Einar Bjørgo

Analysis of merged SMMR‐SSMI time series of Arctic and Antarctic sea ice parameters 1978–1995

The most consistent means of investigating the global sea ice cover is by satellite passive microwave sensors, as these are independent of illumination and cloud cover. The Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSMI) provide information on the global sea ice cover from 1978 to present. The two instruments flew simultaneously during a 6-week overlap period in July and August 1987, thus enabling intercomparison of the two sensors. Brightness temperatures are corrected for instrument drift and calibration differences in order to produce continuous time series of monthly averaged Arctic and Antarctic sea ice extent and sea ice area through the use of the NOrwegian Remote Sensing EXperiment (NORSEX) algorithm, which relates brightness temperatures to ice concentration. Statistical analysis on the time series estimates the decreases in Arctic ice extent and ice area to be 4.5% and 5.7%, respectively, during the 16.8-year observation period. The overall trends established here serve to better define and strengthen earlier assertions of a reduced ice cover, based on analysis of SMMR and SSMI data taken separately. These results are consistent with GCM simulations that suggest retreat of the sea ice cover under global warming scenarios.

Global sea ice monitoring from microwave satellites

The global sea ice covers can be regularly and frequently monitored using satellite passive microwave sensors. Recent studies using passive microwave data have detected significant decreases in Arctic ice extent and ice area, with no significant changes in the Antarctic. The authors analyze microwave data through mid-1995, to identify changes in ice extent, ice area, and, for the first time, overall ice concentration. The authors find continued decreases in the Arctic ice extent and ice area, and establish a decrease in the ice concentration. They also include a seasonal analysis of the Arctic trends, which establishes the greatest decreases to be in summer and spring. The relatively large summer decreases imply a reduction in the multi-year ice area, suggesting reduced ice thickness, though observational data are lacking.

Wind energy mapping from synthetic aperture radar

Satellite based synthetic aperture radar (SAR) measurements from, e.g. ERS-1/ERS-2 can be used for wind energy mapping in coastal regions for wind mill siting. The SAR has clear advantages for high spatial resolution wind field mapping since it is independent of daylight and clouds. The spatial resolution of 30 m is sufficient as is the 100 km wide and several hundred km long spatial coverage along the coastline. Wind maps generated from SAR will be able to provide spatial information about wind energy with a resolution of 400 m. However, before SAR can be used operational for wind energy mapping, the method needs validation and potential limitations must be investigated and described in detail.

A review of the possible applications of satellite earth observation data within EuroGOOS

This paper summarise the current and near future marine applications of satellite earth observation (EO) within the five defined modules of EuroGOOS. The conclusions indicate that several applications of EO data are beneficial or even exclusive for efficient information retrieval. The ultimate use of EO data will be most beneficial through an integrated use with field observations, numerical prediction models, using advanced data assimilation techniques. The operationalization of EO data in the EuroGOOS context has perspectives in current applications, near future operational implementation and longer term development, both with respect to development of methods and new sensor technologies.

Time series analysis of SMMR and SSM/I data for climate change detection

Sea ice is a sensitive component of the climate system, such that reductions in the Arctic and Antarctic sea ice covers could be indicative of greenhouse warming. The most consistent source of information on sea ice parameters is microwave remote sensing from polar-orbiting satellites. Previous analyses of Scanning Multichannel Microwave Radiometer (SMMR) data from 1978-87 revealed a significant decrease in Arctic sea ice extent with no changes in the Antarctic. The authors extend the record to 1994 by including data from the subsequent Special Sensor Microwave Imager (SSM/I). This involves intercalibrating the SMMR-SSM/I data during their six-week overlap period, achieved at the sea ice concentration level using the NORSEX algorithm. Time series analysis of the merged 1978-94 continuous sea ice time series reveals continued decreases in Arctic ice extent and area. Moreover, the authors find a slight decrease in Antarctic ice extent during the period. The brevity of the records reiterates the need for longer, continuous microwave time series to identify long-term trends.

Sea ice concentrations derived from SMMR and SSMI: parameter retrieval and algorithm evaluation

Satellite passive microwave sensors are the most effective means to monitor sea ice on a global scale. 18 GHz horizontal and vertical and 37 GHz vertical polarized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR) are compared to the 19 GHz horizontal and vertical and 37 GHz vertical polarized brightness temperatures from the Special Sensor Microwave Imager (SSMI) over the Arctic and Antarctic during the 1987 overlap period n order to merge the two time series. The Norwegian NORSEX and NASA Team multi-frequency algorithms are used on the overlapping SMMR and SSMI data sets. Sea ice extent and area are calculated and the algorithm performance is compared for both hemispheres. The NORSEX algorithm tends to give approximately 10% higher sea ice concentration values than the NASA Team algorithm.

Operational climate monitoring program of the arctic ice cover

The predominant feature of the Arctic Ocean is the presence of a perennial sea ice cover that shapes the climate of the region, by greatly altering the radiation budget, and restricting heat and mass exchanges between the ocean and atmosphere. The Arctic is believed to be particularly sensitive to global climate change that may result from increases in so-called greenhouse gases. Greenhouse warming scenarios using ocean-atmosphere general circulation models (GCMs) tend to predict enhanced warming in the polar regions, with the Arctic expected to warm about 3–4°C during the next half-century 1 . Some GCMs even show a complete or near-complete removal of the summer ice cover in the Arctic 2 . Thus, systematic, long-term observations of the Arctic ice cover may be useful for the early detection of global climate change 3 . The need is clearly indicated for operational climate monitoring of the Arctic ice cover, in the principles and framework of EuroGOOS. In particular, such a monitoring system fits well within the Climate Monitoring, Assessment and Prediction module. Also, because the Arctic ice cover greatly affects fisheries in the marginal seas, offshore oil and gas activities, and transport operations, such monitoring has relevance to the Marine Meteorological and Oceanographic Operational Services module. Here we describe 1) the existing climate monitoring program of the Arctic ice cover, developed by the Nansen Environmental and Remote Sensing Center (NERSC), Bergen, Norway, and 2) the potential for improvements, including the use of other observations and models of the Arctic ice cover.