Machine learning based dynamic numerical climate multi-model ensemble weighting for high impact weather affecting the energy infrastructure

Machine learning based dynamic numerical climate multi-model ensemble
weighting for high impact weather affecting the energy infrastructure
The infrastructure for renewable energy production and electrical grid itself is affected
by weather and climate conditions and vulnerable to high impact weather and
cascading events. A reliable representation of the meteorological conditions leading to
such events including their uncertainty is therefore needed for both weather and climate
time scales. Individual numerical weather and climate models exhibit systematic
strengths and weaknesses across scales, and geographic regions, despite the
differences in model physics and parametrizations. One way to tackle this and avoid
running single-model ensemble climate simulations are multi-model or poor-man
ensembles consisting of a set of different climate or weather models combined.
Ensemble mixing offers a way to mitigate these weaknesses while providing uncertainty
quantification. Simply ensemble averaging can dilute forecast and climate signals and
penalize outliers and rare extremes. Different approaches have been proposed to tackle
this problem by assigning non-uniform weights to individual model fields and
parameters, however, these methods often rely on domain knowledge such as model
dependencies [1,2].

Here, we propose a machine learning-based multi-model ensemble model-mixing
framework that is domain-agnostic and assigns spatially and temporally dynamic
weights, in addition to an error metric. The domain of interest is the Alps, which exhibit
challenging terrain and localized extreme events, e.g. precipitation extremes that are
difficult to capture in conventional climate models. The CERRA reanalysis data at ~5.5
km resolution serves as the target grid. We build a multi-model ensemble by combining
dynamically downscaled simulations of 2 m air temperature, precipitation, and wind
speed from COSMO-CLM (6 km) and WRF (10 km). Each regional model is driven by two
CMIP6 global climate models (MPI-ESM and EC-Earth) under two scenarios (SSP1-2.6
and SSP5-8.5), with an additional historical period used for training. Static information
such as orography and seasonal dependencies are considered. We evaluate the
ensemble’s performance on selected extreme events (e.g., heavy precipitation,
windstorms, heatwaves) that can (and did) harm energy infrastructure, such as the
European derecho 2022.

[1] Christensen, Jh, E Kjellström, F Giorgi, G Lenderink, and M Rummukainen. 2010.
‘Weight Assignment in Regional Climate Models’. Climate Research 44 (2–3): 179–94.
https://doi.org/10.3354/cr00916.

[2] Merrifield, Anna Louise, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto
Knutti. 2020. ‘An Investigation of Weighting Schemes Suitable for Incorporating Large
Ensembles into Multi-Model Ensembles’. Earth System Dynamics 11 (3): 807–34.
https://doi.org/10.5194/esd-11-807-2020.