---
file_format: mystnb
kernelspec:
  name: python3
---

# Normalization

In this example, the time evolution of type **normalization** is demonstrated.
A historic emission scenario is simulated with OpenAirClim.


## 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`.

```{code-cell} ipython3
import xarray as xr
import matplotlib.pyplot as plt
import openairclim as oac

xr.set_options(display_expand_attrs=False)
```


## Input files

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

* Configuration file `historic.toml`
* Emission inventory `ELK_aviation_2019_res5deg_flat.nc`
* Time evolution file for fuel normalization `time_norm_historic_SSP.nc`

### Emission inventory

* Source: DLR Project EmissionsLandKarte ([ELK](https://elkis.dlr.de/))
* Resolution down-sampled to 5 deg resolution
* Converted into format suitable for OpenAirClim
* Inventory year: 2019

```{code-cell} ipython3
inv = xr.load_dataset("../input/ELK_aviation_2019_res5deg_flat.nc")
display(inv)
```

### Time evolution

* Time evolution with **normalization** of fuel use
* Time period: 1920 - 2019

```{code-cell} ipython3
evo = xr.load_dataset("../input/time_norm_historic_SSP.nc")
display(evo)

fig, ax = plt.subplots()
evo.fuel.plot(ax=ax)
ax.grid(True)
```


## Simulation run

```{code-cell} ipython3
oac.run("historic.toml")
```


## Results

### Time series

* Emission sums
* Concentrations
* Radiative forcings
* Temperature changes

```{code-cell} ipython3
results_ds = xr.load_dataset("results/historic.nc")
display(results_ds)
```

```{code-cell} ipython3
# 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()
```

### Climate metrics

* Absolute Global Temperature Potential (AGTP)
* Absolute Global Warming Potential (AGWP)
* Average Temperature Response (ATR)

```{code-cell} ipython3
metrics_ds = xr.load_dataset("results/historic_metrics.nc")
display(metrics_ds)
```