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Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy Collins, Jonathan Hendricks, Timothy Hoar, Kevin Raeder, and François Massonnet


Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance.

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Robert E. Dickinson, Stephen E. Zebiak, Jeffrey L. Anderson, Maurice L. Blackmon, Cecelia De Luca, Timothy F. Hogan, Mark Iredell, Ming Ji, Ricky B. Rood, Max J. Suarez, and Karl E. Taylor

A common modeling infrastructure ad hoc working group evolved from an NSF/NCEP workshop in 1998, in recognition of the need for the climate and weather modeling communities to develop a more organized approach to building the software that underlies modeling and data analyses. With its significant investment of pro bono time, the working group made the first steps in this direction. It suggested standards for model data and model physics and explored the concept of a modeling software framework. An overall software infrastructure would facilitate separation of the scientific and computational aspects of comprehensive models. Consequently, it would allow otherwise isolated scientists to effectively contribute to core U.S. modeling activities, and would provide a larger market to computational scientists and computer vendors, hence encouraging their support.

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Eugene S. Takle, Christopher J. Anderson, Jeffrey Andresen, James Angel, Roger W. Elmore, Benjamin M. Gramig, Patrick Guinan, Steven Hilberg, Doug Kluck, Raymond Massey, Dev Niyogi, Jeanne M. Schneider, Martha D. Shulski, Dennis Todey, and Melissa Widhalm


Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

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Hsi-Yen Ma, A. Cheska Siongco, Stephen A. Klein, Shaocheng Xie, Alicia R. Karspeck, Kevin Raeder, Jeffrey L. Anderson, Jiwoo Lee, Ben P. Kirtman, William J. Merryfield, Hiroyuki Murakami, and Joseph J. Tribbia


The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.

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Edward J. Zipser, Cynthia H. Twohy, Si-Chee Tsay, K. Lee Thornhill, Simone Tanelli, Robert Ross, T. N. Krishnamurti, Q. Ji, Gregory Jenkins, Syed Ismail, N. Christina Hsu, Robbie Hood, Gerald M. Heymsfield, Andrew Heymsfield, Jeffrey Halverson, H. Michael Goodman, Richard Ferrare, Jason P. Dunion, Michael Douglas, Robert Cifelli, Gao Chen, Edward V. Browell, and Bruce Anderson

In 2006, NASA led a field campaign to investigate the factors that control the fate of African easterly waves (AEWs) moving westward into the tropical Atlantic Ocean. Aircraft and surface-based equipment were based on Cape Verde's islands, helping to fill some of the data void between Africa and the Caribbean. Taking advantage of the international African Monsoon Multidisciplinary Analysis (AMMA) program over the continent, the NASA-AMMA (NAMMA) program used enhanced upstream data, whereas NOAA aircraft farther west in the Atlantic studied several of the storms downstream. Seven AEWs were studied during AMMA, with at least two becoming tropical cyclones. Some of the waves that did not develop while being sampled near Cape Verde likely intensified in the central Atlantic instead. NAMMA observations were able to distinguish between the large-scale wave structure and the smaller-scale vorticity maxima that often form within the waves. A special complication of the east Atlantic environment is the Saharan air layer (SAL), which frequently accompanies the AEWs and may introduce dry air and heavy aerosol loading into the convective storm systems in the AEWs. One of the main achievements of NAMMA was the acquisition of a database of remote sensing and in situ observations of the properties of the SAL, enabling dynamic models and satellite retrieval algorithms to be evaluated against high-quality real data. Ongoing research with this database will help determine how the SAL influences cloud microphysics and perhaps also tropical cyclogenesis, as well as the more general question of recognizing the properties of small-scale vorticity maxima within tropical waves that are more likely to become tropical cyclones.

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David M. Schultz, Altuğ Aksoy, Jeffrey Anderson, Tommaso Benacchio, Kristen L. Corbosiero, Matthew D. Eastin, Clark Evans, Jidong Gao, Almut Gassman, Joshua P. Hacker, Daniel Hodyss, Matthew R. Kumjian, Ron McTaggart-Cowan, Glen Romine, Paul Roundy, Angela Rowe, Elizabeth Satterfield, Russ S. Schumacher, Stan Trier, Christopher Weiss, Henry P. Huntington, and Gary M. Lackmann
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