Impact of sea ice initialization on sea ice and atmosphere prediction skill on seasonal timescales

Abstract

We present a robust assessment of the impact of sea ice initialization from reconstructions of the real state on the sea ice and atmosphere prediction skill. We ran two ensemble seasonal prediction experiments from 1979 to 2012 : one using realistic sea ice initial conditions and another where sea ice is initialized from a climatology, with two forecast systems. During the melting season in the Arctic Ocean, sea ice forecasts become skilful with sea ice initialization until 3–5 months ahead, thanks to the memory held by sea ice thickness. During the freezing season in both the Arctic and Antarctic Oceans, sea ice forecasts are skilful for 7 and 2 months, respectively, with negligible differences between the two experiments, the memory being held by the ocean heat content. A weak impact on the atmosphere prediction skill is obtained.

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

“…Thus, the direct impacts of Baffin Bay and Hudson Bay on the Arctic Ocean interior are considered to be small. Note that the results of this study are not directly comparable with other hindcast studies that focus on pan-Arctic SIE (e.g., Chevallier et al, 2013;Sigmond et al, 2013;Wang et al, 2013;Peterson et al, 2015;Guemas et al, 2016;Sigmond et al, 2016), due to the choice of Arctic Ocean domain. For comparison, the results for the detrended sea ice extent anomaly in the Northern Hemisphere are shown in the supporting information.…”

“…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). In these ensemble hindcasts, it is found that ice thickness and surface or subsurface water temperatures are closely related to the prediction skill, as suggested by idealised or perfect-model experiments with climate models (e.g., Blanchard-Wrigglesworth et al, 2011b;Chevallier and Salas y Mélia, 2012;Day et al, 2014a).…”

Skillful regional prediction of Arctic sea ice on seasonal timescales

“…Next, we consider the sources of summer SIE prediction skill in the FLOR forecast system. Earlier work has shown that SIT is an important source of predictability for summer SIE on seasonal timescales [ Holland et al , ; Blanchard‐Wrigglesworth et al , ; Chevallier and Salas y Mélia , ; Lindsay et al , ; Day et al , ; Germe et al , ; Collow et al , ; Guemas et al , ; Bushuk et al , ]. The ECDA system does not directly assimilate SIT data; however, it may implicitly capture interannual variations in SIT via its assimilation of atmospheric reanalysis data, which provides both thermodynamic and dynamic constraints on SIT.…”

“…Seasonal prediction skill for detrended pan‐Arctic SIE has been assessed in a number of global climate model (GCM)‐based forecast systems. These studies, based on suites of initialized retrospective forecasts (hindcasts), report significant forecast skill relative to the linear trend at lead times of 1–6 months, depending on the target month and model used [ Wang et al , ; Chevallier et al , ; Sigmond et al , ; Merryfield et al , ; Msadek et al , ; Peterson et al , ; Blanchard‐Wrigglesworth et al , ; Guemas et al , ]. Statistical forecast methods have also been shown to skillfully predict detrended pan‐Arctic SIE at lead times up to 6 months [ Lindsay et al , ; Stroeve et al , ; Schröder et al , ; Wang et al , ; Yuan et al , ; Petty et al , ].…”

Summer Enhancement of Arctic Sea Ice Volume Anomalies in the September-Ice Zone

“…The multiyear persistence of sea ice volume and SIT anomalies (Day et al 2014b) implies that knowledge of the SIT state in the preceding winter and spring may be a crucial factor in predicting September SIE. Indeed, a number of recent studies have found that improved SIT initialization leads to improvements in forecast skill on time scales of days (Yang et al 2014) to seasons (Lindsay et al 2012;Day et al 2014a;Collow et al 2015;Guemas et al 2016). The lack of pan-Arctic SIT observations has been a past limitation in sea ice prediction efforts; however, the recent CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) satellite SIT measurements (Kaleschke et al 2012;Laxon et al 2013;Tilling et al 2015), which have data coverage in the melt-pond-free months of October through April, represent a new opportunity for accurate, observation-based initialization of SIT.…”