Circulation Mechanisms of Kenya Rainfall Anomalies

Hastenrath, Stefan; Polzin, Dierk; Mutai, Charles

doi:10.1175/2010jcli3599.1

Cited by 87 publications

(70 citation statements)

References 43 publications

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“…Over the 1981-1993 period, the PC1 WPG relationship was weak (Fig. 3b), consistent with the aforementioned reports of weak zonal connections in the Indo-Pacific region (Hastenrath et al, 2011). The skill of the CIO index, comes more from distinguishing between high PC2 values associated with relatively cool Indian Ocean SSTs between 1981 and 1996 and low PC2 values related to the warm Indian Ocean conditions that have persisted since 1997.…”

Section: Using January Wpg and Cio Indices To Predict March-may Chirpsupporting

confidence: 86%

“…Running correlations with Niño 3.4 SSTs indicate weak relationships before that period . Analyses of 1948-1987 climate data (Hastenrath et al, 2011), furthermore, indicate weak teleconnections between Kenyan boreal spring rainfall and the atmospheric circulation over the Indian Ocean. As the mean Walker circulation has intensified (L'Heureux et al, 2013), however, the East African drought impacts of La Niña-like SST patterns has intensified due to the influence of a stronger WPG (Hoell and Funk, 2013a), resulting in the type of correlation structure shown in Fig.…”

Section: Correlations With January Sstmentioning

confidence: 99%

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Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

Funk

Hoell

Shukla

et al. 2014

Hydrol. Earth Syst. Sci.

112

108

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Abstract. In eastern East Africa (the southern Ethiopia, eastern Kenya and southern Somalia region), poor boreal spring (long wet season) rains in 1999, 2000, 2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity and high levels of malnutrition. Predicting rainfall deficits in this region on seasonal and decadal time frames can help decision makers implement disaster risk reduction measures while guiding climate-smart adaptation and agricultural development. Building on recent research that links more frequent East African droughts to a stronger Walker circulation, resulting from warming in the Indo-Pacific warm pool and an increased east-to-west sea surface temperature (SST) gradient in the western Pacific, we show that the two dominant modes of East African boreal spring rainfall variability are tied to SST fluctuations in the western central Pacific and central Indian Ocean, respectively. Variations in these two rainfall modes can thus be predicted using two SST indicesthe western Pacific gradient (WPG) and central Indian Ocean index (CIO), with our statistical forecasts exhibiting reasonable cross-validated skill (r cv ≈ 0.6). In contrast, the current generation of coupled forecast models show no skill during the long rains. Our SST indices also appear to capture most of the major recent drought events such as 2000, 2009 and 2011. Predictions based on these simple indices can be used to support regional forecasting efforts and land surface data assimilations to help inform early warning and guide climate outlooks.

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Section: Using January Wpg and Cio Indices To Predict March-may Chirpsupporting

confidence: 86%

Section: Correlations With January Sstmentioning

confidence: 99%

Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

Funk

Hoell

Shukla

et al. 2014

Hydrol. Earth Syst. Sci.

112

108

View full text Add to dashboard Cite

show abstract

“…The results for the long rains are not considered here because teleconnections to the large scale were shown to be very weak, in agreement with the conclusions of other studies of this season (e.g. Camberlin and Okoola, 2003;Hastenrath et al, 2011;Mutai and Ward, 2000).…”

Section: Teleconnections To the Large Scale: Sea-surface Temperaturessupporting

confidence: 84%

“…For the short rains of East Africa, it is the low-level zonal wind over the central equatorial Indian Ocean (Hastenrath et al, 2011), but the link to 200 mb winds in the same region is also exceedingly strong (Nicholson, forthcoming). Figure 7b shows running 20-year correlations between Sahel rainfall in JAS and 200 mb zonal winds near the core of the Tropical Easterly Jet over West Africa.…”

Section: Teleconnections To the Large Scale: Atmospheric Factorsmentioning

confidence: 99%

Spatial teleconnections in African rainfall: A comparison of 19th and 20th century patterns

Nicholson

2014

The Holocene

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Rainfall variability over the African continent is remarkably coherent on large-spatial scales. The continent as a whole has exhibited marked fluctuations of rainfall on scales from decades to millennia. A newly created data set commencing in 1800 allows for the comparison between patterns of spatial variability that prevailed on modern and historical time scales. The results show the dominance of a small number of spatial patterns, one of the most common being anomalies of the same sign over most of the continent. Two other common modes are an opposition between equatorial and subtropical regions and an east-west opposition in the equatorial region and in southern Africa. In these modes, the shift between negative and positive poles can be abrupt. These patterns remain stable over time. That is, the spatial patterns and their degree of dominance are markedly similar during the 19th and 20th centuries. On the other hand, the large-scale factors governing the rainfall regime appear to vary on decadal time scales. This is particularly true for ENSO and the Indian Ocean Zonal Mode. The associations with large-scale wind regimes are more stable over time. The robustness of these patterns and varying importance of large-scale factors have implications for the interpretation of paleoclimate data.

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“…1). The UBN basin has one rainy season (May to September) during which more than 80 % of the rainfall occurs, while along the eastern coast of Africa and depending on the location from the Equator, the seasonal cycle of rainfall can have two rainy seasons (Black et al, 2003;Hastenrath et al, 2011). This pattern in the seasonal cycle of rainfall is related to the migration of the intertropical convergence zone (ITCZ) across the Equator.…”

Section: Introductionmentioning

confidence: 99%

Explaining and forecasting interannual variability in the flow of the Nile River

Siam

Eltahir

2015

Hydrol. Earth Syst. Sci.

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Abstract. This study analyzes extensive data sets collected during the twentieth century and defines four modes of natural variability in the flow of the Nile River, identifying a new significant potential for improving predictability of floods and droughts. Previous studies have identified a significant teleconnection between the Nile flow and the eastern Pacific Ocean. El Niño-Southern Oscillation (ENSO) explains about 25 % of the interannual variability in the Nile flow. Here, this study identifies a region in the southern Indian Ocean, with a similarly strong teleconnection to the Nile flow. Sea surface temperature (SST) in the region (50-80 • E and 25-35 • S) explains 28 % of the interannual variability in the flow of the Nile River and, when combined with the ENSO index, the explained variability of the flow of the Nile River increases to 44 %. In addition, during those years with anomalous SST conditions in both oceans, this study estimates that indices of the SSTs in the Pacific and Indian oceans can collectively explain up to 84 % of the interannual variability in the flow of the Nile. Building on these findings, this study uses the classical Bayesian theorem to develop a new hybrid forecasting algorithm that predicts the Nile flow based on global model predictions of indices of the SST in the eastern Pacific and southern Indian oceans.

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Circulation Mechanisms of Kenya Rainfall Anomalies

Cited by 87 publications

References 43 publications

Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

Spatial teleconnections in African rainfall: A comparison of 19th and 20th century patterns

Explaining and forecasting interannual variability in the flow of the Nile River

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