Newsletter No.6 March-1999　

I joined the FRSGC-IARC in January 1998 and has been exposed to different research programs in the polar research community. I am interested in 1) Arctic climate change and predictability based on statistical analyses of historical data, and 2) coupled ice-ocean modeling and nowcast/forecast system in the Arctic. Recently the Arctic Oscillation (AO) has attracted much attention from polar scientists. This may renew our previous knowledge that the North Atlantic Oscillation (NAO) dominates in the Arctic.Working together with the Frontier colleagues, I found that in response to AO the Arctic Sea-Ice Oscillation (ASIO) is the leading mode based on data of 1991-95 and that AO differs from NAO in the following manners: i) both correlates, ii) AO operates seasonwide while NAO works only in winter seasons, and iii) AO has more significant correlation with sea-ice cover in summer. I need to find out the possible signature of the Arctic Ocean Oscillation (AOO) by analyzing the existing historical oceanographic data. This research will improve our understanding of the Arctic air-ice-ocean system toward possible predictability of the interannual and decadal variability to some extent. Because the ultimate goal of our research s to predict the climate changes, the numerical modeling is a very important tool to realize this task. I am implementing a coupled ice-ocean model to the Arctic Ocean. First I need to know how the sea ice and ocean respond to AO; in other words, we may find some important information from the coupled model. The existing coupled ice-ocean models focus on only ice and ocean surface features. I will focus the entire ocean response to AO. Second I need to know the dynamical processes of dense water formation along the coast of the Arctic Ocean. The third step is to set up a nowcast/forecast system of the Arctic ice-ocean system for the short-term prediction in conjunction with data assimilation. The last (third) objective is difficult and may take some time to realize. We all are facing new challenge as we approach the new century. However, I am optimistic to accomplish the scientific topics mentioned above. 若松 剛 Dr.Tsuyoshi Wakamatsu 　98年５月よりIARCに参加している若松です。専門は海洋物理学で、学生時代 は準地衡流方程式より導かれるinvariant-core vortex modelを用い、海洋表 層の中規模渦起因の渦位分布の推定手法開発をテーマとしていました。 　この中で使用したのは変分法にもとづいて導出される随伴方程式を利用するデータ 同化手法で、比較的少ない観測数より海洋構造を推定する場合にこの手法が有効で ある事を示しました。 　一般にデータ同化法とは誤差を含んだモデルと同じく誤差を含んだデータとの最 適な融合により、誤差情報を含んだ物理量の推定を行う技術で、海洋物理学では衛 星観測によるデータ供給量の増加を機会に、過去10年程の間に急速に進歩した研究 分野です。 　この技術の発展は今後、これまで海洋科学が苦手としてきた内部変形半 径程度の空間スケールと数週間から数ヶ月の比較的短い時間スケールを持つ中規模 現象に関わる物理量の定量的なモニタリングへの道を開くと私は考えており、SAR に代表される解像度の高い衛星観測データが供給される極域海洋においても応用の 可能性が高いと思います。 　IARCでの研究は、やはりデータ同化法を用い中緯度海域にはない海氷の各種物 理特性を含む衛星観測データよりどのような情報を取り出すかが焦点となります。 現在は海氷速度データを用いた氷縁域における表層流の構造推定法の確立を目標に、 研究の準備を進めています。将来的には、海氷を含む海洋混合層構造の推定方法の開発と合わせ、極域の縁辺海および沿 岸海洋表層における定量的な熱環境推定の手法開発につなげて行きたいと考えています。 Hello there! My name is Tsuyoshi Wakamatsu, and I have been participating in the IARC since May 1998.My specialization is physical oceanography. In my student years, my subject of research was to develop an estimation technique for potential vorticity distribution caused by meso-scale eddies at the surface of ocean, using an invariant-core vortex model derived from a quasi-geostrophic equation. In this research, I have utilized a data assimilation scheme with use of an adjoint equation, based on the variational method, and have indicated that in the case of a small number of observation, this technique is more efficient in estimating oceanographic structure. In general, the data assimilation is a technique to estimate a physical quantity with its error distribution, by using the optimal blending of data and corresponding model, both containing errors. In the physical oceanography, research in that technique has rapidly progressed over the past ten years, taking advantage of the increased supply of satellite observation data. I think hereafter the development of this technique will break new ground toward quantitative monitoring of a physical quantity concerning mesoscale phenomena, which have a spatial scale of internal deformation radius and a time scale of several weeks through several months. I also suppose that it is highly possible to apply the technique to ocean at the polar region, for which high-resolution satellite observation data, such as SAR are supplied. In the research at the IARC, using the data assimilation technique, the focus may be placed upon how to extract dynamical information from satellite observation data including various physical characteristics of sea ices, which do not exist in the mid-latitude sea area. At present, the research is prepared, aimed at the establishment of a estimation method of surface currents in the Marginal Sea Ice Zone, with the use of ice-drift data. In future, I would like to investigate a quantitative estimation of heat balance at surface of the marginal sea and coastal sea in the polar region, together with the development of estimation techniques of the ocean mixed layer structure including sea ices.