EGU24-3614, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

From climate to weather reconstruction with inexpensive neural networks

Martin Wegmann1,2 and Fernando Jaume-Santero3,4
Martin Wegmann and Fernando Jaume-Santero
  • 1Institute of Geography, University of Bern, Bern, Switzerland (
  • 2Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 3Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
  • 4Geneva School of Business Administration, University of Applied Sciences and Arts of Western Switzerland, Carouge, Switzerland

Understanding atmospheric variability is essential for adapting to future climate extremes. Key ways to do this are through analysing climate field reconstructions and reanalyses. However, producing such reconstructions can be limited by high production costs, unrealistic linearity assumptions, or uneven distribution of local climate records. 

Here, we present a machine learning-based non-linear climate variability reconstruction method using a Recurrent Neural Network that is able to learn from existing model outputs and reanalysis data. As a proof-of-concept, we reconstructed more than 400 years of global, monthly temperature anomalies based on sparse, realistically distributed pseudo-station data.

Our reconstructions show realistic temperature patterns and magnitude reproduction costing about 1 hour on a middle-class laptop. We highlight the method’s capability in terms of mean statistics compared to more established methods and find that it is also suited to reconstruct specific climate events. This approach can easily be adapted for a wide range of regions, periods and variables. As additional work-in-progress we show output of this approach for reconstructing European weather in 1807, including the extreme summer heatwave of that year.

How to cite: Wegmann, M. and Jaume-Santero, F.: From climate to weather reconstruction with inexpensive neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3614,, 2024.