One main concern with RCA2 was that the land surface parameterization, including sea ice, was fairly simplified (Bringfelt et al., 2001). For example, a single energy balance component with one surface temperature for an entire grid square was used. Thus, the representation of subgrid scale surfaces with very different properties, such as ice, snow, open land and forest, were characterized by the same surface temperature. Generally a tile approach, where a separate energy balance is used for each sub surface in a grid box, provides a better representation of important surface processes (Koster and Suarez, 1992). A tiled surface scheme was therefore introduced in RCA3 (Samuelsson et al., 2006). Grid boxes in RCA3 can now include fractions of sea (with fractional ice cover) or lake (with ice or not) and land. The land fraction can be further subdivided into forest and open land, where both can be partly snow covered. Each subgrid scale tile has a separate energy balance equation and individual prognostic surface temperatures. In RCA3, this is true for all surface temperatures, except for the sea-surface temperature (SST), which is prescribed from the boundary condition data set. In the coupled ocean–atmosphere version of RCA, RCAO (Döscher et al., 2009), SSTs are also prognostic.
2.1. The surface scheme
The land-surface scheme (LSS) in RCA3 (Fig. 1) belongs to the second generation of LSSs (Sellers et al., 1997) which means that it has fairly advanced treatments of many physical land-surface processes but it does not account for carbon dioxide (CO2) effects on canopy conductance in evapotranspiration calculations. In RCA2, the evapotranspiration is parameterized according to Noilhan and Planton (1989), which follows the ‘big leaf’ concept by Deardorff (1978) in which no distinction is made between soil and canopy. However, Wallace et al. (1990) showed that the interactions between the fluxes from the soil and the canopy can be significant in sparse canopies, especially for very wet or very dry soil conditions. In the development of RCA3 it was anticipated that such rare conditions are most relevant for forest processes. In addition, from the atmospheric point of view, the forest canopy represents a surface which reacts quickly to changes in energy fluxes due to its low heat capacity. Therefore, the forest subtile (Afor in Fig. 1) was parameterized according to the double-energy balance concept (Shuttleworth and Wallace, 1985) which means that the forest canopy (Tforc) and the forest floor have separate energy balances and separate prognostic temperatures. In RCA3, we also separate the forest floor into a bare soil (Tfors) and a snow covered part (Tforsn), respectively. The parameterization of the double-energy balance in the forest follows Choudhury and Monteith (1988), which includes the formulation of the aerodynamic resistances rb and rd. The canopy air temperature, Tfora, and specific humidity, qfora, are diagnostic quantities which are solved by assuming conservation of fluxes.
Figure 1. A principal sketch of the land-surface scheme in RCA3. Fractions of individual tiles are denoted by Axx. Prognostic temperatures are marked in red and prognostic water and humidity variables in blue. Subscripts ‘am’ refer to the atmospheric lowest model level. The diagnostic canopy air temperature and specific humidity, Tfora and qfora, are marked in black. Aerodynamic and surface resistances are denoted by rxx. Vertical layers in the soil are indicated by zT1–zT5 with respect to temperature and by zθ1 and zθ2 with respect to soil water, respectively. The sub-boxes in the soil are divided by solid and dashed lines. For further details please refer to the text.
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The prognostic snow storages over open land, sn, and in the forest, snfor, are parameterized using a bulk one-layer concept where the top most 15 cm of the snow pack is assumed to be thermally active, characterized by its prognostic snow temperature, Tsn and Tforsn, respectively. The temperature below that layer is in principle unknown which means that the snow-soil heat transfer must be parameterized. The snow can hold 10% of its snow water equivalent as liquid water, wsn and wforsn, respectively. The sources of liquid water are snow melt water and rain that falls on 0°C snow. The prognostic snow density, ρsn and ρsnfor, is set to 100 kg m−3 for fresh snow and increases with time as the snow ages (Douville et al., 1995). The freezing of any liquid water in the snow contributes to an increase of the density using the density of ice. The snow fraction, Asn and Aforsn, is calculated following Lindström and Gardelin (1999). They showed that, according to observations, the snow cover for a deep snow pack is better correlated with the ratio sn/snmax than with sn itself, where snmax is the maximum snow water equivalent reached during the snow season. During snow melt, the snow fraction is not allowed to decrease before sn reaches a certain fraction of snmax. This fraction is set to 0.6 for flat terrain but increases with increased subgrid orography. For open-land snow there is a prognostic snow albedo, allowed to be in the range 0.6–0.85, based on the method described by Douville et al. (1995) which simply makes the albedo decrease with time as snow is ageing. The ageing is faster for melting than for non-melting snow. For snow in forest the albedo is set constant to 0.20.
The open-land subtile, Aopl, consists of a vegetated part and a bare-soil part, both characterized by the same prognostic temperature, Topls. The surface resistance for the evapotranspiration, rsoplv, follows Jarvis (1976) and the soil-surface resistance, rsopls, follows van den Hurk et al. (2000). Interception of rain, wforc for the forest and woplv for open-land vegetation, is parameterized according to Noilhan and Planton (1989).
The aerodynamic resistances in the surface layer, rax, are based on the parameterization of near-surface fluxes of momentum and scalar quantities according to Louis et al. (1982), where individual roughness lengths and stability corrections are used for each subtile. The individual fluxes of heat and momentum from these tiles are weighted to grid-averaged values at the lowest atmospheric model level according to the fractional areas of the tiles.
The soil is divided into five layers with respect to temperature with a no-flux boundary condition at 3.0 m depth. The thicknesses of the layers increase from 1.0 cm for the top-most layer to 1.89 m for the deepest layer. There are separate soil columns below the forest and open-land tiles (Afor and Aopl) and additional soil columns appear when snow is present (Aforsn and Asn). To fulfil the energy balance, heat energy is moved between the snow and non-snow covered soil columns as the snow fraction changes. There are seven different texture classes based on the geographical distribution of soil types FAO-UNESCO (1981) digitized for Europe by the German Weather Service. The heat diffusivity equations follow McCumber and Pielke (1981) and the soil properties Clapp and Hornberger (1978).
For soil water there are two layers, 0.07 and 2.20 m thick, except for in mountain areas where the deep layer is 1.00 m (altitude >600 m and deep soil climatology temperature <7°C). The vertical transport of water is expressed using Richards equation (Hillel, 1980) but the hydraulic conductivity term is replaced by a drainage/runoff parameterization, the β-formulation, as used in the hydrological model HBV (Lindström et al., 1997). The LSS does not include phase changes between liquid water and ice in the soil but instead we parameterize the effect that soil ice would have had on soil heat capacity (Viterbo et al., 1999) and on root extraction of water.
The snow-free land-surface albedo is set to 0.15 for the forest (canopy and forest floor) and to 0.28 for open land. As will be discussed later, the open-land value is high compared to observed values which are around 0.18. The leaf-area index (LAI) is calculated as a function of the soil temperature in layer four (Hagemann et al., 1999) with lower limit set to 0.4 and upper limits set to 2.3 and 4.0 for open land and deciduous forest, respectively. If deep soil moisture reaches the wilting point the LAI is set to its lower limit. LAI for conifers forest is set constant to 4.0.
The land-sea mask is provided from HIRLAM climate fields (Källén, 1996) and the forest fraction is given by Hagemann et al. (1999). However, the more recent physiography data base ECOCLIMAP (Masson et al., 2003) gives generally less fraction of forest than the Hagemann forest fraction. To reach better correspondence with the ECOCLIMAP physiography, the final forest fraction in RCA3 was reduced to 80% of its original value.
Lakes in RCA3 are simulated with the multilayer lake model PROBE (Ljungemyr et al., 1996). PROBE is forced from RCA3 by 2 m air temperature and humidity, 10 m wind speed and downward short-wave (SW) and long-wave (LW) radiation. It simulates water temperature at different levels and the growth of ice. Depth of lakes are given for Swedish lakes but set to 10 m for all lakes outside Sweden. Water surface temperature and ice thickness is provided to RCA which uses these to calculate fluxes of momentum and heat.
The temperature of sea and lake ice is simulated by the heat-transfer equation for an ice cover with two layers assuming constant ice thickness for sea (0.5 m in the Baltic Sea and 1 m for the rest of the ocean) and ice thickness given by PROBE for lakes. Ice albedo is set to 0.5. The heat flux at the ice–water interface is parameterized assuming a constant melting temperature of the ice at the bottom. Snow on ice is simulated as over land but with the prognostic snow albedo limited to the range 0.7–0.85.
Diagnostic variables of temperature and humidity at 2 m and wind at 10 m are calculated using Monin–Obukhov similarity theory. These diagnostic variables are first calculated individually for each tile and then area-averaged for larger subsurfaces or for the whole grid square. When evaluating the RCA diagnostic 2 m air temperature, T2m, against observations we chose to use the simulated T2m over open land fractions of a grid box, T2mopl (snow and snow-free area average), because observational stations generally report temperatures in open land areas or in glades in forest areas. The forest T2m, T2mfor, is defined as the air temperature at 2 m height above the forest floor. As both radiation fluxes and turbulent fluxes at the forest floor are greatly reduced due to the presence of the overlaying canopy T2mfor variability will be much lower than any nearby T2mopl variability. The grid-averaged T2m, T2mgrid, is simply an area weighted average of the individual tiles, sea (water and/or ice), lake (water or ice), forest and open land.
For the Baltic Sea drainage basin the runoff from each grid square can be routed to form river discharge for certain rivers along the Batic Sea coast. This routing is based on the hydrological HBV model and it is specifically calibrated for the Baltic basin (Graham, 2002).
2.2. Changes in the atmospheric parameterization schemes
The radiation scheme in RCA3 is based on the HIRLAM radiation scheme, originally developed for NWP purposes (Savijärvi, 1990; Sass et al., 1994). The scheme is computationally extremely fast but also highly simplified, with only one wavelength band for LW radiation and one for SW. The scheme was modified to include CO2 absorption and an improved treatment of the water vapour continuum by Räisänen et al. (2000). In RCA2, the SW cloud albedo and LW cloud emissivity were calculated from the cloud water content, with a cloud mass absorption coefficient depending only on altitude. In RCA3, cloud emissivity and cloud albedo are now formally linked to cloud liquid water and ice amounts, with a diagnostic calculation of effective radius performed separately for liquid and ice (Wyser et al., 1999). In the radiation scheme, the grid box mean liquid water path is multiplied by a scaling factor of 0.7 before cloud albedo and emissivity are calculated. This is done to account for the fact the RCA radiation code, like the majority of radiation schemes, assumes cloud water to be homogeneously distributed throughout a given grid box cloud fraction, the so-called plane-parallel approximation. As discussed in Barker and Wielicki (1997), Barker (1996), Tiedtke (1996) and Cahalan et al. (1994), use of the plane-parallel approximation will always bias cloud albedo to be higher than equivalent real clouds. In real clouds within-cloud small-scale variability in the cloud-water distribution, with small areas of ascent exhibiting very high cloud water amounts and regions of weaker ascent or even descent having much lower cloud water amounts, leads to an average cloud albedo significantly lower than if the same amount of cloud water were distributed in a plane-parallel sense. Barker et al. (1996) and Barker and Wielicki (1997) discuss more advanced methods for deriving a suitable scaling factor to account for this systematic discrepancy. Such advanced treatment has so far not been tested in RCA.
RCA3 carries a single prognostic equation for the total cloud water mixing ratio, separation into liquid and ice components is diagnosed as a function of local air temperature. In RCA3, this calculation has been modified, compared to RCA2, so that water is now more rapidly put into the ice phase as a function of decreasing temperature. As a result, for a given (cold) cloud, emissivity is reduced in RCA3 relative to RCA2, with a commensurate reduction in downwelling LW radiation. In both the radiation and cloud microphysical schemes, droplet effective radii are based on a prescribed cloud droplet number concentration (CDNC) which is allowed to vary as a function of surface type (land, sea, snow-covered land, ice-covered water). Over land CDNC varies as a linear function of height, decreasing with pressure from a typical surface land value (400 cm−3) to a typical oceanic and free atmospheric value (150 cm−3) at 0.8 times the surface pressure.
To reduce an overestimate of clear-sky SW surface fluxes found in RCA2, in RCA3 the clear-sky water-vapour absorption of SW was modified and the clear-sky SW absorption by aerosols increased. In the RCA2 radiation scheme, emission of LW radiation to the surface from the cloudy fraction of a model grid column occurs from the base of the lowest cloud layer, with the cloud water and cloud fraction treated in a vertically integrated, maximum overlap manner (Sass et al., 1994). The amount of LW radiation emitted from the cloud-base that actually reaches the surface is then scaled by the emissivity of the clear-sky atmosphere below cloud base and normalized by the vertically integrated cloud fraction. Clear-sky LW radiation reaching the surface is considered as an average of the entire vertical atmospheric column emission and assumed to operate over the entire model grid box (i.e. over both clear and cloudy fractions of a grid box). In RCA3, the LW emission is now more formally split into three fractional regions of a given grid box vertical column. The cloud-fraction emission is treated as in RCA2, but now the clear sky emission is separated into two contributions. The first is an assumed emission/absorption process which considers the entire vertical column and is normalized by the clear-sky fraction of the column. The second clear-sky contribution comes from the emission of clear-sky LW radiation below the fractional cloud base, where the clear-sky emissivity is based on the thermodynamic profile below cloud base only. The resulting LW flux is then normalized to cover only the cloudy-part of the grid box. These three contributions, all weighted by their respective cloudy or clear-sky fraction, are then combined into a single, grid-box mean LW flux to the surface.
In RCA2, the turbulence parameterization was a dry prognostic turbulent kinetic energy (TKE) scheme, combined with a diagnostic mixing length (Cuxart et al., 2000). The scheme is updated in RCA3 to include moist processes in the calculation of TKE (Cuijpers and Duynkerke, 1993) and to have a smoother transition between stable and unstable conditions (Lenderink and de Rooy, 2000; Lenderink and Holtslag, 2004). The new scheme has the same basic philosophy as in RCA2, but uses a simpler and faster method to calculate turbulent mixing lengths and better matches these to near surface lengths given by similarity theory in the neutral limit. Furthermore, the new scheme employs an implicit treatment of the TKE equation (Brinkop and Roeckner, 1995) making it numerically more stable.
Moist processes in RCA2 and RCA3 are separated into resolved (large-scale) clouds and subgrid scale (convective) clouds. Large-scale clouds are described using the scheme of Rasch and Kristjánsson (1998). Convective processes are described with an entraining and detraining plume model using the approach of Kain and Fritsch (1990, 1993) and Kain (2004).
The treatment of shallow convective clouds, condensate and precipitation has been substantially modified in RCA3 compared to RCA2. The main change is that in RCA3 the Kain–Fritsch convection scheme now assumes that shallow convection is non-precipitating. Shallow convective cloud water produced by the Kain–Fritsch convection is instead detrained into the environment and a fraction evaporated depending linearly on the local grid box mean relative humidity. The remaining shallow convective cloud water is assumed to reside in a diagnosed shallow cumulus cloud fraction that links cloud amount to the liquid and vapour content of the convective plumes and the local relative humidity (Albrecht, 1981). Microphysical conversion of shallow convective cloud water to precipitation is then performed by the same scheme as for large-scale condensation. The resulting shallow cumulus clouds and cloud water can then interact with the radiation fields. The main impact of these changes is reduced precipitation from shallow convective clouds, a formal shallow convective cloud fraction (not present in RCA2) and thus shallow convective clouds that contain more water, are more reflective and can interact with atmospheric radiation fluxes. A more detailed description of the new parameterization is given in Jones and Sanchez (2002).
In the formulation of large-scale precipitation some minor modifications have been made to the liquid autoconversion calculation in RCA3. These act to reduce the occurrence of weak precipitation, which was too frequent in RCA2. As mentioned earlier, the diagnostic separation of total cloud water into liquid and ice was modified in the RCA3 radiation scheme. This modification was also carried out in the cloud microphysical scheme to maintain consistency through the model.