and rainfall are all sub-grid scale processes whose numerical representations may vary across GCMs (Section 8.2 in IPCC, 2007). Unlike the core fluid-mechanical equations that have a standard representation in a discretized global model, there is no standard representation of these sub-grid scale processes, and thus the improvement of their representation in GCMs continues to be an active area of research. There have been various community efforts to try to accelerate advances in this area by comparing the performance of models, most notably the Coupled Model Intercomparison Project or CMIP (see Meehl et al, 2007), and conducting studies that attempt to score the forecast ability, known as of different models along various dimensions (see Reichler and “skill”, Kim (2008) and Sections 8.3 and 8.4 of IPCC (2007)). Different models have different and thus we advise economics researchers who are studying specific regions or “skill,” processes and are interested in selecting a GCM projection to first consult the appropriate literature as well as specialists in the field. Differences in Model Predictions There are over 20 well-known climate models, all with readily available output. This section demonstrates that the choice of GCM has significant ramifications for impact estimation. In their survey of economic assessments of climate change impacts, Burke et al. (2011) found that 50% of the studies used the model developed by the Hadley Centre to calculate economic climate change impacts across a variety of sectors, of which 17% used only the Hadley model. Among health impact studies, 38% relied on the Hadley model alone. However, there is no evidence that the Hadley model or, in fact, any other model should be the preferred climate model to use. This is supported by the fact that for some climate indicators, such as precipitation, the predictions for certain regions vary drastically across models. In the extreme, some models predict wetter summers for West Africa and others predict drier summers all using the same SRES scenarios. – One solution to overcoming the of having to choose one GCM is to use model or ensemble averages (e.g., Tebaldi and Knutti, 2007). This decreases the reliance on a single model. However we believe it is important to either report the impacts for a number of climate models separately or to average them and indicate the variability in impacts across models. This is not difficult to do and, given the low costs of data storage
and rainfall are all sub-grid scale processes whose numerical representations may vary across GCMs (Section 8.2 in IPCC, 2007). Unlike the core fluid-mechanical equations that have a standard representation in a discretized global model, there is no standard representation of these sub-grid scale processes, and thus the improvement of their representation in GCMs continues to be an active area of research. There have been various community efforts to try to accelerate advances in this area by comparing the performance of models, most notably the Coupled Model Intercomparison Project or CMIP (see Meehl et al, 2007), and conducting studies that attempt to score the forecast ability, known as “skill”, of different models along various dimensions (see Reichler and Kim (2008) and Sections 8.3 and 8.4 of IPCC (2007)). Different models have different “skill,” and thus we advise economics researchers who are studying specific regions or processes and are interested in selecting a GCM projection to first consult the appropriate literature as well as specialists in the field. Differences in Model Predictions There are over 20 well-known climate models, all with readily available output. This section demonstrates that the choice of GCM has significant ramifications for impact estimation. In their survey of economic assessments of climate change impacts, Burke et al. (2011) found that 50% of the studies used the model developed by the Hadley Centre to calculate economic climate change impacts across a variety of sectors, of which 17% used only the Hadley model. Among health impact studies, 38% relied on the Hadley model alone. However, there is no evidence that the Hadley model or, in fact, any other model should be the preferred climate model to use. This is supported by the fact that for some climate indicators, such as precipitation, the predictions for certain regions vary drastically across models. In the extreme, some models predict wetter summers for West Africa and others predict drier summers – all using the same SRES scenarios. One solution to overcoming the of having to choose one GCM is to use model or ensemble averages (e.g., Tebaldi and Knutti, 2007). This decreases the reliance on a single model. However we believe it is important to either report the impacts for a number of climate models separately or to average them and indicate the variability in impacts across models. This is not difficult to do and, given the low costs of data storage