VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

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1 CLIMATE RESEARCH Vl 27: , 2004 Published Octber 7 Clim Res VEMAP Phase 2 biclimatic database I Gridded histrical (20th century) climate fr mdeling ecsystem dynamics acrss the cnterminus USA T G F Kittel 1, 2, 5, *, N A Rsenblm 1, J A Ryle 1, 6, C Daly 3, W P Gibsn 3, H H Fisher 1, P Thrntn 1, D N Yates 1, S Aulenbach 1, C Kaufman 1, R McKewn 2, D Bachelet 4, D S Schimel 1, VEMAP2 Participants 1 Natinal Center fr Atmspheric Research, PO Bx 3000, Bulder, Clrad 80307, USA 2 Natural Resurce Eclgy Labratry, Clrad State University, Frt Cllins, Clrad 80523, USA 3 Spatial Climate Analysis Service and 4 Department f Biengineering, Oregn State University, Crvallis, Oregn 97331, USA Present addresses: 5 INSTAAR, Campus Bx 450, University f Clrad, Bulder, Clrad , USA 6 USDI Fish and Wildlife Service, Laurel, Maryland 20708, USA R Neilsn, J Lenihan, R Drapek (USDA Frest Service, Pacific Nrthwest Research Statin, Crvallis, OR, USA); D S Ojima, W J Partn (Natural Resurce Eclgy Labratry, Clrad State University, Frt Cllins, CO, USA); J M Melill, D W Kicklighter, H Tian (Ecsystems Center, Marine Bilgical Labratry, Wds Hle, MA, USA); A D McGuire (US Gelgical Survey, Alaska Cperative Fish and Wildlife Research Unit, Fairbanks, AK, USA); M T Sykes, B Smith, S Cwling, T Hickler (Physical Gegraphy & Ecsystems Analysis, Lund University, Sweden); I C Prentice (Max Planck Institute fr Bigechemistry, Jena, Germany); S Running (University f Mntana, Missula, MT, USA); K A Hibbard (Cllege f Frestry, Oregn State University, Crvallis, OR, USA); W M Pst, A W King (Oak Ridge Natinal Labratry, Oak Ridge, TN, USA); T Smith, B Rizz (Dept f Envirnmental Sciences, University f Virginia, Charlttesville, VA, USA); F I Wdward (Dept f Animal and Plant Sciences, University f Sheffield, UK) ABSTRACT: Analysis and simulatin f bispheric respnses t histrical frcing require surface climate data that capture thse aspects f climate that cntrl eclgical prcesses, including key spatial gradients and mdes f tempral variability We develped a multivariate, gridded histrical climate dataset fr the cnterminus USA as a cmmn input database fr the Vegetatin/Ecsystem Mdeling and Analysis Prject (VEMAP), a bigechemical and dynamic vegetatin mdel intercmparisn The dataset cvers the perid n a 05 latitude/lngitude grid Climate is represented at bth mnthly and daily timesteps Variables are: precipitatin, mininimum and maximum temperature, ttal incident slar radiatin, daylight-perid irradiance, vapr pressure, and daylight-perid relative humidity The dataset was derived frm US Histrical Climate Netwrk (HCN), cperative netwrk, and snwpack telemetry (SNOTEL) mnthly precipitatin and mean minimum and maximum temperature statin data We emplyed techniques that rely n gestatistical and physical relatinships t create the temprally and spatially cmplete dataset We develped a lcal kriging predictin mdel t infill discntinuus and limited-length statin recrds based n spatial autcrrelatin structure f climate anmalies A spatial interplatin mdel (PRISM) that accunts fr physigraphic cntrls was used t grid the infilled mnthly statin data We implemented a stchastic weather generatr (mdified WGEN) t disaggregate the gridded mnthly series t dailies Radiatin and humidity variables were estimated frm the dailies using a physically-based empirical surface climate mdel (MTCLIM3) Derived datasets include a 100 yr mdel spin-up climate and a histrical Palmer Drught Severity Index (PDSI) dataset The VEMAP dataset exhibits statistically significant trends in temperature, precipitatin, slar radiatin, vapr pressure, and PDSI fr US Natinal Assessment regins The histrical climate and cmpanin datasets are available nline at data archive centers KEY WORDS: Climate dataset Climate variability Climate change Ecsystem dynamics Vegetatin Eclgical mdeling VEMAP Gestatistics Palmer Drught Severity Index PDSI Resale r republicatin nt permitted withut written cnsent f the publisher * kittel@clradedu Inter-Research 2004 wwwint-rescm

2 152 Clim Res 27: , INTRODUCTION 11 Objectives Mdeling bispheric respnses t histrical climate change and variability ver the 20th century is crucial fr validatin f bigechemistry and vegetatin mdels against lng-term bservatins and fr understanding ptential respnses f ecsystems t future climate change Such simulatins require input f spatially and temprally cmplete, multivariate surface climate data that match this purpse (Cramer & Fischer 1996) These data need t be at a spatial reslutin that adequately captures key climatic gradients and f a lng enugh recrd t reflect imprtant mdes f tempral variatin Hwever, even fr the cnterminus USA ne f the best instrumented regins f the glbe such data are difficult t btain Fr example, temperature and precipitatin statin densities in the western USA prir t 1940 are insufficient fr the interplatin f bservatins with cnfidence while accunting fr physigraphic effects such as large lakes and hetergeneus tpgraphy Slar radiatin and humidity bservatins are even mre scattered, largely limited t airprt sites with a bias t valley lcatins in muntainus regins As a result, sphisticated techniques must be emplyed t cnstruct extended time series f all variables at a sufficient spatial reslutin thrughut the dmain, in a manner that (1) is cnsistent with physigraphy and vegetatin (t the extent that vegetatin is an expressin f climate) and (2) maintains physical relatinships amng climate variables Our bjective was t create a histrical biclimate input dataset fr the USA fr simulatin f time-dependent bigechemical and bigegraphical dynamics This effrt was part f an eclgical mdel intercmparisn study, the Vegetatin/Ecsystem Mdeling and Analysis Prject (VEMAP), Phase 2 The verall requirement fr the dataset was that it faithfully represents the biclimate, ie thse aspects f climate that cntrl eclgical prcesses This gal is shared by ther gridded histrical climate datasets fr reginal t glbal dmains, including Cter et al (2000), Thrntn et al (1997), and New et al (2000) Specific requirements fr the VEMAP2 dataset were that it be: (1) Spatially and temprally cmplete, with wallt-wall cverage spanning as much f the histrical perid as can be supprted by the instrumental recrd; (2) temprally realistic, with accurate representatin f climate variability at daily thrugh decadal scales; (3) spatially realistic, reflecting tpgraphic and ther gegraphic cntrls ver climate, and reslved sufficiently t capture key reginal climate gradients and spatial patterns f tempral variability; (4) physically cnsistent acrss variables, in particular at the daily timestep; (5) multivariate, cnsisting f variables recgnized as cntrlling eclgical prcesses and cmmnly used as inputs t eclgical mdels; (6) reslved at mnthly and daily timesteps t match mdel input requirements, with the same climate represented at bth scales 12 VEMAP and its cmmn input datasets VEMAP was a multi-institutinal, internatinal effrt addressing the respnse f ecsystem bigegraphy and bigechemistry t variability in climate and ther drivers in bth space and time dmains Phase 1 (VEMAP1) cmpared a suite f bigechemistry and bigegraphy mdels in their cntrls and equilibrium respnse t changing climate and elevated atmspheric carbn dixide levels acrss the cnterminus USA (VEMAP 1995, Schimel et al 1997, Pan et al 1998, Yates et al 2000) Cnstructin f a cmmn input dataset, the VEMAP1 database, assured that differences in the mdel intercmparisn arse nly frm differences amng mdel algrithms and their implementatin, rather than frm differences in inputs (Kittel et al 1995, 1996) The VEMAP1 database cnsists f lng-term climatlgy (bth mnthly means and a characteristic daily climate), equilibrium climate change scenaris, sil prperties, and ptential natural vegetatin Phase 2 (VEMAP2) evaluated time-dependent respnses f a set f bigechemical mdels and dynamic glbal vegetatin mdels (DGVMs) t histrical and prjected transient climate and atmspheric CO 2 frcings (Schimel et al 2000, Grdn et al 2004) The bigechemistry mdels were TEM (Terrestrial Ecsystem Mdel; Tian et al 1999), Bime-BGC (Bime-Bigechemical Cycles Mdel; Thrntn et al 2002), Century (Partn et al 1994), and GTEC (Glbal Terrestrial Ecsystem Carbn Mdel; Pst et al 1997), while the DGVMs were MC1 (MAPSS [Mapped Atmsphere-Plant-Sil System]-Century Cupled Mdel, Versin 1; Daly et al 2000) and LPJ (Lund- Ptsdam-Jena Mdel; Sitch et al 2003) As in VEMAP1, a cmmn database prvided bth (1) a level playing field fr intercmparisn f mdels and (2) a faithful representatin f the dmain s biclimate, permitting evaluatin f histrical simulatins against bserved eclgical data these features were critical t achieving VEMAP2 gals In this paper, we describe the develpment f the histrical climate dataset fr the cnterminus USA as a cmpnent f the VEMAP2 mdel input database We als develped a cmpanin set f future (21st century) transient climate change scenaris fr the same dmain derived frm cupled atmsphere-cean

3 Kittel et al: VEMAP2 histrical climate dataset fr the USA 153 general circulatin mdel (AOGCM) experiments (Kittel et al 2000) We designed bth the histrical climate and transient climate change scenari sets t meet input requirements fr this class f reginal-glbal eclgical mdels and as required by the experimental design f the VEMAP2 mdel intercmparisn In the fllwing sectins, we present the verall design f the dataset (Sectin 2), techniques used t meet this design (Sectin 3), and derived datasets histrical Palmer Drught Severity Index (PDSI) dataset and a mdel spin-up climate (Sectin 4) In Sectin 5, we describe spatial and tempral prperties f the resulting dataset We prvide infrmatin regarding nline access t the data and analysis and visualizatin tls (Sectin 6) Finally, we summarize key limitatins and attributes f the dataset (Sectin 7) 2 DATASET OVERVIEW 21 Spatial and tempral cverage The histrical climate dataset was develped fr the cnterminus USA The data are n a 05 latitude 05 lngitude grid (Fig 1), with cells bunded by 05 latitude and lngitude lines (as ppsed t cells centered n 05 intersectins) The grid is the same used fr VEMAP1 (Kittel et al 1995, Rsenblm & Kittel 1996) The dataset cvers a 99 yr perid frm A cmpanin histrical climate dataset was develped fr Alaska and adjacent prtins f Canada cvering ; this set is als available nline (Sectin 6) (Kittel et al 2002) A fllw-n prduct with updated, higher reslutin histrical mnthly temperature and precipitatin fr the cnterminus USA is presented in Gibsn et al (2002) and Daly et al (2004) 22 Timestep and variables Climate variables are given in 2 timesteps: daily values and mnthly means (r mnthly ttals fr precipitatin) The dataset includes 7 surface climate variables: (1) and (2) minimum and maximum surface air temperature, (3) precipitatin, (4) surface air vapr pressure, (5) surface air daylight-perid mean relative humidity, (6) ttal incident slar radiatin, and (7) daylight-perid mean irradiance We included 2 humidity variables and 2 slar radiatin variables because, while they represent the same climate infrmatin, different mdels require these inputs in different frms and their intercnversin requires calculatin at the daily level Near-surface wind speed data are als required fr sme ecsystem mdels (eg in MC1 initializatin) but are nly available in the VEMAP database as lng-term seasnal mean climatlgies (Kittel et al 1995; based n Ellitt et al 1986) While we did nt determine daily (and mnthly) wind speed fr this dataset, its estimatin frm ther daily variables has been explred by thers Fig 1 Gridded dmain fr the cnterminus US fr the Vegetatin/Ecsystem Mdeling and Analysis Prject, Phase 2 (VEMAP2) database Backgrund shws tpgraphy (m abve sea level)

4 154 Clim Res 27: , 2004 (eg Hansn & Jhnsn 1998, Parlange & Katz 2000; see Sectin 33) 23 Apprach T create the histrical gridded dataset, ur apprach was t: Step 1 Cmbine histrical mnthly and daily temperature and precipitatin statin datasets t create a unified set with the highest statin density and mst cmplete recrds that were readily achievable (described in Sectin 31) Step 2 Create temprally cmplete mnthly minimum and maximum temperature and precipitatin statin recrds by mdeling gestatistical relatinships amng statins with missing data and neighbring statins (Sectin 322) Step 3 Spatially interplate these temprally cmplete mnthly statin recrds t the 05 grid accunting fr physical relatinships between climate and physigraphy (Sectin 323) Step 4 Disaggregate mnthly climate series t generate daily minimum and maximum temperature and precipitatin using a stchastic weather generatr (Sectin 33) Step 5 Empirically estimate daily (and mnthly) slar radiatin and humidity variables frm daily temperature and precipitatin series, while maintaining physical relatinships amng these variables (Sectin 34) 3 DATASET DEVELOPMENT 31 Step 1 Temperature and precipitatin data Fr the cnterminus USA, we btained mnthly mean minimum and maximum temperature recrds fr 5476 statins and mnthly precipitatin recrds fr 8514 statins (Fig 2a) These data were cmpiled frm the Natinal Climatic Data Center (NCDC) Histrical Climate Netwrk (HCN) database (Easterling et al 1996; Fig 2b), ther primary and cperative netwrk datasets (NCDC undated a, 1994a,b), and Natural Resurces Cnservatin Service (NRCS) SNOTEL (snwpack telemetry) data (NRCS 1996; Fig 2c) While HCN data prvided the lngest and mst hmgeneus recrds, inclusin f additinal data surces allwed interplatin f spatial and tempral climate patterns ver regins with high spatial hetergeneity in factrs cntrlling climate In particular, inclusin f SNOTEL sites significantly imprved statin density in muntainus regins f the western USA (Fig 2c) We als used daily minimum and maximum temperature and precipitatin data frm 526 statins t parameterize the stchastic daily weather generatr (Step 4, Sectin 33) Surces fr daily data included HCN and ther first rder and cperative netwrk daily datasets (NCDC undated b,c, Easterling et al 1999) We used data starting in 1895 (prir t this, precipitatin statins numbered <600, ie average densities < 02 statins per grid cell) and thrugh 1993 (last cmplete year in the HCN set available at the time the set was surveyed) Data quality checks included tests (1) that temperature minimum values did nt exceed crrespnding maxima, (2) fr nnsense precipitatin values (eg less than zer), and (3) fr nnsense metadata (such as bvius errrs in latitude, lngitude, and elevatin, eg frm duble cnversin f feet t meters) The HCN precipitatin and temperature dataset cnsists f statins selected t have lng-term, relatively cmplete recrds which were adjusted fr timef-bservatin differences, instrument changes and mves, statin relcatins, and urbanizatin effects (Easterling et al 1996) HCN prcessing als included infilling f missing data based n neighbring statins The high level f quality checking and attentin t statin histries in mnthly HCN data, alng with extensive cverage acrss the dmain in the earlier part f the recrd, prvided a strng basis fr reliance n this set fr spatial interplatin f mnthly reginal temperature and precipitatin anmalies (Step 2, Sectin 322) This was key fr creating a gridded histrical dataset extending back t the end f the 19th century Hwever, there are imprtant recrd inhmgeneities and ther time-dependent biases that are neither accunted fr in the ther data surces, nr cmpletely in the HCN In additin, lcal anmaly patterns are likely t be less well represented in the earlier part f the recrd because f the lwer density f HCN and ther lng-term statins These limitatins must be kept in mind when evaluating and using the VEMAP2 gridded histrical climate dataset The primary purpse f the dataset is t prvide the best representatin f climate patterns in space and time fr simulating eclgical prcesses T this end, the gal f data cmpleteness in space and time was weighted mre heavily than an alternate bjective f including nly the highest quality and lngest term statin recrds such as wuld be needed fr a rigrus assessment f climate trends and variability 32 Steps 2 and 3 Spatial interplatin 321 Apprach As intrduced, a key issue in the develpment f gridded time series f climate data is spatial interplatin f statin data in physigraphically hetergeneus

5 Kittel et al: VEMAP2 histrical climate dataset fr the USA 155 terrain This is especially difficult in the early part f the histrical recrd when the density f statins with lng-term recrds is lw (Fig 2b, 3) T slve this prblem, we chse t separate interplatin f existing statin data t the grid int 2 spatial statistical mdeling prcesses: a climate anmaly cmpnent and a physigraphically frced cmpnent In the first prcess, we assumed that climate variability is dminated by reginal frcing and that this frcing can be represented by anmaly patterns The use f anmalies remved the mean field, which is strngly determined by physigraphic factrs, revealing spatially cherent climate variability patterns We mdeled this spatial autcrrelatin structure with gestatistical techniques The resulting spatial predictin mdel was used t interplate climate anmalies t lcatins where statin data were missing (Step 2, Sectin 322) The result f this prcess was recnstructin f statin recrds that were discntinuus r limited in length, creating cmplete recrds fr the perid The secnd spatial prcess was interplatin f these recnstructed statin recrds t the grid using a mdel accunting fr physigraphic effects (Step 3, Sectin 323) We emplyed a knwledgebased system that uses ur understanding f hw tpgraphy and ther physigraphic factrs influence the spatial distributin f temperature and precipitatin The utput f this prcess was an time series f 05 gridded mnthly temperature and precipitatin a b c 322 Step 2 Statistical recnstructin f statin recrds kriging predictin mdel Gestatistical mdel T create cmplete precipitatin and temperature statin recrds, we used a lcal (mving windw) kriging predictin methd fllwing Haas (1990, 1995) T impute mnthly anmalies wherever a statin value was missing, this methd takes advantage f the bservatin that temperature and precipitatin anmalies tend t be reginal in scpe In the mving windw apprach, we first mdeled reginal spatial autcrrelatin structure n the rder f 1000 km frm the site Fig 2 (a) Precipitatin statins used t develp the histrical precipitatin dataset, (b) lng-term precipitatin statins, primarily Histrical Climate Netwrk (HCN) statins, (c) snwpack telemetry (SNOTEL) statins

6 156 Clim Res 27: , 2004 Fig 3 Number f precipitatin statins with data as a functin f year Drp after 1990 reflects the end f mnthly time series data in NCDC (1994a); data after that were frm the HCN dataset (Easterling et al 1996) t be predicted using 200 neighbring statins (Fig 4); actual windw size varied based n statin density The use f a lcal, mving windw assumed that spatial structure was lcally unifrm, but allwed fr the spatial crrelatin functin t vary by regin (eg Fig 4b vs d) The crrelgrams were used t build the kriging predictin mdel We applied the kriging mdel t estimate a missing value using the 10 clsest sites with data available at the time pint t be predicted Using a limited number f predictr sites was apprpriate bth cmputatinally, because it greatly reduced the size f the system f linear equatins that must be slved fr each predictin, and theretically, because it kept the predictin area clse t the predicted site when pssible A thinplate spline predictin mdel was als tested; its crssvalidatin predictin errrs were higher than fr the lcal kriging mdel Latitude Predictin Neighbrhd Pairwise Crrelatins (a) (b) Crrelatin Latitude 25 (c) Lngitude Crrelatin 00 (d) Distance (km) Fig 4 Spatial autcrrelatin structure in mnthly precipitatin anmalies fr (a, b) a site in the Midwest where autcrrelatin was strng, (c, d) a site in suthern Califrnia where it was weak (a, c) Maps shw a site and its neighbrhd ( lcal windw ) f 20 statins used t develp site crrelgrams shwn in (b, d) Site crrelgrams shw crrelatins amng all statin pairs as a functin f distance (dts) and best fit line with an expnential mdel In prductin runs, the kriging predictin mdel used 200 statins t mdel the crrelatin functin (Sectin 322)

7 Kittel et al: VEMAP2 histrical climate dataset fr the USA 157 Methd details HCN statin data flagged as infilled (primarily in the temperature dataset) were first deleted s that a cnsistent prcess wuld be used thrughut fr infilling missing values (time f bservatin, statin change, and ther adjustments were retained) Mnthly minimum and maximum temperatures were cnverted t mnthly mean temperature and mean diurnal temperature range t create 2 variables that were rughly independent (Ryle 2000) This allwed us t avid analyzing 2 largely redundant fields and t ensure instead that the analysis wuld emphasize imprtant differences between them Precipitatin data were square-rt transfrmed t nrmalize their distributin and make site variances mre hmgeneus Mnthly anmalies fr cnverted temperature variables and transfrmed precipitatin were calculated relative t crrespnding lng-term mnthly means Univariate lcal kriging was used fr precipitatin and bivariate methdlgy (c-kriging; Cressie 1993a) was implemented fr mean temperature and temperature range anmalies The predictin neighbrhd f 10 nearest statins was selected at each timestep Larger neighbrhds (up t 25 statins) were cnsidered, but with little increase in precisin The entire perid f verlap in data (frm all mnths, all years) amng selected statins and the site f predictin was used t cnstruct a kriging mdel fr a given missing mnth Spatial cvariance structure was assumed t be istrpic within a windw, ie similar in structure in all directins in space Unlike Haas (1995), we did nt allw spatial functins t vary with time, ie by seasn r year Hwever, kriging was based n crrelgrams, with spatial cvariances standardized by statin mnthly variances, which reduced seasnal dependence in spatial structure Istrpy and interannual statinarity were imprtant assumptins that made the mdel numerically mre tractable and better supprted by the amunt f data available, but which did nt accunt fr ptentially imprtant spati-tempral features f climate variability These features include anistrpy within windws arising frm nn-hmgeneus frcing f climatic variability by terrain (Diaz & Bradley 1997, Kittel et al 2002) and decadal cntinental circulatin changes that alter reginal relatinships in climate anmaly patterns (Trenberth & Hurrell 1994) Mdel evaluatin The kriging prcess prduced realistic full-perid time series fr statins with partial recrds (Fig 5), with the recnstructed recrds CM YR-1 CM YR-1 CM YR a b c having interannual variability patterns cnsistent with neighbring statins Predictin errr was evaluated using crss-validatin, where the mdel predicted statin data that had been withheld frm the analysis In a crss-validatin analysis using 100 sites, predicted values clsely estimated bserved values with crrelatin cefficients ranging frm 072 t 096 (Fig 6a) Sites in the Central Lwlands and Pacific castal states tended t have strnger crrelatins (r > 090), while areas in the western muntains and Great Lakes states tended t have the lwest crrelatins (r 080) This pattern indicates that generally lwer errrs ccurred in regins with relatively lw climatic hetergeneity and/r high statin densities, as wuld be expected Magnitude f errrs was generally small relative t interannual variability Quantile analysis f precipitatin crss-validatin errrs shwed a tendency fr underpredictin f the highest values and verpredictin f lwest values, s that there was a slight reductin in variance in predicted time series Crss-validatin errrs fr temperature and precipitatin were higher in the early vs latter part f the recrd due t lwer statin densities (Fig 6b) On average, precipitatin crss-validatin errrs rughly dubled ging frm recent decades back t the early part f the recrd (mean squared errr, MSE = 13 t 25 mm m 1 ; Fig 6b) This dubling crrespnded t an rder f magnitude decrease in statin numbers acrss the dmain (~7000 t ~800 statins, with crrespnding change in average densities f 21 t 02 statins per grid cell; Fig 3) This prprtinally lw respnse f errrs t statin density suggests that the netwrk f lng-term, primarily HCN, precipitatin statins captured majr variability patterns acrss the cnterminus USA Interannual variability was artificially reduced during the first part f recnstructed recrds fr areas DES MOINES, IA CHARLOTTE, NC BOZEMAN, MT RECONSTRUCTED STATION PRECIPITATION RECORDS Fig 5 Precipitatin (cm yr 1 ) recrds fr statins in different climatic regins, as in-filled with the kriging predictin mdel: (a) Des Mines, Iwa; (b) Charltte, Nrth Carlina; (c) Bzeman, Mntana Hrizntal line thrugh the time series is the 40 yr ( ) statin mean, including recnstructed data Hrizntal bar in upper right spans the perid when bserved data were available

8 158 Clim Res 27: , 2004 a CROSS-VALIDATION Crrelatin Between Observed and Predicted 45 Latitude b MSE (mm m 1 ) Lngitude 1912 CROSS-VALIDATION ERROR YEAR Fig 6 Kriging mdel crssvalidatin (a) Crrelatin cefficients between bserved and predicted precipitatin fr 100 crss-validatin sites (b) Time dependence in crssvalidatin errrs fr precipitatin, expressed as mean squared errr (MSE) fr square-rt transfrmed precipitatin Dts are mnthly MSE averaged acrss 100 crss-validatin sites (units = mm m 1 ) Dtted line is a smthing spline fit f MSE values where statin densities were lw early n, as in the muntain and intermuntain west (eg Fig 5c) This was nt an issue in regins where statins densities were high fr mst f the recrd (eg Fig 5a,b) This effect is cnceptually cnsistent with limitatins f the kriging mdel we implemented and is a cnsequence f the mdel generating verly smthed spatial fields when there are t few statin bservatins t capture reginal structure in variability patterns (Cressie 1993b) As statin density is reduced, the mdel reaches further away frm a site t find predictr statins and blends unrelated anmaly patterns frm adjacent regins t predict missing statin data Prly related anmalies tend t cancel each ther when they are blended, diminishing verall variance in recnstructed time series This variance reductin is an artifact f the mdel, as evident frm the bservatin that such shifts generally cincide with a dramatic change in statin density (Fig 3) and are neither seen in lng-term bserved statin recrds, nr in recnstructed recrds where densities remain high Fllw-n analyses demnstrated that ensemble predictins generated stchastically using mdelestimated errrs can adequately generate this missing variance (Fuentes et al 2004) 323 Step 3 Spatial interplatin with physigraphic adjustment PRISM We used PRISM (Parameter-Elevatin Regressin n Independent Slpes Mdel; Daly et al 1994, 2001, 2002) t spatially interplate temprally cmplete statin recrds frm Step 2 t the 05 grid PRISM is an interplatin system that uses a spatial-climate knwledge base t parameterize and cnfigure a weighted climate-elevatin regressin functin The weighted regressin functin is applied t each grid cell in a mving windw fashin; the size f this windw depends n terrain cmplexity and statin density At each grid cell, the mdel assigns weights t nearby statins, based n their perceived climatic similarity

9 Kittel et al: VEMAP2 histrical climate dataset fr the USA 159 t the cell Factrs accunted fr were: (1) Distance: statin regressin weight decreases with distance frm the grid cell (2) Elevatin: weight decreases with increasing elevatin difference (3) Tpgraphic facet: weight is greatest fr statins with similar slpe and aspect as the cell, as determined at several spatial scales (4) Orgraphic effectiveness f terrain: weight is greatest fr statins in terrain that has a similar ability t enhance precipitatin as estimated by steepness (5) Castal prximity: weight is greatest fr statins with similar distance and expsure t a large water bdy (eg lake, cean) (6) Atmspheric inversin: weight is greatest if a statin and the cell are either bth affected by messcale bundary layer prcesses (eg in a valley lcated belw the tp f an inversin), r bth mre strngly influenced by the free atmsphere (eg n ridgetps abve an inversin); this allws fr a different climate-elevatin regressin within each layer (fr bth temperature and precipitatin) if an inversin is supprted by the data (Daly & Jhnsn 1999) (7) Clustering f statins: highly clustered statins are assigned lwer weights t minimize spatial ver-representatin PRISM was applied independently fr each mnth f the 99 yr recrd Statin values were quality checked befre prcessing PRISM prcessing was dne n a fine reslutin 25 (~4 km) grid; these values were aggregated t the VEMAP 05 grid using a mdified Gaussian filter (after Barnes 1964) Pst-prcessing checks included visual inspectin f mnthly gridded maps fr extreme utliers 33 Step 4 Stchastic generatin f daily temperature and precipitatin WGEN We used a daily weather generatr, a mdified versin f WGEN (Richardsn 1981, Katz 1996, Mearns et al 1996, Parlange & Katz 2000), t statistically disaggregate mnthly temperature and precipitatin values t a daily time series fr the perid WGEN uses a first-rder Markv chain mdel t predict ccurrence f precipitatin (wet vs dry days) based n whether the previus day was wet r nt; this allws fr the persistence f wet and dry days WGEN then stchastically predicts precipitatin event size (assuming a gamma distributin) and daily minimum and maximum temperatures Temperature predictin is based n daily temperature means and variances being cnditinal n whether a day is wet vs dry This permitted reduced diurnal temperature range n wet days, maintaining physical relatinships between daily precipitatin and temperature In this mdel versin, all parameters were allwed t vary by lcatin, determined frm statin data (Katz 1996) We did nt use the mdified WGEN s capabilities fr stchastic simulatin f daily slar radiatin, humidity, and near-surface wind speed because f the inadequate number f parameterizatin statins with daily bservatins f these variables, especially in muntain regins (Parlange & Katz 2000) We further mdified WGEN t include separate parameterizatins fr wet vs dry years Such cnditinal parameterizatin better represents daily precipitatin variance structure, because precipitatin event statistics shift under drught vs wet-perid cnditins (Wilks 1989) Parameterizatin f WGEN was based n daily recrds frm 526 HCN and cperative netwrk statins (Sectin 31) fr the perid This parameterizatin perid was selected based n data availability and t span several drught and wet perids in the 20th century fr mst regins Additinal selectin criteria and quality checks n these data were: (1) Statin recrds had t exist fr at least 90% (54 yr) f the 60 yr perid and had t have at least 5 ut f the first 10 yr f the perid (this was t ensure that the 1930s drught perid in the western and central USA was represented) (2) We allwed up t 3 d m 1 f missing precipitatin data and 5 d m 1 f missing temperature data fr a mnth t be cnsidered cmplete (3) All 3 variables (precipitatin and maximum and minimum temperature) had t be present in a statin recrd (4) If recrded precipitatin amunts were accumulated ver >24 h, then the bservatin was eliminated; trace amunts were treated as zer We ran WGEN fr each grid cell fr the 99 yr recrd; mdel utputs are illustrated in Fig 7a Parameterizatins were assigned t cells based n nearest daily statin t cell centers; a given parameterizatin was applied n average t 6 cells WGEN simulatins were cnstrained by gridded mnthly values f temperature and precipitatin (frm Step 3) Because this cnstraint was nt strictly met within WGEN (due t the stchastic nature f the mdel), we frced a match between daily and mnthly climates Fr each cell, we created ensembles f daily series fr each year in the recrd Frm these we selected mnthly daily series whse mnthly statistics best matched the gridded mnthly values Daily values f the selected mnths were nudged s that mnthly statistics f the daily series exactly matched the mnthly data WGEN was run separately fr each cell with n crdinatin in the generatin f series amng adjacent cells The lack f daily spatial cherence restricts the utility f the dataset t applicatin mdels, such as thse in VEMAP, with n cell-t-cell interactin n a daily timestep (see Sectin 71) Because the daily generatin prcess is stchastic, the timing f key events fr a given year (eg date f last and first frst) and date-

10 160 Clim Res 27: , 2004 a b Fig 7 (a) Generated daily climate frm WGEN shwing tempral autcrrelatin (eg persistence f wet and dry days) and crsscrrelatin structure f maximum and minimum temperature and precipitatin Output is fr a characteristic year fr the grid cell fr Missula, Mntana, frm the VEMAP1 dataset (Kittel et al 1995) and reflects behavir f the 99 yr VEMAP2 daily dataset (b) Cmparisn f daily frequency distributins fr statin bservatins vs crrespnding grid cell s WGEN-simulated recrd fr January precipitatin r minimum temperature, fr selected pint in TX TX: central Texas; MN: suthern Minnesta; CO: western Clrad; KS: nrthwestern Kansas Ntch plts shw median (center f ntch), SD (white area = 2 SD), interquartile range (IQR; dark bxes arund median, spanning 25 t 75% f values), data range (brackets), and utliers (bars beynd the brackets; defined as values >15 IQR) Statin ntch plts are fr daily bservatins frm a lng-term statin (usually a daily HCN site), and simulated plts are VEMAP2-generated dailies Precipitatin plt y-axes are in [ln(precipitatin)] space (units = mm m 1 ) The cmparisn cvered the full length f a statin s recrd (perid length in years is given at the base f each plt)

11 Kittel et al: VEMAP2 histrical climate dataset fr the USA 161 sensitive statistics (such as grwing-degree days) will nt precisely match thse in the bserved daily recrd We evaluated this prcess by cmparing the frequency distributins f bserved (statin) vs generated daily recrds fr 10 lcatins that represented different climate regimes acrss the dmain The generated series was created using parameterizatins and mnthly values frm the crrespnding grid cell Fr all sites, the bserved vs generated frequency distributins matched clsely (eg Fig 7b) Mst critically fr precipitatin, the frequency and magnitude f bth high and lw extreme events were well captured a recrd detail that might be expected t be lst in the simplificatin f daily prcesses in a statistical mdel (Fig 7b) The prcess f disaggregating mnthly recrds t generate daily series differed frm appraches used in ther datasets where bserved daily recrds were spatially interplated r assigned t a neighbrhd f grid cells (Thrntn et al 1997, Eischeid et al 2000, Cter et al 2000) These techniques have the advantage f prviding spatial cherence in daily series and f clsely reflecting the bserved daily recrd (s that date-sensitive histrical infrmatin is retained) Disaggregatin was mre apprpriate fr the VEMAP2 dataset because f the need t represent daily climate ver highly hetergeneus terrain early in the 20th century, when daily statin densities were lw We assumed that, under these cnditins, it was mre reasnable t spatially distribute dailystructure parameterizatins than actual daily series In methds that grid daily bservatins, incngruities arise when statin densities are very lw, because cell recrds will be assigned r interplated frm statins at a great distance frm the cell The likely result is that a cell s daily series may reflect that f a markedly different climate regime, with, fr example, different daily frequency and autcrrelatin structure and/r a different seasnal pattern f temperature and precipitatin On the ther hand, in the apprach f assigning parameterizatins t cells as used here, a cell s generated daily series was based n a parameterizatin thrughut the recrd frm the same nearby statin which was likely t clsely apprximate the apprpriate daily climate regime fr that cell This was pssible because parameterizatins were based n a lng (60 yr) prtin f the recrd when daily statin densities were greater than in the early recrd Our apprach f assigning parameterizatins t a cell frm the nearest statin, hwever, des nt accunt fr the pssibility f sharp climate regime changes in the vicinity f a cell This culd have led t a cell s parameterizatin cming frm a statin, fr example, n the ther side f a majr muntain divide In future effrts, this culd be cntrlled fr by pairing cells with statins based nt nly n prximity (as was dne here), but als n ther cmmn factrs that reflect climate regime, such as thse accunted fr in PRISM, eg tpgraphic facet (Daly et al 2002; see Sectin 323) 34 Step 5 Estimatin f slar radiatin and humidity MTCLIM3 We used a physically-based empirical surface climate mdel, MTCLIM3 (Thrntn et al 1997, 2000, Running et al 1987), t estimate daily recrds f ttal incident slar radiatin (SR), daylight-perid irradiance, vapr pressure (VP), and daylight-perid relative humidity frm daily minimum and maximum temperature and precipitatin This apprach maintained physical relatinships amng these surface climate variables n a daily and mnthly basis MTCLIM3 determines daily SR, based n ptential slar inputs (as a functin f latitude and year date), and transmittance estimated frm diurnal temperature range, VP, and ccurrence f precipitatin, as well as elevatin and slar beam gemetry (Bristw & Campbell 1984, Thrntn & Running 1999) The mdel estimates daily VP based n a minimum temperature (as a first apprximatin f dew pint temperature) and evaprative demand based n SR inputs (Kimball et al 1997) As VP and SR are inputs in each ther s calculatin, MTCLIM3 iteratively slves fr these variables Mean daily irradiance was calculated frm SR and day length Relative humidity was calculated frm VP using a mean daylight-perid temperature (Running et al 1987) Mnthly means f radiatin and humidity were derived frm daily values Kimball et al (1997) and Thrntn & Running (1999) evaluated the perfrmance f MTCLIM3 at the site level under different climate regimes Acrss the VEMAP dmain, MTCLIM3-generated VP fields reflect the brad reginal patterns in Marks s gridded VP climatlgy (Marks 1990, Marks & Dzier 1992), which was interplated frm weather statin data with tpgraphic adjustment (Fig 8a,b) MTCLIM3-estimated SR gridded climatlgies cmpared well with mnthly gridded SR climatlgies we develped frm the Slar and Meterlgical Surface Observatin Netwrk (SAMSON) dataset (NREL 1993) (Fig 8c,d) Fr this cmparisn, we calculated 20 yr mnthly mean SR frm 210 SAMSON statin time series, which represent mdeled as well as bserved values Statin means were gridded using kriging with elevatin as an independent pre-

12 162 Clim Res 27: , 2004 a b dictr variable t empirically accunt fr this cntrl (Fig 8d) While brad spatial patterns generated by MTCLIM3 were cnfirmed by cmparisns with gridded SAMSON climatlgies, this evaluatin is limited, because (1) much f the SAMSON data are simulated (with detailed site-level mdels) and (2) a subset f these data (frm statins dminated by bserved values) was used t parameterize MTCLIM3 4 Derived Datasets 41 Palmer Drught Severity Index (PDSI) Fig 8 MTCLIM3 simulated vs bserved vapr pressure (VP) and ttal incident slar radiatin (SR) climatlgies July mean VP (100 Pa) frm (a) MTCLIM3 and (b) Marks (1990), regridded t the VEMAP grid (nte that reds represent high VP and blues lw values) July mean SR (MJ m 2 d 1 ) frm (c) MTCLIM3 and (d) SAMSON statin data (NREL 1993) gridded using kriging with elevatin as an independent predictr variable; differences in map texture result frm lw SAMSON statin densities in (d) MTCLIM3 values (a, c) were simulated using VEMAP1 characteristic-year temperature and precipitatin dailies whse mnthly statistics match crrespnding lng-term means (Kittel et al 1995) These map cmparisns are limited in part by uncertainty in bserved fields (b, d) arising frm the lw density f statins reprting humidity and SR and frm their spatial interplatin while accunting fr elevatin (Sectin 34) c d PDSI indicates the relative status f sil misture supply and is based n a water balance cncept that prvides a standardized measure f misture cnditins, typically n a mnthly basis (Palmer 1965, Alley 1984) We calculated histrical mnthly PDSI based n VEMAP2 histric mnthly precipitatin and temperature data fllwing Alley (1984); required sil infrmatin was als frm Alley (1984) PDSI is a nrmalized, unitless value that varies rughly between 60 (severe dry) and + 60 (extremely wet), where 05 t + 05 is near nrmal It is a cmmn metric fr determining when a dry r a wet spell begins and ends PDSI is a meterlgical index because it integrates the effects f bth precipitatin and temperature (thrugh its cntrl ver evaprative demand) n surface water balance PDSI, hwever, des nt accunt fr ther ptential hydrlgical prcesses and inputs such as runff ruting Key t PDSI is that it has a persistence cmpnent, keeping track f prir sil misture cnditins Fr example, an abnrmally wet mnth in the middle f a lng-term dry perid shuld nt have a majr impact n the index, nr wuld a series f mnths with near-nrmal precipitatin fllwing a serius drught indicate that the drught is ver 42 Detrended mdel spin-up climate TSPIN Mst eclgical mdels that simulate lngterm dynamics f bigechemical pls (eg sil carbn) and vegetatin structure

13 Kittel et al: VEMAP2 histrical climate dataset fr the USA 163 require a spin-up run t initialize these state variables T prvide the input fr such runs, we created a climate series fr each grid cell with the fllwing features: (1) The spin-up series has n lng-term trend, s that it can be lped thrugh as many times as required fr a mdel spin-up run with n discntinuity at the pint where the series is repeated (2) The series retains interannual and decadal variability characteristics f the histrical series This feature is required fr spin-up runs because variability at these time scales has an imprtant effect n bigechemical and vegetatin dynamics (3) a The lng-term mean f the spin-up series matches the mean climate f the beginning f the histrical series, s that there is n discntinuity at the transitin frm the spin-up t the histrical perid We created a 100 yr mnthly and daily spin-up climate (TSPIN) as fllws, fr each grid cell: Detrending Lng-term trends in the mnthly histrical minimum and maximum temperature and precipitatin grid cell time series were remved by passing these series thrugh a 30 yr running average high-pass filter T create a 100 yr series, an additinal year was added t the 99 yr recrds by repeating Year 2 at the start f the recrd (Year 0 value x t0 = x t2 ) Lng-term mean adjustment The lngterm mean f the detrended climate series was adjusted t match the crrespnding mean fr the first 15 yr f the histrical b recrd ( ) Dailies and additinal variables We then used the same prcesses implemented in the develpment f the histrical dataset t generate daily recrds (Step 4) and radiatin and humidity variables (Step 5) (Sectins 33 and 34) tempral prgressin, as seen during the mid-1950s, when the Pacific Nrthwest (ie NW USA) had abve r near-nrmal precipitatin, while much f the central USA sustained a majr drught (see time series in Fig 9a) that brke in 1957 (Fig 9b) Cntrasting reginal patterns f cld vs warm perids are als represented (Fig 11) A time lngitude sectin f August PDSI shws hw drughts in the central and far western USA in the 1920s merged and intensified in the 1930s 5 RESULTS 51 Space and time slices The dataset reflects bth brad latitudinal and lngitudinal patterns f climate, as well as finer-scale physigraphic effects such as seen acrss the muntainus west (Fig 9) The data capture cntinental-scale tempral variability patterns including drughts f the 1930s and 1950s and the increasing trend t mister cnditins frm the late 1950s int the early 1980s (Fig 10a) The dataset illustrates cntrasting reginal patterns and their Fig 9 VEMAP2 precipitatin spatial and tempral variability patterns Annual precipitatin maps fr (a) 1956 and (b) 1957 shw years dminated by drught vs high precipitatin, respectively, acrss much f the suthern and central US Plts belw each map shw time series f annual anmaly ratis averaged fr the Pacific Nrthwest (western Washingtn state) and Central Great Plains (Kansas and Nebraska); anmaly ratis are relative t the crrespnding regin s lng-term spatial average Vertical red bar in time plts indicates year mapped; hrizntal line represents an anmaly rati f 10

14 164 Clim Res 27: , 2004 Table 1 Reginal trends (regressin slpes) in the VEMAP2 Histrical Climate Dataset frm Temperature trends fr the annual mean (Mean), annual mean maximum (Max), and bth annual and winter mean minimum (Min) are in C per century Trends fr annual and summer ttal precipitatin are in mm (%) per century *Regressin slpe statistically 0; α = 005 Regins are US Natinal Assessment megaregins (NAST 2001) (Fig 12) Winter is December, January, February; summer is June, July, August n/a = nt analyzed Temperature Precipitatin Annual Winter Annual Summer Mean Max Min Min (mm) (%) (mm) (%) Nrtheast (54) 2 (08) Sutheast * (89) 9 ( 26) Midwest * (76) 7 (23) Great Plains * 05 52* (105) 14 (79) West 03* 02 04* (102) 10 (143) Pacific Nrthwest * (32) 24* (293) Cnterminus US n/a 59* (82) n/a (08) a (Fig 10b) Animatins shwing the prgressin f precipitatin and temperature during the 20th century are available n the NCAR VEMAP web site (see Sectin 6) 52 Dataset trends While the VEMAP2 dataset was nt designed fr critically evaluating climate trends during the 20th century, we analyzed linear trends in the gridded dataset because f its use in the recent USA Natinal Assessment (NAST 2001) and because these trends influenced results frm the VEMAP2 mdel intercmparisn Over the last century, the histrical climate dataset shwed weak linear trends in mean annual temperature and strnger trends in precipitatin acrss the dmain and fr regins used in the Natinal Assessment (Fig 12, Table 1) The pattern f reginal trends in annual mean temperature and precipitatin was similar t that fund by Karl et al (1996) fr the perid based n NCDC climate divisin averages, and by Hansen et al (2001) fr based n HCN data These studies als shw that the trends were spatially mre variable than can be reslved by the NAST The VEMAP2 b Fig 10 (a) Time series f dmain mean f Palmer Drught Severity Index (PDSI) fr August (b) Develpment f drught as shwn by time-lngitude sectin f PDSI in August Vertical axis is time (year); hrizntal axis is lngitude ( W) Mapped values are lngitudinal averages fr a given year, smthed using a 7 yr running average Nte hw drughts (yellw) in the far western and central US in the 1920s merge and intensify in the 1930s Pacific: Pacific states; G Basin: Great Basin; Rckies: Rcky Muntains; G Plains: Great Plains; C Lwlnds: Central Lwlnds; Appls: Appalachians; Atlantic: Atlantic Castal Plain; N Eng: New England

15 Kittel et al: VEMAP2 histrical climate dataset fr the USA 165 a b Fig 11 As in Fig 9, except fr annual maximum temperature anmalies in years with ppsite reginal temperature anmaly patterns (a) 1933, (b) 1958 Anmalies in maps and time series are abslute differences (in C) frm lng-term averages Hrizntal line in the time plts represents a zer anmaly mean annual temperature trend fr the entire dmain was 02 C per century, but was nt statistically significant (ns) This trend was less than fund by Hansen et al (2001; 03 C per century) and Karl et al (1996; 04 C per century) using slightly different perids and underlying datasets and different prcesses fr spatial distributin f statin data The VEMAP annual precipitatin trend was 6 cm (8%) per century and was statistically significant (p < 005; Table 1) This was similar t the 5% change frm the first 70 yr f the century t the perid fund by Karl et al (1996) Temperature trends were psitive fr all regins, except in Sutheast where there was a tendency fr a decrease in temperatures (Table 1; eg Fig 13b,h) The lack f a trend in the Midwest was a result f averaging acrss a regin with strng psitive trends in the nrth and negative trends in the suth (Karl et al 1996) In the Great Plains, West, and Pacific Nrthwest, psitive trends in annual minimum temperature were significant (p < 005) and greater than fr maximum temperature, reflecting a reduced range in diurnal temperatures (Table 1); the Sutheast shwed the ppsite pattern, resulting in a widening f diurnal temperature range These patterns are cnsistent with analyses f diurnal temperature range trends acrss the USA fr the secnd half f the 20th century (Plantic et al 1990, Easterling et al 1997) Reginal winter minimum temperature trends tended t be strnger whether psitive r negative than crrespnding annual trends; hwever, winter trends were nt significant Precipitatin trends were als similar amng regins, with significant (and generally the strngest) annual trends in the Suth-east, Midwest and Great Plains (p < 005; Fig 13a,d,e), and the strngest and significant summer trend in the Pacific Nrthwest (p < 005; Fig 13f, Table 1) Increases in annual mean VP and decreases in SR in the Midwest and western regins (Table 2) are cnsistent with increases in precipitatin (as expected, given that precipitatin is used in the calculatin f VP and SR) Althugh VP increased, RH changed little, largely because f nearly matched increases in saturatin VP frm higher temperatures PDSI trends were psitive, reflecting wetter cnditins thrughut the dmain, with reginally significant trends in the Sutheast, Great Plains, and West (Table 2) The VEMAP2 dataset generally reflected lng-term climate signals fund in climate detectin studies Dcumenting these trends is imprtant fr interpreting VEMAP mdel utputs (Grdn et al 2004) and ther studies relying n these data (NAST 2001) Hwever, even thugh trends in the dataset generally fllw thse in frmal detectin studies and althugh sme trends were statistically significant, their significance with respect t a rigrus assessment f climate change is limited because f the likelihd f time-dependent biases in nn-hcn data surces underlying the dataset (see Sectins 31 and 71)