research article a comparison of two methods on the ...research article a comparison of two methods...

Research ArticleA Comparison of Two Methods on the Climatic Effects ofUrbanization in the Beijing-Tianjin-Hebei Metropolitan Area

Mingna Wang12 and Xiaodong Yan3

1Key Laboratory of Computational Geodynamics Chinese Academy of Sciences Beijing 100049 China2College of Earth Science University of Chinese Academy of Sciences Beijing 100049 China3State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Beijing 100875 China

Correspondence should be addressed to Mingna Wang wangmnucasaccn

Received 28 September 2014 Revised 25 March 2015 Accepted 8 April 2015

Academic Editor Charles Jones

Copyright copy 2015 M Wang and X YanThis is an open access article distributed under the Creative CommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Both the ldquourban minus ruralrdquo (UMR) and the ldquoobservation minus reanalysisrdquo (OMR) methods were used to quantify the potentialimpacts of urbanization on regional temperature changes in the Beijing-Tianjin-Hebei metropolitan area in China DMSPOLSnighttime light imagery and population census data were used to classify stations into four classes rural small city medium cityand large city groups The regional average annual mean temperature was estimated to increase by 012∘Cdecademinus1 derived fromurban warming accounting for 40 of total climate warming from 1960 to 2009 using the UMR method The OMR method alsoindicates that rapid urbanization has a significant influence on surface warming trend although the urban warming intensity isdependent on reanalysis datasetThe seasonal cycle patterns from all three datasets are consistent with each other which is contraryto the UMR result owing to the cooling effect of agriculture activity in the rural stations confusing the UMR result So in this paperwe found a deficiency of the UMRmethod where it would underestimate the effects of urbanization in summer In this regard theresults from the OMR method are relatively more convincing although we admit it still has many other problems

1 Introduction

In addition to the climate change contributions from green-house gas concentrations and aerosol loading anthropogenicchanges in the large-scale character of land useland coverchange (LUCC) can also significantly influence climatechange [1]

In terms of LUCC urbanization has received muchattention in climatic research However two divergent viewson influence of urbanization on regional and global meantemperature trends currently exist Many researchers supportthat urban heat island effects are real but local and haveno biased large-scale trends the second one holds the otherview that urban heat island (UHI) effects contaminate theglobal and regional temperature time series and urbanizationis an important contribution to climate change by humanactivities Both of these two views have many supporters

IPCC proposed that the effects of urbanization onthe land-based temperature record are negligible (0006∘Cdecademinus1) the release of heat from anthropogenic energy

production can be significant over urban areas but it is notglobally significant [1] Peterson et al identified that well-known global temperature time series from in situ sta-tions were not significantly impacted by urban warming(0005∘Cdecademinus1) [2] and no statistically significant impactof urbanization could be found in annual temperature in thecontiguous United States [3] Jones et al studied station datafrom the Soviet Union eastern Australia and eastern Chinaand then showed that urbanization influences have relativelyminor contributions to regional warming trends [4] Manypapers also agreed with the viewpoint that the UHI effect inChina during the last 50 years was minor compared to thebackground trend of increasing temperature [5 6]

Nevertheless some studies have employed the ldquourbanminus ruralrdquo method (UMR) to indicate that urbanizationmay play a more significant role with regard to temperaturetrends on multiple geographic scales Such results should begivenmore consideration for themitigation of climate change[7] Oke showed that even a city of 1000 people could have anUHI effect and the magnitudes of UHI effects were linearly

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2015 Article ID 352360 12 pageshttpdxdoiorg1011552015352360

2 Advances in Meteorology

Table 1 Results of recent studies of the urban heat island effects in China at regional and national scales

Study area Method Time period UHI warming (∘Cdecademinus1) ReferencesMainland China UMR 1954ndash2004 lt0012 [5]Yangtze River Delta UMR 1961ndash2005 0069 [37]North China UMR 1961ndash2000 011 [10]China Comparison with SST 1951ndash2004 01 [38]Northeast China UMR 1954ndash2005 0027 [6]Southeast China OMR 1979ndash1998 005 [26]East of 110∘E over China OMR 1960ndash1999 012 [39]China OMR 1960ndash1999 014 [40]

East China OMR and UMR 1981ndash2007OMR 0398 0260 0214 0167UMR 0285 0207 0135 0077

for stations from metropolises large citiesmedium cities and small cities

[36]

correlated with the logarithm of the population [8] Wanget al chose 42 pairs of eastern China urban and rural stationsto study UHI effects and revealed that the magnitude of UHIeffects was 008∘Cdecademinus1 from 1954 to 1983 which mustbe carefully considered when attributing causes to observedtrends [9] Ren et al found that the annual mean temperatureof the national basicreference stations in north China wassignificantly impacted by urban warming (011∘Cdecademinus1)[10] Therefore they suggested that the urban warming biasfor the regional average temperature anomaly series shouldbe corrected

A new method to estimate the effect of LUCC on airtemperature changes that subtracts surface temperatures thatare derived from the NCEP-NCAR reanalysis data from thetrends that are observed at surface stations (observationminus reanalysis (OMR)) was proposed [11] The rationalefor the OMR method is that the reanalysis data representlarge-scale climate changes due to greenhouse gases andatmospheric circulation but are insensitive to regional surfaceprocesses because little or no surface data or information(surface observations of temperature moisture and windover land) about land surface changes has been utilized inthe data assimilation process Thus Lim et al studied thesensitivity of surface climate change to land types by theOMRmethod to reveal that warming over barren and urban areas islarger than most other land types [12] The study of Fall et alconfirmed the robustness of the OMR method for detectingnonclimatic changes at the station level and provided robustresults regarding the impact of LUCC on the temperaturetrends over the conterminous United States [13] The resultsindicated that the regions converted into urban areas show apositive OMR trend which agreed well with those obtainedby Lim et al [12] Hu et al used the OMRmethod to estimatethe impact of land surface forcing on mean and extremetemperatures in eastern China and found that LUCC impactscould explain one-third of the observed increase of the annualwarm nights and nearly half of the observed decrease of thecold nights [14]

China has been experiencing a dramatic growth in urbanareas and population since implementing the reform and

open policy All regions in China had more than a 20expansion in urban areas during 1980ndash2005 and the EasternPlain had the largest increase in urban area (25 million ha)[15] which could significantly change the land surface phys-ical processes and surface heat flux There could have beensubstantial impacts on local and regional climates as a resultof the rapid urbanization Therefore determining how toestimate urban and land use contributions to the warmingtrends is a hot research topic currently

Table 1 illustrates some results of recent studies on UHIeffects in China at regional or national scales Overall thecontribution of urban warming on temperature changewhich was determined from the OMR method has beenmore significant than the effect determined from the UMRapproach However no papers explain that in detail regard-less of annual or seasonal changes

Previous studies have predominantly focused on theUHI effect over a single large city [16 17] and the studyof urbanization in Beijing has attracted the most attention[18 19] Ren et al showed that with regard to the annualmean surface air temperature (SAT) at Beijing Station theUHI contribution reached 804 during 1961ndash2000 and613 during 1981ndash2000 [20] However with the increasingdevelopment and growth of the small and medium citiesin Beijing-Tianjin-Hebei metropolitan area the regionalinfluence of urbanization should not be underestimatedThe accurate detection and estimation of surface warmingcaused by urbanization in the Beijing-Tianjin-Hebei areamay contribute significantly to the sustainable developmentand planning of orderly human activities in the regionTherefore in the current study we attempt to use both theUMR and the OMRmethods to quantitatively determine thepotential magnitude of UHI contributions to regional annualor seasonal temperature trends during a rapid urbanizationperiod in the Beijing-Tianjin-Hebei metropolitan area As faras the OMR is concerned previous studies usually used onereanalysis dataset but we know that each reanalysis may havesome deficiencies in itsmodel physics or assimilation processSo we conduct comparative studies with three independentreanalyses (NCEPNCARNCEPDOE and ERA-interim) inorder to show a fair and reasonable urban effect

Advances in Meteorology 3

Deciduous needleleaf forest

GrasslandsWaterEvergreen needleleaf forestEvergreen broadleaf forestDeciduous needleleaf forest

Mixed forestsClosed shrublandsOpen shrublandsWoody savannas

SavannasGrasslandsPermanent wetlandCroplandsUrban and buildupCroplandnatural vegetation mosaicSnow and iceBarren or sparsely vegetated

Beijing

Tianjin

Zhangjiakou Chengde

Tangshan

Qinhuangdao

Baoding

Shijiazhuang Cangzhou

Langfang

43∘N

42∘N

41∘N

40∘N

39∘N

38∘N

37∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Figure 1 Study area and land cover types based on MODIS landcover product

2 Region Definition Data andAnalysis Methods

21 Study Area TheBeijing-Tianjin-Hebeimetropolitan arearefers to the center of Beijing and Tianjin and includesShijiazhuang Baoding Qinhuangdao Langfang CangzhouChengde Tangshan and Zhangjiakou eight large cities inHebei Province The geographical location of these cities andland cover type are shown in Figure 1 The urban types aredisplayed in red in Figure 1 Hebei Province is named Ji forshort in China so the Beijing-Tianjin-Hebei metropolitanarea is usually referred to as JING-JIN-JI area (JJJ area forshort below) This region is defined as the political culturaland economic center of China The JJJ regional plan is animportant regional planning of the national TwelfthFive-YearPlan Because of the rapid economic development this regionhas been identified as the economic center of northernChina

According to the China City Statistical Yearbook (1985ndash2009) the total nonagricultural population in urban districtsof the JJJ area doubled in about 20 years from 1984 to 2008Undoubtedly the large increase in the urban population dueto the rapid urban expansion could considerably impact thelocal and regional climate and environment of the JJJ area

22 Data The meteorological data that were used in thisstudy include the daily mean surface air temperature from 79weather and climate observation stations in JJJ area all cov-ering 50 years from 1960 to 2009The dataset included obser-vations from all of the national reference climate stations

and national basic weather stations in the study area as wellas from some ordinary weather stations in Hebei ProvinceThe inhomogeneity of the national reference climate stationsand national basic weather stations that was caused by siterelocation was adjusted [18 21] The data were also of goodquality and were relatively evenly spatially distributed Allclimate and weather stations in China are maintained byprofessional workers

The US Air Force Defense Meteorological SatelliteProgramrsquos Operational Linescan System (DMSPOLS) hasprovided a capable method for obtaining urban informationat coarse spatial scales through its unique low-light imagingcapability to detect persistent lights even from small humansettlements gas fires and vehicles in the dark background atnight The Version 4 stable nighttime light products (1992ndash2009) with 1 km spatial resolution were used to classify thestationsThey are downloaded from theNationalGeophysicalDataCenter (httpwwwngdcnoaagoveogre directhtml)

In addition three widely used reanalysis datasets wereused in our study These included the National Centers forEnvironmental PredictionNational Center for AtmosphericResearch (NCEPNCAR) reanalysis data (NNR) [22] theNational Centers for Environmental PredictionDepartmentof Energy (NCEPDOE) Atmospheric Model Intercompar-ison Project- (AMIP-) II reanalysis data (NDR) [23] andthe European Centre for Medium-Range Weather Forecasts(ECMWF) reanalysis data (ERA) [24] The NNR and NDRdatasets covered the period from 1948 and 1979 to the presentrespectively with the spatial resolution of the Global T62Gaussian grid (192 times 94)There were some deficiencies in themodel physics of the NNR for example the description ofcloudiness and soil moisture is poor which could bias theestimation of surface temperature [25] Although based onthe widely used NNR the NDR has improved this quality byfeaturing newer physics and observed soil moisture forcingas well as fixing known errors of the NNR [23] Conse-quently the OMR for the NDR data should more accuratelycharacterize near-surface temperature over land [26] Thelatest extension reanalysis ERA-interim was also used in ouranalysis It covers the period from 1979 to the present witha spatial resolution of 15∘ times 15∘ In this work we selectedthe same reference period that is used for comparison from1979 to 2009 The ERA is somewhat more sensitive to landsurface forcing than the NNR or NDR However resultsderived from the ERA would contain a portion of surfacetemperature variation due to land surface forcing [12 27] andthese results would strengthen our conclusions Furthermorean integrated study using a few types of reanalysis datasetsmay help to decrease the uncertainties brought about by datadeficiencies and a comparative analysis of three reanalysesmay perhaps be helpful to confirm the results of the OMRFollowing the same techniques put forward by Kalnay andCai [11] we also interpolated the gridded reanalysis data toobservational sites with bilinear interpolation

23 Methods of Defining Types of Stations The key issue ofestimating the effect of urbanization on temperature trendsis to define the reference or baseline stations The stationslocated in rural areas could be assumed to be reference


Table 2 Numbers of stations for each station group in the JJJ area

Station group name Population criteria(million) Number of stations

Large city station ge05 11Medium city station 01ndash05 31Small city station 005ndash01 26Rural station le005 11

43∘N

42∘N

41∘N

40∘N

39∘N

38∘N

37∘N114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

Figure 2The distribution of the four types of stations in the JJJ areaThe background is DMSPOLS nighttime light data from 2009 (995333large city station + medium city station 998771 small city station ∙ ruralstation)

stations that have not been affected by urbanization andthe temperature trends recorded at the rural stations wouldactually represent large-scale changes

The stations were firstly classified into four types accord-ing to the population data provided by China City StatisticalYearbook (2009) large city stations with nonagriculturalpopulations over 05millionmedium-sized city stations withnonagricultural populations of 01ndash05 million small citystations with nonagricultural populations of 005ndash01 millionand rural stations with nonagricultural populations less than005 million This is consistent with Easterling et al whoconsidered a population of 005 million as the critical valuebetween ldquourbanrdquo and ldquononurbanrdquo stations [28]

Additionally theDMSPOLS nighttime light imagerywasalso applied to define the rural stations The total numberof stations with nonagricultural populations less than 50000was 17 Then the nighttime light data of these sites wereanalyzed Among these 17 stations the nighttime light valuesof six stations increased significantly which indicated thatthese stations experienced a process of urbanization Thesesix sites were then classified as small city stations Thereforein the present study a total of 11 rural stations were chosenThe definitions for the various categories of stations and thenumbers of each station are listed inTable 2 which shows thatthe number of medium city stations was the largest followedby the small city stations

24 Calculation of Temperature Anomalies Figure 2 illus-trates the locations of all stations we analyzed in the JJJ

area Many factors such as topography and altitude couldbias the research results when using average temperaturestherefore we adopted temperature anomalies of the stationobservations and the reanalysis data to denote the interan-nual variation of the regional temperature for each stationcategory All trends were calculated using a simple linearregression and the statistical significance of the linear trendwas assessed using a t-test The Mann-Kendall trend test[29 30] was used to determine abrupt changes and thesignificance of urban warming [31 32]

3 Results

31 Surface Air Temperature (SAT) Trends The observedtrends in the surface air temperature for the different stationgroups in the JJJ area are listed in Table 3 From 1960 to 2009the trends in the annual mean temperature were all positivefor the four station groups with the largest increase occurringat the large city stations (043∘Cdecademinus1) followed bythe medium city stations (030∘Cdecademinus1) and then thesmall city stations (028∘Cdecademinus1) The weakest warmingtrend only 019∘Cdecademinus1 occurred at the rural stationsindicating that urbanization might have exerted a significantinfluence upon the observed temperature warming in theJJJ area The annual mean temperature trends from 1979 to2009 were even larger The values for the seasonal trends areshown along with the annual means for each station groupThe seasonal mean SAT trends for the various station groupsall appeared to be largest in winter followed by the springRelatively weak warming was observed during the autumnand summer The rural stations even witnessed a slightcooling trend in the summertime For the JJJ area as a wholethe annual mean SAT trend was 030∘Cdecademinus1 which issimilar to the results from northern China 029∘Cdecademinus1from 1961 to 2000 [10] The regional averaged SAT warmingtrends during the winter spring summer and autumn were054 046 01 and 01∘Cdecademinus1 respectively

Figure 3 reveals the spatial distribution of the annualmean SAT trends from the observed station records andthe reanalysis data in the JJJ area during 1979ndash2009 Thestation observations (Figure 3(a)) indicated that the spatialdistribution of the warming trend was closely related to theintensity of the nighttime lights The urban stations withrelatively intense nighttime lights exhibited rapid increasesin the temperature Conversely stations with weak warmingtrends were mainly rural or small city stations A broadrange of spatial difference existed in the observed SAT inthe study area with a minimum of 0081 and a maximumof 1012∘Cdecademinus1 In contrast the ERA (Figure 3(b)) NNR(Figure 3(c)) and NDR (Figure 3(d)) reanalysis data allshowed the strongest warming trends from 1979 to 2009However the trend values did not reach the amplitude ofthe observed station records The spatial difference rangedfrom 0399 to 0630∘Cdecademinus1 for the ERA 0284 to0510∘Cdecademinus1 for theNNR and 0227 to 0346∘Cdecademinus1for the NDR The spatial differences of the reanalysis datawere substantially less than that of the station observationsbecause the reanalysis data only reflected the atmospheric


42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

lt02

gt07

02ndash0303ndash0404ndash05

05ndash0606ndash07

(a) OBS

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

03ndash0404ndash05

05ndash0606ndash07

(b) ERA

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

htHigh 63

Low 0

02ndash0303ndash04

04ndash0505ndash06

(c) NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

02ndash0303ndash04

(d) NDR

Figure 3 Annual mean SAT trends from (a) station observations (OBS) (b) ECMWF ERA-interim reanalysis data (ERA) (c) NCEPNCARreanalysis data (NNR) and (d) NCEPDOE reanalysis data (NDR) during 1979ndash2009 (unit ∘Cdecademinus1) Also shown is the DMSPOLSnighttime light imagery from 2009

circulation and greenhouse gas concentrations but wereinsensitive to regional surface processes associated withdifferent land types

32 Effect of Urbanization on SAT Trends Table 3 showstrends for the annual and seasonal mean SAT related tourbanization for various city station groups The values inthe parentheses are the SAT trend differences between theurban station groups and rural sites From the difference inthe observational temperature trends between the urban andrural stations (Table 3) the annual mean UMR trend was024∘Cdecademinus1 for the large city station 011∘Cdecademinus1 forthe medium city station and 009∘Cdecademinus1 for the smallcity station (statistically significant at the 001 confidence levelfor each city station group)The average annual urban warm-ing in the JJJ area was 012∘Cdecademinus1 (statistically significantat the 001 confidence level) The effect of urbanization on

the SAT trend for each city group after 1979 appeared tobe slightly larger than that of 1960ndash2009 Among the fourseasons the urban warming in the summer was the largest(013∘Cdecademinus1) followed by spring (011∘Cdecademinus1) andautumn (010∘Cdecademinus1) The annual SAT trend in winterwas hardly affected by urbanization (004∘Cdecademinus1) Thecontribution of urban warming to the total warming waslargest in the summer reaching 100 for all of the city stationgroups whereas winter witnessed the lowest contributionOne reason may be attributed to the fact that the windvelocities during the summer are weaker than in winter innorthern China as many studies have reported that urbanheat island intensity increases with decreasing wind speed[33ndash35] The effects of UHI in the JJJ area did not signifi-cantly contribute to the observed rapid warming during thewintertime therefore the large-scale climate changes dueto greenhouse gases and atmospheric circulation were likely


1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(a)

1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(b)

Year1960 1970 1980 1990 2000

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(c)

Figure 4 Sequential Mann-Kendall test applied to the observed annual mean temperature for the UMR of large city groups (a) medium citygroups (b) and small city groups (c) The value plusmn196 (the 95 confidence level) is represented with horizontal dashed lines The solid linesrepresent the MK trend for the time series and the dash dotted lines represent the MK trend for the backward series

Table 3 Observed trends of the annual and seasonal mean surface air temperature for different station groups in the JJJ area The values inparentheses are the results of different urban station groups minus the rural station Statistical significance is 005 (single asterisk) and 001(double asterisk) The units for the values are ∘Cdecademinus1

Station group name Winter Spring Summer Autumn Annual (1960ndash2009) Annual (1979ndash2009)Large city station 075lowastlowast (024lowastlowast) 068lowastlowast (031lowastlowast) 021lowastlowast (022lowastlowast) 011lowastlowast (009lowastlowast) 043lowastlowast (024lowastlowast) 066lowastlowast (027lowastlowast)Medium city station 054lowastlowast (003) 049lowastlowast (012lowast) 012lowastlowast (013lowastlowast) 006 (004) 030lowastlowast (011lowastlowast) 050lowastlowast (011lowastlowast)Small city station 047lowastlowast (minus004) 034lowastlowast (minus003) 012lowast (013lowastlowast) 024lowastlowast (022lowastlowast) 028lowastlowast (009lowastlowast) 051lowastlowast (013lowastlowast)Urban station 055lowastlowast 047lowastlowast 012lowastlowast 012 031lowastlowast 053lowastlowast

Rural station 051lowastlowast 037lowastlowast minus001 002 019lowastlowast 039lowastlowast

UMR 004 011lowast 013lowastlowast 010lowastlowast 012lowastlowast 014lowastlowast

Table 4 Linear trends of regional average annual rural and UMRtemperature (∘Cdecademinus1) and the contribution of urban warmingto the total warming for different time periods Statistical signifi-cance is 005 (single asterisk) and 001 (double asterisk)

Total Rural UMR UHI contribution1960ndash1969 minus110lowast minus113 003 0301970ndash1979 039 025 016lowast 41031980ndash1989 046 033 015lowast 32611990ndash1999 128lowastlowast 114lowast 016lowast 12502000ndash2009 026 021 009 34621960ndash2009 030lowastlowast 019lowastlowast 012lowastlowast 4000

the dominant contributors This seasonal conclusion agreeswith the results of Ren et al for northern China [10] and Yanget al in eastern China [36]

Table 4 shows the contribution of urban warming atdifferent time periods The contribution of urban warmingto the overall warming was 40 for the 50 years from 1960 to2009 The warming trends of the background (rural station)

and urban stations were the strongest during the 1990sfollowed by the 1980s and 1970sTheUMR trends in the 1970s1980s and 1990s were the largest (all statistically significant atthe 005 confidence level) This indicated that urbanizationin the JJJ area was consistent with the Chinese progress ofthe reform and open policy since 1978 We know that thestudy area experienced rapid urban development after theTangshan earthquake in 1976 Although theUMR in the 1990swas the largest the contribution of urban warming to theoverall warming was the largest in the 1970s (4103) becausethe background warming trend in the 1990s was so large

Figure 4 shows the results of the sequential Mann-Kendall methods applied to the time series of the annualmean SAT for the UMR of different city groups The sequen-tial Mann-Kendall analysis indicated that the time seriesof the annual mean temperature differences between thecity and rural sites all had very significant positive trendsthat exceeded the 99 confidence level The abrupt changesin the trends of the time series of the annual mean SATbegan in the early 1980s for the large city and medium citystation groups (Figures 4(a) and 4(b)) and the early 1990s


0 5 10 15 20 25 30 35

0

05

1

Nighttime light trends (pixels)minus5

minus05

Tem

pera

ture

anom

aly

trend

s

y = 0013x + 019R = 031lowast

(∘C

deca

deminus1)

Figure 5 Correlation between the trends of the annual mean SATand the intensity of the nighttime lights from 1992 to 2009

for the small city groups (Figure 4(c)) The positive trendsof the large city and medium city stations began to pass the95 confidence level during the 1970s while the small citystations passed the 95 confidence level during the late 1980sTherefore the small city stations clearly experienced weakerand slower urbanization progress than the large and mediumcity stations

Figure 5 shows the trend of the annual mean SAT relatedto the intensity of the nighttime lights and the correlationcoefficient was 031 which is statistically significant at the005 confidence level It is notable that the observed SATwas somewhat affected by the urbanization in the JJJ areaTherefore the increasing UHI effect amplified the trendof the observed SAT Only when the urbanization effect isremoved can these records be considered accurate and betterrepresent the baseline temperatureThewarming trend of theregional average annual SAT in the JJJ area decreased from030∘Cdecademinus1 to 019∘Cdecademinus1 after the adjustment

33 Observation Minus Reanalysis (OMR) Analysis Com-pared with the progress of urbanization the conversions ofland use types caused by other human activities such asagriculture overgrazing and deforestation have been evenmore intense China is one of the most obvious land usechange regions but the climatic effects from land usecoverchanges and greenhouse gas emissions are difficult to dis-tinguish However neglecting the role of land use changescould lead to the wrong assessment of human activities oncontributions to surface warming It is very important toreveal the contributions of urbanization and other land usechanges to climate change In this paper we used the OMRmethod to analyze the contribution of urbanization and otherland use changes to surface warming

Figure 6 shows the observation and reanalysis time seriesof temperature anomalies for the four station groups from1979 to 2009 Both the observed and the reanalysis annualmean temperatures showed increasing trends The reanal-ysis dataset agreed best with the station observation fromrural stations (Figure 6(a)) indicating that the rural stationswere less affected by urbanization and that the backgroundtemperature changes were captured in both the observationsand the reanalysis data The warming trends of the station

observations were stronger than the reanalysis data withthe strongest in the large city groups (Figure 6(d)) followedby the medium city groups (Figure 6(c)) small city groups(Figure 6(b)) and then the rural stations (Figure 6(a))

The OMR values of the NDR were the largest (Figure 6)The ERA-interim [24] indirectly assimilated near-surfacetemperature observations [41] so the ERA was similar tothe observed counterparts in China [42 43] This is shownin Figure 6 and the OMR of the ERA was the smallest(Figure 6) The most substantial increase in the OMR valuesoccurred in the early 2000s in the small and medium citygroups (Figures 6(b)-6(c)) indicating a significant effect ofrapid urbanization on the SAT in the early 2000s The largestincrease of the OMR in the large city groups occurred duringthe late 1980s implying that rapid urbanization developedduring this period (Figure 6(d)) which is relatively consistentwith the UMR abrupt changes tested by the Mann-Kendallmethod

Table 5 presents the annual mean temperature trendsfrom the station observation and three reanalysis datasetsalong with the OMR and UMR values for each station groupThe most significant increase in the station observed annualmean SAT occurred at the largest stations with an annual lin-ear trend of 066∘Cdecademinus1 and the weakest occurred at therural stations with a trend of 039∘Cdecademinus1 The trends ofthe reanalysis datasets among the various station groups werenearly uniformwith a low range of 052 to 054∘Cdecademinus1 forthe ERA 043 to 050∘Cdecademinus1 for the NNR and 024 to026∘Cdecademinus1 for the NDR This predominantly reflectedchanges in circulation and greenhouse warming The annualOMR trends indicated strong warming in the large cities with012 019 and 042∘Cdecademinus1 for the ERA NNR and NDRcontributing 1818 2879 and 6364 respectively Theannual OMR trends were weakest for the rural stations at011 minus006 and minus015∘Cdecademinus1 for the ERA NNR andNDR respectively If these rural values were added to theUMR the results would be consistent with those of the OMRAccordingly both methods showed that urbanization hasimposed significant effects on the SAT in the JJJ area

The seasonal mean temperature trends from threereanalysis datasets all present significant increasing trendsstrongest in the winter and spring In the four seasons thetrends of all the three reanalysis datasets among various sta-tion groups show little differences In addition the seasonalOMR values show strongest warming trends in the largecity stations followed by the medium and small city stationgroupsTheOMR trends in thewinter and spring are strongerthan those in summer and autumn (Table 5) which agreeswith the result obtained byYang et al [40] andHu et al [14] bythe same method for eastern China but no one had pointedout why it appeared contrary to the UMR result The reasonwhy it is contrary to the UMR result is difficult to explain

The OMR values (Figure 7) exhibited a spatial patternsimilar to the trends of the observational temperature(Figure 3(a)) On average the OMRmagnitudes for the NDRwere largest and those for the NNR and ERA were relativelysmall The geographical distribution of both the OMR and


Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

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1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

Observation NDRNNRERA

NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city

Figure 6 Observation and reanalysis time series of temperature anomalies (line graph) and OMR values (bar chart) for (a) rural (b) smallcity (c) medium city and (d) large city groups from 1979 to 2009

the observational temperature trends revealed that landsurface change most likely affected the observed temperatureand that the significant warmingwas affected by urbanizationin the JJJ area especially in Beijing Tianjin and otherlarge cities with the most intense nighttime lighting andstrongest UHI effects We know that the OMR results reflectchanges in land use rather than simply urbanization sothe negative OMR trends that were predominant found atthe rural stations (Table 5 and Figure 7) could be causedby agricultural irrigation and increasing vegetation activity[44] especially in the summer and autumn season (Table 5)which reflected the climatic effects of both urbanization andincreased agricultural planting around the cities In termsof seasonal differences in Table 5 the urban warming effectfrom the OMR method in winter is the largest but thecontribution of urban warming is the largest in summerfrom the UMR result This can be attributed to the factthat the rural stations were affected by agricultural irrigationand increasing vegetation activity in summer so the coolingeffects that we can see in Table 5 and Figure 7 enlarged theresult of urban minus rural method

4 Conclusion and Discussion

The above findings demonstrate that urbanization effectsessentially exert positive promotions on the regional climatewarming in the Beijing-Tianjin-Hebei metropolitan areaUsing the UMR method the urban warming of approxi-mate 012∘Cdecademinus1 accounting for 40 of regional totalclimate warming (030∘Cdecademinus1) from 1960 to 2009 andaccounting for 2569of regional total climatewarming from1979 to 2009 could be found Using the OMR method theurban warming intensity is dependent on reanalysis datasetA very slight UHI could be detected from the ERA whilethe urban warming of 010∘Cdecademinus1 and 026∘Cdecademinus1accounting for 1877 and 5119 of regional total warmingfrom 1979 to 2009 could be calculated from the NNR andNDR respectivelyThe differences might have partly resultedfrom diversities of assimilation systems and input datasetsFor instance ERA indirectly assimilated near-surface tem-perature observations [41] so a very slight UHI could bedetected Thus the ERA dataset is somewhat not optimal forthe OMR method in the study area


42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash00ndash0202ndash04

04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR

Figure 7 Annual mean temperature trends from the observation minus reanalysis method (∘Cdecademinus1) for the (a) ERA (b) NNR and(c) NDR The DMSPOLS nighttime lights imagery from 2009 is also shown

Although there are differences among the annual urbanwarming from ERA NNR and NDR the seasonal cyclepatterns from all three datasets are consistent with eachother A stronger UHI occurs in winter and it is weaker insummer from the OMR results which may reflect stronganthropogenic heating effect in winter However the findingsfrom the UMRmethod demonstrate that the urban warmingcontributes the largest in summer and smallest in winterThereverse seasonal cycle pattern of urban warming from theUMR and OMR method implicates that our understandingof the seasonal cycle of urban effects has some uncertaintiesAs we know that the UMR used the observations entirelyThe urban sites are influenced by UHI Meanwhile the ruralsites are also influenced by other factors such as vegetationtranspiration and moisture evaporation from soil Vegetationgreening enhanced the transpiration and thus cooling of theclimate [45] This cooling effect of agriculture activities can

also be found as suggested for the USA [13] In our study areavegetation has strong activities in summer So the strongesturban warming effect in summer from the UMR methodmight be larger than actual UHI effect Winter witnessedthe lowest contribution from the UMR method that may beattributed to the stronger wind speed in winter in northernChina as UHI intensity decreases with increasingwind speed[33ndash35]

Therefore we found some deficiencies of the UMRmethod that the results from the UMR overestimate theurban warming effect in winter and underestimate it in sum-mer On the other hand the results from the OMR methodare relativelymore convincing althoughwe admit its accuracydepends on the quality of reanalysis dataset

We used nighttime light data to classify the stationgroups but nighttime light can only provide objective urbandevelopment information not details on the physical features


Table 5 Annual and seasonal mean temperature trends from station observation and three reanalysis datasets Shown are the differencesbetween the observations and the reanalysis datasets (OMR) as well as the differences between the urban and rural sites (UMR) for thedifferent station groups from 1979 to 2009 (∘Cdecademinus1) Statistical significance is 005 (single asterisk) and 001 (double asterisk)

Station group name Datasets Winter Spring Summer Autumn Annual

Large city station

OBS 106lowastlowast 100lowastlowast 037lowastlowast minus009lowast 066lowastlowast

NDR 022lowastlowast 032lowastlowast 021lowastlowast 025lowastlowast 024lowastlowast

NNR 068lowastlowast 039lowastlowast 034lowastlowast 032lowastlowast 047lowastlowast

ERA 072lowastlowast 050lowastlowast 049lowastlowast 036lowastlowast 054lowastlowast

OMR (NDR) 084lowastlowast 068lowastlowast 016lowastlowast minus044lowastlowast 042lowastlowast

OMR (NNR) 038lowastlowast 061lowastlowast 003 minus051lowastlowast 019lowastlowast

OMR (ERA) 034lowastlowast 050lowastlowast minus012lowastlowast minus055lowastlowast 012lowastlowast

UMR 017lowastlowast 026lowastlowast 042lowastlowast minus002 029lowastlowast

Medium city station

OBS 083lowastlowast 086lowastlowast 027lowastlowast 008 050lowastlowast




OMR (NDR) 059lowastlowast 053lowastlowast 008 minus016lowastlowast 026lowastlowast

OMR (NNR) 015lowastlowast 047lowastlowast minus006 minus022lowastlowast 000OMR (ERA) 010lowast 038lowastlowast minus018lowastlowast minus028lowastlowast minus003

UMR minus006 012lowastlowast 032lowastlowast 015lowastlowast 013lowastlowast

Small city station

OBS 064lowastlowast 064lowastlowast 036lowastlowast 043lowastlowast 051lowastlowast




OMR (NDR) 043lowastlowast 031lowastlowast 014lowastlowast 017lowastlowast 026lowastlowast

OMR (NNR) 000 024lowastlowast 003 011lowastlowast 008OMR (ERA) minus001 013lowastlowast minus015lowastlowast 011lowastlowast minus001

UMR minus025lowastlowast minus010lowast 041lowastlowast 050lowastlowast 014lowastlowast

Rural station

OBS 089lowastlowast 074lowastlowast minus005 minus007 037lowastlowast




OMR (NDR) 045lowastlowast 041lowastlowast minus028lowastlowast minus032lowastlowast 011lowast

OMR (NNR) 024lowastlowast 033lowastlowast minus039lowastlowast minus039lowastlowast minus006OMR (ERA) 018lowastlowast 023lowastlowast minus056lowastlowast minus041lowastlowast minus015lowastlowast

of the sites and surroundings Urban meteorological obser-vations are more likely to be made within park cool islandsthan in industrial regions [3] Therefore more detailedinformation of the station locations and their surroundingsor extensive remote sensing observation of the stations isneeded in future works Obviously a more realistic treatmentof the land surface and vegetation-climate interaction inmodels would be helpful for improving our understandingof the effects of LUCC on the climate [46] Moreover thesurface observed data did not give any information abouthow high urbanization affects the overlying atmosphere [31]To seek answers to these issues the impact of UHI effect onthe observed regional climate change needs more in-depthresearch so integrated application of the observation analysisand numerical simulationmethodswillmake the conclusionsmore convincing

Although there still exist some uncertainties the resultspresented in this paper suggest that a large portion of the

current surface climate warming over the Beijing-Tianjin-Hebei metropolitan area is caused by urbanization Theresults presented in this work suggest that the climatic effectsof LUCC should be considered in climate change mitigationstrategies during the rapid urbanization of China

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was sponsored by the Major State BasicResearch Development Program of China (973 Program no2010CB950900) and the National Natural Science Founda-tion of China (41405072)


References

[1] Intergovernmental Panel on Climate Change (IPCC) ClimateChange 2007 The Physical Science Basis Contribution of Work-ing Group I to the Fourth Assessment Report of the Intergovern-mental Panel on Climate Change Cambridge University PressCambridge UK 2007

[2] T C Peterson K P Gallo J Lawrimore TW Owen A Huangand D A McKittrick ldquoGlobal rural temperature trendsrdquo Geo-physical Research Letters vol 26 no 3 pp 329ndash332 1999

[3] T C Peterson ldquoAssessment of urban versus rural in situ surfacetemperatures in the contiguous United States no differencefoundrdquo Journal of Climate vol 16 no 18 pp 2941ndash2959 2003

[4] P D Jones P Y Groisman M Coughlan N Plummer W-CWang and T R Karl ldquoAssessment of urbanization effects intime series of surface air temperature over landrdquo Nature vol347 no 6289 pp 169ndash172 1990

[5] Q Li H Zhang X Liu and J Huang ldquoUrban heat island effecton annual mean temperature during the last 50 years in ChinardquoTheoretical andAppliedClimatology vol 79 no 3-4 pp 165ndash1742004

[6] Q Li W Li P Si et al ldquoAssessment of surface air warming innortheast China with emphasis on the impacts of urbaniza-tionrdquo Theoretical and Applied Climatology vol 99 no 3-4 pp469ndash478 2010

[7] R A Pielke Sr ldquoLand use and climate changerdquo Science vol 310no 5754 pp 1625ndash1626 2005

[8] T R Oke ldquoCity size and the urban heat islandrdquo AtmosphericEnvironment (1967) vol 7 no 8 pp 769ndash779 1973

[9] W C Wang Z Zeng and T R Karl ldquoUrban heat islands inChinardquo Geophysical Research Letters vol 17 no 13 pp 2377ndash2380 1990

[10] G Y Ren Y Q Zhou Z Y Chu et al ldquoUrbanization effectson observed surface air temperature trends in North ChinardquoJournal of Climate vol 21 no 6 pp 1333ndash1348 2008

[11] E Kalnay and M Cai ldquoImpact of urbanization and land-usechange on climaterdquoNature vol 423 no 6939 pp 528ndash531 2003

[12] Y K Lim M Cai E Kalnay and L M Zhou ldquoObservationalevidence of sensitivity of surface climate changes to land typesand urbanizationrdquo Geophysical Research Letters vol 32 no 22Article ID L22712 2005

[13] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[14] Y Hu W Dong and Y He ldquoImpact of land surface forcingson mean and extreme temperature in eastern Chinardquo Journalof Geophysical Research Atmospheres vol 115 no D19 ArticleID D19117 2010

[15] M Liu and H Tian ldquoChinarsquos land cover and land use changefrom 1700 to 2005 estimations from high-resolution satellitedata and historical archivesrdquo Global Biogeochemical Cycles vol24 no 3 Article ID GB3003 2010

[16] L Chen W Zhu X Zhou and Z Zhou ldquoCharacteristics ofthe heat island effect in Shanghai and its possible mechanismrdquoAdvances in Atmospheric Sciences vol 20 no 6 pp 991ndash10012003

[17] M Sachweh and P Koepke ldquoRadiation fog and urban climaterdquoGeophysical Research Letters vol 22 no 9 pp 1073ndash1076 1995

[18] Z Li and Z Yan ldquoApplication of multiple analysis of seriesfor homogenization to Beijing daily temperature series (1960ndash2006)rdquoAdvances in Atmospheric Sciences vol 27 no 4 pp 777ndash787 2010

[19] Z Yan Z Li Q Li and P Jones ldquoEffects of site change andurbanisation in the Beijing temperature series 1977ndash2006rdquoInternational Journal of Climatology vol 30 no 8 pp 1226ndash1234 2010

[20] G Y Ren Z Y Chu Z H Chen and Y Y Ren ldquoImplicationsof temporal change in urban heat island intensity observed atBeijing and Wuhan stationsrdquo Geophysical Research Letters vol34 no 5 Article ID L05711 2007

[21] Z Li and Z Yan ldquoHomogenized daily meanmaximummin-imum temperature series for China from 1960ndash2008rdquo Atmo-spheric and Oceanic Science Letters vol 2 no 4 pp 237ndash2432009

[22] E Kalnay M Kanamitsu R Kistler et al ldquoThe NCEPNCAR40-year reanalysis projectrdquo Bulletin of the AmericanMeteorolog-ical Society vol 77 no 3 pp 437ndash471 1996

[23] M Kanamitsu W Ebisuzaki J Woollen et al ldquoNCEP-DOEAMIP-II reanalysis (R-2)rdquo Bulletin of the American Meteorolog-ical Society vol 83 no 11 pp 1631ndash1559 2002

[24] A Simmons S Uppala D Dee and S Kobayashi ldquoERA-interim new ECMWF reanalysis products from 1989 onwardsrdquoECMWF Newsletter vol 110 pp 25ndash35 2007

[25] K E Trenberth ldquoRural land-use change and climaterdquo Naturevol 427 no 6971 p 213 2004

[26] L Zhou R E Dickinson Y Tian et al ldquoEvidence for a signifi-cant urbanization effect on climate in Chinardquo Proceedings of theNational Academy of Sciences of the United States of Americavol 101 no 26 pp 9540ndash9544 2004

[27] O W Frauenfeld T Zhang and M C Serreze ldquoClimatechange and variability using European Centre for Medium-Range Weather Forecasts reanalysis (ERA-40) temperatureson the Tibetan Plateaurdquo Journal of Geophysical Research DAtmospheres vol 110 no 2 Article ID D02101 2005

[28] D R Easterling B Horton P D Jones et al ldquoMaximum andminimum temperature trends for the globerdquo Science vol 277no 5324 pp 364ndash367 1997

[29] M G Kendall Rank Correlation Methods Charles Griffin andCompany London UK 1948

[30] H BMann ldquoNonparametric tests against trendrdquo Econometricavol 13 no 3 pp 245ndash259 1945

[31] Y Ezber O L Sen T Kindap and M Karaca ldquoClimatic effectsof urbanization in Istanbul a statistical and modeling analysisrdquoInternational Journal of Climatology vol 27 no 5 pp 667ndash6792007

[32] M TayancMKaraca andO Yenigun ldquoAnnual and seasonal airtemperature trend patterns of climate change and urbanizationeffects in relation to air pollutants in Turkeyrdquo Journal of Geo-physical Research vol 102 no D2 pp 1909ndash1919 1997

[33] A J Arnfield ldquoTwo decades of urban climate research a reviewof turbulence exchanges of energy and water and the urbanheat islandrdquo International Journal of Climatology vol 23 no 1pp 1ndash26 2003

[34] G T Johnson T R Oke T J Lyons D G Steyn I D Watsonand J A Voogt ldquoSimulation of surface urban heat islands underlsquoIDEALrsquo conditions at night part 1 theory and tests against fielddatardquo Boundary-Layer Meteorology vol 56 no 3 pp 275ndash2941991


[35] T R Oke G T Johnson D G Steyn and I D WatsonldquoSimulation of surface urban heat islands under lsquoidealrsquo condi-tions at night part 2 diagnosis of causationrdquo Boundary-LayerMeteorology vol 56 no 4 pp 339ndash358 1991

[36] X Yang Y Hou and B Chen ldquoObserved surface warminginduced by urbanization in east Chinardquo Journal of GeophysicalResearch Atmospheres vol 116 no D14 Article IDD14113 2011

[37] Y Du Z Xie Y Zeng Y Shi and J Wu ldquoImpact of urbanexpansion on regional temperature change in the Yangtze RiverDeltardquo Journal of Geographical Sciences vol 17 no 4 pp 387ndash398 2007

[38] P Jones D Lister and Q Li ldquoUrbanization effects in large-scaletemperature records with an emphasis on Chinardquo Journal ofGeophysical Research vol 113 Article ID D16122 2008

[39] Z Jingyong D Wenjie W Lingyun W Jiangfeng C Peiyanand D K Lee ldquoImpact of land use changes on surface warmingin Chinardquo Advances in Atmospheric Sciences vol 22 no 3 pp343ndash348 2005

[40] X C Yang Y L Zhang M J Ding L S Liu Z F Wang andD W Gao ldquoObservational evidence of the impact of vegetationcover on surface air temperature change in Chinardquo ChineseJournal of Geophysics vol 53 no 4 pp 261ndash269 2010

[41] A J Simmons P D Jones V da Costa Bechtold et al ldquoCompar-ison of trends and low-frequency variability in CRU ERA-40and NCEPNCAR analyses of surface air temperaturerdquo Journalof Geophysical ResearchD Atmospheres vol 109 no 24 pp 1ndash182004

[42] G Huang ldquoThe assessment and difference of the interdecadalvariations of climate change in Northern part of China with theNCEP-NCAR and ERA-40 reanalysis datardquo Climatic and Envi-ronmental Research vol 11 no 3 pp 310ndash320 2006 (Chinese)

[43] T B Zhao and C B Fu ldquoPreliminary comparison and analysisbetween ERA-40 NCEP-2 reanalysis and observations overChinardquo Climatic and Environmental Research vol 11 no 1 pp14ndash32 2006 (Chinese)

[44] J Fang S Piao J He and W Ma ldquoIncreasing terrestrialvegetation activity in China 1982ndash1999rdquo Science in China SeriesC Life Sciences vol 47 no 3 pp 229ndash240 2004

[45] S J Jeong C H Ho K Y Kim and J H Jeong ldquoReductionof spring warming over East Asia associated with vegetationfeedbackrdquo Geophysical Research Letters vol 36 no 18 ArticleID L18705 2009

[46] R Mahmood R A Pielke Sr K G Hubbard et al ldquoImpactsof land useland cover change on climate and future researchprioritiesrdquo Bulletin of the American Meteorological Society vol91 no 1 pp 37ndash46 2010

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Geological ResearchJournal of


Geology Advances in


Table 1 Results of recent studies of the urban heat island effects in China at regional and national scales

Study area Method Time period UHI warming (∘Cdecademinus1) ReferencesMainland China UMR 1954ndash2004 lt0012 [5]Yangtze River Delta UMR 1961ndash2005 0069 [37]North China UMR 1961ndash2000 011 [10]China Comparison with SST 1951ndash2004 01 [38]Northeast China UMR 1954ndash2005 0027 [6]Southeast China OMR 1979ndash1998 005 [26]East of 110∘E over China OMR 1960ndash1999 012 [39]China OMR 1960ndash1999 014 [40]

East China OMR and UMR 1981ndash2007OMR 0398 0260 0214 0167UMR 0285 0207 0135 0077

for stations from metropolises large citiesmedium cities and small cities

[36]

correlated with the logarithm of the population [8] Wanget al chose 42 pairs of eastern China urban and rural stationsto study UHI effects and revealed that the magnitude of UHIeffects was 008∘Cdecademinus1 from 1954 to 1983 which mustbe carefully considered when attributing causes to observedtrends [9] Ren et al found that the annual mean temperatureof the national basicreference stations in north China wassignificantly impacted by urban warming (011∘Cdecademinus1)[10] Therefore they suggested that the urban warming biasfor the regional average temperature anomaly series shouldbe corrected

A new method to estimate the effect of LUCC on airtemperature changes that subtracts surface temperatures thatare derived from the NCEP-NCAR reanalysis data from thetrends that are observed at surface stations (observationminus reanalysis (OMR)) was proposed [11] The rationalefor the OMR method is that the reanalysis data representlarge-scale climate changes due to greenhouse gases andatmospheric circulation but are insensitive to regional surfaceprocesses because little or no surface data or information(surface observations of temperature moisture and windover land) about land surface changes has been utilized inthe data assimilation process Thus Lim et al studied thesensitivity of surface climate change to land types by theOMRmethod to reveal that warming over barren and urban areas islarger than most other land types [12] The study of Fall et alconfirmed the robustness of the OMR method for detectingnonclimatic changes at the station level and provided robustresults regarding the impact of LUCC on the temperaturetrends over the conterminous United States [13] The resultsindicated that the regions converted into urban areas show apositive OMR trend which agreed well with those obtainedby Lim et al [12] Hu et al used the OMRmethod to estimatethe impact of land surface forcing on mean and extremetemperatures in eastern China and found that LUCC impactscould explain one-third of the observed increase of the annualwarm nights and nearly half of the observed decrease of thecold nights [14]

China has been experiencing a dramatic growth in urbanareas and population since implementing the reform and

open policy All regions in China had more than a 20expansion in urban areas during 1980ndash2005 and the EasternPlain had the largest increase in urban area (25 million ha)[15] which could significantly change the land surface phys-ical processes and surface heat flux There could have beensubstantial impacts on local and regional climates as a resultof the rapid urbanization Therefore determining how toestimate urban and land use contributions to the warmingtrends is a hot research topic currently

Table 1 illustrates some results of recent studies on UHIeffects in China at regional or national scales Overall thecontribution of urban warming on temperature changewhich was determined from the OMR method has beenmore significant than the effect determined from the UMRapproach However no papers explain that in detail regard-less of annual or seasonal changes

Previous studies have predominantly focused on theUHI effect over a single large city [16 17] and the studyof urbanization in Beijing has attracted the most attention[18 19] Ren et al showed that with regard to the annualmean surface air temperature (SAT) at Beijing Station theUHI contribution reached 804 during 1961ndash2000 and613 during 1981ndash2000 [20] However with the increasingdevelopment and growth of the small and medium citiesin Beijing-Tianjin-Hebei metropolitan area the regionalinfluence of urbanization should not be underestimatedThe accurate detection and estimation of surface warmingcaused by urbanization in the Beijing-Tianjin-Hebei areamay contribute significantly to the sustainable developmentand planning of orderly human activities in the regionTherefore in the current study we attempt to use both theUMR and the OMRmethods to quantitatively determine thepotential magnitude of UHI contributions to regional annualor seasonal temperature trends during a rapid urbanizationperiod in the Beijing-Tianjin-Hebei metropolitan area As faras the OMR is concerned previous studies usually used onereanalysis dataset but we know that each reanalysis may havesome deficiencies in itsmodel physics or assimilation processSo we conduct comparative studies with three independentreanalyses (NCEPNCARNCEPDOE and ERA-interim) inorder to show a fair and reasonable urban effect






Beijing

Tianjin

Zhangjiakou Chengde

Tangshan

Qinhuangdao

Baoding


Langfang

43∘N

42∘N

41∘N

40∘N

39∘N

38∘N

37∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E














43∘N

42∘N

41∘N

40∘N

39∘N

38∘N

37∘N114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0







3 Results




42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

lt02

gt07


05ndash0606ndash07

(a) OBS

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

03ndash0404ndash05

05ndash0606ndash07

(b) ERA

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

htHigh 63

Low 0

02ndash0303ndash04

04ndash0505ndash06

(c) NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

02ndash0303ndash04

(d) NDR






1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(a)

1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(b)

Year1960 1970 1980 1990 2000

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(c)













0 5 10 15 20 25 30 35

0

05

1


minus05

Tem

pera

ture

anom

aly

trend

s

y = 0013x + 019R = 031lowast

(∘C

deca

deminus1)












Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city






42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in






Beijing

Tianjin

Zhangjiakou Chengde

Tangshan

Qinhuangdao

Baoding


Langfang

43∘N

42∘N

41∘N

40∘N

39∘N

38∘N

37∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E














43∘N

42∘N

41∘N

40∘N

39∘N

38∘N

37∘N114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0







3 Results




42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

lt02

gt07


05ndash0606ndash07

(a) OBS

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

03ndash0404ndash05

05ndash0606ndash07

(b) ERA

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

htHigh 63

Low 0

02ndash0303ndash04

04ndash0505ndash06

(c) NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

02ndash0303ndash04

(d) NDR






1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(a)

1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(b)

Year1960 1970 1980 1990 2000

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(c)













0 5 10 15 20 25 30 35

0

05

1


minus05

Tem

pera

ture

anom

aly

trend

s

y = 0013x + 019R = 031lowast

(∘C

deca

deminus1)












Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city






42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in





43∘N

42∘N

41∘N

40∘N

39∘N

38∘N

37∘N114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0







3 Results




42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

lt02

gt07


05ndash0606ndash07

(a) OBS

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

03ndash0404ndash05

05ndash0606ndash07

(b) ERA

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

htHigh 63

Low 0

02ndash0303ndash04

04ndash0505ndash06

(c) NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

02ndash0303ndash04

(d) NDR






1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(a)

1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(b)

Year1960 1970 1980 1990 2000

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(c)













0 5 10 15 20 25 30 35

0

05

1


minus05

Tem

pera

ture

anom

aly

trend

s

y = 0013x + 019R = 031lowast

(∘C

deca

deminus1)












Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city






42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

lt02

gt07


05ndash0606ndash07

(a) OBS

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

03ndash0404ndash05

05ndash0606ndash07

(b) ERA

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

htHigh 63

Low 0

02ndash0303ndash04

04ndash0505ndash06

(c) NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

02ndash0303ndash04

(d) NDR






1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(a)

1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(b)

Year1960 1970 1980 1990 2000

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(c)













0 5 10 15 20 25 30 35

0

05

1


minus05

Tem

pera

ture

anom

aly

trend

s

y = 0013x + 019R = 031lowast

(∘C

deca

deminus1)












Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city






42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(a)

1960 1970 1980 1990 2000Year

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(b)

Year1960 1970 1980 1990 2000

10

8

6

4

2

0

minus2

minus4

Man

n-Ke

ndal

l tes

t sta

tistic

s

(c)













0 5 10 15 20 25 30 35

0

05

1


minus05

Tem

pera

ture

anom

aly

trend

s

y = 0013x + 019R = 031lowast

(∘C

deca

deminus1)












Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city






42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


0 5 10 15 20 25 30 35

0

05

1


minus05

Tem

pera

ture

anom

aly

trend

s

y = 0013x + 019R = 031lowast

(∘C

deca

deminus1)












Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city






42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(a) Rural

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)

(b) Small city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus2

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(c) Median city

Tem

pera

ture

anom

aly

(∘C)

2

15

1

05

0

minus05

minus1

minus15

minus2

2

15

1

05

0

minus05

minus1

minus15

minus21979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

Year

OM

R (∘

C)


NDRNNRERA

(d) Large city






42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

Nig

httim

e lig

ht

High 63

Low 0

minus02ndash0


ltminus04

minus04ndashminus02

(a) OBS-ERA

Nig

httim

e lig

ht

High 63

Low 0

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E

minus02ndash0


ltminus04

minus04ndashminus02

(b) OBS-NNR

42∘N

41∘N

40∘N

39∘N

38∘N

114∘E 115∘E 116∘E 117∘E 118∘E 119∘E 120∘E


04ndash06

Nig

httim

e lig

ht

High 63

Low 0

gt06

(c) OBS-NDR









Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in




Large city station









Medium city station








Small city station








Rural station












Acknowledgments



References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


References

























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in























Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

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