Vol. 10(17), pp. 559-569, 15 September, 2015 DOI: 10.5897/SRE2015.6265 Article Number: B6B3E1746894 ISSN 1992-2248 Scientific Research and Essays Copyright©2015 Author(s) retain the copyright of this article http://www.academicjournals.org/SRE

Full Length Research Paper

Maritime continent circulation as a predictor of El Niño-Southern Oscillation (ENSO) influence on Ethiopia rainfall

Mark R. Jury

University of Puerto Rico Mayaguez, PR, USA. University of Zululand, KwaDlangezwa, South Africa.

Received 26 May, 2015; Accepted 2 September, 2015

Summer rainfall over the cropping region of Ethiopia is related to the precursor winter circulation around the Maritime Continent and El Niño–Southern Oscillation (ENSO) development and influence. Investigation of this link reveals that sea surface (SST) in the north Indian Ocean and China Sea are anomalously cold and there are low level north-westerly anomalies around the Maritime Continent prior to dry in Ethiopia. The analysis shows that wind anomalies spread into the Pacific - increasing , and across the Indian Ocean and Africa - suppressing convection. Two indices that represent Asian winter penetration near the Maritime Continent are used to predict Ethiopian summer rainfall at long-lead time. The hindcast fit of the statistical algorithm exceeds 50% during the satellite era (1981-2014).

Key words: prediction, Ethiopia.

INTRODUCTION

Climate variability in Ethiopia is influenced by the Pacific Yeshanew and Jury, 2007; Joly and Voldoire, 2009; El Niño Southern Oscillation (ENSO) (Semazzi et al., Segele et al., 2009; Shaman et al., 2009). 1988; Rowell et al., 1992; Janicot et al., 1996, 2001; Wang and Fan (2009) demonstrate that knowledge on Rowell, 2001) and associated tropical ocean thermocline the evolution of climate patterns in analogue years can (White and Tourre, 2003; Jury and Huang, 2004). During improve the prediction of Asian summer monsoon rainfall. warm phase, atmospheric convection spreads across the During the preceding dry winter (Dec-Mar) the equatorial Pacific causing upper westerly over the circulation is governed by the Tibetan high pressure, jet Atlantic and over much of Africa (Hoskins stream waves that conduct cold fronts across Indo-China and Ambrizzi, 1993; Jury et al., 1994; Branstator, 2002; and circulation patterns over the west Pacific. In the

*Corresponding author. E-mail: [email protected] Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License

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reviews of Chang et al. (2006) and Lau and Wang (2006), departures and linearly detrended. Asian monsoon winter outflow to the Maritime Continent is connected with ENSO phase and equatorially Methods of analysis propagating Madden Julian Oscillation (MJO). Cold air outbreaks over the China Sea are more frequent and join Predictors were assembled from correlation maps analyzed for with westerly MJO surges at the onset of Pacific El Niño December-March in respect of detrended June-September rainfall, and its convection. How this affects the Indian Ocean with values < 80% significance masked. For extraction of predictor circulation and African convection is the focus of this time series, the area for averaging should exceed 15° latitude × 20° longitude in size. The candidate predictor pool was four over a study. training period of 33 years: 1981 to 2014. A statistical algorithm was The main goals here are to understand the ocean- formulated via backward stepwise linear regression onto the target coupling and climatic controls on summer time series. Initially all predictors were included and their partial rainfall in Ethiopia’s crop growing region. The primary correlation was evaluated. Those with lower significance (or co- scientific question is: How does Asian Monsoon winter linearity) were screened out and the algorithm was re-calculated outflow near the Maritime Continent link with ENSO and from the remaining variables. An optimal fit was reached with three predictors, however when repeated for the second half of the time reach across the Indian Ocean to affect African rainfall? (1998-2014), one predictor dropped out (Indian SST 45°-145°E, A spin-off scientific question is: how can this knowledge 5°S-25°N), leaving only the low level winds near the Maritime be exploited to offer operational seasonal forecasts at a Continent as contributors: Indian meridional wind 45°-135°E, 7°S- lead time suitable for intervention? 12°N and Pacific zonal wind 120°-170°E, 15°S-15°N. The performance of the multivariate linear algorithm was 2 evaluated by r fit, adjusted for the number of predictors. Conservatively assuming 16 degrees of freedom, the Pearson DATA AND METHODS 2 product-moment r fit should exceed 35% for statistical significance at 99% confidence. Predicted vs observed scatterplots were The data and methods employed to develop prediction algorithms analyzed for slope, tercile hits and outliers. The algorithm stability are described. The primary index is the observed June to was validated by removing the first and last 7 years and comparing September rainfall averaged over the crop growing region of differences in r2 fit and predictor coefficients. The algorithm is Ethiopia 36.5°-40.5°E and 7°-14°N. The ocean and atmospheric developed from detrended time series to minimize the effect of predictors are drawn from satellite-era reanalysis products in the climate change across the 33 year record. In an operational preceding December to March season. This work expands on situation, detrending is believed to have little influence. earlier studies of Korecha and Barnston (2007) and Jury (2013). To study the interaction of the Asian winter monsoon and Pacific ENSO over the Maritime Continent, precursor and simultaneous composites were analyzed for Ethiopian dry and wet Target and predictor data corresponding with the 850 hPa wind predictors. Dry seasons include 1982, 1987, 1997, 2002, 2014, and wet seasons include: The CHIRPS 5 km resolution global land-only rainfall dataset forms 1988, 1996, 1998, 1999, 2000. Composites of dry minus wet were the basis of this study. Monthly data from over 150 Ethiopian analyzed for NCEPv2 winds, vertical motion and specific National Meteorological Agency gauges are blended with satellite (Kanamitsu et al., 2002), and satellite GPCPv2 rainfall (Adler et al., observations, as described in Funk et al. (2014) over the period 2003) and SST. These datasets are used because NCEP winds 1981 to 2014. The interpolation procedure gives primary influence are available in vertical section and the GPCP rainfall covers both to in-situ observations and gauge climatology, and secondary land and ocean. Similarly composites for the upper ocean were influence to satellite and model data (Janowiak et al., 2001; analyzed longitudinally in the 5°-15°N band using NOAA ocean Huffman et al., 2007, 2011; Saha et al., 2010; Knapp et al., 2011). reanalysis (Behringer, 2007). To quantify the local response of The target area is defined by crop reports from the Ethiopian Ethiopian rainfall to ENSO, correlation maps (5°-15°N, 35°-42°E) Central Statistical Agency (www.csa.gov.et) and the US Dept of were analyzed with respect to June-September Nino3.4 Pacific SST Agriculture Famine Early Warning System (FEWS) that show index (170°-120°W, 5°S-5°N) and with Jul-Oct satellite vegetation production in an eastern highlands zone: 7°-14°N and 36.5°-40.5°E fraction (Tucker et al., 2005). Lag-correlations were calculated (Figure 1a and b) including the states of Amhara and Oromia. This based on the time series of the Ethiopian summer rainfall and zone overlaps with the leading cluster of satellite vegetation fraction Pacific Nino3.4 SST index from -6 (December-March) to +4 months. (Tucker et al 2005) which has a uni-modal peak from July to October that lags 1-month behind rainfall. The mean annual cycle of rainfall (Figure 1c) rises above 100 mm/month from June to RESULTS AND DISCUSSION September, peaking above 200 mm/month in July and August. Hence the season of interest extends across these four months. Summer rainfall forecasts in April would be most useful for Targets and correlation maps planning purposes, so predictors are drawn from the preceding December to March (winter) season. The CHIRPS monthly rainfall The correlation of June-September onto regional is area-averaged to create a time series and then applied to search SST fields in the preceding December-March (Figure 2a) for key predictors in fields of from the reflects anomalous warm conditions in the north Indian Hadley Centre (Kennedy et al., 2011) and 850 mb winds from the European Centre for Medium-range Forecasts (ECMWF) Ocean and northwest Pacific (China Sea), encompassing reanalysis (Dee et al., 2011). Exploratory analysis determined that the zone east of the target area. The corresponding 850 the eastern hemisphere held most of the climate signal: 20°S-35°N hPa V wind correlation field (Figure 2b) shows significant and 0°-180°E. All time series were converted to standardized values over the equatorial Indian Ocean and Maritime

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Nile

a b

350

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50 c 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Figure 1. (a) Loading pattern of first mode of vegetation fraction 1981-2013 shaded light to dark green from zero to one standard deviation with state boundaries and target area. (b) Grain crop growing areas from FEWS shaded yellow to hatch orange with increasing yield, with key cities and rivers. (c) Mean annual cycle of CHIRPS rainfall over the target area (bold) and quintiles.

Continent. The wind correlations, following the edge of normal Ethiopian summer rainfall. An integral feature is the SST correlations, point to diminished tropical zonal winds over the west Pacific, whereby decreased penetration of the Asian winter monsoon prior to above westerlies correspond with an early onset of ENSO in

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a

b

c

Figure 2. Correlation maps with respect to Ethiopia (Jun-Sep) rain at 6 month lead time (Dec-Mar) for (a) Hadley SST, (b, c) ECMWF 850 hPa meridional and zonal wind, all de-trended. Values < 80% confidence are white; boxes identify target (shaded) and predictor areas; arrows signify winds in respect of increased rainfall.

respect of wet conditions in Ethiopia six months later. March season. These indicate an r2 fit > 50% for both two The time series of observed and statistically predicted and three predictor algorithms, values above those of rainfall is given in Figure 3a, scatterplots are provided in Wang and Fan (2009) for Asian summer rainfall. Tercile Figure 3b and c. The corresponding multi-variate hit rates in the dry category are 64%, with misses in algorithm is Ethiopia June-September rain = +0.53*(Indian 1984, 1995, 2009 and 2013. Tercile hits in the wet category are 67%, and outliers are noted in the recent V wind) -0.33*(Pacific U wind) in the preceding December-

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–.33 (Pac U) +.53 (Ind V) a

b c

Figure 3. (a) Time series of observed Jun-Sep target rainfall and 2-predictor model rainfall; scatterplot of linear multi-variate regression of predicted and observed rainfall using (b) 3 predictor model, and (c) two predictor model, with trend fit.

decade: 2003, 2007, 2010 and 2012. steady. In the section below, we switch our analysis from To check for stability of the forecast algorithm, the patterns favouring above normal rainfall to those bringing same 2-predictor model is fitted to the observed rainfall drought conditions to Ethiopia. time series after removing the first and last 7 years. The r2 fit remains constant: 56%, but the coefficients change. The Ind V coefficient is +.37 recent +.57 past, while the Composites of maritime continent ENSO circulation Pac U is –.41 recent –.27 past. Hence the Pacific (Indian) influence grows (shrinks) with time, but predictability is Time series of Ethiopian summer rainfall and preceding

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850 hPa wind GPCP rainfall

a b

850 hPa wind GPCP rainfall

c d m/s mm/day

Figure 4. Composite maps of dry minus wet: (a) preceding Dec-Mar 850 hPa winds and (b) satellite GPCP rainfall, and (c, d) same for Jun- Sep rainy season. Vectors in (a) highlight Asian winter monsoon outflow that anticipates drought. Shading in (a, c) and (b, d) is consistent, and areas with minor differences are unshaded.

winter winds (as indicated by the predictive algorithm) 140°E in the 850 to 600 hPa layer, and constitute an around the Maritime Continent are ranked to provide a enhanced Asian winter monsoon outflow. The dry minus selection of years in lower and upper quintiles. wet scenario evolves to summer with a low level westerly Composite maps of dry (1982, 1987, 1997, 2002, 2014) anomaly over the west Pacific, consistent with a minus wet (1988, 1996, 1998, 1999, 2000) December- developing El Niño. A corresponding dipole develops in March and June-September 850 hPa winds, satellite the humidity field (Figure 6c and d): moist-Pacific, dry- rainfall and sea temperature and vertical motion are Indian. Easterly winds on the east African escarpment analyzed. The atmospheric fields (Figure 4a to d) in derive from the divergent ENSO circulation over the antecedent and simultaneous times show that zonal Indian Ocean, and correspond with expansion of the dry winds east of the Maritime Continent constitute a zone from the Maritime Continent to North Africa, that significant and stable signal dividing a convective Pacific inhibits moisture transport from the Congo Basin to (El Niño) zone from a dry Indian Ocean. The dry minus Ethiopia (Viste and Sorteberg, 2013). wet composite in December-March (year -1) exhibits A complementary depth section of ocean cooler SST over the northwest Pacific (Figure 5a and c), and zonal circulation is given in Figure 7a and b, similarly extending to the NW Indian Ocean and SE Atlantic. The averaged over the 5° to 15°N band. Composite cooler SST correspond with atmospheric subsidence differences for dry minus wet conditions reflect cooling in over the China Sea that shifts to the Maritime Continent the upper ocean to the east and west of the Maritime by summer (Figure 5b and d) and spreads westward Continent in the preceding December-March season. The across the Indian Ocean to Africa. sub-surface ocean circulation differences exhibit zonal The analysis is extended by composite height sections divergence and corresponding upwelling in the northwest averaged 5°-15°N that show upper westerlies over Africa Pacific. The upwelling strengthens in the Pacific as subside toward a region of low humidity over the Maritime westerly currents intensify in the June-September Continent in the preceding winter (Figure 6a to b). season. Ocean temperature differences are < -3°C in Meridional winds are from north (< -1 m/s) from 120°E to depths from 60 to 200 m from 130 to 170°E signifying

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Sea surface temp Vertical motion

COOL

a b

Sea surface temp Vertical motion

COOL c d deg C Pa/s

Figure 5. Composite maps of dry minus wet: (a) preceding Dec-Mar satellite SST and (b) 500 hPa omega, and (c, d) same for Jun-Sep rainy season. Shading in (a, c) and (b, d) is consistent, and areas with minor differences are unshaded.

uplift of the west Pacific thermocline, corresponding with been investigated. A new dataset that blends > 150 rain onset of El Niño in the east Pacific and deficient gauges with satellite estimates provides an accurate over Ethiopia. reflection of local climate. Previous studies have revealed The impact of Pacific ENSO on Ethiopian summer that winter outflow toward the Maritime Continent is rainfall and subsequent response of vegetation is connected with ENSO phase, and could be exploited to analyzed in Figure 8a and b. The map indicates a uncover predictability. Correlation maps at six month lead significant simultaneous correlation of Nino3.4 index and time with respect to Ethiopia rainfall found that the SST rainfall over the southeastern highlands and Rift Valley. and wind circulation around the Maritime Continent were The subsequent response of vegetation to rainfall most influential. A forecast algorithm based on two wind variability tends to concentrate in the Rift Valley and predictors achieved an overall fit of 50% and tercile hit eastern escarpment. The northwestern highlands rate of two-thirds. Atmospheric memory is imparted by (Gonder area) vegetation show little response to rainfall, large-scale coupling with the tropical ocean. Composite suggesting lower vulnerability to climatic anomalies there. differences between five dry and five wet seasons with Lag correlation between rainfall and Nino3.4 indices corresponding winds gave evidence that the signal shifts (Figure 8b) reveals that significant values are reached by from the Asian winter monsoon to the Maritime Continent. two month lead time, too short for intervention and By summer a divergent circulation over the East Indian mitigating actions. Hence our analysis of Maritime Ocean (cf Figure 6c) drives tropical convection into the Continent circulation links to ENSO is essential for central Pacific, and inhibits convection over tropical North predictability at the longer lead times required. Africa. The ocean thermocline lifts in the west Pacific (Figure 7b) signalling the onset of El Niño and corresponding dry weather over Ethiopia. The statistical Conclusion links depend on historical replication, and predictive outliers since 2006 are of concern. How are these results Rainfall over the grain-crop growing areas of Ethiopia has applied? Ethiopia currently experiences food deficits

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Zonal circulation Specific humidity

DRY WET

Africa a Africa b

Zonal circulation Specific humidity

DRY WET

c d India Pacific India Pacific m/s g/kg

Figure 6. Composite longitudinal sections (averaged 5-15 N) of dry minus wet: (a) preceding Dec-Mar zonal wind and (b) specific humidity, and (c, d) same for Jun-Sep season. Zonal circulation vectors (max = 3 m/s) shown in a, c. Meridional wind differences < -.5 m/s grey shaded in b,d, represent Asian monsoon outflow before and during drought in Ethiopia. Topography illustrated, vertical motion exaggerated. Shading in (a, c) and (b, d) is consistent, and areas with minor differences are unshaded.

because of low resource inputs, high population density Continent anticipate shifts in the zonal circulation and and variable climate. Shortfalls can be overcome with tropical convection associated with ENSO (Chang et al., scientific information and practical engineering solutions 2006; Lau and Wang, 2006). Seasonal forecasts of (Goddard et al., 2010). Rainfall predictions are formulated summer rainfall over the grain-crop growing areas of the by the Ethiopian Institute for Agriculture Research and Ethiopia highlands based on December-March predictors shaped into coherent advice to collective decision- near the Maritime Continent will provide the necessary makers/resource managers and community based lead time for strategic decisions to mitigate adverse farming groups. Mitigating actions are suggested by impacts or take advantage of favourable weather. agricultural extension services, with feedback from a Processes underlying the apparent skill of the tropical network of cultivators dealing with impacts. We have wind predictors need to be tested via coupled ensemble sought to enhance our predictive capacity by statistically model simulations. The search for predictability could be analyzing how near-surface winds around the Maritime extended to targets such as vegetation fraction and

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Africa

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-3 Africa

b

Figure 7. Composite longitudinal sections (averaged 5-15 N) of dry minus wet: (a) preceding Dec-Mar sub- surface ocean temperature and zonal circulation (vectors), and (b) same for Jun-Sep season. Vertical motions are exaggerated.

streamflow. ACKNOWLEDGEMENTS

Conflict of Interest This study is part of a Rockefeller Foundation project with the Ethiopian Institute for Agriculture Research, Melkasa, The authors have not declared any conflict of interest. conceived with help from J. Seid and G. Mamo.

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35E 36E 37E 38E 39E 40E 41E 42E

mo nth s

Figure 8. (a) Correlation of Jun-Sep rainfall with Jul-Oct satellite vegetation fraction (shaded) and Jun-Sep Nino3.4 index (contours < -0.4), (b) lag correlation between summer rainfall and Nino3.4 index, negative (months) refer to Pacific SST leading rainfall. The 98% confidence is achieved at - 0.4 (dashed). Red line is mean and green lines are upper and lower quintiles.

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