Skillful regional prediction of Arctic sea ice on seasonal timescales

Abstract

Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan‐Arctic sea ice extent (SIE). In this work, we move toward stakeholder‐relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981–2015 made with a coupled atmosphere‐ocean‐sea ice‐land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea ice thickness initial conditions provide a crucial source of skill for regional summer SIE.

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

“…Improvement in the prediction skill for summer SIE AO is dependent upon refinement of the initial state of the SIT. In fact, higher lagged correlations between the summer SIE AO and the SIV AO suggest that the initialisation of the SIT is important, which is consistent with previous results by Day et al (2014a) and Bushuk et al (2017).…”

“…5), which is consistent with Bushuk et al (2017). Consequently, this persistence contributes to the prediction skill of the September SIE AO .…”

“…Under such maritime activities in the Arctic Ocean, forecasts of the local sea ice distribution rather than the total sea ice extent become of greater interest for marine users. Recent studies have reported the forecast skills of the retreat and advance dates of the sea ice distribution based on statistical methods (e.g., Stroeve et al, 2016;Wang et al, 2016) as well as a dynamical forecast system Bushuk et al, 2017). In the present study, our hindcasts could not reproduce precise seaice edges from summer to fall.…”

“…Motivated by the above studies, we first conduct initialised ensemble hindcasts, using a climate model to assess the seasonal to inter-annual predictability of sea ice extent in the Arctic Ocean, and further investigate sources for prediction skill and clarify the physical processes linking the prediction skill to its sources. In particular, the present study reveals that subsurface ocean heat content originating from the North Atlantic contributes to the predictability of winter sea ice through advection and vertical mixing processes, which is somewhat different from the re-emergence process of the local subsurface ocean temperature suggested by Bushuk et al (2017).…”

“…Bushuk et al (2017) examined the physical mechanisms underlying the prediction skill of regional sea ice extent and showed for the first time the importance of the initialisations of ocean subsurface temperatures and sea ice thickness in their dynamical prediction system. Motivated by the above studies, we first conduct initialised ensemble hindcasts, using a climate model to assess the seasonal to inter-annual predictability of sea ice extent in the Arctic Ocean, and further investigate sources for prediction skill and clarify the physical processes linking the prediction skill to its sources.…”