Implications of Climate Model Selection for Projections of Decision-Relevant Metrics: A Case Study of Chill Hours in California
The research examines how model selection impacts projections for a particular decision-relevant metric – chill hours. The paper highlights the similarities and differences in results based on whether models are chosen for skill in broad-scale physical climatic metrics (such as average or minimum temperature) or for skill in the decision-relevant metric of chill hours.
The research finds that the peculiarities of specific decision-relevant metrics – such as this non-linear threshold-based chill hour metric – can lead to counterintuitive findings that question the validity of some generally accepted recommendations on climate model selection for impact and adaptation studies. The study concludes that the broad regional climate skill of models is not always sufficient to ensure skill for some decision-relevant metrics, and an additional layer of decision-relevant model evaluation may be needed to better understand how models perform on the eventual metrics of relevance to users.
Climate impacts researchers and decision-makers have relatively easy access to climate projections from several different models and downscaled datasets. Yet, there is minimal guidance on the credibility and appropriate use of such models and projections for specific decision-relevant analyses. The few studies that provide recommendations on model choice are often based on evaluations of broad physical climate metrics (such as temperature averages or extremes) at regional scales, without additional examination of local-scale decision-relevant climatic metrics (such as growing degree days or chill hours) that underpin key impacts and resource management strategies. While such broad regional skill may be considered necessary for the overall credibility of models, it is not clear whether it is sufficient to ensure good skill for informing decisions. This paper evaluates the skill of different Global Circulation Models (GCMs) in predicting the decision-relevant metric of chill hours in Fresno, California, and examines how model selection impacts future projections. We find that good skill in predicting broader physical climate metrics in California does not guarantee skill in prediction of chill hours in Fresno. In fact, the models with good regional climatic skill were mutually exclusive of the ones with a good skill for chill hours, which leads to some counterintuitive results for this unique metric. Since many decision-relevant metrics are non-linear derivations of primary physical quantities (like the chill hour metric), more such decision-relevant model evaluations are needed to provide better insights on model credibility and choice.