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For each scenario, the code in CMIP7/esm1p6/atmosphere/co2/cmip7_ES_CO2_interpolate.py reads both the ScenarioMIP CO2 emission forcing data for CO2 and the ScenarioMIP aircraft CO2 emission forcing data, adds the two, then interpolates the time series and regrids the data to produce an ancillary file.
It turns out that the equivalent code in
CMIP7/esm1p6/atmosphere/co2/cmip7_EH_CO2_interpolate.py for the historical forcings interpolated monthly data from the cubes corresponding the the historical forcing data before collapsing the CO2 emission data by sector and the CO2 aircraft emission data by altitude. When this was tried for ScenarioMIP the task ran out of memory when interpolating the time series. The solution applied to both the EH and ES code is to collapse the respective cubes before interpolating the time series. That is why four files are changed.
The debugging work and a small amount of coding was assisted by Google Antigravity using Gemini 3.6 Flash (high).
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Closes #140
For each scenario, the code in CMIP7/esm1p6/atmosphere/co2/cmip7_ES_CO2_interpolate.py reads both the ScenarioMIP CO2 emission forcing data for CO2 and the ScenarioMIP aircraft CO2 emission forcing data, adds the two, then interpolates the time series and regrids the data to produce an ancillary file.
It turns out that the equivalent code in
CMIP7/esm1p6/atmosphere/co2/cmip7_EH_CO2_interpolate.py for the historical forcings interpolated monthly data from the cubes corresponding the the historical forcing data before collapsing the CO2 emission data by sector and the CO2 aircraft emission data by altitude. When this was tried for ScenarioMIP the task ran out of memory when interpolating the time series. The solution applied to both the EH and ES code is to collapse the respective cubes before interpolating the time series. That is why four files are changed.
The debugging work and a small amount of coding was assisted by Google Antigravity using Gemini 3.6 Flash (high).