4 papers
Unmasking Compensating Errors in Numerical Weather Models: A Process-Additive Neural Network Approach
Yuiko Ichikawa, Edward Groot, Jack Atkinson
This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different physical processes can o…
How Do AI Climate Models Respond to Warming Across Climate Zones?
Charlotte C. Merchant, Milan Klöwer, Bradley Stanley-Clamp +4
Regional climate zones are expected to shift under global warming. Whether AI climate models have learned to generalize climate-zone distributions under warming in a physically mea…
How different are deterministic physics suites when coupled to fixed model dynamics and why?
Edward Groot, Hannah Christensen, Xia Sun +5
It is often difficult to attribute uncertainty and errors in atmospheric models to designated model components. This is because sub-grid parameterised processes interact strongly w…
When tiny convective spread affects a midlatitude jet: spread sequence
Edward Groot, Michael Riemer
We investigate the evolution of spread over three days in a numerical ensemble experiment starting from tiny initial condition uncertainty. We simulate a real event during which th…