The majority of climate change impact and adaptation studies conducted to date have been based on only a few forecasts of future climate, using one or a few different Global Climate Models (GCMs) and emissions scenarios. However, there are a large number of models available such as the IPCC recognized GCMs, and the North American high-resolution regional climate models (RCMs). To date, only a few of these models have been used in practical applications due to the vast amount of data, the complexity and variability. The question is, how can valuable data from a number of different models be included in the applications to help improve climate change impact and adaptation decision-making?
Supported by the Ministry of the Environment and Environment Canada, Novus and York University are developing a methodology to perform a hybrid approach to climate change impact assessment using large ensembles of climate change information. This methodology will resolve complexity and variability issues related to the vast amount of available data. Beyond simple scenario analysis, combined statistical downscaling and probabilistic analysis methods utilizes a wealth of additional data mentioned above and provides useful and confident climate change information on regional and municipal scales. The probabilistic and statistical downscaling approach is able to improve adaptation strategies to climate change by presenting a wider envelope or range of possible outcomes.