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ClimateBench

ClimateBench is a benchmark dataset for climate model emulation inspired by WeatherBench. It consists of NorESM2 simulation outputs with associated forcing data processed in to a consistent format from a variety of experiments performed for CMIP6. Multiple ensemble members are included where available.

The processed training, validation and test data can be obtained from Zenodo: 10.5281/zenodo.5196512.

A pre-print of the paper describing ClimateBench and the baseline models can be found here: https://www.essoar.org/doi/10.1002/essoar.10509765.2

Leaderboard

The spatial, global and total NRMSE of the different baseline emulators for the years 2080-2100 against the ClimateBench task of estimating key climate variables under future scenario SSP245. The models are ranked in order of the mean of the total NRMSE across all tasks.

('tas', 'Spatial') ('tas', 'Global') ('tas', 'Total') ('diurnal_temperature_range', 'Spatial') ('diurnal_temperature_range', 'Global') ('diurnal_temperature_range', 'Total') ('pr', 'Spatial') ('pr', 'Global') ('pr', 'Total') ('pr90', 'Spatial') ('pr90', 'Global') ('pr90', 'Total')
Neural Network 0.107294 0.0440271 0.327429 9.91735 1.37219 16.7783 2.1281 0.2093 3.1746 2.61022 0.345709 4.33876
Gaussian Process 0.109106 0.0738238 0.478225 9.20713 2.67495 22.5819 2.34092 0.341453 4.04818 2.5559 0.429154 4.70167
Random Forest 0.107574 0.0584057 0.399602 9.19503 2.65241 22.4571 2.52431 0.502126 5.03494 2.68209 0.543375 5.39896

Installation

The example scripts provided here require ESEm and a few other packages. It is recommended to first create a conda environment with iris or xarray::

$ conda install -c conda-forge iris

Then pip install the additional requirements:

$ pip install esem[gpflow,keras,scikit-learn] eofs

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