Dynamical Seasonal Climate Prediction Using an Ocean–Atmosphere Coupled Climate Model Developed in Partnership between South Africa and the IRI

Beraki, Asmerom F; DeWitt, David G.; Landman, Willem A.; Olivier, C

doi:10.1175/jcli-d-13-00275.1

Cited by 24 publications

(35 citation statements)

References 74 publications

(48 reference statements)

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“…Faced with marginal forecastability, a possibility that it may be a consequence of an observable system with low inherent predictability is sometimes overlooked, and it is stated that an increase in ensemble size will lead to improvements in forecastability. For example, Beraki et al (2014Beraki et al ( , p. 1729) state that ''. For example, whether the statement merely refers to taking advantage of the known relationship between larger ensemble sizes and an increase in forecastability, or refers to a sentiment that no matter what the inherent predictability limit may be, a general increase in forecastability can be achieved by the use of larger ensemble, cannot be readily discerned.…”

Section: Introductionmentioning

confidence: 99%

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

Kumar

Chen

2015

Monthly Weather Review

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Faced with the scenario when prediction skill is low, particularly in conjunction with long-range predictions, a commonly proposed solution is that an increase in ensemble size will rectify the issue of low skill. Although it is well known that an increase in ensemble size does lead to an increase in prediction skill, the general scope of this supposition, however, is that low prediction skill is not a consequence of constraints imposed by inherent predictability limits, but an artifact of small ensemble sizes, and further, increases in ensemble sizes (that are often limited by computational resources) are the major bottlenecks for improving long-range predictions. In proposing that larger ensemble sizes will remedy the issue of low skill, a fact that is not well appreciated is that for scenarios with high inherent predictability, a small ensemble size is sufficient to realize high predictability, while for scenarios with low inherent predictability, much larger ensemble sizes are needed to realize low predictability. In other words, requirements on ensemble size (to realize the inherent predictability) and inherent predictability are complementary variables. A perceived need for larger ensembles, therefore, may also imply the presence of low predictability.

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Section: Introductionmentioning

confidence: 99%

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

Kumar

Chen

2015

Monthly Weather Review

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“…The austral summer precipitation over southern Africa and its variations have been modeled using both the global models [ Joubert and Hewitson , ; Mason and Joubert , ; Landman et al ., ; Landman and Beraki , ; Yuan et al ., ; Beraki et al ., ] and regional models [ Joubert et al ., ; Hansingo and Reason , ; Tadross et al ., ; Kagtuke et al ., ; MacKellar et al ., ; Crétat et al ., ; Sylla et al ., ; Ratnam et al ., ; Ratna et al ., ; Pohl et al ., ]. Few studies [ Landman and Beraki , ; Beraki et al ., ] indicate that using fully coupled global models improves the spatial and temporal precipitation over southern Africa compared to using the two‐tier approach of specifying observed/forecast SSTs to the atmospheric models. Ratnam et al .…”

Section: Introductionmentioning

confidence: 99%

A model study of regional air‐sea interaction in the austral summer precipitation over southern Africa

et al. 2015

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The importance of air-sea interactions in the simulation of southern Africa precipitation is brought out using a fully coupled regional model and by forcing the atmospheric component of the coupled model with the coupled model-simulated sea surface temperature (SST). The coupled model has the Advanced Research Weather Research and Forecasting model as the atmospheric component and the Regional Ocean Modeling System as the oceanic component. The spatial and temporal distribution of the coupled model-simulated SST shows good agreement with observations. The coupled model shows a bias of about 0.25°C near the east coast of southern Africa. Comparison of the precipitation between the coupled and uncoupled model shows that the air-sea interactions play an important role in the simulation of the precipitation during the peak precipitation season January to March, when the precipitation is mostly due to tropical processes. It is found that the two-way specification of SST to the atmospheric model results in significant larger (smaller) precipitation depending on the spatial distribution of SST, due to the increase (decrease) of moisture from the surrounding oceans into the landmass. The results also show that the air-sea interactions are not so important during the initial phases of the precipitation from October to December.

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“…In West Africa, seasonal rainfall prediction (Nicholson 2008(Nicholson , 2009Harada and Sumi 2003;Wu et al 2013) may be assisted by forecasting seasonal forcing (Sultan et al 2010;Zhu et al 2014;Beraki et al 2014;Endris et al 2013). Individual rain events over the Sahara might be predictable because they involve a particular set of interactions between tropical and extratropical waves, jets, and air masses (Lafore et al 2011, p. 9;Knippertz 2003;Davis et al 2013) that are routinely predicted in global weather-forecast models (Waliser et al 2012).…”

Section: Qualitative Descriptions Of Rainfall Patternsmentioning

confidence: 99%

Where the Least Rainfall Occurs in the Sahara Desert, the TRMM Radar Reveals a Different Pattern of Rainfall Each Season

Kelley

2014

Journal of Climate

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Some previous studies were unable to detect seasonal organization to the rainfall in the Sahara Desert, while others reported seasonal patterns only in the less-arid periphery of the Sahara. In contrast, the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) satellite detects four rainy seasons in the part of the Sahara where the TRMM radar saw the least rainfall during a 15-yr period (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). According to the TRMM radar, approximately 208-278N, 228-328E is the portion of the Sahara that has the lowest average annual rain accumulation (1-5 mm yr 21 ). Winter (January and February) has light rain throughout this region but more rain to the north over the Mediterranean Sea. Spring (April and May) has heavier rain and has lightning observed by the TRMM Lightning Imaging Sensor (LIS). Summer rain and lightning (July and August) occur primarily south of 238N. At a maximum over the Red Sea, autumn rain and lightning (October and November) can be heavy in the northeastern portion of the study area, but these storms are unreliable: that is, the TRMM radar detects such storms in only 6 of the 15 years. These four rainy seasons are each separated by a comparatively drier month in the monthly rainfall climatology. The few rain gauges in this arid region broadly agree with the TRMM radar on the seasonal organization of rainfall. This seasonality is reason to reevaluate the idea that Saharan rainfall is highly irregular and unpredictable.

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Dynamical Seasonal Climate Prediction Using an Ocean–Atmosphere Coupled Climate Model Developed in Partnership between South Africa and the IRI

Cited by 24 publications

References 74 publications

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

A model study of regional air‐sea interaction in the austral summer precipitation over southern Africa

Where the Least Rainfall Occurs in the Sahara Desert, the TRMM Radar Reveals a Different Pattern of Rainfall Each Season

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