Range of assumptions of socio-economic drivers (Figure 2.4)¶

Notebook sr15_2.3.1_range_of_assumptions¶

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_2.3.1_range_of_assumptions

IPCC SR15 scenario assessment¶

Assessment of underlying drivers and assumptions¶

This notebook contains the assessment of underlying drivers and assumptions of the scenario ensemble
in Section 2.3.1 and Figure 2.4 for 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 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')
cats_15 = specs.pop('cats_15')
marker= specs.pop('marker')

Set specifications for figures and statistics¶

First, set the list of years included in the plots. Then, define an auxiliary dictionary and function for easier display.

In [5]:

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

Downselect scenario ensemble to years interest for this assessment¶

In [6]:

full_horizon = range(2010, 2101, 10)

In [7]:

df = sr1p5.filter(category=cats, year=full_horizon)

Set plotting settings for illustrative pathways¶

In [8]:

_rc = {
    'S1': dict(linewidth=3, linestyle='--'),
    'S2': dict(linewidth=4, linestyle=':'),
    'S5': dict(linewidth=3, linestyle='-.'),
    'LED': dict(linewidth=3, linestyle='-'),
}

In [9]:

def line_plot_with_markers(ax, _df, name, panel):
    _data = _df.filter(category=cats_15, keep=False).data
    ax.plot(pyam.plotting.reshape_line_plot(_data, 'year', 'value'),
            color='lightgrey')
    ax.scatter(x=[], y=[], c='lightgrey', label='all scenarios')
    y_pos = max(_data.value) - 0.05 * (max(_data.value) - min(_data.value))

    _data = _df.filter(category=cats_15).data
    ax.plot(pyam.plotting.reshape_line_plot(_data, 'year', 'value'),
            color='xkcd:baby blue')
    ax.scatter(x=[], y=[], c='xkcd:baby blue', label='1.5°C pathways')

    for m in marker:
        _data = _df.filter(marker=m).data
        if not _data.empty:
            ax.plot(pyam.plotting.reshape_line_plot(_data, 'year', 'value'),
                    color='xkcd:darkish blue', **_rc[m], label=m)
    
    ax.set_ylabel('{} ({})'.format(name, _data.unit.unique()[0]))
    pyam.plotting.set_panel_label('({})'.format(panel), ax=ax)

In [10]:

fig, ax = plt.subplots(2, 2, figsize=(8, 6))

pop = df.filter(variable='Population')
pop.convert_unit('million', 'billion', factor=1/1000, inplace=True)
line_plot_with_markers(ax[0][0], pop, 'Population', 'a')

gdp = df.filter(variable='GDP|PPP')
gdp.convert_unit('billion US$2010/yr', 'trillion US$2010/yr', factor=1/1000, inplace=True)
line_plot_with_markers(ax[0][1], gdp, 'Gross World Product', 'b')

final = df.filter(variable='Final Energy')
line_plot_with_markers(ax[1][0], final, 'Final Energy Demand', 'c')

food =  df.filter(variable='Food Demand')
line_plot_with_markers(ax[1][1], food, 'Food Demand', 'd')

ax[0][0].legend(loc=1)
fig.tight_layout()

In [11]:

fig.savefig('output/fig2.4_drivers_assumptions.png')

Export timeseries data to `xlsx`¶

In [12]:

writer = pd.ExcelWriter('output/fig2.4_data_table.xlsx')
pyam.utils.write_sheet(writer, 'population', pyam.filter_by_meta(pop.timeseries(), **filter_args),
                       index=True)
pyam.utils.write_sheet(writer, 'gdp', pyam.filter_by_meta(gdp.timeseries(), **filter_args),
                       index=True)
pyam.utils.write_sheet(writer, 'final energy', pyam.filter_by_meta(final.timeseries(), **filter_args),
                       index=True)
pyam.utils.write_sheet(writer, 'food demand', pyam.filter_by_meta(food.timeseries(), **filter_args),
                       index=True)
writer.save()

In [ ]: