Storm-Resolving Models Advance Atmospheric Blocking Simulations and Climate Change Insights

Atmospheric blocking is a key driver of midlatitude weather extremes, including heatwaves and cold spells. Yet general circulation models (GCMs) still struggle to capture the frequency, persistence, and spatial characteristics of blocking. Here, we evaluate atmospheric blocking in next-generation storm-resolving Earth system models from the nextGEMS, EERIE, and DestinE projects, focusing on ICON and IFS-FESOM with ~10 km atmospheric and ~5 km ocean grid spacing. We also provide first insights into the IFS-FESOM under SSP3-7.0 forcing. Blocking frequency, duration, and size are assessed in historical simulations spanning 30 years for IFS and 27 years for ICON, relative to ERA5 reanalysis and a CMIP6 multi-model ensemble of eight models. We further examine links between blocking biases and the background flow, sea surface temperatures (SSTs), and storm-tracks. Performance varies regionally and seasonally: IFS, particularly in its atmosphere-only configuration, reproduces blocking frequency and jet structure more realistically than coupled IFS and ICON over the North Atlantic and North Pacific. ICON shows larger winter biases, including overly zonal jets and underestimated Euro-Atlantic blocking compared to IFS. Several biases identified in the CMIP6 models persist in the storm-resolving models or are even amplified, showing that higher resolution alone does not consistently result in better blocking representation. Atmosphere-only experiments (IFS AMIP) highlight the strong influence of sea surface temperatures (SSTs) and the sensitivity of blocking to ocean–atmosphere coupling. We find a positive relationship between blocking frequency and storm-track activity in JJA in the CMIP6 models, which is weaker or absent in the storm-resolving models. Under SSP3-7.0, IFS projects reduced winter blocking at high latitudes (e.g., northern Europe) and reduced summer blocking frequency over the North Atlantic, northern Europe, and Russia. Changes in magnitude, spatial pattern, and persistence are often of the same order as the model biases, indicating that projected blocking responses are difficult to disentangle from systematic errors related to jet structure, SST biases, and storm-track activity. Overall, storm-resolving models show local improvements in blocking representation, particularly when forced with realistic SSTs. However, coupled simulations still exhibit large biases, underlining the need for further development of ocean–atmosphere coupling representation. These findings highlight both the potential and the current limitations of storm-resolving models for simulating and projecting persistent weather extremes in a warming climate.

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Publication Details

Journal
Open Access CRIS of the University of Bern
Published
2025-10-16
DOI
https://doi.org/10.48620/94339
Primary Topic
Climate variability and models
Type
article
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article

Storm-Resolving Models Advance Atmospheric Blocking Simulations and Climate Change Insights

Stephan Pfahl, Olivia Martius, Edgar Dolores-Tesillos
Open Access CRIS of the University of Bern
Climate variability and models
article

Storm-Resolving Models Advance Atmospheric Blocking Simulations and Climate Change Insights

Stephan Pfahl, Olivia Martius, Edgar Dolores-Tesillos
article en

Abstract

Atmospheric blocking is a key driver of midlatitude weather extremes, including heatwaves and cold spells. Yet general circulation models (GCMs) still struggle to capture the frequency, persistence, and spatial characteristics of blocking. Here, we evaluate atmospheric blocking in next-generation storm-resolving Earth system models from the nextGEMS, EERIE, and DestinE projects, focusing on ICON and IFS-FESOM with ~10 km atmospheric and ~5 km ocean grid spacing. We also provide first insights into the IFS-FESOM under SSP3-7.0 forcing. Blocking frequency, duration, and size are assessed in historical simulations spanning 30 years for IFS and 27 years for ICON, relative to ERA5 reanalysis and a CMIP6 multi-model ensemble of eight models. We further examine links between blocking biases and the background flow, sea surface temperatures (SSTs), and storm-tracks. Performance varies regionally and seasonally: IFS, particularly in its atmosphere-only configuration, reproduces blocking frequency and jet structure more realistically than coupled IFS and ICON over the North Atlantic and North Pacific. ICON shows larger winter biases, including overly zonal jets and underestimated Euro-Atlantic blocking compared to IFS. Several biases identified in the CMIP6 models persist in the storm-resolving models or are even amplified, showing that higher resolution alone does not consistently result in better blocking representation. Atmosphere-only experiments (IFS AMIP) highlight the strong influence of sea surface temperatures (SSTs) and the sensitivity of blocking to ocean–atmosphere coupling. We find a positive relationship between blocking frequency and storm-track activity in JJA in the CMIP6 models, which is weaker or absent in the storm-resolving models. Under SSP3-7.0, IFS projects reduced winter blocking at high latitudes (e.g., northern Europe) and reduced summer blocking frequency over the North Atlantic, northern Europe, and Russia. Changes in magnitude, spatial pattern, and persistence are often of the same order as the model biases, indicating that projected blocking responses are difficult to disentangle from systematic errors related to jet structure, SST biases, and storm-track activity. Overall, storm-resolving models show local improvements in blocking representation, particularly when forced with realistic SSTs. However, coupled simulations still exhibit large biases, underlining the need for further development of ocean–atmosphere coupling representation. These findings highlight both the potential and the current limitations of storm-resolving models for simulating and projecting persistent weather extremes in a warming climate.

Open Access CRIS of the University of Bern
Geographical Institute (HU)
Life below water
Openalex Percentile: Top 31%
Climate variability and models
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