Data that represent average worst fire weather for a particular area are used to index daily fire danger; however, they do not account for different locations or diurnal weather changes that significantly affect fire behavior potential. To study the effects that selected changes in weather databases have on computed fire behavior parameters, weather data for the northern Rocky Mountains were treated as probability distributions, then used in computer simulation to estimate distributions of rate-of-spread (ROS) and fireline intensity (FLI). Sensitivity of ROS and FLl to weather input changes was analyzed by varying the source and amount of weather data, and diurnally adjusting temperature and relative humidity. In eight representative cases, a minimum amount of data produced the lowest cumulative probabilities of ROS and FLl, and data from a higher elevation produced the highest values. For long-term planning, within the region studied, a small subset of weather data distributions was adequate for estimating probabilistic distributions of ROS and FLI. Joint probabilities of ROS and FLI differed substantially among test cases. Fire behavior values obtained with observed data were higher than those obtained with diurnally adjusted data. The simulation techniques used are appropriate for use in long-term fire management planning models.

GKSS SCHOOL OF ENVIRONMENTAL RESEARCH The National Research Laboratory GKSS (member of the Hermann von Helmholtz-Association of German Reserach Centres) located in Geesthacht, near Hamburg, is engaged in environmental research. The main interest of the research center focuses on regional climatology and climate dynamics, interdecadal variations in the state of the Baltic and North Sea and related estuaries, and the flow ofheavy metals, nutrients, and other materials in river catchments to the coastal zones. This research aims at-developing an under standing ofchanges in the environment, both as a result ofinternal (natural) dynamics and as a result of anthropogenic interference. In an effort to dis seminate the results of these research activities, as well as to initiate a broad discussion among senior scientists in the field, and younger colleagues from all areas of the globe, the Institutes of Hydrophysics and Atmospheric Physics at GKSS have instituted the GKSS School of Environmental Research. Appliedenvironmental research has always containedanelement ofaware ness ofthe societal implications and boundary conditions associated with en vironmental concerns. Consequently, the School of Environmental Research adheres to the philosophy that all discussion regarding environmental change should incorporate a social component. This necessity has been well acknowl edged and is apparent by the incorporation ofsocial scientists into the series of lectures. Senior scientists from Europe and North America were invited to give lectures to "students" from all parts of the globe.

A discussion of the direct and indirect mechanisms by which fire and climate interact to influence carbon cycling in North American boreal forests. The first section summarizes the information needed to understand and manage fires' effects on the ecology of boreal forests and its influence on global climate change issues. Following chapters discuss in detail the role of fire in the ecology of boreal forests, present data sets on fire and the distribution of carbon, and treat the use of satellite imagery in monitoring these regions as well as approaches to modeling the relevant processes.

As climate has warmed over recent years, a new pattern of more frequent and more intense weather events has unfolded across the globe. Climate models simulate such changes in extreme events, and some of the reasons for the changes are well understood. Warming increases the likelihood of extremely hot days and nights, favors increased atmospheric moisture that may result in more frequent heavy rainfall and snowfall, and leads to evaporation that can exacerbate droughts. Even with evidence of these broad trends, scientists cautioned in the past that individual weather events couldn't be attributed to climate change. Now, with advances in understanding the climate science behind extreme events and the science of extreme event attribution, such blanket statements may not be accurate. The relatively young science of extreme event attribution seeks to tease out the influence of human-cause climate change from other factors, such as natural sources of variability like El Niño, as contributors to individual extreme events. Event attribution can answer questions about how much climate change influenced the probability or intensity of a specific type of weather event. As event attribution capabilities improve, they could help inform choices about assessing and managing risk, and in guiding climate adaptation strategies. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities.

Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner

Climate change can reasonably be expected to increase the frequency and intensity of a variety of potentially disruptive environmental events-slowly at first, but then more quickly. It is prudent to expect to be surprised by the way in which these events may cascade, or have far-reaching effects. During the coming decade, certain climate-related events will produce consequences that exceed the capacity of the affected societies or global systems to manage; these may have global security implications. Although focused on events outside the United States, Climate and Social Stress: Implications for Security Analysis recommends a range of research and policy actions to create a whole-of-government approach to increasing understanding of complex and contingent connections between climate and security, and to inform choices about adapting to and reducing vulnerability to climate change.

Climate change poses many challenges that affect society and the natural world. With these challenges, however, come opportunities to respond. By taking steps to adapt to and mitigate climate change, the risks to society and the impacts of continued climate change can be lessened. The National Climate Assessment, coordinated by the U.S. Global Change Research Program, is a mandated report intended to inform response decisions. Required to be developed every four years, these reports provide the most comprehensive and up-to-date evaluation of climate change impacts available for the United States, making them a unique and important climate change document. The draft Fourth National Climate Assessment (NCA4) report reviewed here addresses a wide range of topics of high importance to the United States and society more broadly, extending from human health and community well-being, to the built environment, to businesses and economies, to ecosystems and natural resources. This report evaluates the draft NCA4 to determine if it meets the requirements of the federal mandate, whether it provides accurate information grounded in the scientific literature, and whether it effectively communicates climate science, impacts, and responses for general audiences including the public, decision makers, and other stakeholders.