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R. Errico and D. Baumhefner

Abstract

Recently reported results indicate that limited-area mesoscale models with prescribed lateral boundaries do not exhibit the same predictability error growth as observed in large-scale (global) models. These results have been reanalyzed in greater detail. New methods of limited-area initialization and spectral analysis have been used. The new analyses indicate that several model properties act to restrict the growth of perturbation. These include: the Projection of initial perturbations onto gravity waves which interact only weakly with other, more significant motions; the “sweeping out” of errors by correct or perfect lateral boundaries; and the reduction of differences by subgrid dissipation. This last property suggests that there is a strong dynamical forcing of small scales by much larger scales, so that this forcing is only weakly affected by typical, small perturbations in this model. New experiments suggest that some quasi-geostrophic components of the forecasts, away from the inflow boundaries, exhibit local error doubling times of approximately one day within active baroclinic regions. Such doubling is not observed in a lower-resolution, global forecast model.

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J. J. Tribbia and D. P. Baumhefner

Abstract

The reliability of reductions of forecasting error derived from changes in the quality of the initial data or model formulation is considered using a signal-to-noise analysis. Defining the initial data error as the data error source and the model error as the modelling source, we propose the use of the modeling error as a baseline against which potential reductions in data error may be calibrated. In the reverse sense, the data error can also be used to calibrate the reduction in the modeling error. A simple nonlinear model is used to illustrate examples of the above reliability test. Further applications of this test to actual numerical forecast experiments using analyses from both the augmented FWE database and the operational NMC data base are shown. Forecast comparisons using various suites of physical parameterizations are also presented.

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J. J. Tribbia and D. P. Baumhefner

Abstract

An examination of the scale interactions in predictability experiments is made using the NCAR Community Climate Model Version 3 (CCM3) at various horizontal resolutions ranging from T42 to T170. Both identical-model and imperfect-model twin experiments are analyzed, and they show distinctive differences from the classical inverse cascade picture of predictability error growth. In the identical-model twin framework, error growth experiments using initial errors confined to long and short scales are compared and contrasted. In these cases, error growth eventually asymptotes to an exponential growth of baroclinically active scales. In the imperfect-model twin experiments, errors rapidly disperse from scales technically beyond model resolution to a small amplitude, spectrally uniform distribution of errors in resolved scales. The errors in resolved scales further amplify in a quasi-exponential growth of the baroclinically active scales. Finally, the implications of these growth mechanisms for the necessary resolution in short- to medium-range numerical weather prediction are given under the assumption that the accuracy of current initial state estimates of the atmosphere remain fixed at their present level.

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David D. Houghton, David P. Baumhefner, and Warren M. Washington

Abstract

The problem of obtaining initial values for vertical motion and the divergent component of horizontal velocity is examined for a global primitive equation model. Only diagnostic methods are considered, the emphasis being on uniform application over the globe rather than a high degree of accuracy. Results show that a very simple diagnostic equation similar in form to the omega equation provides for realistic values of vertical motion in high and middle latitudes and smooth variations across tropical latitudes. In terms of prediction accuracy, no improvement is noted by using the computed initial vertical motions instead of zero for the initial vertical motions in a six-layer, 5° mesh model. In both cases unrealistic oscillations occur during the first 12 hr.

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J. Shukla, J. Anderson, D. Baumhefner, C. Brankovic, Y. Chang, E. Kalnay, L. Marx, T. Palmer, D. Paolino, J. Ploshay, S. Schubert, D. Straus, M. Suarez, and J. Tribbia

Dynamical Seasonal Prediction (DSP) is an informally coordinated multi-institution research project to investigate the predictability of seasonal mean atmospheric circulation and rainfall. The basic idea is to test the feasibility of extending the technology of routine numerical weather prediction beyond the inherent limit of deterministic predictability of weather to produce numerical climate predictions using state-of-the-art global atmospheric models. Atmospheric general circulation models (AGCMs) either forced by predicted sea surface temperature (SST) or as part of a coupled forecast system have shown in the past that certain regions of the extratropics, in particular, the Pacific–North America (PNA) region during Northern Hemisphere winter, can be predicted with significant skill especially during years of large tropical SST anomalies. However, there is still a great deal of uncertainty about how much the details of various AGCMs impact conclusions about extratropical seasonal prediction and predictability.

DSP is designed to compare seasonal simulation and prediction results from five state-of-the-art U.S. modeling groups (NCAR, COLA, GSFC, GFDL, NCEP) in order to assess which aspects of the results are robust and which are model dependent. The initial emphasis is on the predictability of seasonal anomalies over the PNA region. This paper also includes results from the ECMWF model, and historical forecast skill over both the PNA region and the European region is presented for all six models.

It is found that with specified SST boundary conditions, all models show that the winter season mean circulation anomalies over the Pacific–North American region are highly predictable during years of large tropical sea surface temperature anomalies. The influence of large anomalous boundary conditions is so strong and so reproducible that the seasonal mean forecasts can be given with a high degree of confidence. However, the degree of reproducibility is highly variable from one model to the other, and quantities such as the PNA region signal to noise ratio are found to vary significantly between the different AGCMs. It would not be possible to make reliable estimates of predictability of the seasonal mean atmosphere circulation unless causes for such large differences among models are understood.

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