Predictability of Ice Concentration Anomalies in the High Latitudes of the North Atlantic Using a Statistical Approach

• Main Author: Garcia, Katharine S.
• Other Authors: NAVAL POSTGRADUATE SCHOOL MONTEREY CA
• Format: Text
• Language: English
• Published: 1988
• Subjects: Meteorology; Snow; Ice and Permafrost; *MARINE METEOROLOGY; *STATISTICAL ANALYSIS; *MATHEMATICAL PREDICTION; *METEOROLOGICAL DATA; *SEA ICE; ATMOSPHERIC TEMPERATURE; FORECASTING; TIME SERIES ANALYSIS; AUTOCORRELATION; ACCURACY; THESES; REGRESSION ANALYSIS; BOUNDARIES; MARINE ATMOSPHERES; OCEANOGRAPHIC DATA; SEASONAL VARIATIONS; WINTER; ICE; CLIMATE; STORMS; CONCENTRATION(COMPOSITION); WIND VELOCITY; SPECTRAL ENERGY DISTRIBUTION; STATISTICAL PROCESSES; BAROMETRIC PRESSURE; EQUATIONS; CORRELATION TECHNIQUES; SURFACE TEMPERATURE; BARENTS SEA; HIGH LATITUDES; SEA WATER; NORTH ATLANTIC OCEAN; SUMMER; LAND AREAS; GEOGRAPHIC DISTRIBUTION; ARCTIC REGIONS; GREENLAND SEA; LEAD TIME; PERIODIC VARIATIONS; WIND; PARAMETERS; OCEAN SURFACE; OCEANOGRAPHY; DATA BASES; ANOMALIES; *ICE FORECASTING; SEIC(SEA ICE CONCENTRATION); COADS(COMPREHENSIVE OCEAN ATMOSPHERE DATA SET); ICE CONCENTRATION ANOMALIES; ICE COVER; PERSISTENCE; COADS (COMPREHENSIVE OCEAN- ATMOSPHERE DATA SET); SEIC (SEA ICE CONCENTRATION); ICE FORECASTING; ICE CONCENTRATION; SEA ICE; STATISTICAL ANALYSIS; ARTIC CLIMATE MARINE METEOROLOGY; Arctic; Barents Sea; Greenland; Greenland Sea; Ice; North Atlantic; permafrost; Sea ice; ice covered waters
• Online Access: http://www.dtic.mil/docs/citations/ADA205022; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA205022

Based on a 27 year data record from the COADS and SEIC data sets, a statistical analysis of ice concentration, sea surface temperature (SST), air temperature, U and V wind components, and sea level pressure anomaly data was conducted for five locations in the ice-covered waters of the North Atlantic. Spectral densities and autocorrelations of the time series for each variable were calculated to establish a measure of persistence and periodicity. Regression equations were formulated based on the above data sets to forecast both the winter and summer ice concentration anomalies for each location. The differing effects of land and ice boundaries, currents, storm passages and wind velocity parameters retained by each of the regression equations. In addition to ice concentration anomalies at various lags, the inclusion of meteorological and oceanographic parameters was shown to increase the total explained model variance, which should improve the accuracy of an ice concentration anomaly forecast at lead times of at one season over a forecast based on ice concentration anomaly persistence alone.