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Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis

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  • Philippe Goulet Coulombe
  • Maximilian Gobel

Abstract

On September 15th 2020, Arctic sea ice extent (SIE) ranked second-to-lowest in history and keeps trending downward. The understanding of how feedback loops amplify the effects of external CO2 forcing is still limited. We propose the VARCTIC, which is a Vector Autoregression (VAR) designed to capture and extrapolate Arctic feedback loops. VARs are dynamic simultaneous systems of equations, routinely estimated to predict and understand the interactions of multiple macroeconomic time series. The VARCTIC is a parsimonious compromise between full-blown climate models and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our completely unconditional forecast has SIE hitting 0 in September by the 2060's. Impulse response functions reveal that anthropogenic CO2 emission shocks have an unusually durable effect on SIE -- a property shared by no other shock. We find Albedo- and Thickness-based feedbacks to be the main amplification channels through which CO2 anomalies impact SIE in the short/medium run. Further, conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of CO2 emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050's. Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.

Suggested Citation

  • Philippe Goulet Coulombe & Maximilian Gobel, 2020. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Papers 2005.02535, arXiv.org, revised Mar 2021.
  • Handle: RePEc:arx:papers:2005.02535
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    1. Alan J. Auerbach & Yuriy Gorodnichenko, 2012. "Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 1-27, May.
    2. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    3. Christopher A. Sims, 2012. "Statistical Modeling of Monetary Policy and Its Effects," American Economic Review, American Economic Association, vol. 102(4), pages 1187-1205, June.
    4. Francis X. Diebold & Glenn D. Rudebusch, 2019. "Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections," PIER Working Paper Archive 19-021, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Bachmann, Rüdiger & Sims, Eric R., 2012. "Confidence and the transmission of government spending shocks," Journal of Monetary Economics, Elsevier, vol. 59(3), pages 235-249.
    6. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2019. "Priors for the Long Run," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 565-580, April.
    7. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
    8. Diebold, Francis X. & Rudebusch, Glenn D., 2022. "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, vol. 231(2), pages 520-534.
    9. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    10. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, October.
    11. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    12. Dieppe, Alistair & van Roye, Björn & Legrand, Romain, 2016. "The BEAR toolbox," Working Paper Series 1934, European Central Bank.
    13. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    14. Jean-Marie Dufour & Eric Renault, 1998. "Short Run and Long Run Causality in Time Series: Theory," Econometrica, Econometric Society, vol. 66(5), pages 1099-1126, September.
    15. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    16. Choi,In, 2015. "Almost All about Unit Roots," Cambridge Books, Cambridge University Press, number 9781107482500, September.
    17. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 299-307, October.
    18. Malte F. Stuecker & Cecilia M. Bitz & Kyle C. Armour & Cristian Proistosescu & Sarah M. Kang & Shang-Ping Xie & Doyeon Kim & Shayne McGregor & Wenjun Zhang & Sen Zhao & Wenju Cai & Yue Dong & Fei-Fei , 2018. "Polar amplification dominated by local forcing and feedbacks," Nature Climate Change, Nature, vol. 8(12), pages 1076-1081, December.
    19. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    20. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study: Response," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 313-315, October.
    21. Michael Sigmond & John C. Fyfe & Neil C. Swart, 2018. "Ice-free Arctic projections under the Paris Agreement," Nature Climate Change, Nature, vol. 8(5), pages 404-408, May.
    22. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, October.
    23. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    24. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    25. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    26. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(2), pages 231-272, April.
    27. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    28. Detlef Vuuren & Jae Edmonds & Mikiko Kainuma & Keywan Riahi & Allison Thomson & Kathy Hibbard & George Hurtt & Tom Kram & Volker Krey & Jean-Francois Lamarque & Toshihiko Masui & Malte Meinshausen & N, 2011. "The representative concentration pathways: an overview," Climatic Change, Springer, vol. 109(1), pages 5-31, November.
    29. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    30. Graham Elliott, 1998. "On the Robustness of Cointegration Methods when Regressors Almost Have Unit Roots," Econometrica, Econometric Society, vol. 66(1), pages 149-158, January.
    31. James A. Screen & Ian Simmonds, 2010. "The central role of diminishing sea ice in recent Arctic temperature amplification," Nature, Nature, vol. 464(7293), pages 1334-1337, April.
    32. Steven C. Amstrup & Eric T. DeWeaver & David C. Douglas & Bruce G. Marcot & George M. Durner & Cecilia M. Bitz & David A. Bailey, 2010. "Greenhouse gas mitigation can reduce sea-ice loss and increase polar bear persistence," Nature, Nature, vol. 468(7326), pages 955-958, December.
    33. Aiguo Dai & Dehai Luo & Mirong Song & Jiping Liu, 2019. "Arctic amplification is caused by sea-ice loss under increasing CO2," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
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    Cited by:

    1. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," PIER Working Paper Archive 22-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Philippe Goulet Coulombe & Maximilian Göbel, 2021. "On Spurious Causality, CO 2 , and Global Temperature," Econometrics, MDPI, vol. 9(3), pages 1-18, September.
    3. Philippe Goulet Coulombe & Maximilian Gobel, 2021. "On Spurious Causality, CO2, and Global Temperature," Papers 2103.10605, arXiv.org.
    4. Diebold, Francis X. & Göbel, Maximilian, 2022. "A benchmark model for fixed-target Arctic sea ice forecasting," Economics Letters, Elsevier, vol. 215(C).
    5. Marina Friedrich & Luca Margaritella & Stephan Smeekes, 2023. "High-Dimensional Granger Causality for Climatic Attribution," Papers 2302.03996, arXiv.org, revised Jun 2024.
    6. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Working Papers 22-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    7. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023. "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, vol. 124(C).

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