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Contribution of land-atmosphere coupling to summer climate

variability over the contiguous United States

Jingyong Zhang,1 Wei-Chyung Wang,1 and L. Ruby Leung2

Received 16 March 2008; revised 24 June 2008; accepted 21 August 2008; published 27 November 2008.

[1] TheWeather Research and Forecasting (WRF) model has been used to study the role ofland-atmosphere coupling in influencing interannual summer climate variability over thecontiguous United States. Two long-term climate simulations are performed: a controlexperiment (CTL) allows soil moisture to interact freely with the atmosphere, and anadditional experiment uncouples the land surface from the atmosphere by replacing summersoil moisture at each time step with the climatology of CTL. The CTL simulation reproduceswell the observed summer temperature and precipitation variability, despite somediscrepancies in daily mean and maximum temperature variability in the midwest/OhioValley region and the adjacent areas, and precipitation variability in the Great Plains andsome other areas. Strong coupling of soil moisture with daily mean temperature appearsmainly over the zone from the southwest to the northern Great Plains to the southeast,contributing up to about 30–60% of the total interannual variance of temperature. There isa significantly different influence on daily maximum and minimum temperatures. Soilmoisture plays a leading role in explaining the variability of maximum temperature overthis zone whereas minimum temperature variability is highly constrained by externalfactors including atmospheric circulation and sea surface temperature almost everywhereover land. Soil moisture, mainly through its effects on convection, makes a dominantcontribution to precipitation variability over about half of the northern United States. Thisresult does not support the Global Land-Atmosphere Coupling Experiment (GLACE) hotspot hypothesis over the central United States, at least on the interannual timescale. Themodel’s behavior agrees to a large extent with land-atmosphere relationships diagnosedusing the observations.

Citation: Zhang, J., W.-C. Wang, and L. R. Leung (2008), Contribution of land-atmosphere coupling to summer climate variability

over the contiguous United States, J. Geophys. Res., 113, D22109, doi:10.1029/2008JD010136.

1. Introduction

[2] Increased temperature and precipitation variability canpotentially cause more climate extremes such as more fre-quent and intense heat waves, an increased chance of drought,and increased intensity of precipitation over the midlatitudecontinents in summer [Easterling et al., 2000; Raisanen,2002; Meehl and Tebaldi, 2004]. Land surface is thought tobe an important slowly varying component of the Earthsystem that affects climate variation and variability, especial-ly involving variable of soil wetness [e.g., Dirmeyer, 1995].[3] The soil can ‘‘remember’’ a dry or wet anomaly and

further maintain low or high evaporation and transpirationanomalies for several months, which may in turn play animportant role in the atmosphere evolution [e.g., Shukla andMintz, 1982; Delworth and Manabe, 1988]. The couplingbetween soil moisture conditions and the atmosphere isfound to be sensitive to regional climate regimes [e.g.,

Koster et al., 2000]. The Global Land-Atmosphere Cou-pling Experiment (GLACE) [Koster et al., 2004, 2006a;Guo et al., 2006] study demonstrated that strong land–atmosphere coupling mainly appears in the transitionalzones between dry and wet climates where evaporation issuitably high but still sensitive to soil moisture in the borealsummer season. Zhang et al. [2008] recently showedevidences for soil moisture feedback on precipitation inthe climatic/ecological transitional zones by statistical anal-yses to observed precipitation and soil moisture data fromthe Global Land Data Assimilation System (GLDAS)[Rodell et al., 2004], ERA-40 Reanalysis [Uppala et al.,2005], and limited-area observations. The importance ofsoil moisture to climate variability also varies with season.While soil moisture could have a weak impact on winter-time climate variability which is largely influenced by thelarge-scale circulation associated with sea surface tempera-ture (SST) anomalies, it may play a leading role in sum-mertime variability over the midlatitude continents [Kosteret al., 2000; Douville and Chauvin, 2000; Kushnir et al.,2002; Conil et al., 2007].[4] Surface air temperature is determined by surface

energy balance involving radiation, latent and sensible heat

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D22109, doi:10.1029/2008JD010136, 2008

1Atmospheric Sciences Research Center, State University of New Yorkat Albany, Albany, New York, USA.

2Pacific Northwest National Laboratory, Richland, Washington, USA.

Copyright 2008 by the American Geophysical Union.0148-0227/08/2008JD010136

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fluxes and heat storage, and is thus expected to be highlysensitive to localized interactions between land surface andthe overlying atmosphere. Timbal et al. [2002] demonstratedthe crucial role of soil moisture in temperature variabilityand predictability over Australia. Koster et al. [2006b]suggested that land moisture variables have a first-orderimpact on temporal variability of temperature. Seneviratneet al. [2006] found that the land-atmosphere coupling couldcontribute up to about two thirds of total summer temper-ature variance over the transitional zone in both recent andfuture European climates. While most previous studiesfocused on soil moisture feedback on average temperature,a few addressed soil moisture influence on daily maximumand minimum temperatures [Dai et al., 1999; Durre et al.,2000; Alfaro et al., 2006].[5] Earlier research in soil moisture feedback on climate

variation and variability (including many mentioned above)was largely based on simulations with atmospheric generalcirculation models (AGCMs), which may only providelimited information on land-atmosphere interactions andclimate variability at a regional scale. On the basis of acomparison of observational and simulated relationshipsbetween land surface and atmospheric state variables at afew locations, Dirmeyer et al. [2006] suggested that most ofthe 12 participating AGCMs in the GLACE study do notrepresent the land-atmosphere coupling correctly. Neverthe-less, available data are not sufficient to establish observa-tional land-atmosphere relationships at regional and globalscales, and the statistical approach applied to limited-areaobservations only can offer limited insight into physicalmechanisms. Therefore, if enough care is taken in analysisof models’ behavior, models still are useful for understand-ing the land-atmosphere relationship and provide capacityfor the causality analysis. With high spatial resolution andrealistic representation of key physical processes, regionalclimate models (RCMs) are more skillful at resolving landsurface heterogeneity and other physical processes, and maythus better represent land-atmosphere interactions and theassociated regional climate characteristics as compared toAGCMs [e.g., Dickinson et al., 1989; Giorgi, 1990; Leungand Ghan, 1998; Schar et al., 1999; Wang et al., 2000,2003]. For example, Castro et al. [2007a, 2007b] found thatthe Regional Atmospheric Modeling System (RAMS) canadd value to the representation of summer climate andlong–term climate variability beyond the driving globalreanalysis. By analyses of observations and a 20-yearregional climate simulation, Qian and Leung [2007] dem-onstrated that their RCM can capture surface hydrologicalcycle and precipitation characteristics over East Asia.[6] RCM simulations have previously been used to in-

vestigate the role of soil moisture conditions in NorthAmerican summer climate [e.g., Giorgi et al., 1996;Bosilovich and Sun, 1999; Hong and Pan, 2000; Kanamitsuand Mo, 2003]. These studies, on the whole, exhibited apositive soil moisture feedback on precipitation over NorthAmerica, complying with the GLACE result. Regarding soilmoisture feedback on temperature, Diffenbaugh et al. [2005]recently demonstrated that soil moisture�temperature inter-actions enhance the occurrence of extreme high temperatureevents over the United States. The enhancement was alsoidentified over the Europe [Diffenbaugh et al., 2007; Fischeret al., 2007].

[7] The purpose of the present study is to investigate therole of the land-atmosphere coupling in interannual summerclimate variability over the contiguous United States usingtwo long-term climate simulations produced by the WeatherResearch and Forecasting (WRF) model. Besides meantemperature (Tmean) variability, we are also motivated toexamine if the role of the land-atmosphere coupling differsfor maximum temperature (Tmax) versus minimum temper-ature (Tmin) because surface fluxes associated with largevariation of solar radiation are quite different between dayand night.[8] The paper is organized as follows. Section 2 describes

model and experiments, as well as methods to assess theland-atmosphere coupling. Section 3 evaluates the perfor-mance of the WRF model in simulating interannual climatevariability, and identifies model biases. In section 4, climatevariability is split into respective contributions induced bythe land-atmosphere coupling and external factors (atmo-spheric circulation, SST). Section 5 examines if the resultsare model-dependent by comparing model’s behavior withobservational land-atmosphere relationships. Finally, con-clusion and discussion are given in section 6.

2. Approach

[9] On the basis of previous experimental designs touncouple land surface from the atmosphere [Koster et al.,2000; Seneviratne et al., 2006], two experiments are per-formed in this study: a control integration (CTL), whichcovers the period of March 1981 to August 1996, allowssoil moisture vary freely according to the land surfacescheme; an additional simulation (SoilM), in which the soilmoisture evolution at each time step is replaced with theclimatology of CTL, consist of 15-summer (1982–1996)integration which restarts on 1 June of each year andintegrates for 3 months to 31 August. This effectivelyseparates the contribution of the land-atmosphere couplingto interannual summer climate variability from the externalforcings. The SoilM experiment shares the same modelconfiguration as the CTL simulation.[10] The RCM used in the study is the Weather Research

and Forecasting (WRF) model version 2 [Skamarock et al.,2005] that has been adapted by Leung et al. [2006] forclimate simulations. The physical parameterizations usedinclude the WSM-5 class microphysics [Hong et al., 1998],the new Kain-Fritsch convective parameterization [Kain,2004], the Community Atmospheric Model (CAM3) radia-tion package [Collins et al., 2006], the Yonsei Universityplanetary boundary layer scheme [Noh et al., 2003], and theNoah land surface scheme [Chen and Dudhia, 2001].[11] The Noah land surface scheme calculates soil pro-

cesses at 4 soil layers with 10, 30, 60, and 100 cm thickness,and includes one canopy layer. It simulates soil moisture(both liquid and frozen), soil temperature, skin temperature,snowpack depth, snowpack water equivalent, canopy watercontent, and energy flux and water flux terms of surfaceenergy balance and surface water balance.[12] Model domain and topography utilized in the experi-

ments, as well as the analysis domain are shown in Figure 1.The model domain covers the whole contiguous UnitedStates and the surrounding ocean areas, and extends farenough south to entirely include the Gulf of Mexico. The

D22109 ZHANG ET AL.: SOIL MOISTURE FEEDBACK ON CLIMATE

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simulations use a horizontal grid spacing of 60 km and31 vertical sigma levels. Initial condition and lateral andlower boundary conditions (SST) are derived from theNCEP-NCAR global reanalyses, and updated every 6 h.As described by Leung et al. [2006], our simulationsinclude seasonally varying vegetation fraction and surfacealbedo. The buffer zone in the lateral boundaries consists of10 grid points; a relaxation scheme is used to blend theboundary conditions with the model solution with a linear–exponential functional form for the relaxation coefficients.We use the first 15 months (from March 1981 to May 1982)as model spin-up period to minimize the initialization effectsof soil moisture and soil temperature. The 15 summers thatfollowed the spin-up period are the periods analyzed in ourstudy.[13] We use two methods, variance analysis and the

GLACE-type coupling strength parameter, to objectivelyquantify the land-atmosphere coupling and its contributionto climate variability.[14] In variance analysis, percentage of interannual vari-

ance of summer (June–August) mean climate for a variableV owing to the land-atmosphere coupling is estimated asfollows:

PVv ¼s2V CTLð Þ � s2

V SoilMð Þs2V CTLð Þ

; ð1Þ

where sV2(CTL) and sV

2(SoilM) are interannual variances ofsummer mean V in the CTL and SoilM simulations,respectively. The coupling strength parameter method wasinitially used by Koster et al. [2000] and recently followedin the GLACE study and other studies. For each specificclimate variable, we ignore the first 10 days and group thefollowing 80 days into pentads (5-day periods). This issimilar to the GLAC y and gives 16 pentads per

summer. However, the following data arrangement isdifferent from the GLACE study because the focaltimescales are different in the two studies. We arrange eachpentad for 15 years into one group (15 values in total),representing interannual variation of the climate variable ateach pentad, and thus get 16-group data. The couplingstrength parameter for the climate variable is defined asDWV (WV (CTL) � WV (SoilM)), and WV value is calculatedas follows:

WV ¼16s2

V� s2

V

15s2V

; ð2Þ

where sV2 is the variance of pentad mean climate variable

computed from all values available in 16-group data (across16 group by 15 pentads or 240 values in total) and sV

2 is thevariance of pentad mean climate variable from the 16-groupaverage time series (across 15 values in total). In our study,DWV measures the degree to which the land-atmospherecoupling induces similarity of interannual changes for onespecific climate variable between pentads. Consider, forexample, a wet anomaly induced by a precipitation event atone pentad may in turn induce additional precipitation atsubsequent pentads, and thus increase the precipitationsimilarity across pentads. In the SoilM simulation, suchsimilarity should be absent as climatological land surfaceconditions are prescribed and the land surface is not allowedto respond to the precipitation event.[15] Because the real world cannot present us the states of

uncoupling of land and atmosphere system, the resultsobtained with the variance analysis and coupling strengthanalysis using the model outputs cannot be verified directlyon the basis of observations. To check if simulated land-atmosphere relationships are dependent on the model, we

Figure 1. WRF model domain and topography (in meters), and analysis domain (marked by therectangle) defined in this study.


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further investigate the correlations between antecedent pre-cipitation and temperature in simulations and observations.

3. Model Evaluation

[16] The observed daily Tmean, Tmax, and Tmin data arefrom the U.S. Historical Climatology Network (USHCN)[Karl et al., 1990]. For observed precipitation, we take theClimate Prediction Center (CPC) Merged Analysis of Pre-cipitation (CMAP) data, which are derived from rain gaugeobservations and satellite estimates over the land [Xie andArkin, 1997]. The precipitation data set exhibits similarinterannual variability pattern and range over the UnitedStates to surface-based records [Ruiz-Barradas and Nigam,2005]. Therefore we only present CMAP interannual sum-mer precipitation variability in this study.[17] We first look at the interannual variability of summer

Tmean, Tmax and Tmin, expressed in terms of standarddeviation with respect to the period of 1982–1996 (Figure 2).The Tmean, Tmax and Tmin variability in the observationshas a similar spatial distribution with high values over theareas from the northern Rockies to the midwest/Ohio Valleyregion, which is well simulated by the WRF model. Formagnitude, although the model produces larger Tmean andTmax variability over the midwest/Ohio Valley regionand adjacent areas, it simulates reasonably well Tmeanand Tmax variability ov er regions and Tmin variability

over most areas of the United States as compared to obser-vations.[18] Figure 3 shows 15-year summer mean precipitation,

standard deviation of summer precipitation, and the coeffi-cient of variation (standard deviation divided by the mean)in the observations from the CMAP and model simulations.The WRF model results are in good agreement with theobserved mean precipitation over western United States,much of eastern United States, and northern Mexico, butshow some differences in central United States and someeastern states. The WRF model reproduces poorly theobservational local maximum over the midwest, and alsounderestimates precipitation over other areas in the GreatPlains and over the eastern United States. The standarddeviation of summer precipitation is greatly affected by theprecipitation mean. Overall, it exhibits a similar pattern tothe summer mean precipitation in both WRF simulation andobservations. The WRF model simulates well a generalincrease in precipitation standard deviation from west toeast, but fails to catch the local maximum over the midwest.Because the coefficient of variation can remove the depen-dency of the standard deviation on the mean precipitation, itis a more independent measure of interannual variability[Giorgi et al., 2004]. If we adopt this index, the WRF modelcan simulate well the spatial distribution of precipitationvariability though some discrepancies exist over some areas.In particular, it reproduces the local maximum of precipita-

Figure 2. Standard deviation of temperature (in �C) during summer (June–August) based on 1982–1996 data from (left) U.S. Historical Climatology Network (USHCN) and (right) CTL: (a, d) Tmean,(b, e) Tmax, and (c, f) Tmin.


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tion variability over the midwest though with a too largemagnitude. As is well known, it is a common difficulty forclimate models to reproduce realistically Great Plains pre-cipitation variations [e.g., Anderson et al., 2003], mainlyattributed to complexity in diurnal rainfall associated withmesoscale convective complexes propagating from theRocky Mountain [e.g., Carbone et al., 2002]. The WRFmodel exhibits poor skills in simulating Great Plains pre-cipitation climatology but not the interannual variability (asmeasured by the coefficient of variation).[19] Precipitation is the dominant driver of surface water

balance. We further examine the climatology of the modelsoil moisture and the interannual anomalies from thatclimatology. In a given land model, the range of absolutevalue of soil moisture depends on the maximum holdingcapacity, the field capacity, the soil moisture threshold

below which transpiration starts to become soil moisturelimited, and the soil wilting point. These values are closelyassociated with soil and other land surface properties.Therefore, spatial pattern of soil moisture climatology isnot necessarily consistent with that of precipitation though,at a given grid cell, soil moisture is greatly determined byprecipitation. Figure 4 shows 15-year summer mean andstandard deviation fields of soil moisture in CTL. In generalterms, mean total soil moisture tends to be high in forest-cover areas over the northwest and eastern United States.Note that the precipitation deficits over the Great Plains andthe Gulf of Mexico coastal regions may cause the dry modelbiases in soil moisture. However, the biases could not beobjectively judged owing to the lack of observations. Thelargest variability of soil moisture, as represented by itsstandard deviation, is found over eastern United States, the

Figure 3. The 1982–1996 summer mean precipitation, standard deviation of summer precipitation, andthe coefficient of variation (standard deviation divided by the mean) in CMAP and CTL: (a, b) mean,(c, d) standard deviation, and (e, f) coefficient of variation.

Figure 4. The 1982–1996 summer mean total soil moisture and standard deviation of summer soilmoisture in CTL: (a) the 1982–1996 summer mean total soil moisture (in centimeters) and (b) standarddeviation of s soil moisture (in centimeters).


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portion of the southwest, and parts of the northwest andnorthern Great Plains. Compared to mean precipitationimpact on soil moisture climatology, the pattern of soilmoisture variability is affected by that of precipitation to alarger extent. The ability of soil moisture to affect tem-perature and precipitation variability definitely depends onthe value of soil moisture itself and its variability. Withthis in wind, we subsequently look at the role of the land-atmosphere coupling.

4. Land-Atmosphere Coupling and ClimateVariability

4.1 Coupling of Soil Moisture With Temperature

[20] Figure 5 shows differences between CTL and SoilMin standard deviations of summer Tmean, Tmax and Tmin.Because the land-atmosphere system is uncoupled andinterannual variation of oisture is removed in SoilM,

the fields reflect changes in summer temperature variabil-ity owing to the land-atmosphere coupling.[21] Overall, the land-atmosphere coupling results in an

increase in the standard deviation of summer Tmean,especially over a region extending from the southwest tothe northern Great Plains to the southeast. There is a sharpcontrast between Tmin and Tmax with respect to soilmoisture influence. Changes in Tmax variability have asimilar spatial distribution to that of Tmean, but with largeramplitude, whereas Tmin variability changes are generallysmall and insignificant. This is not unexpected since sensi-ble and latent heat changes induced by soil moisturefeedback are far larger during day than during night. Thesharp day-night contrast dictates that Tmax and Tminbehave differently.[22] We further quantify the role of the land-atmosphere

coupling in interannual summer temperature variabilityusing the variance method and the GLACE-type coupling

Figure 5. Difference in standard deviation of summer temperature (in �C) between CTL and SoilM:(a) Tmean, (b) Tmax, and (c) Tmin. Grid cells with values significant at the 90% level by F-test aremarked by the solid circles.


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strength parameter described in section 2. Figure 6 showsthat the two methods present similar features.[23] 1. The land-atmosphere coupling makes a large

contribution to summer Tmean variability in a zone extend-ing from the southwest to the northern Great Plains to thesoutheast, explaining about 30–60% of the total interannualvariance. In particular, the Tmean variability is greatlyaffected by soil moisture feedback over Arkansas, Tennes-see, and portions of Mississippi, Alabama, Missouri, Illi-nois, Kentucky, and New Mexico.[24] 2. The land-atmosphere coupling plays a leading role

in summer Tmax variability almost over all regions whereTmean is highly influenced, generally accounting for morethan 50% of the total variance.[25] 3. Summer Tmin variability is highly constrained by

external factors over most areas of the United States; soilmoisture exerts some impacts mainly in southern California,Arizona and New Mexico that are influenced by the NorthAmerican summer monsoon, and some isolated small areas.[26] Compared with Figure 4b, the zone in which strong

coupling of soil moisture with Tmean and Tmax exists alsohas high soil moisture variability in CTL. The existence ofhigh soil moisture variability is a necessary, but not suffi-cient, condition for strong land-atmosphere feedback ontemperature to exist, as a region with small variabilitywould not be expected to have strong feedback. For

example, some areas over the northwest that exhibit highsoil moisture variability do not reveal strong soil moisture�temperature coupling. Further analyses find that the zonewith strong coupling generally corresponds to the transi-tional zone between cold and warm climates (Figure 7b).Most of grid cells with DWTmean > 0.08 and/or more than40% of total variance owing to the land-atmosphere cou-pling fall within the temperature range of 23�–29�C. Thesensitivity of soil moisture impacts to temperature regime(in addition to soil moisture regime) suggests that if a RCMor AGCM behaves unrealistically in simulating Tmean, itmay be not able to represent the interactions between soilmoisture and temperature correctly. Comparison with obser-vations (Figure 7a) shows that the WRF model reproducesTmean features properly despite a local warm bias overcentral United States and some other differences in magni-tudes. With the warm bias, the overlapping region with themodel transitional zone in the observations has coldertemperature bounds of 1�–2�C compared to the CTLsimulations over the central United States.[28] This leads us to ask why strong soil moisture�

temperature coupling preferably appears in the transitionaltemperature zone with high soil moisture variability. Aseries of previous studies have demonstrated that the tem-perature variability is closely associated with evapotranspi-ration (ET) anomalies [e.g., Diffenbaugh et al., 2005;

Figure 6. (left) Percentage of interannual summer temperature variance due to land-atmospherecoupling and (right) the GLACE-type coupling strength parameter: (a, d) Tmean, (b, e) Tmax, and (c, f)Tmin.


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Koster et al., 2006a; Seneviratne et al., 2006]. The GLACEstudy used the product of the coupling strength parameterfor ET and the standard deviation of ET to characterize theability of a local ET signal to support land-atmospherefeedback [Guo et al., 2006]. It was found that an evapora-tion rate that varies strongly and consistently with soilmoisture tends to lead to a higher coupling strength. Herewe follow the same procedure to examine the coupling ofsoil moisture with ET and sensible heat (Figure 8). Betts[2004] found that the relationship between sensible heat andsoil wetness is usually stronger than that for latent heat andsoil wetness across several domains from the deep tropics toboreal forests. This characteristic is borne out in our studyas geographical pattern of the diagnostic product for sensi-ble heat agree to a larger degree with pattern of soilmoisture anomalies (Figure 4b) than that of latent heat.Clearly, the product for sensible heat appears to explain wellthe geographical variation in the coupling of soil moisturewith Tmean and Tmax. In addition, over the zone withstrong soil moisture�temperature coupling the ET signalalso tends to vary strongly and consistently with soilmoisture. From the perspective of surface energy budget,soil moisture, along with other land surface conditions,

determines the partitioning of available energy into latentheat and sensible heat. The sensible heat directly warmsnear-surface air while the latent heat is released at higherlevel to fuel precipitation. Therefore, it is not surprising thatsoil moisture anomalies affect temperature variability morevia sensible heat than latent heat. The consistency betweenthe diagnostic product for sensible heat and the soil mois-ture�temperature coupling also implies that soil moistureimpacts on temperature are largely local.

4.2. Coupling of Soil Moisture With Precipitation

[29] Figure 9 presents differences between CTL andSoilM in standard deviations of total precipitation, convec-tive precipitation, and large-scale precipitation. Overall, theland-atmosphere coupling causes an increase in interannualsummer variability of total precipitation and convectiveprecipitation over northern United States (north of 36�N),eastern sea board, and the southwest. In contrast, neitherpositive nor negative sign dominates in central UnitedStates and other regions. Changes in large-scale precipita-tion variability, though significant at the 90% level by F-testover many areas, are generally small as compared toconvective precipitation variability changes.

Figure 7. The 1982–1996 summer mean Tmean (in �C) in (a) USHCN and (b) CTL. In Figure 7b, gridcells in Figure 6a with values larger than 40% and in Figure 6d with DWTmean > 0.08 are marked withcircles and crosses, respectively. Note that strong coupling mainly appears the transition zone betweencold and warm climate (23–29�C in model and 22–27�C in observations).


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[30] There are generally more spatial heterogeneity anduncertainty with respect to changes in precipitation vari-ability than changes in temperature variability. For example,differences in standard deviations of precipitation betweenCTL and SoilM often jump from positive to negative valuesacross very small areas over southern Great Plains andsoutheastern states. To avoid the effects of precipitationheterogeneity, model data are smoothed in each directionwith a nine-point filter prior to quantifying the coupling ofsoil moisture with precipitation.[31] Figure 10 examines the percentage of total interan-

nual summer variance induced by the land-atmospherecoupling and the GLACE-type coupling strength parameterfor total precipitation, convective precipitation, and large-scale precipitation using the spatially smoothed data. Bothmeasures agree that the land-atmosphere coupling signifi-cantly contributes to total precipitation variability in a swathcovering about 50% of northern United States, accountingfor about half of the interannual variance. Further analysisof respective contributions of convective precipitation andlarge-scale precipitation shows that the effects of convectiondominate the coupling with total precipitation over mostareas of the swath, while large-scale precipitation is onlyimportant in north central United States and portion of theGreat Lakes region. Similarly, the GLACE study also foundthat convective precipitation bears most of the signal of thesoil moisture’s impact on precipitation on the global scale,due in large part to the dominance of convective precipita-tion during boreal summer.[32] Although positive soil moisture feedback is domi-

nant over most areas, ne ative contribution of soil moisture

to precipitation variability is also seen, but limited to smallareas. Possible reasons for negative changes may includelarge-scale effects, negative soil moisture feedback, andstatistical sampling.[33] From Figure 10, the locations of strong soil moistur-

e�precipitation coupling are generally identical to thosediagnosed from the GLDAS soil moisture analysis of Zhanget al. [2008]. However, there are some disagreements withthe GLACE study that estimated a hot spot of the land-atmosphere coupling in the Great Plains over North America,although the northern Great Plains are identified as acommon key region in the two studies. Dirmeyer et al.[2006] demonstrated that, compared to the observed rela-tionships between surface and atmospheric state variables infew locations, most of the AGCMs in the GLACE studycannot validate well, suggesting that these models do notrepresent the land-atmosphere coupling correctly. Over theGreat Plains, it is evident that the AGCMs often producepoor simulations of climate variations [e.g., Fennessy andXue, 1997]. This implies that the estimated hot spot in theGreat Plains in the GLACE study could be more uncertain.Indeed, only half of the participating models in the GLACEstudy showed strong soil moisture impacts over the GreatPlains [Koster et al., 2004]. Ruiz-Barradas and Nigam[2005, 2006] found the dominance of large-scale moistureflux in accounting for Great Plains precipitation variationsboth from the observations and the North American RegionalReanalysis (NARR) [Mesinger et al., 2006], and suggestedthat the GLACE hot spot in the Great Plains could beattributed to the undue influence of a minority of models(3 out of 12).

Figure 8. Distribution of the product of the GLACE-type coupling strength parameter and standarddeviation: (a) latent heat and (b) sensible heat.


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[34] Meanwhile, the results of this study agree qualita-tively with those of the GLACE study at two aspects. Thetwo studies consistently agree that, as a whole, there exists apositive soil moisture�precipitation feedback over the con-tiguous United States. In addition, the GLACE found thatstrong land-atmosphere coupling appears over the transi-tional zones between dry and wet climates, emphasizing therole of soil moisture regime [e.g., Koster et al., 2006a].Similarly, our results also show that strong coupling withtemperature and precipitation are not visible over many wetareas over the northwest and the northeast and dry southernGreat Plains in model.[35] Strong soil moisture�precipitation coupling would

not appear over the areas where highest product of thecoupling strength parameter for ET and the standard devi-ation of ET occurs (Figure 8a). Rather, over regions ofstrong coupling the soil moisture only exhibits moderateability to affect the ET l. This suggests that the ET-

precipitation link that involving the interactions amongsurface processes, the atmospheric boundary layer, andclouds also play an important role in the geographicvariations of soil moisture�precipitation coupling strength.

5. Comparison With Observational Land-Atmosphere Relationships

[36] Validation of the simulated coupling is limited by alack of observational soil moisture, ET, and other surfacefluxes data at regional and global scales. Zhang et al. [2008]recently assessed Northern Hemisphere summer land sur-face and precipitation coupling using available observationsand soil moisture data from the GLDAS. They found thatstrong coupling exists in the northern part of the UnitedStates. As mentioned in section 4.2, their diagnostic con-clusion and the quantified coupling in our simulations verifywell with each other, although the former focused on local

Figure 9. Differences in standard deviation of summer precipitation (in mm/d) between CTL andSoilM: (a) total precipitation, (b) convective precipitation, and (c) large-scale precipitation. Grid cellswith values significant at the 90% level by F-test are marked by solid circles.


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subsurface soil moisture impacts, and estimated a smallerpercentage of total variance owing to soil moisture feed-back.[37] To test if simulated relationships between soil mois-

ture and temperature are model-dependent, we follows thesame strategy used in previous studies [e.g., Huang and Vanden Dool, 1993; Koster et al., 2003]. First, we examinecorrelations between antecedent precipitation and tempera-ture in the observations, and a significant correlation ishypothesized to point to soil moisture impacts. We thendetermine if the significant correlation agrees with thatcalculated from the CTL simulation. Finally, the samecalculation is performed using data from SoilM to deter-mine whether or not the consistent correlation sign, if any,found in the observations and CTL, is attributed to soilmoisture feedback.[38] Here we use CMAP precipitation and Willmott and

Matsuura [1995] temperature data. Prior to correlationcalculations, observed temperature data at a resolution of0.5� � 0.5� and model data are first aggregated onto thesame 2.5� � 2.5� spatial resolution as CMAP precipitation.A three-point filter is then applied in both meridional andzonal directions at each grid cell with mean annual cyclesand interannual trends of each month removed. Finally,correlations are calculated using June–July and July–Augustdata (That is, 2 � 15 years = 30 samples in total). Note that

the smoothness adopted allows us to obtain better signifi-cance because the correlations depend partly on spatialscales.[39] Figure 11 presents the correlations between antecedent

precipitation and temperature in the observations and CTLand SoilM simulations. Observational correlations are sig-nificant at 95% confidence level over northern and easternUnited States and portion of the southwest. The overallpattern in CTL agrees with that in the observations to a largeextent. At the same time, there exist some differences in thenorthern Rockies and midwest/Ohio Valley region, whereCTL has stronger correlations. Figure 11c shows that theatmospheric circulation and SST can explain part of thecorrelations in the two regions. If their effects are removedfrom the CTL simulation, the pattern in Figure 11b tends toagree more with Figure 11a. On one hand, the betterconsistency indicates that simulated land-atmosphere rela-tionships are not model specific. On the other hand, itsuggests that most of the observed correlations are inducedsolely by soil moisture feedback.

6. Conclusion and Discussion

[40] This study isolates the role of the land-atmospherecoupling in climate variability from external factors in-cluding atmospheric circulation and SST over the contig-

Figure 10. (left) Percentage of interannual summer precipitation variance due to land-atmospherecoupling and (right) the GLACE-type coupling strength parameter: (a, d) total precipitation, (b, e)convective precipitation, and (c, f) large-scale precipitation.


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uous United States with two RCM simulations. The land-atmosphere coupling makes a significant (dominant) contri-bution to interannual summer Tmean (Tmax) variability overthe zone extending from the southwest to the northern GreatPlains to the southeast, but plays a very limited role in Tminvariability. Summer precipitation variability is dominated bythe land-atmosphere coupling in a swath over northernUnited States. Convective precipitation, as a key componentof the pathway linking soil moisture variations and precipi-tation, is more sensitive to land surface moisture variationsthan large-scale precipitation. Comparison with diagnosedland-atmosphere relationships from Zhang et al. [2008] and

observational correlations between antecedent precipitationand temperature shows that the WRF model realisticallyrepresents the land-atmosphere coupling over the contiguousUnited States to a large extent.[41] It is noteworthy that the existence of high soil

moisture variability, though is a necessary, does not byitself guarantee a strong coupling of soil moisture withTmean and Tmax. The coupling of soil moisture withTmean and Tmax may also be sensitive to temperatureregime (in addition to soil moisture regime), which was notidentified in earlier AGCM studies. While sensible heattends to be limited at low temperature, high temperature

Figure 11. Correlations between antecedent precipitation (1-month lead) and temperature in(a) observations, (b) CTL, and (c) SoilM. Correlations of ±0.36, ±0.46, ±0.5, and ±0.57 are significantat the 95%, 99%, 99.5%, and 99.9% levels, respectively. The correlation is calculated using June–Julyprecipitation and July–August temperature over the period 1982–1996 (That is, 2� 15 years = 30 samplesin total). Grid cells with correlation in SoilM significant at the 95% level are marked by solid circles inFigure 11b.


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may make the soil dry out very quickly, and thus damp soilmoisture anomalies. Therefore, in both cold and warmclimates, either small soil moisture changes or small sensibleheat changes limit the temperature response to soil moistureanomalies. This is traced to the coupling between soilmoisture and sensible heat, which is larger in the tempera-ture range of 23�–29�C between the cold and warmregimes. At a higher spatial resolution that resolves region-al-scale forcings, the WRF model can represent moredetailed climate features and more realistic land-atmosphereinteractions, and may thus allow the role of temperatureregime to stand out. Conversely, because the resolution ofAGCMs is too coarse to capture some important regionalclimate details, they may lack the ability to identify the roleof temperature regime in soil moisture�temperature cou-pling correctly.[42] Strong coupling between soil moisture and precipi-

tation appear over northern United States in which the ETsignals are in general moderately, but not most highly,sensitive to soil moisture anomalies. This suggests that, inaddition to the ability of soil moisture to affect ET, the ET-precipitation connection also plays an important role in soilmoisture�precipitation feedbacks over the United States.Further analysis of variations of the atmospheric boundarylayer and clouds will offer insight into this connection.[43] Meanwhile, there are several relevant issues that

warrant discussion. Although the WRF model generallyreproduces the major characteristics of temperature andprecipitation variability, it contains biases in simulatingTmean and Tmax variability in the midwest/Ohio Valleyregion and adjacent areas, and precipitation variability in theGreat Plains and some other areas. In the midwest/OhioValley region which has large biases in temperature vari-ability, soil moisture also has high variability and exhibitsstrong feedbacks. If too high soil moisture variability issimulated over this region, the model errors would beamplified by surface moisture effects. Further study isneeded to improve our understanding of the complexinterplay between the land-atmosphere coupling and themodel errors. Previous studies demonstrated that RCMsimulations of precipitation are highly sensitive to thechoice of cumulus parameterization [e.g., Giorgi andShields, 1999; Leung et al., 2003]. Liang et al. [2004]found that the Kain-Fritsch cumulus scheme yields a largeprecipitation deficit over the midwest when compared to theGrell scheme. Experiments with different physical repre-sentations especially cumulus options should be pursued inthe future to clarify model uncertainties over areas withlarge precipitation biases. Giorgi and Bi [2000] found thatthe difference in summer mean rainfall, or internal modelvariability, due to random perturbations of initial atmospher-ic conditions can reach 5–15% of the average rainfall at thesubregional level. They also compared the results of theperturbation experiments with corresponding simulations inwhich the Leaf Area Index (LAI) throughout the modeldomain was divided by a factor of 3. Precipitation changesinduced by the decreasing LAI were essentially not distin-guishable from those of the perturbation experiments. Qianand Leung [2007] suggested that the internal model vari-ability may contribute to low RCM skill in simulatinginterannual summer precipitation anomalies. More researchis clearly needed to ide at, to what extent, the internal

model variability biases the contribution of land-atmospherecoupling to summer precipitation variability over the UnitedStates.[44] Nevertheless, this study represents an early attempt

to use long-term RCM simulations to assess the role of theland-atmosphere coupling in climate variability over theUnited States. The main conclusions are encouraging be-cause they are not model specific, at least qualitatively,when compared to the observational land-atmosphere rela-tionships. Given the importance of the land-atmospherecoupling to interannual summer climate variability, andhence initial land surface states to climate prediction atseasonal to interannual timescales, improved monitoring ofsoil moisture and land surface fluxes especially over keyregions with strong land surface feedback is highly desirable.

[45] Acknowledgments. We thank two anonymous reviewers forhelpful comments and suggestions. This work is supported by grants (toSUNY Albany) from the U.S. Department of Energy’s Office of ScienceBiological and Environmental Research and the Climate Dynamics Divi-sion, National Science Foundation. Pacific Northwest National Laboratoryis operated for the U.S. DOE by Battelle Memorial Institute under contractDE-AC06-76RLO1830.

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��L. R. Leung, Pacific Northwest National Laboratory, Richland, WA

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