EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Origins of variability and predictability in the North Atlantic region

Daniela Domeisen
Daniela Domeisen
  • ETH Zürich, Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, Zürich, Switzerland (

The atmosphere over the North Atlantic sector exhibits significant interannual and interdecadal variability, as well as long-term trends due to global change. This variability is accompanied by changes in predictability. The origins of North Atlantic variability can to a large extent be traced back to the ocean and the land surface, the upper atmosphere, the tropics, as well as circum-global patterns. In particular, the tropical Pacific and the upper atmosphere have a strong influence on interannual and decadal variability in the North Atlantic region. As an example, the tropical Pacific affects the North Atlantic both through a tropospheric pathway across North America and through an indirect pathway through the stratosphere. Hence, due to the large number of factors influencing the North Atlantic region, their inter-dependence and their non-stationarity, the influence of these different factors is difficult to disentangle. Furthermore, models are often not able to capture the inter-dependence and superposition of these factors, which affects to what extent models are able to predict the North Atlantic region. This submission will explore the contribution to variability and predictability for several of these remote influences.


How to cite: Domeisen, D.: Origins of variability and predictability in the North Atlantic region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7815,, 2020

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Display material version 2 – uploaded on 03 May 2020
  • CC1: Model performance in seasonal skill, Lisa Degenhardt, 05 May 2020

    Dear Daniela,

    thank you for this great display, I really enjoyed to look through it. Your presentations is a great summary from seasonal to decadel variability, which is kind of the basic in my PhD project.

    Therefore, a more general question. Do you have a publication where I can find this nice first time-scale-figure with the different factors?

    And another question: Do you found reasons why GloSea5 and MPI-ESM-MR show such a good forecast skill compared to other models? (Slide 6)

    Thank you,



    • AC1: Reply to Lisa Degenhardt, Daniela Domeisen, 05 May 2020

      Hi Lisa,

      Thanks for your interest!

      1) time-scale figure: I haven't published this figure in a publication (yet), but I think EGU presentations are citable, so if you like you could just use it and cite the EGU display. Feel free let me know if you have questions about this. 

      2) NAO model skill: It is not fully clear why GloSea5 and MPI-ESM have a comparably higher NAO skill as compared to other models. One potential explanation is higher resolution (compare e.g. MPI-ESM-MR to MPI-ESM-LR, which has lower vertical resolution than the MR version (L47 instead of L95, but the same horizontal resolution); and GloSea5 to L85GloSea4, which has lower horizontal resolution, while the vertical resolution is the same). Note also that this figure is from 2016, and many modeling systems have meanwhile improved, especially e.g. the ECMWF system. 



Display material version 1 – uploaded on 03 May 2020, no comments