Towards dynamical adjustment of the full temperature distribution

Internal variability due to atmospheric circulation can dominate the thermodynamical signal present in the climate system for small spatial or short temporal scales, thus fundamentally limiting the detectability of forced climate signals. Dynamical adjustment techniques aim to enhance the signal-to-noise ratio of trends in climate variables such as temperature by removing the influence of atmospheric circulation variability. Forced thermodynamical signals unrelated to circulation variability are then thought to remain in the residuals, allowing a more accurate quantification of changes even at the regional or decadal scale. The majority of these methods focus on climate variable's averages, thus discounting important distributional features. Here we propose a machine learning dynamical adjustment method for the full temperature distribution that recognizes the stochastic nature of the relationship between the dynamical and thermodynamical components. Furthermore, we illustrate how this method enables evaluating how specific events would have unfolded in a different, counterfactual climate from a few decades ago, thereby characterizing the emergent effect of climatic changes over decadal time scales. We apply our method to observational data over Europe and over the past 70 years.