| Home > Newsevents > Training > Rcourse_notes > PARAMETRIZATION > RADIATION_TRANSFER > |
Radiation Transfer
March 2000
By Jean-Jacques
Morcrette
European Centre for Medium-range Weather
Forecasts, Shinfield Park, Reading Berkshire RG2 9AX, United Kingdom
Table of contents >>
Next Section >>
Previous Section >>
6 . Conclusions and perspectives
Recent studies have focussed at the direct and/or indirect radiative impact of aerosols in climate simulations (Joussaume, 1990; Genthon, 1992; Boucher and Lohmann, 1995; Chylek et al., 1995; Mitchell et al., 1995; Lohmann and Feichter, 1997; Tegen et al., 1997; Timmreck et al., 1997; Cusack et al., 1998; Le Treut et al., 1998; Schulz et al., 1998). Since the pioneering work of Tanre et al. (1984), the ECMWF model has included an annual mean, but geographically distributed climatology of aerosols. The geographical distribution of the maritime, continental, urban and desert aerosols are given in Figs. 6.1 and 6.2 , where as the vertical distribution is illustrated in Fig. 6.3 . As discussed in Cusack et al. (1998), the presence of these aerosols in the ECMWF model is certainly one of the reasons why the surface downward SW radiation is less overestimated in the ECMWF model than in other leading climate GCMs (Garratt, 1994; Garratt et al., 1998). However, a monthly specification of the aerosol distributions or better a prognosed aerosol loading is a possible future development for the ECMWF model.
Recent studies by Wang and Rossow (1995, 1998) and Stubenrauch et al. (1997) have focussed at the potentially large impact on the atmospheric circulation linked to uncertainties in the vertical distribution of clouds and related radiative heating/cooling rates. Similar results were also obtained with the ECMWF model (Morcrette and Jakob, 2000). Fig. 6.4 presents the cloud overlaps that the operational radiation scheme can handle. One-dimensional calculations are performed for different cloud configurations often encountered in three-dimensional simulations. The first one corresponds to a typical convective system and includes a convective tower of fractional cover 0.15 from 820 to 290 hPa topped by stratiform anvil of cover 0.4 and 70 hPa thick. The second case includes three layers of stratiform cloud of cover 0.3 between 890 and 820 hPa (low-level), 640 and 545 hPa (middle-level), and 290 and 220 hPa (high-level), respectively. The profiles of the cloud longwave, shortwave and net radiative forcing for these two cases are presented in Fig. 6.5 , left and right panels respectively. The cloud forcing term is simply the difference between the heating obtained for the cloudy atmosphere minus the clear-sky heating. The different COAs are treated exactly in the LW part and only approximately in the SW part of the radiation scheme. Therefore, the LW MRN and MAX results are essentially the same for case 1, and the same holds for the LW MRN and RAN results for case 2. In the SW, the agreement on the SW forcing profiles is also very good. In the first case (see Fig. 6.5 , left panels), the anvil produces a strong LW cooling whereas a relative heating is found in the rest of the convective tower below. Compared to MRN/MAX, the RAN LW shows a smaller cooling peak in the anvil, smaller heating below within the tower, and larger warming in the clear PBL. All these features are consistent with the larger effective cloudiness that the RAN assumption produces between any two cloudy layers. This larger effective cloudiness (i) prevents the radiation from the lower atmospheric layers from escaping to space, thus the heating in the PBL, (ii) decreases the possible radiative exchanges within the tower, thus the smaller radiative heating, and (iii) decreases the upward LW flux at the base of the anvil, hence the smaller LW divergence in the anvil. Similarly, a stronger SW heating is linked in RAN to the larger cloud fraction available to screen the downward radiation and to the more diffuse character of this radiation in the cloudy fraction of the column. Consequently a smaller amount of SW radiation is available below the cloud base and this translates into a relative cooling. The smaller heating of the anvil in RAN is linked to the smaller SW flux divergence through a larger upward radiation due to the increased reflection on the larger effective amount of lower level clouds.
In the second case (see Fig. 6.5 , right panels), each of the three stratiform cloud layers produces a LW cooling inside and a LW heating immediately underneath. Differences between MAX and MRN/RAN results are consistent with the smaller total effective cloudiness given by MAX. The cooling in the low-level cloud is reduced in MAX because the middle- and high-level clouds prevent the radiation at the top of the low-level cloud from escaping to space. Similarly, the cooling in the high-level cloud is increased in MRN/RAN because the low- and middle-level clouds prevent the warmer radiation from the lower layers to reach the base of the high-level cloud. The smaller total cloudiness in MAX also explains the smaller LW heating below the lowest cloud. In the SW, the difference in heating is very small within the clouds. The slightly larger relative cooling immediately below the clouds in MAX comes from the larger fraction of radiation being directly transmitted, as, in MRN/RAN, the radiation in the cloudy fraction of the column is more diffuse and therefore more likely to be absorbed. The smaller SW heating of the upper cloud with MRN/RAN is again linked to the increased reflection. These one-dimensional computations show the validity of the treatment of the different COAs in the ECMWF radiation scheme. They also show the larger impact of a change of COA on the LW than on the SW radiative profiles.
Fig. 6.6 presents the total cloudiness (TCC) averaged over the last three months (DJF) of the EPR simulations with the MRN, MAX and RAN COAs. Not surprisingly, the set of MAX simulations display the minimum total cloud cover at 0.609, followed by the MRN at 0.639, whereas the RAN simulations have a much larger total cloud cover at 0.714. The increase from MRN to RAN (respectively, the decrease from MRN to MAX) is apparent over most of the globe, but is more pronounced over the sub-tropics, particularly those of the Southern hemisphere. Are these changes simply reflecting the different geometrical distribution of the cloud elements on the vertical or are they linked to actual changes in the volume of the cloud elements? A simple check whether the cloud volume is actually modified is obtained by diagnosing the total cloud cover for all sets of simulations and applying the operational maximum-random overlap assumption to the various cloud elements. Fig. 6.7 presents the MRN-equivalent TCC for the EPR experiments. The main result is that most of the change in TCC is not reflected in the MRN-equivalent TCC, showing that a large fraction of the signal is due to the geometrical overlapping in the radiation scheme, and not to a real significant change in the cloud volume.
Radiation transfer parametrisation is generally the most expensive in computer time among the physical parametrisations used in a GCM. In the ECMWF operational forecast model, full radiation computations are performed only every 3 hours and on a reduced grid (1 point out of 4 along the longitude direction). Even so, radiation accounts for about 15%t of the total cost of the model. Although such spatial and temporal sampling of the radiation forcing does not appear detrimental on the short time scales encompassed in the operational analyses of meteorological observations and the 10-day forecasts made with the high resolution (TL319) ECMWF weather forecast model (Fig. 6.8 for the anomaly correlation of the geopotential at 500 hPa, Figs. 6.9 and 6.10 for the mean error in temperature at 850, 500, 200 and 50 hPa), the impact becomes systematic and (possibly) detrimental on seasonal time-scales and/or at lower resolution (Morcrette, 2000). For example, Figures 6.11 and 6.12 present the zonal mean cloudiness and temperature, with the operational configuration (S4T3h = spatial sampling 1 point out of 4 in the tropics, full radiation calculation only every 3 hours), and the difference with configurations for which radiation is either called at every grid point (S1) and/or for every time step (T1). When the radiation calculations are synchronous with the rest of the physics (and of the model), the stability in the ITCZ is decreased, with a resultant (small) increase in high-level clouds (Fig. 6.11 ), but a more spatially extensive decrease in stratospheric temperatures (Fig. 6.12 ).
New algorithms are being tested based on the neural network approach of Cheruy et al. (1996) and Chevallier et al. (2000a, 2000b), or on the linearized approach proposed by Chou and Neelin (1996), both of which introduce major savings in computer time. They would therefore allow for more frequent and/or non spatially sampled radiation calculations. For example, Fig. 6.13 present the bias and standard deviation of the LW cooling rates computed by a neural network method relative to the operational LW scheme, whereas. Figs. 6.14 and 6.15 display standard objective scores, the anomaly correlation of the geopotential at 500 hPa (Fig. 6.14 ) and the mean error in temperature at various levels in the atmosphere (Fig. 6.15 ). Such results indicate that the neural network approach is potentially a viable replacement. for traditional LW parametrizations.
Other areas of development concern the role of the cloud inhomo/heterogeneity on the model horizontal sub-grid scale, and that of the vertical overlapping of cloud layers. On the first point, (not equivalent to a full account of the three-dimensional cloud-induced radiative effects, but going away from the plane-parallel approach used over the last 25 years), Tiedtke (1996) proposed a simple parametrisation of this effect, while Barker (1996, 1998) modified a SW scheme similar to the ECMWF one and incorporated an approximate treatment of the impact of the liquid water inhomogeneities via a gamma-distribution approach. Figs. 6.16 and 6.17 present the differences in transmissivity, reflectivity and absorptivity for some standard liquid and ice water clouds in the two spectral intervals of the SW scheme. The impact is important over most of the range of optical thickness and for all quantities. Such an "horizontal" effect, to be accounted for also in the LW part of the spectrum is likely to change the effect of the clouds on the radiative distribution within the whole atmospheric column. In view of the large effects of the sub-grid scale distribution of the condensed water on the radiative fluxes, it is likely that, in the coming years, GCMs will incorporate an equation to at least diagnose this sub-grid scale variability and account for its effect on the radiation fields.
Up to now, the validation of the large-scale radiative fields produced by a climate or a weather forecast GCM has mainly consisted of checks on the total cloud cover, and related top of the atmosphere and surface longwave and shortwave radiation fields. These fields (for example, Darnell et al., 1992; Gupta et al., 1993; Laszlo and Pinker, 1993; Li and Leighton, 1993; Rossow, 1993; Zhang et al., 1995) are provided by dedicated satellite observations such as ERB (Stowe et al., 1989), ERBE (Harrison et al., 1988), ScaRaB (Kandel et al., 1998), CERES (Wielicki et al., 1996) and/or operational satellite observations as part of ISCCP (Rossow et al., 1987). Although very useful, these essentially two-dimensional (3-D if time history is considered) validation efforts are only a first step towards what would really be required to ascertain the adequacy of the representation of the cloud-radiation interactions: the 4-dimensional distribution of cloud volume and cloud water loading together with relevant 4-dimensional radiation parameters: a real challenge for the future!
Training Course Notes Front Page >>
Table of contents >>
Next Section >>
Previous Section >>
 |
12.06.2002 |
 |
© ECMWF |
