Regarding Uncertainty

As described in the section on Quantification of Uncertainty, it is important to note that only a limited part of the total uncertainty associated with future climate change is covered by the NARCCAP simulations. Only one emissions scenario and four AOGCMs are used. A total of 23 different AOGCMs participated in the Fourth Assessment Report of the IPCC and are available at the PCMDI web site. However, based on earlier work (e.g., Tebaldi et al., 2005) the climate changes simulated by the four AOGCMs can be placed in the context of probabilities of future climate change generated using the full suite of climate models. This will allow NARCCAP results to be placed in the context of the large number of AOGCM results. The uncertainty related to spatial scale is relatively well-covered since six different regional models are used. As always, however, uncertainties that cannot be readily quantified through use of even the full suite of global climate models still must be recognized. These include such uncertainties as processes that are missing from the climate models (e.g., a fully coupled carbon cycle) processes that are not understood well enough to model successfully (e.g., certain aspects of ice sheet dynamics), and processes that we are still unaware of.

Regarding the Data

The data from the climate model simulations are provided as-is. We have implemented quality control procedures to correct any errors occurring in the creation of the data sets, but these procedures are not perfect. In addition, data corruption can occur during storage and download. When errors are found in published data, we correct them and attempt to notify those users who have downloaded the affected files.

Note also that the NCEP-driven simulations and current climate simulations using boundary conditions from the AOGCMs do contain biases compared to observed data for the same time periods. Climate model simulations are not perfect. We will, however, provide some insights into the credibility of the future climate simulations through detailed analysis of the model results, including an assessment of the quality of the current model simulations and their representation of processes which are responsible for the projected climate changes.

References

Tebaldi, C., R. Smith, D. Nychka, and L. O. Mearns, 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multi-model ensembles. J. Climate 18:1524-1540.