SPECIAL ISSUE

Bulgarian mountains air temperatures and precipitation—statistical downscaling of global climate modelsand some projections

Peter Nojarov

Received: 21 November 2011 /Accepted: 7 February 2012 /Published online: 15 July 2012# Springer-Verlag 2012

Abstract Measured air temperature and precipitation datafrom three high mountainous Bulgarian stations were usedalong with data from 18 global climate models (GCMs). Airtemperature and precipitation outputs of preindustrial con-trol experiment were compared with actually observed val-ues. GCM with the best overall performance is BCCR BCM2.0 for air temperatures (period 1941–2009) and CGCM 3.1/T47 for precipitation (period 1947–2009). Statistical meth-ods were used in this research—nonparametric Spearmancorrelation, Mann–Whitney test, multiple linear regression,etc. Projections were made for the following future decades:2015–2024, 2045–2054 and 2075–2084. The best months,described by multiple linear regression (MLR) model of airtemperatures, are November, January, March, and May. Theworst described are summer months. There is not any pat-tern in the relationship between constructed MLR modelsand measured precipitation. Models that perform the best indifferent months at the three investigated stations are MIUBECHO-G, GISS AOM, CGCM 3.1/T63, and CNRM CM3for air temperatures and GFDL CM 2.1, GISS AOM, andMIUB ECHO-G for precipitation. The fit between statisticalmodels' outputs and values observed at stations is different,better in cold part of the year. There will be mixed futurechanges of air temperatures at all the three high mountain-ous stations. An increase of temperatures is expected inApril, November, and December. A decrease will happenin February, July, and October. Mean annual temperaturesare expected to rise by 0.1 °C (Botev) to 0.2 °C (Musala andCherni vrah) in the decade 2075–2084, but mean annualtemperatures at the end of the period with measurements

(2009) has already exceeded by far projected values. Trendsin precipitation are mixed both in spatial and in temporaldirections. Observed decrease of precipitation, especially inthe warm half of the year, is not described well in MLRmodels. The same is valid for annual amounts, which areprojected to be higher than those measured in the end ofinstrumental period (2009). This is opposite to observedtrends in recent decades, especially at stations Cherni vrahand Botev, where a significant decrease of precipitationamounts has happened.

1 Introduction

Global Climate Models (GCM) are designed to represent inmaximum detail all the factors of the system called Earth, aswell as their interactions and relationships. Thus, along withatmosphere, oceans, lithosphere, cryosphere, biosphere, andastronomical factors (mainly incoming solar energy) areconsidered. Relationships and interactions are expressedby mathematical equations. Obtained results generally sim-ulate some of the basic climatic elements such as tempera-ture and precipitation. Model outputs can have differenttemporal and spatial frameworks. Temporally, their direc-tion is usually forward, following certain scenarios in orderto project some climatic elements (Climate change 2007).These scenarios are commonly based on existing presenttrends and are associated with changes in some componentsof the investigated system. An example is increasing amountof greenhouse gasses, mainly CO2. Models can be used alsoto simulate climatic elements backwards in time. The goal isverification of a certain model through comparison ofobtained output results with the observed values. Whenlarge differences exist, models should be improved in oneor another direction. Spatial resolution of models is notparticularly high—up to about 100×100 km. This also

P. Nojarov (*)National Institute of Geophysics, Geodesy and Geography,Bulgarian Academy of Sciences,Akad. G. Bonchev str., bl.3, room 327,1113 Sofia, Bulgariae-mail: pnojarov@abv.bg

Theor Appl Climatol (2012) 110:631–644DOI 10.1007/s00704-012-0709-8

brings certain inaccuracies in the results when it comes toresearch a certain territory, which in most cases is repre-sented by one to several ground stations. All these requirefurther work on refining these models to enable results to bemore adequate to climate trends in the studied area.

Researches, which use GCMs for the territory of Bulga-ria, are not many. In all cases, results of simulation of thedifferent climate elements are taken as they are. Examinedare only a few existing models, but not all of them. Thearticles assess the impact of future climate changes onagriculture (Alexandrov 1997a, 1997b, 1999; Alexandrovand Hoogenboom 2000; Hartig et al. 1997), on wetlands(Hartig et al. 1997), and on water resources (Alexandrovand Genev 2003; Chang et al. 2002; Knight et al. 2004).These studies do not include mountain areas, which mainlyhave an impact on water resources and tourism. Improvingof model's outputs for the territory of Bulgaria or some of itsparts remains still unsolved problem, despite the constantdevelopment of GCMs. Also, only partially solved is theproblem as to which of the models represents the bestBulgarian climate. All this requires further work in thisdirection.

The aim of this research is statistical downscaling ofGCMs for high mountainous parts of Bulgaria. This wasdone for two basic elements of climate—air temperaturesand precipitation. Data from both models and from certainhigh elevation stations were used. The best models for eachof the two elements were determined. Based on employedscenario, mean values of studied elements for periods 2015–2024, 2045–2054, and 2075–2084 were calculated.

2 Data and method

Air temperature and precipitation data from three highmountainous Bulgarian stations (Fig. 1) were used—peaksMusala (2,927 ma.s.l.), Cherni vrah (2,290 ma.s.l.), andBotev (2,378 ma.s.l.). The first one is operational since1933, the second one since 1936, and the third one since1941. The research period for air temperatures is 1941–2009. Because station Botev has reliable measurements ofprecipitation, since the first half of 1946, the research periodfor precipitation is 1947–2009. Monthly and annual databased on actual measurements were used. The period ofstudy is long enough and allows to draw statistically signif-icant conclusions.

Air temperature data at peak Musala station are completewith one exception—1996. This year was recovered usingthe method of differences. Base station is Cherni vrah,which has reliable data for 1996. The data homogeneitywas checked through comparison with Botev station. Ananalysis of the history of the station at peak Musala was alsomade together with a data check through nonparametric

criterion of Mann–Whitney. They both proved the homoge-neity of collected numbers.

There are complete month-by-month precipitation data atpeak Musala for the whole investigated period. Homogene-ity check (using the method of Mann–Whitney) showedsignificant deviations with highest value between 1969 and1970. Analysis of the history of the station revealed thatabout that time, the rain gage has been moved with 70 m innorthwestern direction. This has led to a decrease of annualprecipitation amounts of about 380 mm. Month-by-monthanalyses using the method of Mann–Whitney showed thatchanges are due to months of the cold part of the year—from January to May and November and December. Monthsfrom June till October are homogeneous. That is why cor-rections of some monthly precipitation sums up until 1969were made. The way is to reduce older data using themethod of ratios. This was made for every month of thecold part of the year.

There are no air temperature data at Cherni vrah stationonly for 1989. It was recovered using the method of differ-ences. The base station is peak Musala, which has reliabledata for this year. The method of differences was used inorder to check the homogeneity of mean monthly and meanannual values. An analysis of the history of the station atCherni vrah peak was also made. In the end of April 1965,the meteorological cage was relocated. Observed valueswere checked through the method of differences using sev-eral other stations—peaks Musala, Botev, Murgash, andSofia. Results show that in 1965, there is a decrease of meanair temperatures at Cherni vrah station of about 0.3 °C (0.2–0.4 °C for different months). That is why all mean monthlynumbers up to April 1965 were corrected using the respec-tive differences.

There are no monthly precipitation data at Cherni vrahpeak for the period 1982–1989. Also missing is annualprecipitation sum for the year of 1982. They were notrecovered due to the large spatial variability of precipitation.Homogeneity check (using the method of Mann–Whitney)showed significant deviations with highest value between1973 and 1974. History of the station revealed a relocationof rain gauge with about 50 m in northern direction, whichhappened about that time. This has led to a decrease ofannual precipitation amounts of about 200 mm. Month-by-month analyses using the method of Mann–Whitneyshowed that changes are due to months of the cold part ofthe year—from January to April and November and Decem-ber. Months from May till October are homogeneous. Thatis why corrections of some monthly precipitation sums upuntil 1973 were made. Method is the same as for peakMusala.

There are no air temperatures data for the followingperiods at Botev station: September 1943–November1943, January 1944–January 1946. There are also some

632 P. Nojarov

missing data for the following months: June 1993, January1995, and April 2000. They were recovered using the meth-od of differences based on the data from the station at peakMusala. Homogeneity of this new data row was checkedusing the method of differences as well as Mann–Whitneynonparametric test.

Botev peak station has complete monthly and annualprecipitation data for the whole research period. Homoge-neity check did not show any significant deviations.

The main methods for statistical downscaling of GCMsare grouped into three groups, each with its own strengthsand weaknesses (Wilby et al. 2004). The first group includesmethods of weather typing, the second weather generators,and the third regression methods, which include multiplelinear regressions (MLR), used in this study. Linear relation-ships are associated with sample normal distribution. Airtemperature meets this condition, which warrants the use ofMLR. Evaluation and comparison of different methods forstatistical downscaling, which belong to the third of theabove groups, was made in the work of Huth (1999). Itwas concluded that the best performing method at highmountainous stations in the Alps is MLR. This is due tothe better link of climate elements at those stations withprocesses occurring in the free atmosphere, which are de-scribed fairly well by GCMs. The case investigated in this

article is similar. Significance of different predictors withrespect to air temperature was examined in many worksdealing with statistical downscaling (Huth 1999; Kettleand Thompson 2004). Results show that the most appropri-ate predictors to use are sea level pressure and air tempera-ture at 850 hPa level (T850). Usually, they are included asresults of various outputs of GCMs. In these outputs, how-ever, data for 2-m surface air temperatures are also included.It should be directly associated with the climatic element (airtemperature) investigated in this paper. That is why thesedata are used as predictors in further calculations.

In the paper of Schmidli et al. (2007), which investigatesprecipitation, several regional climate models (RCM) aswell as statistical downscaling models (SDM) were com-pared based on examples from the Alps. The research,however, has been done for daily precipitation amounts,and it was found that RCMs perform better. The cause isthat SDMs underestimate to a large-degree inter-annualvariability of precipitation. In monthly sums, however, thisvariability decreases. In the article of Labraga (2010), whichdeals with monthly precipitation amounts in the Andes,MLR models have been used successfully. Predictors usedin statistical downscaling of precipitation are different. Forexample, in the work of Busuioc et al. (2006), atmosphericpressure at sea level and specific humidity were the

Fig. 1 Location of the three high mountainous stations—Musala, Cherni vrah, and Botev

Downscaling of global climate models and projections 633

predictors, which are common outputs of different GCMs.These outputs, however, usually also include total precipi-tation per second over a certain cell. This amount can beeasily recalculated into a monthly value. In this study, thispredictor was used for statistical downscaling of GCMs atthe Bulgarian high mountainous stations.

A total of 18 GCMs were used in this study. Data sourcewas the World Climate Research Programme's (WCRP's)Coupled Model Intercomparison Project phase 3 (CMIP3)multi-model dataset (Meehl et al. 2007). Air temperatureand precipitation data of preindustrial control experiment(PICNTRL, run 1), covering the respective research periods(1941–2009 for air temperature and 1947–2009 for precip-itation) were compared with actually observed values at thethree investigated mountain stations. Then, through statisti-cal methods (nonparametric Spearman correlation), themodel that best describes processes in studied areas wasdetermined. Results for air temperatures are shown inTable 1. It could be seen that the model that shows the bestpositive correlation is BCCR BCM 2.0. The model ofGFDL CM 2.0 has also good results. In two of the threestations, it has statistically significant relationship with ob-served air temperatures. However, correlation coefficientsare not particularly high due to the low resolution of models.Comparison of precipitation data from different models withactually observed at the three stations is shown in Table 2. Itcould be seen that there are not any statistically significant

correlations in the entire table. The highest positive correla-tions shows the model CGCM 3.1/T47. General conclusionis that precipitation in mountainous areas of Bulgaria is notdescribed well by GCMs.

The choice of PICNTRL was determined by the fact thatonly this scenario has output data from large amount ofGCMs (18) for the entire research period (1941–2009).The most of other scenarios used in IPCC AR4 have outputdata only for the 21st century or later. This fact does notallow determination of which one is closest to the actualstate of climate. Ten-year common period (2000–2009) istoo short for statistical comparisons and conclusions. It isalso not possible to do a validation for a long enough periodof time. PICNTRL experiments run with constant preindus-trial levels of greenhouse gasses. That is, this scenarioshows evolution of climate without human interference,under the impact of only natural causes. In this sense, itcould serve as an assessment of the additional anthropogen-ic load. Any deviations or disagreements will confirm this.Projections are made for the following future decades:2015–2024, 2045–2054, and 2075–2084. This is a way tosee climate trends in high mountainous parts of Bulgaria.Output data from all 18 GCMs were used as predictors inthe MLR model, while observed values at the respectivestations were dependent variable in the models. Thus, coef-ficient of determination R2 serves as an indicator of the fit ofthe model to the actual state of climate elements, i.e., this isthe verification of the model, which is based on the whole

Table 1 Spearman correlation between observed and modeled(PICNTRL experiment, run 1) air temperatures at three Bulgarianmountainous stations for the period 1941–2009

Models Musala Cherni vrah Botev

GFDL CM 2.0 0.07 0.08 0.08

GFDL CM 2.1 0.01 0 −0.01

CGCM 3.1/T47 0.01 −0.01 −0.02

CGCM 3.1/T63 −0.02 −0.02 −0.01

BCCR BCM 2.0 0.11 0.09 0.1

CSIRO Mk 3.0 −0.04 −0.05 −0.06

CSIRO Mk 3.5 0.04 0.04 0.04

UKMO HadCM3 0.03 0.03 0.02

UKMO HadGEM −0.03 −0.04 −0.02

INM CM 3.0 0.04 0.06 0.05

IPSL CM4 −0.02 −0.02 −0.01

IAP FGOALS-g 1.0 −0.05 −0.03 −0.01

CNRM CM3 0.05 0.06 0.05

MIUB ECHO-G 0.01 0.01 0.01

MRI CGCM 2.3.2 −0.05 −0.06 −0.03

GISS AOM −0.05 −0.06 −0.04

GISS EH 0.02 0.03 0.03

GISS ER 0.02 0.01 0.01

Statistically significant data are set in italics

Table 2 Spearman correlation between observed and modeled(PICNTRL experiment, run 1) precipitation at three Bulgarian moun-tainous stations for the period 1947–2009

Models Musala Cherni vrah Botev

GFDL CM 2.0 −0.05 −0.01 −0.01

GFDL CM 2.1 −0.01 0.02 −0.02

CGCM 3.1/T47 0.02 0.01 0.02

CGCM 3.1/T63 −0.04 −0.04 −0.06

BCCR BCM 2.0 0 −0.02 0.04

CSIRO Mk 3.0 0.01 −0.01 0

CSIRO Mk 3.5 −0.03 −0.05 −0.07

UKMO HadCM3 −0.03 0.03 −0.03

UKMO HadGEM −0.02 −0.04 −0.03

INM CM 3.0 0.01 0.02 −0.02

IPSL CM4 −0.06 −0.07 −0.07

IAP FGOALS−g 1.0 −0.01 −0.02 −0.02

CNRM CM3 0.02 0.01 0.01

MIUB ECHO-G −0.04 0.02 −0.02

MRI CGCM 2.3.2 0 0.07 −0.02

GISS AOM −0.01 0.03 0.02

GISS EH 0.03 0.01 0

GISS ER −0.05 −0.02 −0.02

634 P. Nojarov

research period. In this regard, no calibration was donebecause output data from only one scenario were used andthere was not any possibility for a choice.

Main methods employed in this research are statistical(Wilks 2006). Statistical significance everywhere is at levelp<0.05. The approach accepted in this article was to take asmany as possible GCMs in order to use their outputs aspredictors in MLR models. This is so-called multi-modelapproach. Data used are exclusively from outputs for 2-msurface air temperature (for downscaling of air temperatures)and total precipitation (for downscaling of precipitationamounts). As it will be seen later, results are encouraging.So far, researches on this topic have used different combina-tions of predictors related to temperature, humidity, and cir-culation characteristics of the atmosphere.

3 Results and discussion

The base period for research of air temperatures is 1941–2009 because of the available measurements at the threemountain stations. Horizontal resolution of GCMs is differ-ent and varies approximately from 1 to 5 ° along latitude andlongitude. Thus, the three investigated stations fall intodifferent cells in different models. Usually, the differencecomes from the longitude. In any case, Cherni vrah stationfalls within the western cell and Botev station within theeastern. Musala station is located on the border betweenboth cells. There are some cases (coarse GCMs) when allthree stations belong to one cell.

Results of MLR models of air temperatures at the threeinvestigated stations are shown in Table 3. Coefficient ofdetermination R2 shows to what degree models' outputs cor-respond to observed values at different stations. It could beseen that R2 is from 0.073 (June, Botev) to 0.496 (November,Musala) in the case of 18 input GCMs. Values of R2 inNovember at all the three stations are statistically significant.Also, high values of this coefficient could be seen in January,March, and May. This means that these months are describedwell by the chosen scenario. It also means that they “run” on anatural course as they would develop without the presence ofthe anthropogenic influence from the last two centuries.Months with the lowest R2 are June, July, and September,i.e, they are described not so well by the chosen scenario. Thismeans that there are some other factors, most likely withanthropogenic origin. Accordingly, it would be better to usesome other scenario for them. Overall picture of the year isvariegated and cannot be described well by a single scenario.At least within those used in preparing of IPCC AR4. It alsomeans that the balance of impact of various factors (natural/anthropogenic) changes throughout the year. This should betaken into account in creation of future GCMs.

Table 3 Coefficient of determination R2 of MLR model of air tem-perature for 18 input GCMs for the period 1941–2009

Musala Cherni vrah Botev

January 0.294 0.296 0.305

February 0.236 0.269 0.286

March 0.327 0.326 0.387

April 0.265 0.293 0.264

May 0.285 0.316 0.28

June 0.148 0.159 0.073

July 0.192 0.223 0.176

August 0.241 0.237 0.196

September 0.108 0.153 0.122

October 0.215 0.231 0.282

November 0.496 0.466 0.434

December 0.244 0.224 0.178

Statistically significant data are set in italics

Table 4 The most significantGCM according to multiple lin-ear regression of air tempera-tures for the period 1941–2009(out of 18)

Musala Cherni vrah Botev

January MIUB ECHO-G MIUB ECHO-G MIUB ECHO-G

February MIUB ECHO-G MIUB ECHO-G MIUB ECHO-G

March GISS AOM CGCM 3.1/T63 CGCM 3.1/T63

April INM CM 3.0 INM CM 3.0 INM CM 3.0

May GISS EH GISS EH GISS AOM

June CNRM CM3 CNRM CM3 GISS AOM

July GISS AOM MIUB ECHO-G UKMO HadGEM

August IAP FGOALS-g 1.0 IAP FGOALS-g 1.0 CNRM CM3

September CGCM 3.1/T63 GISS EH GISS EH

October BCCR BCM 2.0 IAP FGOALS-g 1.0 CNRM CM3

November CGCM 3.1/T63 CNRM CM3 CNRM CM3

December CSIRO Mk 3.0 CSIRO Mk 3.0 CSIRO Mk 3.0

Downscaling of global climate models and projections 635

Each GCM model has a different significance (force) inthe final output of constructed MLR model. The most sig-nificant GCMs in different months at the three investigatedstations are shown in Table 4. At Cherni vrah station, themost significant relation with air temperatures show outputsfrom MIUB ECHO-G model—it leads in 3 months of theyear. At Musala, the best models are MIUB ECHO-G, GISSAOM, CGCM 3.1/T63 (each one leading in 2 months of theyear). At Botev, the best model is CNRM CM3—it leads in3 months of the year. It is clear however that there is not anyGCM, which is the best in more than 3 months of the year.This speaks for a certain equivalence of the models. On theother hand, Table 1 showed that when all months of the yearare taken, the best model is BCCR BCM 2.0, which, how-ever, appears only once in Table 4. Overall, it is clear thatdepending on the temporal or spatial scale, different modelsshow different strengths and it could not be determinedwhich one is the best. The right decision in this case is touse as many of them as possible.

Figure 2 shows the best and worst, described by MLR(18 GCMs) model, months at Cherni vrah station. The bestdescribed month, according to coefficient of determinationR2, is November. It could be seen that the course of modeledair temperature is very close to observed values. Positiveand negative anomalies are accounted for well. Some un-derestimation of measured values exists in the period 1959–1969, and overestimation in the beginning of the researchperiod until 1950. The worst described month is September.The graph clearly shows that course gaps are serious. Modelcurve is very flat and does not account well for the extremevalues of mean monthly air temperatures.

The same data for Musala station are shown in Fig. 3.The picture is similar to that at Cherni vrah station. The bestdescribed month is November. The graph shows that the twocurves coincide to a large extent. Some underestimation ofobserved values exist in the period 1959–1964, and overes-timation – in the beginning until 1950. The worst describedmonth is September. Here again the model curve is very flat

Fig. 2 September andNovember fit of MLR modelsof air temperatures to observedvalues at Cherni vrah station(1941–2009)

Fig. 3 September andNovember fit of MLR modelsof air temperatures to observedvalues at Musala station(1941–2009)

636 P. Nojarov

and does not account well for inter-annual changes in airtemperatures.

Botev peak data are shown in Fig. 4. The best describedmonth again is November. Model graph here is close to thecourse of observed air temperatures. There are some under-estimation in the period 1959–1964, and overestimation inthe beginning until 1950. The worst described month isJune. Model graph is too flat and does not reflect well thereal course of air temperatures during this month. Overall,the graphs show that predictor 2-m surface air temperature(using as much as possible input GCMs) has a decentperformance in certain months, which belong mostly to thecold part of the year. In summer MLR models do not workso well. Inclusion of other predictors, mainly related tocirculation, or other scenarios could improve statisticaldownscaling and will be subject of future research.

Downscaled mean air temperatures in different months ofthe year at the three investigated stations are shown inFigs. 5, 6, and 7. Mean monthly temperatures for the entire

research period (1941–2009) as well as mean monthly airtemperatures for the last decade (2000–2009) are shownalso in order to have some basis for comparison. Figure 5(Cherni vrah station) shows mixed trends. According tochosen scenario a significant rise in air temperatures is notexpected. The months, with temperature increase in thedecade 2075–2084 compared to present time, are April,November, and December. Some months will cool. Theseare February, July, and October. Mixed trends prevail in theremaining months in the three investigated future decades.Perhaps this would be the situation in the presence of onlynatural factors. Mean air temperatures for the period 2000–2009 are indicative of current trends. They confirm trends inFebruary, April, November, and December. To some extent,March and September also could be included in this group.In summer months (May–August) and October, the differ-ence is big. It could be seen that observed air temperaturesin the last decade significantly exceed projections for futureperiods. As seen also from coefficient of determination R2,

Fig. 4 June and November fit ofMLR models of air temperaturesto observed values at Botevstation (1941–2009)

Fig. 5 Air temperature monthlymeans for different periods atCherni vrah station

Downscaling of global climate models and projections 637

these months are not described well by MLR models. Thismeans that they follow a different scenario than the oneselected in this study.

Figure 6 (Musala station) again shows mixed trends inprojections of future air temperatures. An increase, due onlyto natural causes, is expected in April, November, andDecember. A decrease of air temperatures in 2075–2084 isexpected in February, July, and October. Other months showmixed trends. Relatively low January air temperatures in thedecade 2015–2024 should be pointed out here. Tempera-tures in the last decade (2000–2009) confirm projections forFebruary, April, November, and December; to a lesser ex-tent also for March. Summer (May–August) as well asOctober temperatures significantly exceed projected ones.This means that they follow a different scenario.

Figure 7 (Botev station) shows that the distribution of airtemperatures in the three projected decades is various.Months, with expected increase of air temperatures, result-ing from natural causes, are April, May, November, and

December. A decrease of temperatures is expected in Feb-ruary, July, August, and October. Trends are mixed in othermonths. Very low temperatures in January in the decade2015–2024 could be seen again here. The last decade(2000–2009) confirms projected trends for March, April,May, November, and December. Significant discrepancyoccurs in June, July, August, and October. This means thatthey are not described well by the chosen scenario. Overall,it can be concluded that scenario PICNTRL experiment setsmixed future changes of air temperatures at the three inves-tigated high mountainous stations. It is based only on theeffects of natural causes. In this regard, an increase of airtemperatures is expected in April, November, and Decem-ber. A decrease will happen in February, July, and October.The last decade of measurements (2000–2009) confirmsprojected trends in April, November, and December. Theseare the positive trends. Negative ones are not confirmed.The sign of the trends in these months is even opposite. Thismeans that they follow another scenario, perhaps with the

Fig. 6 Air temperature monthlymeans for different periods atMusala station

Fig. 7 Air temperature monthlymeans for different periods atBotev station

638 P. Nojarov

presence of anthropogenic factors. Results for the SwissAlps (for lower stations, however) mentioned in the paperof Beniston (2006) show an increase in winter temperaturesof about 4 °C and in summer temperatures with about 5.5–6 °C at the end of the 21st century. This bigger increase isdue to the scenario used, which predicts a threefold rise ofCO2 by the end of this century.

Figure 8 shows mean annual air temperatures at the threeinvestigated stations for the periods 2015–2024, 2045–2054, and 2075–2084. 2009 was taken as a starting pointwith value for this year calculated by means of linear re-gression. The graph shows that there is an increase of airtemperatures by the last projected decade (2075–2084) at allthe three stations. In the two western stations (peaks Musalaand Cherni vrah), this trend is straight, while at peak Botev,there is a reverse of the positive trend in the last projecteddecade. Mean air temperatures are expected to rise by 0.1(Botev) to 0.2 °C (Musala and Cherni vrah). Mean annualtemperature at the end of the period of measurements (2009)has already exceeded, predicted by scenario temperature atall the three stations. This once again confirms the conclu-sion that air temperatures in Bulgarian mountains are influ-enced by natural as well as by anthropogenic causes.

The period of research for precipitation is 1947–2009. 18GCMs cover completely this period as well as projectedfuture periods 2015–2024, 2045–2054, and 2075–2084.PICNTRL experiment scenario is used again here.

Coefficient of determination R2 shows to what degreemodels' outputs fit measured precipitation at the three sta-tions. Results are shown in Table 5. It could be seen thatvalues range from 0.139 (April, Musala) to 0.445 (October,Cherni vrah). There are no statistically significant values ofR2 in the table. There are some high values in differentmonths at different stations but only Cherni vrah stationshow stable high numbers throughout the whole year. Thiscould be due to the lack of data for the period 1982–1989. It

is difficult to highlight any particular month in which pre-cipitation is described well by MLR model at all the threepoints. October is closest to this requirement. Accordingly,it is not possible to find the worst, described by MLR model,month. Precipitation has large spatial variability and obvi-ously is more difficult to be described by models and sce-narios. The only thing which is obvious here is that thelower two stations (Cherni vrah and Botev) fit better intoconstructed MLR models as this is valid for all of theirmonths.

The most significant, on a monthly basis, GCMs used forstatistical downscaling of precipitation in Bulgarian moun-tains are shown in Table 6. At Cherni vrah station, the mostsignificant models are GFDL CM 2.1, GISS AOM, andMIUB ECHO-G—each one leading in 2 months of the year.At Musala, the most significant is GISS AOM—leading in3 months of the year. At Botev, the most significant is GISSAOM—leading in 3 months of the year. Obviously, GISS

Fig. 8 Mean annual airtemperatures at the threeinvestigated stations fordifferent periods

Table 5 Coefficient of determination R2 of MLR model of precipitationfor 18 input GCMs for the period 1947–2009

Musala Cherni vrah Botev

January 0.32 0.405 0.172

February 0.194 0.296 0.264

March 0.39 0.356 0.361

April 0.139 0.213 0.406

May 0.177 0.356 0.259

June 0.292 0.363 0.217

July 0.316 0.305 0.25

August 0.26 0.378 0.23

September 0.262 0.404 0.22

October 0.244 0.445 0.384

November 0.341 0.299 0.25

December 0.238 0.339 0.192

Downscaling of global climate models and projections 639

AOM model performs very well at all the three stations.Again, as for air temperatures, the best performing generallymodel (CGCM 3.1/T47) appears only twice in Table 6.

Figure 9 shows the best and worst, described by MLRmodel, months at Cherni vrah station. October is the bestdescribed month. It could be seen that precipitation is de-scribed well, including its minimum and maximum monthlysums. Some deviations exist only in the beginning (around1950) and in the end of the investigated period. The worstdescribed month is April. Deviations from actual course arebig, especially in the beginning until 1968 and in the endafter 1988. These deviations are in both directions, under-estimation or overestimation, of precipitation during themonth.

The best and worst, described by MLR model, months atMusala station are shown in Fig. 10. March is the bestdescribed month. Modeled precipitation sums follow well-observed course. Deviations exist only in the mid 50s and inthe 90s of the 20th century. The worst described month is

April. There are serious discrepancies between predicted byMLR model precipitation and actually measured. This isespecially obvious in the beginning of the investigated pe-riod as the gap is in both directions—overestimation orunderestimation.

The best and worst, described by MLR model, months atBotev peak are shown in Fig. 11. The best described monthis April. It has a good agreement with measured amountsmainly in the second half of the research period. In thebeginning, discrepancies are more. The worst, describedby MLR model, month is January. Deviations here are mostserious in direction of underestimation of precipitation in thebeginning of the research period until the end of the 60s andin direction of overestimation of precipitation in the 80s and90s of the 20th century. Generally, there is not any pattern inthe relationship between constructed MLR models and mea-sured precipitation at all the three investigated stations.

Downscaled and observed precipitation sums at the threestations for the periods 1947–2009, 2000–2009, 2015–

Table 6 The most significantGCM according to multiple linearregression of precipitation for theperiod 1947–2009 (out of 18)

Musala Cherni vrah Botev

January UKMO HadCM3 CSIRO Mk 3.5 IAP FGOALS-g 1.0

February CGCM 3.1/T63 UKMO HadGEM GISS AOM

March GISS AOM CGCM 3.1/T47 CNRM CM3

April CNRM CM3 CNRM CM3 BCCR BCM 2.0

May INM CM 3.0 GFDL CM 2.1 IAP FGOALS-g 1.0

June GISS ER GISS AOM GISS AOM

July UKMO HadCM3 MIUB ECHO-G GISS AOM

August CGCM 3.1/T63 CGCM 3.1/T63 CGCM 3.1/T63

September GISS ER INM CM 3.0 BCCR BCM 2.0

October GISS AOM GISS AOM MRI CGCM 2.3.2

November CNRM CM3 MIUB ECHO-G CGCM 3.1/T47

December GISS AOM GFDL CM 2.1 GISS ER

Fig. 9 April and October fit ofMLR models of precipitation toobserved values at Cherni vrahstation (1947–2009)

640 P. Nojarov

2024, 2045–2054, and 2075–2084 are shown in Figs. 12,13, and 14. Figure 12 shows values for station Cherni vrah.Precipitation have different trends. A decrease towards thelast projected decade is expected in June, August, andDecember. In June, there will be an increase firstly and thendecrease. Precipitation increase is expected in April, May,July, and September. In July, before the increase, there willbe a decrease. There is not any clear trend in the remainingmonths of the year. Data from the last decade show presenttendencies. They confirm projections for September andDecember. There are some discrepancies in the othermonths. They are most notable in April, May, June, July,August, October, and November. In almost all of thesemonths (with exception of August) measured in the lastdecade (2000–2009), precipitation amounts were lower thanmodeled. Generally, they show a downward trend, a featurethat does not appear in MLR models. This means that thechosen scenario, with only natural driving climate factors in

Bulgarian mountains, is not quite accurate. Obviously, otherscenarios are needed to account for anthropogenic factorsthat have significant impact in the warm part of the year.Conclusions for air temperatures were also in this direction.

Results for Musala peak are shown in Fig. 13. A decreaseof precipitation is expected in January and February. Anincrease of precipitation is expected in April, August, Sep-tember, and November, as in the last 2 months before theincrease there will be a decrease. There is not any clear trendin other months. Data from the last decade (2000–2009)confirm projections for August, September, and November.Certain deviations are typical for the other months. Anincrease of January and February precipitation is observedin the last decade with measurements, which is opposite tomodeled trends. The decrease of precipitation in April, May,and June is not sufficiently described by projections.

Results for Botev peak are shown in Fig. 14. A decreaseof precipitation is expected in May and December. An

Fig. 10 March and April fit ofMLR models of precipitation toobserved values at Musalastation (1947–2009)

Fig. 11 January and April fit ofMLR models of precipitation toobserved values at Botevstation (1947–2009)

Downscaling of global climate models and projections 641

increase will happen in August and October. Trends aremixed in other months, and only July may be pointed outwith some decrease of precipitation amounts towards thelast projected decade. Data from the last decade (2000–2009) confirm projected trends for May, July, and Decem-ber. In the other months (with exception of April), a de-crease of precipitation prevails, which is not reflected wellin projections. This is due to the chosen scenario and factorsincluded in it. An addition of other scenarios is recommend-able, but due to the high spatial and temporal variability ofprecipitation, a better final result is not guaranteed. Mixedpicture of precipitation at mountain stations is described alsoin the research of Beniston (2006).

Figure 15 shows mean annual precipitation sums at thethree investigated stations for the periods 2015–2024, 2045–2054, and 2075–2084. 2009 is also shown with its lineartrend value as a basis for comparison. Generally, the firstand last projected periods (2015–2024 and 2075–2084)

have more precipitation sums at the three investigated sta-tions, while the decade 2045–2054 is expected to be moredry. However, at Cherni vrah and Botev peaks, precipitationsums of every one of the three projected decades will exceedthose of the end of the instrumental period (2009). Only thedecade 2045–2054 at Musala peak will have slightly lowersum than that of 2009. This means that in the presence ofonly natural causes in Bulgarian mountains, precipitationwould increase in the next years. But present trends aremostly opposite, which means that effects of anthropogeniccauses are also important.

4 Conclusion

The scenario which was used in this research is PICNTRLexperiment. It runs with constant preindustrial levels ofgreenhouse gasses. Thus, it reveals only the influence of

Fig. 12 Mean monthlyprecipitation sums for differentperiods at Cherni vrah station

Fig. 13 Mean monthlyprecipitation sums for differentperiods at Musala station

642 P. Nojarov

natural factors on climate system. Out of 18 GCMs used, thebest connection with observed air temperatures at the threemountain stations for the investigated period (1941–2009)showed the model of BCCR BCM 2.0, which has a statis-tically significant correlation. The best, described by MLRmodel of air temperatures, month is November. January,March, and May could also be included in this group. Theworst described are summer months—June, July, and Sep-tember. Models that perform the best in different months atthe three investigated stations are MIUB ECHO-G, GISSAOM, CGCM 3.1/T63, and CNRM CM3. The fit betweenstatistical models' outputs and actual values observed atstations is different, better in cold part of the year. In sum-mer, statistical models do not work so well. ScenarioPICNTRL experiment sets different future changes of airtemperatures at all the three high mountainous stations. Anincrease of temperatures is expected in April, November,and December. A decrease will happen in February, July,and October. Mean annual temperatures are expected to rise

by 0.1 °C (Botev) to 0.2 °C (Musala and Cherni vrah) in thedecade 2075–2084, but mean annual temperatures at the endof the period with measurements (2009) has alreadyexceeded by far projected values. This means that air tem-peratures in Bulgarian mountains are influenced by natural,as well as by anthropogenic factors.

Out of 18 GCMs used, the best connection with measuredprecipitation sums at the three stations for the period 1947–2009 shows the model of CGCM 3.1/T47. However, there arenot any statistically significant correlation coefficients. Thesame lack of statistical significance refers to coefficient ofdetermination R2, which indicates the fit of MLR models toobserved values in different months of the year. The mostsignificant, on a monthly basis, models are GFDL CM 2.1,GISS AOM, and MIUB ECHO-G. Generally, there is not anypattern in the relationship between constructed MLR modelsandmeasured precipitation at all the three investigated stations.Trends in precipitation are mixed both in spatial and in tem-poral directions. Observed decrease of precipitation, especially

Fig. 14 Mean monthlyprecipitation sums for differentperiods at Botev station

Fig. 15 Mean annualprecipitation sums at the threeinvestigated stations fordifferent periods

Downscaling of global climate models and projections 643

in the warm half of the year, is not described well in MLRmodels. The same is valid for annual amounts, which areprojected to be higher than those measured in the end ofinstrumental period (2009). This is opposite to observed trendsin recent decades, especially at stations Cherni vrah and Botev,where a significant decrease of precipitation amounts hashappened. All these mean that the influence of anthropogenicfactors on precipitation in Bulgarian mountains is importantand should be considered.

Acknowledgments The author acknowledges the modeling groups, theProgram for Climate Model Diagnosis and Intercomparison (PCMDI), andthe WCRP's Working Group on Coupled Modelling (WGCM) for theirroles in making available the WCRP CMIP3 multi-model dataset. Supportof this dataset is provided by the Office of Science, U.S. Department ofEnergy.

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