EGU2020-12620, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-12620
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modeling and Extrapolating Arctic Feedback Loops using Macroeconometric Techniques

Philippe Goulet Coulombe1 and Maximilian Göbel2
Philippe Goulet Coulombe and Maximilian Göbel
  • 1University of Pennsylvania, Economics, United States of America (gouletc@sas.upenn.edu)
  • 2University of Lisbon

The minimum extent of arctic sea ice (SIE) in 2019 ranked second-to-lowest in history and is trending downward. Hence, there is an immediate need for flexible statistical modeling approaches that both explain endogenously the trend of SIE and permits its extrapolation to generate a long-run forecast. To that end, we propose the VARCTIC, which is a Vector Autoregression (VAR) specifically designed to capture and extrapolate feedback loops that characterize the Arctic system.  VARs are dynamic simultaneous systems of equations routinely estimated in economics to predict and understand the interactions of multiple macroeconomic time series. The VARCTIC is a compromise between fully structural/deterministic modeling and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our "business as usual" completely unconditional forecast has September SIE hitting 0 around the middle of the century. By studying the impulse response functions of Bayesian VARs including different sets of variables, we single out CO2 shocks as main drivers of the long-run evolution of SIE. Additionally, we document that the corresponding responses of Sea Ice Albedo and Thickness largely amplify the long-run impact of CO2 on SIE.  Finally, we conduct conditional forecasts analysis of remedies like reducing CO2 emissions or the implementation of Albedo-enhancing Geo-Engineering technologies.

How to cite: Goulet Coulombe, P. and Göbel, M.: Modeling and Extrapolating Arctic Feedback Loops using Macroeconometric Techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12620, https://doi.org/10.5194/egusphere-egu2020-12620, 2020

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  • CC1: Comment on EGU2020-12620, David Docquier, 05 May 2020

    Dear Philippe,

    This is a very interesting study. I have 2 questions:

    1) I guess you have seen this abstract (and related display) by Diebold and Rudebush: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-3717.html. Do you have any comment regarding their earlier estimate of SIE=0 compared to yours?

    2) Would ocean heat transport be also an interesting variable to consider in your VAR approach? I know that you already use SST, but OHT (which is integrated over the whole ocean depth and includes ocean velocity) is a known driver of Arctic sea-ice extent reduction: https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-18-0750.1.

    Thanks for your feedback.

    David

    • AC1: Reply to CC1, Philippe Goulet Coulombe, 05 May 2020

      Hi. Thanks for your interest in our work.

      i) Yes, we do know that paper very well.  First, methodologies differ. Our results depends on the path of CO2 and how it interacts with the included variable in the VAR. In their case, it is a quadractic trend fitted by months, which only implicetely may account for forcing. However, when conditionnig on RCP 8.5, we get forecasts closer to them. In our case, we also discuss at some point in the paper the possibility of extending the VAR framework so that dynamics would evolve throuhgout seasons, which could potentially bridge the gap. 

      ii) Indeed, we discuss that issue in the paper.  Some studies have found that anomalies in the temperature of the upper-ocean layers and anomalies in SST do coincide (Park & al. 2015). Further, the fact that we include some atmospheric variables in the regression framework can purge SST from atmospheric interference. We also consider a version of of our model with 10 additional variables, some of them proxying for what you raise, and find similar results about our  ain variables of interest.

       

       

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

        Thanks for your quick replies.

        Could you provide reference for your paper (if already published)?

        David

        • AC2: Reply to CC2, Philippe Goulet Coulombe, 07 May 2020

          Here it is  

          thanks

          Philippe

          • CC3: Reply to AC2, David Docquier, 07 May 2020

            Hi Philippe,

            I can't see the link.

            Thanks,

            David