Multiple emission inventories

In this example, no time evolution file is given, but multiple emission inventories are given as input. OpenAirClim will interpolate between discrete inventory years.

Imports

If the openairclim package cannot be imported, make sure that you have installed the package with pip or added the oac source folder to PYTHONPATH.

import xarray as xr
import matplotlib.pyplot as plt
import zenodo_get
import openairclim as oac

xr.set_options(display_expand_attrs=False)

<xarray.core.options.set_options at 0x7f7ae980c6e0>

Input files

In order to be able to execute this example simulation, two types of input are required.

• Configuration file multi_inv.toml

• Emission inventories emi_inv_20XX.nc

Emission inventories

• Source: DLR research study DEPA 2050

• Inventory years: 2030, 2040, 2050

• Available for download in suitable OpenAirClim format

%%capture
# Download inventories from zenodo
zenodo_get.zenodo_get(["https://doi.org/10.5281/zenodo.11442322", "-g", "emi_inv_20[3-5]0.nc", "-o", "source/demos/input/"])

Simulation run

oac.run("source/demos/03_multi_inv/multi_inv.toml")

Results

Time series

• Emission sums

• Concentrations

• Radiative forcings

• Temperature changes

results_ds = xr.load_dataset("source/demos/03_multi_inv/results/multi_inv.nc")
display(results_ds)

<xarray.Dataset> Size: 4kB
Dimensions:        (ac: 2, time: 21)
Coordinates:
  * ac             (ac) <U7 56B 'DEFAULT' 'TOTAL'
  * time           (time) int64 168B 2030 2031 2032 2033 ... 2047 2048 2049 2050
Data variables:
    emis_CO2       (ac, time) float64 336B 1.075e+03 1.101e+03 ... 1.712e+03
    emis_distance  (ac, time) float64 336B 6.371e+10 6.478e+10 ... 8.757e+10
    emis_H2O       (ac, time) float64 336B 431.4 441.9 452.5 ... 671.8 686.8
    conc_CO2       (ac, time) float64 336B 0.1379 0.2718 0.4038 ... 2.823 2.984
    RF_CO2         (ac, time) float64 336B 0.00208 0.004084 ... 0.03992 0.04207
    RF_cont        (ac, time) float64 336B 0.04683 0.04708 ... 0.05769 0.05863
    RF_H2O         (ac, time) float64 336B 0.002381 0.002407 ... 0.003386
    dT_CO2         (ac, time) float64 336B 0.0001585 0.000452 ... 0.01868
    dT_cont        (ac, time) float64 336B 0.002105 0.003987 ... 0.01958 0.02008
    dT_H2O         (ac, time) float64 336B 0.0002068 0.0003929 ... 0.002155
Attributes: (4)

xarray.Dataset

• Dimensions:
  • ac: 2
  • time: 21
• Coordinates: (2)
  • ac
    (ac)
    <U7
    'DEFAULT' 'TOTAL'

    long_name :

    aircraft identifier

    array(['DEFAULT', 'TOTAL'], dtype='<U7')

  • time
    (time)
    int64
    2030 2031 2032 ... 2048 2049 2050

    long_name :

    time

    units :

    years

    array([2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041,
           2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050])

• Data variables: (10)
  • emis_CO2
    (ac, time)
    float64
    1.075e+03 1.101e+03 ... 1.712e+03

    long_name :

    CO2 Emission

    units :

    Tg

    array([[1075.02554317, 1101.31766886, 1127.60979456, 1153.90192026,
            1180.19404595, 1206.48617165, 1232.77829734, 1259.07042304,
            1285.36254874, 1311.65467443, 1337.94680013, 1375.30528236,
            1412.66376458, 1450.02224681, 1487.38072904, 1524.73921126,
            1562.09769349, 1599.45617572, 1636.81465795, 1674.17314017,
            1711.5316224 ],
           [1075.02554317, 1101.31766886, 1127.60979456, 1153.90192026,
            1180.19404595, 1206.48617165, 1232.77829734, 1259.07042304,
            1285.36254874, 1311.65467443, 1337.94680013, 1375.30528236,
            1412.66376458, 1450.02224681, 1487.38072904, 1524.73921126,
            1562.09769349, 1599.45617572, 1636.81465795, 1674.17314017,
            1711.5316224 ]])

  • emis_distance
    (ac, time)
    float64
    6.371e+10 6.478e+10 ... 8.757e+10

    long_name :

    distance Emission

    units :

    km

    array([[6.37100278e+10, 6.47809647e+10, 6.58519015e+10, 6.69228384e+10,
            6.79937753e+10, 6.90647122e+10, 7.01356491e+10, 7.12065860e+10,
            7.22775228e+10, 7.33484597e+10, 7.44193966e+10, 7.57345198e+10,
            7.70496430e+10, 7.83647662e+10, 7.96798894e+10, 8.09950126e+10,
            8.23101358e+10, 8.36252590e+10, 8.49403822e+10, 8.62555054e+10,
            8.75706286e+10],
           [6.37100278e+10, 6.47809647e+10, 6.58519015e+10, 6.69228384e+10,
            6.79937753e+10, 6.90647122e+10, 7.01356491e+10, 7.12065860e+10,
            7.22775228e+10, 7.33484597e+10, 7.44193966e+10, 7.57345198e+10,
            7.70496430e+10, 7.83647662e+10, 7.96798894e+10, 8.09950126e+10,
            8.23101358e+10, 8.36252590e+10, 8.49403822e+10, 8.62555054e+10,
            8.75706286e+10]])

  • emis_H2O
    (ac, time)
    float64
    431.4 441.9 452.5 ... 671.8 686.8

    long_name :

    H2O Emission

    units :

    Tg

    array([[431.39065446, 441.94126889, 452.49188332, 463.04249774,
            473.59311217, 484.14372659, 494.69434102, 505.24495544,
            515.79556987, 526.34618429, 536.89679872, 551.88816527,
            566.87953183, 581.87089838, 596.86226493, 611.85363149,
            626.84499804, 641.8363646 , 656.82773115, 671.8190977 ,
            686.81046426],
           [431.39065446, 441.94126889, 452.49188332, 463.04249774,
            473.59311217, 484.14372659, 494.69434102, 505.24495544,
            515.79556987, 526.34618429, 536.89679872, 551.88816527,
            566.87953183, 581.87089838, 596.86226493, 611.85363149,
            626.84499804, 641.8363646 , 656.82773115, 671.8190977 ,
            686.81046426]])

  • conc_CO2
    (ac, time)
    float64
    0.1379 0.2718 ... 2.823 2.984

    long_name :

    CO2 Concentration

    units :

    ppmv

    array([[0.13794532, 0.27178481, 0.40379622, 0.53525637, 0.66688545,
            0.79909381, 0.93211907, 1.06610226, 1.20113011, 1.33725868,
            1.47452637, 1.61438128, 1.75676831, 1.90165372, 2.04901555,
            2.19883832, 2.35110999, 2.50582041, 2.66296035, 2.82252107,
            2.984494  ],
           [0.13794532, 0.27178481, 0.40379622, 0.53525637, 0.66688545,
            0.79909381, 0.93211907, 1.06610226, 1.20113011, 1.33725868,
            1.47452637, 1.61438128, 1.75676831, 1.90165372, 2.04901555,
            2.19883832, 2.35110999, 2.50582041, 2.66296035, 2.82252107,
            2.984494  ]])

  • RF_CO2
    (ac, time)
    float64
    0.00208 0.004084 ... 0.04207

    long_name :

    CO2 Radiative Forcing

    units :

    W/m²

    array([[0.00208036, 0.00408427, 0.00604672, 0.00798727, 0.00991691,
            0.01184188, 0.01376579, 0.01569078, 0.01761815, 0.01954874,
            0.02148313, 0.02344146, 0.02542345, 0.02742841, 0.02945584,
            0.03150533, 0.03357654, 0.03566914, 0.03778283, 0.03991732,
            0.04207233],
           [0.00208036, 0.00408427, 0.00604672, 0.00798727, 0.00991691,
            0.01184188, 0.01376579, 0.01569078, 0.01761815, 0.01954874,
            0.02148313, 0.02344146, 0.02542345, 0.02742841, 0.02945584,
            0.03150533, 0.03357654, 0.03566914, 0.03778283, 0.03991732,
            0.04207233]])

  • RF_cont
    (ac, time)
    float64
    0.04683 0.04708 ... 0.05769 0.05863

    long_name :

    cont Radiative Forcing

    units :

    W/m²

    array([[0.04683353, 0.04707616, 0.04731878, 0.04756141, 0.04780404,
            0.04804666, 0.04828929, 0.04853192, 0.04877454, 0.04901717,
            0.0492598 , 0.0501967 , 0.05113361, 0.05207052, 0.05300742,
            0.05394433, 0.05488123, 0.05581814, 0.05675504, 0.05769195,
            0.05862886],
           [0.04683353, 0.04707616, 0.04731878, 0.04756141, 0.04780404,
            0.04804666, 0.04828929, 0.04853192, 0.04877454, 0.04901717,
            0.0492598 , 0.0501967 , 0.05113361, 0.05207052, 0.05300742,
            0.05394433, 0.05488123, 0.05581814, 0.05675504, 0.05769195,
            0.05862886]])

  • RF_H2O
    (ac, time)
    float64
    0.002381 0.002407 ... 0.003386

    long_name :

    H2O Radiative Forcing

    units :

    W/m²

    array([[0.00238147, 0.00240682, 0.00243216, 0.00245751, 0.00248286,
            0.0025082 , 0.00253355, 0.0025589 , 0.00258424, 0.00260959,
            0.00263494, 0.00271008, 0.00278523, 0.00286038, 0.00293553,
            0.00301068, 0.00308582, 0.00316097, 0.00323612, 0.00331127,
            0.00338641],
           [0.00238147, 0.00240682, 0.00243216, 0.00245751, 0.00248286,
            0.0025082 , 0.00253355, 0.0025589 , 0.00258424, 0.00260959,
            0.00263494, 0.00271008, 0.00278523, 0.00286038, 0.00293553,
            0.00301068, 0.00308582, 0.00316097, 0.00323612, 0.00331127,
            0.00338641]])

  • dT_CO2
    (ac, time)
    float64
    0.0001585 0.000452 ... 0.01868

    long_name :

    CO2 Temperature change

    units :

    K

    array([[0.00015845, 0.00045199, 0.00086253, 0.00137549, 0.00197877,
            0.00266209, 0.00341661, 0.00423464, 0.00510945, 0.00603513,
            0.00700648, 0.00802041, 0.00907416, 0.01016524, 0.01129138,
            0.01245057, 0.01364098, 0.01486095, 0.01610902, 0.01738383,
            0.0186842 ],
           [0.00015845, 0.00045199, 0.00086253, 0.00137549, 0.00197877,
            0.00266209, 0.00341661, 0.00423464, 0.00510945, 0.00603513,
            0.00700648, 0.00802041, 0.00907416, 0.01016524, 0.01129138,
            0.01245057, 0.01364098, 0.01486095, 0.01610902, 0.01738383,
            0.0186842 ]])

  • dT_cont
    (ac, time)
    float64
    0.002105 0.003987 ... 0.02008

    long_name :

    cont Temperature change

    units :

    K

    array([[0.00210462, 0.00398711, 0.00567241, 0.00718266, 0.00853752,
            0.00975443, 0.01084889, 0.01183464, 0.0127239 , 0.01352751,
            0.01425508, 0.01494637, 0.01560548, 0.01623611, 0.01684148,
            0.01742451, 0.01798774, 0.01853346, 0.01906368, 0.0195802 ,
            0.02008462],
           [0.00210462, 0.00398711, 0.00567241, 0.00718266, 0.00853752,
            0.00975443, 0.01084889, 0.01183464, 0.0127239 , 0.01352751,
            0.01425508, 0.01494637, 0.01560548, 0.01623611, 0.01684148,
            0.01742451, 0.01798774, 0.01853346, 0.01906368, 0.0195802 ,
            0.02008462]])

  • dT_H2O
    (ac, time)
    float64
    0.0002068 0.0003929 ... 0.002155

    long_name :

    H2O Temperature change

    units :

    K

    array([[0.00020678, 0.00039287, 0.00056059, 0.000712  , 0.00084894,
            0.00097304, 0.00108573, 0.0011883 , 0.00128189, 0.00136751,
            0.00144606, 0.00152265, 0.00159752, 0.00167087, 0.00174287,
            0.0018137 , 0.00188348, 0.00195234, 0.0020204 , 0.00208776,
            0.0021545 ],
           [0.00020678, 0.00039287, 0.00056059, 0.000712  , 0.00084894,
            0.00097304, 0.00108573, 0.0011883 , 0.00128189, 0.00136751,
            0.00144606, 0.00152265, 0.00159752, 0.00167087, 0.00174287,
            0.0018137 , 0.00188348, 0.00195234, 0.0020204 , 0.00208776,
            0.0021545 ]])

• Attributes: (4)

  title :

  multi_inv

  created :

  2025-11-19 10:54:57

  user :

  runner

  oac version :

  0.13.0

# Plot Radiative Forcing and Temperature Changes

ac = "TOTAL"
rf_cont = results_ds.RF_cont.sel(ac=ac) * 1000
rf_co2 = results_ds.RF_CO2.sel(ac=ac) * 1000
rf_h2o = results_ds.RF_H2O.sel(ac=ac) * 1000
dt_cont = results_ds.dT_cont.sel(ac=ac) * 1000
dt_co2 = results_ds.dT_CO2.sel(ac=ac) * 1000
dt_h2o = results_ds.dT_H2O.sel(ac=ac) * 1000

fig, ax = plt.subplots(ncols=2, figsize=(10,5))
ax[0].grid(True)
ax[1].grid(True)
rf_cont.plot(ax=ax[0], color="deepskyblue", label="cont")
rf_co2.plot(ax=ax[0], color="k", label="CO2")
rf_h2o.plot(ax=ax[0], color="steelblue", label="H2O")
dt_cont.plot(ax=ax[1], color="deepskyblue", label="cont")
dt_co2.plot(ax=ax[1], color="k", label="CO2")
dt_h2o.plot(ax=ax[1], color="steelblue", label="H2O")
ax[0].set_ylabel("Radiative Forcing [mW/m²]")
ax[1].set_ylabel("Temperature Change [mK]")
ax[0].legend()
ax[1].legend()

<matplotlib.legend.Legend at 0x7f7ae967a900>