This research is addressing the questions: How is anthropogenic forcing likely to affect regional climate? With what certainty? To what degree? The work consists of two main parts: (1) Verification of the performance of 20th Century temporal characteristics, such as the trends in multi-decadal means and the interannual variability about those means, from the Atmosphere-Ocean Global Circulation Models (AOGCM)s used for 21st Century climate change predictions; and, (2) Construction of probabilistic multi-model scenarios for 21st Century climate and its variability, using the results from (1) as an objective basis for assigning weights to the model predictions.
For the verification analysis, a probabilistic skill metric will be applied to the AOGCM ensembles relative to an ensemble of synthetic observations, each of which possesses the same temporal characteristics of the observed climate over the 20th Century. Similarly, sub-sets of the observations will be used to construct the probability distributions of the expected skill score. For regions where the probability distributions of the Monte Carlo skill scores for the AOGCMs and observations are significantly similar, the model performance for the 20th Century will be deemed credible.
For the probabilistic multimodel change scenarios, a Bayesian approach will be applied using the prior assumption that 21st Century variability may be represented by the observed 20th Century variability. The analyses will be performed spatially from the grid scale to the regional scale for seasonal (i.e. 3-month mean) near-surface air temperature and precipitation.
The results from this research are intended for inclusion in the Fourth Assessment Report of the Intergovernmental Panel for Climate Change (IPCC). The methods employed in this research, applying techniques of multi-model ensembling that are based on model performance, have not yet been applied in the context of climate change predictions and can pilot further research for future IPCC reports. The findings of this research also can be used to set the longer-range context for seasonal-to-interannual climate variability and predictions. The suggestion here is that proper synthesis of seasonal forecasts, and longer-term assessments, taking into account the uncertainties of each, provides the best opportunity to minimize losses, take advantage of opportunity, and work toward sustainable practices.
Broader impacts include the training of a post-doc and the societal benefits to be realized from credible estimates of the performance of climate prediction models.
