DOI: 10.5281/zenodo.10949057
title: Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations"

description: All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".While most of the code is a frozen clone of the original Repository, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.

The dataset for training and evaluation can be found at data/nextsim, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the SASIP shared data OpenDAP server:



The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework" (Boutin et al., 2023) and available as Zenodo dataset (Boutin et al., 2022).

The forcing data is based on the ERA5 reanalysis dataset published in the paper: "The ERA5 global reanalysis" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the hourly reanalysis data on single levels and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. Disclaimer: The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.


The neural network weights are included under data/models and split into weights for the deterministic models and the diffusion models.These neural network weights have been used to generate the results presented in the paper.

In this capsule, the notebooks folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.

Generally, we recommend to just download the data.tar.gz file and use otherwise the original Repository, since the here included code can be outdated. We further refer to the repository for additional information.

 

Contained in this capsule:



configs.tar.gz: The configuration files for the experiments.

data.tar.gz: The dataset and neural network weights.

diffusion_nextsim.tar.gz: The main code for the neural network etc.

environment.yaml: The anaconda environment file, can be used to install the needed packages.

notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.

readme.md: The readme file from the repository.

scripts.tar.gz: The scripts used for the experiments.

setup.py: the file to install the diffusion_nextsim package in a python environment.


References:

Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523

Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.

Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47.

Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999–2049. https://doi.org/10.1002/qj.3803

 
description: This research has received financial support from the project SASIP (grant no. 353) funded by Schmidt Science – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies.