Publication: Global, Arctic, and Antarctic sea ice volume predictions: using score-driven threshold climate models
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2024-01-25
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Abstract
The literature on sea ice predictions uses a variety of general circulation models (GCMs),
which suggest diverse predictions of the date of ice-free or almost ice-free oceans, and focus mainly
on the Arctic. According to the same literature, GCMs are not sensitive enough to tipping points in the
Atlantic meridional overturning circulation (AMOC), and they underestimate the sensitivity of Arctic
sea ice to carbon emissions. In this paper, we use a novel time series model, named the score-driven
threshold climate (SDTC) model, and we report global, Arctic, and Antarctic sea ice predictions. For
the SDTC model, the estimations are computationally less demanding than those of the GCMs. We
combine long-run 1,000-year frequency climate data from 798,000 to 1,000 years ago, and short-run
annual data from year 850 to year 2014. We present the evolution of long-run and short-run climate
data with descriptive statistics. We estimate the SDTC model using annual data from 850 to 2014
for Arctic and Antarctic sea ice volume Ice𝑡 and Antarctic land surface temperature Temp𝑡 . We use
the atmospheric CO2,𝑡 concentration as a clustering variable to define periods of climate change. We
report in-sample interval forecasts of global, Arctic, and Antarctic sea ice from 1980 to 2014. Observed
global and Arctic sea ice volumes are below the forecasted interval from 2003. Observed Antarctic sea
ice volume is below the forecasted interval from 2011. We report out-of-sample interval forecasts of
sea ice from 2015 to 2314. The out-of-sample forecasts, 𝜇[𝜇 ± 2𝜎], indicate that if the current trend
of climate change continues, then Arctic sea ice will disappear around 2058[2049, 2068], and global
and Antarctic sea ice will disappear around 2174[2123, 2270].
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Climate Change, Sea Ice Volume, Atmospheric Co2 Concentration, Dynamic Conditional Score (Dcs), Generalized Autoregressive Score (Gas), General Circulation Models (Gcms)