Categorisation of pathways by climate impact and computation of metadata indicators¶
Notebook sr15_2.0_categories_indicators¶
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).
IPCC SR15 scenario assessment¶
Scenario categorization and indicators¶
This notebook assigns the categorization by warming outcome and computes a range of descriptive indicators
for the scenario assessment of the IPCC's "Special Report on Global Warming of 1.5°C".
It generates a sr15_metadata_indicators.xlsx spreadsheet, which is used in other notebooks for this assessment
for categorization and extracting descriptive indicators.
Scenario ensemble data¶
The scenario data used in this analysis can be accessed and downloaded at https://data.ene.iiasa.ac.at/iamc-1.5c-explorer.
Bibliographic details of the scenario ensemble and all studies that contributed scenarios to the ensemble are included in this repository as Endnote (enl), Reference Manager (ris), and BibTex (bib) format.
License and recommended citation¶
This notebook is licensed under the Apache License, Version 2.0.
Please refer to the README for the recommended citation of the scenario ensemble and the notebooks in this repository.
Import dependencies and define general notebook settings¶
import math
import io
import yaml
import re
import pandas as pd
import numpy as np
from IPython.display import display
Introduction and tutorial for the pyam package¶
This notebook (and all other analysis notebooks in this repository) uses the pyam package,
an open-source Python package for IAM scenario analysis and visualization
(pyam-iamc.readthedocs.io).
For an introduction of the notation and features of the pyam package,
please refer to this tutorial.
It will take you through the basic functions and options used here,
and provide further introduction and guidelines.
import pyam
logger = pyam.logger
Import Matplotlib and set figure layout defaults in line with SR1.5 guidelines¶
import matplotlib.pyplot as plt
plt.style.use('style_sr15.mplstyle')
Import scenario snapshot and define auxiliary dictionaries¶
This notebook only assigns indicator based on global timeseries data.
The dictionary meta_tables is used to collect definitions
of categories and secondary scenario classification throughout this script.
These definitions are exported to the metadata/categorization Excel workbook
at the end of the script for completeness.
The dictionary meta_docs collects definitions used for the documentation tags
in the online scenario explorer.
The dictionary specs collects lists and the run control specifications to be exported to JSON
and used by other notebooks for the SR1.5 scenario analysis.
The plotting_args dictionary assigns the default plotting arguemnts in this notebook.
sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')
meta_tables = {}
meta_docs = {}
specs = {}
plotting_args = {'color': 'category', 'linewidth': 0.2}
specs['plotting_args'] = plotting_args
Verify completeness of scenario submissions for key variables¶
Verify that every scenario except for Shell Sky and the historical reference scenarios reports CO2 Emissions in 2030.
sr1p5.require_variable(variable='Emissions|CO2', year=2030, exclude_on_fail=False)
Check MAGICC postprocessing prior to categorization¶
Assign scenarios that could not be postprocessed by probabilistic MAGICC to respective categories:
- data not available for full century
- insufficient reporting of emission species
- reference scenario
sr1p5.set_meta(name='category', meta= 'uncategorized')
reference = sr1p5.filter(model='Reference')
pd.DataFrame(index=reference.meta.index)
sr1p5.set_meta(meta='reference', name='category', index=reference)
no_climate_assessment = (
sr1p5.filter(category='uncategorized').meta.index
.difference(sr1p5.filter(year=2100, variable='Emissions|CO2').meta.index)
)
pd.DataFrame(index=no_climate_assessment)
sr1p5.set_meta(meta='no-climate-assessment', name='category', index=no_climate_assessment)
Categorization of scenarios¶
This section applies the categorization of scenario as defined in Chapter 2 of the Special Report for unique assignment of scenarios.
The category specification as agreed upon at LAM 3 in Malmö is repeated here for easier reference.
The term $P_{x°C}$ refers to the probability of exceeding warming of $x°C$ throughout the century in at least one year and $P_{x°C}(y)$ refers to the probability of exceedance in a specific year $y$.
| Categories | Subcategories | Probability to exceed warming threshold | Acronym | Color |
|---|---|---|---|---|
| Below 1.5°C | Below 1.5°C (I) | $P_{1.5°C} \leq 0.34$ | Below 1.5C (I) | xkcd:baby blue |
| Below 1.5°C (II) | $0.34 < P_{1.5°C} \leq 0.50$ | Below 1.5C (II) | ||
| 1.5°C return with low OS | Lower 1.5°C return with low OS | $0.50 < P_{1.5°C} \leq 0.67$ and $P_{1.5°C}(2100) \leq 0.34$ | (Lower) 1.5C low OS | xkcd:bluish |
| Higher 1.5°C return with low OS | $0.50 < P_{1.5°C} \leq 0.67$ and $0.34 < P_{1.5°C}(2100) \leq 0.50$ | (Higher) 1.5C low OS | ||
| 1.5°C return with high OS | Lower 1.5°C return with high OS | $0.67 < P_{1.5°C}$ and $P_{1.5°C}(2100) \leq 0.34$ | (Lower) 1.5C high OS | xkcd:darkish blue |
| Higher 1.5°C return with high OS | $0.67 < P_{1.5°C}$ and $0.34 < P_{1.5°C}(2100) \leq 0.50$ | (Higher) 1.5C high OS | ||
| Lower 2.0°C | $P_{2.0°C} \leq 0.34$ (excluding above) | Lower 2C | xkcd:orange | |
| Higher 2.0°C | $0.34 < P_{2.0°C} \leq 0.50$ (excluding above) | Higher 2C | xkcd:red | |
| Above 2.0°C | $P_{2.0°C} > 0.50$ for at least 1 year | Above 2C | darkgrey |
Category definitions to Excel¶
The following dictionary repeats the category definitions from the table above
and saves them as a pandas.DataFrame to a dictionary meta_tables.
Throughout the notebook, this dictionary is used to collect definitions
of categories and secondary scenario classification.
These definitions are exported to the metadata/categorization Excel workbook
at the end of the script for easy reference.
dct = {'Categories of scenarios':
['Below 1.5°C',
'',
'1.5°C return with low overshoot',
'',
'1.5°C return with high overshoot',
'',
'Lower 2.0°C',
'Higher 2.0°C',
'Above 2.0°C'],
'Subcategories':
['Below 1.5°C (I)',
'Below 1.5°C (II)',
'Lower 1.5°C return with low overshoot',
'Higher 1.5°C return with low overshoot',
'Lower 1.5°C return with high overshoot',
'Higher 1.5°C return with high overshoot',
'',
'',
''],
'Criteria for assignment to category':
['P1.5°C ≤ 0.34',
'0.34 < P1.5°C ≤ 0.50',
'0.50 < P1.5°C ≤ 0.67 and P1.5°C(2100) ≤ 0.34',
'0.50 < P1.5°C ≤ 0.67 and 0.34 < P1.5°C(2100) ≤ 0.50',
'0.67 < P1.5°C and P1.5°C(2100) ≤ 0.34',
'0.67 < P1.5°C and 0.34 < P1.5°C(2100) ≤ 0.50',
'P2.0°C ≤ 0.34 (excluding above)',
'0.34 < P2.0°C ≤ 0.50 (excluding above)',
'P2.0°C > 0.50 during at least 1 year'
],
'Acronym':
['Below 1.5C (I)',
'Below 1.5C (II)',
'Lower 1.5C low overshoot',
'Higher 1.5C low overshoot',
'Lower 1.5C high overshoot',
'Higher 1.5C high overshoot',
'Lower 2C',
'Higher 2C',
'Above 2C'],
'Color':
['xkcd:baby blue',
'',
'xkcd:bluish',
'',
'xkcd:darkish blue',
'',
'xkcd:orange',
'xkcd:red',
'darkgrey']
}
cols = ['Categories of scenarios', 'Subcategories', 'Criteria for assignment to category', 'Acronym', 'Color']
categories_doc = pd.DataFrame(dct)[cols]
meta_tables['categories'] = categories_doc
meta_docs['category'] = 'Categorization of scenarios by global warming impact'
meta_docs['subcategory'] = 'Sub-categorization of scenarios by global warming impact'
other_cats = ['no-climate-assessment', 'reference']
cats = ['Below 1.5C', '1.5C low overshoot', '1.5C high overshoot', 'Lower 2C', 'Higher 2C', 'Above 2C']
all_cats = cats + other_cats
subcats = dct['Acronym']
all_subcats = subcats + other_cats
specs['cats'] = cats
specs['all_cats'] = all_cats
specs['subcats'] = subcats
specs['all_subcats'] = all_subcats
Subcategory assignment¶
We first assign the subcategories, then aggregate those assignment to the main categories. The categories assigned above to indicate reasons for non-processing by MAGICC are copied over to the subcategories.
Keep in mind that setting a category will re-assign scenarios (in case they have already been assigned). So in case of going back and forth in this notebook (i.e., not executing the cells in the correct order), make sure to reset the categorization.
def warming_exccedance_prob(x):
return 'AR5 climate diagnostics|Temperature|Exceedance Probability|{} °C|MAGICC6'.format(x)
expected_warming = 'AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|Expected value'
median_warming = 'AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED'
sr1p5.set_meta(meta=sr1p5['category'], name='subcategory')
pyam.categorize(sr1p5, exclude=False, subcategory='uncategorized',
value='Below 1.5C (I)', name='subcategory',
criteria={warming_exccedance_prob(1.5): {'up': 0.34}},
color='xkcd:baby blue')
pyam.categorize(sr1p5, exclude=False, subcategory='uncategorized',
value='Below 1.5C (II)', name='subcategory',
criteria={warming_exccedance_prob(1.5): {'up': 0.50}},
color='xkcd:baby blue')
To categorize by a variable using multiple filters (here: less than 66% probability of exceeding 1.5°C at any point during the century and less than 34% probability of exceeding that threshold in 2100) requires to perform the assignment in three steps - first, categorize to an intermediate low OS category and, in a second step, assign to the category in question. The third step resets all scenarios still categorized as intermediate after the second step back to uncategorized.
pyam.categorize(sr1p5, exclude=False, subcategory='uncategorized',
value='low overshoot', name='subcategory',
criteria={warming_exccedance_prob(1.5): {'up': 0.67}})
pyam.categorize(sr1p5, exclude=False, subcategory='low overshoot',
value='Lower 1.5C low overshoot', name='subcategory',
criteria={warming_exccedance_prob(1.5): {'up': 0.34, 'year': 2100}},
color='xkcd:bluish')
pyam.categorize(sr1p5, exclude=False, subcategory='low overshoot',
value='Higher 1.5C low overshoot', name='subcategory',
criteria={warming_exccedance_prob(1.5): {'up': 0.50, 'year': 2100}},
color='xkcd:bluish')
Display scenarios that satisfy the low overshoot criterion
but are not assigned to Lower 1.5C lower overshoot or Higher 1.5C lower overshoot.
Then, reset them to uncategorized.
sr1p5.filter(subcategory='low overshoot').meta
sr1p5.set_meta(meta='uncategorized', name='subcategory', index=sr1p5.filter(subcategory='low overshoot'))
Determine all scenarios with a probability to exceed 1.5°C greater than 66% in any year throughout the century.
The function categorize() cannot be used for this selection, because it would either check for the criteria being true for all years or for a particular year.
df = sr1p5.filter(exclude=False, subcategory='uncategorized', variable=warming_exccedance_prob(1.5)).timeseries()
sr1p5.set_meta(meta='high overshoot', name='subcategory',
index=df[df.apply(lambda x: max(x), axis=1) > 0.66].index)
pyam.categorize(sr1p5, exclude=False, subcategory='high overshoot',
value='Lower 1.5C high overshoot', name='subcategory',
criteria={warming_exccedance_prob(1.5): {'up': 0.34, 'year': 2100}},
color='xkcd:darkish blue')
pyam.categorize(sr1p5, exclude=False, subcategory='high overshoot',
value='Higher 1.5C high overshoot', name='subcategory',
criteria={warming_exccedance_prob(1.5): {'up': 0.50, 'year': 2100}},
color='xkcd:darkish blue')
Reset scenarios that satisfy the high overshoot criterion
but are not assigned to Lower 1.5C high overshoot or Higher 1.5C high overshoot.
sr1p5.set_meta(meta='uncategorized', name='subcategory', index=sr1p5.filter(subcategory='high overshoot'))
pyam.categorize(sr1p5, exclude=False, subcategory='uncategorized',
value='Lower 2C', name='subcategory',
criteria={warming_exccedance_prob(2.0): {'up': 0.34}},
color='xkcd:orange')
pyam.categorize(sr1p5, exclude=False, subcategory='uncategorized',
value='Higher 2C', name='subcategory',
criteria={warming_exccedance_prob(2.0): {'up': 0.50}},
color='xkcd:red')
pyam.categorize(sr1p5, exclude=False, subcategory='uncategorized',
value='Above 2C', name='subcategory',
criteria={warming_exccedance_prob(2.0): {'up': 1.0}},
color='darkgrey')
Aggregation of subcategories to categories¶
rc = pyam.run_control()
def assign_rc_color_from_sub(cat, sub):
rc.update({'color': {'category': {cat: rc['color']['subcategory'][sub]}}})
sr1p5.set_meta(meta='Below 1.5C', name='category',
index=sr1p5.filter(subcategory=['Below 1.5C (I)', 'Below 1.5C (II)']).meta.index)
assign_rc_color_from_sub('Below 1.5C', 'Below 1.5C (II)')
sr1p5.set_meta(meta='1.5C low overshoot', name='category',
index=sr1p5.filter(subcategory=['Lower 1.5C low overshoot', 'Higher 1.5C low overshoot']))
assign_rc_color_from_sub('1.5C low overshoot', 'Lower 1.5C low overshoot')
sr1p5.set_meta(meta='1.5C high overshoot', name='category',
index=sr1p5.filter(subcategory=['Lower 1.5C high overshoot', 'Higher 1.5C high overshoot']))
assign_rc_color_from_sub('1.5C high overshoot', 'Lower 1.5C high overshoot')
cats_non15 = ['Lower 2C', 'Higher 2C', 'Above 2C']
df_2c = sr1p5.filter(subcategory=cats_non15)
sr1p5.set_meta(meta=df_2c['subcategory'], name='category')
for c in cats_non15:
assign_rc_color_from_sub(c, c)
Additional assessment of categorization¶
Check whether there are any scenarios that return to 1.5°C by the end of the century and exceed the 2°C threshold with a likelyhood higher than 34% or 50% (i.e., the Lower 2C or the Higher 2C categories respectively). Having scenario categorized as 1.5C but with a higher-than-50% probability of exceeding 2°C at some point in the century may need to be considered separately in subsequent assessment.
cats_15 = ['Below 1.5C', '1.5C low overshoot', '1.5C high overshoot']
specs['cats_15'] = cats_15
cats_15_no_lo = ['Below 1.5C', '1.5C low overshoot']
specs['cats_15_no_lo'] = cats_15_no_lo
cats_2 = ['Lower 2C', 'Higher 2C']
specs['cats_2'] = cats_2
df = sr1p5.filter(exclude=False, category=cats_15, variable=warming_exccedance_prob(2.0)).timeseries()
ex_prob_2 = df.apply(lambda x: max(x))
if max(ex_prob_2) > 0.34:
logger.warning('The following 1.5C-scenarios are not `Lower 2C` scenarios:')
display(df[df.apply(lambda x: max(x), axis=1) > 0.34])
if max(ex_prob_2) > 0.50:
logger.warning('The following 1.5C-scenarios are not `2C` scenarios:')
display(df[df.apply(lambda x: max(x), axis=1) > 0.50])
Counting and evaluation of scenario assignment categories¶
Count the number of scenarios assigned to each category.
This table is the basis for Tables 2.1 and 2.A.11 in the SR1.5.
lst = sr1p5.meta.groupby(['category', 'subcategory']).count()
(
lst
.reindex(all_cats, axis='index', level=0)
.reindex(all_subcats, axis='index', level=1)
.rename(columns={'exclude': 'count'})
)
Check whether any scenarios are still marked as uncategorized. This may be due to missing MAGICC postprocessing.
if any(sr1p5['category'] == 'uncategorized'):
logger.warning('There are scenarios that are no yet categorized!')
display(sr1p5.filter(category='uncategorized').meta)
Validation of Kyoto GHG emissions range (SAR-GWP100)¶
Validate all scenarios whther aggregate Kyoto gases are outside the range as assessed by the Second Assessment Report (SAR) using the Global Warming Potential over 100 years (GWP100). These scenarios are excluded from some figures and tables in the assessment.
invalid_sar_gwp = sr1p5.validate(criteria={'Emissions|Kyoto Gases (SAR-GWP100)':
{'lo': 44500, 'up': 53500, 'year':2010}}, exclude_on_fail=False)
name='Kyoto-GHG|2010 (SAR)'
sr1p5.set_meta(meta='in range', name=name)
sr1p5.set_meta(meta='exclude', name=name, index=invalid_sar_gwp)
meta_docs[name] = 'Indicator whether 2010 Kyoto-GHG reported by the scenario (as assessed by IPCC SAR) are in the valid range'
Assignment of baseline scenarios¶
This section assigns a baseline reference for scenarios from selected model intercomparison projects and indivitual submissions.
def set_baseline_reference(x):
m, s = (x.name[0], x.name[1])
b = None
if s.startswith('SSP') and not 'Baseline' in s:
b = '{}Baseline'.format(s[0:5])
if s.startswith('CD-LINKS') and not 'NoPolicy' in s:
b = '{}NoPolicy'.format(s[0:9])
if s.startswith('EMF33') and not 'Baseline' in s:
b = '{}Baseline'.format(s[0:6])
if s.startswith('ADVANCE') and not 'NoPolicy' in s:
b = '{}NoPolicy'.format(s[0:8])
if s.startswith('GEA') and not 'base' in s:
b = '{}base'.format(s[0:8])
if s.startswith('TERL') and not 'Baseline' in s:
b = s.replace('15D', 'Baseline').replace('2D', 'Baseline')
if s.startswith('SFCM') and not 'Baseline' in s:
b = s.replace('1p5Degree', 'Baseline').replace('2Degree', 'Baseline')
if s.startswith('CEMICS') and not s == 'CEMICS-Ref':
b = 'CEMICS-Ref'
if s.startswith('SMP') and not 'REF' in s:
if s.endswith('Def') or s.endswith('regul'):
b = 'SMP_REF_Def'
else:
b = 'SMP_REF_Sust'
if s.startswith('DAC'):
b = 'BAU'
# check that baseline scenario exists for specific model `m`
if (m, b) in sr1p5.meta.index:
return b
# else (or if scenario name not in list above), return None
return None
name = 'baseline'
sr1p5.set_meta(sr1p5.meta.apply(set_baseline_reference, raw=True, axis=1), name)
meta_docs[name] = 'Name of the respective baseline (or reference/no-policy) scenario'
Assignent of marker scenarios¶
The following scenarios are used as marker throughout the analysis and visualization, cf. Figure 2.7 (SOD):
| Marker | Model & scenario name | Reference | Symbol |
|---|---|---|---|
| S1 | AIM/CGE 2.0 / SSP1-19 | Fujimori et al., 2017 | white square |
| S2 | MESSAGE-GLOBIOM 1.0 / SSP2-19 | Fricko et al., 2017 | yellow square |
| S5 | REMIND-MAgPIE 1.5 / SSP5-19 | Kriegler et al., 2017 | black square |
| LED | MESSAGEix-GLOBIOM 1.0 / LowEnergyDemand | Grubler et al., 2018 | white circle |
dct = {'Marker':
['S1',
'S2',
'S5',
'LED'],
'Model and scenario name':
['AIM/CGE 2.0 / SSP1-19',
'MESSAGE-GLOBIOM 1.0 / SSP2-19',
'REMIND-MAgPIE 1.5 / SSP5-19',
'MESSAGEix-GLOBIOM 1.0 / LowEnergyDemand'],
'Reference':
['Fujimori et al., 2017',
'Fricko et al., 2017',
'Kriegler et al., 2017',
'Grubler et al., 2018'],
'Symbol':
['white square',
'yellow square',
'black square',
'white circle']
}
cols = ['Marker', 'Model and scenario name', 'Reference', 'Symbol']
markers_doc = pd.DataFrame(dct)[cols]
meta_tables['marker scenarios'] = markers_doc
meta_docs['marker'] = 'Illustrative pathways (marker scenarios)'
specs['marker'] = ['S1', 'S2', 'S5', 'LED']
sr1p5.set_meta('', 'marker')
rc.update({'marker': {'marker': {'': None}}})
m = 'S1'
sr1p5.set_meta(m, 'marker',
sr1p5.filter(model='AIM/CGE 2.0', scenario='SSP1-19'))
rc.update({'marker': {'marker': {m: 's'}},
'c': {'marker': {m: 'white'}},
'edgecolors': {'marker': {m: 'black'}}}
)
m = 'S2'
sr1p5.set_meta(m, 'marker',
sr1p5.filter(model='MESSAGE-GLOBIOM 1.0', scenario='SSP2-19'))
rc.update({'marker': {'marker': {m: 's'}},
'c': {'marker': {m: 'yellow'}},
'edgecolors': {'marker': {m: 'black'}}})
m = 'S5'
sr1p5.set_meta(m, 'marker',
sr1p5.filter(model='REMIND-MAgPIE 1.5', scenario='SSP5-19'))
rc.update({'marker': {'marker': {m: 's'}},
'c': {'marker': {m: 'black'}},
'edgecolors': {'marker': {m: 'black'}}})
m = 'LED'
sr1p5.set_meta(m, 'marker',
sr1p5.filter(model='MESSAGEix-GLOBIOM 1.0', scenario='LowEnergyDemand'))
rc.update({'marker': {'marker': {m: 'o'}},
'c': {'marker': {m: 'white'}},
'edgecolors': {'marker': {m: 'black'}}})
Visual analysis of emission and temperature pathways by category¶
First, we plot all carbon dioxide emissions trajectories colored by category and the CO2 emissions from the AFOLU sector. Then, show the warming trajectories by category.
horizon = list(range(2000, 2020, 5)) + list(range(2020, 2101, 10))
df = sr1p5.filter(year=horizon)
df.filter(exclude=False, variable='Emissions|CO2').line_plot(**plotting_args, marker='marker')
df.filter(exclude=False, variable='Emissions|CO2|AFOLU').line_plot(**plotting_args, marker='marker')
df.filter(exclude=False, variable=expected_warming).line_plot(**plotting_args, marker='marker')
Import scientific references and publication status¶
The following block reads in an Excel table with the details of the scientific references for each scenario.
The main cell of this section loops over all entries in this Excel table, filters for the relevant scenarios, and assigns a short reference and the publication status. If multiple references are relevant for a scenario, the references are compiled, and the 'highest' publication status is written to the metadata.
ref_cols = ['project', 'model', 'scenario', 'reference', 'doi', 'bibliography']
sr1p5.set_meta('undefined', 'reference')
sr1p5.set_meta('unknown', 'project')
refs = pd.read_csv('../bibliography/scenario_references.csv', encoding='iso-8859-1')
_refs = {'index': []}
for i in ref_cols:
_refs.update({i.title(): []})
for cols in refs.iterrows():
c = cols[1]
filters = {}
# check that filters are defined
if c.model is np.NaN and c.scenario is np.NaN:
logger.warn('project `{}` on line {} has no filters assigned'
.format(c.project, cols[0]))
continue
# filter for scenarios to apply the project and publication tags
filters = {}
for i in ['model', 'scenario']:
if c[i] is not np.NaN:
if ";" in c[i]:
filters.update({i: re.sub(";", "", c[i]).split()})
else:
filters.update({i: c[i]})
df = sr1p5.filter(**filters)
if df.scenarios().empty:
logger.warn('no scenarios satisfy filters for project `{}` on line {} ({})'
.format(c.project, cols[0], filters))
continue
# write to meta-tables dictionary
_refs['index'].append(cols[0])
for i in ref_cols:
_refs[i.title()].append(c[i])
sr1p5.meta.loc[df.meta.index, 'project'] = c['project']
for i in df.meta.index:
r = c['reference']
sr1p5.meta.loc[i, 'reference'] = r if sr1p5.meta.loc[i, 'reference'] == 'undefined' \
else '{}; {}'.format(sr1p5.meta.loc[i, 'reference'], r)
cols = [i.title() for i in ref_cols]
meta_tables['references'] = pd.DataFrame(_refs)[cols]
meta_docs['reference'] = 'Scientific references'
meta_docs['project'] = 'Project identifier contributing the scenario'
Peak warming and indicator of median global warming peak-and-decline¶
Determine peak warming (relative to pre-industrial temperature) and end-of century warming and add this to the scenario metadata. Then, compute the "peak-and-decline" indicator as the difference between peak warming and warming in 2100.
def peak_warming(x, return_year=False):
peak = x[x == x.max()]
if return_year:
return peak.index[0]
else:
return float(max(peak))
median_temperature = sr1p5.filter(variable=median_warming).timeseries()
name = 'median warming at peak (MAGICC6)'
sr1p5.set_meta(median_temperature.apply(peak_warming, raw=False, axis=1), name)
meta_docs[name] = 'median warming above pre-industrial temperature at peak (°C) as computed by MAGICC6'
name = 'year of peak warming (MAGICC6)'
sr1p5.set_meta(median_temperature.apply(peak_warming, return_year=True, raw=False, axis=1), name)
meta_docs[name] = 'year of peak median warming as computed by MAGICC6'
name = 'median warming in 2100 (MAGICC6)'
sr1p5.set_meta(median_temperature[2100], name)
meta_docs[name] = 'median warming above at peak above pre-industrial temperature as computed by MAGICC6'
name = 'median warming peak-and-decline (MAGICC6)'
peak_decline = sr1p5['median warming at peak (MAGICC6)'] - sr1p5['median warming in 2100 (MAGICC6)']
sr1p5.set_meta(peak_decline, name)
meta_docs[name] = 'median warming peak-and-decline from peak to temperature in 2100 (°C) as computed by MAGICC6'
Add mean temperature at peak from 'FAIR' model diagnostics¶
median_temperature_fair = sr1p5.filter(variable='AR5 climate diagnostics|Temperature|Global Mean|FAIR|MED')\
.timeseries()
name = 'median warming at peak (FAIR)'
sr1p5.set_meta(median_temperature_fair.apply(peak_warming, raw=False, axis=1), name)
meta_docs[name] = 'median warming above pre-industrial temperature at peak (°C) as computed by FAIR'
name = 'year of peak warming (FAIR)'
sr1p5.set_meta(median_temperature_fair.apply(peak_warming, return_year=True, raw=False, axis=1), name)
meta_docs[name] = 'year of peak median warming as computed by FAIR'
fig, ax = plt.subplots()
sr1p5.filter(category=cats).scatter(ax=ax,
x='median warming at peak (MAGICC6)',
y='median warming at peak (FAIR)', color='category')
ax.plot(ax.get_xlim(), ax.get_xlim())
import matplotlib
matplotlib.__version__
fig, ax = plt.subplots()
sr1p5.scatter(ax=ax, x='year of peak warming (MAGICC6)', y='year of peak warming (FAIR)', color='category')
ax.plot(ax.get_xlim(), ax.get_xlim())
Computation of threshold exceedance year and 'overshoot' year count¶
Determine the year when a scenario exceeds a specific temperature threshold, and for how many years the threshold is exceeded.
This section uses the function exceedance() to determine the exceedance and return years.
The function overshoot_severity() computes the cumulative exceedance of the 1.5°C threshold
(i.e., the sum of temperature-years above the threshold).
def exceedance(temperature, years, threshold):
exceedance_yr = None
return_yr = None
overshoot_yr_count = None
prev_temp = 0
prev_yr = None
for yr, curr_temp in zip(years, temperature):
if np.isnan(curr_temp):
continue
if exceedance_yr is None and curr_temp > threshold:
x = (curr_temp - prev_temp) / (yr - prev_yr) # temperature change per year
exceedance_yr = prev_yr + int((threshold - prev_temp) / x) + 1 # add one because int() rounds down
if exceedance_yr is not None and return_yr is None and curr_temp < threshold:
x = (prev_temp - curr_temp) / (yr - prev_yr) # temperature change per year
return_yr = prev_yr + int((prev_temp - threshold) / x) + 1
prev_temp = curr_temp
prev_yr = yr
if return_yr is not None and exceedance_yr is not None:
overshoot_yr_count = int(return_yr - exceedance_yr)
if exceedance_yr is not None:
exceedance_yr = int(exceedance_yr)
if return_yr is not None:
return_yr = int(return_yr)
return [exceedance_yr, return_yr, overshoot_yr_count]
exceedance_meta = median_temperature.apply(exceedance, axis=1, raw=True,
years=median_temperature.columns, threshold=1.5)
name = 'exceedance year|1.5°C'
sr1p5.set_meta(exceedance_meta.apply(lambda x: x[0]), name)
meta_docs[name] = 'year in which the 1.5°C median warming threshold is exceeded'
name = 'return year|1.5°C'
sr1p5.set_meta(exceedance_meta.apply(lambda x: x[1]), name)
meta_docs[name] = 'year in which median warming returns below the 1.5°C threshold'
name = 'overshoot years|1.5°C'
sr1p5.set_meta(exceedance_meta.apply(lambda x: x[2]), name)
meta_docs[name] = 'number of years where 1.5°C median warming threshold is exceeded'
def overshoot_severity(x, meta):
exceedance_yr = meta.loc[x.name[0:2]]['exceedance year|1.5°C']
return_yr = meta.loc[x.name[0:2]]['return year|1.5°C'] - 1
# do not include year in which mean temperature returns to below 1.5
if exceedance_yr > 0 and return_yr > 0:
return pyam.cumulative(x, exceedance_yr, return_yr) - (return_yr - exceedance_yr + 1) * 1.5
name = 'exceedance severity|1.5°C'
sr1p5.set_meta(median_temperature.apply(overshoot_severity, axis=1, raw=False, meta=sr1p5.meta), name)
meta_docs[name] = 'sum of median temperature exceeding the 1.5°C threshold'
exceedance_meta = median_temperature.apply(exceedance, axis=1, raw=True,
years=median_temperature.columns, threshold=2)
name = 'exceedance year|2.0°C'
sr1p5.set_meta(exceedance_meta.apply(lambda x: x[0]), name)
meta_docs[name] = 'year in which the 2.0°C median warming threshold is exceeded'
name = 'return year|2.0°C'
sr1p5.set_meta(exceedance_meta.apply(lambda x: x[1]), name)
meta_docs[name] = 'year in which median warming returns below the 2.0°C threshold'
name = 'overshoot years|2.0°C'
sr1p5.set_meta(exceedance_meta.apply(lambda x: x[2]), name)
meta_docs[name] = 'number of years where 2.0°C median warming threshold is exceeded'
Secondary categorization and meta-data assignment according to CO2 emissions¶
Defining the range for cumulative indicators and units¶
All cumulative indicators are computed over the time horizon 2016-2100 (including the year 2100 in every summation).
baseyear = 2016
lastyear = 2100
def filter_and_convert(variable):
return (sr1p5
.filter(variable=variable)
.convert_unit('Mt CO2/yr', 'Gt CO2/yr')
.timeseries()
)
unit = 'Gt CO2/yr'
cumulative_unit = 'Gt CO2'
co2 = filter_and_convert('Emissions|CO2')
name = 'minimum net CO2 emissions ({})'.format(unit)
sr1p5.set_meta(co2.apply(np.nanmin, axis=1), name)
meta_docs[name] = 'Minimum of net CO2 emissions over the century ({})'.format(unit)
Indicators from cumulative CO2 emissions over the entire century (2016-2100)¶
Compute the total cumulative CO2 emissions for secondary categorization of scenarios.
Cumulative CO2 emissions are a first-order proxy for global mean temperature change.
Emissions are interpolated linearly between years. The last_year value is included in the summation.
The function pyam.cumulative() defined below aggregates timeseries values from first_year until last_year,
including both first and last year in the total. The function assumes linear interpolation for years where no values are provided.
name = 'cumulative CO2 emissions ({}-{}, {})'.format(baseyear, lastyear, cumulative_unit)
sr1p5.set_meta(co2.apply(pyam.cumulative, raw=False, axis=1, first_year=baseyear, last_year=lastyear), name)
meta_docs[name] = 'Cumulative net CO2 emissions from {} until {} (including the last year, {})'.format(
baseyear, lastyear, cumulative_unit)
ccs = filter_and_convert('Carbon Sequestration|CCS')
cum_ccs_label = 'cumulative CCS ({}-{}, {})'.format(baseyear, lastyear, cumulative_unit)
sr1p5.set_meta(ccs.apply(pyam.cumulative, raw=False, axis=1, first_year=baseyear, last_year=lastyear), cum_ccs_label)
meta_docs[cum_ccs_label] = 'Cumulative carbon capture and sequestration from {} until {} (including the last year, {})'\
.format(baseyear, lastyear, cumulative_unit)
beccs = filter_and_convert('Carbon Sequestration|CCS|Biomass')
cum_beccs_label = 'cumulative BECCS ({}-{}, {})'.format(baseyear, lastyear, cumulative_unit)
sr1p5.set_meta(beccs.apply(pyam.cumulative, raw=False, axis=1, first_year=baseyear, last_year=lastyear), cum_beccs_label)
meta_docs[cum_beccs_label] = 'Cumulative carbon capture and sequestration from bioenergy from {} until {} (including the last year, {})'.format(
baseyear, lastyear, cumulative_unit)
Issue #9 requested to add the data for scenario where timeseries data for bioenergy with CCS was not provided explicitly (and hence not captured by the computation above) but could implicitly by assessed from the CCS timeseries data.
filled_ccs = sr1p5.meta[sr1p5.meta[cum_ccs_label] == 0][cum_beccs_label]
sr1p5.set_meta(name=cum_beccs_label, meta=0, index=filled_ccs[filled_ccs.isna()].index)
seq_lu = filter_and_convert('Carbon Sequestration|Land Use')
name = 'cumulative sequestration land-use ({}-{}, {})'.format(baseyear, lastyear, cumulative_unit)
sr1p5.set_meta(seq_lu.apply(pyam.cumulative, raw=False, axis=1, first_year=baseyear, last_year=lastyear), name)
meta_docs[name] = 'Cumulative carbon sequestration from land use from {} until {} (including the last year, {})'.format(
baseyear, lastyear, cumulative_unit)
Cumulative CO2 emissions until peak warming¶
def get_from_meta_column(df, x, col):
val = df.meta.loc[x.name[0:2], col]
return val if val < np.inf else max(x.index)
name = 'cumulative CO2 emissions ({} to peak warming, {})'.format(baseyear, cumulative_unit)
sr1p5.set_meta(co2.apply(lambda x: pyam.cumulative(x, first_year=baseyear,
last_year=get_from_meta_column(sr1p5, x,
'year of peak warming (MAGICC6)')),
raw=False, axis=1), name)
meta_docs[name] = 'cumulative net CO2 emissions from {} until the year of peak warming as computed by MAGICC6 (including the year of peak warming, {})'.format(
baseyear, cumulative_unit)
(
sr1p5
.filter(category=cats)
.scatter(x='cumulative CO2 emissions (2016 to peak warming, {})'.format(cumulative_unit),
y='median warming at peak (MAGICC6)', color='category')
)
Cumulative CO2 emissions until net-zero of total emissions¶
def year_of_net_zero(data, years, threshold):
prev_val = 0
prev_yr = np.nan
for yr, val in zip(years, data):
if np.isnan(val):
continue
if val < threshold:
x = (val - prev_val) / (yr - prev_yr) # absolute change per year
return prev_yr + int((threshold - prev_val) / x) + 1 # add one because int() rounds down
prev_val = val
prev_yr = yr
return np.inf
name = 'year of netzero CO2 emissions'
sr1p5.set_meta(co2.apply(year_of_net_zero, years=co2.columns, threshold=0, axis=1), name)
meta_docs[name] = 'year in which net CO2 emissions reach zero'
name = 'cumulative CO2 emissions ({} to netzero, {})'.format(baseyear, cumulative_unit)
sr1p5.set_meta(co2.apply(lambda x: pyam.cumulative(x, first_year=baseyear,
last_year=get_from_meta_column(sr1p5, x,
'year of netzero CO2 emissions')),
raw=False, axis=1), name)
meta_docs[name] = 'net CO2 emissions from {} until the year of peak warming (including the last year, {})'.format(
baseyear, cumulative_unit)
name = 'warming at netzero (MAGICC6)'
sr1p5.set_meta(median_temperature.apply(lambda x: x[get_from_meta_column(sr1p5, x,
'year of netzero CO2 emissions')],
raw=False, axis=1), name)
meta_docs[name] = 'median warming above pre-industrial temperatures in the year of net-zero CO2 emission (MAGICC, °C)'.format(
baseyear, cumulative_unit)
(
sr1p5
.scatter(x='cumulative CO2 emissions (2016 to netzero, {})'.format(cumulative_unit),
y='warming at netzero (MAGICC6)', color='category')
)
fig, ax = plt.subplots()
(
sr1p5
.scatter(ax=ax, x='cumulative CO2 emissions (2016 to peak warming, {})'.format(cumulative_unit),
y='cumulative CO2 emissions (2016 to netzero, {})'.format(cumulative_unit),
color='category')
)
ax.plot(ax.get_xlim(), ax.get_xlim())
fig, ax = plt.subplots()
(
sr1p5
.scatter(ax=ax, x='median warming at peak (MAGICC6)',
y='warming at netzero (MAGICC6)', color='category')
)
x = np.linspace(*ax.get_xlim())
ax.plot(ax.get_xlim(), ax.get_xlim())
fig, ax = plt.subplots()
(
sr1p5
.scatter(ax=ax, x='median warming in 2100 (MAGICC6)',
y='warming at netzero (MAGICC6)', color='category')
)
x = np.linspace(*ax.get_xlim())
ax.plot(ax.get_xlim(), ax.get_xlim())
Categorization and meta-data assignment according to final energy demand¶
Add a categorization column to the metadata categorization based on final energy demand at the end of the century.
horizon = list(range(2000, 2020, 5)) + list(range(2020, 2101, 10))
df = sr1p5.filter(year=horizon)
fe_df = df.filter(variable='Final Energy')
fe_df.line_plot(**plotting_args, marker='marker')
fe = fe_df.timeseries()
name = 'final energy|2100'
sr1p5.set_meta(fe[2100], name)
meta_docs[name] = 'Final energy demand at the end of the century (EJ/yr)'
Indicators on carbon price development¶
Retrieve the carbon price timeseries and derive the following indicators:
- carbon price in 2030, 2050, and 2100 given both in 2010$ as reported by models and as NPV
- simple average of NPV (2030-2100)
- annual compounded NPV (2030-2100)
- continuously compounded NPV (2030-2100)
All net present values (NPV) are given relative to the year 2020. They are calculated assuming a 5% discount rate.
dct = {'Indicator type':
['Price by year',
'Price by year (as NPV)',
'Average (Avg) NPV',
'Annual compounded (AC) NPV)',
'Continuously compounded (CC) NPV',
'', 'Note on NPV'
],
'Description':
['Global carbon price as reported by each scenario',
'Global carbon price as reported by each scenario discounted to 2020 NPV',
'Cumulative NPV carbon price from 2030 until 2100 divided by number of years (71)',
'Annual compounded NPV carbon price from 2030 until 2100 divided by number of years (71)',
'Continuously compounded NPV carbon price from 2030 until 2100 divided by number of years (71)',
'', 'All NPV indicators are discounted to 2020 using a 5% discount rate'
]
}
meta_tables['carbon_price'] = pd.DataFrame(dct)[['Indicator type', 'Description']]
carbonprice_df = sr1p5.filter(variable='Price|Carbon', year=range(2020, 2101, 10))
Plot the carbon price trajectories by category¶
There are some outlier scenarios with very high carbon prices, so the following cell manually sets the limits of the y-axis.
fig, ax = plt.subplots()
carbonprice_df.filter(year=range(2020, 2101, 10)).line_plot(ax=ax, **plotting_args)
plt.ylim(ymax=10000, ymin=0)
Show outliers for verification and assessment¶
carbon_price = carbonprice_df.timeseries()
carbon_price[carbon_price.apply(lambda x: max([i for i in x if not np.isnan(i)]) > 10000, axis=1)]
Compute net-present values of the carbon price¶
carbon_price_npv = carbon_price.copy()
r = 0.05
discount_year = 2020
npv_first_year = 2030
npv_last_year = 2100
for y in carbon_price_npv.columns:
carbon_price_npv[y] = carbon_price_npv[y] / math.pow(1 + r, y - discount_year)
discount_docstring = ' from {} until {} including the last year, '.format(npv_first_year, npv_last_year) + \
'discounted to {} using a {}% discount rate'.format(discount_year, r)
for y in [2030, 2050, 2100]:
name = 'carbon price|{}'.format(y)
sr1p5.set_meta(carbon_price[y], 'carbon price|{}'.format(y))
meta_docs[name] = 'carbon price in {} (2010USD/tCO2)'.format(y)
name = 'carbon price|{} (NPV)'.format(y)
sr1p5.set_meta(carbon_price_npv[y], name)
meta_docs[name] = 'net-present value of carbon price in {} (2010USD/tCO2), discounted to {}'.format(
y, discount_year)
name = 'carbon price|Avg NPV (2030-2100)'
sr1p5.set_meta(carbon_price_npv.apply(pyam.cumulative, first_year=2030, last_year=2100,
raw=False, axis=1)/71, name)
meta_docs[name] = 'continuously compounded net-present value of carbon prices (2010USD/tCO2)' + discount_docstring
def compute_w1_annual(r, dt):
value = 0
for i in range(1, dt): # range does not include the last element, but this is 0 in this formula
value += math.pow(1 / (1 + r), i) * (1 - i / dt)
return value
def compute_w2_annual(r, dt):
value = 0
for i in range(1, dt + 1): # range does not include the last element
value += math.pow(1 / (1 + r), i - dt) * (i / dt)
return value
w1_annual = {}
w2_annual = {}
def compute_w1_compounded(r, dt):
return 1 / r - (1 - math.exp(-r * dt)) / (r * r * dt)
def compute_w2_compounded(r, dt):
return - 1 / r + (math.exp(r * dt) - 1) / (r * r * dt)
w1_compounded = {}
w2_compounded = {}
# this function is an adaptation of 'pyam.timeseries:cumulative()'
def npv_weighted(x, first_year, last_year, w1, w2, w1_function, w2_function, r):
if min(x.index) > first_year or max(x.index) < last_year:
return np.nan
x[first_year] = pyam.fill_series(x, first_year)
x[last_year] = pyam.fill_series(x, last_year)
years = [i for i in x.index if i >= first_year and i <= last_year
and ~np.isnan(x[i])]
years.sort()
# loop over years
if not np.isnan(x[first_year]) and not np.isnan(x[last_year]):
value = 0
for (i, yr) in enumerate(years[:-1]):
next_yr = years[i+1]
dt = next_yr - yr
if dt not in w1.keys():
w1[dt] = w1_function(r, dt)
if dt not in w2.keys():
w2[dt] = w2_function(r, dt)
# the summation is shifted to include the first year fully in sum,
# otherwise, would return a weighted average of `yr` and `next_yr`
value += w1[dt] * x[yr] + w2[dt] * x[next_yr]
# the loop above does not include the last element in range `last_year`,
# therefore added explicitly
value += x[last_year]
return value / (last_year - first_year + 1)
name = 'carbon price|AC NPV (2030-2100)'
sr1p5.set_meta(carbon_price_npv.apply(npv_weighted, first_year=npv_first_year, last_year=npv_last_year,
w1=w1_annual, w2=w2_annual,
w1_function=compute_w1_annual, w2_function=compute_w2_annual, r=r,
raw=False, axis=1), name)
meta_docs[name] = 'anually compounded net-present value of carbon prices (2010USD/tCO2)' + discount_docstring
name = 'carbon price|CC NPV (2030-2100)'
sr1p5.set_meta(carbon_price_npv.apply(npv_weighted, first_year=npv_first_year, last_year=npv_last_year,
w1=w1_compounded, w2=w2_compounded,
w1_function=compute_w1_compounded, w2_function=compute_w2_compounded, r=r,
raw=False, axis=1), name)
meta_docs[name] = 'continuously compounded net-present value of carbon prices (2010USD/tCO2)' + discount_docstring
View categorization and indicators, export metadata and grouping specifications¶
The metadata (categorization and diagnostic indicators) is exported as an xlsx spreadsheet to be used by other scripts for subsequent analysis and visualization. The specifications of groupings and markers are exported as a yaml file to be imported by other notebooks.
[i for i in sr1p5.meta.columns if i not in meta_docs.keys()]
sr1p5.meta.head()
_meta_docs = pd.DataFrame(meta_docs, index=[0]).T.reset_index()
_meta_docs.rename(columns={'index': 'Category or indicator', 0: 'Description'}, inplace=True)
writer = pd.ExcelWriter('sr15_metadata_indicators.xlsx')
pyam.utils.write_sheet(writer, 'meta', sr1p5.meta, index=True)
pyam.utils.write_sheet(writer, 'categories_indicators_doc', _meta_docs, index=False)
for name, df in meta_tables.items():
pyam.utils.write_sheet(writer, 'def_{}'.format(name), df)
writer.save()
specs['run_control'] = rc
with io.open('sr15_specs.yaml', 'w', encoding='utf8') as outfile:
yaml.dump(specs, outfile, default_flow_style=False, allow_unicode=True)