aThe numbers provided by UNOLS in this table differ slightly from those reported by NSF/OCE in Appendix F ($66.5 M and $106.5 M, respectively).
NOTE: The intent of this table is not to single out NSF, which has been one of the most consistent supporters of seagoing science, but rather to illustrate a significant trend, that is, the fraction of at-sea operations in the ocean sciences is much smaller today than it was just 10 years ago and accounts for a much smaller fraction of the budget. Only the addition of new fleet users, such as NAVO, has kept fleet operations at constant levels.
shortage of research vessel time by the mid-1990s. The problem is more a change of emphasis than a shortage. The increase in coastal programs by NSF, ONR, and NOAA may require new vessels capable of berthing multi-investigator teams of researchers to work in shallow coastal waters. If the strategic planning for science and facilities, including ships, is coordinated (as recommended in Chapter 4), then as the ships are retired and replaced, the capabilities of the fleet can change in response to the scientific needs of the oceanographic community.
Modeling, Synthesis and Data Assimilation
There may be indications that a gap exists in a major oceanographic program when the modeling and data collection components appear disjointed. Data assimilation, whereby data and models are used together to improve the understanding of a particular ocean system or process, can naturally bridge gaps between the modeling and data collection components of a major ocean program. In addition, as data collected in the framework of one program are likely to be useful to other programs, data assimilation and data exchange can also act to bridge gaps between programs.
TOGA dealt with the issue of follow-on activities in modeling and data assimilation through its culmination in the formation of NOAA's Climate Diagnostics and Experimental Prediction Centers and the International Research Institute (IRI). These facilities were established after a basic understanding of interannual variability