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David W. Pierce, Daniel R. Cayan, Tapash Das, Edwin P. Maurer, Norman L. Miller, Yan Bao, M. Kanamitsu, Kei Yoshimura, Mark A. Snyder, Lisa C. Sloan, Guido Franco, and Mary Tyree


Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California's mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (>60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions.

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Mark S. Kulie, Claire Pettersen, Aronne J. Merrelli, Timothy J. Wagner, Norman B. Wood, Michael Dutter, David Beachler, Todd Kluber, Robin Turner, Marian Mateling, John Lenters, Peter Blanken, Maximilian Maahn, Christopher Spence, Stefan Kneifel, Paul A. Kucera, Ali Tokay, Larry F. Bliven, David B. Wolff, and Walter A. Petersen

BAMS Capsule:

Profiling radar and ground-based in situ observations reveal the ubiquity of snowfall produced by shallow clouds, the importance of near-surface snowfall enhancement processes, and regime-dependent snow particle microphysical variability in the Northern Great Lakes Region.

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Sarah J. Doherty, Stephan Bojinski, Ann Henderson-Sellers, Kevin Noone, David Goodrich, Nathaniel L. Bindoff, John A. Church, Kathy A. Hibbard, Thomas R. Karl, Lucka Kajfez-Bogataj, Amanda H. Lynch, David E. Parker, I. Colin Prentice, Venkatachalam Ramaswamy, Roger W. Saunders, Mark Stafford Smith, Konrad Steffen, Thomas F. Stocker, Peter W. Thorne, Kevin E. Trenberth, Michel M. Verstraete, and Francis W. Zwiers

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) concluded that global warming is “unequivocal” and that most of the observed increase since the mid-twentieth century is very likely due to the increase in anthropogenic greenhouse gas concentrations, with discernible human influences on ocean warming, continental-average temperatures, temperature extremes, wind patterns, and other physical and biological indicators, impacting both socioeconomic and ecological systems. It is now clear that we are committed to some level of global climate change, and it is imperative that this be considered when planning future climate research and observational strategies. The Global Climate Observing System program (GCOS), the World Climate Research Programme (WCRP), and the International Geosphere-Biosphere Programme (IGBP) therefore initiated a process to summarize the lessons learned through AR4 Working Groups I and II and to identify a set of high-priority modeling and observational needs. Two classes of recommendations emerged. First is the need to improve climate models, observational and climate monitoring systems, and our understanding of key processes. Second, the framework for climate research and observations must be extended to document impacts and to guide adaptation and mitigation efforts. Research and observational strategies specifically aimed at improving our ability to predict and understand impacts, adaptive capacity, and societal and ecosystem vulnerabilities will serve both purposes and are the subject of the specific recommendations made in this paper.

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Clifford F. Mass, Mark Albright, David Ovens, Richard Steed, Mark Maciver, Eric Grimit, Tony Eckel, Brian Lamb, Joseph Vaughan, Kenneth Westrick, Pascal Storck, Brad Colman, Chris Hill, Naydene Maykut, Mike Gilroy, Sue A. Ferguson, Joseph Yetter, John M. Sierchio, Clint Bowman, Richard Stender, Robert Wilson, and William Brown

This paper examines the potential of regional environmental prediction by focusing on the local forecasting effort in the Pacific Northwest. A consortium of federal, state, and local agencies have funded the development and operation of a multifaceted numerical prediction system centered at the University of Washington that includes atmospheric, hydrologic, and air quality models, the collection of real-time regional weather data sources, and a number of real-time applications using both observations and model output. The manuscript reviews northwest modeling and data collection systems, describes the funding and management system established to support and guide the effort, provides some examples of regional real-time applications, and examines the national implications of regional environmental prediction.

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David M. Tratt, John A. Hackwell, Bonnie L. Valant-Spaight, Richard L. Walterscheid, Lynette J. Gelinas, James H. Hecht, Charles M. Swenson, Caleb P. Lampen, M. Joan Alexander, Lars Hoffmann, David S. Nolan, Steven D. Miller, Jeffrey L. Hall, Robert Atlas, Frank D. Marks Jr., and Philip T. Partain


The prediction of tropical cyclone rapid intensification is one of the most pressing unsolved problems in hurricane forecasting. The signatures of gravity waves launched by strong convective updrafts are often clearly seen in airglow and carbon dioxide thermal emission spectra under favorable atmospheric conditions. By continuously monitoring the Atlantic hurricane belt from the main development region to the vulnerable sections of the continental United States at high cadence, it will be possible to investigate the utility of storm-induced gravity wave observations for the diagnosis of impending storm intensification. Such a capability would also enable significant improvements in our ability to characterize the 3D transient behavior of upper-atmospheric gravity waves and point the way to future observing strategies that could mitigate the risk to human life caused by severe storms. This paper describes a new mission concept involving a midinfrared imager hosted aboard a geostationary satellite positioned at approximately 80°W longitude. The sensor’s 3-km pixel size ensures that the gravity wave horizontal structure is adequately resolved, while a 30-s refresh rate enables improved definition of the dynamic intensification process. In this way the transient development of gravity wave perturbations caused by both convective and cyclonic storms may be discerned in near–real time.

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David J. Diner, Thomas P. Ackerman, Theodore L. Anderson, Jens Bösenberg, Amy J. Braverman, Robert J. Charlson, William D. Collins, Roger Davies, Brent N. Holben, Chris A . Hostetler, Ralph A. Kahn, John V. Martonchik, Robert T. Menzies, Mark A. Miller, John A. Ogren, Joyce E. Penner, Philip J. Rasch, Stephen E. Schwartz, John H. Seinfeld, Graeme L. Stephens, Omar Torres, Larry D. Travis, Bruce A . Wielicki, and Bin Yu

Aerosols exert myriad influences on the earth's environment and climate, and on human health. The complexity of aerosol-related processes requires that information gathered to improve our understanding of climate change must originate from multiple sources, and that effective strategies for data integration need to be established. While a vast array of observed and modeled data are becoming available, the aerosol research community currently lacks the necessary tools and infrastructure to reap maximum scientific benefit from these data. Spatial and temporal sampling differences among a diverse set of sensors, nonuniform data qualities, aerosol mesoscale variabilities, and difficulties in separating cloud effects are some of the challenges that need to be addressed. Maximizing the longterm benefit from these data also requires maintaining consistently well-understood accuracies as measurement approaches evolve and improve. Achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the earth system can be achieved only through a multidisciplinary, interagency, and international initiative capable of dealing with these issues. A systematic approach, capitalizing on modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies, can provide the necessary machinery to support this objective. We outline a framework for integrating and interpreting observations and models, and establishing an accurate, consistent, and cohesive long-term record, following a strategy whereby information and tools of progressively greater sophistication are incorporated as problems of increasing complexity are tackled. This concept is named the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON). To encompass the breadth of the effort required, we present a set of recommendations dealing with data interoperability; measurement and model integration; multisensor synergy; data summarization and mining; model evaluation; calibration and validation; augmentation of surface and in situ measurements; advances in passive and active remote sensing; and design of satellite missions. Without an initiative of this nature, the scientific and policy communities will continue to struggle with understanding the quantitative impact of complex aerosol processes on regional and global climate change and air quality.

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David A. R. Kristovich, George S. Young, Johannes Verlinde, Peter J. Sousounis, Pierre Mourad, Donald Lenschow, Robert M. Rauber, Mohan K. Ramamurthy, Brian F. Jewett, Kenneth Beard, Elen Cutrim, Paul J. DeMott, Edwin W. Eloranta, Mark R. Hjelmfelt, Sonia M. Kreidenweis, Jon Martin, James Moore, Harry T. Ochs III, David C Rogers, John Scala, Gregory Tripoli, and John Young

A severe 5-day lake-effect storm resulted in eight deaths, hundreds of injuries, and over $3 million in damage to a small area of northeastern Ohio and northwestern Pennsylvania in November 1996. In 1999, a blizzard associated with an intense cyclone disabled Chicago and much of the U.S. Midwest with 30–90 cm of snow. Such winter weather conditions have many impacts on the lives and property of people throughout much of North America. Each of these events is the culmination of a complex interaction between synoptic-scale, mesoscale, and microscale processes.

An understanding of how the multiple size scales and timescales interact is critical to improving forecasting of these severe winter weather events. The Lake-Induced Convection Experiment (Lake-ICE) and the Snowband Dynamics Project (SNOWBAND) collected comprehensive datasets on processes involved in lake-effect snowstorms and snowbands associated with cyclones during the winter of 1997/98. This paper outlines the goals and operations of these collaborative projects. Preliminary findings are given with illustrative examples of new state-of-the-art research observations collected. Analyses associated with Lake-ICE and SNOWBAND hold the promise of greatly improving our scientific understanding of processes involved in these important wintertime phenomena.

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William J. Gutowski Jr., Raymond W. Arritt, Sho Kawazoe, David M. Flory, Eugene S. Takle, Sébastien Biner, Daniel Caya, Richard G. Jones, René Laprise, L. Ruby Leung, Linda O. Mearns, Wilfran Moufouma-Okia, Ana M. B. Nunes, Yun Qian, John O. Roads, Lisa C. Sloan, and Mark A. Snyder


This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.

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Richard Rotunno, Leonard J. Pietrafesa, John S. Allen, Bradley R. Colman, Clive M. Dorman, Carl W. Kreitzberg, Stephen J. Lord, Miles G. McPhee, George L. Mellor, Christopher N. K. Mooers, Pearn P. Niiler, Roger A. Pielke Sr., Mark D. Powell, David P. Rogers, James D. Smith, and Lian Xie

U.S. Weather Research Program (USWRP) prospectus development teams (PDTs) are small groups of scientists that are convened by the USWRP lead scientist on a one-time basis to discuss critical issues and to provide advice related to future directions of the program. PDTs are a principal source of information for the Science Advisory Committee, which is a standing committee charged with the duty of making recommendations to the Program Office based upon overall program objectives. PDT-1 focused on theoretical issues, and PDT-2 on observational issues; PDT-3 is the first of several to focus on more specialized topics. PDT-3 was convened to identify forecasting problems related to U.S. coastal weather and oceanic conditions, and to suggest likely solution strategies.

There were several overriding themes that emerged from the discussion. First, the lack of data in and over critical regions of the ocean, particularly in the atmospheric boundary layer, and the upper-ocean mixed layer were identified as major impediments to coastal weather prediction. Strategies for data collection and dissemination, as well as new instrument implementation, were discussed. Second, fundamental knowledge of air–sea fluxes and boundary layer structure in situations where there is significant mesoscale variability in the atmosphere and ocean is needed. Companion field studies and numerical prediction experiments were discussed. Third, research prognostic models suggest that future operational forecast models pertaining to coastal weather will be high resolution and site specific, and will properly treat effects of local coastal geography, orography, and ocean state. The view was expressed that the exploration of coupled air-sea models of the coastal zone would be a particularly fruitful area of research. PDT-3 felt that forecasts of land-impacting tropical cyclones, Great Lakes-affected weather, and coastal cyclogenesis, in particular, would benefit from such coordinated modeling and field efforts. Fourth, forecasting for Arctic coastal zones is limited by our understanding of how sea ice forms. The importance of understanding air-sea fluxes and boundary layers in the presence of ice formation was discussed. Finally, coastal flash flood forecasting via hydrologic models is limited by the present accuracy of measured and predicted precipitation and storm surge events. Strategies for better ways to improve the latter were discussed.

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Robert Wood, Michael P. Jensen, Jian Wang, Christopher S. Bretherton, Susannah M. Burrows, Anthony D. Del Genio, Ann M. Fridlind, Steven J. Ghan, Virendra P. Ghate, Pavlos Kollias, Steven K. Krueger, Robert L. McGraw, Mark A. Miller, David Painemal, Lynn M. Russell, Sandra E. Yuter, and Paquita Zuidema
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