..•
Journal of
Hydrology
ELSEVIER
Journal of Hydrology 155 (1994) 325-336
[3]
The impact of changes in the runoff formulation of a general circulation model on surface and near-surface parameters Pedro Viterbo*, Lodovica Illari European Centrefor Medium Range Weather Forecasting, Shinfield Park, Reading RG2 9AX, UK
(Received 28 February 1992; revision accepted 30 April 1993)
Abstract
The surface and near-surface properties of the European Centre for Medium Range Weather Forecasting (ECMWF) general circulation model are shown to be sensitive to the parametrization of runoff. If a broader subgrid-scale distribution of precipitation is assumed when computing runoff, the infiltration increases, more water becomes available for evaporation and the model surface cools. The averaged Bowen ratio over land is shown to decrease from 1.5 to 0.9 in a Northern Hemisphere summer experiment. Possible implications for the estimation of soil moisture and evapotranspiration using a global data assimilation-forecasting system are discussed.
1. Introduction
Over the last decade there has been growing interest in the representation of subgrid-scale heterogeneities of the land surface and their impact on parametrization for general circulation models (GCMs). By assuming simple statistical spatial distributions of a few relevant variables, it is possible to derive grid point values of other derived quantities as an area-mean over the grid box. A typical example is the definition of runoff from an assumed subgrid-scale distribution of precipitation. A recent review of modelling approaches, and of theoretical and observational evidence on this subject has been given by Thomas and Henderson-Sellers (1991). In convective situations in nature the observed distribution of precipitation over a grid box for a timestep of the model is far from uniform. The size of a typical convective element is much smaller than the grid box used in present generation G C M s , whereas its lifetime is of the order of the timestep used (At ~ 0 . 5 - 1 h ) . * Corresponding author. uu22-1694/94/$07.00 © 1994 Elsevier Science B.V. All rights reserved SSDI 0022-1694(93)0505 I-K
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During a timestep, an individual convective element will be advected; hence the fraction of the grid box wetted by the cloud will be larger than the value observed at any instantaneous time. The point precipitation intensity will follow some distribution law, with shape and amplitude dependent on the grid size and the timestep considered. Eagleson and Wang (1985) analysed the distributions of precipitation in the Great Plains of the USA using detailed rain-gauge data over a long period of time, and Austin and Houze (1972) classified radar observations of precipitation in New England, finding a different typical size, duration and intensity associated with synoptic, large or small mesoscale systems and convective events. Based on satellite observations of cloud clusters in the Western Pacific, Ruprecht and Gray (1976) derived a histogram of intensity vs. horizontal extent of precipitation area over the oceans. Hourly precipitation amounts for a typical cloud cluster of size 40 × 40 indicated that the rainfall was not evenly distributed, with more than half of the area free of rain. From the above observational evidence, it is necessary to take into account the subgrid-scale variability of precipitation when computing the runoff. Entekhabi and Eagleson (1989) and Famiglietti and Wood (1991) presented an approach for computing the runoff over a grid box assuming an arbitrary probability density function for the spatial distribution of precipitation. This has been applied in several surface parametrization models starting with the work of Warrilow et al. (1986) (see also Sellers et al., 1986; Pitman et al., 1990; Xue et al., 1991). The European Centre for Medium Range Weather Forecasting (ECMWF) model uses the simple assumption that convective precipitation, CP, during one timestep covers a fraction k of the grid box, with amplitude C P / k . The runoff is then computed from this modified flux of precipitation. The surface characteristics of the ECMWF model were found to be sensitive to the value of k used when computing the runoff. Pitman et al. (1990) and Thomas and Henderson-Sellers (1991) reported a similar sensitivity in off-line calculations with a surface parametrization, using prescribed forcing from the atmosphere. The purpose of this paper is to present results in the context of a GCM experiment with a fully interactive surface formulation. We will show that when a broader distribution of precipitation is used for the computation of runoff, more water infiltrates into the soil. This increase in soil moisture implies a change in the energy balance at the surface, which increases the latent heat flux and reduces the sensible heat flux, together with lower values of surface and nearsurface air temperatures. Section 2 presents an overview of the model with a brief description of the land surface parametrization. Section 3 describes some deficiencies of the ECMWF model and the impact of the changes to the formulation of runoff on summer integrations. The concluding section discusses possible impacts of this change on the estimates of soil moisture and evapotranspiration given by the current global data assimilation-forecasting systems.
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2. The forecast model The model used in the simulations presented below is the ECMWF spectral model (Simmons et al., 1989). It has 19 vertical levels with a hybrid vertical coordinate (Simmons and Striifing, 1981) and typically five levels within the planetary boundary layer. The physical parametrization used by the model is fairly comprehensive, including a mass-flux convective scheme (Tiedtke, 1989), gravity wave drag (Miller et al., 1989), a radiation parametrization (Morcrette, 1990) and an interactive diagnostic cloud scheme (Slingo, 1987; Morcrette, 1990). The boundary-layer parametrization is based on stability-dependent exchange coefficients (Louis, 1979). The land surface parametrization has been described in detail by Blondin (1991) or in the ECMWF internal documentation (European Centre for Medium Range Weather Forecasting (ECMWF, 1991). The relevant features are described below. There are two active layers in the soil with specified values in a third (lowest) layer acting as a seasonally varying boundary condition. Each layer is forced thermally by the fluxes across the interface, specified by a Fickian diffusion law. The forcing from the atmosphere is given at the top as the sum of the net surface radiation, and sensible and latent heat fluxes. For each grid point the evaporative flux is calculated separately over (a) the skin reservoir fraction (which combines the ponded bare soil, the wet vegetation and the snow-covered fraction), (b) the dry bare soil fraction and (c) the dry vegetation fraction. A fixed vegetation cover is assigned to each grid point according to Wilson and Henderson-Sellers 0985). The model also treats separately the heat and water balance in the snow mantle, including sublimation and melting. The skin reservoir intercepts precipitation, collects dew and evaporates at the potential rate. Infiltration into the soil is given by the difference between the precipitation not intercepted by the skin reservoir and the runoff. Surface runoff, Y, has three components. The first component represents the runoff owing to sloping terrain, and is parametrized according to Y1 =
Pw" max(O, Vor_- Vormin.x
(1)
Vormin -[- Vormax.]
wherc Vor rcprcsents the subgrid-scalcvariance of the orography, dcpendent on the grid point, and Vor~i~and Vorm~ arc typical values. Thc sccond component of surface runoff is determined by an infiltrationlimit Ifm~xfor thc soil: Y2 = max(0, Pw - Y1 -/fmax )
(2)
The maximum rate of infiltration, Ifm,x' is computed in terms of the estimated water flux at the surface under conditions of saturation (Mahrt and Pan, 1984). It decreases when the soil water content increases. The third component, Y3, represents saturation excess runoff, and is given at each soil layer as the difference between the water contents at the end of a timestep and the saturation value. Total runoff is then defined as the sum of the components related to orography, permeability and
P. Viterbo, L. lllari / Journal of Hydrology 155 (1994,) 325-336
328
saturation excess: Y:
Y I + Y 2 + Y3
Although three of the main factors influencing runoff are considered in the formulation described above, we realize that there are still problems to be solved to obtain a physically based parametrization of runoff. Among the factors omitted or badly described we can list: (1) the crude parametrization of the orographic effect, YI, representing only a larger value of runoff in 'rougher' terrain; (2) the assumption of orographically constant water-holding capacity; (3) the lack of consideration of subgrid-scale variability of infiltration. The spatial distribution of precipitation and evaporation will be affected by the details of the runoff formulation used by the model. In Eqs. (1) and (2), Pw is a modified precipitation flux, computed by applying the heterogeneity assumptions to the precipitation that overspilled the skin reservoir. Specifically, the amount of water available from convective precipitation is assumed to be concentrated in a fraction k of the grid box, with an amplitude factor 1/k. Large-scale precipitation is assumed to be uniform. In the control simulations presented below, k = 0.05 for convective precipitation. This value was chosen to represent the instantaneous cloud cover of a convective system and it does not take into account the advection of the system inside the grid box during a timestep. It was used by the operational T106 E C M W F forecasting system up to May 1990, but seems to be too small when compared with observational evidence. Observations of rainfall over a catchment area of 40 k m × 40 km in the Great Plains, USA, spanning more than 10 years of data, suggest that k should be 0.66 for convective showers of 0.Sh duration (Entekhabi and Eagleson, 1989). Theoretical considerations by the same workers suggest a value of k = 0.5 for the tropics (Eagleson et al., 1987). A new value, k -- 0.5, for convective precipitation has been adopted and used in the new experiment. We shall see that the change leads to more water available to wet the surface, and hence to a moister soil. The surface Bowen ratio decreases, owing to an increase in latent heat flux and a decrease of the surface heat flux. The sum of the two should remain unchanged in summer conditions, as it is controlled by the net radiation at the surface, assuming negligible fluxes into the ground on a time-scale of a few days.
3. GCM
experimentation
One of the recognized deficiencies of the E C M W F operational model was its tendency to produce excessively dry soil when compared with climatology and, consequently, surface temperatures that were too high. Underestimation of evaporation over land in the first days of the forecast (Arpe, 1991) was further evidence of this bias. This error is evident not only at operational resolution (TI06) but also at lower resolutions. In the following analysis, we will call forecast bias the difference between (a) the 90-day time average of the forecast values and (b) the mean of 90 very short term (6 h)
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Fig. 1. (a) Ninety day mean forecast bias o f soil wetness for a control run. Contour interval I turn (5 % o f the field capacity). Dashed line/negative indicates a drying error. (b) Ninety day mean forecast bias of surface temperature for a control run. Contour interval I K , soild lines/positive indicate a warming drift.
forecasts with initial dates spanning the integration period. As soil moisture is not an observed quantity, the bias represents the best approximation of the drift of the surface model. For consistency, we compare the soil temperature in the same way. Figure l(a) shows the mean forecast bias for the soil wetness, in a 90 day T42 control (k = 0.05) run, with initial date 1 June 1988. The capacity of the first reservoir is 20 mm of water, extending over a depth of 70 mm (volumetric units 2/7). A change of 2 mm in this picture represents 10% of the total capacity. The figure shows a general drying everywhere, especially for the Amazon basin, Europe, Equatorial Africa, Canada, southern Australia and Asia. Associated with this drying, the model tends to warm continental areas. Figure 1(b) shows the mean 90 day forecast bias for
P. Viterbo, L. lllari / Journal of Hydrology 155 (1994) 325-336
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Fig. 2. (a) Ninety day mean forecast difference in soil moisture between the new (k = 0.5) and the control (k = 0.05) run. C o n t o u r interval 2 mm 0 0 % o f field capacity). Solid lines indicate new wetter than c o n t r o l (b) N i n e t y day mean difference in surface temperature between new and control runs (contours as in Fig. l(b)).
the soil temperature, Ts, showing a warming over continental areas (up to 7 K in the USA and Siberia). A corresponding bias is seen in screen-level temperature, T2m (not shown)• To test the revised formulation of runoff, one 90 day summer run, with initial date 1 June 1988, has been performed with the new parametrization ( k = 0.5)• To compensate for the initial dry bias, both experiments start from an initial state with soil moisture set to climatological values. Figure 2(a) shows the difference in the soil wetness between new and control runs, averaged throughout the 90 day forecast. There is a general wetting of the surface reservoir in all areas where significant precipitation occurs, up to 6mm of water in the Amazon basin and 8 m m in
331
P. Viterbo, L. lllari / Journal of Hydrology 155 (1994) 325-336
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Fig. 3. Zonal mean average of the precipitation over land points for the new (solid line) and the control (dashed line) run. Each value in the graph represents the average of all land points in the corresponding latitude line. Equatorial Africa. These values are respectively 30% and 40% of the field capacity. This produces an increase in the surface latent heat flux of 50-60 W m -2 over land, and a similar decrease of sensible heat flux. This decrease in the Bowen ratio from a global land averaged value of 1.5 in the control experiment to 0.9 in the new experiment, gives an increased precipitation in the tropics and the summer hemisphere (Fig. 3), and a colder and moister planetary boundary layer (PBL), reducing the lower troposphere stability error in the extratropics. A small increase in low-level and convective cloudiness was found in those areas with increased soil moisture. The warm bias evident in the PBL over land is reduced by up to 2 K for Canada and 3 K for Mongolia (Fig. 4). A direct consequence of this modification is to cool the surface (compare Fig. 2(b) with Fig. l(b)). The drift of the model in the control run, indicated in Fig. 5 by a soil that is too dry everywhere, is reduced in the new run (Fig. 6). In both figures, the drift has been separated into its monthly components. The model soil wetness drifts very quickly to its own steady state; the picture for the first m o n t h of the run is not substantially different from that for the last month. When compared with short-range forecast values, the soil in the forecast with the revised runoff is wetter in Central Africa, Mexico, Northeast China and Mongolia, and drier in the N o r t h American Midwest, Eastern Australia and South America. The only exception to the general improvement in the moisture bias is the apparent wetting error south of the Sahara Desert. The precipitation bias (not shown) reveals a northward shift of the African branch of the intertropical convergence zone in the new run, which is directly reflected in the negative/positive pattern in the soil moisture bias. Similar beneficial results were obtained when the revised runoff formulation was tested on a T106 data assimilation and forecast experiment leading to its operational implementation.
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P. Witerbo, L. Illari / Journal of Hydrology 155 (1994) 325 336 150°W
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P. Viterbo, L. Illari / Journal of Hydrology 155 (1994) 325-336 150'W
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334
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P. Viterbo, L. lllari / Journal of Hydrology 155 (1994) 325-336
335
4. Summary and conclusions This paper considers the sensitivity of surface and near-surface parameters to a change in the runoff parametrization of a GCM. It has been shown that the dry-warm surface bias of the ECMWF model is reduced when a broad subgrid-scale distribution of precipitation is assumed. A lower tropospheric cooling is evident in summer over continental surfaces, extending in the vertical direction to the top of the planetary boundary layer. Associated with this cooling, the wetter surface induces a larger surface evaporation and smaller sensible heat flux. Climate studies require a better description of that part of the hydrological cycle which is related to the earth's surface. Of all the quantities involved in the surface water budget, soil moisture and runoff are among the least known on a planetary scale. No ground observations of soil moisture are routinely reported; only satellite estimates are available on a weekly basis (Choudhury, 1991). Runoff data from the large network of streamflow gauges is available for comparison with GCM output only for selected areas and not yet in real time. The only practical way of estimating globally the geographical distribution of the various terms of the surface hydrological budget on a day-to-day basis is to use data assimilation within a global forecasting system such as that used routinely at the ECMWF. Conventional synoptic data plus satellite observations of the atmosphere can be combined with a very short range (6 h) forecast to produce an analysed state of the atmosphere. Short-range integrations starting from these initial fields can provide an internally consistent (although model-dependent) picture of precipitation, evaporation and runoff. It is important to keep in mind that GCM estimates of surface variables (model products) are dependent on the model formulation. Results presented in this paper show one example of the sensitivity of these products to a parametrization assumption. The value of k to be used in a GCM should depend on both spatial resolution and timestep, and, possibly, be different for tropics and mid-latitudes. In view of the results presented here, further observational studies are needed, such as the GEWEX continental-scale experiment planned for the Mississippi valley in the second half of this decade.
Acknowledgements It is a pleasure to thank our colleagues at the ECMWF research department, Martin Miller, Anton Beljaars, Michael Tiedtke and Tony Hollingsworth. The manuscript benefited considerably from their suggestions and comments.
References Arpe, K., 1991. The hydrologicalcyclein the ECMWFshort range forecasts. Dyn. Atmos. Oceans, 16: 33-59.
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Austin, P.M. and Houze, R.A., 1972. Analysis of the structure of precipitation patterns in New England. J. Climate Appl. Meteorol., 11: 926-935. Blondin, C., 1991. Parametrization of land-surface processes in numerical weather prediction. In: T.J. Schmugge and J.-C. Andr6 (Editors), Land Surface Evaporation: Measurement and Parametrization. Springer, Berlin, pp 31-54. Choudhury, B.J., 1991. Passive microwave remote sensing contribution to hydrological variables. Surv. Geophys., 12: 63-84. Eagleson, P.S. and Wang, Q., 1985. Moments of catchment storm area. Water Resour. Res., 21:1185-1194. Eagleson, P.S., Fennesey, N.M., Wang, Q. and Rodriguez-Iturbe, I., 1987. Application of spatial Poisson models to air mass thunderstorm rainfall. J. Geophys. Res., 92D: 9661-9678. Entekhabi, D. and Eagleson, P.S., 1989. Land surface hydrology parametrization for atmospheric general circulation models including subgrid scale spatial variability. J. Climate, 2:816-831. European Centre for Medium Range Weather Forecasting (ECMWF), 1991. ECMWF Research Manual 3. ECMWF forecast model. Physical parametrization. ECMWF Meteorological Bulletin M1.6/2. ECMWF, Reading, 90 pp. Famiglietti, J.S. and Wood, E.F., 1991. Evaporation and runoff from large land areas: land surface hydrology for atmospheric general circulation models. Surv. Geophys., 12: 179-204. Louis, J.-F., 1979. A parametric model of vertical eddy fluxes in the atmosphere. Boundary-Layer Meteorol., 17: 187-202. Mahrt, L. and Pan, H. 1984. A two-layer model of soil hydrology. Boundary-Layer Meteorol., 29: 1-20. Miller, M.J., Palmer, T.N. and Swinbank, R.W., 1989. Parametrization and influence of subgrid-scale orography in general circulation and numerical weather predicton models. Meteor. Atmos. Phys., 40: 84-109. Morcrette, J.-J., 1990. Impact of changes to the radiation transfer parametrizations plus cloud optical properties in the ECMWF model. Mon. Weather Rev., 118: 847-873. Pitman, A.J., Henderson-Sellers, A. and Yang, Z.L., 1990. Sensitivity of regional climates to localised precipitation in global models. Nature, 346: 734-737. Ruprecht, E. and Gray, W.M., 1976. Analysis of satellite-observed tropical cloud clusters. Tellus, 28: 414-425, Sellers, P.J., Mintz, Y., Sud, Y.C. and Dalcher, A., 1986. A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43: 505-531. Simmons, A.J. and Striifing, R., 1981. An energy and angular-momentum conserving finite-difference scheme, hybrid coordinates and medium-range weather prediction. ECMWF Tech. Rep. 28. ECMWF, Reading, 68 pp. Simmons, A.J., Burridge, D.M., Jarraud, M., Girard, C. and Wergen, W., 1989. The ECMWF mediumrange prediction models, development of the numerical formulations and the impact of increased resolution. Meteor. Atmos. Phys., 40: 28-60. Slingo, J.M., 1987. The development and verification of a cloud prediction model for the ECMWF model. Q. J. R. Meteorol. Soc., 111: 1071-1085. Thomas, G. and Henderson-Sellers, A., 1991. An evaluation of proposed representations of subgrid hydrologic processes in climate models. J. Climate, 4: 898-910. Tiedtke, M., 1989. A comprehensive massflux scheme for cumulus parametrization in large-scale models. Mon. Weather Rev., 117: 1777-1798. Warrilow, D.A., Sangster, A.B. and Slingo, A., 1986. Modelling of land-surface processes and their influence on European climate. DCTN 38, Dynamical climatology. Meteorological Office, Bracknell, 92 pp. Wilson, M.F. and Henderson-Sellers, A., 1985. Cover and soils datasets for use in general circulation models. J. Climatol., 5:119-143. Xue, Y., Sellers, P.J., Kinter, J.L. and Shukla, J., 1991. A simplified biosphere model for global climate studies. J. Climate, 4: 345-364.
Journal of
Hydrology
ELSEVIER
Journal of Hydrology 155 (1994) 325-336
[3]
The impact of changes in the runoff formulation of a general circulation model on surface and near-surface parameters Pedro Viterbo*, Lodovica Illari European Centrefor Medium Range Weather Forecasting, Shinfield Park, Reading RG2 9AX, UK
(Received 28 February 1992; revision accepted 30 April 1993)
Abstract
The surface and near-surface properties of the European Centre for Medium Range Weather Forecasting (ECMWF) general circulation model are shown to be sensitive to the parametrization of runoff. If a broader subgrid-scale distribution of precipitation is assumed when computing runoff, the infiltration increases, more water becomes available for evaporation and the model surface cools. The averaged Bowen ratio over land is shown to decrease from 1.5 to 0.9 in a Northern Hemisphere summer experiment. Possible implications for the estimation of soil moisture and evapotranspiration using a global data assimilation-forecasting system are discussed.
1. Introduction
Over the last decade there has been growing interest in the representation of subgrid-scale heterogeneities of the land surface and their impact on parametrization for general circulation models (GCMs). By assuming simple statistical spatial distributions of a few relevant variables, it is possible to derive grid point values of other derived quantities as an area-mean over the grid box. A typical example is the definition of runoff from an assumed subgrid-scale distribution of precipitation. A recent review of modelling approaches, and of theoretical and observational evidence on this subject has been given by Thomas and Henderson-Sellers (1991). In convective situations in nature the observed distribution of precipitation over a grid box for a timestep of the model is far from uniform. The size of a typical convective element is much smaller than the grid box used in present generation G C M s , whereas its lifetime is of the order of the timestep used (At ~ 0 . 5 - 1 h ) . * Corresponding author. uu22-1694/94/$07.00 © 1994 Elsevier Science B.V. All rights reserved SSDI 0022-1694(93)0505 I-K
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During a timestep, an individual convective element will be advected; hence the fraction of the grid box wetted by the cloud will be larger than the value observed at any instantaneous time. The point precipitation intensity will follow some distribution law, with shape and amplitude dependent on the grid size and the timestep considered. Eagleson and Wang (1985) analysed the distributions of precipitation in the Great Plains of the USA using detailed rain-gauge data over a long period of time, and Austin and Houze (1972) classified radar observations of precipitation in New England, finding a different typical size, duration and intensity associated with synoptic, large or small mesoscale systems and convective events. Based on satellite observations of cloud clusters in the Western Pacific, Ruprecht and Gray (1976) derived a histogram of intensity vs. horizontal extent of precipitation area over the oceans. Hourly precipitation amounts for a typical cloud cluster of size 40 × 40 indicated that the rainfall was not evenly distributed, with more than half of the area free of rain. From the above observational evidence, it is necessary to take into account the subgrid-scale variability of precipitation when computing the runoff. Entekhabi and Eagleson (1989) and Famiglietti and Wood (1991) presented an approach for computing the runoff over a grid box assuming an arbitrary probability density function for the spatial distribution of precipitation. This has been applied in several surface parametrization models starting with the work of Warrilow et al. (1986) (see also Sellers et al., 1986; Pitman et al., 1990; Xue et al., 1991). The European Centre for Medium Range Weather Forecasting (ECMWF) model uses the simple assumption that convective precipitation, CP, during one timestep covers a fraction k of the grid box, with amplitude C P / k . The runoff is then computed from this modified flux of precipitation. The surface characteristics of the ECMWF model were found to be sensitive to the value of k used when computing the runoff. Pitman et al. (1990) and Thomas and Henderson-Sellers (1991) reported a similar sensitivity in off-line calculations with a surface parametrization, using prescribed forcing from the atmosphere. The purpose of this paper is to present results in the context of a GCM experiment with a fully interactive surface formulation. We will show that when a broader distribution of precipitation is used for the computation of runoff, more water infiltrates into the soil. This increase in soil moisture implies a change in the energy balance at the surface, which increases the latent heat flux and reduces the sensible heat flux, together with lower values of surface and nearsurface air temperatures. Section 2 presents an overview of the model with a brief description of the land surface parametrization. Section 3 describes some deficiencies of the ECMWF model and the impact of the changes to the formulation of runoff on summer integrations. The concluding section discusses possible impacts of this change on the estimates of soil moisture and evapotranspiration given by the current global data assimilation-forecasting systems.
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2. The forecast model The model used in the simulations presented below is the ECMWF spectral model (Simmons et al., 1989). It has 19 vertical levels with a hybrid vertical coordinate (Simmons and Striifing, 1981) and typically five levels within the planetary boundary layer. The physical parametrization used by the model is fairly comprehensive, including a mass-flux convective scheme (Tiedtke, 1989), gravity wave drag (Miller et al., 1989), a radiation parametrization (Morcrette, 1990) and an interactive diagnostic cloud scheme (Slingo, 1987; Morcrette, 1990). The boundary-layer parametrization is based on stability-dependent exchange coefficients (Louis, 1979). The land surface parametrization has been described in detail by Blondin (1991) or in the ECMWF internal documentation (European Centre for Medium Range Weather Forecasting (ECMWF, 1991). The relevant features are described below. There are two active layers in the soil with specified values in a third (lowest) layer acting as a seasonally varying boundary condition. Each layer is forced thermally by the fluxes across the interface, specified by a Fickian diffusion law. The forcing from the atmosphere is given at the top as the sum of the net surface radiation, and sensible and latent heat fluxes. For each grid point the evaporative flux is calculated separately over (a) the skin reservoir fraction (which combines the ponded bare soil, the wet vegetation and the snow-covered fraction), (b) the dry bare soil fraction and (c) the dry vegetation fraction. A fixed vegetation cover is assigned to each grid point according to Wilson and Henderson-Sellers 0985). The model also treats separately the heat and water balance in the snow mantle, including sublimation and melting. The skin reservoir intercepts precipitation, collects dew and evaporates at the potential rate. Infiltration into the soil is given by the difference between the precipitation not intercepted by the skin reservoir and the runoff. Surface runoff, Y, has three components. The first component represents the runoff owing to sloping terrain, and is parametrized according to Y1 =
Pw" max(O, Vor_- Vormin.x
(1)
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(2)
The maximum rate of infiltration, Ifm,x' is computed in terms of the estimated water flux at the surface under conditions of saturation (Mahrt and Pan, 1984). It decreases when the soil water content increases. The third component, Y3, represents saturation excess runoff, and is given at each soil layer as the difference between the water contents at the end of a timestep and the saturation value. Total runoff is then defined as the sum of the components related to orography, permeability and
P. Viterbo, L. lllari / Journal of Hydrology 155 (1994,) 325-336
328
saturation excess: Y:
Y I + Y 2 + Y3
Although three of the main factors influencing runoff are considered in the formulation described above, we realize that there are still problems to be solved to obtain a physically based parametrization of runoff. Among the factors omitted or badly described we can list: (1) the crude parametrization of the orographic effect, YI, representing only a larger value of runoff in 'rougher' terrain; (2) the assumption of orographically constant water-holding capacity; (3) the lack of consideration of subgrid-scale variability of infiltration. The spatial distribution of precipitation and evaporation will be affected by the details of the runoff formulation used by the model. In Eqs. (1) and (2), Pw is a modified precipitation flux, computed by applying the heterogeneity assumptions to the precipitation that overspilled the skin reservoir. Specifically, the amount of water available from convective precipitation is assumed to be concentrated in a fraction k of the grid box, with an amplitude factor 1/k. Large-scale precipitation is assumed to be uniform. In the control simulations presented below, k = 0.05 for convective precipitation. This value was chosen to represent the instantaneous cloud cover of a convective system and it does not take into account the advection of the system inside the grid box during a timestep. It was used by the operational T106 E C M W F forecasting system up to May 1990, but seems to be too small when compared with observational evidence. Observations of rainfall over a catchment area of 40 k m × 40 km in the Great Plains, USA, spanning more than 10 years of data, suggest that k should be 0.66 for convective showers of 0.Sh duration (Entekhabi and Eagleson, 1989). Theoretical considerations by the same workers suggest a value of k = 0.5 for the tropics (Eagleson et al., 1987). A new value, k -- 0.5, for convective precipitation has been adopted and used in the new experiment. We shall see that the change leads to more water available to wet the surface, and hence to a moister soil. The surface Bowen ratio decreases, owing to an increase in latent heat flux and a decrease of the surface heat flux. The sum of the two should remain unchanged in summer conditions, as it is controlled by the net radiation at the surface, assuming negligible fluxes into the ground on a time-scale of a few days.
3. GCM
experimentation
One of the recognized deficiencies of the E C M W F operational model was its tendency to produce excessively dry soil when compared with climatology and, consequently, surface temperatures that were too high. Underestimation of evaporation over land in the first days of the forecast (Arpe, 1991) was further evidence of this bias. This error is evident not only at operational resolution (TI06) but also at lower resolutions. In the following analysis, we will call forecast bias the difference between (a) the 90-day time average of the forecast values and (b) the mean of 90 very short term (6 h)
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forecasts with initial dates spanning the integration period. As soil moisture is not an observed quantity, the bias represents the best approximation of the drift of the surface model. For consistency, we compare the soil temperature in the same way. Figure l(a) shows the mean forecast bias for the soil wetness, in a 90 day T42 control (k = 0.05) run, with initial date 1 June 1988. The capacity of the first reservoir is 20 mm of water, extending over a depth of 70 mm (volumetric units 2/7). A change of 2 mm in this picture represents 10% of the total capacity. The figure shows a general drying everywhere, especially for the Amazon basin, Europe, Equatorial Africa, Canada, southern Australia and Asia. Associated with this drying, the model tends to warm continental areas. Figure 1(b) shows the mean 90 day forecast bias for
P. Viterbo, L. lllari / Journal of Hydrology 155 (1994) 325-336
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the soil temperature, Ts, showing a warming over continental areas (up to 7 K in the USA and Siberia). A corresponding bias is seen in screen-level temperature, T2m (not shown)• To test the revised formulation of runoff, one 90 day summer run, with initial date 1 June 1988, has been performed with the new parametrization ( k = 0.5)• To compensate for the initial dry bias, both experiments start from an initial state with soil moisture set to climatological values. Figure 2(a) shows the difference in the soil wetness between new and control runs, averaged throughout the 90 day forecast. There is a general wetting of the surface reservoir in all areas where significant precipitation occurs, up to 6mm of water in the Amazon basin and 8 m m in
331
P. Viterbo, L. lllari / Journal of Hydrology 155 (1994) 325-336
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P. Viterbo, L. Illari / Journal of Hydrology 155 (1994) 325-336 150'W
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334
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4. Summary and conclusions This paper considers the sensitivity of surface and near-surface parameters to a change in the runoff parametrization of a GCM. It has been shown that the dry-warm surface bias of the ECMWF model is reduced when a broad subgrid-scale distribution of precipitation is assumed. A lower tropospheric cooling is evident in summer over continental surfaces, extending in the vertical direction to the top of the planetary boundary layer. Associated with this cooling, the wetter surface induces a larger surface evaporation and smaller sensible heat flux. Climate studies require a better description of that part of the hydrological cycle which is related to the earth's surface. Of all the quantities involved in the surface water budget, soil moisture and runoff are among the least known on a planetary scale. No ground observations of soil moisture are routinely reported; only satellite estimates are available on a weekly basis (Choudhury, 1991). Runoff data from the large network of streamflow gauges is available for comparison with GCM output only for selected areas and not yet in real time. The only practical way of estimating globally the geographical distribution of the various terms of the surface hydrological budget on a day-to-day basis is to use data assimilation within a global forecasting system such as that used routinely at the ECMWF. Conventional synoptic data plus satellite observations of the atmosphere can be combined with a very short range (6 h) forecast to produce an analysed state of the atmosphere. Short-range integrations starting from these initial fields can provide an internally consistent (although model-dependent) picture of precipitation, evaporation and runoff. It is important to keep in mind that GCM estimates of surface variables (model products) are dependent on the model formulation. Results presented in this paper show one example of the sensitivity of these products to a parametrization assumption. The value of k to be used in a GCM should depend on both spatial resolution and timestep, and, possibly, be different for tropics and mid-latitudes. In view of the results presented here, further observational studies are needed, such as the GEWEX continental-scale experiment planned for the Mississippi valley in the second half of this decade.
Acknowledgements It is a pleasure to thank our colleagues at the ECMWF research department, Martin Miller, Anton Beljaars, Michael Tiedtke and Tony Hollingsworth. The manuscript benefited considerably from their suggestions and comments.
References Arpe, K., 1991. The hydrologicalcyclein the ECMWFshort range forecasts. Dyn. Atmos. Oceans, 16: 33-59.
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Austin, P.M. and Houze, R.A., 1972. Analysis of the structure of precipitation patterns in New England. J. Climate Appl. Meteorol., 11: 926-935. Blondin, C., 1991. Parametrization of land-surface processes in numerical weather prediction. In: T.J. Schmugge and J.-C. Andr6 (Editors), Land Surface Evaporation: Measurement and Parametrization. Springer, Berlin, pp 31-54. Choudhury, B.J., 1991. Passive microwave remote sensing contribution to hydrological variables. Surv. Geophys., 12: 63-84. Eagleson, P.S. and Wang, Q., 1985. Moments of catchment storm area. Water Resour. Res., 21:1185-1194. Eagleson, P.S., Fennesey, N.M., Wang, Q. and Rodriguez-Iturbe, I., 1987. Application of spatial Poisson models to air mass thunderstorm rainfall. J. Geophys. Res., 92D: 9661-9678. Entekhabi, D. and Eagleson, P.S., 1989. Land surface hydrology parametrization for atmospheric general circulation models including subgrid scale spatial variability. J. Climate, 2:816-831. European Centre for Medium Range Weather Forecasting (ECMWF), 1991. ECMWF Research Manual 3. ECMWF forecast model. Physical parametrization. ECMWF Meteorological Bulletin M1.6/2. ECMWF, Reading, 90 pp. Famiglietti, J.S. and Wood, E.F., 1991. Evaporation and runoff from large land areas: land surface hydrology for atmospheric general circulation models. Surv. Geophys., 12: 179-204. Louis, J.-F., 1979. A parametric model of vertical eddy fluxes in the atmosphere. Boundary-Layer Meteorol., 17: 187-202. Mahrt, L. and Pan, H. 1984. A two-layer model of soil hydrology. Boundary-Layer Meteorol., 29: 1-20. Miller, M.J., Palmer, T.N. and Swinbank, R.W., 1989. Parametrization and influence of subgrid-scale orography in general circulation and numerical weather predicton models. Meteor. Atmos. Phys., 40: 84-109. Morcrette, J.-J., 1990. Impact of changes to the radiation transfer parametrizations plus cloud optical properties in the ECMWF model. Mon. Weather Rev., 118: 847-873. Pitman, A.J., Henderson-Sellers, A. and Yang, Z.L., 1990. Sensitivity of regional climates to localised precipitation in global models. Nature, 346: 734-737. Ruprecht, E. and Gray, W.M., 1976. Analysis of satellite-observed tropical cloud clusters. Tellus, 28: 414-425, Sellers, P.J., Mintz, Y., Sud, Y.C. and Dalcher, A., 1986. A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43: 505-531. Simmons, A.J. and Striifing, R., 1981. An energy and angular-momentum conserving finite-difference scheme, hybrid coordinates and medium-range weather prediction. ECMWF Tech. Rep. 28. ECMWF, Reading, 68 pp. Simmons, A.J., Burridge, D.M., Jarraud, M., Girard, C. and Wergen, W., 1989. The ECMWF mediumrange prediction models, development of the numerical formulations and the impact of increased resolution. Meteor. Atmos. Phys., 40: 28-60. Slingo, J.M., 1987. The development and verification of a cloud prediction model for the ECMWF model. Q. J. R. Meteorol. Soc., 111: 1071-1085. Thomas, G. and Henderson-Sellers, A., 1991. An evaluation of proposed representations of subgrid hydrologic processes in climate models. J. Climate, 4: 898-910. Tiedtke, M., 1989. A comprehensive massflux scheme for cumulus parametrization in large-scale models. Mon. Weather Rev., 117: 1777-1798. Warrilow, D.A., Sangster, A.B. and Slingo, A., 1986. Modelling of land-surface processes and their influence on European climate. DCTN 38, Dynamical climatology. Meteorological Office, Bracknell, 92 pp. Wilson, M.F. and Henderson-Sellers, A., 1985. Cover and soils datasets for use in general circulation models. J. Climatol., 5:119-143. Xue, Y., Sellers, P.J., Kinter, J.L. and Shukla, J., 1991. A simplified biosphere model for global climate studies. J. Climate, 4: 345-364.
