Ecology

Annual cycle

Engineering

Artificial intelligence

General Circulation Model

Circulation (fluid dynamics)

Geology

Climate change

Aerospace engineering

Meteorology

Biology

Climate model

Geography

Weather prediction

Computer science

Climatology

Environmental science

Artificial neural network

Geoscientific model development

Abstract. Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom–up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple general circulation models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.

Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground