Example notebook - How to calculate climate metrics for aviation¶

In [1]:

# --- Import libraries ---
import numpy as np
from aerocm.metrics.aviation_climate_metrics_calculation import AviationClimateMetricsCalculation
from aerocm.utils.functions import emission_profile_function

# --- Import libraries --- import numpy as np from aerocm.metrics.aviation_climate_metrics_calculation import AviationClimateMetricsCalculation from aerocm.utils.functions import emission_profile_function

In [2]:

# --- Set parameters ---

## Climate model
climate_model = "FaIR" # Choose a climate model among 'IPCC', 'GWP*', 'LWE' and 'FaIR'
start_year = 1940 # Choose a start year for the climate simulation
### The end_year parameter will be automatically calculated using time horizon (Option 1)
### or the length of the species inventory, with a check for time horizon (Option 2)

## Metrics
time_horizon = [20, 50, 100] # Choose an integer or a list of integers

## Species
species_profile = 'pulse' # Choose between 'pulse', 'step', 'combined', 'scenario'

## Option 1 ('pulse', 'step', 'combined'): standard profiles
### This option will directly generate standard emission profiles (pulse/step)
profile_start_year = 2020 # Choose the start year for the pulse/step (for FaIR: profile_start_year>start_year)
species_list = ["Contrails", "Soot"] # Choose the species to study among the ones available in the climate model

## Aditional settings
### You can use species_settings or model_settings as in the case of the AviationClimateSimulation class

# --- Set parameters --- ## Climate model climate_model = "FaIR" # Choose a climate model among 'IPCC', 'GWP*', 'LWE' and 'FaIR' start_year = 1940 # Choose a start year for the climate simulation ### The end_year parameter will be automatically calculated using time horizon (Option 1) ### or the length of the species inventory, with a check for time horizon (Option 2) ## Metrics time_horizon = [20, 50, 100] # Choose an integer or a list of integers ## Species species_profile = 'pulse' # Choose between 'pulse', 'step', 'combined', 'scenario' ## Option 1 ('pulse', 'step', 'combined'): standard profiles ### This option will directly generate standard emission profiles (pulse/step) profile_start_year = 2020 # Choose the start year for the pulse/step (for FaIR: profile_start_year>start_year) species_list = ["Contrails", "Soot"] # Choose the species to study among the ones available in the climate model ## Aditional settings ### You can use species_settings or model_settings as in the case of the AviationClimateSimulation class

In [3]:

# --- Run simulation in the case of Option 1 ---
results = AviationClimateMetricsCalculation(
    climate_model,
    start_year,
    time_horizon,
    species_profile,
    profile_start_year,
    species_list   
).run(
    include_absolute_metrics=False,  # <-- whether to include absolute metrics (default is False)
    return_df=True  # <-- whether to return resulsts as a dataframe (default is a nested dict)
)

# --- Run simulation in the case of Option 1 --- results = AviationClimateMetricsCalculation( climate_model, start_year, time_horizon, species_profile, profile_start_year, species_list ).run( include_absolute_metrics=False, # <-- whether to include absolute metrics (default is False) return_df=True # <-- whether to return resulsts as a dataframe (default is a nested dict) )

In [4]:

# display results
results

# display results results

Out[4]:

	time_horizon	species	gwp_rf	gwp_erf	egwp_rf	egwp_erf	gtp	igtp	ratr
0	20	Contrails	87.317157	36.673206	36.673206	36.673206	10.693590	48.347866	48.347866
1	20	Soot	3940.518599	3940.518599	3940.518599	3940.518599	1038.841521	5110.737201	5110.737201
2	50	Contrails	41.451008	17.409423	17.409423	17.409423	2.678263	21.323234	21.323234
3	50	Soot	1871.510333	1871.510333	1871.510333	1871.510333	193.765518	2193.817748	2193.817748
4	100	Contrails	23.155547	9.725330	9.725330	9.725330	1.573622	11.704448	11.704448
5	100	Soot	1048.526748	1048.526748	1048.526748	1048.526748	106.366923	1171.040073	1171.040073

In [5]:

# More readable as a pivot table
results.pivot_table(index=["species", "time_horizon"])

# More readable as a pivot table results.pivot_table(index=["species", "time_horizon"])

Out[5]:

		egwp_erf	egwp_rf	gtp	gwp_erf	gwp_rf	igtp	ratr
species	time_horizon
Contrails	20	36.673206	36.673206	10.693590	36.673206	87.317157	48.347866	48.347866
	50	17.409423	17.409423	2.678263	17.409423	41.451008	21.323234	21.323234
	100	9.725330	9.725330	1.573622	9.725330	23.155547	11.704448	11.704448
Soot	20	3940.518599	3940.518599	1038.841521	3940.518599	3940.518599	5110.737201	5110.737201
	50	1871.510333	1871.510333	193.765518	1871.510333	1871.510333	2193.817748	2193.817748
	100	1048.526748	1048.526748	106.366923	1048.526748	1048.526748	1171.040073	1171.040073

In [6]:

# Select specific time horizon and/or specie and metrics
results.loc[
    (results["time_horizon"] == 50) & (results["species"] == "Contrails"),  # <- rows to select
    #["gwp_rf", "gtp", "agwp_rf"]  # <- columns to select
]

# Select specific time horizon and/or specie and metrics results.loc[ (results["time_horizon"] == 50) & (results["species"] == "Contrails"), # <- rows to select #["gwp_rf", "gtp", "agwp_rf"] # <- columns to select ]

Out[6]:

	time_horizon	species	gwp_rf	gwp_erf	egwp_rf	egwp_erf	gtp	igtp	ratr
2	50	Contrails	41.451008	17.409423	17.409423	17.409423	2.678263	21.323234	21.323234

In [7]:

# --- Run simulation in the case of Option 2 ---

## Species
species_profile_scenario = 'scenario' # Choose between 'pulse', 'step', 'combined', 'scenario'

## Option 2 ('scenario'): species inventory
### This option will directly generate the emission profiles of your choice

### Intermediate parameters for generating the inventory
if type(time_horizon) == int:
    time_horizon_max = time_horizon
else:
    time_horizon_max = max(time_horizon)
profile_start_year = 2020

### Inventory
species_inventory = {
    "CO2": emission_profile_function(start_year,
                                            profile_start_year,
                                            time_horizon_max,
                                            profile="pulse",
                                            unit_value=10**10
                                            ), # in kg
    "Contrails": emission_profile_function(start_year,
                                            profile_start_year,
                                            time_horizon_max,
                                            profile="pulse",
                                            unit_value=10**10
                                            ), # in km
    "Soot": emission_profile_function(start_year,
                                            profile_start_year,
                                            time_horizon_max,
                                            profile="pulse",
                                            unit_value=10**14
                                            ),  # in kg
}

results_scenario = AviationClimateMetricsCalculation(
    climate_model,
    start_year,
    time_horizon,
    species_profile_scenario,
    species_inventory=species_inventory,
).run(include_absolute_metrics=True, return_df=True)

# --- Run simulation in the case of Option 2 --- ## Species species_profile_scenario = 'scenario' # Choose between 'pulse', 'step', 'combined', 'scenario' ## Option 2 ('scenario'): species inventory ### This option will directly generate the emission profiles of your choice ### Intermediate parameters for generating the inventory if type(time_horizon) == int: time_horizon_max = time_horizon else: time_horizon_max = max(time_horizon) profile_start_year = 2020 ### Inventory species_inventory = { "CO2": emission_profile_function(start_year, profile_start_year, time_horizon_max, profile="pulse", unit_value=10**10 ), # in kg "Contrails": emission_profile_function(start_year, profile_start_year, time_horizon_max, profile="pulse", unit_value=10**10 ), # in km "Soot": emission_profile_function(start_year, profile_start_year, time_horizon_max, profile="pulse", unit_value=10**14 ), # in kg } results_scenario = AviationClimateMetricsCalculation( climate_model, start_year, time_horizon, species_profile_scenario, species_inventory=species_inventory, ).run(include_absolute_metrics=True, return_df=True)

In [8]:

results_scenario

results_scenario

Out[8]:

	time_horizon	species	agwp_rf	agwp_erf	aegwp_rf	aegwp_erf	agtp	iagtp	atr	gwp_rf	gwp_erf	egwp_rf	egwp_erf	gtp	igtp	ratr
0	20	Contrails	2.230000e-12	9.366000e-13	9.366000e-13	9.366000e-13	6.024035e-15	4.766615e-13	2.383307e-14	87.317157	36.673206	36.673206	36.673206	10.693590	48.347866	48.347866
1	20	Soot	1.006372e-10	1.006372e-10	1.006372e-10	1.006372e-10	5.852120e-13	5.038674e-11	2.519337e-12	3940.518599	3940.518599	3940.518599	3940.518599	1038.841521	5110.737201	5110.737201
2	20	CO2	2.553908e-14	2.553908e-14	2.553908e-14	2.553908e-14	5.633313e-16	9.858997e-15	4.929498e-16	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	50	Contrails	2.230000e-12	9.366000e-13	9.366000e-13	9.366000e-13	1.416490e-15	5.610897e-13	1.122179e-14	41.451008	17.409423	17.409423	17.409423	2.678263	21.323234	21.323234
4	50	Soot	1.006844e-10	1.006844e-10	1.006844e-10	1.006844e-10	1.024795e-13	5.772711e-11	1.154542e-12	1871.510333	1871.510333	1871.510333	1871.510333	193.765518	2193.817748	2193.817748
5	50	CO2	5.379845e-14	5.379845e-14	5.379845e-14	5.379845e-14	5.288839e-16	2.631354e-14	5.262707e-16	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
6	100	Contrails	2.230000e-12	9.366000e-13	9.366000e-13	9.366000e-13	8.045229e-16	6.102080e-13	6.102080e-15	23.155547	9.725330	9.725330	9.725330	1.573622	11.704448	11.704448
7	100	Soot	1.009786e-10	1.009786e-10	1.009786e-10	1.009786e-10	5.438068e-14	6.105183e-11	6.105183e-13	1048.526748	1048.526748	1048.526748	1048.526748	106.366923	1171.040073	1171.040073
8	100	CO2	9.630522e-14	9.630522e-14	9.630522e-14	9.630522e-14	5.112556e-16	5.213471e-14	5.213471e-16	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000