EGU24-19593, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816

Peter Stucki1, Lucas Pfister1, Stefan Brönnimann1, Yuri Brugnara1,2, Chantal Hari1,3,4, and Renate Varga1,5
Peter Stucki et al.
  • 1University of Bern, Oeschger Centre for Climate Change Research and Institute of Geography, Bern, Switzerland (
  • 2Empa, Dübendorf, 8600, Switzerland
  • 3Wyss Academy for Nature, University of Bern, Bern, 3011, Switzerland
  • 4Physics Institute, University of Bern, Bern, 3012, Switzerland
  • 5REWE International AG, Wiener Neudorf, 2355, Austria

The “Year Without Summer” of 1816 was characterized by extraordinarily cold and wet periods in Central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective and beyond its tragic consequences, the summer of 1816 represents a rare occasion to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D-variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. 
Our downscaling simulations reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations. In turn, data assimilation requires careful selection, preprocessing and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

How to cite: Stucki, P., Pfister, L., Brönnimann, S., Brugnara, Y., Hari, C., and Varga, R.: Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19593,, 2024.

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