IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis

8.2.3 Terrestrial Processes

Few multi-model analyses of terrestrial processes included in the models in Table 8.1 have been conducted. However, significant advances since the TAR have been reported based on climate models that are similar to these models. Analysis of these models provides insight on how well terrestrial processes are included in the AR4 models. Surface Processes

The addition of the terrestrial biosphere models that simulate changes in terrestrial carbon sources and sinks into fully coupled climate models is at the cutting edge of climate science. The major advance in this area since the TAR is the inclusion of carbon cycle dynamics including vegetation and soil carbon cycling, although these are not yet incorporated routinely into the AOGCMs used for climate projection (see Chapter 10). The inclusion of the terrestrial carbon cycle introduces a new and potentially important feedback into the climate system on time scales of decades to centuries (see Chapters 7 and 10). These feedbacks include the responses of the terrestrial biosphere to increasing carbon dioxide (CO2), climate change and changes in climate variability (see Chapter 7). However, many issues remain to be resolved. The magnitude of the sink remains uncertain (Cox et al., 2000; Friedlingstein et al., 2001; Dufresne et al., 2002) because it depends on climate sensitivity as well as on the response of vegetation and soil carbon to increasing CO2 (Friedlingstein et al., 2003). The rate at which CO2 fertilization saturates in terrestrial systems dominates the present uncertainty in the role of biospheric feedbacks. A series of studies have been conducted to explore the present modelling capacity of the response of the terrestrial biosphere rather than the response of just one or two of its components (Friedlingstein et al., 2006). This work has built on systematic efforts to evaluate the capacity of terrestrial biosphere models to simulate the terrestrial carbon cycle (Cramer et al., 2001) via intercomparison exercises. For example, Friedlingstein et al. (2006) found that in all models examined, the sink decreases in the future as the climate warms.

Other individual components of land surface processes have been improved since the TAR, such as root parametrization (Arora and Boer, 2003; Kleidon, 2004) and higher-resolution river routing (Ducharne et al., 2003). Cold land processes have received considerable attention with multi-layer snowpack models now more common (e.g., Oleson et al., 2004) as is the inclusion of soil freezing and thawing (e.g., Boone et al., 2000; Warrach et al., 2001). Sub-grid scale snow parametrizations (Liston, 2004), snow-vegetation interactions (Essery et al., 2003) and the wind redistribution of snow (Essery and Pomeroy, 2004) are more commonly considered. High-latitude organic soils are included in some models (Wang et al., 2002). A recent advance is the coupling of groundwater models into land surface schemes (Liang et al., 2003; Maxwell and Miller, 2005; Yeh and Eltahir, 2005). These have only been evaluated locally but may be adaptable to global scales. There is also evidence emerging that regional-scale projection of warming is sensitive to the simulation of processes that operate at finer scales than current climate models resolve (Pan et al., 2004). In general, the improvements in land surface models since the TAR are based on detailed comparisons with observational data. For example, Boone et al. (2004) used the Rhone Basin to investigate how land surface models simulate the water balance for several annual cycles compared to data from a dense observation network. They found that most land surface schemes simulate very similar total runoff and evapotranspiration but the partitioning between the various components of both runoff and evaporation varies greatly, resulting in different soil water equilibrium states and simulated discharge. More sophisticated snow parametrizations led to superior simulations of basin-scale runoff.

An analysis of results from the second phase of AMIP (AMIP-2) explored the land surface contribution to climate simulation. Henderson-Sellers et al. (2003) found a clear chronological sequence of land surface schemes (early models that excluded an explicit canopy, more recent biophysically based models and very recent biophysically based models). Statistically significant differences in annually averaged evaporation were identified that could be associated with the parametrization of canopy processes. Further improvements in land surface models depends on enhanced surface observations, for example, the use of stable isotopes (e.g., Henderson-Sellers et al., 2004) that allow several components of evaporation to be evaluated separately. Pitman et al. (2004) explored the impact of the level of complexity used to parametrize the surface energy balance on differences found among the AMIP-2 results. They found that quite large variations in surface energy balance complexity did not lead to systematic differences in the simulated mean, minimum or maximum temperature variance at the global scale, or in the zonal averages, indicating that these variables are not limited by uncertainties in how to parametrize the surface energy balance. This adds confidence to the use of the models in Table 8.1, as most include surface energy balance modules of more complexity than the minimum identified by Pitman et al. (2004).

While little work has been performed to assess the capability of the land surface models used in coupled climate models, the upgrading of the land surface models is gradually taking place and the inclusion of carbon in these models is a major conceptual advance. In the simulation of the present-day climate, the limitations of the standard bucket hydrology model are increasingly clear (Milly and Shmakin, 2002; Henderson-Sellers et al., 2004; Pitman et al., 2004), including evidence that it overestimates the likelihood of drought (Seneviratne et al., 2002). Relatively small improvements to the land surface model, for example, the inclusion of spatially variable water-holding capacity and a simple canopy conductance, lead to significant improvements (Milly and Shmakin, 2002). Since most models in Table 8.1 represent the continental-scale land surface more realistically than the standard bucket hydrology scheme, and include spatially variable water-holding capacity, canopy conductance, etc. (Table 8.1), most of these models likely capture the key contribution made by the land surface to current large-scale climate simulations. However, it is not clear how well current climate models can capture the impact of future warming on the terrestrial carbon balance. A systematic evaluation of AOGCMs with the carbon cycle represented would help increase confidence in the contribution of the terrestrial surface resulting from future warming.