Recent years have seen a substantial progress in the understanding of the nonlinear and stochastic processes responsible for important dynamical aspects of the complex Earth system. The Earth system is a complex system with a multitude of spatial and temporal scales which interact nonlinearly with each other. For understanding this complex system new methods from dynamical systems, complex systems theory, complex network theory, statistics, machine learning and climate and Earth sciences are needed.
In this context the session is open to contributions on all aspects of the nonlinear and stochastic dynamics of the Earth system, including the atmosphere, the ocean and the climate system. Communications based on theoretical and modeling studies, as well as on experimental investigations are welcome. Studies that span the range of model hierarchy from idealized models to complex Earth System Models (ESM), data driven models, use observational data and also theoretical studies are particularly encouraged.
vPICO presentations: Wed, 28 Apr
Recently, there has been much interest in issuing subseasonal to seasonal (S2S) forecasts, although their skill is often debated. In addition to large systematic errors, ensemble systems are often overconfident, i.e. have incorrect information about the uncertainty of a particular forecast. Stochastic parameterization schemes are used routinely to remedy the problem of overconfidence, but also have the potential to reduce systematic model errors.
Here, we study the impact of adding a stochastic parameterization scheme in coupled simulations with the climate model CESM. Physical processes associated with S2S-predictability, like the Madden-Julian Oscillation (MJO) and Northern Hemispheric blocking are analyzed. In the simulations with a stochastic parameterization scheme, the northward propagation of the MJO is captured better, leading to an improved MJO lifecycle. The impact on other atmospheric fields like precipitation and winds will be discussed.
How to cite: Berner, J.: Benefits of stochastic parameterizations in subseasonal to seasonal (S2S) forecasts , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16321, https://doi.org/10.5194/egusphere-egu21-16321, 2021.
The discovery of dynamical equations governing time-evolving observations issued from a complex dynamical system requires a statistical formulation, since information concerning neglected variables or unobserved degrees of freedom is necessarily incomplete. At the same time, an equation that is closed within a small number of observables is often obtained only by approximations. Thus, the relevance of approximations must be understood before any attempt to derive a closed set of equations. This is where closure formalisms are of usefulness and the corresponding mathematical structures serve as a guide for knowing what to approximate. Many such formalisms are available from turbulence theory, quantum field theories, to statistical physics.
Observables of interest often include response functions, spectra of fluctuations, or low-order moments, etc. These quantities correspond to moments of the full probability density function (PDF), the mother of all system's statistics but itself beyond the reach of standard closure theories, except in special cases. Yet, to have, for a given choice of observables, a (good) class of closure models able to produce out-of-sample reliable occurrences, is of prime importance. When derived on a firm basis, such closure models may indeed allow for analyzing in greater details certain features of a given phenomenon for which available data are limited, by e.g. drawing a large ensemble of statistical emulations of this phenomenon, from the closure model.
This is the goal that will be pursued here for a special but common class of clouds, namely continental shallow cumulus (Cu) that can be found from low to mid/high latitudes, across a wide range of scales, and that play a growing role in the Earth's radiative budget. These clouds typically organize through a variety of patterns such as cloud streets, clusters, or mesoscale arcs. Based on observables suitably extracted from high-resolution satellite observations, it will be shown that the efficient learning of hidden, stochastic, variables along with their interaction laws with the observed variables is key for the derivation of relevant stochastic data-driven models. To do so, our approach will rely on the Mori-Zwanzig closure theory to guide the search of the constitutive elements, on one hand, while their learning will exploit recent advances in data-driven stochastic modeling techniques, on the other.
As a byproduct, dynamical equations involving a few variables are learned from high-resolution satellite observations of continental shallow Cu. These equations will be shown to take the form of differential equations that include lagged effects, and are driven by a spatially correlated white noise. It will be finally shown that the combined effects of these terms allow to generate easily statistical ensembles of shallow Cu that exhibit a wide range of spatio-temporal variability while displaying consistency with the shallow Cu's organizational and multiscale features, from observations. Based on such large ensembles, new physical insights are attainable and their interpretation will be discussed. This work is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [Grant Agreement No. 810370].
How to cite: Chekroun, M. D., Dror, T., Altaratz, O., and Koren, I.: Data-driven stochastic model discovery of organized clouds dynamics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14142, https://doi.org/10.5194/egusphere-egu21-14142, 2021.
Stochastic subgrid-scale parametrizations aim to incorporate effects of unresolved processes in an effective model by sampling from a distribution usually described in terms of resolved modes. This is an active research area in climate, weather and ocean science where processes evolved in a wide range of spatial and temporal scales. In this study, we evaluate the performance of conditional generative adversarial network (GAN) in parametrizing subgrid-scale effects in a finite-difference discretization of stochastically forced Burgers equation. We define resolved modes as local spatial averages and deviations from these averages are the unresolved degrees of freedom. We train Wesserstein GAN (WGAN) conditioned on the resolved variables to learn the distribution of subgrid flux tendencies for resolved modes and, thus, represent the effect of unresolved scales. Resulting WGAN is then used in an effective model to reproduce the statistical features of resolved modes. We demonstrate that various stationary statistical quantities such as spectrum, moments, autocorrelation, etc. are well approximated by this effective model.
How to cite: Timofeyev, I. and Alcala, J.: Subgrid-scale parametrization of unresolved scales in forced Burgers equation using Generative Adversarial Networks (GAN), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6910, https://doi.org/10.5194/egusphere-egu21-6910, 2021.
Intrinsic ocean variability is essential for climate prediction because it is less sensitive to stochastic process, but it is very difficult to be identified due to internal climate variability. Here we use regional interactive ensemble applied on ocean-atmosphere interface (RIE-OA) to suppress atmosphere stochastic variability and to reveal intrinsic variability as well as to understand climate dynamic across multiple timescales. Five atmosphere general circulation models (AGCM) are coupled to an ocean general circulation model (OGCM) over the North Atlantic basin (20oN to Denmark Strait and Greenland-Scotland ridge). The OGCM interacts with fluxes from a selected AGCM globally except over the North Atlantic basin where the OGCM interacts with the ensemble averaged fluxes from the five AGCMs. The five AGCMs, on the other hand, feel the same ocean states. Hence, the atmosphere stochastic variability impacting the ocean is one-fifth weaker than stand-alone configuration (control case). This leads to reduction of the local climate variability, such as Atlantic Multidecadal Variability, but should not reduce intrinsic variability. Comparing control cases and RIE-OA case, we found the intrinsic ocean variability, a narrow-banded low-frequency (about 8 to 20 years) signal over the North Atlantic Subtropical Gyre, is not influenced by the weakened stochastic variability. More details will be discussed.
How to cite: Shen, M.-L., Keenlyside, N., and Chiu, P.-G.: Understanding intrinsic ocean variability by suppressing regional stochastic variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14346, https://doi.org/10.5194/egusphere-egu21-14346, 2021.
I will present a comprehensive inter-comparison of linear regression, stochastic and deep-learning-based models for reduced-order statistical modelling of the simplified ocean circulation. The reference dataset is provided by the top 150 empirical orthogonal functions (EOFs) and principal components (PCs) of an idealized, eddy-resolving, double-gyre ocean model. Our goal is to have a systematic and comprehensive assessment of the skills, costs and complexities of all the models considered.
The model based on linear regression is considered as a baseline. Additionally, we investigate stochastic models (linear regression plus additive-noise and a multi-level approach), deep-learning models (a feed-forward Artificial Neural Network (ANN), a Long Short Term Memory (LSTM)), and deep-learning augmented linear regression models (also called hybrid models). We also explored stochastically improved deep learning methods by adding spatially correlated white noise in the deep learning models to account for the residuals and left out variance in the discarded PCs. The assessment metrics considered are climatology, variance, RMSE, instantaneous correlation coefficients, frequency map, prediction horizon, and computational costs for training and predictions.
Until now, we found that the hybrid LSTM models perform the best, followed by the multi-level linear stochastic model and multiplicative white noise model. Additionally, hybrid models found to perform better when augmented by spatially correlated white noise. This suggests that an amalgam of physics, memory effects, and stochasticity provides the best strategy for low-order representation of oceanic process. However, LSTM was also found to be most expensive to train and forecast amongst all. Skills of simple stochastic models are similar to those of the linear regression model but superior to those of the pure deep learning models, as evidenced by relatively better frequency maps, infinite prediction horizon, and low running cost.
Overall, our analysis promotes multi-level stochastic methods, with memory effects, and stochastic hybrid methods for low-dimensional ocean models as a more practical option when compared to pure deep-learning solutions as they are more accurate, stable, and low-cost. Furthermore, this is an ongoing research project and more updated results will be discussed at the time of presentation.
How to cite: Agarwal, N., Kondrashov, D., Dueben, P., Ryzhov, E., and Berloff, P.: A comparison of data-driven approaches to build low-dimensional ocean models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10194, https://doi.org/10.5194/egusphere-egu21-10194, 2021.
Moist equatorial waves, responsible for a large fraction of synoptic and intraseasonal tropical variability, are visible in satellite observations of cloud top temperature and outgoing longwave radiation, with familiar dispersion relations appearing in space time spectra once a background red-like spectrum is removed (Kiladis et al., 2009).
Studies have suggested that the large-scale planetary waves in the equatorial region can be excited by smaller scale gravity waves (Yang & Ingersoll, 2013), baroclinic waves from the extratropics (Wedi & Smolarkiewicz, 2010), or localized synoptic scale heating (Gill 1980, 1982). In this study we examine the possibility that a continuous forcing of anomalies at the mesoscale can, through a turbulent upscale cascade, excites these waves, thus explaining both: the peaks along dispersion relations and the background red spectrum.
Our underlying assumption is that within the tropics (excluding wave forcing from the extratropics), a prerequisite to form coherent heating of ≈1000 km zonal length on an aqua planet is self-aggregation. Over the last two decades, self-aggregation has been studied over a wide range of scenarios up to the atmospheric mesoscale. In this study we examine the aggregation from the mesoscale up to the planetary scale, by applying mesoscale stochastic forcing in idealized spherical shallow water model. In particular, we examine the dependence of the large scale spectra on the field being stochastically forced, and on the existence of moisture.
We find that indeed, continuous stochastic forcing at the mesoscale can excite two-dimensional turbulence and linear tropical wave modes. When vorticity or moisture are forced in the simulations at wavenumber 100, a classical -5/3 slope of the eddy kinetic energy spectrum forms in an upscale energy cascade up to the planetary scale. Furthermore, equatorial waves emerge and Wheeler-Kiladis plots reveal a rich temporal and spatial structure of Rossby, Kelvin, Yanai, and Inertial Gravity waves. On the other hand, stochastic forcing of the divergence, or height fields only leads to a turbulent field when applied at planetary scales. Some explanations for this strong dependence on the type of forcing, and the role of moisture, will be discussed.
How to cite: Schröttle, J., Suhas, D., Harnik, N., and Sukhatme, J.: Coexistence of equatorial waves & turbulence in moist shallow water simulations , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7722, https://doi.org/10.5194/egusphere-egu21-7722, 2021.
Atmosphere and ocean dynamics display many complex features and are characterized by a wide variety of processes and couplings across different timescales. Here we use Multivariate Empirical Mode Decomposition (MEMD; Rehman and Mandic, 2010) to investigate the multivariate and multiscale properties of a low-order model of the ocean-atmosphere coupled dynamics (Vannitsem, 2017). The MEMD allows us to decompose the original data into a series of oscillating patterns with time-dependent amplitude and phase by exploiting the local features of data and without any a priori assumptions on the decomposition basis. Moreover, each oscillating pattern, usually named Multivariate Intrinsic Mode Function (MIMF), can be used as a source of local (in terms of scale) fluctuations and information. This information allows us to derive multiscale measures when looking at the behavior of the generalized fractal dimensions at different scales (Hentschel and Procaccia, 1983) that can be seen as a sort of multivariate and multiscale generalized fractal dimensions (Alberti et al., 2020). With these two approaches, we demonstrate that the coupled ocean-atmosphere dynamics presents a rich variety of common features, although with a different nature of the fractal properties between the ocean and the atmosphere at different timescales. The MEMD results allow us to capture the main dynamics of the phase-space trajectory that can be used for reconstructing the skeleton of the phase-space dynamics, while the evaluation of the fractal dimensions at different timescales characterize the intrinsic complexity of oscillating patterns that can be related to the attractor properties. Our results support the interpretation of the coupled ocean-atmosphere dynamics as well as the investigation of general deterministic-chaotic dissipative dynamical systems in terms of invariant manifolds, bifurcations, as well as (strange) attractors in their phase-space, whose geometric and topological properties are a reflection of the dynamical regimes of the system at different scales. We compare the results obtained for the low-order dynamical model with those derived from the reanalysis data and demonstrate that a similar scale-dependent behavior is found, thus also confirming the suitability of the proposed system to model the ocean-atmosphere dynamics at different timescales and to describe topological and geometrical features of its phase-space.
Alberti, T., Consolini, G., Ditlevsen, P. D., Donner, R. V., Quattrociocchi, V. (2020). Multiscale measures of phase-space trajectories. Chaos 30, 123116.
Alberti, T., Donner, R. V., and Vannitsem, S. (2021). Multiscale fractal dimension analysis of a reduced order model of coupled ocean-atmosphere dynamics. Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2020-96, in review.
Hentschel, H. G. E., Procaccia, I. (1983). The infinite number of generalized dimensions of fractals and strange attractors. Physica D 8, 435–444.
Rehman, N., Mandic, D. P. (2010). Multivariate empirical mode decomposition. Proceedings of the Royal Society A, 466, 1291–1302.
Vannitsem S., Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics, Chaos, 27, 032101, 2017.
How to cite: Alberti, T., Donner, R., and Vannitsem, S.: On the multiscale fractal features of a low-order coupled ocean-atmosphere model in comparison with reanalysis data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-135, https://doi.org/10.5194/egusphere-egu21-135, 2020.
We used the Hurst Space Analysis (HSA), a technique that we recently developed to cluster or differentiate records from an arbitrary complex system based on the presence and influence of cycles in their statistical functions, to classify climatic data from climatically homogeneous regions according to their long-term persistent (LTP) character. For our analysis we selected four types of HadCRUT4 cells of temperature records over regions homogeneous in both climate and topography, which are sufficiently populated with ground observational stations. These cells bound: Pannonian and West Siberian plains, Rocky Mountains and Himalayas mountainous regions, Arctic and sub-Arctic climates of Island and Alaska, and Gobi and Sahara deserts.
It was shown for LTP records across different complex systems that their statistical functions are rarely, as in theory, and due to their power-law dynamics, ideal linear functions on log-log graphs of time scale dependence. Instead, they frequently exhibit existence of transient crossovers in behavior, signs of trends that arise as effects of periodic or aperiodic cycles. HSA was developed so to use methods of scaling analysis – the time dependent Detrended Moving Average (tdDMA) algorithm and Wavelet Transform spectral analysis (WTS) – to analyse these cycles in data. In HSA we defined a space of p-vectors hts (that we dubbed the Hurst space) that represent record ts in any dataset, which are populated by tdDMA scaling exponents α calculated on subsets of time scale windows of time series ts that bound cyclic peaks in their WTS. In order to be able to quantify any such time series ts with a single number, we projected their relative unit vectors sts = (hts – m) / (∑i=1n (hits - mi)2)1/2 (with mi = 1/n ∑ts=1n hits) onto a unit vector e of an assigned preferred direction in the Hurst space. The definition of the ’preferred’ direction depends on the characteristic behavior one wants to investigate with HSA - projection of unit vectors sts of any record with a ’preferred’ behavior onto the unit vector e is then always positive.
By using HSA we were able to cluster records from our selected climatically homogeneous regions according to the 'preferred' characteristic that those do not 'belong to the ocean'. We further extended HSA constructed from our dataset to group teleconnection indices that may influence their climate dynamics. In this way our results suggested that there probably exists a necessity to examine cycles in climate records as important elements of natural variability.
How to cite: Blesic, S., Sarvan, D., Tosic, M., and Borovinic, M.: Classification of time series of temperature variations from climatically homogeneous regions using Hurst Space Analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-238, https://doi.org/10.5194/egusphere-egu21-238, 2020.
It has been well recognized that, for most climatic records, their current states are influenced by both past conditions and current dynamical excitations. However, how to properly use this idea to improve the climate predictive skills, is still an open question. In this study, we evaluated the decadal hindcast experiments of 11 models (participating in phase 5 of the Coupled Model Intercomparison Project, CMIP5) in simulating the effects of past conditions (memory part, M(t)) and the current dynamical excitations (non-memory part, ε(t)). Poor skills in simulating the memory part of surface air temperatures (SAT) are found in all the considered models. Over most regions of China, the CMIP5 models significantly overestimated the long-term memory (LTM) of SAT. While in the southwest, the LTM was significantly underestimated. After removing the biased memory part from the simulations using fractional integral statistical model (FISM), the remaining non-memory part, however, was found reasonably simulated in the multi-model means. On annual scale, there were high correlations between the simulated and the observed ε(t) over most regions of the country, and for most cases they had the same sign. These findings indicated that the current errors of dynamical models may be partly due to the unrealistic simulations of the impacts from the past. To improve predictive skills, a new strategy was thus suggested. As FISM is capable of extracting M(t) quantitatively, by combining FISM with dynamical models (which may produce reasonable estimations of ε(t)), improved climate predictions with the effects of past conditions properly considered may become possible.
How to cite: Xiong, F., Yuan, N., Ma, X., Lu, Z., and Gao, J.: On memory and non-memory parts of surface air temperatures over China: can they be simulated by decadal hindcast experiments in CMIP5?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3628, https://doi.org/10.5194/egusphere-egu21-3628, 2021.
It is well recognized that climate predictability has three origins: (i)climate memory (inertia of the climate system) that accumulated from the historical conditions, (ii) responses to external forcings, and (iii) dynamical interactions of multiple processes in the climate system. However, how to systematically identify these predictable sources is still an open question. Here, we combine a recently developed Fractional Integral Statistical Model (FISM) with a Variance Decomposition Method (VDM), to systematically estimate the potential sources of predictability. With FISM, one can extract the memory component from the considered variable. For the residual parts, VDM can then be applied to extract the slow varying covariance matrix, which contains signals related to external forcings and dynamical interactions of multiple processes in climate. To show the improvement of our methodology, we have tested it on realistic data, using monthly temperature observations over China during 1960-2017. It is found that the climate memory component contributes a large portion of the seasonal predictability in the temperature records. Our results offer the potential for more skillful seasonal predictions compared with the results obtained using FISM or VDM alone.
How to cite: Nian, D., Yuan, N., Ying, K., Liu, G., Fu, Z., Qi, Y., and Franzke, C. L. E.: Identifying the sources of seasonal potential predictability using Fractional Integral Statistical Model with a Variance Decomposition Method, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3866, https://doi.org/10.5194/egusphere-egu21-3866, 2021.
The soil temperature (ST) is closely related to the surface air temperature (AT), but their coupling may be affected by other factors. In this study, by using linear analysis and nonlinear causality analysis—convergent cross mapping (CCM) and its time-lagged version (time-lagged CCM), significant effects of the AT on the underlying ST were found, and the time taken to propagate downward to 320 cm can be up to 10 months. Besides the AT, the ST is also affected by memory effects—namely, its prior thermal conditions. At deeper depth (i.e., 320 cm), the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season. At shallower layers (i.e., < 80 cm), the effects of the AT may be blocked by the snow cover, resulting in a poorly synchronous correlation between the AT and the ST. In northeastern China, this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring. Due to the thermal insulation effect of the snow cover, the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period. These findings may be instructive for better understanding ST variations, as well as land−atmosphere interactions.
How to cite: Zhang, H., Yuan, N., Ma, Z., and Huang, Y.: Understanding the Soil Temperature Variability at Different Depths: Effects of Surface Air Temperature, Snow Cover, and the Soil Memory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3943, https://doi.org/10.5194/egusphere-egu21-3943, 2021.
One of the most used metrics to gauge the effects of climate change is the equilibrium climate sensitivity, defined as the long-term (equilibrium) temperature increase resulting from instantaneous doubling of atmospheric CO2. Since global climate models cannot be fully equilibrated in practice, extrapolation techniques are used to estimate the equilibrium state from transient warming simulations. Because of the abundance of climate feedbacks – spanning a wide range of temporal scales – it is hard to extract long-term behaviour from short-time series; predominantly used techniques are only capable of detecting the single most dominant eigenmode, thus hampering their ability to give accurate long-term estimates. Here, we present an extension to those methods by incorporating data from multiple observables in a multi-component linear regression model. This way, not only the dominant but also the next-dominant eigenmodes of the climate system are captured, leading to better long-term estimates from short, non-equilibrated time series.
How to cite: Bastiaansen, R., Dijkstra, H., and von der Heydt, A.: Multivariate Estimations of Equilibrium Climate Sensitivity from Short Transient Warming Simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-187, https://doi.org/10.5194/egusphere-egu21-187, 2020.
How to cite: Lembo, V., Lucarini, V., and Ragone, F.: Predicting Climate Change through Response Operators in a Coupled General Circulation Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2221, https://doi.org/10.5194/egusphere-egu21-2221, 2021.
Nonlinear atmospheric dynamics produce rare events that are hard to predict and attribute due to many interacting degrees of freedom. A sudden stratospheric warming is a spectacular example in which the winter polar vortex in the stratosphere rapidly breaks down, inducing a shift in midlatitude tropospheric weather patterns that persist for up to 2-3 months. In principle, lengthy numerical simulations can be used to predict and understand these rare transitions. For complex models, however, the cost of the direct numerical simulation approach is often prohibitive. We describe an alternative approach which in principle only requires relatively short duration computer simulations of the system. Applying this methodology to a classical idealized stratospheric model with stochastic forcing, we compute optimal forecasts of sudden warming events and quantify the limits of predictability. Statistical analysis relates these optimal forecasts to a small number of easy-to-interpret physical variables.Remarkably, we are able to estimate these quantities using a data set of simulations much shorter than the return time of the warming event.
How to cite: Finkel, J., Webber, R. J., Gerber, E. P., Abbot, D. S., and Weare, J.: Forecasting rare stratospheric transitions using short simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16323, https://doi.org/10.5194/egusphere-egu21-16323, 2021.
In this work, we consider turbulence closures of LES (Large Eddy Simulation) type for classical decaying 2D turbulence in a priori and a posteriori experiments using explicit filtering approach. According to Germano 1986 decomposition, full subfilter stress for Gaussian filter is decomposed into three Galilean-invariant parts: Leonard, Cross and Reynolds stresses. By analysing spectral transfer of energy and enstrophy, we show that Leonard stress redistributes resolved energy toward large scales and dissipates substantial part of enstrophy, while Cross stress provides an additional enstrophy dissipation at subfilter scales and Reynolds stress predominantly injects energy into middle scales (i.e., Kinetic Energy Backscatter). Substantial part of enstrophy dissipation is located on subfilter scales, and it should be accounted for by choosing base filter wide enough compared to mesh step of LES model. Otherwise, significant fraction of enstrophy dissipation will correspond to subgrid scale stress, which is less universal and harder to approximate. As a result of a priori analysis, we propose LES closure consisting of three parts: SSM (Scale Similarity Model), which is equivalent to Leonard stress, biharmonic Smagorinsky damping as a Cross stress counterpart and ADM (Approximate Deconvolution Model) approximation for Reynolds stress ("backscatter"). The proposed model have two free parameters: Smagorinsky constant and amplitude of the backscatter. These parameters are estimated in a posteriori experiments utilizing dynamic approach and energy-enstrophy balance equation, correspondingly. The proposed model have the following distinctive features: it reproduces energy and enstrophy transfer spectra in accordance to the individual components of the subfilter forces, "reproduces" base filter and reproduces energy growth in accordance to the filtered DNS (Direct Numerical Simulation) solution.
The work was supported by the Russian Foundation for Basic Research (projects 19-35-90023, 18-05-60184) and Moscow Center for Fundamental and Applied Mathematics (agreement with the Ministry of Education and Science of the Russian Federation No. 075-15-2019-1624).
How to cite: Perezhogin, P. and Glazunov, A.: A priori and a posteriori analysis in Large eddy simulation of the two-dimensional decaying turbulence using Explicit filtering approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2382, https://doi.org/10.5194/egusphere-egu21-2382, 2021.
In this study, when using reflection seismic data to study the wakes of the Batan Islands, a method for estimating the fluid dynamics parameters such as the relative vorticity (relative Rossby number) and the relative potential vorticity is proposed. Although the relative Rossby number estimation method proposed in this study cannot guarantee absolute accuracy in the calculation value, this method is more accurate in describing the positive and negative vorticity distribution for the wakes, and the resolution of the positive and negative vorticity distribution described by this method is higher than the result of the reanalysis data. For the wakes developed in the Batan Islands, the reflection events in the wake development area have the larger inclination than the reflection events in the western Pacific water distribution area. It is also found that the negative vorticity wakes are mainly distributed on the west side of the island/ridge, and the positive vorticity wakes are mainly distributed on the east side of the island/ridge. This is consistent with the understanding of previous wakes simulations. The strong vorticity values in the study area are mainly distributed at depths above 300m, and the maximum impact depth of wakes can reach 600m. At the downstream position of the wake on the survey line 7, it can be seen that the bottom boundary layer has separated, and there is the negative vorticity wakes on the west side intruding into the positive vorticity wakes on the east side , which is presumed to be caused by the disturbance of the small anticyclone existing near the Batan Islands. For the survey line 7, the negative potential vorticity is mainly distributed on the west side of the island/ridge, and the influence range can reach the sea depth of 600m. In the negative potential vorticity region, there is strong energy dissipation and vertical shear. In this study, we don’t find the existence of submesoscale coherent vortices on the survey line 7, but find the reflection structure similar to filaments on the seismic section. Combined with the analysis of the balanced Richardson number angle of survey line 7, we speculate that the wake in the negative potential vorticity distribution area has the characteristics of symmetrical instability, and the symmetrical instability may destroy the process of filaments forming submesoscale coherent vortices.
How to cite: Fan, W., Song, H., Zhang, K., Gong, Y., Yang, S., and Wu, D.: Using reflection seismic method to estimate the fluid dynamics parameters related to wakes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9332, https://doi.org/10.5194/egusphere-egu21-9332, 2021.
Apart from its known impact to variations in the Earth’s length-of-day (LOD) variations, the role of long-period tidal forcing cycles in geophysical behaviours has remained elusive. To explore this further, tidal forcing is considered as a causative mechanisms to the following cyclic processes: El Niño Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), and the Chandler wobble. Annualized impulse reponse formulations and nonlinear solutions to Navier-Stokes-based Laplace's Tidal Equations are required to make the connection to the observed patterns as the underlying periods are not strictly commensurate in relation to harmonics of the tidal cycles. If equatorial climate phenomena such as QBO and ENSO can be explained as deterministic processes then the behavior that may be predictable. This paper suggests that QBO, ENSO, and the Chandler wobble may share a common origin of lunar and solar tidal forcing, but with differences arising due to global symmetry considerations. Through analytical approximations of nonlinear fluid dynamics and detailed time-series analysis, matching quantitative models of these behaviors can be shown.
How to cite: Pukite, P.: Nonlinear long-period tidal forcing with application to ENSO, QBO, and Chandler wobble, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10515, https://doi.org/10.5194/egusphere-egu21-10515, 2021.
Wetlands, affected by the hydro-climatic condition and human activities, are key elements in providing valuable ecosystem services for ecology, environment, and human. Wetlands can exist in various states (e.g., area, volume, depth, etc.) driven by both natural and human forcing, and are often distributed in a wetlandscape. In these specific landscapes, wetlands (node) and dispersal path (link) of inhabiting species organize ecological networks. Here, we generated the three ecological networks with three dispersal models (threshold distance, exponential kernel, and heavy-tailed dispersal model) and analyzed network characteristics (degree, efficiency and clustering coefficient) associated with the seasonal change of hydro-climatic condition on wetland hydrology. To identify the role of small wetlands, we analyzed two different scenarios in which the sum of wetland areas are similar but their area distributions are distinct. In the first scenario, most of the small wetlands are hydrologically disappeared while the second scenario maintains the small wetlands with a shrunk area of large wetlands. When the area of large wetlands was reduced, a slight decrease in the values of network metrics was observed due to an increase in distances between wetlands. On the other hand, when a number of small wetlands were hydrologically disappeared, all the metric values were significantly decreased compared to the network in which all wetlands were hydrologically maintained. Especially, when the disappeared wetlands were not recovered even after rainfall, possibly due to long-term dehydration of supporting soil, the network characteristics also did not recover even if the total area of wetlands were recovered. However, when the dried small wetlands were hydrologically recovered, the network characteristics also recovered rapidly. Based on our observation, we confirmed that the small wetlands, despite their extremely low areal portion in the entire wetlandscape, play a key role in maintaining the ecological network resilience. Our findings can be used for a decision-making process for wetland conservation and restoration by reflecting the functional importance of small wetlands with physical characteristics requirements such as wetland areas.
How to cite: Kim, B. and Park, J.: The role of small wetlands for resilient ecological networks in a wetlandscape , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13712, https://doi.org/10.5194/egusphere-egu21-13712, 2021.
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