Modeling and Extrapolating Arctic Feedback Loops using Macroeconometric Techniques
- 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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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
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.
Thanks for your quick replies.
Could you provide reference for your paper (if already published)?
David
Here it is
thanks
Philippe
Hi Philippe,
I can't see the link.
Thanks,
David