Long-Lead Forecasts of Seasonal Precipitation in Africa Using CCA

Barnston, Anthony G.; Thiao, Wassila; Kumar, Vadlamani

doi:10.1175/1520-0434(1996)011<0506:llfosp>2.0.co;2

Cited by 64 publications

(32 citation statements)

References 9 publications

(10 reference statements)

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“…Lamb, 1978a,b;Folland et al, 1986Folland et al, , 1991Lamb and Peppler, 1992;Hastenrath et al, 1995;Ward, 1998;Giannini et al, 2003). Furthermore, Barnston et al (1996) demonstrated that the time-space behaviour of the SST field alone influences the seasonal rainfall in certain regions/seasons for Africa, both on inter-annual and inter-decadal time-scales. For Ethiopia, Segele and Lamb (2005) examined the possible associations with ENSO and global SST for the onset, cessation, and growing length of the main rainy season (Kiremt).…”

Section: Variations In Global Sst Associated With June-september Ethimentioning

confidence: 97%

Large‐scale atmospheric circulation and global sea surface temperature associations with Horn of Africa June–September rainfall

Segele

Lamb

Leslie

2008

Intl Journal of Climatology

143

171

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This study uses correlation, regression, and composite analyses for the period 1970-1999 to explore the relationships between the June-September rainfall in the Horn of Africa (especially Ethiopian) and large-scale regional atmospheric circulation patterns across Africa and the Atlantic and Indian Oceans, and global sea surface temperature (SST) anomalies. Abundant rainfall in the Horn of Africa is associated with enhanced westerlies across western and central Africa. These westerlies are produced by a stronger north-east directed mean sea level pressure (MSLP) gradient resulting from MSLP intensification over the Gulf of Guinea and deepening of the monsoon trough across the Arabian Peninsula. This is reflected by a strong correlation (−0.71) between 5-day (pentad) Ethiopian rainfall and the Gulf of Guinea minus the Arabian Peninsula MSLP difference. This correlation decreases to −0.39 when the seasonal cycles are removed from both time series. A wet Horn of Africa monsoon is also associated with deep moist air extending up to mid-troposphere and large water vapour transport convergence across much of Ethiopia, a strong Somali low-level jet, and a strong tropical easterly jet (TEJ). Although there are large changes in TEJ strength, the position of the jet axis shows little variation between wet and dry events. Associated with the TEJ, the strongest upper level divergence occurs at 100 hPa, where the raw/de-seasonalized zonal wind speed correlates negatively (−0.71/−0.23) with the corresponding Ethiopian rainfall at the pentad time-scale. Furthermore, SSTs over the equatorial Pacific, Indian, and southern Atlantic Oceans correlate strongly with contemporary Ethiopian summer rainfall. In general, Ethiopian rainfall is suppressed during El Niño and enhanced during La Niña. This identification and documentation of the regional atmospheric circulation patterns and global SST anomalies directly linked to rainfall variability over Ethiopia/Horn of Africa, are crucial for developing statistical prediction schemes and designing climate model simulations for the region on intra-seasonal to inter-annual time-scales.

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Section: Variations In Global Sst Associated With June-september Ethimentioning

confidence: 97%

Large‐scale atmospheric circulation and global sea surface temperature associations with Horn of Africa June–September rainfall

Segele

Lamb

Leslie

2008

Intl Journal of Climatology

143

171

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“…This knowledge led to the development of objective operational seasonal forecasting systems for South Africa, but only as recently as the 1990s (e.g., Jury 1996;Jury et al 1999;Landman and Mason 1999;Mason 1998). Although the prediction problem over southern Africa was also addressed by modelers from outside the region (e.g., Barnston et al 1996), the South African-based institutions that started with prediction system development include the South African Weather Service (SAWS) and the Universities of the Witwatersrand, Pretoria, Cape Town, and Zululand. Although most of these efforts have initially been largely fragmented, the institutions in South Africa involved with the operational running of global circulation models started in 2003 to display their forecasts on the Global Forecasting Centre for Southern Africa web site (www.gfcsa.net).…”

Section: Introductionmentioning

confidence: 99%

Seasonal Rainfall Prediction Skill over South Africa: One- versus Two-Tiered Forecasting Systems

Landman

DeWitt

Lee

et al. 2012

Weather and Forecasting

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Forecast performance by coupled ocean-atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5-version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML-NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the twotiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable.

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“…The first one has been developed in season forecast [3,4]. So far, the experimental models for season forecast with different methods have been studied and developed [5][6][7][8][9]. Statistical method is a popular tool, divided into 3 main groups [10]: traditional statistical method; Statistical Downscaling (SD); Model Output Statistics (MOS).…”

Section: Introductionmentioning

confidence: 99%

Precipitation Scenarios in Ho Chi Minh City in the Context of Climate Change

Phung¹

2018

JST

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This work aimed at developing precipitation scenarios in Ho Chi Minh City (HCMC) corresponding to scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP8.5. By means of data collecting and processing and SimCLIM software, results showed the average annual precipitation in HCMC would increase over the years and RCP scenarios: from 13.4 % to 24 % in 2100 compared to that in the period of 1986-2005. The dry seasonal precipitation would tend to decrease while the rainy seasonal one would increase. By space, average annual and seasonal precipitations in HCMC decrease from the northwest to the southeast: the highest one are in the north (Cu Chi, Hoc Mon) and the city center while the lowest are in the coastal area (Can Gio). These results are an important basis for assessing impacts and vulnerability due to precipitation variations in particular and climate change in general in HCMC.

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Long-Lead Forecasts of Seasonal Precipitation in Africa Using CCA

Cited by 64 publications

References 9 publications

Large‐scale atmospheric circulation and global sea surface temperature associations with Horn of Africa June–September rainfall

Large‐scale atmospheric circulation and global sea surface temperature associations with Horn of Africa June–September rainfall

Seasonal Rainfall Prediction Skill over South Africa: One- versus Two-Tiered Forecasting Systems

Precipitation Scenarios in Ho Chi Minh City in the Context of Climate Change

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