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ncc-stripes

Python code to merge geological, instrumental and climate model land surface air temperature anomalies and visualise them in the form of the Climate Stripes by lead scientist: Professor Ed Hawkins and uses data from PAGES2k, HadCRUT5 and FaIR v1.6.3.

Contents

  • plot_ncc_stripes_futures.py - climate stripes 500-2200 CE with 5 climate model forecasts from FaIR: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5
  • plot_ncc_stripes_pleistocene.py - climate stripes 2.58 Myr ( < 2015 ) to 2200 CE for user-choice of SSP
  • plot_ncc_stripes_multiaxis.py - climate stripes 65.5 Myr ( < 2015 ) to 2200 CE with epochs plotted in segments for user-choice of SSP
  • plot_ncc_stripes_rebinning.py - climate stripes, bars and timeseries 65.5 Myr ( < 2015 ) to 2200 CE with log-linear re-binning implementation for user-choice of SSP

The first step is to clone the latest ncc-stripes code and step into the check out directory:

$ git clone https://github.com/patternizer/ncc-stripes.git
$ cd ncc-stripes

Using Standard Python

The code should run with the standard CPython installation and was tested in a conda virtual environment running a 64-bit version of Python 3.8.11+.

ncc-stripes can be run from sources directly, once the following data requirements are present in the DATA/ directory:

  • paleo_data_compilation.xls - multi-proxy temperature estimate 65.5 Myr ( < 2015 ) to 1 CE ( source: Greg Fergus, see also the discussion by Gavin Schmidt at RealClimate )
  • PAGES2k.txt - 7000-member ensemble median global mean temperature reconstruction 1-1850 CE ( source: PAGES2k Consortium )
  • HadCRUT5.csv - instrumental record 1850-2020 CE ( source: UKMO Hadley Centre )
  • variability_realisation0.txt - 1350-1546 proxy for climate model internal variability ( source: Professor Tim Osborn )
  • variability_realisation1.txt - 1550-1746 proxy for climate model internal variability ( source: Professor Tim Osborn )
  • fair_rcp3pd.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for RCP3pd using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_rcp45.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for RCP4.5using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_rcp6.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for RCP6 using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_rcp85.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for RCP8.5 using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_ssp119.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for SSP1-1.9 using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_ssp126.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for SSP1-2.6 using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_ssp246.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for SSP2-4.5 using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_ssp370.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for SSP3-7.0 using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )
  • fair_ssp585.csv - FaIR v1.6.3 climate model forecast for 2021-2200 CE for SSP5-8.5 using an ensemble median constrained by HadCRUT5 observations ( source: FaIR v1.6.3 )

Run with for example:

$ python plot_ncc_stripes_futures.py
$ python plot_ncc_stripes_pleistocene.py
$ python plot_ncc_stripes_multiaxis.py
$ python plot_ncc_stripes_rebinning.py

License

The code is distributed under terms and conditions of the Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/ and is designed to visualise land surface air temperature data and provide an implementation akin to https://showyourstripes.info/ by Professor Ed Hawkins.

Contact information