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Timing and Patterns of the ENSO Signal in 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 , the Gulf of coast, and 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 of South Africa. In October– November, negative correlations over Ethiopia, , and Uganda strengthen while positive correlations emerge in the 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 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 , 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 , 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)

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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. index. Section 3 gives a rapid overview of the methods For our purposes we selected the period July 1981– and approach used. Results are presented in section 4 June 2008 and upscaled the data to 16-km resolution and for the three main regions detected according to the a monthly time step by simple spatial and temporal av- ENSO signal timing: the summer rainfall regions, the erages. Following Martiny et al. (2006), pixels with NDVI winter rainfall regions, and the equinoctial rainfall re- mean values ,0.12, corresponding to bare soils and gions. For each region and at the pixel scale, we assess desert areas, were excluded from the study. These areas 1) the link between the timing of the ENSO and the pat- are the , the Namibian coast, northern Kenya, tern in NDVI response through correlations, 2) asym- northern Somalia, and northeastern Ethiopia, which are metries in the NDVI response between the warm and all sparsely inhabited, hyperarid regions. cold ENSO events, and 3) NDVI sensitivity to rainfall, Figures 1a and 1b present the months of mean green- especially in winter rainfall regions and humid and up and dormancy onsets of vegetation. Following subhumid environments where that sensitivity has been Philippon et al. (2007), the green-up (dormancy) onset less explored as compared to the semiarid environments. is defined as the month when NDVI extends up (down) To complement these results at the pixel scale, we also to the annual mean level. This simple approach pro- present for six regional NDVI indices the rainfall and posed for the Sahelian semiarid environment is none- total cloud cover seasonal cycle anomalies that develop theless adequate for the whole of Africa since even the during the warm and cold ENSO events. Finally in equatorial evergreen vegetation, despite high NDVI section 5, we synthesize and discuss the findings in the values, has a marked phenological rhythm (Gond et al. context of the African continent as a whole. 1997). Moreover, at the monthly time step our method leads to results that are consistent with those obtained in other studies using more complex methods (Brown 2. Data and de Beurs 2008; Vrieling et al. 2011; Zhang et al. 2003). Where bimodal regimes exist, only the green-up a. NDVI and dormancy onsets that respectively follow and pre- For our study purposes we have worked with the cede the lowest mean monthly NDVI value have been longest available NDVI data, collected by the Advanced retained. This artificially increases the vegetative season Very High Resolution Radiometer (AVHRR) sensor duration but there are years and regions for which onboard the National Oceanic and Atmospheric Ad- the short dry season is sometimes suppressed (e.g., as in ministration (NOAA) satellites. These data were - 1997/98 in ; Anyamba et al. 2002). tained from the Famine Early Warning Systems Network Green-up (Fig. 1a) starts in March north of the (FEWS NET) Africa data portal (http://earlywarning. equator and shifts northward to the Sahara margins in usgs.gov/fews/africa/index.php). This portal provides August [at an average rate of ;0.05 km per day ac- 10-day composite NDVI images of Africa at 8-km spa- cording to Zhang et al. (2005)]. South of the equator, tial resolution from July 1981 to the present, processed green-up is much more uniform with large areas showing by the Global Inventory Monitoring and Modeling Stud- an onset in November or December without any clear ies group (Tucker et al. 2005) at the National Aero- north–south propagation. The , Kenya, south- nautical and Space Administration (NASA). NDVI is ern Uganda, and northern Tanzania equatorial regions calculated from the near-infrared (NIR) and red (VIS) are subject to bimodal regimes, and experience a main top-of-atmosphere reflectances, using the following al- onset of green-up by October–November. Lastly, the gorithm: NDVI 5 (NIR 2 VIS)/(NIR 1 VIS). Values two winter rainfall regions located at the northwest and for vegetated land generally range from about 0.1 to 0.8, southwest tips of Africa (i.e., the Mediterranean mar- with values lower than 0.15 indicating sparse vegetation gins of Morocco and Algeria and the Atlantic margin of and values greater than 0.6 indicating dense vegetation. South Africa, respectively) experience a green-up start GIMMS NDVI data have been corrected for 1) strato- in December and June, respectively. These results are spheric aerosols due to volcanic eruptions during April consistent with those obtained by Zhang et al. (2004, 1982–December 1984 and June 1991–December 1993, 2005) using Moderate Resolution Imaging Spectror- 2) artifacts due to satellite drift, which is especially adiometer (MODIS) NDVI. The dormancy onset (Fig. 1b) important in tropical regions, and 3) subpixel cloud con- pattern is much more homogeneous than the green-up tamination (Pinzon et al. 2005). The same desert calibra- onset pattern. Dormancy onset occurs in October– tion has been applied for all the sensors (NOAA-7 to November in north of the equator and in

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FIG. 1. (a) Month of the vegetative season onset (average over 27 yr) defined as the month when NDVI extends up the annual mean level. (b) Month of the vegetative season end, defined as the month when NDVI extends down the annual mean level. the winter rainfall region of South Africa. Dormancy delivery and processing of data at GPCC, then the Afri- onset occurs in May–June south of the equator, in East can rain gauge network has suffered strong deteriorations Africa as well as in the winter rainfall region of Northern since the 1990s. There are fewer and fewer operating Africa. This makes vegetative seasons lasting 1) be- stations, while data of those that are operating are not fed tween 8 and 4 months from the Guinean Gulf coast systematically into the international system. In addition, to the Sahel, 2) between 7 and 5 months from 108Sto the quality of the data is often poor with gaps of missing South Africa, 3) around 7–8 months in East Africa, and data and inefficient quality controls (UNECA 2011). This 4) 9 months and upward in the equatorial region. In the leads to a poor spatial distribution of data with large rest of the study area, those pixels/months that did not regions (i.e., , Democratic Republic of Congo, fall within the vegetative season (i.e., between the green- Nigeria; Fig. 2a) undocumented over the last decade. up and dormancy months) were excluded from the analysis. This ensured that we interpreted signals in c. Cloud cover NDVI that are truly related to vegetation greenness and The cloud cover data downloaded from the Koninklijk not to soil reflectance values, for example, as may happen Nederlands Meteorologisch Instituut (KNMI) climate in semiarid environments. explorer(http://climexp.knmi.nl)originatesfromthe widely used ISCCP D2 dataset. We have worked with b. Rainfall the total cloud amount (%), which is provided globally Rainfall data used in this study originate from the on an equal-area map grid with 280-km spatial resolu- Global Precipitation Climatology Center (http://gpcc. tion and a monthly time step over the period from July dwd.de/), which provides gauge-based gridded monthly 1983 to June 2006. The ‘‘D’’ dataset improves over the precipitation datasets for the global land surface, in dif- previous one (‘‘C’’) in terms of radiance calibration, ferent spatial resolutions. We selected the GPCC full cloud detection, and radiative modeling. Moreover the data reanalysis product (Rudolf and Schneider 2005), spatial and temporal resolutions have been increased which compiles the most comprehensive global collec- and the temporal coverage extended, and additional tion of in situ monthly precipitation data from 1901 to products are also available (Rossow et Schiffer 1999). 2009. We chose a 0.5830. 58 spatial resolution and ex- d. Nino-3.4~ index tracted grid points covering Africa for the period January 1981–December 2008. Note that over that period and Finally, the Nino-3.4~ (N3.4) sea surface temperature area, the number of stations has gradually decreased (SST) index was downloaded from the Climate Pre- from ;3642 in January 1981 to ;1150 in December diction Center database (http://www.cpc.ncep.noaa.gov/ 2005 and ;483 in December 2007 (Fig. 2b). If the latter data/indices/). It documents the sea surface temperature decrease is not attributed solely to the delay in the over the area 58N–58S, 1708–1208W and has been computed

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FIG. 2. (a) Map of GPCC grid points documented by rain gauges only in January 1981 (crosses) and both in January 1981 and December 2004 (circle). (b) Evolution from January 1981 to December 2007 of the number of rain gauges used in the GPCC database over Africa.

from the Extended Reconstructed SST (ERSST) V3b (Fig. 3, spread between the small open gray circles). database (Smith et al. 2008). We selected the period Note as well that any significant trend is observed in the January 1981–December 2008 and a monthly time step. time series for each of these four phases. Other ENSO We upscaled data to a monthly overlapping, 3-month ‘‘representative’’ indices such as the multivariate ENSO time step. Our analyses focus on 4 of the 6 phases of the index (MEI) also exist and recently distinct types of ENSO seasonal cycle (Larkin and Harrison 2002), which ENSO variability (e.g., eastern Pacific and central Pa- is presented in Fig. 3. These are the ONSET from cific), having somewhat different timings and impacts, ;May0 to July0, the PEAK from ;July0 to December0, have also been highlighted (Kao and Yu 2009; Newman the DECAY from ;January11 to April11, and lastly et al. 2011; Ren and Jin 2011). However, the objective the POST phase from May11. On average, the lowest here is to provide a general picture of the relationship T values (below 278C; Fig. 3, large gray dots) are re- with ENSO for Africa. There is no justification for corded during the PEAK and DECAY phases, which considering specific types of ENSO, which may result in are also characterized by a large year to year variability a slightly different NDVI signal in a given region only.

FIG. 3. Seasonal cycle (full large gray dots and dashed line) and scatterplot over 1981–2007 (small opened gray circles) of 3-month NINO-3.4~ data; black thin line: trend lines (none is significant at the 95% level). The onset, peak, and decay phases start in MJJ, ASO, and JFM respectively.

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3. Methods and approach contains years/trimesters when the N3.4 anomaly is be- low or equal to 20.5 std. The ‘‘normal’’ sample contains To assess precisely and understand the signal of years when the N3.4 anomaly is between 20.5 and 0.5 std. ENSO on NDVI in the whole of Africa during the last Differences in NDVI monthly field between the warm three decades, we have conducted two sets of analyses. and normal sample as well as the cold and normal sample In the first set, we have computed correlations between are presented as monthly maps in Figs. 5a and 5b, re- monthly overlapping, 3-month SST values over the spectively. As for the correlations maps, differences that Nino-3.4~ region and monthly NDVI values with 1-month are not significant at the 90% level according to a Stu- lag. For example, the April–June N3.4 value is corre- dent’s t test are masked out. It is instructive to compare lated with July NDVI, the May–July N3.4 value is cor- the results of the correlation and composite analyses. related with August NDVI and so on. The 1-month lag Indeed, for those pixels that are significantly correlated enables the time response of vegetation to climatic per- to ENSO, the composite maps show whether the cor- turbations to be taken into account. The 1-month time relation signal comes primarily from the ENSO cold or step enables the evolution of the correlation patterns warm phases or from both. Those pixels that are not during the vegetation phenological cycle (green-up, significantly correlated to ENSO but that show large ... peak, senescence ) to be followed precisely. This is an deviations from normal in the composite analysis in- improvement over preceding studies, which have usually dicate that the relationship with ENSO is not linear and worked with a seasonal time step (e.g., Williams and is strongly asymmetrical (i.e., only the cold or warm Hanan 2011) that can mask rapid month-to-month ENSO events relate to disturbances in the local climate changes in the correlation patterns and dilute signals by and subsequently to vegetation photosynthetic activity). aggregating months that do not show concordant re- It will be seen in the results section that the Guinean lationships with ENSO. Results are presented as monthly region of western Africa well exemplifies this. maps in Fig. 4 where correlations that are not significant In a second set of analyses, to understand the origin of at the 90% level are masked out (dark gray shades) and the ENSO signal detected in NDVI, we have reassessed the in four tables documenting southern, western, eastern, sensitivity of NDVI to rainfall. It is presented in Fig. 6 as and (see Fig. 7, left top panel for their correlation maps between monthly overlapping 3-month location). For each month, the total number of pixels rainfall values and monthly NDVI values with 1-month analyzed and the percentage of pixels significantly cor- lag (i.e., the April–June rainfall amount is correlated ~ related with Nino-3.4 are shown. Also, the limits of these with July NDVI, the May–July rainfall amount is cor- four large regions have been chosen according to several related with August NDVI, and so on). Given the dif- criteria: they are coherent in terms of 1) mean climate ference of spatial resolution between NDVI (;16 km) [e.g., the western Africa (WAF) region is influenced by and rainfall (;50 km) data, several pixels of NDVI fall the West African monsoon], 2) rainfall interannual within the same pixel covered by the rainfall grid. This variability [see Poccard et al. (2000), who extracted four can enhance the signal particularly when vegetation is ho- dominant modes of rainfall variability across Africa, mogeneous across the rainfall pixel. Note that the 1-month with our WAF, southern Africa (SAF), and eastern lag is the lag for which the correlations between vege- Africa (EAF) regions matching well with three of these tation and rainfall mean seasonal cycles are the highest modes], 3) seasonality of the ENSO–rainfall tele- for most of the pixels regardless of vegetation type (not connection (see Camberlin et al. 2001; our four regions shown). This is coherent with findings by Martiny et al. roughly correspond to their regions 1, 2, 3, and 5) and (2006) and Klein and Rohrig€ (2006), who show for sev- 4) NDVI–rainfall relationships (results in section 4 and eral semiarid and semihumid regions in western, east- Fig. 6). Note that these regions are roughly equal in ern, and southern Africa that the vegetation response terms of number of pixels analyzed. These tables are usually lags behind rainfall by 1 to 1.5 months. Similarly, a valuable summary of the different regional ENSO the 3-month cumulative rainfall amount has already signals in vegetation and rainfall. been found to be best correlated with NDVI in semiarid To document possible asymmetries between the cold environments in particular (Nicholson et al. 1990; Klein and warm events signals, and to assess the level of NDVI and Rohrig€ 2006). Indeed, vegetation usually does not anomalies (expressed in % of departure from the mean), respond directly to rainfall but to soil moisture, which we have also conducted composite analyses. Three sam- is related to rainfall accumulated over several months ples of years were created according to N3.4 anomalies (Malo and Nicholson 1990). Such NDVI–rainfall cor- (note that N3.4 values follow a normal distribution). The relation maps for the whole of Africa and over the whole ‘‘warm’’ sample contains years/trimesters when the N3.4 seasonal cycle have never been produced before and pro- anomaly is above or equal to 0.5 std. The ‘‘cold’’ sample vide interesting new insights as compared to maps produced

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FIG. 4. July to June correlation maps between 3-month Nino-3.4~ and monthly NDVI with 1-month lag (i.e., April–June Nino-3.4~ correlated to July NDVI). Pixels/months that do not fall within the vegetative season are in light gray. Pixels for which the correlation is not significant at the 90% level are in dark gray. The significance level at 90% (95%) equals 0.32 (0.38).

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FIG. 5a. July to June composite maps of NDVI anomalies (in % of the 1981–2007 mean) during warm ENSO events. Pixels/months that do not fall within the vegetative season are in light gray. Pixels for which the com- posite does not pass the significance test at 90% are in dark gray.

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FIG. 5b. As in (a), but for cold ENSO events.

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FIG. 6. July to June correlation maps between 3-month GPCC rainfall amounts and monthly NDVI with 1-month lag. Pixels/months that do not fall within the vegetative season are in light gray. Pixels for which the correlation is not significant at the 90% level are in dark gray. The significance level at 90% (95%) equals 0.32 (0.38). The 3-month isohyets are superimposed as black contours.

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TABLE 1. Southern Africa (and southwestern South Africa from TABLE 3. As in Table 1, but for East Africa (and north of East July to October). 1) Number of pixels of NDVI studied (bold in- Africa in June–October). dicates the largest and the smallest number of pixels); 2) percent- age of pixels significantly correlated to Nino-3.4~ at the 90% level 1234 (the number in parentheses and italics is the median correlation); No. of %ofR 3) percentage of pixels significantly correlated to rainfall at the pixels %ofR rainfall in 90% level; and 4) percentage of pixels significantly correlated to studied % of R N3.4 rainfall R N3.4 rainfall among those significantly correlated to Nino-3.4.~ Note that the significance level at 90% (95%) equals 0.32 (0.38). July 7635 8.8 (35) 33.7 34 August 8129 16.7 (238) 31.2 26 1234September 8055 22.3 (242) 35.2 45.6 October 8243 24.1 (238) 31.9 47.3 No. of %ofR November 9149 27 (37) 63.3 77.1 pixels %ofR rainfall in December 9557 35 (46) 66.2 86.5 studied % of R N3.4 rainfall R N3.4 January 10 237 42.9 (44) 66.4 85.9 July 2097 17.3 (235) 57 59.7 February 10 276 27.1 (39) 51.4 73.9 August 1882 24.1 (40) 49 64.9 March 10 320 17.9 (38) 40.5 57.2 September 1633 9.5 (34) 61.5 41.9 April 10 739 13.1 (234) 32.4 26.8 October 1463 6.1 (34) 65.8 24.7 May 11 948 10.2 (237) 31.4 43.2 November 2320 17.2 (39) 74.3 78.4 June 10 280 22 (238) 40.9 45.3 December 10 404 11 (237) 73 76.3 January 12 925 20.2 (239) 61.8 78.7 February 13 855 23 (239) 65.5 85.5 March 14 453 41.5 (240) 63.4 83.2 (column 3) and the percentage of pixels where the NDVI– April 14 566 30.1 (241) 75.3 91.7 rainfall correlation is significant amongst those pixels May 13 717 34.8 (241) 76.9 90.4 where the NDVI–ENSO correlation is significant (col- June 5069 21.4 (239) 56.1 72.6 umn 4). To complement and synthesize these pixel-scale analyses, composite analyses have also been performed for six regional indexes chosen for their representative- in previous studies at the annual time scale (e.g., Camberlin ness of the vegetative and climatic context and of the et al. 2007) or regional spatial scale (e.g., Brown et al. NDVI–ENSO relationship. These indexes document 1) 2010), particularly with regard to the evolution of veg- a semiarid area experiencing a bimodal rainfall cycle in etation sensitivity to rainfall during the phenological Tanzania, 2) a semiarid summer rainfall area in Botswana, cycle. These maps, when related to the NDVI–ENSO 3) a semiarid winter rainfall area in the western Cape correlation maps in Fig. 4 that are built in the same way, region of South Africa, 4) a subhumid region in the indicate the potential role of rainfall in transmitting the Guinean domain of western Africa, and two humid re- ENSO signal to vegetation. In addition, Tables 1–4 give gions in 5) and 6) the Democratic Republic of for each month the percentage of pixels where the Congo (Fig. 7, left top panel). Generally speaking the NDVI–rainfall correlation is significant at the 90% level three last humid regions usually attract little attention.

TABLE 2. As in Table 1, but for western Africa (and northwestern Africa from January to March). TABLE 4. As in Table 1, but for central Africa.

1234 1234 No. of %ofR No. of %ofR pixels %ofR rainfall in pixels %ofR rainfall in studied % of R N3.4 rainfall R N3.4 studied % of R N3.4 rainfall R N3.4 July 14 749 8.8 (234) 28.1 31.1 July 6674 6.8 (234) 12.9 14.8 August 16 015 10.8 (238) 24.3 31.9 August 6756 19.1 (239) 10 10.8 September 16 023 5 (233) 26.4 21 September 6754 16.3 (239) 6.6 10.8 October 16 024 7.6 (33) 39.3 48.3 October 8773 8 (36) 15.1 16.1 November 12 294 4.2 (33) 48.1 37.7 November 11 934 6.9 (34) 15.6 14.6 December 2280 6.1 (233) 40.8 55.4 December 11 486 5.4 (235) 10.3 9.4 January 2523 14.2 (39) 50.6 63.5 January 11 527 7.7 (35) 12.9 12.1 February 2775 13.7 (32) 38.5 30.3 February 11 604 24.8 (39) 10 10.5 March 2958 6.7 (234) 39.9 17.6 March 12 686 17.3 (36) 10.5 16.8 April 4665 12.1 (237) 45.2 48.9 April 14 638 6.8 (236) 10.1 20.8 May 8736 10.8 (237) 34.6 35 May 13 636 15 (235) 19.9 33.4 June 10 610 12.1 (232) 28.8 25 June 8434 8 (236) 25.1 43.4

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FIG. 7. Location of the six regional indices and four large regions studied. Also shown are mean (full line) and composite (dashed line for cold and dashed dotted line for warm ENSO events) seasonal cycles of NDVI (unitless), rainfall (mm), and the residues of total cloud cover (%) for the six regional indices. Stars (circles) denote month for which the cold (warm) composite is significantly different from the mean (at the 90% level).

Figure 7 presents the six indexes’ NDVI and rainfall with the effect of the synchronous rainfall removed. mean seasonal cycles, based on all years (thick full line) These residuals have been obtained from the linear re- and for composite ENSO years (thin dashed and dotted gression of the rainfall mean seasonal cycle on the cloud dashed lines, for the cold and warm event samples re- cover mean seasonal cycle. The reason for working with spectively). In addition, for the three humid regions residuals is that cloud cover variations are partly ex- where rainfall is not as strong a limiting factor as for the pressed in rainfall. Thus the residuals might sign the po- semiarid regions, the total cloud cover mean and com- tential effect of nonprecipitating clouds on vegetation as posite seasonal cycles are given to explore the potential they also act as a barrier to the light. These regional- impact of ENSO through light availability. Note that we scale analyses of NDVI, rainfall, and cloud cover sea- have worked with the residuals of the total cloud cover sonal cycles are instructive for they show clearly the shifts

Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 1APRIL 2014 P H I L I P P O N E T A L . 2521 and amplitude changes in the seasonal cycles, the asym- core (February–April), a signal potentially interesting metries in the response to ENSO warm and cold events, for forecasting purposes. This is coherent with findings and the synchronicity (in time and/or intensity) between by Richard et al. (2002), who have shown that preseason the responses of the three variables. (October) rainfall anomalies in South Africa tend to be out of phase with rainfall anomalies during the rainy season (December–April), which is a pattern and mode 4. ENSO signal in vegetation photosynthetic of variability significantly related to ENSO. activity The instructiveness of the composite maps (Figs. 5a,b) Figure 4 suggests that Africa can be divided into three about the linearity of the relationship to ENSO and the main regions according to the sign and timing of the symmetry of the cold and warm event impacts in southern correlations between NDVI and N3.4. These are the Africa is obvious. The timing, patterns, and intensity of summer rainfall regions, the winter rainfall regions and the warm and cold event impacts are clearly different. the equinoctial rainfall regions. Results for each of these During cold events (Fig. 5b), vegetation photosynthesis three regions are presented in sections 4a–4c, respectively. is significantly enhanced at the beginning of the vege- tative season (January) in Botswana and at the end of a. Summer rainfall regions the vegetative season (April–May) in Namibia and north The three summer rainfall regions of Africa are the of 208S. During warm events (Fig. 5a), vegetation pho- southern African region (358–108S), the western African tosynthesis increases in November in the southeast part region (58–168N, 208W–208E), and the northeastern of South Africa, then decreases substantially in South African region (48–168N, 208–408E). Africa, Botswana, and Namibia for ;4 months in a row from February to April. 1) SOUTHERN AFRICA Looking at Table 1 (columns 3 and 4) and Fig. 6, it is In southern Africa, where the vegetative growing evident that the impact of ENSO on NDVI in southern season spans from approximately December to May Africa is mainly through the effect of rainfall. Vegeta- (Figs. 1a,b), the significant correlations with N3.4 (Fig. 4) tion sensitivity to rainfall is very high over the region: are mainly negative (shades of yellow), indicating that from November to May (Table 1, column 3), 60% to vegetation photosynthetic activity tends to be damp- 75% of the pixels have significant 1-month lag correla- ened during the warm phases of ENSO and to increase tions between monthly NDVI and 3-month rainfall during the cold ones. The negative correlations appear amounts. Moreover, most of the correlation values are as early as December (Fig. 4) at the borders of South above 0.6 or 0.8 (Fig. 6). The concordance between some Africa, Zimbabwe, and Mozambique. By March, these isohyets and the patterns of significant correlations (e.g., correlations have intensified and spread over most of the the isohyets 60 and 210 mm in January, and 60 and region south of 188S (the Namibia/Angola border area) 360 mm in February and May) is also remarkable. This is while north of 188S positive correlations are recorded consistent with previous findings performed at the an- (actually they appear as early as January) so that ;41% of nual time step (Malo and Nicholson 1990; Martiny et al. the pixels are significantly correlated to ENSO (Table 1, 2006; Camberlin et al. 2007) showing that the relation- column 2). According to the vegetation map of the ship between NDVI and rainfall in Africa is strong and Global Land Cover 2000 project [which has provided linear for those areas with an annual rainfall between for the year 2000 a harmonized land cover database 200 and 1000 mm and with open grasslands and rainfed over the whole globe making use of a dataset of 14 crops as dominant vegetation cover. Note as well in Fig. 6 months of preprocessed daily global data acquired by the gradual increase in the sensitivity of vegetation to the VEGETATION instrument on board the Satellite rainfall (i.e., higher correlations—0.55 on average in Pour l’Observation de la Terre (SPOT) 4 satellite (Mayaux January as against 0.61 in March—which are also more et al. 2004); not shown] the transition from shrub/grass coherent in space) during the vegetative season, which is dominant vegetation (in the south) to tree dominant veg- an effect of the lagged response of vegetation to rainfall etation (in the north) is located near 188S. From April to and of the intraseasonal persistence of vegetation anom- June the negative correlations gradually shift to the north alies (Philippon et al. 2007). Table 1 (column 4) further and the east reaching Angola, Zambia, and Malawi, and indicates that between 75% and 90% of the pixels sig- then the north of Mozambique and the south of Tan- nificantly correlated to ENSO are also significantly cor- zania. Lastly, it is noteworthy that in northern Namibia related to rainfall accumulated during the previous and southeast South Africa, NDVI correlations switch trimester. Lastly, looking at the example of Botswana in from being positive at the beginning of the vegetative Fig. 7, the amplitude of the NDVI seasonal cycle is strongly season (November–December) to being negative at the decreased during warm ENSO events in accordance with

Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 2522 JOURNAL OF CLIMATE VOLUME 27 the rainfall seasonal cycle. The increase in the amplitude and Pohl et al. (2009) show that the ENSO–rainfall tele- of the NDVI seasonal cycle is less pronounced during connection at the seasonal scale arises from a modulation the cold events although rainfall amounts seem to in- of the frequency of the tropical–temperate troughs crease noticeably in January–February. Two hypotheses (TTT), which are the dominant rain-bearing systems. can be evoked here to explain this apparent weak re- 2) WESTERN AFRICA sponse of NDVI to rainfall excesses recorded during cold ENSO events. First, the rainfall anomalies can be Western Africa (58–178N, 208W–208E) records its main due to few very intense rain events that are not partic- growing season from ;May to November (Figs. 1a,b). In ularly effective for vegetative growth since much of the comparison with southern Africa, correlations between water is lost through runoff (this could also explain why ENSO and vegetation photosynthetic activity are weaker— the rainfall anomalies do not pass the significance test). according to Table 2, column 2, barely 10% of the pixels Second, vegetation is on average very close to its opti- are significantly correlated with ENSO—and less spa- mum and cannot increase its photosynthetic productivity tially coherent. The most consistent signals are observed or be any denser in cover and biomass than it already is. in July–August over Senegal and Mali and along the These results agree with and complement the results coast with negative correlations (Fig. 4). from several previous studies that have explored the This is consistent with Propastin et al. (2010) and Williams impact of ENSO on vegetation and rainfall in southern and Hanan (2011), who found West African vegetation Africa. The north–south dipole by 188S in the NDVI– to be less influenced by ENSO warm events than the ENSO correlation pattern is evident in the studies by southern Africa vegetation. With regard to the Sahel, Anyamba and Eastman (1996) and Anyamba et al. (2001), the composite maps show that only the warm events who have mapped the evolution of NDVI anomalies (Fig. 5a) have a significant signal, in July–August mainly during the ENSO years 1986–89 and 1997–98. It is also over the western (Senegal) and eastern (Chad) parts of coherent with composite maps of Williams and Hanan the Sahelian band. Brown et al. (2010) note that ENSO (2011) for the season December–February (DJF), where events more strongly impact the start of the vegetative the limit around ;188S is clearer during cold than warm season (delayed during warm events) than its core (see ENSO events. However, these authors have worked their Fig. 7). This weak sensitivity of vegetation to ENSO with net photosynthesis modeled by the Simple Bio- over the Sahel could be explained by both a weak sen- sphere Model version 3, and not with observed NDVI. sitivity of vegetation to rainfall and a weak sensitivity of Similarly, Brown et al. (2010) obtained coherent pat- climate (rainfall especially) to ENSO. First, on average terns of negative correlations between the multivariate NDVI–rainfall correlations barely exceed 0.55 (com- ENSO index and NDVI cumulated over March–May at pared to 0.61 for southern Africa). Nicholson et al. (1990) the border between South Africa, Zimbabwe, and Mo- and Martiny et al. (2006) computed the rain use effi- zambique, and in northern Namibia/southern Angola. ciency (RUE; expressed as the NDVI to rainfall ratio) Over South Africa, these authors obtain either a non- for small regions in western, southern, and eastern Af- significant or positive correlation between March–May rica and noted that the Sahel had the smallest RUE. NDVI and MEI. It is obvious from our results that ENSO They attribute it to the shortness and intensity of its has its strongest negative impact in South Africa in rainy season. Second, during the Sahelian vegetative January–March and not April–May when the vegetative season ENSO is either in its onset or post phases (and season has come to an end in part of the country (see the not in its peak or decay phases as is the case for South May map; Fig. 4) and when the ENSO has entered into Africa; Fig. 3). Moreover, the teleconnection between its post phase. This points to the importance of consid- ENSO and the West African monsoon is affected by the ering a monthly time step to accurately follow the spatial strong decadal variations of the climate background evolution of the ENSO signal during the phenological state. Whereas the teleconnection was high in the 1970s cycle. With regard to the ENSO impact on rainfall in and 1980s (Janicot et al. 2001), it has decreased over the southern Africa and the mechanisms involved, when two last decades in favor of the analyzing both observations and atmospheric general (Rodrıguez-Fonseca et al. 2011; Fontaine et al. 2011). circulation model (AGCM) outputs, Richard et al. (2000) This could explain the weak impact we found in our and Mason (2001) note that convection and rainfall over study. southern Africa (the southwest ) are de- The composite maps for the Guinean region (Figs. 5a,b) creased (increased) during warm ENSO years and the provide evidence for a stronger signal of ENSO than subtropical high pressure belt is weakened, leading to that suggested by the correlation analyses alone. Indeed, reduced moisture fluxes toward the continent. Working during cold events (Fig. 5b) large positive anomalies of at the intraseasonal time scale, Fauchereau et al. (2009) NDVI are recorded from June to September over West

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Africa south of 108N up to northern Gabon. As a con- during the warm events (Fig. 5a). This suggests ENSO- sequence, in terms of the NDVI seasonal cycle (Fig. 7 induced changes in the phase of the seasonal cycle. A and the example of Guinea), the cold events are asso- last interesting signal seen only in the cold event com- ciated with a unimodal cycle (the July–August vegeta- posite maps is the positive anomalies of NDVI over tion photosynthesis decrease is suppressed). With regard western Ethiopia from June to August. to the parameters involved in the NDVI–N3.4 re- This signal of ENSO in NDVI is mainly through the lationship over the Guinean region, it is obvious that effect of rainfall. As seen in Table 3, column 4, between even if weak positive correlations between NDVI and 45% and 47% of the pixels significantly correlated with rainfall accompanies the northward shift of the ITCZ N3.4 in September–October are also significantly cor- and associated West African monsoon (April–June; Fig. 6), related with rainfall accumulated over the previous tri- vegetation sensitivity to rainfall accumulated the pre- mester. In addition, the ENSO–NDVI correlation patterns vious trimester is generally low over the region. More- for September–October fit well with the rainfall–NDVI over, few pixels are correlated to both ENSO and rainfall, correlation patterns shown in Fig. 6, falling within the suggesting that the ENSO signal in vegetation might not 60–360-mm isohyets. In their study, Williams and Hanan be through rainfall. Figure 7 brings interesting insights. (2011) did not find any ENSO impact on net photosyn- First, the positive and significant anomalies of NDVI thesis in the northern part of eastern Africa probably from July to September associated with cold events are because of the seasonal time scale considered [June– synchronous with positive and significant anomalies of August (JJA) and September–November (SON)], which rainfall. As opposed to the semiarid environments, the merges months with different, or opposite, ENSO sig- delay in the vegetation response to rainfall during these nals. But the negative impact of ENSO on summer rainfall months and over that region appears small. Enhanced in Uganda and northwest Kenya has been documented rainfall might allow vegetation to reach its potential by numerous studies such as those of Ogallo (1988), photosynthetic level, which is of a magnitude close to Camberlin (1995), Phillips and McIntyre (2000), and that observed in June or October. Second, the large Indeje et al. (2000) while the negative impact of ENSO positive anomaly in NDVI in August is also associated on summer rainfall in Ethiopia has been documented by with a negative anomaly for cloud cover. That additional Gissila et al. (2004). supply of light could contribute to the increase in veg- b. Winter rainfall regions etation photosynthesis. It is not incompatible for that region and for the months investigated to have higher The two semiarid, winter rainfall regions of Africa are rainfall amounts associated with a reduced cloud cover. located in western South Africa (primarily along the Indeed, the July–August little dry season usually expe- Atlantic coast) and northwestern Africa (along the At- riences a uniform cover of nonprecipitating stratus lantic and Mediterranean coasts). clouds (Knippertz et al. 2011) that brings less light and 1) WESTERN SOUTH AFRICA less rainfall than the broken cover of vertically de- veloped cumulus clouds of the intertropical convergence In western South Africa, the vegetative growing sea- zone (ITCZ). son spans the period from July to October (Figs. 1a,b), the winter. Little is known about 3) NORTHERN PART OF EASTERN AFRICA the impact of ENSO on vegetation over this region as The northern part of eastern Africa encompasses most of the studies devoted to southern African NDVI South Sudan, western and central Ethiopia, Uganda, variability have focused on the summer rainfall region. and western Kenya. This region under the double in- First, as opposed to the summer rainfall region, correla- fluence of the West African monsoon and the East Af- tions for this winter rainfall region (Fig. 4) are positive, rican equinoctial rainy seasons has a vegetative season meaning that enhanced (diminished) vegetation photo- spanning from approximately May to November (Figs. synthetic activity is expected during warm (cold) ENSO 1a,b). The most consistent correlations with N3.4 are events. However, looking at the composite maps (Figs. 5a, observed at the end of the season (i.e., August–October; b),NDVIanomaliespassthesignificance test during cold Fig. 4) and are negative. That pattern of negative cor- events only. Second, correlations with N3.4 are weaker relations fits very well with the area of intensive culti- and are significant mainly in August (Fig. 4), with 24% vation (GLC2000 map, not shown). It seems to be only of the pixels analyzed significantly correlated to N3.4 triggered by the both cold and warm events but whereas (Table 1, column2). This could be attributed first to the the largest positive anomalies of NDVI are observed in type of vegetation itself. Shrubs dominate the October during cold events (Fig. 5b), the largest negative and biomes, which are endemic to the anomalies of NDVI are recorded in August–September region. Shrubs are known to exhibit a lower amplitude in

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NDVI values between seasons (see the western Cape weak and significant in January and over the index NDVI seasonal cycle in Fig. 7, with an amplitude Mediterranean-facing region of Morocco and Algeria of 0.05 only). They are also known to be less sensitive to only (Fig. 4 and Table 2, column 2, with barely 10% of rainfall variations. In the study by Camberlin et al. the pixels showing significant correlations with N3.4). (2007) there is no significant correlation between annual According to the composite analyses, the ENSO signal NDVI and annual rainfall. At the monthly time step, cor- in NDVI in Algeria is related to the warm events mainly relation values between June–October NDVI and rainfall but very few pixels pass the significance test and the accumulated the previous trimester (Fig. 6) are on av- NDVI positive anomalies recorded from January to erage around 0.56 [against 0.7 for the summer rainfall March are very weak (;15% above the mean; Fig. 5a). region; cf. section 4a(1)], and are significant for 50%– As for the other semiarid environments studied, rainfall 65% of the pixels (Table 1, column3). The Succulent variations are the main driver of NDVI variability over Karoo and the Fynbos biomes respond to rainfall in the region. From January to May (Table 2, column 3) different ways. Vegetation of the Succulent Karoo biome ;40%–50% of the pixels analyzed show significant is the most sensitive (Fox et al. 2005). Its growth starts correlations between monthly NDVI and rainfall accu- with the first significant rains at the end of the summer mulated over the previous trimester (October–April), (April–May), continues throughout the winter, and then with correlation values that rise above 0.7 (Fig. 6). drops sharply in spring (October) as rainfall declines As for western South Africa, little attention has been (Esler and Rundel 1999). Conversely, vegetation within paid to vegetation and rainfall variability in this region. the Fynbos biome exhibits a bimodal pattern with Mariotti et al. (2002) have analyzed the composite pat- NDVI peaks in August and November (Hoare and tern in seasonal rainfall according to ENSO warm and Frost 2009) that do not match well with the seasonal cold events over the region. They have observed that the cycle of rainfall, which varies from a winter regime in relationship shifts from positive to negative between the west to a nonseasonal and equinoctial regime in the autumn and spring. Thus, the positive correlation we eastern part of the biome (Rouault and Richard 2003). observe between ENSO and NDVI in winter could result Moreover, the relatively long summer drought period from a combination of the vegetation sensitivity to au- appears not to be a limiting factor to photosynthetic tumn rainfall added to persistence in NDVI anomalies. activity (Stock and Allsopp 1992). These points explain c. Equinoctial rainfall regions the somewhat lower NDVI sensitivity to rainfall over this region as compared to the summer rainfall area of The equatorial regions of Africa, namely East Africa southern Africa. (108N–108S, 328–458E) and central Africa (;128S–58N, As for the semiarid, summer rainfall environments, 88–328E), have equinoctial rainfall regimes, that is, two the signal of ENSO is through rainfall as suggested by rainy seasons coinciding with the northward and then Table 1 (column 4). The 42%–65% of pixels that show the southward passage of the ITCZ, which produces a significant correlation with ENSO in July–September rain in October–December (the short rains) and also show a significant correlation to rainfall. As shown in March–May(thelongrains)inEastAfrica(Tanzania the rainfall seasonal cycle of the western Cape in Fig. 7, 1) index in Fig. 7), and in September–November and most of the precipitation in the region falls from May to March–May in central Africa (Gabon and DRC indexes August, and 2) June–July monthly amounts are strongly in Fig. 7; Nicholson and Grist 2003; Balas et al. 2007; lowered during cold ENSO events. This is consistent Samba and Nganga 2012). with the findings by Philippon et al. (2011), who have 1) EAST AFRICA recently highlighted a positive correlation between ENSO and the May–July (MJJ) rainfall amount over the The signal of ENSO in the East African vegetation region (r ;0.5 over the period 1979–99). During ENSO photosynthetic activity is asymmetrical between the two years the rain-bearing systems (extratropical troughs rainy seasons. During the short rains, positive correla- mainly) in MJJ are more frequent and deeper and are tions between NDVI and N3.4 (Fig. 4) suggest that shifted toward the north, thus carrying more rainfall warm (cold) ENSO events are associated with positive over the region. (negative) NDVI anomalies. Significant correlations emerge in October over eastern Ethiopia/southern So- 2) NORTHWESTERN AFRICA malia first [note as well the west–east dipole by 408E due In northwestern Africa the vegetative season spans to the persistence of NDVI anomalies that have emerged the period from January to May (Figs. 1a,b), the winter in August to the west; cf. section 4a(3)]. They next of the . As for the western South spread southward to northern and eastern Kenya by African region, correlations with N3.4 are positive but November, and then to northern Tanzania by January

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(Fig. 4). By that time, more than 40% of the pixels the low-level (high level) winds blowing to the east studied display significant correlations with N3.4 (Table 3, (west) along the equator are weakened or turn westward column 2). Significant correlations first start to fade away (eastward), bringing moisture to the region and driving over Tanzania in February, then in Kenya and southern convective rains. Mapande and Reason (2005) focused Somalia in March. When looking at the composite maps, on western Tanzania, a region at the transition between the asymmetry of the cold and warm ENSO events sig- the opposite-signed ENSO-impacted eastern and southern nal is obvious. While warm events are associated with African regions, and reported that above-normal October– significant large positive NDVI anomalies from October April rainfall was associated with warm ENSO events. to February (Fig. 5a), only a few pixels in December The correlation patterns of NDVI-N3.4 during the show significant negative anomalies during cold events long rains [March–May (MAM); Fig. 4] are radically (Fig. 5b). This asymmetry clearly appears on the Tan- different from those that occur during the short rains. zania index as well (Fig. 7). The NDVI seasonal cycle Few pixels have significant correlations with N3.4 (be- amplitude is particularly enhanced during warm events tween 10% and 18% according to Table 3, column2). with a pronounced peak in January. These results com- From March to April correlations switch very quickly plete and agree with previous studies on NDVI per- from largely positive to somewhat negative (except in formed at the seasonal scale for the East African region. southern Tanzania). By May, the negative correlations For example, the NDVI anomaly pattern associated with have vanished. In June, sparse (and low) positive cor- the 1997/98 warm ENSO event shown by Linthicum et al. relations appear in northwestern Kenya, Somalia, and (1999) and Anyamba et al. (2002) featured strong posi- eastern Ethiopia. This weak impact of ENSO on vege- tive anomalies from December to March in Kenya and tation during the long rains as compared to the short the southern parts of Somalia, Ethiopia, and Sudan. rains first arises from a weaker impact of ENSO on cli- This marked response of vegetation to ENSO in gen- mate and rainfall in particular. Camberlin and Philippon eral and to warm events in particular, during and shortly (2002) found a weak negative correlation between Nino-3~ after the short rains, arises from both a high sensitivity of and the March–April rainfall amount, switching to vegetation to rainfall and a high sensitivity of rainfall to positive for May rainfall. This is consistent with our ENSO. Table 3 shows in column 3 that from November NDVI–N3.4 correlation maps, but on the whole ENSO to January, monthly NDVI is significantly correlated to only explains a very small part of the long rains vari- the rainfall accumulated over the previous trimester for ability, as pictured as well in the Tanzania rainfall sea- more than 60% of the pixels, while column 4 shows that sonal cycle in Fig. 7. Second, the vegetation sensitivity to from November to February more than 70% of the rainfall variability is also lower than during the short pixels of NDVI that are significantly correlated to ENSO rains: between 30% and 40% of the pixels have a sig- are also significantly correlated to rainfall. The NDVI– nificant 1-month lag correlation between monthly NDVI rainfall correlations often rise above 0.7 (Fig. 6). An and 3-month rainfall amounts (against 50% to 66% interesting point highlighted as well in column 3 of during the short rains and the January–February dry Table 3 is that the vegetation sensitivity to rainfall does season; Table 3, column3). Correlation values are also not decline in the dry period stretching between the lower as seen in Fig. 6 (0.5–0.6 vs 0.7–0.8 in November– short rains and the long rains. On the contrary, in February). This is consistent with Martiny et al. (2006), Kenya, the highest correlations (.0.8) are observed who noticed a higher RUE during the short rains than between November–January (NDJ) rains and Febru- during the long rains. ary NDVI (Fig. 6). This can be related to the vegetation 2) CENTRAL AFRICA time response to rainfall, which is about one month (Davenport and Nicholson 1993; Martiny et al. 2006), Vegetation response to climate and ENSO in central but also to the shortness of the January–February dry Africa has been less studied and is less well known as season, which can also be relatively wet, as was the case in compared to the semiarid regions. This lack of studies 1998, and thus sustain high vegetation photosynthetic and interest for the region comes primarily from data activity between the two rainy seasons (Linthicum et al. issues: 1) NDVI saturates during the greenest months 1999; Anyamba et al. 2001). and is also perturbed by cloudiness, 2) the large rainfall The teleconnection between equatorial East African amounts do not seem to be a limiting factor to vegeta- (Kenya, Uganda, Tanzania) short rains and ENSO has tion growth, and 3) long-term climate data in the region been documented by many authors, including Indeje et al. are scarce. Nonetheless, two interesting signals in the (2000) and Mutai and Ward (2000). The teleconnection NDVI–N3.4 correlation patterns are observed: one at operates through a modification of the Walker cell cir- the beginning of the driest of the two rainy seasons (i.e., culation over the Indian Ocean. In warm ENSO years, from February to March) and one during one of the two

Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 2526 JOURNAL OF CLIMATE VOLUME 27 dry seasons (i.e., from July to September); see the mean during cold events NDVI values are systematically equal seasonal cycles in Fig. 7. In February–March positive to (1984, 1999) or above (1983, 1988, 1987, 1998) the correlations stretch from 158S to approximately the equa- mean. Interestingly, significant deviations from the mean tor (Fig. 4), a signal that comes mainly from warm events are also recorded in rainfall in August with higher with significant positive anomalies of NDVI to the west amounts during cold events. This additional water supply of Angola, the Democratic Republic of Congo (DRC), could help vegetation to sustain a high photosynthetic 2 Congo, and Gabon (Fig. 5a). This is in line with the re- activity during the dry season (roughly 50 mm month 1). sults obtained by Anyamba et al. (2001) for the 1997/98 Note that unlike for Guinea there is not any clear and ENSO event and Los et al. (2001), where positive anom- significant signal in the cloud cover residuals for alies of NDVI are obvious over the whole of the Congo Gabon. For the DRC the deviation is positive during basin in March (see their Fig. 2) during warm ENSO cold events, which does not support the hypothesis of events. From March to April–May, a band of weak neg- more active vegetation due to an additional supply of ative correlations shifts from the equator to 78N, a signal light. However, as illustratedinFig.2a,veryfewrain associated with cold events mainly (Fig. 5b). It suggests gauge data have been fed into the GPCC database that ENSO might modulate the northward propagation over the DRC for the last decade. Thus the quality of of the ITCZ during the boreal spring with propagation rainfall and cloud cover residuals composite cycles is sped up during cold events. In July–September, which questionable. are relatively dry months over the region, sparse nega- Climate variability in central Africa and how it relates tive correlations are observed at the equatorial Atlantic to ENSO has been the subject of very few studies. Whereas coast (Gabon mainly) and to the east of the DRC (Fig. 4). ENSO appears as the primary driver of temperature These correlations seem respectively in line with the variations in all the tropical rain forest regions (South negative correlations observed in Guinea [see section America, Southeast , and central Africa), and of 4a(2)] and to the north of East Africa [see section 4a(3)], precipitation in and Southeast Asia, and seem mainly triggered by warm events (negative Malhi and Wright (2004) could not find any significant anomalies of NDVI in Fig. 5a). relationship between ENSO and precipitation in central To further document these two signals, the NDVI, Africa. However, it should be noted that these authors rainfall, and residual cloud cover mean seasonal cycles computed interannual correlations for all months to- during warm and cold events for the Gabon and DRC gether, assuming the absence of any seasonality in the are presented in Fig. 7. First, as opposed to the semiarid ENSO–rainfall relationships. The central Africa rainfall– environments (i.e., Tanzania, Botswana) where a 1- to ENSO relationships have also been documented in 2-month lag is observed, there is barely a lag between studies by Nicholson and Kim (1997), Poccard et al. NDVI and rainfall mean seasonal cycles: the two NDVI (2000), and Camberlin et al. (2001), and more specifically maxima (minima) are concurrent with the two rainfall in the studies by Balas et al. (2007) and Misra (2010). maxima (minima), that is, MAM and SON (JJA and Camberlin et al. (2001) and Balas et al. (2007) show that DJF). Thus considering a 1-month lag and 3-month ac- negative rainfall anomalies in October–December (OND) cumulated rainfall to explore the NDVI–rainfall re- and/or DJF over Gabon and Congo are associated with lationships for that region might not be as suitable as for warm events in the equatorial Pacific while Misra (2010) the semiarid regions (very few pixels display significant shows that positive anomalies of rainfall in DJF are re- NDVI–rainfall correlations in Fig. 6 and Table 4, col- corded over the eastern parts of the DRC during warm umn 3). Second, it is noteworthy that the highest NDVI events in association with anomalous upper-level di- peak is recorded during the driest of the two rainy sea- vergent winds over the western Indian Ocean. None of sons (MAM), which appears also as the one with the these studies considers the July–September dry season, lowest residual cloud cover for Gabon. This agrees with which nonetheless seems critical for photosynthetic ac- results by Gond et al. (2013), who have mapped vege- tivity of vegetation. tation in central Africa by classifying seasonal profiles of the MODIS enhanced vegetation index (EVI). For 5. Discussion and summary the forest classes, the highest EVI peak coincides with the driest of the two rainy seasons. In agreement with the The aim of this study was to provide a precise and correlation and composite maps, large deviations from updated picture of the timing and patterns of the ENSO the mean are observed in NDVI during warm and cold signal during the seasonal cycle in the whole of Africa events in July–August for both Gabon and the DRC and over the last three decades. We used the normalized (Figs. 5a,b). Although they do not pass the significance difference vegetation index rather than climate data test, scatterplots for August (not shown) reveal that for that purpose. This was because NDVI has a higher

Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 1APRIL 2014 P H I L I P P O N E T A L . 2527 spatial resolution and is more frequently updated than in the end of the ENSO decay phase and the beginning of situ climate databases. Moreover, by using the longest its post phase (April–June) a northward (to 108S) and available NDVI database (i.e., the one built from the eastward (to the south of Tanzania) spread of the neg- NOAA AVHRR missions) it is possible to take into ative correlations south of 188S occurs. Thus, the NDVI– account the interannual variability. Therefore, using NDVI N3.4 teleconnections feature regional-scale dipoles and brings out signals that are potentially stronger, or occur propagative patterns both in space (from one region to at a higher spatial resolution, than when using short- another one) and time (from one month to another). term remotely sensed or long-term in situ climate prod- These patterns have not been identified with such ac- ucts, respectively. However, one of the drawbacks of curacy in previous studies. They suggest that ENSO in- using NDVI is that signals are often difficult to explain fluences the location of the ITCZ (its southward and for the humid environments (e.g., central Africa) where northward but also eastward and westward displace- NDVI saturates and is biased due to cloudiness, and ments) and the convection within it during most of the vegetation responses to climate variations are complex seasonal cycle. and not well understood. They are also difficult to in- Although correlation analyses at the monthly time terpret for cultivated areas under irrigation where water step point out interesting patterns, composite analyses supply may induce variations in photosynthetic activity performed at the pixel scale (Figs. 5a,b) or the regional that are likely to obscure the natural response to climate. scale (Fig. 7) highlight the strong asymmetry of the im- We have mapped first the month-by-month evolution pact of the cold and warm ENSO events for numerous of the 1-month lag correlations between the Nino-3.4~ regions. For instance, in Gabon, southern and eastern index (3-month values) and NDVI (monthly values) Africa vegetation photosynthetic activity is modulated over the whole of Africa (Fig. 4). Although patterns are during warm ENSO events mainly, with an activity not necessarily the same from one event to the next dampened in Gabon in July–August and southern Af- (Myneni et al. 1996; Kogan 2000; Lyon and Mason rica from December to May, and increased in eastern 2007), the use of a monthly time step adds important Africa from October to February. But the clearest ex- new insights about the timing and patterns of ENSO ample is for the Guinean domain in western Africa, signal to the findings of previous studies, which are which records strong positive anomalies of NDVI in based largely on annual or seasonal time scales and on July–August during cold ENSO events, a signal barely a regional spatial scale. Starting from July (the begin- perceptible in correlation analyses. Moreover, the value ning of the peak phase of ENSO) and going to June (the of the complementary regional-scale approach (i.e., re- post phase of ENSO), the teleconnection patterns over gional indices; Fig. 7) is particularly obvious. Signals that the whole of Africa evolve as follows. From July to were not that noticeable at the pixel scale (both in the September, negative correlations between NDVI and correlation and composite analyses) are much more N3.4 are observed north of the equator (including the evident at the regional scale, which is known to enhance Sahel, the Gulf of Guinea coast, and regions from the the signal-to-noise ratio. northern Democratic Republic of Congo to Ethiopia) Figure 8 summarizes our findings. When the four large but they are not uniform in space and are moderate African regions studied are classified according to the (;0.3). Conversely, positive correlations are recorded strength of the ENSO signal in NDVI, it appears that the over the winter rainfall region of South Africa. In the regions with the largest ENSO signal both in terms of period from October to November, negative correla- spatial extent (i.e., the number of pixels having a signif- tions over Ethiopia/Sudan/Uganda disappear while icant correlation with N3.4) and correlation levels are positive correlations emerge in the Horn of Africa (so southern Africa and eastern Africa. In southern Africa that a west–east dipole is observed in October), and in ENSO impacts NDVI of the southern Africa winter the southeast coast of South Africa. By December, the rainfall region in JAS and in the southeast region in end of the peak phase of ENSO, with the settlement of November–December (Fig. 8; class 4), and of the sum- the ITCZ south of the equator, positive correlations mer rainfall region south of 188S from December to May over the Horn of Africa spread southward and westward (Fig. 8; class 2). However, for the latter region it must be while negative correlations appear over Mozambique, remembered that its teleconnection with ENSO is sub- Zimbabwe, and South Africa. This pattern strengthens ject to a strong decadal variability, so the ENSO signal and a dipole at 188S is well established in February– could evolve in the next decades. In eastern Africa, March (the decay phase of ENSO), with reduced pho- ENSO impacts NDVI of the northwestern region under tosynthetic activity during ENSO years south of 188S the influence of the West African monsoon from August and enhanced activity north of 188S. In the meantime, by to October (Fig. 8; class 2) and of the October–December ;28N negative correlations spread northward. Lastly, at short rains and January–February little dry season (Fig. 8;

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FIG. 8. (top) Month-by-month and region-by-region number of pixels showing a significant NDVI–Nino-3.4~ correlation (correlation is negative for class 2 and positive for class 4) and a significant positive NDVI–rainfall correlation (note that the significant negative NDVI– rainfall correlations are very few and thus have not been considered). SAF indicates southern Africa, EAF East Africa, WAF , and CAF central Africa. (bottom) Solid lines indicate median NDVI–rainfall correlations month by month for the four regions for (left) class 2 and (right) class 4. As a benchmark, the thin lines display the median NDVI–rainfall correlations for pixels showing a significant and positive NDVI–rainfall correlation but for which the NDVI–Nino-3.4~ correlation is not necessarily significant. Red indicates SAF, green EAF, blue WAF, and black CAF. The significance level considered for all the correlations is set at 90%. class 4). It is interesting to note that all these regions are rainfall region of South Africa is comparable to that in semiarid but not all of them have their rainy seasons the summer rainfall region (Table 1, August vs March, phased with the peak of ENSO. For all these regions and and Fig. 8, August class 4 vs March class 2). Generally months the NDVI–N3.4 correlations get stronger during speaking, more attention should be given to the winter the progress of the vegetative season (Tables 1 and 3), in rainfall regions of Africa since they hold biodiversity agreement with the similar increase of vegetation sen- hotspots, are densely inhabited, and sustain an intense sitivity to rainfall (Fig. 8, bottom panels and thick lines). agricultural export industry, and because of their loca- Clearly, the impact of ENSO on vegetation there is tion at the transition between the midlatitudes and the through rainfall. Indeed, pixels for which the NDVI– tropics, which is an area that is expected to be strongly N3.4 correlation is significant display higher correlations affected by climate change (Mc Kellar et al. 2007; Giorgi with rainfall than pixels for which the NDVI–N3.4 cor- and Lionello 2008). relation is not necessarily significant (Fig. 8; compare the Conversely, the less impacted regions are western and thick and thin lines; e.g., class 4, January, and eastern central Africa. With regard to the Sahel and northwestern Africa with correlation values of 0.7 vs 0.6). Lastly, veg- Africa semiarid regions, the relatively weak impact of etation sensitivity to ENSO and to rainfall in the winter ENSO on NDVI illustrates first the weak impact of ENSO

Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 1APRIL 2014 P H I L I P P O N E T A L . 2529 on climate and for the Sahel over rainfall in particular of Africa and its climate variability seems to be largely over the last two decades, a fact pointed out by Fontaine independent of ENSO, at least its rainfall variability. et al. (2011). As for southern Africa, the Sahel–ENSO From the vegetation side, its sensitivity to rainfall is not teleconnection is subject to strong decadal variations. well known and has not yet been properly evaluated, nor Although vegetation in the Sahel and northwestern its sensitivity to light or temperature. The time response Africa is somewhat less sensitive to rainfall than vege- of vegetation to rainfall might be longer or shorter than tation in eastern and southern Africa, the main driver the one opted for in that study (i.e., 3 months accumu- of its variability is nonetheless rainfall. The western lated rainfall and a 1-month lag). Moreover, the vari- Africa region where vegetation appears to be the most ability of the length and intensity of the dry season, affected by ENSO is the Guinean wet region (Fig. 8, class which has not been explored in this study, might play 2, July–August). According to the composite analyses, a much more dominant role in the photosynthetic ac- the July–August vegetation photosynthesis is signifi- tivity of vegetation than that of rainfall accumulated cantly enhanced during cold events. Interestingly, this during the rainy season. It must be also noticed that response is associated with significant and synchronous great disparities exist among the different tree species in increases of precipitation and decreases of cloud cover, terms of their sensitivity and time response to climate two features that have never been shown before. Al- variability, and that can weaken the signal. For instance, though the quality of the NOAA AVHRR NDVI data is Couralet et al. (2010) noticed from a survey of semi- questionable for that region given the extensive and deciduous, overstorey trees in the Mayombe forest of shallow cover of nonprecipitating clouds that affect it in the DRC that while the growth of some species is mainly July and August, these results call for a further study of dependent on the early rainy season rains, the growth of the rainfall–cloud cover–NDVI relationships to un- other species is impacted by rains at the end of the rainy derstand the respective contribution of rainfall and light season. With regard to data quality, in situ rainfall data to vegetation photosynthesis in that humid environ- available in the region are very scarce and irregularly ment. Indeed, it is not clear what role exactly rainfall distributed as illustrated by Fig. 2a. Hence, rainfall in- plays on vegetation in this region given that we have formation for most of the grid points is determined by always considered in our correlation analyses rainfall the interpolation of data from a neighboring grid point accumulated over 3 months and a 1-month lag with rather than from the average values derived from rain NDVI, which is not strictly valid for every region of gauges. Therefore, the accuracy of the rainfall and re- Africa. Moreover, we have considered the total cloud sidual cloud cover composite cycles for the Gabon and cover only. An analysis of cloud types would corrobo- DRC indexes in particular (Fig. 7) is questionable and rate our hypothesis of a uniform cover of low clouds could explain the weak match with the NDVI composite replaced by a more broken cover of vertically developed cycles. One way to circumvent that problem would be to clouds during cold ENSO events enabling a larger illu- use rainfall estimates derived from satellites. Unfor- mination of vegetation. We have found very few ENSO- tunately, these estimates, especially those based on in- related signals in NDVI for central Africa, and the lack frared data, are still unreliable for the region. For of studies related to vegetation and climate variability example, the multisatellite rain product from the Global for this region is a handicap to the interpretation and Precipitation Climatology Project (GPCP) overestimates evaluation of the robustness of our results. The main by a factor of 2 the estimates derived from rain gauges signals concern the months of February–March and from the Global Precipitation Climatology Center July–August (NDVI low). In February–March the (GPCC) (Mc Collum et al. 2000). The NOAA AVHRR photosynthetic activity is enhanced during warm ENSO NDVI data themselves over the region are compounded events. In July–August the photosynthetic activity is by the persistence of clouds, which, despite the 10-day decreased during warm ENSO events and increased ‘‘filtering,’’ contaminate the data and make the vegeta- during cold ones. However, as compared to the Guinean tion signal uncertain. It would also be worth checking region of western Africa, the associated signals in rain- some of these results using other NDVI databases such fall and cloud cover are not very coherent and signifi- as the Moderate Resolution Imaging Spectroradiometer cant. That lack of response to ENSO might be related to (MODIS) as well as another vegetation index such as different factors: 1) the low sensitivity of climate and the enhanced vegetation index (EVI). For instance, rainfall in particular to ENSO, 2) the low sensitivity of Huete et al. (2002) have evaluated the performance of vegetation to rainfall, and 3) the quality of rainfall and the MODIS NDVI and EVI products over a wide range vegetation data. From the climate side, it must be of biomes (from the semiarid grassland of Arizona to the stressed that unfortunately the spatial coherence of tropical broadleaf forest of Brazil) and compared it to rainfall in central Africa is lower than in most other parts the products derived from NOAA AVHRR. They note

Unauthenticated | Downloaded 10/01/21 04:45 AM UTC 2530 JOURNAL OF CLIMATE VOLUME 27 that when compared to NOAA AVHRR NDVI, sensed photosynthetic activity and rainfall in tropical Africa. MODIS NDVI has a greater seasonal dynamic range Remote Sens. Environ., 106, 199–216. over the wet environments and during the wet growing Chen, D., M. A. Cane, A. Kaplan, S. E. Zebiak, and D. Huang, 2004: Predictability of El Nino~ over the past 148 years. Nature, season. They attribute the differences to the atmosphere 428, 733–736. water vapor content that strongly affects the AVHRR Couralet, C., F. J. Sterck, U. Sass-Klaassen, J. Van Acker, and near-infrared band and decreases NDVI values. The H. Beeckman, 2010: Species-specific growth responses to cli- authors note as well that when compared to MODIS mate variations in understory trees of a central African rain NDVI, MODIS EVI does not become as easily satu- forest. Biotropica, 42, 503–511. Cretat, J., Y. Richard, B. Pohl, M. Rouault, C. 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