glimu Uynumia Springer-Verlag 1993

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1 Climate Dynamics (1993) 9:33-41 glimu Uynumia Springer-Verlag 1993 Sub-grid scale precipitation in AGCMs: re-assessing the land surface sensitivity using a single column model Andrew J Pitman, Zong-Liang Yang, Ann Henderson-Sellers School of arth Sciences, Macquarie University, North Ryde, 2109, Sydney, Australia Received: 18 September 1992/Accepted: 5 January 1993 Abstract. The sensitivity of a land surface scheme to the distribution of precipitation within a general circulation model's grid element is investigated. arlier experiments which showed considerable sensitivity of the runoff and evaporation simulation to the distribution of precipitation are repeated in the light of other results which show no sensitivity of evaporation to the distribution of precipitation. Results show that while the earlier results overestimated the sensitivity of the surface hydrology to the precipitation distribution, the general conclusion that the system is sensitive is supported. It is found that changing the distribution of precipitation from falling over 100% of the grid square to falling over 10% leads to a reduction in evaporation from 1578mmy -1 to 1195 mm y-1 while runoff increases from 278 mm y-1 to 602 mm y-~. The sensitivity is explained in terms of evaporation being dominated by available energy when precipitation falls over nearly the entire grid square, but by moisture availability (mainly intercepted water) when it falls over little of the grid square. These results also indicate that earlier work using stand-alone forcing to drive land surface schemes 'off-line', and to investigate the sensitivity of land surface codes to various parameters, leads to results which are non-repeatable in single column simulations. 1 Introduction This paper examines the role of sub-grid scale precipitation in atmospheric general circulation models (AGCMs) by using a single column model (SCM) of the Central Amazon. AGCMs predict large- and small-scale rainfall separately, so the amounts of each precipitation type could be provided independently to the land surface. A problem with AGCMs is that their grid resolutions are necessarily coarse and processes with length or time scales below those of the AGCM must be parameterized. Although AGCM grid resolutions are increasing, the length and time scales of surface processes, including soil moisture (Wetzel and Chang 1987) and tur- Correspondence to: AJ Pitman bulent energy exchange (Mahrt and k 1993), remain sub-grid scale. Prognostic variables produced by AGCMs describe atmospheric conditions over the entire grid element, obscuring sub-grid scale variability. AGCMs predict the occurrence, magnitude and generation process (e.g. frontal or convective) of precipitation at each time step. Thus if a small-scale rainfall event (e.g. a cumulus shower) is simulated, the precipitation is spread out over the entire area of the grid element. This leads to a widespread, low intensity shower rather than a localized and intense storm, and thereby an underestimation of the precipitation intensity occurs in regions where smallscale precipitation is common. Although large-scale precipitation events could occur over an area equivalent to that of an AGCM's grid element, sub-grid scale variability does exist, and precipitation usually covers only a fraction of a grid element. This paper argues that it is necessary to parameterize the characteristics of precipitation distribution more accurately in AGCMs and that the land surface is sensitive to the assumptions made. This discussion is divided into three parts. A brief review of how sub-grid scale precipitation can be incorporated into climate models and how the sensitivity of these models can be assessed is followed by a re-evaluation of Pitman et al.'s (1990) results using a SCM. Finally, the sensitivity of hydrological predictions by AGCMs to sub-grid scale precipitation is shown to be significant, but the sensitivity can only be determined if both surface-atmospheric feedbacks and a canopy sub-model are included. 2 Sub-grid scale hydrology in AGCMs As precipitation intensity increases, the percentage intercepted by a canopy decreases (Rutter 1975, Fig. 5). At high intensities a canopy can saturate rapidly leading to considerable throughfall and increased soil moisture store. At low rainfall intensities, a dense canopy can intercept in excess of 60% of the rainfall (Pereira 1973) leading to high interception loss and relatively depleted soil moisture stores. While intercepted water on a cano-

2 34 Pitman et al.: Land surface sensitivity to precipitation py evaporates within hours, precipitation which reaches the soil may remain there for months or even years if it reaches deep ground water stores. Most AGCMs simulate grid area averaged precipitation and tend to underpredict precipitation intensity (see Pitman et al. 1990). Low precipitation intensities could lead to fundamentally misleading hydrological simulations since precipitation re-cycling from the surface to the atmosphere, in any AGCM incorporating a vegetation canopy, will be overestimated at the expense of soil moisture or runoff. To overcome this problem it is necessary to account for both the spatial extent and intensity of precipitation in AGCMs. In order to retain the spatial extent and intensity of precipitation reaching the surface, Warrilow et al. (1986 unpublished, Meteorological Office; UK) (hereafter W86) derived expressions for surface runoff and Shuttleworth (1988a, Appendix 2) for canopy drip following Milly and agleson (1982). The local precipitation rate over the rain covered fraction of a grid element (p) is assumed to follow a probability distribution (see Sect. 4.3). Sato et al. (1989) have suggested an alternative scheme to W86 and Shuttleworth's (1988a) models although they do not indicate how sensitive their AGCM is to this parameterization. Pitman et al. (1990) examined these canopy drip and surface runoff models using the Biosphere-Atmosphere- Transfer-Scheme (BATS, Dickinson et al. 1986) coupled to stand-alone (zero-d) atmospheric forcing (which prevents atmospheric feedbacks). They showed that the partitioning of precipitation between runoff and evaporation was highly sensitive to /~. In contrast, Dolman and Gregory (1992), using a SCM, compared Shuttleworth's (1988a) model with a similar model developed by Gregory and Smith (1990) and found net evaporation from a tropical forest to be insensitive to p. The use of stand-alone forcing has been questioned by Koster and agleson (1990) who compared simulations from stand-alone experiments, a SCM and an AGCM and showed that the results from the standalone experiments were incompatible with the other two techniques. In addition, Jacobs and de Bruin (1992) showed that planetary boundary layer feedbacks, explicitly prevented when using stand-alone techniques, are important in assessing the sensitivity of the land surface to changes in the land surface parameterization. In order to investigate why Dolman and Gregory's (1992) results directly contradicted Pitman et al.'s (1990) we have repeated the experiments of Pitman et al. (1990) using the SCM used by Dolman and Gregory (1992). We show that while Pitman et al.'s (1990) results did overestimate the sensitivity, the overall conclusion that modifying p led to a significant redistribution of precipitation between evaporation and runoff is still valid. We argue here that the lack of a full canopy parameterization in the model used by Dolman and Gregory (1992) explains the lack of sensitivity in their results to changes in ~. 3 Single Column Model forcing of BATS The UK Meteorological Office (UKMO) SCM described in W86 includes the basic physical processes incorpo- rated in the UKMO 1 l-layer AGCM such as interactive radiation with four cloud types (low, medium, high and convective), a mass flux convection scheme, large scale precipitation, a boundary layer parameterization based on Clarke (1970) and the W86 land surface scheme. As discussed by W86 and Dolman and Gregory (1992), the SCM incorporates a method for treating heat and moisture advection at the edge of the grid element (assumed to be a circular element with radius An). The local rate of change of a quantity X, averaged over the grid element, due to large-scale advection is AX I V. l (Xr- JO coax - (1) At An Ap where At is the model timestep, I V~l is the absolute value of the horizontal velocity in the direction of the positive gradient n, co is the vertical p-velocity, p is the pressure and Xr, is a reference value of X outside the grid element. W86 prescribe V~, co and X, assuming a Gaussian probability distribution with means and standard deviations taken from Oort (1983). A dew point depression (/9) is used to specify a moisture reference profile while the standard deviation of D is assumed linearly related to temperature. Since the mean values of X, refer to the centre of the grid element and a reference profile is required at the edge of the element, a correction is applied to account for the gradient of a quantity (dx) across the grid element. In these experiments using the SCM, data from the Central Amazon (3 S 60 W) are used to provide the advective forcing. Data for January and July are prescribed (see Dolman and Gregory 1992) and a sinusoidal interpolation procedure is used to calculate data for other months. A random number generator is used to specify the precise forcing on any single day from the assumed Gaussian distributions described. Values are interpolated from one day to the next to prevent large step-changes in the atmospheric forcing. The use in these experiments of the SCM continues a trend established by W86, Koster and agleson (1990) and Dolman and Gregory (1992). The inclusion of surface atmospheric feedbacks, ignored in stand-alone forcing, permits changes in the partitioning of available energy to impact on the temperature and moisture in the lowest layer of the SCM. This modifies the moisture and temperature profiles and subsequent turbulent energy fluxes. Surface-atmospheric feedbacks are therefore included, making the results from SCM experiments more readily extrapolated to the AGCM (Koster and agleson 1990). 4 Parameterizing the land surface in the SCM 4.1 The lancl surface in AGCMs There are numerous methods for parameterizing the land surface in AGCMs (Henderson-Sellers and Dickinson 1992). The standard land surface scheme in the SCM was developed by W86 and includes a four-layer soil model designed to produce a reasonable prediction of surface temperature according to the radiative forc-

3 Pitman et al.: Land surface sensitivity to precipitation 35 W86 BATS 1 t,(f(t, vpd... ) t ~(f(k))'( Vm ~ l 002.5ram_ 2"9m-- I l,h i V c T,~, T,w 1 ~ i ~ Z4 ~"~ l Intercepted water Canopy layer... --Y,-w- - - T,w ;////2 H m m m Fig. 1. A diagrammatic representation of BATS (version 1) and W86 showing the basic differences between the two land surface models. Symbols are included where a quantity is predicted prognostically: w is soil moisture content, T is temperature, q is specific humidity, V is wind speed at the lowest model layer (m) and at canopy height (c), is evaporation, H is sensible heat flux,, is transpiration (expressed as a function of either a constant (k) or temperature, vapour pressure deficit etc. (see q. 5)) and Z are soil depths from Z1 to Z,. Soil depths are drawn approximately to scale although upper depths are exaggerated so that they can be seen ing in the frequencies from half a day to a year. An important aspect of the model is its treatment of the hydrological aspects of the canopy component. W86 represent vegetation as a single solid body with a single temperature equal to that of the top soil layer temperature. There is no explicit treatment of canopy temperature and hence the model is fundamentally different from BATS which includes three soil layers and a separate canopy layer. Since the results in this paper compare simulations by Dolman and Gregory (1992) using W86 with results using BATS, a brief description of some relevant components from both models is given. 4.2 Warrilow et al. "s (1986) model and Dickinson et al. "s (1986) model, BATS BATS and W86 parameterize evaporation from the soil, wet canopy and dry canopy. Although there are similarities between the two models, Fig. 1 shows that in BATS, the canopy temperature is calculated using a full energy balance and that the canopy is elevated, porous and distinct from the surface with canopy-like characteristics. In W86, the canopy temperature and soil temperature are assumed to be equal, and in terms of heat capacity, density and air flow, the canopy has the characteristics of a soil layer. In W86, the evaporation from the soil-vegetation surface (~0 is given by (q~* - q~) ~ (2) (r~ + r~) where q is the specific humidity of the surface (s) and the air (a), * refers to a saturated value, r~ is the aerodynamic resistance and rs is the surface resistance to water vapour transfer. In q. (2), the saturated value for q~ is used, giving the potential evaporation rate, which is reduced to the actual rate by modifying the 'unstressed' surface resistance to account for the soil moisture concentration [see Warrilow et al. 1986, q. (4.4)]. The W86 model predicts evaporation from both wet and dry canopy fractions. vaporation from a wet canopy is given by q. (2) with rs set to zero. In BATS, evaporation is calculated from the same surfaces as in the W86 model, but each surface has a separate temperature, specific humidity, etc. Therefore in BATS, qs depends on the soil and canopy temperatures. In the area simulated here (Central Amazon), BATS is initialized so that the canopy covers of the grid element and hence qs is calculated as qs = 0.9tic q* + 0.1fig q* (3) where q* is the saturated specific humidity of the canopy (c) and the soil (g), which are calculated from the temperatures of each surface, and fl is the fractional wetness of the canopy and soil surfaces (as described by Dickinson et al. 1986). Hence the soil component of temperature is relatively unimportant in BATS in contrast to W86 where q~= qg. The aerodynamic resistance needed in q. (2) is given following Dickinson et al. (1986). Transpiration is calculated in W86 based on a specified constant stomatal resistance. In BATS the stomatal resistance (rstom) is derived from a series of factors [see Dickinson et al. 1986, q. (51)] including the amount of intercepted photosynthetically active radiation, air temperature and soil moisture concentration. The version of BATS used here also includes a vapour pressure deficit feedback in the calculation of stomatal resistance. This method of calculating stomatal resistance is more sensitive to overall changes in the model's climatology and is one of the factors that explains why BATS shows greater sensitivity to changes in the distribution of precipitation compared to W86. In addition, the W86 model incorporates only the hydrological aspects of the canopy such as interception and re-evaporation of rainfall. A wet surface can evaporate quickly and cool, but in the SCM the surface which cools is characteristic of a soil layer with non-negligible heat capacity. This leads to the potential underestimation of the cooling and thereby an overestimation of the

4 36 sensible heat flux. In BATS, the canopy energy balance is solved explicitly and hence evaporation can cool the surface rapidly and avoid overestimating the sensible heat flux. W86 does, however, include an attempt to incorporate a sub-grid scale distribution of precipitation for runoff which BATS ignores. The model can account for the differences in the characteristics of large-scale and small-scale precipitation events only in terms of the simulation of surface runoff, but can be extended to include the canopy drip component (Shuttleworth 1988a; Dolman and Gregory 1992). This methodology is discussed below. 4.3 The Warrilow/Shuttleworth precipitation distribution scheme In order to represent a realistic spatial distribution and intensity of precipitation in AGCMs, W86 and Shuttleworth (1988a) derived expressions for surface runoff and canopy drip following Milly and agleson (1982). The local precipitation rate, over the rain affected fraction of a grid element (p), is assumed to follow a probability distribution (the expressions for Rsurf and Rd~ip are derived by W86 and Shuttleworth (1988a) and are not repeated here). According to W86 the surface runoff rate (R~u~f) can be defined as Rsurf = Ps exp [ ~ F~] (4) where Ps is the net flux of water at the surface and Fs is the maximum surface infiltration rate, assumed constant over the grid element (see W86). The canopy drip rate (Rdrip) over the fraction of the grid element covered by vegetation is modelled following Shuttleworth (1988a) as Rdrip = Pc exp / Pc J (5) where Pc is the precipitation intercepted by the canopy, and Fc is the maximum canopy infiltration rate, assumed constant over the grid element. Rdrip requires the calculation of Fc which is analogous to F~. Shuttleworth (1988a) defines Fc as S-C Fc - (6) At where S is the maximum canopy storage capacity, C is the water stored on the canopy and At is the time step. Finally, R~f and Rdrip are constrained such that if the infiltration rate (F~) is greater than P~ (i.e. the soil can absorb all the precipitation), there is no surface runoff. Similarly with the canopy, if the intercepted precipitation does not exceed Fc then there is no drip, quations (4) to (6) provide a quite different description of canopy and soil surface runoff than those currently incorporated in BATS. In this study, the effects of incorporating these equations into BATS are described and the implications for re-modelling the land surface in this way in coarse resolution AGCMs are discussed. Pitman et al.: Land surface sensitivity to precipitation 4.4 Parameterization of land-surface hydrology in BATS BATS contains, by necessity, a number of simplifications in the parameterizations of surface runoff and canopy hydrology. In BATS, surface runoff is defined as [_ Wsat J where Wsat is the saturated soil water fraction, w is the soil water fraction weighted toward the surface and G is the net flux of water at the soil surface (the sum of precipitation, dew fall and canopy drip). The value of G used here is the grid element average flux. Thus, Rsurf > 0 if G > 0, irrespective of the intensity of precipitation, dew fall or canopy drip. Since in BATS there is no infiltration capacity, it is probable that the model would overestimate Rsure particularly at low precipitation intensities, as even a light shower will lead to some runoff. However, the overestimation of Rs~re will be minor when the soil is not close to saturation. BATS parameterizes the canopy hydrology in an analogous way. If the amount of water intercepted by the canopy (C) (which, in BATS, is all the precipitation which falls over the vegetated fraction of the grid element) exceeds the storage capacity (the leaf and stem area index L~ai multiplied by 0.1 expressed in kg m-2), then the canopy storage is set to the maximum value and the canopy drip (Rdrip) over the vegetated fraction of the grid element is C- 0.1 Ls~ (8) Rdrip -- At The parameterization of Rarip and Rsurf incorporated in BATS is probably a reasonable simplification if precipitation is assumed to fall homogeneously within the grid element. However, it will be shown that if the parameterization described by Shuttleworth (1988a) is included in BATS instead of qs. (7) and (8), the simulated results are quite different. It must also be emphasized that these simulations discuss only sub-grid scale variations in precipitation. At the end of each time step soil moisture and canopy water are assumed to be distributed uniformly over each individual grid element. This is clearly an inconsistency, but at present no computationally efficient method exists to avoid this and to permit the propagation of sub-grid scale variations in these quantities between time steps. In addition, no account is taken of sub-grid scale heat advection which might result from non-uniform precipitation. These are all areas requiring further research before incorporation into AGCMs. 5 Control experiments 5.1 Modification of the SCM to include BA TS Linking BATS to the SCM involved a series of modifications. For instance, the soil depth, soil characteristics, roughness length, albedo etc. were all modified from the

5 Pitman et al.: Land surface sensitivity to precipitation 37 default values used in the SCM (parameter values used by BATS are listed in Table 1). Linking BATS with the SCM leads to the removal of the W86 land surface model but the atmospheric component remains unchanged. Note that in these control experiments, the SCM includes the standard version of BATS which does not include sub-grid scale precipitation. 5.2 Results from seasonal simulations We have conducted a series of two year simulations using the SCM (thereby avoiding initialization and 'spinup' problems) but only results from the second year are shown here. These results are independent of initial conditions, but it is interesting to note that the SCM seems significantly less sensitive to initial conditions as compared to stand-alone simulations, suggesting that the surface-atmospheric feedbacks incorporated in the SCM are included realistically, or advective forcing dominates the response. Figure 2 shows results from the SCM including BATS (hereafter SCMB). The simulation of precipitation from the SCMB can be compared to the same observed data used by Dolman and Gregory (1992) (Lloyd 1990, 15 years average) and those described by Shuttleworth (1988b, Fig. 8) for one year (1984). The control simulation reproduces the long-term average observed precipitation to within 2ram d -1 for all but three months of the year. However, the SCMB predicts monthly precipitation amounts to within Lloyd's (1990) estimate of the standard error for only five months of the year. Of the seven months where predicted precipitation is outside the standard error estimate of Lloyd (1990), the predicted precipitation is very close to the data described by Shuttleworth (1988b) for The version of the SCM used here seriously underestimates rainfall amounts in April and May, which is clearly a problem in using this model in a predictive mode. However, since the experiments discussed here are all sensitivity experiments, the underestimation of precipitation is not a significant problem except in March, April and May. The difficulties in predicting precipitation were explained by Dolman and Gregory (1992) in terms of problems in the cumulus convection scheme, the random nature of the forcing, or the characteristics of the forcing data. However, the simulations presented here show much more serious errors in March, April and May compared to Dolman and Gregory (1992). This implies that great care must be taken comparing these results with observed data and in interpreting the significance of the results in an absolute sense. The amounts of precipitation simulated by the SCMB in March, April and May are, however, high enough to maintain soil moisture stores and a generally wet surface. Soil moisture reservoirs do not become depleted and so the low precipitation does not impact the evaporation rates in subsequent months (Fig. 3a). The prediction of evaporation (Fig. 3a) by the SCMB is reasonable. Shuttleworth (1988b) observes that the total evaporation is relatively constant throughout the Table 1. Parameter values in simulations using BATS for a tropical forest ecotype Quantity Units Value Maximum fractional cover of vegetation Difference between maximum fractional vegetation cover and fractional cover at 269 K Roughness length m 2.0 Zero plane displacement height m 18.0 Minimum stomatal resistance s m Maximum leaf area index (ratio unit cover per unit ground) Minimum leaf area index Stem area index Inverse square root of leaf dimension m --1/2 5.0 Light sensitivity factor mzw Depth of upper soil layer mm Depth of rooting zone mm Depth of total soil mm Vegetation albedo <0.7 gm Vegetation albedo _> 0.7 gm Soil colour Soil porosity Minimum soil suction mm Maximum hydraulic conductivity mms Fraction of water content at which permanent wilting occurs Clapp and Hornberger "b" parameter 9.2 Ratio of soil thermal conductivity to that of loam 0.8 "o L_ 0~ o_ F 16 i i J i i i i i i i i i --- Observed J 14 - Modelled 4 12 Shutfleworfh" i V' 2 i..i / 0 I i I I t I r { I i i r J F M A M J J A S 0 N D Fig. 2. Simulation of precipitation from the SCM + BATS for an area representative of Manaus, Brazil (3 S, 60 W). The solid line is modelled precipitation from the second year of a two year integration. The observed data (dashed fine) is from Lloyd (1990) and is the average rainfall from at the Reserva Ducke climate station 25 km from Manaus. The error bars on the observed data are Lloyds' estimate of the standard error of the mean. Point values (O) are from Shuttleworth (1988b) and are observed monthly precipitation amounts for one year (1984). All quantities are expressed in mm d- 1 i

6 38 Pitman et al.: Land surface sensitivity to precipitation i i i i i - - Precipitation --- vaporation... Runoff i i i i - - Precipitation --- vaporation... Runoff >. "0 L >. O 23 i._ Q_ [\ /// _ / I 2 / / 1' \ 0, ~ "'l... T t "'"+-'-T---P... f' I ~ ~ "-i... 7 ~ --T--~ ~ ~ T J F M A M J J A S 0 N D J F M A M a O A S 0 N Precipitation 16 i i i i - - Precipitation vaporation... Runoff vaporation... Runoff g" 10 X3 x_ 09 ^ 8 [3- / \\\ / \ 6 > (3 L_ 0 O- ff '\ /. \ \ / \, \ "' /,/ N'NX 5,,v -.._- -_ %, C 0,... i...,... ~--T... i"--~...,---,...,----,... J F M A M J J A S 0 N D 0 d F M A M J J A S 0 N O Fig. 3a-d. Simulation of precipitation, evaporation and runoff from the SCM including BATS for an ecotype representative of a tropical forest for a the control version of BATS; h the SCM with BATS and the sub-grid scale parameterization of runoff and canopy drip with/~ = 1.0; e # =0.5 and d p=0.1. All quantities are expressed in mm d -1 Table 2. Annual precipitation and evaporation totals from the observed (quoted by Dolman and Gregory 1992), the control version of BATS and the experiments with BATS incorporating the p parameterization. Differences from the observed are also included xperiment Results from SCMB Results from SCMB Results from Pitman et al. (1990) Precipitation (mm y-l) vaporation (mm y- 1) Runoff (mm y - 1) Soil moisture vaporation (mm y-x) Annual Difference Annual Difference Annual total total from observed total from observed Change in root Annual total Difference zone moisture from observed Observed Control # = = = The final two columns show results from the stand-alone forcing experiments discussed by Pitman et at. (1990) which are included for comparison purposes. Note that in Pitman et al.'s (1990) simu- lations, the precipitation was a constant 2871 mm y-~ and could not vary due to the characteristics of stand-alone forcing

7 Pitman et al.: Land surface sensitivity to precipitation year at about ll0mm month -1 (about 3.7mind -1) although Shuttleworth (1988b, Fig. 8) shows a range in observed evaporation from -80mmmonth-1 to mm month- 1 The SCMB predicts maximum evaporation in March (160 mm month -~ or 5 mm d -') and minimum evaporation in July (97 mm month -1 or - 3 mm d-l). The SCMB predicts a total annual evaporation of 1578mm or 131 mmmonth -~ (see Table 2) which, considering inevitable differences in radiative forcing (the SCMB consistently overpredicts net radiation by 20-30% compared to Shuttleworth and Dickinson 1989, Fig. 1), is a reasonable prediction. However, Table 2 also shows that the control version of BATS overestimates the fraction of precipitation which evaporates by 37%. Part of this error is due to the underestimation of precipitation in March, April and May as more rainfall would tend to lead to a smaller percentage loss by evaporation, while part is due to the overestimation of net radiation. The prediction of runoff (Fig. 3a) is closely related to the precipitation pattern with maximum runoff in February. Runoff occurs throughout the year, with maximum values of about 3 mm d- 1 in February and minimum values of 0.1 mm d - i in July and September. SCMB underpredicts precipitation and overpredicts evaporation (by up to 1 mm d- 1) but overall reproduces the observations adequately for sensitivity studies. The comparison between the SCMB and the observed precipitation data should not be examined too closely since the results from the SCMB are monthly averages over one year and year-to-year variability in the observed precipitation is quite large (see Shuttleworth 1988b, Fig. 8). The precise match between modelled and predicted precipitation is not crucial in the following experiments since they are examining the sensitivity of a parameterization rather that the predictive quality. However, the poor simulation of precipitation in March, April and May and the overestimation of net radiation must be remembered when the model results are compared to the observed data. 6 Sensitivity experiments A version of BATS which incorporated the parameterization of runoff and canopy drip developed by W86 and Shuttleworth (1988a) was linked to the SCM and a series of simulations performed where the only change was the prescription of the distribution of precipitation (p in qs. 4 and 5). Four simulations were performed, one without the sub-grid scale parameterization (the control version of BATS discussed in Sect. 4.4), and three experiments with/2 set to 1.0, 0.5 and 0.1. Incorporating the/2 parameterization led to significant changes in the partitioning of precipitation between evaporation and runoff. The first experiment using ~t= 1.0 (Fig. 3b) implied that precipitation was distributed across the whole grid element before reaching the surface. Using/2 = 1.0 lead to a reduction in the effective precipitation intensity and increased the amount of precipitation which was intercepted by the canopy. Figure 3b shows that the evaporation increased from 88% of precipitation in the control to when/2 = 1.0 (Table 2). vaporation shows greater seasonality which, coupled to higher evaporation rates, lead to a poorer overall simulation compared to the control. There was little change in runoff except in February when BATS did not predict significant surface runoff since the amount of water reaching the soil surface was reduced as the precipitation intensity was reduced due to the larger value of /2. Under these conditions, BATS predicted only runoff through gravitational drainage. The reduced prediction of runoff in BATS including the/2 parameterization leads to an increased availability of moisture for evaporation. This, in turn, lead to unrealistically high evaporation in March as soil evaporation reaches high values since, in these simulations, soil moisture and not available energy (which is overestimated by 48% compared to Shuttleworth and Dickinson 1989, Fig. 1 in the SCMB) was the limiting factor in the magnitude of evaporation. In addition, the distribution of precipitation in the SCMB in February and March shows high amounts of rainfall each day, in contrast to later in the year where days of rainfall are separated by dry days. The high and frequent rainfall, high interception and excessive net radiation combine to lead to the overprediction of evaporation in March, particularly. Reducing the value of/2 to 0.5 led to a slight decrease in evaporation to 88% of precipitation (Table 2) and a small increase in runoff as shown by Fig. 3c. BATS still only predicts runoff through gravitational drainage but the increased amount of precipitation reaching the surface (via canopy drip) leads to an increased soil moisture store and thereby to enhanced gravitational drainage. The differences between the simulation of precipitation, evaporation and runoff are shown in Fig. 4. Changing/2 from 1.0 to 0.5 has little effect on precipitation. vaporation decreased slightly in most months (by 0.2 mm d- 1) but increased in March by about the same amount. The changes in runoff are complementary to the changes in evaporation with a small increase in runoff of about 0.2 mm d -1. None of the effects of changing/2 from 1.0 to 0.5 were large and were much smaller than Pitman et al. (1990) found (see the final three columns of Table 2). Changing/2 from 1.0 to 0.5 leads to a change of only about 2% in the total evaporation in the SCMB, compared to 14% in the stand-alone experiments of Pitman et al. (1990). The final experiment changed/2 from 1.0 to 0.1 (i.e. the same amount of precipitation now falls over only 10% of the grid element but at ten times the intensity). The specification that rainfall covers 10% of an AGCM's grid square in the Central Amazon is probably still an overestimation but was chosen to present a balance between convective showers which cover very small areas and larger-scale events. Using/2 = 0.1 lead to significant changes in the prediction of evaporation and runoff by the SCMB (Fig. 3d). vaporation was reduced to 67% of precipitation since the higher precipitation intensities reduced interception and lead to greater amounts of water reaching the soil surface. Figure 3d shows an increase in total runoff due to increased surface runoff. These differences are clearly shown in Fig. 4 39

8 ,0-0, I '\ \ o '\,/ '\ 0.5 '\ _ -- _./ 0,0 F'~ I I J F M -- Precipitation: Precipitation: vaporation: vaporation: 0.1-I.0 '\ ~ -,\ / \./ k / '\, / / -~ / \, A M J J 'x / "/ - - Runoff: Runoff: 0.1-LO,\ /,\ \ /. / \.i \ A S 0 N D Fig. 4. Differences between three experiments using BATS and the p-sub-grid scale parameterization for runoff and drip. The top panel shows precipitation, middle panel shows evaporation and lower panel shows runoff. ach case shows experiment (p = 0.5 or # =0.1) minus control (p= 1.0). Units are mm d -1 Although the changes in precipitation were again small, much larger changes in evaporation and runoff occurred. vaporation was reduced by mm d- 1 except in February (which brings it very close to Shuttleworth's [1988b] observations). Runoff was increased by mm d- 1. The changes in runoff largely balance the changes in evaporation although there were also changes in the soil moisture store as/t was decreased (Table 2). Again, the sensitivity to changing/t using the SCMB was less than found in stand-alone experiments by Pitman et al. (1990). Changing/t from 1.0 to 0.1 in stand-alone experiments led to a change in evaporation of about which was reduced to 23% in SCMB integrations (Table 2). The sensitivity in the prediction of evaporation and runoff to changes in/t supports the basic results of Pitman et al. (1990), although the magnitude of the sensitivity was reduced (Table 2). This contrasts with the results of Dolman and Gregory (1992) who found no sensitivity to changes in/t using the SCM with the W86 land surface scheme and with W86 linked with Shuttleworth's (1988a) canopy drip formulation. 7 Discussion and concluding remarks These results show that the earlier simulations by Pitman et al. (1990) using stand-alone forcing overestimated the sensitivity of the land surface hydrology predicted by BATS to changes in the spatial distribution of precipitation. However, the overall conclusions of that study are confirmed: Changing the distribution of precipitation within a grid element does lead to a modifica- Pitman et al.: Land surface sensitivity to precipitation tion in the partitioning of precipitation between evaporation and runoff in BATS and the total evaporation predicted by the SCMB does depend on the value of/t (Table 2). Unfortunately, there is a problem in the control version of the SCM with a poor prediction of precipitation early in the year (Fig. 2) and an overestimate in the predicted net radiation due to an underestimation of cloud cover in the model. It is therefore not possible to identify the most realistic overall simulation since the simulated climate was significantly different from the observed climate. Although the quality of the control climate is imperfect, the sensitivity of the SCMB to changes in/t are still valid although the quantitative assessment of sensitivity shown in Table 2 is affected by the poor simulation of precipitation during March, April and May. Figure 4 shows that the sensitivity of evaporation to p is insensitive to the amount of precipitation. Figure 4 also shows that as precipitation increases, runoff appears to become more sensitive to /t. Overall, the sensitivity of BATS to/t shown in Table 2 is probably reasonable although the poor simulation of March, April and May precipitation may have led to an underestimation of the role of/t in the simulation of runoff, but this will require an improvement in the SCMB to be confirmed. Table 2 shows that as/t is increased from 0.1 to 1.0, BATS predicts an increase in the fraction of precipitation evaporated from 67.1 o70 to There is relatively little sensitivity in the evaporation or the interception rate to/t at high values of/t. Table 2 shows that the total annual evaporation varies by only 27 mm y-1 between the control,/t = 1.0 and/t = 0.5 while the canopy evapotranspiration changes by only 22 mm y-1. This lack of sensitivity suggests that at higher values of/t, the evaporation is limited by the available energy rather than moisture and small changes in interception due to changes in/t do not lead to changes in monthly evaporation since there is enough moisture to realize the atmospheric demand. It is the overprediction of net radiation which leads to the overprediction in evaporation shown by Table 2. When/t is reduced to 0.1 there is a significant change in the partitioning of precipitation between evaporation and runoff. vaporation is dramatically reduced, largely due to a fall in the canopy evapotranspiration from 1517 mm y-1 to 1123 mm y-l, while runoff increase to compensate. The increased intensity of precipitation reduces interception and increases throughfall, resulting in a higher net water flux to the surface and an increase in runoff. Under these circumstances, moisture becomes the limiting factor for evaporation rather than net radiation since the canopy dries and cannot support the atmospheric demand. This basic pattern reproduces the results of Pitman et al. (1990) but the sensitivity of the hydrological system is markedly reduced due to the incorporation of surface-atmospheric feedbacks in the SCM (see Jacobs and de Bruin 1992). There remains the issue of how to implement a/t-type parameterization into climate models. This study has shown that the prediction of runoff and evaporation on longer time scales is relatively insensitive to large values

9 Pitman et al.: Land surface sensitivity to precipitation 41 of p. The p-parameterization is therefore particularly important when p becomes low (e.g. 0.1) which is likely to be the case in tropical regions and semi-arid regions where intense convective activity is more common. It may be sufficient, for the time being, to follow W86's approach and prescribe a value for p for model-predicted large- and small-scale rainfall. However, the choice of p for small-scale events is critical and dependent on AGCM resolution. Rather than prescribing p, a conceptual approach to its prediction needs to be developed since p-based schemes are already being used within AGCMs (Sato et al. 1989). If p is predicted rather than prescribed, the advantages of incorporating qs. (4) and (5) increase. AGCMs could estimate the fraction of the grid element surface receiving precipitation either from simulated fractional cloud cover or from the "plume fraction of the grid element that is active during moist-convection events" (ntekhabi and agleson 1989), although as pointed out by Dolman and Gregory (1992), the difficulties in predicting values for p should not be underestimated. Overall, the importance of the parameterization of sub-grid scale precipitation has been confirmed using BATS linked to a SCM. The results described here show a significantly reduced sensitivity compared to Pitman et al.'s (1990) and indicates that the results obtained using stand-alone forcing are questionable. These results also show that reducing the distribution of precipitation from p = 1.0 to p = 0.1 leads to a reduction in evaporation and an increase in runoff. This has implications in regard to those land surface models which already include parameterizations of sub-grid scale precipitation (e.g. W86). Changes in the amount of precipitation predicted by AGCMs from convective to larger-scale (or vice versa) will impact in a larger (and perhaps more realistic) way than in those models which do not include sub-grid scale parameterizations. This makes comparing the results from climate change experiments more difficult since it adds an additional physical difference between the land surface models. We have shown that BATS linked to the SCM is sensitive to the distribution of precipitation. However, the SCM did not simulate the precipitation or net radiation well enough to assess which value of p led to the most realistic prediction. In addition, the overprediction of net radiation was shown to lead to an overestimate in the predicted evaporative flux. This supports the analysis by Shuttleworth and Dickinson (1989) who showed that BATS was sensitive to the overprediction of net radiation in an AGCM and supports the suggestion by Shuttleworth and Dickinson (1989) that this overprediction of evaporation could be improved by using a p type parameterization. However, we concur with Shuttleworth and Dickinson (1989) who argue that the most pressing problem in improving the modelling of the land surface climate is to improve the prediction of abovesurface quantities, particularly cloud cover and net radiation. Acknowledgements. We thank Dr. P. Rowntree, Dr. R.. Dickinson, Dr. P. Sellers and an anonymous referee for their val- uable and perceptive comments, AJP acknowledges Australian Research Council and Macquarie University Research Grants. Z-L Y acknowledges the support of a Macquarie University Postgraduate scholarship. A H-S acknowledges the support of MCCA, the Australian Research Council and DAST. 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