2023/08/08 Storm Hans in Scandinavia
Heavy precipitation in Storm Hans mostly strengthened by human-driven climate change and natural variability
Press Summary (First Published 2023/09/05, Updated 2023/11/22)
Storms similars to Hans are no more intense in the present than they would have been in the past in terms of sea-level pressure. Precipitations are more intense than they would have been in the past in Southern Scandinavia and the Baltic region, and less intense in the interior of the Scandinavian Peninsula.
Storm Hans was a largely unique event.
Natural climate variability likely played a role in driving the pressure pattern and the associated heavy precipitation linked to storm Hans.
Event Description
In early August 2023, a summer extratropical storm named Hans affected the Scandinavian countries as well as Finland and the Baltics. The storm, which led to some of the heaviest precipitation on the 8th August, but persisted over land for several days, caused significant damage and resulted in 2 casualties. It featured heavy precipitation, with recorded values exceeding 80 mm/day in Sweden, and a weather warning for precipitation of up to 100 mm/day in Norway. Unusually powerful for the season and region, the storm's impacts were chiefly associated with heavy precipitation, and spanned from derailed trains to extensive flooding, mudslides and landslides.
Storm Hans displayed clear negative Surface Pressure Anomalies over Scandinavia, Finland, the Baltics and the northernmost parts of continental Europe. Precipitation during the storm in the reanalysis data used here was above 30 mm/day over a large swath of Scandinavia, and at some locations exceeded 60 mm/day.
Climate and Data Background for the Analysis
In Chapter 11 of the IPCC AR6 report, it is highlighted that there is low confidence in recent total extratropical storm changes globally, but medium confidence in a poleward storm track shift since the 1980s. Understanding past-century extratropical storm trends is hindered by interannual variability and assimilation data variations in reanalyses, particularly for the pre-satellite era. Agreement is better for stronger cyclones in reanalyses and tracking algorithms. Data for the Northern Hemisphere supports a decreased central pressure for cyclones (<970 hPa) in summer and winter during 1979–2010, but with non-monotonic trends. The background mean sea level pressure's seasonal and regional variations complicate assessing extratropical storm dynamical intensity trends based on absolute central pressure.
Our analysis approach rests on looking for weather situations similar to those of the event of interest having been observed in the past. For Hans, 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 Hans 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 [0°E 30°E 50°N 70°N]. The Surface Pressure Changes show that the pressure over the storm area is largely unchanged in the present relative to the past. Temperature Changes show that similar events produce hotter (2-3 °C) in the present than it would have been in the past in Central Europe, while no significant temperature changes are observed in the area of interest. Precipitation Changes show that similar events produce precipitation that is up to 7 mm/day more intense in the present than it would have been in the past in Southern Scandinavia and the Baltic region, yet less intense in the interior of the Scandinavian Peninsula. Storms similar to Hans are associated with warmer temperatures and heavier precipitation in Copenhagen (Denmark), Gothenburg (Sweden) and Oslo (Norway) than they would have been in the past, although the precipitation changes are only significant in Oslo. We also note that Similar Past Events have become more common in June or September, and less frequent in the high summer months of July and August.
Finally, we find that sources of natural climate variability, notably the Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation, may have influenced the event. This means that the changes we see in the event compared to the past may be due to natural climate variability.
Conclusion
Based on the above, we conclude that storms similar to Hans have not changed in terms of sea-level pressure with respect to those occurring in the past, but they lead warmer temperatures and regionally heavier precipitation than in the past. We interpret this storm as a largely unique extreme event for which natural climate variability played a role.
Contact Author
Gabriele Messori, Uppsala University, 📨gabriele.messori@geo.uu.se 🗣️Swedish, French, Italian, English
Davide Faranda, IPSL-CNRS, France 📨davide.faranda@lsce.ipsl.fr 🗣️French, Italian, English"'
Additional Information : Complete Output of the Analysis
NB1: The following output is specifically intended for scientists and contain details that are fully understandable only by reading the methodology described in Faranda, D., Bourdin, S., Ginesta, M., Krouma, M., Noyelle, R., Pons, F., Yiou, P., and Messori, G.: A climate-change attribution retrospective of some impactful weather extremes of 2021, Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, 2022.
NB2: Colorscales may vary from the ClimaMeter figure presented above.
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.