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A global analysis of the fractal properties of clouds revealing anisotropy of turbulence across scales Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-03-18 Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, Jérôme C. Riedi
Abstract. The deterministic motions of clouds and turbulence, despite their chaotic nature, nonetheless follow simple statistical power-law scalings: a fractal dimension D relates individual cloud perimeters p to measurement resolution, and turbulent fluctuations scale with separation distance through the Hurst exponent ℌ. It remains uncertain whether atmospheric turbulence is best characterized by
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Phytoplankton retention mechanisms in estuaries: a case study of the Elbe estuary Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-03-13 Laurin Steidle, Ross Vennell
Abstract. Due to their role as primary producers, phytoplankton are essential to the productivity of estuarine ecosystems. However, it is important to understand how these nearly passive organisms are able to persist within estuaries when river inflow results in a net outflow to the ocean. Estuaries also represent challenging habitats due to a strong salinity gradient. Little is known about how phytoplankton
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Variational techniques for a one-dimensional energy balance model Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-03-08 Gianmarco Del Sarto, Jochen Bröcker, Franco Flandoli, Tobias Kuna
Abstract. A one-dimensional climate energy balance model (1D EBM) is a simplified climate model for the zonally averaged global temperature profile, based on the Earth's energy budget. We examine a class of 1D EBMs which emerges as the parabolic equation corresponding to the Euler–Lagrange equations of an associated variational problem, covering spatially inhomogeneous models such as with latitude-dependent
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Scaling and intermittent properties of oceanic and atmospheric pCO2 time series and their difference Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-03-05 Kévin Robache, François G. Schmitt, Yongxiang Huang
Abstract. In this study the multi-scale dynamics of 38 oceanic and atmospheric pCO2 time series from fixed Eulerian buoys recorded with three-hour resolution are considered. The difference between these time series, the sea surface temperature and the sea surface salinity data were also studied. These series possess multi-scale turbulent-like fluctuations and display scaling properties from three hours
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A comparison of two causal methods in the context of climate analyses Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-02-27 David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, Stéphane Vannitsem
Abstract. Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely, the Liang–Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and we apply them to four different artificial models
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Extraction of periodic signals in Global Navigation Satellite System (GNSS) vertical coordinate time series using the adaptive ensemble empirical modal decomposition method Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-02-21 Weiwei Li, Jing Guo
Abstract. Empirical modal decomposition (EMD) is an efficient tool for extracting a signal from stationary or non-stationary time series and is enhanced in stability and robustness by ensemble empirical mode decomposition (EEMD). Adaptive EEMD further improves computational efficiency through adaptability in the white noise amplitude and set average number. However, its effectiveness in the periodic
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A two-fold deep-learning strategy to correct and downscale winds over mountains Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-02-13 Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, Nora Helbig
Abstract. Assessing wind fields at a local scale in mountainous terrain has long been a scientific challenge, partly because of the complex interaction between large-scale flows and local topography. Traditionally, the operational applications that require high-resolution wind forcings rely on downscaled outputs of numerical weather prediction systems. Downscaling models either proceed from a function
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Prognostic Assumed-PDF (DDF) Approach: Further Generalization and Demonstrations Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-02-09 Jun-Ichi Yano
Abstract. A methodology for directly predicting the time evolution of the assumed parameters for the distribution densities based on the Liouville equation, as proposed earlier, is extended to multi–dimensional cases as well as when the systems are constrained by integrals over a part of the variable range. The extended methodology is tested against a convective energy cycle system as well as the Lorenz’s
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Dynamically-optimal models of atmospheric motion Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-02-06 Alexander Voronovich
Abstract. A derivation of a dynamical core for the dry atmosphere in the absence of dissipative processes based on the least action (i.e., Hamilton’s) principle is presented. This approach can be considered the finite-element method applied to the calculation and minimization of the action. The algorithm possesses the following characteristic features: (1) For a given set of grid points and a given
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Part 2: Joint multifractal analysis of available wind power and rain intensity from an operational wind farm Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-02-02 Jerry Jose, Auguste Gires, Ernani Schnorenberger, Yelva Roustan, Daniel Schertzer, Ioulia Tchiguirinskaia
Abstract. Wind power production plays an important role in achieving UN’s (United nations) Sustainable development goal (SDG) 7 – affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding the uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its
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Part 1: Multifractal analysis of wind turbine power and the associated biases Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-02-02 Jerry Jose, Auguste Gires, Yelva Roustan, Ernani Schnorenberger, Ioulia Tchiguirinskaia, Daniel Schertzer
Abstract. The inherent variability in atmospheric fields, which extends over a wide range of temporal and spatial scales, also gets transferred to energy fields extracted off them. In the specific case of wind power generation, this can be seen in the theoretical power available for extraction in the atmosphere as well as the empirical power produced by turbines. Further the power produced by turbines
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Leading the Lorenz-63 system toward the prescribed regime by model predictive control coupled with data assimilation Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-30 Fumitoshi Kawasaki, Shunji Kotsuki
Abstract. In recent years, concerns have been raised regarding the intensification and increase of extreme weather events such as torrential rainfall and typhoons. To mitigate the damage caused by weather-induced disasters, recent studies have started developing weather control technologies to lead the weather to a desirable direction with feasible manipulations. This study proposes introducing the
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A new approach to understanding fluid mixing in process-study models of stratified fluids Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-30 Samuel George Hartharn-Evans, Marek Stastna, Magda Carr
Abstract. While well-established energy-based methods of quantifying diapycnal mixing in process-study numerical models are often used to provide information about when mixing occurs, and how much mixing has occurred, describing how and where this mixing has taken place remains a challenge. Moreover, methods based on sorting the density field struggle when the model is under-resolved and when there
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A Comparison of Two Nonlinear Data Assimilation Methods Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-24 Vivian A. Montiforte, Hans E. Ngodock, Innocent Souopgui
Abstract. Advanced numerical data assimilation (DA) methods, such as the four-dimensional variational (4DVAR) method, are elaborate and computationally expensive. Simpler methods exist that take time-variability into account, providing the potential of accurate results with a reduced computational cost. Recently, two of these DA methods were proposed for a nonlinear ocean model. The first method is
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Multifractal structure and Gutenberg-Richter parameter associated with volcanic emissions of high energy in Colima, México (years 2013–2015) Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-24 Marisol Monterrubio-Velasco, Xavier Lana, Raúl Arámbula-Mendoza
Abstract. The evolution of multifractal structures in physical processes, for instance, climatology, seismology or volcanology, contributes to detecting changes in the corresponding phenomena. The evolution of the multifractal structure of volcanic emissions of low, moderate, and high energy (Colima, México years 2013–2015) contributes to this research to detect quite evident signs of the immediacy
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Sensitivity of the polar boundary layer to transient phenomena Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-19 Amandine Kaiser, Nikki Vercauteren, Sebastian Krumscheid
Abstract. Numerical weather prediction and climate models encounter challenges in accurately representing flow regimes in the stably stratified atmospheric boundary layer and the transitions between them, leading to an inadequate depiction of regime occupation statistics. As a consequence, existing models exhibit significant biases in near-surface temperatures at high latitudes. To explore inherent
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Aggregation of slightly buoyant microplastics in 3D vortex flows Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-17 Irina I. Rypina, Lawrence J. Pratt, Michael Dotzel
Abstract. Although the movement and aggregation of microplastics at the ocean surface have been well studied, less is known about the subsurface. Within the Maxey–Riley framework governing the movement of small, rigid spheres with high drag in fluid, the aggregation of buoyant particles is encouraged in vorticity-dominated regions. We explore this process in an idealized model that is qualitatively
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Application of Advection-Diffusion Equation for Nonlinearly Evolving Precipitation Field Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-15 Ji-Hoon Ha
Abstract. Analytic solutions for the Advection-Diffusion equation have been explored in diverse scientific and engineering domains, aiming to understand transport phenomena, including heat and mass diffusion, along with the movement of water resources. Precipitation, a vital component of water resources, presents a modeling challenge due to the complex interplay between advection-diffusion effects
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On dissipation time scales of the basic second-order moments: the effect on the Energy and Flux-Budget (EFB) turbulence closure for stably stratified turbulence Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-15 Evgeny Kadantsev, Evgeny Mortikov, Andrey Glazunov, Nathan Kleeorin, Igor Rogachevskii
Abstract. The dissipation rates of the basic turbulent second-order moments are the key parameters controlling turbulence energetics and spectra, turbulent fluxes of momentum and heat, and playing a vital role in turbulence modelling. In this paper, we use the results of Direct Numerical Simulations (DNS) to evaluate dissipation rates of the basic turbulent second-order moments and revise the energy
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High-frequency Internal Waves, High-mode Nonlinear Waves and K-H Billows on the South China Sea's Shelf Revealed by Marine Seismic Observation Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-12 Linghan Meng, Haibin Song, Yongxian Guan, Shun Yang, Kun Zhang, Mengli Liu
Abstract. From July to September 2009, a set of multi-channel seismic data was collected in the northern shelf area of the South China Sea. After the data was processed, we observed a series of shoaling events on one of the survey lines, including high-frequency internal waves, high-mode nonlinear internal waves, and shear instability. Using theoretical results from previous numerical simulations and
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Stability and uncertainty assessment of geoelectrical resistivity model parameters: a new hybrid metaheuristic algorithm and posterior probability density function approach Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-10 Kuldeep Sarkar, Jit V. Tiwari, Upendra K. Singh
Abstract. Estimating a reliable subsurface resistivity structure using conventional techniques is challenging due to the nonlinear nature of the inverse problems. The performance of the inversion techniques can be pretty ambiguous based on the optimal error, although traditional methods have proven to be quite effective. In this work, the impacts of the constraints accessible from a borehole are examined
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Brief Communication: A modified Korteweg–de Vries equation for Rossby–Khantadze waves in a sheared zonal flow of the ionospheric E layer Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-09 Laila Zafar Kahlon, Hassan Amir Shah, Tamaz David Kaladze, Qura Tul Ain, Syed Assad Bukhari
Abstract not available
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The dynamic of ion Bernstein-Greene-Kruskal holes in plasmas with regularized κ-distributed electrons Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-08 Qiu Ping Lu, Cai Ping Wu, Hui Chen, Xiao Chang Chen, San Qiu Liu
Abstract. The dynamics of ion holes (IHs) in plasmas where electrons follow the regularized Kappa distribution (RKD) and ions follow the Maxwellian distribution (MD) are investigated based on the Bernstein-Greene-Kruskal (BGK) method. The results show that the depth of the IHs, the allowed combination of width and amplitude to support physically plausible IHs equilibrium depend on the spectral index
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Multi-level data assimilation for simplified ocean models Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2024-01-04 Florian Beiser, Håvard Heitlo Holm, Kjetil Olsen Lye, Jo Eidsvik
Abstract. Multi-level Monte Carlo methods have established as a tool in uncertainty quantification for decreasing the computational costs while maintaining the same statistical accuracy as in single-level Monte Carlo. Lately, there have also been theoretical efforts to use similar ideas to facilitate multi-level data assimilation. By applying a multi-level ensemble Kalman filter for assimilating sparse
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Existence and influence of mixed states in a model of vegetation patterns Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-12-11 Lilian Vanderveken, Marina Martínez Montero, Michel Crucifix
Abstract. The Rietkerk vegetation model is a system of partial differential equations, which has been used to understand the formation and dynamics of spatial patterns in vegetation ecosystems, including desertification and biodiversity loss. Here, we provide an in-depth bifurcation analysis of the vegetation patterns produced by Rietkerk's model, based on a linear stability analysis of the homogeneous
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Uncertainties, complexities and possible forecasting of Volcán de Colima energy emissions (Mexico, years 2013–2015) based on a fractal reconstruction theorem Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-12-08 Marisol Monterrubio-Velasco, Xavier Lana, Raúl Arámbula-Mendoza
Abstract. The effusive–explosive energy emission process in a volcano is a dynamic and complex physical phenomenon. The importance of quantifying this complexity in terms of the physical and mathematical mechanisms that govern these emissions should be a requirement for deciding to apply a possible forecasting strategy with a sufficient degree of certainty. The complexity of this process is determined
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Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-12-08 John Bjørnar Bremnes, Thomas N. Nipen, Ivar A. Seierstad
Abstract. During the last two years, tremendous progress in global data-driven weather models trained on numerical weather prediction (NWP) re-analysis data has been made. The most recent models trained on the ERA5 at 0.25° resolution demonstrate forecast quality on par with ECMWF's high-resolution model with respect to a wide selection of verification metrics. In this study, one of these models, the
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Bridging classical data assimilation and optimal transport Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-12-05 Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, Yelva Roustan
Abstract. Because optimal transport acts as displacement interpolation in physical space rather than as interpolation in value space, it can potentially avoid double penalty errors. As such it provides a very attractive metric for non-negative physical fields comparison – the Wasserstein distance – which could further be used in data assimilation for the geosciences. The algorithmic and numerical implementations
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Selecting and weighting dynamical models using data-driven approaches Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-12-01 Pierre Le Bras, Florian Sévellec, Pierre Tandeo, Juan Ruiz, Pierre Ailliot
Abstract. In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. To obtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted. The simplest method, namely the model democracy, gives equal weights to all models, while more advanced approaches base weights on agreement with available observations. Here, we
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Downscaling of surface wind forecasts using convolutional neural networks Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-29 Florian Dupuy, Pierre Durand, Thierry Hedde
Abstract. Near-surface winds over complex terrain generally feature a large variability at the local scale. Forecasting these winds requires high-resolution numerical weather prediction (NWP) models, which drastically increase the duration of simulations and hinder them in running on a routine basis. Nevertheless, downscaling methods can help in forecasting such wind flows at limited numerical cost
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Improving Ensemble Data Assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC) Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-28 Man-Yau Chan
Abstract. Small forecast ensemble sizes (< 100) are common in the ensemble data assimilation (EnsDA) component of geophysical forecast systems, thus limiting the error-constraining power of EnsDA. This study proposes an efficient and embarrassingly parallel method to generate additional ensemble members: the Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC; "peace gee see"). Such
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Stieltjes functions and spectral analysis in the physics of sea ice Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-23 Kenneth M. Golden, N. Benjamin Murphy, Daniel Hallman, Elena Cherkaev
Abstract. Polar sea ice is a critical component of Earth’s climate system. As a material, it is a multiscale composite of pure ice with temperature-dependent millimeter-scale brine inclusions, and centimeter-scale polycrystalline microstructure which is largely determined by how the ice was formed. The surface layer of the polar oceans can be viewed as a granular composite of ice floes in a sea water
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Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by high-frequency radar, and its relation to wind regimes using the maximum-entropy principle Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-21 Sofia Flora, Laura Ursella, Achim Wirth
Abstract. Two years (2021–2022) of high-frequency-radar (HFR) sea surface current data in the Gulf of Trieste (northern Adriatic Sea) are analysed. Two different timescales are extracted using a superstatistical formalism: a relaxation time and a larger timescale over which the system is Gaussian. We propose obtaining an ocean current probability density function (PDF) combining (i) a Gaussian PDF
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A quest for precipitation attractors in weather radar archives Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-21 Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis Vasileios Sideris, Urs Germann, Isztar Zawadzki
Abstract. Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space dimensions of the attractor are defined using the domain-scale statistics of precipitation fields
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Robust weather-adaptive post-processing using model output statistics random forests Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-20 Thomas Muschinski, Georg J. Mayr, Achim Zeileis, Thorsten Simon
Abstract. Physical numerical weather prediction models have biases and miscalibrations that can depend on the weather situation, which makes it difficult to post-process them effectively using the traditional model output statistics (MOS) framework based on parametric regression models. Consequently, much recent work has focused on using flexible machine learning methods that are able to take additional
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Computation of covariant lyapunov vectors using data assimilation Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-15 Shashank Kumar Roy, Amit Apte
Abstract. Computing Lyapunov vectors from partial and noisy observations is a challenging problem. We propose a method using data assimilation to approximate the Lyapunov vectors using the estimate of the underlying trajectory obtained from the filter mean. We then extensively study the sensitivity of these approximate Lyapunov vectors and the corresponding Oseledets' subspaces to the perturbations
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Evolution of small-scale turbulence at large Richardson numbers Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-15 Lev Ostrovsky, Irina Soustova, Yuliya Troitskaya, Daria Gladskikh
Abstract. The theory of stratified turbulent flow developed earlier by the authors is applied to data from the upper oceanic level to confirm that small-scale turbulence can be amplified and supported at a quasi-stationary level even at large gradient Richardson numbers due to the exchange between kinetic and potential energies. Using the mean profiles of Brunt-Väisälä frequency and vertical current
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Rate-induced tipping in ecosystems and climate: the role of unstable states, basin boundaries and transient dynamics Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-11-03 Ulrike Feudel
Abstract. The climate system as well as ecosystems might undergo relatively sudden qualitative changes in the dynamics when environmental parameters or external forcings vary due to anthropogenic influences. The study of these qualitative changes, called tipping phenomena, requires the development of new methodological approaches that allow phenomena observed in nature to be modeled, analyzed and predicted
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Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-23 Kenta Kurosawa, Shunji Kotsuki, Takemasa Miyoshi
Abstract. This study explores coupled land–atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. This study integrates a land DA component into a global atmospheric DA system of the Nonhydrostatic ICosahedral Atmospheric Model and the local ensemble transform Kalman filter (NICAM-LETKF) and performs both strongly and weakly coupled
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Representation learning with unconditional denoising diffusion models for dynamical systems Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-20 Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, Charlotte Durand
Abstract. We propose denoising diffusion models for data-driven representation learning of dynamical systems. In this type of generative deep learning, a neural network is trained to denoise and reverse a diffusion process, where Gaussian noise is added to states from the attractor of a dynamical system. Iteratively applied, the neural network can then map samples from isotropic Gaussian noise to the
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Learning Extreme Vegetation Response to Climate Forcing: A Comparison of Recurrent Neural Network Architectures Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-17 Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, Karin Mora
Abstract. Vegetation state variables are key indicators of land-atmosphere interactions characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the potential of neural networks in capturing vegetation state responses, including extreme behavior driven by atmospheric conditions. While machine learning methods, particularly neural networks
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Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-17 Weiwei Li, Jing Guo
Abstract. Ensemble Empirical Mode Decomposition (EEMD) has been widely used in the data analysis. Adaptive EEMD further improves computational efficiency through the adaptability in the white noise amplitude and set average number. However, its effectiveness of the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding on the inevitable missing data
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The joint application of a metaheuristic algorithm and a Bayesian statistics approach for uncertainty and stability assessment of nonlinear magnetotelluric data Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-10 Mukesh,, Kuldeep Sarkar, Upendra K. Singh
Abstract. In this paper, we have developed three algorithms, namely hybrid weighted particle swarm optimization (wPSO) with the gravitational search algorithm (GSA), known as wPSOGSA; GSA; and PSO in MATLAB to interpret one-dimensional magnetotelluric (MT) data for some corrupted and non-corrupted synthetic data, as well as two examples of MT field data over different geological terrains: (i) geothermally
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Review article: Interdisciplinary Perspectives on Climate Sciences – Highlighting Past and Current Scientific Achievements Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-10 Vera Melinda Galfi, Tommaso Alberti, Lesley De Cruz, Christian L. E. Franzke, Valerio Lembo
Abstract. In the online seminar series “Perspectives on Climate Sciences: from Historical Developments to Future Frontiers”, which took place during 2020–2021, well-known and established scientists from several fields – including mathematics, physics, climate science and ecology – presented their perspectives on the evolution of climate science and on relevant scientific concepts. This special issue
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Modeling of terrain effect in magnetotelluric data from Garhwal Himalaya Region Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-05 Suman Saini, Deepak Kumar Tyagi, Sushil Kumar, Rajeev Sehrawat
Abstract. The magnetotelluric method (MT) is one of the most effective geophysical techniques for studying the deep structure of the Earth's crust, particularly in steep terrain like the Garhwal Himalaya region. The MT responses are distorted as a result of the undulated/rugged terrain. Such responses, if not corrected, can lead to a misinterpretation of MT data for the geoelectrical structures. In
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A comparison of two causal methods in the context of climate analyses Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-05 David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, Stéphane Vannitsem
Abstract. Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely the Liang-Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and apply them to four different artificial models of
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Review article: Dynamical systems, algebraic topology and the climate sciences Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-05 Michael Ghil, Denisse Sciamarella
Abstract. The definition of climate itself cannot be given without a proper understanding of the key ideas of long-term behavior of a system, as provided by dynamical systems theory. Hence, it is not surprising that concepts and methods of this theory have percolated into the climate sciences as early as the 1960s. The major increase in public awareness of the socio-economic threats and opportunities
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Phytoplankton Retention Mechanisms in Estuaries: A Case Study of the Elbe Estuary Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-10-05 Laurin Steidle, Ross Vennell
Abstract.Due to their role as primary producers, phytoplankton are essential to the productivity of estuarine ecosystems. However, it is important to understand how these nearly passive organisms are able to persist within estuaries, when river inflow results in a net outflow to the ocean. Estuaries are also representing challenging habitats due to a strong salinity gradient. So far, little is known
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The Sampling Method for Optimal Precursors of ENSO Event Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-09-20 Bin Shi, Junjie Ma
Abstract. El Niño-Southern Oscillation (ENSO) is one of the significant climate phenomena, which appears periodically in the tropic Pacific. The intermediate coupled ocean-atmosphere Zebiak-Cane (ZC) model is the first and classical one designed to numerically forecast the ENSO events. Traditionally, the conditional nonlinear optimal perturbation (CNOP) approach has been used to capture optimal precursors
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How far can the statistical error estimation problem be closed by collocated data? Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-09-19 Annika Vogel, Richard Ménard
Abstract. Accurate specification of the error statistics required for data assimilation remains an ongoing challenge, partly because their estimation is an underdetermined problem that requires statistical assumptions. Even with the common assumption that background and observation errors are uncorrelated, the problem remains underdetermined. One natural question that could arise is as follows: can
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Transformation of internal solitary waves under ridged ice cover Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-09-19 Kateryna Terletska, Vladimir Maderich, Elena Tobisch
Abstract. Internal wave-driven mixing is an important factor in the balance of heat and salt fluxes in the polar regions of the ocean. The breaking internal waves at the edge of the ice cover can essentially enhance the mixing and melting of ice in the Arctic Ocean and Antarctica. The internal solitary waves (ISWs) are generated by various sources, including tidal currents over the bottom topography
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Quantification of Magnetosphere – Ionosphere coupling timescales using mutual information: response of terrestrial radio emissions and ionospheric/magnetospheric currents Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-09-13 Alexandra Ruth Fogg, Caitriona M. Jackman, Sandra C. Chapman, James E. Waters, Aisling Bergin, Laurent Lamy, Karine Issautier, Baptiste Cecconi, Xavier Bonnin
Abstract. Auroral kilometric radiation (AKR) is a terrestrial radio emission, excited by the same accelerated electrons which excite auroral emissions. Although it is well correlated with auroral and geomagnetic activity, the coupling timescales between AKR and different magnetospheric/ionospheric regions are yet to be determined. Estimation of these coupling timescales is non-trivial as a result of
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Variational Techniques for a One-Dimensional Energy Balance Model Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-09-12 Gianmarco Del Sarto, Jochen Bröcker, Franco Flandoli, Tobias Kuna
Abstract. A one-dimensional climate energy balance model (1D-EBM) is a simplified climate model that describes the evolution of Earth's temperature based on the planet's energy budget. In this study, we examine a 1D-EBM that incorporates a bifurcation parameter representing the impact of carbon dioxide on the energy balance. Firstly, independent of the value of the additive parameter, we demonstrate
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A new approach to understanding fluid mixing in process-study models of stratified fluids Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-09-06 Samuel George Hartharn-Evans, Marek Stastna, Magda Carr
Abstract. While well established energy-based methods of quantifying diapycnal mixing in process-study numerical models are often used to provide information about when mixing occurs, and how much much mixing has occurred, describing how and where this mixing has taken place remains a challenge. Moreover, methods based on sorting the density field struggle with under resolution and uncertainty as to
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Review article: Scaling, dynamical regimes, and stratification. How long does weather last? How big is a cloud? Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-08-16 Shaun Lovejoy
Abstract. Until the 1980s, scaling notions were restricted to self-similar homogeneous special cases. I review developments over the last decades, especially in multifractals and generalized scale invariance (GSI). The former is necessary for characterizing and modelling strongly intermittent scaling processes, while the GSI formalism extends scaling to strongly anisotropic (especially stratified)
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Quantum Data Assimilation: A New Approach to Solve Data Assimilation on Quantum Annealers Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-08-08 Shunji Kotsuki, Fumitoshi Kawasaki, Masanao Ohashi
Abstract. Data assimilation is a crucial component in the Earth science field, enabling the integration of observation data with numerical models. In the context of numerical weather prediction (NWP), data assimilation is particularly vital for improving initial conditions and subsequent predictions. However, the computational demands imposed by conventional approaches, which employ iterative processes
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What Drives Plate Motion? Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-08-03 Yongfeng Yang
Abstract. Plate motion is a remarkable Earth process that is widely ascribed to two primary driving forces: ridge push and slab pull. With the release of the first- and second-order stress fields in 1989, it was found that the observed stresses are mainly distributed on the uppermost brittle part of the lithosphere. A modeling analysis, however, reveals that the stress produced by ridge push is mainly
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Aggregation of Slightly Buoyant Microplastics in Three-Dimensional Vortex Flows Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-07-27 Irina I. Rypina, Lawrence J. Pratt, Michael Dotzel
Abstract. Although the movement and aggregation of microplastics at the ocean surface has been well studied, less is known about the subsurface. Within the Maxey-Riley framework governing the movement of small spheres with high drag in fluid, aggregation of buoyant particles is encouraged in vorticity-dominated regions. We explore this process in an idealized model of a three-dimensional eddy with
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An approach for projecting the timing of abrupt winter Arctic sea ice loss Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-07-21 Camille Hankel, Eli Tziperman
Abstract. Abrupt and irreversible winter Arctic sea ice loss may occur under anthropogenic warming due to the disappearance of a sea ice equilibrium at a threshold value of CO2, commonly referred to as a tipping point. Previous work has been unable to conclusively identify whether a tipping point in winter Arctic sea ice exists because fully coupled climate models are too computationally expensive
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Using orthogonal vectors to improve the ensemble space of the ensemble Kalman filter and its effect on data assimilation and forecasting Nonlinear Process. Geophys. (IF 2.2) Pub Date : 2023-07-21 Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, Yung-An Lee
Abstract. The space spanned by the background ensemble provides a basis for correcting forecast errors in the ensemble Kalman filter. However, the ensemble space may not fully capture the forecast errors due to the limited ensemble size and systematic model errors, which affect the assimilation performance. This study proposes a new algorithm to generate pseudomembers to properly expand the ensemble