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DTSTAMP:20260506T171115Z
SUMMARY:Elements of sea ice variability and predictability in the high nort
 h
DESCRIPTION:We determine robust modes of the northern hemisphere (NH) sea i
 ce variability on seasonal to interannual time scales disentangled from th
 e long-term climate change. This study focuses on sea ice thickness (SIT)\
 , reconstructed with an ocean–sea-ice general circulation model\, becaus
 e SIT has a potential to contain most of the interannual memory and predic
 tability of the NH sea ice system. We use the Kmeans cluster analysis to d
 etermine three NH SIT clusters/modes in a historical reconstruction of SIT
  from 1958 to 2013. Compositing analysis of the NH surface climate conditi
 ons associated with each cluster indicates that wind forcing seem to be th
 e key factor driving the formation of interannual SIT cluster patterns dur
 ing the winter. Furthermore\, we explore the prediction skill of these NH 
 SIT modes of variability in a state-of-the-art coupled forecast system wit
 h respect to two statistical forecast benchmarks. More specifically\, we u
 se the EC-Earth2.3 coupled climate model to produce five-member 12-month-l
 ong monthly forecasts of the NH SIT modes initialized on 1 May and 1 Novem
 ber every year from 1979 to 2010. We use a three-state first-order Markov 
 chain and climatological probability forecasts determined from the histori
 cal SIT mode reconstruction as two statistical reference forecasts. The an
 alysis of ranked probability skill scores (RPSSs) relating these three for
 ecast systems shows that the dynamical SIT mode forecasts typically have a
  higher skill than the Markov chain forecasts\, which are overall better t
 han climatological forecasts. The evolution of RPSS in forecast time indic
 ates that the transition from the sea-ice melting season to growing season
  in the EC-Earth2.3 forecasts\, with respect to the Markov chain model\, t
 ypically leads to the improvement of prediction skill. The reliability dia
 grams overall show better reliability of the dynamical forecasts than that
  of the Markov chain model\, especially for 1 May start dates\, while dyna
 mical forecasts with 1 November start dates are overconfident. The relativ
 e operating characteristics (ROC) diagrams confirm this hierarchy of forec
 ast skill among these three forecast systems.\nFučkar\, N.S.\, Guemas\, V
 .\, Johnson\, N.C. et al. Clim Dyn (2016) 47: 1527.https://doi.org/10.1007
 /s00382-015-2917-2\nFučkar\, N.S.\, Guemas\, V.\, Johnson\, N.C. et al. C
 lim Dyn (2018). https://doi.org/10.1007/s00382-018-4318-9
URL:https://www.imperial.ac.uk/events/97291/elements-of-sea-ice-variability
 -and-predictability-in-the-high-north/
DTSTART;TZID=Europe/London:20190226T113000
DTEND;TZID=Europe/London:20190226T123000
LOCATION:United Kingdom
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DTSTART:20190226T113000
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