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``.

.. jupyter-execute::
    
    import xarray as xr
    import matplotlib.pyplot as plt
    import zenodo_get
    import openairclim as oac

    xr.set_options(display_expand_attrs=False)


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

.. _DEPA 2050: https://elib.dlr.de/142185/

.. jupyter-execute::

    %%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
--------------

.. jupyter-execute::

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


Results
-------

Time series
^^^^^^^^^^^

* Emission sums
* Concentrations
* Radiative forcings
* Temperature changes

.. jupyter-execute::

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

.. jupyter-execute::

    # 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()