Regional climate model simulation of winter climate over Central-Southwest Asia, with emphasis on...

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 30: 220–235 (2010)Published online 26 February 2009 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1887

Regional climate model simulation of winter climate overCentral–Southwest Asia, with emphasis on NAO and ENSO

effects

F. S. Syed,a,b* F. Giorgi,c J. S. Palc and Kevin Keayd

a Department of Meteorology, Stockholm University, 10691, Stockholm, Swedenb Pakistan Meteorological Department, 44000, Islamabad, Pakistan

c Abdus Salam International CentreCenter for Theoretical Physics, 34014, Trieste, Italyd School of Earth Sciences, University of Melbourne, 3010, Melbourne, Victoria, Australia

ABSTRACT: We present a regional climate modeling study of the winter climate of Central–Southwest Asia (CSWA),focusing on the mean model climatology of temperature and precipitation, the simulated storm characteristics and the effectsof El-Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on storm activity and precipitation. Forty-three winter seasons are simulated with the model RegCM3 driven by ERA40 reanalysis fields over a region encompassingCSWA and the Mediterranean sector. The model shows a good performance in reproducing the observed mean surfaceclimate of the region as well as the characteristics of the western disturbances affecting CSWA. Observations show thatboth the NAO and ENSO have a substantial influence on the CSWA climate, particularly in the region of Northern Pakistan,Afghanistan and Tajikistan, where precipitation increases (decreases) during the positive (negative) NAO phase and warm(cold) ENSO phase. The model captures well the observed signals and a detailed storm analysis shows that they are mostlyassociated with an intensification of western disturbances originating in the Eastern Mediterranean and Middle East regionsand moving eastward across a 500 hPa trough situated over the CSWA during the positive NAO and warm ENSO phases.The NAO and ENSO signals are reinforced by enhanced moisture sources from the Mediterranean, Caspian and ArabianSeas. Our study provides encouraging indications towards the use of the model RegCM3 for climate simulations overCSWA. Copyright 2009 Royal Meteorological Society

KEY WORDS NAO; ENSO; Precipitation variability; western disturbances; extratropical storms; cyclone tracking; regionalclimate model RegCM3

Received 1 May 2008; Revised 16 November 2008; Accepted 31 January 2009

1. Introduction

Central–Southwest Asia (CSWA), a region extendingfrom the Middle East to the western reaches of theHimalayas chain (Figure 1), has been little investigatedby the regional climate modeling community. As anexample, regional climate model (RCM) simulationsaimed at investigating the effects of Aral Sea desiccationon the surrounding climate were conducted by Smallet al. (1999a,b). As additional examples, an RCM-basedstudy of the response of the Caspian Sea level to globalwarming was presented by Elguindi and Giorgi (2006)while Akhtar et al. (2008) used an RCM to study thehydrology of Hindukush–Karakorum-Himalaya region.

These limited studies, however, focused on specificbasins of CSWA and not on the full region. On the otherhand, analyses of global model projections indicate thatCSWA may be profoundly affected by global change(Giorgi et al., 2001, Giorgi and Bi, 2005), and the

* Correspondence to: F. S. Syed, Department of Meteorology, Stock-holm University, 10691, Stockholm, Sweden.E-mail: [email protected]

complex morphology of the region calls for the use ofhigh resolution modeling tools. It is therefore importantthat RCMs are tested for their ability to reproduce theclimatic features of CSWA so that they can be moreeffectively used to investigate climate change over theregion.

Although a large portion of Asia receives most pre-cipitation during the summer monsoon season, the win-ter is most important for CSWA. It is the rainy seasonover large portions of the region and it is the seasonwhen the water reservoir accumulates in the form ofsnowpack. Winter precipitation over CSWA is mostlyassociated with western disturbances that originate fromthe northeastern Atlantic, Europe and the Mediterraneanand travel eastward to reach CSWA (Martyn, 1992). Thevariability of Atlantic and European storms is in turnaffected by the North Atlantic Oscillation (NAO) andthe El-Nino Southern Oscillation (ENSO) (e.g. Hurrell,1995; Dickson et al., 2000; Trigo et al., 2004). It maythus be expected that NAO and ENSO influence thewinter climate of CSWA, and indeed the observationalanalysis of Syed et al. (2006) (hereafter referred to asS06) show that a strong signal of both ENSO and NAO

Copyright 2009 Royal Meteorological Society

REGIONAL CLIMATE MODEL SIMULATION OF WINTER CLIMATE OVER CENTRAL–SOUTHWEST ASIA 221

Figure 1. Model domain and topography (m).

(a) (b)

(c) (d)

Figure 2. Simulated (a) and observed (b) climatology (1961–2000) of December to March (DJFM) precipitation (mm/day). Simulated (c) andobserved (d) climatology (1961–2000) of DJFM surface air temperature (°C).

effects can be found on the temperature and precipita-tion climatology of CSWA. In particular, S06 show thatsome areas of CSWA, most noticeably Northern Pakistan,

Afghanistan and Tajikistan, receive increased precipita-tion during the positive phase of the NAO and duringwarm ENSO events in relation to the intensification of

Copyright 2009 Royal Meteorological Society Int. J. Climatol. 30: 220–235 (2010)

222 F. S. SYED ET AL.

a trough over the region. They then hypothesize that theeffect of NAO and ENSO mostly results from an inten-sification of western disturbances as they deepen whenreaching the intensified trough over the CSWA region.

Following these considerations, in this paper wepresent an evaluation of the ability of the Abdus SalamInternational Centre for Theoretical Physics (ICTP)regional climate modeling system RegCM3 (Pal et al.,2007) in simulating winter climate over CSWA.

Forty-three winter seasons are simulated, from 1958 to2000, with the model driven at the lateral boundaries byECMWF analyses of observations (ERA40). The focus ofthe model analysis is on average precipitation and temper-ature climatology, western disturbance statistics and theeffects of NAO and ENSO on the variability of CSWAprecipitation. Because of the importance of western dis-turbances for the winter climate of CSWA, we devoteparticular attention to the analysis of storms and for this

Figure 3. Tracks of storms lasting at least 2 days, December to March (DJFM, 1991–1993). Solid circles represent locations of cyclogenesisand hollow circles represent locations of cyclolysis.

(a) (b)

(c)

Figure 4. ERA40 500 hPa storm climatology (1961–2000): (a) system density [degree latitude square (DLSQ)]; (b) cyclogenesis (1000DLSQ/day); (c) cyclolysis (1000 DLSQ/day).



purpose we employ the storm “finding and tracking”scheme of (Simmonds and Murray, 1999). This allows usto investigate the effects of sub-synoptic scale structureand mesoscale cyclogenetic processes (e.g. Alpert andNeeman, 1992; Trigo et al., 1999, 2002; Trigo, 2006). Interms of the NAO and ENSO effects, we use the obser-vational work of S06 as reference.

We first describe the model, numerical experimentsand storm finding and tracking schemes (section 2).Results are discussed in section 3, while summary andconclusions are presented in section 4.

2. Model, data and methods

2.1. Model and numerical experiments

The numerical experiments are carried out using theICTP regional climate modeling system in its latest ver-sion RegCM3. This version is documented by Pal et al.(2007). It is an augmented version of the model of Giorgiet al. (1993a,b). The dynamical core of RegCM3 is basedon the hydrostatic version of the fifth-generation Pennsyl-vania State University–National Center for AtmosphericResearch (PSU–NCAR) Mesoscale Model (MM5; Grellet al., 1994). We use the nonlocal, counter gradientplanetary boundary layer of Holtslag et al. (1990) andthe Biosphere–Atmosphere Transfer Scheme (BATS IE)

land surface process schemes of Dickinson et al. (1993)as described in Giorgi et al. (1993a, 1993b). Radiativetransfer processes are described using the scheme ofthe NCAR CCM3 (Community Climate Model 3; Kiehlet al., 1996) as implemented by (Giorgi et al., 1999;Giorgi F and Mearns LO, 1999) while ocean–atmospherefluxes follow the parameterization of Zeng et al. (1998).

RegCM3 includes several options for describing con-vective precipitation. The scheme used here is that ofGrell, (1993), which is a mass flux-based parameteriza-tion accounting for penetrative updrafts and downdraftsand including different closure assumptions. Resolved-scale precipitation is described by the scheme of Pal et al.(2000), which is an explicit moisture scheme account-ing for the sub grid-scale variability in clouds and theevaporation and accretion of precipitation.

The model was integrated for 4.5 month-long sim-ulations, beginning on November 15 and ending onMarch 31. The first 15 days of these simulations are dis-carded from the analysis to allow for model spin up,so that the analysis period goes from December throughMarch and is referred to as DJFM. Forty-three DJFMsimulations are performed, from 1958 to 2000. The modeldomain is shown in Figure 1. It is a large domain encom-passing the entire Euro-CSWA region (approximately−5° to 90 °E and 10° to 65°N), with 123 and 78 grid points

(a) (b)

(c)

Figure 5. Simulated storm climatology (1961–2000): (a) system density [degree latitude square (DLSQ)]; (b) cyclogenesis (1000 DLSQ/day);(c) cyclolysis (1000 DLSQ/day).



in the zonal and meridional directions, respectively, and ahorizontal grid spacing of 70 km and 18 vertical levels.This domain includes the main Mediterranean cycloge-netic regions relevant for winter precipitation in CSWA.

2.2.1. Cyclone tracking and finding scheme

Because winter precipitation over CSWA is mainly asso-ciated with synoptic scale western disturbances, it isimportant to carry out a detailed storm analysis to under-stand the processes that determine the storm characteris-tics. A number of studies are available in the literaturewhich use different cyclone tracking procedures (Sanders,1988; Parker et al., 1989, Bell and Bosart, 1989, 1994;Lefevre and Nielsen-Gammon, 1995; Lambert, 1988;Konig et al., 1993; Serreze et al., 1993; Blender et al.,1997; Sinclair, 1994; Lionello et al., 2002). These pro-cedures can be manual or automatic and are based ondifferent variables to identify cyclones, such as sea levelpressure (SLP), 500 mb geopotential height and vorticity.

In this study, we adopt the cyclone ‘finding’ and ‘track-ing’ scheme developed by Murray and Simmonds (1991)and refined by Simmonds and coworkers (Simmonds andMurray, 1999). Various refined versions of this schemehave been used to examine SLP from operational anal-yses and reanalyses (e.g. Jones and Simmonds, 1993a,

1993b, 1994; Simmonds and Keay, 2000) and from theoutput of model climate and sensitivity experiments (e.g.Simmonds and Wu, 1993; Simmonds and Law, 1995;Murray and Simmonds, 1995).

There are three stages to the scheme, all performedwithout intervention once the governing parameter set-tings have been decided. This allows an objective com-parison across the cyclone statistics obtained from dif-ferent datasets. Essentially, the algorithm searches forlows on a spline-fitted surface, allowing both closed andopen depressions to be found. It can also be applied to500 hPa fields. For example, (Simmonds and Murray,1999) deemed that an SLP-based cyclone analysis haslittle meaning when the topography exceeds 1 km. Onthe other hand, the 500 hPa height is always above thesurface, except around the highest mountains, and henceall systems identified can be considered as ‘real’. Basedon these considerations, we perform our analysis over ourCSWA domain using the 500 hPa field, however we havealso included model topography in the cyclone analysiscode for more realistic positions of the cyclones.

The details of the scheme can be found in (Simmondsand Murray, 1999). Briefly stated, the finding routinebegins by searching for ∇2p, a local maximum in theLaplacian of the pressure (or, in our present case, of

(a) (b)

Figure 6. North Atlantic Oscillation (NAO) precipitation composite (mm/day). (a) Simulated; (b) observed.

(a) (b)

Figure 7. North Atlantic Oscillation (NAO) surface air temperature composite (°C). (a) Simulated; (b) observed.



the 500 hPa geopotential height) compared with thatof the surrounding eight grid points. This indicates theproximity of a maximum of relative cyclonic geostrophicvorticity (ξg), since

ξg ≈ 1

ρf∇2p

Where ‘ρ’ is the air density and ‘f’ is the Coriolisparameter. From this point, the location of the associatedheight minimum is sought iteratively using ellipsoidalminimization techniques. The lows are then categorizedas ‘open’ or ‘closed’, as detailed in Murray and Sim-monds (1991). In the case of a closed system, the centerof the cyclone is defined as the point of minimum geopo-tential height, the point of inflection in the field beinga suitable equivalent in the case of an open system.The systems (both open and closed) are then requiredto satisfy a ‘minimum concavity criterion’ to qualify asmeteorologically significant phenomena. This is done byemploying a threshold ‘area-averaged Laplacian’ over aspecified radial distance from the cyclone center (2° lati-tude was used in this study). This is the main parameterin need of adjustment to capture significant mid-latitudesystems without identifying the large number of shallowdepressions in the tropics.

2.2.2. Cyclone diagnostic parameters

Different cyclone statistics have been defined by Murrayand Simmonds (1991). ‘Cyclone density’ is calculatedby summing contributions from all sampled positions(recorded or interpolated along the tracks) and normal-izing this sum by the area and the number of sam-pling times. Stated differently, it is the mean numberof cyclones per analysis to be found in the prescribedreference area. “Cyclogenesis” and “cyclolysis” refer tothe formation and disappearance of a cyclone, respec-tively, per reference area per day. Another quantity thescheme produces is the ‘net velocity’, i.e. the averagespeed and direction with which the cyclone centers moveat a given point. Finally, the terms “intensity”, “depth”and “radius” of a cyclone refer to particular characteris-tics of a cyclonic structure (Simmonds and Keay, 2000).Briefly, for an axially symmetric paraboloidal depressionon a flat field, the depth D is related to the intensity ∇2h

(where h is the field under consideration), and the radiusR by

D = R2

4∇2h

Since the Laplacian is not spatially uniform, butdecreases rapidly away from the cyclone center, anaverage value of ∇2h over a radius of a few degreesfrom the center is used in the determination of the depth.

(a) (b)

(c) (d)

Figure 8. North Atlantic Oscillation (NAO) sea level pressure (SLP) composite (hPa) for (a) simulation and (b) ERA40 reanalysis; (c–d) NAO500 hPa height composite (gpm) for (c) simulation and (d) ERA40 reanalysis.



2.3. Methods

In this study, the NAO is measured by the winter (DJFM)NAO index (NAOI) of Hurrell (1995). The NAOI isbased on the normalized SLP difference between Lisbon,Portugal and Stykkisholmur/Reykjavik and Iceland. TheSLP anomalies at each station are normalized by divid-ing each seasonal mean pressure by the correspondingstandard deviation calculated over the long-term period(1864–1983) (http://www.cgd.ucar.edu/cas/jhurrell/nao.stat.winter.html). The normalization is used to avoid theseries being dominated by the greater variability of thenorthern station. In this study we consider the period from1948 to 2000 (see below).

The Southern Oscillation Index (SOI) is one measureof the large-scale fluctuations in air pressure betweenthe western and eastern tropical Pacific (i.e. the state ofthe Southern Oscillation) during El Nino and La Ninaepisodes. Traditionally, this index is calculated from thepressure anomaly difference between Tahiti and Darwin,Australia. In general, smoothed time series of the SOIare consistent with changes in ocean temperature acrossthe eastern tropical Pacific. The negative phase of theSOI represents below-normal air pressure at Tahiti andabove-normal air pressure at Darwin. Prolonged periodsof negative SOI values coincide with abnormally warmocean waters across the eastern tropical Pacific (El Nino

episode or warm ENSO phase) while prolonged periodsof positive SOI values coincide with abnormally coldocean waters across the eastern tropical Pacific (La Ninaepisode or cold ENSO phase), (Allan et al., 1996).

To calculate the NAOI and SOI we use the large-scale circulation mean monthly data (SLP, 500 hPa and200 hPa geopotential heights) from the ERA40 reanalysis(Uppala et al., 2005). For climate observations overland, we use the Climate Research Unit, CRU TS 2.0dataset (Mitchell et al., 2004), which includes monthlysurface air temperature and precipitation for the period1901–2000 on a regular global 0.5 degree land surfacegrid. This dataset revises and extends the original datasetof New et al. (2000). We focus on the period 1951–2000because of the availability of both the ERA40 data andCRU observations in this period and to be consistent withthe study of S06.

The initial and lateral boundary conditions for theRegCM3 simulations are derived from the ERA40 reanal-ysis (Uppala et al., 2005), available at a horizontal res-olution of 2.5° latitude by 2.5° longitude and a tempo-ral resolution of 6 h (00 : 00, 06 : 00, 12 : 00 and 18 : 00UTC). Sea surface temperature (SST) for the RegCM3 isobtained by interpolation of the monthly average valuesof the Reynolds and Smith (1994) dataset. The modeltopography (Figure 1) and land use are obtained from

(a) (b)

(c)

Figure 9. Simulated North Atlantic Oscillation (NAO) storm composite: (a) system density [degree latitude square (DLSQ)]; (b) cyclogenesis(1000 DLSQ/day); (c) cyclolysis (1000 DLSQ/day).



the 10-min United States Geological Survey (USGS) andGlobal Land Cover Characterization (GLCC, Lovelandet al., 2000) datasets, respectively.

We identify NAO and ENSO years from our NAOIand SOI calculations. A year is considered to be in apositive (negative) NAO phase when the NAOI is morethan one standard deviation above (below) the averageNAOI for 1951–2000. An analogous definition is appliedto warm and cold SOI years. Composites are calculated asthe difference between the ensemble average of positiveNAOI (or SOI) years and that of negative NAOI (or SOI)years. During the simulation period 1958–2000 we findten positive NAOI years (1973, 1981, 1983, 1989, 1990,1992, 1993, 1994, 1995, 2000), 8 negative NAOI years(1962, 1963, 1964, 1965, 1969, 1977, 1979, 1996), sixpositive SOI years (1971, 1974, 1976, 1989, 1999, 2000)and seven negative SOI years (1978, 1983, 1987, 1990,1992, 1993, 1998).

3. Results

3.1. Analysis of model climatology

We first present an evaluation of the model climatologyfor the relevant variables analyzed here, namely precip-itation, temperature (section 3.1.1) and cyclone statistics(section 3.1.2).

3.1.1. Model precipitation and temperature climatology

Figure 2 compares the simulated and observed DJFMprecipitation and temperature climatologies over themodel domain for the benchmark 1961–2000 period.The model captures the observed regional precipitationpatterns and, in particular, the effects of topography.Focusing on the CSWA portion of the domain, observedand simulated maxima are found over the Karakorumrange in Northern Pakistan, across the mountain rangesof Southern Iran and the Middle East coastal areas,and over the Caucasus mountains west of the CaspianSea. The simulated maxima show higher precipitationamounts than observed, however it should be recalledthat observed precipitation is likely to be underestimatedfor various reasons, including a low elevation station biasand a gauge undercatch bias. In winter conditions thisunderestimate can be up to 30% or even more (Adamet al., 2003). Therefore, the model overestimate is likelyartificially inflated by these problems in the observations.

There is a generally good agreement between theobserved and simulated climatology for surface air tem-perature (Figure 2(c) and (d)). The spatial gradients andtopographically induced structure are captured and sim-ulated values are within a few degrees of the observedvalues. Note that the difference between model and realtopography is not considered in Figure 2, however thisdoes not have a large effect since the resolutions of the

(a) (b)

(c)

Figure 10. Simulated North Atlantic Oscillation (NAO) storm composite: (a) Net velocity (m/s); (b) depth (gpm); (c) central heights (gpm).



model and observations are close to each other. Figure 2thus shows that, overall, the model is capable of simu-lating the observed DJFM surface climate of the CSWAregion.

3.1.2. ERA40 and model cyclone climatology

We should first recall that, since our cyclone analysisis performed on the 500 hPa geopotential height, theposition of cyclones we identify would be somewhatshifted compared to those identified at the surface. Withthis caveat in mind, Figure 3 shows examples (years1991–1993) of model-simulated storm tracks for DJFMlasting at least 2 days. Solid circles represent locationsof storm cyclogenesis while hollow circles representlocations of storm cyclolysis. It is evident that two majorstorm tracks can be identified for the CSWA region.The first extends from eastern and northeastern Europe

to Northern Kazakhstan. These storms mostly originateover the northern Atlantic (therefore outside our modeldomain) and travel eastward across Europe into thenorthern CSWA regions. The second major storm trackoriginates from the Mediterranean, especially its easternregions, and extends into Tajikistan, Northern Pakistanand Afghanistan. Figure 3 clearly shows that CSWA ismostly a cyclolysis region.

Figure 4 shows the ERA40 500 hPa climatology forsystem density, cyclogenesis and cyclolysis, while Figure5 shows the model climatology for the same variables. Itcan be seen that the major cyclogenesis and cyclolysisregions are essentially the same in the ERA40 andRegCM3. However, more regional detail and higher localvalues are found in the model storm climatology due itshigher resolution. The Mediterranean basin encompassesseveral cyclogenesis regions (Trigo et al., 1999). For

Figure 11. Simulated North Atlantic Oscillation (NAO) composite of vertically integrated (1000–500 hPa) moisture convergence (mm/day) andmoisture transport (m/s).

(a) (b)

Figure 12. El-Nino Southern Oscillation (ENSO) precipitation composite (mm/day). (a) Simulated; (b) observed.



example, the gulf of Genoa, where cyclones are formedmostly in the lee of the Alps (Buzzi and Tibaldi,1978), constitutes one of the major cyclogenesis areasthroughout the year, and the model captures this regioneven if it is of relatively small spatial scale (Figure 5(b)).

Cyclogenesis in the eastern part of our domain, whichis of primary interest for our study, is distributed amongfour main subregions (Trigo et al., 1999).

(1) The Aegean Sea is one of the major sources for win-ter cyclones. Due to its sub-synoptic scale, Aegeancyclogenesis (Flocas and Karacostas, 1996) has beenfrequently underestimated in previous Mediterraneancyclone studies (e.g., Alpert et al., 1990). The modelgenerally captures the effect of this region, bothin terms of cyclogenesis (Figure 5(b)) and systemdensity (Figure 5(a)), although in our 500 hPa anal-ysis this is shifted somewhat to the north of theAegean Sea.

(2) A major cyclogenesis center occurs over the easternBlack Sea throughout the year, but most noticeably insummer (Trigo et al., 1999). The model also capturesthis center, although this is not pronounced in thewinter.

(3) Finally, the most pronounced cyclogenesis area inthe winter is the Middle East, extending from theEastern Mediterranean coastal regions to Syria andIraq (Trigo et al., 1999). The model captures thiscyclogenesis center quite well.

Figure 5(a) presents the average system density, whichclearly shows the two major storm tracks identifiedabove, one from Northeastern Europe to Northern Kaza-khstan and one from the Eastern Mediterranean regionsto Tajikistan, Northern Pakistan and Afghanistan. As alsoshown by Figure 5(c), these regions are also primarycyclolysis areas for CSWA.

Figure 5(b) shows that our simulation is able to cap-ture the main winter cyclogenesis areas found in theMediterranean and affecting the formation of westerndisturbances that move over CSWA. As also shown by

Figure 5(c), these regions are also primary cyclolysisareas for CSWA.

The comparison of cyclone statistics between theERA40 (Figure 4) and RegCM3 (Figure 5) data showssimilarities as well as differences. The ERA40 datashow an area of maximum storminess across a bandextending from Italy to Turkey, which is also found inthe RegCM3. However, the ERA40 misses the maximumover the Eastern Mediterranean, which is evidently dueto small scale cyclogenesis processes not captured bythe coarse ERA40 data. Both the ERA40 and RegCM3data show a maximum over Northern Pakistan whilean area where a significant difference between the twomodels is found is east of the northern Caspian Sea.Here the ERA40 data show relatively high storminess,while the RegCM3 shows a relative storminess minimum.It is possible that, again, the different scales of thetwo datasets might be influencing storm formation anddevelopment as identified by the storm tracking code(Blender and Schubert, 2000). In general, the RegCM3shows greater fine scale spatial detail than the ERA40 aswell as higher values of storm statistics.

We also calculated the climatology of cyclone centralheight, net velocity and depth (not shown for brevity).The central heights show a gradual meridional gradientdecreasing towards the north, while the net cyclonevelocity shows two maxima, one over Egypt and oneover Iran, Turkmenistan and Afghanistan. The cyclonedepth has a maximum over the northern Mediterraneanand, more generally, the northern parts of the domain.

In summary, the simulated winter cyclone statisticsshow marked regional features that are consistent withprevious analyses of observations (Trigo et al., 1999);both in terms of cyclone formation and evolution.

3.2. Effect of NAO and ENSO

In this section, we turn our attention to the effects ofNAO and ENSO on the climate of CSWA. This is mostlyperformed through a composite analysis, where NAO andENSO composites are taken over the ensembles of ENSOand NAO years identified in section 2.3.

(a) (b)

Figure 13. El-Nino Southern Oscillation (ENSO) surface air temperature composite (°C). (a) Simulated; (b) observed.



3.3. NAO3.3.1. Composite analysis of precipitation andtemperature

Figure 6 shows the simulated and observed composites(positive minus negative NAO phase years) of DJFMprecipitation over the Euro-Asia region. The observedcomposite shows an area of maximum positive valuesof 0.4–1.0 mm/day, extending from the Hindu Kushmountain range in Afghanistan to Northern Pakistan andadjacent areas of Northern India. These values can becompared with an average precipitation of 2–4 mm/dayover this region. A positive precipitation composite isalso observed in northern and northwestern Iran, whichis consistent with the findings of Jafar and Zeinolabedin(2003). The westernmost areas of CSWA, including theEastern Mediterranean region and central and SouthernIran, show negative composite precipitation. Also notethe well known north–south dipole pattern of NAOprecipitation composite over the European region. Theregional patterns of precipitation composite are capturedwell by the model (Figure 6(a)), although the simulationstend to show a stronger NAO signal over the CSWAregion than observed (see also below).

Figure 7 shows the simulated and observed NAO sur-face air temperature composites. A north–south dipolepattern is found in the temperature composite, with a

trough extending over the CSWA region. The modelessentially reproduces this pattern, although the simulatedtemperature trough extends further south than observed,with some positive values simulated also over southeast-ern Pakistan and western India.

To understand the precipitation and temperature com-posite patterns, we can analyze the composites of SLPand 500 hPa height presented in Figure 8. These show aprominent trough over the CSWA region, which appearsto be an eastward and southeastward extension of the Ice-landic low. Hurrell and Van Loon (1997) and Thompsonet al. (2000) point out that the positive phase of the NAOyields enhanced advection of warm air over extratropi-cal Eurasia north of about 45°N. The positive phase ofthe NAO is thus likely an important cause of the winterhalf-year warming in Siberia and northern Europe (Hur-rell, 1996). This composite advection pattern also causesthe relatively warm composite temperatures over CSWA(Figure 7).

Two caveats should be mentioned. First, our analysisof NAO variability is only for the period 1958–2000due to the availability of the ERA40 data used to drivethe RegCM3. During this period a strong NAO trendwas present, so that the patterns we find may not becompletely stable. Second, because of the small samplesize of NAO-related cases it is difficult to establish thestatistical significance of the related signals. Towards this,

(a) (b)

(c) (d)

Figure 14. El-Nino Southern Oscillation-sea level pressre (ENSO-SLP) composite (hPa): (a) simulated; (b) ERA40 reanalysis; ENSO 500 hPaheights (gpm) composite: (c) simulated; (d) ERA40 reanalysis.



we calculated the significance of mean differences shownin Figure 6 using a 2-tailed t-test for unequal variancesand found that these differences were not statisticallysignificant for precipitation (due to its high variability)while they were significant at the 95% confidence levelover the CSWA trough region for temperature, SLP and500 hPa height.

Compared to the ERA40 reanalysis, the model tendsto overestimate the intensity and southward extent ofthe composite trough. This overestimate at least partiallycontributes to the overestimate of the precipitation andtemperature composites found over CSWA (Figures 6and 7). S06 hypothesized that the composite NAOprecipitation signal is associated with an intensificationof storms as they reach the SLP trough in CSWA. Weinvestigate this hypothesis via the storm analysis of thenext section.

3.3.2. Composite analysis of storm characteristics

Figure 9(a)–(c) shows the NAO composite of cyclonedensity, cyclogenesis and cyclolysis, while Figure 10(a)–(c) shows the NAO composites for velocity, depth andcentral height.

In the composites of cyclone density, we find aconsistent increase during the positive NAO phase overa region extending from the Eastern Mediterranean,

across CSWA, up to Northern Pakistan and Afghanistan,indicating a higher number of storms affecting thisregion. Figure 9(b) suggests that this increase of thenumber of storms is associated with an increase incyclone formation over the Eastern Mediterranean andIran centers followed by eastward storm motion acrossthe CSWA region (recall that the cyclogenesis dipolenear the western boundary of the domain is artificiallyproduced by the storms entering the domain).

Other features in Figures 9 and 10 are of importance.First, we note that the net cyclone velocity shows apositive composite over Pakistan and Afghanistan. Thismay play an important role in increasing orographicallyproduced precipitation over the Himalayan foothills ofNorthern Pakistan, a region where the largest positiveprecipitation composite is found. In fact, higher veloc-ities imply greater lifting and thus higher precipitationamounts. We also note that the composite cyclone depthincreases and the composite central pressure decreasesover most of CSWA, which is consistent with the hypoth-esis put forward by S06 of an intensification of storms asthey enter the SLP and 500 hPa trough in CSWA.

Figure 11 shows the NAO composite of verticallyintegrated moisture convergence and moisture transport,integrated from 100 to 500 hPa. It clearly shows thetransport of extra moisture during the positive phase

(a) (b)

(c)

Figure 15. Simulated El-Nino Southern Oscillation (ENSO) storm composite: (a) system density [degree latitude square (DLSQ)]; (b) cyclogenesis(1000 DLSQ/day); (c) cyclolysis (1000 DLSQ/day).



of NAO from the Mediterranean, Caspian Sea, PersianGulf and Arabian Sea to the region extending fromsoutheastern Iran to Afghanistan and Northern Pakistan.In addition, there is a net moisture divergence overthe Mediterranean (negative values are not shown inFigure 11). Confirming the mechanism proposed by S06,this availability of extra moisture contributes to enhancethe NAO precipitation signal over the region.

3.4. El Nino Southern Oscillation3.4.1. Composite analysis of precipitation andtemperature

Results from the observed and simulated ENSO com-posite analysis of precipitation and temperature are com-pared in Figures 12 and 13. For precipitation, the obser-vations show a positive maximum of 0.4–1.0 mm/dayalong the Himalayas and Karakoram Range in North-ern Pakistan and the Pamir Mountains in Tajikistan,which can be compared to an average precipitation of2–4 mm/day over this region (1951–2000) (Figure 2(b)).Positive observed composite precipitation values are alsofound over most of Iran (especially the southern coastalareas) and the central regions of Afghanistan and Pak-istan. Small areas of negative composite precipitationoccur over Northern Iran and Eastern Turkey. Most ofthese features of the precipitation composite are very wellcaptured by the model, although similar to the NAO case,

the model tends to show a stronger ENSO signal overthe CSWA region. This overestimate is likely associatedto the generally larger precipitation amounts found in thesimulation compared to observations. It is also interestingto note that the ENSO signal shows similar spatial char-acteristics to the NAO signal, although the underlyingprocesses are different (see below).

The ENSO observed temperature composite showswarm conditions over Mongolia and Siberia in correspon-dence to the warm ENSO phase (Figure 13). In addition,a tongue of warm air composite extends from NorthernAfghanistan to Southern Iran and Pakistan. These resultsare consistent with those of Hurrell (1996). Similarly tothe case of precipitation, the model captures well theobserved spatial structure of the temperature compositeover CSWA.

Simulated and observed ENSO composites of SLP and500-hPa heights are shown in Figure 14(a)–(d). Similarto the NAO case, we find a trough over CSWA; althoughthe structure of the ENSO-related trough is similar tothat of the NAO-related trough over the CSWA area, theunderlying process appears different. While the NAO-related trough is associated with an eastward extensionof the Icelandic low, the ENSO-related trough appears tooccur in conjunction with a weaker than normal establish-ment of the Siberian High. In addition, the ENSO-relatedtrough has a more pronounced north–south orientation

(a) (b)

(c)

Figure 16. Simulated El-Nino Southern Oscillation (ENSO) storm composite: (a) net velocity (m/s); (b) depth (gpm); (c) central heights (gpm).



than the NAO-related one, which extends more in aneast–west direction. The presence of similar troughs inthe NAO and ENSO composites, although of differentorigin, suggests a similar storm intensification processfor the two cases, as hypothesized by S06. Similarly tothe NAO case, also in the ENSO case the composite sig-nal is not statistically significant at the 95% confidencelevel for precipitation, while it is for temperature, SLPand 500-hPa height.

3.4.2. Composite analysis of storm characteristics

The composite analysis of storm statistics for the ENSOcase is shown in Figures 15 and 16. Comparison withFigures 9 and 10 shows marked differences with theNAO case. First, the ENSO composites show much morenoisy signals than the NAO composites, especially forcyclogenesis and cyclolysis. The system density showsa positive composite throughout most of the domainexcept over the Eastern Mediterranean and NorthernPakistan region. Therefore, in the ENSO case the positiveprecipitation composite over Northern Pakistan is notassociated with an increase in the number of stormsoriginating from the Eastern Mediterranean.

On the other hand, along the Eastern Mediter-ranean–CSWA track the net velocity and depth of stormshave a positive composite and the central pressure a neg-ative one (Figure 16(a)–(c)). Therefore, it appears thatthe positive precipitation composite over the NorthernPakistan region associated with the warm ENSO phase isrelated to an intensification of western disturbances ratherthan an increase in the number of storms. This is againconsistent with the hypothesis put forward by S06.

Figure 17 shows the ENSO composite of verticallyintegrated moisture convergence and moisture transport

from 1000 to 500 hPa. Similar to the NAO case, we find astrong area of convergence over a region extending fromsoutheastern Iran to Northern Pakistan. Moisture diver-gence appears to be prevalent over the Mediterranean,Caspian Sea and Red Sea areas, although this signal ismuch weaker than in the NAO case. In the case of ENSO,it appears that the excess moisture over the CSWA region,which contributes to enhancing precipitation there duringthe warm ENSO phase, originates from the Persian Gulfand Arabian Sea south of the area covered by our modeldomain.

4. Summary and conclusions

This paper presents an analysis of the performance of aregional climate model (the ICTP RegCM3) in simulatingwinter climate over CSWA, with a focus on regionalprecipitation, temperature and storm statistics. Using 43simulated winter seasons, we analyzed the averagedmodel climatology and the effects of NAO and ENSOon the regions’ climate which S06 found to be importantin the observed climate.

We first show that the model driven at the lateralboundaries by meteorological fields from the ERA40reanalysis is able to reproduce well the climatology oftemperature, precipitation and storms over CSWA. Ourresults show that the regCM3 captures the observed influ-ence of NAO and ENSO on the CSWA climate, particu-larly in the region of Northern Pakistan, Afghanistan andKazhakistan. A detailed analysis of the simulated stormcharacteristics showed that the NAO and ENSO precipi-tation signal over the CSWA region is mostly associatedwith an intensification of western disturbances originat-ing in the Eastern Mediterranean and Middle East regions

Figure 17. Simulated El-Nino Southern Oscillation (ENSO) composite of vertically integrated (1000–500 hPa) moisture convergence (mm/day)and moisture transport (m/s).



and moving eastward across CSWA during the positiveNAO and the warm ENSO phases.

This intensification is associated with the effect ofan enhanced SLP and 500 hPa trough which developsover the region during these NAO and ENSO phases.Although the structure of the trough is similar for theNAO and ENSO cases, its origin is different. The troughoriginates from an eastward extension of the Icelandiclow during the positive NAO phase and from a weakestablishment of the Siberian High during the warmENSO phase. In this regard, our study supports thehypothesis put forward by S06. In the positive phaseof the NAO and (to a lesser extent) the warm phase ofENSO, the composite of vertically integrated moistureconvergence from 1000 to 500 hPa indicates that thetransport of extra moisture from the Mediterranean,Caspian Sea and Arabian Sea contribute to the observedprecipitation signals.

Our results give encouraging indications towards theuse of our regional climate modeling system to study theclimate of the CSWA region, which is characterized byquite complex climatological and topographical features.We therefore plan to use the RegCM system nested withindifferent global climate models to simulated climatechange over CSWA due to increased greenhouse gasconcentrations.

Acknowledgements

F. S. Syed thanks the Abdus Salam International Centrefor Theoretical Physics, Trieste, Italy, for inviting him asa junior associate and the part of the work on this paperwas completed during this visit. Finally, we thank theanonymous reviewers for their careful reviews and veryuseful comments.

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