Toward an Integrated Seasonal Forecasting System for South America

Coelho, Caio A. S.; Stephenson, David B.; Balmaseda, Magdalena; Doblas‐Reyes, F. J.; Oldenborgh, G. J. van

doi:10.1175/jcli3801.1

Cited by 83 publications

(64 citation statements)

References 37 publications

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“…This is especially true when combined with methods that can provide forecasts for the large-scale aspect of monsoons, such as statistical methods (e.g., Moron et al, 2009a,b;Stolbova et al, 2016), dynamic prediction of monsoon indexes (e.g., Vellinga et al, 2013;Alessandri et al, 2015), and even the integration of statistical and dynamical prediction (e.g., Coelho et al, 2006).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

et al. 2017

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Sub-seasonal to seasonal (S2S) retrospective forecasts from three global coupled models are used to evaluate the predictability of the onset and demise dates of the rainy season over monsoonal regions. The onset and demise dates of the rainy season are defined using only precipitation data. The forecasts of the onset and demise dates of the rainy season are based on a hybrid methodology that combines observations and simulations. Although skillful model precipitation predictions remain challenging in many regions, our results show that they are skillful enough to identify onset and demise dates of the rainy season in many monsoon regions at sub-seasonal (∼30 days) lead-times in retrospective forecasts. We verify sub-seasonal prediction skill for the onset and demise dates of the rainy season over South America, East Asia, and Northern Australia. However, we find low prediction skill for the onset and demise of the rainy season on sub-seasonal scales over the Indian monsoon region. This information would be valuable to sectors related to water management.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

et al. 2017

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“…Recent studies for target regions have shown that a careful post-processing (Coelho et al, 2006) and combination of information from different forecast systems, including statisticalempirical schemes (Stephenson et al, 2005), and predictor's expertise (Schubert et al, 2007) can significantly increase the forecast quality of seasonal predictions. This is certainly possible in our case, since the three forecast systems described here possess complementary strengths in their abilities to sample structural, parameter and initial-condition uncertainties.…”

Section: F J Doblas-reyes Et Almentioning

confidence: 99%

Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts

Doblas‐Reyes

Weisheimer

Déqué

et al. 2009

Quart J Royal Meteoro Soc

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123

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ABSTRACT:The relative merits of three forecast systems addressing the impact of model uncertainty on seasonal/annual forecasts are described. One system consists of a multi-model, whereas two other systems sample uncertainties by perturbing the parametrization of reference models through perturbed parameter and stochastic physics techniques. Ensemble reforecasts over 1991 to 2001 were performed with coupled climate models started from realistic initial conditions. Forecast quality varies due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. Both the stochastic-physics and perturbed-parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Although the multi-model experiment has an ensemble size larger than the other two experiments, most of the assessment was done using equally-sized ensembles. The three ensembles show similar levels of skill: significant differences in performance typically range between 5 and 20%. However, a nine-member multi-model shows better results for seasonal predictions with lead times shorter than five months, followed by the stochastic-physics and perturbed-parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed-parameter ensemble gives more often better results. All systems suggest that spread cannot be considered a useful predictor of skill. Annual-mean predictions showed lower forecast quality than seasonal predictions. Only small differences between the systems were found. The full multi-model ensemble has improved quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions.

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“…Notwithstanding this progress, the north-eastern interior remains an area where SSTs are producing high forecast skill, although the GCM and GCM-MOS systems also produce high skill there. It should be worthwhile to investigate whether or not a combination of physical model output and statistical forecast models that use SSTs as predictors can further improve skill (Coelho et al, 2006) over South Africa.…”

Section: Discussionmentioning

confidence: 99%

Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

Landman

Kgatuke

Mbedzi

et al. 2008

Intl Journal of Climatology

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ABSTRACT:The ability of advanced state-of-the-art methods of downscaling large-scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December-January-February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea-surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large-scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead-time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10-year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post-processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied.

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Toward an Integrated Seasonal Forecasting System for South America

Cited by 83 publications

References 37 publications

Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts

Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

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