Predictability of weather and climate

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:

In 2021, Canada experienced one of the most extreme heatwaves ever seen anywhere on the globe. We use a weather forecast model to attribute health impacts to climate change. We simulate the heatwave as a present-day forecast, a preindustrial-counterfactual scenario, and a future-counterfactual scenario. Despite the extremeness of the event, our analysis shows that, under current climate conditions, we could have still seen up to 30% more heat-related deaths than the number observed. We show that between 11 and 15% of the observed human mortality was attributable to climate change during this event, depending on the conditioning of the atmospheric circulation. We also show that, had "the same event" occurred in the future, the mortality toll is nonlinear compared with the warming trend, and so the future attribution would be even more extreme, 16 to 31%. We argue that this method gives particularly reliable impact attribution results and is therefore strongly defensible in decision-making and legal settings.

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>