2024/02/01 Storm Ingunn

Storm Ingunn mostly strengthened by human-driven climate change 

Press Summary (First published 2024/02/01)

Event Description

On January 31, 2024, a depression named Ingunn by the Norwegian Meteorological Institute, was moving to the east across the North Sea, expected to hit Norway on February 1. The storm kept an unusually northerly trajectory, and went through a rapid intensification of 35 hPa over 24 hours, becoming an explosive cyclone as it tracked over the Faroe Islands, producing record winds of 155 mph (250 km/h) and a minimum pressure of 940 hPa. The following day, Ingunn made landfall in Norway with an intensity comparable to a category 1 hurricane, pummeling the coast with record winds and 10-meters waves, and becoming the strongest storm to hit Norway in the last 30 years. Other than structural damage from the wind, Norwegian news outlets reported tens of thousands people without electric power and disruption of all means of transportation (roads, railways and ferry lines), as the storm caught the attention of international media.

The Surface Pressure Anomalies reveal a large negative (cyclonic) anomaly over northern Norway. In the context of Atlantic extratropical storms, this setup has the tendency to transport moist and warm air from the Atlantic to the Norwegian coasts, resulting in large positive Temperature Anomalies. This setup has led to significant precipitation amounts on the Norwegian coasts, with up to 30mm/day. This precipitation fell as rain, sleet, or snow depending on the altitude in the Norwegian fjords. Windspeed Data show that the coasts have experienced extreme winds, reaching values of average wind speed over 100 km/h.

Climate and Data Background for the Analysis

The IPCC AR6 WG1 report states that climate change impacts on storminess in Europe with negative repercussions that are exacerbated by rising sea levels and heavy precipitation. Shifts in atmospheric circulation patterns are anticipated due to the unequal warming of land and ocean. This differential warming may lead to reduced continental near-surface relative humidity and contribute to localized decreases in precipitation. Climate models consistently indicate a potential increase in the frequency and intensity of severe thunderstorms characterized by tornadoes, hail, and winds.

Projections indicate that mean wind speeds are likely to decrease in Mediterranean areas and possibly in Northern Europe for global warming levels of 2°C or higher, particularly beyond the middle of the century. On the contrary, a modest increase in the frequency and intensity of extratropical cyclones, strong winds, and extratropical storms is expected for northern, central, and western Europe for the same warming levels. Changes in winter storminess are instead complex and varied. In the Euro-Atlantic region, significant changes in winter storminess are primarily expected after surpassing the 1.5°C warming threshold. These changes are influenced by shifts in the atmospheric stratification and temperature gradients at different altitudes.

Our analysis approach rests on looking for weather situations similar to those of the event of interest having been observed in the past. For Storm Ingunn we have low confidence in the robustness of our approach given the available climate data, as the event is largely unique in the data record.

ClimaMeter Analysis

We analyse here (see Methodology for more details) how events similar to Storm Ingunn have changed in the present (2001–2022) compared to what they would have looked like if they had occurred in the past (1979–2000) in the region [-5°E 30°E 55°N 75°N]. Surface pressure changes indicate that storms resembling Ingunn exhibit no difference in pressure anomalies in the present compared to the past. However, Temperature, Wind and Precipitation changes suggest that these storms are now up to 3 °C warmer over most of Scandinavia, up to 10 mm/day rainier in Norway and up to 10 km/h windier in central Norwegian coast. From the analysis in the urban areas we found that Tromsø, Trondheim and Bergen are about 1.5 °C warmer and 1.5-3 mm/day rainier than in the past. We also note that the seasonality of Similar Past Events has not changed significantly.

Finally, we find that sources of natural climate variability, notably the Atlantic Multidecadal Oscillation, may have only partly influenced the event. This means that the changes we see in the event compared to the past may be mostly due to human driven climate change.


Based on the above, we conclude that windstorms similar to Storm Ingunn are up to 3°C warmer and up to 10mm/day (i.e.  2-15 cm snow, that is 10-30%)  rainier and snowier, and up to 10km/h windier  in the present than they have been in the past. We interpret Storm Ingunn as a largely unique event whose characteristics can mostly be ascribed to human driven climate change.

Contact Authors

Flavio Pons, IPSL-CNRS, France  📨flavio.pons@lsce.ipsl.fr  🗣️French, Italian, English

Davide Faranda, IPSL-CNRS, France  📨davide.faranda@lsce.ipsl.fr  🗣️French, Italian, English

Additional Information : Complete Output of the Analysis

The figure shows the average of surface pressure anomaly (msl) (a), average 2-meter temperatures anomalies (t2m) (e), cumulated total precipitation (tp) (i),  and average wind-speed (wspd) in the period of the event. Average of the surface pressure analogs found in the counterfactual [1979-2000] (b) and factual periods [2001-2022] (c), along with corresponding 2-meter temperatures (f, g),  cumulated precipitation (j, k), and wind speed (n, o).  Changes between present and past analogues are presented for surface pressure ∆slp (d),  2 meter temperatures ∆t2m (h), total precipitation ∆tp (i), and windspeed ∆wspd (p): color-filled areas indicate significant anomalies with respect to the bootstrap procedure. Violin plots for past (blue) and present (orange) periods for Quality Q analogs (q), Predictability Index D (r), Persistence Index Θ (s), and distribution of analogs in each month (t). Violin plots for past (blue) and present (orange) periods for ENSO (u), AMO (v) and PDO (w).  Number of the Analogues occurring in each subperiod (blue) and linear trend (black).  Values for the peak day of the extreme event are marked by a blue dot. Horizontal bars in panels (q,r,s,u,v,w) correspond to the mean (black) and median (red) of the distributions.