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Masao Kanamitsu, Wesley Ebisuzaki, Jack Woollen, Shi-Keng Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter

The NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the “50-year” (1948–present) NCEP–NCAR Reanalysis Project. NCEP–DOE AMIP-II re-analysis covers the “20-year” satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP–NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP–NCAR reanalysis are made in land surface parameters and land–ocean fluxes. This analysis can be used as a supplement to the NCEP–NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.

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Thomas M. Hamill, Gary T. Bates, Jeffrey S. Whitaker, Donald R. Murray, Michael Fiorino, Thomas J. Galarneau Jr., Yuejian Zhu, and William Lapenta

A multidecadal ensemble reforecast database is now available that is approximately consistent with the operational 0000 UTC cycle of the 2012 NOAA Global Ensemble Forecast System (GEFS). The reforecast dataset consists of an 11-member ensemble run once each day from 0000 UTC initial conditions. Reforecasts are run to +16 days. As with the operational 2012 GEFS, the reforecast is run at T254L42 resolution (approximately 1/2° grid spacing, 42 levels) for week +1 forecasts and T190L42 (approximately 3/4° grid spacing) for the week +2 forecasts. Reforecasts were initialized with Climate Forecast System Reanalysis initial conditions, and perturbations were generated using the ensemble transform with rescaling technique. Reforecast data are available from 1985 to present.

Reforecast datasets were previously demonstrated to be very valuable for detecting and correcting systematic errors in forecasts, especially forecasts of relatively rare events and longer-lead forecasts. What is novel about this reforecast dataset relative to the first-generation NOAA reforecast is that (i) a modern, currently operational version of the forecast model is used (the previous reforecast used a model version from 1998); (ii) a much larger set of output data has been saved, including variables relevant for precipitation, hydrologic, wind energy, solar energy, severe weather, and tropical cyclone forecasting; and (iii) the archived data are at much higher resolution.

The article describes more about the reforecast configuration and provides a few examples of how this second-generation reforecast data may be used for research and a variety of weather forecast applications.

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W. Lawrence Gates, James S. Boyle, Curt Covey, Clyde G. Dease, Charles M. Doutriaux, Robert S. Drach, Michael Fiorino, Peter J. Gleckler, Justin J. Hnilo, Susan M. Marlais, Thomas J. Phillips, Gerald L. Potter, Benjamin D. Santer, Kenneth R. Sperber, Karl E. Taylor, and Dean N. Williams

The Atmospheric Model Intercomparison Project (AMIP), initiated in 1989 under the auspices of the World Climate Research Programme, undertook the systematic validation, diagnosis, and intercomparison of the performance of atmospheric general circulation models. For this purpose all models were required to simulate the evolution of the climate during the decade 1979–88, subject to the observed monthly average temperature and sea ice and a common prescribed atmospheric CO2 concentration and solar constant. By 1995, 31 modeling groups, representing virtually the entire international atmospheric modeling community, had contributed the required standard output of the monthly means of selected statistics. These data have been analyzed by the participating modeling groups, by the Program for Climate Model Diagnosis and Intercomparison, and by the more than two dozen AMIP diagnostic subprojects that have been established to examine specific aspects of the models' performance. Here the analysis and validation of the AMIP results as a whole are summarized in order to document the overall performance of atmospheric general circulation–climate models as of the early 1990s. The infrastructure and plans for continuation of the AMIP project are also reported on.

Although there are apparent model outliers in each simulated variable examined, validation of the AMIP models' ensemble mean shows that the average large-scale seasonal distributions of pressure, temperature, and circulation are reasonably close to what are believed to be the best observational estimates available. The large-scale structure of the ensemble mean precipitation and ocean surface heat flux also resemble the observed estimates but show particularly large intermodel differences in low latitudes. The total cloudiness, on the other hand, is rather poorly simulated, especially in the Southern Hemisphere. The models' simulation of the seasonal cycle (as represented by the amplitude and phase of the first annual harmonic of sea level pressure) closely resembles the observed variation in almost all regions. The ensemble's simulation of the interannual variability of sea level pressure in the tropical Pacific is reasonably close to that observed (except for its underestimate of the amplitude of major El Niños), while the interannual variability is less well simulated in midlatitudes. When analyzed in terms of the variability of the evolution of their combined space–time patterns in comparison to observations, the AMIP models are seen to exhibit a wide range of accuracy, with no single model performing best in all respects considered.

Analysis of the subset of the original AMIP models for which revised versions have subsequently been used to revisit the experiment shows a substantial reduction of the models' systematic errors in simulating cloudiness but only a slight reduction of the mean seasonal errors of most other variables. In order to understand better the nature of these errors and to accelerate the rate of model improvement, an expanded and continuing project (AMIP II) is being undertaken in which analysis and intercomparison will address a wider range of variables and processes, using an improved diagnostic and experimental infrastructure.

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S. Pawson, K. Kodera, K. Hamilton, T. G. Shepherd, S. R. Beagley, B. A. Boville, J. D. Farrara, T. D. A. Fairlie, A. Kitoh, W. A. Lahoz, U. Langematz, E. Manzini, D. H. Rind, A. A. Scaife, K. Shibata, P. Simon, R. Swinbank, L. Takacs, R. J. Wilson, J. A. Al-Saadi, M. Amodei, M. Chiba, L. Coy, J. de Grandpré, R. S. Eckman, M. Fiorino, W. L. Grose, H. Koide, J. N. Koshyk, D. Li, J. Lerner, J. D. Mahlman, N. A. McFarlane, C. R. Mechoso, A. Molod, A. O'Neill, R. B. Pierce, W. J. Randel, R. B. Rood, and F. Wu

To investigate the effects of the middle atmosphere on climate, the World Climate Research Programme is supporting the project “Stratospheric Processes and their Role in Climate” (SPARC). A central theme of SPARC, to examine model simulations of the coupled troposphere–middle atmosphere system, is being performed through the initiative called GRIPS (GCM-Reality Intercomparison Project for SPARC). In this paper, an overview of the objectives of GRIPS is given. Initial activities include an assessment of the performance of middle atmosphere climate models, and preliminary results from this evaluation are presented here. It is shown that although all 13 models evaluated represent most major features of the mean atmospheric state, there are deficiencies in the magnitude and location of the features, which cannot easily be traced to the formulation (resolution or the parameterizations included) of the models. Most models show a cold bias in all locations, apart from the tropical tropopause region where they can be either too warm or too cold. The strengths and locations of the major jets are often misrepresented in the models. Looking at three-dimensional fields reveals, for some models, more severe deficiencies in the magnitude and positioning of the dominant structures (such as the Aleutian high in the stratosphere), although undersampling might explain some of these differences from observations. All the models have shortcomings in their simulations of the present-day climate, which might limit the accuracy of predictions of the climate response to ozone change and other anomalous forcing.

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