Example notebook - How to run a climate simulation for aviation¶

In [1]:

# --- Import libraries ---
import numpy as np
from pandas import read_csv
from aerocm.climate_models.aviation_climate_simulation import AviationClimateSimulation
from aerocm.utils.functions import plot_simulation_results, show_model_info
from aerocm.climate_models.fair_climate_model import FairClimateModel

# --- Import libraries --- import numpy as np from pandas import read_csv from aerocm.climate_models.aviation_climate_simulation import AviationClimateSimulation from aerocm.utils.functions import plot_simulation_results, show_model_info from aerocm.climate_models.fair_climate_model import FairClimateModel

In [2]:

# --- Set parameters ---
start_year = 1940  # start year of simulation
end_year = 2019  # end year of simulation

# Time evolution of emissions for each species
## Here an example with historical emissions from aviation
commercial_aviation_historical_data_df = read_csv("../climate_data/aviation_emissions_data.csv", delimiter=";")
commercial_aviation_historical_data = commercial_aviation_historical_data_df.values
years_data = commercial_aviation_historical_data[:80, 0]
co2_emissions_data = commercial_aviation_historical_data[:80, 1]
nox_emissions_data = commercial_aviation_historical_data[:80, 2]
h2o_emissions_data = commercial_aviation_historical_data[:80, 3]
soot_emissions_data = commercial_aviation_historical_data[:80, 4]
sulfur_emissions_data = commercial_aviation_historical_data[:80, 5]
distance_data = commercial_aviation_historical_data[:80, 6]
species_inventory = {
    'CO2': co2_emissions_data * 1e9, # [Mt to kg]
    'Contrails': distance_data,
    'NOx - ST O3': nox_emissions_data * 1e9,
    'NOx - CH4 and induced': nox_emissions_data * 1e9,
    'H2O': h2o_emissions_data * 1e9,
    'Soot': soot_emissions_data * 1e9,
    'Sulfur': sulfur_emissions_data * 1e9,
}

# --- Set parameters --- start_year = 1940 # start year of simulation end_year = 2019 # end year of simulation # Time evolution of emissions for each species ## Here an example with historical emissions from aviation commercial_aviation_historical_data_df = read_csv("../climate_data/aviation_emissions_data.csv", delimiter=";") commercial_aviation_historical_data = commercial_aviation_historical_data_df.values years_data = commercial_aviation_historical_data[:80, 0] co2_emissions_data = commercial_aviation_historical_data[:80, 1] nox_emissions_data = commercial_aviation_historical_data[:80, 2] h2o_emissions_data = commercial_aviation_historical_data[:80, 3] soot_emissions_data = commercial_aviation_historical_data[:80, 4] sulfur_emissions_data = commercial_aviation_historical_data[:80, 5] distance_data = commercial_aviation_historical_data[:80, 6] species_inventory = { 'CO2': co2_emissions_data * 1e9, # [Mt to kg] 'Contrails': distance_data, 'NOx - ST O3': nox_emissions_data * 1e9, 'NOx - CH4 and induced': nox_emissions_data * 1e9, 'H2O': h2o_emissions_data * 1e9, 'Soot': soot_emissions_data * 1e9, 'Sulfur': sulfur_emissions_data * 1e9, }

In [3]:

# You can now select a model and show its properties
climate_model = "LWE"  # 'IPCC', "GWP*", "LWE", "FaIR"  or another one such as FairClimateModel which respects the template (and with settings)
show_model_info(climate_model)

# You can now select a model and show its properties climate_model = "LWE" # 'IPCC', "GWP*", "LWE", "FaIR" or another one such as FairClimateModel which respects the template (and with settings) show_model_info(climate_model)

Class: LWEClimateModel

Attributes:
  • available_species: ['CO2', 'Contrails', 'NOx - ST O3', 'NOx - CH4 and induced', 'H2O', 'Soot - ARI', 'Soot - ACI', 'Sulfur - ARI', 'Sulfur - ACI', 'H2 leakage - ST O3', 'H2 leakage - CH4 and induced']
  • available_species_settings: {'CO2': {'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.0}}, 'Contrails': {'sensitivity_rf': {'type': <class 'float'>, 'default': 2.23e-12}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 0.42}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'NOx - ST O3': {'sensitivity_rf': {'type': <class 'float'>, 'default': 7.64e-12}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.37}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'NOx - CH4 and induced': {'ch4_production_per_nox': {'type': <class 'float'>, 'default': -3.9}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.18}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'H2O': {'sensitivity_rf': {'type': <class 'float'>, 'default': 5.2e-15}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.0}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'Soot - ARI': {'sensitivity_rf': {'type': <class 'float'>, 'default': 1e-10}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.0}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'Soot - ACI': {'sensitivity_rf': {'type': <class 'float'>, 'default': 0.0}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.0}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'Sulfur - ARI': {'sensitivity_rf': {'type': <class 'float'>, 'default': -2e-11}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.0}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'Sulfur - ACI': {'sensitivity_rf': {'type': <class 'float'>, 'default': 0.0}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.0}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'H2 leakage - ST O3': {'sensitivity_rf': {'type': <class 'float'>, 'default': 4e-13}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.37}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}, 'H2 leakage - CH4 and induced': {'ch4_production_per_nox': {'type': <class 'float'>, 'default': 0.34}, 'ratio_erf_rf': {'type': <class 'float'>, 'default': 1.18}, 'efficacy_erf': {'type': <class 'float'>, 'default': 1.0}}}
  • available_model_settings: {'tcre': {'type': <class 'float'>, 'default': 0.00045}}

Methods:
  • run()
  • validate_inventory()
  • validate_model_settings()
  • validate_specie_settings()

In [4]:

# Set climate parameters relative to each species, according to the available parameters of the selected climate model
species_settings = {
    "CO2": {"ratio_erf_rf": 1.0},
    "NOx - ST O3": {"sensitivity_rf": 7.6e-12, "ratio_erf_rf": 1.37, "efficacy_erf": 1.0},
    "NOx - CH4 and induced": {"ch4_change_per_species": -3.9, "ratio_erf_rf": 1.18, "efficacy_erf": 1.0},
    "Contrails": {"sensitivity_rf": 2.23e-12, "ratio_erf_rf": 0.42, "efficacy_erf": 1.0},
    "H2O": {"sensitivity_rf": 5.2e-15, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0},
    "Soot - ARI": {"sensitivity_rf": 1.0e-10, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0},
    "Soot - ACI": {"sensitivity_rf": 0.0, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0},
    "Sulfur - ARI": {"sensitivity_rf": -2.0e-11, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0},
    "Sulfur - ACI": {"sensitivity_rf": 0.0, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0},
}

# Climate model settings
model_settings = {"tcre": 0.00045} 

# Set climate parameters relative to each species, according to the available parameters of the selected climate model species_settings = { "CO2": {"ratio_erf_rf": 1.0}, "NOx - ST O3": {"sensitivity_rf": 7.6e-12, "ratio_erf_rf": 1.37, "efficacy_erf": 1.0}, "NOx - CH4 and induced": {"ch4_change_per_species": -3.9, "ratio_erf_rf": 1.18, "efficacy_erf": 1.0}, "Contrails": {"sensitivity_rf": 2.23e-12, "ratio_erf_rf": 0.42, "efficacy_erf": 1.0}, "H2O": {"sensitivity_rf": 5.2e-15, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0}, "Soot - ARI": {"sensitivity_rf": 1.0e-10, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0}, "Soot - ACI": {"sensitivity_rf": 0.0, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0}, "Sulfur - ARI": {"sensitivity_rf": -2.0e-11, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0}, "Sulfur - ACI": {"sensitivity_rf": 0.0, "ratio_erf_rf": 1.0, "efficacy_erf": 1.0}, } # Climate model settings model_settings = {"tcre": 0.00045}

In [5]:

# --- Run simulation ---
results = AviationClimateSimulation(
    climate_model,
    start_year,
    end_year,
    species_inventory,
    species_settings,
    model_settings
).run(return_xr=True)
results

# --- Run simulation --- results = AviationClimateSimulation( climate_model, start_year, end_year, species_inventory, species_settings, model_settings ).run(return_xr=True) results

Out[5]:

<xarray.Dataset> Size: 37kB
Dimensions:                      (species: 18, year: 80)
Coordinates:
  * species                      (species) <U21 2kB 'CO2' ... 'Total'
  * year                         (year) int64 640B 1940 1941 1942 ... 2018 2019
Data variables:
    effective_radiative_forcing  (species, year) float64 12kB 5.99e-05 ... 0....
    radiative_forcing            (species, year) float64 12kB 5.99e-05 ... 0....
    temperature                  (species, year) float64 12kB 1.581e-05 ... 0...

xarray.Dataset

• Dimensions:
  • species: 18
  • year: 80
• Coordinates: (2)
  • species
    (species)
    <U21
    'CO2' 'Contrails' ... 'Total'

    array(['CO2', 'Contrails', 'NOx - ST O3', 'NOx - CH4 and induced', 'H2O',
           'Soot - ARI', 'Soot - ACI', 'Sulfur - ARI', 'Sulfur - ACI', 'NOx - CH4',
           'NOx - CH4 induced O3', 'NOx - CH4 induced H2O', 'NOx', 'Soot',
           'Sulfur', 'Aerosols', 'Non-CO2', 'Total'], dtype='<U21')

  • year
    (year)
    int64
    1940 1941 1942 ... 2017 2018 2019

    array([1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951,
           1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963,
           1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975,
           1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
           1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,
           2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
           2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])

• Data variables: (3)
  • effective_radiative_forcing
    (species, year)
    float64
    5.99e-05 0.0001207 ... 0.09983

    array([[ 5.99021403e-05,  1.20684712e-04,  1.83126530e-04, ...,
             3.66434874e-02,  3.78181963e-02,  3.89883622e-02],
           [ 6.98134522e-04,  7.54105837e-04,  8.14382638e-04, ...,
             4.83173208e-02,  5.08377114e-02,  5.26359834e-02],
           [ 1.41917082e-03,  1.53294955e-03,  1.65548049e-03, ...,
             4.30318330e-02,  4.50118253e-02,  4.55398233e-02],
           ...,
           [-1.89057510e-04, -2.04214757e-04, -2.20537946e-04, ...,
            -5.73256656e-03, -5.99633496e-03, -6.06667320e-03],
           [ 1.86012596e-03,  1.89445603e-03,  1.94111191e-03, ...,
             5.62801930e-02,  5.95558928e-02,  6.08415661e-02],
           [ 1.92002810e-03,  2.01514075e-03,  2.12423844e-03, ...,
             9.29236804e-02,  9.73740891e-02,  9.98299282e-02]],
          shape=(18, 80))

  • radiative_forcing
    (species, year)
    float64
    5.99e-05 0.0001207 ... 0.1653

    array([[ 5.99021403e-05,  1.20684712e-04,  1.83126530e-04, ...,
             3.66434874e-02,  3.78181963e-02,  3.89883622e-02],
           [ 1.66222505e-03,  1.79549009e-03,  1.93900628e-03, ...,
             1.15041240e-01,  1.21042170e-01,  1.25323770e-01],
           [ 1.03589111e-03,  1.11894128e-03,  1.20837992e-03, ...,
             3.14100971e-02,  3.28553469e-02,  3.32407469e-02],
           ...,
           [-1.89057510e-04, -2.04214757e-04, -2.20537946e-04, ...,
            -5.73256656e-03, -5.99633496e-03, -6.06667320e-03],
           [ 2.46011901e-03,  2.56006419e-03,  2.67590510e-03, ...,
             1.16123971e-01,  1.22504313e-01,  1.26281992e-01],
           [ 2.52002115e-03,  2.68074890e-03,  2.85903163e-03, ...,
             1.52767459e-01,  1.60322509e-01,  1.65270354e-01]],
          shape=(18, 80))

  • temperature
    (species, year)
    float64
    1.581e-05 3.289e-05 ... 0.04128

    array([[ 1.58111496e-05,  3.28899215e-05,  5.13338250e-05, ...,
             1.54084116e-02,  1.59098936e-02,  1.64172582e-02],
           [ 1.90731109e-04,  2.17710132e-04,  2.45386131e-04, ...,
             1.95800244e-02,  2.05632530e-02,  2.13680465e-02],
           [ 3.87719009e-04,  4.42562082e-04,  4.98821970e-04, ...,
             1.87871310e-02,  1.95560133e-02,  1.99438289e-02],
           ...,
           [-5.16507169e-05, -5.89567400e-05, -6.64515068e-05, ...,
            -2.50276299e-03, -2.60519109e-03, -2.65685467e-03],
           [ 5.08188432e-04,  5.48708158e-04,  5.89396372e-04, ...,
             2.28660697e-02,  2.41218076e-02,  2.48584513e-02],
           [ 5.23999581e-04,  5.81598079e-04,  6.40730197e-04, ...,
             3.82744813e-02,  4.00317013e-02,  4.12757094e-02]],
          shape=(18, 80))

• Indexes: (2)
  • species
    PandasIndex

    PandasIndex(Index(['CO2', 'Contrails', 'NOx - ST O3', 'NOx - CH4 and induced', 'H2O',
           'Soot - ARI', 'Soot - ACI', 'Sulfur - ARI', 'Sulfur - ACI', 'NOx - CH4',
           'NOx - CH4 induced O3', 'NOx - CH4 induced H2O', 'NOx', 'Soot',
           'Sulfur', 'Aerosols', 'Non-CO2', 'Total'],
          dtype='object', name='species'))

  • year
    PandasIndex

    PandasIndex(Index([1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951,
           1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963,
           1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975,
           1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
           1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,
           2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
           2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019],
          dtype='int64', name='year'))

• Attributes: (0)

In [6]:

# --- Plot results ---
plot_simulation_results(results, data_var="temperature", species=["CO2", "Non-CO2"], stacked=True)

# --- Plot results --- plot_simulation_results(results, data_var="temperature", species=["CO2", "Non-CO2"], stacked=True)

Out[6]:

<Axes: title={'center': 'Temperature'}, xlabel='Year', ylabel='Temperature'>

In [ ]: