Development of a seasonal forecast model for Kuwait winter precipitation

Journal of Arid Environments (2001) 48: 233–242 doi:10.1006/jare.2000.0746, available online at http://www.idealibrary.com on

Development of a seasonal forecast model for Kuwait winter precipitation

H. A. Nasrallah*, R. C. Balling, Jr-, N. J. Selover- & R. S. Vose*College of Health Sciences, Department of Environmental Sciences, Public Authority of Applied Education and Training, State of Kuwait -Office of Climatology, Arizona State University, Tempe, Arizona 85287, U.S.A. (Received 31 January 2000, accepted 10 October 2000, published electronically 22 March 2001) Winter rainfall in Kuwait is important to a variety of activities in the region, and accurate forecasts a season in advance would serve many useful functions. In this investigation, we develop a statistical forecast model that explains over 70% of the variance in Kuwait winter precipitation. Regional sea surface temperatures and several teleconnection indices provide the key predictor variables. These six variables are all well documented in terms of their contributions to moisture, instability, and a triggering mechanism in the atmospheric circulation. The predictor variables are all easily obtained making the model highly operational.  2001 Academic Press Keywords: winter precipitation; Kuwait

Introduction Kuwait lies in an area of marginal, sporadic, and unreliable precipitation; 80% of the rainfall received in Kuwait falls in the winter months from December through March (Fig. 1). Given the importance of this winter precipitation to the country, it is critical to investigate the predictability of this seasonal rainfall well in advance of the winter months. With accurate predictive models, policymakers in Kuwait would have advanced information regarding winter-season precipitation. A variety of dynamical, physical, and statistical forecasting models have proved useful in other dryland areas throughout the world including the Sahel, East Africa, and northeast Brazil (e.g. Bunting et al., 1975; Farmer, 1988; Folland et al., 1991; Mutai et al., 1998; Uvo et al., 1998). In this investigation, we develop a winter-season rainfall forecast model for Kuwait. Forecasts of winter-season precipitation could be useful in a variety of applications including greening projects, agricultural production and irrigation scheduling, sand movement control, anticipating dust-storm frequencies, water resources planning, natural vegetation changes, wildlife habitat, soil moisture, and outdoor recreation. By establishing climatological and statistical relationships between predictor variables and regional precipitation, it should be possible to assess the temporal precipitation patterns in Kuwait in the broader context of climate forcing. With the global climate change issue on the minds of many (Houghton et al., 1996), it is important to be able to place any climate variations in the context of non-greenhouse forcing. 0140}1963/01/060233#10 $35.00/0

 2001 Academic Press

234

H. A. NASRALLAH ET AL.

Figure 1. Distribution of monthly rainfall in Kuwait. ( ) Umm Al-Aish; ( ) Al-Omariyah; ( ) Ahmadi; ( ) Mena Al-Ahmadi; ( ) Shuwaikh; ( ) KuwaitAP; ( ) Failaka Island.

Regional Climatology Hot summers, mild winters, and a dry summer precipitation regime characterize the desert of Kuwait. During winter, the North African anticyclone moves westward and the Siberian High generates north-easterly winds across the Arabian Peninsula, bringing mild temperatures. The ocean circulation in the Indian Ocean and Arabian Sea is counterclockwise, pushing warm water up the Arabian Gulf, providing additional moisture for the winter rainfall. While occasional frontal systems from Europe cross the Mediterranean and penetrate the Arabian Peninsula, most lose their moisture before reaching Kuwait. During summer, the intertropical convergence zone (ITCZ) migrates north into the Arabian Sea, the Asian anticyclone breaks down, and a strong cyclone develops over northern India. The ocean circulation in the Indian Ocean and Arabian Sea becomes clockwise, pushing warm water toward the west coast of India. The clockwise circulation of the subtropical high over North Africa generates strong westerly winds over the Mediterranean and north-westerly winds over the Arabian Peninsula. North-westerly winds down the Tigris and Euphrates valleys, combined with northerly winds generated by the northern India cyclone, block the Indian monsoon moisture from moving up the Arabian Gulf. The result is a nearly total lack of moisture during the hot summer months (Fig. 1). Predictor Variables Based on the circulation regimes described above, we collected a suite of 106 climatological variables (34 surface and 72 upper air) that might be useful in forecasting Kuwait winter precipitation. These indicators include sea surface temperatures, the Southern Oscillation Index, the Cold Tongue Index, 14 monthly teleconnection indices, surface air temperature anomalies, antecedent rainfall records, and upper air wind and moisture patterns. All variables were collected for the period 1958–1998 for the months September, October, and November. Although most of the predictor variables were

DEVELOPMENT OF FORECAST MODEL FOR WINTER PRECIPITATION

235

available for the entire period, a large proportion of the Middle East upper air and Kuwait surface temperature data were missing, and were ultimately not used in the model. Sea surface temperatures Sea surface temperatures (SSTs) are important to the probability of rainfall because they influence the intensity of the ITCZ and its migration north and south (Williams & Balling, 1996). The ITCZ in turn affects the intensity and direction of wind flow over the Indian Ocean, the Arabian Peninsula, and the Mediterranean Sea. On the regional scale, SSTs reflect the relative potential for evaporation. In Kuwait, the adjacent seas provide the moisture for rainfall, with the circulation patterns drawing the moist air into the region ahead of cold fronts. Sea surface temperature data for our analyses were extracted from the UK Meteorological Office 1;13 gridded data set. The waters adjacent to the Arabian Peninsula serve as moisture sources for Kuwait winter rainfall. The 25 gridded SST values centered at each of the following locations were averaged to generate areal mean SSTs: the Eastern Mediterranean (32)53N; 32)53E), the Red Sea (22)53N; 37)53E), the Gulf of Aden (12)53N; 47)53E), the Arabian Sea (12)53N; 52)53E and 17)53N; 57)53E), the Gulf of Oman (22)53N; 62)53E), an the Arabian Gulf (27)53N; 52)53E). The areal averages helped reduce collinearity problems with the SST data. Southern Oscillation Index In general, the Southern Oscillation Index is considered the primary teleconnection worldwide (Ropelewski & Jones, 1987), and thus it is typically included in any seasonal climatic forecasting venture. However, in the Middle East its efficacy is occasionally swamped by other factors such as SSTs, the location of the ITCZ, and upper air circulation patterns. For example, Ropelewski & Halpert (1987) found that the relationship between the Southern Oscillation Index and Mideast rainfall changed sign from 1935–1953, compared to earlier and later periods. Cold Tongue Index The Cold Tongue Index (CTI) represents variations in the sea surface temperatures in the Eastern Equatorial Pacific Ocean. The index and its relationship to El Nin o/Southern Oscillation (ENSO) and extreme cold and warm Pacific Ocean episodes are described by Deser (1990) and Mitchell (1996). It is important to note that in the warm season, the eastern equatorial cold tongue breaks down during a warm episode, but in the cold season the cold tongue remains, regardless of whether the ENSO event is warm or cold. Teleconnection indices Fourteen other teleconnection indices that reflect large-scale circulation patterns were obtained for this analysis. They include the North Atlantic oscillation (NAO), East Atlantic pattern (EA), East Atlantic jet (EAJET), West Pacific pattern (WP), East Pacific pattern (EP), North Pacific pattern (NP), Pacific/North American pattern (PNA), East Atlantic/Western Russia pattern (EAWR), Scandinavia pattern (SCA), Tropical/Northern Hemisphere pattern (TNH), Polar/Eurasian pattern (POL), Pacific

236

H. A. NASRALLAH ET AL.

Transition pattern (PT), Subtropical Zonal pattern (SZ), and the Asia Summer pattern (ASU). Five of these indices, which do not have values in September, October or November (EAJET, NP, POL, PT, ASU), were not included as predictor variables. Wallace & Gutzler (1981) and Barnston & Livezey (1987) have defined these widelyused teleconnection indices, described their seasonality and their potential impacts on climate patterns throughout the world. Surface air temperature anomalies Temperature gradients across the Middle East control both the surface winds and evaporation. Surface temperature anomalies from the Jones (1994) 5;53 land-based gridded monthly temperature anomaly data set were used to capture these gradients. Four grid cells were selected which may be important to predicting Kuwait winter rainfall, and each cell is centered along the 47330 E longitude meridian. The northern two are located at 52330N and 47330N (north of the Caspian Sea), and the southern two are located at 32330N and 27330N (over Kuwait and the Arabian Gulf ). These surface temperature anomalies indicate the severity and timing of the onset of winter and can be used to establish the relative strength of near-surface temperature gradients across the study area. Rainfall records Many investigators (Walker & Rowntree, 1977; Atlas et al., 1993; Bounova & Krishnamurti, 1993a, b; Lare & Nicholson, 1994) have looked at the effects of antecedent rainfall and soil moisture on seasonal precipitation levels in drylands and most have found a positive feedback (e.g. higher levels of soil moisture promote an increase in rainfall). The antecedent (November) Kuwait International Airport rainfall data were used as a proxy for antecedent soil moisture. The values ranged from 0 to 107 mm, with an average around 15 mm. Upper air wind and moisture patterns Since rainfall patterns follow the migration of pressure belts, the monthly changes in synoptic scale circulation should have predictive value. In particular, the 500 mb heights and 300 mb winds indicate the strength and position of the ridge-trough patterns and the flow around them. The 850 and 700 mb heights, dew point depression and wind direction indicate the relative temperature and moisture of air being advected into the region and the source areas. In addition, 700 mb winds provide the steering for air mass thunderstorms (Chaston, 1995). The upper air data for this analysis were extracted from the Comprehensive Aerological Reference Data Set (CARDS), which contains radiosonde data for locations throughout the Middle East for the period. The Dependent Variable: Kuwait Winter Precipitation After collecting the necessary predictor variables, the next step in the analysis was to identify a single measure of Kuwait winter precipitation that could act as the dependent variable in the forecast model. This was accomplished by examining both the spatial and seasonal variability in precipitation at individual weather stations throughout the region. To assess the spatial/seasonal variability in rainfall, Spearman rank correlation coefficients were computed on the mean monthly totals for the following locations: Kuwait stations Shuwaikh, Umm Al-Aish, Ahmadi, Fahaaheel, Al-Omariyah, Mena

DEVELOPMENT OF FORECAST MODEL FOR WINTER PRECIPITATION

237

Al-Ahmadi, Failaka Island, Kuwait International Airport; Bahrain Airport, in Bahrain; Iraq stations Basrah, Najaf, Diwaniya, and Nasiraya; and Abadan, Iran. Correlation coefficients through time ranged from 0)85 to 0)99 (Table 1). The actual rainfall totals are slightly higher in the north-east in winter, however, which is consistent with frontal activity crossing the Mediterranean, cold air outbreaks through the Caucasus Mountains, and a slight orographic effect of the Zagros Mountains to the northeast of Kuwait in Iran. The mean monthly rainfall values for seven Kuwait stations for the period 1961–1980 were graphed to illustrate the seasonal cycle (Fig. 1). During this period, no station ever received any rainfall from June through September. In contrast, all stations exhibit a December through March maximum, with May, October, and November being transitional in nature. These analyses suggested that Kuwait exhibits relatively high spatial coherence in the temporal variability of winter rainfall throughout the region. For this reason, the winter (December through March) rainfall total at Kuwait International Airport was used to develop the forecast model. The choice of Kuwait International Airport has the added advantage of being easily updated in future years. The Forecast Model Stepwise multiple regression analysis was employed to develop the forecast model. An independent variable was added to the regression model if and only if (1) it was normally distributed; (2) it had at least 30 non-missing values; (3) the regression coefficient was statistically significant (we used the 0)05 level of confidence); and (4) the addition of the variable created a significant increase in the multiple r value. Any predictor added to the equation that was significantly related to existing predictors was tested for undesirable multicollinearity effects. The final equation is presented in Table 2; this model accounts for 70)20 percent of the variance in Kuwait winter precipitation during the 1958–1998 period and is significant at the 0)00 level. All six predictor variables were normally distributed according to a battery of tests including calculation of the standardized coefficients of skewness and kurtosis as well as the Kolmogorov-Smirnov One-Sample Test. The majority of the root mean square error was unsystematic, and the residuals proved to be normally distributed. The predicted and observed winter rainfall values are shown in Fig. 2. The statistical model appears to produce no obvious biases in terms of over- or under-predicting winter precipitation during wet or dry years. However, as seen in the figure, the statistical model performs surprisingly poorly in 1992, 1994, and 1995. Observed precipitation values in the 1990s revealed more variability than seen previously, including the highest precipitation seen in the record. Whatever the cause, our statistical model did not capture the antecedent signal in those odd years. We ran a series of experiments in which selected years were excluded from the model development, and the three odd years (1992, 1994, and 1995) carried substantial leverage into the model development (e.g. their inclusion or deletion had a disproportionate impact on the results). The best result, and the one we are presenting in this paper, did not include these three years. Importantly, all statistical models included the SOI and WP indices along with the Gulf of Aden and Gulf of Oman SSTs. An intercorrelation matrix of the six predictor variables showed an average absolute correlation of 0)20, with only one high correlation, 0)88, existing between the interrelated Cold Tongue Index and SOI. We conducted a series of tests and could find no evidence that this multicollinearity introduced to the model influenced the stability or reliability of the results. We then interpreted the physical mechanisms that must underlie the statistical relationships.

238

Table 1. Spearman’s rho correlation matrix for rainfall

Umm Al-Aish

1)000 0)989 0)983 0)994 0)881 0)955 0)885 0)885 0)868 0)908 0)937

1)000 0)983 0)983 0)847 0)932 0)852 0)863 0)863 0)896 0)904

Ahmadi Fahaaheel Bahrain AP Basrah Najaf Diwaniya Nasiraya Abadan Kuwait AP

1)000 0)977 0)870 0)927 0)885 0)902 0)885 0)885 0)909

Correlations are all significant to the 0)01 level (1-tailed).

1)000 0)876 0)949 0)880 0)880 0)863 0)902 0)932

1)000 0)944 0)947 0)947 0)936 0)936 0)954

1)000 0)936 0)941 0)941 0)980 0)982

1)000 0)967 0)944 0)922 0)952

1)000 0)967 0)933 0)924

1)000 0)972 0)929

1)000 0)963

1)000

H. A. NASRALLAH ET AL.

Shuwaikh Umm Al-Aish Ahmadi Fahaaheel Bahrain AP Basrah Najaf Diwaniya Nasiraya Abadan Kuwait AP

Shuwaikh

DEVELOPMENT OF FORECAST MODEL FOR WINTER PRECIPITATION

239

Table 2. Stepwise multiple regression results

Variable Southern Oscillation Index (Sept.) Western Pacific (Sept.) Gulf of Aden Sept. SST Gulf of Oman Sept. SST East Pacific (Nov.) Cold Tongue Index (Oct.) Intercept

Regression coefficient

Standardized reg. coefficient

Significance level

22)00 !23)14 52)93 !92)80 !12)84 32)75 976)94

4)23 !5)12 3)60 !6)49 !2)50 3)10 2)44

0)00 0)00 0)00 0)00 0)02 0)00 0)02

Sea surface temperature predictors The model contains two SST predictor variables and both are from September. The Gulf of Oman SST is negatively correlated with Kuwait winter precipitation, while the Gulf of Aden SST is positively correlated with winter rainfall. Based on the standardized regression coefficients, the Gulf of Oman SST produces the greatest influence on Kuwait winter rainfall compared to the other variables. The model shows that a decrease in the September water temperature at the Gulf of Oman, and a corresponding increase of the September water temperature at the Gulf of Aden, brings a wetter winter to Kuwait. The summertime clockwise circulation in the Arabian Sea draws the relatively cold waters of the deep Red Sea out into the Gulf of Aden, while drawing warm water from the shallow Arabian Gulf into the Gulf of Oman. The negative anomaly for Gulf of

Figure 2. Plot of observed ( ) vs. predicted ( ) winter precipitation values in Kuwait.

240

H. A. NASRALLAH ET AL.

Oman and corresponding positive anomaly for the Gulf of Aden indicate an early reversal of the currents to the winter pattern. The counterclockwise winter circulation forces the abnormally warm water back into the Arabian Gulf, increasing evaporation and moisture available for winter rainfall. Teleconnection Indices The model contains four teleconnection index predictor variables. The September southern oscillation index (SOI) and the October cold tongue index (CTI) are positively correlated with Kuwait winter precipitation. The September West Pacific and November East Pacific teleconnection indices are negatively correlated with Kuwait winter rainfall. A positive SOI, a cold event, for September indicates a strong counter-clockwise circulation in the Arabian Sea as the relative pressure over the Arabian Peninsula becomes much weaker than the Darwin pressure. This sets up a strong winter ocean circulation, which traps warm water in the Arabian Gulf in late summer. Coupled with the increased convection due to the atmospheric low pressure over the region, evaporation and atmospheric moisture are enhanced in the region. The CTI represents the sea surface temperature component of ENSO. A positive CTI in October would indicate a shift from the cold event, which set up during late summer, into a wintertime warm event (El Nin o), which brings heavier rainfall to the Middle East. Since the CTI indicator lags the SOI indicator by a month, it indicates a breakdown of the Pacific circulation pattern. Although there is some collinearity between the SOI and CTI, in the cold season the CTI remains in place regardless of whether the ENSO event is cold or warm. Therefore the CTI and SOI have both overlapping and separate contributions to the Kuwait rainfall variability. It would be very useful to have a complete upper air record for the Middle East to understand the specific relationship between these teleconnection indices and the Kuwait winter precipitation. A negative East Pacific (EP) teleconnection index for November indicates a weak subtropical ridge with split flows around it, where the strong subtropical jet brings moisture and significant rainfall to the south-western United States. The zonal or meridional character of flow around the Northern Hemisphere tends to be fairly consistent across the Western Hemisphere where the oceans and land masses are of similar size. A split flow around a weak subtropical ridge over central Africa would bring warm unstable air into the Kuwait region from the Mediterranean. This pattern is highly persistent, though it is generally strongest in October. Years with high winter rainfall in Kuwait are generally characterized by a fairly uniform distribution across all four winter months. A negative West Pacific (WP) teleconnection index for September indicates a meridional flow across Southeast Asia. As this pattern is nearly opposite in phase with the East Pacific (EP) pattern, it appears that a strong meridional pattern in September, which breaks down by November, becoming zonal, brings higher rainfall amounts to the Middle East. This is probably due to some collinearity between the SOI and the SST indicators, in terms of the ocean circulation. The sea surface temperatures are stronger indicators of ocean circulation, so they account for a larger proportion of the explained variance. Conclusions The final statistical model accounts for over 70% of the variance in winter precipitation as measured at Kuwait International Airport. It has been determined in these analyses that the Kuwait International Airport precipitation is representative of the rainfall over the entire region. The model has a sound basis in theoretical climatology, as the

DEVELOPMENT OF FORECAST MODEL FOR WINTER PRECIPITATION

241

predictor variables are all well documented in terms of their contributions to moisture and instability. Development of this model has provided both theoretical insight into the climate forcing mechanisms of the Middle East, and a technique for seasonal rainfall prediction. The predictor variables are all easily obtained making the model highly operational. This model did a good job with normal and below normal rainfall values, making it a valuable tool in water resource planning and management as well as in agricultural resource allocation, production and irrigation scheduling. Forecasting winter-season precipitation with this model may lead to reductions in sand movement and dust-storm frequencies by discouraging development activities during dry winters. It is possible that a reversal of the SOI}Middle East rainfall relationship, as documented by Ropelewski & Halpert (1987), has begun in the 1990s, but this change is too recent to be identified at the end of the data record. If such a reversal could be established, it should result in a significant model improvement. As the seasonal precipitation pattern throughout Kuwait, Bahrain, and the flat, desert areas of Iran and Iraq, is relatively uniform, this model could be expanded to predict seasonal precipitation across the entire region. Further analysis of the extreme rainfall events of the 1990s and future years relative to the Middle East upper air circulation patterns should result in model improvements. Unfortunately there were not enough consistent upper air data available to evaluate their contribution to the Kuwait winter rainfall regime. Once we establish the statistical and climatological relationships between the predictor variables and regional precipitation, we can evaluate the temporal precipitation patterns in Kuwait and the Arabian Peninsula in the broader context of climate forcing.

References Atlas, R., Wolfson, N. & Terry, J. (1993). The effect of SST and soil moisture on GLA model simulations of the 1998 U.S. summer drought. Journal of Climate, 6: 2034–2048. Barnston, A.G. & Livezey, R.E. (1987). Classification, seasonality and persistence of lowfrequency atmospheric circulation patterns. Monthly Weather Review, 115: 1083–1126. Bounova, L. & Krishnamurti, T.N. (1993a). Influence of soil moisture on the Sahelian climate prediction I. Meteorology and Atmospheric Physics, 52: 183–203. Bounova, L. & Krishnamurti, T.N. (1993b). Influence of soil moisture on the Sahelian climate prediction II. Meteorology and Atmospheric Physics, 52: 205–224. Bunting, A.H., Dennett, M.D., Elston, J. & Milford, J.R. (1975). Seasonal rainfall forecasting in West Africa. Nature, 253: 622–623. Chaston, P.R. (1995). Weather maps: how to read and interpret all the Basic Weather Charts. Kearney, MO: Chaston Scientific, Inc. 166 p. Deser, C. & Wallace, J.M. (1990). Large-scale atmospheric circulation features of warm and cold episodes in the tropical Pacific. Journal of Climate, 3: 1254–1281. Farmer, G. (1988). Seasonal forecasting of the Kenya coast short rains, 1901–1984. Journal of Climatology, 8: 489–497. Folland, C.K., Owen, J.A., Ward, M.N. & Colman, A.W. (1991). Prediction of seasonal rainfall in the Sahel region using empirical and dynamical methods. Journal of Forecasting, 10: 21–56. Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A. & Maskell, K. (Eds). (1996). Climate Change 1995: The Science of Climate Change. Cambridge: Cambridge University Press. 572 pp. Jones, P.D. (1994). Hemispheric surface air temperature variations: a reanalysis and an update to 1993. Journal of Climate, 7: 1794–1802. Lare, A.R.L & Nicholson, S.E. (1994). Contrasting conditions of surface water balance in wet years and dry years as a possible land surface-atmosphere feedback mechanism in the West African Sahel. Journal of Climate, 7: 653–668. Mitchell, T.P. & Wallace, J.M. (1996). ENSO seasonality: 1950–1978 versus 1979–1992 . Journal of Climate, 9: 3149–3161.

242

H. A. NASRALLAH ET AL.

Mutai, C.C., Ward, M.N. & Colman, A.W. (1998). Towards the prediction of the East Africa short rains based on sea-surface temperature-atmosphere coupling. International Journal of Climatology, 18: 975–997. Ropelewski, C.F. & Halpert, M.S. (1987). Global and regional scale precipitation patterns associated with El Nino/Southern Oscillation. Monthly Weather Review, 115: 1606–1626. Ropelewski, C.F. & Jones, P.D. (1987). An extension of the Tahiti-Darwin Southern Oscillation Index. Monthly Weather Review, 115: 2161–2165. Uvo, C.B., Repelli, C.A., Zebiak, S.E. & Kushnir, Y. (1998). The relationships between tropical Pacific and Atlantic SST and Northeast Brazil monthly precipitation. Journal of Climate, 11: 551–562. Walker, J. & Rowntree, P.R. (1977). The effect of soil moisture on circulation and rainfall in a tropical model. Quarterly Journal of the Royal Meteorological Society, 103: 29–46. Wallace, J.M. & Gutzler, D.S. (1981). Teleconnections in the geopotential height field during the Northern Hemisphere winter. Monthly Weather Review, 109: 784–812. Williams, M.A.J. & Balling, R.C., Jr (1996). Interactions of Desertification and Climate. London, Arnold, 230 pp.