Effective radiative forcing of short-lived climate forcer (Figure 2.8)¶

Notebook sr15_2.3.3_short-lived_climate_forcers_radiative_forcing¶

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_radiative_forcing

IPCC SR15 scenario assessment¶

Analysis of radiative forcing from short-lived climate-forcers¶

This notebook plots the radiative forcing from short-lived climate forcers (SLCF) shown in Figure 2.8 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, 2020, 2030, 2050, 2100]

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/fig2.8_effective_radiative_forcing.{}'

In [10]:

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

In [11]:

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

Compute radiative forcing from short-lived climate forcers¶

In [12]:

df.filter(variable='AR5 climate diagnostics|Forcing|CO2|*').variables()

Out[12]:

0       AR5 climate diagnostics|Forcing|CO2|FAIR|MED
1    AR5 climate diagnostics|Forcing|CO2|MAGICC6|MED
Name: variable, dtype: object

In [13]:

rf_llcf_vars = [
    'AR5 climate diagnostics|Forcing|CO2|FAIR|MED',
    'AR5 climate diagnostics|Forcing|N2O|FAIR|MED'
]

In [14]:

for v in ['AR5 climate diagnostics|Forcing|FAIR|MED'] + rf_llcf_vars:
    df.require_variable(v, exclude_on_fail=True)

df.filter(exclude=False, inplace=True)

pyam.core - INFO: 1 scenario does not include required variable `AR5 climate diagnostics|Forcing|FAIR|MED`, marked as `exclude: True` in metadata
pyam.core - INFO: 1 scenario does not include required variable `AR5 climate diagnostics|Forcing|CO2|FAIR|MED`, marked as `exclude: True` in metadata
pyam.core - INFO: 1 scenario does not include required variable `AR5 climate diagnostics|Forcing|N2O|FAIR|MED`, marked as `exclude: True` in metadata

In [15]:

rf_all = df.filter(variable='AR5 climate diagnostics|Forcing|FAIR|MED').timeseries()
rf_all.index = rf_all.index.droplevel([2, 3, 4])

In [16]:

rf_llcf = df.filter(variable=rf_llcf_vars).timeseries()
rf_llcf = rf_llcf.groupby(pyam.META_IDX).sum()

In [17]:

rf_slcf = rf_all - rf_llcf

In [18]:

data = pyam.filter_by_meta(rf_slcf, **filter_args)
boxplot_by_cat(data, **plotting_args(),
               ylabel='Effective radiative forcing (W/m2)', )

Export timeseries data to `xlsx`¶

In [19]:

data.reset_index().to_excel('output/fig2.8_data_table.xlsx')

In [ ]: