scholarly journals Sea-surface temperature — South African rainfall associations, 1910–1989

1995 ◽  
Vol 15 (2) ◽  
pp. 119-135 ◽  
Author(s):  
S. J. Mason
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
South African ◽  
Sea Surface ◽  
African Rainfall

The relationship between Indian Ocean sea–surface temperature and East African rainfall

2005 ◽  
Vol 363 (1826) ◽  
pp. 43-47 ◽  
Author(s):  
Emily Black
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Sea Surface ◽  
East African ◽  
African Rainfall ◽  
The Pacific ◽  
The Indian Ocean ◽  
The Relationship

Knowledge of the processes that control East African rainfall is essential for the development of seasonal forecasting systems, which may mitigate the effects of flood and drought. This study uses observational data to unravel the relationship between the Indian Ocean Dipole (IOD), the El Niño Southern Oscillation (ENSO) and rainy autumns in East Africa. Analysis of sea–surface temperature data shows that strong East African rainfall is associated with warming in the Pacific and Western Indian Oceans and cooling in the Eastern Indian Ocean. The resemblance of this pattern to that which develops during IOD events implies a link between the IOD and strong East African rainfall. Further investigation suggests that the observed teleconnection between East African rainfall and ENSO is a manifestation of a link between ENSO and the IOD.

scholarly journals Biases in CMIP5 Sea Surface Temperature and the Annual Cycle of East African Rainfall

2020 ◽  
Vol 33 (19) ◽  
pp. 8209-8223
Author(s):  
Bradfield Lyon
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Annual Cycle ◽  
General Circulation ◽  
Regional Scale ◽  
Sea Surface ◽  
East African ◽  
Coupled Models ◽  
African Rainfall ◽  
Short Rains

AbstractIn much of East Africa, climatological rainfall follows a bimodal distribution characterized by the long rains (March–May) and short rains (October–December). Most CMIP5 coupled models fail to properly simulate this annual cycle, typically reversing the amplitudes of the short and long rains relative to observations. This study investigates how CMIP5 climatological sea surface temperature (SST) biases contribute to simulation errors in the annual cycle of East African rainfall. Monthly biases in CMIP5 climatological SSTs (50°S–50°N) are first identified in historical runs (1979–2005) from 31 models and examined for consistency. An atmospheric general circulation model (AGCM) is then forced with observed SSTs (1979–2005) generating a set of control runs and observed SSTs plus the monthly, multimodel mean SST biases generating a set of “bias” runs for the same period. The control runs generally capture the observed annual cycle of East African rainfall while the bias runs capture prominent CMIP5 annual cycle biases, including too little (much) precipitation during the long rains (short rains) and a 1-month lag in the peak of the long rains relative to observations. Diagnostics reveal the annual cycle biases are associated with seasonally varying north–south- and east–west-oriented SST bias patterns in Indian Ocean and regional-scale atmospheric circulation and stability changes, the latter primarily associated with changes in low-level moist static energy. Overall, the results indicate that CMIP5 climatological SST biases are the primary driver of the improper simulation of the annual cycle of East African rainfall. Some implications for climate change projections are discussed.

scholarly journals S<sup>4</sup>CAST v2.0: sea surface temperature based statistical seasonal forecast model

2015 ◽  
Vol 8 (5) ◽  
pp. 3971-4018 ◽  
Author(s):  
R. Suárez-Moreno ◽  
B. Rodríguez-Fonseca
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
West African ◽  
Sea Surface ◽  
Tropical Oceans ◽  
African Rainfall ◽  
Decadal Climate ◽  
Stationary Behavior

Abstract. Sea Surface Temperature is the key variable when tackling seasonal to decadal climate forecast. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. Recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability, the Sea Surface Temperature based Statistical Seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of S4CAST model whose operation is focused on the study of the predictability of any variable related to sea surface temperature. An application focused on West African rainfall predictability has been implemented as a benchmark example.

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