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D. A. Paolino, Q. Yang, B. Doty, J. L. Kinter III, J. Shukla, and David M. Straus

Results are presented from a retrospective analysis of 19 months (May 1982–November 1983) of global atmospheric observations. The National Meteorological Center Global Data Assimilation System was used in tandem with the atmospheric general circulation model of the Center for Ocean–Land–Atmosphere Studies to produce four-times-daily representations of the global atmosphere. Statistics were compiled regarding the use of data by the analysis and the decisions of the quality control procedures. Comparison of the reanalyses with both observation and the archived contemporaneous analyses showed substantial improvements in the representation of the global atmospheric circulation, possibly excepting the Southern Hemisphere south of 60°S. A list of data products from the reanalysis is given in an appendix.

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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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M.J. Fennessy, J.L. Kinter III, B. Kirtman, L. Marx, S. Nigam, E. Schneider, J. Shukla, D. Straus, A. Vernekar, Y. Xue, and J. Zhou


A series of sensitivity experiments are conducted in an attempt to understand and correct deficiencies in the simulation of the seasonal mean Indian monsoon with a global atmospheric general circulation model. The seasonal mean precipitation is less than half that observed. This poor simulation in seasonal integrations is independent of the choice of initial conditions and global sea surface temperature data used. Experiments are performed to test the sensitivity of the Indian monsoon simulation to changes in orography, vegetation, soil wetness, and cloudiness.

The authors find that the deficiency of the model precipitation simulation may be attributed to the use of an enhanced orography in the integrations. Replacement of this orography with a mean orography results in a much more realistic simulation of Indian monsoon circulation and rainfall. Experiments with a linear primitive equation model on the sphere suggest that this striking improvement is due to modulations of the orographically forced waves in the lower troposphere. This improvement in the monsoon simulation is due to the kinematic and dynamical effects of changing the topography, rather than the thermal effects, which were minimal.

The magnitude of the impact on the Indian monsoon of the other sensitivity experiments varied considerably, but was consistently less than the impact of using the mean orography. However, results from the soil moisture sensitivity experiments suggest a possibly important role for soil moisture in simulating tropical precipitation, including that associated with the Indian monsoon.

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