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17 changes: 15 additions & 2 deletions docs/dataset-overviews/anthropogenic-slcf-co2-emissions.md
Original file line number Diff line number Diff line change
Expand Up @@ -285,8 +285,21 @@ Note there is a variable naming inconsistency for these file - described in the

The data for ScenarioMIP comes from a range of IAMs,
rather than the CEDS consortium.
[TODO: any information about how ScenarioMIP files differ from the DECK files.
There shouldn't be any major differences]
The file naming and general format is the same as the historical.
The key difference is that the data is not contiguous in time,
but instead is provided as monthly data for specific years.
Comment on lines +288 to +290

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@jkikstra @eahoegner @mzecc can you please double check this

Your code will have to handle this,
and you will have to interpolate between these timesteps yourself.
If you do not have any existing interpolation methods,
our suggestion is to linearly interpolate based on the monthly data
i.e. $E(y, m, ...) = E(y_0, m, ...) + \frac{y - y_0}{y_1 - y_0} \cdot (E(y_1, m, ...) - E(y_0, m, ...))$,
where $y$ is the year and $m$ is the month for which your are generating interpolated data,
$...$ represents non-time (i.e. spatial) dimensions,
$y_0$ is the previous year for which there is data in the forcings dataset
and $y_1$ is the next year for which there is data in the forcings dataset.
For example, assuming the data is reported for 2030 and 2035 in the forcings dataset,
values for a given month in 2032 would be given by
$E(2032, m, ...) = E(2030, m, ...) + \frac{2032 - 2030}{2035 - 2030} \cdot (E(2035, m, ...) - E(2035, m, ...))$.
Comment on lines +293 to +302

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@jkikstra @vnaik60 are you happy with this guidance? Or is there something smarter that we can write here?

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Just make it clear that months need to be interpolated separately to preserve the monthly variation within each interpolated year.


<!--- begin-revision-history -->
<!--- Do not edit this section, it is automatically updated when the docs are built -->
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17 changes: 15 additions & 2 deletions docs/dataset-overviews/open-biomass-burning-emissions.md
Original file line number Diff line number Diff line change
Expand Up @@ -315,8 +315,21 @@ Apply the 1850 values on repeat.

The data for ScenarioMIP comes from a range of IAMs,
rather than the CEDS consortium.
[TODO: any information about how ScenarioMIP files differ from the DECK files
There shouldn't be any major differences except maybe naming of VOCs.]
The file naming and general format is the same as the historical.

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@jkikstra @eahoegner @mzecc can you please double check this. I think we have a difference in names for NMVOCs?

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The CEDS and IIASA-IAMC datasets differ in their definition of sector. Also the CEDS datasets include separate percentage files and the IIASA-IAMC datasets do not.

See ACCESS-NRI/CMIP7-Input#134

The key difference is that the data is not contiguous in time,
but instead is provided as monthly data for specific years.
Your code will have to handle this,
and you will have to interpolate between these timesteps yourself.
If you do not have any existing interpolation methods,
our suggestion is to linearly interpolate based on the monthly data
i.e. $E(y, m, ...) = E(y_0, m, ...) + \frac{y - y_0}{y_1 - y_0} \cdot (E(y_1, m, ...) - E(y_0, m, ...))$,
where $y$ is the year and $m$ is the month for which your are generating interpolated data,
$...$ represents non-time (i.e. spatial) dimensions,
$y_0$ is the previous year for which there is data in the forcings dataset
and $y_1$ is the next year for which there is data in the forcings dataset.
For example, assuming the data is reported for 2030 and 2035 in the forcings dataset,
values for a given month in 2032 would be given by
$E(2032, m, ...) = E(2030, m, ...) + \frac{2032 - 2030}{2035 - 2030} \cdot (E(2035, m, ...) - E(2035, m, ...))$.

<!--- begin-revision-history -->
<!--- Do not edit this section, it is automatically updated when the docs are built -->
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