El Niño climate disturbance in northern Madagascar and in the Comoros

El Niño climate disturbance in northern Madagascar and in the Comoros Rabeharisoa J. M.¹, Ratiarison A.¹, Rakotovao N.¹, Salim Ahmed Ali¹ ² (*) ¹ Laboratoire de Dynamique de l Atmosphère, du Climat et des Océans (DyACO) - University of Antananarivo - Madagascar ² Faculty of Sciences and Technology - University of Comoros - Moroni Comoros (*) Author: Salim Ahmed Ali - salim_s1@yahoo.fr Abstract: El Niño is a phenomenon of climate disturbance that occurs in the equatorial pacific. It is characterized by a positive anomaly (negative anomaly in case of La Niña) of Sea Surface Temperatures above 1 C (or below -1 C for La Niña). These "climate changes" are repeated every 2 to 7 years, and have repercussions on the whole globe climate. Our study is concerned with the characterization of the El Niño Southern Oscillation (ENSO) manifestation in the climate variability in northern Madagascar and in the Comoros from a historical analysis from 1979 to 2013. The study parameters are: the ENSO3.4 index in the Pacific, the monthly Sea Surface Temperature (SST) anomalies in the study area and the monthly rainfall anomalies. The methodological tools used are: spectral analysis (with Discrete Fourier Transform), cross-correlations study, and analysis from graphic and cartographic anomalies observations. The objective is to identify the ENSO signature in this region and to determine its real impact in the variability of SST and rainfall. The results of our study showed that the relationship between the ENSO3.4 index in the Pacific and SST anomalies and rainfall anomalies in the target area is not as linear as might be expected. Indeed, both El Niño and La Niña can be manifested by positive or negative SST/rainfall anomalies. A Fourier Transform spectral study identified some significant peaks including the one at 46.7 months over the study range from 1979 to 2013. As applications, results of this study may be of interest to the economic and social sectors from a perspective of forecasting and adaptation to climate variability. Keywords: El Niño, La Niña, ENSO, Sea Surface Temperatures anomaly (SST), rainfall anomaly, cross correlations, Discrete Fourier Transform (FFT), northern Madagascar, Comoros. 1. Introduction ENSO (El Niño Southern Oscillation) is the phenomenon of oscillation between El Niño and La Niña conditions in the equatorial eastern Pacific Ocean. It is characterized by a positive anomaly (case of El Niño) of Sea Surface Temperatures or negative anomaly (case of La Niña). This phenomenon is now well known and attracts the attention of many scientists as it disrupts the climate of the whole globe. It generally provides an excess rain in usually dry areas or a deficit rain in wet areas. At the global scale, the average temperature tends to be abnormally high [1]. These "climate changes" can have dramatic consequences such as in the economic sectors (perturbation of agriculture, tourism, fishing...), in the social sectors (floods, droughts, population displacement...). But they can offer some advantages such as supply water in usually devoid areas. The phenomenon ENSO has an occurrence of about 2 to 7 years and can last between 12 to 18 months [3]. At the local scale, there could be some variations on the influence of ENSO. The objective of this work is to describe the influence of ENSO on the Sea Surface Temperature (SST) and on the rainfall in the Indian Sea particularly northern Madagascar and the Comoros (10 S 17 S, 42 E 52 E), from historical data from 1979 to 2013. This region is characterized by a tropical maritime climate with a rainy and warm season from December to April (DJFMA) and a dry and fresh season from June to October (JJASO). First, a comparative study between the evolution of the ENSO3.4 index 1 and those of both monthly anomalies of SST and rainfall was carried out. This study was followed by an analysis of the crosscorrelations between these three parameters in order to extract the rate of similarity between them. The Discrete Fourier Transform (FFT) was applied to determine the ENSO spectral signature in both SST anomalies and in rainfall anomalies. Finally, an analysis by comparison of the neutral episode with the El Niño / La Niña episodes allowed to describe the real impact of ENSO in the variability of SST and rainfall. Figure 1: Study area: northern Madagascar and the Comoros (left map). Climate conditions in the Pacific during ENSO (right map). 1 ENSO3.4 index is calculated in zone 3.4 of the pacific corresponding to the coordinates: (5 N-5 S ; 170 W- 120 W) 1

2. Data The data for our study come from the European Center Medium range Weather Forecast (ECMWF), and are stored in the European Research Area ERAinterim database. These are data of daily re-analyses obtained from 1979 to 2013. The study parameters are described below: - ENSO3.4 index: it is the difference between the monthly SST and a climatological average calculated over the last 30 years of the chronicle, calculated in the ENSO3.4 area of the Pacific. - SST anomaly: it is the difference between the actual SST in the study area and a climatological average calculated over the neutral episodes 2. - Rainfall anomaly: it is the difference between the actual monthly rainfall and a climatological average calculated over the neutral episodes. 3. Methodological approach 3.1 Cross-correlations evaluation The cross-correlation study aims to extract the rate of similarity between two one-dimensional time signals [4]. The mathematical definition of the crosscorrelation coefficient rxy() τ is as follows: N τ r (τ ) x x y y σ σ (1) xy i i τ x y i1 where x and y are the two sets of values of the two series; σx and σ y, the respective standard deviations; x and y, the respective arithmetic means; N, the total length of each series; and τ, the lag (or phase shift) between the two series. This phase shift measures the time difference between two signals that are similar. This phase shift is obtained for a maximum value of the cross-correlation function. As for the linear correlation coefficient, when the phase shift between the two signals is equal to zero, it measures the degree of the similarity between two non-phase-shifted signals. Its interpretation is summed up as follows: - If 0,5 r xy (0) 1, the linear link between the two series is strong and positive or negative depending on the sign of r () τ (respectively positive or negative). - And if 0 r xy (0) 0,5, the linear link is weak. xy 3.2 Spectral Analysis by Discrete Fourier Transform The calculation of the spectrum allows a description of the temporal signal in the frequency domain. Generally, the objective is to identify periodic phenomena in a series (the ENSO index in this case). In addition, it applied to SST anomalies series and rainfall in order to determine the ENSO spectral signature. The power spectral density X(ω) is obtained from the following relationship [5]: N 1 i0 i ωt j2π i N X(ω ) x( t )e (2) where x is the set of values of the time series; ω, the frequency; and N, the length of the series. In practice, we used the Fast Fourier Transform (FFT) algorithm to calculate this spectral density. 3.3 Approach adopted for the description of the ENSO impact in the study area According to norms dictated by the World Meteorological Organization (WMO), an El Niño event occurs when the average ENSO index over three consecutive months is greater than 0.5 C (less than - 0.5 C in case of La Niña). However, in this study, an ENSO phase is active as soon as the ENSO index of one month is, in absolute value, greater than or equal to 0.5 C. To describe the ENSO impact on SST and rainfall, we carried out a comparative study of the three values below: - Long-term average of each month over El Niño episodes - Long-term average of each month over La Niña episodes - Long-term average of each month over Neutral episodes The SST/rainfall Impact is equal to the difference between the mean of SST/rainfall anomalies over El Niño/La Niña episodes and the mean of SST/rainfall anomalies over Neutral episodes. If the Impact is close to zero, then there is no ENSO impact for the month in question, the situation is like normal (Neutral episodes). The Impact is described by the relation below: Im pact Mean X Mean X (3) ENSO Neutral where X is SST anomalies or rainfall anomalies. 2 Neutral episodes are the inactive ENSO phases. 2

4. Results 4.1 Comparative analysis of study parameters Figure 2 shows the evolution of the ENSO3.4 index and those of temperature anomalies (SST) and rainfall anomalies from 1979 to 2013. Regarding the impact of the ENSO on the temperature (Figure 2, middle graph), positive ENSO indices (El Niño episodes) globally correspond to positive SST anomalies (sometimes exceeding +0.8 C), with the exception of the 1993, 1994 and 1995 episodes. Negative ENSO indices (La Niña episodes) are related to both positive SST anomalies (usually at the beginning of the episode), and negative SST anomalies (usually at the end of the episode, sometimes exceeding -0.8 C), except for the 2010 episode. However, the positive anomalies during the start of the La Niña phase could be explained by a continuation of the El Niño effect on the zone. This will however not be discussed here. Regarding the impact of the ENSO on rainfall (Figure 2, bottom graph), positive anomalies (sometimes more than 20 mm/month) or negative anomalies (less than -100 mm/month in a certain months) are observed, depending on the ENSO episode. However, no obvious relationship between the ENSO indices and the rainfall anomalies can be identified from a simple observation. Figure 2: Comparative evolution of the ENSO index in the Pacific (top); SST anomalies (middle); and rainfall anomalies (bottom) in northern Madagascar and in the Comoros. Study period: 1979-2013. 4.2 Cross-correlations analysis between study parameters Figure 3 shows the cross-correlation study which reveals 4-month phase shifts between the ENSO index and the SST anomalies (Figure 3, left graph), on the one hand; and 1 month between the ENSO index and the rainfall anomalies (Figure 3, middle graph), on the other hand. The associated cross-correlation coefficients are respectively 0.4 and 0.2. Although these values are significantly different from zero, they remain low and do not allow concluding to a linear relationship (with a lag) neither between ENSO index and SST anomalies, nor between ENSO index and rainfall anomalies. The linear correlation coefficient (evaluated at zero phase shift) is 0.23 between the ENSO index and SST anomalies, and 0.12 between the ENSO index and rainfall anomalies. These values are sufficiently low to conclude that there is no linear correlation between the study parameters. Regarding correlation between SST anomalies and rainfall anomalies (Figure 3, right graph), the phase shift is 329 months, with a coefficient equal to 0.13. The linear correlation coefficient is close to 0.1. These coefficients are low and indicate the absence of linear dependencies between SST anomalies and rainfall anomalies in the studied area. 3

Figure 3: Study of the cross-correlations between the study parameters: ENSO index and SST (left); ENSO Index and Rainfall (middle); and SST and Rainfall (right). p-value = 0.0; significant level α=0.05. 4.3 Spectral analysis of study parameters The spectral analysis of the ENSO index series values (Figure 4, left graph) reveals significant peaks that are characteristic of pseudo periodic phenomena with a dominant mode at 46.7 months. This means an average occurrence every 4 years. Both the spectrum of SST anomalies (Figure 4, middle graph) and the spectrum of rainfall anomalies (Figure 4, right graph) show significant peaks, including those at 46.7 months, which characterize the ENSO spectral signature in the studied area. Figure 4: Spectra of the ENSO index (left); the SST anomaly (middle); and the rainfall anomaly (right) 4.4 ENSO Impact on climate variability Figure 5 describes the ENSO impact on monthly SST and on rainfall, both averaged over El Niño episodes (red bars), over La Niña episode (blue bars), and over Neutral episode (the origin) (see equation (3)). Figure 5a presents the ENSO impact on SST in a spatial average. It shows that during El Niño episodes (red bars), every month of the year record positive SST anomalies, with an amplitude ranging from +0.04 C (in July) to more than +0.5 C(in March); February, March and April being the critical months. During La Niña episodes (blue bars), positive SST anomalies (lower than +0.3 C) appear for the July, August, September and November months, and negative anomalies (greater than -0.23 C) are Figure 5a: Monthly variation of El Niño Impact (red bars) and La Niña Impact (blue bars) on SST. 4

associated with January, March, April and May months. February, June, October and December are close to normal phases. These results are consistent with the graphical analysis in section 4.1. Figure 5b presents the box plot distribution of the ENSO impact on rainfall in a spatial average. The bars are the mean of a series made up of all El Niño/La Niña Impact in the evaluated month. They show that during El Niño episodes (red bars), every month record an excess of rain accumulation (varying between +2.12 mm and +46.3 mm/month), except for March and May which show a rain deficit between - 7.9 mm and -15.8 mm/month. During La Niña episodes (blue bars), the months with excess rain are December, January, February and June (between +4 mm and +26.5 mm/month), and the months with deficit rain are March, April and May (between - 12.3 mm and -40 mm/month). Other months are close to normal conditions. For example during the rainy season (DJFMA) (Figure 6a), SST increases (between +0.19 C and +0.42 C) over the whole zone during the El Niño episode (left map), but during the La Niña episode (right map), SST are almost the same as those during Neutral episode in everywhere (with amplitude varying between -0.15 C and +0.06 C). During the dry season (JJASO) (Figure 6b), SST increases (between +0.11 C and +0.21 C) over the whole zone during both the El Niño episode (left map), and during the La Niña episode (between +0.10 C and +0.24 C) (right map). Figure 6b: Representation of spatial variability of ENSO Impact on the SST during dry season in the study area, with El Niño episode (left); Neutral episode (middle); and La Niña episode (right). Figure 5b: Box plot distribution of monthly variation of El Niño Impact (top) and La Niña Impact (bottom) on rainfall. Moreover, regarding the median (central mark), the box and whiskers are extended, particularly on October, November, December, January, February, March and April. It means that the ENSO impact is not homogeneous. As for rainfall (Figure 6c), during the rainy season, El Niño episode provides rainfall over most of the area (+80 mm in spatial average) with a marked spatial variability reaching a maximum of +275 mm in some places. During La Niña episodes, the amount of rain decreases in the Northeast and Northwest, East of Madagascar and in the North of the Comoros, with a minimum spatial variability close to -189 mm. On the contrary, the amount of rain increases in the centre of Madagascar (maximum at +169 mm). 4.5 Spatial variability of the ENSO impact In the Neutral episodes, SST and rainfall are not distributed homogeneously over the study area, as shown in Figure 6a (middle map). Figure 6a: Representation of spatial variability of ENSO Impact on the SST during the rainy season in the study area, with El Niño episode (left); Neutral episode (middle); and La Niña episode (right). Figure 6c: Representation of spatial variability of ENSO Impact on the rainfall during rainy season, with El Niño episode (left); Neutral episode (middle); and La Niña episode (right). During the dry season (Figure 6d), El Niño episode brings a surplus of rain (+106 mm maximum) over the whole zone, except in the South of the centre of Madagascar which remains close to normal conditions. During La Niña episode, rainfall increases (+70 mm maximum) on the East coast of Madagascar, in the Eastern part of the Comoros and in the center of the study area. Rainfall decreases in the centre of Madagascar and in the East of the study area (-48 mm minimum). 5

Figure 6d: Representation of spatial variability of ENSO Impact on the rainfall during dry season, with El Niño episode (left); Neutral episode (middle); and La Niña episode (right). 5. Conclusion In this study, the ENSO impact on the variability of SST and rainfall in northern Madagascar and in the Comoros was demonstrated by the presence of the ENSO spectral signature (dominant mode at 46.7 months) in SST anomalies and rainfall. The cross-correlations study concluded that there were no linear dependence relationships between the ENSO index and the other study parameters. On the contrary, the comparative analysis showed that the SST anomalies were positive (between +0.04 C and more than +0.5 C) every month during El Niño episode, but were variable during La Niña episode (between -0.23 C and +0.3 C). Regarding rainfall, the El Niño episode provided, on average, an excess rainfall over the rainy season (on a spatial average, +80 mm) and also during the dry season (+40 mm), with a marked spatial variability. During La Niña episode, there was, on average, an excess rainfall over the dry season (+16 mm) and at the beginning of the rainy season (DJF) (+38 mm) but a deficit at the end of the rainy season (MA) (-52 mm). In general, the El Niño events impact on climate variability was much greater than that of La Niña events. But, both El Niño and La Niña impacts were not homogeneous during the episodes. The ENSO impact was not linear in the studied area. To better understand this variation, we could study the influence on the behavior of winds and other climate parameters. Moreover, a differentiated study of other climate indices (like IOD Indian Oscillation Dipole) would allow a better description of the ENSO impact. References [1] Voituriez B., Jacques G., 1999: El Niño. Réalité et fiction, Editions UNESCO. [2] Brian Fagan, 1999. Floods, Famines, and Emperors: El Nino and the Fate of Civilizations, published by Basic Books. [3] Beucher F., 2010: Météorologie tropicale: des alizés aux cyclones, Météo France [4] Chatfield C., The analysis of time series: an introduction. [5] Steven W. Smith, 1999: The Scientist and Engineer's Guide to Digital Signal Processing, Second Edition by California Technical Publishing 6