More accurate forecasts of climate conditions over time periods of weeks to a few years could help people plan agricultural activities, mitigate drought, and manage energy resources, amongst other activities; however, current forecast systems have limited ability on these time- scales. Models for such climate forecasts must take into account complex interactions among the ocean, atmosphere, and land surface. Such processes can be difficult to represent realistically. To improve the quality of forecasts, this book makes recommendations about the development of the tools used in forecasting and about specific research goals for improving understanding of sources of predictability. To improve the accessibility of these forecasts to decision-makers and researchers, this book also suggests best practices to improve how forecasts are made and disseminated.

Improving the reliability of long-range forecasts of natural disasters, such as severe weather, droughts and floods, in North America, South America, Africa and the Asian/Australasian monsoon regions is of vital importance to the livelihood of millions of people who are affected by these events. In recent years the significance of major short-term climatic variability, and events such as the El Nino/Southern Oscillation in the Pacific, with its worldwide effect on rainfall patterns, has been all to clearly demonstrated. Understanding and predicting the intra-seasonal variability (ISV) of the ocean and atmosphere is crucial to improving long range environmental forecasts and the reliability of climate change projects through climate models. In the second edition of this classic book on the subject, the authors have updated the original chapters, where appropriate, and added a new chapter that includes short subjects representing substantial new development in ISV research since the publication of the first edition.

This is the first comprehensive review of intra-seasonal variability (ISV); the contents are balanced between observation, theory and modeling. Starting with an overview of ISV and historical observations, the book addresses the coupling between ocean and atmosphere, and the worldwide role of ISV in monsoon variability. Also considered are the connections between oscillations like the Madden, Julian and El Nino/Southern and short-term climate.

As the nation's economic activities, security concerns, and stewardship of natural resources become increasingly complex and globally interrelated, they become ever more sensitive to adverse impacts from weather, climate, and other natural phenomena. For several decades, forecasts with lead times of a few days for weather and other environmental phenomena have yielded valuable information to improve decision-making across all sectors of society. Developing the capability to forecast environmental conditions and disruptive events several weeks and months in advance could dramatically increase the value and benefit of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices. Over the past decade, the ability to forecast weather and climate conditions on subseasonal to seasonal (S2S) timescales, i.e., two to fifty-two weeks in advance, has improved substantially. Although significant progress has been made, much work remains to make S2S predictions skillful enough, as well as optimally tailored and communicated, to enable widespread use. Next Generation Earth System Predictions presents a ten-year U.S. research agenda that increases the nation's S2S research and modeling capability, advances S2S forecasting, and aids in decision making at medium and extended lead times.

Monthly and seasonal climate prediction of variables such as precipitation, temperature, and sea surface temperature (SST) is of current interest in the scientific research community, and also has implications for users in the agricultural and water management domains, among others. This dissertation studies a variety of approaches to seasonal climate prediction of variables over North America, including both climate prediction systems and methods of analysis. We utilize the North American Multi-Model Ensemble (NMME) System for Intra-Seasonal to Inter-Annual Prediction (ISI) to study seasonal climate prediction skill over North America. We also use the Community Climate System Model version 4.0 (CCSM4) to preformed targeted climate prediction experiments to study contributions to skill or predictability from SSTs, land and atmosphere initialization, and ocean-atmosphere coupling. While all can be considered important for predictions, we show that for winter predictions, SST errors are a leading cause in forecast degradation, and improvement of SSTs causes a significant improvement in skill. Climate models, including those involved in NMME, typically overestimate eastern Pacific warming during central Pacific El Niño events, which can affect precipitation predictions regions that are influenced by teleconnections, such as the southeast US. Land and atmosphere initialization, and the minimization of errors in these initial states, shows moderate improvement in skill, expected for the first seasonal lead. Finally, ocean-atmosphere coupling, in the context of this experiment design and in relation to prescribed SST versus fully coupled hindcasts, is a comparatively weak contribution to prediction skill and predictability.

It has been known for some time that the behavior of the short-term fluctuations of the earth's atmosphere resembles that of a chaotic non-linear dynamical system, and that the day-to-day weather cannot be predicted beyond a few weeks. However, it has also been found that the interactions of the atmosphere with the underlying oceans and the land surfaces can produce fluctuations whose time scales are much longer than the limits of deterministic prediction of weather. It is, therefore, natural to ask whether it is possible that the seasonal and longer time averages of climate fluctuations can be predicted with sufficient skill to be beneficial for social and economic applications, even though the details of the day-to-day weather cannot be predicted beyond a few weeks. The main objective of the workshop was to address this question by assessing the current state of knowledge on predictability of seasonal and interannual climate variability and to investigate various possibilities for its prediction.