do global models properly represent the feedback between ... · do global models properly represent...

Do Global Models Properly Represent the Feedback between Land and Atmosphere?

PAUL A. DIRMEYER

Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

RANDAL D. KOSTER

Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

ZHICHANG GUO

Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

(Manuscript received 10 August 2005, in final form 5 January 2006)

ABSTRACT

The Global Energy and Water Cycle Experiment/Climate Variability and Predictability (GEWEX/CLIVAR) Global Land–Atmosphere Coupling Experiment (GLACE) has provided an estimate of theglobal distribution of land–atmosphere coupling strength during boreal summer based on the results froma dozen weather and climate models. However, there is a great deal of variation among models, attributableto a range of sensitivities in the simulation of both the terrestrial and atmospheric branches of the hydro-logic cycle. It remains an open question whether any of the models, or the multimodel estimate, reflects theactual pattern and strength of land–atmosphere coupling in the earth’s hydrologic cycle. The authorsattempt to diagnose this by examining the local covariability of key atmospheric and land surface variablesboth in models and in those few locations where comparable, relatively complete, long-term measurementsexist. Most models do not encompass well the observed relationships between surface and atmospheric statevariables and fluxes, suggesting that these models do not represent land–atmosphere coupling correctly.Specifically, there is evidence that systematic biases in near-surface temperature and humidity among allmodels may contribute to incorrect surface flux sensitivities. However, the multimodel mean generallyvalidates better than most or all of the individual models. Regional precipitation behavior (lagged auto-correlation and predisposition toward maintenance of extremes) between models and observations is alsocompared. Again a great deal of variation is found among the participating models, but remarkably accuratebehavior of the multimodel mean.

1. Introduction

If robust interactions between the slowly varyingland surface state and the atmosphere on weather-to-climate time scales could be demonstrated, predictionsof the climate system could be enhanced. Monitoring ofland surface states to initialize numerical models withthe proper coupling between terrestrial and atmo-spheric processes should lead to improved forecasts.There are many global and regional modeling studiesthat suggest such interactions exist, especially involvingthe land surface state variable of soil wetness. But thesemodeling results have largely been based on long simu-

lations, ensemble simulations, or large area averagesthat outstrip the coverage of current observationaldatasets. Therefore, observational evidence to back upthe finding of the models is scarce. In addition, differentmodels have shown different character or degrees ofresponse, casting an additional shadow of uncertaintyover the prospect of exploiting land–atmosphere inter-actions for enhanced predictability. Even in analyticalmodels with highly controlled parameters, varied andconflicting results arise (e.g., Findell and Eltahir2003a,b; D’Odorico and Porporato 2004; Kochendorferand Ramirez 2005; Teuling et al. 2005). Two questionsarise. Is there a model consensus regarding land–atmosphere feedbacks? Are any models (or is the con-sensus) close to being correct?

Recently, an international initiative was undertakenby a dozen weather and climate modeling groups in-cluding both operational and research centers to deter-

Corresponding author address: Dr. Paul A. Dirmeyer, Centerfor Ocean–Land–Atmosphere, 4041 Powder Mill Road, Suite 302,Calverton, MD 20705-3016.E-mail: [email protected]

VOLUME 7 J O U R N A L O F H Y D R O M E T E O R O L O G Y DECEMBER 2006

© 2006 American Meteorological Society 1177

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mine the degree to which the atmospheric branch of thehydrologic cycle is coupled to the land surface withinglobal coupled land–atmosphere models. The project,called the Global Land–Atmosphere Coupling Experi-ment (GLACE), is jointly sponsored by the Global En-ergy and Water Cycle Experiment (GEWEX) GlobalLand–Atmosphere System Study (GLASS) and theClimate Variability and Predictability (CLIVAR)Working Group on Seasonal–Interannual Prediction(WGSIP). Participating GCMs include those from theBureau of Meteorology Research Centre (BMRC) andCommonwealth Scientific and Industrial Research Or-ganisation Conformal-Cubic 3 (CSIRO-CC3) in Aus-tralia, the Canadian Climate Centre (CCCma), theCenter for Climate System Research (CCSR) at theUniversity of Tokyo, the Hadley Centre in the UnitedKingdom (HadAM3), and seven from the followingcenters in the United States: the Center for Ocean–Land–Atmosphere Studies (COLA), the GeophysicalFluid Dynamics Laboratory (GFDL), the NationalCenter for Atmospheric Research [NCAR; CommunityAtmosphere Model 3 (CAM3)], the National Centersfor Environmental Prediction [NCEP; Global ForecastSystem/Ohio State University (GFS/OSU)], the Uni-versity of California at Los Angeles (UCLA), and twofrom the National Aeronautics and Space Administra-tion (NASA) Goddard Space Flight Center [Geosta-tionary Earth-Orbiting Satellite–Climate RadiationBranch (GEOS-CRB) and NASA Seasonal-To-Interannual Prediction Project (NSIPP)].

As described in detail by Koster et al. (2006), eachmodeling group in GLACE was asked to perform anensemble of 16 three-month simulations with its gen-eral circulation model (GCM) beginning on 1 June andusing the same specified sea surface temperature for allsimulations (case W). The ensemble members vary onlyin their initialization, preferably taken from 1 Junestates of a multidecade, parallel integration, so that themembers would be as independent as possible. Eachgroup chose one member to be the basis of test caseensembles, and saved all land surface state variables atevery model time step from that member. Two testensembles were made—one with all land surface statevariables specified (i.e., prescribed at each time step) tomatch the chosen member from the control ensemble(case R), and the other having only soil wetness speci-fied for soil layers below the thin surface layer (case S).Comparisons between the test ensembles and the con-trol ensemble show to what degree elements of the landsurface affect seasonal climate.

Koster et al. (2004, 2006, hereafter referred to as K04and K06, respectively) showed the global distribution

of the strength of land–atmosphere feedback, mani-fested in precipitation, as calculated across the 12 mod-els. “Hot spots” appeared for boreal summer over sev-eral parts of the world, including the Great Plains ofNorth America, sub-Saharan Africa north of the equa-tor, India, and parts of China. The signal was generallyweak over the Southern Hemisphere (austral winter),high latitudes, and very arid or humid regions. K06 alsoshowed a great deal of variation among models, both interms of patterns and the overall strength of feedbacks.The multimodel pattern of hot spots is not plainly evi-dent in several of the individual models.

Guo et al. (2006, hereafter G06) showed that thepathway for strong feedbacks in the models requiresboth a robust coupling of surface fluxes to soil wetnessin the land surface component of the model, and astrong link between precipitation and surface fluxes inthe atmospheric model through convection. G06 wasable to separately quantify these two segments of thefeedback loop in the models and show that weakness ineither branch hindered the overall link between soilwetness and precipitation. Furthermore, the land sur-face segment was found generally to be weak in humidregions (due to a lack of evapotranspiration response tochanges in soil wetness) and arid regions (because oflow variability), while the atmospheric segment wasweak in arid zones. This leaves the transitional regionsbetween arid and humid as the only regions where bothsegments can propagate information about soil wetnessanomalies to the convective parameterizations and ex-ert some control on precipitation.

K06 and G06 use the metric of “coupling strength,”denoted by the symbol �, in the multimodel assessmentof land–atmosphere feedbacks; � is a measure of thecoherence of a seasonal time series of a prognostic ordiagnostic model variable (e.g., precipitation or evapo-ration) across a range of ensemble members that havebeen initialized differently. In essence, it is the fractionof the total variance of a variable that is “explained,” orforced, by the prescription of all boundary conditions inthe model. The coupling strength between land andatmosphere is quantified by the change in � (��) be-tween an ensemble with differently initialized, freelyevolving land surface state variables, and an ensemblewhere the land surface state variables (namely, subsur-face soil wetness) are specified to match one case fromthe control ensemble. The idea is that if the land surfaceis exerting some controlling influence on surface fluxesand atmospheric processes, the restriction in the timeevolution of the land surface state variables should re-sult in an increase in the coherence among the timeseries of surface fluxes and meteorological states. Feed-backs are implied by a nonzero value of �� (which

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varies nominally from 0 to 1), with the degree of cou-pling measured by the magnitude of ��.

The parameter � is a handy construct for model com-parisons and analysis, but it is not a physical quantity. Itis an artifact of ensemble simulations. The real worlddoes not present us with an ensemble of parallel histo-ries, but only one realization. Therefore, there is nodirect way to calculate a field of �, much less ��, fromobservations. This is but one of the impediments toquantifying the land–atmosphere coupling strength inthe environment.

Another difficulty is the lack of global measurementsof soil moisture and surface fluxes, which are key ele-ments of the coupling pathway. There have been effortsto infer feedbacks from the observational record. Bettset al. (1996) show from field data collected at middleand high latitudes that the interaction of the land sur-face and the atmosphere is primarily through its influ-ence on the character of the planetary boundary layer(its depth, moisture content, rate of entrainment of airfrom above, and its ability to trigger convection) as aresult of surface properties such as soil wetness, veg-etation, and the diffusivity of heat in the soil column.Findell and Eltahir (1997) showed a positive correlationbetween variations in the observed soil moisturerecords in the Illinois Climate Network and rainfall inthe subsequent three weeks, which they claimed wasobservational evidence of a positive hydrologic feed-back between land and atmosphere. Salvucci et al.(2002) subsequently showed that the formulation of thecalculation by Findell and Eltahir (1997) biased the re-sults by allowing some future soil wetness informationto affect the correlation. Koster and Suarez (2004)showed that there is a statistically significant separationin the probability density functions of monthly rainfallduring summer over midlatitude land, conditioned onthe rainfall anomaly during the previous month. Paral-lel analyses of GCM simulations suggest that other fac-tors (e.g., alteration of the circulation due to remoteSST anomalies) are not responsible for the separation,and thus the separation implies a positive feedback be-tween land and atmosphere.

We have in the GLACE results, a multimodel-basedestimate of the strength and spatial variation of land–atmosphere coupling, and its relationship to state vari-ables and fluxes within global models. Can we confirmor refute the GLACE results using the observationalrecord? Where thorough surface flux and land stateobservations exist, we attempt to validate the GLACEmodels and to establish relationships among measuredand unmeasured (purely model-derived) variables thatmay allow us to infer more about the veracity of theGLACE results. The recent paper by Betts (2004; here-

after B04) provides a framework, based on a series ofrelationships found in an independent global model,which can be followed to quantify underlying elementsof land–atmosphere coupling strength from measurablequantities at the surface and in the boundary layer.

Section 2 describes the observational datasets that weused. In section 3, we attempt to link the models’ �parameter for evapotranspiration to observable quan-tities and validate the performance of the model simu-lations. The model validation to other relationships isexpanded in section 4. In section 5 we include the at-mospheric segment of coupling by comparing the mod-els’ behavior to observational evidence of persistence inprecipitation anomalies. Conclusions are given in sec-tion 6.

2. Observational data

To compare the model representation of land–atmosphere coupling strength to that in the real world,complete observations of land surface state variables,near-surface atmospheric states, and fluxes betweenland and atmosphere are needed. These observationsmust span a long enough period of time to provide alarge sample covering the range of variability of thesevariables and to provide adequate statistical signifi-cance for the results. Finally, we are interested in thesame season as the GLACE experiments, spanningJune, July, and August. There are very few sources ofobservational data that can meet all these require-ments. Two are identified for this study.

The U.S. Department of Energy operates the Atmo-spheric Radiation Measurement (ARM) Program(Ackerman and Stokes 2003). In particular, the south-ern Great Plains site consists of a Central Facility and anumber of Extended Facilities across a large area ofOklahoma and southern Kansas, each having instru-ment clusters to measure radiation, near-surface me-teorology, surface fluxes, soil moisture, and tempera-ture. For our application, data from the Energy BalanceBowen Ratio (EBBR; Cook 2005) system is appropri-ate. The EBBR is a ground-based sensor system in-stalled over grass that uses observations of net radia-tion, soil heat flow (25 mm below the surface), surfacesoil moisture (top 50 mm layer), and the vertical gradi-ents of temperature and relative humidity to estimatethe vertical heat fluxes at the local surface by a Bowenratio energy balance technique. The complete set ofnear-surface meteorological variables is measured aswell. Data archives exist for 14 stations.

We use the B1-level 30-min-average data and aver-age them further to daily time scales for consistencywith the model output from GLACE. The summer data

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for the years 2001–04 are used. Applying a rather strictquality filter to the data, we reject any day’s data for avariable if there is not at least 21 h of data with noquality control issues flagged. Then stations that haveexcessive missing data are screened out. We have twocriteria; one is that the station must have at least 75%of the days with all terms of the surface heat fluxes(latent, sensible, and ground heat fluxes) available. Thesecond is that 75% of the days must have soil moisturemeasurements. These criteria eliminate five stationsfrom consideration. Earlsboro (E27) only came onlinein late 2003, and has intermittent measurements in thearchive. Plevna (E4) has intermittent data throughoutthe 4-yr period. Cement (E26) is missing significantamounts of data during 2001 and 2002. Ashton (E9) hasno flux data for 2001 and about half of 2002. Ringwood(E15) is missing most of the soil wetness measurementsfor 2002–04. This leaves nine stations with sufficientdata for comparison with the models. These stations are

shown with their station codes in the lower-left panel ofFig. 1, along with land-cover type (indicated by thesymbols). The station data are examined individuallyand combined to represent averages over scales similarto a GCM grid box.

The second source of data comes from the FLUXNETnetwork of micrometeorological tower sites (Baldocchiet al. 2001). Though designed primarily to measure theexchanges of carbon dioxide, water vapor, and energybetween the biosphere and atmosphere, they also in-clude standard meteorological measurements, and insome cases subsurface water and temperature data. In aquest for data that are quality controlled, we havedrawn upon the long-term archive at the Oak RidgeNational Laboratory Distributed Active Archive Cen-ter. Daily gap-filled data (Falge et al. 2003) from theAmeriFlux and EUROFLUX regional networks areavailable for a number of years. Two AmeriFlux sites[Bondville, Illinois (Meyers and Hollinger 2004), and

FIG. 1. Location of ARM Extended Facilities (lower left beside station codes starting with “E”) and FLUXNET sites used in thisstudy. Symbols indicate the type of vegetation cover.


Little Washita, Oklahoma (Meyers 2001)] have multi-year records of fluxes and soil moisture (averaged overtop 100 mm). None of the EUROFLUX sites in thegap-filled dataset record soil moisture, but four sites(Bayreuth, Tharandt, Loobos, and Hyytiala) have 12 ormore summer months of other relevant observations inthe archive. Data used from the EUROFLUX sitescover the summers of 1996–2000 except for Bayreuth,which covers 1996–99. The locations and vegetationcover of these sites are also given in Fig. 1.

In addition to sample size and the list of variables, thedata must also represent a reasonably closed surfacewater and energy balance in order to be useful formodel validation. Figures 2 and 3 show the surface en-ergy balances (measured net radiation versus the sumof measured surface latent, sensible, and ground heatfluxes) for the ARM and FLUXNET sites, respectively.The bold line is the least squares linear regression ofthe surface heat fluxes on the net radiation. Also shownare the RMSE, explained variance (r2), and bias withrespect to the perfect fit line. The ARM sites show verytight closure in most cases, with a tendency towardslight positive biases (heat fluxes exceed net radiation).These are Bowen ratio stations, so the good fit is notsurprising. As might be expected, the average acrossthe ARM sites has the highest r2 and the lowest ofRMSE. The fit for the FLUXNET sites is not as good,with a tendency for negative biases and greater scatter.Only Tharandt has a bias as low as the ARM sites. Dayswith greater than 50% gap filling in surface flux termsor radiation are not included in Fig. 3 or the calcula-tions. Note that at the Hyytiala site there are no groundheat flux measurements, so the terms of the surfaceenergy balance are not completely specified, possiblycontributing to the appearance of a strong negative biasthere. At the other stations in Fig. 3, removal of theground heat flux term would contribute an average ad-ditional bias of �8.0 W m�2 to the relationships.

It is worth reminding the reader that the ARM fa-cilities and the Little Washita AmeriFlux site lie nearthe center of the North American “hot spot” for land–atmosphere coupling identified by GLACE (K04).Thus, we begin with an expectation that the observa-tions from these sites may provide the strongest avail-able evidence for land surface feedbacks on weatherand climate. However, there is also evidence thatGCMs often do a poor job of simulating climate in thisarea (e.g., Fennessy and Xue 1997; Koster and Suarez2003; Sud et al. 2003; GFDL Global AtmosphericModel Development Team 2004) The European sitesare in a more quiescent region for land–atmospherecoupling, according to the GLACE models, providing

an opportunity to compare and contrast among themodels and observations.

All of the scatter diagrams and other comparisonsuse 6-day averages as in K06 and G06, similar to B04.The analysis in section 5 utilizes two observed precipi-

FIG. 2. Validation of the energy balance from 6-day means atselected ARM Extended Facility sites for June–August 2001–04,and the average across all sites (upper left). Units: W m�2. Thediagonal dashed gray line shows exact balance; the black solid lineis the best-fit linear regression through the data points.


tation datasets. The first is the multidecadal (1948–97),1/4° daily precipitation reanalysis of Higgins et al.(2000), which is based on rain gauge reports from over10 000 sources in the United States. The second datasetconsists of global monthly precipitation fields gener-ated by Global Precipitation Climatology Project(GPCP; version 2; see Huffman et al. 1997). This globaldataset, which combines in situ (gauge) and satellitemeasurements, covers the period 1979–2001 at a spatialresolution of 2.5° � 2.5°.

3. Observable analogs to “coupling strength”

In this study we use only data from cases W and S, asdescribed in the introduction. The definition of thechange in intraensemble coherence of model evapo-transpiration (ET) from the control case to the casewhere subsurface soil wetness (SWet; wilting point � 0,saturation � 1) is specified, referred to as �E(S) ��E(W) in G06 but here simply called ��E, carries clearimplications. It suggests that increased coherence mustbe the result of a strong functional dependence of ETon SWet. If there is no relationship between ET and

SWet, the specification of a particular time series ofSWet as a boundary condition common to all ensemblemembers should have no statistically detectable effecton the ET time series. Of course, the land surface pa-rameterizations in every one of the 12 GLACE modelsspecify SWet as a term on the rhs of their respectiveequations for evapotranspiration [or more likely, forlatent heat flux (LHF)]. LHF is not a univariate func-tion, but also depends on other state variables (andparameters that are functions of state variables). Thedegree to which SWet specifically and uniquely deter-mines LHF likely varies among models, geographicallywithin a model, and even temporally at any grid boxwithin a model, depending on the impacts of the otherpredictors in the equation.

In fact, one would expect to determine most stronglynot the absolute LHF, but rather the partitioning ofavailable energy between LHF and sensible heat flux(SHF). We may examine this effect through the nor-malized latent heat (NLH) defined as the ratio of LHFto net surface radiation. We can compare the functionaldependence of NLHF on SWet among models and toobservations, as well as calculate values of �NLH and��NLH.

Figure 4 shows scatter diagrams of NLH as a functionof SWet for all members of the control ensemble fromnine of the GLACE GCMs at the grid cell containingthe center of the ARM region (some of the models didnot provide the complete set of output or had otherproblems that precluded computing all of the necessaryquantities for this part of the study). Six-day means areshown beginning at day 8 of the 92-day integrations(like those used in the calculations by K06 and G06).However, we display the 6-day means beginning everyday through day 87—a total of 80 points per ensemblemember instead of 14. This helps to show the evolutionof the two terms in time for some of the models, butsignificance tests use the degrees of freedom for thesmaller set of consecutive 6-day means. A few modelsshow a very smooth and tight relationship betweenNLH and SWet (e.g., NSIPP) while others appear tohave little relationship at all (e.g., CSIRO-CC3). Othercontrasts exist. Some models span the entire range ofSWet (e.g., GFDL) while others have a very limitedrange (e.g., CAM3) or a very uneven distribution (e.g.,GFS/OSU). There is also a great deal of discrepancy inthe ranges of SWet among models, although they allspan most of the range of NLH.

The blue lines in the panels of Fig. 4 are a best fit tothe scatter of points, based on 20 bins of equal popu-lation of points along the SWet axis spanning the rangeof SWet for that model and location. For each bin, the

FIG. 3. As in Fig. 2 but for selected FLUXNET sites. NoteHyytiala lacks ground heat flux measurements.


average value of NLH is calculated. The line connectsthose values. The limited sample size contributes to thezigzag nature of this line for some models. The advan-tage of this approach is that no a priori assumption ismade regarding the functional relationship betweenNLH and SWet.

To match ��NLH to the degree of dependence ofNLH on SWet, we need to quantify the strength of thefunctional relationship between the two quantities witha single value at each grid box. It is fairly easy to discernby eye from Fig. 4 which models exhibit a strong de-pendence of NLH on SWet and which do not, but weneed an objective, quantitative means to do so. Weestimate the strength of the functional relationship as aratio. The numerator is the standard deviation of theLHF values in each bin i about the bin average, totaledover all bins:

s � ��i�ni

�NLHni� NLHi�

2

�i

ni �1�2

. �1�

The denominator is the total range of the 20 bin-averaged NLH values:

R � max�NLHi� � min�NLHi�. �2�

The result is an estimate of “goodness of fit”:

g �s

R, �3�

where g is a positive number whose value decreases asthe fit improves. We have conducted Monte Carlosimulations showing that for data distributed in a Gauss-ian-random fashion on both x and y axes, g also has a

FIG. 4. Relationship of NLH to SWet in the 16 ensemble members of nine GCMs at the grid box encompassing the ARM CentralFacility. Solid blue line is fit through the means of 20 bins of equal number of points. Red points show the ensemble member used asbasis for fixed SWet integrations. Here g is a goodness-of-fit metric.


Fig 4 live 4/C

Gaussian distribution, and values of g below 0.36 aresignificant at the 99% level. The values of g for eachmodel at the grid box encompassing the core of theARM network are also shown in Fig. 4. Only CSIRO-CC3 fails to achieve statistical significance at this level.

The ensemble member chosen as the source for thefixed SWet runs is shown in the scatterplots of Fig. 4 byred symbols. This illuminates another shortcoming ofthe design of the GLACE experiment. The resultingvalue of ��E, ��NLH, and potentially ��P at any lo-cation for a given model may be a result of the randomchoice made in selecting the basis for specified SWet,especially for the majority of models that do not appearto span the entire range of possible SWet values duringone seasonal integration. We can see from Fig. 4 thatfor this location the chosen SWet time series in theCAM3 model happened to be the wettest of all en-semble members. This may have depressed the esti-mate of ��NLH at this location. On the other hand,CCCma chose an anomalously dry case where the slopeof the fitted curve is large and sensitivity is unusuallyhigh.

Ideally, to avoid this problem, the sensitivity experi-ments in GLACE would have been carried out 16times, once with each control ensemble member as thesource of specified SWet. This was an impractical de-mand to make on the modeling groups. Of course,through the averaging of ��E across the globe, thiseffect is averaged out, allowing the GLACE design toprovide an accurate overall assessment of an individualmodel’s coupling strength (see Fig. 6 in K06). In addi-tion, at a given location, averaging across the modelsshould filter out much of this source of error.

We also see from Fig. 4 that some models appear notto have a strict dependence of NLH on SWet, but astrong codependence on other factors. The CCCma,CSIRO-CC3, HadAM3, and GFS/OSU models (andCOLA and CAM3 to a lesser extent) show evidence ofthis as oscillations or deviations in the track of pointsfrom the “best fit” envelope. We checked whetherthese models had a high incidence of drizzle that mightdrive a large proportion of evaporation to come frominterception loss (which would occur independently ofSWet), but that was not the case. Other factors mustexert significant control on NLH (and LHF, not shown)in these models.

Figure 5 shows global maps of ��NLH for eachmodel. There is a fairly strong agreement between��NLH and ��E for each model (not shown), but gen-erally ��E is larger. Shown in each panel is the globalmean (land only, north of 60°S) value of ��NLH. Con-sistent with the findings of G06, the GFDL model hasthe strongest ��NLH and the GFS/OSU model is the

weakest. Figure 6 shows the global distribution of thegoodness-of-fit parameter g for each model. The shad-ing is chosen so that statistically significant functionalrelationships of NLH on SWet are shown in shades ofblue where they exceed the 99% confidence level, or-ange for confidence between 90% and 99%, and yellowfor values below 90%. Shown in each panel are thefraction of the nonblank land area where confidenceexceeds 99%, as well as the global mean of g and itsspatial correlation with ��NLH. Field significance ishigh for all models, and every model except CCCmahas a statistically significant spatial correlation betweeng and ��NLH, implying that the goodness-of-fit diag-nostic is indeed relevant to coupling strength. Blankareas over land are either very dry (low soil wetness orlow variance of soil wetness) or have no valid value of��NLH.

There exists a resemblance between the spatial pat-terns of the multimodel values of ��NLH and g, shownin Fig. 7. The global mean of g is 0.467 for the nine-model mean, and the spatial correlation between thetwo fields is �0.57. It seems clear that once the noisefrom the original limited set of GLACE integrations isfiltered out by aggregation, a firmer relationship is es-tablished between the lower branch of the land–atmosphere feedback loop and locally observable quan-tities. However, when we consider calculations of g and�� based on LHF instead of NLH, the multimodelaverage shows a much higher spatial correlation be-tween the global fields of �0.73, explaining over half ofthe variance. The stronger connection for LHF than forNLH in the models is counter to what is suggested inthe observations, as we will show later.

This exercise suggests that within the realm ofweather and climate models we may relate the unmea-surable coupling indices ��NLH and ��LHF to an indexthat is not dependent on ensembling. This opens thepossibility that we can quantify aspects of couplingstrength between land and atmosphere in the realworld, given a sufficiently large and high quality set ofmeasurements over several seasons at locations of in-terest. Additionally, this relationship gives us a meansto validate the coupling characteristics of these models,given the caveats mentioned earlier. At the very least,we can test whether these models simulate the correctranges and sensitivities of surface fluxes and state vari-ables.

Table 1 shows how the individual models compare tothe observations of SWet, LHF, and g calculated forboth NLH and LHF at Bondville, Little Washita, andthe average of the ARM sites. Averaging over theARM domain helps scale these observations to GCMgrid scales, and avoid errors from local surface variabil-


ity and nonlinearities (Crow and Wood 2002). Al-though the HadAM3 and CCCma are consistentlyamong the best models in terms of error for all thequantities shown, none of the models is especially im-pressive. The type of variability among models shownin Fig. 4 is typical for all of the locations examined, andnone of the models is comfortably accurate in its rep-resentation of the observed relationships betweenSWet and NLH or LHF. As can be seen in Table 1 andFig. 4, the models struggle to represent the correct dis-tribution of soil wetness, and rarely come within 20% ofthe observed mean values of any quantity. It is alsoworth noting that in most cases the models have a bet-ter fit for the functional relationship of LHF on SWetthan of NLH on SWet. The station data suggest theopposite is true. It seems that most of the models favora stronger dependence of ET on SWet than for thepartitioning of net radiation on SWet. The GFDLmodel bucks the trend in this regard but shows muchtoo strong a relationship between SWet and surface

fluxes at all locations. CAM3 has the correct stratifica-tion of g at two of three locations, and has a muchbetter goodness of fit than GFDL and most other mod-els. The multimodel average (last column) ranks in thetop four in 12 of 15 rows, giving further credence to itsuse as the best model-based representation of the realworld.

In Fig. 8 we show how the observations behave overthe ARM domain in terms of the relationship betweenNLH and SWet. Comparison to Fig. 4 shows just howdifferent the models are from the observations. Notethat only four years of measurements have gone intoFig. 8—no more than one-quarter the amount of data inthe model plots. Thus Fig. 8 may underrepresent theobserved range of SWet due to the small sample sizeand the possible lack of measurements at very lowSWet. Nevertheless, the “best” models are NSIPP,which has the right goodness of fit but appears to puttoo much energy toward ET, and HadAM3, whichoverlaps the range of SWet and NLH rather well but

FIG. 5. The ��NLH for boreal summer in each model. Global mean (land only) value is shown in the bottom left corner of eachpanel.


Fig 5 live 4/C

exhibits a slow mode of variability (the trains of pointsarcing up and down on a slight tilt) that increasesspread and is not evident in the observations. TheARM data do show a positive correlation betweenNLH and SWet. The observed fit is much weaker in theobservations than in the models. Because of the lack ofobservational data in very dry conditions, we cannotsay whether that tail of the relationship follows an ex-ponential curve like the GFDL or GEOS-CRB models,or an S-shaped curve like NSIPP and COLA. Overall,comparisons at the individual ARM sites, Bondvilleand Little Washita, portray a similar picture.

4. Other relationships with surface variables

Our comparison with observational sites is greatlyrestricted by the need for long time series of soil wet-ness measurements. However, there may be relation-ships between other more commonly measured surface

variables that we can use for validation and compari-son. B04 found a number of striking associationsamong surface and lower-atmospheric quantities in theECMWF reanalyses that can be used as a guide for thisinvestigation. For instance, the relationship betweensurface SHF and SWet was found by B04 to be strongerthan between LHF and SWet across several domainsfrom the deep Tropics to boreal forests. This charac-teristic is largely borne out in the observations (Table2). Only Elk Falls in the ARM network and Bondvilleshow a significantly lower value of g for the relationshipbetween SWet and LHF than between SWet and SHF.However, when we normalize by net surface radiation,the relationship reverses. Only at Elk Falls and LittleWashita is the value of g appreciably lower for normal-ized sensible heat (NSH) than for NLH. At the sametime, the goodness of fit increases going from LHF toNLH at every station, while it decreases going fromSHF to NSH at all but three stations.

FIG. 6. As in Fig. 4 but for g. Also shown at the bottom center of each panel is the global spatial correlation between ��NLH and gfor each model.


Fig 6 live 4/C

The GLACE models have a very different behavior.Table 3 shows that there is a stronger functional rela-tionship between LHF and SWet than for SHF andSWet for every model over the ARM region, with theexception of CSIRO-CC3, which has a very weak rela-tionship to either. The same is nearly always true atindividual sites. Values of g for the average of the ARMsites are also shown for comparison to the model gridbox values. Every model except GFS/OSU shows astronger relationship between NLH and SWet than forNSH and SWet, consistent with observations, but fiveof nine models show a degradation in goodness of fitgoing from LHF to NLH, and every model except

CCCma and CSIRO-CC3 has a tighter relationship be-tween NSH and SWet than between SHF and SWet,contrary to the observed data. The model values of gfor NLH are the same as in Fig. 4. The implication isthat the GLACE models all have a fundamentally dif-ferent (and perhaps wrong) interplay between soil wet-ness and surface fluxes, at least in this region.

One possible explanation for this behavior is that theGCMs emphasize a different factor controlling surfaceheat flux than does the real world. For example thePenman–Monteith equation and similar relationshipsthat are widely used to parameterize evapotranspira-tion in land surface parameterizations has two main

FIG. 7. The multimodel mean of (a) g and (b) ��NLH.


Fig 7 live 4/C

terms: one based on potential evapotranspiration (ef-fectively net radiation) and the other on the humiditygradient between the land surface and near-surface air.There is a lack complete information (namely, aerody-namic resistance) that would allow us to directly com-pare the relative magnitudes of each term for eachmodel and for observations. However, the main com-

ponents of each term among the models and observa-tions can be compared.

Figure 9 shows the frequency distribution of net ra-diation (top), the difference between actual and satu-ration specific humidity (middle), and the temperature(bottom) for the ARM region. Observed distributionsare shown by bars. The vertical lines show the rangeamong the GCMs in each bin, with the marker indicat-ing the nine-model mean frequency. Note that theGCMs have a reasonable distribution of net radiation(only one GCM has a distinct high bias, which princi-pally affects the ranges in the panel). However, allGCMs have a propensity for unrealistically large spe-

FIG. 8. As in Fig. 3 but for observed average over ARMExtended Facility sites.

TABLE 2. Observed goodness of fit between various surface fluxvariables and SWet at individual sites in North America.

g(LHF,SWet)

g(SHF,SWet)

g(NLH,SWet)

g(NSH,SWet)

E8—Coldwater 0.313 0.283 0.247 0.448E22—Cordell 0.350 0.282 0.284 0.310E7—Elk Falls 0.330 0.368 0.316 0.298E19—El Reno 0.763 0.523 0.640 0.652E2—Hillsboro 0.508 0.514 0.353 0.848E13—Lamont 0.496 0.472 0.438 0.476E20—Meeker 0.330 0.199 0.197 0.193E18—Morris 1.057 0.631 0.563 0.844E12—Pawhuska 0.339 0.173 0.215 0.451Bondville 0.354 0.419 0.285 0.705Little Washita 0.294 0.199 0.214 0.170

TABLE 1. Comparison of observations, models, and multimodel average estimates of SWet (dimensionless), LHF (W m�2), andgoodness of fit of NLH and LHF to SWet for two North American FLUXNET locations and the average over ARM Extended Facilitysites.

Obs

CC

Cm

a

CO

LA

CSI

RO

-CC

3

GE

OS-

CR

B

GF

DL

Had

AM

3

CA

M3

GF

S/O

SU

NSI

PP

M.M

.

Bon

dvill

e g(NLH, SWet) 0.285 0.282 0.150 0.518 0.089 0.058 0.501 0.200 0.280 0.241 0.332g(LHF, SWet) 0.354 0.242 0.128 0.467 0.118 0.138 0.408 0.198 0.258 0.462 0.264SWet mean 0.757 0.218 0.515 0.149 0.388 0.437 0.392 0.006 0.144 0.269 0.258SWet range 0.573 0.585 0.907 0.141 1.124 0.990 0.399 0.047 0.308 0.314 0.565LHF mean 100.8 118.5 136.9 121.5 120.9 116.8 121.9 63.2 88.7 149.3 104.8

Lit

tle

Was

hita g(NLH, SWet) 0.214 0.319 0.207 0.958 0.125 0.055 0.349 0.239 0.219 0.138 0.270

g(LHF, SWet) 0.294 0.253 0.183 1.173 0.114 0.094 0.296 0.265 0.203 0.120 0.257SWet mean 0.347 0.030 0.595 0.014 0.012 0.113 0.223 0.017 0.032 0.229 0.128SWet range 0.289 0.256 0.741 0.072 0.760 0.989 0.467 0.068 0.080 0.476 0.433LHF mean 57.0 68.5 72.1 86.5 26.5 42.6 85.4 88.0 39.6 77.8 58.9

AR

Mav

erag

e g(NLH, SWet) 0.201 0.319 0.194 0.855 0.102 0.058 0.280 0.221 0.247 0.151 0.340g(LHF, SWet) 0.427 0.253 0.186 0.988 0.085 0.103 0.284 0.291 0.223 0.146 0.248SWet mean 0.367 0.030 0.705 0.033 0.022 0.205 0.289 0.018 0.043 0.291 0.165SWet range 0.338 0.256 0.558 0.099 1.021 0.999 0.442 0.059 0.118 0.531 0.450LHF mean 104.2 68.5 79.5 74.7 35.8 67.6 95.4 107.8 42.4 108.5 68.2


cific humidity depressions (and thus low relative hu-midity). In fact, nearly a third of the days in the typicalmodel have values above the observed range. This ap-pears to be a result of excessively high temperatures inthe GCMs (bottom panel). Whereas there are no oc-currences of surface air temperatures warmer than33°C in the observational data (a mean over the ARMstations), individual models simulate anywhere from10% to 55% of their days with mean temperaturesabove this level. Given the highly nonlinear increase ofsaturation specific humidity with temperature at thesehigh values, it appears likely that the GCMs’ evapo-transpiration is too strongly driven by humidity gradi-ents (e.g., by the vapor pressure deficit term in thePenman equation), and thus responds relatively weaklyto variations in net radiation.

This result, though striking, is for only one location.Do the models show this apparent overdependence onvapor pressure deficit globally? Figure 10 shows theratio of g calculated for LHF versus g for NLH. Blueareas show where the models overall have a strongerdependence on SWet by LHF than NLH. This includesmost of the midlatitudes, including the areas in Europeand North America where this study has observationaldata. Table 4 compares the global mean values of g andthe area where LHF has a stronger dependence onSWet than does NLH for the GLACE models. Nomodel shows a dominance for NLH in the global mean,and only the GFDL and CSIRO-CC3 models showg(NLH, SWet) dominating over a majority of the landarea. Similar comparisons between g(LHF, SWet) andg(SHF, SWet) show that most models have a strongerdependence of LHF on SWet than SHF on SWet.

So overall, most models do not show the relativedependencies of surface fluxes on soil wetness that aresuggested by B04 or the limited observational dataavailable This may result, at least in part, from biases insimulated vapor pressure deficit. These flaws, however,

do not necessarily invalidate the pattern and degree ofland–atmosphere coupling found by K04. B04 contendsthat the strong relationship between SHF and SWet isnot necessarily direct, but through the strong interac-tions each have with the height above ground (in pres-sure coordinates) of the lifting condensation level(PLCL). The proposed mechanism is that SWet exerts astrong control on PLCL through its effect on the near-surface dewpoint depression, which then determines

FIG. 9. (top) Categorical frequency of occurrence of net radia-tion, and (middle) the difference between actual and saturationspecific humidity and (bottom) temperature over the ARM regionfor observations (bars), and the mean of the GCMs (markers).Vertical lines span the range of models for each bin.

TABLE 3. As in Table 2, but for models and observations for theaverage over ARM Extended Facility sites.

g(LHF,SWet)

g(SHF,SWet)

g(NLH,SWet)

g(NSH,SWet)

Observations 0.427 0.232 0.201 0.248CCCma 0.253 0.337 0.319 0.342COLA 0.186 0.260 0.194 0.204CSIRO-CC3 0.988 0.743 0.855 1.045GEOS-CRB 0.085 0.176 0.102 0.118GFDL 0.103 0.151 0.058 0.126HadAM3 0.284 0.371 0.280 0.308CAM3 0.291 0.335 0.221 0.228GFS/OSU 0.223 0.295 0.247 0.245NSIPP 0.146 0.219 0.151 0.153


the size of the available heat reservoir in the mixedlayer, and thus the rate of SHF that can be sustained. Anearly uniform heating rate of the boundary layer of 3.8K day�1 was found by B04 for SHF in the ECMWFmodel forecasts, yielding a linear relationship betweenthe mass of air in the boundary layer and the sensibleheat flux rate when averaged over 5-day intervals. SoSWet may impact cloud processes, and thus precipita-tion, via both LHF and SHF.

We calculate the observed and model relationshipsbetween PLCL and SWet following the approximationfor PLCL based on near-surface temperature and hu-

midity from Bolton (1980). The average of the obser-vations over the ARM region (Fig. 11) shows a fairlystrong relationship similar to B04. The range of soilwetness is limited in this area, so the tails of the distri-bution for very wet and dry soil conditions cannot beseen. The models (Fig. 12) exhibit an assortment ofbehaviors, but all GCMs except GFDL have a high biasin PLCL and most have a clear negative correlation be-tween SWet and PLCL. The variety is striking. GFDL,for example, has a very tight connection between PLCL

and SWet, while some other models show a rather weakrelationship between these variables (e.g., HadAM3 orGFS/OSU) or no relationship at all (e.g., CCCma orCSIRO-CC3). The models stratify just as they did forthe other goodness-of-fit relationships. It appears thatmany of these GCMs do not simulate the proper cou-pling between surface moisture and the cloud base. Thepositive biases in cloud-base height are consistent withbiases toward low relative humidity shown in Fig. 9,suggesting a connection between these errors in themodels.

What about the relationship between PLCL and SHF?We can now introduce the data from the EUROFLUXsites into the validation exercise. The models inGLACE did not report SHF, but we can deduce theterm SHF GHF (ground heat flux) from LHF andnet radiation. In Table 5 the observations are shown forthe implied heating rates and the r2 with PLCL usingboth SHF and SHF GHF where available to provide

TABLE 4. Global mean values from models and the multimodelmean of goodness of fit, the ratio of the global means of goodnessof fit, and the fraction of global land surface grid points where thedependence of LHF on SWet is stronger than for NLH on SWet.

g(LHF,SWet)

g(NLH,SWet) Ratio

Areag(LHF, SWet) g(NLH, SWet)

CCCma 0.483 1.248 0.39 85%COLA 0.443 0.493 0.88 62%CSIRO-CC3 0.413 0.429 0.96 42%GEOS-CRB 0.357 0.442 0.81 67%GFDL 0.229 0.275 0.83 27%HadAM3 0.496 0.548 0.91 55%CAM3 0.556 0.654 0.85 59%GFS/OSU 0.377 0.458 0.82 70%NSIPP 0.383 0.439 0.87 54%MM 0.400 0.447 0.89 69%

FIG. 10. Ratio of the multimodel mean of g(LHF, SWet) to g(NLH, SWet).


Fig 10 live 4/C

a means of translation to the results of B04. Over theARM sites the heating rate deduced in B04 appearsquite appropriate, but for the other FLUXNET sites arange of heating rates from 2.9 to 6.0 K day�1 is appar-ent. Inclusion of GHF in the calculation tends to in-crease the slope, and thus the derived heating rate, andcuriously also improves the fit of the linear regressionin most cases.

Large differences in the value of r2 between modelsand observations suggest that those models do not rep-resent the relative importance of SHF as a source ofboundary layer heating (or cooling) compared to otherthermodynamic processes such as radiative cooling,thermal advection, diffusion, and dry and moist convec-tive processes. However, a high value of r2 does notguarantee a correct heating rate, because even if a par-ticular model is producing a good simulation of SHF,the other heating terms in the boundary layer may beamiss. Table 5 suggests that while some models clearly

FIG. 11. As in Fig. 7 but for the relationship between height ofcloud base (hPa) and SWet.

FIG. 12. As in Fig. 3 but for the relationship between height of cloud base (hPa) and SWet.


Fig 12 live 4/C

do better than others, none is without problems. Yetagain, the multimodel average gives the most reason-able overall representation of variations in heatingrates and correlations among the stations, althoughthere are still biases in evidence.

Comparison of the observed relationships betweensurface and near-surface state variables, fluxes, and at-mospheric parameters to those presented in B04 withforecasts from the ECMWF model, which did not par-ticipate in GLACE, shows that the ECMWF model hastoo little spread in many of the scatter diagrams. Thisresembles the GFDL model, which has the strongestcoupling between SWet and surface fluxes in GLACE.The implication is that the ECMWF model might have

a similarly strong coupling between land and atmo-sphere. Another interesting aspect of the GFDL modelis that over the ARM area it is the only model to showa clearly bimodal distribution of soil wetness (evident inFig. 4). D’Odorico and Porporato (2004) argued thatthis can be a result of feedbacks between soil wetnessand precipitation, which G06 showed to be strongest inthe GFDL simulations.

5. Land–atmosphere coupling and precipitationmemory

As discussed in the introduction, validation of thecoupling strengths quantified in GLACE is difficult be-

TABLE 5. Comparison of the percentage of explained variance between SHF and PLCL, and the derived boundary layer heating rates(K day�1) for observations and models for the ARM region average as well as all available FLUXNET sites. The right column showsthe results for the multimodel mean. The bottom rows show the average across all locations.

Obs

(SH

F)

Obs

(SH

F

GH

F)

BM

RC

CC

Cm

a

CO

LA

CSI

RO

-CC

3

GE

OS-

CR

B

GF

DL

Had

AM

3

CA

M3

GF

S/O

SU

NSI

PP

Mod

elav

g

AR

M r2 0.59 0.63 0.03 0.70 0.72 0.21 0.37 0.74 0.65 0.78 0.36 0.49 0.51

Heating rate 3.7 4.1 0.7 6.1 3.2 1.9 5.2 3.7 2.1 3.2 2.8 3.3 3.2

Bon

dvill

e r2 0.27 0.22 0.03 0.58 0.89 0.60 0.55 0.73 0.70 0.69 0.57 0.00 0.53

Heating rate 4.1 4.8 �0.4 3.9 4.9 2.2 3.6 3.2 3.1 3.4 3.4 3.0

Lit

tle

Was

hita r2 0.59 0.65 0.03 0.70 0.58 0.16 0.37 0.72 0.69 0.76 0.37 0.53 0.49

Heating rate 2.9 4.1 0.7 6.1 2.5 1.6 5.7 4.0 2.7 3.7 3.2 3.6 3.4

Bay

reut

h r2 0.40 0.51 0.01 0.31 0.09 0.52 0.00 0.00 0.60 0.61 0.18 0.15 0.25

Heating rate 5.8 5.8 0.4 5.8 �2.0 2.2 4.0 3.9 5.3 3.5 2.9

Hyy

tial

a r2 0.55 N/A 0.46 0.30 0.61 0.81 0.58 0.60 0.76 0.58 0.25 0.36 0.53

Heating rate 4.5 N/A 3.7 4.9 8.6 4.1 7.7 5.4 5.7 7.0 6.2 7.3 6.1

Loo

bos r2 0.51 0.57 0.27 0.38 0.63 0.39 0.46 0.32 0.65 0.62 0.21 0.17 0.41

Heating rate 6.0 7.2 �5.3 7.6 4.0 3.5 6.8 10.7 3.2 5.3 7.8 �14.4 2.9

Tha

rand

t r2 0.39 0.49 0.01 0.66 0.49 0.38 0.00 0.23 0.45 0.61 0.14 0.15 0.31

Heating rate 3.1 3.7 0.4 7.5 3.6 2.2 5.5 2.9 3.4 4.3 3.5 3.7

Avg r2 0.47 0.52 0.12 0.52 0.57 0.44 0.33 0.48 0.64 0.66 0.30 0.26 0.43

Heating rate 4.3 4.9 0.0 6.0 3.5 2.5 5.8 5.4 3.4 4.3 4.7 1.1 3.6


cause direct large-scale observations of land–atmo-sphere feedback do not exist. We can, however, derivecertain diagnostic quantities from large-scale observa-tions that are tied indirectly to the feedback. Thesequantities, if examined with caution, allow an indirectevaluation of modeled coupling strength.

The two diagnostic quantities examined in this sec-tion are those described in detail by Koster et al. (2003)and Koster and Suarez (2004, hereafter referred to asK03 and KS04, respectively). Both K03 and KS04 fol-lowed the same analysis strategy: (a) a feature of inter-est—hypothesized as being related to land–atmospherefeedback—is identified in the observational data rec-ord; (b) the feature is then sought and identified in afull GCM simulation; (c) the GCM simulation is re-peated with all land–atmosphere feedback artificiallyremoved, and the absence of the feature is noted. Thefinal two steps unequivocally identify land–atmospherefeedback as the source of the feature of interest withinthe GCM. Given the feature’s presence in the observa-tions, we are left with two possible conclusions: eitherland–atmosphere feedback does indeed occur in na-ture, or the presence of the feature in both the obser-vations and the model is coincidental.

The features identified by K03 and KS04 involve thespatial patterns of precipitation autocorrelation overthe conterminous United States and the area-averagedconditional expected value of monthly precipitation fol-lowing extreme precipitation months. Each feature isdiscussed here in the context of the GLACE results.

a. Patterns in the temporal correlations ofprecipitation

K03 speculated that land–atmosphere feedback, if itexists, should be reflected in the temporal correlationsof precipitation. The idea is simple. If feedback oper-ates in nature, an anomalously high precipitation eventduring one week should lead to high evaporation ratesand thus high precipitation rates in subsequent weeks,strengthening the temporal correlation. K03 focusedtheir analysis of the correlations on the continentalUnited States, for which a precipitation dataset of ac-ceptable length and quality is available (Higgins et al.2000). Fifty years of daily July precipitation data, bothfrom the observations and from the NSIPP-1 GCM(with or without enabled feedback), were aggregated to5-day, or pentad, precipitation totals. Correlations werethen computed between twice-removed pentads—thatis, between the precipitation anomalies for 1–5 July and10–15 July, between those for 6–10 July and 16–20 July,and so on. Correlations between consecutive pentadswere not considered because these are overly influ-enced by storms that straddle the time divisions. A

statistically significant signal appeared in the observa-tions for July and August. The NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) GCM cap-tured the overall shape of this signal, but significantlyoverestimated its magnitude. When feedback was dis-abled the correlations in the GCM essentially disap-peared. Thus, feedback was responsible for the corre-lation signal in the GCM.

Using the pentad precipitation rates from the 16 Ju-lys in experiment W (the free-running GLACE experi-ment, with no specification of surface states), we com-puted the correlations between twice-removed pentadsfor each GLACE model. (As in K03, the observationaland model data fields were aggregated to the same 2° �2.5° resolution and treated with a 3-point filter prior tothe calculation of the statistics.) The correlation fieldsare very noisy—not necessarily because the models arepoor, but because the number of truly independentdata pairs contributing to the correlation calculation foreach model is small, of order 30. Still, several modelsshow a rough indication of positive correlation in thecenter of the continent. For presentation here (Fig. 13),we filter out some of the sampling error by averagingthe correlations across the continental United Statesand presenting the averaged results, for each of thethree simulation months (June, July, and August), inhistogram form. Individual models vary the exact loca-tion of the North American hotspot, so we use a largeaveraging area at the expense of reducing the values inthe histograms.

In each panel of Fig. 13, the means for the observa-tions are shown as dotted histogram bars. The obser-vations show a maximum of correlation in July, asmaller amount in August, and a correlation in Junethat is close to zero. The models, as expected, show arange of behavior, with some models strongly overesti-mating the correlation (e.g., GFDL, CCCma) and oth-ers strongly underestimating it (e.g., GFS/OSU). Ingeneral, the models do not capture the observed sea-sonal cycle of the correlation.

Of course, given the pervasive sampling error, theseresults are hard to interpret, even with the spatial av-eraging. For reliable estimates of precipitation autocor-relation—particularly regarding nuances in seasonaland geographical distribution—hundreds of seasonsshould be examined, not just the 16 examined here.Still, the multimodel results shown at the bottom of thefigure are encouraging. When sampling error and evenmodel error is smoothed out further by averaging thespatially integrated values across the 12 models, theresults for July and August are remarkably close to theobserved results. The models still strongly overestimate


correlations in June, though they correctly identifyJune as the weakest month for the correlations.

b. Conditional expected values of rainfall acrossmidlatitude land

In KS04, observed monthly data were analyzed todetermine the conditional expected value of a monthlyprecipitation anomaly given that the anomaly in a pre-ceding month (one, two, or three months beforehand)was of a certain sign and magnitude. To increase thesample space and thereby allow meaningful distinctionsbetween computed probability density functions

(PDFs), ergodicity was assumed: monthly precipitationtotals in all grid cells covering midlatitude land (30°–60°N) were standardized and included in the construc-tion of conditional probability distributions. To stan-dardize the data, each monthly rainfall had the localmean subtracted from it, and the resulting anomaly wasdivided by the local standard deviation. The observedconditional expected values are statistically distinct.When the observed rainfall at a given location is in thelowest 20% (i.e., the lowest quintile) of all rainfalls atthat location, the rainfall there in the following monthsalso tends, on average, to be reduced. Similarly,

FIG. 13. Correlations between twice-removed 5-day precipitation totals averaged across the continental United States, as estimatedfrom GLACE control ensemble output for each model (solid lines) and for observations (dashed lines).


monthly rainfalls in the highest quintile tend to leadto higher-than-average rainfall rates in subsequentmonths.

KS04 then examined the precipitation rates gener-ated in GCM simulations. The observed conditional ex-pectations are reproduced by the GCM when land–atmosphere feedback is enabled, but they are destroyedwhen the feedback is artificially disabled. The effect ofocean variability on the signal is relatively small (seebelow). Thus, the GCM suggests that the observed con-ditional expectations are a signature of feedback.

The GLACE data allow the quantification of condi-tional expectations across a number of GCMs for com-parison with the observations. Precipitation rates fromthe 16 Junes, Julys, and Augusts of the control en-semble (case W) were processed onto the same hori-zontal grid and then used to generate conditional PDFsfollowing the strategy of KS04, with two slight modifi-cations: (a) instead of binning the monthly rates intoquintiles, which is difficult with 16 values, we binned

them into quartiles instead, and (b) rather than aver-aging the one-month-lagged results across the monthsstudied, we separately examine July rainfall condi-tioned on June rainfall and August rainfall conditionedon July rainfall. Results are shown in Fig. 14. The ob-servations (Huffmann et al. 1997) show that if Junerainfall is in the top quartile, the standardized July rain-fall will have an expected value of 0.2 (the unshaded,positive histogram bar). If, on the other hand, Junerainfall is in the bottom quartile, the expected value ofstandardized July rainfall will be about �0.13 (thecrosshatched negative bar). The results from the vari-ous models are generally similar in magnitude, but theystill vary, with some models (notably GFDL) producinglarger values and some others (BMRC, GFS/OSU,CSIRO-CC3) producing lower values. Results for Au-gust conditioned on July are similar. The averages ofthe conditional expectations across the models (the fi-nal bars in each panel) are close to, but slightly lowerthan, the observed values. For August conditioned on

FIG. 14. Conditional expected mean of standardized precipitation anomaly given an antecedent monthly anomaly in the topmostquartile (clear bars) and in the bottommost quartile (striped bars). Results are shown for observations, the individual models, and themultimodel average. Results from KS04 are also shown: ALO refers to an AGCM run with atmospheric, land, and ocean variabilityacting; AL to a run with only atmospheric and land variability acting; AO to a run with only atmospheric and ocean variability acting;and A to a run with only atmospheric variability acting.


June, the conditional expectations are greatly reduced,especially for the models. The multimodel averages ofthe conditional expectation for the two-month-laggedcase are considerably less than the observed values.

The proper interpretation of the comparison be-tween observations and models in Fig. 14 requires thecareful consideration of ocean impacts. The conditionalexpectations for the observations may partially reflectan influence of sea surface temperature (SST) anoma-lies that span the summer season and that are differentfrom year to year. Rainfall in the models cannot besimilarly influenced, since all ensemble members utilizethe same SST distribution. Again, the KS04 study sug-gests that land feedbacks dominate the signal. This isshown graphically in Fig. 14 by the histogram bars la-beled ALO (for a control simulation, in which the at-mosphere, land, and ocean all contribute to precipita-tion variability), AL (for a simulation in which theocean’s contribution is artificially suppressed), AO (fora simulation in which the land’s contribution is artifi-cially suppressed), and A (for a simulation in which thecontributions of both the land and the ocean are sup-pressed). These are the original KS04 results: they arebased on quintiles, and they are averaged across thefive months that KS04 studied. KS04 concluded thatland feedback dominates the diagnostic because with-out it (simulations AO and A), the conditional expec-tations are close to zero. Nevertheless, the histogramsindicate that the ocean does have a nonnegligible im-pact. A comparison of the results for simulations ALOand AL (for the one-month-lagged cases) suggests thatif the conditional expectations for the observationswere not influenced by SSTs, the observational resultsmight be reduced to about 90% of their plotted values.Considered in that light, the multimodel conditionalexpectations—at least for the one-month-lagged case—are seen to be very close to those from the observa-tional data.

6. Discussion

We have revisited the output from the participatingmodels of the GLACE experiment, which quantifiedthe strength and distribution of land–atmosphere cou-pling within 12 GCMs and estimated a model-independent global distribution of land–atmospherecoupling. The results of K04, K06, and G06 are basedon properties of the individual model ensembles. Thepresent study attempts to validate to the fullest extentpossible the behavior of the GLACE models with insitu observations. We look separately at the relation-ship between local surface properties and fluxes, and atthe memory signal evident regionally in precipitation.

The g parameter (the goodness of fit of the empiri-cally fitted dependence of NLH on SWet) correlateswell with ��NLH and is thus considered an observablemetric for a critical element of land–atmosphere cou-pling—the link between the soil moisture variationsand surface fluxes. (The g parameter can be derivedfrom observations wherever soil moisture measure-ments and flux towers are collocated, whereas ��NLH isa property of the ensembling of model integrations andis thus intrinsically unobservable.) Unfortunately, thereare very few locations where contemporaneous mea-surements of surface fluxes and SWet have been col-lected over a sufficiently long period to provide statis-tically stable relationships. The ARM Extended Facili-ties and a subset of FLUXNET sites do providesufficient data. At these locations, we find that indi-vidual models often poorly validate with regard tosimulations of SWet, NLH, and the relationship be-tween the two, but the multimodel average validatesbetter. We also find that the models show a strongerrelationship between LHF and SWet than betweenNLH and SWet, whereas observations show the re-verse, suggesting at first glance that there may be someproblems with the flux parameterizations in today’sland surface schemes. Further investigation shows,however, that in the region studied (the ARM region),all of the models simulate excessively warm tempera-tures and unrealistically low daytime relative humidity,thereby reducing the relative impact of net radiationvariations on the surface fluxes.

B04 provides a set of relationships, found within theECMWF model, that allow us to extend the analysis toother variables such as SHF and PLCL—variables thatare measured or can be estimated at FLUXNET siteswhere SWet is not recorded. B04 and field data suggestthat the relationship between SHF and SWet is usuallystronger than that for LHF and SWet, but the GLACEmodels do not exhibit that characteristic. (The modelsdo agree with observations that NLH and NSH havesimilar relationships with SWet, with NLH beingslightly stronger.) Likewise, the relationship betweenSWet and PLCL and between SHF and PLCL found byB04 is generally borne out in the observations, butpoorly represented by many of the GLACE models.Most models simulate too high a PLCL, which is consis-tent with the excessive simulated surface temperatures.Most GCMs appear not to simulate properly the cou-pling between the land surface and atmospheric bound-ary layer in midlatitude summer. Several GLACE mod-els show too weak a relationship, and the ECMWFmodel of B04 along with a few of the GLACE modelsappear to be too strongly coupled. Thus, perhaps, themultimodel estimate of land surface coupling strength


is not an unreasonable approximation of reality. Itshould be noted that the results of B04 were based ondata from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis(ERA-40), whereas the GLACE models were not con-strained by data assimilation. Nudging of the state vari-ables would not affect the calculation of fluxes, butcould limit the range of SWet or alter the apparentrelationship between SHF and PLCL, since PLCL is afunction of near-surface temperature and dewpoint. Itis unclear what affect this might have on the apparentcoupling strength of the ECMWF model. Stated an-other way, the GLACE models may appear poor in thiscomparison not because the parameterizations under-lying land–atmosphere coupling are poor, but becausebiases in the climate model shift the model climates intounrealistic regimes at the validation sites.

Large-scale relationships for precipitation over theconterminous United States also show that the multi-model mean represents quite well the observed behav-ior of lagged autocorrelations of pentad rainfall. Per-sistence of categorical anomalies in monthly rainfallduring boreal summer across Northern Hemispheremidlatitudes is also well represented by the multimodelmean. There is again a large degree of variation amongmodels in the strength of these metrics for precipitationmemory, but the results of K03 and KS04 suggest thatthe land surface is a likely culprit in supplying this per-sistence to the precipitation signal.

Overall, it appears that there is still much that can bedone to improve the behavior (i.e., the parameteriza-tions) related to land–atmosphere interactions in theGCMs widely used for weather and climate predictionand research. Variations among models can arise formany, often subtle reasons having to do with details ofthe parameterizations and the interplay of componentsand tunings of the models (Teuling and Troch 2005).Liu et al. (2005) have shown what can be accomplishedtoward improved model performance simply by consid-ering the land and atmosphere parameters together inexisting parameterizations when calibrating coupledmodels. The multimodel approach like that of GLACEis not an antidote but does alleviate the symptoms ofindividual model errors and biases.

We cannot disprove the results of GLACE over thelimited areas where there are sufficient data to estimatelocally the land–atmosphere coupling strength. Rather,we can argue that we still do not have sufficient data toquantify the actual strength of coupling between landand atmosphere. Long-term collocated measurementsof SWet, surface fluxes, and near-surface meteorologyshould be distributed around the globe in order to aidmodel development and assess the potential for SWet

as a predictor for climate via land–atmosphere feed-back. In the mean time, land–atmosphere model devel-opment efforts could benefit by paying more attentionto local validation of land surface and boundary layerparameterizations with available in situ data.

Acknowledgments. The authors thank G. Hughes forproviding us with the ARM data for the SGP ExtendedFacility sites. We would also like to thank Tim DelSolefor useful and enlightening discussions as this work pro-gressed. The time and effort of individuals at all of theparticipating GLACE modeling centers have made thisstudy possible: G. Bonan, E. Chan, P. Cox, C. T. Gor-don, S. Kanae, E. Kowalczyk, D. Lawrence, P. Liu,C.-H. Lu, S. Malyshev, B. McAvaney, J. L. McGregor,K. Mitchell, D. Mocko, T. Oki, K. W. Oleson, A. Pit-man, Y. C. Sud, C. M. Taylor, D. Verseghy, R. Vasic, Y.Xue, and T. Yamada. This work was conducted undersupport from National Aeronautics and Space Admin-istration Grant NAG5-11579.

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