Dr Scott Osprey FRMetS

Abstract:

The Quasi-Biennial Oscillation (QBO) is the dominant mode of tropical stratospheric variability that modulates global circulation and climate. Although a long-term weakening of QBO amplitude has been observed under global warming, the relative roles of different tropical oceans remain unclear. We perform sensitivity experiments forced by sea surface temperature perturbations over the tropical Pacific, Atlantic, and Indian Oceans, as well as their combined warming, to separate individual and joint effects. Pacific warming produces the strongest weakening and slowest descent of the QBO, whereas Atlantic warming slightly strengthens the amplitude and extends the vertical structure. Indian Ocean warming slightly weakens the amplitude and accelerates the descent. When all three oceans warm simultaneously, the QBO exhibits a weaker amplitude and faster descent, consistent in sign with the combined single-basin responses but with a reduced magnitude owing to diminished zonal and inter-basin SST gradients. Momentum budget analyses further show that basin-dependent competition between equatorial wave forcing and tropical upwelling underlies these contrasting responses.

Abstract:

Abstract. This study uses an ensemble of climate model experiments coordinated by the Quasi-Biennial Oscillation initiative (QBOi) to analyze the Madden-Julian Oscillation (MJO) in the presence of either perpetual El Niño or La Niña sea surface temperatures during boreal winter. In addition to the prescribed El Niño Southern Oscillation (ENSO) conditions, the nine models internally generate QBOs, meaning each may influence the MJO. Objectives of our analyses are to assess the response of the MJO to strong idealized ENSO forcing and look for evidence of a QBO influence on the MJO in a multi-model context. The diagnostics used include wavenumber-frequency spectra of tropical convective and dynamical fields, measures of MJO lifetime, an evaluation of MJO diversity and visualization of MJO vertical structure, as well as an assessment of QBO morphology and the QBO's impact on tropical convection. Kelvin wave spectral power increases in the El Niño simulations whereas equatorial Rossby waves power is stronger in the La Niña simulations. All models simulate faster MJO propagation under El Niño conditions. This change in speed is corroborated by the MJO diversity analysis, which reveals that models better reproduce the observed “fast propagating” and “standing” MJO archetypes given perpetual El Niño and La Niña, respectively. Regardless of ENSO, QBO descent into the lower stratosphere is underestimated and we detect little QBO influence on tropical tropopause stability and MJO activity. With little influence from the QBO on the MJO activity in these runs, we can be confident that the aforementioned changes in the MJO indeed arise from the different ENSO boundary conditions.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>Atmospheric flow underpins virtually all meteorological and climatological phenomena, yet extracting meaningful features from its dynamics remains a major scientific challenge due to its high dimensionality, multi-scale behaviour, and inherent nonlinearity. In this study, we investigate the potential of a network-based framework to reveal the relationships between distinct flow structures. Specifically, we apply three techniques, independent of any particular phenomenon or model, to explore patterns of coherence and information transfer, vortical interactions, and Lagrangian coherent structures. We assess their utility using a rotating shallow-water model of the stratospheric polar vortex, which reproduces key aspects of wintertime dynamics, including sudden stratospheric warming split events. Our results support three central claims. First, the transformation of fluid flow data into a network representation preserves essential dynamical information. Second, this representation enables a more accessible and structured analysis of the underlying dynamical structures. Third, multiple types of networks can be constructed from atmospheric flow data, each offering distinct yet complementary insights into the system’s collective behaviour. Together, these findings highlight the potential of network-based approaches as valuable tools in atmospheric research.</jats:p>