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DOI

Teleconnection patterns of different El Nino types revealed by climate network curvature

Ricci-curvature applied to climate networks to study teleconnection patterns of ENSO diversity.

An intuition on network curvature:

toymodel

Clone the repo and install all required packages

1. Clone repository with submodules:

git clone --recurse-submodules https://github.com/jakob-schloer/netcurvature.git

2. Installing packages

To reproduce our analysis described in the paper follow these steps: We recommend to create a new environment and install all required packages by running:

conda env create -f submodules/climnet/condaEnv.yml 
conda activate climnetenv 
pip install graphriccicurvature 
pip install -e submodules/climnet

3. Download data

Download 2m temperature data from ERA5 and store the merged files in the data folder.

Reproduce plots

  1. Create the corresponding networks
python bin/t2m_create_net.py -data 'datapath' -s 'standard'
python bin/t2m_create_net.py -data 'datapath' -s 'Nino_EP'
python bin/t2m_create_net.py -data 'datapath' -s 'Nino_CP'
  1. Reproduce plots in the paper by running
python bin/paperplots.py -d 'datapath' -ep 'epNetPath' -cp 'cpNetPath' -normal 'normalNetPath'

The figures should look somehow similiar to the following:

fig2

fig3

fig4

The networks used for this plots are stored in 'outputs/t2m_1950-2020_nino_nets'.