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Abstract:

Nudging remains a cost-effective data assimilation technique in coupled climate models, yet conventional schemes with fixed spatial strengths struggle to represent heterogeneous ocean processes. This study introduces an adaptive nudging framework in which a spatially varying gain matrix dynamically balances model and observational errors, providing a more physically consistent determination of nudging coefficients. Implemented in the SPEEDY-NEMO coupled model, the method is systematically evaluated against a traditional latitude-dependent scheme. Results show substantial improvements in subsurface temperature assimilation across key regions, including the Niño3.4, tropical Indian Ocean, North Pacific, North Atlantic, and northeastern Pacific. The most pronounced gains occur above and within the thermocline, where strong stratification renders fixed nudging strengths inadequate, yielding a 20–30% reduction in RMSE and a 30–50% increase in correlation. In mid- to high-latitude regions, improvements extend to greater depths, consistent with deeper thermocline structures. The adaptive framework corrects both systematic bias and variance, enhancing not only the mean state but also variability representation. Additional benefits are found in salinity, currents, and sea surface height, demonstrating that spatially adaptive nudging provides a more effective and practical alternative for improving ocean state estimation in coupled models.

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>