Impacts of Sea Ice Thickness Initialization on Seasonal Arctic Sea Ice Predictions

Abstract

A promising means for increasing skill of seasonal predictions of Arctic sea ice is improving sea ice thickness (SIT) initial conditions; however, sparse SIT observations limit this potential. Using the Canadian Climate Model, version 3 (CanCM3), three statistical models designed to estimate SIT fields for initialization in a real-time forecasting system are applied to initialize sea ice hindcasts over 1981–2012. Hindcast skill is assessed relative to two benchmark SIT initialization methods (SIT-IMs): a climatological initialization currently used operationally and SIT values from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Based on several measures of skill, sea ice predictions are generally improved relative to a climatological initialization. The accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC), with the most robust improvements obtained with SIT-IMs that best represent both processes. Similar skill to that achieved by initializing with PIOMAS, including skillful predictions of detrended September SIA from May, is obtained by initializing with two of the statistical models. Regional skill for September SIC is also enhanced using improved SIT-IMs, with an increase in the spatial coverage of statistically significant skill from 10% to 60%–70% of the appreciably varying ice pack. Reduced skill is seen, however, in the Nordic seas using the improved SIT-IMs, resulting from an inherent cold sea surface temperature bias in CanCM3 that is amplified by a thicker initial ice cover.

A Spring Barrier for Regional Predictions of Summer Arctic Sea Ice

“…By performing multiple hindcast simulations using a fully coupled GCM, Bushuk et al (2017a) provide the first extensive analysis of regional Arctic SIE prediction skill, highlighting both key physical mechanisms underlying skillful predictions and assessing forecast skill in each region. Consistent with previous work that relates summer sea ice prediction skill to sea ice thickness (Blanchard-Wrigglesworth, Armour, et al, 2011;Chevallier & Salas-Mélia, 2012;Collow et al, 2015;Dirkson et al, 2017), Bushuk et al (2017a) demonstrate that skillful regional predictions of summertime sea ice depend on initialization of sea ice thickness, while wintertime predictions hinge on initialization of subsurface ocean temperatures.…”

The potential of sea ice leads as a predictor for summer Arctic sea ice extent

“…For example, sea ice concentration and surface temperature in spring are introduced into a multiple linear regression model to forecast the minimum Arctic sea ice extent (Drobot, 2007;Drobot et al, 2006). Some studies suggested that accurate sea ice thickness can increase forecast skill 2 months ahead (Day et al, 2014;Dirkson et al, 2017). Recently, the spring melt pond fraction has been employed to improve the skill of forecasting September sea ice extent (Liu et al, 2015;Schröder et al, 2014).…”

CSIB v1: a sea-ice biogeochemical model for the NEMO community ocean modelling framework

“…The monthly-mean ice thickness and ice concentration gridded data products of PIOMAS were also used for spatial comparison (Dirkson et al, 2016).…”