Orals: Mon, 28 Apr | Room -2.93
Understanding and predicting Earth system processes requires more than accurate forecasts—it demands uncovering the underlying relationships among variables and constructing models that are interpretable, physically consistent, and mathematically robust. While machine learning has demonstrated exceptional predictive capabilities, its models often neglect fundamental physical laws, raising concerns about reliability, interpretability, and trust. This work explores the integration of domain knowledge with machine learning through hybrid and causal modeling approaches, aiming to bridge data-driven insights with the principles of the physical sciences. By leveraging these methods, we can enhance our understanding of the data-generating processes and achieve results that are both consistent and explainable. I will present recent advances and strategies in this field, highlighting their potential to revolutionize Earth system research. This effort represents a step toward a long-term AI agenda for developing algorithms that drive knowledge discovery in Earth sciences.
https://arxiv.org/pdf/2010.09031.pdf
https://arxiv.org/abs/2104.05107.pdf
https://doi.org/10.1016/j.physrep.2023.10.005
How to cite: Camps-Valls, G.: Advancing AI for Earth sciences with hybrid and causal models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8388, https://doi.org/10.5194/egusphere-egu25-8388, 2025.
Complex chaotic systems exhibit nontrivial internal variability, with a power spectrum typically characterised by resonant broad peaks standing out on a continuous background of frequencies. These resonances correspond to nonlinear excitable modes of the system's evolution that can be generally attributed to long-lasting persistent events, weakly damped instabilities, or critical settings where the chaotic attractor is approaching a crisis [1].
In the context of the climate system, examples of such resonant behaviour are the El Niño Southern Oscillation, caused by a weakly damped instability of the atmosphere-ocean system [2], or the transition between the Warm and Snowball state of the Earth system due to a boundary crisis due to a change of the ice-albedo feedback [3].
On top of describing the relevant features of the internal variability of systems, such resonances also represent the fundamental modes shaping the resilience of the system to external perturbations. In particular, the linear response of the system to general forcing scenarios is solely determined by the Green’s function, for which a decomposition in terms of resonances can be obtained [4].
It is possible to establish a link between the system's nonlinear resonances and the spectral properties of the Koopman operator underlying the evolution in time of the system's observables.
Based on our work in [5], I will show that data-driven techniques developed to investigate the properties of the Koopman operator can be used to extract both resonances and dynamical modes from data. I will provide numerical evidence that the dynamical evolution of the statistical properties of the system can be interpreted as a superposition of such modes. In particular, by employing a projection of generic observables of the system onto the set of nonlinear modes, I will show that it is possible to reconstruct not only correlation functions but also the response of virtually any observable of interest.
Even though so far restricted to low dimensional systems, our results highlight the importance of such nonlinear modes in shaping the variability and response of chaotic systems and provide a way to (a) interpret the relevance of observables as a proxy to investigating dynamical properties of the system and (b) explain the difference between intrinsic variability of observables and their response to perturbations.
References
[1] Chekroun et al., Journal of Statistical Physics (2020) 179:1366–1402, https://doi.org/10.1007/s10955-020-02535-x
[2] Tantet et al., Journal of Statistical Physics (2020) 179:1449–1474, https://doi.org/10.1007/s10955-020-02526-y
[3] Tantet et al., 2018 Nonlinearity 31 2221, https://iopscience.iop.org/article/10.1088/1361-6544/aaaf42
[4] Manuel Santos Gutiérrez and Valerio Lucarini, 2022 J. Phys. A: Math. Theor. 55 425002, https://iopscience.iop.org/article/10.1088/1751-8121/ac90fd
[5] Zagli, Colbrook, Lucarini, Mezić, Moroney, “Bridging the gap between Koopmanism and Response Theory: Using Natural Variability to predict Forced Response”, https://doi.org/10.48550/arXiv.2410.01622
How to cite: Zagli, N., Moroney, J., Lucarini, V., Colbrook, M., and Mezić, I.: Bridging the gap between Koopmanism and Response Theory: Using Natural Variability to predict Forced Response, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18492, https://doi.org/10.5194/egusphere-egu25-18492, 2025.
Intermittency has been initially linked to systems alternating regular and irregular states, while nowadays, it also encompasses systems that switch between two or more regimes. These include geophysical processes such as turbulence, convection, and precipitation patterns, not to mention applications in plasma physics, medicine, neuroscience, and economics. Traditionally, the study of intermittency has focused on global statistical indicators, such as the average frequency of regime changes under fixed conditions, or how these vary as a function of the system’s parameters or forcing, like in the case of climate change. However, these global indicators fail in capturing the local spatio-temporal nature of the regime transitions.
In this work, we use global and local perspectives to analyze intermittent systems and uncover reliable indicators of regimes’ changes. In particular, using tools such as the Lyapunov exponents and Covariant Lyapunov Vectors (CLVs), we have been able to characterize both global dynamics and local transitions in five different systems, of various complexities, and for three types of intermittency. We identified some key indicators and precursors of the regime transition that are common, despite the differences in the intermittency mechanism and in the dynamical model properties. At the same time, intermittency-type related mechanisms have been unveiled. For instance, we discover very peculiar behaviors in the Lorenz 96 (L96; Lorenz, 1996) and in the Kuramoto-Shivanshinki models (KS; Kuramoto, Y. and Tsuzuki, T., 1976; G. Sivashinsky, 1977) that, despite their notoriety, have been so far unseen. In the L96 we identified crisis-induced intermittency with pseudo-periodic intermissions. In the KS equations we detected a spatially global intermittency which follows the scaling of type-I intermittency.
In our local analysis, we uncover the relation between the CLVs mutual alignment and the regimes’ change, a connection that is present in all of the systems and for all types of intermittency considered. In the case of the on-off intermittency, the angle between CLVs is an effective precursor of the jump to the “on” regime. Furthermore, in all systems, the last CLV (the most stable), was found to carry important information about the dynamical features of the intermittency. In the case of type-I intermittency it allowed us to reconstruct the limit cycle around which the intermittency develops, in merging-crisis type of intermittency the last CLV successfully detected the pseudo-periodic intermissions, while in the case of on-off intermittency it allowed us to indicate the “off” state.
The identification of these general and fundamental mechanisms driving intermittent behaviours, and in particular the detection of indicators spotting the regimes’ change, have the potential to be impactful in the study of turbulent geophysical fluids, rainfall patterns or atmospheric deep convection. In particular, they could be used to define a latent space of reduced dimension in which a neural network can be trained to automatically predict regime changes.
How to cite: Barone, A., Savary, T., Demaeyer, J., Vannitsem, S., and Carrassi, A.: Characterization of dynamical properties and indicators of regime change in intermittent chaotic systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18862, https://doi.org/10.5194/egusphere-egu25-18862, 2025.
In the spirit of Hasselmann’s program, Climate Tipping Points are often studied in systems described by stochastic differential equations where a combination of noise and a parameter approaching or crossing a bifurcation threshold leads to tipping. However, in some cases, a multistable system forced by another potentially chaotic system is a more appropriate description and might give rise to unexpected effects.
We show how tipping in such chaotically forced systems is affected by varying the relative timescale between the chaotic forcing and the forced multistable system. Further, we explain how periodic orbits of the forcing system can help to understand this effect and how they can be used to characterise the chaotic tipping window.
How to cite: Roemer, R. and Ashwin, P.: The chaotic tipping window and its dependence on relative timescales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20233, https://doi.org/10.5194/egusphere-egu25-20233, 2025.
We first present briefly recent insights on the effects of time-dependent forcing on systems with intrinsic variability, such as anthropogenic forcing on the climate system. These insights are applied next to the problem of periodic forcing of the wind-driven double-gyre problem. The topological perspective here is provided by applying recent advances in the algebraic topology of autonomously chaotic dynamic systems subject to time-dependent forcing. These advances are applied to the problem at hand.
The application starts by finding a topological representation of the underlying structure of the system’s flow in phase space by the construction of a cell complex that approximates its branched manifold and of a directed graph on this complex. The directed graph corresponds to the way that the flow in phase space moves from one cell of the complex to another.
Fundamental ingredients of the above representation, called generatexes and stripexes, delineate distinct ways of following the dynamical paths on the complex, namely the nonequivalent ways of travelling through the flow in phase space. These mathematically defined pathways will be shown to correspond to physical modes of variability.
How to cite: Ghil, M., Charó, G., Sciamarella, D., Ruiz, J., and Pierini, S.: A topological perspective on the wind-driven ocean circulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12270, https://doi.org/10.5194/egusphere-egu25-12270, 2025.
We use a quasi-geostrophic model of the wind-driven double-gyre to explore qualitative changes in the system's behavior through a novel topological framework called Templex. This method characterizes and classifies the system's attractors in phase space across various regimes, from low-energy, temporally smooth dynamics to highly energetic and chaotic states. Our study focuses on stationary wind stress forcing to deepen the understanding of the underlying dynamics, starting from this simplified scenario to identify the physical processes involved, while the spatial resolution is gradually increased up to 5 km x 5 km.
The original system of partial differential equations describing the flow is converted into a set of ordinary differential equations using finite-difference methods. We take advantage of the Julia programming language to build the model, apply continuation methods, and perform stability analyses along the branches of a bifurcation tree, subject to pseudo-adiabatic variations in wind intensity. Our findings emphasize the effectiveness of topological methods in revealing the structural aspects of bifurcations, and in examining new pathways to study dynamical systems in geosciences. Moreover, we present novel insights on the existence of an attractor in the infinite-dimensional system, bridging the gap between topological results obtained numerically and the original mathematical model.
Through this presentation, we aim to foster discussion on the potential of topological approaches to advance the understanding of nonlinear systems in geophysical fluid dynamics.
How to cite: Bodnariuk, N., Sciamarella, D., Speich, S., Simonnet, E., and Ghil, M.: The Wind-Driven Double-Gyre Circulation: A topological characterization of attractors across regimes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11928, https://doi.org/10.5194/egusphere-egu25-11928, 2025.
Perhaps the most dynamic component of the Arctic sea ice cover is the marginal ice zone (MIZ), the transitional region between dense pack ice and open ocean. It widens severalfold and moves poleward in a dramatic annual cycle, impacting the climate system, ecological processes, and human accessibility to the Arctic. We’ll discuss multiscale partial differential equation models for MIZ dynamics and the sea ice concentration field. The MIZ is viewed as a liquid-solid phase transition region, or mushy layer, to obtain a model that captures the seasonal cycle. Parameters in the model depend on finer scale structure and are computed using rigorous homogenization methods. We also consider a related floe-scale model with advective forcing to study ice transport processes, jamming, and anomalous diffusion observed in ice floe GPS data.
How to cite: Golden, K.: Multiscale PDE Models for Marginal Ice Zone Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5286, https://doi.org/10.5194/egusphere-egu25-5286, 2025.
An important component of Earth's climate system is the land surface atmosphere interaction. For theoretical and empirical reasons, the strongest constraints to vegetation productivity, and thus drawdown of atmospheric CO2, as well as water use (evapotranspiration) are edaphic in nature. The appropriate treatment of the soil is related only to one of the topics listed in the call for abstract submissions, namely statistical mechanics. A new water balance treatment developed based on combining ecological optimality with distinct percolation scaling results for solute transport in directed as well as random networks as applied to root growth and soil formation delivers observed results for net primary productivity of vegetation, streamflow elasticity, evapotranspiration, plant species richness, and the scale dependence of the water cycle with a single parameter, which can be evaluated in a 2D or a 3D fractal model of plant roots. Most of the results summarized are generated from the 2D (thin soil) model, meaning that most of the above are consistent with predictions that use no adjustable or unknown parameters at all, merely their universal values from percolation theory.
How to cite: Hunt, A. G. and Ghanbarian, B.: With only stochastic and non-linear dynamics methods, the Earth surface component of climate cannot be modeled correctly, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12251, https://doi.org/10.5194/egusphere-egu25-12251, 2025.
Reconstructing spatiotemporal systems from sparse observations remains a long-standing challenge in several domains, including geoscience, air pollution and fluid dynamics. While various data assimilation (DA) and machine learning (ML) methods have shown potential, they still face significant challenges (see [1]):
- The computational burden of conventional DA algorithms (including error covariance specification), particularly for multivariate, high-dimensional systems.
- Sparse and movable sensor placement, which makes conventional ML models (typically requiring fixed and regularly distributed input data) cumbersome.
- The ill-defined nature of the sparse reconstruction problem, which poses significant risks of overfitting.
We will present our recent works aimed at addressing these challenges. More specifically, we developed latent DA algorithms [2] to reduce the computational burden of variational DA methods. These algorithms demonstrate great potential in efficiently assimilating sparse observations within a reduced-order latent space constructed by neural networks, thanks to the TorchDA library [3]. The latter enables GPU implementation of mainstream data assimilation methods and supports non-explicit state-observation transformation functions, provided they can be learned by a neural network.
We have also employed advanced deep learning techniques, including Voronoi-tessellation CNNs [4] and Vision Transformer-based autoencoders [5], to learn mappings from sparse observations to the complete physical space. These approaches effectively address challenges such as movable sensor placements and varying sensor numbers. Their integration with DA algorithms has also been evaluated.
Finally, our recent work explores [6] the utility of generative AI techniques, particularly denoising diffusion models, for field reconstruction from sparse observations. Generative AI methods offer two main advantages: first, they produce a sample from a probability distribution rather than predicting the mean as a fixed output, which can help mitigate overfitting caused by the illy-defined problem. Second, they inherently function as ensemble predictors by generating several samples, facilitating uncertainty quantification, which is essential in data assimilation. The numerical results tested on cases ranging from fluid dynamics benchmarks to semi-operational air pollution simulations will also be discussed.
[1] Cheng, S., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., Xiao, D., Janjic, T., Ding, W., Guo, Y., Carrassi, A., Bocquet, M. and Arcucci, R, 2023. Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review. IEEE/CAA Journal of Automatica Sinica
[2] Cheng, S., Chen, J., Anastasiou, C., Angeli, P., Matar, O.K., Guo, Y.K., Pain, C.C. and Arcucci, R., 2023. Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models. Journal of Scientific Computing
[3] Cheng, S., Min, J., Liu, C. and Arcucci, R., 2025. TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions. Computer Physics Communications
[4] Cheng, S., Liu, C., Guo, Y. and Arcucci, R., 2024. Efficient deep data assimilation with sparse observations and time-varying sensors. Journal of Computational Physics
[5] Fan, H., Cheng, S., de Nazelle, A.J. and Arcucci, R., 2024. ViTAE-SL: a vision transformer-based autoencoder and spatial interpolation learner for field reconstruction. Computer Physics Communications
[6] Zhuang, Y., Cheng, S. and Duraisamy, K., 2025. Spatially-aware diffusion models with cross-attention for global field reconstruction with sparse observations. Computer Methods in Applied Mechanics and Engineering
How to cite: Cheng, S., Fan, H., Zhuang, Y., Finn, T. S., Lugon, L., Sartelet, K., Duraisamy, K., Arcucci, R., and Bocquet, M.: Hybrid data assimilation and machine learning algorithms for sparse observational data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13544, https://doi.org/10.5194/egusphere-egu25-13544, 2025.
The Maximum Likelihood Ensemble Filter (MLEF) with State Space Localization (MLEF-SSL) was recently developed as a new ensemble data assimilation method that incorporates state space covariance localization in model space. The main motivation for developing this method was to enable global numerical optimization and assimilation of vertically integrated observations in an ensemble data assimilation system with covariance localization. MLEF-SSL uses random projection to compute the localized forecast error covariance and reduce the analysis dimensions to a manageable space. MLEF-SSL is being applied to a global NWP system named Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with the assimilation of atmospheric observations, i.e., the NICAM-MLEF-SSL system, to explore its capability under a realistic high-dimensional dynamical application.
To apply MLEF-SSL in a high-dimensional system such as NICAM, a substantially large number of ensembles of the order of O(104) - O(105) is necessary to represent the localized forecast error covariance, thus, requiring special attention on the algorithmic development for the NICAM-MLEF-SSL system. This presentation will discuss the practical implementation of the NICAM-MLEF-SSL system on the RIKEN supercomputer Fugaku with an emphasis on the use of advanced math libraries for parallel computing.
In addition, the performance of NICAM-MLEF-SSL in assimilation of real atmospheric observations will be evaluated in detail and compared to that of NICAM-LETKF, which is the global data assimilation system that is currently used at RIKEN.
Future plans related to the use of strong dynamical balance constraints and Artificial Intelligence (AI) techniques in NICAM-MLEF-SSL will also be discussed.
How to cite: Wu, T.-C., Zupanski, M., and Miyoshi, T.: Global Atmospheric Ensemble Data Assimilation using NICAM global icosahedral model and Maximum Likelihood Ensemble Filter with State Space Localization: Real Observation Experiment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2633, https://doi.org/10.5194/egusphere-egu25-2633, 2025.
Magnetotelluric (MT) sounding is a vital geophysical exploration method, renowned for its ability to investigate deep geological structures, detect low-resistivity anomalies, and support diverse applications. It is widely used in mineral and geothermal resource exploration, hydrogeological surveys, and deep structural studies, effectively delineating subsurface structures from hundreds of meters to hundreds of kilometers. However, MT inversion, essential for quantitatively analyzing subsurface electrical structures, faces challenges due to the inherent non-uniqueness of MT methods and noise in field data. Traditional deterministic inversion methods, which rely on gradient-based optimization, typically produce a single model or a set of models fitting the data but fail to quantify uncertainties, complicating interpretation and reducing reliability.
To address these challenges, this study adopts a Bayesian inference framework for MT inversion. Unlike deterministic methods, Bayesian inversion treats model parameters as random variables and iteratively updates their prior distributions using observational data, ultimately obtaining posterior probability distributions. This approach enables a quantitative assessment of inversion uncertainties. However, the computational demands of Bayesian inversion, particularly for 2D and 3D MT problems, pose significant challenges, with forward modeling efficiency being a critical bottleneck.
To overcome this, we propose an efficient MT forward modeling method based on the Extended Fourier Deep Operator Network (EFDO), a physics-informed neural operator network. EFDO leverages the principles of Fourier transforms and deep neural operator networks to learn the functional mapping between model conductivity inputs and MT forward responses. By embedding physical laws into the network, EFDO ensures accurate predictions while significantly improving computational efficiency. Once trained, EFDO predicts forward responses in milliseconds, achieving a speedup of 300 times compared to traditional Finite Volume Methods (FVM) while maintaining high accuracy. A multi-GPU distributed parallel training strategy further accelerates EFDO training, drastically reducing preparation time.
Additionally, we integrate Voronoi and Delaunay parameterization techniques with the reversible-jump Markov Chain Monte Carlo (rjMCMC) method to enhance model sampling efficiency. This establishes a robust 2D MT trans-dimensional Bayesian inversion framework. Numerical experiments and tests on the Coprod2 dataset demonstrate the method’s computational efficiency and reliability.
In summary, this study introduces a novel approach combining the physics-informed EFDO network and advanced parameterization techniques to improve efficiency and uncertainty quantification in MT Bayesian inversion, paving the way for rapid, high-dimensional geophysical exploration.
How to cite: Liao, W., Hu, X., and Peng, R.: Efficient 2D MT Forward Modeling and Trans-Dimensional Bayesian Inversion with Physics-Informed Neural Operator Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7723, https://doi.org/10.5194/egusphere-egu25-7723, 2025.
By its simplicity and intuitive appeal, the geometric interpretation of analysis provides a complementary understanding of minimum variance estimation. The geometric interpretation is made possible by using a Hilbert space representation of random variables. In this presentation we will argue how actually a geometric approach can help to explore/discover new relationships, in identifying assumptions, and provide an alternative pathway of understanding the concept of analysis and estimation of error covariances.
For example, relationships between analysis increments in cross-validation could be easily derived. An interpretation of sequential observation processing also follows a simple interpretation. Important considerations in establishing relationships for an arbitrary number of collocated data sets could also be established. Then we examine how we can relax the assumption of an optimal analysis. This will guide us in deriving a new diagnostic of observation statistics with correlated errors.
How to cite: Ménard, R., Deshaies-Jacques, M., and Vogel, A.: A geometric interpretation of analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13502, https://doi.org/10.5194/egusphere-egu25-13502, 2025.
Sampling error is a fundamental limitation of assimilation schemes, such as the EnKF, that employ the sample covariance from an ensemble of forecasts. Despite the fact that the EnKF is typically applied in situations where the ensemble size is small compared to the system dimension, most of what is known about the effect of sampling error comes from low-dimensional examples or asymptotic results valid when the ensemble size is large. For high-dimensional systems and small ensembles, progress can be made by leveraging (i) the diagonal form of the Kalman-filter update in the optimal coordinates of Snyder and Hakim (2022) and (ii) basic approximations from the theory of random matrices. These yield novel, explicit expressions for the EnKF gain, the (sample) mean and covariance of the EnKF posterior ensemble, and the error covariance of that posterior mean. The expressions show that a single EnKF update will remove almost all variance from the ensemble, unless the observations are very uninformative. They also identify those directions in the state space for which the EnKF update is effective, improving the state estimate despite sampling errors, and those directions for which sampling errors in the EnKF overwhelm observational information and degrade the state estimate.
How to cite: Snyder, C.: Sampling error in the ensemble Kalman filter for small ensembles and high-dimensional states, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14641, https://doi.org/10.5194/egusphere-egu25-14641, 2025.
Detecting subtle, spatially localized changes in climate datasets is essential for understanding evolving regional dynamics and extreme events—key topics in Earth and planetary sciences. We present EagleEye, a novel distribution-free approach for identifying local density anomalies between two multivariate datasets. By leveraging a nearest-neighbor strategy and a binomial-based test, EagleEye pinpoints regions of significant deviations without relying on complex model assumptions.
We illustrate its potential using temperature reanalysis products, where EagleEye reveals localized shifts in historical climate patterns—including notable positive anomalies around Greenland—that may indicate emerging changes in temperature fields. Owing to its scalability and interpretability, EagleEye can handle large, high-dimensional datasets and integrate multiple climatic variables within a single region. This framework offers an innovative avenue for probing evolving climate dynamics, highlighting areas undergoing rapid change, and supporting an enhanced understanding of climate variability—making it a valuable tool for geoscience applications and beyond.
How to cite: Springer, S.: EagleEye: Unsupervised Detection and Quantification of Local Density Anomalies in Climate Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21320, https://doi.org/10.5194/egusphere-egu25-21320, 2025.
How to cite: Brolly, M. T.: Online learning of stochastic closures of quasigeostrophic turbulence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4165, https://doi.org/10.5194/egusphere-egu25-4165, 2025.
To understand the mechanisms underlying the magnetic fields of planets, including the Earth and the Sun, previous studies have proposed disk dynamo models as a suitable reduction of the mean-field dynamo equations. One of these is the Rikitake-Hide model, which combines the Rikitake model (a two-disk model for geomagnetic reversal) and the Hide model (a disk model with a motor and mechanical friction). This model reproduces nearly equal intervals of magnetic field reversals with modulated cycles, resembling the fluctuations of sunspots. In this presentation, we discuss the Jacobi stability and aperiodicity of the Rikitake-Hide model using geometrical invariants in the KCC (Kosambi–Cartan–Chern) theory. In the KCC theory, the second and third KCC invariants are related to the Jacobi stability and aperiodicity of trajectories in the system, expressed through variables (electrical currents and angular velocities) and parameters. By calculating the Jacobi stability of the Rikitake-Hide model using the second KCC invariant, we find that the model is Jacobi unstable when fluctuations in magnetic energy (square of the electric current) reach local minima. The instability at local minima manifests as branches in the trajectories of electric currents in the model. Based on the third KCC invariant for the trajectories of electric currents in the Rikitake-Hide model, the aperiodicity of the model may arise from individual electric currents. Although the model is always accompanied by aperiodicity, nearly equal intervals of magnetic field reversals are preserved through the cancellation of aperiodicity by symmetric cyclical fluctuations of electric currents. On the other hand, asymmetric electric currents originating from the motor and mechanical friction in the model alter the periods of magnetic field intervals. Finally, we consider the correspondence between the fluctuations of magnetic energy in the Rikitake-Hide model and sunspots on the Sun and discuss the implications for solar activity.
How to cite: Hirano, M., Nagahama, H., and Yajima, T.: Jacobi stability and aperiodicity of the Rikitake-Hide dynamo model based on KCC-theory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2556, https://doi.org/10.5194/egusphere-egu25-2556, 2025.
Polar vortices are observed in the atmospheres of most solar-system planets, arising as a single cyclone centred on or close to the pole. In contrast, Jupiter’s polar vortices have an unprecedented structure. As revealed by NASA’s Juno spacecraft, they consist of geometric patterns of cyclonic vortices surrounding a central cyclonic vortex at the pole. These crystalline structures were not predicted prior to being observed, and the mechanisms explaining their formation and evolution remain poorly understood. One possible mechanism is that moist convection produces small vortices in the polar regions, with the cyclones then migrating polewards via the ‘beta-drift’ mechanism and merging. Nevertheless, models including these processes do not spontaneously produce polygonal patterns like those on Jupiter. In contrast, this study investigates the stability of an initialized pattern of fully formed polar vortices subjected to these small-scale short-lived processes. This forced-dissipative system is modelled using the shallow-water equations describing a single layer of fluid on a polar gamma-plane. The initialized cyclones are subjected to a stochastic forcing with a short decorrelation time and the factors affecting their stability and time-evolution are studied. These include their degree of shielding (an anticyclonic ring around each cyclone), their depth and the properties of the forcing, in addition to the role of potential vorticity mixing.
How to cite: Cope, L., Thomson, S., Seviour, W., and Shipton, J.: The Dynamics of Jovian Polar Cyclones, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18489, https://doi.org/10.5194/egusphere-egu25-18489, 2025.
Extreme rainfall events (EREs) have recently been observed to exhibit teleconnection patterns across long spatial distances. Here we investigate the EREs over the Yellow River basin (YRB) using complex network-based event synchronization analysis. We found that EREs in the YRB are significantly synchronized with multiple regions worldwide, including both local regions and remote regions, and the spatial synchronization patterns exhibit time-scale dependence. Particularly, we found significant synchronization between the EREs in the YRB and those in Europe, with the YRB lags Eastern Europe by 3-5 days lag, while the YRB lags Western Europe by 5–7 days lag. Further analysis reveals that Rossby wave propagation plays a key role in the synchronization of EREs between Europe and the YRB. Wave trains originating in Europe propagate downstream of the Eurasian jet, inducing anomalous circulations over the YRB that enhance vertical upward motion and moisture transport, ultimately triggering EREs. Two distinct wave trains are observed across the Eurasian continent: one associated with Eastern Europe-YRB synchronization, occurring in the mid-latitude region and resembling Rossby wave patterns along the mid-latitude jet stream, with wave energy reaching the YRB after approximately 3 days; and another associated with Western Europe-YRB synchronization, positioned at higher latitudes and relates to Rossby waves along the polar front jet, with wave energy reaching the YRB in about 5 days. Our findings provide valuable insights into the predictability of EREs, offering critical guidance for improving forecasting and early warning capabilities for EREs in the YRB.
How to cite: Cai, L., Yuan, N., Boers, N., and Kurths, J.: Teleconnection of Extreme Rainfall Events between the Yellow River Basin and Europe: A Complex Network Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15195, https://doi.org/10.5194/egusphere-egu25-15195, 2025.
The Dynamic State Index (DSI) is a scalar diagnostic field that indicates deviations from a steady, adiabatic and inviscid atmospheric basic state. It is defined as Jacobian determinant of the potential voticity, the Bernoulli stream function and the potential temperature. Several works have been demonstrated that the DSI provides a suitable parameter to indicate diabatic processes across all scales. So far, DSI variants for different atmospheric models have been derived and applied to diagnose e.g. the onset and presence of precipitation or the organization of storms. In a novel approach DSI variants that identify specific polytropic states (isobaric, isochoric, isentropic, isothermal) of the atmosphere can be used for an analysis of cloud processes at different climate zones.
How to cite: Rudolph, A., Lisa, S., Peter, N., Storelvmo, T., and Vercauteren, N.: The DSI - a dynamic weather and climate index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15845, https://doi.org/10.5194/egusphere-egu25-15845, 2025.
The Paris Agreement legally commits the international community to keep anthropogenic global warming well below 2.0°C, while major efforts shall be made to hold the 1.5°C-line. This is supported by ample scientific evidence indicating that climate change becomes difficult to manage beyond those lines due to highly nonlinear impacts (like tipping processes). The time range when the Paris guardrails will be transgressed under business as usual is highly relevant for precautionary adaptation measures and for justifying the rapid transformation towards a post-fossil economy. Fully-fledged Earth System Models (ESMs) are usually employed for the pertinent projections, yet they are not only computationally expensive but also lack explicit accounting for natural climate system variability. The latter may significantly distort (and invalidate) the ESM overshoot-timing projections. As an alternative, we present here a purely data-driven stochastic approach based on the persistence properties of the observed global temperatures. We are able to quantify, in a probabilistic way, the natural variability that must be superimposed on the anthropogenic trends in order to retrieve the observed warming behavior. When assuming that the anthropogenic warming continues at the current rate, we actually arrive at comparable overshoot timing estimates as the ESMs and provide an explanation for this finding.
How to cite: Ludescher, J., Yuan, N., Schellnhuber, H. J., and Bunde, A.: Natural variability-focused assessment of climate overshoot timing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13518, https://doi.org/10.5194/egusphere-egu25-13518, 2025.
Traditional abrupt climate change detection method often overlooks the process of the abrupt change. Considering the abrupt change process of climate change is conducive to further understanding the details of climate change. We propose the concept of the transition process of abrupt climate change and develop a climate transition process detection technology based on nonlinear models. Through dynamic segmented fitting, we identify the start time (state) and end time (state) of the abrupt change, as well as the stability parameters of the transition process, which are physical quantities that characterize the change process, and finely depict the characteristics of the transition process. Furthermore, based on the principle of critical slowing down, we derive generalized velocity and generalized force as early warning signals of abrupt climate change. During the change process, we also demonstrate the quantitative relationship between parameters during the system's change process, that is, the product of the degree of change stability and the square of the change amplitude is directly proportional to the change speed. This quantitative relationship has been confirmed in the observational data of global sea surface temperature. On this basis, prediction technology for climate turning points can be developed. This prediction technology has successfully predicted a transition process of the Pacific Decadal Oscillation.
How to cite: Yan, P., Zhao, C., and Li, H.: Transition process detection method of abrupt climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17167, https://doi.org/10.5194/egusphere-egu25-17167, 2025.
Real rocks are heterogeneous and contain cracks of various scales. The influence of these cracks on hydraulic fracturing was first studied in the 1980s, when experimental studies showed that the presence of natural fractures can affect the trajectory of the hydraulic fracture. Another type of inhomogeneous material is a homogeneous matrix with fragments of a different material with significantly different mechanical and filtration properties. By varying the composition and size of these fragments, it is possible to create samples with different characteristics. The interface between the matrix and the fragments acts as a weakened area, similar to cracks in a nonuniform fractured material.
Here we present the results of experiments conducted to study the propagation of hydraulic fracture in a heterogeneous material composed of a mixture of gypsum, cement, and fillers. Marbles chips and fine gravel were used as additives in the mixture. The porosity of the original samples without additives was 0.44 and the permeability was 4.15 mD. The porosity and permeability of samples with marble chips were 0.32 and 6.9 mD, respectively, while those with gravel were 0.30 and 2.74 mD. The height of the samples was 60 mm with an outer diameter of 104 mm. Brass inserts with an internal diameter of 10 mm and a length of 18 mm were placed in the center of both sides of each sample during manufacturing. The length of the uncased part of the well was approximately 24 mm. The prepared sample was placed between two circular aluminum bases, with piezoelectric transducers mounted in the recessed working surfaces. Silicone liquid PMS-5, with a kinematic viscosity of 5 centistokes, was used to saturate the sample. During hydraulic fracturing, silicone liquid PMS-200, with a viscosity of 200 centistokes, was applied. The samples were subjected to radial stresses of 0.5 MPa, along the lateral surface, and axial stresses of 3 MPa were applied along the axis. The main focus of the experiments was on studying the acoustic emission accompanying the propagation of the hydraulic fracturing crack.
The experiments have shown that the presence of inclusions significantly affects the shape and development of hydraulic fractures: under conditions of radially symmetric lateral compression, fractures with three branches form in heterogeneous materials, while fractures with two wings form in homogenous samples. The fracturing pressure in samples with fillers is higher than in homogenous ones, and the highest fracturing pressure values are achieved when there is a more rigid and durable filler present. It has been established that acoustic emission pulses in an inhomogenous material have a wider frequency range than in a homogeneous one. In samples containing fillers, more acoustic emission pulses are recorded, especially when there are rigid fillers present. It is also worth noting that acoustic emission occurs earlier in experiments with homogenous samples relative to the time at which the maximum pressure is reached during hydraulic fracturing. The results of the acoustic emission source location are consistent with the observed patterns of cracks on the surface of the samples.
How to cite: Turuntaev, S., Zenchenko, E., Chumakov, T., and Zenchenko, P.: Laboratory study of hydraulic fracturing in heterogeneous materials, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5113, https://doi.org/10.5194/egusphere-egu25-5113, 2025.
Snapback, a post-peak response often observed in brittle rocks, is crucial for accurately characterising fracture properties, particularly regarding size and strain rate effects. This study emphasises the application of the AUSBIT method, which utilises an indirect control approach, to maintain strain rate inside the fracture process zone (FPZ) within the quasi-static range. By selecting different specimen sizes, the AUSBIT technique effectively captures the snapback response while minimising dynamic effects.
The experimental results reveal significant variations in tensile strength measured using the AUSBIT method compared to traditional Brazilian Disc testing. This discrepancy highlights the effectiveness of AUSBIT in accurately reflecting the underlying fracture mechanisms through indirect strain-rate control. These findings offer important insights into rock fracture behaviours and support the potential of AUSBIT as a valuable tool for studying size and rate effects in brittle materials.
How to cite: Dharani Raj, S. V., Nguyen, T. N., Huang, Z., Nguyen, G. D., Karakus, M., Bui, H. H., and Phan, D.: Influence of loading control on tensile strength of rocks , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14623, https://doi.org/10.5194/egusphere-egu25-14623, 2025.
Abstract: With the continuous advancement of tunnel construction in cold regions, the shotcrete technique has been widely utilized in tunnel support, forming a concrete-rock composite structure. Influenced by the low-temperature climate in the cold region, the freezing of moisture around the tunnel will exert a significant frost heaving force on this structure, resulting in various degrees of freeze-thaw damage to the structure. Hence, investigating the damage characteristics of the rock-mass concrete material under freeze-thaw cycling conditions is of great significance for the protection of tunnel lining. In this study, an environmental scanning electron microscope (ESEM) test was conducted on the rock-concrete composites specimens, and the micro-damage deterioration mechanism and failure mode of the specimens subjected to uniaxial compression freeze-thaw cycling were analyzed from a microscopic perspective. The test results indicate that: (1) The freeze-thaw cycling causes irreversible damage to the interface area between the rock and concrete, with the number and length of micro-cracks continuously increasing, leading to the fracture and separation of some areas at the interface between the two. It directly affects the macroscopic mechanical performance of the specimens. (2) The freeze-thaw cycling weakens the cementation between particles. It can cause the structure between mineral particles to become loose, resulting in mineral shedding and fracture. Some fibrous minerals are damaged, and the size and quantity of pore structures continuously increase, while the integrity and bonding degree of minerals are damaged. (3) The composite specimens mainly experience splitting failure, sliding failure, and the combined mixed failure under uniaxial compression conditions. The splitting failure belongs to the tensile-shear failure dominated by tensile failure, and the sliding failure is a simple shear failure. Additionally, it is found that the interface failure mode of composite specimens (the inclination angle is 90°) is the tensile-shear failure dominated by tensile failure.
Key words: ESEM; freeze-thaw cycles; rock-concrete specimen; uniaxial compression
How to cite: Zhou, Z., Li, Y., Hu, Y., and Li, K.: Microscopic Deterioration Mechanism and Failure Mode of Rock-Concrete Composites Subjected to Uniaxial Compression and Freeze-Thaw Cycles using ESEM Technology , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15099, https://doi.org/10.5194/egusphere-egu25-15099, 2025.
The weak connections between individual blocks in blocky rock mass lead to the possibility of block rotations. Rotation of non-spherical blocks pushes the adjacent blocks away; the phenomenon termed elbowing [1]. Furthermore, rotations in both directions create the lateral displacements in the same direction such that the behaviour becomes non-linear of absolute value type. In order to investigate the resulting pattern of elbowing blocks, we considered a simple one-dimensional structure (chain) of stiff square blocks holding together by springs. Only the end block (driving block) with a fixed centroid position is rotated, while each passive block has two degrees of freedom: translational and rotational. A one-dimensional physical model, analytical model, and numerical model (using the commercial discrete element software UDEC) are developed. It was observed that the passive blocks do not consistently rotate in the same direction as the driving block; instead, when the driving block reaches a certain critical angle, it triggers the change of the direction of rotation and the blocks start inverse rotation. We define the angle of the driving block, when the rotation of the passive blocks returns to zero, as the ‘angle of interior zero’. It was found that this angle is influenced by the magnitude of contact friction and the number of blocks. The result demonstrates the complexity of block motion pattern even in such a simple blocky system.
[1] Pasternak, E., Dyskin, A.V., Estrin, Y. (2006) Deformations in transform Faults with rotating crustal blocks. Pure Appl. Geophys. 163 2011-2030.
Acknowledgement: The authors are grateful to Dr I. Shufrin and the School of Engineering workshop for the help with designing and manufacturing of an initial version of the experimental device.
How to cite: Zhang, M., Dyskin, A., and Pasternak, E.: Triggering inverse rotation in 1D model of blocky rock mass, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4782, https://doi.org/10.5194/egusphere-egu25-4782, 2025.
Posters on site: Mon, 28 Apr, 10:45–12:30 | Hall X3
Mechanics of rock failure in uniaxial compression remains a challenge since the wing cracks produced by pre-existing defects/cracks in uniaxial compression are shown to wrap around the initial defect effectively arresting their further growth. As a result, they cannot grow to the extent sufficient for splitting rock samples. This presentation proposes another mechanism of rock failure based on extensive fracture growth caused by zones of tensile stresses formed as parts of self-equilibrating stress field induced by defects distributed in rock (both pre-existing and produced in the process of loading). The fracture driven by localised tensile stresses grows avoiding the zones of compressive stresses thus forming areas of interruption or overlapping. These areas work as distributed bridges constricting the fracture opening. Fractures with constricted opening have the stress intensity factors increasing with fracture growth making the fracture growth unstable and capable to break the rock.
Due to the necessity to avoid the compression zones the growing fracture will deviate form the straight path. If the sample size is not sufficiently large as compared to the size of the compression zones these deviations can be seen as inclined in one direction making the fracture oblique. This can be passed for the conventional shear fracture, which in fact cannot play a role in the process, as the shear fractures do not grow in their own plane forming wings instead. Therefore, the fractures observed in rocks failed in uniaxial compression, in both splitting and oblique failure types are tensile fractures with constricted opening formed and driven by the stress field induced by distributed defects. Whether the resulting failure is splitting or oblique (“shear” failure) depends upon the ratio of the sample size to the compression zones dimensions. We term this dependence “the scale effect in failure type”.
The proposed concept will form a basis for developing models of rock failure in compression necessary for analysing and predicting large scale failures in the rock mass, especially during mining operations.
How to cite: Dyskin, A. and Pasternak, E.: Defect-induced stress field triggering extensive tensile fracture growth in uniaxial compression, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5131, https://doi.org/10.5194/egusphere-egu25-5131, 2025.
The presentation proposes a 2D model of sliding over a rough fault of finite length. Sliding over such a fault involves interaction between asperities of its opposite sides. The interaction proceeds in two stages. At Stage 1 (the initial stage) the interaction involves climbing asperities over the asperities on the opposite side of the fault, causing dilation. As a result, additional resistance to sliding is induced working as friction such that the friction angle becomes the sum of the material friction angle representing friction between the asperity contacts and the average angle of inclination of the tangential line of the asperity contacts. With the increase of shear stress above the critical value of the friction stress shear over the fault is initiated. This corresponds to the conventional interpretation of sliding over rough fault.
This conventional sliding however only proceeds until it reaches the half asperity length, that is when the corresponding local dilation (fault opening) reaches its maximum, which is the asperity height. Under uniform shear stress this is obviously reached at the fault centre. At this point Stage 2 commences. At Stage 2 the asperities which produced maximum dilation (the size of two asperity height) start reducing the dilation creating the effect of local negative friction since the pressure at this stage drives sliding. Subsequently, in the fault zone sliding in Stage 2 the average friction angle drops to that of the material friction angle. As a result, the central zone slides under reduced friction. It was found that there exists a critical magnitude of shear stress, which triggers self-sustained extension of the zone with reduced friction.
Sliding of this type involves creation of loci of negative friction, which can affect the microseismic signals. The presented model provides an additional mechanism of fault sliding and the associated induced seismicity.
How to cite: Pasternak, E. and Dyskin, A.: Rough fault with dilation. Triggering of full sliding, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5137, https://doi.org/10.5194/egusphere-egu25-5137, 2025.
Local dimension computed using Extreme Value Theory (EVT) is usually used as a tool infer dynamical properties of a given state ζ of the chaotic attractor of the system. The dimension computed in this way is also known as the pointwise dimension in dynamical systems literature, and is defined using a limit for infinitely small neighborhood in the phase space around ζ. Since it is numerically impossible to achieve such limit, and because dynamical systems theory predicts that this local dimension is almost constant over the attractor, understanding the properties of this tool for a finite scale R is crucial. We show that the dimension can considerably depend on R, and this view differs from the usual one in geophysics literature, where it is often considered that there is one dimension for a given dynamical state or process. We also systematically assess the reliability of the computed dimension given the number of points to compute it.
This interpretation of the R-dependence of the local dimension is illustrated on the Lorenz 63 system for ρ = 28, but also in the intermittent case ρ = 166.5. The latter case shows how the dimension can be used to infer some geometrical properties of the attractor in phase space. The Lorenz 96 system with n = 50 dimensions is also used as a higher dimension example. A dataset of radar images of precipitation (the RADCLIM dataset) is finally considered, with the goal of relating the computed dimension to the (un)stability of a given rain field.
How to cite: Bonte, M. and Vannitsem, S.: Finite-size local dimension as a tool for extracting geometrical properties of attractors of dynamical systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4017, https://doi.org/10.5194/egusphere-egu25-4017, 2025.
The primitive three-dimensional viscous equations for atmospheric dynamics with the phase transformation of water vapor are studied.
According to the actual physical process, we give the heating rate, mass of water, and precipitation rate, which are related to temperature and
pressure. In fact, in this system, the anelastic approximation is not used, and more complex boundary conditions and dissipation coefficients are given. Providing H2 initial data and boundary conditions with physical significance, we prove the local well-posedness of a unique strong solution to the moist atmospheric equations by the contractive mapping principle and the energy method in the H2 framework.
How to cite: Ma, J., Lian, R., and Zeng, Q.: Local well-posedness of strong solution to a climate dynamic model with phase transformation of water vapor, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8234, https://doi.org/10.5194/egusphere-egu25-8234, 2025.
Air pollutant emissions represent key input information for modeling atmospheric chemical composition or evaluating air pollution control policies. Over the last decades, different approaches have been proposed for estimating emissions. These approaches include collecting activity data combined with emission factors to build bottom-up emission inventories, developing appropriate spatial and temporal disaggregation proxies applied to national or regional total estimates to construct top-down emission inventories, and creating complex data assimilation workflows around chemistry-transport models combined with observations to perform air pollution emission inverse modeling.
In the last decade, deep neural networks (DNNs) have demonstrated exceptional ability to model complex spatiotemporal data. Meanwhile, advances in Earth observation systems, such as the Tropospheric Monitoring Instrument (TROPOMI), have enabled the collection of high-resolution atmospheric composition data in near real-time. These developments open up opportunities to integrate the predictive power of DNNs with satellite observations to deliver rapid and accurate estimates of pollutant emissions in near real-time.
Deterministic CTMs offer insights into the forward relationship between emissions and atmospheric composition, and some studies are already suggesting that DNN might be able to estimate with reasonable predictive skills the chemical concentrations obtained from these physics-based models. While the forward mapping is well-defined, the inverse mapping—from atmospheric composition to emissions—is inherently ill-posed. Our objective is ultimately to exploit DNNs for doing air pollutant emission inverse modeling, without using the traditional data assimilation approach. This presents challenges, requiring the application of regularization techniques to address the ambiguity raised by this ill-posed problem.
Here, we will present the preliminary results of our study on training regularized DNNs for inverting the NOx emissions in Spain , utilizing training data derived from the MONARCH air quality model. We will take advantage of the flexibility offered by these models to create different training datasets and assess the performance of our models across different data scenarios.
How to cite: Petticrew, J., Petetin, H., Mas Magre, I., Guevara Vilardell, M., Jorba, O., and Pérez García-Pando, C.: Inverting Spanish NOx emissions using a neural network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8593, https://doi.org/10.5194/egusphere-egu25-8593, 2025.
The source term of Chernobyl 2020 wildfires is a tensor consisting of five dimensions: spatial location described by longitude and latitude in a given area with potentially many sources, time profiles, height above ground level, and the size of particles carrying the material. Since the number of concentration measurements is limited, the estimation of this source term is an ill-posed problem. Prior information is thus essential to obtain a reproducible estimate. We show that deep image prior that utilizes the structure of a deep neural network to regularize the inversion is suitable for this problem. The deep network is initialized randomly without the need to train it on any dataset first. The networks is used to represent both the mean and variance of the posterior estimate. The resulting variational Bayes procedure thus introduces smoothness in the spatial estimate of the emissions and reduces the number of unknowns by enforcing a prior covariance structure in the source term. The estimate of the 137Cs emissions during the Chernobyl wildfires in 2020 is compared to the Tikhonov method. The spatial distribution of the proposed method is close to the distribution obtained from satellite observations.
Acknowledgment:
This research has been supported by the Czech Science Foundation (grant no. GA24-10400S).
How to cite: Smidl, V., Brozova, A., Tichy, O., and Evangeliou, N.: Estimation of Spatial-temporal Source Term of Chernobyl Wildfires using Deep Neural Network Prior, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11939, https://doi.org/10.5194/egusphere-egu25-11939, 2025.
Bayesian inverse modelling systems are a valuable tool for quantifying sources and sinks of greenhouse gases using atmospheric observations. They are increasingly used to verify national emission inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC). Recent studies have noted the value of hierarchical Bayesian inverse methods for improved uncertainty quantification in inverse modelling systems, and the importance of including physical constraints such as non-negativity in emissions. However, systems that exhibit these properties can suffer from severe computational bottlenecks, exacerbated by the growing volume of atmospheric observations and the demand for higher spatiotemporal resolution in emission estimates. As a result, spatial dimension reduction and spatiotemporal independence are often placed on emissions estimates, to make the algorithms computationally feasible. This research aims to explore these limits, before introducing novel approaches to address them.
We use a hierarchical Bayesian approach, using Markov chain Monte Carlo (MCMC) sampling, to explore the limits of the spatiotemporal resolution of flux estimates considering different multivariate Gaussian correlations. We demonstrate this by quantifying emissions of methane in the UK. In addition, we demonstrate the utility of a multivariate log-normal emissions distribution, simultaneously maintaining the non-negativity of emissions, as well as the explicit representation of emissions covariance. The results are compared with the emissions calculated using a spatially and temporally independent emissions prior, demonstrating the developments associated with a multivariate approach.
The computational costs associated with MCMC sampling means that the potential for extending the approach to large spatiotemporal parameter spaces is limited. Therefore, alternative inferential methods are explored, with a focus on sequential algorithms - such as Kalman filtering - that are augmented for non-Gaussian, hierarchal inference, in higher dimensional parameter spaces. This work provides the foundation for developing scalable non-Gaussian hierarchical frameworks that combine computational feasibility with improved emissions estimates.
How to cite: Pearson, S., Western, L., Ganesan, A., and Rigby, M.: Scalable Approaches for Hierarchical Non-Gaussian Inverse Modelling for Emissions Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13641, https://doi.org/10.5194/egusphere-egu25-13641, 2025.
The Madden Julian Oscillation (MJO) is the dominant element of the atmospheric variability in the tropics on intraseasonal zonal timescales. The MJO manifests itself as a slowly eastward propagating envelope coupling large scale circulation and convection. Despite recent progress in the understanding of the MJO, a comprehensive theory of the MJO is still missing, partly due to its complexity associated with moist physics and nonlinearity.
Here, we use a normal mode decomposition of atmospheric reanalysis datasets to show that the MJO can be understood in terms of a superposition of interacting Rossby, Kelvin and inertio-gravity modes. We discuss the nature of the interaction between these modes, which can be either linear, due to large-scale variations in the moisture, or simply due to the inherent nonlinearity of the equations of the atmosphere.
How to cite: Raphaldini, B., S. W. Teruya, A., Mayta, V., F.M. Raupp, C., Dias, P., and Peixoto, P.: Normal mode interactions in the Madden Julian Oscillation envelope, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13822, https://doi.org/10.5194/egusphere-egu25-13822, 2025.
Limited-area models (LAMs) often suffer from degradation in their representation of large-scale features compared to that of global models (GMs) due to the restricted domain size and limited observational coverage. To address this, we propose a novel flow-dependent large-scale blending (LSB) method for LAM data assimilation (DA). LSB methods incorporate large-scale information from a GM into the LAM DA system using scale-dependent weights. Our approach, termed as nested EnVar, extends the previously proposed static variational LSB method (nested 3DVar) to an ensemble-based framework. Unlike static LSB methods, nested EnVar simultaneously assimilates both observational and large-scale GM information into LAM forecasts with dynamically adjusting the weight given to GM information based on its estimated flow-dependent uncertainty.
Through idealized assimilation experiments using a nested system of simplified chaotic models with a single spatial dimension, we demonstrate that nested EnVar effectively reduces large-scale errors in LAM DA as existing LSB methods, and offers better forecasts than GM downscaling. Compared to both traditional DA and other LSB methods, nested EnVar provides more accurate analyses and forecasts when dealing with dense and unevenly distributed observations. By dynamically accounting for GM uncertainty, nested EnVar improves the stability and accuracy of the analysis across scales.
Our findings suggest that nested EnVar offers a promising alternative to traditional LSB methods for high-resolution simulations of complex, hierarchically structured phenomena. This novel approach has the potential to enhance the effectiveness of high-resolution LAM DA for spatially localized convective-scale observations.
How to cite: Nakashita, S. and Enomoto, T.: Flow-dependent large-scale blending for limited-area ensemble assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16805, https://doi.org/10.5194/egusphere-egu25-16805, 2025.
It is well-established that large eddies significantly influence the turbulent transport of heat and scalars in the atmospheric surface layer. However, the mechanistic understanding of how large eddies originating from both the ground (updrafts) and aloft (downdrafts) regulate flux convergence (FC) and divergence (FD) remains relatively unexplored. Based on turbulence data measured at 12 levels, spanning from 1.2 m to 60.5 m above the ground, we observe a notable increase in the variability of sensible heat flux magnitudes with height. Our results show that FC and FD of sensible heat are primarily linked to variations in the respective transport efficiencies (RwT) at different heights. Using the cross-wavelet transform, we find that in FC cases, the regions with high wavelet coherence expand with height, resulting in higher RwT at higher levels compared to low ones. Conversely, in FD cases, the regions with high wavelet coherence decrease with height, leading to lower RwT at higher levels. Large eddies with length scales of approximately 120 to 500 m have a significant impact on amplifying or attenuating RwT at higher levels compared to lower levels. Using conditional sampling to extract the updrafts and downdrafts of large eddies, distinct patterns are observed in the characteristics of updrafts and downdrafts between FC and FD groups, especially in their flux contribution and transport efficiencies. This work emphasizes the significant contribution of asymmetric turbulent transport by updrafts and downdrafts to the discrepancy between the observed turbulent fluxes and those predicted by the Monin-Obukhov similarity theory.
How to cite: Gao, Z., Li, L., Liu, H., and Yang, B.: Flux Convergence and Divergence Linked to Asymmetric Transport by Large Turbulent Eddies , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20158, https://doi.org/10.5194/egusphere-egu25-20158, 2025.
Recent studies found that the concept of typicality of extreme events is relevant in case of several weather and climate extremes related to atmospheric circulation anomalies, such as heatwaves or cold spells. This concept refers to the property of extreme events of being similar among each other while being at the same time anomalous with respect to usual weather conditions, referring to both spatial patterns and temporal evolutions. The aim of typicality analysis is to clarify whether this property applies to the extreme events of interest and to quantify the strength of typicality. In this presentation, I will discuss different types of typicality and give a practical guidance on how to perform a typicality analysis using climate datasets. I will present the most important steps of the analysis, discussing also several tools we can use to quantify typicality, such as standard statistical measures, Taylor diagrams, dynamical systems-based indicators.
How to cite: Galfi, V. M.: Typicality analysis for extreme climate events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21616, https://doi.org/10.5194/egusphere-egu25-21616, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 4
EGU25-5033 | ECS | Posters virtual | VPS20
Research on dispersion relation and dynamic properties of pendulum‑type waves in 1D blocky rock masses with complex hierarchical structuresFri, 02 May, 14:00–15:45 (CEST) | vP4.12
Rock masses are characterized by the complex hierarchical structures involving various scale levels. The deformation of rock masses is primarily controlled in weak structural layers between rocks, whereas the rock block can be regarded as a non-deformable block and can move as a whole. In consequence, a new dynamic phenomenon, namely the pendulum-type wave, has emerged, which is a kind of nonlinear displacement wave caused by the overall movement of relatively intact large-scale rock blocks. Aiming at the complex hierarchical structures of rock masses and low-frequency characteristics of pendulum-type waves, the blocky rock masses composed of granite blocks and rubber interlayers are simplified into the block-spring model and wave motion model. Based on Bloch theorem and d’Alembert’s principle, the dispersion relation and equations of motion of 1D blocky rock masses are determined. Research shows that with the increase of the rock size and geomechanical invariant, the initial frequency of the first attenuation zone gradually decreases, and only the low-frequency waves lower than that frequency can propagate in blocky rock masses, which reveals the mechanism of low-frequency characteristics of pendulum-type waves theoretically. The equivalent substitution for the two models and their errors are given, and the results show that the equivalent substitution of the two models is not universal and unconditional. Finally, the influence of hierarchical structures on the dispersion relation and dynamic response is further studied. The larger the stiffness ratio, or the higher the order of hierarchical structures, the smaller is the effect of ignoring the high-order hierarchical structures.
How to cite: Jiang, K. and Qi, C.: Research on dispersion relation and dynamic properties of pendulum‑type waves in 1D blocky rock masses with complex hierarchical structures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5033, https://doi.org/10.5194/egusphere-egu25-5033, 2025.
