Anupama K. Xavier

Assessments of Amazon rainforest dieback have primarily focused on the magnitude of forest loss under transient climate change, while less attention has been paid to the rate, variability, and commitment of ecosystem decline under alternative climate intervention pathways. The concept of realized and committed ecosystem change has shown that Amazon forest loss can be effectively locked in long before it becomes observable (Jones, C. et al., 2009), while recent policy-oriented assessments demonstrate that such commitments remain a substantial risk even under 1.5 °C stabilization and overshoot pathways (Munday, G. et al., 2025). This study explores whether solar geoengineering modifies long-term Amazon forest commitment or primarily alters the timing and variability of dieback.Here, we propose to extend the concept of committed ecosystem change to quantify the speed and temporal variability of Amazon forest dieback under scenarios with and without stratospheric aerosol injection (SAI). Using the Met Office Hadley Centre climate carbon cycle model HadCM3, we will analyse transient and equilibrium forest responses to stabilized forcing states derived from high emission baseline scenarios.The analysis follows the realized and committed ecosystem framework developed in earlier coupled climate vegetation studies using HadCM3 (Jones, C. et al., 2009) without solar geoengineering. We first reproduce this analysis under a non-SAI baseline and subsequently apply the same diagnostics to simulations from the UK Earth System Model (UKESM) incorporating stratospheric aerosol injection, enabling a consistent comparison of Amazon dieback speed and variability across scenarios.For each scenario, we distinguish between realized (time evolving) and committed (equilibrium) states of Amazon forest cover, using equilibrium diagnostics to estimate the long-term ecosystem response to fixed climate conditions. Dieback speed will be defined as the rate of fractional forest loss per degree of global mean temperature change, while variability will be assessed through interannual and decadal fluctuations in forest cover and associated hydroclimatic drivers. This analysis is expected to provide preliminary insights into whether SAI delays or modifies the rate and variability of Amazon forest dieback, while potentially leaving committed long-term losses largely unchanged once critical climatic thresholds are exceeded. As this study is at an early stage, the results will be exploratory in nature and intended to provide a first-order assessment of potential ecosystem risks associated with solar geoengineering rather than definitive projections.ReferenceJones, C., Lowe, J., Liddicoat, S. et al. Committed terrestrial ecosystem changes due to climate change. Nature Geosci 2, 484–487 (2009). https://doi.org/10.1038/ngeo555Munday, G., Jones, C.D., Steinert, N.J. et al. Risks of unavoidable impacts on forests at 1.5 °C with and without overshoot. Nat. Clim. Chang. 15, 650–655 (2025). https://doi.org/10.1038/s41558-025-02327-9

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.

Atmospheric blocking exerts a profound influence on mid-latitude circulation, yet its predictability remains elusive due to intrinsic non-linearities 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 CMIP6 MIROC6 simulations. Blocking structures are identified using geopotential height gradient reversal, with their temporal evolution analysed through trajectory tracking and error growth metrics. Results reveal that Eastern blocks exhibit lower predictability, characterized by rapid error divergence and heightened mean logarithmic growth rates, whereas Western blocks display dynamical stability. 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.

North Pacific blocking patterns, defined by persistent high-pressure systems that disrupt atmospheric circulation, are pivotal elements of mid-latitude weather dynamics. These blocking events play a significant role in shaping regional weather extremes, such as prolonged cold spells or heatwaves, and can redirect storm tracks across the Pacific. For instance, the 2021 Pacific Northwest heatwave demonstrated the profound impact of blocking on terrestrial temperatures, where an upstream cyclone acted as a diabatic source of wave activity, intensifying the blocking system. This led to heat-trapping stable stratification, which elevated surface temperatures to unprecedented levels (Neal et al., 2022). Similarly, marine heatwaves in the Northeast Pacific have been linked to high-latitude blocking events, which weaken westerly winds, suppress southward Ekman transport, and enhance ocean stratification, thereby increasing sea surface temperatures (Niu et al., 2023). The predictability of North Pacific blocking events is governed by the intricate interplay of large-scale atmospheric dynamics, ocean-atmosphere interactions, and internal variability (Smith et al., 2020).This study investigates the differences in predictability between eastern and western North Pacific blocking events, using a modified version of the Davini et al. (2012) blocking index to distinguish their geographical locations. Identified blocking events were tracked using a block-tracking algorithm until they dissipated. Predictability was assessed by identifying an analogue pair for each blocking event. Specifically, after classifying blocks as eastern or western, geopotential height maps for each event were compared to all other days in the dataset. The analogue pair for an event was defined as the day with the smallest root mean square (RMS) distance. Predictability was then evaluated by averaging the error evolution of the tracks between events in each analogue pair.Using CMIP6 model simulations and ERA5 reanalysis data, the study revealed that eastern blocks are significantly more persistent and stable than their western counterparts. Eastern blocks exhibited longer durations and greater resistance to atmospheric variability, resulting in improved forecast accuracy. In contrast, western blocks were found to be more transient and challenging to predict due to their susceptibility to dynamic instabilities.ReferencesDavini, P., Cagnazzo, C., Gualdi, S. and Navarra, A., 2012. Bidimensional diagnostics, variability, and trends of Northern Hemisphere blocking. Journal of Climate, 25(19), pp.6496-6509.Neal, E., Huang, C.S. and Nakamura, N., 2022. The 2021 Pacific Northwest heat wave and associated blocking: Meteorology and the role of an upstream cyclone as a diabatic source of wave activity. Geophysical Research Letters, 49(8), p.e2021GL097699.Niu, X., Chen, Y. and Le, C., 2023. Northeast Pacific marine heatwaves associated with high-latitude atmospheric blocking. Environmental Research Letters, 19(1), p.014025.

Low-frequency variability (LFV) encompasses atmospheric and climate processes on time scales from a few weeks to decades.​ This includes atmospheric blockings, heat waves, cold spells, and at longer time scales long-term oscillations like the MJO, the NAO, ENSO….. Better understanding of LFV, could contribute to improved long term forecasts​. In the results described in Xavier et al, 2023, who used a reduced order atmosphere-land model (Demaeyer et al, 2020), weather patterns that involve atmospheric blocking to the west of a given topographical feature tend to have reduced predictability and show instability when contrasted with blocking occurrences situated to the east of such topographical elements. This finding aligns with actual meteorological occurrences, such as the persistence of North Pacific blocking patterns (Breeden et al., 2020; Kim and Kim, 2019). The shape and characteristics of the identified blocking events closely resemble North Pacific blocks, where a high-pressure system exists either on the western or eastern side of the underlying topography. In the physical world, these positions correspond to Asian and American continents on either side of the Pacific. Despite quasi-geostrophic models being overly simplified, using such reduced order models in this study allowed us to undertake such mathematical analysis. Thus allowing for a comparison with the real world…In the current study, we aim to analyze these predictability differences based on the morphology of the blocking situations in the real-world scenario using the CMIP6 dataset. Blocking situations are identified using two different indices, the anomaly-based blocking index proposed by Sausen et al., 1995 and the local reversal of the meridional flow-based index proposed by Davini et al., 2012. Predictability is quantified using local dimension metrics and analogue studies in the identified blocking events. The findings are discussed from the perspective of the current literature on the predictability of blocking.ReferencesXavier, 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.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.Breeden, M. L., Hoover, B. T., Newman, M., and Vimont, D. J.: Optimal North Pacific blocking precursors and their deterministic subseasonal evolution during boreal winter, Monthly Weather Review, 148, 739–761, 2020Kim, S.-H. and Kim, B.-M.: In search of winter blocking in the western North Pacific Ocean, Geophysical Research Letters, 46, 9271–9280, 2019.Sausen, R., König, W., & Sielmann, F. (1995). Analysis of blocking events from observations and ECHAM model simulations. Tellus A, 47(4), 421–438.Davini, P., Cagnazzo, C., Gualdi, S., & Navarra, A. (2012). Bidimensional diagnostics, variability, and trends of Northern Hemisphere blocking. Journal of Climate, 25(19), 6496–6509.

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.

This study delves into the predictability of atmospheric blocking, zonal, and transition patterns utilizing a simplified coupled model. This model, implemented in Python, emulates midlatitude atmospheric dynamics with a two-layer quasi-geostrophic channel atmosphere on a β plane, encompassing simplified land effects. Initially, we comprehensively scrutinize the model's responses to environmental parameters like solar radiation, surface friction, and atmosphere–ground heat exchange. Our findings confirm that the model faithfully replicates real-world Earth-like flow regimes, establishing a robust foundation for further analysis. Subsequently, employing Gaussian mixture clustering, we successfully delineate distinct blocking, zonal, and transition flow regimes, unveiling their dependencies on surface friction. To gauge predictability and persistence, we compute the averaged local Lyapunov exponents for each regime. Our investigation uncovers the presence of zonal, blocking, and transition regimes, particularly under conditions of reduced surface friction. As surface friction increases further, the system transitions to a state characterized by two blocking regimes and a transition regime. Intriguingly, periodic behavior emerges under specific surface friction values, returning to patterns observed under low friction coefficients. A model resolution increase impacts the system in such a way that only two regimes are then obtained with the clustering: the transition phase disappears and the predictability drops to roughly 2 d for both of the remaining regimes. In accordance with previous research findings, our study underscores the fact that when all three regimes coexist, zonal patterns exhibit a more extended predictability horizon compared to blocking patterns. Remarkably, transition patterns exhibit reduced predictability when coexisting with the other regimes. In addition, within a specified range of surface friction values where two blocking regimes are found, it is observed that blocked atmospheric situations in the west of the applied topography are marked by instabilities and reduced predictability in contrast to the blockings appearing on the eastern side of the topography.

This study delves into the predictability of atmospheric blocking, zonal, and transition patterns utilizing a simplified coupled model. This model, implemented in Python, emulates midlatitude atmospheric dynamics with a two-layer quasi-geostrophic channel atmosphere on a beta-plane, encompassing simplified land effects. Initially, we comprehensively scrutinize the model's responses to environmental parameters like solar radiation, surface friction, and atmosphere-ground heat exchange. Our findings confirm that the model faithfully replicates real-world Earth-like flow regimes, establishing a robust foundation for further analysis. Subsequently, employing Gaussian mixture clustering, we successfully delineate distinct blocking, zonal, and transition flow regimes, unveiling their dependencies on surface friction. To gauge predictability and persistence, we compute the averaged local Lyapunov exponents for each regime. Our investigation uncovers the presence of zonal, blocking, and transition regimes, particularly under conditions of reduced surface friction. As surface friction increases further, the system transitions to a state characterized by two blocking regimes and a transition regime. Intriguingly, periodic behavior emerges under specific surface friction values, returning to patterns observed under low friction coefficients. Model resolution increase impacts the system in a way that only two regimes are then obtained with the clustering: the transition phase disappears and the predictability drops to roughly 2 days for both of the remaining regimes. In accordance with previous research findings, our study underscores that when all three regimes coexist, zonal patterns exhibit a more extended predictability horizon compared to blocking patterns. Remarkably, transition patterns exhibit reduced predictability when coexisting with the other regimes. In addition, within a specified range of surface friction values where two blocking regimes are found, it is observed that blocked atmospheric situations in the west of the applied topography are marked by instabilities and reduced predictability in contrast to the blockings appearing on the eastern side of the topography.

This study delves into the predictability of atmospheric blocking, zonal, and transition patterns utilizing a simplified coupled model. This model, implemented in Python, emulates midlatitude atmospheric dynamics with a two-layer quasi-geostrophic channel atmosphere on a beta-plane, encompassing simplified land effects. Initially, we comprehensively scrutinize the model's responses to environmental parameters like solar radiation, surface friction, and atmosphere-ground heat exchange. Our findings confirm that the model faithfully replicates real-world Earth-like flow regimes, establishing a robust foundation for further analysis. Subsequently, employing Gaussian mixture clustering, we successfully delineate distinct blocking, zonal, and transition flow regimes, unveiling their dependencies on surface friction. To gauge predictability and persistence, we compute the averaged local Lyapunov exponents for each regime. Our investigation uncovers the presence of zonal, blocking, and transition regimes, particularly under conditions of reduced surface friction. As surface friction increases further, the system transitions to a state characterized by two blocking regimes and a transition regime. Intriguingly, periodic behavior emerges under specific surface friction values, returning to patterns observed under low friction coefficients. Model resolution increase impacts the system in a way that only two regimes are then obtained with the clustering: the transition phase disappears and the predictability drops to roughly 2 days for both of the remaining regimes. In accordance with previous research findings, our study underscores that when all three regimes coexist, zonal patterns exhibit a more extended predictability horizon compared to blocking patterns. Remarkably, transition patterns exhibit reduced predictability when coexisting with the other regimes. In addition, within a specified range of surface friction values where two blocking regimes are found, it is observed that blocked atmospheric situations in the west of the applied topography are marked by instabilities and reduced predictability in contrast to the blockings appearing on the eastern side of the topography.

This study delves into the predictability of atmospheric blocking, zonal, and transition patterns utilizing a simplified coupled model. This model, implemented in Python, emulates midlatitude atmospheric dynamics with a two-layer quasi-geostrophic channel atmosphere on a beta-plane, encompassing simplified land effects. Initially, we comprehensively scrutinize the model's responses to environmental parameters like solar radiation, surface friction, and atmosphere-ground heat exchange. Our findings confirm that the model faithfully replicates real-world Earth-like flow regimes, establishing a robust foundation for further analysis. Subsequently, employing Gaussian mixture clustering, we successfully delineate distinct blocking, zonal, and transition flow regimes, unveiling their dependencies on surface friction. To gauge predictability and persistence, we compute the averaged local Lyapunov exponents for each regime. Our investigation uncovers the presence of zonal, blocking, and transition regimes, particularly under conditions of reduced surface friction. As surface friction increases further, the system transitions to a state characterized by two blocking regimes and a transition regime. Intriguingly, periodic behavior emerges under specific surface friction values, returning to patterns observed under low friction coefficients. Model resolution increase impacts the system in a way that only two regimes are then obtained with the clustering: the transition phase disappears and the predictability drops to roughly 2 days for both of the remaining regimes. In accordance with previous research findings, our study underscores that when all three regimes coexist, zonal patterns exhibit a more extended predictability horizon compared to blocking patterns. Remarkably, transition patterns exhibit reduced predictability when coexisting with the other regimes. In addition, within a specified range of surface friction values where two blocking regimes are found, it is observed that blocked atmospheric situations in the west of the applied topography are marked by instabilities and reduced predictability in contrast to the blockings appearing on the eastern side of the topography.