底層水の生成域を内部に持ち, 南北両半球にひろがる海洋の冷却過程を数値的に研究した. モデル海洋は初期に一様な水温の海水で占められている. 計算の過程では, 表面を一様に加熱する一方, 南半球の最高緯度帯で底層水を生成させた.
計算初期においては, 生成された底層水は, ケルビン波的な密度流の形態を呈しつつ南半球海洋の西端に沿って北上した後, 赤道に沿って東進する. 海洋の東端に達した底層水は, ケルビン波的な密度流の形態で極向きに移動すると同時に, ロスビー波的な密度流を形成することによって西方へも広がり, 内部領域に極向きの流れを引き起こす. 底層水が西端に達すると, そこに西端境界流が形成される. この時から, 南半球海洋の西岸を北上する底層水が, 赤道を越えた北半球海洋の西岸境界流に直接補給されるようになる. 中深層においては, 内部領域および沿岸での湧昇によって下層から順に冷やされて行くが, その過程で, 底層水と周囲の暖かい海水とが混合し, 温度躍層は, 加熱層 (最上層) の下端に留まらず, より深層にまで広がる傾向が見られる. その結果, 湧昇速度は温度躍層の下部で最大となり, ストムメル・アーロンズが示したような内部領域での流れの形態は底層付近にのみ再現される. また, 赤道に沿っては鉛直高次モードの運動が卓越し, 流速場は, 東向流と西向流が互い違いに重なった構造 ( structure) になる.

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In order to provided a novel maximised approach to the generation of accurate, comprehensive, consensus sequences of the expressed human genome, we have developed and produced a system for a novel-representation, broad gene coverage, consensus database of expressed human gene fragments (ESTs). To perform clustering of ESTs, we have developed and employed D2-cluster, an algorithm based on the d2-search algorithm (Hide et al. 1994) specifically for EST clustering. D2-cluster does not require alignment in order to perform clustering (Burke, Davison and Hide, in prep). We have incorporated d2-cluster into a portable and novel system to perform clustering, alignment and automated error analysis of publicly available expressed sequence tags (STACKIPACK). The system includes a statistically robust algorithm that can detect and compensate for error within an aligned cluster of ESTs. We have manufactured a database of partial human consensus sequences from 552 013 ESTs from dbEST 040896 and TIGR. The database is termed Sequence Tag Alignment and Consensus Knowledgebase (STACK). STACK 1.0 contains 18 divisions based on tissue annotation identifying 204 431 unique sequences and generating 76 131 consensi which represent 321 134 ESTs. The consensus sequences have an average length of 497 bases, a 39% increase over the 357 base average length of the input data set. Clone Ids are used to join 92 759 unique sequences and 48 858 consensi into 61 632 linked sequences, averaging 900 bases each. The distribution of clusters compares favourably with UniGene, reflecting the difference in methodology of clustering and the higher input number of sequences into STACK. SANIGENE high accuracy database is also generated, consisting of sequences which agree in at least two ESTs. STACK is a distributable, core information resource upon which a comprehensive knowledgebase can be built.

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