Using IPCC Color Palettes

pyam supports the use of explicit IPCC AR5 and AR6 color palettes by providing the RCP and/or SSP of interest via the pyam.run_control() feature.

The full list of the IPCC color palette colors available in pyam can be retrieved by the following code.

import pandas as pd

import pyam

colors = pyam.plotting.PYAM_COLORS
pd.DataFrame({"name": list(colors.keys()), "color": list(colors.values())})

	name	color
0	AR6-SSP1	#1e9583
1	AR6-SSP2	#4576be
2	AR6-SSP3	#f11111
3	AR6-SSP4	#e78731
4	AR6-SSP5	#8036a7
5	AR6-SSP1-1.9	#00a9cf
6	AR6-SSP1-2.6	#003466
7	AR6-SSP2-4.5	#f69320
8	AR6-SSP3-7.0	#df0000
9	AR6-SSP3-LowNTCF	#e61d25
10	AR6-SSP4-3.4	#2274ae
11	AR6-SSP4-6.0	#b0724e
12	AR6-SSP5-3.4-OS	#92397a
13	AR6-SSP5-8.5	#980002
14	AR6-RCP-2.6	#003466
15	AR6-RCP-4.5	#709fcc
16	AR6-RCP-6.0	#c37900
17	AR6-RCP-8.5	#980002
18	AR5-RCP-2.6	#0000FF
19	AR5-RCP-4.5	#79BCFF
20	AR5-RCP-6.0	#FF822D
21	AR5-RCP-8.5	#FF0000
22	AR6-C1	#97CEE4
23	AR6-C2	#778663
24	AR6-C3	#6F7899
25	AR6-C4	#A7C682
26	AR6-C5	#8CA7D0
27	AR6-C6	#FAC182
28	AR6-C7	#F18872
29	AR6-C8	#BD7161
30	AR6-IMP-LD	#4FA7BF
31	AR6-IMP-Ren	#2B7C8B
32	AR6-IMP-SP	#004D52
33	AR6-IMP-Neg	#84A12B
34	AR6-IMP-GS	#6E7895
35	AR6-IP-ModAct	#F29424
36	AR6-IP-CurPol	#E31F2B

Display scenario data with default colours

We use the scenario ensemble from the first-steps tutorial (here on GitHub and on read the docs). Let’s pull out two example scenarios (implemented by multiple modelling frameworks each) and plot them with the default color scheme.

scenarios = ["CD-LINKS_NoPolicy", "CD-LINKS_NPi2020_400"]

df = pyam.IamDataFrame(data="tutorial_data.csv").filter(
    variable="Emissions|CO2", region="World", scenario=scenarios
)

df.plot(color="scenario")

[INFO] 12:04:12 - pyam.core: Reading file tutorial_data.csv

/home/docs/checkouts/readthedocs.org/user_builds/pyam-iamc/checkouts/latest/pyam/utils.py:318: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
  .stack(dropna=True)

<Axes: title={'center': 'region: World - variable: Emissions|CO2'}, xlabel='Year', ylabel='Mt CO2/yr'>

As an example, we assume that each of these two sets of scenarios correspond to categorizations in the AR6 context. We can utilize the specific colors by following two steps:

1. Update pyam.run_control() telling it which scenario name maps to which AR6 color

2. Call line_plot using that color mapping

Updating the run control

We need to tell pyam that whenever it sees a certain scenario name, it should use a specific color from the IPCC palette.

color_map = {
    "CD-LINKS_NPi2020_400": "AR6-SSP2-4.5",
    "CD-LINKS_NoPolicy": "AR6-SSP5-8.5",
}

pyam.run_control().update({"color": {"scenario": color_map}})

The illustration above is implemented directly in Python code, but it also works by specifying the desired mapping in a yaml configuration file and loading that file into run_control().

Use the new colors

Now, it’s as simple as calling the plot function again!

df.plot(color="scenario")

<Axes: title={'center': 'region: World - variable: Emissions|CO2'}, xlabel='Year', ylabel='Mt CO2/yr'>