Temporal dynamics of precipitation in an extreme mid-latitude monsoonal climate

ORIGINAL PAPER

Temporal dynamics of precipitation in an extrememid-latitude monsoonal climate

E. A. Grigorieva & C. R. de Freitas

Received: 18 December 2012 /Accepted: 6 May 2013# Springer-Verlag Wien 2013

Abstract Trends of precipitation over the twentieth centuryare examined by a variety of methods to more fully describehow precipitation has changed in the Russian Far East. Dataused are considered to represent conditions of the extrememonsoonal climate of the high-to-mid-latitude climate of theRussian Far East region for the period 1911 to 2005. The studyexamines within-year characteristics of the 95-year time se-ries. The results show variability of precipitation is high in allmonths, but especially so during the cold season. Trends in thedata indicate that both the wettest and driest months of theyear are getting wetter. There are some distinct shifts in thetrend patterns. Most noticeable is a shift from positive trendsto negative trends. Overall, the results show the highest twen-tieth century precipitation in the early 1960s and in the late1970s, with a general decrease since the mid-1980s. Thisdiffers from trends and means for Russia as a whole. Theresults also show standard normals to be different from acomplete record of monthly precipitation data. Further, itmay not enough to use a limited period times series such asa 30-year normal to represent a steady average for a year or aseason, as the mean changes through time; in particular, for asteady cold season, mean one should use the full period.

1 Introduction

The risk of climate change resulting from increasing concen-trations of greenhouse gases in the atmosphere has led to

searches for signals in temperature and precipitation records(e.g. Kärner and de Freitas 2012; Roshydromet 2008; Hansenand Lebedeff 1987; Yu and Neil 1993). Efforts to model andaccount for broad-scale climate signals have had some success,but the major models have precipitation discrepancies at theregional scale (Solomon et al. 2007). As a result, precipitationprojections into the future at the sub-regional scale are suspect(De Luis et al. 2009). Added to this are problems interpretingprecipitation time series due to inter-annual and spatial vari-ability, which needs to be better understood if analyses oftemporal trends or periodicities are to improve. In light of this,the IPCC (Solomon et al. 2007; Roshydromet 2008) suggeststhat the sub-regional variability in precipitation should beanalysed in detail. This requires a data set going back as faras possible in time (De Luis et al. 2009; Lana and Burgueno2000; Huntington 2006). There are also other considerations.

Preparation of climatological data for use in research or foroperational purposes often requires selection of part of therecord as a ‘best estimate’ of ‘period averages’. The WorldMeteorological Organization (WMO) defined the latter as ar-ithmetical means of climatological data (World MeteorologicalOrganization 2007). This forms the basis for describing climaticnormals, which are a base reference statistic for characterisingthe climate of a location or region. Climatic normals are definedby the WMO as “period averages computed for a uniform andrelatively long period comprising of at least three consecutiveten-year periods” (World Meteorological Organization 2007, p.6). Normals in their most straightforward form are annualmeans and totals of daily atmospheric variables, usually airtemperature and precipitation. WMO recommends that coun-tries update an official set of 30-year climate normals every10 years. The reasoning is centred on the need to base funda-mental planning decisions on averages and extremes in non-stationary climate conditions.

Climate normals are often used as basic information toclassify a region’s climate and make decisions for a widevariety of purposes involving, for instance, agriculture and

E. A. GrigorievaInstitute for Complex Analysis of Regional Problems, Far EasternBranch, Russian Academy of Sciences, Birobidzhan, Russiae-mail: [email protected]

C. R. de Freitas (*)School of Environment, University of Auckland,Auckland, New Zealande-mail: [email protected]

Theor Appl ClimatolDOI 10.1007/s00704-013-0925-x

natural vegetation, energy use, transportation, tourism andresearch in many fields. Climatic normals are also used for avariety of other purposes such that their reliability is crucial tothe accuracy of the results. For example, modelling or homog-enization of daily data is required when a climate stationmoves, instrument changes and missing data or proceduralchange in observing have led to fragmentation of climaterecords. The model employs a climatic normal and is consid-ered helpful in adjusting the data or fitting missing data so thatthe station record appears to be complete. However, the new

data set is synthetic and, although it may appear complete, itmay be far from an accurate or a truthful representation if thenormal applied is unrepresentative of the true period mean. Insuch cases, averaging the data from a new or different set over30 years or longer might be a better description of the precip-itation climate.

Climatic normals are also commonly used for identifying andthen quantifying precipitation trends by comparing the differ-ences between the normal climate (mean annual precipitation forthe current 30-year period) and that of a series of individual years

Fig. 1 Map of Russia showing geographical regions and the location of the climate stations Khabarovsk, Nikolaevsk-on-Amur and Smidovich.The North-East, Priamurye and Primorye regions make up Russian Far East of which Khabarovsk is the capital

Table 1 Frequency of wind direction during January and July for three climate stations in the Russian Far East for the period 1950–1980

Climate station Month Wind direction (%)

N NE E SE S SW W NW

Khabarovsk January 3 8 1 1 3 65 18 1

July 6 25 10 8 10 25 13 3

Nikolaevsk-on-Amur January 9 4 2 0 1 6 49 29

July 7 19 42 19 2 2 6 3

Smidovich January 5 8 2 2 6 34 38 5

July 5 23 16 9 13 21 10 3

Maximum values are shown in bold

E.A. Grigorieva, C.R. de Freitas

a series of years (or months). In light of the above-mentioned, this study also examines how well standardpublished normals describe the period mean for the fulldata record. The study area is located within the Far Eastadministrative territory of Russia consisting of the geo-graphical regions of Priamurye and Primorye and theNorth-East (Fig. 1). The data used are those for the southernpart, specifically the climate station located at Khabarovsk(Fig. 1). The site is considered to represent conditions of theextreme monsoonal climate of the region. Using precipita-tion for the period 1911 to 2005, we examine the extent towhich a climatic normal is appropriate to use in statisticalassessments compared to the use of the mean of the data forthe full time series.

2 Method

The Khabarovsk climate station is situated at latitude 48°31′N, longitude 135°10′ E, at altitude 88 m above sea level(WMO index 31735), in the southeastern part of the RussianFar East (Fig. 1). It is located within a temperate monsoonclimatic zone characterised by an extreme continental re-gime of annual temperatures and seasonal precipitation(Alisov 1956). Monsoon climates are characterised by aseasonal reversal of prevailing wind flow accompanied byseasonal extremes in precipitation. The central role thatmonsoons play in determining climates around the worldhas made their study a high priority for climatologists.

The major monsoon systems of the world consist of theWest African and Asia–Australian monsoons. The Asian

situation reveals the most pronounced features of both thesummer and winter monsoons. The summer monsoon ischaracterised by a moist maritime air flow from the IndianOcean and warm western Pacific Ocean towards a large lowpressure situation over the hot Asian continental interior.The warm, moist unstable air results in copious amounts ofprecipitation. The intensity and duration, however, are notuniform from year to year. In contrast, the winter monsoonis characterised by the outflow of cold, dry stable air fromthe high pressure situated over the extremely cold Asiancontinental interior. Generally, conditions are dry and pre-cipitation accumulations small.

The climate of Khabarovsk conforms to the simple sum-mer maximum and winter minimum distribution of preci-pitation caused by the summer and winter monsoons,respectively. Winter monsoons bring strong westerly tonorth–westerly winds from the continent (Asian anticy-clone) with dry, cold air. Summer monsoon is characterisedby warm, humid south-to-southeasterly winds from thePacific Ocean that bring heavy rain to Khabarovsk, espe-cially in July and August. A weak monsoon rainy seasonmay cause drought, crop failures and hardship for peopleand wildlife.

Data used for this study are monthly mean precipitationfor Khabarovsk climate station for the period 1911–2005archived by the Hydrometeorological Information Center inKhabarovsk. The data are processed statistically to deriveperiod means, standard deviations, variances, extremes,moving averages and trends.

2.1 Monsoonality of the Khabarovsk study site

The concept of monsoonality, according to Khromov(1956), refers to the onset, ending, intensity and locationof monsoons, which is reflected in the percentage frequencyof wind direction during the peak of the summer and wintermonsoons. The Monsoonality Index (J) introduced byKhromov (1956) is given as:

J ¼ F1þ F7ð Þ.2 ð1Þ

where F1 is percentage frequency of the main wind directionin January, and F7 is the percentage frequency of main winddirection in July. If J>60 %, the region is classified as

Table 2 Precipitation in July and in January, the precipitation ratio andMonsoonality Index for three climate stations in the Far East, namelyKhabarovsk, Nikolaevsk-on-Amur and Smidovich, for the period1937–1980

Climate station Precipitation(mm)

July/Januaryprecipitationratio

MonsoonalityIndex

January July

Khabarovsk 12 120 10 45.0

Smidovich 7 154 22 45.5

Nikolaevsk-on-Amur 24 84 3.5 30.5

Table 3 Monthly, seasonal and annual precipitation statistics for Khabarovsk showing the mean, mean root square deviation (σ) and coefficient ofvariation (V), for the period 1911–2005

Month/statistic I II III IV V VI VII VIII IX X XI XII Cold season,November–March

Warm season,April–October

Year

Mean (mm) 10 9 14 35 57 73 119 128 85 42 19 13 76 529 605

σ (mm) 8.1 6.9 10.3 21.6 25.5 37.6 68.7 68.2 47.3 29.6 12.5 10.7 25.2 137.3 139.7

V (%) 81 77 74 62 45 52 58 54 56 71 66 82 33 25 23

Temporal dynamics of precipitation in an extreme mid-latitude monsoonal climate

monsoonal. Regions where J is between 40 and 60 % aredescribed as having a climate with monsoonal tendency, whilethose in which J<40 % are considered to outside the zone ofmonsoonal influences. The Monsoonality Index forKhabarovsk is derived using (1) comparison with two otherclimate stations. The first is Smidovich (48 36′N, 133º 49′E),which is just 95 km from Khabarovsk (48° 31′N, 135º 10′E).The other station is Nikolaevsk-on-Amur (53 08′N, 140º44′E), which is located 651 km from Khabarovsk. Table 1gives the frequency of wind direction for these climatestations during the mid-winter month of January and themid-summer month of July. It shows the main winddirection is from west- to south–west in January and fromeast- to north–east in July. Table 2 gives MonsoonalityIndex for climate stations Khabarovsk, Nikolaevsk-on-Amur and Smidovich calculated using Eq. (1), along withthe ratio of precipitation in July to precipitation in Januaryat the same stations.

3 Results

3.1 Climatic setting

Mean annual precipitation for Khabarovsk for the period 1911–2005 is 605mm. Generally, minimum of precipitation occurs incold period month of November to March. The mean for thisperiod is 76 mm, which is 13 % of the annual precipitation(Table 3). The maximum precipitation occurs in warm period(April to October) with amean of 529mm,which is 87% of themean annual precipitation (Table 3). This inter-seasonal dy-namic is explained by peculiarities of the monsoon climate atthe southern part of the Russian Far East described above.

Precipitation at Khabarovsk is characterised by consider-able intra-annual and inter-annual variability. As a measure oftemporal variability of precipitation, the mean root squaredeviation (σ) and coefficient of variation (V) are used. V givesa normalised or relative measure of deviation, whereas σ gives

Fig. 2 Trends in precipitationbased on seven 30-year periodsof mean precipitation forKhabarovsk over the 90-yearperiod 1911–2000. The barsindicate the standard deviationfor each 30-year period. Thedashed line shows the mean forthe full period, 1911–2000,which is 610 mm

Fig. 3 Temporal dynamics of deviation of each year from 1911 to2005 from the mean for the full period of data (1911–2005) forKhabarovsk. The “Number of standard deviations from the full-periodmean” on the y-axis is the deviation of annual precipitation from the

full period mean divided by the standard deviation. For example, 1911:mean full-period precipitation is 609 mm, which is 49 mm lower thanthe 1911 mean of 654. If σ=139.5, then the number of σ=49/139.5=0.35 (for year 1911)


an absolute deviation. Table 3 describes absolute and relativedeviation for the year as a whole, seasons and for individualmonths. In terms of σ, Table 3 shows that annual precipitationis characterised by high temporal heterogeneity, generally dueto considerable absolute variability in the total amount ofprecipitation that is greatest in the warm period from year toyear. On the other hand, V is higher in the cold season. Moredetailed scrutiny of intra-annual dynamics show that in indi-vidual months, V values reaches 80 %, indicating considerabledeviations from the mean (Table 3). This is a characteristic ofthe climate, manifesting itself a frequent occurrence ofdroughts and floods of varyingmagnitudes (Table 3). V is moreuseful for describing features of the climate.

3.2 Climatic normals

To examine precipitation for trends in putative normals, thedata period 1911 to 2000 was divided into seven 30-yearperiods at 10-year intervals. Mean precipitation amounts foreach of the seven periods and standard deviation are given inFig. 2 along with the full-period mean shown as a dashed line.The results reveal a clear upward trend, levelling off with signsof a slight decline in the 1971–2000 period. It can be seen that

only the 1941–1970 normal corresponds closely to the full-period mean. There is close to a 100 % difference between thatdeviation from the full-period mean for the 1911–1940 period(−98 mm) as compared with the 1961–1990 period (Fig. 2).The averaging for 30-year periods has revealed a clearly showntrend of both an increase in precipitation and its inter-annualvariability, peaking in the 1961–1990 normal. Later, it isrevealed that the peak decade was the 1980s (Fig. 3).

Data were examined to assess the effect of calculating ofnormals from a number of years greater than 30 years, forexample, averaging on 50-year interval. The results in Table 4show that this leads to a deviation of values from mean to 11 %for a year, and to 27% for separatemonths (Table 4). This meansthat there is a considerable divergence in mean values as theaveraging period is changed. It is necessary to use the full dataperiod as a base period to calculate a ‘normal’ or steady periodmean to serve as a base reference for precipitation in the climateregime represented by observations made at Khabarovsk.

3.3 Analysis of year to year and within-year variability

To examine within-year variability, the deviation of themean for each year was compared with the full-period mean.

Table 4 Ratio of mean precipi-tation (in percent) at Khabarovskfor annual, seasonal and monthlydata for 1911–2005 comparedfor five different 50-year periods

50-year periods Year Cold season Warm season January April July October

1911–1960 −11 −23 −10 −27 −20 −14 −11

1921–1970 −4 −14 −3 −13 −13 −2 −4

1931–1980 0 0 1 −4 4 0 −2

1941–1990 7 7 7 −3 11 7 9

1951–2000 11 20 10 15 19 11 17

Fig. 4 Plots of 30-year running means for precipitation in July and in August at Khabarovsk, from the start of 1911 and to the end of 2005


The results in Fig. 3 show an abrupt increase in variabilityaround the end of the 1950s. Prior to 1956, there was onlyone case (in 1941) when the variability was greater than−2.0 σ. Post-1956, there are three cases where the variabilitywas greater than +2.0 σ, namely 1962 (+2.4 σ), 1971(+2.2σ) and 1981 (+3.6 σ), shown in Fig. 3.

To examine within-year characteristics, the mean month-ly data are examined. The results in Table 3 show thatrelative variability of precipitation is high in all months,but especially so during the cold season. There is a largeabsolute variation in the warm season. July and Augustsstand as the months of heaviest accumulations, whereasJanuary and February are the months with the lowest pre-cipitation. Trends in the data are revealed using 30-yearrunning means (Figs. 4 and 5). Figures 4 and 5 show thatboth the wettest and driest months of the year are gettingwetter. In case of the wet months of July and August, Fig. 4shows that for the first part of the century, August is thewettest month. Around about the middle of the century,mean values for July and August converge and remainsimilar until the mid-1950s when August resumes its place.In the case of the cold season, January and February, the

months of minimum precipitation, follow a very similarpattern (Fig. 5). The general finding here is that the monthof seasonal extremes (mean maximum and mean minimum)for the full data period are shifted 1 month later. The monthof mean maximum moves from July to August and themonth of mean minimum from January to February.

Analysis of the continuous time series from 1911 to 2005revealed some features of precipitation in Khabarovsk overthe 95-year period. Maximum annual value was observed in1981 (1,105 mm), a minimum in 1941 (334 mm), withanother low value of 382 mm in 2001 (382 mm). Extrememonthly and seasonal values of precipitation forKhabarovsk are presented in Table 5.

The lowest warm season precipitation of 223 mm wasobserved in 1941, which is 42 % of the mean long-termvalue. The highest warm season precipitation of 1,048 mmoccurred in 1981, which is 73 % mean annual value ofprecipitation. The driest cold period precipitation was26 mm in 1952 and the wettest in 1997 (143 mm).

Trends were analysed over different time periods. Thefirst is over the complete 95-year time series (1911–2005)shown in Fig. 6. The second is for four different 30-year

Fig. 5 Plots of 30-year runningmeans for precipitation inJanuary and in February atKhabarovsk, from the start of1911 and to the end of 2005

Table 5 Extreme precipitation (in millimetre) for individual months and cold and warm periods during 1911–2005 in Khabarovsk

Month/period I II III IV V VI VII VIII IX X XI XII Cold period Warm period

Maximum

Precipitation (mm) 36 33 46 130 146 199 301 434 304 126 71 43 143 1,048

Year 1982 1979 1994 1983 1994 1991 1962 1981 1956 1972 1996 1990 1997 1981

Minimum

Precipitation (mm) 0 0 0 2 15 2 7 25 15 2 1 1 26 223

Year 1978 1951 1933 1917 1929 1986 1974 1989 1976 1976 1958 1938 1952 1941

1962


periods: 1911 to 1940, 1941 to 1970, 1971 to 2000 and 1976to 2005 (Fig. 7). The results show that there are somedistinct shifts in the trend patterns. Most noticeable is a shiftfrom positive trends to negative trends. However, the size ofthese trends is small and likely insignificant compared tointer-annual variability. Decadal-scale variations are:+16 mm/10 years for 1911–1940, +80 mm/10 years for1941–1970, −2 mm/10 years for 1971–2000 and−55 mm/10 years for 1976–2005 (Fig. 7). Inter-annual var-iations can reach ±400–450 mm.

4 Discussion

In light of the above-mentioned, the following three ques-tions arise: (1) Does a 30-year climatic normal represent asteady mean for reference purpose in analyses? (2) If not,what length of time series is required? (3) Are steady periodmeans (‘normals’) for cold season and warm seasons appro-priate to use in within-year analyses?

In addressing the first two questions and using consecutiveyear analysis, Shver (1984) suggested that the length of time

Fig. 6 Precipitation inKhabarovsk, 1911–2005. Thedark line is the 11-year runningmean. The thin straight line isthe linear trend

Fig. 7 Trends over four 30-year periods for Khabarovsk, 1911 to 1940, 1941 to 1970, 1971 to 2000 and 1976 to 2005


series required for the period mean to settle at a representativeconstant value did not depend on the length of the observationperiod. Our findings show that this is not necessarily the case.The results of our analysis has shown that it is not enough touse a limited period time series or normal to represent a steadyaverage for any year or warm season, as the mean changesthrough time. For a steady cold season mean, we should usethe full-period (85-year) time series. By and large, this is inkeeping with guidance offered by the WMO:

A number of studies have found that 30 years is notgenerally the optimal averaging period for normals usedfor prediction. The optimal period for temperatures isoften substantially shorter than 30 years, but the optimalperiod for precipitation is often substantially greaterthan 30 years. (World Meteorological Organization2011, pp. 4–16)

In normals for 30 years, there is also a displacement of themaximum and minimum values in the annual dynamic.Drozdov et al. (1963) examined the question of determininga steady long-term precipitation mean and concluded that it isnot enough to use short (25–30 years) time series. Our resultssupport this finding, as discussed in the shift of the month ofseasonal extremes for the full data period (Figs. 4 and 5).

Considering general long-term trends, our results forKhabarovsk show the highest twentieth century precipitationin the early 1960s and in the late1970s, with a general decreasesince the mid-1980s (Fig. 6). Gruza et al. (2007) have lookedinto the temporal and spatial patterns of precipitation in Russiathat include geographical two regions in the Russian Far East,namely Priamurye and Primorye (Fig. 1). The patterns are notgreatly dissimilar to those reported here for Khabarovsk, in thatthe data for this region showing an upward trend to the early1960s, followed by a steady decline (Gruza et al. 2007).However, trends in Priamurye and Primorye differ from trendsfor Russia as a whole: data for which show an increase ofprecipitation for the period 1907–2006, most notably over theperiod 1976–2000. By and large, changes and trends in pre-cipitation are very similar to those from the work ofNovorotskii (2007) for the Lower Amur basin.

5 Conclusion

The case study reported here is based on the analysis of datafrom Khabarovsk climate station situated in the extreme mon-soonal climate of the Russian Far East. Standard normals weredetermined to be different from a complete record of monthlyprecipitation data. The results show that, by and large, thelength of time series required for the period mean to settle at arepresentative constant value depends on the length of theobservation period. Our findings show that it may not beenough to use a limited period times series such as a 30-year

normal to represent a steady average for a year or a warmseason, as the mean changes through time; in particular, for asteady cold season mean, we should use the full-period (95-year) time series.

The study also examined within-year characteristics ofthe 95-year time series. The results show that variability ofprecipitation is high in all months, but especially so duringthe cold season. The month of seasonal extremes (meanmaximum and mean minimum) for the full data period areshifted 1 month later.

Trends in the data are revealed using 30-year runningmeansshowing that both the wettest and driest months of the year aregetting wetter. Analysis of the continuous precipitation timeseries from 1911 to 2005 shows that the maximum annualvalue was observed in 1981 (1,105 mm), a minimum in 1941(334 mm), with another low value of 382 mm in 2001. Trendswere analysed over different time periods. The results showthat there are some distinct shifts in the trend patterns. Mostnoticeable is a shift from positive trends to negative trends.However, the sizes of these trends are small and likely insig-nificant compared to inter-annual variability, which can be ashigh as ±450 mm. Overall the results for Khabarovsk show themaximum twentieth century precipitation in the early 1960sand in the late 1970s, with a general decrease since the mid-1980s. This differs from trends and means for Russia as awhole: data for which show an increase of precipitation forthe period 1907–2006, most notably over the period 1976–2000 (Gruza et al. 2007).

Climatic normals are used for a variety of purposes suchthat their reliability is crucial to the accuracy of the results, forexample, when they are used for homogenization of daily datawhen a climate station moves or when there are instrumentchanges, missing data or procedural change in observing thathas led to fragmentation of climate records. However, the newdata set is synthetic. Our results suggest that the quality of thehomogenised data set could be questionable since the normalused may be unrepresentative of the true period mean. In suchcases, averaging the data from a new or different set over30 years or longer might be a better description of the precip-itation climate. The same caution would be required whenclimatic normals are used for identifying and then quantifyingprecipitation trends. Perhaps the most recent 30-year normalwould be more useful than an older one because of inertia orpersistence in the system; more useful therefore for analysis ofthe latest data.

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Temporal dynamics of precipitation in an extreme mid-latitude monsoonal climate