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The ARGO project archives huge quantities of upper ocean salinity/temperature time series measurements that are related to climate issues such as global warming. Fuzzy inter-transaction association rules are derived from ARGO data using a reduced prefix-projected item set algorithm that has a small space and time complexity. After mining the frequent 1-itemsets the proposed algorithm exploits a reduced prefix projection strategy to extract the frequent inter-itemsets. Based on the extracted fuzzy inter-transaction association rules a fuzzy inference model is proposed for identifying salinity/temperature anomalies. Experimental results verify that the proposed model is effective in predicting the occurrence of abnormal salinity/temperature variations.