8.8.3 Earth System Models of Intermediate Complexity

Pictorially, EMICs can be defined in terms of the components of a three-dimensional vector (Claussen et al., 2002): the number of interacting components of the climate system explicitly represented in the model, the number of processes explicitly simulated and the detail of description. Some basic information on the EMICs used in Chapter 10 of this report is presented in Table 8.3. A comprehensive description of all EMICs in operation can be found in Claussen (2005). Actually, there is a broad range of EMICs, reflecting the differences in scope. In some EMICs, the number of processes and the detail of description are reduced to simulate feedbacks between as many components of the climate system as feasible. Others, with fewer interacting components, are utilised in long-term ensemble experiments to investigate specific aspects of climate variability. The gap between some of the most complicated EMICs and AOGCMs is not very large. In fact, this particular class of EMICs is derived from AOGCMs. On the other hand, EMICs and simple climate models differ much more. For instance, EMICs as well as AOGCMs realistically represent the large-scale geographical structures of the Earth, like the shape of continents and ocean basins, which is certainly not the case for simple climate models.

Since the TAR, EMICs have intensively been used to study past and future climate changes (see Chapters 6, 9 and 10). Furthermore, a great deal of effort has been devoted to the evaluation of those models through coordinated intercomparisons.

Figure 8.17 compares the results from some of the EMICs utilised in Chapter 10 (see Table 8.3) with observation-based estimates and results of GCMs that took part in AMIP and CMIP1 (Gates et al., 1999; Lambert and Boer, 2001). The EMIC results refer to simulations in which climate is in equilibrium with an atmospheric CO₂ concentration of 280 ppm. Figures 8.17a and 8.17b show that the simulated latitudinal distributions of the zonally averaged surface air temperature for boreal winter and boreal summer are in good agreement with observations, except at northern and southern high latitudes. Interestingly, the GCM results also exhibit a larger scatter in these regions, and they somewhat deviate from data there. Figures 8.17c and 8.17d indicate that EMICs satisfactorily reproduce the general structure of the observed zonally averaged precipitation. Here again, at most latitudes, the scatter in the EMIC results seems to be as large as the scatter in the GCM results, and both EMIC and GCM results agree with observational estimates. When these EMICs are allowed to adjust to a doubling of atmospheric CO₂ concentration, they all simulate an increase in globally averaged annual mean surface temperature and precipitation that falls largely within the range of GCM results (Petoukhov et al., 2005).

Figure 8.17. Latitudinal distributions of the zonally averaged surface air temperature (a, b) and precipitation rate (c, d) for boreal winter (DJF) (a, c) and boreal summer (JJA) (b, d) as simulated at equilibrium by some of the EMICs used in Chapter 10 (see Table 8.3) for an atmospheric CO₂ concentration of 280 ppm. In (a) and (b), observational data merged from Jennings (1975), Jones (1988), Schubert et al. (1992), da Silva et al. (1994) and Fiorino (1997) are shown by crosses. In (c) and (d), observation-based estimates from Jaeger (1976; crosses) and Xie and Arkin (1997; open circles) are shown. The vertical grey bars indicate the range of GCM results from AMIP and CMIP1 (see text). Note that the model versions used in this intercomparison have no interactive biosphere and ice sheet components. The MIT-UW model is an earlier version of MIT-IGSM2.3. Adapted from Petoukhov et al., 2005.

The responses of the North Atlantic MOC to increasing atmospheric CO₂ concentration and idealised freshwater perturbations as simulated by EMICs have also been compared to those obtained by AOGCMs (Gregory et al., 2005; Petoukhov et al., 2005; Stouffer et al., 2006). These studies reveal no systematic difference in model behaviour, which gives added confidence to the use of EMICs.

In a further intercomparison, Rahmstorf et al. (2005) compared results from 11 EMICs in which the North Atlantic Ocean was subjected to a slowly varying change in freshwater input. All the models analysed show a characteristic hysteresis response of the North Atlantic MOC to freshwater forcing, which can be explained by Stommel’s (1961) salt advection feedback. The width of the hysteresis curve varies between 0.2 and 0.5 Sv in the models. Major differences are found in the location of the present-day climate on the hysteresis diagram. In seven of the models, the present-day climate for standard parameter choices is found in the bi-stable regime, while in the other four models, this climate is situated in the mono-stable regime. The proximity of the present-day climate to Stommel’s bifurcation point, beyond which NADW formation cannot be sustained, varies from less than 0.1 Sv to over 0.5 Sv.

A final example of EMIC intercomparison is discussed in Brovkin et al. (2006). Earth System Models of Intermediate Complexity that explicitly simulate the interactions between atmosphere, ocean and land surface were forced by a reconstruction of land cover changes during the last millennium. In response to historical deforestation of about 18 x 10⁶ km², all models exhibited a decrease in globally averaged annual mean surface temperature in the range of 0.13°C to 0.25°C, mainly due to the increase in land surface albedo. Further experiments with the models forced by the historical atmospheric CO₂ trend reveal that, for the whole last millennium, the biogeophysical cooling due to land cover changes is less pronounced than the warming induced by the elevated atmospheric CO₂ level (0.27°C–0.62°C). During the 19th century, the cooling effect of deforestation appears to counterbalance, albeit not completely, the warming effect of increasing CO₂ concentration.