Partitioning the sources of uncertainty in statistically downscaled and bias-adjusted climate simulations

With the growing number of statistically downscaled datasets available, it can become difficult for users to choose what to focus on when selecting an ensemble and to understand the impact of this choice. To assist in this task, the authors use a systematic approach to quantify the uncertainty sources of statistically downscaled and bias-adjusted climate simulations. Classical uncertainty partitioning of climate simulations includes internal variability, greenhouse gases scenario and global climate model. Bias adjusted and statistically downscaled datasets descend a level deeper in the cascade of uncertainty. To study this, the authors include two new dimensions: observational reference used in bias-adjustment and bias-adjustment method itself. The fraction of uncertainty associated with each of these five dimensions is calculated for precipitation-based, temperature-based and multivariate indicators. Eastern Canada is used as a case study, focusing on three locations with contrasting climates and observational network densities. This analysis reveals that, while the method is only responsible for a small portion of the variance, the uncertainty associated with the observational reference dataset can play a major role, even becoming the leading source of uncertainty in many cases. This finding underscores the importance of this, often overlooked, dimension in the evaluation of datasets by users and impact modelers. Further, it highlights the ethical responsibility for data providers to clearly communicate the full uncertainty structure of their products.