The strong interaction and balance among all the elements in the figure are crucial. End-to-end seasonal to interannual prediction requires the development of coupled atmosphereocean-land models. It requires that observations be available and a procedure developed for initializing the forecasts. It means that remote and in situ observations must be combined for this initialization and that an efficient data system must be established for this combination. It requires a procedure for validating predictions. It requires that poorly understood or modeled processes be investigated and sets priorities for these processes. Since climate information, to be useful, must be brought down to the local level, it requires adding local information and making region-specific forecasts. Then, the sector of application and its normal mode of operation in the absence of additional information must be identified and understood. Finally, the information must be combined with the forecast and presented to the user in a way that guarantees maximum utility. The basic implication of this concept is that it guides, in a focused way, what needs to be done; provides a measure of the value of an activity in terms of its role in the end-to-end system; indicates gaps or imbalances in the activities (what is not being done); provides useful results on both a short-term and an ongoing basis; and has a built-in means of evaluation: the skill of prediction and the success of the applications. Conversely, this end-to-end activity is integral: no part of it can be compromised without affecting the ultimate skill of the prediction and the usefulness of the applications. The working group participants identified some priorities within individual components of this integrated program on seasonal to interannual climate prediction. APPENDIX A Models 39 Research is needed to enhance the understanding of a crucial, but poorly understood, aspect of climate models: (1) land-atmosphere interactions, with initial emphasis on landatmosphere interactions over the Mississippi and the Amazon basins, and (2) the characteristics and predictability of precipitation in this region and other land regions that affect seasonal to interannual predictability (GEWEX). Observing System General Principle A general observing system for end-to-end predictions must be some combination of in situ and remote observations and must lead to model-assimilated data. The reasons for this principle are numerous: Remote systems generally require surface information continuously. This information is used for continuous calibration and to ameliorate gaps that always arise from remote observations. Conversely, in situ observations can never be global; they require remote measurements to achieve global coverage. Both types of observations must contribute to the initialization and validation of predictions and, therefore, to a modelassimilated data product. We can identify the priorities for seasonal to interannual prediction: Atmosphere: upper air data as given by the World Weather Watch--precipitation, water vapor distributions and profiles, top-of-the-atmosphere radiation, cloud and aerosol properties and distributions in the vertical and horizontal; • Ocean: sea surface temperature, sea surface winds, upper ocean subsurface temperatures, precipitation, sea level, salinity, sea ice Land: soil moisture, soil type, topography, vegetation, surface temperature, precipitation, snow cover, runoff, and fields of surface radiation coordinated with top-of-the-atmosphere radiation. The quantities are not prioritized among atmosphere, land, and ocean, and only for the ocean are relative priorities identified (italicized quantities represent the highest priorities). Note that precipitation occurs in all three lists. Maintenance of the CLIVAR/GOALS observing system in the tropical Pacific and its appropriate expansion combining in situ and remote observations (including Mission to Planet Earth) over other oceans and over land are essential. 40 Process Studies APPENDIX A Process studies can be observational, theoretical, or computational and can range from pencil-and-paper calculations to large observational field programs. In order to apply to end-to-end prediction, they must focus on those inadequacies in the models, observations, or applications that affect the skill of prediction or the success of applications. The skill in seasonal to interannual prediction within the U.S. is still insufficient to be used effectively but it is being developed in a planned, phased process. This process begins by further improving the skill of predicting of El Niño in the tropical Pacific; then expanding the regions of application around the tropics (including the monsoon regions of North America, especially Arizona, Texas, and New Mexico; South America; and Southeast Asia); next investigating predictability in midlatitude areas (including the U.S. West Coast and Southeast) that derive their predictability from the remote effects of El Niño; and finally, investigating whatever predictability may be further exploited from atmosphere-ocean-land interactions totally outside the tropics (CLIVAR/GOALS and GEWEX). These process studies are best pursued via U.S. contributions to the high-priority international programs CLIVAR/GOALS and GEWEX, and via successful implementation of the U.S. SCPP, including establishment of an IRI. EVALUATION OF USGCRP PROGRAM MANAGEMENT Accomplishments thus far have resulted in a new paradigm in which the concept of end-to-end prediction motivates and guides all program components and determines the priorities and balance among program elements. The concept of end-to-end prediction can also be used to focus and evaluate relevant research by imposing a discipline on the process and defining the priorities for a carefully balanced program. This balance is crucial: since all elements depend on each other, no element can be compromised without damaging the entire enterprise. It presents a method of R&D in which success can be demonstrated by the development of forecast skill and by the money and lives saved by applications of predictive information. The program requires careful coordination, good advice and oversight, and a stable and balanced funding profile, with focused contributions by the agencies involved in seasonal to interannual prediction. This country has an enthusiastic and able body of scientists eager to tackle the scientific problems involved in developing end-to-end prediction on these time scales. The return for investment now will pay off in the short run and eventually lead to a permanent prediction capability that will benefit the entire country. In this context, the working group identified some program management principles that must apply in supporting and managing a demonstration research program on end-to-end seasonal to interannual prediction. Success requires a management structure in USGCRP (with OMB, the Office of Science and Technology Policy (OSTP), and the Congress) that will APPENDIX A 41 ensure that the highest-priority programs are protected both within and between agencies; ensure that support is focused on the highest-priority programs and that balance is maintained among program components, and ensure that participating agencies contribute (or not withdraw) resources for the highest-priority programs. The working group emphasized that these requirements are not currently being fully met. OPPORTUNITIES FOR INTERACTION WITH OTHER ELEMENTS OF USGCRP Seasonal to interannual climate variability interacts strongly with other elements of the USGCRP. Only a few examples are given here. Decadal to Centennial Variability and Change The attachment to this appendix provides some details on the connections between research on seasonal to interannual climate variability and investigations of decadal to centennial climate change. Examples include the following: El Niño has a predominantly interannual time scale but is also modulated on decadal time scales. This decadal modulation has teleconnection to higher latitudes and has been shown to be responsible for the greater warming over land and cooling over ocean during the winter than during the summer. Therefore, El Niño processes are an important source of decadal climate variability. The subtropics of the Atlantic have a dipole in sea surface temperature that helps determines the location of rainfall in both northeastern Brazil and the Sahel. The variability of this dipole is both interannual and decadal and therefore is a natural contact point between the two scientific areas. Atmospheric Chemistry Since cumulus convection in the tropical Pacific has the time dependence of El Niño, and since it both directly transports water vapor (and other trace gases) into the stratosphere and affects the height of the tropopause, there will be a modulation of stratospheric- tropospheric exchange. 42 APPENDIX A Tropospheric temperature, especially in the tropics, varies with El Niño and, through temperature and water vapor, affects all aspects of tropospheric chemistry. Under normal conditions, the tropical Pacific is a net source of carbon dioxide and contributes 1 gigaton per year to the atmosphere. During warm El Niño conditions, this flux of carbon dioxide is severely reduced or completely eliminated. El Niño modulations of carbon dioxide are therefore important components of the natural carbon budget of the atmosphere. Large-Scale Ecology All growing systems near the surface respond to sunlight and water at the surface. Interannual modulations of both water and sunlight affect the characteristics and response of these ecological systems. Extreme conditions during El Niño (e.g., rainfall in the normally arid Peruvian coastal plains) can stress ecosystems used to more subtle variations. MISSION TO PLANET EARTH/EARTH OBSERVING SYSTEM (MTPE/EOS) 1. GOALS, GEWEX, and SCPP look to MTPE to help provide the capability to expand prediction skill around the globe and to higher latitudes (including land), and to better assess the impacts of seasonal to interannual variability. It can do this by measuring the high-priority quantities subject to the principle that all USGCRP observations are combinations of in situ and remote measurements leading to model-assimilated data products when possible and desirable, guaranteeing the continuity and quality of measurements by overlapping in situ and remote measurements, overlapping remote measurements, and continuing in situ validation of remote measurements, and ⚫ supporting and enhancing the core programs GOALS, GEWEX, and SCPP, including the IRI. 2. The Earth Observing System/Data Information System (EOSDIS) should provide products that contribute to data assimilation for initialization of end-to-end seasonal to |