EGU General Assembly 2020
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the Creative Commons Attribution 4.0 License.

North Atlantic – European weather regimes in a changing climate: present and future

Luise J. Fischer1,2, Dominik Büeler3, Christian M. Grams3, Urs Beyerle1, David N. Bresch2, and Heini Wernli1
Luise J. Fischer et al.
  • 1Institute for Atmospheric and Climate Science (IAC), ETH Zürich, Switzerland
  • 2Institute for Environmental Decisions (IED), ETH Zürich, Switzerland
  • 3Institute of Meteorology and Climate Research (IMK-TRO), Department Troposphere Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

We present findings from an analysis of weather regimes over the North Atlantic and Europe in present and future climate conditions. Weather regimes strongly influence the statistical distribution of surface weather variables. We use a recently developed, all-season North Atlantic - European weather regime classification with seven regimes. These regimes were originally identified in ERA-Interim reanalyses and, in this study, we investigate how they are represented in climate simulations using the CESM1 large ensemble for present-day and future (RCP8.5) climate conditions. With these regimes, the classification of the flow conditions in the considered region goes beyond the classical categorization according to the North Atlantic oscillation index; the weather regimes explicitly capture different flavors of strong zonal flows and the occurrence of blocking over Greenland, Scandinavia, and Central Europe, respectively. In ERA-Interim they explain 70% of the variability in geopotential height at 500 hPa year-round. Our analysis quantifies how well CESM1 represents the statistics of the weather regimes in present-day climate and how strongly their frequencies change in the future climate scenario. In addition, we identify statistical relationships between weather regimes and their resulting impacts on spatial patterns of surface variables such as precipitation. We compare those patterns and characteristics of the weather regimes identified in ERA-Interim to their characteristics in simulations of present and future climate conditions.

This analysis leads to insight into the representation of and changes in atmospheric circulation in one particular climate model, and, at the same time, it quantifies how well the climate model captures the observed link between surface weather and weather regimes. This approach contributes to improving our understanding of atmospheric circulation changes and their impact on a regional scale, and it may benefit the interpretation and communication of climate projections.

How to cite: Fischer, L. J., Büeler, D., Grams, C. M., Beyerle, U., Bresch, D. N., and Wernli, H.: North Atlantic – European weather regimes in a changing climate: present and future, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4589,, 2020

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Presentation version 2 – uploaded on 29 Apr 2020
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  • CC1: Comment on EGU2020-4589, Leon Hermanson, 07 May 2020

    Hi Luise, have you looked at transitions between weather regimes? Do you think that AT could lead to EuBL (or vice versa)? It would be interesting if the two regimes you see increase with climate change actually are linked!

    • AC1: Reply to CC1, Luise J. Fischer, 07 May 2020
      Hi Leon,
      Thank you for raising this interesting question. I have not yet had time to look at the transitions between the wether regimes in the climate simulations. It is possible that the two weather regimes, Atlantic Trough (AT) and European Blocking (EuBL), (which in the mean increase from present-day to future climate conditions) lead into each other more frequently than leading into one of the other five regimes. It will be interesting to see if the regimes that increase or decrease in future climate conditions are linked. Looking at the transitions is thus an interesting aspect to assess.
      A detail that could make this assessment challenging is the seasonal ‘preference’ of one regime versus another. Although, with the large amount of model simulations available in both climate conditions, this could partially be addressed with statistical sampling methods.
Presentation version 1 – uploaded on 27 Apr 2020 , no comments