Spatial changes of Extended De Martonne climatic zones affected by climate change in Iran

ORIGINAL PAPER

Spatial changes of Extended De Martonne climaticzones affected by climate change in Iran

Jaber Rahimi & Meisam Ebrahimpour & Ali Khalili

Received: 1 December 2011 /Accepted: 25 July 2012 /Published online: 10 August 2012# Springer-Verlag 2012

Abstract In order to better understand the effect associatedwith global climate change on Iran’s climate condition, it isimportant to quantify possible shifts in different climatictypes in the future. To this end, monthly mean minimum andmaximum temperature, and precipitation from 181 synopticmeteorological stations (average 1970–2005) have beencollected from the meteorological organization of Iran. In thispaper, to study spatial changes of Iran’s climatic zones affectedby climate changes, Extended De Martonne’s classification(originally formulated by De Martonne and extended byKhalili (1992)) was used. Climate change scenarios were simu-lated in two future climates (average conditions during the2050s and the 2080s) under each of the SRES A1B and A2,for the CSIRO-MK3, HadCM3, and CGCM3 climate models.Coarse outputs of GCMswere downscaled by delta method.Weproduced all maps for three time periods (one for the current andtwo for the future) according to Extended De Martonne’s clas-sification. Finally, for each climatic zone, changes between thecurrent and the future were compared. As the main result,simulated changes indicate shifts to warmer and drier zones.For example, in the current, extra arid-cold (A1.1m2) climate iscovering the largest area of the country (21.4 %), whereas inboth A1B and A2 scenarios in the 2050s and the 2080s, extraarid-moderate (A1.1m3) and extra arid-warm (A1.1m4) will bethe climate and will occupy the largest area of the country, about21 and 38 %, respectively. This analysis suggests that the global

climate change will have a profound effect on the future distri-bution of severe aridity in Iran.

1 Introduction

There is a general scientific agreement that global climaticchange is a real problem and will affect all climatic aspects.There is considerable confidence that for obtaining crediblequantitative estimates of future climate change, particularly atcontinental and larger scales, the most advanced tools aregeneral circulation models (GCMs) (IPCC 2007b). GCMs aremathematical formulation of atmospheric, oceanic, and landsurface processes that are based on classical physical principlesused to simulate climatic patterns.Many international modelinggroups have completed simulations of present climate andfuture climate under selected IPCC SRES (Special Report onEmissions Scenarios) storylines (Nakićenović and Swart 2000)used to prepare the IPCC 4th Assessment Report (Meehl et al.2005). Among the important consequences resulting fromclimate change in arid and semi-arid lands, rank changes inclimatic zones. Identification of climate zones and theircharacteristics under climate change condition can assistpolicy makers in making decisions on climate-related planning(Virmani 1980).

Basically, climate classifications were constructed todesignate the various existing local climates to an adequatenumber of climate types, and to determine the spatialdistribution of these types based on climatic data for areference period. Thus, climate classifications are introducedin order to reflect the mean spatial climate characteristics(Beck et al. 2006). Classification methods have manyapplications in climatological studies (Golian et al. 2010).There are several classification models based on variousapproaches found in the literature (e.g. De Martonne 1926;Koppen 1936; Hare 1951; Thornthwaite and Mather 1955;

J. Rahimi (*) :M. Ebrahimpour :A. KhaliliMeteorological Division, Department of Irrigationand Reclamation Engineering,College of Soil and Water Engineering, University of Tehran,Karaj, Irane-mail: [email protected]

M. Ebrahimpoure-mail: [email protected]

A. Khalilie-mail: [email protected]

Theor Appl Climatol (2013) 112:409–418DOI 10.1007/s00704-012-0741-8

Gadgil and Inyengar 1980). Divisions of climate in Iran havebeen studied based on different methods such as Köppen(Ganji 1955; Adle 1960; Jawadi 1966), Thornthwaite (KalimiNikfard 1982) and De Martonne (Khalili 1992). Althoughthese methods are suitable for capturing climatic character-istics, their compatibility with ecological zones and vegetationcover in Iran either has not been considered or the resultsindicated incompatibility.

In many researches, the classification has been used todetect recent changes in global and regional climate types(Diaz and Eischeid 2007; Beck et al. 2006; Rubel andKottek 2010). For example, the most extreme climatic zonesof the earth in the widely used Koppen classification systemshowed statistically significant shifts; the global areas coveredby tropical climate were expanded, whereas the tundra regionswere reduced in size (Fraedrich et al. 2001; Wang andOverland 2005). Shifts from colder to warmer climate typeshave occurred in Europe (Gerstengarbe and Werner 2009;Jylhä et al. 2010). Also, shifts in agro-climatic zones areconsidered for the application of the climate change scenarios(Metzger et al. 2006; Rounsevell et al. 2006). Climate modelshave been used for studying climate and climate change inorder to project the moves of the boundaries of climate zonesin the future (Lohmann et al. 1993; Kalvova et al. 2003;

Gnanadesikan and Stouffer 2006; de Castro et al. 2007; Gaoand Giorgi 2008; Jylhä et al. 2010; Jokinen and Jylhä 2011).

Khalili (1993) showed that the original De Martonnearidity index (De Martonne 1926) does not match withvegetation cover in Iran, and therefore he modified it togain a better consistency between divisions of climateand vegetation. In this paper, the Extended De Martonneclassification method is briefly reviewed. The main objectiveof this study is mapping climate zones of Iran according to thatmethod, considering three climatic periods, one currentclimate: 1970–2005, and two future climates: the 2050s andthe 2080s affected by climate change (based on three GCMsoutputs and A1B and A2 scenarios), and showing howchanges will occur among different climate zones across Iran.

2 Materials and methods

2.1 Study area

Iran is situated in the south-western part of Asia with area ofabout 1,648,000 km2. This country is stretched in 25–45°Nand 44–64°E. Iran has a very variable climate due to someeffective factors like widespread area along latitude, presence

Fig. 1 The location of thestudy area and distribution ofsynoptic weather stations

410 J. Rahimi et al.

of the Elburz Chain in the north, the Zagros Chain in the west,two large deserts (the Lut desert and the Kavir desert) lying inthemiddle of Iranian Plateau and intensive elevation variability(from −25 m in coastal regions of the Caspian Sea to 5,600 min central Elburz Chains) (Fig. 1). Since the country is placedin the arid belt of northern hemisphere (30–60°N), arid andsemi-arid climate cover the majority of area (Ganji 1968).

2.2 Data and method of data analysis

2.2.1 Climate data

The mean monthly value records of temperature andprecipitation were obtained for total period of historicrecords of 181 synoptic stations from the Islamic Republic ofIran Meteorology Organization (IRIMO 2007). The length ofthe record common among the stations was 1970 to 2005. Thespatial distribution of the weather stations across the country isrelatively uniform. In general, all regions and relief formspossess meteorological stations measuring air temperatureand precipitation. The most part of the central regions areplaced where topography is generally flat and there is notintensive climatic gradient. So, using rather few synopticstations in these regions is unlikely to affect the results.Figure 1 shows the distribution of synoptic stations in studyarea. Data reconstruction, where required, was performedusing a multivariate regression method between surroundingstations (Eischeid et al. 1995).

2.2.2 GCM outputs data

In this study, having used SRESA1B (balance across sources)and A2 (highest emissions scenario analyzed), we obtainedtemperature and precipitation outputs with three GCMs(CSIRO-MK3, HadCM3 and CGCM3) from CanadianClimate Change Scenarios Network to project the futurecondition (http://www.cccsn.ec.gc.ca). A larger rectangulardomain than Iran boundaries (latitude 40.45–24.85°N,longitude 43.24–64.34°E) was selected in order to generatebaseline and future maps. All gridded network points, insidethe given domain, in three averaged time periods (baseline,1970–2005; the 2050s and the 2080s) were interpolated usinggeographical information system (GIS; ArcGIS 9.2) and mapsof spatial distributions were created.

2.2.3 Extended De Martonne classification

De Martonne's climatic classification (De Martonne 1926) isbased on aridity index values, Ai ¼ P T þ 10ð Þ= , where P isthe mean annual precipitation (millimeters) and T is the meanannual temperature (degrees Celsius). Many researchescarried out using this classification in the last decades showedthat despite this index have practical usage in some specific

regions (Krishnan 1980), yet it is not individually able toidentify climatic characteristics of regions with complextopography like Iran (Khalili 2004). According to DeMartonne's formula, the effect of temperature on aridity indexin mountainous areas and high-cold regions is more importantthan the effect of rainfall amount. That is why the aridity indexin high Zagros Range is almost equal to the rainy Caspian Seacoast of Iran, where the vegetation covers are very different.There is the same condition in desert regions and southernwarm regions. Khalili (1992) modified De Martonne'sclassification, attempting tomatch it to Iran's vegetation cover.Therefore, the list of trees and shrubs (Sabeti 1976) were

Table 1 Extended De Martonne classification (Khalili 1992)

Main climate Thermal sub-climate Climate type

Extra arid Very cold (m<−7) A1.1m1

A1.1 (aridity index<5) Cold (−7≤m<0) A1.1m2

Moderate (0≤m<5) A1.1m3

Warm (m≥5) A1.1m4

Arid Very cold (m<−7) A1.2m1

A1.2 (5≤aridity index<10) Cold (−7≤m<0) A1.2m2

Moderate (0≤m<5) A1.2m3

Warm (m≥5) A1.2m4

Semi-arid Very cold (m<−7) A2m1

A2 (10≤aridity index<20) Cold (−7≤m<0) A2m2

Moderate (0≤m<5) A2m3

Warm (m≥5) A2m4

Mediterranean Very cold (m<−7) A3m1

A3(20≤aridity index<24) Cold (−7≤m<0) A3m2


Warm (m≥5) A3m4

Sub humid Very cold (m<−7) A4m1



Warm (m≥5) A4m4

Humid Very cold (m<−7) A5m1



Warm (m≥5) A5m4

Per-humid A Very cold (m<−7) A6m1



Warm (m≥5) A6m4

Per-humid B Very cold (m<−7) A7m1

A7 (aridity index≥55) Cold (−7≤m<0) A7m2


Warm (m≥5) A7m4

m mean minimum temperature in the coldest month of year (degreesCelsius)

Spatial changes of Extended De Martonne climatic zones 411

http://www.cccsn.ec.gc.ca

gathered and categorized according to De Martonne's aridityindex and their thermal conditions.

According to this revision, each climate is identifiedusing two parts (Table 1). “Ai” is represented in ExtendedDe Martonne's index similar to the original De Martonneclassification, but there is only a small difference due to aridzones in the central deserts; the arid parts will be dividedinto two parts: “extra arid” and “arid”. So, this classificationincludes eight main classes (Table 1). The second part “mj” isdistinguished by considering the meanminimum temperaturesof coldest month of the year. Therefore, the Extended DeMartonne's index is comprised from four thermal groupsincluding “very cold, m1”, “cold, m2”, “moderate, m3”, and“warm, m4”, and eight types of main climates, so DeMartonne classification is extended into 32 categoriesof climate. Hence, preparing appropriate classified list ofvegetations for each climate is possible. Also, the map ofIran's climate for 1964–1984 was prepared according to thismethod (Khalili et al. 1992). Thermal thresholds used inthis classification are those that Emberger et al. (1963) ap-plied to explain ecological zones of Mediterranean climateregions (Sabeti 1969).

2.2.4 Mapping current climate

We produced our maps according to three time periods;the current (averaged from historical records until 2005),mid-period of the century (averaged over the 2050sdecade) and late period of the century (averaged overthe 2080s decade). Current climate maps were producedby the means of 181 synoptic stations' monthly historicalrecords. For each month, we obtained mean monthlyminimum temperature, maximum temperature (meanmonthly temperature used to Extended De Martonne'sclassification calculated from average of minimum andmaximum temperatures), and mean monthly precipitation.The coldest month of the year was obtained from meanmonthly temperatures. We used the Kriging interpolationtechnique (with a spherical semivariogram, variable searchradius and based on the ten nearest data points (Legendreand Fortin 1989)) to create minimum temperature, maximumtemperature, and precipitation maps at a 2×2 arc-minutespatial resolution. These surfaces were used to createExtended De Martonne's classification according to itsformula.

Fig. 2 Map of the mainclimatic zones according tothe Extended De Martonneclassification in Iran underthe current climate condition(1970–2005)


2.2.5 Projection future climate: delta method

In this method, simply, an interpolated layer of changes inclimate (deltas or anomalies) was produced—with exactlythe same characteristics of the current climate map—basedon SRES A1B and A2 emissions scenarios (IPCC 2007b),then this layer and the current climate map were used toproject future condition. Since the greatest source of uncertaintyassociated with climate change studies arises from therange of future scenarios produced by GCMs (Wiley andPalmer 2008), and also none of these models can beclearly identified as being more accurate than the others

(Luedeling et al. 2009), averaged from outputs of threegeneral circulation models (CSIRO-MK3, HadCM3 andCGCM3) were used. On the other hand, the individualmodel errors are eliminated and the ensemble uncertaintyreduces by applying the average of an ensemble ofGCMs (Sperna Weiland et al. 2011).

The delta method uses differences between current andsimulated future climate conditions from three generalcirculation models added to observed time series of climatevariables (Hay et al. 2000). The method assumes that futuremodel biases for both mean and variability will be the same asthose in present-day simulations (Bader et al. 2008; Ramirez

Table 2 Area transfers among different climates of Extended De Martonne classification in three climatic periods based on A1B emission scenario

Climate classification Areas (km2, %)

Current 2050s 2080s

km2 % % km2 % % km2 % %

A1.1 m1 13,103.1 0.795 46.085 16,486.4 1.000 56.544 13,185.5 0.800 56.210m2 352,738.4 21.404 284,798.8 17.281 287,586.2 17.451

m3 196,069.6 11.897 347,608.5 21.093 328,132.9 19.911

m4 197,572.3 11.989 282,946.6 17.169 297,434.1 18.048

A1.2 m1 73,139.0 4.438 24.510 79,607.9 4.831 23.775 59,802.2 3.629 26.131m2 228,396.6 13.859 213,754.9 12.971 238,567.1 14.476

m3 56,132.5 3.406 52,485.1 3.185 61,801.1 3.750

m4 46,254.8 2.807 45,956.6 2.789 70,475.3 4.276

A2 m1 198,561.9 12.049 22.909 92,353.3 5.604 17.409 59,160.4 3.590 16.065m2 149,485.0 9.071 171,521.1 10.408 172,859.8 10.489

m3 28,441.9 1.726 22,061.4 1.339 25,233.9 1.531

m4 1,044.6 0.063 971.9 0.059 7,500.5 0.455

A3 m1 37,421.6 2.271 3.198 9,737.8 0.591 1.121 4,217.9 0.256 0.760m2 13,249.7 0.804 4,786.4 0.290 3,851.1 0.234

m3 2,034.2 0.123 3,667.7 0.223 2,145.6 0.130

m4 0.0 0.000 275.1 0.017 2,310.7 0.140

A4 m1 16,200.2 0.983 1.421 2,365.7 0.144 0.452 660.2 0.040 0.257m2 4,270.0 0.259 2,164.0 0.131 953.6 0.058

m3 2,950.5 0.179 2,017.2 0.122 1,027.0 0.062

m4 0.0 0.000 898.6 0.055 1,595.5 0.097

A5 m1 13,011.4 0.790 1.190 495.1 0.030 0.255 110.0 0.007 0.191m2 2,968.8 0.180 696.9 0.042 220.1 0.013

m3 3,628.5 0.220 2,108.9 0.128 1,797.2 0.109

m4 0.0 0.000 898.6 0.055 1,027.0 0.062

A6 m1 1,832.6 0.111 0.653 36.7 0.002 0.445 18.3 0.001 0.385m2 934.6 0.057 0.0 0.000 0.0 0.000

m3 7,990.1 0.485 7,170.4 0.435 4,236.2 0.257

m4 0.0 0.000 128.4 0.008 2,090.6 0.127

A7 m1 36.7 0.002 0.034 0.0 0.000 0.000 0.0 0.000 0.000m2 0.0 0.000 0.0 0.000 0.0 0.000

m3 531.5 0.032 0.0 0.000 0.0 0.000

m4 0.0 0.000 0.0 0.000 0.0 0.000

100 100 100


and Jarvis 2010). While these assumptions might holdtrue in a number of cases, they could be wrong inhighly heterogeneous landscapes. Anomalies are interpolatedamong GCM grid outputs, and are then applied to a currentclimate map as the baseline period, using absolute sumfor temperatures, and addition of relative changes forprecipitation.

3 Results

Current main climatic zones in Iran according to theExtended De Martonne classification, based on precipitationand temperature data from 181 synoptic stations of IRIMO,are illustrated in Fig. 2. Under the current climate (averagedclimate from 1970 to 2005), extra arid (A1.1) climate occupies∼46 % of the total area, and arid (A1.2) climate, semi-arid(A2), Mediterranean (A3), semi-humid (A4), humid(A5),per-humid type A (A6), and per-humid type B (A7) contribute24.51, 22.90, 3.19, 1.42, 1.19, 0.65, and 0.03 %, respectively(Table 2). At first glance, obviously, about half the total area ofthe country is covered by extra-arid climate; and humid

climates including humid, per-humid A, and per-humidB cover about only 2 %.

Having applied three CGMs outputs (CSIRO-MK3,HadCM3 and CGCM3) and delta method downscaling,future mean monthly minimum temperature, maximumtemperature and precipitation (in sum, 36 maps for eachperiod) were compiled. Projected data set by three GCMsunder SRES A1B showed that annual mean temperature willrise by ∼3.1 and ∼3.9 °C for 2050s and 2080s, respectively.Moreover, precipitation projections indicated future decreases∼26 mm (12.26 %) for 2050s and ∼29 mm (13.68 %) for2080s. Similar results were reported according to IPCC(2007b).

Climate classification maps for two periods (the 2050sand the 2080s) based on aridity index “Aimj” distributionfor two scenarios (A1B and A2) were projected. Wewithdrew display of all 32 classes of Extended De Martonnein maps because of complication in illustrations; hence, wepresented in Figs. 3 and 4 only eight main types of climates. Inaddition, A1.1 climate, covering considerable area in thecountry, with combination of m2, m3 and m4 was selectedas examples to represent probable shifts in Extended De

Fig. 3 Map of the mainclimatic zones according to theExtended De Martonneclassification in Iran under thefuture climate condition (2080s,A1B)


Martonne categories (Fig. 5). In Figs. 3 and 4, aridity indexdistribution of 2080s climates under SRES A1B and A2 arefigured. It may be concluded that in the future condition, wewill experience profound changes in distribution of aridityindex. For example, the area assigned to arid climate (A1.1and A1.2) under A1B scenario, ∼71 % in the current climate,will reach to ∼82% in the 2080s; and under A2 scenario it willreach to ∼79 %. In both scenarios A1B and A2, very cold (m1)and cold (m2) climates dominantly will change to moderate(m3) andwarm (m4) climates. The largest part of the country isexpected to shift into extra-arid–moderate (A1.1m3, ∼21 %)climate under A1B scenario and extra-arid–warm (A1.1m4,∼38%) climate under A2 scenario due to global warming,whereas it is extra-arid–cold (A1.1m2, ∼21 %) in thepresent climate (Tables 2 and 3). These results areclearly observable in Fig. 5.

Considering the thermal part of classification, for examplethe warm sub-climate (m4), with a mean of minimumtemperature in the coldest month of at least 5 °C (Table 1), thearea of ∼14% in the current climate will increase to about ∼20and ∼23 % in the 2050s and the 2080s, respectively,based on A1B scenario. Also, changes inside semi-arid

class show that A2m1 and A2m2, representing very coldand cold areas, will decrease from ∼12 and ∼9 % in thecurrent climate to ∼1 and ∼8 % in the 2080s under A2scenario, respectively (Table 3).

Although per-humid (B) climate is occupying a small areaof the country in the current, in both emission scenarios, it willdisappear from Iran by the 2050s. On the other hand, we willexperience newwarmer types of climates that have not existedso far (A3m4, A4m4, A5m4, and A6m4).

4 Conclusion

Climate change may be recognized in shifts of regionalmean climate, and also, in the frequency and intensity ofthe changes in different climatological extremes associated toboth temperature and precipitation (IPCC 2007a). Shift inclimatic zones is one of the most significant impacts of theclimate change. This could alter climate-related patterns andextreme weather events, such as drought, which are likely tooccur more frequently in the future. Most visible climatechanges may be found in the Northern hemispheric

Fig. 4 Map of the mainclimatic zones according tothe Extended De Martonneclassification in Iran underthe future climate condition(2080s, A2)


30–80° belt (Rubel and Kottek 2010). A better understandingof how to move specific climates associated with potentialclimate change impacts will aid decision makers in planningappropriate response strategies.

This study is the first of its kind in Iran to assess thepotential effects of climate change on climatic types. Insummary, we analyzed climate change simulations producedby the three general circulation models: CSIRO-MK3,

HadCM3, and CGCM3 based on the SRES A1B and A2.A series of Extended De Martonne's classification mapsdepicting spatial distribution of its classes in the current(1970–2005) and the future (2050s and 2080s) were presentedin this study. The analysis of climatic zones' changes in Iranbased on GCMs shows that since Extended De Martonneincludes thermal and humidity features of climate, in general,simulated changes indicate shifts to warmer and drier zones.

Fig. 5 Maps of the climaticzones according to theExtended De Martonneclassification in Iran forA1.1m2, A1.1m3 andA1.1m4 under current andfuture climate conditions(asterisk (*), percentageof the area which iscovered by the relevantclimate category)


The area and proportion of each climatic zones of ExtendedDe Martonne showed that in the current climate A1.1m2 isoccupying the largest area of Iran, whereas according to A1Band A2 scenarios in the 2050s and the 2080s, A1.1m3 andA1.1m4will occupy the largest area of the country, respectively.

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http://dx.doi.org/10.1029/2006GL028098

http://dx.doi.org/10.1371/journal.pone.0006166

http://dx.doi.org/10.1371/journal.pone.0006166

http://dx.doi.org/10.1127/0941-2948/2010/0430

http://dx.doi.org/10.1127/0941-2948/2010/0430

Documents

Spatial changes of Extended De Martonne climatic zones affected by climate change in Iran