EGU2020-4729
https://doi.org/10.5194/egusphere-egu2020-4729
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learnt from 5 Large Ensembles and CMIP5 Simulations

Daniel Topal1,2,3, Qinghua Ding3, Jonathan Mitchell4,5, Ian Baxter3, Mátyás Herein6,7, Tímea Haszpra6,7, Rui Luo3, and Qingquan Li8
Daniel Topal et al.
  • 1Eötvös Loránd University, Institute of Geography and Earth Sciences, Department of Meteorology, Hungary (topaldani@gmail.com)
  • 2Institute for Geological and Geochemical Research, Research Centre for Astronomy and Earth Sciences, Budapest, Hungary
  • 3Department of Geography, Earth Research Institute, University of California, Santa Barbara, Santa Barbara, California, USA
  • 4Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, USA
  • 5Department of Earth, Planetary and Space Sciences, University of California, Los Angeles, California, USA
  • 6Institute of Theoretical Physics, Eötvös Loránd University, Budapest, Hungary
  • 7MTA–ELTE Theoretical Physics Research Group, Eötvös Loránd University, Budapest, Hungary
  • 8Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China

Arctic sea ice melting processes in summer due to internal atmospheric variability have recently received considerable attention. A regional barotropic atmospheric process over Greenland and the Arctic Ocean in summer (June-July-August), featuring either a year-to-year change or a low-frequency trend toward geopotential height rise, has been identified as an essential contributor to September sea ice loss, in both observations and the CESM1 Large Ensemble (CESM-LE) of simulations [1-2]. This local melting is further found to be sensitive to remote sea surface temperature (SST) variability in the East Central Pacific [3]. Here, we utilize five available single-model large ensembles and 31 CMIP5 models’ pre-industrial control simulations to show that the same atmospheric process, resembling the observed one and the one found in the CESM-LE, also dominates internal sea ice variability on interannual to interdecadal time scales in pre-industrial, historical and future scenarios, regardless of the modeling environment. However, all models exhibit limitations in replicating the correct magnitude of the observed local atmosphere-sea ice coupling and its sensitivity to remote tropical SST variability. These biases cast a shadow over models’ credibility in simulating interactions of sea ice variability with the Arctic and global climate systems. Further efforts toward identifying possible causes of these model limitations may provide profound implications for alleviating the biases and improving interannual and decadal time scale sea ice prediction and future sea ice projection.

 

[1] Ding, Q., and Coauthors, (2017): Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Climate Change, 7, 289-295.

[2] Ding, Q., and Coauthors, (2019): Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nat. Geosci., 12, 28–33.

[3] Baxter, I., and Coauthors, (2019): How tropical Pacific surface cooling contributed to accelerated sea ice melt from 2007 to 2012 as ice is thinned by anthropogenic forcing. J. Climate, 32, 8583–8602 https://doi.org/10.1175/JCLI-D-18-0783.1 

How to cite: Topal, D., Ding, Q., Mitchell, J., Baxter, I., Herein, M., Haszpra, T., Luo, R., and Li, Q.: An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learnt from 5 Large Ensembles and CMIP5 Simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4729, https://doi.org/10.5194/egusphere-egu2020-4729, 2020

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  • CC1: Comment on EGU2020-4729, David Docquier, 05 May 2020

    Dear Daniel,

    Thank you for your interesting presentation. I have some questions:

    1) In Slide 6, do you have any idea why there is such a difference in correlation spatial patterns between observations and models (especially in terms of temperature, right column)?

    2) In Slide 7, am I right when I say that you computed internal variability as the difference between the fast melting and slow melting groups? I am wondering what's exactly the rationale behind that choice. Does it really represent internal variability or rather the atmospheric response to sea-ice loss (which also includes part of the forced signal)? Have you tried to use the standard deviation of the LEs instead?

    Thank you very much for your feedback.

    David

     

    • AC1: Reply to CC1, Daniel Topal, 05 May 2020

      Dear David,

      Thank you for your comment.

      1) In Slide 6, do you have any idea why there is such a difference in correlation spatial patterns between observations and models (especially in terms of temperature, right column)?

          One possibility is the weak sensitivity of temperature and humidity fields to circulation variability in the Arctic, in addition to the inappropriate representation of cloud variability in the Arctic mid-low troposphere. The atmopsheric mechanism that is discussed in the presentation is based on Ding et al 2017 (see the ref in the abstract), who showed how atmopsheric circulation drives Arctic surface temperature and sea ice variability in summer. Various modeling experiments suggest, that the small skin temperature difference between the ocean surface and sea ice in summer allows only really weak atmopsheric response to sea ice loss. Instead, the atmosphere acts as a driver upon sea ice variability. This mechanism is not captured by CMIP5/6 models with full strength, which results in the differences in the abovementioned correlation patterns.

      2) In Slide 7, am I right when I say that you computed internal variability as the difference between the fast melting and slow melting groups? I am wondering what's exactly the rationale behind that choice. Does it really represent internal variability or rather the atmospheric response to sea-ice loss (which also includes part of the forced signal)? Have you tried to use the standard deviation of the LEs instead?

           Yes, I took the fast and slow melting group of members (for a given time period) and looked at the difference of Z200, T, Z trends between the slow and fast melting groups. I chose 15% of the total ensemble members to the fast and slow melting groups, which is approx. 1 standard deviation. 

      The choice relies on the supposed mechanism, that those members that show enhanced melting (relative to other members) show concomitant high pressure in the Arctic and they do indeed, as the composite ensures. As Ding et al 2017 showed, the atmopshere shows only really weak response to sea ice melt in model simulations, however the way around, atmosphere can indeed be a driver of sea ice variability.

      I understand your concern, why only 15% of the members are chosen, however, Maximum Covariance Analysis ensured, that the choice is suffice.

      I hope I could answer your questions, would be happy to discuss further via email.

      Best,

      Dani

      • CC2: Reply to AC1, David Docquier, 05 May 2020

        OK thanks a lot for your detailed responses.

        Looking forward to the session this afternoon,

        David