Pan-Arctic and Regional Sea Ice Predictability: Initialization Month Dependence

Abstract

Seasonal-to-interannual predictions of Arctic sea ice may be important for Arctic communities and industries alike. Previous studies have suggested that Arctic sea ice is potentially predictable but that the skill of predictions of the September extent minimum, initialized in early summer, may be low. The authors demonstrate that a melt season “predictability barrier” and two predictability reemergence mechanisms, suggested by a previous study, are robust features of five global climate models. Analysis of idealized predictions with one of these models [Hadley Centre Global Environment Model, version 1.2 (HadGEM1.2)], initialized in January, May and July, demonstrates that this predictability barrier exists in initialized forecasts as well. As a result, the skill of sea ice extent and volume forecasts are strongly start date dependent and those that are initialized in May lose skill much faster than those initialized in January or July. Thus, in an operational setting, initializing predictions of extent and volume in July has strong advantages for the prediction of the September minimum when compared to predictions initialized in May. Furthermore, a regional analysis of sea ice predictability indicates that extent is predictable for longer in the seasonal ice zones of the North Atlantic and North Pacific than in the regions dominated by perennial ice in the central Arctic and marginal seas. In a number of the Eurasian shelf seas, which are important for Arctic shipping, only the forecasts initialized in July have continuous skill during the first summer. In contrast, predictability of ice volume persists for over 2 yr in the central Arctic but less in other regions.

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

“…The lagged correlations with the observations (Ishii et al, 2006;Ishii and Kimoto, 2009) decrease within the first few months for all of the start months, and those originating between January and June subsequently rise again in the winter (November through March). Significant skill in the control run is obtained for greater lead times than in the observations, which is consistent with previous studies (e.g., BlanchardWrigglesworth et al, 2011b;Day et al, 2014b). For the SIE in the Northern Hemisphere (Fig.…”

“…For the SIE in the Northern Hemisphere (Fig. S1a in the Supplement), the correlation patterns are similar to those in Day et al (2014b), except for a lead time of one month for May which may be due to differences in the observational time period (Fig. S1d).…”

“…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). The observed detrended Arctic sea ice extent, based on ensemble hindcasts can be predicted up to 2-7 and 5-11 months ahead for summer and winter, respectively (e.g., Chevallier et al, 2013;Sigmond et al, 2013;Wang et al, 2013;Msadek et al, 2014;Peterson et al, 2015;Guemas et al, 2016;Sigmond et al, 2016).…”

Dynamical prediction of Arctic sea ice modes of variability

“…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.…”

“…This is compatible with findings of Holland et al (2011) with the NCAR Community Climate System Model, version 3, where summer thermodynamic forcing reduces inherent predictability. Similarly, Day et al (2014) shows that a melt season "predictability barrier" is a robust feature of five global climate models.…”