Abstract/Summary

Variations in the location, intensity and duration of precipitation impact ecosystems and human activities such as agriculture, energy generation, and water reserves for human and animal consumption. Observation shows that persistent and intense precipitation often happens under synoptic-scale filaments of high moisture concentration; such events are typically referred to as convergence zones. Filaments of high tracer concentration emerge as a consequence of the deformation of fluid parcels during the process of fluid mixing. However, the mathematical basis to objectively identify coherent mixing features is relatively recent and the associated computational tools are lacking for meteorological datasets. Thus, it is unquantified the extent at which global precipitation depends on the horizontal mixing at synoptic timescales. It is also not known whether this framework can provide a basis for the development of convergence zone detection algorithms. This thesis adapts the framework of mixing to identify atmospheric structures where the synoptic-scale kinematics favours the accumulation and filamentation of moisture in reanalyses and climate model data. Case-studies show that precipitation and moisture organise around coherent mixing features while climatological analyses show that precipitation anomalies during high mixing events are consistent with the convergence zone literature. It is shown that more than 60% of the monthly precipitation variability can be explained by synoptic-scale mixing in many tropical and subtropical regions. Globally, more than half of precipitation falls during convergence zone events. The capability of climate models to reproduce convergence zone precipitation is evaluated in comparison with observational estimates: all of the analysed models overestimate the contribution of convergence zones to the total precipitation between approximately 7% and 20%. The proposed framework is revealed to be a promising process-based diagnostic to investigate mechanisms of precipitation variability and identifying circulation errors in weather and climate models which lead to errors in predicted precipitation.