tions has been demonstrated, and the capability to use these predictions is being developed. In deploying a dedicated observing system in the tropical Pacific, a coherent observational base was created to test and improve predictive models. Data from this observing system have also been made available in real time over the Internet, demonstrating the possibilities of making data freely available in an environment where data and conclusions have commercial and strategic value.

Research Imperatives that must be met to understand climate on seasonal to interannual timescales include the following:

  • ENSO. Maintain and improve the capability to make ENSO predictions.

  • Global monsoon. Define and predict global seasonal to interannual variability, especially the global monsoon systems, and understand the extent to which variability is predictable.

  • Land surface exchanges. Understand the roles of land surface energy and water exchanges and their correct representation in models for seasonal to interannual prediction.

  • Downscaling. Improve the ability to interpret the effects of large-scale climate variability on a local scale.

  • Terrestrial hydrology. Understand the seasonal to interannual factors that influence land surface manifestations of the hydrological cycle, such as floods, droughts, and other extreme weather.

INTRODUCTION

Understanding climate variability on seasonal to interannual timescales, especially with regard to the hydrological cycle, offers some of the most direct benefits in all of global change research. In particular, better prediction of precipitation is of special interest because it can change the way people interact with the environment, perhaps in revolutionary ways. Precipitation is a fundamental determinant of climate and human habitability through its relationship to land surface conditions, including soil moisture, snow cover, vegetation, evaporation, stream discharge, and surface temperature. An improved capability to model and predict precipitation variability on seasonal to interannual timescales is therefore of potentially great socioeconomic benefit for water and energy resource management, agriculture, and a variety of other factors related to general human well-being.

In this chapter, case studies highlight recent applications of seasonal to interannual climate prediction, in particular the prediction of precipitation, in geographical areas from Asia to Brazil to the central United States. These cases indicate the promise of climate forecasting and also the issues that such forecasting raises in particular applications.

Some important advances have come from the study of ocean-atmosphere interactions. Some aspects of ENSO are predictable a year or more in advance.1 This predictive capability for ENSO must not only be maintained and improved



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