Impact of inferred soil water capacity on simulated climate

Rather than using a model with a fully coupled interactive vegetation component, of which only a few are currently being developed, the inferred field of wmax is used in a sensitivity study to test the impact of the additional satellite information on the climate simulated by the ECHAM-4 general circulation model (GCM). ECHAM-4 [Roeckner et al., 1996] is the fourth generation of the ECHAM GCMs, a series of spectral climate models developed at the Max-Planck-Institut für Meteorologie, Hamburg. Its land surface scheme takes into account vegetational effects on the energy and moisture cycles, such as the interception of precipitation or the stomatal control of evapotranspiration, including a parameterisation of soil moisture stress in dry regions.

The land surface characteristics of ECHAM-4 are described by a set of global annual mean land surface parameters [Claussen et al., 1994], including quantities like surface background albedo, leaf area index and fractional vegetation cover. This data set has been constructed by allocating parameter values from different sources to major ecosystem complexes [Olson et al., 1983]. The global distribution of the total soil water-holding capacity was derived from a high-resolution data set [Patterson, 1990].

In order to assess the impact of the newly derived distribution of soil water reservoirs, two global experiments are conducted. One simulation is performed with the standard version of the ECHAM-4 GCM and serves as a control simulation, while in the other run the total soil water-holding capa city is replaced by in accordance with the convention used in ECHAM-4 to convert total water-holding capacity to the plant-available amount (see above). Both model versions are integrated for a 10-year period after at least five years of spin-up to exclude any remaining impact of soil moisture initialization. A T42 spatial resolution was chosen which is equivalent to about 2.8° x 2.8° on a latitude-longitude grid. Both simulations use an annual cycle of monthly mean climatological sea surface temperatures. As Fig. 3c shows, there is no change in soil water holding capacity for unvege-tated regions, so that only the effect of the addional satellite-based information is assessed.

Before comparing simulations with the modified wx to the standard control run, Fig. 6 is used to compare the simulated control climate of ECHAM-4 with the climate map of Legates and Willmott [1990a]. Comparing to Fig. 4, it turns out that there is some agreement between the tropical semi-arid areas experiencing increased dry-season soil water content after assimilation, and those where ECHAM-4 overestimates the 2m temperature. Although much of this difference can probably be attributed to model dynamics and radiation parameterisation, soil moisture might also play a role here.

As Fig. 7 shows, the satellite-inferred changes from the standard wmax derived by the BETHY model are able to compensate some of this discrepancy when used in ECHAM, at least for the southern tropics. Increased soil water storage leads to increased evapotranspiration, cooling the air near the surface. In southern Africa, where the a priori soil water capacity was rather low (cf. Fig. 3), and for parts of South America, these changes can amount to as much as 3°C. However, for March (not shown), there is only very little change, in the South because soil water reservoirs are filled in both cases, and for the northern tropics, because the change in soil water storage is rather small. Consequently, the simulated temperature in summer and early fall is reduced, which is shown in Fig. 8a; compared to the climatology by Legates and Willmott [1990a, b], it is actually reduced into the right direction.

It is likely that other factors than soil moisture contribute to the described differences between near-surface temperature simulated by ECHAM-4 and the climatology by Legates and Willmott. Too little precipitation leading to too much surface drying should not be the reason, at least in the case of southern Africa, as Fig. 8b shows. For example, the Arabian Desert is also simulated too warm, and there is certainly no vegetation not accounted for in the climate model. However, it could also be expected that the assimilation procedure underestimates wmax, because interannual changes in precipitation are not taken into account.

March

September

180 120W SOW 0 60 E 120E 180

Figure 6. Difference between the 10-year mean 2m air temperatures in °C simulated with the standard ECHAM-4 GCM, used as control simulation, and the climatology of Legates and Willmott [1990a], for March (dry season in the northern tropics) and September (dry season for southern tropics)

Higher year-to-year variability would force the vegetation to develop even deeper roots than estimated here with a mean climate. This is suggested by the single-point simulations shown in Fig. 5, using precipitation data from the relatively dry year of 1992. Some estimates of maximum rooting depth by vegetation type [Canadell et al., 1996] also show rather large values, suggesting that some factors determining rooting strategy may be missing in this analysis. All taken together, soil moisture storage does appear to have a significant impact on climate, and the inclusion of vegetation leads to further possibilities of validating the results of climate model simulations against global satellite data.

Was this article helpful?

0 0

Post a comment