Diagnostics and predictability of east African rainfall with tropospheric circulation parameters
Abstract
Rainfall is one of the weather and climate parameters that is highly variable in time
and space with extremes sometimes manifested as excessive rainfall or floods and severe
rainfall deficits or droughts. The national economies of East Africa are heavily dependent on
rainfall which mainly occurs during the two major rainfall seasons, namely March-May (long
rains) and October-December (short rains) making the predictability of seasonal rainfall very
crucial. Any major change in the onset, duration and intensity in seasonal rainfall has severe
adverse impacts on several socio-economic activities in the region. There is to some extent
some prediction potential in the predictability of seasonal rainfall in the region. However,
most of the current prediction tools are based on distant global and regional climate systems
associated with rainfall anomalies in the region. The main goal of the present study is to
develop new reliable local predictors to improve the skill in the prediction of seasonal rainfall
and enhance provision of timely climate early warning products and services for application
in the planning and management of various climate sensitive socio-economic activities in the
East African region. The specific objectives of the study is to determine the best set of upper
tropospheric variables that could provide new predictors of the two major rainfall seasons
and improve the seasonal rainfall predictability in the region. The upper tropospheric
variables investigated included 300 hPa temperature anomaly (thermal index) in relation to
rainfall and linkages with strong Indian Ocean Dipole (I0D) and EI Nino/Southern
Oscillation (ENSO) climate systems; 300 hPa. geopotential anomaly (geopotential Index)
and the geopotential derived Energy Index expressed as the variation of geopotential
anomalies at 500 hPa and 300 hPa surfaces.
The data sets used in the study included monthly and seasonal rainfall; upper-air
monthly temperature and geopotential heights; sea surface temperatures (SSTs) and Southern
Oscillation Index (SOl) covering the period 1956-2000. The rainfall stations used in the
study were selected from homogenous rainfall zones derived from Principal Component
Analysis (PCA) by IGAD Climate Prediction and Applications Centre (lCPAC) for the two
major rainfall seasons in the East African region. The suitability of the estimated missing
rainfall data were however examined first by fitting straight lines on rainfall mass curves to
ensure quality and homogeneity of the data before inclusion in the analyses.
The methods adopted included diagnostic analysis to determine the best set of upper
tropospheric variables that could provide new and reliable local predictors of seasonal
rainfall. The diagnostic tools used included composite and correlation analyses. Existence of
any unique linkages between upper tropospheric thermal anomalies during the years of strong
IOD and ENSO associated with some regional seasonal rainfall anomalies in the region were
also investigated. The prediction potentials of the upper-tropospheric predictors in the timely
seasonal rainfall outlooks were also examined using both correlation and regression analyses.
The regression analysis was used to provide the best empirical equation that can describe the
relationship between the predict and and the new predictors.
The analysis results of monthly rainfall from homogeneous rainfall zones displayed
three types of rainfall patterns namely unimodal, bimodal and trimodal where the majority of
the stations in the region were dominated by the bimodal rainfall pattern. The analysis of the
fitted straight lines to cumulative rainfall curves were indicative of good quality and
homogeneity of the rainfall data that were used in the study. Results from normalised rainfall
indices indicated common existence of extreme droughts and floods in the rainfall seasons of
March-May and October-December. It was discernible from the results that it is not common
for both the March-May and October-December rainfall seasons to have similar rainfall
anomaly signals during the same year. It was also noted that many severe floods/droughts
observed in a given season were often followed by droughts/floods respectively in the
following season/year. This may be due to strong linkages between regional rainfall and
ENSO events. However, some of the extreme rainfall events also occurred during the strong
lOD years. Results from the study indicated evidence of significant relationships between
seasonal rainfall anomalies and 300 hPa temperature anomaly (thermal index) also linked to
strong lOD and ENSO events; 300 hPa. geopotential anomaly (geopotential index) and the
Energy Index. The spatial correlation patterns indicated that there were some reasonable time
lags between seasonal rainfall anomalies and the various upper-troposphere indices with
significant predictive values. For example onset of warm/cold upper-troposphere thermal
index regime starts appearing in January that could be useful for the prediction of the March-
May seasonal rains while the July and August values could also be used as predictors of the
October-December seasonal rains. The thermal index displayed the strongest signal in the
prediction of March-May rainfall season. The regression analysis results indicated that
prediction of seasonal rainfall using upper tropospheric temperature and geopotential indices
had higher skill during the October-December season compared to the March-May season.
The skill of the seasonal forecast however varied significantly from one location to another
with percentages variance ranging from 30 to 70%.
The study has for the first time found new local and reliable prediction tools to
provide improved seasonal rainfall predictions and enhance the early warning and disaster
preparedness systems for timely planning, management and mitigation from adverse impacts
of the weather and climate related catastrophes on the socio-economic activities in the East African region.
Citation
Doctor of Philosophy in MeteorologySponsorhip
University of NairobiPublisher
Institute for meteorological training and research Kenya meteorological department