This repository provides code and data to reproduce results of the article

Achieving net zero greenhouse gas emissions critical to limit climate tipping risks

Tessa Möller, Annika Ernest Högner, Carl-Friedrich Schleussner, Samuel Bien, Niklas H. Kitzmann, Robin D. Lamboll, Joeri Rogelj, Jonathan F. Donges, Johan Rockström, Nico Wunderling

Files

---

CODE

• 01_temp_extension.py Python code to produce linearly extended long-term temperature trajectories.
• 02_temp_conversion.py Python code to convert temperature trajectories to .txt input for MAIN_script.py
• 03_monte_carlo_ensemble.py Python code to produce the ensemble members to propagate tipping related uncertainties.
• 04_MAIN_script.py Python code to calculate tipping risks.
• 05_overshoots_evaluation.R R code to produce tipping risk .csv from MAIN_script output.
• core pycascades model scripts
• earth_sys pycascades model scripts

pycascades is developed at the Potsdam Institute for Climate Impact Research, Potsdam, Germany.

Description paper: N. Wunderling, J. Krönke, V. Wohlfarth, J. Kohler, J. Heitzig, A. Staal, S. Willner, R. Winkelmann, J.F. Donges, Modelling nonlinear dynamics of interacting tipping elements on complex networks: the PyCascades package, The European Physical Journal Special Topics (2021).

DATA

INPUT data:

• kyoto_emissions.csv: PROVIDEv1.2 emissions
• tier1_temperature_summary.csv: PROVIDEv1.2 temperature trajectories

from Scenario emissions and temperature data for PROVIDE project (Robin Lamboll, Joeri Rogelj, Carl-Friedrich Schleussner, 2022)

OUTPUT data:

• results450.csv: Tipping risk results for the medium-term (450 model years, until 2300)
• resultsLT.csv: Tipping risk results for the long-term (50,000 model years)

Required modules

---

python:

• numpy
• pandas
• matplotlib
• cycler
• glob
• re
• sys
• os
• scipy
• seaborn
• pyDOE
• time
• itertools
• PyPDF2
• netCDF4
• networkx

R:

• dplyr
• tidyverse

Description

---

The executable scripts need to be run in the indicated order. For execution, core and earth_sys need to be saved in the same folder as 04_MAIN_script.py.

DEMO: By default, 04_MAIN_script.py is set to run with a test system of one ensemble member (using the central estimates from Armstrong McKay et al., Science 2022, for tipping limits and tipping time scales) and in this way works as demo on an ordinary pc. Running all scripts in demo mode takes about 1 hour. Expected output is shared as expected_output.pdf.

The full model simulation needs to be run on a parallel computing cluster. 03_monte_carlo_ensemble.py generates a .txt file with the Monte Carlo ensemble members; this needs to be referred to as input in the bash shell script file that also executes 04_MAIN_script.py, depending on your system.

This code was implemented in Python 3.9. and R 4.2.1. on MAC OS 10.13.6. For each script, it is advised to first check dependencies.

---

A.E. Högner & T. Möller, 17.06.2024