Orals: Fri, 8 May, 14:00–15:45 | Room -2.15
Cross-scale biodiversity–ecosystem functioning (BEF) relationships are widely used to evaluate how biodiversity relates to ecosystem functioning across space. Theory predicts that when species turnover is incomplete across space, the BEF slope follows a characteristic hump-shaped scaling pattern, strengthening with increasing scale before weakening at broader scales. In real landscapes, however, biodiversity and ecosystem function often co-vary along environmental gradients, and spatial autocorrelation naturally increases with scale, potentially confounding regression-based BEF inference.
We combined simulations and field data to quantify how explicitly accounting for spatial autocorrelation (SAC) affects BEF scaling. In simulations, biodiversity and ecosystem function were generated under joint control of an environmental gradient and a spatial stochastic component, allowing SAC to emerge in both predictors and responses. In empirical analyses, we used forest inventory data from two temperate forests. We constructed a sequence of spatial scales by aggregating plots using a k-nearest-neighbor procedure, with k increasing from small to large neighborhoods. At each scale, we estimated BEF as the slope of species richness (SR) on biomass increment, while controlling for climate, soil, and trait covariates. We then contrasted non-spatial models with spatial models that include SAC in the residual structure, and quantified ΔBEF as the difference in SR slopes between spatial and non-spatial fits.
Across simulations and observations, ignoring SAC produced an apparently monotonic strengthening of BEF with scale. However, when SAC was included, the BEF scaling curve followed the predicted hump-shaped pattern. Moreover, ΔBEF increased with residual Moran’s I, indicating that stronger spatial dependence systematically inflates non-spatial BEF estimates as scale increases. Finally, the BEF slopes were negatively correlated with excess species richness and positively correlated with species turnover after correcting for SAC, consistent with the theory that species turnover plays a key role in BEF scaling. Our study emphasizes that accounting for SAC is essential for accurate BEF scaling and provides a useful approach for future studies.
How to cite: Wang, Z., Li, Y., Zhang, F., Yu, J., Wang, C., Chen, L., and Gou, X.: Accounting for spatially autocorrelated errors is necessary to infer cross-scale biodiversity–ecosystem functioning patterns in natural world, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8215, https://doi.org/10.5194/egusphere-egu26-8215, 2026.
Excavation and unloading of deep rock mass under varying in-situ stress levels is a typical non-linear geomechanical process, Specifically, in the context of the widely used drilling and blasting (D&B) method, the excavation damage zone (EDZ) around underground opening induced by transient unloading represents a dynamic response problem governed by multiple factors. While the exact theoretical solution of stress state in surrounding rock during transient excavation can describe the stress state and eventually converge to the Kirsch solution after rock mass excavation completed, it cannot fully capture the dynamic damage process. Therefore, a circular tunnel model for transient excavation was established in this study using a dynamic finite element code LS-DYNA. An equivalent released nodal force method was implemented to stably control the transient unloading path under non-hydrostatic in-situ stress conditions after stress initiation, which realizing the synchronous release of radial and tangential stresses in the excavated zone. Moreover, the validity of the linear elastic transient excavation model was verified through comparison with an analytical solution. Then the dynamic stress redistribution, as well as the EDZ evolution process were numerically simulated under various stress unloading paths and lateral pressure coefficients, utilizing an elastoplastic constitutive model. This study provides a basis for simulating transient excavation under various paths and understanding failure of surrounding rock in non-hydrostatic stress states.
How to cite: Xiang, G., Tao, M., Li, X., Zhao, Q., Huang, L., Yin, T., Zhao, R., and Chen, J.: Numerical Study on the Path-Dependent Evolution of the Excavation Damage Zone under Transient Unloading, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4711, https://doi.org/10.5194/egusphere-egu26-4711, 2026.
Recently, Paluš et al. (2024) demonstrated that information-theoretic generalization of Granger causality – based on conditional mutual information/transfer entropy – when reformulated in terms of Rényi entropy, provides a time-series analysis tool suitable for identifying the causes of extreme values in affected variables.
Investigating the causes of warm summer surface air temperature extremes in Europe, Rényi information transfer highlights the role of blocking events among large-scale circulation patterns and modes of variability. Soil moisture interacts with air temperature on a daily scale, exhibiting bidirectional causal effects on the mean, whereas its influence on temperature extremes emerges over longer time scales, from a fortnight to a month. In contrast, the causal effect of blocking on temperature extremes is primarily observed at the daily scale. Using tools from Rényi information theory, we aim to disentangle this complex, multicausal, multiscale phenomenon and identify the regions in Europe where these factors modulate the probability of extreme summer heat.
This research was supported by the Johannes Amos Comenius Programme (P JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks; and by the Czech Science Foundation, Project No. 25-18105S.
Paluš, M., Chvosteková, M., & Manshour, P. (2024). Causes of extreme events revealed by Rényi information transfer. Science Advances, 10(30), eadn1721.
How to cite: Paluš, M., Manshour, P., Ghosh, A., Tabachová, Z., Holtanová, E., and Mikšovský, J.: Understanding extreme heat: Causes and time scales revealed by Rényi information transfer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14920, https://doi.org/10.5194/egusphere-egu26-14920, 2026.
The background continuum of climate variability recorded in proxy records is often modelled using parametric spectral models, such as power-laws, auto-regressive processes, or stochastic differential equations.
However, fitting such models to proxy data is usually done in an ad-hoc manner, such as by using least-squares fitting in log-log space.
Here I will discuss two formal Bayesian methods for fitting parametric stochastic models to proxy data. One is a spectral-domain approach based the Whittle likelihood. The other is a time-domain approach based on Gaussian Processes.
In both cases, I show how the standard approaches can be modified to account for some of the ways in which climate proxies alter spectral slopes: measurement error, time uncertainty, uneven sampling, and smoothing (e.g. from diffusion or bioturbation). Finally, I use synthetic data generated from power-law and Matern processes, and proxy-system models, to show expected skill of the two approaches for different proxies.
I find that these formal approaches provide significant bias reduction relative to typical ad-hoc approaches, allowing for much more accurate calibration of stochastic models of climate variability across scales.
How to cite: Proistosescu, C.: Bayesian methods for fitting spectral models to noisy, sparse, proxy data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15967, https://doi.org/10.5194/egusphere-egu26-15967, 2026.
Geological time is punctuated by events that define biostrata and the Geological Time Scale’s (GTS) hierarchy of eons, eras, periods, epochs, ages. Paleotemperatures and macroevolution rates, have already indicated that the range ≈ 1 Myr to (at least) several hundred Myrs) is a scaling (hence hierarchical) “megaclimate” regime. We apply analysis techniques including Haar fluctuations, structure functions trace moment and extended self-similarity to the temporal density of the boundary events (r(t)) of two global and four zonal series. We show that r(t) itself is a new paleoindicator and we determine the fundamental multifractal exponents characterizing the mean fluctuations, the intermittency and the degree of multifractality. The strong intermittency allows us to show that the (largest) megaclimate scale is at least ≈ 0.5 Gyr. We also analyze a Precambrian series going back 3.4Gyrs directly confirming this limit and allowing us to quantatively compare the Phanerozoic with the Proterozoic eons.
We find that the probability distribution of the intervals (“gaps”) between boundaries and find that its tail is also scaling with an exponent qD≈ 3.3 indicating huge variability with occasional very large gaps such that it’s third order statistical moment barely converges. The scaling in time implies that record incompleteness increases with its resolution (the “Resolution Sadler effect”), while scaling in probability space implies that incompleteness increases with sample length (the “Length Sadler effect”).
The density description of event boundaries is only a useful characterization over time intervals long enough for there to be typically one or more events. In order to model the full range of scales (and low to high r(t)), we introduce a compound Poisson-multifractal model in which the multifractal process determines the probability of a Poisson event. The model well reproduces all the observed statistics.
Scaling changes our understanding of life and the planet and it is needed for unbiasing many statistical paleobiological and geological analyses, including unbiasing spectral analysis of the bulk of geodata that are derived from cores.
How to cite: Lovejoy, S., Spiridonov, A., Hebert, R., and Lambert, F.: From Eons to Epochs: multifractal Geological Time and a compound multifractal-Poisson model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14999, https://doi.org/10.5194/egusphere-egu26-14999, 2026.
It is well known that the predictability of the climate varies over time and will depend on the initial conditions, especially when considering non-linear systems such as El Niño Southern Oscillation (ENSO). While recent decades have seen a few extreme ENSO events, proxy data reveal a large amplitude in tropical Pacific sea surface temperatures low frequency modulations over past millennia. To better interpret what is observed in proxies, a useful approach is to combine the climate information derived from natural archives with the physics of GCMs using paleoclimate data assimilation (PDA). Recently, efficient online ensemble-based data assimilation techniques have been developed relying on climate model emulators and the predictable components of the climate system. The skill of these ensemble forecasts is a key factor for the success of PDA especially when considering Particle Filters. Such predictability may however change according to the host-GCM, the emulator skills in capturing the host-GCM non-linear behaviours and the dimension of the problem. In this study, we explore these issues in a perfect model framework across PMIP3 and PMIP4 climate model simulations for the past millennium, relying on various types of architectures and climate model emulators. Our results indicate that relying on such a hierarchy of multi-model approaches provides a promising way to better quantify uncertainties and decipher the relative contribution from internal dynamics and external forcings embedded in proxy records, particularly regarding ENSO.
How to cite: Gaudin, A. and Khodri, M.: New classes of climate model emulators to improve paleoclimate reconstructions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19188, https://doi.org/10.5194/egusphere-egu26-19188, 2026.
The dynamics driving Earth's weather are commonly presumed to be governed by a hierarchy of distinct dynamical mechanisms, each operating over some limited range of spatial scales. The largest scales are argued to be driven by quasi-two-dimensional turbulence, the mesoscales by gravity waves, and the smallest scales by 3D isotropic turbulence. In principle, such a hierarchy should result in observable breaks in atmospheric kinetic energy spectra at discrete points as one mechanism transitions to the next. Using global radiosonde and dropsonde datasets, we show that this view is not supported in observations. Between 200m and 8km, we find that structure functions calculated along the vertical direction display a Hurst exponent of H_v \approx 0.6, which is inconsistent with either gravity waves (H_v = 1) or 3D turbulence (H_v = 1/3). In the horizontal directions, large-scale structure functions between 200km and 1800km display a Hurst exponent of H_h \approx 0.4, which is inconsistent with quasi-geostrophic dynamics (H_h = 1). We show that these observations are instead consistent with a lesser-known theory of stratified turbulence proposed by Lovejoy and Schertzer in 1985, where at all scales the dynamics obey a single anisotropic turbulent cascade with H_v=3/5 and H_h =1/3.
Our results suggest a reinterpretation of atmospheric dynamics: rather than being controlled by a hierarchy of distinct dynamical elements, atmospheric flow should instead be thought of as a superposition of anisotropic turbulent eddies that continually cascade from large scales to small scales. We show how this view may be interpreted literally and used to construct photorealistic and quantitatively accurate simulations of atmospheric volumes, and without integration of the hydrodynamic equations. We argue that the model also provides a more intuitive basis for interpreting both the intermittent and the anisotropic aspects of the observed statistics of the atmosphere.
How to cite: DeWitt, T., Garrett, T., Rees, K., and Oppong, S.: Global sonde datasets do not support a mesoscale transition in the turbulent energy cascade, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15166, https://doi.org/10.5194/egusphere-egu26-15166, 2026.
In the era of the data-driven research, the zero-values of geophysical fields require increased attention in order to improve understanding of their effective impacts on the prediction of extreme geophysical phenomena.
In everyday life, we use the idea that zero denotes the absence of quantity, whereas in geophysics, it refers to a chosen reference point, not necessarily the absence of a physical phenomenon. It then results from the removal of the background field, either by design of the measured quantity or due to the current limitations of empirical detection.
Regardless of their origin, the presence of zeros in data significantly alters the resulting statistical distributions and influences the estimates of statistical parameter. Regarding universal multifractals (UM), two approaches have been favoured over the last thirty years to mimic the appearance of zeros and/or quantify their influence on the resulting UM estimates. The first, among the most widely used, relies on multiplying of a UM field by an independent fractal model, the ‘beta-model’, i.e. to assume the field has physically a fractal support. The second consist of thresholding the UM singularities and ignoring the fluctuations below the threshold, i.e. assuming that there is a detection of low field values.
This presentation will revisit these two approaches, emphasizing the significant resulting differences in the theoretical behaviour of the multifractal phase transitions, which are responsible for the behaviour of multifractal extremes. Then practical methods for preliminary detection of the most appropriate zero-creation mechanism within the data will be illustrated with concrete examples from geophysical fields.
How to cite: Tchiguirinskaia, I., Gires, A., and Schertzer, D.: Geophysical extremes, scaling and fractal support induced by zero-values, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20114, https://doi.org/10.5194/egusphere-egu26-20114, 2026.
Scaling dynamics, intermittency, and multifractality in complex natural systems remain a central challenge across physics, geoscience, and hazard science. Earth system dynamics exhibit strongly non-equilibrium behaviour, long-range codependences, irreversible energy and information flows, and multiscale spatiotemporal coevolution, including dynamically adaptive interactions across spatial, temporal, and organizational domains.
The present contribution introduces and explores our latest advances in information physical intelligence for addressing these challenges, further building from our recent developments in non-ergodic nonlinear open quantum systems, where systems non-recurrently exchange energy, matter and information with structural-functional coevolutionary environments. In this setting, entropy production, information backflow, coherence, and decoherence are anchored on cross-scaling organizing principles spanning from microphysical foundations to emergent macrophysical behaviour, dynamically traceable and solvable through our novel nonlinear quantum developments.
Our new nonlinear quantum intelligence framework is then equipped with our latest non-ergodic information physical categorical algebraic infrastructure and associated mathematical physics apparatus, underlying the natural emergence of coevolutionary cyber-physical cognitive systems. These are then tested in controlled synthetic and free-range natural experiments, in order to provide operational insights on their ability to autonomously unfold and shape structural-functional emergence of complex system dynamics including scaling mechanisms in nonlinear non-ergodic multiscale stochastic-dynamical systems exhibiting scale-dependent entropy production rates, anomalous dissipation, and multidirectional cascades, on an inherent information physical thermodynamic process for far-from-equilibrium coevolutionary multifractal scaling.
One of the advances herein brings out a novel coevolutionary far-from-equilibrium thermodynamic renormalization of non-ergodic open quantum dynamics, where delocalization and aggregation across scales induces effective non-Markovianity, memory kernels, and scale-dependent effective energetics. These features are then shown to map naturally onto formal multifractal signatures observed in turbulence, precipitation fields, seismicity, geomagnetic activity, and climate variability.
Within this framework, coevolutionary multifractality emerges as a signature of competing irreversible processes operating across coevolving subsystems, rather than as a purely statistical or kinematic geometric construct. The corresponding generalization of information-theoretic quantities, including quantum relative entropy, Fisher information, and entropy production fluctuations, provide structural descriptors of scaling regimes and phase-transition-like behaviour in Earth system dynamics.
From theory to operation, we demonstrate how these information physical foundations and developments enable cross-domain integration in multiscale, multidomain Earth system modeling and more broadly across our System-of-Systems for Multi-Hazard Risk Intelligence Networks (SoS4MHRIN) platform. In doing so, we unveil and elicit coevolutionary scaling mechanisms linking traditional quantum information to meso and macroscale complexity, and harness elusive predictability pertaining to far-from-equilibrium non-ergodic non-recurrent emergence, intermittence and persistence of structural-functional changes, critical transitions and extreme events, along with their interactions and impacts.
This is particularly relevant for compound, cascading, coevolutionary and synergistic multi-hazards, where earthquakes, volcanic eruptions, extreme weather, floods, wildfires, and landslides interact across scales and domains. Far-from-equilibrium entropy production and information physical flows act as early warning indicators and organizing variables for multi-hazard interactions and tipping dynamics.
By synergistically articulating non-ergodic information physics, nonlinear open quantum thermodynamics, scaling theory, and Earth system science, this work provides a physically grounded, scale-aware framework for better understanding and operating on complexity, predictability, and resilience in the Earth system under ongoing structural-functional multiscale coevolution.
How to cite: Perdigão, R. A. P. and Hall, J.: Nonlinear Quantum Intelligence Framework for Coevolutionary Scaling and Multifractality across Far-from-Equilibrium Earth System Dynamics and Multi-Hazards, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7773, https://doi.org/10.5194/egusphere-egu26-7773, 2026.
Posters on site: Fri, 8 May, 10:45–12:30 | Hall X4
This study investigated the complex temporal behavior of cosmogenic Beryllium-7 (7Be) by analyzing daily activity concentrations from 21 monitoring stations in the CTBTO network, spanning the years 2010 through 2017. By applying multifractal detrended fluctuation analysis (MF-DFA), it was established that 7Be time series exhibit significant nonlinear scaling behaviors. The results indicate a broad multifractal spectrum (Δα ranging from 0.17 to 0.66), with statistically significant multifractality observed at all locations except RN45 and RN47. Leveraging the extracted spectral width and Hölder exponents, current study utilized the K-means algorithm to categorize the global stations into three distinct clusters based on their dynamic signatures. Furthermore, this study assessed the external forcing of 7Be variations via multifractal cross-correlation analysis against five major indices: the Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), and solar activity markers (Total, Northern, and Southern hemisphere sunspot numbers). While cross-correlations varied across indices, the NAO emerged as the dominant driver. Notably, station RN16 (Yellowknife, Canada) displayed the highest sensitivity to these external drivers, suggesting a unique coupling between atmospheric/solar indices and isotope concentration at this latitude.
How to cite: Ogunjo, S.: Global Beryllium-7 Dynamics: Nonlinear Scaling Properties, Spatial Classification, and Sensitivity to Atmospheric Teleconnections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-963, https://doi.org/10.5194/egusphere-egu26-963, 2026.
Various empirical methods exist to calculate fractal dimension of geospatial objects with the box-counting principle being a popular one. However, these methods generally require geospatial data to be projected to Euclidean space. While this works fine at small geographic scales, computation at larger or global scales introduces distortions inevitable with projection due to the curvature of the earth. I show from mathematical principles how Discrete Global Grid Systems (DGGSs) – hierarchical spatial data structures composed of polygonal cells that are increasingly being used for modelling geospatial data – can be employed creatively to act as the covering set for calculating the Minkowski-Bouligand dimension using the box-counting principle. This enables computation of the fractal dimension of geospatial data in spherical coordinates without having to project the data in question on a planar surface. Results on synthetic datasets are within 1% of their theoretical fractal dimensions. A case study on opaque cloud fields obtained from a geostationary meteorological remote sensing satellite image yields a result of 1.577±0.0207 when aggregated using three different geodesic DGGSs based on the Icosahedral Snyder Equal Area (ISEA) projection, in line with values reported in the literature. As the cells of a DGGS are generally pre-defined and fixed to the earth, this method also brings some relief associated with the box-counting method in general, particularly the choice of cell-sizes to be sampled as well as the placement and orientation of the grid that acts as the covering set – issues that are usually circumvented by rules of thumb and conventions. I comment on the possibility to extend the method for use with raster data. Ways to improve the method using low-aperture DGGSs to better capture the self-similarity and possibilities of developing custom DGGSs for this purpose are also noted. Being a computationally intensive method, development of software libraries making use of parallel computing to enhance performance and scalability is also proposed. With climatic variables exhibiting spatiotemporal autocorrelation with long-range effects, I believe this method would be of interest to climate scientists interested in studying their fractal properties at continental and global scales.
How to cite: Ghosh, P.: Computing fractal dimension at large geographic scales using Discrete Global Grid Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4299, https://doi.org/10.5194/egusphere-egu26-4299, 2026.
Freshwater input from melting polar ice sheets can profoundly alter ocean circulation, in particular the Atlantic Meridional Overturning Circulation (AMOC), with far-reaching climatic consequences. Yet the sensitivity of the AMOC to freshwater forcing remains highly uncertain: models exhibit divergent responses depending on source location, background climate state, and circulation regime, while the instrumental record is too short to unambiguously detect and characterise a melt-driven weakening.
Palaeoclimate archives, especially from the last deglaciation, provide ample evidence of melt events through indicators such as surface-ocean δ¹⁸O and biomarkers (e.g. BIX) in sediment cores and speleothems. However, the spatial and temporal characteristics of the underlying meltwater forcing remain poorly constrained. While meltwater discharge into the North Atlantic may be local, rapid, and event-like, its redistribution and impact on the AMOC unfold over centuries, complicating direct inference from surface-ocean proxies. Consequently, in deglacial general circulation model simulations, meltwater forcing is typically inferred indirectly from ice-sheet reconstructions or expected climate responses, resulting in a wide spread of applied forcings that propagates into substantial uncertainty.
Here we introduce a new forward-modelling approach aimed at strengthening the estimation and detection of regionally distinct and temporally evolving surface-ocean meltwater signals in proxy archives. We develop an empirical Green’s-function (impulse-response) framework based on a new suite of HadCM3 simulations, in which conservative tracers track meltwater originating from different source regions under distinct AMOC modes representative of deglacial conditions. Signals at terrestrial proxy sites are inferred using atmospheric back-trajectory analysis. The resulting kernels encode the system’s response for different source regions across multiple time lags, allowing any transient meltwater history to be reconstructed through discrete convolution with a derived 500-year response function. Applied to the last deglaciation, the framework demonstrates how differences between ice-sheet reconstructions (e.g. GLAC-1D versus ICE-6G) translate into distinct surface-ocean meltwater anomalies in the North Atlantic. The model highlights the critical role of meltwater amount, timing, and injection location, as well as the underlying AMOC circulation mode, in shaping surface-ocean proxy signals. It further provides quantitative estimates of how meltwater-related surface anomalies propagate to proxy sites distributed across the North Atlantic. Notably, transitions between AMOC modes can effectively mask even massive meltwater pulses, such as Meltwater Pulse 1A, at certain proxy locations. This forward-modelling approach thus offers an alternative perspective on deglacial freshwater forcing in the proxy realm and represents a step towards data-constrained reconstructions of past surface-ocean freshening and AMOC resilience.
How to cite: Endres, L., Ivanovic, R., Romé, Y., and Stoll, H.: A Dye-Tracer Forward-Modeling Framework for Deglacial Meltwater Reconstruction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21023, https://doi.org/10.5194/egusphere-egu26-21023, 2026.
A significant fraction of multidecadal fluctuations in the reanalysis-based gridded estimates of the observed climate variability over the past century and a half lie outside of the envelope generated by ensembles of climate-model historical simulations. Several pattern-recognition methods have been previously used to map out a truly global reach of the observed vs. simulated climate-data differences; in our own work we dubbed these global discrepancies the stadium wave to highlight their most striking spatiotemporal characteristic. Here we used a novel combination of such methods in conjunction with a large multi-model ensemble and two popular twentieth-century reanalysis products to: (i) succinctly describe the geographical evolution of the observed stadium wave in the annually sampled near-surface atmospheric temperature and mean sea-level pressure fields in terms of three basic patterns; (ii) show the robustness of this identification with respect to methodological details, including the demonstration of the truly global character of the stadium wave; and (iii) provide essential clues to its dynamical origin. All input time series were first decomposed into the forced signal and the residual internal variability; multi-model forced-signal estimates were also decomposed into their common-evolution part and the individual-model residuals. Analysis of the latter residuals suggests a contribution to the stadium-wave dynamics from a delayed climate response to variable external forcing despite the observed stadium-wave patterns’ exhibiting the magnitudes and the level of global teleconnectivity unmatched by the forced-signal residuals.
How to cite: Kravtsov, S., Westgate, A., and Gavrilov, A.: Global-scale multidecadal climate variability: The stadium wave, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7074, https://doi.org/10.5194/egusphere-egu26-7074, 2026.
A robust understanding of the potential range of Earth system dynamics is essential for effectively simulating future climate change. Previous studies have reported increasing discrepancies in modelled temperature variability from global to local scale, and beyond decadal timescales, based on paleoclimate reconstructions. The instrumental record is most complete for the last 145 years. This limits a spatio-temporal assessment of historical temperature variability to multidecadal timescales at the upper end. To this day, model-observation comparisons of regional climate variability have mostly focused on sea surface temperature.
Here, we compare historical near-surface air temperatures from an ensemble of 50 CMIP6 models with similar initial conditions and two single-model initial-condition large ensembles (SMILE) with reanalysis and observation datasets. Following a robust like-for-like approach, all datasets are interpolated to a common grid of about 2.8 degrees and compared over the period of 1880 to 2015. Spectral analysis and filters reveal the structure of temperature variability over different spatial and temporal scales. Specifically, we focus on temperature variability on timescales of 10 to 30 years from global to local scale.
On the global scale, models consistently display higher temperature variance in bands from 10 to 30 years than reanalysis data. Masking the analysis to regions with a consistent observational record confirms this trend. On the local scale, observed temperature variability can deviate substantially from the mean of stacked model standard deviation fields. For example, observed temperature variability in Europe lies in the lower tail of the model distribution. Vice versa, observed temperature in the southern Atlantic is representative of the model distributions' upper tail. Consistently over the multi-model ensemble and two SMILEs, decadal temperature variability is overestimated on land, but underestimated over the ocean. Nevertheless, there are exceptions to this pattern. For example, in the northern Atlantic, modelled variability overestimates observations consistent with the literature. Overall, these regional inconsistencies suggest that multiple, regionally heterogeneous processes are involved.
How to cite: Schürmann, T. and Rehfeld, K.: CMIP6 simulations overestimate historical decadal temperature variability over most land areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9513, https://doi.org/10.5194/egusphere-egu26-9513, 2026.
Whether observed Atlantic Multidecadal variability (AMV) is truly an intrinsic internal mode of climate variability or an externally forced response remains contentious, with conflicting literature that North Atlantic SST variability arises from internal dynamics or external forcing. The availability of several single-model initial-condition large ensembles (SMILEs) and new insights into potential biases in sea surface temperature (SST) variations offer a fresh opportunity to reassess this question. We show that SMILE ensembles provide strong evidence that AMV-like variability is largely externally forced. New insights into potential SST biases also raise questions about apparent early 20th-century oscillatory behaviour, suggesting that discrepancies between observations and climate model simulations may not arise solely from model deficiencies. SMILE models with stronger multidecadal variability show weaker agreement with observed AMV phasing, even in the best-performing individual ensemble members, suggesting that large internal model variability may obscure the forced signal. We conclude that future variations in North Atlantic SST will very likely be driven primarily by future anthropogenic activities.
How to cite: Liang, Y., Hawkins, E., McCarthy, G., and Thorne, P.: Atlantic Multidecadal Variability-like behaviour since 1850 is largely externally forced, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11706, https://doi.org/10.5194/egusphere-egu26-11706, 2026.
Empirical, data-driven models provide a complementary approach to dynamical models for simulating and forecasting weather and climate variability across daily to subseasonal timescales. We present ongoing work toward the development of a global, data-driven weather emulator for temperature and precipitation based on higher-order Linear Inverse Models (LIMs) formulated within the Empirical Model Reduction (EMR) framework. This formulation enables the representation of effective low-order dynamics, memory effects, and scale-dependent variability embedded in high-dimensional atmospheric fields. Rather than relying on a fixed EOF-based spatial decomposition, we explore a state-space approach in which the spatial basis is parameterized and optimized using Kalman filtering, thereby learning an optimal dynamical representation directly from the data.
The model is trained using a combination of NASA satellite observations and atmospheric reanalysis products. Near-surface temperature is modeled directly, while precipitation is represented using a pseudo-precipitation variable: precipitation equals observed rainfall where it occurs and otherwise corresponds to the negative air-column integrated water-vapor saturation deficit, defined as the amount of water vapor required to bring the atmospheric column to saturation at each vertical level. This formulation yields a continuous and dynamically meaningful representation of moist processes that facilitates the analysis of variability statistics across scales.
Model performance is evaluated in terms of its ability to reproduce observed variability statistics, temporal persistence, and subseasonal prediction skill, while dynamical diagnostics will be used to investigate the underlying sources of forecast skill. By focusing on the statistical and dynamical representation of variability, this work contributes to ongoing efforts to bridge data-driven modeling and theoretical perspectives on weather to climate variability across scales.
How to cite: Hébert, R. and Kravtsov, S.: A Global Data-Driven Weather Emulator for Temperature and Precipitation Based on Higher-Order Linear Inverse Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10397, https://doi.org/10.5194/egusphere-egu26-10397, 2026.
Rainfall exhibits strong variability, intermittency and a heavy-tailed distributions across a wide range of scales. Understanding and characterizing these features is needed for numerous applications such as quantifying the extremes or merging measurements from various sensors operating at different space-time scales.
This study presents a comprehensive multifractal analysis of high-resolution (30 s) 1D rainfall time series from the Paris region (2018 – 2024) using the Universal Multifractals (UM) framework. The data was collected with the help of optical disdrometers installed on the campus of Ecole nationale des Ponts et chausséee campus (https://hmco.enpc.fr/portfolio-archive/taranis-observatory/) UM framework has been widely used to characterize and simulate rainfall across wide range of scales with the help of only three parameters: the mean intermittency C₁, the multifractality index α and the non-conservation parameter H.
Spectral analysis identifies a clear scale break around 1 h, separating two distinct regimes. Coarse scales (>1h) are characterized by smooth, low-intermittency variability (spectral slope β ≈ 0.4), while fine scales (<1h) exhibit stronger spectral slope (β > 1). Accordingly, a regime-dependent analysis strategy is adopted: actual rainfall series are used at coarse scales to preserve large scale structure, while absolute values of fluctuation series are preferred at fine scales to reduce to study underlying conservative field and obtain cleaner scaling behaviour.
Analyses reveal strong multifractality (α ≈ 1.6 –1.7) and moderate intermittency (C₁ ≈ 0.12 – 0.45) at fine scale regimes. At coarser scale regimes, rainfall exhibits smoother variability with moderate multifractality (α < 1)and lower intermittency (C₁ ≈ 0.15–0.18). The UM parameters display good inter annual stability over 2018 – 2024, mild seasonal modulation (slightly higher C₁ in summer), and individual rain-event analyses were performed to examine event-to-event variability, indicating substantial heterogeneity between events.
These results demonstrate the relevance of the UM framework for quantitatively characterizing rainfall variability in the Paris region. Initial attempts to interpret the observed differences between fine and coarse scales regimes using a unique model will be presented.
Authors acknowledge partial financial support by the European Union as part of the Horizon Europe programme, Marie Skłodowska-Curie Actions, call COFUND-2022 and under grant agreement number 101126720; the France-Taiwan Ra2DW project (grant number by the French National Research Agency – ANR-23-CE01-0019-01).
How to cite: Balamurugan, A., Gires, A., Schertzer, D., and Tchiguirinskaia, I.: Universal Multifractals characterization of high-resolution rainfall in the Paris region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12081, https://doi.org/10.5194/egusphere-egu26-12081, 2026.
Urban areas are increasingly exposed to localized extreme rainfall events, with evidence suggesting a trend toward higher precipitation volumes and more frequent short-duration, high-intensity storms, posing major challenges to infrastructure resilience and public safety.
Urban hydrometeorology is characterized by highly nonlinear processes, strong interactions with geophysical systems, and pronounced variability across spatial and temporal scales, making both scientific understanding and operational management particularly demanding.
Within this context, the Fresnel platform is a state-of-the-art urban hydrometeorological observatory combining conceptual modeling approaches with extensive field measurements. One of its components, RadX, is a Software-as-a-Service (SaaS) platform that provides real-time and historical data from high-resolution sensors, together with a graphical user interface (GUI) for Multi-Hydro, a fully distributed and physically based hydrological model developed at École nationale des ponts et chaussées (ENPC). Multi-Hydro relies on four open-source software components representing different processes of the urban water cycle. The RadX GUI allows users to efficiently run simulations using dedicated high-performance computing resources, configure multiple scenarios for a given catchment, modify land-use parameters, and assess their impacts on drainage system discharges.
The originality of this contribution lies in the development of a new 3D isometric graphical interface based on an open-source game engine. Unlike conventional interfaces relying on the editing of raster matrices, this approach provides a more intuitive and spatially explicit visualization of land-use configurations. It enables a clearer representation and manipulation of Nature-based Solutions (NbS), such as porous pavements, whose implementation often remains abstract when expressed solely through raster data.
Beyond hydrological modeling, RadX also supports integrating shared value principles into business models to enhance resilience and sustainability. Within the PIA3 TIGA-CFHF project (“Construire au futur, habiter le futur”), it promotes an integrated vision where economic activities are situated within a complex socio-environmental system, aligning economic performance with environmental and societal objectives.
To support this transition, RadX aims to incorporates multifractal and advanced socio-economic analysis tools that enable organizations to assess performance and develop shared value–oriented strategies aligned with measurable environmental objectives.
The RadX platform is continuously improved through an iterative development process driven by feedback from students, academic researchers, and industry practitioners, and may integrate additional visualization or forecasting components in future developments.
How to cite: Drouen, G., Schertzer, D., Gires, A., Versini, P.-A., and Tchiguirinskaia, I.: Extending the Fresnel Platform with a 3D Isometric Graphical Interface for Land-Use Scenario Design in Hydrological Modeling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19829, https://doi.org/10.5194/egusphere-egu26-19829, 2026.
Clay shrink-swell (CSS) behavior arises from fluctuations in soil moisture driven by seasonal cycles of rainfall and drought. This phenomenon causes ground movements that can damage building foundations and infrastructure. In France, where approximately 54% of constructions are exposed to this hazard, CSS ranks as the second most significant category of natural disaster insurance claims.
The French central reinsurance fund reports that the average annual cost, calculated over a five-year sliding window, remained below €300 million in 2016. Since 2017, this figure has increased, reaching about €1.35 billion as of 2025. Climate change is expected to amplify droughts, heatwaves, and precipitation extremes, further intensifying CSS processes and potentially rendering their financial burden unsustainable for insurers.
To address this issue, we analyze meteorological data from the SAFRAN reanalysis provided by Météo-France, which offers daily observations at an 8 km spatial resolution across France since 1958. Our study applies geostatistical and multifractal techniques to characterize spatiotemporal variability, identify scale breaks, estimate extreme values, and examine spectral properties of key climatic variables. Specifically, we compute:
- Multifractality index (α): It measures the speed of change in intermittency;
- Mean singularity (C₁): Average singularity, characterizes intermittency;
- Maximum probable singularity (γₛ): maximum probable singularity.
Tracking these parameters from 1958 to 2025 enables us to identify regions most affected by changes in extremes. Analyses focus on variables influencing CSS behavior, including precipitation, temperature, evapotranspiration, and soil moisture index.
Finally, we compare the evolution of extremes in these climatic parameters with trends in CSS occurrence, quantified through insurance claims. This spatial and temporal comparison between multifractal indicators and affected areas provides insights into the relationship between the intensification of extreme meteorological events and the dynamics of clay shrink-swell processes.
This work is part of the IRGAK (inhibition of clay shrinkage-swelling by K+ ion injection) project, founded by the French Agency for Ecological Transition (ADEME). Its objective is to model the link between climate variability and CSS, and to propose adaptation strategies to mitigate a risk that is expected to increase significantly with climate change, leading to escalating insurance costs and growing socio-economic impacts.
How to cite: Tixier, C., Versini, P.-A., and Dardé, B.: Linking meteorological extremes to clay shrink–swell hazard: Insights from 65 years of climate data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12716, https://doi.org/10.5194/egusphere-egu26-12716, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot 1b
EGU26-23043 | Posters virtual | VPS23
Analysis of Vector-Field Multifractal CascadesThu, 07 May, 14:06–14:09 (CEST) vPoster spot 1b
How to cite: Thurler Rondon da Fonseca, J. F., Schertzer, D., da Silva Rocha Paz, I., and Tchiguirinskaia, I.: Analysis of Vector-Field Multifractal Cascades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23043, https://doi.org/10.5194/egusphere-egu26-23043, 2026.
