FILE = Path(
    "tests/regression/cmip7-scenariomip/test-data/salted-202507-scenariomip-input.csv"
)

EXAMPLE_INPUT_FILE = Path( "tests/regression/cmip7-scenariomip/test-data/salted-202507-scenariomip-input.csv" )

In [6]:

start = load_timeseries_csv(
    EXAMPLE_INPUT_FILE,
    index_columns=["model", "scenario", "region", "variable", "unit"],
    out_columns_type=int,
    out_columns_name="year",
)

start = load_timeseries_csv( EXAMPLE_INPUT_FILE, index_columns=["model", "scenario", "region", "variable", "unit"], out_columns_type=int, out_columns_name="year", )

In [7]:

relplot_in_emms = partial(
    sns.relplot,
    kind="line",
    linewidth=2.0,
    alpha=0.7,
    facet_kws=dict(sharey=False),
    x="year",
    y="value",
    col="variable",
    col_wrap=3,
)

data = start.openscm.to_long_data(time_col_name="year")
data = data[
    data["variable"].isin(
        [
            "Emissions|CO2",
            "Emissions|CH4",
            "Emissions|Sulfur",
            "Emissions|BC",
            "Carbon Removal|Geological Storage|Biomass",
            "Carbon Removal|Geological Storage|Direct Air Capture",
            "Carbon Removal|Enhanced Weathering",
        ]
    )
    & (data["region"].isin(["World"]) | data["region"].str.startswith("model_1"))
]

fg = sns.relplot(
    data=data,
    hue="region",
    style="scenario",
    kind="line",
    linewidth=2.0,
    alpha=0.7,
    facet_kws=dict(sharey=False),
    x="year",
    y="value",
    col="variable",
    col_wrap=2,
)

for ax in fg.axes.flatten():
    if "CO2" in ax.get_title():
        ax.axhline(0.0, linestyle="--", color="gray")
    else:
        ax.set_ylim(ymin=0.0)

relplot_in_emms = partial( sns.relplot, kind="line", linewidth=2.0, alpha=0.7, facet_kws=dict(sharey=False), x="year", y="value", col="variable", col_wrap=3, ) data = start.openscm.to_long_data(time_col_name="year") data = data[ data["variable"].isin( [ "Emissions|CO2", "Emissions|CH4", "Emissions|Sulfur", "Emissions|BC", "Carbon Removal|Geological Storage|Biomass", "Carbon Removal|Geological Storage|Direct Air Capture", "Carbon Removal|Enhanced Weathering", ] ) & (data["region"].isin(["World"]) | data["region"].str.startswith("model_1")) ] fg = sns.relplot( data=data, hue="region", style="scenario", kind="line", linewidth=2.0, alpha=0.7, facet_kws=dict(sharey=False), x="year", y="value", col="variable", col_wrap=2, ) for ax in fg.axes.flatten(): if "CO2" in ax.get_title(): ax.axhline(0.0, linestyle="--", color="gray") else: ax.set_ylim(ymin=0.0)

Pre-process¶

The first step is pre-processing the emissions scenario(s) to compile the sectors which are used for gridding. This step also produces data sets that can be used with the 'standard' global-only workflows.

Re-aggregator¶

The major challenge in processing is knowing which re-aggregator to use. At present, we only support one re-aggregation method, but we may need to support more in future, hence this idea.

For this data, we need to use the basic ReaggregatorBasic class. When initialising our re-aggregator, we also need to tell it which model regions to use (models often report data in aggregate regions too, and we need to make sure this doesn't trip things up).

In [8]:

model_regions = [
    r
    for r in start.index.get_level_values("region").unique()
    if r.startswith("model_1")
]
reaggregator = (
    gcages.cmip7_scenariomip.pre_processing.reaggregation.basic.ReaggregatorBasic(
        model_regions=model_regions
    )
)

model_regions = [ r for r in start.index.get_level_values("region").unique() if r.startswith("model_1") ] reaggregator = ( gcages.cmip7_scenariomip.pre_processing.reaggregation.basic.ReaggregatorBasic( model_regions=model_regions ) )

Pre-processor¶

Having defined our re-aggregator, we can initialise our pre-processor and do the pre-processing.

In [9]:

pre_processor = CMIP7ScenarioMIPPreProcessor(
    reaggregator=reaggregator,
    n_processes=None,  # run serially
    run_checks=True,
)

pre_processor = CMIP7ScenarioMIPPreProcessor( reaggregator=reaggregator, n_processes=None, # run serially run_checks=True, )

In [10]:

res_pre_processed = pre_processor(start)

res_pre_processed = pre_processor(start)

/home/docs/checkouts/readthedocs.org/user_builds/gcages/envs/latest/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

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1it [00:00,  3.11it/s]

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As an aside, it is possible to let gcages guess the re-aggregator to use, it's also more confusing when it goes wrong though.

In [11]:

pre_processor_guess_reaggregator = CMIP7ScenarioMIPPreProcessor(
    # Don't specifiy the re-aggregator, let gcages guess
    # reaggregator=reaggregator,
    n_processes=None,  # run serially
    run_checks=True,
)

# The guessing is not so intelligent
# (it can't figure out which regions are model native and which aren't by itself),
# so we need to give it a hand
# by stripping out everything except 'World' data
# and model-region data
# (removing all other regional aggregations).
start_guessable = start.loc[
    (start.index.get_level_values("region") == "World")
    | start.index.get_level_values("region").str.startswith("model_1")
]
# The result is the same as the above
_ = pre_processor_guess_reaggregator(start_guessable)

pre_processor_guess_reaggregator = CMIP7ScenarioMIPPreProcessor( # Don't specifiy the re-aggregator, let gcages guess # reaggregator=reaggregator, n_processes=None, # run serially run_checks=True, ) # The guessing is not so intelligent # (it can't figure out which regions are model native and which aren't by itself), # so we need to give it a hand # by stripping out everything except 'World' data # and model-region data # (removing all other regional aggregations). start_guessable = start.loc[ (start.index.get_level_values("region") == "World") | start.index.get_level_values("region").str.startswith("model_1") ] # The result is the same as the above _ = pre_processor_guess_reaggregator(start_guessable)

0it [00:00, ?it/s]

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1it [00:00,  3.26it/s]

Results¶

The result contains a few pieces of information.

Global workflow emissions¶

The first bit of information is data which can be used with the global workflow.

In [12]:

fg = sns.relplot(
    data=res_pre_processed.global_workflow_emissions.openscm.to_long_data(
        time_col_name="year"
    ),
    hue="scenario",
    kind="line",
    linewidth=2.0,
    alpha=0.7,
    facet_kws=dict(sharey=False),
    x="year",
    y="value",
    col="variable",
    col_order=sorted(
        res_pre_processed.global_workflow_emissions.index.get_level_values(
            "variable"
        ).unique()
    ),
    col_wrap=4,
    height=3.0,
    aspect=1.0,
)

for ax in fg.axes.flatten():
    if "CO2" in ax.get_title():
        ax.axhline(0.0, linestyle="--", color="gray")
    else:
        ax.set_ylim(ymin=0.0)

fg = sns.relplot( data=res_pre_processed.global_workflow_emissions.openscm.to_long_data( time_col_name="year" ), hue="scenario", kind="line", linewidth=2.0, alpha=0.7, facet_kws=dict(sharey=False), x="year", y="value", col="variable", col_order=sorted( res_pre_processed.global_workflow_emissions.index.get_level_values( "variable" ).unique() ), col_wrap=4, height=3.0, aspect=1.0, ) for ax in fg.axes.flatten(): if "CO2" in ax.get_title(): ax.axhline(0.0, linestyle="--", color="gray") else: ax.set_ylim(ymin=0.0)

Gridding workflow emissions¶

The second bit of information is data which can be used with the gridding workflow. This contains data at the region-sector level for a specific set of sectors.

In [13]:

data = split_sectors(
    res_pre_processed.gridding_workflow_emissions
).openscm.to_long_data(time_col_name="year")
species_to_plot = "CO2"
data = data[data["species"] == species_to_plot]
# data = data[data["sectors"] == "Energy Sector"]

fg = sns.relplot(
    data=data,
    style="scenario",
    style_order=sorted(data["scenario"].unique()),
    hue="sectors",
    hue_order=sorted(data["sectors"].unique()),
    col="region",
    col_order=sorted(data["region"].unique()),
    kind="line",
    linewidth=2.0,
    alpha=0.7,
    facet_kws=dict(sharey=False),
    x="year",
    y="value",
    col_wrap=3,
    height=4.0,
    aspect=1.0,
)

for ax in fg.axes.flatten():
    ax.axhline(0.0, linestyle="--", color="gray")

fg.fig.suptitle(species_to_plot, y=1.02)

data = split_sectors( res_pre_processed.gridding_workflow_emissions ).openscm.to_long_data(time_col_name="year") species_to_plot = "CO2" data = data[data["species"] == species_to_plot] # data = data[data["sectors"] == "Energy Sector"] fg = sns.relplot( data=data, style="scenario", style_order=sorted(data["scenario"].unique()), hue="sectors", hue_order=sorted(data["sectors"].unique()), col="region", col_order=sorted(data["region"].unique()), kind="line", linewidth=2.0, alpha=0.7, facet_kws=dict(sharey=False), x="year", y="value", col_wrap=3, height=4.0, aspect=1.0, ) for ax in fg.axes.flatten(): ax.axhline(0.0, linestyle="--", color="gray") fg.fig.suptitle(species_to_plot, y=1.02)

Out[13]:

Text(0.5, 1.02, 'CO2')

As part of this reporting, we also show which timeseries were assumed to be zero during the processing. (Lots of assumptions are not an issue, we simply include this for clarity and transparency.)

Harmonisation¶

The next step is harmonisation. This is the process of aligning the scenarios with historical emissions estimates. We do this at both the global level and the level used for gridding.

Historical emissions aligned with the rest of the CMIP7 exercise are used for this. These were processed in this repository: https://github.com/iiasa/emissions_harmonization_historical and are archived at https://zenodo.org/records/17845154.

Under the hood, the harmonisation uses the aneris package.

Global¶

In [14]:

CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE = Path(
    "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/history_cmip7_scenariomip.csv"
)
ANERIS_GLOBAL_OVERRIDES_FILE = Path(
    "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/aneris-overrides-global.csv"
)

CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE = Path( "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/history_cmip7_scenariomip.csv" ) ANERIS_GLOBAL_OVERRIDES_FILE = Path( "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/aneris-overrides-global.csv" )

In [16]:

harmoniser_global = create_cmip7_scenariomip_global_harmoniser(
    cmip7_scenariomip_global_historical_emissions_file=CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE,
    aneris_global_overrides_file=ANERIS_GLOBAL_OVERRIDES_FILE,
    n_processes=None,  # run serially for this demo
)

harmoniser_global = create_cmip7_scenariomip_global_harmoniser( cmip7_scenariomip_global_historical_emissions_file=CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE, aneris_global_overrides_file=ANERIS_GLOBAL_OVERRIDES_FILE, n_processes=None, # run serially for this demo )

In [17]:

harmonised_global = harmoniser_global(res_pre_processed.global_workflow_emissions)
harmonised_global

harmonised_global = harmoniser_global(res_pre_processed.global_workflow_emissions) harmonised_global

Scenarios to harmonise: 0it [00:00, ?it/s]

INFO:root:Harmonizing with reduce_ratio_2080

INFO:root:Harmonizing with reduce_offset_2150_cov

Scenarios to harmonise: 1it [00:00,  2.20it/s]

Scenarios to harmonise: 1it [00:00,  2.20it/s]

Out[17]:

				year	2023	2024	2025	2026	2027	2028	2029	2030	2031	2032	...	2091	2092	2093	2094	2095	2096	2097	2098	2099	2100
model	scenario	region	variable	unit
model_1	SSP1 - Low Emissions	World	Emissions|BC	Mt BC/yr	7.219245	7.144170	7.069115	6.600127	6.131266	5.662533	5.193926	4.725447	4.630838	4.536256	...	1.985668	1.982659	1.979650	1.976642	1.973633	1.970625	1.967616	1.964607	1.961599	1.958590
			Emissions|CH4	Mt CH4/yr	363.489901	365.526092	367.565757	355.928260	344.265560	332.577658	320.864553	309.126246	300.639794	292.134973	...	132.359170	130.664527	128.969883	127.275240	125.580596	123.885953	122.191309	120.496666	118.802023	117.107379
			Emissions|CO	Mt CO/yr	774.053434	774.666130	775.256992	724.758119	673.815787	622.429994	570.600742	518.328030	509.817833	501.221767	...	347.673297	348.702968	349.732638	350.762309	351.791979	352.821649	353.851320	354.880990	355.910661	356.940331
			Emissions|NH3	Mt NH3/yr	69.113401	69.584726	70.051607	69.079299	68.111815	67.149154	66.191318	65.238306	63.677284	62.125110	...	21.296855	20.939381	20.581906	20.224432	19.866958	19.509483	19.152009	18.794534	18.437060	18.079586
			Emissions|NOx	Mt NO2/yr	127.510494	122.853346	118.219603	110.532086	102.884767	95.277648	87.710727	80.184006	78.589364	77.002322	...	35.380763	35.193219	35.005675	34.818131	34.630588	34.443044	34.255500	34.067956	33.880412	33.692869
			Emissions|OC	Mt OC/yr	34.147891	34.304770	34.461479	32.212148	29.965001	27.720038	25.477258	23.236662	22.787355	22.338477	...	12.146310	12.190877	12.235444	12.280011	12.324578	12.369145	12.413712	12.458280	12.502847	12.547414
			Emissions|SOx	Mt SO2/yr	71.902038	69.148596	66.420641	62.236616	58.094191	53.993366	49.934143	45.916520	44.113645	42.328073	...	14.818543	14.779546	14.740548	14.701550	14.662553	14.623555	14.584558	14.545560	14.506562	14.467565
			Emissions|NMVOC	Mt VOC/yr	191.244286	190.557045	189.869826	174.813753	159.758163	144.703056	129.648433	114.594292	112.251944	109.909671	...	61.627644	61.896796	62.165949	62.435102	62.704254	62.973407	63.242559	63.511712	63.780864	64.050017
			Emissions|N2O	kt N2O/yr	11289.003671	11418.909664	11548.313212	11339.894890	11132.116080	10924.976783	10718.476998	10512.616726	10265.983337	10020.130856	...	3867.749693	3814.733648	3761.717603	3708.701558	3655.685513	3602.669468	3549.653423	3496.637378	3443.621333	3390.605287
			Emissions|CO2|Fossil	Mt CO2/yr	38712.217040	39021.233879	39330.487888	38844.085817	38357.277992	37870.064412	37382.445077	36894.419988	35372.834524	33850.010402	...	-608.686933	-750.368775	-892.050616	-1033.732458	-1175.414299	-1317.096140	-1458.777982	-1600.459823	-1742.141664	-1883.823506
			Emissions|CO2|Biosphere	Mt CO2/yr	3627.550667	3351.646901	3075.743135	2812.000589	2548.258043	2284.515497	2020.772951	1757.030406	1452.976240	1148.922074	...	-3178.876123	-3227.579799	-3276.283475	-3324.987151	-3373.690827	-3422.394503	-3471.098178	-3519.801854	-3568.505530	-3617.209206

11 rows × 78 columns

You can see the modification to the pathways as a result of the harmonisation in the plot below.

In [18]:

pdf = (
    pix.concat(
        [
            res_pre_processed.global_workflow_emissions.pix.assign(
                stage="pre_processed"
            ),
            harmonised_global.pix.assign(stage="harmonised"),
            harmoniser_global.historical_emissions.pix.assign(
                stage="history", scenario="history", model="history"
            ).loc[
                pix.isin(
                    variable=res_pre_processed.global_workflow_emissions.pix.unique(
                        "variable"
                    )
                )
            ],
        ]
    )
    .melt(ignore_index=False, var_name="year")
    .reset_index()
)

fg = relplot_in_emms(
    data=pdf,
    hue="scenario",
    style="stage",
    dashes={
        "history": (1, 1),
        "pre_processed": (3, 3),
        "harmonised": "",
    },
)

fg.axes.flatten()[0].axhline(0.0, linestyle="--", color="gray")
fg.axes.flatten()[1].set_ylim(ymin=0.0)

pdf = ( pix.concat( [ res_pre_processed.global_workflow_emissions.pix.assign( stage="pre_processed" ), harmonised_global.pix.assign(stage="harmonised"), harmoniser_global.historical_emissions.pix.assign( stage="history", scenario="history", model="history" ).loc[ pix.isin( variable=res_pre_processed.global_workflow_emissions.pix.unique( "variable" ) ) ], ] ) .melt(ignore_index=False, var_name="year") .reset_index() ) fg = relplot_in_emms( data=pdf, hue="scenario", style="stage", dashes={ "history": (1, 1), "pre_processed": (3, 3), "harmonised": "", }, ) fg.axes.flatten()[0].axhline(0.0, linestyle="--", color="gray") fg.axes.flatten()[1].set_ylim(ymin=0.0)

Out[18]:

(0.0, 406.88376766567256)

Region-sector (i.e. gridding level)¶

In [19]:

# TBD

# TBD

You can see the modification to the pathways as a result of the harmonisation in the plot below. Here we obviously only show a selection of timeseries.

In [20]:

# pdf = (
#     pix.concat(
#         [
#             pre_processed.pix.assign(stage="pre_processed"),
#             harmonised.pix.assign(stage="harmonised"),
#             harmoniser.historical_emissions.pix.assign(
#                 stage="history", scenario="history", model="history"
#             ).loc[pix.isin(variable=pre_processed.pix.unique("variable"))],
#         ]
#     )
#     .melt(ignore_index=False, var_name="year")
#     .reset_index()
# )

# fg = relplot_in_emms(
#     data=pdf,
#     hue="scenario",
#     style="stage",
#     dashes={
#         "history": (1, 1),
#         "pre_processed": (3, 3),
#         "harmonised": "",
#     },
# )

# fg.axes.flatten()[0].axhline(0.0, linestyle="--", color="gray")
# fg.axes.flatten()[1].set_ylim(ymin=0.0)

# pdf = ( # pix.concat( # [ # pre_processed.pix.assign(stage="pre_processed"), # harmonised.pix.assign(stage="harmonised"), # harmoniser.historical_emissions.pix.assign( # stage="history", scenario="history", model="history" # ).loc[pix.isin(variable=pre_processed.pix.unique("variable"))], # ] # ) # .melt(ignore_index=False, var_name="year") # .reset_index() # ) # fg = relplot_in_emms( # data=pdf, # hue="scenario", # style="stage", # dashes={ # "history": (1, 1), # "pre_processed": (3, 3), # "harmonised": "", # }, # ) # fg.axes.flatten()[0].axhline(0.0, linestyle="--", color="gray") # fg.axes.flatten()[1].set_ylim(ymin=0.0)

Infilling¶

The next step is infilling. This is the process of inferring any emissions which are not included in the scenarios, but are needed for running climate models. In our example, this is things like CFC11, CFC12 and other gases covered under the Montreal Protocol and other greenhouse gases with a warming impact much smaller than the ones already included.

In CMIP7 ScenarioMIP, a specific infilling database was used. You can see where it is archived below.

Under the hood, the AR6 infilling uses the silicone package.

In [21]:

BASE_DIR = Path("tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs")
CMIP7_SCENARIOMIP_INFILLING_FILE = BASE_DIR / "infilling_db_cmip7_scenariomip.csv"
GHG_INVERSION_FILE = BASE_DIR / "cmip7_ghg_inversions.csv"

BASE_DIR = Path("tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs") CMIP7_SCENARIOMIP_INFILLING_FILE = BASE_DIR / "infilling_db_cmip7_scenariomip.csv" GHG_INVERSION_FILE = BASE_DIR / "cmip7_ghg_inversions.csv"

With the infilling databases, we can initialise our infiller.

In [23]:

infiller = CMIP7ScenarioMIPInfiller.from_cmip7_scenariomip_config(
    cmip7_scenariomip_infilling_leader_emissions_file=CMIP7_SCENARIOMIP_INFILLING_FILE,
    cmip7_ghg_inversions_file=GHG_INVERSION_FILE,
    cmip7_scenariomip_global_historical_emissions_file=CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE,
)

infiller = CMIP7ScenarioMIPInfiller.from_cmip7_scenariomip_config( cmip7_scenariomip_infilling_leader_emissions_file=CMIP7_SCENARIOMIP_INFILLING_FILE, cmip7_ghg_inversions_file=GHG_INVERSION_FILE, cmip7_scenariomip_global_historical_emissions_file=CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE, )

And infill

In [24]:

complete = infiller(harmonised_global)
complete

complete = infiller(harmonised_global) complete

[WARNING] 09:41:24 - pint.util: Redefining 'kt' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'EUR_2005' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'EUR' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'C' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'N' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'NOX' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'gNOX' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'tNOX' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:24 - pint.util: Redefining 'S' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'yr' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'a' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'h' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'd' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'degreeC' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'degreeF' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'kt' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'Tt' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

[WARNING] 09:41:25 - pint.util: Redefining 'ppm' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)

Out[24]:

				year	2023	2024	2025	2026	2027	2028	2029	2030	2031	2032	...	2091	2092	2093	2094	2095	2096	2097	2098	2099	2100
model	scenario	region	variable	unit
model_1	SSP1 - Low Emissions	World	Emissions|BC	Mt BC/yr	7.219245	7.144170	7.069115	6.600127	6.131266	5.662533	5.193926	4.725447	4.630838	4.536256	...	1.985668	1.982659	1.979650	1.976642	1.973633	1.970625	1.967616	1.964607	1.961599	1.958590
			Emissions|CH4	Mt CH4/yr	363.489901	365.526092	367.565757	355.928260	344.265560	332.577658	320.864553	309.126246	300.639794	292.134973	...	132.359170	130.664527	128.969883	127.275240	125.580596	123.885953	122.191309	120.496666	118.802023	117.107379
			Emissions|CO	Mt CO/yr	774.053434	774.666130	775.256992	724.758119	673.815787	622.429994	570.600742	518.328030	509.817833	501.221767	...	347.673297	348.702968	349.732638	350.762309	351.791979	352.821649	353.851320	354.880990	355.910661	356.940331
			Emissions|NH3	Mt NH3/yr	69.113401	69.584726	70.051607	69.079299	68.111815	67.149154	66.191318	65.238306	63.677284	62.125110	...	21.296855	20.939381	20.581906	20.224432	19.866958	19.509483	19.152009	18.794534	18.437060	18.079586
			Emissions|NOx	Mt NO2/yr	127.510494	122.853346	118.219603	110.532086	102.884767	95.277648	87.710727	80.184006	78.589364	77.002322	...	35.380763	35.193219	35.005675	34.818131	34.630588	34.443044	34.255500	34.067956	33.880412	33.692869
			Emissions|OC	Mt OC/yr	34.147891	34.304770	34.461479	32.212148	29.965001	27.720038	25.477258	23.236662	22.787355	22.338477	...	12.146310	12.190877	12.235444	12.280011	12.324578	12.369145	12.413712	12.458280	12.502847	12.547414
			Emissions|SOx	Mt SO2/yr	71.902038	69.148596	66.420641	62.236616	58.094191	53.993366	49.934143	45.916520	44.113645	42.328073	...	14.818543	14.779546	14.740548	14.701550	14.662553	14.623555	14.584558	14.545560	14.506562	14.467565
			Emissions|NMVOC	Mt VOC/yr	191.244286	190.557045	189.869826	174.813753	159.758163	144.703056	129.648433	114.594292	112.251944	109.909671	...	61.627644	61.896796	62.165949	62.435102	62.704254	62.973407	63.242559	63.511712	63.780864	64.050017
			Emissions|N2O	kt N2O/yr	11289.003671	11418.909664	11548.313212	11339.894890	11132.116080	10924.976783	10718.476998	10512.616726	10265.983337	10020.130856	...	3867.749693	3814.733648	3761.717603	3708.701558	3655.685513	3602.669468	3549.653423	3496.637378	3443.621333	3390.605287
			Emissions|CO2|Fossil	Mt CO2/yr	38712.217040	39021.233879	39330.487888	38844.085817	38357.277992	37870.064412	37382.445077	36894.419988	35372.834524	33850.010402	...	-608.686933	-750.368775	-892.050616	-1033.732458	-1175.414299	-1317.096140	-1458.777982	-1600.459823	-1742.141664	-1883.823506
			Emissions|CO2|Biosphere	Mt CO2/yr	3627.550667	3351.646901	3075.743135	2812.000589	2548.258043	2284.515497	2020.772951	1757.030406	1452.976240	1148.922074	...	-3178.876123	-3227.579799	-3276.283475	-3324.987151	-3373.690827	-3422.394503	-3471.098178	-3519.801854	-3568.505530	-3617.209206
			Emissions|C2F6	kt C2F6/yr	2.431873	2.528807	2.625905	2.672723	2.719619	2.766591	2.813640	2.860766	2.911256	2.961829	...	1.021122	1.014390	1.007657	1.000924	0.994192	1.033516	1.072841	1.112165	1.151490	1.190814
			Emissions|C6F14	kt C6F14/yr	0.062998	0.064466	0.065935	0.066375	0.066815	0.067255	0.067695	0.068136	0.068972	0.069808	...	0.127935	0.129267	0.130599	0.131930	0.133262	0.134586	0.135910	0.137234	0.138558	0.139883
			Emissions|CF4	kt CF4/yr	15.117718	15.299725	15.480824	14.740297	14.002787	13.268293	12.536815	11.808353	12.060031	12.310516	...	13.483391	13.591301	13.699211	13.807121	13.915031	14.091919	14.268807	14.445695	14.622584	14.799472
			Emissions|HFC125	kt HFC125/yr	124.074300	129.016434	133.904686	131.476090	129.064917	126.671164	124.294832	121.935922	103.016576	84.281500	...	27.627230	27.648761	27.670291	27.691822	27.713352	28.150706	28.588059	29.025412	29.462766	29.900119
			Emissions|HFC134a	kt HFC134a/yr	291.788600	296.075875	300.295373	291.655916	283.096269	274.616432	266.216404	257.896185	273.531352	288.962665	...	109.414646	107.893343	106.372040	104.850737	103.329433	102.231552	101.133670	100.035789	98.937908	97.840026
			Emissions|HFC143a	kt HFC143a/yr	35.833800	37.368378	38.902956	38.374447	37.845938	37.317429	36.788920	36.260411	37.539522	38.818632	...	9.208669	9.406711	9.604752	9.802794	10.000835	10.104266	10.207696	10.311126	10.414556	10.517986
			Emissions|HFC227ea	kt HFC227ea/yr	6.645300	6.652549	6.659049	5.435314	4.228291	3.037980	1.864381	0.707494	1.666058	2.610431	...	1.065298	0.926165	0.787032	0.647899	0.508766	0.501147	0.493527	0.485907	0.478287	0.470667
			Emissions|HFC23	kt HFC23/yr	14.051843	11.877941	9.704039	8.181493	6.658948	5.136402	3.613856	2.091311	3.118982	4.146654	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
			Emissions|HFC245fa	kt HFC245fa/yr	15.504500	16.346968	17.198894	14.549765	11.865979	9.147537	6.394438	3.606682	3.171099	2.729941	...	5.760548	5.845571	5.930593	6.015615	6.100637	6.192335	6.284033	6.375731	6.467429	6.559127
			Emissions|HFC32	kt HFC32/yr	105.566000	110.444916	115.276979	100.579615	86.007847	71.561676	57.241101	43.046123	35.934222	28.884940	...	21.607166	21.928615	22.250064	22.571513	22.892962	23.206831	23.520700	23.834569	24.148438	24.462307
			Emissions|HFC4310mee	kt HFC4310/yr	0.882600	0.886145	0.889689	0.729938	0.570187	0.410436	0.250685	0.090935	0.255454	0.419973	...	0.607384	0.532124	0.456863	0.381602	0.306341	0.303097	0.299853	0.296609	0.293365	0.290121
			Emissions|SF6	kt SF6/yr	9.352094	9.435333	9.516460	9.173983	8.835452	8.500867	8.170230	7.843538	7.852858	7.861174	...	1.541841	1.450642	1.359442	1.268242	1.177042	1.145030	1.113019	1.081007	1.048995	1.016983
			Emissions|CCl4	kt CCl4/yr	40.297308	37.507899	34.970595	32.456073	30.041196	27.591942	25.100321	23.540679	23.662151	24.613811	...	51.885560	51.908060	51.866591	51.830099	51.798163	51.770306	51.816629	51.871686	51.829248	51.792121
			Emissions|CFC11	kt CFC11/yr	55.993337	53.802418	51.585468	49.404109	47.385642	45.470128	43.660703	41.897968	40.119867	38.450704	...	3.268634	3.094453	2.991418	2.962178	2.820386	2.624553	2.562224	2.510741	2.406025	2.307588
			Emissions|CFC113	kt CFC113/yr	2.030656	2.624086	2.662652	2.794922	3.019316	3.167136	3.238011	3.316106	3.400552	3.576931	...	7.591940	7.648563	7.621687	7.596121	7.571520	7.547174	7.609181	7.588081	7.509434	7.483586
			Emissions|CFC114	kt CFC114/yr	0.873953	1.124991	1.159712	1.197559	1.237278	1.278910	1.322527	1.368196	1.415482	1.542378	...	3.197824	3.191741	3.186103	3.180476	3.174835	3.168677	3.240427	3.235465	3.078017	3.000740
			Emissions|CFC115	kt CFC115/yr	0.027629	0.026451	0.025678	0.024929	0.024647	0.000000	0.000000	0.022034	0.020872	0.020580	...	0.000000	0.000000	0.043234	0.042116	0.000000	0.000000	0.000000	0.039164	0.107593	0.107043
			Emissions|CFC12	kt CFC12/yr	20.278901	18.606179	17.087091	15.723169	14.517109	13.305941	12.144250	11.197171	10.357148	9.516056	...	0.007780	0.017593	0.032351	0.052369	0.077664	0.108615	0.090490	0.022536	0.000000	0.000000
			Emissions|CH3Br	kt CH3Br/yr	140.486974	140.445253	140.445528	140.445535	140.445536	140.445536	140.445536	140.445536	140.445536	140.445536	...	140.445536	140.445536	140.445536	140.445536	140.445536	140.445536	140.445536	140.445536	140.445536	140.445536
			Emissions|CH3CCl3	kt CH3CCl3/yr	1.925368	1.984467	2.015470	2.121806	2.243025	2.295690	2.316133	2.424701	2.584005	2.711650	...	5.689864	5.689864	5.689864	5.689864	5.689863	5.689863	5.689863	5.689863	5.689863	5.689863
			Emissions|CH3Cl	kt CH3Cl/yr	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	...	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241	5376.602241
			Emissions|HCFC141b	kt HCFC141b/yr	57.429166	57.680778	57.214803	56.483254	55.671149	54.851644	54.015283	53.027147	51.577654	49.895208	...	6.602050	6.387501	6.184757	5.994172	5.762463	5.531226	5.364562	5.156822	4.896512	4.701716
			Emissions|HCFC142b	kt HCFC142b/yr	20.176795	20.998765	20.830485	20.617558	20.395064	20.167233	19.883953	19.591142	19.197580	18.693290	...	6.748363	6.677946	6.562452	6.492394	6.472634	6.412347	6.352457	6.343138	6.293446	6.222596
			Emissions|HCFC22	kt HCFC22/yr	324.526866	323.798943	316.207542	306.350001	295.744097	285.946353	276.890203	266.147380	251.390469	235.222718	...	21.863943	21.698471	21.501999	21.306995	21.151981	21.036735	20.961812	20.849738	20.569984	20.259407
			Emissions|Halon1202	kt Halon1202/yr	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
			Emissions|Halon1211	kt Halon1211/yr	2.562978	2.471289	2.304956	2.213393	2.205219	2.131462	1.984032	1.837040	1.689530	1.616350	...	0.064282	0.045382	0.101339	0.166331	0.081848	0.064133	0.129589	0.045532	0.000000	0.000000
			Emissions|Halon1301	kt Halon1301/yr	1.232019	1.165094	1.096459	1.162428	1.161851	1.093271	1.091831	1.090003	1.088166	1.086341	...	0.352499	0.345555	0.338213	0.330847	0.323018	0.382600	0.443897	0.371047	0.298030	0.293164
			Emissions|Halon2402	kt Halon2402/yr	0.000000	0.000000	0.074954	0.057748	0.041236	0.024741	0.008247	0.000000	0.000000	0.000000	...	0.049483	0.049482	0.049483	0.049506	0.050338	0.000000	0.000000	0.000000	0.025644	0.008322
			Emissions|C3F8	kt C3F8/yr	0.721178	0.749924	0.778719	0.792603	0.806510	0.820439	0.834392	0.848367	0.863340	0.878338	...	0.302816	0.300820	0.298823	0.296827	0.294830	0.306492	0.318154	0.329815	0.341477	0.353139
			Emissions|C4F10	kt C4F10/yr	0.101586	0.105636	0.109692	0.111647	0.113606	0.115569	0.117534	0.119503	0.121612	0.123724	...	0.042655	0.042374	0.042093	0.041812	0.041530	0.043173	0.044816	0.046458	0.048101	0.049744
			Emissions|C5F12	kt C5F12/yr	0.061383	0.063830	0.066280	0.067462	0.068646	0.069831	0.071019	0.072208	0.073483	0.074759	...	0.025774	0.025604	0.025434	0.025264	0.025094	0.026087	0.027080	0.028072	0.029065	0.030057
			Emissions|C7F16	kt C7F16/yr	0.186453	0.193885	0.201330	0.204919	0.208515	0.212116	0.215723	0.219336	0.223208	0.227085	...	0.078290	0.077774	0.077258	0.076741	0.076225	0.079240	0.082255	0.085270	0.088285	0.091300
			Emissions|C8F18	kt C8F18/yr	0.136638	0.142084	0.147539	0.150170	0.152805	0.155444	0.158088	0.160735	0.163572	0.166414	...	0.057373	0.056995	0.056616	0.056238	0.055860	0.058069	0.060279	0.062488	0.064698	0.066907
			Emissions|cC4F8	kt cC4F8/yr	2.248599	2.275690	2.302645	2.192422	2.082648	1.973323	1.864447	1.756019	1.793480	1.830763	...	2.005339	2.021401	2.037462	2.053524	2.069586	2.095915	2.122244	2.148572	2.174901	2.201230
			Emissions|SO2F2	kt SO2F2/yr	4.594033	4.649381	4.704452	4.479260	4.254984	4.031625	3.809184	3.587660	3.664195	3.740367	...	4.097036	4.129852	4.162667	4.195482	4.228297	4.282089	4.335880	4.389672	4.443463	4.497254
			Emissions|HFC236fa	kt HFC236fa/yr	0.354300	0.373552	0.393019	0.332483	0.271155	0.209034	0.146122	0.082418	0.072464	0.062383	...	0.131637	0.133580	0.135523	0.137465	0.139408	0.141504	0.143599	0.145695	0.147790	0.149885
			Emissions|HFC152a	kt HFC152a/yr	54.869900	55.090259	55.310617	45.379146	35.447675	25.516204	15.584733	5.653263	15.881160	26.109058	...	37.760156	33.081313	28.402470	23.723627	19.044783	18.843101	18.641418	18.439735	18.238053	18.036370
			Emissions|HFC365mfc	kt HFC365mfc/yr	5.241900	5.318920	5.394722	5.239516	5.085745	4.933407	4.782503	4.633032	4.913914	5.191133	...	1.965603	1.938274	1.910944	1.883614	1.856284	1.836561	1.816838	1.797115	1.777392	1.757669
			Emissions|CH2Cl2	kt CH2Cl2/yr	1612.125558	1632.639346	1652.828830	1611.490692	1570.534427	1529.960034	1489.767514	1449.956867	1524.768137	1598.604001	...	739.501246	732.222103	724.942959	717.663816	710.384673	705.131521	699.878369	694.625218	689.372066	684.118914
			Emissions|CHCl3	kt CHCl3/yr	400.499380	406.058514	411.627042	414.312063	417.001490	419.695323	422.393562	425.096207	427.991806	430.892135	...	319.593466	319.207350	318.821234	318.435117	318.049001	320.304241	322.559481	324.814722	327.069962	329.325202
			Emissions|NF3	kt NF3/yr	4.010291	4.045985	4.080774	3.933915	3.788749	3.645275	3.503494	3.363405	3.367401	3.370967	...	0.661160	0.622053	0.582945	0.543838	0.504730	0.491003	0.477276	0.463549	0.449822	0.436095

52 rows × 78 columns

You can see infilled pathways compared to raw pathways in the below.

This is not a deep analysis. If you want to see the full details of how the infilling works, see Lamboll et al., 2020.

In [25]:

pdf = (
    pix.concat(
        [
            res_pre_processed.global_workflow_emissions.loc[:, 2023:].pix.assign(
                stage="pre_processed"
            ),
            harmonised_global.pix.assign(stage="harmonised"),
            complete.pix.assign(stage="infilled"),
        ]
    )
    .loc[pix.ismatch(variable=["**CO2|Fossil", "**CH4", "**N2O", "**CO"])]
    .melt(ignore_index=False, var_name="year")
    .reset_index()
)

fg = relplot_in_emms(
    data=pdf,
    hue="scenario",
    style="stage",
    dashes={
        "pre_processed": (3, 3),
        "harmonised": "",
        "infilled": (1, 1),
    },
)

fg.axes.flatten()[0].axhline(0.0, linestyle="--", color="gray")
fg.axes.flatten()[1].set_ylim(ymin=0.0)

pdf = ( pix.concat( [ res_pre_processed.global_workflow_emissions.loc[:, 2023:].pix.assign( stage="pre_processed" ), harmonised_global.pix.assign(stage="harmonised"), complete.pix.assign(stage="infilled"), ] ) .loc[pix.ismatch(variable=["**CO2|Fossil", "**CH4", "**N2O", "**CO"])] .melt(ignore_index=False, var_name="year") .reset_index() ) fg = relplot_in_emms( data=pdf, hue="scenario", style="stage", dashes={ "pre_processed": (3, 3), "harmonised": "", "infilled": (1, 1), }, ) fg.axes.flatten()[0].axhline(0.0, linestyle="--", color="gray") fg.axes.flatten()[1].set_ylim(ymin=0.0)

Out[25]:

(0.0, 989.1820568503626)

SCM Running¶

The next step is running a simple climate model (SCM) or models (SCMs). This provides information about the climate implications of the scenario(s).

In CMIP7 ScenarioMIP, MAGICCv7.6.0 and FaIR [version TBD] were used to inform this climate assessment.

Under the hood, the AR6 SCM running uses the OpenSCM-Runner package.

In [26]:

# Get MAGICC from somewhere public
# FaIR should be pre-installed

# Get MAGICC from somewhere public # FaIR should be pre-installed

In [27]:

# combine to create complete timeseries using sum of gridded
# and the global harmonised timeseries or provide both as input
# complete_scenarios = pd.concat([harmonised, infilled])
# complete_scenarios

# combine to create complete timeseries using sum of gridded # and the global harmonised timeseries or provide both as input # complete_scenarios = pd.concat([harmonised, infilled]) # complete_scenarios

In [28]:

MAGICC_EXE_PATH = Path(
    "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/magicc-v7.6.0a3/bin"
)
MAGICC_CMIP7_SCENARIOMIP_PROBABILISTIC_CONFIG_FILE = Path(
    "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/magicc-v7.6.0a3/configs/magicc-ar7-fast-track-drawnset-v0-3-0.json"
)

MAGICC_EXE_PATH = Path( "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/magicc-v7.6.0a3/bin" ) MAGICC_CMIP7_SCENARIOMIP_PROBABILISTIC_CONFIG_FILE = Path( "tests/regression/cmip7-scenariomip/cmip7-scenariomip-workflow-inputs/magicc-v7.6.0a3/configs/magicc-ar7-fast-track-drawnset-v0-3-0.json" )

In [30]:

if platform.system() == "Darwin":
    if platform.processor() == "arm":
        MAGICC_EXE = MAGICC_EXE_PATH / "magicc-darwin-arm64"
        os.environ["DYLD_LIBRARY_PATH"] = "/opt/homebrew/opt/gfortran/lib/gcc/current/"

elif platform.system() == "Linux":
    MAGICC_EXE = MAGICC_EXE_PATH / "magicc"

elif platform.system() == "Windows":
    MAGICC_EXE = MAGICC_EXE_PATH / "magicc.exe"

if platform.system() == "Darwin": if platform.processor() == "arm": MAGICC_EXE = MAGICC_EXE_PATH / "magicc-darwin-arm64" os.environ["DYLD_LIBRARY_PATH"] = "/opt/homebrew/opt/gfortran/lib/gcc/current/" elif platform.system() == "Linux": MAGICC_EXE = MAGICC_EXE_PATH / "magicc" elif platform.system() == "Windows": MAGICC_EXE = MAGICC_EXE_PATH / "magicc.exe"

With the set up done, we can initialise our SCM runner.

In [31]:

scm_runner = CMIP7ScenarioMIPSCMRunner.from_cmip7_scenariomip_config(
    # Generally, you want to run SCMs in parallel
    n_processes=multiprocessing.cpu_count(),
    magicc_exe_path=MAGICC_EXE,
    magicc_prob_distribution_path=MAGICC_CMIP7_SCENARIOMIP_PROBABILISTIC_CONFIG_FILE,
    historical_emissions_path=CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE,
    output_variables=("Surface Air Temperature Change", "Effective Radiative Forcing"),
    batch_size_scenarios=15,
)

scm_runner = CMIP7ScenarioMIPSCMRunner.from_cmip7_scenariomip_config( # Generally, you want to run SCMs in parallel n_processes=multiprocessing.cpu_count(), magicc_exe_path=MAGICC_EXE, magicc_prob_distribution_path=MAGICC_CMIP7_SCENARIOMIP_PROBABILISTIC_CONFIG_FILE, historical_emissions_path=CMIP7_SCENARIOMIP_GLOBAL_HISTORICAL_EMISSIONS_FILE, output_variables=("Surface Air Temperature Change", "Effective Radiative Forcing"), batch_size_scenarios=15, )

If you're reading this on RtD, note that we run a greatly reduced number of ensemble members. You will likely want to skip this step if running yourself.

In [32]:

if os.environ.get("READTHEDOCS", False):
    scm_runner.climate_models_cfgs["MAGICC7"] = scm_runner.climate_models_cfgs[
        "MAGICC7"
    ][:10]

if os.environ.get("READTHEDOCS", False): scm_runner.climate_models_cfgs["MAGICC7"] = scm_runner.climate_models_cfgs[ "MAGICC7" ][:10]

And then run

In [33]:

scm_results = scm_runner(complete)
scm_results

scm_results = scm_runner(complete) scm_results

Climate models:   0%|          | 0/1 [00:00<?, ?it/s]

Scenario batch:   0%|          | 0/1 [00:00<?, ?it/s]

Climate models:   0%|          | 0.00/1.00 [00:00<?, ?it/s]

[WARNING] 09:41:29 - openscm_runner.adapters.magicc7.magicc7: Historical data has not been checked

/home/docs/checkouts/readthedocs.org/user_builds/gcages/envs/latest/lib/python3.11/site-packages/scmdata/run.py:2632: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
  pd.concat([ret._meta.to_frame(), *to_join_metas]).astype("category")

Writing SCEN7 files:   0%|          | 0.00/1.00 [00:00<?, ?it/s]

Writing SCEN7 files: 100%|██████████| 1.00/1.00 [00:00<00:00, 3.38it/s]

Front serial:   0%|          | 0.00/2.00 [00:00<?, ?it/s]

Front serial: 100%|██████████| 2.00/2.00 [00:01<00:00, 1.54it/s]

Front parallel:   0%|          | 0.00/2.00 [00:00<?, ?it/s]

Front parallel: 100%|██████████| 2.00/2.00 [00:01<00:00, 1.71it/s]

Parallel runs:   0%|          | 0.00/6.00 [00:00<?, ?it/s]

Parallel runs: 100%|██████████| 6.00/6.00 [00:03<00:00, 1.75it/s]

Climate models: 100%|██████████| 1.00/1.00 [00:09<00:00, 9.09s/it]

Climate models: 100%|██████████| 1.00/1.00 [00:09<00:00, 9.09s/it]

Scenario batch: 100%|██████████| 1/1 [00:09<00:00,  9.11s/it]

Scenario batch: 100%|██████████| 1/1 [00:09<00:00,  9.11s/it]

Climate models: 100%|██████████| 1/1 [00:09<00:00,  9.12s/it]

Climate models: 100%|██████████| 1/1 [00:09<00:00,  9.12s/it]

Out[33]:

						time	1750	1751	1752	1753	1754	1755	1756	1757	1758	1759	...	2091	2092	2093	2094	2095	2096	2097	2098	2099	2100
climate_model	model	region	run_id	scenario	unit	variable
MAGICCv7.6.0a3	model_1	World	0	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.024862	0.118897	0.116943	0.101121	0.073664	0.010479	-0.012348	0.037891	0.084870	0.115642	...	3.083843	3.075493	3.054449	3.029675	3.006603	2.982615	2.963324	2.944188	2.930397	2.921243
					K	Surface Air Temperature Change	0.000000	0.025523	0.035245	0.038705	0.037403	0.035409	0.028824	0.013995	0.019164	0.031390	...	2.111727	2.108801	2.104123	2.097292	2.089272	2.080448	2.071740	2.063266	2.055657	2.049453
			1	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.040476	0.188135	0.175385	0.153909	0.121071	0.013471	-0.034662	0.058462	0.133571	0.179039	...	3.094397	3.093155	3.071267	3.043145	3.017493	2.990062	2.969977	2.949997	2.938670	2.934664
					K	Surface Air Temperature Change	0.000000	0.031040	0.042205	0.048116	0.050462	0.050914	0.043648	0.027046	0.034479	0.048696	...	3.503449	3.507925	3.510118	3.508996	3.505763	3.500927	3.495672	3.490288	3.485688	3.482789
			2	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.045260	0.197474	0.192773	0.165397	0.121218	0.011162	-0.031142	0.057258	0.136335	0.189962	...	3.189095	3.186040	3.159951	3.126917	3.096682	3.064538	3.040696	3.017066	3.003200	2.997172
					K	Surface Air Temperature Change	0.000000	0.030692	0.040997	0.045351	0.045125	0.044089	0.036419	0.019364	0.027058	0.041304	...	2.255736	2.254983	2.251817	2.245242	2.236770	2.227074	2.217516	2.208366	2.200556	2.194913
			3	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.075002	0.176557	0.155117	0.115360	0.065439	-0.030846	-0.065528	0.012842	0.099842	0.167591	...	3.024171	3.030908	2.999572	2.956839	2.918830	2.877727	2.850460	2.823628	2.813078	2.815474
					K	Surface Air Temperature Change	0.000000	0.031590	0.038679	0.038038	0.032724	0.026720	0.016755	0.001728	0.011589	0.029319	...	1.669894	1.670524	1.667166	1.658750	1.648003	1.635818	1.624364	1.613752	1.605480	1.600646
			4	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.064249	0.152303	0.142644	0.102969	0.045903	-0.022696	-0.045883	0.007949	0.082311	0.142061	...	2.920006	2.922528	2.892401	2.852596	2.816974	2.778781	2.752477	2.726529	2.714500	2.714121
					K	Surface Air Temperature Change	0.000000	0.027147	0.035592	0.036302	0.030512	0.026315	0.019135	0.006278	0.013671	0.028335	...	2.139012	2.139391	2.136262	2.128616	2.118696	2.107241	2.096132	2.085539	2.076827	2.071158
			5	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.063361	0.161519	0.136322	0.105250	0.070650	-0.020938	-0.059445	0.019417	0.102558	0.157535	...	2.930197	2.932259	2.902117	2.862398	2.826766	2.788597	2.762183	2.736166	2.723925	2.722748
					K	Surface Air Temperature Change	0.000000	0.027591	0.032539	0.031000	0.026948	0.021737	0.012411	-0.001149	0.008632	0.024716	...	1.291494	1.291044	1.287469	1.280211	1.271488	1.261900	1.253056	1.244913	1.238576	1.234806
			6	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.028969	0.142720	0.143538	0.124446	0.090351	0.014523	-0.014325	0.047448	0.102313	0.140134	...	3.147702	3.136302	3.110177	3.079644	3.050937	3.021023	2.996444	2.972010	2.953826	2.940899
					K	Surface Air Temperature Change	0.000000	0.023076	0.031425	0.034695	0.033929	0.033021	0.027644	0.013909	0.019043	0.029946	...	1.723372	1.719082	1.713451	1.706168	1.698065	1.689363	1.680731	1.672209	1.664298	1.657414
			7	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.061555	0.134827	0.123996	0.087731	0.038717	-0.028246	-0.044861	0.004328	0.070991	0.127741	...	3.723759	3.729312	3.703411	3.668161	3.636964	3.603372	3.581207	3.559355	3.550740	3.552999
					K	Surface Air Temperature Change	0.000000	0.027917	0.037654	0.038351	0.031970	0.024944	0.016686	0.002665	0.008787	0.025196	...	2.570896	2.573917	2.573343	2.567761	2.559461	2.549407	2.539579	2.530336	2.523045	2.518969
			8	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.027534	0.145660	0.143361	0.126693	0.097244	0.016528	-0.014465	0.051436	0.107386	0.143041	...	3.237184	3.227599	3.203926	3.176016	3.149821	3.122462	3.100197	3.078061	3.061840	3.050692
					K	Surface Air Temperature Change	0.000000	0.021493	0.027976	0.030634	0.030421	0.029793	0.024080	0.012661	0.018823	0.028366	...	1.754271	1.751256	1.746683	1.740487	1.733516	1.725890	1.718351	1.710839	1.703944	1.698049
			9	SSP1 - Low Emissions	W/m^2	Effective Radiative Forcing	0.089170	0.247113	0.224403	0.175709	0.109414	-0.024954	-0.078921	0.031023	0.144344	0.230172	...	3.191125	3.205830	3.175017	3.130727	3.092397	3.050749	3.025845	3.001593	2.996725	3.006899
					K	Surface Air Temperature Change	0.000000	0.038188	0.048259	0.050171	0.046314	0.041752	0.030263	0.011439	0.022490	0.041930	...	2.117250	2.121848	2.122067	2.116812	2.108982	2.099439	2.090574	2.082487	2.076912	2.075073

20 rows × 351 columns

With these outputs, we can look at raw (i.e. before pre-processing) variables.

In [34]:

scm_results.loc[
    pix.isin(variable=["Effective Radiative Forcing"])
].openscm.plot_plume_after_calculating_quantiles(
    style_var="variable",
    quantile_over="run_id",
    quantiles_plumes=(
        (0.5, 0.8),
        ((0.05, 0.95), 0.3),
    ),
)

scm_results.loc[ pix.isin(variable=["Effective Radiative Forcing"]) ].openscm.plot_plume_after_calculating_quantiles( style_var="variable", quantile_over="run_id", quantiles_plumes=( (0.5, 0.8), ((0.05, 0.95), 0.3), ), )

Out[34]:

<Axes: xlabel='time', ylabel='W/m^2'>

Post-processing¶

The last step is post-processing. This handles calculation of key pieces of metadata.

In [35]:

post_processor = CMIP7ScenarioMIPPostProcessor.from_cmip7_scenariomip_config()
post_processed_results = post_processor(
    scm_results.loc[pix.isin(variable=["Surface Air Temperature Change"])]
)

post_processor = CMIP7ScenarioMIPPostProcessor.from_cmip7_scenariomip_config() post_processed_results = post_processor( scm_results.loc[pix.isin(variable=["Surface Air Temperature Change"])] )

For example, the scenario category.

In [36]:

post_processed_results.metadata_categories.unstack("metric")

post_processed_results.metadata_categories.unstack("metric")

Out[36]:

		metric	category	category_name
climate_model	model	scenario
MAGICCv7.6.0a3	model_1	SSP1 - Low Emissions	C5	C5: limit warming to 2.5°C (>50%)

Exceedance thresholds.

In [37]:

post_processed_results.metadata_exceedance_probabilities.unstack("threshold")

post_processed_results.metadata_exceedance_probabilities.unstack("threshold")

Out[37]:

						threshold	0.50	1.00	4.01
climate_model	model	region	scenario	unit	variable	threshold_unit
MAGICCv7.6.0a3	model_1	World	SSP1 - Low Emissions	%	Exceedance probability	K	100.0	100.0	0.0

Key warming metrics.

In [38]:

post_processed_results.metadata_quantile.loc[
    pix.isin(quantile=[0.05, 0.5, 0.95])
].unstack(["quantile", "metric"]).round(2).sort_index(axis="columns")

post_processed_results.metadata_quantile.loc[ pix.isin(quantile=[0.05, 0.5, 0.95]) ].unstack(["quantile", "metric"]).round(2).sort_index(axis="columns")

Out[38]:

					quantile	0.05			0.50			0.95
					metric	2100	max	max_year	2100	max	max_year	2100	max	max_year
climate_model	model	region	scenario	unit	variable
MAGICCv7.6.0a3	model_1	World	SSP1 - Low Emissions	K	Surface Temperature (GSAT)	1.26	1.45	NaN	1.93	2.03	NaN	2.96	3.01	NaN
				yr	Surface Temperature (GSAT)	NaN	NaN	2053.5	NaN	NaN	2070.0	NaN	NaN	2088.05

Assessed surface temperatures.

In [39]:

post_processed_results.timeseries_quantile.loc[:, 2000:].openscm.plot_plume(
    style_var="variable",
    quantiles_plumes=(
        (0.5, 0.8),
        ((0.05, 0.95), 0.3),
    ),
)

post_processed_results.timeseries_quantile.loc[:, 2000:].openscm.plot_plume( style_var="variable", quantiles_plumes=( (0.5, 0.8), ((0.05, 0.95), 0.3), ), )

Out[39]:

<Axes: xlabel='time', ylabel='K'>