Oisín Hamilton

Atmospheric blocking exerts a profound influence on midlatitude circulation, yet its predictability remains elusive, due to intrinsic nonlinearities and sensitivity to initial conditions. While blocking dynamics have been extensively studied, the impact of geographical positioning on predictability remains largely unexplored. This study provides a comparative assessment of the predictability of western and eastern North Pacific blocking events, leveraging analogue‐based diagnostics applied to Coupled Model Intercomparison Project Phase 6 (CMIP6) Model for Interdisciplinary Research on Climate, version 6 (MIROC6) simulations. Blocking structures are identified using geopotential height gradient reversal, with their temporal evolution analyzed through trajectory tracking and error growth metrics. Results reveal that eastern blocks exhibit lower predictability, characterized by rapid error divergence and increased mean logarithmic growth rates compared with western blocks. Persistence analysis gives no significant difference between eastern and western North Pacific blocking events. Sensitivity analyses across varying detection thresholds validate the robustness of these findings.

Auto-AUTO (or AUTO 2) is a Python package that acts as an intermediate layer between the user and AUTO-07p continuation software (Doedel et al., 2007) (AUTO from here on). auto-AUTO automates the running of AUTO by monitoring the continuation, while also keeping track of bifurcation points. The package can automatically continue along these branching points, and continue branching along further bifurcation points to attempt to construct full bifurcation trees. To achieve this in a reliable way, AUTO computations are managed by auto-AUTO by setting appropriate stopping conditions, such as meeting other branches, looping branches, and specified bifurcations. These stopping conditions supplement the usual AUTO bounds and ensure that branches of detected solutions are unique and well defined.

Unstable Periodic Orbits (UPOs) were used to identify regimes, and transitions between regimes, in a reduced-order coupled atmosphere-land spectral model. In this paper we describe how the chaotic attractor of this model was clustered using the numerically derived set of UPOs. Using continuation software, the origin of these clusters were also investigated. The flow of model trajectories can be approximated using UPOs, a concept known as shadowing. Here we extend that idea to look at the number of times a UPO shadows a model trajectory over a fixed time period, which we call cumulative shadowing. This concept was used to identify sets of UPOs that describe different life cycles of each cluster. The different regions of the attractor that were identified in the current work, and the transitions between these regions, are linked to specific atmospheric features known as atmospheric blocks.

Reduced order quasi-geostrophic land-atmosphere coupled models display qualitatively realistic mid-latitude atmosphere behaviour, meaning that such models can produce typical atmospheric dynamical features such as atmospheric blocking. At the same time, due to a low number of degrees of freedom, they are still simple enough to allow for analysis of the system dynamics. These features mean that these models are well suited to investigating bifurcations in atmospheric dynamics, and use a dynamical systems approach to better understand the corresponding atmospheric behaviour. This project introduces a symbolic python workflow for using the flexible land-atmosphere (qgs, 2020) spectral model with the continuation software AUTO. This work builds on the results of Xavier et al. (2023) to understand how the model variability and predictability is impacted by the model resolution. We also use bifurcation diagrams to better understand how parameters such as atmosphere-land friction impact the atmospheric blocking, and in turn the model atmosphere predictability. This is done for a range of model resolutions to investigate how the number of degrees of freedom impacts both the realism of the model, but also the structures found in the dynamics. Demaeyer, Jonathan & De Cruz, Lesley & Vannitsem, S.: qgs: A flexible Python framework of reduced-order multiscale climate models. Journal of Open Source Software. 5. 2597. 10.21105/joss.02597, 2020. Xavier, A. K., Demaeyer, J., and Vannitsem, S.: Variability and Predictability of a reduced-order land atmosphere coupled model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2257, 2023.

Multistabilities in the ocean–atmosphere flow were found in a reduced‐order ocean–atmosphere coupled model, by solving the nonlinear temperature equations numerically. In this article, we explain how the full nonlinear Stefan–Bolzmann law was implemented numerically and the resulting change to the system dynamics was compared with the original model where these terms were linearised. Multiple stable solutions were found that display distinct ocean–atmosphere flows, as well as different Lyapunov stability properties. In addition, distinct low‐frequency variability (LFV) behaviour was observed in multiple attractors. We investigated the impact on these solutions of changing the magnitude of the ocean–atmospheric coupling, as well as the atmospheric emissivity, to simulate an increasing greenhouse effect. Where multistabilities exist for fixed parameters, the possibility for tipping between solutions was investigated, but tipping did not occur in this version of the model where there is a constant solar forcing. This study was undertaken using a reduced‐order coupled quasigeostrophic ocean–atmosphere model.

Reduced order quasi-geostrophic ocean-atmosphere coupled models provide a platform that preserve key atmosphere behaviours, while still being simple enough to allow for analysis of the system dynamics. These models produce typical atmospheric dynamical features like atmospheric blocking and other low-frequency variability, while having a low number of degrees of freedom. For this reason, these models are well suited to investigating tipping points or bifurcations in the Earth's climate due to their simplified but insightful dynamics. In our present work we compare the dynamics of an ocean-atmosphere coupled model, previously implemented with linearised temperature equations (Vannitsem et al., 2015), but here we solve the equations including the non-linear Stefan-Boltzmann law in the radiative temperature term. When compared with the original version of the model with linearised temperature equations, the modified version of the model is found to produce multiple stable flows in the coupled ocean-atmosphere system. We find, for increasing atmospheric emissivity, there is an increase in the number of stable attractors, and these stable attractors present distinct flows in the ocean and atmosphere and distinct Lyapunov stability properties.Vannitsem, S., Demaeyer, J., De Cruz, L., & Ghil, M. (2015). Low-frequency variability and heat transport in a low-order nonlinear coupled ocean–atmosphere model. Physica D: Nonlinear Phenomena, 309, 71-85.

Multistabilities were found in the ocean-atmosphere flow, in a reduced order ocean-atmosphere coupled model, when the non-linear temperature equations were solved numerically. In this paper we explain how the full non-linear Stefan-Bolzmann law was numerically implemented, and the resulting change to the system dynamics compared to the original model where these terms were linearised. Multiple stable solutions were found that display distinct ocean-atmosphere flows, as well as different Lyapunov stability properties. In addition, distinct Low Frequency Variability (LFV) behaviour was observed in stable attractors. We investigated the impact on these solutions of changing the magnitude of the ocean-atmospheric coupling, as well as the atmospheric emissivity to simulate an increasing green-house effect. Where multistabilities exist for fixed parameters, the possibility for tipping between solutions was investigated, but tipping did not occur in this version of the model where there is a constant solar forcing. This study was undertaken using a reduced-order quasi-geostrophic ocean-atmosphere model, consisting of two atmosphere layers, and one ocean layer, implemented in the Python programming language.