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Influence of Spatially Variable Instrument GEOPHYSICALRESEARCH LETTERS, VOL. 18, NO. 12, PAGES 2249-2251, DECEMBER 1991 INFLUENCEOF SPATIALLYVARIABLE INSTRUMENT NETWORKS ON CLIMATICAVERAGES C. J. Willmort and S. M. Robeson Centerfor Climatic Research, Department ofGeography, University ofDelaware J. J. Feddema DepartmentofGeography, University ofCalifornia, Los Angeles Abstract.Instrument networks for measuringsurface air temperature(T) andprecipitation (P)have varied consid- erablyover the last century. Inadequate observing-station locationshave produced incomplete, uneven, and biased samplesof the spatialvariability in climateand, in turn, terrestrialand globalscale averages of T and P havebeen biased. New high-resolutionclimatologies [Legates and Willmort,1990a; 1990b] are intensively sampled and inte- gratedto illustratethe effects of thesenontrivial sampling biases.Since station networks may not representspatial climaticvariability adequately, their ability to represent climatethrough time is suspect. Introduction Increasingdependence of the worldeconomy on climate- influencedrenewable resources (e.g., crops) has heightened our interestsin climate,climatic 'variability and climatic forecasting.In recentyea•s, general circulation or global climatemodels (GCMs) have simulated dramatic climatic changescenarios, intensifying scientific interest as well as economicand socialconcerns among the public and gov- ernment.Efforts to verifyor refuteGCM prognostications haveprovided new impetusfor climatologiststo exarnine the instrumentalrecord for evidenceof climatic change. Within this paper, we focuson how well the instrumen- tal recordrepresents areally averaged climate and climatic variability. While a number of climatic variablesare mea- suredroutinely, shelter-height air temperatureand precip- itation are examinedsince they havebeen the mostexten- sivelysampled in both spaceand time. How well temper- ature and precipitationstation networks represent spatial variability is of particular concernbecause ill-conditioned networkscan and have injected bias into spatial averages of climate. Fig. 1. Spatialdistributions of air temperaturestations From a climatologicalstandpoint, the instrumentalrec- in the NCAR surfacestation climatologyfor three se- ord is both incompleteand uneven. It is incompletein lectedyears: (a) 1886,(b) 1920,and (c) 1970.Equal area that there are few variables for which there is sufficient Molleweideprojection allows comparison of stationdensi- large-scalespatial and temporalcoverage for us to make ties betweenregions. confidentstatements about climatic variability. It is un- evenin severalrespects. Spatial and temporalvariability, for example,are better resolved for the recentpast and for or preciselythe instrumentalrecord represents climate at certainregions, such as North America,Europe, and In- the measurement(station) locations, the mostcommonly dia(Figure 1). Overviewsof thehistorical (instrumental) availablevariables, and the temporalvariability that can record[Ellsaesser et al., 1986]usually summarize the avail- be detectedin the instrumentalrecord. Documentederrors able instrumental data as well as what cax•be inferred from that affectclimatic data include: changes or discrepancies those data. Such reviews have focusedon how accurately in observingpractices [Mitchell, 1953; Karl eta!., 1986; Schaaland Dale, 1977],movement and siting of stations Copyright1991 by the AmericanGeophysical Union. [Mitchell,1953; Quinlan eta!., 1987],changes in thelocal environment[Mitchell, 1953; Karl andJones, 1989; Kukla Papernumber 91GL02844 et al., 1986],and problems in the waythat time-averages 0094-8534/91/91GL-02844503.00 are constructedat the stationlocations [Mitchell, 1953; 2249 2250 Willmort et al.: SpatiallyVariable Networks and Climatic Edwards,1987]. Whether the availablestation networks trial meanas that obtainedfrom the entirehigh-resolution canfaithfully represent areas (particularly large areas) has climatology;however, marked discrepancies occur. Early beenincompletely examined. Tacit neglectof spatialrep- in the record(1880s to 1920s),when less than 500 NCAR resentativenessproblems by climatologistsis illustratedby stationsare available,estimates of terrestriallyaveraged the paucityof maps showingpertinent station networks, temperatureare highly variableand differ from the 'true' stationdensities, or spatialbiases [Ellsaesser et al., 1986]. value(the horizontaldashed line in Figure2) by as much as0.6 øC.Later in the record,the estimatesappear to ap- TerrestrialAir Temperature proachan equilibriumvalue that is about0.2 øC higher than the 'true' average. Of all climatic elements,mean air temperatureis the Moststudies of globaltemperature change do not explic- most commonlyused to characterizethe overall climate. itly addressthe problemsof unevenspatial sampling. How- Althoughmuch attention has been paid to errorsat in- ever,in a studyNorthern Hemisphere temperature change, dividualtemperature stations, perhaps even more uncer- usingan augmentedNCAR data set, an attempt to esti- tainty lies in the uneven distribution of stations and how mate the effectsof incompletespatial coverage was made this distributionhas changedthrough time. Data con- [Joneset al., 1986]. Insteadof varyingthe stationloca- tainedin the NCAR surfacestation climatology [Spangler tions and keepingthe underlyingfield fixed (as we have andJenne, 1988] commonly form the coreof globaltemper- done), an examinationof the differencebetween a fixed aturechange research [Jones et al., 1986;Hansen and Lebe- grid and a time-variablegrid was made, while the station deft, 1987]. While the NCAR archivewas createdso that locationsand temperature data alsochanged through time. spatial coveragewas as uniform as possible,dearly both Temperaturedeviations from stationaverages, rather than the numberand distributionof stationschange through- temperatureitself, also were used. Although thesedif- out the periodof record(Figures 1 and 2). ferencesmake direct comparison with our resultsdifficult, To examine the effects of uneven station distributions, largevariability (> 0.5 øC) occursearly in the time series the yearly NCAR. temperature station networksare eval- of Joneset al. [1986]as well. uatedusing a high-resolution(17,986 terrestrial stations) clima]tology[Legates and Willmort, 1990a]. The ability TerrestrialPrecipitation of each yearly N CAR temperature network to represent terrestrial-scalevariability is evaluatedby samplingfrom the high-resolutionfield at the NCAR stationlocations, in- Precipitationis probablythe mostextensively sampled terpolatingto a regular grid, and numericallyintegrating climatevariable. The numberof raingagesworldwide, to obtaina terrestrialaverage (Figure 2). The interpola- for instance,likely exceeds 120,000 [Mintz, 1981]while tionsare made using a spherically-based,weighted-average monthlyaverages are availablefor well over 24,000ter- algorithm[Willmort et al., 1985]that is reliable[Bussi&es restrialstations [Legates and Willmort, 1990b]. In spiteof and Hogg,1989]. Ideally, temperatures sampled at NCAR numerousobservations, station location bias still produces station locationsshould produce nearly the same terres- markedover- or underestimatesofregional (and ultimately global)averages of precipitation.As with air temperature stations,precipitation gages tend to be locatednear human 15.1 , -2500 settlements,with highconcentrations of gages in Europe, NorthAmerica, India, and Japan. Few gages are located o / 2000 in the desertsor mountainregions. • 14.914.7 o i co Re-samplingagain is usedto illustratethe effectsof un- (13 o •_ o o . • evenstation networks on large-scale spattally averaged pre- :2 o • v \ l 1500 --• 14.5 ' cipitation.A high-resolutionannual mean precipitation field[Legates and Willmort, 1990b] is uaed to representthe • 1•.3 • 1000 'true'spatial variability in precipitation.Numerical (spa- tial)integration ofthe annual average field can be thought • 14.1 ........................ b%• ....................... of asthe 'real'globally averaged rate of precipitation.As • ....• 500 • 13.9 o __•....... before,the ability of eachyearly NCAR raingage network ..•-.-'- to representterrestrial average precipitation can be exam- 13.7 , ,,, , , , , .,........ , inedby sampling from the •true' field at thegage locations, interpolatingto a regulargrid [Willmort et al•, 1985]and Ye• thennumerically integrating to obtainestimates of spa- Fig. 2. Sevenpoint Gaussianfilter (solidline) applied tiallyaveraged precipitation. Yearly NCAR networks again to annualaverage terrestrial (excluding Antarctica and are of interestbecause they haveformed the basesof sew Green]and)air temperatureestimates (circles) computed eralstudies of temporalvariability in .spa•tiallyaveraged kom of precipitationstatistics [Bradley et al., 1987;Diaz et al., high-resolutionclimatology [Legates and Willmort, 1990a]. 1989]. Samplepoints were the yearly stationlocations in th• Raingagenetworks within the NCAR archive are highly NCARsurface station climatology. Total number of NCAR variablefrom year to year(Figure 3). Sparsestation net- [emperatures•a•ions also is given (dotted line). A bestes- works,particularly in the late 1800'sto the !ate 1920's, timateof the%rue' value of •errestrial average air temper- producesizable overestimates ofterrestrially averaged pre- amre(using allof the stations inthe climatology) isshown cipitation (•sa,). Losses of importantstations in Oceania as •he horizontal dashed line. duringWorld War II leadto underestimationof Pr in the Willmortet al.' SpaticilyVariable
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