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

Very rare heat extremes: how anomalous could they get?

Claudia Gessner, Erich Fischer, Urs Beyerle, and Reto Knutti
Claudia Gessner et al.
  • Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland (

Extreme heat waves as in 2003 and 2010 can have severe consequences for the economy and society. This raises the question how anomalous they could have gotten. Addressing this question is challenging given the lack of long coherent reliably daily data. Multi-millennial GCM simulations and single-model initial condition large ensembles offer a new opportunity to investigate the very upper tail of temperature distribution. Here, we use a nearly 5,000-year long pre-industrial control run and a 84-member large initial condition ensemble performed with CESM1.2. Evaluations show that the simulated climate variability and temperature response to circulation anomalies agree well with the ERA5 reanalysis over large parts of the global land regions.

We show that highest temperature extremes in the long pre-industrial control simulation exceed the temperature records of 2003 by several degrees in the related hotspot region over Western Europe. The anomalies are caused by large anticyclonic circulation anomalies and very dry land surface conditions, leading to amplifying feedbacks in the surface energy budget. Moreover, the simulation results reveal that summer temperature maxima as a function of return period have an asymptotic , suggesting an upper temperature limit.

In a next step, we use a novel method of ensemble boosting to generate even more extreme temperatures. To that end, 100-member ensembles are reinitialized with perturbed atmospheric conditions weeks before the most intense events. Thereby, we gain insight into short-term mechanisms that underly these hot extremes. The result of the ensemble calculation shows that using this method even more extreme event anomalies can be generated, substantially exceeding highest values in the long pre-industrial control simulations. We investigate how the physical mechanisms of these rare and unseen simulated events differ from more moderate events. We further compare the simulated very rare events with maximum anomalies estimated based on statistical methods.

How to cite: Gessner, C., Fischer, E., Beyerle, U., and Knutti, R.: Very rare heat extremes: how anomalous could they get?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1628,, 2019

Comments on the presentation

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Presentation version 1 – uploaded on 01 May 2020
  • CC1: Comment on EGU2020-1628, Ana Casanueva, 05 May 2020

    Hi Claudia, many thanks for preparing your presentation in this format, which made it much easier to follow, and for taking part in the dicussions this morning.

    I have one question (I am sorry if that is too obvious) about the ensemble boosting. In your slide 4, which peak/extreme is used for ensemble boosting, is that a single event (summer 2003)? or all the 20-year block maxima in the control run?


    • AC1: Reply to CC1, Claudia Gessner, 05 May 2020

      Hi Ana,

      this is a good question and important for the interpretation of the results. I am sorry that I have not added this information to the caption.

      I applied ensemble boosting to the most extreme heat wave in the 5,000-year pre-industrial simulation. Here, the most extreme heat wave refers to the event, achieving the highest Tx7d anomaly (7-day running mean of daily maximum 2m-temperature) in the summer months JJA. This highest Tx7d anomaly is shown as the red horzontal line in the figure on slide 4. It also corresponds to the most extreme 20-year block maxima (point most right) in the return period plot on the previous slide 3.

      If you want to compare the results to, e.g., the European heat wave 2003, first keep in mind that the shown hottest pre-industrial event already exceeds the records of 2003 by up to a few degree, as shown on slide 3. Taking ensemble boosting into account, the results show that about one quarter of all ensemble members (with lead times 7-16 days) exceeds the highest Tx7d anomaly of the pre-industrial simulation but remain within the estimated range of upper Tx7d bound, estimated by the GEV-distribution on slide 3.

      I hope that I could answer your question. If there is still something unclear, please do not hestitate to contact me again.

      • CC3: Reply to AC1, Ana Casanueva, 06 May 2020

        ok, I got it. thanks for clarifying!

  • CC2: Comment on EGU2020-1628, Clemens Schwingshackl, 05 May 2020

    Thank you for the very nice and clear presentation, Claudia!
    I am coming back to my question I asked during the chat session: Do you think a similar approach could be used for precipitation extremes? Are you planning to do anything in this direction? I guess for convective precipitation it could be difficult, bur for large scale events it might be possible. What do you think?

    • AC2: Reply to CC2, Claudia Gessner, 05 May 2020

      Hi Clemens,

      we have not yet tested ensemble boosting for precipitation extremes, but basically the method could be used for other variables too. Maybe you would need smaller lead times, as large-scale precipitation events could easily get lost in the driving field or the affected regions differ. Due to the coarse resolution of the model and the small time and spatial scales of convective precipitation, these events are inappropriated.

      I guess an application of ensemble boosting on large-scale precipitation events would be very interesting. However, we have not (yet) planned anything on precipitation extremes. Nevertheless, thank you for your suggestion, we will keep this in mind.

      I hope that I could answer your question. Please contact me again, if there is still something unclear.