Gas baskets

Gas baskets#

Gas baskets like KyotoGHG are essentially the sum of individual emissions. Usually, gas baskets are specified in units of warming equivalent CO2, so they necessarily always have to specify a global warming potential metric as well.

We offer a few specialized functions for handling gas baskets.

Summation#

To sum the contents of gas baskets , the function xarray.Dataset.pr.gas_basket_contents_sum() is available.

Let’s first create an example dataset.

import primap2
import xarray as xr
import numpy as np

# select example dataset
ds = primap2.open_dataset("../minimal_ds.nc").loc[{"time": slice("2000", "2003")}][
    ["CH4", "CO2", "SF6"]
]
ds

<xarray.Dataset> Size: 496B
Dimensions:      (time: 4, area (ISO3): 4, source: 1)
Coordinates:
  * area (ISO3)  (area (ISO3)) <U3 48B 'COL' 'ARG' 'MEX' 'BOL'
  * source       (source) <U8 32B 'RAND2020'
  * time         (time) datetime64[ns] 32B 2000-01-01 2001-01-01 ... 2003-01-01
Data variables:
    CH4          (time, area (ISO3), source) float64 128B [CH4·Gg/a] 0.7543 ....
    CO2          (time, area (ISO3), source) float64 128B [CO2·Gg/a] 0.6635 ....
    SF6          (time, area (ISO3), source) float64 128B [SF6·Gg/a] 0.0018 ....
Attributes:
    area:     area (ISO3)
# add (empty) gas basket with corresponding metadata
ds["KyotoGHG (AR4GWP100)"] = xr.full_like(ds["CO2"], np.nan).pr.quantify(units="Gg CO2 / year")
ds["KyotoGHG (AR4GWP100)"].attrs = {"entity": "KyotoGHG", "gwp_context": "AR4GWP100"}

ds

<xarray.Dataset> Size: 624B
Dimensions:               (time: 4, area (ISO3): 4, source: 1)
Coordinates:
  * area (ISO3)           (area (ISO3)) <U3 48B 'COL' 'ARG' 'MEX' 'BOL'
  * source                (source) <U8 32B 'RAND2020'
  * time                  (time) datetime64[ns] 32B 2000-01-01 ... 2003-01-01
Data variables:
    CH4                   (time, area (ISO3), source) float64 128B [CH4·Gg/a] ...
    CO2                   (time, area (ISO3), source) float64 128B [CO2·Gg/a] ...
    SF6                   (time, area (ISO3), source) float64 128B [SF6·Gg/a] ...
    KyotoGHG (AR4GWP100)  (time, area (ISO3), source) float64 128B [CO2·Gg/a] ...
Attributes:
    area:     area (ISO3)

Now, we can compute KyotoGHG from its contents (assuming for the moment that this only includes CO\(_2\), SF\(_6\) and CH\(_4\))

# compute gas basket from its contents, which have to be given explicitly
ds.pr.gas_basket_contents_sum(
    basket="KyotoGHG (AR4GWP100)",
    basket_contents=["CO2", "SF6", "CH4"],
)

<xarray.DataArray 'KyotoGHG (AR4GWP100)' (time: 4, area (ISO3): 4, source: 1)> Size: 128B
<Quantity([[[   60.55826022]
  [11537.15564864]
  [15441.09112246]
  [ 2832.13708223]]

 [[10913.89923578]
  [12017.91242534]
  [18293.41941003]
  [18461.14522004]]

 [[12164.44190136]
  [14106.2809531 ]
  [ 5472.05066779]
  [ 7054.12526007]]

 [[12163.56107579]
  [11353.23339466]
  [ 9916.31088755]
  [15594.44908077]]], 'gigagram * CO2 / year')>
Coordinates:
  * area (ISO3)  (area (ISO3)) <U3 48B 'COL' 'ARG' 'MEX' 'BOL'
  * source       (source) <U8 32B 'RAND2020'
  * time         (time) datetime64[ns] 32B 2000-01-01 2001-01-01 ... 2003-01-01
Attributes:
    gwp_context:  AR4GWP100
    entity:       KyotoGHG

Note that like all PRIMAP2 functions, xarray.Dataset.pr.gas_basket_contents_sum() returns the result without overwriting anything in the original dataset, so you have to explicitly overwrite existing data if you want that:

ds["KyotoGHG (AR4GWP100)"] = ds.pr.gas_basket_contents_sum(
    basket="KyotoGHG (AR4GWP100)",
    basket_contents=["CO2", "SF6", "CH4"],
)

Filling in missing information#

To fill in missing data in a gas basket, use xarray.Dataset.pr.fill_na_gas_basket_from_contents()

# delete all data about the years 2002-2003 (inclusive) from the
# KyotoGHG data
ds["KyotoGHG (AR4GWP100)"].loc[{"time": slice("2002", "2003")}].pint.magnitude[:] = np.nan
ds["KyotoGHG (AR4GWP100)"]

<xarray.DataArray 'KyotoGHG (AR4GWP100)' (time: 4, area (ISO3): 4, source: 1)> Size: 128B
<Quantity([[[   60.55826022]
  [11537.15564864]
  [15441.09112246]
  [ 2832.13708223]]

 [[10913.89923578]
  [12017.91242534]
  [18293.41941003]
  [18461.14522004]]

 [[           nan]
  [           nan]
  [           nan]
  [           nan]]

 [[           nan]
  [           nan]
  [           nan]
  [           nan]]], 'gigagram * CO2 / year')>
Coordinates:
  * area (ISO3)  (area (ISO3)) <U3 48B 'COL' 'ARG' 'MEX' 'BOL'
  * source       (source) <U8 32B 'RAND2020'
  * time         (time) datetime64[ns] 32B 2000-01-01 2001-01-01 ... 2003-01-01
Attributes:
    gwp_context:  AR4GWP100
    entity:       KyotoGHG
ds.pr.fill_na_gas_basket_from_contents(
    basket="KyotoGHG (AR4GWP100)", basket_contents=["CO2", "SF6", "CH4"]
)

<xarray.DataArray 'KyotoGHG (AR4GWP100)' (time: 4, area (ISO3): 4, source: 1)> Size: 128B
<Quantity([[[   60.55826022]
  [11537.15564864]
  [15441.09112246]
  [ 2832.13708223]]

 [[10913.89923578]
  [12017.91242534]
  [18293.41941003]
  [18461.14522004]]

 [[12164.44190136]
  [14106.2809531 ]
  [ 5472.05066779]
  [ 7054.12526007]]

 [[12163.56107579]
  [11353.23339466]
  [ 9916.31088755]
  [15594.44908077]]], 'gigagram * CO2 / year')>
Coordinates:
  * area (ISO3)  (area (ISO3)) <U3 48B 'COL' 'ARG' 'MEX' 'BOL'
  * source       (source) <U8 32B 'RAND2020'
  * time         (time) datetime64[ns] 32B 2000-01-01 2001-01-01 ... 2003-01-01
Attributes:
    gwp_context:  AR4GWP100
    entity:       KyotoGHG

The reverse case is that you are missing some data in the timeseries of individual gases and want to fill those in using downscaled data from a gas basket. Here, use xarray.Dataset.pr.downscale_gas_timeseries()

# delete all data about the years 2005-2009 from the individual gas data
sel = {"time": slice("2002", "2003")}
ds["CO2"].loc[sel].pint.magnitude[:] = np.nan
ds["SF6"].loc[sel].pint.magnitude[:] = np.nan
ds["CH4"].loc[sel].pint.magnitude[:] = np.nan
ds

<xarray.Dataset> Size: 624B
Dimensions:               (time: 4, area (ISO3): 4, source: 1)
Coordinates:
  * area (ISO3)           (area (ISO3)) <U3 48B 'COL' 'ARG' 'MEX' 'BOL'
  * source                (source) <U8 32B 'RAND2020'
  * time                  (time) datetime64[ns] 32B 2000-01-01 ... 2003-01-01
Data variables:
    CH4                   (time, area (ISO3), source) float64 128B [CH4·Gg/a] ...
    CO2                   (time, area (ISO3), source) float64 128B [CO2·Gg/a] ...
    SF6                   (time, area (ISO3), source) float64 128B [SF6·Gg/a] ...
    KyotoGHG (AR4GWP100)  (time, area (ISO3), source) float64 128B [CO2·Gg/a] ...
Attributes:
    area:     area (ISO3)
# This determines gas shares at the points in time where individual gas
# data is available, interpolates the shares where data is missing, and
# then downscales the gas basket data using the interpolated shares
ds.pr.downscale_gas_timeseries(basket="KyotoGHG (AR4GWP100)", basket_contents=["CO2", "SF6", "CH4"])

<xarray.Dataset> Size: 624B
Dimensions:               (time: 4, area (ISO3): 4, source: 1)
Coordinates:
  * area (ISO3)           (area (ISO3)) <U3 48B 'COL' 'ARG' 'MEX' 'BOL'
  * source                (source) <U8 32B 'RAND2020'
  * time                  (time) datetime64[ns] 32B 2000-01-01 ... 2003-01-01
Data variables:
    CH4                   (time, area (ISO3), source) float64 128B [CH4·Gg/a] ...
    CO2                   (time, area (ISO3), source) float64 128B [CO2·Gg/a] ...
    SF6                   (time, area (ISO3), source) float64 128B [SF6·Gg/a] ...
    KyotoGHG (AR4GWP100)  (time, area (ISO3), source) float64 128B [CO2·Gg/a] ...
Attributes:
    area:     area (ISO3)
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