In summary, the plan does not synthesize recent work in the area (including what has been learned from this work and how that has influenced the proposed priorities) or even list what the current and recently supported GAPP projects have been (e.g., those funded by NOAA’s Office of Global Programs and NASA). That context would add a tremendous amount of credibility to the plan for the future. The following paragraphs provide some specific comments and suggestions for improvement. Furthermore, the plan lacks a mechanism for assessing and evaluating the progress of GAPP-funded independent research by the principal investigators.
In Chapter 2 of the plan, the overall objective is well stated and is in fact central to the GAPP mission. One broad-scale issue that is missing is the relation, with respect to implementation, between this chapter and Chapter 6, “Operational Seasonal Climate Prediction: Components from GAPP.” The committee speculates that the distinction, with respect to implementation, is that Chapter 2 is more of a call for studies from principal investigators (PIs), whereas Chapter 6 is more of a guide for the Core Project. If this is the case, it should be made clear.
In the GAPP science background document (NOAA 2004), topography is discussed as having a specific relationship to predictability through processes such as indirect effects on convection and mesoscale circulation. In the plan, however, topography is essentially dropped from this sense (it is specifically discounted as not having memory). Throughout the rest of the GAPP science background document, topography is stressed as important for accuracy in spatial downscaling of predictions. Unfortunately, this distinction is not made in the GAPP plan. The plan should be clear with respect to the distinction between predictability (long memory issues) and prediction accuracy, and it should indicate which of these (or both) is a priority with respect to topography.
In Section 2.1.1 (NOAA-NASA 2004, pp. 9-11) there are four main bullets. The first bullet is presented as quantification of the strength of land-memory processes, but what is described is data analysis. No methodologies are discussed or suggested for this topic. Instead, a set of specific data sets are suggested, which may be too proscriptive. The committee wonders why no modeling analysis was suggested here and why advanced multivariate time series statistical methods are not discussed to address this quantification. Additionally, no sense is given of what exactly (in terms of a quantifiable metric) is even meant by the “strength” of land memory (is it just a decay timescale of soil moisture, or something more subtle?).
The second bullet (NOAA-NASA 2004, p. 10) of the science background section describes an interesting issue related to the spatial and temporal characteristics of memory, but again is not clear as to a metric. A long list of activities is given whose relation to the primary goal is unclear. For example, why does the need to learn about the spatial and temporal characteristics of memory “consequently” involve acquiring or collecting new data for the improvement or validation of models? What are the current shortcomings that require this activity? Strengths here include attempting to use remote sensing observations, since these by definition have good space-time coverage, and testing model abilities for lateral processes, which determines the ability to predict space-time memory (e.g., lowlands have more persistent wetness).
The third bullet (NOAA-NASA 2004, pp. 10-11) of the science background section should provide a diagnostic measure for predictability and, specifically, a metric for improvement of predictability when land-memory processes are included. A significant