1APRIL 2014 P H I L I P P O N E T A L . 2509 Timing and Patterns of the ENSO Signal in Africa over the Last 30 Years: Insights from Normalized Difference Vegetation Index Data N. PHILIPPON,N.MARTINY, AND P. CAMBERLIN Centre de Recherches de Climatologie, Biogeosciences UMR6282 CNRS, Universite de Bourgogne, Dijon, France M. T. HOFFMAN Plant Conservation Unit, Department of Biological Sciences, University of Cape Town, Rondebosch, South Africa V. GOND CIRAD, ES, UPR 105 Biens et Services des Ecosyst emes Forestiers Tropicaux, Campus de Baillarguet, Montpellier, France (Manuscript received 16 May 2013, in final form 3 December 2013) ABSTRACT A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature. The month-by-month NDVI–Nino-3.4~ correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic Republic of Congo to Ethiopia. However, they are not uniform in space and are moderate (;0.3). Conversely, positive correlations are recorded over the winter rainfall region of South Africa. In October– November, negative correlations over Ethiopia, Sudan, and Uganda strengthen while positive correlations emerge in the Horn of Africa and in the southeast coast of South Africa. By December with the settlement of the ITCZ south of the equator, positive correlations over the Horn of Africa spread southward and westward while negative correlations appear over Mozambique, Zimbabwe, and South Africa. This pattern strengthens and a dipole at 188S is well established in February–March with reduced (enhanced) greenness during ENSO years south (north) of 188S. At the same time, at ;28N negative correlations spread northward. Last, from April to June negative correlations south of 188S spread to the north (to 108S) and to the east (to the south of Tanzania). 1. Introduction (Latif et al. 1998; Chen et al. 2004) and forecasts, based either on numerical and statistical models or a combi- El Nino–Southern~ Oscillation (ENSO) is one of the nation of the two, are now routinely performed. They main modes of natural climate variability at the global are used in early warning systems in different parts of and interannual (2–7 yr) scale (de Viron et al. 2013). During the world and help in mitigating ENSO impacts. Be- ENSO events, the atmospheric circulation and precipi- cause of Africa’s strong reliance on primary production tation patterns are strongly disturbed for several months for its agricultural needs and its many socioeconomic worldwide and more particularly in the tropics (e.g., Dai problems such as endemic poverty, low human devel- and Wigley 2000). For that reason, the predictability and opment index, poor governance, and armed conflicts, it prediction of ENSO events have long been considered is highly vulnerable to climate variability and change (Thornton et al. 2006). The ENSO impacts on the African climate and envi- Corresponding author address: N. Philippon, Centre de Recherches de Climatologie, Biogeosciences UMR6282 CNRS/Universitede ronment have been the subject of numerous studies. Bourgogne, 6 Blvd. Gabriel, 21000 Dijon, France. Impacts on climate have been assessed primarily by an E-mail: [email protected] analysis of rainfall and temperature data while impacts DOI: 10.1175/JCLI-D-13-00365.1 Ó 2014 American Meteorological Society Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 2510 JOURNAL OF CLIMATE VOLUME 27 on the environment have been assessed primarily by an patterns of ENSO signal in the whole of Africa over the analysis of remotely sensed data of vegetation photo- three last decades. With this aim in mind we analyze the synthetic activity. However, most of these analyses have normalized difference vegetation index (NDVI), which been performed at seasonal to annual time scales (Williams provides an indirect estimate of the vegetation photo- and Hanan 2011; Kogan 2000) or have focused on the synthetic activity (Tucker 1979), rather than rainfall and impact of particular ENSO events [e.g., the 1997/98 temperature to circumvent the issue of in situ data event in Anyamba et al. (2001, 2002); Verdin et al. 1999] scarcity and reliability over the last decades, and to or on specific regions within Africa (e.g., Martiny et al. document the ENSO signal at fine space scales. Indeed, 2009; Brown et al. 2010) and rarely on the continent as the frequent and regular update of the NDVI data, their a whole (e.g., Nicholson and Kim 1997). Therefore, high spatial and temporal resolutions, and their cover- neither the precise timing of the signal of ENSO nor the age of the whole of Africa are among the advantages of patterns (e.g., propagations, dipoles) have been fully using NDVI over rainfall. Moreover, NDVI is a good documented. In addition, while the signal of ENSO in indicator of the climatic conditions. NDVI variability in the summer rainfall areas and semiarid tropics (Sahel, the African summer rainfall semiarid environments is southern Africa, Horn of Africa) is regularly inves- closely related to rainfall and soil moisture availability tigated (e.g., Segele et al. 2008; Fauchereau et al. 2009; (Camberlin et al. 2007; Martiny et al. 2006; Nicholson Martiny et al. 2009; Brown et al. 2010; Cretat et al. 2012; et al. 1990, among others). In both the African winter Fontaine et al. 2011; Mohino et al. 2011, among many rainfall semiarid environments and the subhumid to others), the humid to subhumid regions and those ex- humid environments, NDVI variability and its climatic periencing winter rainfall at the northwest and south- controls have been far less studied as compared to the west tips of the continent (the Mediterranean fringes of summer rainfall semiarid environments or the Amazo- Morocco and Algeria and the Atlantic fringe of South nian humid environments. It has been shown that the Africa, respectively) have drawn less attention (see variability in photosynthetic activity in the primary Knippertz et al. 2003; Malhi and Wright 2004; Balas forests of Amazonia is primarily controlled by light et al. 2007; Philippon et al. 2011). However, several of variability (Nemani et al. 2003) especially during the dry these regions are densely populated and are net ex- season (Huete et al. 2006; Xiao et al. 2006), and that the porters of high-quality agricultural products. This calls ENSO impact on these type of forests is through the for a better knowledge of their climate variability, par- modulation of cloudiness and illumination (Graham ticularly as it relates to ENSO. Furthermore, most of the et al. 2003; Pau et al. 2010). While over equatorial re- studies performed on the African climate and ENSO gions NDVI data are constrained by saturation issues teleconnections consider long time periods (usually (i.e., above 0.7 NDVI is no longer an indicator of growth 50 years from the 1950s). However, these teleconnections during the wettest months) and biases related to water are subject to a strong decadal variability, and for many vapor content and cloudiness, these aspects, especially regions in Africa, they have become much more intense a possible ENSO signal, have not yet been explored for since the end of the 1970s (e.g., Janicot et al. 1996; Africa. Richard et al. 2000; Knippertz et al. 2003; Philippon et al. We also consider NDVI at the monthly time step in 2011). In parallel, since the end of the 1970s, in situ cli- order to follow the spatial patterns and evolution during mate data in Africa have become more and more scarce the seasonal cycle of NDVI anomalies associated with and less reliable [see Malhi and Wright (2004) for a dis- ENSO. This time step is of greater interest than the cussion on the decline in the number of temperature and seasonal time step for the scientific community working precipitation stations in humid Africa and its conse- on vegetation phenology because it enables one to point quence on gridded products]. Unfortunately, rainfall es- out the ENSO signal at different vegetation phenologi- timates from satellites still do not adequately capture the cal stages. A secondary underlying objective is also to climatology and variability in rainfall over the continent reconsider the climatic variables through which ENSO and its subregions, and they feature important biases in impacts NDVI. To that end, the NDVI relationships rainfall patterns and quantities (Nicholson et al. 2003; Ali with rainfall are reassessed. In particular, comparing the et al. 2005; Dinku et al. 2007; McCollum et al. 2000). rainfall–NDVI relationship intensity to the ENSO– High-resolution products are also not available for a suf- NDVI relationship is informative. For the humid envi- ficiently long period of time to enable a consistent and ronments, few analyses are performed considering the precise mapping of the ENSO signal on local climate links between NDVI, rainfall, and cloud cover. conditions and environment. The paper is organized into five sections. Section 2 Thus, the primary objective of this study is to provide presents the different databases used, namely Global a more complete and updated picture of the timing and Inventory Modeling and Mapping Studies (GIMMS) Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 1APRIL 2014 P H I L I P P O N E T A L . 2511 NDVI, Global Precipitation Climatology Centre NOAA-17). No correction has been applied for atmo- (GPCC) rainfall, International Satellite Cloud Clima- spheric effects due to tropospheric aerosols, water va- tology Project (ISCCP) total cloud cover, and the Nino-3.4~ por, Rayleigh scattering, or stratospheric ozone.