Plotting & other codes for Ch.10 “Transport” of IPCC AR6 WGIII

Contents: Important links & notes — Running the code — CLI --help text — History — References

This code generates plots used in figures in Chapter 10, mainly Section 10.7, titled “Scenarios from integrated, sectoral, and regional models”. The code does not generate other Chapter 10 plots or figures not mentioned.

The code, including its entire edit history, is available on GitHub at: https://github.com/transportenergy/ipcc-ar6-wg3-ch10

• Each plot responds to multiple configuration options; thus, there are at least 30 variants of each plot, many with multiple pages (one per spatial region).

  The figures in the final text of Chapter 10 each contain 1 or more pages from 1 or 2 variants of each plot, selected to best support the assessment text. The commands which will generate the specific plots used in the figures are:

  • Figure 10.14: fig_9, created using: python -m ar6_wg3_ch10 plot --ar6-data=world --bw10 9
  • Figure 10.17: fig_10, created using:
    • python -m ar6_wg3_ch10 plot --ar6-data=world --bw10 9 (IAM scenarios; i.e. same as Figure 10.14, but a different page)
    • python -m ar6_wg3_ch10 plot --tem-data=IMO --bw=8 10 (IMO scenarios)
  • Figure 10.18: fig_1, created using:
    • python -m ar6_wg3_ch10 plot --ar6-data=world --bw=9 1 (world panel)
    • python -m ar6_wg3_ch10 plot --ar6-data=R6 --bw=9 1 (regional panels)
  • Figure 10.19: fig_2, created using:
    • python -m ar6_wg3_ch10 plot --ar6-data=world --bw=9 2 (world panel)
    • python -m ar6_wg3_ch10 plot --ar6-data=R6 --bw=9 2 (regional panels)
  • Figure 10.20: fig_6, created using: python -m ar6_wg3_ch10 plot --ar6-data=world --bw=8 --recat=A 6.
  • Figure 10.21: fig_4, created using: python -m ar6_wg3_ch10 plot --ar6-data=world --bw=9 4.
  • Figure 10.22: fig_7, created using: python -m ar6_wg3_ch10 plot --ar6-data=world --bw=9 --recat=A 7.
  • Figure 10.23 (in Box 10.5): fig_8, created using: python -m ar6_wg3_ch10 plot --ar6-data=IP 8.
• Earlier versions of figures used by the chapter authors in earlier drafts of the report are uploaded on Box.com (access for report contributors only), in the 7 Scenarios/plots/ folder.
  • Subfolders are named by date.
  • There are multiple variants of each figure, as indicated by the file name:
    • AR6-world, AR6-R5, AR6-R10, AR6-country—these indicate which snapshot of data from the AR6 database is used.
  • Each dated folder contains a data/ subfolder with ZIP files containing CSV dumps of the data used each plot. The file names match figure file names, with additions, e.g.:
    • _plot.csv: the actual values, e.g. descriptive statistics (median etc.) displayed in the plot.
    • _iam.csv: the individual scenario values used to compute these statistics.
    • _indicator.csv: a subset of scenario values for the Chapter 3-designated indicator scenarios.
    • _tem.csv: data from the G-/NTEM (sectoral and national) models.

• The file NOTES.rst contains some earlier plans and notes, not all up to date. Refer to the code for latest information, comments, pending TODOs, etc. The files all follow a similar pattern.

  For instance, for information on fig2, refer to the file ar6_wg3_ch10/fig_2.py.

Running the code

Generating all plots

1. Download the snapshots from the AR6 Scenario Explorer website.

   This data is submitted by a variety of parties, and processed in various ways by the Chapter 3 team. The code here uses the metadata produced by Chapter 3 to select data to be plotted.

2. Place the contents in the directory data/raw/.

   Data must be converted to .csv.gz format, using a process like:

   • Unpack the .zip snapshot and enter the directory created.
   • Compress the data using the Gzip command-line program, available on most *nix systems: gzip *.csv.
   • Move the file created to data/raw/.

   Refer to the file common.py for the expected file names.

3. Install requirements: pip install -r requirements.txt

4. Run python -m ar6_wg3_ch10 plot-all (about 30 minutes) or other commands

(see below).

Other actions

To retrieve raw data from the Scenario Explorer API, modify config-example.json to create a file named config.json with content like:

{
  "credentials": {
    "username": "your-user-name",
    "password": "your-password"
  },
  "remote": {
    "upload": "ipcc:IPCC CH10/7 Scenarios/plots"
  }
}

(NB. do not commit this file to the git repository; your password will become a permanent part of the history, and you will need to change it.)

# Cache all raw data
$ python -m ar6_wg3_ch10 cache refresh AR6  # about 60 minutes

Note that the final process to generate figures used in the report does not use this raw data directly, but rather the post-processed "snapshots" provided by the Chapter 3 team, as mentioned above.

CLI help text

The top-level CLI and each command provide help text; use --help. For example, python -m ar6_wg3_ch10 --help gives:

Command-line interface for IPCC AR6 WGIII Ch.10 figures.

Reads a file config.json in the current directory. See config-
example.json.

Verbose log information for certain commands is written to a timestamped
.log file in output/.

Options:

--skip-cache Don't use cached intermediate data. --verbose Also print DEBUG log messages to stdout. --help Show this message and exit.

Commands:

clear-cache Clear cached/intermediate data matching PATTERN. count Count model and scenario names in final data. coverage Report coverage of transport variables. debug Demo or debug code. plot Plot figures, writing to output/. plot-all Generate all plots. prepare Prepare files for submission. refs Retrieve reference files to ref/. remote Retrive data from remote databases to data/cache/SOURCE/. upload Sync output/ to a remote directory using rclone. variables Write lists of variables for each data source.

History

Use git log on the command line or the “commits” tab on the GitHub website.

References

These are only for convenience; the chapter/section Mendeley collections should be used to store all key references.

• AR5 WGIII chapters & figures