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Č Branković
,
T. N. Palmer
, and
L. Ferranti

Abstract

Results from a set of 120-day ensemble integrations of a T63L19 version of the European Centre for Medium-Range Weather Forecasts (ECMWF) model are described. The integrations, started from observed initial conditions, used observed global sea surface temperature (SST) as a lower boundary condition. Each ensemble comprised three members initiated from consecutive analyses one day apart. The ensembles were analyzed over the last 90 days of the integration period, corresponding to conventional calendar seasons. Interannual variations in the atmosphere for the period 1986 to 1990 were studied in this way. The sign and magnitude of tropical Pacific SST anomalies were chosen to define an El Niño-Southern Oscillation (ENSO) index. Difference fields were formed from seasons in which this index was 1) large and of opposite sign and 2) small (and of opposite sign). The skill and spread of the ensemble simulations were determined over nine areas covering the globe. In general, the skill of the ensemble difference fields was higher for the strong ENSO-index years than for the weak ones, both in the tropics and the extratropics. In the northern extratropics, there was a tendency for the skill of the ensemble mean to be highest in the spring season. This was consistent with the fact that the internal spread of the ensemble also tended to be smallest in spring. Differences in zonally averaged zonal mean wind revealed that in the tropical and subtropical troposphere, the model simulations were quite accurate, particularly for the strong ENSO-index years. For both strong and weak ENSO-index years, the model correctly simulated differences in the tropical stratosphere associated with the quasi-biennial oscillation (QBO). Further experimentation confirmed that this was associated with a memory of initial conditions over the 120 days of the integration, and suggested some influence of the QBO in the upper tropical troposphere. From wind differences and analysis of changes to regime residence frequencies, it was concluded that while the SST anomalies associated with strong ENSO-index years had a significant influence on the extratropical circulation (including both North America and Europe), there was considerable intra-ensemble variability that affected the tropical Pacific area itself, including surface wind stress over the tropical Pacific. Intraensemble variability was also shown to be substantial in parts of the tropics associated with the summer monsoons over India and Southeast Asia. By contrast, rainfall over sub-Saharan Africa was more stable.

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L. Ferranti
,
F. Molteni
,
Č Branković
, and
T. N. Palmer

Abstract

Properties of the general circulation simulated by the ECMWF model are discussed using a set of seasonal integrations at T63 resolution. For each season, over the period of 5 years, 1986–1990, three integrations initiated on consecutive days were run with prescribed observed sea surface temperature (SST).

This paper presents a series of diagnostics of extratropical variability in the model, with particular emphasis on the northern winter. Time-filtered maps of variability indicate that in this season there is insufficient storm track activity penetrating into the Eurasian continent. Related to this the maximum of lower-frequency variance in the Euro-Atlantic region is erroneously shifted eastward in the model. By contrast the simulated fields of both high- and low-frequency variability for northern spring are more realistic.

Blocking is defined objectively in terms of the geostrophic wind at 500 mb. Consistent with the low-frequency transience, in the Euro-Atlantic sector the position of maximum blocking in the model is displaced eastward. The composite structure of blocks over the Pacific is realistic, though their frequency is severely underestimated at all times of year.

Shortcomings in the simulated wintertime general circulation were also revealed by studying the projection of 5-day mean fields onto empirical orthogonal functions (E0Fs) of the observed flow. The largest differences were apparent for statistics of EOFs of the zonal mean flow. Analysis of weather regime activity, defined from the EOFS, suggested that regimes with positive PNA index were overpopulated, while the negative PNA regimes were underpopulated. A further comparison between observed and modeled low-frequency variance revealed that underestimation of low-frequency variability occurs along the same axes that explain most of the spatial structure of the error in the mean field, suggesting a common dynamical origin for these two aspects of the systematic error.

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T. N. Palmer
,
Č Branković
,
P. Viterbo
, and
M. J. Miller

Abstract

Results from a set of 90-day integrations, made with a T42 version of the ECMWF model and forced with a variety of specified sea surface temperature (SST) datasets, are discussed. Most of the integrations started from data for 1 June 1987 and 1 June 1988. During the summer of 1987, both the Indian and African monsoons were weak, in contrast with the summer of 1988 when both monsoons were much stronger. With observed SSTs, the model is able to simulate the interannual variations in the global-scale velocity potential and stream-function fields on seasonal time scales. On a regional basis, rainfall over the Sahel and, to a lesser extent, India showed the correct sense of interannual variation, though in absolute terms the model appears to have an overall dry bias in these areas.

Additional integrations were made to study the impact of the observed SST anomalies in individual oceans. Much of the interannual variation in both Indian and African rainfall can be accounted for by the remote effect of the tropical Pacific SST anomalies only. By comparison with the effect of the Pacific, interannual variability in Indian Ocean, tropical Atlantic Ocean, or extratropical SSTs had a relatively modest influence on tropical large-scale flow or rainfall in the areas studied.

Integrations run with identical SSTs but different initial conditions indicated that for large-scale circulation diagnostics, the impact of anomalous ocean forcing dominated the possible impact of variations in initial conditions. In terms of local rainfall amounts, on the other hand, the impact of initial conditions is comparable with that of SST anomaly over parts of India and Southeast Asia, less so over the Sahel. While this may suggest that a nonnegligible fraction of the variance of month-to-seasonal mean rainfall on the regional scale in the tropics may not be dynanamically predictable, it is also quite possible that the disparity in the apparent predictability of rainfall and circulation anomalies is a reflection of model systematic error.

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K. R. Sperber
,
C. Brankovic
,
M. Déqué
,
C. S. Frederiksen
,
R. Graham
,
A. Kitoh
,
C. Kobayashi
,
T. Palmer
,
K. Puri
,
W. Tennant
, and
E. Volodin

Abstract

Ensembles of hindcasts from seven models are analyzed to evaluate dynamical seasonal predictability of 850-hPa wind and rainfall for the Asian summer monsoon (ASM) during 1987, 1988, and 1993. These integrations were performed using observed sea surface temperatures and from observed initial conditions. The experiments were designed by the Climate Variability and Predictability, Working Group on Seasonal to Interannual Prediction as part of the Seasonal Prediction Model Intercomparison Project. Integrations from the European Union Prediction of Climate Variations on Seasonal to Interannual Timescales experiment are also evaluated.

The National Centers for Environmental Prediction–National Center for Atmospheric Research and European Centre for Medium-Range Weather Forecasts reanalyses and observed pentad rainfall form the baseline against which the hindcasts are judged. Pattern correlations and root-mean-square differences indicate errors in the simulation of the time mean low-level flow and the rainfall exceed observational uncertainty. Most models simulate the subseasonal EOFs that are associated with the dominant variations of the 850-hPa flow during the ASM, but not with the fidelity exhibited by the reanalyses as determined using pattern correlations. Pattern correlations indicate that the first EOF, associated with the tropical convergence zone being located over the continental landmass, is best simulated. The higher-order EOFs are less well simulated, and errors in the magnitude and location of their associated precipitation anomalies compromise dynamical seasonal predictability and are related to errors of the mean state. In most instances the models fail to properly project the subseasonal EOFs/principal components onto the interannual variability with the result that hindcasts of the 850-hPa flow and rainfall are poor. In cases where the observed EOFs are known to be related to the boundary forcing, the failure of the models to properly project the EOFs onto the interannual variability indicates that the models are not setting up observed teleconnection patterns.

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T.N. Palmer
,
C. Brankovic
,
F. Molteni
,
S. Tibaldi
,
L. Ferranti
,
A. Hollingsworth
,
U. Cubasch
, and
E. Klinker

Results from a 3 1/2-yr experimental program of extended-range integrations of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model are summarized. The topics discussed include

Our results are broadly consistent with those from other major centers evaluating the feasibility of dynamical extended-range prediction. We believe that operational extended-range forecasting using the ECMWF model may be viable to day 20—and possibly beyond—following further research on techniques for Monte Carlo forecasting, and when model systematic error in the tropics has been reduced significantly.

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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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