The First of Two Arctic Sea Ice Trilogies

I never bogged on the following Arctic sea ice "trilogy" with Glen Rudebusch et al., as much of the research was done when the blog was dormant in the early 2020s. It is concerned with the timing of the first ice-free Arctic September -- what statistical time-series models robustly predict; why, in contrast, the large-scale structural climate models tend to get things so wrong; and why you should care. I'm posting on it now not only because I simply think it's interesting and important (and I hope you will too), but also because it complements and contrasts with my next post (on a related but different Arctic sea ice trilogy). Stay tuned! 

Diebold, F.X. and Rudebusch, G.D. (2022), “Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections,” Journal of Econometrics, 231, 520-534.

---> Compares statistical and large-scale climate model forecasts, and finds that the stat models forecast a much earlier near-ice-free Arctic.

Diebold, F.X., Rudebusch, G.D., Goebel, M., Goulet Coulombe, P. and Zhang, B. (2023), “When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," Journal of Econometrics, 236, 105479.

---> Drills down much deeper on the DR (2022) statistical models, exploring many variations (extent, area, thickness, volume; polynomial vs carbon trends; much more...), establishing robustness of the DR (2022) results and providing more refined forecasts.

Diebold, F.X. and Rudebusch, G.D. (2023), “Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions,” Energy Economics, 126, 107012.

---> Asks WHY the large-scale models fail so badly in DR (2022) and traces the failure to insufficient carbon sensitivity of sea ice in the large-scale models.