1. Introduction
The evolution of the diurnal valley winds is the result of complex interactions between solar and thermal radiation, the land surface, turbulence, and the thermally induced flows themselves of various scales, from slope flows to plain-to-mountain circulations (e.g., Whiteman 2000; Weigel et al. 2006; Schmidli and Rotunno 2010). In a recent valley wind model intercomparison study, for example, Schmidli et al. (2011) find large differences in the evolution of the valley wind among nine mesoscale models, even for key aggregated quantities such as the along-valley wind averaged over the entire valley volume. For convenience, the evolution of the mean valley wind for a subset of the nine models is reproduced in Fig. 1. It was further found that there are quite large differences in the evolution of the surface sensible heat flux among the models. This leads to the question: are the differences in the simulated valley wind just the result of differences in the thermal forcing, or are there genuine differences among the models in their simulation of the valley wind?
Here we introduce a new diagnostic, the diabatic pressure difference, which can be used to synchronize the evolution of thermally induced flows among different models and different configurations of the same model. As a measure of the thermal forcing history of the valley wind system, the new diagnostic allows us to distinguish between differences originating from diabatic forcing (surface sensible heat flux, radiation flux divergence) to those originating from the model dynamics (e.g., dynamical core, turbulence scheme).
2. The diabatic pressure difference
It should be noted that Δpd is a measure of the time-integrated bulk diabatic forcing of the valley–plain system and thus only indirectly related to the evolution of the along-valley wind. By design, it does not take into account the influence of the heat exchange between the valley and its surroundings or the influence of upper-level pressure gradients on the evolution of the valley wind, in contrast to Δpsfc, nor does it take into account local gradients of temperature or pressure, which can be important in some situations (Schmidli and Rotunno 2010, 2012). It should be further added that as a measure of the thermal forcing history, it is not primarily the absolute value of the diabatic pressure difference, but the relative values of different cases that are of importance.
3. Application
As an example, we apply the concept of the diabatic pressure difference to a subset of models from the valley wind model intercomparison study of Schmidli et al. (2011). Time series of the diabatic pressure difference for the same models as in Fig. 1 are shown in Fig. 2. As the valley volume factor τυ is identical for all models, the differences in Δpd are solely due to differences in the evolution of the surface sensible heat flux over the plain and in the valley (neglecting the very small contributions from the radiation flux divergence). It can be seen that these differences in the thermal forcing amount to temporal differences of up to 2 h. While the fastest model attains an integrated forcing of 2 hPa at 1200 LT, the slowest model attains the same forcing only at 1400 LT. There are also notable difference in the maximum magnitude of the forcing, ranging from 3.0 to 3.7 hPa.
Next we can use the diabatic pressure difference to synchronize the model simulations according to the integrated thermal forcing. Figure 3 shows the evolution of the mean valley wind as a function of the diabatic pressure difference Δpd. It can be seen that for these six models the evolution of the mean valley wind, when corrected for differences in the thermal forcing, is very similar up to an integrated forcing of Δpd ≈ 2 hPa, which is reached, on average, at about 1200 LT. This implies that either only diabatic forcing is significant for the evolution of the mean valley wind, or that the other relevant processes, such as, for example, heat exchange with the valley surroundings and surface friction, are of almost identical magnitude in all six models (prior to 1200 LT). Further analysis shows that the latter is the case (Schmidli and Rotunno 2010, 2012). After about 1200 LT, as the cross-valley circulation and turbulence intensify, the differences among the models become larger. The intensification leads to larger differences in the heat exchange between the valley and its surroundings, which reduces the initially strong correlation between the evolution of Δpd and the net forcing of the along-valley wind.
To conclude, Fig. 3 provides a concise summary of the evolution of two key quantities of the valley wind system, the integrated thermal forcing of the system, as measured by Δpd, and its reaction to the forcing in terms of the mean valley wind.
4. Conclusions
In conclusion, the diabatic pressure difference is a concise measure of the thermal forcing history of the valley–plain system. As illustrated in the present note, it can be used to synchronize the evolution of thermally induced flows among different models and thus help to distinguish between differences orginating from the diabatic forcing to those originating from the model dynamics (e.g., dynamical core, turbulence scheme, and numerical smoothing). More generally, it can be used to account for one large source of differences between model simulations of thermally induced valley winds—be it different models or the same model with different configurations—namely, differences in the evolution of the diabatic (surface) forcing. Clearly this is useful, as often differences in surface properties (e.g., vegetation type and soil moisture) or in the land surface models are a (the) major source of uncertainty in the simulation of valley winds in idealized and real-case model setups (e.g., Chow et al. 2006; Schmidli et al. 2009, 2011). The application of the diabatic pressure difference is, however, not restricted to model intercomparison and sensitivity studies. As a measure of the thermal forcing history of the system, it can be used as a general tool for the analysis of thermally induced valley winds. It could, for example, also be used to analyze day-to-day or seasonal variability of the valley winds for particular locations.
Acknowledgments
The author is grateful for helpful comments from the reviewers.
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