scholarly journals An observational study of the variability of East African rainfall with respect to sea surface temperature and soil moisture

2018 ◽  
Vol 144 (S1) ◽  
pp. 384-404 ◽  
Author(s):  
C. Wenhaji Ndomeni ◽  
E. Cattani ◽  
A. Merino ◽  
V. Levizzani
Keyword(s):  
Soil Moisture ◽  
Observational Study ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
East African ◽  
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 Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

2014 ◽  
Vol 11 (3) ◽  
pp. 3111-3136 ◽  
Author(s):  
C. Funk ◽  
A. Hoell ◽  
S. Shukla ◽  
I. Bladé ◽  
B. Liebmann ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Rainfall Variability ◽  
Sea Surface ◽  
Southern Ethiopia ◽  
East African ◽  
Central Pacific ◽  
Boreal Spring ◽  
Central Indian Ocean

Abstract. In southern Ethiopia, Eastern Kenya, and southern Somalia, poor boreal spring 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 droughts in that region to a stronger Walker Circulation, warming in the Indo-Pacific warm pool, and an increased western Pacific sea surface temperature (SST) gradient, we show that the two dominant modes of East African boreal spring rainfall variability are tied, respectively, to western-central Pacific and central Indian Ocean SST. Variations in these rainfall modes can be predicted using two previously defined SST indices – the West Pacific Gradient (WPG) and Central Indian Ocean index (CIO), with the WPG and CIO being used, respectively, to predict the first and second rainfall modes. These simple indices can be used in concert with more sophisticated coupled modeling systems and land surface data assimilations to help inform early warning and guide climate outlooks.

scholarly journals Analyzing the Effects of Sea Surface Temperature (SST) on Soil Moisture (SM) in Coastal Areas of Eastern China

2020 ◽  
Vol 12 (14) ◽  
pp. 2216
Author(s):  
Yingying Liu ◽  
Yuanzhi Zhang ◽  
Jingze Cai ◽  
Jin Yeu Tsou
Keyword(s):  
Soil Moisture ◽  
Correlation Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Eastern China ◽  
Sea Surface ◽  
Causality Analysis ◽  
China Sea ◽  
Regional Correlation ◽  
Four Seasons

In this paper, we applied the re-analysis data cobe-SST (cobe-sea surface temperature) and Global Land Data Assimilation System (GLDAS) surface soil moisture (SM) data from 1961 to 2011 by using regional correlation analysis and time series causality analysis to trace annual variations in and identify the abnormal relationship of sea surface temperature (SST) in the eastern China Sea and SM in eastern China (EC). We also used satellite Moderate Resolution Imaging Spectroradiometer (MODIS) SST and AMSR-E SM data to examine the correlation of SST and SM in EC from 2004–2009. The results show that the SST in the eastern China Sea has experienced a warming trend since 1987, whereas the SM in EC has shown a drying trend since 1978. Before 1967 and after 1997, SST and SM changed during opposite phases, whereas from 1967 to 1997 they changed during the same phase. The differences between them may result from the abnormal summer precipitation causing abnormal SM. According to the regional correlation analysis, SST of the East China Sea is significantly related to SM in the southeast coastal area, and temporal sequence causality analysis shows that SST is correlated with and has higher influence on SM than vice versa. SM during spring and autumn shows a similar correlation with SST during the four seasons, so that SM in spring and autumn is positively correlated with SST in autumn and negatively correlated with SST in other seasons. SM in summer and winter correlated with SST in the four seasons, contradicting the foregoing conclusions. All these findings indicate that the thermodynamic state of the eastern China Sea has affected SM in EC.

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