In order to improve the understanding of the parameterization of land surface processes, the Project for Intercomparison of Land-Surface Parameterization Schemes (PILPS) was initiated in 1992 as a World Climate Research Programme project. The overall goals of PILPS are to improve the performance of land-surface schemes, as they are used in climate and weather prediction models. The progress to date and planned future activities of PILPS are described in detail by Henderson-Sellers et al. (1995).
In Phase 2 of PILPS, land-surface schemes are being compared in off-line experiments that employ observed data. The Cabauw experiment (Phase 2a) used observation from Cabauw, the Netherlands (51°58′N, 4°56′E) (Beljaars and Viterbo 1994; Beljaars and Bosveld 1997), as the atmospheric forcing to drive 23 land-surface schemes (Table 1). Point-based observations of surface energy fluxes, net radiation, and upward longwave radiation data were used for validation of the simulations (Chen et al. 1997). In addition to these intercomparisons, two sensitivity experiments were undertaken using modified versions of the Cabauw forcing in which the surface air temperature was increased or decreased by 2 K at every model time step. Hereafter, the experiment with +2-K forcing is referred to as “Plus2” and that with −2-K forcing as “Minus2.”
The results from the off-line simulations in previous phases of PILPS (Pitman et al. 1993; Shao and Henderson-Sellers 1996) show that there were large discrepancies among the existing land-surface schemes in terms of the partitioning of surface net radiation into sensible heat and latent heat flux and partitioning of precipitation into evapotranspiration and soil water components (soil water storage, runoff, and drainage). Any attempt to understand the reasons for these discrepancies requires a systematic examination and intercomparison of individual parameterizations and processes within the models. To address the similar diversity in GCM simulations (i.e., when GCM simulations showed quite differing climatic responses to prescribed forcing such as increasing CO2 (Schlesinger and Mitchell 1987), Wetherald and Manabe (1988) and Hansen et al. (1981) used a procedure in which basic variables, such as temperature, water vapor, surface albedo, and cloud cover, were individually varied to assess individual feedback processes. Cess and Potter (1988) presented a computationally more efficient means for both understanding and intercomparing climate feedback mechanisms in GCM simulations by using surface temperature perturbations as a surrogate climatic change for the purpose of studying atmospheric feedback processes.
Such techniques are also very useful for understanding and intercomparing the land-surface parameterization schemes. The sensitivity experiments discussed in this paper are analogous to the Cess et al. (1990) experiments, which evaluated cloud forcing sensitivities in GCMs by artificially increasing and then decreasing prescribed sea surface temperatures by 2 K. The purpose of these sensitivity experiments was to obtain a first-order estimate of the sensitivity of PILPS schemes to changed air temperatures and to determine whether different schemes respond to such changes differently and the extent to which any differences could be traced to different parameterizations. Although different experiments in which other forcing variables are also altered can be constructed, this paper describes only these first-order tests.
In this paper, the sensitivity of latent heat fluxes in current land-surface schemes to the change of air temperature will be described and analyzed. The behavior of the latent and sensible heat flux is discussed in section 2a, including the theoretical aspects of the relationship between latent heat flux and air temperature. The effect of the “classic β” formulation on the parameterization of latent heat is discussed in section 3a, together with the influence of stress factors in the sensitivity experiments in section 3b. Finally, some conclusions are drawn in section 4.
2. Sensitivity of latent heat fluxes in the Plus2 and the Minus2 experiments
a. Behavior of the latent and sensible heat flux
Since the incoming radiation was not altered in the sensitivity experiments discussed here, the increase in air temperature must lead to changes in the latent or sensible heat. This is achieved by an adjustment of surface temperature. Figure 1 shows the differences in the annual mean effective temperatures, Ts (the combined mean surface radiative temperature of the canopy and the ground) between Plus2 and Control (i.e.,
Generally, most of the schemes exhibit similar, but opposite, behavior in Plus2 and Minus2. Figure 2 shows the difference of latent heat flux L and sensible heat flux H (W m−2) between Plus2 (Minus2) and Control for all 23 land-surface schemes. Compared to the control experiment, latent heat flux increases by about 6 W m−2 in Plus2, with BUCKET showing the lowest increase (2.1 W m−2) and CAPS the highest (9.6 W m−2), and decreases by about 11 W m−2 in Minus2, with UGAMP2 showing the lowest decrease (5.6 W m−2) and VIC-3L the highest (17.0 W m−2) (Fig. 2a). SPONSOR is the only scheme that produces a small decrease of latent heat flux in Plus2. A possible reason for this behavior will be discussed in section 3b(3). Sensible heat flux decreases by about 12 W m−2 in Plus2, with CAPS showing the largest decrease (16.2 W m−2) and SPONSOR the lowest (4.7 W m−2), and increases about 16 W m−2 in Minus2, with VIC-3L showing the highest increase (23.7 W m−2) and SWAP the lowest (9.9 W m−2) (Fig. 2b). It should be noted here that for Plus2 the annual means of sensible and latent heat fluxes have ranges across the schemes of 31 and 23 W m−2, respectively, and for Minus2 of 24 and 28 W m−2, respectively. These ranges are of a similar magnitude as those for the control experiment, which is significantly larger than the uncertainties of the measurements (Chen et al. 1997). After Beljaars and Bosveld (1997), the observational errors of the Cabauw dataset are within ±5 W m−2 for sensible heat flux and ±10 W m−2 for surface net radiation and latent heat flux.
Figure 3 shows the differences of L and H between Plus2 and Minus2. It can be seen that |H+2 − H−2| is larger than |L+2 − L−2| for all schemes; that is, sensible heat flux is more sensitive to the prescribed change of air temperature than latent heat flux. This is because of the linear relationship between H and Ts, which changes with the change of air temperature [Eq. (3)]. It can also be seen that if L of a certain scheme is sensitive to the change of air temperature, H is also sensitive.
The interesting result with regard to the behavior of latent heat flux is that the increases or decreases of latent heat flux in Plus2 and Minus2 are not linear with respect to the prescribed equal and opposite changes in air temperature (+2 K and −2 K). Figure 4 illustrates this nonlinearity. For clarity, 6 out of 23 schemes are shown, representative of median (UKMO, CLASS, SSIB, CSIRO9, ECHAM) and extreme (BUCKET) performance in the control experiment (cf. Chen et al. 1997). For latent heat flux, the nonlinear relation to the change of air temperature is evident.
b. Theoretical aspects of the relationship between Ls and air temperature
Equation (2) shows clearly that there is a nonlinear relation between surface temperature and Ls induced by the nonlinearity between surface temperature and saturation specific humidity. A numerical evaluation of Eq. (2) will be used here to illustrate the response of Ls to the change of air temperature. Fixing all variables in Eq. (2) except Ts and λ (λ changes with Ts) and assuming ρ = 1.292 (kg m−3), U = 5.0 (m s−1), CE = 0.00597 (appendix of Chen et al. 1997), qa = 0.005 (kg kg−1), we allow Ts to increase or decrease 1 K because Fig. 1 shows that annual mean effective surface temperature increases or decreases by about 1 K when air temperature increases or decreases by 2 K. These changes in Ts are imposed upon a base surface temperature that varies from −10° to 30°C to match the annual range of air temperature in Cabauw (Beljaars and Bosveld 1997). In this numerical test, we use the symbol
Figure 5 shows the difference (absolute values) between
The interesting point here is that the nonlinear change of Ls with regard to the linear change of temperature is shown in a way that the amount of increase of Ls for +1 K Ts is larger than the amount of decrease of Ls for −1 K Ts, that is, |
3. The formulation of latent heat fluxes in current land-surface parameterizations
a. The β adjustment
As mentioned in section 2a, latent heat flux is commonly parameterized by using Eq. (1) in PILPS schemes. Most of the schemes determine Ls by using the aerodynamic method [Eq. (2)], while a few schemes (Table 1) use the Penman–Monteith formulation. We first consider the effect of βg on the latent heat flux in these sensitivity experiments and, for clarity, assume drag coefficient CE does not change in the sensitivity experiments.
Here we demonstrate that the involvement of βg formulation in the parameterization of latent heat flux and the change of βg induced by the increase and decrease of air temperature in the sensitivity experiments is the reason that causes rL < 1 for the schemes using the aerodynamic method. For simplicity, we assume that βg is only a function of soil moisture and assess its effects on the latent heat flux in the sensitivity experiments.
Figure 6 shows |L+1 − LC| and |L−1 − LC| at different base surface temperatures and for two different sets of βg value. In Fig. 6a, the βg values in Table 2 are used, that is,
The analysis above shows that the involvement of the βg formulation in the parameterization of latent heat flux causes the nonlinearity of L with regard to the linear change of air temperature in the form that the increase of latent heat flux in Plus2 is much smaller than its decrease in Minus2, that is, rL ≪ 1. Without the involvement of the βg formulation, the nonlinearity is different, namely, the increase of latent heat flux in Plus2 is larger than its decrease in Minus2, as discussed in section 2b.
For the schemes using the Penman–Monteith formulation to determine the scaling evaporation, the situation is more complicated. The following analysis will show, however, that rL < 1 is still attributable to the involvement of a βg formulation even for those schemes using a Penman–Monteith formulation.
b. Influence of the stress factors on the change of βg in the sensitivity experiments
From the discussions in section 3a, it can be seen that (i) the involvement of the βg formulation in the parameterization of latent heat flux and (ii) the change of βg induced by the change of air temperature is responsible for rL < 1 both for the schemes using the aerodynamic method to determine the scaling evaporation, and for the schemes using Penman–Monteith. As βg is a function of ra and a series of stress factors, we will discuss in the following section which factors most strongly influence the change of βg and therefore the change of latent heat flux in the sensitivity experiments.
1) The influence of air temperature on drag coefficient
As drag coefficient CE (some models use alternatively aerodynamic resistance) for water vapor transfer, which is involved in βg [Eq. (11)], depends on thermal stability, increasing or decreasing forcing air temperature also influences CE. The following analysis shows that the changes in CE induced by air temperature changes of ±2 K have a significant influence on monthly or annual latent heat flux for most of the schemes.
It should be noted that for a cloudy day with lower net radiation, the differences of the calculated Richardson number between Plus2 and control and also between Minus2 and control could be quite large during the day, such as on 13 September. For Plus2, for example,
It should be pointed out that for the models without explicit stomatal control of transpiration and with no or small changes in predicted soil moisture for Plus2 and Minus2, the change in CE may play a dominant role in resulting rL < 1. Models SPONSOR and SWAP are examples of this situation. The transpiration in these two models is estimated by modifying potential evaporation through a βT formulation that is a function of soil moisture only. Since the predicted soil moisture for Plus2, Minus2, and control is nearly the same and larger than Wcr [see section 3b(3)], βT is nearly the same for Plus2, Minus2, and control. Therefore, rL < 1 is mainly caused by decreases in CE induced by the more unstable stratification in Plus2 in the calculation of the potential evaporation.
2) Influence of air temperature on bulk stomatal resistance
Of particular importance is the factor Mf, which accounts for the effects of soil moisture stress on latent heat flux. Usually, Mf varies between 0 and 1 when soil moisture Wg varies between Wwilt and Wcr. In many schemes, Mf is a simple and explicit function of soil moisture. In some schemes (e.g., ISBA, CSIRO9, and VIC-3L), Mf takes the same form as Eq. (13). The change of predicted soil moisture in Plus2 and Minus2 leads to quite large change in Mf. For example, from the estimation given in section 3a, it can be seen that if Mf takes the form given in Eq. (13), the annual mean of Mf decreases from 0.65 for the control experiment to 0.52 for Plus2. That means an increase of rs of about 25% for Plus2 compared to control, which will have a significant influence on latent heat flux. Thus, it can be seen that changes in the soil moisture in these sensitivity experiment induced (indirectly) by the increase or decrease of forcing air temperature plays an important role on the change of the scaling parameter βg and therefore is one of the major factors affecting the behavior of the changes in latent heat flux.
3) Behavior of soil moisture and its effect on latent heat flux in the sensitivity experiments
Figure 11a gives the difference of the annual mean soil moisture of the top 1-m soil layer W between Plus2 (Minus2) and control for all schemes. It can be seen that soil moisture decreases in Plus2 and increases in Minus2 for most of the schemes; the exception being SEWAB, which shows no change. This response is attributable to the behavior of latent heat flux, which increases in Plus2 and decreases in Minus2. However, the extent of the decrease (increase) of soil moisture in Plus2 (Minus2) is very different among the PILPS schemes. This can be also seen in Fig. 11b, which gives the absolute value of the difference of soil moisture between Plus2 and Minus2, that is, |W+2 − W−2|. Some schemes (BASE, BUCKET, CAPS, GISS, ECHAM, PLACE, MOSAIC, CSIRO9, CAPSNMC, VIC-3L) show large |W+2 − W−2| over 20 kg m−2, with VIC-3L showing the highest value (85.8 kg m−2). This implies that the soil moisture in these schemes is quite sensitive to the prescribed changes of air temperature. Other schemes (BATS, SSIB, SWAP, SEWAB, SPONSOR) show very small |W+2 − W−2|, while GISS and MOSAIC show a large increase of soil moisture in Minus2 (37.9 and 29.3 kg m−2, respectively) but only a small decrease in Plus2 (9.6 and 8.9 kg m−2, respectively). Another “outlier” is PLACE, which produces a small increase of soil moisture in Minus2 (9.0 kg m−2) but a large decrease in Plus2 (19.7 kg m−2).
The very small |W+2 − W−2| values for BATS, SSIB, SWAP, SEWAB, and SPONSOR are due to the fact that the soil moisture below 1-m depth was prescribed as saturated in these schemes. Since the schemes allow water movement crossing the 1-m interface, the decrease of root zone soil moisture in Plus2 induced by high evaporation under imposed higher air temperature can be compensated by the water supply from the soil below 1 m (water table). For SWAP and SPONSOR, the predicted soil moisture for Plus2, Minus2, and control is in fact nearly the same and near or larger than Wcr. There is no straightforward correlation between soil moisture and latent heat flux in terms of their sensitivity to the prescribed changes in air temperature (Figs. 11b,c). For example, both BATS and SSIB show large |L+2 − L−2|, but very small |W+2 − W−2|.
As discussed in section 3b(2), for most schemes soil moisture stress factors most strongly influence the change of βg and therefore the change of latent heat flux in the sensitivity experiments. In Plus2, the decrease of soil moisture under higher temperature (+2 K) leads to the decrease of βg through the decrease of Mf, which is an indication of soil moisture stress, and hence the increase of rs, which, in turn, leads to lower evaporation.
The effect of the soil moisture stress in Plus2 is quite strong for some schemes, for which monthly latent heat flux for Plus2 is smaller than that for control in summer months. Figure 12 shows the monthly variation of latent heat flux for BUCKET, CAPS, CAPSNMC, ISBA, CLASS, CSIRO9, and ECHAM. It can be seen in Fig. 12 that monthly mean L of these schemes in Plus2 is lower than or nearly the same as that in the control experiment in summer months, mostly in July. The situation L+2 < LC can only happen when a βg formulation is involved and L+2 is reduced by a small βg, in which Mf reflects strong soil moisture stress in Plus2 so that L+2 is even smaller than or equal to LC. This argument seems to be supported by reviewing the variation of daily latent heat flux and root zone soil moisture in July for some schemes.
Figures 13a,b show the daily latent heat flux and root zone soil moisture in July (day 182–day 212) for CLASS. It can be seen that L+2 < LC from day 192 to day 197 (Fig. 13a), during which the predicted root zone soil moisture for Plus2, W+2, goes down to the lowest level of the year (Fig. 13b). Figures 13c,d show the case for ISBA. It can be seen that L+2 < LC from day 186 to day 197, which also corresponds to the time when the predicted root zone soil moisture for Plus2 shows its lowest values of the year. For some schemes (BASE, PLACE, SWB, VIC-3L), although the monthly mean L for July in Plus2 is larger than that in control, the daily latent heat flux for Plus2 is smaller than that for control for some periods in July. Figures 13e,f show the case for VIC-3L as an example. It can be seen that VIC-3L shows L+2 < LC from day 190 to day 197, during which the predicted root zone soil moisture shows its lowest values of the year.
It should be noted that most schemes use different formulations for Mf to describe the limitation of soil moisture to latent heat flux. This implies that different schemes may have different criteria on soil moisture stress, which is caused by (i) differences in Mf formulation involved in individual schemes, (ii) differences in the definition of critical soil moisture, and (iii) by using different soil moisture in Mf; for example, some models use soil moisture for the root zone in Mf, while some other models may consider the root distribution and use soil moisture for the surface layer. We can see from Fig. 13 that different schemes suffer soil moisture stress at totally different soil moisture levels. For Plus2, CLASS shows soil moisture stress (L+2 < LC) at root zone soil moisture, W+2, being around 390 mm (Fig. 13b), ISBA at W+2 around 310 mm (Fig. 13d), and VIC-3L at W+2 around 260 mm (Fig. 13f). From these results we may derive that the difference in the parameterization of the latent heat flux versus soil moisture relationship (both Mf and β for bare soil) across the models might be one of the important reasons for discrepancies among the models. More studies are needed on this issue.
Furthermore, we can also see that the use of the Mf formulation makes it difficult to identify the effect of soil moisture on latent heat flux, because soil moisture has only an indirect relation to latent heat flux through its presence in βg. In this case, latent heat flux is directly modified by βg every model time step and is thus sensitive to changes in βg. Therefore, the formulation of Mf and β (for soil evaporation) parameterizations in βg would have significant influences on predicted latent heat, even if the soil moisture is accurately estimated. This relationship will also become further complicated when the feedbacks between soil moisture and evaporation are considered.
4. Summary and conclusions
Using 23 land-surface schemes, driven off-line by observations from Cabauw, the Netherlands, two sensitivity experiments have been undertaken in which the forcing air temperature was increased or decreased by 2 K and all other parameters remained as in the control experiment. The results show the following.
On an annual timescale, all schemes exhibit qualitatively similar and plausible responses to the prescribed 2-K increase or decrease in air temperature, although there are quantitatively significant differences among the schemes. In Plus2 (Minus2), all schemes show that (i) Ts increases (decreases), (ii) latent heat flux increases (decreases), (iii) sensible heat flux decreases (increases), and (iv) soil moisture decreases (increases).
The change of latent heat and sensible heat flux is not linear with respect to the change of air temperature. Specifically, the increase of latent heat flux in the Plus2 experiment is smaller than the decrease of latent heat flux in the Minus2 experiment. This is partly due to the βg formulation involved in the parameterization of latent heat flux, which is a function of a series of stress factors that limit the scaling evaporation, and partly due to the changes in drag coefficient induced by the change in stratification as a consequence of the imposed change in air temperature (±2 K).
Changes of soil moisture play an important role in the changes of βg in these sensitivity experiments. For most schemes, one of the reasons for the fact that |L+2 − LC| is much smaller than |L−2 − LC| in the sensitivity experiments is the decrease of soil moisture in Plus2, which leads to a smaller βg, indicating soil moisture stress. Except for the schemes that specify their soil moisture below 1-m depth as saturated, the effect of soil moisture stress is especially strong for some schemes (BUCKET, CAPS, CAPSNMC, ISBA, CLASS, CSIRO9, ECHAM, BASE, PLACE, SWB, VIC-3L) for which the latent heat flux in Plus2 is even smaller than that in the control experiment in summer months.
PILPS is funded by grants from the Australian Research Council and through NOAA to The University of Arizona. We also acknowledge support from National Greenhouse Advisory Committee of the Department of Environment, Sports and Territories, Australia. The PILPS team acknowledges the Royal Netherlands Meteorological Institute (KNMI) for providing the Cabauw data, which are the result of a long-term boundary layer monitoring program in the Netherlands, and the active participation of all “PILP-ers.”
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List of models participating in PILPS phase 2a and some basic information about these landsurface schemes.
|L+1 − LC| and |L+1 − LC| in the case with and without βg adjustment.