Search for: All records

Abstract. Basic statistical metrics such as autocorrelations and across-region lagcorrelations of sea ice variations provide benchmarks for the assessments offorecast skill achieved by other methods such as more sophisticatedstatistical formulations, numerical models, and heuristic approaches. In thisstudy we use observational data to evaluate the contribution of the trend tothe skill of persistence-based statistical forecasts of monthly and seasonalice extent on the pan-Arctic and regional scales. We focus on the BeaufortSea for which the Barnett Severity Index provides a metric of historicalvariations in ice conditions over the summer shipping season. The varianceabout the trend line differs little among various methods of detrending(piecewise linear, quadratic, cubic, exponential). Application of thepiecewise linear trend calculation indicates an acceleration of the winterand summer trends during the 1990s. Persistence-based statistical forecastsof the Barnett Severity Index as well as September pan-Arctic ice extent showsignificant statistical skill out to several seasons when the data includethe trend. However, this apparent skill largely vanishes when the data aredetrended. In only a few regions does September ice extent correlatesignificantly with antecedent ice anomalies in the same region more than 2months earlier. The springtime “predictability barrier” in regionalforecasts based on persistence of ice extent anomalies is not reduced by theinclusion of several decades of pre-satellite data. No region showssignificant correlation with the detrended September pan-Arctic ice extent atlead times greater than a month or two; the concurrent correlations arestrongest with the East Siberian Sea. The Beaufort Sea's ice extent as farback as July explains about 20 % of the variance of the Barnett SeverityIndex, which is primarily a September metric. The Chukchi Sea is the onlyother region showing a significant association with the Barnett SeverityIndex, although only at a lead time of a month or two. 

Abstract A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.