Using regional wind fields to improve general circulation model forecasts of July–September Sahel rainfall

Ndiaye, Ousmane; Goddard, Lisa; Ward, Martin

doi:10.1002/joc.1767

Cited by 27 publications

(39 citation statements)

References 31 publications

(55 reference statements)

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“…For AGCM forecasts of Sahel rainfall, the published literature contains no clear systematic improvements on this approach. Applying the persistence approach, although June SST showed good skill in Ndiaye et al (2009), prediction skill from May and April SST was substantially reduced, even after applying the MOS system.…”

Section: Introductionmentioning

confidence: 83%

“…The approach taken in Ndiaye et al (2009), to address the problem with AGCM simulations of Sahel rainfall, was to focus on indices of the regional circulation system predicted by the AGCM (ECHAM4.5) and to use the indices in a model output statistics (MOS) to predict Sahel rainfall. The ECHAM4.5 AGCM is typical in its poor level of skill for Sahel rainfall; however, using the low-level wind field over the tropical Atlantic and western part of West Africa, a level of skill in predicting Sahel rainfall is achieved that is comparable with the small number of AGCMs that have been able to successfully represent the impact of SST directly on Sahel rainfall (e.g., Giannini et al 2003).…”

Section: Introductionmentioning

confidence: 99%

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Predictability of Seasonal Sahel Rainfall Using GCMs and Lead-Time Improvements Through the Use of a Coupled Model

2011

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The ability of several atmosphere-only and coupled ocean-atmosphere general circulation models (AGCMs and CGCMs, respectively) is explored for the prediction of seasonal July-September (JAS) Sahel rainfall. The AGCMs driven with observed sea surface temperature (SST) over the period 1968-2001 confirm the poor ability of such models to represent interannual Sahel rainfall variability. However, using a model output statistics (MOS) approach with the predicted low-level wind field over the tropical Atlantic and western part of West Africa yields good Sahel rainfall skill for all models. Skill is mostly captured in the leading empirical orthogonal function (EOF1), representing large-scale fluctuation in the regional circulation system over the tropical Atlantic. This finding has operational significance for the utility of AGCMs for short lead-time prediction based on persistence of June SST information; however, studies have shown that for longer lead-time forecasts, there is substantial loss of skill, relative to that achieved using the observed JAS SST.The potential of CGCMs is therefore explored for extending the lead time of Sahel rainfall predictions. Some of the models studied, when initialized using April information, show potential to at least match the levels of skill achievable from assuming persistence of April SST. One model [NCEP Climate Forecasting System (CFS)] was found to be particularly promising. Diagnosis of the hindcasts available for the CFS (from lead times up to six months for 1981-2008) suggests that, especially by applying the same MOS approach, skill is achieved through capturing interannual variations in Sahel rainfall (primarily related to El Niñ o-Southern Oscillation in the period of study), as well as the upward trend in Sahel rainfall that is observed over 1981-2008, which has been accompanied by a relative warming in the North Atlantic compared to the South Atlantic. At lead times up to six months (initialized forecasts in December), skill levels are maintained with the correlation between predicted and observed Sahel rainfall at approximately r 5 0.6. While such skill levels at these long lead times are notably higher than previously achieved, further experiments, such as over the same period and with comparable AGCMs, are required for definitive attribution of the advance to the use of a coupled ocean-atmosphere modeling approach. Nonetheless, the detrended skill achieved here by the January-March initializations (r 5 0.33) must require an approach that captures the evolution of the key ocean-atmosphere anomalies from boreal winter to boreal summer, and approaches that draw on persistence in ocean conditions have not previously been successful.Corresponding author address: Ousmane Ndiaye,

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Predictability of Seasonal Sahel Rainfall Using GCMs and Lead-Time Improvements Through the Use of a Coupled Model

2011

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“…Light spatial smoothing has also been done to reduce noise. Other modifications include selected spatial model output statistics (MOS) corrections of systematic errors of the individual AGCMs for precipitation for specific regions using CCA (Ndiaye et al 2009;Tippett et al 2003;Landman and Goddard 2002); a nudging toward reduction (enhancement) of probabilities for below-normal (above normal) temperature, partly in response to a diagnostic verification of IRI's forecasts during 1997-2000 (Wilks and Godfrey 2002); and making the forecasts more consistent with those of other meteorological centers or regional climate outlook forums.…”

Section: Final Forecastmentioning

confidence: 99%

Verification of the First 11 Years of IRI’s Seasonal Climate Forecasts

Barnston

Mason

et al. 2010

Journal of Applied Meteorology and Climatology

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142

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This paper examines the quality of seasonal probabilistic forecasts of near-global temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from late 1997 through 2008, using mainly a two-tiered multimodel dynamical prediction system. Skill levels, while modest when globally averaged, depend markedly on season and location and average higher in the tropics than extratropics. To first order, seasons and regions of useful skill correspond to known direct effects as well as remote teleconnections from anomalies of tropical sea surface temperature in the Pacific Ocean (e.g., ENSO related) and in other tropical basins. This result is consistent with previous skill assessments by IRI and others and suggests skill levels beneficial to informed clients making climate risk management decisions for specific applications. Skill levels for temperature are generally higher, and less seasonally and regionally dependent, than those for precipitation, partly because of correct forecasts of enhanced probabilities for above-normal temperatures associated with warming trends. However, underforecasting of above-normal temperatures suggests that the dynamical forecast system could be improved through inclusion of time-varying greenhouse gas concentrations. Skills of the objective multimodel probability forecasts, used as the primary basis for the final forecaster-modified issued forecasts, are comparable to those of the final forecasts, but their probabilistic reliability is somewhat weaker. Automated recalibration of the multimodel output should permit improvements to their reliability, allowing them to be issued as is. IRI is currently developing single-tier prediction components.

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“…Model projections of future climate vary dramatically between much wetter and much drier conditions (Druyan 2011), and this has heightened the ambition to identify which models are more or less credible. There are already good examples of process-based model evaluation for West Africa, including investigation of the meridional circulation (Cook and Vizy 2006;James et al 2015), the AEJ (Caminade et al 2006;Abiodun et al 2011), AEWs , the Saharan heat low (Biasutti et al 2009), jet-rainfall coupling (Whittleston et al 2017), and analysis across a range of time scales (Cook and Vizy 2006;Ndiaye et al 2009;Birch and Parker 2014;Diasso and Abiodun 2017;Vellinga et al 2016). This research has shown that many models do not produce a monsoon at all: the rainfall maximum does not move onto the continent during the boreal summer (Cook and Vizy 2006), at least in part because of a warm SST bias in the Gulf of Guinea (Roehrig et al 2013).…”

Section: Pan-africanmentioning

confidence: 99%