1116 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 52

Ethiopian Highlands Crop- Prediction: 1979–2009

MARK R. JURY University of Zululand, KwaDlangezwa, South , and University of Puerto Rico, Mayaguez, Puerto Rico

(Manuscript received 28 May 2012, in final form 17 November 2012)

ABSTRACT

This study compares different methods of predicting crop-related climate in the Ethiopian highlands for the period 1979–2009. A target index (ETH4) is developed as an average of four variables in the June–September season—rainfall, rainfall minus evaporation, estimated latent heat flux, and vegetation, following correlation with crop yields at Melkassa, (8.48N, 39.38E, 1550 m elevation). Predictors are drawn from gridded near-global fields of surface temperature, surface air pressure, and 200-hPa zonal wind in the preceding December–March season. Prediction algorithms are formulated by stepwise multivariate regression. The first set of predictors derive from objective principal component (PC) time scores with tropical loading patterns, and the second set is based on key areas determined from correlation with the target index. The second PC of upper zonal wind reveals a tropical–subtropical dipole that is correlated with ETH4 at two-season lead time (corre- lation coefficient r 520.53). Point-to-field regression maps show high-latitude signals in surface temperature (positive in and negative in Eurasia) and air pressure (negative in the North Pacific and positive in the South Pacific). Upper zonal winds are most strongly related with ETH4 over the tropical Pacific and Africa at two-season lead time. The multivariate algorithm that is based on PC predictors has an adjusted r2 fit of 0.23, and the algorithm using key-area predictors achieves r2 5 0.37. In comparison, numerical model forecasts reach r2 5 0.33 for ECMWF simulations but are low for other models. The statistical results are specific to the ETH4 index, which is a climate proxy for crop yields in the Ethiopian highlands.

1. Introduction time scales, meridional patterns of surface temperature (SST) can shift the Hadley cell and Past climate research has demonstrated a predictable associated rainfall regimes (Rowell et al. 1995; Jury component of seasonal rainfall and temperature that 1996; Ward 1998; Yeshanew and Jury 2007). Subtropical derives from coupling between anomalous ocean heat trade winds provide a link between the Atlantic SST content and shifts in atmospheric convection and gradient and Pacific ENSO (Enfield and Mayer 1997; circulation (Palmer and Anderson 1994). Climate pre- Servain et al. 1998; Latif and Grotzner 2000). Our diction efforts have employed both statistical and knowledge of processes driving SST variability in the numerical techniques; the former consider the history of is developing (Venzke et al. 2000). Zonal environmental patterns with respect to an index that gradients of Indian Ocean SST covary with Pacific is area and time averaged. The physics behind the ENSO (Tourre and White 1997; Yamagata et al. 2004) relationships are partially established, and there is and often generate opposing rainfall anomalies over progress in understanding the limits of predictability. southern and eastern Africa (Hastenrath et al. 1995; For time scales above seasonal, the ocean’s thermal Tennant 1996; Rocha and Simmonds 1997; Mason 1998). inertia tends to prevail over atmospheric initial condi- Land surface moisture and vegetation can provide sour- tions (Lorenz 1990; Rosati et al. 1997; Zeng et al. 1999; ces of memory in perennial regions (Fennessey Brankovic and Palmer 2000), as exemplified by the and Shukla 1999; Douville and Chauvin 2000). Although Pacific Ocean El Nino–Southern~ Oscillation (ENSO) most climate forecasts rely on ocean conditions, skill may and its global atmospheric circulations (Bjerknes 1972; be improved by use of atmospheric predictors (Jury 1998; McCreary 1983; Philander and Seigel 1985). At decadal Jury et al. 1999; Philippon and Fontaine 1999). Implicit in the application of statistical forecasts is that Corresponding author address: Mark Jury, Dept. of Physics, antecedent relationships will persist in the future University of Puerto Rico, Rte. 108, Mayaguez, PR 00681. (Barnston et al. 1996; Mattes and Mason 1998). Yet in- E-mail: [email protected] stabilities in environment–target relationships exist as

DOI: 10.1175/JAMC-D-12-0139.1

Ó 2013 American Meteorological Society Unauthenticated | Downloaded 10/05/21 09:06 AM UTC MAY 2013 J U R Y 1117 a result of nonlinear interactions between the atmo- sphere and ocean and among the three tropical ocean basins (Hastenrath and Greischar 1993; Annamalai 1995; Krishna Kumar et al. 1999; Sahai et al. 2000). Studies of climate variability in discrete frequency bands have uncovered some of the cross-basin interactions (Jury et al. 2004; Jury and Gouirand 2011), but appli- cation to statistical forecasts may be limited by inflated skill (Wilks 1995; Zhang and Casey 2000). Climate predictions are often aimed at seasonal rainfall and temperature or at variables such as crop yield and river flow (Jury 2011). Although routine weather observations have been made since the 1800s, station coverage across Africa is best for the period of 1950–80 (Jones 1994). Since then, satellite estimates have provided useful data (Xie and Arkin 1997) not only for climate impacts but for environmental fields that enable forecasts. The objective of this paper is to compare two statistical methods for climate prediction: the first is objectively derived from the climate and the second derives from regression between a target index and environmental fields. Further themes in this paper include an analysis of predictor teleconnections from tropical and high lati- tudes, composite analysis of climatic responses over the Ethiopian highlands, and consideration of numerical model forecasts.

2. Data and methods To characterize crop-climate variability in northeast- ern Africa for the period of 1979–2009, various monthly gridded datasets were considered: 1) Global Precipitation Climatology Center (GP) rainfall (Rudolf and Schneider 2005), 2) European Centre for Medium-Range Weather Forecasts Interim Reanalysis (EC) precipitation minus evaporation (Dee et al. 2011), 3) National Centers for Environmental Prediction Coupled Forecast System reanalysis (CF) latent heat flux (Saha et al. 2010), and 4) National Ocean and Atmospheric Administration sat- ellite vegetation color (NV; Zeng et al. 1999). Most data were drawn from the Climate Explorer Internet site FIG. 1. (a) Average satellite vegetation over Ethiopia, with geo- (http://climexp.knmi.nl), with the exception that NV is graphical names, 1200-m elevation contour (thin white line), dashed highlands target area, and circled Melkassa crop yields. (b) JJAS from the International Research Institute Climate Library ETH4 climate index, and (c) its wavelet spectral energy; shaded (http://iridl.ldeo.columbia.edu) and National Aeronautics areas give confidence at 5% intervals beginning with 80% (light and Space Administration (http://disc.sci.gsfc.nasa.gov) gray), with a cone of validity indicating decadal cycling on the edge. Internet sites. The predictand data resolutions are 0.58 for GP, 0.78 for EC, 0.38 for CF, and 0.088 for NV. These Ethiopian Institute for Agricultural Research at Mel- are area averaged over 6.58–148N, 358–40.58E (Fig. 1a), kassa, Ethiopia (Fig. 1a: 88–108N, 388–408E). Significant which encompasses the crop-growing region of the correlation coefficients r were found mainly in the third Ethiopian highlands and includes the zone of runoff into quarter, depending on crop (Table 1): r 5 0.46 for GP the River (Korecha and Barnston 2007; Block and and beans, r 5 0.48 for EC and maize, r 5 0.46 for CF Rajagopalan 2007). A subset of the environmental time and sorghum, r 5 0.56 for NV and maize and for NV and series was cross correlated with crop yields from the sorghum, and r 5 0.51 for NV and all crops (for 20

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TABLE 1. Cross correlation between Melkassa crop yields (circled area in Fig. 1a; letters in table head refer to crop subtypes) and meteorological variables, where numbers 2, 3, or 4 refer to the quarter of the year, CRU is Climate Research Unit, P-E is precipitation minus evaporation, lhf is latent heat flux, and Tx is maximum temperature. Boldface values are significant at the 90% confidence level for 20 degrees of freedom. Data are area averages for 88–108N, 388–408E.

Meteorological variable Maize K Maize M Sorghum 7 Sorghum G Beans R Beans A All EC P-E2 0.06 20.37 0.13 0.09 20.02 0.18 0.03 EC P-E3 0.48 0.17 0.26 0.05 0.56 0.59 0.24 EC P-E4 0.13 0.45 0.14 0.09 0.21 0.45 0.20 CF lhf2 0.05 20.13 0.37 0.13 0.05 0.23 0.10 CF lhf3 0.12 0.23 0.46 0.42 0.43 0.53 0.29 CF lhf4 0.06 0.08 0.34 0.12 0.54 0.55 0.22 CF rainfall 0.20 0.33 0.26 0.09 0.37 0.56 0.21 CF Tx 20.27 20.22 20.35 0.01 20.68 20.58 20.26 CRU Tx 0.04 20.19 20.21 0.09 20.43 20.29 20.15 GP rainfall 20.12 20.10 20.04 20.04 0.19 0.46 0.05 NV 0.32 0.56 0.33 0.56 0.28 20.04 0.51 degrees of freedom, r . 0.36 achieves 90% confidence). averaged fields in the global domain 408S–458N, and the Many of the highlands-area seasonal time series were three leading modes for each field (four for tempera- significantly correlated with each other (Table 2): GP–EC ture) were retained to represent the natural variability. had r 5 0.52 and CF–NV had r 5 0.63. Yet each index For the targeted method, regression maps were com- exhibits peculiar features; EC precipitation minus puted for the JJAS ETH4 index with respect to the same evaporation (P 2 E) is low for the period of 1990–95, DJFM environmental fields. Because these results and CF latent heat flux is low for the period of 2001–05. revealed high-latitude signals, the domain was expanded An average of four June–September (JJAS) standard- to 558S–608N. Key areas were defined where regression ized values (GP, EC, CF, and NV) is made to form a values exceed 90% confidence over an area of more target index (‘‘ETH4’’; Fig. 1b) that combines the data than 158 latitude 3 208 longitude, and their standard- so as to minimize the impact of changing observational ized DJFM time series were extracted. The Northern density. The consequent statistical results are specific to Hemisphere climate was also analyzed with respect to this index and should not be generalized. ETH4 by regression of CF 200-hPa geopotential heights Large-scale environmental patterns were studied at zero-, one-, and two-season leads. using three fields: 1) National Climatic Data Center Pairwise cross correlations were computed between (NC) surface temperature, version 3 (Smith et al. 2008), antecedent DJFM PC scores and key-area series, that incorporates sea surface temperature boundary and the JJAS ETH4 index (with 30 degrees of freedom, conditions, 2) Hadley Centre (HAD) sea level air r . 0.35 achieves 95% confidence). Multivariate re- pressure, version 2 (Allan and Ansell 2006), and 3) CF gression algorithms were developed to fit the ETH4 200-hPa zonal winds. To determine predictors by an index from either 10 PC scores or seven key-area series objective method, principal components (PC) analysis at two-season lead time. A backward stepwise technique was applied to standardized December–March (DJFM) was employed to drop less-influential predictors on the

TABLE 2. Cross correlation between meteorological variables as defined in Table 1. Boldface values are significant at the 95% confidence level for 30 degrees of freedom. Data are area averages for 6.58–148N, 358–40.58E.

EC P-E2 EC P-E3 EC P-E4 CF lhf2 CF lhf3 CF lhf4 CF rainfall CF Tx CRU Tx GP rainfall EC P-E3 0.29 EC P-E4 0.30 0.07 CF lhf2 0.56 0.14 0.23 CF lhf3 0.25 0.19 0.49 0.70 CF lhf4 0.22 0.05 0.54 0.58 0.76 CF rainfall 0.31 0.19 0.49 0.50 0.76 0.87 CF Tx 20.14 20.31 20.34 20.60 20.73 20.76 20.66 CRU Tx 0.00 20.01 20.14 20.33 20.38 20.52 20.39 0.71 GP rainfall 0.52 0.14 0.21 0.43 0.27 0.42 0.48 20.37 20.42 NV 0.16 20.16 0.41 0.32 0.24 0.63 0.55 20.27 20.14 0.52

Unauthenticated | Downloaded 10/05/21 09:06 AM UTC MAY 2013 J U R Y 1119 basis of their statistical significance in partial correlation. signal,’’ T1 also reflects the variability of the Indian Ocean. A final multivariate regression was determined with an Other noteworthy surface temperature modes include r2 fit adjusted for the number of predictors used (two or T2 (variance 5 17.6%), representing ENSO (for T2 vs three). The predictors were screened for colinearity and Nino-3,~ r 510.90 at zero-season lag), and T4 (variance 5 whether their coefficients were the same sign as was 5.6%), which shows the tropical Atlantic dipole (for T4 vs found in pairwise correlation. Similar procedures have ETH4, r 510.35 at two-season lead). The surface air been followed in earlier work (Gissila et al. 2004). pressure P mode-1 (variance 5 34%) pattern exhibits an Numerical models were evaluated for the period of east–west dipole, with positive values across the tropical 1979–2009 by extraction of 1 May ensemble mean Atlantic and eastern Indian Ocean. Negative loading forecasts of JJAS rainfall over the Ethiopian highlands. values are located over the eastern Pacific and extend Model forecasts were evaluated from EC system 3, the south of the . The P1 time score has a weak CF operational system, the Met Office operational sys- downward trend that is consistent with hydrostatic tem, and ‘‘ECHAM,’’ version 4.5, from the Max Planck adjustment to global warming and also relates to ENSO Institute. To study the local circulation reponse to large- (for T2 vs P1, r 510.91 at zero-season lag). The upper scale forcing, composite differences between the four zonal wind U patterns exhibit positive loading over the wettest and four driest JJAS seasons in the ETH4 series tropics. Component U1 (variance 5 were calculated for CF winds and GP rainfall. The years 30.8%) has negative loading over the central Pacific include wet (1986, 1996, 1998, and 2007) and dry (1982, and positive loading over the central Atlantic that is 1992, 2002, and 2009) cases. To link the climate and relatedtoENSO(forT2vsU1,r 510.90 at zero- weather scale, daily GP and EC rainfall was analyzed season lag), and U2 (variance 5 12.3%) has positive and a case study of 1800 UTC 12 June 2007 was studied loading along an axis across the southern tropics and using CF winds and Meteosat IR imagery. The spectral negative loading in the subtropics that is relatively in- energy in key-area time series was evaluated by wavelet dependent of ENSO (for T2 vs U2, r 520.21 at zero- analysis (Torrence and Compo 1998). To summarize season lag). Note that U2 achieves the highest pairwise the approach, three methods for predicting Ethiopian correlationwithETH4(r 520.53 at two-season lead). climate, as represented by a multivariate index, are From this analysis, 10 predictors (PC time scores) are compared: 1) a statistical method from PC analysis, 2) a retained for multivariate stepwise regression with the statistical method from key-area correlation, and 3) a target index. numerical method using a numerical ensemble average. c. Targeted regression and predictors 3. Results The JJAS ETH4–DJFM field regression maps are given in Figs. 3a–c. For NC surface temperature, re- a. Target index gression values exceed 18C per standard deviation of Most climate prediction studies use area-averaged ETH4 north of 408N across North America (positive) seasonal rainfall; rainfall, however, is intermittent and and Eurasia (negative). In other areas, values are low or heterogeneous and may be poorly observed or under- cover small zones. Such a pattern suggests the assymetry reported. A combination of different types of data from of the winter circulation that resembles the Pa- independent sources may be advantageous, particularly cific–North America (PNA) climate mode. Consistent if significantly correlated with crop yields (Table 1) with this pattern is the regression map for HAD surface and each other (Table 2). The ETH4 index, which is air pressure, which shows low values in the North Pa- based on rainfall, P 2 E, latent heat flux, and vegetation cific–Aleutian zone and high values in the Icelandic– color, yields a time series (Fig. 1b) whose wavelet Russian zone exceeding 2 hPa per standard deviation. spectra (Fig. 1c) have significant energy at 2.0–3.2 yr There are noteworthy positive values in the southwest- and 610 yr. ern and southern regions of the Pacific Ocean. Both surface air temperature and pressure regression maps b. Objective PC modes and predictors show weak values at low latitudes. It is only at one- The global PC loading patterns of leading modes, season lead time that temperatures in the tropical Pacific whose scores relate to ETH4, are shown in Figs. 2a–f. and pressures in the Atlantic achieve significant re- The surface temperature T mode-1 (T1) pattern (variance 5 gression values (not shown). The regression map for CF 25.6%) shows positive loading in the tropics with higher upper zonal wind, on the other hand, reveals negative 2 values across the Indian Ocean, Africa, and . values across the tropical eastern Pacific (23ms 1 per The T1 time score has a marked rising trend (r2 5 0.54). standard deviation) and African regions. There are Although this may be considered to be a ‘‘climate change positive zones along 308N and 308S at two-season lead

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FIG. 2. (left) PC loading patterns and (right) time scores computed from standardized DJFM environmental fields in the period 1979–2009 for (a) T1, (b) T2, (c) T4, (d) P1, (e) U1, and (f) U2. Map loadings refer to a single color scale from 22to12 std dev. Significant linear trends are analyzed with an r2 fit.

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FIG. 3. Regression maps between the JJAS ETH4 index and DJFM environmental fields: (a) 2 NC temperature (8C), (b) HAD air pressure (hPa), and (c) CF 200-hPa zonal wind (m s 1). Dashed boxes in (b) and (c) highlight key areas used in the algorithm. time. Pair-wise cross correlations of DJFM key-area Increasing the number of predictors does not help model time series with JJAS ETH4 are highest for South Pacific performance because of colinearity; the adjusted r2 is P (10.47) and African U (20.42). From this targeted 0.23. There are no ENSO predictors in the model, unlike approach, seven time series are retained as predictors: two in Korecha and Barnston (2007) at shorter lead time. In for high-latitude T, three for high-latitude P, and two for the PC-based algorithm, U2 is the dominant predictor. tropical U; many are consistent with Block and Raja- The T4 Atlantic gradient is the ‘‘next best’’ PC predictor gopalan (2007) and Diro et al. (2011). at two-season lead time, consistent with Diro et al. (2011). Using the key-area series and the ETH4 index, d. Model outcomes the multivariate regression algorithm after stepwise Considering the PC time scores and the ETH4 index, reduction is 20.47(North Pacific P) 1 0.16(South Pacific P) the multivariate regression algorithm after stepwise 20.45(African U), with partial p values of 0.00, 0.38, elimination takes the form 10.10(T4) 2 0.58(U2), and 0.02, respectively. Dropping the least-influential where partial p values are 0.59 and 0.01, respectively. South Pacific P reduces performance. With three

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FIG. 4. Regression statistics and scatterplots for the two algorithms. predictors, the adjusted r2 is 0.37. Model statistics and except ECMWF (system 3). Its r2 fit over the period is scatterplots are given in Fig. 4 for each of the two sta- 0.33 for rainfall, which is better than the adjusted fit of the tistical methods. PC-score model (0.23) but is not as high as the key-area Cross validation of the statistical algorithms was model (0.37) for the hybrid ETH4 index. done by removing the first and last 10 yr and predicting e. Composite structure those years on the basis of the algorithm for the re- maining years. The results show that the fit over the The local climate scenario is described using com- whole period is a good indicator of tercile hit rate: 65% in posite differences between the four wettest and four the case of the key-area statistical model. For compari- driest JJAS seasons on the basis of the ETH4 index (cf. son, numerical model forecasts of rainfall for the Ethio- Fig. 1b). The 850-hPa winds exhibit a trough over the 2 pian highlands at one-season lead time were analyzed (Fig. 5a); 11gkg 1 specific humid- from the Climate Explorer Internet site. Although data ity differences extend southwestward across Ethiopia. were available for a number of independent models, all The trough is related to the northern circumpolar flow as failed to reproduce a statistically significant forecast described below (see Fig. 6). An interesting feature is

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21 FIG. 5. Composite JJAS wet-minus-dry (a) 850-hPa wind speed (shaded and thick-arrowed areas show regions of .1ms , with 2 0.3 m s 1 contour interval; dashed line represents the axis of surplus moisture), (b) 150-hPa wind speed (shaded and thick-arrowed 2 2 2 areas show regions of .4ms 1,with0.5ms 1 interval), and (c) GP rain (shaded areas show regions of .20 mm month 1,with 2 10 mm month 1 interval). Also shown are latitude–height sections of composite wind averaged over 308–458E for (d) zonal and (e) meridional components, with key features labeled and the terrain profile shown.

that the low-level winds associated with the trough are conditions in the lower Nile River basin during wet channeled by the and . Composite seasons over the highlands. differences show that the tropical easterly jet (–U150) To study the circumpolar flow, strengthens downstream and equatorward of the high- regression maps between the JJAS ETH4 index and CF lands (Fig. 5b), in synchrony with global zonal over- 200-hPa geopotential height Z200 were analyzed at var- turning circulations (cf. U1; Fig. 2e). Composite GP ious lead times (Figs. 6a–c). In the DJFM season used 21 rainfall differences of greater than 40 mm month for prediction, Z200 exhibits low values in midlatitudes (Fig. 5c) extend across the highlands westward into (220 m per standard deviation). Higher values build . Considering a composite in latitude–height over during the MAMJ season. In JJAS a ridge 2 2 (Figs. 5d,e), there are 25ms 1 (15ms 1) zonal wind extends from the Mediterraean Sea to the , differences at 308N (508N) north of Ethiopia in the indicating a poleward excursion of the subtropical jet

300–150-hPa layer, indicative of an anomalous ridge stream. In the tropics, the Z200 regression map exhibits 2 over Turkey. There is also a significant 212 m s 1 dif- low values (210 m per standard deviation), particularly ference at 30 hPa over the equator consistent with the over the Pacific. These features were compared with east phase of the quasi-biennial oscillation (QBO). In known climate signals. The DJFM Z200 pattern re- the meridional component there is an upper diffluence sembles the PNA climate mode, and the JJAS pattern over 408N. Northerly wind differences extend to Ethio- has ENSO attributes. Thus, the anomalous Northern pia, suggesting interaction between the Hadley circula- Hemisphere circulation that links with Ethiopian rain- tion and the subtropical jet stream by means of the fall appears to be related to certain features of the global Arabian trough. This is further supported by dry climate system.

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FIG. 6. Regression maps between JJAS ETH4 and CF 200-hPa geopotential height for the (a) DJFM, (b) MAMJ, and (c) JJAS seasons. Key features and the target area are labeled in the polar projections. f. The weather scale spring to summer, then forecasts can be misleading. The low fit of the tropical PC-score algorithm indicates this In analyzing daily weather patterns over the Ethio- problem is significant at two-season lead time. At pian highlands for wet years such as 2007, it is found that shorter lead times, ENSO predictors may be useful, but the early- and late-season rains are most affected by the forecasts after April give little time for mitigating ac- Arabian trough. Heaviest rains in June (12, 14, and 16) tions. The closer fit of the key-area algorithm suggests and September (8, 11, and 12) 2007 coincide with an that high-latitude predictors may be helpful. Further 850-hPa trough over the Arabian Peninsula (composite work is needed to determine whether changes in the Z anomaly 5230 m), a 200-hPa ridge east of Turkey Arctic circumpolar flow develop in a stable manner. (1160 m), and surplus precipitable water (16 mm) in There may be a push (winter)–pull (summer) effect that a southwest band across Ethiopia (not shown). Rainfall 2 mediates interaction between upper winds and convec- exceeds 10 mm day 1, an equivalent volume of ;1010 m3 tion. Another issue is whether the target falls naturally across the Ethiopian highlands. The northwestern Indian into a coherent regime. Analysis of satellite rainfall PC Ocean shares in the convective outbreak: composite modes over the African hemisphere indicates that our outgoing longwave radiation and SST anomalies are 2 ETH4 area is located on the eastern edge of ‘‘’’ 260 W m 2 and 118C, respectively, consistent with mode 1, which extends across (88–178N). If Gissila et al. (2004). The daily rainfall series for 2007 the Ethiopian highlands are on the fringe, then that re- (Fig. 7a) has significant spectral energy below 20 days in gion’s teleconnections with ocean–atmosphere coupled June and September, which is typical of forcing by signals are potentially unstable. Longer datasets would subtropical waves. In contrast, the main convective better represent the decadal oscillations and predictability outbreak at the end of July exhibits spectral energy (cf. Fig. 1c), but satellite data and modern reanalysis are above 20 days (Fig. 7b), consistent with forcing by needed to form the target index. equatorial long waves. The case of 12 June 2007 is chosen to demonstrate links with the Arabian trough that con- tributes to seasonal rains (Korecha and Barnston 2007). 4. Conclusions Low-level southerly flow (below 98N) converges with This study has compared methods of predicting crop- westerlies and northerlies (above 128N), creating bands related climate in the Ethiopian highlands for the of thunderstorms that sweep across the Ethiopian high- period of 1979–2009. A target index, ETH4, for the lands and join orographic, diurnal convection (Fig. 7c). June–September season was developed as an average of four variables—rainfall, rainfall minus evaporation, g. Inherent limitations latent heat flux, and vegetation color, after considering An understanding of teleconnections arises from this their correlation with local crop yield. Predictors were work, but predictability is limited by ENSO uncertainty drawn from gridded near-global fields of surface tem- in northern spring and the relative strength of ;2-yr perature, surface air pressure, and 200-hPa zonal wind oscillations. None of the predictors exhibits significant in the preceding December–March season. Statistical autocorrelation at 11 yr except the climate-change algorithms were formulated by stepwise multivariate signal T1. If ocean–atmosphere coupling ‘‘flips’’ from regression. The first set of predictors were derived from

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maps exhibited high-latitude signals in surface tempera- ture (positive in North America and negative in Eurasia) and air pressure (negative in the North Pacific and posi- tive in the South Pacific). Upper zonal winds were most strongly related to ETH4 over the tropical Pacific and Africa at the specified lead time (DJFM / JJAS). A multivariate algorithm that was based on the PC pre- dictors had an adjusted r2 fit of only 0.23, whereas the model that used key-area regression achieved r2 5 0.37. In comparison, numerical model forecasts reached r2 5 0.33 for ECMWF but were low for other models. A key feature of this study was the use of a hybrid index to characterize local crop climate that combines station, satellite, and reanalysis information. The cli- mate index may be useful as a proxy for crop yield and thus is ‘‘attractive’’ for prediction. Although the tar- geted approach to statistical prediction appears to be best suited to the Ethiopian highlands crop climate at two-season lead time, inferences from the objective PC method also provided useful insights. Further studies are recommended to uncover the relationships among trough activity over the Arabian Peninsula, tropical moisture fluxes, and large-scale forcing from high latitudes. Al- ternative crop-climate indices can be formulated that may elucidate statistical links to climate that are different than those found here for ETH4.

Acknowledgments. This study is an outcome of a workshop presented at the Ethiopian Institute for Agri- cultural Research, Melkassa, 26–30 December 2011. Funding was provided by the Rockefeller Foundation, with the organizational direction of Dr. G. Mamo and crop-yield data from A. Shimeles.

FIG. 7. (a) Daily rainfall during 2007 over the Ethiopian highlands REFERENCES as an average of GP and EC values, with the case study circled, and (b) its wavelet spectral energy; shaded areas give confidence at 5% inter- Allan, R., and T. Ansell, 2006: A new globally complete monthly vals beginning with 80%, with cone of validity also shown. (c) The historical gridded mean sea level pressure dataset (HadSLP2): 1800 UTC 12 Jun 2007 satellite IR image and 850-hPa winds. 1850–2004. J. Climate, 19, 5816–5842. Annamalai, H., 1995: Intrinsic problems in the seasonal prediction of the Indian summer monsoon rainfall. Meteor. Atmos. Phys., objective principal component time scores with tropi- 55, 61–76. cal loading patterns, and the second set was based on Barnston, A. G., W. M. Thiaw, and V. Kumar, 1996: Long-lead key areas determined from regression with our target forecasts of seasonal precipitation in Africa using CCA. Wea. index. All data were standardized, but trends were Forecasting, 11, 506–520. retained. The first PC of the surface temperature re- Bjerknes, J., 1972: Large-scale atmospheric response to the 1964– 1965 Pacific equatorial warming. J. Phys. Oceanogr., 2, 212–217. flected climate change and Indian Ocean variability, Block, P., and B. Rajagopalan, 2007: Interannual variability and and the second PC represented the Pacific ENSO sig- ensemble forecast of upper basin Kiremt season nal. The first PC of air pressure and upper zonal wind precipitation. J. Hydrometeor., 8, 327–343. exhibited a dipole between the Eastern and Western Brankovic, C., and T. N. Palmer, 2000: Seasonal skill and pre- Hemispheres: an atmospheric component of ENSO. dictability of ECMWF PROVOST ensembles. Quart. J. Roy. Meteor. Soc., 126, 2035–2068. The second PC of upper zonal wind revealed a tropical– Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: subtropical dipole that was correlated with ETH4 at two- Configuration and performance of the data assimilation sys- season lead time (r 520.53). Point-to-field regression tem. Quart. J. Roy. Meteor. Soc., 137, 553–597.

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Diro, G. T., D. I. F. Grimes, and E. Black, 2011: Teleconnections Philippon, N., and B. Fontaine, 1999: A new statistical pre- between Ethiopian summer rainfall and sea surface temper- dictability scheme for July–September Sahel rainfall (1968– ature: Part II. Seasonal forecasting. Climate Dyn., 37, 121–131. 1994). C. R. Acad. Sci. II, 329, 1–6. Douville, H., and F. Chauvin, 2000: Relevance of soil moisture for Rocha, A., and I. Simmonds, 1997: Interannual variability of south- seasonal climate predictions: A preliminary study. Climate eastern African summer rainfall. Part I: Relationships with Dyn., 10/11, 719–736. air–sea interaction processes. Int. J. Climatol., 17, 235–265. Enfield, D. B., and D. A. Mayer, 1997: Tropical Atlantic sea surface Rosati, A., K. Miyakoda, and R. Gudgel, 1997: The impact of ocean temperature variability and its relation to El Nino–Southern~ initial conditions on ENSO forecasting with a coupled model. Oscillation. J. Geophys. Res., 102, 929–945. Mon. Wea. Rev., 125, 754–772. Fennessey, M. J., and J. Shukla, 1999: Impact of initial soil wetness Rowell, D. P., C. K. Folland, K. Maskell, and N. M. Ward, 1995: on seasonal atmospheric prediction. J. Climate, 12, 3167–3180. Variability of summer rainfall over tropical Gissila, T., E. Black, D. I. F. Grimes, and J. M. Slingo, 2004: Sea- (1906–1992): Observations and modelling. Quart. J. Roy. sonal forecasting of the Ethiopian summer rains. Int. J. Cli- Meteor. Soc., 121, 669–704. matol., 24, 1345–1358. Rudolf, B., and U. Schneider, 2005. Calculation of gridded pre- Hastenrath, S., and L. Greischar, 1993: Changing predictability of cipitation data for the global land-surface using in-situ gauge Indian monsoon rainfall anomalies. Proc. Indian Acad. Sci., observations. Proc. Second Workshop of the Int. Precipitation 102, 35–47. Working Group, Monterey, CA, EUMETSAT, 231–247. ——, ——, and J. van Heerden, 1995: Prediction of the summer Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System rainfall over South Africa. J. Climate, 8, 1511–1518. Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057. Jones, P. D., 1994: Hemispheric surface air temperature variations: Sahai, A. K., M. K. Soman, and V. Satyan, 2000: All India summer A reanalysis and an update to 1993. J. Climate, 7, 1794. monsoon rainfall prediction using an artificial neural network. Jury, M. R., 1996: Regional teleconnection patterns associated with Climate Dyn., 16, 291–302. summer rainfall over South Africa, Namibia and Zimbabwe. Servain, J., A. J. Busalacchi, M. J. McPhaden, A. D. Moura, Int. J. Climatol., 16, 135–153. G. Reverdin, M. Vianna, and S. E. Zebiak, 1998: A Pilot ——, 1998: Statistical analysis and prediction of KwaZulu-Natal Research Moored Array in the Tropical Atlantic (PIRATA). climate. Theor. Appl. Climatol., 60, 1–10. Bull. Amer. Meteor. Soc., 79, 2019–2031. ——, 2011: Climatic factors modulating Nile River flow. Nile River Smith, T. M., and Coauthors, 2008: Improvements to NOAA’s Basin, Part IV, A. M. Melesse, Ed., Springer, 267–280. historical merged land–ocean surface temperature analysis ——, and I. Gouirand, 2011: Decadal climate variability of the 1880–2006. J. Climate, 21, 2283–2293. eastern . J. Geophys. Res., 116, D00Q03, doi:10.1029/ Tennant, W. J., 1996: Influence of Indian Ocean sea-surface tem- 2010JD015107. perature anomalies on the general circulation of southern ——, H. M. Mulenga, and S. J. Mason, 1999: Development of Africa. S. Afr. J. Sci., 92, 289–295. statistical long-range models to predict summer climate vari- Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet ability over . J. Climate, 12, 1892–1899. analysis. Bull. Amer. Meteor. Soc., 79, 61–78. ——, W. B. White, and C. J. Reason, 2004: Modelling the dominant Tourre, Y. M., and W. B. White, 1997: Evolution of the ENSO signal climate signals around southern Africa. Climate Dyn., 23, 717– over the Indo-Pacific domain. J. Phys. Oceanogr., 27, 683–696. 726. Venzke, S., M. Latif, and A. Villock, 2000: The coupled GCM Korecha, D., and A. G. Barnston, 2007: Predictability of June– ECHO-2. Part II: Indian Ocean response to ENSO. J. Climate, September rainfall in Ethiopia. Mon. Wea. Rev., 135, 628– 13, 1371–1383. 650. Ward, N. M., 1998: Diagnosis and short-lead prediction of summer Krishna Kumar, K., B. Rajagopalan, and M. A. Cane, 1999: On the rainfall in tropical North Africa at interannual and multi- weakening relationship between the Indian monsoon and decadal timescales. J. Climate, 11, 3167–3191. ENSO. Science, 284, 2156–2159. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Latif, M., and A. Grotzner, 2000: The equatorial Atlantic oscilla- Academic Press, 467 pp. tion and its response to ENSO. Climate Dyn., 16, 213–218. Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly Lorenz, E. N., 1990: Can chaos and intransitivity lead to in- analysis based on gauge observations, satellite estimates, and nu- terannual variability? Tellus, 42A, 378–389. merical model outputs. Bull. Amer. Meteor. Soc., 78, 2539–2558. Mason, S. J., 1998: Seasonal forecasting of South African rainfall Yamagata,T.,S.Behera,J.Luo,S.Masson,M.Jury,andS.Rao,2004: using a non-linear discriminant analysis model. Int. J. Climatol., Coupled ocean–atmosphere variability in the tropical Indian 18, 147–164. Ocean. ’s Climate: The Ocean–Atmosphere Interaction, Mattes, M., and S. J. Mason, 1998: Evaluation of a seasonal forecasting Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 189–212. procedure for Namibian rainfall. S. Afr. J. Sci., 94, 183–185. Yeshanew,A.,andM.R.Jury,2007:NorthAfricanclimatevari- McCreary, J. P., 1983: A model of tropical ocean–atmosphere ability. Part 2: Tropical circulation systems. Theor. Appl. Climatol., interaction. Mon. Wea. Rev., 111, 370–387. 89, 37–49. Palmer, T. N., and D. L. T. Anderson, 1994: The prospects for Zeng, N., J. D. Neelin, K.-M. Lau, and C. J. Tucker, 1999: En- seasonal forecasting. Quart. J. Roy. Meteor. Soc., 120, 755–793. hancement of interdecadal climate variability in the Sahel by Philander, S. G. H., and A. D. Seigel, 1985: Simulation of El Nino~ of vegetation interaction. Science, 286, 1537–1540. 1982–1983. Coupled Ocean–Atmosphere Models, J. Nihoul, Zhang, H., and T. Casey, 2000: Verification of categorical proba- Ed., Elsevier, 517–541. bility forecasts. Wea. Forecasting, 15, 80–89.

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