the influence of the north atlantic oscillation and european circulation regimes on the daily to...

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. (2011)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.2383

The influence of the North Atlantic Oscillationand European circulation regimes on the daily

to interannual variability of winterprecipitation in Israel

Emily Black*NCAS-Climate, Department of Meteorology, University of Reading, Reading RG6 6BB, UK

ABSTRACT: The arid climate of the Middle East means that variations in rainfall on all timescales from days to yearshave enormous impact on the people who live in the region. Understanding this variability is crucial if we are to interpretmodel simulations of the region’s climate and make meaningful predictions of how the climate may change in the future.This study investigates the links between the European synoptic regimes that favour rainfall in Israel, interannual variabilityin the Mediterranean storm track, and large-scale modes of variability such as the North Atlantic Oscillation (NAO). It isshown that particular European circulation regimes favour rainfall in Israel, and that these regimes occur more frequentlywhen the NAO is in its positive phase. This is reflected by increased likelihood of very high rainfall totals in the Middle Eastduring NAO positive winters. In contrast, the circulation regimes associated with an increased probability of dry weatherare not associated with NAO negative conditions. This non-linearity, along with the known lack of stationarity in NAOteleconnections explains the weak correlation between the NAO index and precipitation in the Jordan and Israel. Copyright 2011 Royal Meteorological Society

KEY WORDS Israel; Jordan; precipitation; NAO; GWL

Received 30 March 2011; Accepted 26 May 2011

1. Introduction

Understanding the factors that control the variability inregional precipitation is crucial for assessing climatemodel performance, and hence for interpreting projec-tions of climate change. This is particularly true in theMiddle East, which has a complex climate affected byboth tropical and mid-latitude processes (for exampleEvans, 2004). Previous studies of this region have notexplicitly considered the connections between variabil-ity on different timescales, instead focusing either ondaily variability or on interannual and longer timescales.Here, we address this gap in understanding by consider-ing the way that the regional circulation conditions thatdetermine the daily probability of rain in Israel (character-ized by synoptic regimes) relate to interannual variabilityand to large-scale circulation patterns, such as the NorthAtlantic Oscillation (NAO). To this end, we consider thefollowing questions:

• What European synoptic regimes favour precipitationin Israel and Jordan?

∗ Correspondence to: E. Black, NCAS-Climate, Department of Mete-orology, University of Reading, Reading RG6 6BB, UK.E-mail: [email protected]

• How do patterns of rainfall and circulation over Europeand the Mediterranean differ for wet and dry seasonsin Israel?

• How do these daily to interannual patterns of variabil-ity relate to remote modes such as the NAO and theEast Atlantic/Western Russia (EAWR) pattern?

Jordan and Israel have a climate that is modified byboth mid-latitude and tropical processes. In the winterand spring, most rainfall arises from storms crossing theMediterranean (Enzel et al., 2003), whereas the summeris dry as a result of descent related to the Indian monsoonand Hadley circulation (Rodwell and Hoskins, 1996;Ziv et al., 2004). During autumn, most rainfall resultsfrom northward extension of the Red Sea troughs thatarise from the Sudanese low (Tsvieli and Zangvil, 2005).Annually, most rainy events in Jordan and Israel resultfrom the passage of mid-latitude cyclones. Nevertheless,events entering the region from the south, such asRed Sea troughs affect the region throughout the year.The importance of water vapour input from the southwas demonstrated in Evans and Smith (2006), whichshowed that although most events emanate from the west,southerly water vapour flux contributes significantly toannual precipitation totals. Furthermore, when a Red Seatrough coincides with a Mediterranean cyclone, extreme

Copyright 2011 Royal Meteorological Society

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rainfall and serious flooding may ensue (Krichak et al.,2000b; Ziv et al., 2005).

Israel’s proximity to Europe raises the possibility thatits climate is influenced by the Eurasian/Atlantic modesof variability, such as the NAO (Hurrell et al., 2003).Although the influence of the NAO has been shown toextend as far east as Turkey (Cullen and Demenocal,2000; Cullen et al., 2002), there is little consensusabout its influence on precipitation in Israel and Jordan.In particular, the character of the NAO teleconnectionvaries geographically throughout the region. This haslead to apparently contradictory results. For exampleBen-Gai et al. (2001) and Ziv et al. (2006) report thatrainfall over Israel is poorly correlated with the NAO,whereas Eshel and Farrell (2000, 2001), whose studyarea reaches further north, suggest that rainfall overthe eastern Mediterranean region is modulated by thelarge-scale atmospheric circulation of the whole Atlantic-Mediterranean.

Another prominent teleconnection pattern, which af-fects winter variability in the Mediterranean region, isthe EAWR mode. The EAWR consists of two anomalycentres: one over the Caspian Sea and the other overWestern Europe. In its positive phase, there is lowpressure over south-western Russia and Western Europe,and high pressure over north-western Europe, whichis associated with dry conditions over Europe and theMediterranean and wet conditions in the Middle East;the converse is true for the negative phase (Krichak et al.,2000a). Krichak et al. (2002) found a link between eastMediterranean rainfall and the NAO/EAWR pattern, andargued that variability in the region is primarily affectedby the EAWR, with secondary influence from the NAO(Krichak et al., 2002; Krichak and Alpert, 2005a, 2005b).The lack of consistency between the conclusions of thesestudies arguably reflects the weak and indirect nature ofthe teleconnection. In any case, discrepancies betweenstudy regions, time periods, and rainfall datasets make itdifficult to compare results from different studies.

Upper atmosphere variability has also been found toaffect Middle East precipitation – particularly in Israel.An upper level trough extending from Eastern Europetowards the eastern Mediterranean affects rainfall in thesouthern Levant. Both the position and intensity of thetrough vary, so that during rainy years, it is centredover Israel, and during dry years, it is centred furtherwest (Ziv et al., 2006). Another example of the impor-tance of upper level processes is the teleconnection withthe North Caspian Pattern (NCP). The NCP is a modeof upper level variability with centres of action overEastern Europe/Asia and the Channel. During NCP nega-tive years, there is an increased southwesterly circulationtowards western Turkey. Conversely, during NCP posi-tive years, there is an increased northwesterly circulationtowards East Europe and increased northeasterly circula-tion towards the Black Sea (Barnston and Livezey, 1987).Although its influence on European rainfall is generallysmall in comparison to other modes of variability, such as

the NAO, the NCP is significantly correlated with rainfallin Israel (Kutiel and Paz, 1998; Kutiel et al., 2002).

In this study, we first look at the large-scale synopticconditions that favour rainfall in Israel, and how theseproject onto interannual variability. We then relate thesesynoptic regimes and interannual variability in circulationto the aforementioned indices. This way, we aim toaddress (1) to what degree the larger scale circulationover the Mediterranean affects Middle East precipitationand (2) how the circulation patterns that favour rainfallare related to European/Atlantic teleconnection patterns.

2. Data

2.1. Rainfall data

The monthly precipitation data used in this study werebased on gauge records. Figure 1 shows the locationof rain gauges within the study area. It can be seenthat many parts of the region contain few gauges fromthe Global Historical Climate Network (GHCN), whichprovided much of the data to the Global PrecipitationClimatology Centre (GPCC). Because of the sparsity ofobservations included within the product for this region,many of the grid points would have been based on

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Figure 1. Location of rain gauges. Top: Global Historical ClimateNetwork (GHCN) gauges within Europe, Middle East and North Africa.Bottom: Gauge data within the Middle East. Circles indicate GHCNmonthly data; diamonds are gauges from the World MeteorologicalOrganisation GSOD (daily data of very variable quality); crosses arestations with daily data, provided by the Israeli Meteorological Serviceand listed in Table I. This figure is available in colour online at

wileyonlinelibrary.com/journal/joc

Copyright 2011 Royal Meteorological Society Int. J. Climatol. (2011)

NAO AND ISRAEL PRECIPITATION

interpolations over large distances. Therefore, to avoidbeing misled by artefacts of the interpolation, analysesof monthly to interannual variability were carried outusing either individual rain gauges or a gridded versionof the available gauge data that was developed especiallyfor this study. Away from the study area, only theGHCN data were included. In the study area, thesedata were supplemented by station data provided by theIsraeli Meteorological Service, and data extracted fromthe World Meteorological Office Global Summary of theDay (GSOD) archive. The gridding was carried out atmonthly time intervals on a 1 × 1° grid for 1950–1999.

The gridding of the data was done in several stages toavoid biasing the results. First, stations were selected forinput into the dataset. To be included the station had tohave less than 10% missing data over the time period con-sidered and pass a basic quality control test that checkedthe data for homogeneity, for repeated data (e.g. severalyears reporting exactly the same values) and for identicaldata from different stations. Many of the GSOD sta-tions failed the quality control tests – primarily becauseof missing data. A rainfall climatology was calculated forall grid squares for which data were available. To avoidbias, before deriving the grid box mean, the data fromindividual stations were standardized.

All daily analyses were carried out using individ-ual stations. Daily rainfall data for 1985–1999 fromnine stations along the River Jordan were providedby the Israeli Meteorological Service (Table I). Else-where in Jordan and Israel, data were taken from theWorld Meteorological Organisation GSOD archive (seehttp://lwf.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html for details of the GSOD stations). Similar to themonthly data, all daily data were subjected to a basicquality control process that involved checking the datafor homogeneity, for repeated data and for identical datafrom different stations.

2.2. Other data and indices

The NAO index we used is based on station measure-ments of sea-level pressure in Iceland and the IberianPeninsula. Details of how the index was calculated can befound in Jones et al. (1997) and www.cru.uea.ac.uk/cru/data/nao.htm. Other indices were taken from the ClimatePrediction Center (CPC) website (http://www.cpc.ncep.

noaa.gov/data/teledoc/telecontents.shtml). Sea-level pres-sure data were taken from the National Centers forEnvironmental Prediction (NCEP) reanalysis, which isa 2.5 × 2.5° product based on an optimal interpolationbetween observational and modelled data (Kalnay et al.,1996). NCEP reanalysis precipitation data were used forthe daily rainfall composites described in Section 4.1.There are serious problems with the way that the reanal-ysis represents both spatial and temporal variability (Diroet al., 2009). For this reason, all the quantitative anal-ysis of both monthly and daily data were carried outusing gauge-based data (as described above) and reanaly-sis rainfall was only used for the qualitative survey shownin Figure 4.

3. The mean climate and seasonal cycle

3.1. Seasonal cycle

Figure 2 shows the mean seasonal cycle for variousrainfall statistics between 1985 and 1999 for severalstations in the Jordan valley. The plot of precipitationtotals shows that most precipitation is experienced duringthe Mediterranean cyclone season between Novemberand March, and that the summer is completely dry.The large interannual standard deviation in the numberof rainy days (particularly in the winter and autumn)suggests that number of rainy events strongly influencesthe interannual variability in total rainfall.

The maximum rainfall and mean rain per rainy day atthe peak of the rainy season (November, December, Jan-uary, and February) remains steady, and decreases at themargins of the season, perhaps reflecting a reduction ofthe intensity of cyclones and the increasing dominance ofsmaller scale convective events over large-scale cyclones.The lowermost figure shows the probability of rain givenrain the day before and the probability of rain given norain the day before, which are measures of the durationand frequency of rainy events. During the peak of therainy season (December and January), both the durationand frequency of events stays fairly constant. In the earlypart of the rainy season (October and November), the fre-quency of rainy events increases gradually, although theduration of each event is the same as for December andJanuary.

Table I. Daily rainfall gauge data provided by the Israeli Meteorological service.

Station identifier Station name Longitude Latitude Data period

320 650 DEGANIA BET 35.6 32.6 1984–1999320 800/1 BET ZERA 35.6 32.7 1984–1999320 900 MASSADA 35.6 32.68 1984–1999321 148 ASHDOT YAAQOV MEUHAD 35.6 32.65 1984–1999321 552 MAOZ HAYYIM 32.55 32.5 1984–1999321 800 SEDE ELIYYAHU 35.5 32.43 1984–1999321 850 TIRAT ZEVI 35.52 32.42 1984–1999323 402 GILGAL 35.6 32.6 1988–1999330 370 KALIA 35.5 31.8 1984–1999


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Figure 2. Seasonal cycle in various rainfall statistics for the Israeli Meteorological Service stations shown in the map to the right and listed inTable I. The x axis gives the month and the y axis the statistic in question. The error bars represent the interannual standard deviation from oneof the stations. From top to bottom, the statistics are: total monthly rainfall (mm), mean number of rainy days in the month (days/month), meanrain per rainy day (mm/day), mean maximum daily rainfall in the month (mm), probability of rain given rain the day before (upper group of

curves), and probability of rain given no rain the day before (lower group of curves).

3.2. Mediterranean cyclones and larger scale patternsof precipitation

As has been described above, most boreal winter precip-itation in Jordan and Israel results from the passage ofmid-latitude cyclones. Many of these systems, however,pass north over Syria and Turkey and thus do not deliverprecipitation to Jordan and Israel (Enzel et al., 2003). Thepath that cyclones follow over the Mediterranean is thusa key control on precipitation variability in the MiddleEast. To explore this further, weather systems crossingthe Mediterranean were tracked in reanalysis data. Low-level weather systems in the Mediterranean were identi-fied and tracked using the TRACK software developed

at the Natural Environment Research Council, Environ-mental Systems Science Centre (see http://www.nerc-essc.ac.uk/∼kih/TRACK/Track.html and Hodges, 1994).This method automatically and objectively identifies fea-tures in a meteorological field and tracks them in 6-hourly data. The data used here were 850 mb vorticity,derived from the ERA40 reanalysis, which were filteredto exclude very long and short wavelength features. Thetracks were then collated and the track density calculatedusing the methodology is described in Hodges (1994,1995).

Figure 3 shows the boreal winter total precipitation,mean sea-level pressure (MSLP), and track density (basedon tracking of features in the 850 mb vorticity field) over



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Figure 3. Mean climate over the Mediterranean. From left to right: seasonal total precipitation (mm), seasonal MSLP (mb), seasonal mean trackdensity (number of tracks per 5° spherical cap). This figure is available in colour online at wileyonlinelibrary.com/journal/joc

the Mediterranean, Southern Europe, and North Africa.The mean path that the mid-latitude cyclones followover the Mediterranean (the Mediterranean storm track)is shown as a low in the MSLP and as a maximum inthe track density. Israel and Jordan lie on the edge ofthe Mediterranean storm track, which means that evensmall variations in its orientation or intensity will have aprofound effect on rainfall in the region. This is consistentwith Saaroni et al. (2009), which demonstrates the highsensitivity of the rainfall in Israel to the location ofcyclone centres.

4. Daily to interannual variability

4.1. Variability on daily to interannual timescales

The question of how circulation in the Mediterraneanand Atlantic affects the daily probability of rainfall iskey to understanding the large-scale controls on rainfallat all timescales. Synoptic variability over Europe canbe characterized by considering the circulation as beingin a particular regime. Regimes can be thought ofas consecutive synoptic patterns that follow particularspatial developments. These regimes can last anythingfrom a few days to a fortnight or more, and are associatedwith characteristic weather patterns. There are variousmethods of defining synoptic regimes, ranging frommanual identification of a regime on a particular day tofully automated cluster techniques. Manual identificationis time intensive and subject to error, whereas the fullyautomated techniques often produce poor results. Toovercome these problems, a semi-objective method thatcombines an automatic method of characterizing eachday with the well-established Grosswetterlagen (GWL)catalogue (a list of subjectively determined synopticregime over Europe for every day by the German WeatherService that was originally described in Baur et al., 1944and updated in Gerstengabe et al., 1999). This methodis fully described in James (2007). The basic idea is thatcomposites of MSLP for each regime, based on the GWLcatalogue are derived. The MSLP for each day is thencompared to these composites and the day is assigned tothe regime for which the pattern correlation is highest. Anadditional criterion is that regimes must last for more than3 days. The semi-objective GWL catalogue was used inthis study for two reasons. First, it was developed for

Europe and the Atlantic region, making it a good choicefor studying the association between the larger scalecirculation and Israel rainfall. Second, as was describedabove, part of the selection of the regimes requires themto be persistent over a few days – a particularly usefulfeature for this study, which aims to describe the linksbetween daily rainfall in Israel, circulation conditions,and longer timescale variability. If regimes were allowedto change daily, the results could be noisy because dayswith small-scale and short-lived low pressure anomaliesover the region of interest (such as can occur during anyregime) are likely to be associated with rainfall.

To determine which synoptic weather patterns areassociated with high rainfall in Israel, composites ofMSLP and rainfall for the regimes with the highestprobability of rainfall were derived for an example stationin the Jordan valley (Figure 2). The rainfall probabilitiesduring December–February at the example station aregiven in Table II, which summarizes data for each ofthe GWLs considered. It should be noted that the sameregimes would have been selected for any of the stationsshown in Figure 2 although, as would be expected, therainfall probabilities vary subtly. Although 29 objectiveGWLs can be defined, only the most common 15were considered because of the short time series. Thisapproach is preferable to compositing MSLP on rainydays, because it avoids the problem of superposingdifferent synoptic regimes.

A Monte Carlo approach was used to test whetherthe rainfall probability when a given regime was activewas significantly different to the climatology. The yearswithin the rainfall time series were shuffled randomly(preserving the daily sequences within the seasons)so that the GWL and rainfall time series were beingcompared for different years. The probability of rainfor each regime was then calculated for the randomlyshuffled time series. This was repeated 3000 times.The regime was considered to be associated with asignificantly perturbed rainfall probability if the observedprobability fell outside 90% of the shuffled values.

The four regimes associated with the largest proba-bilities of rainfall were found to be: anti-cyclonic west-erly (WA), anti-cyclonic southwesterly (SWA), cyclonicsouthwesterly (SWZ), and cyclonic northwesterly (NWZ)(Figure 4). The first three of these are associated withhigh pressure and low rainfall in central and Western


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Table II. The 15 most common GWL regimes and their relationship to Jordan Valley rainfall and large-scale modes of variability.The first two columns give the names of the regimes and their abbreviations. The third column gives the percentage probability ofthe regime. The fourth column gives the probability of rain when the regime is active (bold indicates that the rainfall probabilityis significantly different to the mean - see text for further details). The fifth and sixth columns give the ratio of the probabilityof the regime during NAO/EAWR positive phases and its probability when the NAO/EAWR is negative (considered this way toavoid biases from different numbers of NAO/EAWR positive and negative days). The NAO and EAWR indices were calculated

monthly.

Regime Abbreviation PercentageProbability

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Ratio of theprobability of the re-gime during NAO+

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Ratio of theprobability of the re-gime during EAWR+

and EAWR− years

Anticyclonic Westerly WA 11.1 40.3 4.0 2.4Cyclonic Westerly WZ 12.8 27.4 2.7 0.4South-Shifted Westerly WS 3.1 39.3 0.6 0.2Maritime Westerly (Block E. Europe) WW 7.8 30 2.2 0.7Anticyclonic South-Westerly SWA 6.7 44.6 3.2 0.7Cyclonic South-Westerly SWZ 3.8 46.2 1.3 2.3Anticyclonic North-Westerly NWA 5.1 37.3 0.6 5.5Cyclonic North-Westerly NWZ 4.6 40 1.7 0.5High over Central Europe HM 5.8 24.6 0.9 7.1Zonal Ridge over Central Europe BM 4.5 20 2.3 0.9Low over Central Europe TM 1.4 11.1 2.3 0.5Anticyclonic Northerly NA 1.2 38.9 0.2 0.2Cyclonic Northerly NZ 1.2 16.7 1.1 1.0Icelandic High, Ridge C. Europe HNA 4.4 31.0 0.4 3.0Icelandic High, Trough C. Europe HNZ 1.5 26.3 0.1 1.9



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Figure 5. Composites of (from top to bottom) precipitation, track density, and sea-level pressure during January based on the five wettest anddriest seasons in a box with minimum longitude 34°, maximum longitude 36°, minimum latitude 31°, and maximum latitude 33°. This figure is

available in colour online at wileyonlinelibrary.com/journal/joc

Europe, and higher rainfall in the southeast Mediter-ranean. The fourth is associated with a large low pressurecentre and high rainfall over central and east Europeand the whole east Mediterranean. The regimes that leastfavour rainfall (and are significantly different to the cli-matology) are: a low over Central Europe, a zonal ridgeover Central Europe, cyclonic westerly, and cyclonicnortherly. The low over Central Europe and the ridge overCentral Europe regimes both feature high pressure overmuch of Southern Europe and widespread dry conditions.The cyclonic westerly and cyclonic northerly regimes areassociated with more patchy precipitation anomalies inthe eastern Mediterranean – with localized areas of lowprecipitation near the station for which the probabilitieswere calculated. In general, the association between Euro-pean circulation regimes and low rainfall probabilities isweaker than for high rainfall probabilities, and the resultsare more difficult to interpret in terms of the large-scaledynamics.

The patterns described above were related to the inter-annual variability, by comparing the daily regime com-posites in Figure 4 to composites of wet and dry seasons.To this end, Figure 5 compares precipitation, MSLP, andtrack density during the five rainiest and driest seasons(November–February) in a box with minimum longitude34°, maximum longitude 36°, minimum latitude 31°, andmaximum latitude 33°. The precipitation and track den-sity are shown for the Mediterranean region, and theMSLP is shown for a wider region. The resemblancebetween the differences between the wettest and dri-est years, and the first three synoptic regimes shown inFigure 4 is striking. Like the regimes associated with highrainfall probabilities described above, during wet years,the rainfall is low in Western Europe and high throughout

Israel and Jordan. The track density composites fit in withthis picture, with high rainfall in Israel and Jordan asso-ciated with a weakening and southward shift of the stormtrack. This is consistent with the MSLP patterns, whichindicate that high rainfall in the study area is associatedwith high pressure over Europe and an intensified Cypruslow, resulting in a slackening of the west-east MSLPgradient. On a larger scale, during rainy years, there istendency to high pressure in the subtropical Atlantic andlow pressure in the North Atlantic.

The fourth regime shown in Figure 4 (NWZ) has alarge low pressure centre over the whole of CentralEurope, Israel and Jordan, and the eastern Mediterranean,which is associated with rainfall throughout the region.The NWZ regime does not resemble the compositesof wet–dry years – reflecting the fact that it occursapproximately equally frequently in wet and dry years(4.9% of the time during the three wettest seasons and6.5% during the three driest seasons). Consistent withthis, the NWZ regime is associated with 11% of the rainydays during the driest years as compared to 5.5% duringthe three wettest years – when the other rainy regimesdominate. The superposition of the MSLP anomaliesfrom the four rainy synoptic regimes is clear on thislarge scale, with the lows associated with the SWA andSWZ regimes partially cancelling out the highs that occurduring the WA and NWZ regimes in the central Atlantic.

4.2. The impact of large-scale modes of variability oninterannual precipitation variability

The inconsistencies between the published studies de-scribed at the beginning of this section reflect the factthat the link between east Mediterranean regional pre-cipitation and the Eurasian modes of climate variability


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is not clear. Figure 6 shows that distribution of rain-fall for positive and negative phases of three modesof variability: the NAO, the EAWR, and the El NinoSouthern Oscillation (ENSO) (as defined by warm andcold Sea Surface Temperature (SST) anomalies in theNino3 region of the Pacific). The analysis was carriedout for November–January seasonal means over a 50-year period (1950–1999). It can be seen that there isno clear difference between the rainfall histograms forpositive and negative phases of the East Atlantic patternor ENSO, and this is borne out by a Student’s t-test,which shows that the means of the distributions are notsignificantly different, even at a 90% level. The rain-fall histograms for the positive and negative phases ofthe EAWR pattern hint at a link – with negative phasesof the EAWR pattern associated with somewhat lowerrainfall (based on a Student’s t-test, the means of thedistributions are almost significantly different at the 95%level). This is consistent with the correlations between theEAWR pattern and rainfall variability in Israel reportedin Krichak et al. (2002) and Krichak and Alpert (2005b).However, the clearest relationship is with the NAO. Dur-ing NAO positive years, there is greater variability, whichis reflected by a greater proportion of very rainy years.In fact, nine out of the ten rainiest seasons during the last50 years have occurred when the NAO is in its positivephase (significant at the 95% level – based on the imple-mentation of Fisher’s exact test described in Appendix Bof Mason and Goddard, 2001). The means of the rainfalldistributions are significantly different at the 99% levelbased on a Student’s t-test. The relationship between thepositive phase of the NAO and high rainfall is not, how-ever, universal: during some positive NAO years, rainfallis well below average. Moreover, at R = 0.44, the linearcorrelation between the NAO index and rainfall total isonly just significant at the 95% level (based on a Stu-dent’s t-test).

The question of whether the NAO and EAWR co-vary and, moreover, whether the NAO and EAWR actingtogether have a stronger influence on rainfall than theydo separately is relevant both to this work and to pre-viously published studies (Krichak et al., 2002; Krichakand Alpert, 2005b). The data used in this study showedno clear evidence of significant co-variation between the

NAO and EAWR indices. Specifically, when the NAOwas positive, the EAWR had a 57% chance of beingpositive – compared to the climatological value of 49%.Moreover, the correlation between the EAWR and NAOfor November–January is 0.12 (not significant at the 90%level).

To investigate the joint influence of the NAO andEAWR on Israel precipitation, histograms of precipitationtotals during all possible combinations of positive andnegative NAO and EAWR were plotted and compared toclimatology (Figure 7). Comparison between Figures 6and 7 gives no indication that the NAO influence on rain-fall is stronger when it is in the same state as the EAWR.Indeed, although further quantitative interpretation wasnot possible because of the low number of cases, Figure 7hints that the opposite is true. In other words, a positiveEAWR reduces the impact of a positive NAO. Specifi-cally, there is a lack of very rainy years when the EAWRis positive – perhaps reflecting the relatively low likeli-hood of the climatologically common SWA GWL regime.

The association between the NAO and Middle Eastrainfall on interannual timescales can be related to thelikelihood of the synoptic regimes discussed in Section4.1. Table II shows that the four regimes most favourablefor rainfall are all more likely when the NAO is in itspositive phase. In other words, when the NAO is posi-tive, circulation regimes over Europe that strongly favourrainfall in the Middle East are more likely to occur. Thisleads to more rainy days during NAO positive years andhence to higher rainfall over the season. There is no con-verse relationship between the regimes associated withthe lowest probabilities of rain and NAO negative condi-tions. In particular, the regime with the lowest probabilityof rain (MSLP low over Central Europe – rainfall prob-ability 11.1%) is more than twice as likely during NAOpositive years than during NAO negative years. More-over, the regimes most strongly associated with NAOnegative conditions (Icelandic High, Trough C. Europeand Anti-cyclonic Northerly) have climatological or highrainfall probabilities.

The association between EAWR positive conditionsand regimes that favour high rainfall is clear, but some-what weaker than for the NAO. In particular, the WAregime – which is the second most common regime, and



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Figure 7. Histograms of total rainfall anomalies during November–January for the box defined for Figure 5 for years selected by the state ofboth the NAO and the EAWR (grey shading) compared against all years (no shading). The NAO and EAWR states are labelled on the figure

with the number of events given in brackets.

which strongly favours rainfall – occurs 2.4 times as fre-quently when the EAWR is positive, compared to 4.0times when the NAO is positive. Furthermore, as men-tioned above, the SWA regime, which strongly favoursrainfall, is more common when the EAWR is nega-tive. The link between low rainfall and EAWR negativeconditions is, however, clearer than the correspondingrelationship for the NAO. For example, the regime thatmost strongly favours dry weather (a low over CentralEurope), which, as was described above, is associatedwith NAO positive conditions, is twice as likely whenthe EAWR is negative. Moreover, the most common ofthe four regimes that favour dry weather (cyclonic west-erly) is 2.5 times more likely when the EAWR is negative.Again, this regime is associated with the positive phaseof the NAO. The other two of the four are approximatelyequally common during EAWR positive and negativephases.

It should be noted that the probability of rain for agiven synoptic regime does not depend on the state ofthe NAO or EAWR (data not shown here) suggesting,perhaps not surprisingly, that the teleconnection betweenlarge-scale modes of variability and precipitation in theMiddle East arises via perturbations to the circulationover the Mediterranean. This implies that longer termchanges in the NAO or EAWR are likely to affect Israelrainfall only insofar as they affect the circulation overthe Mediterranean. Any change in the links betweenthese modes of variability and the circulation patternsover the Mediterranean would therefore be expected toaffect their statistical relationships with Israel rainfall.

The changing strength of the teleconnection between theEuropean circulation and the NAO has previously beennoted (Jones et al., 2003), raising the possibility that theteleconnection between the NAO and Israel rainfall is notconstant in time.

The results of this study are broadly consistent withthe published literature. In particular, the anti-correlationbetween seasonal total precipitation in Europe/westernMediterranean and the southeast (including Jordan andIsrael) has previously been recognized (Kutiel and Paz,1998; Krichak et al., 2000a). Consistent with this, severalother studies have reported that the association betweenprecipitation and large-scale modes of variability such asthe NAO and EAWR pattern have opposite polarities inWestern Europe and the Middle East (Krichak and Alpert,2005b; Ziv et al., 2006).

5. Discussion

The daily probability of rainfall in Israel is linked to thecirculation patterns over Europe – with rain more likelyduring regimes when MSLP is either high over West-ern/Central Europe, or low over the whole Mediterraneanand Southern Europe. These patterns can be related tothe trajectory of cyclones crossing the Mediterranean.High pressure over Central/Southern Europe results ina southward diversion of cyclones, and hence a greaterlikelihood that they will enter Jordan and Israel, whereaslow pressure over the whole region is associated withlarge events that affect the whole Mediterranean, South-ern Europe and Israel and Jordan. Not surprisingly, the


E. BLACK

precipitation that occurs in the Middle East as a result ofthe diversion of cyclones by high pressure over Europe isassociated with dry conditions in Southern Europe, whilethe rain that results from large events is more widespread.Overall, most rainfall in the region results from the diver-sion of cyclones, with widespread events less common.

This daily variability projects onto the interannualtimescale. Anomalously dry seasons in Israel and Jordanare associated with wet conditions in Europe, and viceversa. Although this would initially appear to suggestthat the system is linear, with wet seasons associated withsimilar but opposite polarity circulation anomalies to dryseasons, the composites in Figure 5, show that this isnot the case. During wet years, the high pressure oversoutheastern Europe, and a modest intensification of theCyprus low, divert the Mediterranean storm track leadingto increased precipitation over the Middle East. Duringdry years, however, the negative MSLP anomalies arestrongest over the northern Mediterranean and Turkey,reflecting a focused and strengthened storm track thatbrings higher than usual precipitation to Southern Europeand less than usual to the Middle East.

Our analysis of daily to interannual variability in Israelrainfall and European circulation provides a frameworkfor understanding the correlations between Middle Eastclimate and Euro-Atlantic modes of variability such asthe NAO and EAWR. As was described above, the pat-terns of circulation and hence the position and orientationof the storm track during wet and dry years are not sym-metrical (i.e. the MSLP and storm track anomalies duringwet years are not equal and opposite to those during dryyears). Specifically, during wet years, high pressure insoutheastern Europe tends to divert the Mediterraneanstorm track southwards, resulting in increased precipita-tion in the Middle East. During dry years, on the otherhand, an intensification of the storm track in the northernMediterranean, results in fewer cyclones and hence lowerprecipitation in the Middle East. The circulation has aparticularly strong NAO type character during wet years,when the high pressure centre over Southern Europe thatis associated with NAO positive conditions diverts thestorm track south over Israel. This is consistent with theobservation that the link between a positive NAO and awet Israel is weaker than that between a negative NAOand a dry Israel. The European MSLP anomalies associ-ated with the EAWR are more widespread and strongerthan those associated with the NAO. The EAWR patternthus resembles the MSLP anomalies during dry years,when the storm track activity is generally strong andfocused over continental Europe. Consistent with this,and in contrast to the NAO, the negative state of theEAWR is more clearly associated with low rainfall inIsrael than the positive state is with high rainfall.

The lack of symmetry in the association betweenthe NAO and Israel rainfall explains, in part, the lowcorrelations reported in some studies (Ben-Gai et al.,2001; Ziv et al., 2006). This is not, however, the wholestory. The previous analysis suggests that the associationbetween the NAO and rainfall in Israel is associated

primarily with its influence on European circulation. Ondecadal and longer timescales, the European circulationpatterns associated with NAO variability have varied.A previously published comparison between 1901–1950and 1951–1998 shows marked differences between thecorrelations of NAO index and temperature/precipitation,with the strength and coherence at the centres of actionin the Atlantic being stronger in the later period. Despitethis, in the earlier period the influence of the NAO seemsto extend further east. The signal in the region of Turkeyand the Middle East appears particularly variable (Joneset al., 2003). The non-stationarity in the teleconnectionexplains the apparent inconsistencies between studies thathave considered different data periods. It also explainswhy the decadal variability in the NAO does not relatedirectly to decadal variability in Middle East rainfall.

In the future, the NAO is projected to become morepositive (IPCC, 2007). These same simulations, however,project a decrease in Middle East rainfall (Black, 2009;Evans, 2009, 2010). There is thus an apparent incon-sistency between the observed association between highMiddle East rainfall and a positive state of the NAO oninterannual timescales, and the projected decrease in Mid-dle East rainfall under increasingly NAO positive condi-tions. This suggests that changes in regional precipitationin Jordan and Israel are not solely explicable by the vari-ability in circulation described in this paper. Rather thechanges in precipitation may be driven by changes in themean state of the Mediterranean storm track (Black et al.,2010) and the thermodynamic response of the hydrolog-ical cycle to climate change (Held and Soden, 2006).

6. Conclusions

Much of the Middle East is arid, with annual rainfallless than 200 mm. The small amount of rain is crucialfor the local population, with both spatial and temporalvariability having a significant impact on society. Thisstudy has shown that daily to interannual variability inrainfall is influenced by the large-scale circulation. Thedaily probability of rainfall is affected by the synopticregime over Europe and the Atlantic, while the occur-rence of these regimes is determined, in turn, by slowlyvarying large-scale modes of variability, such as the NAOand the EAWR pattern. The teleconnection between theNAO and the circulation over Europe is however, non-stationary in time. Moreover, the association between dryconditions in the Middle East, the circulation regime overEurope and NAO negative conditions is less clear thanfor NAO positive conditions. These complexities help toexplain the low correlations between the NAO index andinterannual variability in Middle East rainfall.

Acknowledgements

The author is grateful to Paul James for providing hisGWL catalogue for use in this analysis and to KevinHodges his help with the TRACK software. The work



described in this paper benefited from discussion withinthe NCAS-Climate tropical group and with the Water,Life and Civilization climate modelling group: DavidBrayshaw, Brian Hoskins, Julia Slingo. The manuscriptwas greatly improved by the comments of two anony-mous reviewers. The Israeli Meteorological Service pro-vided precipitation data. This work was supported by theLeverhulme Trust funded Water, Life and Civilizationprogramme and the climate division of the National Cen-tre for Atmospheric Science.

References

Barnston AG, Livezey RE. 1987. Classification, seasonality andpersistence of low-frequency atmospheric circulation patterns.Monthly Weather Review 115: 1083–1126.

Baur F, Hess P, Nagel H. 1944. Kalendar der GrosswetterlagenEuropas 1881–1939. DWD: Bad Homburg.

Ben-Gai T, Bitan A, Manes A, Alpert P, Kushnir Y. 2001. Tempera-ture and surface pressure anomalies in Israel and the North AtlanticOscillation. Theoretical and Applied Climatology 69: 171–177.

Black E. 2009. The impact of climate change on daily precipitationstatistics in Jordan and Israel. Atmospheric Science Letters 10:192–200.

Black E, Brayshaw D, Rambeau C. 2010. Past, present and futureprecipitation in the Middle East: insights from models andobservations. Philosophical Transactions of the Royal Society A 368:5173–5184.

Cullen HM, Demenocal PB. 2000. North Atlantic influence on Trigris-Euphrates streamflow. International Journal of Climatology 20:853–863.

Cullen HM, Kaplan A, Arkin PA, Demenocal PB. 2002. Impact of theNorth Atlantic Oscillation on Middle Eastern climate and streamflow.Climatic Change 55: 315–338.

Diro GT, Grimes DIF, Black E, O’Neill A, Pardo-Iguzquiza E.2009. Evaluation of reanalysis rainfall estimates over Ethiopia.International Journal of Climatology 29: 67–78.

Enzel Y, Bookman R, Sharon D, Gvirtzman H, Dayan U, Ziv B,Stein M. 2003. Late Holocene climates of the Near East deducedfrom Dead Sea level variations and modern regional winter rainfall.Quaternary Research 60: 263–273.

Eshel G, Farrell BF. 2000. Mechanisms of eastern Mediterraneanrainfall variability. Journal of the Atmospheric Sciences 57:3219–3232.

Eshel G, Farrell BF. 2001. Thermodynamics of Eastern Mediterraneanrainfall variability. Journal of the Atmospheric Sciences 58: 87–92.

Evans JP. 2009. 21st century climate change in the Middle East,Climatic Change 92: 417–432.

Evans JP. 2010. Global warming impact on the dominant precipitationprocesses in the Middle East. Theoretical and Applied Climatology99: 389–402.

Evans JP, Smith RB. 2006. Water vapor transport and the productionof precipitation in the Eastern Fertile Crescent. Journal ofHydrometeorology 7: 1295–1307.

Evans JP, Smith RB, Oglesby RJ. 2004. Middle East climatesimulation and dominant precipitation processes. InternationalJournal of Climatology 24: 1671–1694.

Gerstengabe F-W, Werner PC, Ruuge U. 1999. Katalog der Gross-wetterlagen Europas 1881–1998 nach P. Hess und H. Brezowsky.Potsdam-Inst. F. Klimafolgenforschung: Potsdam.

Held IM, Soden BJ. 2006. Robust responses of the hydrological cycleto global warming. Journal of Climate 19: 5686–5699.

Hodges KI. 1994. A general-method for tracking analysis and itsapplication to meteorological data. Monthly Weather Review 122:2573–2586.

Hodges KI. 1995. Feature tracking on the unit-sphere. Monthly WeatherReview 123: 3458–3465.

Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (eds). 2003. Geophys-ical Monograph Series. The North Atlantic Oscillation Climate Sig-nificance and Environmental Impacts. American Geophysical Union:Washington DC.

IPCC. 2007. Fourth Assessment Report: Working Group II Report“Impacts, Adaptation and Vulnerability”. Available at http://www.ipcc.ch/ipccreports/ar4-wg2.htm. (Retrieved on March, 2011)

James PM. 2007. An objective classification method for Hess andBrezowsky Grosswetterlagen over Europe. Theoretical and AppliedClimatology 88: 17–42.

Jones PD, Jonsson T, Wheeler D. 1997. Extension to the NorthAtlantic oscillation using early instrumental pressure observationsfrom Gibraltar and south-west Iceland. International Journal ofClimatology 17: 1433–1450.

Jones PD, Osborn TJ, Briffa KR. 2003. Pressure-based measures ofthe North Atlantic Oscillation (NAO): A comparison and anassessment of changes in the strength of the NAO and its influenceon surface climate parameters. In The North Atlantic OscillationClimate Significance and Environmental Impacts Eds J. W. Hurrell,Y. Kushnir, G. Ottersen and M. Visbeck Washington DC: AmericanGeophysical Union pp. 51–62.

Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L,Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M,Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C,Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D. 1996. TheNCEP/NCAR 40-year reanalysis project. Bulletin of the AmericanMeteorological Society 77: 437–471.

Krichak SO, Alpert P. 2005a. Decadal trends in the east Atlantic-west Russia pattern and Mediterranean precipitation. InternationalJournal of Climatology 25: 183–192.

Krichak SO, Alpert P. 2005b. Signatures of the NAO in theatmospheric circulation during wet winter months over theMediterranean region. Theoretical and Applied Climatology 82:27–39.

Krichak SO, Kishcha P, Alpert P. 2002. Decadal trends of mainEurasian oscillations and the Eastern Mediterranean precipitation.Theoretical and Applied Climatology 72: 209–220.

Krichak SO, Tsidulko M, Alpert P. 2000a. Monthly synoptic patternsassociated with wet/dry conditions in the Eastern Mediterranean.Theoretical and Applied Climatology 65: 215–229.

Krichak SO, Tsidulko M, Alpert P. 2000b. November 2, 1994, severestorms in the southeastern Mediterranean. Atmospheric Research 53:45–62.

Kutiel H, Maheras P, Turkes M, Paz S. 2002. North Sea CaspianPattern (NCP) – an upper level atmospheric teleconnection affectingthe eastern Mediterranean-implications on the regional climate.Theoretical and Applied Climatology 72: 173–192.

Kutiel H, Paz S. 1998. Sea level pressure departures in theMediterranean and their relationship with monthly rainfall conditionsin Israel. Theoretical and Applied Climatology 60: 93–109.

Mason SJ, Goddard L. 2001. Probabilistic precipitation anomaliesassociated with ENSO. Bulletin of the American MeteorologicalSociety 81: 619–638.

Rodwell MJ, Hoskins BJ. 1996. Monsoons and the dynamics ofdeserts. Quarterly Journal of the Royal Meteorological Society 122:1385–1404.

Saaroni H, Halfon N, Ziv B, Alpert P, Kutiel H. 2009. Links betweenthe rainfall regime in Israel and location and intensity of Cypruslows. International Journal of Climatology 30: 1014–1025.

Tsvieli Y, Zangvil A. 2005. Synoptic climatological analysis of ‘wet’and ‘dry’ Red Sea Troughs over Israel. International Journal ofClimatology 25: 1997–2015.

Ziv B, Dayan U, Kushnir Y, Roth C, Enzel Y. 2006. Regional andglobal atmospheric patterns governing rainfall in the southernLevant. International Journal of Climatology 26: 55–73.

Ziv B, Dayan U, Sharon D. 2005. A mid-winter, tropical extremeflood-producing storm in southern Israel: Synoptic scale analysis.Meteorology and Atmospheric Physics 88: 53–63.

Ziv B, Saaroni H, Alpert P. 2004. The factors governing the summerregime of the eastern Mediterranean. International Journal ofClimatology 24: 1859–1871.


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