Predictability of weather and climate

Abstract:

Cities worldwide are increasingly facing the challenges of heat stress, a problem expected to worsen with ongoing climate change. The lack of detailed, city-specific data hinders effective response measures and limits the adaptive capacity of urban populations. In this data descriptor, we introduce a comprehensive database providing climate and heat stress information for 142 cities globally, covering the present and extending projections up to 2100 across three distinct climate scenarios, including two overshoot scenarios. This dataset includes 34 heat stress indicators at a spatial resolution of 100 meters, offering a unique database to identify vulnerable areas and deepen the understanding of urban heat risks. The data is presented through an accessible, user-friendly dashboard, enabling policymakers, researchers, and city planners, as well as non-experts, to easily visualise and interpret the findings, supporting more informed decision-making and urban adaptation strategies.

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>