Going with the Flow? Flow-dependent Multi-timescale Model Diagnostics

Abstract:

Common approaches to diagnose systematic model errors involve the computation of statistical metrics aimed at providing an overall summary of the performance of the model in reproducing the particular variables of interest in the study, normally tied to specific spatial and temporal scales. However, the evaluation of model performance is not always tied to the understanding of the physical processes that are correctly represented, distorted or even absent in the model world. As the physical mechanisms are more often than not related to interactions taking place at multiple time and spatial scales, cross-scale model diagnostic tools are not only desirable but required. Here, a recently proposed circulation-based diagnostic framework is extended to consider systematic errors in both spatial and temporal patterns at multiple timescales. The proposed framework, which uses a weather-typing --or flow-dependent-- dynamical approach, quantifies spatial biases in the magnitudes, location and tilt of modeled atmospheric circulation patterns, as well as biases associated with their temporal characteristics, such as frequency of occurrence, duration, persistence and transitions. Relationships between these biases and climate teleconnections (e.g., SST patterns, ENSO and MJO) are explored using different models. Some concrete applications are discussed.