Rainfall Mechanisms for the Dominant Rainfall Mode over Zimbabwe Relative to ENSO and/or IODZM

Abstract

Zimbabwe's homogeneous precipitation regions are investigated by means of principal component analysis (PCA) with regard to the underlying processes related to ENSO and/or Indian Ocean Dipole zonal mode (IODZM). Station standardized precipitation index rather than direct rainfall values represent the data matrix used in the PCA. The results indicate that the country's rainfall is highly homogeneous and is dominantly described by the first principal mode (PC1). This leading PC can be used to represent the major rainfall patterns affecting the country, both spatially and temporarily. The current practice of subdividing the country into the two seasonal rainfall forecast zones becomes irrelevant. Partial correlation analysis shows that PC1 is linked more to the IODZM than to the traditional ENSO which predominantly demonstrates insignificant association with PC1. The pure IODZM composite is linked to the most intense rainfall suppression mechanisms, while the pure El Niño composite is linked to rainfall enhancing mechanisms.

Multi‐century tree‐ring precipitation record reveals increasing frequency of extreme dry events in the upper Blue Nile River catchment

“…Our results are in line with other studies that found ENSO-and IOD-rainfall-teleconnections across eastern and southern Africa (Li et al, 2013;Manatsa & Mukwada, 2012;Saji et al, 1999;Tierney, Smerdon, Anchukaitis, & Seager, 2010). Hence, examining the influence of ENSO and IOD on eastern Africa rainfall is vital to improve the understanding of mechanisms affecting regional weather conditions and the extent of global weather teleconnections.…”

“…All Pearson correlations were performed using SPSS (IBM, SPSS statistics, 20.0.0). To account for such lag-effects, we characterized the El Niño/positive-IOD and the La Niña/negative-IOD based on 6-month (June-November) averages of SSTs and IOD events (Manatsa & Mukwada, 2012;Trenberth, 1997). Furthermore, ENSO/IOD events generally have an immediate impact on rainfall extremes, but these impacts may not instantaneously be reflected by responses of biological systems (e.g., tree growth), because tree growth varies not only with the amount of rain falling in a given season but may also be affected by growing conditions during previous seasons (Maza-Villalobos, (14) reported that identified rainfall extremes from the reconstructed rainfall just after ENSO/IOD years as the effects of previous year ENSO/IOD events on the UBNRC rainfall patterns.…”

“…The reconstructed regional rainfall showed highfrequency (interannual) and low-frequency (decadal and multidecadal) F I G U R E 9 (a) UBNRC wet-season total rainfall. Variability at 2.2 to 3.8 years may be linked to the remote influence of Pacific El Niño events and to the partially coupled Indian Ocean Dipole (Behera, Luo, & Masson, 2005;Manatsa & Mukwada, 2012) or to SST variations in the tropical Atlantic (Camberlin et al, 2001;Nicholson, 2000;Schreck & Semazzi, 2004). 1940-1941 152 1965-1966 À211 1943 112 1971-1974 À243 1948 97 1978 À46 1950 90 1979 À88 1954-1956 255 1981 weather extremes has serious implications in the global and regional ecosystem functions (IPCC, 2007;Jentsch, Kreyling, & Beierkuhnlein, 2007;Jentsch, Kreyling, Boettcher-Treschkow, & Beierkuhnlein, 2009;Smith, 2011).…”

“…Therefore, it is imperative to understand climate history, trends, and frequencies of drought events and its climate forcing mechanisms to develop relevant climate resilient adaptation and mitigation plans (Gebrekirstos, Br€ auning, Van, & Mitl€ ohner, 2011;Masson-Delmotte et al, 2013). Among other global climate forcing mechanisms, El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) variations play a significant role in modulating climatic and weather conditions in the tropics, with high temporal and spatial variability (Manatsa & Mukwada, 2012;Saji, Goswami, Vinayachandran, & Yamagata, 1999). El-Niño/positive-IOD and La-Niña/negative-IOD are the warm and cold phases of the ENSO/IOD cycle, impacting weather and climate patterns worldwide through largescale atmospheric teleconnections (Li et al, 2013).…”

ENSO–Kalahari Desert linkages on southern Africa summer surface air temperature variability

“…Overhanging this region is an intense high pressure anomaly, having the greatest positive anomalies located between 300 and 600 mb (Figure (g)). This strong high pressure anomaly reflects a strengthening of the Botswana Upper High (Manatsa and Mukwada, ) leading to reduced values of relative humidity and wind speed, limited cloud cover, lower PET, curtailed atmospheric water vapour absorption of incoming radiation and suppressed mechanical mixing. Directly below the high pressure anomaly is a region of weak low pressure anomaly located between 21° and 27°S coinciding with the warmest temperature anomalies (Figure (h)) where near surface thermal forcing is largest and associated with strong heating of the land‐surface.…”