Global characteristics of short-lived non-CO2 emissions (Figure 2.7)¶

Notebook sr15_2.3.3_short-lived_climate_forcers¶

This notebook is based on the Release 1.1 of the IAMC 1.5C Scenario Explorer and Data and refers to the published version of the IPCC Special Report on Global Warming of 1.5C (SR15).

The notebook is run with pyam release 0.5.0.

The source code of this notebook is available on GitHub (release 2.0.2).

sr15_2.3.3_short-lived_climate_forcers

IPCC SR15 scenario assessment¶

Analysis of short-lived non-CO2 emissions¶

This notebook plots emissions of CH4, F-gases, BC and SO2 as shown in Figure 2.7 of the IPCC's "Special Report on Global Warming of 1.5°C".

The scenario data used in this analysis can be accessed and downloaded at https://data.ene.iiasa.ac.at/iamc-1.5c-explorer.

Load `pyam` package and other dependencies¶

In [1]:

import pandas as pd
import numpy as np
import io
import itertools
import yaml
import math
import matplotlib.pyplot as plt
plt.style.use('style_sr15.mplstyle')
%matplotlib inline
import pyam

from utils import boxplot_by_cat

pyam - INFO: Running in a notebook, setting `pyam` logging level to `logging.INFO` and adding stderr handler

Import scenario data, categorization and specifications files¶

The metadata file with scenario categorisation and quantitative indicators can be downloaded at https://data.ene.iiasa.ac.at/iamc-1.5c-explorer.
Alternatively, it can be re-created using the notebook sr15_2.0_categories_indicators.

The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook.

In [2]:

sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')

pyam.utils - INFO: Reading `../data/iamc15_scenario_data_world_r2.0.xlsx`

In [3]:

sr1p5.load_meta('sr15_metadata_indicators.xlsx')

pyam.core - INFO: Importing metadata for 416 scenarios (for total of 416)

In [4]:

with open("sr15_specs.yaml", 'r') as stream:
    specs = yaml.load(stream, Loader=yaml.FullLoader)

rc = pyam.run_control()
for item in specs.pop('run_control').items():
    rc.update({item[0]: item[1]})
cats = specs.pop('cats')
all_cats = specs.pop('all_cats')
subcats = specs.pop('subcats')
all_subcats = specs.pop('all_subcats')
plotting_args = specs.pop('plotting_args')
marker= specs.pop('marker')

Downselect scenario ensemble to categories of interest for this assessment¶

In [5]:

years = [2010, 2030, 2050]

In [6]:

cats.remove('Above 2C')

In [7]:

sr1p5.meta.rename(columns={'Kyoto-GHG|2010 (SAR)': 'kyoto_ghg_2010'}, inplace=True)

In [8]:

df = sr1p5.filter(kyoto_ghg_2010='in range', category=cats, year=years)

Set specifications for filter and plotting¶

In [9]:

save_name = 'output/fig2.7{}.{}'

In [10]:

filter_args = dict(df=sr1p5, category=cats, marker=None, join_meta=True)

In [11]:

compare_years = [2030, 2050]
base_year = 2010

In [12]:

def plotting_args(name, filetype='png'):
    return {'categories': cats, 'column': 'category', 'years': years,
            'add_marker': marker,
            'save': save_name.format(name, filetype)}

In [13]:

data = []

Plot different emissions pathways by category¶

In [14]:

ch4 = df.filter(variable='Emissions|CH4').timeseries()

In [15]:

name = 'ch4'
label = 'Global CH4 emissions'
unit = 'MtCH4/yr'
_data = pyam.filter_by_meta(ch4, **filter_args)
fig = boxplot_by_cat(_data, ylabel='{} ({})'.format(label, unit),
                     **plotting_args('a_{}'.format(name)))
_data['species'] = name
data.append(_data)

In [16]:

fgases = df.filter(variable='Emissions|F-Gases').timeseries()

In [17]:

name = 'f-gases'
label = 'Global F-gas emissions'
unit = 'GtCO2e/yr'

_data = pyam.filter_by_meta(fgases, **filter_args)
fig = boxplot_by_cat(_data, ylabel='{} ({})'.format(label, unit),
                     **plotting_args('b_{}'.format(name)), legend=False)
_data['species'] = name
data.append(_data)

In [18]:

bc = df.filter(variable='Emissions|BC').timeseries()

In [19]:

name = 'bc'
label = 'Global BC emissions'
unit = 'MtBC'

_data = pyam.filter_by_meta(bc, **filter_args)
fig = boxplot_by_cat(_data, ylabel='{} ({})'.format(label, unit),
                     **plotting_args('c_{}'.format(name)), legend=False)
_data['species'] = name
data.append(_data)

In [20]:

so2 = df.filter(variable='Emissions|Sulfur').timeseries()

In [21]:

name = 'so2'
label = 'Global SO2 emissions'
unit = 'MtSO2'
_data = pyam.filter_by_meta(so2, **filter_args)
fig = boxplot_by_cat(_data, ylabel='{} ({})'.format(label, unit),
                     **plotting_args('d_{}'.format(name)), legend=False)
_data['species'] = name
data.append(_data)

Export timeseries data to `xlsx`¶

In [22]:

data = pd.concat(data).set_index(['species', 'category', 'marker'], append=True)

In [23]:

data.head()

Out[23]:

								2010	2030	2050
model	scenario	region	variable	unit	species	category	marker
IMAGE 3.0.1	IMA15-RenElec	World	Emissions\|CH4	Mt CH4/yr	ch4	1.5C high overshoot	NaN	349.608185	243.733200	190.165695
REMIND 1.7	ADVANCE_2020_Med2C	World	Emissions\|CH4	Mt CH4/yr	ch4	Higher 2C	NaN	371.620600	348.322900	264.427200
AIM/CGE 2.0	SFCM_SSP2_SupTech_1p5Degree	World	Emissions\|CH4	Mt CH4/yr	ch4	Lower 2C	NaN	372.161500	199.236700	144.036800
MESSAGE-GLOBIOM 1.0	EMF33_Med2C_cost100	World	Emissions\|CH4	Mt CH4/yr	ch4	Higher 2C	NaN	326.445555	282.830349	234.067868
MESSAGE-GLOBIOM 1.0	ADVANCE_2020_1.5C-2100	World	Emissions\|CH4	Mt CH4/yr	ch4	1.5C low overshoot	NaN	325.287412	238.415911	169.963534

In [24]:

data.reset_index().to_excel('output/fig2.7_data_table.xlsx')

In [ ]: