incompleteness shared by the other data sets discussed in this chapter, and furthermore exhibit temporal and spatial correlation. An eventual question to address is the role of phytoplankton distribution in climate change, but first a quantitative analysis of the distribution itself is necessary. Factors such as bathymetry, nutrients, eddy kinetic energy, wind stress, cloud cover, meltwater formation, and Ekman upwelling are believed to be potential influences on the phytoplankton distribution, but the relationships are as yet unknown. Currently available data on many of these factors are sparse, and a great deal of spatial and temporal aggregation is necessary in order to assess such potential relationships. Future satellite observations are expected to ameliorate the data issues basic to the study of these important biological and chemical oceanographic processes, but the statistical problems discussed in Chapters 2 through 8 will remain the same.
In physical oceanography, the development and application of statistical analysis techniques are somewhat more advanced than in other disciplines of oceanography. In large part, a greater need for sophisticated statistical techniques in physical oceanography has been driven by rapid technological advances over the past 30 years or so that have resulted in larger volumes of observational data spanning a broader range of space and time scales than are available in the other oceanographic disciplines. There has also been intensive development of a theoretical foundation to explain the observations. As a result of these two parallel efforts and recognition of the importance of physical oceanographic processes in many of today’s important global issues, there are many significant opportunities for applications of statistics, both where descriptive analyses of the observational data are needed and where there is a need to relate observations to theory. Even the limited scope of physical oceanography presents a rather daunting task for those who would explore it, since the discipline encompasses a very broad range of topics. Input to the panel was sought and was generously provided by several outside experts (see the preface) to broaden the span of topics outlined in this report.
It should be emphasized at the outset that statistical analyses of physical oceanographic data have not been developed in total isolation from developments in the field of statistics. On the contrary, statistical techniques are already used to an unusual degree of sophistication compared with their use in some other scientific disciplines, partly because of the need to develop techniques to understand the almost overwhelming quantity of observational data available. In this regard, physical oceanography has benefitted from the parallel development of techniques of statistical analysis in the field of atmospheric sciences, in which researchers also need to interpret the large volumes of atmospheric data available. Physical oceanographers are generally well versed in traditional and many modern statistical analysis techniques. In addition, several books and monographs have been written specifically on applications of statistical techniques in the atmospheric sciences and physical oceanography (e.g., Gandin, 1965; Thiebaux and Pedder, 1987; Preisendorfer, 1988; Daley, 1991; Ghil and Malanotte-Rizzoli, 1991; Bennett, 1992). Many statistical techniques tailored to specific analyses of oceanographic data have also been published in journal articles.
This report consists of a collection of sections (Chapters 2 through 8) outlining research problems that the panel believes could serve as fruitful areas for collaboration between statisticians and oceanographers. In Chapter 9, the panel presents its conclusions, observations, and suggestions on encouraging successful collaborations between statistics and