SAR, SSMI and numerical model characterisation of Arctic Ocean coastal polynyas Sverre Thune Dokken (1) Peter Winsor (2) Thorsten Markus (3) Jan Askne (1) (1) Chalmers University of Technology Department of Radio and Space Science, Gothenburg, Sweden Email: sverre@rss.chalmers.se, askne@rss.chalmers.se (2) Gothenburg University Department of Oceanography, Gothenburg, Sweden. Email: pewi@oce.gu.se (3) NASA GSFC-UMBC JCET, Greenbelt, MD 20771, USA. Email: thorsten @ beaufort.gsfc.nasa.gov ABSTRACT Coastal polynyas in the Arctic basin from the winter period (January to March) are characterised using ERS-1/2 SAR images [full resolution (PRI) and Browse image product], passive microwave data [the polynya SSM/I signature model (PSSM), the Bootstrap and the NASA Team algorithms] and a numerical polynya model (NPM). A SAR polynya algorithm is used to delineate open water, new ice, young ice, and to define the size and shape of polynyas. In order to extract the radiometric and contextual information in the ERS SAR PRI images, different image classification routines are developed and applied. No in-situ data has been available for verification of the polynya shapes and sizes, but the ice classification routines have to some extent been verified. The PSSM calculates polynya shape and size, and delineates open water and thin ice. It compares significantly better with the SAR compared to the NASA Team and the Bootstrap algorithms, but especially the calculation of thin ice can be improved with the help of SAR data. The wind-driven NPM computes offshore coastal polynya widths, heat exchange, ice production, and salt ejection. SAR PRI images are the most useful data set to validate the NPM and the use of SAR in combination with the NPM significantly contributes to characterise and to gain knowledge about the dynamics of polynyas. Envisat (and Radarsat 2) will have full polarisation and increased temporal and spatial coverage (compared to today’s SAR sensors) that will further emphasise the applicability of the SAR. More SAR satellite data is highly necessary to assess these features in the Arctic climate study contexts. INTRODUCTION Global climate models predict that the possible temperature rise will be amplified in the Arctic because of various feedback mechanisms associated with a strong coupling between the Arctic ice cover, the atmosphere and the ocean (see e.g. [1], [2]). Some of these feedback mechanisms are related to salt and ice production in polynyas, heat transfer between ocean and atmosphere in polynyas and leads, and the albedo of polynyas, leads and meltponds. Polynyas are responsible for 2-3 orders of magnitude larger heat exchange from the ocean to the atmosphere compared to the surrounding ice masses and an ice production of up to 5 m per event or some 50 % of the seasonal mean in some areas [3]. Siberian Shelf Polynyas are capable of producing 20-60 % of the Arctic Intermediate Water [4] and Arctic polynyas in general about 30 % of the estimated salt flux necessary to maintain the cold halocline layer of the Arctic Ocean [3]. Knowledge of the distribution and frequency of coastal polynyas and leads is therefore important in understanding large-scale climate processes in the Arctic Basin. Presently the Arctic Ocean is only coarsely represented in climate models. Many processes that potentially affect global climate depend upon complicated and inadequately described mechanisms (see e.g. [5], [6]). Especially the distribution and frequency of polynyas and leads are not well represented in these models. Detailed observations are therefore needed to gain knowledge and to optimise these large-scale models. Large scale monitoring of the Arctic ice cover has to come through satellite observations. It is the only possibility to obtain a regional or global overview of these inaccessible areas having extreme weather conditions. The satellite