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.

Seasonal forecasts of Arctic sea ice using dynamical models are inherently uncertain and so are best communicated in terms of probabilities. Here, we describe novel statistical postprocessing methodologies intended to improve ensemble-based probabilistic forecasts of local sea ice concentration (SIC). The first of these improvements is the application of the parametric zero- and one-inflated beta (BEINF) probability distribution, suitable for doubly bounded variables such as SIC, for obtaining a smoothed forecast probability distribution. The second improvement is the introduction of a novel calibration technique, termed trend-adjusted quantile mapping (TAQM), that explicitly takes into account SIC trends and is applied using the BEINF distribution. We demonstrate these methods using a set of 10-member ensemble SIC hindcasts from the Third Generation Canadian Climate Coupled Global Climate Model (CanCM3) over the period 1981–2017. Though fitting ensemble SIC hindcasts to the BEINF distribution consistently improves probabilistic hindcast skill relative to a simpler “count based” probability approach in perfect model experiments, it does not itself correct model biases that may reduce this improvement when verifying against observations. The TAQM calibration technique is effective at removing SIC biases present in CanCM3 and improving forecast reliability. Over the recent 2000–17 period, TAQM-calibrated SIC hindcasts show improved skill relative to uncalibrated hindcasts. Compared against a climatological reference forecast adjusted for the trend, TAQM-calibrated hindcasts show widespread skill, particularly in September, even at 3–4-month lead times.

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

We formulate seasonal probabilistic forecasts of Arctic sea ice concentration from a multimodel (MM) ensemble constructed from six state‐of‐the‐art climate models. Trend‐adjusted quantile mapping is applied to postprocess individual model forecasts prior to MM combination, and a comparison is made against two benchmark MM ensembles: one uncorrected and another where individual models are adjusted for mean and trend bias. Focusing on September hindcasts over 2000–2015 initialized monthly from April–August, calibration improves forecast skill for all models tested, but greatest skill is achieved by the calibrated MM ensemble. Compared against a climatology adjusted for trends, skill is seen over most of the Arctic for all MM formulations and at all lead times, with highest skill obtained by the calibrated MM ensemble. Furthermore, despite the overall skill for each MM formulation, we find evidence of the “spring predictability skill barrier” for forecasts initialized before June.

There is a high demand and expectation for sub-seasonal to seasonal (S2S) prediction which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve sub-seasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and two-meter temperature globally averaged over forecast weeks 3 and 4, and weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multi-model combination. These forecast improvements should benefit the use of S2S forecasts in applications.