GCM simulations of the Indian Ocean dipole influence on East African rainfall: Present and future

Abstract

[1] Six coupled GCMs are assessed in terms of their ability to simulate observed characteristics of East African rainfall, the Indian Ocean dipole and their temporal correlation. Model results are then used to analyze the future behaviour of rainfall and the DMI. All models simulate reasonably well the spatial distribution and variability of annual and seasonal rainfall over the 1961 -1990 period. Model simulation of observed DMI characteristics is less consistent with observations, however, five models reproduce similar correlations to those observed between the DMI and East African short rains (SON). In the future, there are no clear inter-model patterns of rainfall or DMI behaviour. In this sample of models four (two) out of six simulate modest increases (decreases) in annual rainfall by the 2080s. For SON, three of the six models indicate a trend towards increasingly positive phase of the DMI, two indicate a decrease and one shows no substantial change. Citation: Conway, D., C. E.Hanson, R. Doherty, and A. Persechino (2007), GCM simulations of the Indian Ocean dipole influence on East African rainfall: Present and future, Geophys. Res. Lett., 34, L03705,

Adaptation to climate change in international river basins in Africa: a review / Adaptation au changement climatique dans les bassins fluviaux internationaux en Afrique: une revue

“…The main and most understood climate drivers of inter-annual and decadal rainfall variability in Africa are Atlantic (and other) Ocean SST patterns (West Africa and the Sahel), ENSO behaviour (West, southern and East Africa) and Indian Ocean dynamics (East and southern Africa). At present, model simulations of future climate do not show clear tendencies in the future behaviour of these large-scale drivers (Merryfield, 2006;Conway et al, 2007).…”

Differential Heating in the Indian Ocean Differentially Modulates Precipitation in the Ganges and Brahmaputra Basins

“…The well-known IO Dipole Mode Index (DMI saji ) defined by [5] characterizes the pattern of differential heating in the tropical IO and is composed of SST anomalies averaged across specific areas in the west (50 • E-70 • E, 10 • S-10 • N) and the east (90 • E-110 • E, 10 • S-Equator) [5]. It is now well known that the tropical DMI and ENSO are largely independent coupled phenomena [5] that have a strong influence on the precipitation of east Africa [8], southern Africa [9], Indonesia [10], Sri Lanka [11], Australia [12], and India [13,14] with spatial differences [15]. The influence of these climate modes is likely to persist into the future as the DMI is likely to remain active and El Niño events may likely intensify due to the warming climate, according to the Intergovernmental Panel on Climate Change's Fifth Assessment Report (AR5) on the physical basis of climate change [16].…”

Comparison of GCM Precipitation Predictions with Their RMSEs and Pattern Correlation Coefficients

“…Table 2 also shows the NRMSE estimated for each GCM. The NRMSE is a dimensionless measure, calculated by dividing the error of the GCM predictions by the mean of the observed data [39]. The NRMSE counts both the mean difference (or bias) and the variability of the difference between two dataset.…”

“…The pattern correlation coefficient, as well as the NRMSE given in Equation (2), was generally used to compare the major CSEOFs, derived from the GCM predictions, and those from the observed data [11,39,[58][59][60][61]. The pattern correlation coefficient indicated the correlation coefficient of the data, given as forms of a matrix or vector.…”