Jupyter notebooks to reproduce the main figures of the paper by Rackow et al. (2021). The notebooks and data are stored in two separate folders as detailed below.
Time series of observed September sea ice (1979–2018) and projections until the end of the 21st century.
September Antarctic sea-ice extent in the high-resolution (HR; grey/black) and low-resolution
(LR; light green/green) AWI-CM configurations. Control simulations with atmospheric greenhouse gas concentrations fixed at
1950 levels are shown in light colors. The red line depicts historical SIE observations (OBS) over the satellite era.
└─ notebooks/:
| └─ Figure1.ipynb: Read data and reproduce Figure 1 (R script)
| └─ Figure2.ipynb: Read data and reproduce Figure 2
| └─ Figure3a.ipynb: Read data and reproduce panel a) in Figure 3
| └─ Figure3b.ipynb: Read data and reproduce panel b) in Figure 3
| └─ Figure3c.ipynb: Read data and reproduce panel c) in Figure 3
| └─ Figure4.ipynb: Read data and reproduce all panels of Figure 4 (except sea ice in panel a))
| └─ Figure4_NSIDCice.ipynb: Read data and reproduce sea ice in panel a) of Figure 4
| └─ Figure5ad.ipynb: Read data and reproduce panels a) and d) of Figure 5
| └─ Figure5bcef.ipynb: Read data and reproduce panels b), c) and e),f) of Figure 5
| └─ colorbars.py: contains some colorbars
└─ data/:
| └─ Figure1.csv: data for Figure 1
| └─ data_Fig2.pickle: data for Figure 2
| └─ data_Fig3a.pickle: data for Figure 3, panel a)
| └─ data_Fig3b.pickle: data for Figure 3, panel b)
| └─ data_Fig3c_HR.pickle: HR data for Figure 3, panel c) (use git lfs)
| └─ data_Fig3c_LR.pickle: LR data for Figure 3, panel c)
| └─ data_Fig4.pickle: data for Figure 4 (except ice), all panels (use git lfs)
| └─ data_Fig4_ice.pickle: HR and LR sea ice data for Figure 4, panels b) and c)
| └─ mean.sep.1979-2020.s.bil: OBS sea ice data for Figure 4, panel a)
| └─ data_Fig5a.pickle: model data for Figure 5, panel a)
| └─ data_Fig5d.pickle: model data for Figure 5, panel d)
| └─ CMIP5_OHF_ensmeans_allmodels_1990-2019.csv: CMIP5 meridional heat flux, 1990-2019 for Fig. 5d
| └─ CMIP5_OHF_ensmeans_allmodels_2070-2099.csv: CMIP5 meridional heat flux, 2070-2099 for Fig. 5d
| └─ data_Fig5bcef.pickle: model data for Figure 5, panels b),c) and e),f)
| └─ CMIP5_iceedge_latitudes_1990-2019.csv: CMIP5 location of sea ice edge, 1990-2019 for Fig. 5e
| └─ CMIP5_iceedge_latitudes_2070-2099.csv: CMIP5 location of sea ice edge, 2070-2099 for Fig. 5e
| └─ ...: some more data
[some of the data are stored with Git Large File Storage (lfs). Although deprecated, try 'git lfs clone'
with older git clients if you experience problems, instead of the usual 'git clone']
Interactive jupyter notebook are provided to reproduce the figures of this paper. You can install Jupyter and then launch the notebooks here.
Download e.g. a miniconda installer from here and then type
conda create -n natcommun netCDF4 numpy matplotlib xarray scipy
conda install -c anaconda basemap
conda install jupyter python=3.8
conda install seawater joblib
conda install cmocean
to create a "natcommun" environment with necessary dependencies.
In order to plot Figure 4, the pyfesom package needs to be installed and the path to your local installation included:
import sys
sys.path.append("/path/to/pyfesom/")
import pyfesom as pf
To install an R kernel for Figure 1, you can follow the instructions here. In order to run Figure4_NSIDCice.ipynb, you need to have 'gdal' installed. This can sometimes be problematic on some operating systems like macOS, but you can try the following recipe:
conda create -n gdal python=3.8
conda activate gdal
conda install -c conda-forge gdal
conda install numpy matplotlib
conda install jupyter
Information on the usage of Git LFS you can find here.