Produce aerosol ancillaries for the ACCESS-ESM1.6 `ScenarioMIP` h and vl experiments

[penguian]

See https://input4mips-cvs.readthedocs.io/en/latest/dataset-overviews/anthropogenic-slcf-co2-emissions/ https://input4mips-cvs.readthedocs.io/en/latest/dataset-overviews/open-biomass-burning-emissions/ and #53.

Unlike #53, the datasets used for ScenarioMIP aerosols are under /g/data/qv56/replicas/input4MIPs/CMIP7/ScenarioMIP/IIASA-IAMC/ at
IIASA-IAMC-h-1-1-1 and [IIASA-IAMC-vl-1-1-1. These datasets have a structure that is different from the datasets used for the historical experiment, so there needs to be some new Python code.

[penguian]

Example dataset:

OC openburning emissions / (kg s-1 m-2) (time: 228; sector: 4; latitude: 360; longitude: 720)
    Dimension coordinates:
        time                                 x            -            -               -
        sector                               -            x            -               -
        latitude                             -            -            x               -
        longitude                            -            -            -               x
    Cell methods:
        0                               time: mean
    Attributes:
        Conventions                     'CF-1.8'
        activity_id                     'input4MIPs'
        comment                         'Gridded emissions produced after harmonization and downscaling as part ...'
        contact                         'kikstra@iiasa.ac.at, hoegner@iiasa.ac.at, zecchetto@iiasa.ac.at'
        creation_date                   '2026-03-26T17:19:37Z'
        data_structure                  'grid'
        dataset_category                'emissions'
        doi                             '10.5281/zenodo.17981825'
        end_date                        '210012'
        external_variables              'gridcell_area'
        frequency                       'mon'
        further_info_url                'https://doi.org/10.5281/zenodo.17981825'
        global_total_emissions_2022     '20.25 Tg OC/year'
        global_total_emissions_2100     '13.6 Tg OC/year'
        grid                            '0.5x0.5 degree latitude x longitude'
        grid_label                      'gn'
        institution                     'International Institute for Applied Systems Analysis - Integrated Assessment ...'
        institution_id                  'IIASA-IAMC'
        license                         'Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses). ...'
        mip_era                         'CMIP7'
        nominal_resolution              '50 km'
        product                         'derived'
        realm                           'atmos'
        references                      'See: https://github.com/IAMconsortium/concordia and https://github.com/iiasa/emissions_harmonization_historical ...'
        reporting_unit                  'Mass flux of OC'
        source                          'Scenarios generated as part of the ScenarioMIP-CMIP7 project, see http ...'
        source_id                       'IIASA-IAMC-vl-1-1-1'
        source_version                  '1.1.1'
        start_date                      '202201'
        table_id                        'input4MIPs'
        target_mip                      'ScenarioMIP'
        title                           'Future openburning emissions of OC in vl'
        tracking_id                     'hdl:21.14100/027a0ad7-a285-474e-8038-749d99d5831e'
        variable_id                     'OC_em_openburning'

where the sector dimension coordinate contains:

{'_metadata_manager': MetadataManager(
        standard_name=None,
        long_name='sector',
        var_name='sector',
        units=Unit('unknown'),
        attributes={'ids': '0: Agricultural Waste Burning; 1: Forest Burning; 2: Grassland Burning; 3: Peat Burning'},
        coord_system=None,
        climatological=False,
        circular=False),
 '_mesh_timestamps': [],
 '_values_dm': DataManager(array([0., 1., 2., 3.])),
 '_bounds_dm': DataManager(array([[-0.5,  0.5],
        [ 0.5,  1.5],
        [ 1.5,  2.5],
        [ 2.5,  3.5]])),
 '_ignore_axis': False}

[penguian]

Google AI has this to say about the mapping between the DRES-CMIP-BB4CMIP7-2-0 and the ScenarioMIP IIASA-IAMC sector classification:

The Python dictionary-like string attributes={'ids': '0: Agricultural Waste Burning; 1: Forest Burning; 2: Grassland Burning; 3: Peat Burning'} defines the integer-to-label mapping for the sector coordinate variable within the NetCDF header metadata of harmonized CMIP6/CMIP7 biomass burning datasets.
In the context of DRES-CMIP-BB4CMIP7-2-0 (which uses 6 granular sectors) and IIASA-IAMC (which uses 4 broad categories), this attribute string facilitates the aggregation of granular fire data into the simplified format required by Integrated Assessment Models (IAMs).

1. The Exact Technical Relationship

This string is a standard CF-compliant metadata attribute found in the header of the emission NetCDF files (specifically for the sector variable). It tells the software reading the file how to interpret the integer index of the sector dimension:

• Index 0 $\rightarrow$ Agricultural Waste Burning
• Index 1 $\rightarrow$ Forest Burning
• Index 2 $\rightarrow$ Grassland Burning
• Index 3 $\rightarrow$ Peat Burning ...

2. Sector Mapping Crosswalk

This 4-sector schema represents a simplified aggregation of the 6 more granular sectors found in the raw DRES-CMIP-BB4CMIP7-2-0 (and GFED) data.

ID (NetCDF) [2]	Broad Sector Label (IIASA-IAMC)	Maps to DRES-CMIP-BB4CMIP7-2-0 Sector Codes
0	Agricultural Waste Burning	AGRI (Agricultural waste burning)
1	Forest Burning	DEFO (Tropical deforestation/degradation) TEMF (Temperate forest fires) BORF (Boreal forest fires)
2	Grassland Burning	SAVA (Savanna, grassland, and shrubland fires)
3	Peat Burning	PEAT (Peat fires)

Summary for Data Users

• If you are using the raw DRES-CMIP-BB4CMIP7-2-0 files: You will see variables broken down by the 6 codes (AGRI, DEFO, TEMF, BORF, SAVA, PEAT).
• If you are using the Harmonized Emissions (IAMC-formatted) files: The data has been re-gridded and summed along the "Forest" dimension, resulting in the 4-ID structure defined by the attribute string you found.

[...]

[penguian]

The ncdump output for a sample biomass burning NetCDF file:

$ ncdump -h /g/data/qv56/replicas/input4MIPs/CMIP7/ScenarioMIP/IIASA-IAMC/IIASA-IAMC-vl-1-1-1/atmos/mon/SO2_em_openburning/gn/v20260409/SO2-em-openburning_input4MIPs_emissions_ScenarioMIP_IIASA-IAMC-vl-1-1-1_gn_202201-210012.nc 
netcdf SO2-em-openburning_input4MIPs_emissions_ScenarioMIP_IIASA-IAMC-vl-1-1-1_gn_202201-210012 {
dimensions:
        time = 228 ;
        sector = 4 ;
        lat = 360 ;
        lon = 720 ;
        bound = 2 ;
variables:
        double SO2_em_openburning(time, sector, lat, lon) ;
                SO2_em_openburning:_FillValue = NaN ;
                SO2_em_openburning:units = "kg s-1 m-2" ;
                SO2_em_openburning:cell_methods = "time: mean" ;
                SO2_em_openburning:long_name = "SO2 openburning emissions" ;
        double time_bnds(time, bound) ;
        double sector_bnds(sector, bound) ;
        double lat_bnds(lat, bound) ;
        double lon_bnds(lon, bound) ;
        double lat(lat) ;
                lat:_FillValue = NaN ;
                lat:bounds = "lat_bnds" ;
                lat:units = "degrees_north" ;
                lat:long_name = "latitude" ;
                lat:standard_name = "latitude" ;
                lat:axis = "Y" ;
                lat:realtopology = "linear" ;
        double lon(lon) ;
                lon:_FillValue = NaN ;
                lon:bounds = "lon_bnds" ;
                lon:long_name = "longitude" ;
                lon:standard_name = "longitude" ;
                lon:units = "degrees_east" ;
                lon:axis = "X" ;
                lon:modulo = 360. ;
                lon:realtopology = "circular" ;
                lon:topology = "circular" ;
        double time(time) ;
                time:_FillValue = NaN ;
                time:bounds = "time_bnds" ;
                time:long_name = "time" ;
                time:axis = "T" ;
                time:realtopology = "linear" ;
                time:standard_name = "time" ;
                time:units = "days since 2022-01-01" ;
                time:calendar = "365_day" ;
        double sector(sector) ;
                sector:_FillValue = NaN ;
                sector:long_name = "sector" ;
                sector:bounds = "sector_bnds" ;
                sector:ids = "0: Agricultural Waste Burning; 1: Forest Burning; 2: Grassland Burning; 3: Peat Burning" ;

// global attributes:
                :Conventions = "CF-1.8" ;
                :activity_id = "input4MIPs" ;
                :comment = "Gridded emissions produced after harmonization and downscaling as part of the ScenarioMIP-CMIP7. See https://github.com/iiasa/emissions_harmonization_historical, https://github.com/IAMconsortium/concordia, and https://github.com/iiasa/aneris for documentation on the processes." ;
                :contact = "kikstra@iiasa.ac.at, hoegner@iiasa.ac.at, zecchetto@iiasa.ac.at" ;
                :data_structure = "grid" ;
                :dataset_category = "emissions" ;
                :external_variables = "gridcell_area" ;
                :frequency = "mon" ;
                :grid = "0.5x0.5 degree latitude x longitude" ;
                :grid_label = "gn" ;
                :license = "Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law." ;
                :institution = "International Institute for Applied Systems Analysis - Integrated Assessment Modeling Consortium" ;
                :institution_id = "IIASA-IAMC" ;
                :mip_era = "CMIP7" ;
                :nominal_resolution = "50 km" ;
                :realm = "atmos" ;
                :references = "See: https://github.com/IAMconsortium/concordia and https://github.com/iiasa/emissions_harmonization_historical for references" ;
                :source = "Scenarios generated as part of the ScenarioMIP-CMIP7 project, see https://wcrp-cmip.org/mips/scenariomip/" ;
                :further_info_url = "https://doi.org/10.5281/zenodo.17981825" ;
                :doi = "10.5281/zenodo.17981825" ;
                :table_id = "input4MIPs" ;
                :target_mip = "ScenarioMIP" ;
                :product = "derived" ;
                :start_date = "202201" ;
                :end_date = "210012" ;
                :creation_date = "2026-03-26T17:20:11Z" ;
                :tracking_id = "hdl:21.14100/f5fef888-74ff-49af-9b54-8e433b30165f" ;
                :source_version = "1.1.1" ;
                :source_id = "IIASA-IAMC-vl-1-1-1" ;
                :variable_id = "SO2_em_openburning" ;
                :title = "Future openburning emissions of SO2 in vl" ;
                :reporting_unit = "Mass flux of SO2" ;
                :global_total_emissions_2022 = "2.7 Tg SO2/year" ;
                :global_total_emissions_2100 = "1.87 Tg SO2/year" ;
}

[penguian]

The ncdump output for a sample anthropogenic emissions NetCDF file:

$ ncdump -h /g/data/qv56/replicas/input4MIPs/CMIP7/ScenarioMIP/IIASA-IAMC/IIASA-IAMC-vl-1-1-1/atmos/mon/SO2_em_anthro/gn/v20260409/SO2-em-anthro_input4MIPs_emissions_ScenarioMIP_IIASA-IAMC-vl-1-1-1_gn_202201-210012.nc 
netcdf SO2-em-anthro_input4MIPs_emissions_ScenarioMIP_IIASA-IAMC-vl-1-1-1_gn_202201-210012 {
dimensions:
        time = 228 ;
        sector = 8 ;
        lat = 360 ;
        lon = 720 ;
        bound = 2 ;
variables:
        double SO2_em_anthro(time, sector, lat, lon) ;
                SO2_em_anthro:_FillValue = NaN ;
                SO2_em_anthro:units = "kg s-1 m-2" ;
                SO2_em_anthro:cell_methods = "time: mean" ;
                SO2_em_anthro:long_name = "SO2 anthropogenic emissions" ;
        double time_bnds(time, bound) ;
        double sector_bnds(sector, bound) ;
        double lat_bnds(lat, bound) ;
        double lon_bnds(lon, bound) ;
        double lat(lat) ;
                lat:_FillValue = NaN ;
                lat:units = "degrees_north" ;
                lat:long_name = "latitude" ;
                lat:axis = "Y" ;
                lat:bounds = "lat_bnds" ;
                lat:realtopology = "linear" ;
                lat:standard_name = "latitude" ;
        double lon(lon) ;
                lon:_FillValue = NaN ;
                lon:units = "degrees_east" ;
                lon:long_name = "longitude" ;
                lon:axis = "X" ;
                lon:bounds = "lon_bnds" ;
                lon:modulo = 360. ;
                lon:realtopology = "circular" ;
                lon:standard_name = "longitude" ;
                lon:topology = "circular" ;
        double time(time) ;
                time:_FillValue = NaN ;
                time:bounds = "time_bnds" ;
                time:long_name = "time" ;
                time:axis = "T" ;
                time:realtopology = "linear" ;
                time:standard_name = "time" ;
                time:units = "days since 2022-01-01" ;
                time:calendar = "365_day" ;
        double sector(sector) ;
                sector:_FillValue = NaN ;
                sector:long_name = "sector" ;
                sector:bounds = "sector_bnds" ;
                sector:ids = "0: Agriculture; 1: Energy; 2: Industrial; 3: Transportation; 4: Residential, Commercial, Other; 5: Solvents Production and Application; 6: Waste; 7: International Shipping" ;

// global attributes:
                :Conventions = "CF-1.8" ;
                :activity_id = "input4MIPs" ;
                :comment = "Gridded emissions produced after harmonization and downscaling as part of the ScenarioMIP-CMIP7. See https://github.com/iiasa/emissions_harmonization_historical, https://github.com/IAMconsortium/concordia, and https://github.com/iiasa/aneris for documentation on the processes." ;
                :contact = "kikstra@iiasa.ac.at, hoegner@iiasa.ac.at, zecchetto@iiasa.ac.at" ;
                :data_structure = "grid" ;
                :dataset_category = "emissions" ;
                :external_variables = "gridcell_area" ;
                :frequency = "mon" ;
                :grid = "0.5x0.5 degree latitude x longitude" ;
                :grid_label = "gn" ;
                :license = "Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law." ;
                :institution = "International Institute for Applied Systems Analysis - Integrated Assessment Modeling Consortium" ;
                :institution_id = "IIASA-IAMC" ;
                :mip_era = "CMIP7" ;
                :nominal_resolution = "50 km" ;
                :realm = "atmos" ;
                :references = "See: https://github.com/IAMconsortium/concordia and https://github.com/iiasa/emissions_harmonization_historical for references" ;
                :source = "Scenarios generated as part of the ScenarioMIP-CMIP7 project, see https://wcrp-cmip.org/mips/scenariomip/" ;
                :further_info_url = "https://doi.org/10.5281/zenodo.17981825" ;
                :doi = "10.5281/zenodo.17981825" ;
                :table_id = "input4MIPs" ;
                :target_mip = "ScenarioMIP" ;
                :product = "derived" ;
                :start_date = "202201" ;
                :end_date = "210012" ;
                :creation_date = "2026-03-26T17:12:39Z" ;
                :tracking_id = "hdl:21.14100/ec43deaa-fddc-469d-a25b-d5f9c58eb398" ;
                :source_version = "1.1.1" ;
                :source_id = "IIASA-IAMC-vl-1-1-1" ;
                :variable_id = "SO2_em_anthro" ;
                :title = "Future anthropogenic emissions of SO2 in vl" ;
                :reporting_unit = "Mass flux of SO2" ;
                :standard_name = "cell_area" ;
                :cell_methods = "time: mean" ;
                :long_name = "SO2 anthropogenic emissions" ;
                :history = "18-04-2025 03:28:52 AM UTC; College Park, MD, 20742" ;
                :license_id = "CC BY 4.0" ;
                :global_total_emission_2000 = "107.19 Tg/year" ;
                :global_total_emission_2023 = "68.76 Tg/year" ;
                :data_usage_tips = "Note that these are monthly average fluxes." ;
                :datetime_start = "200001" ;
                :datetime_end = "202312" ;
                :time_range = "200001-202312" ;
                :global_total_emissions_2022 = "68.56 Tg SO2/year" ;
                :global_total_emissions_2100 = "1.5 Tg SO2/year" ;
}

[penguian]

Here is an implementation plan that I used Google Antigravity to ask Gemini 3.5 Flash (Medium) to create:

Implementation Plan: ScenarioMIP Aerosol Forcing Generation

This plan outlines the generation of cmip7_SM*.py scripts and related code refactoring to support ScenarioMIP (future scenario) aerosol emissions generation in the CMIP7-Input repository. This includes migrating the processing pipeline to handle the structured differences of the IIASA-IAMC datasets.

Goal

Add support for generating future aerosol forcing ancillaries (2022–2100) for the h (high emission) and vl (very low emission) ScenarioMIP experiments. This is done by implementing ScenarioMIP-specific (_SM_) scripts for BC, OC, SO2, and Biomass Burning (Bio) using the datasets located under /g/data/qv56/replicas/input4MIPs/CMIP7/ScenarioMIP/IIASA-IAMC/ (source IDs IIASA-IAMC-h-1-1-1 and IIASA-IAMC-vl-1-1-1 respectively), aligning them with the established _SM_ patterns in the repository.

---

User Review Required

Important

Biomass Burning Structural Shift
Unlike the historical (DRES) biomass burning dataset which uses separate 3D percentage files, the IIASA-IAMC ScenarioMIP dataset consolidates all emissions into a single 4D file (time, sector, lat, lon) containing absolute mass flux values (kg s-1 m-2).

We will refactor the core cmip7_aerosol_biomass.py module to:

• Directly index the 4-sector dimension of IIASA-IAMC files when processing ScenarioMIP data.
• Extract low-level emissions by summing sector indices 0 (Agricultural Waste), 2 (Grassland), and 3 (Peat).
• Extract high-level emissions by slicing sector index 1 (Forest).
• Bypass percentage multiplication and division by 100 since the variables contain absolute fluxes.

Note

Anthropogenic Emissions Alignment
The IIASA-IAMC anthropogenic dataset (e.g. SO2_em_anthro) has been verified to use the exact same 8 sectors as the historical PNNL-JGCRI (CEDS) dataset:
0: Agriculture; 1: Energy; 2: Industrial; 3: Transportation; 4: Residential, Commercial, Other; 5: Solvents Production and Application; 6: Waste; 7: International Shipping

Therefore, no custom sector mapping or slicing logic is required for future anthropogenic emissions. The existing .collapsed(["sector"], iris.analysis.SUM) pipeline is fully compatible and will work without modifications.

Tip

Concordia & common-definitions Validation
GitHub discussions in IAMconsortium/common-definitions#151 and IAMconsortium/concordia#63 confirm the transition to the Concordia tool for CMIP7 emissions harmonization and downscaling (superseding the CMIP6 aneris / emissions_downscaling pipeline).
These issues validate that:

• Concordia merges CEDS and GFED definitions into a unified format (CEDS+ / 9+ Sectors).
• The open biomass burning sectors are officially defined as the 4 categories: Agricultural Waste Burning, Forest Burning, Grassland Burning, and Peat Burning (matching the IIASA-IAMC NetCDF structure).
• The harmonization base year is set to 2023, defining the transition boundary between the historical DRES/CEDS data and the ScenarioMIP projections.

---

Proposed Changes

We will modify existing shared aerosol utilities to dynamically support both CMIP (historical) and ScenarioMIP (future) activity configurations, and then create the new _SM_ generation scripts.

1. Shared Utilities Refactoring

[MODIFY] cmip7_aerosol_anthro.py

• Update cmip7_aerosol_anthro_rootpath to return the path under IIASA-IAMC instead of PNNL-JGCRI when the activity is ScenarioMIP.
• Update filepath builders (cmip7_aerosol_anthro_filepath and cmip7_aerosol_air_anthro_filepath) to format the filename with the ScenarioMIP activity tag and the dash-separated variable name (e.g., SO2-em-anthro instead of SO2_em_anthro in the filename).

[MODIFY] cmip7_aerosol_biomass.py

• Add a loader that reads the 4D IIASA-IAMC NetCDF files, mapping activity to ScenarioMIP and institution to IIASA-IAMC.
• Implement a new low/high height split function specifically for IIASA-IAMC datasets that directly slices/sums the sector dimensions:
  • low = sector(0) + sector(2) + sector(3)
  • high = sector(1)
• Integrate this into the save_cmip7_aerosol_biomass pipeline when operating under ScenarioMIP mode.

---

2. New ScenarioMIP Aerosol Scripts

[NEW] cmip7_SM_aerosol.py

• Define the ScenarioMIP temporal boundaries:
  • CMIP7_SM_AEROSOL_BEG_YEAR = CMIP7_SM_BEG_YEAR - 1 (2021)
  • CMIP7_SM_AEROSOL_END_YEAR = CMIP7_SM_END_YEAR + 1 (2101)
• Define the output directory helper:
  • Path(args.ancil_target_dirname) / "scenarios" / args.scenario / "atmosphere" / "aerosol"

[NEW] cmip7_SM_aerosol_anthro.py

• Implement ScenarioMIP-specific loaders for anthropogenic emissions and aircraft (air) anthropogenic emissions using the ScenarioMIP time boundaries and IIASA-IAMC root paths.

[NEW] cmip7_SM_BC_interpolate.py

• Create ScenarioMIP entry point for BC (Black Carbon) interpolation.

[NEW] cmip7_SM_OC_interpolate.py

• Create ScenarioMIP entry point for OC (Organic Carbon) interpolation.

[NEW] cmip7_SM_SO2_interpolate.py

• Create ScenarioMIP entry point for SO2 interpolation.

[NEW] cmip7_SM_Bio_interpolate.py

• Create ScenarioMIP entry point for biomass burning (Bio) emissions interpolation, invoking the refactored IIASA-IAMC loader.

---

Verification Plan

Automated Verification

• Execute syntax and import checks on all newly added scripts.
• Run dry runs of the scripts with sample files/metadata to confirm that filenames are built correctly, coordinate systems are set, and dimensions are manipulated without raising ValueError or axis errors.

References

• Produce aerosol ancillaries for the ACCESS-ESM1.6 ScenarioMIP h and vl experiments (Issue #134)
• IAMC-to-CMIP7 emissions mapping and harmonization (Issue #151)
• Variable list or Example template for CMIP7/ScenarioMIP harmonization (Issue #63)
• input4MIPs Open Biomass Burning Emissions readthedocs

[penguian]

I will not follow the implementation plan exactly, but will use it as a general guide. I asked Gemini 3.5 Flash (medium) via Google Antigravity for a code snippet to replace split_frac_low_high and it replied with the following:

Viewed cmip7_aerosol_biomass.py:66-140

In ScenarioMIP, the equivalent to the bulk-percentage partition (split_frac_low_high) is done by directly slicing and summing the 4D (time, sector, lat, lon) emission cubes of BC and OC along the sector dimension.

Since the variables contain absolute mass flux values rather than percentages, we do not load separate percentage files. Instead, the logic maps the 4 sectors directly to the two injection heights:

• Low injection height: Sum of Sector 0 (Agricultural Waste), Sector 2 (Grassland), and Sector 3 (Peat).
• High injection height: Sector 1 (Forest).

Here is the proposed Python implementation snippet to be integrated into cmip7_aerosol_biomass.py:

def split_scenario_low_high(bc_cube, oc_cube):
    """
    Slices and sums the 4D ScenarioMIP BC and OC emission cubes to partition
    low-level and high-level injection height emissions.
    
    Dimensions of input cubes: (time, sector, lat, lon)
    Sectors: 0=Agricultural Waste, 1=Forest, 2=Grassland, 3=Peat
    """
    # Sum low-level sectors: index 0 (Agri), 2 (Grassland), 3 (Peat)
    bc_low = bc_cube[:, 0] + bc_cube[:, 2] + bc_cube[:, 3]
    oc_low = oc_cube[:, 0] + oc_cube[:, 2] + oc_cube[:, 3]
    
    # Extract high-level sector: index 1 (Forest)
    bc_high = bc_cube[:, 1]
    oc_high = oc_cube[:, 1]
    
    # Remove the scalar sector coordinate from the sliced cubes to prevent
    # coordinate mismatch warnings when summing or saving
    for cube in (bc_low, oc_low, bc_high, oc_high):
        if cube.coords("sector"):
            cube.remove_coord("sector")
            
    # Combine the species into bulk low and high emission cubes
    low = bc_low + oc_low
    high = bc_high + oc_high
    
    return low, high

This functions as a drop-in replacement for the historical combination pipeline in save_cmip7_aerosol_biomass when processing ScenarioMIP data.

[penguian]

The anthropogenic sectors for ScenarioMIP match the sectors for CMIP:

$ ncdump -h /g/data/qv56/replicas/input4MIPs/CMIP7/CMIP/PNNL-JGCRI/CEDS-CMIP-2025-04-18/atmos/mon/SO2_em_anthro/gn/v20250421/SO2-em-anthro_input4MIPs_emissions_CMIP_CEDS-CMIP-2025-04-18_gn_175001-179912.nc
netcdf SO2-em-anthro_input4MIPs_emissions_CMIP_CEDS-CMIP-2025-04-18_gn_175001-179912 {
dimensions:
        lon = 720 ;
        lat = 360 ;
        sector = 8 ;
        time = UNLIMITED ; // (600 currently)
        bound = 2 ;
variables:
        double lon(lon) ;
                lon:units = "degrees_east" ;
                lon:long_name = "longitude" ;
                lon:axis = "X" ;
                lon:bounds = "lon_bnds" ;
                lon:modulo = 360. ;
                lon:realtopology = "circular" ;
                lon:standard_name = "longitude" ;
                lon:topology = "circular" ;
        double lat(lat) ;
                lat:units = "degrees_north" ;
                lat:long_name = "latitude" ;
                lat:axis = "Y" ;
                lat:bounds = "lat_bnds" ;
                lat:realtopology = "linear" ;
                lat:standard_name = "latitude" ;
        int sector(sector) ;
                sector:long_name = "sector" ;
                sector:bounds = "sector_bnds" ;
                sector:ids = "0: Agriculture; 1: Energy; 2: Industrial; 3: Transportation; 4: Residential, Commercial, Other; 5: Solvents production and application; 6: Waste; 7: International Shipping" ;
        double time(time) ;
                time:units = "days since 1750-01-01 0:0:0" ;
                time:long_name = "time" ;
                time:calendar = "365_day" ;
                time:axis = "T" ;
                time:bounds = "time_bnds" ;
                time:realtopology = "linear" ;
                time:standard_name = "time" ;
        float SO2_em_anthro(time, sector, lat, lon) ;
                SO2_em_anthro:units = "kg m-2 s-1" ;
                SO2_em_anthro:_FillValue = 1.e+20f ;
                SO2_em_anthro:long_name = "SO2 Anthropogenic Emissions" ;
                SO2_em_anthro:cell_methods = "time: mean" ;
                SO2_em_anthro:missing_value = 1.e+20f ;
        double lat_bnds(lat, bound) ;
        double lon_bnds(lon, bound) ;
        double time_bnds(time, bound) ;
        double sector_bnds(sector, bound) ;

// global attributes:
                :Conventions = "CF-1.6" ;
                :creation_date = "2025-04-03T03:32:38Z" ;
                :data_structure = "grid" ;
                :dataset_category = "emissions" ;
                :source_version = "2025-04-18" ;
                :external_variables = "gridcell_area" ;
                :frequency = "mon" ;
                :grid = "0.5x0.5 degree latitude x longitude" ;
                :grid_label = "gn" ;
                :nominal_resolution = "50 km" ;
                :history = "03-04-2025 03:32:38 AM UTC; College Park, MD, 20742" ;
                :mip_era = "CMIP7" ;
                :product = "primary-emissions-data" ;
                :realm = "atmos" ;
                :source_id = "CEDS-CMIP-2025-04-18" ;
                :target_mip = "CMIP" ;
                :variable_id = "SO2_em_anthro" ;
                :license = "The input4MIPs data linked to this entry is licensed under a Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/). Consult https://pcmdi.llnl.gov/CMIP6Plus/TermsOfUse for terms of use governing CMIP7 output, including citation requirements and proper acknowledgment. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law." ;
                :license_id = "CC BY 4.0" ;
                :global_total_emission_1750 = "0.38 Tg/year" ;
                :global_total_emission_1799 = "0.62 Tg/year" ;
                :data_usage_tips = "Note that these are monthly average fluxes." ;
                :reporting_unit = "Mass flux of SOx, reported as SO2" ;
                :datetime_start = "175001" ;
                :datetime_end = "179912" ;
                :time_range = "175001-179912" ;
                :tracking_id = "hdl:21.14100/4a8b9894-103c-11f0-afd1-18c04d79ed00" ;
                :activity_id = "input4MIPs" ;
                :comment = "Research data produced using the Community Emissions Data System (CEDS)" ;
                :contact = "Rachel Hoesly (rachel.hoesly@pnnl.gov); Steve Smith (ssmith@pnnl.gov)" ;
                :further_info_url = "https://www.pnnl.gov/projects/ceds" ;
                :institution = "Pacific Northwest National Laboratory - Joint Global Change Research Institute, College Park, MD 20740, USA" ;
                :institution_id = "PNNL-JGCRI" ;
                :references = "Hoesly et al., 2018 https://doi.org/10.5194/gmd-11-369-2018; Feng et al., 2020 https://doi.org/10.5194/gmd-13-461-2020" ;
                :title = "CEDS Annual Anthropogenic Emissions of SO2 prepared for input4MIPs" ;
                :source = "CEDS-2025-04-18: Community Emissions Data System (CEDS) for Historical Emissions" ;
                :doi = "10.5281/zenodo.15127477" ;
}

[penguian]

See #53. For the SO2 forcing for piControl and historical used yearly repeating DMS from the CMIP6 ancillaries. This will also be done for ScenarioMIP, with the dates adjusted.

[MartinDix]

The input4MIPS historical emissions go to the end of 2023. The scenario emissions have data for every 5th year from 2025 to 2100 and are supposed to be linearly interpolated between the end of historical emissions and 2025. This seems true for everything but biomass burning where there's an inconsistency.

ESM1.5 scenario runs require data for December 2021.

Timeseries and maps of the new ancillaries all look ok.

SO2

Black carbon

Organic carbon

Biomass burning

[MartinDix]

Extra checks on continuity between the historical and H ESM.6 files. Historical is plotted to Jan 2022 and H from Dec 2021 to account for the extra months used in the model time interpolation. The match is good for everything except the BB high emissions but I'm happy to accept that as of no practical significance.