Understanding Recent Eastern Horn of Africa Rainfall Variability and Change

Liebmann, Brant; Hoerling, Martin P.; Funk, Chris; Bladé, Ileana; Dole, Randall M.; Allured, Dave; Quan, Xiao-Wei; Pegion, Philip; Eischeid, Jon

doi:10.1175/jcli-d-13-00714.1

Cited by 211 publications

(261 citation statements)

References 59 publications

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“…16.2a). The well-documented post-1999 decline (Funk and Hoell 2015;Funk et al 2014;Hoell and Funk 2013b;Liebmann et al 2014;Lyon and DeWitt 2012;Yang et al 2014) has resulted in a 10 to 30% reduction in rainfall. Dry and warm conditions have been much more likely since 1998 across the eastern portions of EA.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…Additionally, in model experiments, the increased GHG forcing during the last 34 years dries the region. Liebmann et al 2014;Lyon and DeWitt 2012;Shukla et al 2014a). Interagency research by Famine Early Warning Systems Networks (FEWS NET) scientists has been focusing on using SST gradients to predict some of these spring droughts Shukla et al 2014a;Shukla et al 2014b).…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Diagnostic analyses of simulations from the ECHAM5 ) and GFSv2 (Hoell and Funk 2013b) models help confirm these relationships, and WP SST gradients were used to predict the 2014 boreal spring drought . Extreme WPI conditions represent a combination of warming west Pacific SST (Funk and Hoell 2015) and La Niña-like cool central Pacific SST, which enhances rainfall and moisture convergence over the Indo-Pacific warm pool while decreasing it over eastern East Africa (Hoell and Funk 2013b;Hoell et al 2014;Liebmann et al 2014;Williams and Funk 2011).…”

Section: Acknowledgmentsmentioning

confidence: 99%

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Explaining Extreme Events of 2014 from a Climate Perspective

Herring¹,

Hoerling²,

Kossin³

et al. 2015

Bulletin of the American Meteorological Society

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117

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Section: Methods and Resultsmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

See 1 more Smart Citation

Explaining Extreme Events of 2014 from a Climate Perspective

Herring¹,

Hoerling²,

Kossin³

et al. 2015

Bulletin of the American Meteorological Society

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117

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“…Rainfall variability over southern and eastern Africa show strong correlation to ENSO (Goddard et al, 2001;Black et al, 2003;Liebmann et al, 2014;Smith and Semazzi, 2014). However, the Indian Ocean Dipole (IOD) is also known to drive climate variability over East Africa, especially during the 'short rains' (OND) (Goddard et al, 2735(Goddard et al, 2001Saji and Yamagata, 2003;Black, 2005;Owiti and Ogallo, 2007;Owiti et al, 2008) either acting alone or together with ENSO variability.…”

Section: Introductionmentioning

confidence: 99%

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Ogutu

Franssen

Supit

et al. 2016

Intl Journal of Climatology

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This study evaluates the potential use of the ECMWF System-4 seasonal forecasts (S4) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15-member ensemble of 7-month forecasts initiated every month, and tested forecast skill of precipitation (tp), near-surface air temperature (tas) and surface downwelling shortwave radiation (rsds). We validated the 30-year (1981-2010) hindcast version of S4 against the WFDEI reanalysis (WATCH Forcing Data ERA-Interim) and to independent relevant observational data sets. Probabilistic skill is assessed using anomaly correlation, ranked probability skill score (RPSS) and the relative operating curve skill score (ROCSS) at both grid cell and over six distinct homogeneous rainfall regions for the three growing seasons of East Africa (i.e. MAM, JJA and OND).S4 exhibits a wet bias in OND, a dry bias in MAM and a mix of both in JJA. Temperature biases are similar in all seasons, constant with lead-time and correlate with elevation. Biases in rsds correlate with cloud/rain patterns. Bias correction clears biases but does not affect probabilistic skills.Predictability of the three variables varies with season, location and lead-time. The choice of validating dataset plays little role in the regional patterns and magnitudes of probabilistic skill scores. The OND tp forecasts show skill over a larger area up to 3 months lead-time compared to MAM and JJA. Upper-and lower-tercile tp forecasts are 20-80% better than climatology. Temperature forecasts are skillful for at least 3 months lead-time and they are 40-100% better than climatology. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher in all lead months against the alternative datasets. The forecast system captures El-Niño Southern Oscillation (ENSO)-related anomalous years with region-dependent skill.

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“…For example, various studies detected a robust statistical relationship between Australian monsoonal precipitation and the ENSO state during previous months (Cai et al, 2011;Ummenhofer et al, 2009). A significant influence of El Niño events was also found for monsoonal precipitation amounts in eastern and southern Africa (Liebmann et al, 2014;Ratnam et al, 2014) and the Sahel region (Parhi et al, 2015). For the south Asian monsoon a negative response to El Niño events has been frequently perceived (Krishnaswamy et al, 2014;Lau and Wu, 2001;Surendran et al, 2015).…”

Section: Introductionmentioning

confidence: 98%

A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia

Gerlitz

Vorogushyn

Apel

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. The study presents a statistically based seasonal precipitation forecast model, which automatically identifies suitable predictors from globally gridded sea surface temperature (SST) and climate variables by means of an extensive data-mining procedure and explicitly avoids the utilization of typical large-scale climate indices. This leads to an enhanced flexibility of the model and enables its automatic calibration for any target area without any prior assumption concerning adequate predictor variables. Potential predictor variables are derived by means of a cell-wise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability-based cluster analysis. Finally, for every month and lead time, an individual randomforest-based forecast model is constructed, by means of the preliminary generated predictor variables. Monthly predictions are aggregated to running 3-month periods in order to generate a seasonal precipitation forecast.The model is applied and evaluated for selected target regions in central and south Asia. Particularly for winter and spring in westerly-dominated central Asia, correlation coefficients between forecasted and observed precipitation reach values up to 0.48, although the variability of precipitation rates is strongly underestimated. Likewise, for the monsoonal precipitation amounts in the south Asian target area, correlations of up to 0.5 were detected. The skill of the model for the dry winter season over south Asia is found to be low.A sensitivity analysis with well-known climate indices, such as the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the East Atlantic (EA) pattern, reveals the major large-scale controlling mechanisms of the seasonal precipitation climate for each target area. For the central Asian target areas, both ENSO and NAO are identified as important controlling factors for precipitation totals during moist winter and spring seasons. Drought conditions are found to be triggered by a cold ENSO phase in combination with a positive state of NAO in northern central Asia, and by cold ENSO conditions in combination with a negative NAO phase in southern central Asia. For the monsoonal summer precipitation amounts over southern Asia, the model suggests a distinct negative response to El Niño events.

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Understanding Recent Eastern Horn of Africa Rainfall Variability and Change

Cited by 211 publications

References 59 publications

Explaining Extreme Events of 2014 from a Climate Perspective

Explaining Extreme Events of 2014 from a Climate Perspective

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia

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