Near surface temperature and dew point temperature

Observed versus simulated near surface temperatures are presented in Fig. 4.6. Shown in each graph is the mean diurnal cycle of temperature for each month of the year. All in all there is quite a good correspondence between simulation and observations. In summer when near surface temperature is predominantly determined by the incoming solar radiation, the timing of the temperature increase and its slope in the morning are simulated almost perfectly by the model. The slight timeshift of about 15 minutes between model and observations can mostly be attributed to the practice of observers to perform their measurements about 10 minutes before the full hour (although the values are labeled with the full hour). It should be mentioned here that the radiation scheme of MM5 is called every 20 minutes, and the value of the solar inclination angle used for the calculations is centred in time for this period. Thus, the way of calcu- lating radiation should not significantly contribute to the time shift. In the afternoon hours, however, a bias of up to -2 K has to be stated that gets reflected in a general timeshift in the maximum temperature of about 1 hour in the simulation. Note that a similar finding is reported e.g. by Hohenegger et al. (2008) for CLM. The possible rea- sons for this behaviour are manifold. For example, one seemingly obvious explanation would be that the PBL-scheme imposes somewhat too strong mixing that transports too much heat away from lower parts of the atmosphere. Strong mixing could also lead to excessive drying of the near surface atmosphere, leading to more evaporation from the soil and eventually to an altered bowen ratio, so that a too small fraction of the solar radiation absorbed by the underlying soil is transformed into sensible heat. However, the notion of too intense mixing is not supported by the dew point temper-

52 4.2. PERFORMANCE OF REFERENCE CONFIGURATION OF MM5 1 2 3 4 5 6 7 8 9 10 11 12 0 40 80 120 160 200 rainsum [mm] sim obs_stat obs_grid tot a) 1 2 3 4 5 6 7 8 9 10 11 12 0 40 80 120 160 200 _sim obs_stat obs_grid afl b) 1 2 3 4 5 6 7 8 9 10 11 12 0 40 80 120 160 200 _sim obs_stat obs_grid alp c) 1 2 3 4 5 6 7 8 9 10 11 12 -40 -20 0 20 40 error [mm] tot afl alp bias obs_stat d) 1 2 3 4 5 6 7 8 9 10 11 12 0 10 20 30 40 50 tot afl alp rmse obs_stat e) 1 2 3 4 5 6 7 8 9 10 11 12 0 10 20 tot afl alp nae obs_stat f) 1 2 3 4 5 6 7 8 9 10 11 12 month -40 -20 0 20 40 error [mm] tot afl alp bias obs_grid g) 1 2 3 4 5 6 7 8 9 10 11 12 month 0 10 20 30 40 50 tot afl alp rmse obs_grid h) 1 2 3 4 5 6 7 8 9 10 11 12 month 0 10 20 tot afl alp nae obs_grid i)

Figure 4.5: Upper row: accumulated monthly rain for years 1991 to 2000 simulated by MM5 (’sim’), averaged from station data (’obs stat’) and from aggregated high- resolution fields (’obs grid’) for total validation area (’tot’), Alpine foreland (’afl’) and Alpine area (’alp’) respectively. For areas see also Fig. 3.3. Second row gives values in subdomains for ’bias’, ’rmse’ and ’nae’ in relation to averaged station data; third row shows corresponding values referring to gridded observational fields.

ature validation (see below). A similar error in the bowen ratio could also be related to the formulation of the evaporation from plants and soil, or simply to the vegetation fractions and soil types assumed in the model. Cooley et al. (2005), for example, per- form simulation experiments with MM5 and a coupled landsurface module including effects of harvesting on regional climate. Here, the removal of evaporating plants, i.e. changing the vegetation fractions, has significant effects on the bowen ratio and the near surface temperature. Furthermore, inaccuracies in the calculation of downward longwave radiation might contribute to the temperature bias observed. Fernandez et al. (2007), however, do not find substantial differences in simulated 2-m temperature for MM5 simulations over the Iberian Peninsula resorting to a more sophisticated (and computationally more costly) longwave radiation scheme in comparison to the respec- tive parameterization used for the present study. Last but not least, the formation of clouds in the afternoon may exert a substantial influence on near surface temperature (Hohenegger et al. 2008). Yet further investigations on these issues are beyond the scope of the present work and should be addressed in future studies, focusing also e.g. on different formulations of the PBL. The overall features of the observed and simulated diurnal temperature cycle in July prove to be more or less representative for all months of the year (Fig. 4.6). Even in winter the behaviour does not change substantially as could have been expected from the fact that the near-surface temperature then is less strongly controlled by the incoming solar radiation, and advection or large scale forcing has a relatively larger influence compared to local processes (Chen et al. 2003). Nevertheless, observed and simulated temperatures are still in rather good agreement, and the timing of the temperature rise in the morning is well captured by the model again. Evaporation from soil or even plants will, of course, have a small impact in winter. However, the formulation of ground and soil heat fluxes might be relevant in winter (Chen et al. 1997). In particular, the existence and extent of snow cover is crucial for modelling of the wintertime near surface temperature (Zhang 2005). More detailed month-by-month analyses (not shown) also revealed that the largest biases occur in months with persistent low stratus conditions, suggesting deficiencies in the ability of the model to simulate stratus clouds to a realistic extent. This could, for example, be related to deficits in the cloud-radiation interaction (Guan et al. 2000) or to unrealistic drizzle formation by the microphysics scheme (Lynn et al. 2005).

To shed some light on the simulated moisture budget in the lower atmosphere, Fig. 4.7 shows the mean diurnal cycle of the 2-m dew point temperature for each month of the year. In summer the correspondence between model and measurements at nighttime and in the early morning is again quite good. The onset of early morning evaporation at sunrise and the amount of released water vapor seem to be simulated quite realistically. Also, nighttime formation of dew obviously is captured very well. After 8 UTC in the morning, however, when the observed dew point temperature decreases and finally forms its ideal bimodal diurnal wave, the simulation follows a substantially different path. Hohenegger et al. (2008) also found pronounced overprediction of near surface