  Exploring the climate impacts of various anthropogenic emissions scenarios is
key to making informed decisions for climate change mitigation and adaptation.
State-of-the-art Earth system models can provide detailed insight into these
impacts, but have a large associated computational cost on a per-scenario
basis. This large computational burden has driven recent interest in developing
cheap machine learning models for the task of climate model emulation. In this
manuscript, we explore the efficacy of randomly wired neural networks for this
task. We describe how they can be constructed and compare them to their
standard feedforward counterparts using the ClimateBench dataset. Specifically,
we replace the serially connected dense layers in multilayer perceptrons,
convolutional neural networks, and convolutional long short-term memory
networks with randomly wired dense layers and assess the impact on model
performance for models with 1 million and 10 million parameters. We find that
models with less complex architectures see the greatest performance improvement
with the addition of random wiring (up to 30.4% for multilayer perceptrons).
Furthermore, out of 24 different model architecture, parameter count, and
prediction task combinations, only one saw a statistically significant
performance deficit in randomly wired networks compared to their standard
counterparts, with 14 cases showing statistically significant improvement. We
also find no significant difference in prediction speed between networks with
standard feedforward dense layers and those with randomly wired layers. These
findings indicate that randomly wired neural networks may be suitable direct
replacements for traditional dense layers in many standard models.
