Sensitivity of the mid-latitude waveguide to the dynamics and observations of Arctic tropopause-based vortices
Heating due to infrared radiation is a great source of uncertainty in weather and climate prediction models, and high impact weather events are often strongly influenced by sub-synoptic-scale features that affect such heating. This project will closely examine specific processes active within tropopause polar vortices (TPVs) before they encounter the jet stream. Improved knowledge gained through detailed analysis of the processes, and their interactions with the jet stream, will lead to improvements in weather and climate prediction by improving their representation in numerical models. TPVs are unique because they can exist for long periods of time before affecting the jet stream, and are therefore a key feature to observe and accurately represent. The principal investigator will develop human capacity and teach students how to advance science through the entire spectrum of research. Quality-controlled datasets, developed during the course of the research, will be made openly available to the research community for use as a long-term tool for predictability studies. Researchers will test the hypothesis: diabatic heating from radiation, coupled with weak deformation, is required to maintain the structure of tropopause polar vortices (TPVs) as they encounter increasing vertical shear associated with the waveguide. The project will use three approaches: (1) analyzing the statistical characteristics of TPVs based upon downstream impact, (2) isolating and identifying important physical processes important for TPV development and maintaining TPV intensity as they approach the waveguide through controlled numerical modeling experiments, and (3) diagnosing key uncertainties to identify specific methods of improving process representation in numerical models. Experiments will be conducted using the atmospheric version of the Model for Prediction Across Scales (MPAS) and parallel simulations using the Advanced Weather Research and Forecasting (ARW) model with the Data Assimilation Research Testbed (DART).