A review on Arctic sea‐ice predictability and prediction on seasonal to decadal time‐scales

Guémas, Virginie; Blanchard‐Wrigglesworth, Edward; Chevallier, Matthieu; Day, Jonathan J.; Déqué, Michel; Doblas‐Reyes, F. J.; Fučkar, Neven S.; Germe, Agathe; Hawkins, Ed; Keeley, Sarah; Köenigk, Torben; Mélia, David Salas y; Tietsche, Steffen

doi:10.1002/qj.2401

Cited by 217 publications

(247 citation statements)

References 198 publications

(307 reference statements)

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“…Figure 7 shows a higher 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 skill of the dynamical forecast initialized in autumn than in spring over the first 5 months, but on the longer forecast horizons this relationship reverses with the switch between melting and growing seasons. Such behavior is in accord with findings that SIT and sea ice volume have typically greater skill in winter than in any other season (Day et al 2014;Guemas et al 2014b). Overall, the RPSS medians in Fig.…”

Section: Apd Csd Catsupporting

confidence: 88%

“…The focus is on SIT because it has a capability to act as a buffer of climate signals on intraseasonal and longer time scales (e.g. Blanchard-Wrigglesworth et al 2011;Guemas et al 2014b). The K-means nonhierarchical clustering is a type of unsupervised statistical learning method complementary to the PCA, but not constrained by the orthogonality and linearity assumption inherent to the PCA (Hastie et al 2009;Wilks 2011).…”

Section: Summary Conclusion and Future Directionsmentioning

confidence: 99%

“…Such behavior indicates that the transition from the sea-ice melting season to growing season in EC-Earth2.3 typically leads to improvement of skill. This is also likely related to a higher inherent predictability of SIT in winter than in other seasons (Day et al 2014;Guemas et al 2014b). The reliability diagrams of EC-Earth2.3 forecasts show high reliability of all modes after initialization on 1 May, while after initialization on 1 November the dynamical system appears to be overconfident (possibly due to a small ensemble size).…”

Section: Summary Conclusion and Future Directionsmentioning

confidence: 99%

“…Sea ice thickness (SIT) is an integrating medium of the surface ocean and atmosphere conditions: it has the capability to contain climate information on time scales longer than seasonal (Blanchard-Wrigglesworth et al 2011;Chevallier and Salas y Mélia 2012;Guemas et al 2014b). Fučkar et al (2016) extended the conceptual framework of recurrent large-scale modes to the sea ice system and identified modes of the northern hemisphere (NH) sea ice cover variability that persist from intraseasonal to interannual time scales.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical prediction of Arctic sea ice modes of variability

et al. 2018

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This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates 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, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.

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Section: Apd Csd Catsupporting

confidence: 88%

Section: Summary Conclusion and Future Directionsmentioning

confidence: 99%

Section: Summary Conclusion and Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamical prediction of Arctic sea ice modes of variability

et al. 2018

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“…This is primarily because of the persistence of ice thickness anomalies from summer to summer and the persistence of sea surface temperature anomalies from the melt to growth seasons (BlanchardWrigglesworth et al, 2011a;Guemas et al, 2014). These features are also found in the results of experiments comparing multiple climate models (Day et al, 2014b;Tietsche et al, 2014).…”

Section: Introductionmentioning

confidence: 93%

Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

et al. 2018

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A posteriori adjustment of near‐term climate predictions: Accounting for the drift dependence on the initial conditions

Fučkar

Volpi

Guémas

et al. 2014

Geophysical Research Letters

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Climate predictions initialized from an observationally based state (OBS) drift toward the state of the unconstrained model, which makes the use of a posteriori correction methods essential to disentangle the climate signal of interest from the model bias. We propose that applying a linear regression of the predictions and corresponding OBS on the OBS initial conditions (IC), and substituting the latter for the former, offers an effective method for bias correction. The impact of this new method is examined on monthly means of large-scale sea surface temperature indices and the Northern Hemisphere sea ice extent in EC-Earth2.3 predictions. This postprocessing adjustment through a linear regression on the averaged OBS over the first forecast month as a temporarily smoothed proxy for OBS IC shows a reduction of model error with respect to the two established bias correction methods. Improvements are seen for at least two seasons and for some variables up to 5 years.

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A review on Arctic sea‐ice predictability and prediction on seasonal to decadal time‐scales

Abstract: International audienc

Cited by 217 publications

References 198 publications

Dynamical prediction of Arctic sea ice modes of variability

Dynamical prediction of Arctic sea ice modes of variability

Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

A posteriori adjustment of near‐term climate predictions: Accounting for the drift dependence on the initial conditions

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