ClimateLearn: Benchmarking Machine Learning for Weather and Climate
  Modeling
Tung Nguyen Jason Jewik Hritik Bansal Prakhar Sharma Aditya Grover
Modeling weather and climate is an essential endeavor to understand the near-
and long-term impacts of climate change, as well as inform technology and
policymaking for adaptation and mitigation efforts. In recent years, there has
been a surging interest in applying data-driven methods based on machine
learning for solving core problems such as weather forecasting and climate
downscaling. Despite promising results, much of this progress has been impaired
due to the lack of large-scale, open-source efforts for reproducibility,
resulting in the use of inconsistent or underspecified datasets, training
setups, and evaluations by both domain scientists and artificial intelligence
researchers. We introduce ClimateLearn, an open-source PyTorch library that
vastly simplifies the training and evaluation of machine learning models for
data-driven climate science. ClimateLearn consists of holistic pipelines for
dataset processing (e.g., ERA5, CMIP6, PRISM), implementation of
state-of-the-art deep learning models (e.g., Transformers, ResNets), and
quantitative and qualitative evaluation for standard weather and climate
modeling tasks. We supplement these functionalities with extensive
documentation, contribution guides, and quickstart tutorials to expand access
and promote community growth. We have also performed comprehensive forecasting
and downscaling experiments to showcase the capabilities and key features of
our library. To our knowledge, ClimateLearn is the first large-scale,
open-source effort for bridging research in weather and climate modeling with
modern machine learning systems. Our library is available publicly at
https://github.com/aditya-grover/climate-learn.