Arctic air masses have direct impacts on the weather and climatic extremes of midlatitude areas such as central North America. Arctic physical processes pose special and very important problems for global atmospheric models used for climate simulation and numerical weather prediction. At present, the observational database is inadequate to support research aimed at overcoming these problems. Three interdependent Arctic field programs now being planned will help to remedy this situation: SHEBA, which will operate an ice camp in the Arctic for a year; ARM, which will supply instruments for use at the SHEBA ice camp and which will also conduct longer-term measurements near Barrow, Alaska; and FIRE, which will conduct one or more aircraft campaigns, in conjunction with remote-sensing investigations focused on the SHEBA ice camp. This paper provides an introductory overview of the physics of the Arctic from the perspective of large-scale modelers, outlines some of the modeling problems that arise in attempting to simulate these processes, and explains how the data to be provided by the three field programs can be used to test and improve large-scale models.

Significant advances are being made in our understanding of the Arctic sea ice-climate system. The mean circulation of the Arctic sea icecover is now well defined through analysis of data from drifting stations and buoys. Analysis of nearly 20 years of daily satellite data from optical, infrared, and passive microwave sensors has documented the regional variability in monthly ice extent, concentration, and surface albedo. Advances in modeling include better treatments of sea ice dynamics and thermodynamics, improved atmosphere-ice-ocean coupling, and the development of high resolution regional models. Diagnostic studies of monthly and interannual sea ice variability have benefited from better sea ice data and geostrophic wind analyses that incorporate drifting buoy data. Some evidence exists for a small retreat of Arctic sea ice over the last 2 decades, but there are large decadal fluctuations in regional ice extent. Antiphase relationships between ice anomalies in different sectors can be related to changes in atmospheric circulation. Evidence suggests that episodes of significant salinity reduction in the North Atlantic, associated with extensive sea ice in the Greenland Sea, may be a manifestation of a decadal oscillation in the Arctic climate system. Aspects of the Arctic system in need of further attention include the surface energy budget and its variability, particularly with respect to the roles of cloud cover and surface types in summer. Sea ice thickness distribution data remain meager, and there are many unknowns regarding the circulation and hydrologic cycle of the Arctic Ocean and its links to the world ocean.Planned measurements from a new generation of satellites, supported by field programs, will provide much needed data to address these issues.1. ] stress the critical roles that observations, modeling and remote sensing must play if the Arctic sea ice-climate system is to be more fully understood. Basic to this goal is an improved ability to monitor variability in sea ice extent and concentration (fractional cover), ice thickness, surface albedo, turbulent heat transfer, and brine production in the polar ocean, and the relative roles played by dynamic and thermodynamic forcings on such variability. This paper reviews our current state of knowledge of the Arctic sea ice-climate system, sea ice data sets, advances in modeling, recent research findings, and directions for future work. LAPTEV EAST SoeA ß 70 o CANADIAN BASIN CHUKCHI SoeA BEAUFORT SoeA 3O0O Figure 1. Location map of the Arctic Ocean, showing geographic locations and ocean bathymetr¾ [after Aagaard, 1989]. 2. GENERAL CHARACTERISTICS OF THE ARCTIC OCEAN SEA ICE COVER The Arctic Ocean is 9.5 x 10 6 km 2 in area, roughly 4 times the size of the Mediterranean Sea. Approximately one third of this area comprises shallow shelf seas, the Chukchi, East Siberian, Laptev, Kara, and Barents seas, which are less than 200 m in depth. The major deep features are the Canada Basin and the smaller Eurasian Basin, which is over 4000 m in depth, separated ...

In order to develop an operational method for the U.S. Navy/NOAA Joint Ice Center to extract ice velocity vectors from sequential advanced very high resolution radiometer (AVHRR) imagery, we have combined the maximum cross correlation (MCC) method with a spatial filtering technique on the image inferred ice motion vectors. We compute the cross correlations between images directly from the image brightness values rather than computing FFTs. The direct method allows greater flexibility in computational parameter settings and allows one to compute motion vectors near coastlines where irregular windows are required. By using a combination of statistical and spatial filters we can then retrieve coherent ice motion vectors in the presence of cloud contaminated imagery. A series of six satellite images of the Fram Strait region, from April 1986, was used to compute sea ice motion from pairs of sequential images. The resulting ice motion vectors were taken as a representation of the surface flow field derived objectively from the satellite imagery. Resulting vector motion fields were found to match well with manually tracked vectors for the same images, thus verifying the validity of the objective MCC method of computing ice motion. These techniques were applied to both the visible and infrared AVHRR channels and to images with different spatial resolutions yielding an overall bias accuracy of about 0.5 cm/s and standard deviations of about 0.9 cm/s. The MCC ice motion results were also compared with wind‐driven numerical model simulations of the region. Marked differences between the MCC image‐derived velocities and those from the numerical model were thought to be primarily due to a stronger ocean current than was present in the model.

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