Sources of Variability of Evapotranspiration in Californiatenaya.ucsd.edu/~dettinge/hidalgo_eto.pdf · Sources of Variability of Evapotranspiration in California ... Hourly cloud-cover

VOLUME 6 FEBRUARY 2005J O U R N A L O F H Y D R O M E T E O R O L O G Y

q 2005 American Meteorological Society 3

Sources of Variability of Evapotranspiration in California

HUGO G. HIDALGO

Climate Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

DANIEL R. CAYAN

Climate Research Division, Scripps Institution of Oceanography, University of California, San Diego, and Water Resources Division,U.S. Geological Survey, La Jolla, California

MICHAEL D. DETTINGER

Water Resources Division, U.S. Geological Survey, and Climate Research Division, Scripps Institution of Oceanography,University of California, San Diego, La Jolla, California

(Manuscript received 23 February 2004, in final form 12 July 2004)

ABSTRACT

The variability (1990–2002) of potential evapotranspiration estimates (ETo) and related meteorological var-iables from a set of stations from the California Irrigation Management System (CIMIS) is studied. Data fromthe National Climatic Data Center (NCDC) and from the Department of Energy from 1950 to 2001 were usedto validate the results. The objective is to determine the characteristics of climatological ETo and to identifyfactors controlling its variability (including associated atmospheric circulations). Daily ETo anomalies are strong-ly correlated with net radiation (Rn) anomalies, relative humidity (RH), and cloud cover, and less with averagedaily temperature (Tavg). The highest intraseasonal variability of ETo daily anomalies occurs during the spring,mainly caused by anomalies below the high ETo seasonal values during cloudy days. A characteristic circulationpattern is associated with anomalies of ETo and its driving meteorological inputs, Rn, RH, and Tavg, at daily toseasonal time scales. This circulation pattern is dominated by 700-hPa geopotential height (Z700) anomalies overa region off the west coast of North America, approximately between 328 and 448 latitude, referred to as theCalifornia Pressure Anomaly (CPA). High cloudiness and lower than normal ETo are associated with the low-height (pressure) phase of the CPA pattern. Higher than normal ETo anomalies are associated with clear skiesmaintained through anomalously high Z700 anomalies offshore of the North American coast. Spring CPA, cloud-iness, maximum temperature (Tmax), pan evaporation (Epan), and ETo conditions have not trended significantlyor consistently during the second half of the twentieth century in California. Because it is not known how cloudcover and humidity will respond to climate change, the response of ETo in California to increased greenhouse-gas concentrations is essentially unknown; however, to retain the levels of ETo in the current climate, a declineof Rn by about 6% would be required to compensate for a warming of 138C.

1. Introduction

Potential evapotranspiration (ETo) is the atmosphericdemand for water from soil and free water surfaces(Rind et al. 1990). As it is estimated from the energyavailable for the vaporization of water, ETo is a potentialrate, with no regard for the actual availability of waterto evaporate. As a result, ETo represents an upper boundon actual evapotranspiration (ETa) rates. In California’sCentral Valley, the average ratios of ETa/ETo range from1.5% during the summer to 100% during the winter (N.Knowles, U.S. Geological Survey, 2003, personal com-

Corresponding author address: Dr. Hugo G. Hidalgo, Climate Re-search Division, Scripps Institution of Oceanography, 9500 GilmanDrive, MC 0224, La Jolla, CA 92093.E-mail: [email protected]

munication). In many cases, however, estimates of thepotential rate can be as useful as the actual rate (Rindet al. 1990). For example, the long-term difference (orratio) between ETo and precipitation (P) has been con-sidered a measure of aridity used in several climateclassification schemes (Koppen 1936; Thornthwaite1948; Prentice 1990). Climatological aridity is a criticalenvironmental factor that helps to determine the char-acter and sustainability of natural vegetation and ter-restrial ecosystems, and consequently is related to ero-sivity, water demands, and the condition of water re-sources. The variation of this difference (or ratio) is alsoan index of drought (Rind et al. 1990). According to arecent modeling study, anthropogenic climate changemay soon yield increases in the frequency and severityof droughts and the expansion of deserts (Manabe et al.2004). These changes would have profound impacts in

4 VOLUME 6J O U R N A L O F H Y D R O M E T E O R O L O G Y

FIG. 1. Location of CIMIS stations with available meteorologicaldata from 1995 to 2002 (open circles), CIMIS stations with availablemeteorological data from 1990 to 2002 (filled circles), NCDC stationswith available daily cloud-cover data from 1984 to 1994 (squaremarkers), DOE stations with monthly cloud-cover data from 1893 to1995 (solid squares), NCDC stations with available pan evaporationdata from 1965 to 2001 (triangle markers), and NCDC stations withprecipitation, maximum temperature, and minimum temperature from1950 to 2001 (plus symbols).

water resources, ecosystems, and socioeconomic struc-tures. Thus, study of changes and covariations of EToand P are needed and may provide the basis for earlywarnings of such changes.

Probably the most common practical use of ETo es-timates is in scheduling irrigation in agricultural regions(ITRC 2003). On farmed land, the amount of water thathas to be applied to the soil (water demand) generallymust be close to the potential evapotranspiration rate.Under California’s Mediterranean climate, ETo ratesreach their maxima during summer months and are lowduring the winter, while P is largely restricted to winterand spring. Importantly, in California, a large fractionof the winter precipitation is locked as snow in the highelevations until spring and summer so that the largestdemands for irrigation water (in the lowlands of theCentral Valley) are supplied by spring snowmelt thatarrives ‘‘just in time.’’ This arrangement is crucial tothe success of many of the state’s water supply systems,but also imposes challenges to managers of water re-sources as the supplies and demands vary from year toyear. In this study, we investigate the climatic influenceson one of the major demands, the demand for irrigationwater.

A better understanding of the climatic processes as-sociated with ETo variability is also needed for the im-provement of hydrologic models. Climatic mechanismsrelated to the variability of ETo may provide insightneeded to close regional water and energy budgets, toassess influences of changing land surface conditions,and to anticipate changing demands on water supplysystems at a variety of time scales.

In the present study, daily, seasonal, and longer-termvariations of ETo and of estimated associated meteo-rological variables are evaluated. In addition, these var-iations are related to large-scale climatic forcings bycomparing them with atmospheric circulation patternsat the 700-hPa pressure level, and to meteorologicalobservations including cloudiness, humidity, tempera-ture, and wind speed. Identifying the primary atmo-spheric mechanisms responsible for ETo variation inCalifornia should help to understand how evaporationdemand will be affected by climate variability and cli-mate change.

2. Data

a. Meteorological records

California’s strong agricultural economy (especiallyin the Central Valley) justified the establishment of anautomated network of meteorological stations to esti-mate ETo for irrigation scheduling. The resulting Cal-ifornia Irrigation Management Information System(CIMIS; see Web site online at http://www.cimis.water.ca.gov, hereafter referred to as CIMIS Web site)was established in 1982 and is currently managed bythe California Department of Water Resources. Data

from CIMIS are primarily being used to support agri-cultural activities throughout the state. However, nowthat more than 20 years of CIMIS data have been col-lected, the network’s high spatial density makes it usefulfor climate analyses. To date, CIMIS data have beenused in published studies with the objective of cali-brating models to predict pasture growth rates (Lile andGeorge 1993) and for evaluating the suitability of em-pirical ETo equations based on air temperature (Hopeand Evans 1992; Hargreaves and Allen 2003; Alexan-dris and Kerkides 2003). The regional ETo signals con-tained in these data and the climate patterns that deter-mine that signal have not been investigated previously.

Meteorological variables measured at CIMIS stationsinclude solar radiation, air temperature, soil tempera-ture, humidity, wind speed, and wind direction. Thevariables are used to estimate hourly ETo rates at CIMISsites, using a modified Penman equation (see next sec-tion; Penman 1948; Pruitt and Doorenbos 1977; CIMISWeb site), and those rates are integrated into daily andmonthly values. For this study, daily ETo estimates,together with daily values of net radiation (Rn), meanair temperature (Tavg), relative humidity (RH), windspeed (Uavg), and wind direction (Udir), were obtainedfrom 52 of the CIMIS stations (Fig. 1). These sites used

FEBRUARY 2005 5H I D A L G O E T A L .

are the subset of the CIMIS network stations that hadless than 7% missing data from 1995 to 2002. In severalanalyses, a smaller subset of 29 stations with data span-ning the period from 1990 to 2002 were used to capturea broader sampling of ETo variations (Fig. 1). Missingdata were filled by averaging neighboring data values,and the resulting time series were detrended and visuallyinspected for outliers. The mean seasonal cycles of eachvariable were estimated from 30-day moving averagesof period-of-record medians for each day of the calendaryear, and the resulting smoothed seasonal cycles weresubtracted from all daily variables to obtain daily anom-alies. The daily anomalies are used in several of thesubsequent analyses. Trends were computed using linearregression applied to the seasonal means of the raw data.

Daily gridded (2.58 3 2.58) geopotential heights ofthe 700-hPa pressure level (Z700), from the NationalCenters for Environmental Prediction–National Centerfor Atmospheric Research (NCEP–NCAR) reanalysis(Kalnay et al. 1996; Kistler et al. 2001), were used tocharacterize atmospheric circulations from 1950 to2002. The daily Z700 were deseasonalized following thesame procedure as the CIMIS data. Gridded daily cloud-cover fractions, and monthly mean values of surfacerelative humidity, surface temperature, precipitationrate, and outgoing longwave radiation (OLR; a proxyfor cloudiness) from the same time period and samesource were used to characterize large-scale processesaffecting California’s surface variables. El Nino–South-ern Oscillation (ENSO) indices, obtained from the ar-chives of the Joint Institute for the Study of the At-mosphere and the Ocean, are used to determine possibleENSO influences on ETo variations.

Hourly cloud-cover observations from 1984 to 1994at two Central Valley weather stations were obtainedfrom the National Climatic Data Center (NCDC) (Fig.1). Cloud-cover observations from 6:00 A.M. to 6:00P.M. each day were averaged to produce daytime means,since inspection of hourly data showed insignificantnighttime contributions to the daily ETo totals. In ad-dition, monthly mean cloud cover data for stations inSan Diego, Los Angeles, Fresno, Red Bluff, and SanFrancisco from 1893 to 1987 were obtained from theDepartment of Energy (DOE) Carbon Dioxide Infor-mation Analysis Center (Steurer and Karl 2004). How-ever, only the cloud-cover data from 1950 to 1987 wereused, because changes in cloud-observing practices inthe 1930s and 1940s may have introduced spurioustrends (Karl and Steurer 1990). These data were usedto verify long-term trends in cloud cover. The strengthand position of the North Pacific high (NPH) were char-acterized using the indices described in Hameed et al.(1995). Daily pan evaporation (Epan) rates at seven Cal-ifornia stations, from 1965 to 2001, were also obtainedfrom NCDC. The Epan data were used as a check on theCIMIS ETo estimates and to verify the regional climaticpatterns that are found to be associated with ETo in thisstudy. Daily precipitation and daily maximum and min-

imum temperatures, from 1950 to 2001, at 165 coop-erative observer stations in California were obtainedfrom the NCDC and were used to analyze long-termtrends. Only stations with less than 7% of missing datawere considered. The data were checked for outliers,and missing data were filled using neighboring values,but no additional manipulation was performed on thedata (i.e., tests for homogeneity). A similar dataset, com-posed of 256 daily precipitation stations covering thesame period of the CIMIS data (1990–2000), was alsoobtained from the Cooperative National Weather Ser-vice dataset through NCDC. These data were used tocharacterize climatological precipitation in high eleva-tions, since most of the CIMIS data and the 165 NCDCmeteorological stations previously mentioned are lo-cated in lower elevations. Statistics of total populationby county for 2002 were obtained from the U.S. FederalCensus [see ‘‘2002 Data Profiles’’ on the U.S. CensusBureau Web site (http://www.census.gov/acs/www/Products/index.htm)]. These data were used to classifythe temperature records as coming from either urban orrural stations in order to minimize biases introduced byurbanization (Goodridge 1992).

b. Estimates of ETo

CIMIS ETo estimates are calculated by applying amodified version of Penman’s (1948) equation for agrass reference crop to mean hourly values of net ra-diation [ (W m22)], relative humidity [RHhour (%)],Rnhour

mean 2-m air temperature [Thour (8C)], and wind speed[Uhour (m s21)]. The hourly estimates are then aggregatedinto daily ETo totals. With the exception of , all ofRnhour

these variables are measured at each CIMIS site. Netradiation is estimated from an empirical fit of mea-Rnhour

sured (pyranometer) solar radiation, Thour and RHhour, aswell as other solar and cloud parameters [Eq. (9); Donget al. 1992; Monteith 1973]. Net radiation is thenRnhour

converted from units of watts per meter squared to mil-limeters per hour using the latent heat of vaporizationaccording to Eq. (10). The CIMIS version of the Penmanhourly evapotranspiration (ETohour) equation is (Penman1948; Pruitt and Doorenbos 1977; Dong et al. 1992;Satterlund 1979; Monteith 1973; CIMIS Web site)

D DETo 5 R 1 1 2 (e 2 e ) f ,hour n s a Uhour 1 2D 1 g D 1 g

21(mm h ), (1)

where

4099es 21D 5 (kPa 8C ), (2)2(T 1 237.3)hour

P cpatm 21g 5 (kPa 8C ), (3)l«

27 2P 5 101.3 2 0.0115z 1 5.44 3 10 zatm

(kPa), (4)


21l 5 (2.50 2 0.0022T ) (MJ kg ), (5)hour

T 17.27houre 5 0.6108 exp (kPa), (6)s 1 2T 1 237.3hour

RHhoure 5 e (kPa), (7)a s100

if R # 0:nhour

21 21f 5 0.125 1 0.0439U (mm h kPa ), (8a)U hour

if R . 0:nhour

21 21f 5 0.030 1 0.0576U (mm h kPa ), (8b)U hour

22(W m ) 4R 5 0.89{(1 2 a)Rs 1 s(T 1 273.15)n hourhour

3 [« (O)(1 2 c) 1 c 2 0.98]}a

22(W m ), (9)22(W m )Rn21 hour(mm h ) 21R 5 (mm h ), (10)nhour 277.78l

Rsif $ 0.375:

I

a 5 0.001 58Q 1 0.386 exp(20.0188Q), (11a)

Rsif , 0.375:

I

a 5 0.26, (11b)22I 5 I cos(Z ) (W m ), (12)o

Z 5 90 2 Q (degrees), (13)(T 1273.15)/2016hour« (O) 5 1.08{1 2 exp[2(10e ) ]}, (14)a a

0.294Rsc 5 1.33 2 1.33 , (15a)1 2Ra

if c . 1: c 5 1, (15b)

if c , 0: c 5 0, (15c)

37522Ra 5 0.79 2 I (W m ), (16)1 2Q

where D is the slope of the Clausius–Clapeyron equationrelating saturation vapor pressure to temperature, g isthe psychometric constant, es is the saturation vaporpressure, ea is the actual vapor pressure, cp is the specificheat at constant pressure (1.013 3 1023 MJ kg21 8C21),« is the ratio of the molecular weight of water vapor tothat of dry air (0.622), and Patm is the barometric pres-sure, estimated from the station elevation z (m) usingEq. (4). Equation (8) is an empirical wind function thatwas developed by the University of California (CIMISWeb site) to estimate the influence of winds on ETo.

Details of the formulations leading to Eqs. (9) and(11)–(16) can be found in Dong et al. (1992). In theseequations, a is the surface albedo over well-watered

grass, Rs (W m22) is the measured solar radiation re-ceived at a horizontal plane at each CIMIS station, sis the Stefan–Boltzman constant (5.670 3 1028 J K24

m22 s21), «a(O) is the clear-sky emissivity [Eq. (14);Satterlund 1979], I (W m22) is the irradiance on a hor-izontal surface at the top of the atmosphere, Io is thesolar constant (1367 W m22), c is the fractional cloudcover (0 $ c # 1) estimated from Eq. (15), Q (degrees)is the solar altitude, Z (degrees) is the zenith angle [Eq.(13)], and Ra (W m22) is the potential clear-sky solarradiation [Eq. (16)].

Potential evapotranspiration ETo formulations basedon the Penman equation sometimes consider a measuredor estimated ground heat flux (G), which is subtractedfrom Rn, in order to account for heat transference at theair–soil interface (Monteith 1965). In order to determineif CIMIS’s ETo estimates would be affected significantlyby the exchange of heat with the soil, G was approxi-mated using Eq. (17) (Shuttleworth 1993; van Wijk andde Vries 1963), which is based on the day-to-day dif-ference of soil temperatures [Tsoil (8C)]:

0.3821G 5 (Tsoil 2 Tsoil ) (mm day ). (17)day1 day2 day1l

Equation (17) is intended for estimating G at daily timescales for average soil conditions and properties. Sincethe CIMIS data is based on hourly ETo estimates, al-ternative ETo estimates based on Eq. (1) were computedusing daily mean values of Rn, RH, U, and Tavg. Thedaily anomalies of these alternative estimates proved tobe strongly correlated (median correlation 5 0.84, p ,0.01) with the values reported by CIMIS (which werecomputed at hourly time scales and integrated into dailyvalues). These correlations did not change significantlywhen G was included (median correlation 5 0.83, p ,0.01). Although there are a few exceptional days whenG becomes an important fraction of ETo (greater than10%), the median contribution of G is less than 0.1%of ETo for the set of California stations used here. Othercorrelations, such as the ones between ETo and Epan

anomalies did not change significantly (less than 60.02change) when the G term was added. Therefore, theground flux is not included in subsequent analyses,which are instead based on the standard ETo estimatesreported by CIMIS.

CIMIS ETo estimates and nearby pan evaporation sta-tion data (Epan) are well correlated at monthly scales(Table 1; see also Grismer et al. 2002). However, theEpan rates are usually higher than their correspondingETo estimates. The evaporation from a pan can varysignificantly from the surrounding vegetation (evenwell-watered) or nearby lakes. This difference is usuallyaccounted through pan coefficients (kpan) such that

21ETo 5 k E (mm day ).pan pan (18)

Pan coefficients are usually less than one, but they varywidely according to pan characteristics, U, RH, and thelength of upwind fetch of green crop (Shuttleworth


TA

BL

E1.

Com

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betw

een

pan

evap

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(Epa

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IMIS

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Dat

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Lat

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Std

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NC

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837

934

835

911

9858

912

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149

4.69

3.43

2.45

1.71

0.52

0.50

0.39

0.85

Dav

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388

329

388

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1218

459

1218

479

15 186.

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252.

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690.

630.

500.

87

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378

3937

819

1218

4912

0846

912 29

7.39

3.87

5.38

2.42

0.73

0.63

0.43

0.74

Fri

ant

Gov

ernm

ent

Cam

pF

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tate

Epa

nE

ToN

CD

CC

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368

599

368

499

1198

439

1198

449

125

103

5.25

3.83

3.68

2.44

0.70

0.64

0.31

0.74

TABLE 2. Mean, standard deviation, and coefficients of variationof ETo and P water year totals (Oct–Nov) from California stationsfrom 1990 to 2001. The ETo and PCIMIS values shown represent themedian of 29 CIMIS stations shown as filled circles in Fig. 1. ThePNCDC correspond to the median values for 256 stations shown witha ‘‘1’’ and ‘‘*’’ symbols in Fig. 1.

ETo PCIMIS PNCDC

Mean (mm)Std dev (mm)Coef var

1344700.05

321152

0.48

971443

0.44

1993). Daily Epan anomalies have higher standard de-viations than ETo anomalies and are only moderatelycorrelated with ETo anomalies, but at monthly timescales Penman ETo estimates seem to be significantlycorrelated with Epan data (Table 1). Similar results havebeen found in other regions of the world (Chiew andMcMahon 1992; Xu and Singh 1998). There are manyreasons why potential evaporation estimates from thePenman equation (Epen) would differ from the pan dataestimations even when some limitations of the Penmanand pan methods are considered. One of the most ob-vious differences is that Epan and Epen are based on datacollected from different evaporating surfaces (soil ver-sus water). The roughness and the exchange of energyof the air with water and soil surfaces are different. Inaddition, if the soil is not kept continuously saturated,the water from deeper layers has to be drawn throughcapillarity until pores with the lower matric potentialare depleted (Lee 1980). It is unfortunate that ETo couldnot be further validated as there are almost no dataavailable in California from other methods such as ly-simeter, Bowen ratio, or eddy covariance.

3. Variability of ETo and related observations

a. Climatology and seasonality

Annual ETo totals in California are greater than an-nual precipitation, a relation that is characteristic of itssemiarid climate. When only the CIMIS stations areconsidered, precipitation is on average only 24% of an-nual ETo (Table 2). However, the average annual pre-cipitation for California computed from CIMIS stationsis underestimated, as there are only a few stations inthe high-producing areas in the high elevations. Con-sidering the contribution of a larger range of elevationscontained in 256 NCDC stations, precipitation in Cal-ifornia is on average 72% of ETo annual totals (Table2). There is, however, large spatial variability in thedistribution of the water deficit. The average (1990–2002) water deficit (ETo minus P) ranges from approx-imately 200 mm yr21 at northern coastal sites to morethan 1800 mm yr21 in the southern deserts (Fig. 2); Pis also much more variable from year to year than ETo.The coefficient of variation of annual P is on the orderof 40%, while that for ETo is only 5% (Table 2).Throughout the network, ETo reaches its maximum in


FIG. 2. Average annual total (a) ETo and (b) ETo 2 P (mm yr21)from 1995 to 2002. Contours are every 1000 m starting at 500-melevation.

FIG. 3. Median daily averages of ETo (dotted line), P from CIMISstation (thick solid line), and P from NCDC stations (thin solid line)in mm day21.

FIG. 4. Distributions of the daily ETo data from 1995 to 2002 forselected CIMIS stations; ETo values inside (outside) the interquartilerange are shown with a light dot (dark plus) symbol.

July or August during the dry summer season whenclouds are few, insolation is long and intense, and tem-peratures are high. The seasonal minimum of ETo is inJanuary, during the wet (cloudy and cool) winters (Fig.3); P and ETo are thus modulated by conditions in dis-tinctly different, evidently independent seasons.

California’s precipitation is episodic and providedmostly by relatively few winter storms each year. Incontrast, ETo is more persistent and seasonal in char-acter, although significant anomalies around the sea-sonal cycle occur. The daily ETo anomalies are char-acterized by some extremely low values (Fig. 4). Themajority of these negative anomalies extend from thewinter through the spring until the midsummer (Fig. 4),and they are infrequent for the rest of the summer. Aswill be seen in following sections, these extremely neg-ative daily ETo anomalies are associated with cloudydays during the spring. Anomalies of ETo are morepersistent than P anomalies at daily time scales, but, on

the monthly scale, the lag 1 autocorrelation coefficientsof ETo and P are similar, suggesting comparable mem-ories (Table 3). ETo rates are relatively low in winterand autumn, and summer ETo values—although largestoverall—are relatively least variable. The median springstandard deviation of the daily ETo anomalies is 1.36mm day21, and for summer is 0.89 mm day21. SpringETo is both large and highly variable. Consequently, therest of this paper tends to focus on the warm seasonand, especially, spring ETo variations.

The amplitude of the seasonal cycles of ETo, as well


TABLE 3. Median lag-1 autocorrelation coefficient (r1) of ETo andP anomalies and coefficient of variation (c.v.) obtained by dividingthe std dev of the ETo and P anomalies by the mean of their actualvalues. The values represent the median of 29 CIMIS stations from1990 to 2002.

ETo

r1 c.v.

P

r1 c.v.

Daily5 days

10 days30 days90 daysAnnual

0.550.370.310.320.23

20.02

0.250.180.150.110.080.05

0.270.270.270.330.03

20.23

4.822.852.211.561.130.47

TABLE 4. Median intercorrelations at different time scales of me-teorological variables for 29 CIMIS stations in California from 1990to 2002.

(a) Daily anomalies (intraseasonal variations)ETo Rn RH Tavg Uavg

EToRn

RHTavg

Uavg

1.00 0.811.00

20.6720.46

1.00

0.430.11

20.271.00

0.2120.0420.08

0.001.00

(b) Month-to-month (seasonal cycle included)ETo Rn RH Tavg Uavg

EToRn

RHTavg

Uavg

1.00 0.991.00

20.7520.69

1.00

0.920.89

20.681.00

0.560.55

20.210.361.00

(c) Year-to-year (interannual variations)ETo Rn RH Tavg U

EToRn

RHTavg

Uavg

1.00 0.761.00

20.6720.37

1.00

0.380.00

20.231.00

0.240.010.100.071.00

as of the other CIMIS variables (except the wind pa-rameters), is strongly modulated by the strength of theinfluence of North Pacific air masses, which are roughlyproportional to the distance from the ocean. For ex-ample, the difference between daily ETo from summerto winter ranges from 3.3 mm day21 in coastal stationsto 7.3 mm day21 inland. The moist near-surface air alongthe coast dampens seasonal cycles in contrast to thesemiarid environment farther inland. The logarithm ofdistance to the coast explains 62% and 87% of the spa-tial variation of the amplitudes of the ETo and Tavg sea-sonal cycles, respectively, for the 52 CIMIS stationsshown in Fig. 1. This modulation of seasonal cycles bythe marine environment is stronger among the CIMISsites than is the influence of elevation. However, theCIMIS network, and the subset of stations used here, isdenser in the Central Valley and along the central andsouthern California coast. The underrepresentation ofhigh altitudes in the CIMIS network may lead to anunderemphasis of elevation effects, and the paucity ofsouthern California desert stations probably yields anunderemphasis of the role of north–south climate con-trasts.

b. Meteorological factors of ETo variability

Among the CIMIS meteorological variables, at theregional scale considered here, Rn and RH fluctuationscontribute most to the variability of daily ETo anoma-lies. The analysis of this section is based on correlationsbetween the anomalies of ETo and the anomalies of therelated variables (Rn, Tavg, RH, and U). It should benoted that when ETo is computed from these relatedvariables, the seasonal cycles are included in the equa-tion. ETo’s seasonal cycle can be approximated fairlyaccurately using the seasonal cycle of Rn (or even Tavg).For this reason, in order to find the origin of the day-to-day ETo anomalies the seasonal cycles were removedfrom the data. The sense of the correlations of the anom-alies of the related variables with ETo are such thatpositive anomalies of Rn (higher than normal solar ir-radiation) and negative anomalies of RH (drier than nor-mal conditions) are associated with higher than normalETo anomalies (Table 4a). Wind speed anomalies, Uavg,

are poorly correlated with ETo anomalies. For this rea-son, the results for Uavg are not shown in the followinganalyses. The Tavg anomalies correlations with EToanomalies are weaker than the Rn anomalies correlationsbecause the terms associated with Tavg in the Penmanequation are highly nonlinear. The nonlinear relation-ship between Tavg and ETo also appears in other tem-perature-based ETo equations such as the Hargreaves’equation (Hargreaves et al. 1985; Hargreaves and Allen2003). Physically, this probably reflects the relativelymodest excursions of vapor pressure (esat and D) overthe range of temperatures encountered in California. TheTavg and Rn anomalies are moderately to weakly corre-lated, indicating that the two variables provide some-what independent influences on ETo anomalies. At dailytime scales, Rn anomalies are more linearly related onrelative humidity anomalies and other variables, suchas cloudiness, than air temperatures.

When the seasonal cycles are included and the high-frequency variations are dampened by integrating theraw data into monthly means, Rn and ETo are still thehighest intercorrelated variables. However, the corre-lations between Tavg and ETo (and Tavg and Rn) are alsoquite strong, suggesting that the seasonal cycles of EToand Tavg are strongly correlated (Table 4b), although thiscorrelation does not imply causality. In comparison, theseasonal cycles of RH and ETo are not as strongly re-lated. In terms of the interannual variations, Rn showedthe strongest correlations and RH explains a similarfraction of the variability of ETo as it did at daily timescales. Notably, for interannual time scales, Tavg doesnot explain a large fraction of the variation of ETo (Table4c).


FIG. 5. Median lag autocorrelations of daily warm-season (Apr–Sep) anomalies of different meteorological variables from 29 CIMISstations (1990–2002).

c. Variability

The characteristic time scales of daily CIMIS fluc-tuations differ from variable to variable. Daily variationsof ETo are most similar to those of Rn (as noted pre-viously) and this similarity is reflected in their char-acteristic time scales. Daily correlograms of CIMIS var-iables during the warm season (spring plus summer) areshown in Fig. 5. Variations of both ETo and Rn havedecorrelated (have e-folding times) after about 1 to 2days, on average, in all seasons. In contrast, RH andTavg are most persistent, decorrelating only after about3 to 4 days. Although the lag correlation for each var-iable becomes small by about 5 days, each has persis-tently nonzero values to at least 10 to 12 days (especiallyRH), reflecting long-term intraseasonal to interseasonalexcursions (e.g., some springs are just clearer or warmerthan others). After about 8 days most of the high-fre-quency autocorrelation is low for all variables and isonly marginally reduced from this point to 15 or 30days. The correlogram of RH is fairly similar to Tavg atshort lags, albeit with a wider range of variability be-tween stations. Presumably, differences in their decor-relation time scales reflect differences in the processcontrolling each of these variables. These processeswould include the larger-scale atmospheric circulationthat strongly governs daily temperature and humidity(larger-scale controls) and smaller-scale cloud processesthat govern Rn anomalies. After a single day Uavg de-correlates completely and presumably reflects strongshort-term influences. As noted previously, ETo reflectsa combination of radiation and humidity time scales,with its time scale falling between the two. However,as noted previously, ETo anomalies are most strongly

influenced by Rn, and thus the time scale of its fluctu-ations around its strong seasonal cycle is quite short.

The amplitudes of daily variations of the CIMIS var-iables also differ, between variables, seasonally, andgeographically. The standard deviations of the dailyCIMIS anomalies for ETo, Rn, RH, and Tavg are mappedin Fig. 6. Much like the amplitudes of seasonal cycles,the standard deviations of daily Tavg anomalies dependto a considerable extent upon proximity to the coast,with the largest variations at the interior stations, es-pecially in the northeastern corner of California on thelee side of the Sierra Nevada. In contrast, standard de-viations of ETo are not strongly related to the distanceto the coast. Other factors, such as the variation of cloudcover and other influences (i.e., large-scale circulation),must determine the size of the ETo variations.

Daily variations of ETo, Rn, and Tavg anomalies arethe largest in spring and somewhat smaller during thesummer. Winter and autumn variations of ETo and Rn

anomalies are smallest. The larger ETo variability dur-ing the spring reflects a general increase in the standarddeviations, but notably much of this spread reflects theoccurrence of low values during cloudy days, as thenegative anomalies are generally larger in magnitude asthe positive anomalies (Fig. 4). These negative anom-alies become much less frequent (in some settings quiteabruptly) by midsummer. The abrupt transition betweenthe broad spring and narrow summer ETo distributionsreflects the rapid reduction of cloudiness once summercirculations are established (Nogues-Paegle and Mo1987). The tendency toward higher variability duringspring parallels, and is largely driven by, a similar ten-dency in Rn at most of the stations. The springtimenegative ETo anomalies correspond to Rn reductions as-sociated with partially (and completely) cloudy days thatinterrupt the seasonal progression from cool-season towarm-season ETo norms. The much lower autumn andwinter ETo values are only marginally reduced byclouds, and the variability during these seasons is small.Summertime variability of ETo and Rn anomalies aresmall, mostly because clouds are scattered and infre-quent throughout the state in this season, especially inthe Central Valley (Davis and Fresno State stations inFig. 4).

As was mentioned before, ETo deviations above themedian (positive anomalies) tend to be smaller than arethe lower than median excursions (negative anomalies),except for a few stations, mostly in the Sacramento Ba-sin (i.e., Davis in Fig. 4). The episodes of extremelyhigh spring ETo in the Sacramento Basin are associatedwith combinations of exceptionally clear skies with hightemperatures and low RH in the basin. An analysis ofweather reports from two Central Valley stations (Fig.7) reveals that the variation of ETo is inversely corre-lated with clouds, fog, and rain during the spring, andthat the presence of haze does not preclude high ETo.Rainy days during the spring are almost exclusivelyassociated with low ETo days (and high cloudiness),


FIG. 6. Std devs of daily anomalies for spring and summer,1995–2002.

FIG. 7. Percentage of spring days in which haze, fog, rainfall, orclear skies (total sky cover ,30%) were reported at the SacramentoExecutive Airport (Sacramento Basin) and at the Fresno/YosemiteInternational Airport (San Joaquin Basin) stations. The data wereclassified using terciles of ETo CIMIS data at nearby Davis and Fresnostations, respectively. Haze, fog, and rain occurrence data were ob-tained from NCDC Present Weather reports from 1984 to 1994. Totalsky cover data were obtained by averaging hourly NCDC station datafrom 6:00 A.M. to 6:00 P.M. for the same years.

while high ETo days are exclusively related with drydays (and low cloudiness). Normal ETo days are almostalways observed in days with no rain but with someamount of cloud coverage.

4. Influence of large-scale climate

a. Regional atmospheric circulation

Regional atmospheric circulations have strong effectson the variability of CIMIS variables at all time scales,and a single, shared circulation pattern is associated withanomalies of ETo and its driving meteorological inputs,Rn, RH, and Tavg. The correlations between daily anom-alies of Z700 and each of these variables are shown inFig. 8 and reveal a consistent underlying circulationpattern. The correlation pattern that all the variablesshare is dominated by Z700 anomalies over a region offthe west coast of North America, approximately be-tween 328 and 448 latitude. The pattern is displacedsomewhat inland in the Tavg correlations. During thespring, the shape of the Rn correlation pattern is verysimilar to the ETo pattern (Fig. 8), consistent with thestrong dependence of ETo and Rn (Table 4). The ETocorrelation pattern during summer is more similar to the


FIG. 8. Median day-to-day correlations between anomalies of several meteorological pa-rameters and anomalies of 700-hPa geopotential height data. The contours represent the mediancorrelation of a subset of 29 CIMIS stations from 1990 to 2002. Contours every 0.10. Dashed(solid) lines were used for negative (positive) correlations. Zero contour (thick line) is labeled.


FIG. 9. (left) Average composites of daily springtime anomalies of Z700 (contours) and total sky cover (color shading)during high and low ETo days minus the average conditions during normal ETo days and (right) average wind speed(shading) and direction (arrows) according to each ETo condition. The ETo days were classified as terciles of the meanstandardized ETo anomalies for 29 CIMIS stations from 1990 to 2002. Contours are every 5 m. Negative contours aredashed.

Tavg pattern than during spring. Of the CIMIS variables,Tavg is the most strongly correlated with the circulation,especially during summer, but has less influence on ETothan Rn and RH. Very similar correlation patterns areobtained at all time scales and at several pressure levels(not shown). In general, the center of the correlationpatterns tilt southward with height in the atmosphere.

The correlation pattern shown in Fig. 8 suggests thatETo (and the other variables) are strongly influenced byinterplays between coastal-marine and continental airmasses. During the spring, days with low pressures(Z700) offshore are associated with anomalously south-westerly winds and more than normal cloud cover inmost of California (Fig. 9) and, as a consequence, lowerthan normal ETo. Spring days with high pressures off-

shore experience a blocking of westerly flows into thestate with less cloudiness and are characterized by highETo rates (Fig. 9). A similar configuration of pressuresoperates in the summer, although the correlations aresignificantly lower, and the center of the ETo circulationpattern is displaced slightly inland, yielding broad at-mospheric subsidence, drying and warming of the airover the state, and resembling more the Tavg correlationpattern (Fig. 8).

The relation of these circulation patterns to the po-tential for evapotranspiration can be verified, in part, byconsidering the longer Epan records (1965–2001) froma few locations in California. Correlations between Epan

and Z700 were shown to be very similar to the corre-sponding ETo pattern (Fig. 8a), regardless of time period


FIG. 10. Time series of spring seasonal-mean CPAI (thin line), standardized ETo (dotted line),cloud cover multiplied by 21 (circular markers), and Epan (thick gray line) data from Californiastations. The ETo values represent the mean of 29 stations, the Epan values represent the mean of7 stations, and the cloud cover is the mean of 5 stations.

considered (not shown). The pattern and magnitude ofCIMIS Tavg daily and monthly correlations with Z700

(Fig. 8) were also verified using temperatures from 165NCDC stations (not shown).

b. Relations of regional atmospheric circulations withlarge-scale climate

The springtime circulation pattern associated withETo variations (Figs. 8 and 9) represents the long-termaverage connection between Z700 and the CIMIS surfacevariables. The pattern is an average and thus wouldobscure some of the different circulation patterns thatcontribute to ETo variations, especially those which in-frequently recur over the region.

The springtime pattern is similar to the correlationpatterns between winter sea level pressure and Decem-ber–August streamflow in the Sacramento River (Fig.6 of Cayan and Peterson 1989). The streamflow-cor-relation pattern, called the California Pressure Anomaly(CPA; Cayan and Peterson 1989), has roughly the sameareal extent but is centered (408N, 1308W) farther norththan the patterns of Figs. 8 and 9. Low wintertime pres-sures in the CPA region are associated with large stream-flows. Similar patterns were found by Mo (1999) andMo and Higgins (1998) in several atmospheric circu-lation variables associated with California wintertimeprecipitation. Another possibility, given that our focusis on spring and summer, is that the patterns shown inFigs. 8 and 9 might actually be related to the positionand influences of the North Pacific High (NPH) pattern(Hameed et al. 1995). The NPH is a synoptic featurecharacterized by a very persistent region of high pres-sure off the coast of North America. The spring patternshown here, however, is not the NPH as such, as theNPH is not stationary in space and it is generally locatedfarther offshore, although it sometimes could ridge overthe Pacific Northwest (Navy 2004). During the winter-time the NPH presents lower intensity and displacessouth (Dorman and Winant 1995; Navy 2004). The nor-mal position of the NPH during the summer is north ofHawaii (around 308N), and during the winter it is be-tween San Francisco and the north of Hawaii (around408N; Dorman and Winant 1995; Navy 2004). The cor-relation (1950–95) of spring means of the CPA-like

index (CPAI) and an index representing the intensity ofthe NPH (Shi 1999; Hameed et al. 1995) is 10.31 (pù 0.05). The correlation of the spring-mean CPAI andthe latitude of the spring center of action of the NPHis 10.36 (p , 0.05). Similar correlation for the lon-gitude of the spring center of action of the NPH is 10.37(p , 0.05).

On a seasonal basis, the CPAI developed here isstrongly correlated with ETo, Epan, and cloud cover[rmedian 5 0.72 (p , 0.01), 0.61 (p , 0.01), and r 50.40 (p , 0.05), respectively]. From 1950 to 2002, aweighted average of daily Z700 values was calculated forpoints in Fig. 8 with correlations greater than or equalto 0.40, weighted by those correlations. The springtimeCPAI series, and standardized springtime means of ETo,Epan, and cloud cover are compared in Fig. 10. The CPAIhas a local influence on precipitation and a regionalinfluence on temperature, as the CPAI pattern is sig-nificantly correlated with springtime temperature vari-ations into Nevada and Arizona (Fig. 11). The CPAIinfluence on surface RH is also relatively localized toCalifornia (Fig. 11). It is interesting to note that thecorrelations of the CPAI with RH have opposite signsfor the land and the sea, with steep gradients between,suggesting greater RH land–sea contrasts during ex-treme values of the CPAI (and thus also of ETo). Cor-relations of the CPAI with outgoing longwave radiation(a proxy for cloudiness) are consistent with the precip-itation correlation pattern. The OLR interannual cor-relation pattern also is similar to the OLR-compositepatterns found by Mo (1999; cf. Mo’s Fig. 5) and Moand Higgins (1998; cf. their Fig. 3a), which are thoughtto be related to intraseasonal precipitation oscillations(10–90-day period) in California during the winter. Cor-relations of the filtered (10–90 days) CPAI and OLRdaily anomalies during the spring presented a charac-teristic three-cell pattern depicted in Mo and Higgins(1998; not shown). The three-cell pattern is also seenin the OLR and precipitation rate season-mean corre-lations with the CPAI (Fig. 11). Heavy wintertime pre-cipitation in California (middle cell of the three-cellpattern) is associated with dry conditions over Wash-ington, British Columbia, Canada, and the eastern Gulfof Alaska (northernmost cell of the pattern), and alsowith reduced precipitation over the subtropical eastern


FIG. 11. Correlations between springtime seasonal-mean CPAI and data from the NCEP–NCAR reanalysis.

Pacific and Mexico (southernmost cell; Mo and Higgins1998). The seesaw between northern British Columbiaand California is a more consistent pattern, which isrelated to moisture transport. The seesaw between Cal-ifornia and the subtropics, although having a dynamicalsupport, depends on the strength of the Hadley cell,which is a reflection of convection in the subtropics (Moand Higgins 1998; K. C. Mo 2004, personal commu-nication). In our analysis, the local (middle) pattern overCalifornia defined by the CPAI was correlated with theother poles of the three-cell pattern during the spring,at 10–90 days and at interannual time scales (using sea-sonal-mean correlations). However, the connections ofCalifornia precipitation (or cloudiness) with the sub-tropics during the spring have to be verified with moreresearch, as the Mo and Higgins (1998) study focusedon winter processes and the Northern Hemisphere Had-ley cell weakens progressively from winter to summer(Dima and Wallace 2003).

Although the very strong El Nino events of 1982/83and 1997/98 yielded unusually low spring CPAI values,this tendency was not generally evident in other El Nino

events during 1950–2002. This may reflect the tendencyfor large El Nino events to persist through boreal winterinto the spring season. La Nina springs also are notconsistently associated with high spring CPAI. There-fore, there is no strong ENSO signal in springtime CPAIor ETo. For example, the correlation between springtimeNino-3 SST anomalies and the CPAI is nonsignificant[r 5 20.22 (p . 0.05)]. The same type of correlationfor the winter is 20.41 (p , 0.01), suggesting thatduring this season, the CPAI may be a feature relatedto the linear part of the extratropical response to ENSO.

5. Historical trends and implications for climatechange

Declining Epan rates have been reported in some re-gions of the United States and have been attributed tochanges in radiation, sunlight reaching the surface, andcloudiness (Roderick and Farquhar 2002; Peterson et al.1995; Lawrimore and Peterson 2000). In contrast, nosignificant springtime Epan trends are present at five ofseven California stations (Table 5). Even the signs of


TABLE 5. Annual and springtime trends of (a) pan evaporation dataand (b) cloud-cover data for California stations. Significant trends atthe 0.05 level are italic.

(a) Pan evaporation trends (1965 to 2001)

NameAnnual trend

(mm yr21)Spring trend(mm yr21)

Cachuma DamDavisDeath ValleyFriant Government CampSan Luis DamShasta DamLodi

21.501913.8009

233.670524.126723.468714.121225.6219

10.313610.582029.178911.149420.563110.481821.8858

(b) Cloud-cover trends (1950 to 1987)

NameAnnual trend

(% yr21)Spring trend

(% yr21)

EurekaRed BluffFresnoLos AngelesSacramentoSan FranciscoSan Diego

20.109710.118010.191720.023410.076910.034710.1758

20.133810.024010.037520.089120.0217310.017610.08123

TABLE 6. Annual and spring-mean trends in 1) precipitation and air temperature for two sets of NCDC California stations and 2) the springCPAI. Trends significant at the 0.05 level are italic. Low population density stations are those located in areas where population density isless than 120 habitants km22. Data are from 1950 to 2000.

Variable

Annual

Median trend

Percentage ofstations reporting

positive trends

Spring

Median trend

Percentage ofstations reporting

positive trends

Surface variables:All stations (165 stations):

PrecipitationMaximum temperatureMinimum temperature

11.0644 mm yr21

10.00718C yr21

10.02418C yr21

88%65%84%

13.2804 mm yr21

10.01698C yr21

10.03258C yr21

89%77%94%

Low population density stations (116 stations):PrecipitationMaximum temperatureMinimum temperature

10.9617 mm yr21

10.00428C yr21

10.02218C yr21

86%60%81%

12.8688 mm yr21

10.01318C yr21

10.97088C yr21

88%70%92%

Atmospheric variables:California Pressure Anomaly index 20.0242 yr21

Epan trends at stations relatively close to each other (e.g.,Davis and Lodi) have differed (Table 5). Thus, therehave been no strong or consistent overall springtimeEpan trend in California.

In California, radiation (and cloudiness) changes thatmay cause Epan and ETo trends (in the future) are as-sociated with springtime atmospheric circulations thatcan be indexed by the CPAI. However, historically, thespringtime and annual CPAI show no significant trendsfrom 1950 to 2002, and few of the cloud-cover stationsreported significant trends (Tables 5 and 6). Over thesame period, however, there have been significant trendstoward more springtime precipitation at many NCDCsites. This precipitation trend is not particularly consis-tent with the lack of trends in cloud cover, but inspection

of the precipitation time series reveals that many of theprecipitation trends reflect a step change toward wetterconditions in 1976/77. This climatic shift is part of aseemingly natural, large-scale circulation change thataffected many environmental and physical variables inNorth America (Ebbesmeyer et al. 1991; Mantua et al.1997). Overall, it appears that the lack of historicalspringtime Epan and ETo trends reflects a weakness orlack of trends in their primary radiative forcings.

Temperature trends may also affect ETo in California(eventually). In California, ETo’s temperature depen-dence is largely limited to daytime temperatures, whichcan be indexed by daily maximum temperatures (Tmax).Springtime and annual Tmax series in California havesmall (usually nonsignificant) and less consistent pos-itive trends than have minimum temperatures (Tmin). Atnonurban stations (those where population density ,120 habitants km22), the Tmax trends are even smallerand less significant (Table 5; Goodridge 1992).

Although historical springtime evaporation demandsin California have not trended significantly, future cli-mate changes may affect them. Current climate modelsproject twenty-first century warmings of between about38 and 68C over California in response to ‘‘business-as-usual’’ greenhouse-gas emission scenarios (e.g., Dettin-ger 2004). In response to a 138C uniform warming ofTavg, Eq. (1) (with unchanged 1990–2002 CIMIS me-teorological data applied otherwise) indicates that an-nual ETo totals could increase by 6% (Fig. 12). Notethat although temperatures play only a secondary rolein determining California ETo variability, Tavg trendscould still be reflected as ETo trends, as the lack ofstrong intercorrelation between Tavg and ETo anomalies(presented in section 3b) does not preclude this effectaccording to Eq. (1). That is, ETo’s driving meteoro-logical inputs (Rn, Tavg, RH, and U) used in Eq. (1)include trends and seasonal cycles of the variables,while the correlations between these inputs and ETo


FIG. 12. Median percentage change in total annual ETo(%) due toprescribed uniform increases in T and percentage changes of Rn con-sidering no change in RH and Uavg. The contours were plotted usingthe median values of meteorological inputs from 29 California sta-tions during 1990–2002. Positive (negative) contours solid (dashed)black (gray) curves.

(section 3b) were computed from the anomalies of thevariables (seasonal cycles and trends removed). Projec-tions of Rn trends are generally not available and havenot been studied. Projections of twenty-first century pre-cipitation might provide some hints about the future ofRn but are notably less unanimous than the temperatureprojections, with even the signs of projected precipi-tation changes differing from projection to projection(Dettinger 2004). However, to put the role of future Rn

changes into perspective, we note that a reduction of Rn

by about 6% would be required to mitigate the projected38C warming influence on ETo (Fig. 12); any increasesin Rn would rapidly aggravate the warming-induced ETotrends. For reference, the standard deviation of annualETo is typically around 5% (Table 2), so that ETo re-sponses of these magnitudes would be large and ob-servable in CIMIS data.

6. Concluding discussion

The annual demand of water is greater than the supplyof accumulated precipitation for all of the relatively lowelevation stations for which we can estimate ETo. How-ever, precipitation in California arrives in sporadicstorms, with many dry days in between, while ETo ismuch more smoothly distributed throughout the seasons.Although the variability of annual precipitation totals isalso much larger than those of ETo totals, ETo vari-ability is important because of its impacts upon waterresources and agricultural management. The ETo vari-ations are largest in spring when precipitation variabilityis muted and thus have significant influences upon theoverall hydrologic cycle in California.

Daily ETo anomalies in California are most variableduring spring. This large variability is associated withthe generally large ETo values, due to the season’s highsurface irradiance (compared to winter and autumn),

which is interrupted by occasional ETo reductions as-sociated with cloudy days. This combination of highseasonal values with strong cloud interference is notcommon to the other seasons. Daily ETo anomalies aremost strongly correlated with Rn and RH, and less sowith Tavg (especially during spring). The strong relationbetween ETo and Rn compared to Tavg is also evidencedby the shorter decorrelation times shared by ETo andRn, suggesting faster and more local controls on EToand Rn (i.e., cloudiness) than on Tavg.

ETo and associated meteorological variations aredriven by the regional atmospheric circulation. A char-acteristic circulation pattern influences daily, monthly,and seasonal variations of ETo, Rn, Tavg, RH, and Uavg

anomalies. High cloudiness and lower than normal ETovalues are associated with low-height (pressure) anom-alies centered immediately offshore from central Cali-fornia, in a CPA-like circulation pattern (Cayan andPeterson 1989). Conversely, higher than normal EToanomalies are associated with clear skies maintained bya high pressure center off the central California coast.

Statewide springtime cloudiness and Epan and EToconditions in California did not trend appreciably duringthe last part of the twentieth century. Average Tmin tem-peratures for 165 California stations showed significantincreasing trends from 1950 to 2001, but Tmax—whichis more directly associated with ETo variability—didnot generally trend significantly during the twentiethcentury. However, significant temperature increases inthe twenty-first century under the influences of increas-ing greenhouse gases are suggested by the climate pro-jections from general circulation models (GCMs). Thetypical range of temperature increase projected by theGCMs would increase ETo significantly, unless miti-gated by changes in Rn. The future of Rn remains un-certain, in large part because the disposition of cloudcover under climate change is not known. Moderate (onestandard deviation) changes in annual ETo would beprojected from the Penman equation [(1)] in responseto realistic future changes in temperature and radiation.However, more subtle changes should also be investi-gated; for example, those related to the changes in thehourly distributions of cloud cover, changes related tononuniform increases in temperature and radiation, andto ETo’s differing sensitivities to Tmax and Tmin.

This paper was focused on the variability of ETo be-cause of its importance for quantifying water demandand its widespread use for scheduling irrigation. In otherapplications, such as for closing water balances, climatechange studies, and hydrological models, it may be nec-essary to analyze ETa estimates. Although approximatemethods are available for the estimation of ETa fromETo and Rn (Bouchet 1963) and from hydrologic models(Knowles 2002; Knowles and Cayan 2002), more ly-simeter measurements are needed in order to improveETa estimates. High-resolution (,1 km) land-cover datawould also improve our estimates of hydrological andevapotranspiration conditions, while more high-eleva-


tion meteorological stations are needed to provide a bet-ter understanding of forest evapotranspiration processes.Additional CIMIS-like stations in northernmost Cali-fornia and in the southern deserts are needed to com-plement the information provided by the CIMIS system.

Acknowledgments. This work was funded by grantsfrom the California Department of Water Resources(Contract 4600002292), the California Energy Com-mission through the California Climate Change Centerat SIO, and the U.S. Department of Energy. Figure 11was created using the tools provided by NOAA’s Cli-mate Prediction Center with the help of Cathy Smith.We thank Bekele Temesgen from CIMIS who provideduseful information about the dataset and Kingtse Mofor her helpful comments. We thank the three anony-mous reviewers and the chief editor of the Journal ofHydrometeorology, Dennis Lettenmaier, for their con-structive comments, which enhanced the overall impactof this paper.

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