The data and code in this repository relates to the following paper:
McKenna C. M., A. C. Maycock, P. M. Forster, C. J. Smith, K. B. Tokarska, Stringent mitigation substantially reduces risk of unprecedented near-term warming rates, Nature Climate Change, 11, 126-131 (2021). https://doi.org/10.1038/s41558-020-00957-9.
The data was processed with funding from the European Union’s CONSTRAIN project as part of the Horizon 2020 Research and Innovation Programme under grant agreement number 820829. A.C.M. was also supported by the Natural Environment Research Council (grant no. NE/M018199/1) and Leverhulme Trust. C.J.S. was also supported by a NERC/IIASA Collaborative Research Fellowship (no. NE/T009381/1).
Code to setup and run FaIR simulations (see description of these simulations below).
Time-series of annual global mean surface air temperature (GSAT) anomalies for 500 FaIR simple climate model simulations using historical emissions and future emissions scenarios (NDCs, SSP1-1.9, SSP1-2.6, SSP3-7.0, and SSP5-8.5). Anomalies are calculated with respect to 1850-1900. The 500 simulations were generated from a broad uncertainty analysis of possible futures, based on lines of evidence for ECS, TCR, and ERF that reflect our latest understanding since IPCC SR1.5. Files listed use two different time-series of aerosol ERF: the default CMIP6 aerosol ERF, or the aerosol ERF from AR5.
Distributions of ECS, TCR, and ERF, used to generate each set of 500 simulations. Also contains time-series of the ERF, both for its total and for individual forcing agents. Both versions of the aerosol ERF time-series described above are given.
Time-series of emissions for each emissions scenario; the SSP datasets were obtained from https://www.rcmip.org/ and the NDCs dataset was provided by Joeri Rogelj. The NDC-like scenario is representative of the scenarios described in Chapter 3 of the UNEP Emissions Gap Report (https://wedocs.unep.org/bitstream/handle/20.500.11822/30797/EGR2019.pdf?sequence=1&isAllowed=y), and also of the pathways for the NDC-like projections in Vrontisi et al. 2018 (https://iopscience.iop.org/article/10.1088/1748-9326/aab53e/pdf). Units are given for all major GHGs and SLCFs, and for minor GHGs the unit is kilotons/year.
Time-series of concentrations of all GHGs and SLCFs for each emissions scenario.
Warming trends and anomalies for 2021-2040, using a constraint based on the observed warming trend for 1981-2017 (based on Tokarska et al., Sci Adv. 2020: https://doi.org/10.1126/sciadv.aaz9549). Trends and anomalies are calculated from CMIP6 GSAT data for the future emissions scenarios SSP1-2.6, SSP3-7.0, and SSP5-8.5. See Tokarska et al. 2020 for further details on the CMIP6 and observational data used. Data are provided both as a raw text file, and in a form for use with the relevant Python code provided.
Time-series of annual global mean surface temperature from different observational datasets, which are used to calculate observed temperature trends over the historical period. Datasets were downloaded from the following links:
HadCRUT4.6.0.0: https://crudata.uea.ac.uk/cru/data/temperature/HadCRUT4-gl.dat
Berkeley Earth Land-Ocean: http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_summary.txt
Cowtan-Way version 2 updated with HadSST3: https://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_annual_v2_0_0.txt
GISTEMP version 4: https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv
Observation-based estimates of internal variability in 20-year GSAT trends and anomalies. This data is provided here in processed form only for use with the relevant Python code provided. The raw data used to calculate these are based on Haustein et al. 2019 (https://doi.org/10.1175/JCLI-D-18-0555.1) and were provided by Karsten Haustein.
Python code used to conduct the analysis and produce the figures/tables. Any additional data required to run this code (e.g. the CMIP6 pre-industrial control simulation GSAT data), and not listed above, is provided in the folder for the relevant figure/table.