IceNet: Seasonal Arctic sea ice forecasting with probabilistic deep learning
This codebase accompanies the Nature Communications paper Seasonal Arctic sea ice forecasting with probabilistic deep learning. It includes code to fully reproduce all the results of the study from scratch. It also includes code to download the data generated by the study, published on the Polar Data Centre, and reproduce all the paper's figures.
The flexibility of the code simplifies possible extensions of the study.
The data processing pipeline and custom
IceNetDataLoader class lets you
dictate which variables are input to the networks, which climate simulations are
used for pre-training, and how far ahead to forecast.
The architecture of the IceNet model can be adapted in
The output variable to forecast could even be changed by refactoring the
The guidelines below assume you're working in the command line of a Unix-like machine with a GPU. If aiming to reproduce all the results of the study, 1 TB of space should safely cover the storage requirements from the data downloaded and generated.
If you run into issues or have suggestions for improvement, please raise an issue or email me (firstname.lastname@example.org).
Steps to plot paper figures using the paper's results & forecasts
To reproduce the paper figures directly from the paper's results and forecasts, run the following after setting up the conda environment (see Step 1 below):
./download_paper_generated_data.sh. Downloads raw data from the paper. From here, you could start to explore the results of the paper in more detail.
python3 icenet/download_sic_data.py. This is needed to plot the ground truth ice edge. Note this download can take anywhere from 1 to 12 hours to complete.
python3 icenet/plot_paper_figures.py. Figures are saved in
Steps to reproduce the paper's results from scratch
0) Preliminary setup
I use conda for package management. If you don't yet have conda, you can download it here.
To be able to download ERA5 data, you must first set up a CDS account and populate your
.cdsapircfile. Follow the 'Install the CDS API key' instructions here.
To download the ECMWF SEAS5 forecast data for comparing with IceNet, you must first register with ECMWF here. If you are from an ECMWF Member State, you can then gain access to the ECMWF MARS Catalogue by contacting your Computing Representative. Once registered, obtain your API key here and fill the ECMWF API entries in
To track training runs and perform Bayesian hyperparameter tuning with Weights and Biases, sign up at https://wandb.ai/site. Obtain your API key from here and fill the Weights and Biases entries in
icenet/config.py. Ensure you are logged in by running
wandb loginafter setting up the conda environment.
1) Set up conda environment
After cloning the repo, run the commands below in the root of the repository to set up the conda environment:
- If you don't have mamba already, install
it to your base env for faster conda operations:
conda install -n base mamba -c conda-forge.
- For upgradeability use the versioned direct dependency
mamba env create --file environment.yml
- For reproducibility use the locked environment file:
mamba env create --file environment.locked.yml
- Activate the environment before running code:
conda activate icenet
2) Download data
The CMIP6 variable naming convention
is used throughout this project - e.g.
tas for surface air temperature,
sea ice concentration, etc.
Warning: some downloads are slow and the net download time can take 1-2 days. It may be advisable to write a bash script to automatically execute all these commands in sequence and run it over a weekend.
python3 icenet/gen_masks.py. This obtains masks for land, the polar holes, monthly maximum ice extent (the 'active grid cell region'), and the Arctic regions & coastline.
python3 icenet/download_sic_data.py. Downloads OSI-SAF SIC data. This computes monthly-averaged SIC server-side, downloads the results, and bilinearly interpolates missing grid cells (e.g. polar hole). Note this download can take anywhere from 1 to 12 hours to complete.
./download_era5_data_in_parallel.sh. Downloads ERA5 reanalysis data. This runs multiple parallel
python3 icenet/download_era5_data.pycommands in the background to acquire each ERA5 variable. The raw ERA5 data is downloaded in global latitude-longitude format and regridded to the EASE grid that OSI-SAF SIC data lies on. Logs are output to
./download_cmip6_data_in_parallel.sh. Downloads CMIP6 climate simulation data. This runs multiple parallel
python3 icenet/download_cmip6_data.pycommands in the background to download each climate simulation. The raw CMIP6 data is regridded from global latitude-longitude format to the EASE grid that OSI-SAF SIC data lies on. Logs are output to
./rotate_wind_data_in_parallel.sh. This runs multiple parallel
python3 icenet/rotate_wind_data.pycommands in the background to rotate the ERA5 and CMIP6 wind vector data onto the EASE grid. Logs are output to
./download_seas5_forecasts_in_parallel.sh. Downloads ECMWF SEAS5 SIC forecasts. This runs multiple parallel
python3 icenet/download_seas5_forecasts.pycommands to acquire 2002-2020 SEAS5 forecasts for each lead time via the ECMWF MARS API and regrid the forecasts to EASE. The forecasts are saved to
data/forecasts/seas5/in the folders
EASE/. Logs are output to
python3 icenet/biascorrect_seas5_forecasts.py. Bias corrects the SEAS5 2012+ forecasts using 2002-2011 forecasts. Also computes SEAS5 sea ice probability (SIP) fields. The bias-corrected forecasts are saved as NetCDFs in
(target date, y, x, lead time).
3) Process data
3.1) Set up IceNet's custom data loader
python3 icenet/gen_data_loader_config.py. Sets up the data loader configuration. This is saved as a JSON file dictating IceNet's input and output data, train/val/test splits, etc.The config file is used to instantiate the custom
IceNetDataLoaderclass. Two example config files are provided in this repository in
dataloader_configs/. Each config file is identified by a dataloader ID, determined by a timestamp and a user-provided name (e.g.
2021_06_15_1854_icenet_nature_communications). The data loader ID, together with an architecture ID set in the training script, provides an 'IceNet ID' which uniquely identifies an IceNet ensemble model by its data configuration and architecture.
3.2) Preprocess the raw data
python3 icenet/preproc_icenet_data.py. Normalises the raw NetCDF data and saves it as monthly NumPy files. The normalisation parameters (mean/std dev or min/max) are saved as a JSON file so that new data can be preprocessed without having to recompute the normalisation. A custom IceNetDataPreProcessor class
The observational training & validation dataset for IceNet is just 23 GB, which can fit in RAM on some systems and significantly speed up the fine-tuning training phase compared with using the data loader. To benefit from this, run
python3 icenet/gen_numpy_obs_train_val_datasets.pyto generate NumPy tensors for the train/val input/output data. To further benefit from the training speed improvements of
tf.data, generate a TFRecords dataset from the NumPy tensors using
python3 icenet/gen_tfrecords_obs_train_val_datasets.py. Whether to use the data loader, NumPy arrays, or TFRecords datasets for training is controlled by bools in
4) Train IceNet
4.1) OPTIONAL: Run the hyperparameter search (skip if using default values from paper)
icenet/train_icenet.pyup for hyperparameter tuning: Set pre-training and temperature scaling bools to
Falsein the user input section.
wandb sweep icenet/sweep.yaml
- Then run the
wandb agentcommand that is printed.
- Cancel the sweep after a sufficient picture on optimal hyperparameters is built up on the wandb.ai page.
4.2) Run training
- Train IceNet networks with
python3 icenet/train_icenet.py. This takes hyperameter settings and the random seed for network weight initalisation as command line inputs. Run this multiple times with different settings of
--seedto train an ensemble. Trained networks are saved in
trained_networks/<dataloader_ID>/<architecture_ID>/networks/. If working on a shared machine and familiar with SLURM, you may want to wrap this command in a SLURM script.
5) Produce forecasts
python3 icenet/predict_heldout_data.py. Uses
xarrayto save predictions over the validation and test years as (2012-2020) as NetCDFs for IceNet and the linear trend benchmark. IceNet's forecasts are saved in
data/forecasts/icenet/<dataloader_ID>/<architecture_ID>/. For IceNet, the full forecast dataset has dimensions
(target date, y, x, lead time, ice class, seed), where
seedspecifies a single ensemble member or the ensemble-mean forecast. An ensemble-mean SIP forecast is also computed and saved as a separate, smaller file (which only has the first four dimensions).
Compute IceNet's ensemble-mean temperature scaling parameter for each lead time:
python3 icenet/compute_ensemble_mean_temp_scaling.py. The new, ensemble-mean temperature-scaled SIP forecasts are saved to
data/forecasts/icenet/<dataloader_ID>/<architecture_ID>/icenet_sip_forecasts_tempscaled.nc. These forecasts represent the final ensemble-mean IceNet model used for the paper.
6) Analyse forecasts
python3 icenet/analyse_heldout_predictions.py. Loads the NetCDF forecast data and computes forecast metrics, storing results in a global
(model, ensemble member, lead time, target date)and columns for each metric (binary accuracy and sea ice extent error). Uses
daskto avoid loading the entire forecast datasets into memory, processing chunks in parallel to significantly speed up the analysis. Results are saved as CSV files in
results/forecast_results/with a timestamp to avoid overwriting. Optionally pre-load the latest CSV file to append new models or metrics to the results without needing to re-analyse existing models. Use this feature to append forecast results from other IceNet models (identified by their dataloader ID and architecture ID) to track the effect of design changes on forecast performance.
python3 icenet/analyse_uncertainty.py. Assesses the calibration of IceNet and SEAS5's SIP forecasts. Also determines IceNet's ice edge region and assesses its ice edge bounding ability. Results are saved in
7) Run the permute-and-predict method to explore IceNet's most important input variables
python3 icenet/permute_and_predict.py. Results are stored in
8) Generate the paper figures and tables
python3 icenet/plot_paper_figures.py. Figures are saved in
figures/paper_figures/. Note, you will need the Sea Ice Outlook error CSV file to plot Supp. Fig. 5:
wget -O data/sea_ice_outlook_errors.csv 'https://ramadda.data.bas.ac.uk/repository/entry/get/sea_ice_outlook_errors.csv?entryid=synth%3A71820e7d-c628-4e32-969f-464b7efb187c%3AL3Jlc3VsdHMvb3V0bG9va19lcnJvcnMvc2VhX2ljZV9vdXRsb29rX2Vycm9ycy5jc3Y%3D'
icenet/utils.pydefines IceNet utility functions like the data preprocessor, data loader, ERA5 and CMIP6 processing, learning rate decay, and video functionality.
icenet/models.pydefines network architectures.
icenet/losses.pydefines loss functions.
icenet/callbacks.pydefines training callbacks.
icenet/metrics.pydefines training metrics.
Project structure: simplified output from
. ├── data │ ├── obs │ ├── cmip6 │ │ ├── EC-Earth3 │ │ │ ├── r10i1p1f1 │ │ │ ├── r12i1p1f1 │ │ │ ├── r14i1p1f1 │ │ │ ├── r2i1p1f1 │ │ │ └── r7i1p1f1 │ │ └── MRI-ESM2-0 │ │ ├── r1i1p1f1 │ │ ├── r2i1p1f1 │ │ ├── r3i1p1f1 │ │ ├── r4i1p1f1 │ │ └── r5i1p1f1 │ ├── forecasts │ │ ├── icenet │ │ │ ├── 2021_06_15_1854_icenet_nature_communications │ │ │ │ └── unet_tempscale │ │ │ └── 2021_06_30_0954_icenet_pretrain_ablation │ │ │ └── unet_tempscale │ │ ├── linear_trend │ │ └── seas5 │ │ ├── EASE │ │ └── latlon │ ├── masks │ └── network_datasets │ └── dataset1 │ ├── meta │ ├── obs │ ├── transfer │ └── norm_params.json ├── dataloader_configs │ ├── 2021_06_15_1854_icenet_nature_communications.json │ └── 2021_06_30_0954_icenet_pretrain_ablation.json ├── figures ├── icenet ├── logs │ ├── cmip6_download_logs │ ├── era5_download_logs │ ├── seas5_download_logs │ └── wind_rotation_logs ├── results │ ├── forecast_results │ │ └── 2021_07_01_183913_forecast_results.csv │ ├── permute_and_predict_results │ │ └── permute_and_predict_results.csv │ └── uncertainty_results │ ├── ice_edge_region_results.csv │ ├── sip_bounding_results.csv │ └── uncertainty_results.csv └── trained_networks └── 2021_06_15_1854_icenet_nature_communications ├── obs_train_val_data │ ├── numpy │ └── tfrecords │ ├── train │ └── val └── unet_tempscale └── networks ├── network_tempscaled_36.h5 ├── network_tempscaled_37.h5 :
Thanks to James Byrne (BAS) and Tony Phillips (BAS) for direct contributions to this codebase.