Does the IOD Independently Influence Seasonal Monsoon Patterns In

atmosphere

Article Does the IOD Independently Inﬂuence Seasonal Monsoon Patterns in Northern Ethiopia?

Daniel Gebregiorgis 1,* , David Rayner 2 and Hans W. Linderholm 2

1 Department of Geosciences, Georgia State University, Atlanta, GA 30303, USA 2 Regional Climate Group, Department of Earth Sciences, University of Gothenburg, 41320 Gothenburg, Sweden * Correspondence: [email protected]

Received: 25 June 2019; Accepted: 23 July 2019; Published: 26 July 2019

Abstract: The dominant large-scale interannual modes in the tropical Pacific and Indian Oceans—El Niño southern oscillation (ENSO) and the Indian Ocean Dipole (IOD)—dominate seasonal rainfall patterns in Ethiopia. However, there is a clear interaction between ENSO and the IOD, and it is unclear whether the IOD has an independent influence on seasonal monsoon patterns in Northern Ethiopia. We use monthly rainfall records from 15 stations from two drought–prone regions in Northern Ethiopia (Afar and Amhara) for the period 1966–2006 to explore relationships between rainfall and circulation patterns and sea surface temperature (SST) anomalies over the tropical Indo-Pacific region. Our analysis confirms that regional summer monsoon (Kiremt) rainfalls in these regions are predominantly modulated by ENSO. Warm and cold ENSO episodes (El Niño/La Nina) are associated with below and above average summer monsoon rainfall, respectively. Lagged relationship between the IOD and Kiremt rainfall shows that positive/negative phases of the IOD are generally conducive to Kiremt rainfall increases/decreases over large parts of Ethiopia. Regression models based on the large-scale circulation indices NINO3.4 and a Dipole Mode Index (DMI)NO-ENSO representing the “ENSO-free IOD” also highlight the role of ENSO. However, the relative-weights for the models with DMINO-ENSO, calculated using Akaike Information Criteria (AIC), were 1.5 and 1.1 times the weights for the ENSO only models for the Afar and Amhara regions, respectively. This suggests that the IOD has an independent regional influence. This is in line with the conception of the IOD as a unique coupled-mode in the tropics, and may have important implications in boosting seasonal forecasting skills in the regions. No statistically significant trends were found in the regional and modeled rainfall time-series.

Keywords: ENSO-free IOD; IOD; ENSO; monsoon

1. Introduction The East African monsoon system is predominantly linked to changes in sea surface temperatures (SSTs) in the Indian and Paciﬁc Oceans [1–7] and, to a much lesser extent, the North Atlantic Ocean [8]. Northern Ethiopia lies within the core moisture-sink region of the East African monsoon system, and rainfall changes over the region are associated with the El Niño southern oscillation (ENSO) and the Indian Ocean Dipole (IOD) [9,10]. Above/below average monsoon rainfall over large parts of Ethiopia mostly coincides with strong cold/warm phases of ENSO and positive/negative IOD years; for example, [3,11]. However, ENSO and the IOD are interrelated phenomena, and the extent to which the IOD is considered as a distinct oscillation of the climate system is still widely debated; for example, [12–16]. It is therefore unclear whether the IOD exerts inﬂuence on seasonal monsoon patterns in Northern Ethiopia, independent of ENSO.

Atmosphere 2019, 10, 432; doi:10.3390/atmos10080432 www.mdpi.com/journal/atmosphere Atmosphere 2019, 10, 432 2 of 14

Significant progress has been made over the years towards better understanding of the large-scale influences on Ethiopian rainfall; for example, [1,3–5,17,18]. ENSO-related climate variability, in particular, has been a subject of intense research over the last three decades, to the extent that sea surface temperature (SST) anomalies in the tropical Pacific are primarily used as main predictors of the Ethiopian monsoon in the operational forecasts of the National Meteorology Agency (NMA) [5]. The operational forecasts rely on lead and lag relationships between observed and predicted SSTs and seasonal rainfall patterns, because current dynamical models show little skill [19,20]. A better understanding of the independent influence of the IOD on seasonal monsoon patterns is, therefore, crucial for improving seasonal forecasting models for the region. Here, we build composite records for two drought-prone regions in Northern Ethiopia for the period 1966–2006 using monthly rainfall records from 15 stations, and we assess the contribution and impact of the “independent” component of the IOD on seasonal rainfall patterns. To this end, we develop an empirical forecast model for the monsoon rains in Northern Ethiopia, also locally known as Kiremt (July–September) using regression with two sets of large-scale indices, after removing the direct and lagged ENSO signal from the IOD [21]. Long-term trends in regional rainfall and station records are also examined.

2. Data and Methods

2.1. Rainfall Data The National Metrological Services Agency (NMSA) of Ethiopia provided rainfall data for 17 stations (Table1). The study area (Figure1) lies within the Afar and Amhara regional states, and stations were selected based on lengths of the precipitation records and the geographical distribution. Data were missing or limited for some stations for a few months and/or years, but this did not impact the composite records (Table1). The rainfall records extend from the mid-1960s to the mid-2000s. Station histories are unknown, and it is likely that there have been station relocations and changes in instrumentation. Consequently, all records were examined for inhomogeneity using a standard normal homogeneity test (SNHT) [22]. The rainfall stations were then divided into two groups based on topography and geographical proximity, while also taking account of the inter-station correlation (1981–2006) between Kiremt rainfall series as a function of inter-station distance. Given the influence of topography on the spatial variation in rainfall patterns and the strong relationship between rainfall and elevation in both regions, grouping the stations according to topography offers better representation of the station records. To ensure that regional rainfall trends were not dominated by unrepresentative observations from a single station, they were compared with seasonal trends at all individual gauge stations using the nonparametric Mann-Kendall (MK) test [23]. Confidence levels of 95% and 99% were set as thresholds to classify the significance of positive or negative trends, while trends at significance below the 95% confidence level were classified as showing no-trend. Trend magnitude in all time series was determined using Sen’s estimator method [24]. Atmosphere 2019, 10, 432 3 of 14 Atmosphere 2019, 10, x FOR PEER REVIEW 3 of 15

FigureFigure 1. 1.( a(a)) Mean Mean summer summer monsoonmonsoon precipitationprecipitation (Kiremt) and and anomalous anomalous vector vector wind wind at at 850 850 hPa, hPa, basedbased on on HadGEM3 HadGEM3 downscaled downscaled ERA-Interim ERA-Interim (mm /day)(mm/day) data for data 1990–2008 for 1990–2008 and on Global and Precipitationon Global ClimatologyPrecipitation CenterClimatology (GPCC) Center data, respectively.(GPCC) data, (brespectively.) Topographic (b) mapTopographic of Ethiopia map (elevation of Ethiopia data interpolated(elevation data from interpolated GTOPO30). from Yellow GTOPO30). and red circles Yellow in (aand) and red (b )circles show thein locations(a) and (b of) theshow rainfall the stationslocations in of Afar the andrainfall Amhara, stations respectively. in Afar and Amhara, Black arrows respectively. in (a) indicate Black arrows the atmospheric in (a) indicate surface the 1 circulationatmospheric pattern surface during circulation the Kiremt pattern monsoon during season.the Kiremt Reference monsoon arrow season. given Reference in m s− . arrow given in ms−1. Table 1. Details of the 15 rainfall stations used in the study. Note that two other stations (Awara Melka andTable Awash 1. Details 7 kilo) of were the excluded15 rainfall from stations the analysis used in becausethe study. of concernsNote that with two dataother integrity stations (see (Awara text). Melka andStation Awash Name 7 kilo) Altitudewere excluded (m) from Observation the analysis because Missing of (years) concerns with Cluster data integrity (see text). Assayita 430 1966–2006 7 Afar StationBati Name Altitude 1501 (m) Observation 1987–2005 Missing 0 (years) Cluster Amhara BokokesaAssayita 1673430 1975–20051966–2006 9 7 Amhara Afar Dessie 2402 1971–2006 2 Amhara Bati 1501 1987–2005 0 Amhara Dubti 377 1981–2006 5 Afar BokokesaHayq 20301673 1966–20061975–2005 1 9 Amhara Amhara KarakoreDessie 16962402 1966–20031971–2006 1 2 Amhara Amhara KombolchaDubti 1916377 1966–20061981–2006 05 Amhara Afar Logia 400 1980–2006 3 Afar Hayq 2030 1966–2006 1 Amhara Mersa 2300 1981–2006 3 Amhara KarakoreMille 5171696 1966–20061966–2003 3 1 Amhara Afar KombolchaSerdo 4451916 1981–20051966–2006 7 0 Amhara Afar SrinkaLogia 2080400 1981–20061980–2006 43 AmharaAfar WerebaboMersa 16502300 1981–20061981–2006 2 3 Amhara Amhara Woldia 2010 1966–2006 6 Amhara Mille 517 1966–2006 3 Afar Serdo 445 1981–2005 7 Afar 2.2. Large–Scale Circulation Indices Srinka 2080 1981–2006 4 Amhara Different atmosphericWerebabo and oceanic1650 indices are1981–2006 used as a measure of 2 ENSO. The Amhara oceanic component of ENSO is measuredWoldia by the Niño3, Niño3.4,2010 and Niño41966–2006 SST indices over the 6 eastern (5Amhara◦ N–5◦ S, 90◦–150◦ W), east central (5◦ N–5◦ S, 120◦–170◦ W), and central (5◦ N–5◦ S, 160◦ E–150◦ W) tropical Pacific correspondingly. Monthly2.2. Large–Scale values ofCirculation the Niño 3.4Indices index for the period 1966–2006 were obtained from the National Oceanic and AtmosphericDifferent atmospheric Administration and (NOAA) oceanic Climateindices Predictionare used Centeras a measure (CPC) database of ENSO. [25]. The oceanic componentThe IOD of is ENSO a coupled is measured ocean–atmosphere by the Niño3, mode Ni inño3.4, the tropicaland Niño4 Indian SST Ocean indices [26 over]. The the Dipole eastern Mode (5° IndexN–5° (DMI)S, 90°–150° is an indicator W), east ofcentral the east-west (5° N–5° temperature S, 120°–170° gradient W), and of central the sea surface(5° N–5° temperature S, 160° E–150° anomaly W) (SSTA)tropical across Pacific the correspondingly. tropical Indian Ocean,Monthly and values is defined of the asNiño the difference3.4 index for between the period the western 1966–2006 box

Atmosphere 2019, 10, x FOR PEER REVIEW 4 of 15 were obtained from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) database [25]. The IOD is a coupled ocean–atmosphere mode in the tropical Indian Ocean [26]. The Dipole ModeAtmosphere Index2019, 10(DMI), 432 is an indicator of the east-west temperature gradient of the sea surface4 of 14 temperature anomaly (SSTA) across the tropical Indian Ocean, and is defined as the difference between the western box (50° E–70° E, 10° S–10° N) and eastern box (90° E–110° E, 10° S–eq). (50◦ E–70◦ E, 10◦ S–10◦ N) and eastern box (90◦ E–110◦ E, 10◦ S–eq). Monthly values of DMI (1966–2006) wereMonthly downloaded values of from DMI the (1966–2006) Japan Agency were for download Marine-Earthed from Science the and Japan Technology Agency (JAMSTEC)for Marine-Earth [27]. ScienceThe and set Technology of large-scale (JAMSTEC) drivers measured [27]. by the indices is not entirely independent. In particular, ENSOThe appears set of large-scale to exert influence drivers onmeasured the IOD, by and the vice indi versa.ces is not Following entirely on independent. Werner, et al. In [ 21particular,], lagged ENSOregression appears between to exert monthly influence Niño 3.4on SSTthe andIOD, DMI and was vice used versa. to removeFollowing the ENSOon Werner, effect andet al. extract [21], laggedthe ENSO-free regression IOD between (Figure 2monthly). The ENSO-free Niño 3.4 SST IOD and is a DMI better was variable used to useremove to evaluate the ENSO the effect IOD andcontribution extract the to ENSO-free rainfall variability IOD (Figure and 2). examine The ENSO-fr its hindcastee IOD skill.is a better This variable method to takes use to account evaluate of thestatistically IOD contribution significant leadto /rainfalllag correlations variability (at pand< 0.05 examine and p < 0.01)its hindcast between skill. ENSO This and IODmethod to remove takes accountthe ENSO of influencestatistically from significant the variable. lead/lag By regressingcorrelations the (at monthly p < 0.05 DMIand p over < 0.01) NINO3.4 between time ENSO series and at IODENSO to lead remove/lags upthe to ENSO eight months,influence and from subtracting the variable. the sum By of regressing these contributions, the monthly it was DMI possible over NINO3.4to remove time the ENSOseries signalat ENSO from lead/lags the IOD indexup to with eight no months, significant and correlations subtracting evident the sum between of these the contributions, it was possible to remove the ENSO signal from the IOD index with no significant new ENSO-free IOD index (DMINOENSO) and the other ENSO indices (e.g., SOI, NINO3, NINO4) for correlations evident between the new ENSO-free IOD index (DMINOENSO) and the other ENSO leads/lags of up to 12 months, while the original DMI time series and DMINOENSO are significantly indicescorrelated (e.g., (p

Figure 2. NormalizedNormalized monthly monthly time time series series of ofNINO3.4 NINO3.4 sea seasurface surface temperature temperature anomaly anomaly (SSTA), (SSTA), the originalthe original Dipole Dipole Mode Mode Index Index (DMI), (DMI), and and the the new new El El Niño Niño southern southern oscillation oscillation (ENSO)-independent (ENSO)-independent DMI (DMI ) for the period 1966–2006. DMI (DMINOENSO) for the period 1966–2006. 2.3. Identification of Rainfall Predictors 2.3. Identification of Rainfall Predictors The relationships between the standard indices during the pre-monsoon and monsoon seasons, and The relationships between the standard indices during the pre-monsoon and monsoon correlations between regional rainfalls and large-scale ocean–atmospheric variables, were examined. seasons, and correlations between regional rainfalls and large-scale ocean–atmospheric variables, This approach of correlation-analysis using ocean–atmospheric circulation variables as predictors were examined. This approach of correlation-analysis using ocean–atmospheric circulation provides insights into large-scale controls on Ethiopian rainfall, and is used to enhance Ethiopia’s variables as predictors provides insights into large-scale controls on Ethiopian rainfall, and is used seasonal forecasting capabilities [5]. Seasonal relationships between regional rainfall, SSTs, and vector to enhance Ethiopia’s seasonal forecasting capabilities [5]. Seasonal relationships between regional wind fields are further explored using composite analysis of global SST and vector wind fields during rainfall, SSTs, and vector wind fields are further explored using composite analysis of global SST unusually wet and dry years. Interannual departures expressed as Rainfall Anomaly Indices (RAI) and vector wind fields during unusually wet and dry years. Interannual departures expressed as were calculated by dividing the regional rainfall anomaly with respect to the mean by the standard Rainfall Anomaly Indices (RAI) were calculated by dividing the regional rainfall anomaly with deviation of regional Kiremt rainfall over the period of observation, and is used to define the driest and respect to the mean by the standard deviation of regional Kiremt rainfall over the period of wettest years. Zonal and meridional wind components at 300 and 850 hPa have been extracted from observation, and is used to define the driest and wettest years. Zonal and meridional wind the NCEP/NCAR reanalysis data set and are used to describe the changes in circulation and Kiremt components at 300 and 850 hPa have been extracted from the NCEP/NCAR reanalysis data set and rainfall based on GPCC data [28]. are used to describe the changes in circulation and Kiremt rainfall based on GPCC data [28]. We performed a multiple-linear regression analysis of regional Kiremt rainfall based on the predictor indices. We regressed regional Kiremt and predictor indices using data for 1966–2006, and used two sets of predictor indices to develop regional models. Correlation analysis of regional Atmosphere 2019, 10, 432 5 of 14

Kiremt rainfalls vs. each potential predictor did not suggest any non-linear relations. Thus, the following linear relation was assumed and employed in the ﬁrst models:

Modeled Kiremt = β + βs xs + et 0 × where xs is the variable vectors of predictor index NINO3.4, β represents model coeﬃcient, and et is the additive error term. To assess the inﬂuence of IOD, we used a model with the form:

Modeled Kiremt = β + βs xs + β x + et 0 × i × i where xs and xi are variable vectors corresponding to predictor indices NINO3.4 and DMINOENSO. We characterized the best model using Akaike Information Criterion (AIC) [29] after sample size adjustment using residual sum of squares (RSS) [30]. The AIC measures the relative quality of models, and combines a measure of goodness-of-ﬁt with a penalty term that increases with the number of free parameters. The AIC can be computed as:

AIC = n ln(RSS/n) + 2 k + (2 k* k + 1))/(n k 1) × × × × − − where k is the number of free parameters in the model, and n is the sample size. Models are usually compared by normalizing the relative likelihood exp((AIC AIC )/2) to calculate Akaike weights min − i (wi), analogous to weights in a weighted average. Higher AIC weights indicate better models.

3. Results and Discussions

3.1. Trends in Regional Rainfall and Correlations with Large-Scale Circulation Indices Temporal trends in regional and individual rainfall data series were investigated in detail. Trend analysis of regional and station rainfall data series showed no statistically significant long–term trends. Amhara Kiremt rainfalls showed slightly increasing trends, while Kiremt in Afar showed a slightly decreasing trend during the same period (none statistically significant). Weak positive trends in Kiremt at Logia and Dubti in Afar were not statistically significant. Similarly, trends in Kiremt rainfalls were insignificant for all stations in Amhara, although Dessie and Mersa stations showed weak positive trends with negative trends at Karakore (none statistically significant). A number of other studies have also concluded that there is no evidence of any long-term trend or change in rainfall for different parts of the country, including Afar and Amhara [31–34]. Neither modeled rainfall time-series (see Section 3.2), nor the predictor indices, showed any statistically significant trends, although trends in modeled Afar rainfall show a weak positive trend in contrast to weak negative regional trends. Likewise, the distribution of residuals against predicted values (1966–2006) of Kiremt rainfall showed no trend for either Afar or Amhara, and no changes in rainfall caused by other factors were detected. Seasonal and interannual correlations between regional rainfalls and circulation indices suggest that positive/negative Kiremt anomalies are strongly linked to ENSO (Figure3). The same is also reflected in the composite maps of seasonal SST anomalies during the months of June to July for the driest and wettest years in Afar and Amhara (Figure4). The driest years in Afar and Amhara coincide with warm anomalies in the equatorial Pacific corresponding to El Niño conditions, while the opposite is true for the wettest years. During the driest years in Afar and Amhara, maximum upper-level easterly anomalies are found over Northern Ethiopia and the Arabian Peninsula (Figure5), implying the tropical westerlies in the upper troposphere are significantly weakened with zonal wind anomalies become increasingly easterly across much of eastern Africa during the driest years in Afar and Amhara. Meanwhile, during the wettest years in Amhara and Afar, there is an increase in the counterclockwise circulation anomaly over the northern Indian Ocean. Atmosphere 2019, 10, 432 6 of 14

While ENSO is found to have the strongest relationships with Kiremt rainfall in both Amhara andAtmosphere Afar, statistically2019, 10, x FOR signiﬁcant PEER REVIEW correlations were also evident between Kiremt rainfall in Afar6 and of 15 Atmosphere 2019, 10, x FOR PEER REVIEW 6 of 15 DMINOENSO during the preceding month of March (Figure6), with weaker correlations in Amhara.

TheThe correlation correlation between between DMI DMINOENSONOENSO and regionalregional rainfallrainfall isis slightlyslightly weakerweakerthan than with with the the original original The correlation between DMINOENSO and regional rainfall is slightly weaker than with the original DMIDMI index. index. Note, Note, however, however, that that if if we we exclude exclude the the weaker weaker IOD IOD years years from from the the analysis analysis (i.e., (i.e.,<+ <+0.50.5 DMI index. Note, however, that if we exclude the weaker IOD years from the analysis (i.e., <+0.5 standardstandard score score for for positive positive IOD IOD years years and and> >0.5−0.5 standard standard score score for for negative negative IOD IOD years), years), DMI DMINOENSONOENSO standard score for positive IOD years and >−−0.5 standard score for negative IOD years), DMINOENSO hashas a a stronger stronger correlation correlation with with regional regional Kiremt Kiremt rainfall rainfall (R (R= =0.41 0.41 for for Afar Afar and and R R= =0.35 0.35 for for Amhara). Amhara). has a stronger correlation with regional Kiremt rainfall (R = 0.41 for Afar and R = 0.35 for Amhara). A strong positive relationship between summer rainfall and the IOD during the boreal spring suggests A strongstrong positivepositive relationshiprelationship betweenbetween summersummer ra rainfallinfall and and the the IOD IOD during during the the boreal boreal spring spring strengthening of the regional monsoon after the peaking phase (i.e., between September and November) suggests strengtheningstrengthening ofof thethe regionalregional monsoon monsoon after after the the peaking peaking phase phase (i.e., (i.e., between between September September of the IOD with a lead-time of ~8 months. In the next sections, we turn to the main focus of this study: and November)November) ofof thethe IODIOD with with a a lead-time lead-time of of ~8 ~8 mont months.hs. In In the the next next sections sections, we, we turn turn to tothe the main main Whether the IOD has an inﬂuence on the seasonal monsoon patterns in Northern Ethiopia that is focusfocus ofof thisthis study:study: WhetherWhether thethe IODIOD hashas anan influence influence on on the the seasonal seasonal monsoon monsoon patterns patterns in in independent of ENSO. Northern EthiopiaEthiopia thatthat is is independent independent of of ENSO. ENSO.

FigureFigureFigure 3. 3.3.Correlations CorrelationsCorrelations between betweenbetween regional regional regional KiremtKiremt Kiremt rainfallrainfall rainfall andand and July–SeptemberJuly–September July–September NINO3.4 NINO3.4 sea sea sea surface surface surface temperaturestemperaturestemperatures (SSTs) (SSTs)(SSTs) averages averages averages forfor for ((a a()a) Amhara)Amhara Amhara andand and ((b b()b) Afar.)Afar. Afar. Color Color shades shades represent represent represent individual individual individual years years years betweenbetweenbetween 1966 19661966 and and and 2006. 2006. 2006.

FigureFigure 4. 4.June June to to July July global global SSTA SSTA during during (a) driest(a) driest years years in Amhara in Amhara and and Afar Afar (RAI (RAI1.0; ≤ − Years1.0; Years 1982, Figure 4. June to July global SSTA during (a) driest years in Amhara and Afar≤ (RAI − ≤ −1.0; Years 1983,1982, 1984, 1983, 1987), 1984, and1987), (b )and wettest (b) wettest years in years Afar in and Afar Amhara and Amhara (RAI (RAI1.0; Years≥ 1.0; 1975,Years1988, 1975,1994, 1988, 1998).1994, 1982, 1983, 1984, 1987), and (b) wettest years in Afar and Amhara≥ (RAI ≥ 1.0; Years 1975, 1988, 1994, 1998). 1998).

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FigureFigureFigure 5. Composite5. 5.Composite Composite maps maps maps of summerof of summer summer (July–September) (July–September (July–September anomalous) )anomalous anomalous vector vector windvector atwind wind 850 hPaat at850 (reference850 hPa hPa vector(reference(reference given vector in vector m/s) given andgiven precipitationin in m/s) m/s) and and precipitation anomaliesprecipitation duringanomalies anomalies the (duringa )during driest the the and (a )( a (driestb) )driest wettest and and years(b ()b wettest) wettest in Afar andyearsyears Amhara. in in Afar Afar and and Amhara. Amhara.

FigureFigureFigure 6. Monthly6. 6.Monthly Monthly correlations correlations correlations between between between regional regi regi Kiremtonalonal Kiremt rainfallKiremt rainfall andrainfall DMI and NOENSOand DMI DMINOENSOduringNOENSO duringthe during preceding the the monthprecedingpreceding of March month month for of ( aof )March AmharaMarch for for and(a )( aAmhara) ( bAmhara) Afar. and Colorand (b ()b shadesAfar.) Afar. Color representColor shades shades individual represent represent years individual individual between years years 1966 andbetweenbetween 2006. 1966 1966 and and 2006. 2006. 3.2. New Statistical Models for Regional Kiremt Rainfall 3.2.3.2. New New Statistical Statistical Models Models for for Regional Regional Kiremt Kiremt Rainfall Rainfall We developed multi-linear regression models for predicting regional Kiremt rainfalls in Afar and WeWe developed developed multi-linear multi-linear regression regression models models for for predicting predicting regional regional Kiremt Kiremt rainfalls rainfalls in in Afar Afar Amhara,andand Amhara, Amhara, and evaluated and and evaluated evaluated the contribution the the contribution contribution of the large-scaleof of th the elarge-scale large-scale circulation circulation circulation indices indices after indices removing after after removing removing the direct andthethe lagged direct direct ENSO and and laggedsignal lagged fromENSO ENSO the signal IOD.signal Thefrom from regression the the IOD. IOD. coe The Theffi cientsregression regression for predicting coefficients coefficients normalized for for predicting predicting regional Kiremtnormalizednormalized rainfalls regional fromregional normalized Kiremt Kiremt rainfalls predictorrainfalls from from indices normalized normalized (NINO3.4) predictor predictor for July–September indices indices (NINO3.4) (NINO3.4) and DMI for NOENSOfor July– July–for March–AprilSeptemberSeptember areand and shown DMI DMINOENSO inNOENSO Table for for2 .March–April CorrelationMarch–April between are are shown shown Kiremt in in Table rainfallTable 2. 2. andCorrelation Correlation NINO3.4 between andbetween DMINO Kiremt KiremtENSO wererainfallrainfall calculated. and and NINO3.4 TheNINO3.4 NINO3.4 and and DMINO isDMINO the mostENSOENSO influentialwere were calculated. calculated. predictor The The of KiremtNINO3.4 NINO3.4 rainfalls is isthe the in most Afarmost andinfluential influential Amhara. Thepredictorpredictor highest of correlationsof Kiremt Kiremt rainfalls rainfalls between in in Afar Afar Kiremt and and Amhara rainfallAmhara. The. and The highest NINO3.4highest correlations correlations are obtained between between at zeroKiremt Kiremt months rainfall rainfall lag, andandand there NINO3.4 NINO3.4 is no furtherare are obtained obtained significant at at zero zero relationshipmonths months lag, lag, and after and th 3there monthsere is isno no further lag;further whereas significant significant the relationship highest relationship correlations after after 3 3 between Kiremt rainfall and DMINOENSO were obtained for March–April. Input predictors were

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months lag; whereas the highest correlations between Kiremt rainfall and DMINOENSO were obtained constructed based on these observations as potential predictor sets for Kiremt rainfall. The predictors for March–April. Input predictors were constructed based on these observations as potential were admitted in to the model after employing stepwise selection procedure. The regression equations predictor sets for Kiremt rainfall. The predictors were admitted in to the model after employing were fitted to all data and cross-validated over the 1966–2006 period. There is no significant serial stepwise selection procedure. The regression equations were fitted to all data and cross-validated correlation in the residuals for either region. over the 1966–2006 period. There is no significant serial correlation in the residuals for either region. Figure7a,b show modeled and observed regional rainfall for the period 1966–2006 using the new Figure 7a,b show modeled and observed regional rainfall for the period 1966–2006 using the predictor set (NINO3.4 + DMINOENSO). The same is shown using the NINO3.4 as the only predictor new predictor set (NINO3.4 + DMINOENSO). The same is shown using the NINO3.4 as the only for Kiremt rainfall in Figure7c,d. The new model using the predictor set (NINO3.4 + DMI ) predictor for Kiremt rainfall in Figure 7c,d. The new model using the predictor set (NINO3.4NOENSO + has a higher correlation (r2 = 0.18 and r2 = 0.14 for Afar and Amhara, respectively), with a higher DMINOENSO) has a higher correlation (r2 = 0.18 and r2 = 0.14 for Afar and Amhara, respectively), with predictor weight for DMI in the Amhara region. The models were then compared using a higher predictor weightNOENSO for DMINOENSO in the Amhara region. The models were then compared Akaikeusing Akaike information information criteria criteria (Table3 ).(Table The results 3). The clearly results suggest clearly that suggest the predictor that the indicespredictor (NINO3.4 indices + DMI ) are preferred for the Afar region. These results provide empirical evidence that the (NINO3.4NOENSO + DMINOENSO) are preferred for the Afar region. These results provide empirical evidence predictor set (NINO3.4 + DMI ) is a significantly better model of regional rainfall; that is, that the predictor set (NINO3.4NOENSO + DMINOENSO) is a significantly better model of regional rainfall; that whileis, while ENSO ENSO predominantly predominantly controls controls regional regional Kiremt Kiremt rainfall, rainfall, the IOD the also IOD substantially also substantially modulates regionalmodulates rainfall. regional Both rainfall. models underestimatedBoth models underest Kiremtimated rainfall Kiremt for the rainfall year 1997, for where the year the exceptionally1997, where strongthe exceptionally El Niño year strong of 1997 El didNiño not year result of 1997 in a significantdid not result reduction in a significant of Kiremt reduction rainfall in of Afar Kiremt and Amhara,rainfall in as Afar generally and Amhara, predicted as forgenerally El Niño predicted years. for El Niño years.

FigureFigure 7.7. StandardizedStandardized modeledmodeled KiremtKiremt rainfallrainfall (NINO3.4(NINO3.4 ++ DMIDMINOENSONOENSO) )and and observed observed regional regional rainfallrainfall for for the the same same period period for for (a )(a Amhara) Amhara and and (b) ( Afarb) Afar regions. regions. NINO3.4-based NINO3.4-based modeled modeled rainfall rainfall with observedwith observed regional regional rainfall rainfall for 1966–2006 for 1966–2006 for (c )for Amhara (c) Amhara and ( dand) Afar (d) regions.Afar regions.

Table 2. Regression coeﬃcients for prediction of normalized regional Kiremt rainfall from normalized Table 2. Regression coefficients for prediction of normalized regional Kiremt rainfall from predictor indices, and the relative weights of each predictor index on the regional Kiremt rainfall normalized predictor indices, and the relative weights of each predictor index on the regional model estimates. Kiremt rainfall model estimates.

IndicesIndices Regression Regression CoeCoefficientsﬃcients Predictor Predictor Weights Weights AfarAfar Amhara Amhara Afar Amhara Amhara NINO3.4NINO3.4 0.29 0.29 0.31 0.31 61% 53% 53% DMINOENSO 0.18 0.27 39% 47% DMINO ENSO 0.18 0.27 39% 47%

Table 3. Model comparison based on Akaike Information Criterion (AIC) and weights (wi) with K Table 3. Model comparison based on Akaike Information Criterion (AIC) and weights (wi) with K number of free parameters. Lower AIC/higher weights indicate preferred models. number of free parameters. Lower AIC/higher weights indicate preferred models. Model K RSS AIC ∆ (AIC min AIC) w Model K RSS AIC Δi (AICi i − mini − AIC) wi i NINO3.4+ +DMI DMINOENSONOENSO 44 29 29 −4.454.45 −4.45 4.45+ 4.45+ 4.45= 0.00= 0.00 0.60 0.60 Afar − − NINO3.4 3 32 3.02 3.05 + 4.45 = 1.30 0.40 Afar Afar NINO3.4 3 32 −3.02− −3.05− + 4.45 = 1.30 0.40 NINO3.4+ +DMI DMINOENSONOENSO 44 30 30 −2.972.97 −2.97 2.97+ 2.97+ 2.97= 0.00= 0.00 0.51 0.51 Amhara − − ra NINO3.4NINO3.4 3 332 32 −2.822.82 −2.82 2.82+ 2.97+ 2.97= 0.15= 0.15 0.49 0.49 Amha − −

3.3. The Role of the IOD in Eastern African Rainfall

Atmosphere 2019, 10, 432 9 of 14

3.3. The Role of the IOD in Eastern African Rainfall Previous studies have identified a variety of drivers of seasonal rainfall in Ethiopia. Large scale interannual modes in the tropical Pacific and Indian Oceans, the ENSO and IOD respectively, have been shown to dominate the climate and rainfall patterns in East Africa over the last centuries [35], while little is known in regards to the influence of the IOD (i.e., independent of the ENSO). The IOD is a coupled mode of SST variability in the tropical Indian Ocean, and is correlated with interannual variability of precipitation from East Africa to Australia to India [36–42]. Although, the impact of the IOD on eastern African rainfall is thought to be strongest from September to November [41], it has also been shown that extreme positive IOD events are associated with above-average precipitation over southern East Africa during the monsoon season [36]. However, there is little evidence as to whether IOD has an influence independent of ENSO. The IOD is a coupled mode of SST variability in the tropical Indian Ocean and is often considered as a direct extension of the ENSO. In order to assess the contribution and impact of the “independent” IOD on seasonal rainfall patterns and quantify the statistical relation between the IOD and Kiremt rainfall, correlation analysis was conducted using the new DMINOENSO index and Ethiopian rainfall based on the GPCC dataset. A recent study [43] shows that the GPCC dataset is in good agreement with seasonal observations and spatiotemporal patterns over longer periods over the region. Correlations between Kiremt rainfall and DMINOENSO suggest that the IOD has significant positive lag-impact on Kiremt rainfall over large areas of Ethiopia (Figure8). The lagged relationship between the IOD and Kiremt rainfall is relatively weaker within three-months lead-time compared to the ENSO (Figure9). Nonetheless, the link between Kiremt rainfall and the IOD is statistically significant over large parts of Ethiopia, and suggests positive phases of the IOD are linked to strengthening of Kiremt over large parts of Ethiopia. As the IOD peaks in boreal fall, it has a strong influence on the contemporaneous rainfall variations over the next rainy season, with a lead-time of up to three months, while the ENSO has a much shorter lead-time (Figure9). A strong positive relationship between Kiremt and NINO3.4 suggests that the anomalous response of Kiremt rainfall over Northern Ethiopia is dominantly controlled by the ENSO, while a matured IOD phase can also serve as a potential predictor for Kiremt rainfall. Statistically, Kiremt over Northern Ethiopia can be predicted at six months lead-time if IOD is used as a precursor, particularly following positive phases of the IOD (Figure 10). To further examine the IOD’s influence on Kiremt rainfall, we have selected the strongest positive IOD (>+1 standard score) and negative IOD years (< 1 standard score) based on the new DMI − NOENSO index for the months between September–November. Accordingly, the years of 1988, 1994, and 1997 constitute the positive phases of the IOD, while 1980, 1992, and 1996 constitute the negative phases of the IOD. All negative IOD years occur during weak El Niño/La Niña years, while 1988 and 1997 coincide with strong La Niña and El Niño years, respectively. Note that El Niño and La Niña years are strongly linked to above/below average Kiremt rainfall over large parts of Ethiopia [3,11]. The fact that the exceptionally strong El Niño year of 1997 did not result in a significant reduction of Kiremt rainfall (Figure7) as generally predicted for El Niño years highlights the important role the IOD may play in modulating seasonal monsoon patterns across the region. The rainfall anomaly composites for the following Kiremt seasons are shown in Figure 10. As the IOD typically peaks, it has a strong influence on the contemporaneous rainfall variations over the next rainy season (Figure8). During positive phases of the IOD, cool SST anomalies are observed over the eastern equatorial Indian Ocean, while relatively warmer SST anomalies dominate the western equatorial Indian Ocean [15]. This zonal SST gradient reinforces the south westerly monsoon winds bringing heavy rains to the East African continent, while leading to droughts over the Indonesian region; for example, [37]. The rainfall anomaly composites during the years following the strongest positive IOD years also show that rainfall over large parts of the summer monsoon over Northern Ethiopia was significantly strengthened (Figure 10). Above average Kiremt rainfall occurred in most parts of central and southern Ethiopia during the same years (Figure 10). This is likely a response to the weakening of low-level easterlies and warming of the western Indian Ocean associated with the positive phase of the IOD [15,16], which leads to a Atmosphere 2019, 10, 432 10 of 14 convergence over East Africa [42]. During negative phases of the IOD, these anomalous oscillations seesaw to the opposite phase and Northern Ethiopia experiences a reversal of the dipole pattern, and drier summer conditions prevail over the region (Figure 10). However, there appears to be an asymmetry between the two opposite phases of the IOD, with regional rainfall increasing during positive phases of the IOD, while the impact of the IOD on regional rainfall during the negative phases is not as pronounced. This is likely driven by the asymmetry in the teleconnection pathways of ENSO and the IOD [38]. Overall, these results confirm that the IOD exerts a significant independent regional influence on regional Kiremt rainfalls in Afar and Amhara that is consistent with the conception of the IOD as a unique coupled mode in the tropics. Atmosphere 2019, 10, x FOR PEER REVIEW 10 of 15

FigureFigure 8.8.Comparisons Comparisons of of the the lagged lagged correlation correlation between between Kiremt Kiremt rainfall rainfall andthe and new the DMI newNOENSO DMINOENSOindex duringindex (duringa) March–May (a) March–May (MAM), (MAM), (b) April–May (b) April–May (AMJ), (c )(AMJ), May–July (c) (MJJ),May–July and (MJJ), (d) June–August and (d) June– (JJA). DottedAugust points (JJA). indicateDotted statisticallypoints indicate significant statistically grid pointssignificant at the grid 95% points confidence at the level95% (one-tailedconfidence test).level (one-tailed test).

Atmosphere 2019, 10, 432 11 of 14 Atmosphere 2019, 10, x FOR PEER REVIEW 11 of 15

FigureFigure 9.9. ComparisonsComparisons of the lagged correlation between Kiremt Kiremt rainfall rainfall and and NINO3.4 NINO3.4 index index during during (a(a)) March–May March–May (MAM), (MAM), (b )(b April–May) April–May (AMJ), (AMJ), (c) May–July(c) May–July (MJJ), (MJJ), and (andd) June–August (d) June–August (JJA). Dotted(JJA). pointsDotted indicate points statisticallyindicate statistically signiﬁcant significant grid points grid at thepoints 95% at conﬁdence the 95% confidence level (one-tailed level test).(one-tailed test).

Atmosphere 2019, 10, 432 12 of 14 Atmosphere 2019, 10, x FOR PEER REVIEW 12 of 15

FigureFigure 10. 10.Composite Composite maps maps of summer of summer (June–September) (June–Septemb anomalouser) anomalous vector windvector at 850wind hPa at (reference 850 hPa vector(reference given invector m/s) andgiven precipitation in m/s) and anomalies precipitation during anomalies the years during following the (yearsa) positive following (1988, (a 1994,) positive and 1997)(1988, and 1994, (b) negative and 1997) IOD and years (b (1980,) negative 1992, IOD and years 1996) determined(1980, 1992, basedand 1996) on the determined new DMINOENSO based index.on the 4. Conclusionsnew DMINOENSO index.

InvestigationTo further examine of the relationship the IOD’s betweeninfluence regional on Kiremt Kiremt rainfall, rainfalls we have and large-scaleselected the circulation strongest indicespositive show IOD that (>+1 ENSO standard has strongscore) and and negative widespread IOD influencesyears (<−1 onstandard seasonal score) monsoon based patternson the new in NorthernDMINOENSO Ethiopia. index Infor that, the Elmonths Niño /Labetween Niña eventsSeptember–November. are associated with Accordingly, below/above the average years of Kiremt 1988, rainfall1994, (oftenand 1997 used constitute as the main the positive proxy defining phases droughtof the IOD, and while wet years1980, in1992, the and regions). 1996 constitute Our analysis the confirmsnegative that phases regional of Kiremtthe IOD. rainfalls All negative in Afar andIOD Amhara years occur are predominantly during weak modulatedEl Niño/La by Niña the ENSO,years, butwhile also show1988 and that the1997 IOD coincide exerts with a significant strong La independent Niña and regionalEl Niño influence.years, respectively. This is consistent Note that with El theNiño conception and La ofNiña the IODyears as are a unique strongly coupled linked mode to ab inove/below the tropics, average and may Kiremt have rainfall important over implications large parts inof boosting Ethiopia seasonal [3,11]. forecastingThe fact that skills the inexceptionally these regions, strong where El seasonal Niño year monsoon of 1997 rainfall did not variability result in isa mainlysignificant controlled reduction by combined of Kiremt e ffrainfallects of (Figure various 7) large-scale as generally coupled predicted Ocean for Atmosphere El Niño years oscillations. highlights the important role the IOD may play in modulating seasonal monsoon patterns across the region. AuthorThe rainfall Contributions: anomalyConceptualization, composites for D.G.,the following D.R., and H.W.L.;Kiremt Methodology, seasons are D.G.,shown D.R., in andFigure H.W.L; 10. FormalAs the analysis, D.G.; Investigation, D.G.; Writing, D.G. with contribution from D.R. and H.W.L. IOD typically peaks, it has a strong influence on the contemporaneous rainfall variations over the Funding:next rainyThis season research (Figure received 8). no During external positive funding. phases of the IOD, cool SST anomalies are observed Acknowledgments:over the easternWe equatorial are grateful Indian to the EthiopianOcean, while National relatively Metrological warmer Services SST Agency anomalies for providing dominate rainfall the data.western We thank equatorial three anonymous Indian reviewersOcean [15]. for theirThis carefulzonalreading SST gradient of the manuscript reinforces and the helpful south comments. westerly Conflictsmonsoon of Interest: winds bringingThe authors heavy declare rains no to conflict the East of interest. African continent, while leading to droughts over the Indonesian region; for example, [37]. The rainfall anomaly composites during the years Referencesfollowing the strongest positive IOD years also show that rainfall over large parts of the summer monsoon over Northern Ethiopia was significantly strengthened (Figure 10). Above average Kiremt 1. Gissila, T.; Black, E.; Grimes, D.I.F.; Slingo, J.M. Seasonal forecasting of the Ethiopian summer rains. rainfallInt. J. occurred Climatol. 2004 in most, 24, 1345–1358. parts of central [CrossRef and] southern Ethiopia during the same years (Figure 10). 2. ThisIndeje, is likely M.; Semazzi,a response F.H.; to Ogallo, the weakening L.J. ENSO of signals low- inlevel East easterlies African rainfall and warming seasons. Int.of the J. Climatol. western2000 Indian, 20, Ocean19–46. associated [CrossRef with] the positive phase of the IOD [15,16], which leads to a convergence over East 3. AfricaKorecha, [42]. D.;During Barnston, negative A.G. Predictabilityphases of the of IOD, June–September these anomalous Rainfall oscillations in Ethiopia. seesawMon. Weather to the Rev.opposite2007, phase135, 628–650.and Northern [CrossRef Ethiopia] experiences a reversal of the dipole pattern, and drier summer 4. conditionsSegele, Z.T.; prevail Lamb, over P.J.; the Leslie, region L.M. (Figure Large-scale 10). atmosphericHowever, there circulation appears and to global be an sea asymmetry surface temperature between theassociations two opposite with phases Horn of of Africa the IOD, June–September with regional rainfall. rainfallInt. increasing J. Climatol. 2009during, 29, positive 1075–1100. phases [CrossRef of the] IOD, while the impact of the IOD on regional rainfall during the negative phases is not as pronounced. This is likely driven by the asymmetry in the teleconnection pathways of ENSO and

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