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 host, with floe sizes ranging from centimeters to tens of kilometers. A principal challenge in modeling sea ice and its role in climate is how to use information on smaller-scale structures to find the effective or homogenized properties on larger scales relevant to process studies and coarse-grained climate models. That is, how do you predict macroscopic behavior from microscopic laws, like in statistical mechanics and solid state physics? Also of great interest in climate science is the inverse problem of recovering parameters controlling small-scale processes from large-scale observations. Motivated by sea ice remote sensing, the analytic continuation method for obtaining rigorous bounds on the homogenized coefficients of two-phase composites was applied to the complex permittivity of sea ice, which is a Stieltjes function of the ratio of the permittivities of ice and brine. Integral representations for the effective parameters distill the complexities of the composite microgeometry into the spectral properties of a self-adjoint operator like the Hamiltonian in quantum physics. These techniques have been extended to polycrystalline materials, advection diffusion processes, and ocean waves in the sea ice cover. Here we discuss this powerful approach in homogenization, highlighting the spectral representations and resolvent structure of the fields that are shared by the two-component theory and its extensions. Spectral analysis of sea ice structures leads to a random matrix theory picture of percolation processes in composites, establishing parallels to Anderson localization and semiconductor physics and providing new insights into the physics of sea ice.
{"title":"Stieltjes functions and spectral analysis in the physics of sea ice","authors":"Kenneth M. Golden, N. Benjamin Murphy, Daniel Hallman, Elena Cherkaev","doi":"10.5194/npg-30-527-2023","DOIUrl":"https://doi.org/10.5194/npg-30-527-2023","url":null,"abstract":"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 host, with floe sizes ranging from centimeters to tens of kilometers. A principal challenge in modeling sea ice and its role in climate is how to use information on smaller-scale structures to find the effective or homogenized properties on larger scales relevant to process studies and coarse-grained climate models. That is, how do you predict macroscopic behavior from microscopic laws, like in statistical mechanics and solid state physics? Also of great interest in climate science is the inverse problem of recovering parameters controlling small-scale processes from large-scale observations. Motivated by sea ice remote sensing, the analytic continuation method for obtaining rigorous bounds on the homogenized coefficients of two-phase composites was applied to the complex permittivity of sea ice, which is a Stieltjes function of the ratio of the permittivities of ice and brine. Integral representations for the effective parameters distill the complexities of the composite microgeometry into the spectral properties of a self-adjoint operator like the Hamiltonian in quantum physics. These techniques have been extended to polycrystalline materials, advection diffusion processes, and ocean waves in the sea ice cover. Here we discuss this powerful approach in homogenization, highlighting the spectral representations and resolvent structure of the fields that are shared by the two-component theory and its extensions. Spectral analysis of sea ice structures leads to a random matrix theory picture of percolation processes in composites, establishing parallels to Anderson localization and semiconductor physics and providing new insights into the physics of sea ice.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"281 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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, such as the mean precipitation, fraction of rain, spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales, climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000 x 4000 km2, 21 years) and the Swiss Alpine region (≈500 x 500 km2, 6 years).
摘要。综合气象雷达图像档案是研究降水可预测性的宝贵资源。本文比较了从雷达降水场构造经验低维吸引子的两种不同方法。在第一种方法中，利用降水场的域尺度统计来定义吸引子的相空间维度，如平均降水、降雨比例、空间和时间相关性。第二类吸引子考虑降水的空间分布，采用主成分分析(PCA)建立。对于这两个吸引子，我们研究了相空间中轨迹的密度、模拟态误差的增长和分形性质。为了代表不同的尺度、气候和地形条件，分析使用了美国大陆(≈4000 x 4000 km2, 21年)和瑞士阿尔卑斯地区(≈500 x 500 km2, 6年)的多年雷达档案。
{"title":"A quest for precipitation attractors in weather radar archives","authors":"Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis Vasileios Sideris, Urs Germann, Isztar Zawadzki","doi":"10.5194/npg-2023-24","DOIUrl":"https://doi.org/10.5194/npg-2023-24","url":null,"abstract":"<strong>Abstract.</strong> 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, such as the mean precipitation, fraction of rain, spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales, climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000 x 4000 km<sup>2</sup>, 21 years) and the Swiss Alpine region (≈500 x 500 km<sup>2</sup>, 6 years).","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"214 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 for the fast fluctuations and (ii) a convolution of exponential PDFs for the slowly evolving variance of the Gaussian function rather than for the thermodynamic β=1/σ2 in a system with a few degrees of freedom, as the latter has divergent moments. The Gaussian PDF reflects the entropy maximization for real-valued variables with a given variance. On the other hand, if a positive variable, as a variance, has a specified mean, the maximum-entropy solution is an exponential PDF. In our case the system has 2 degrees of freedom, and therefore the PDF of the variance is the convolution of two exponentials. In the Gulf of Trieste there are three distinct main wind forcing regimes: bora, sirocco, and low wind, leading to a succession of different sea current dynamics on different timescales. The universality class PDF successfully fits the observed data over the 2 observation years and also for each wind regime separately with a different variance of the variance PDF, which is the only free parameter in all the fits.
{"title":"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","authors":"Sofia Flora, Laura Ursella, Achim Wirth","doi":"10.5194/npg-30-515-2023","DOIUrl":"https://doi.org/10.5194/npg-30-515-2023","url":null,"abstract":"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 for the fast fluctuations and (ii) a convolution of exponential PDFs for the slowly evolving variance of the Gaussian function rather than for the thermodynamic β=1/σ2 in a system with a few degrees of freedom, as the latter has divergent moments. The Gaussian PDF reflects the entropy maximization for real-valued variables with a given variance. On the other hand, if a positive variable, as a variance, has a specified mean, the maximum-entropy solution is an exponential PDF. In our case the system has 2 degrees of freedom, and therefore the PDF of the variance is the convolution of two exponentials. In the Gulf of Trieste there are three distinct main wind forcing regimes: bora, sirocco, and low wind, leading to a succession of different sea current dynamics on different timescales. The universality class PDF successfully fits the observed data over the 2 observation years and also for each wind regime separately with a different variance of the variance PDF, which is the only free parameter in all the fits.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"1148 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 weather-related predictors into account during post-processing beyond the forecast of the variable of interest only. Some of these methods have achieved impressive results, but they typically require significantly more training data than traditional MOS and are less straightforward to implement and interpret. We propose MOS random forests, a new post-processing method that avoids these problems by fusing traditional MOS with a powerful machine learning method called random forests to estimate weather-adapted MOS coefficients from a set of predictors. Since the assumed parametric base model contains valuable prior knowledge, much smaller training data sizes are required to obtain skillful forecasts, and model results are easy to interpret. MOS random forests are straightforward to implement and typically work well, even with no or very little hyperparameter tuning. For the difficult task of post-processing daily precipitation sums in complex terrain, they outperform reference machine learning methods at most of the stations considered. Additionally, the method is highly robust in relation to changes in data size and works well even when less than 100 observations are available for training.
{"title":"Robust weather-adaptive post-processing using model output statistics random forests","authors":"Thomas Muschinski, Georg J. Mayr, Achim Zeileis, Thorsten Simon","doi":"10.5194/npg-30-503-2023","DOIUrl":"https://doi.org/10.5194/npg-30-503-2023","url":null,"abstract":"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 weather-related predictors into account during post-processing beyond the forecast of the variable of interest only. Some of these methods have achieved impressive results, but they typically require significantly more training data than traditional MOS and are less straightforward to implement and interpret. We propose MOS random forests, a new post-processing method that avoids these problems by fusing traditional MOS with a powerful machine learning method called random forests to estimate weather-adapted MOS coefficients from a set of predictors. Since the assumed parametric base model contains valuable prior knowledge, much smaller training data sizes are required to obtain skillful forecasts, and model results are easy to interpret. MOS random forests are straightforward to implement and typically work well, even with no or very little hyperparameter tuning. For the difficult task of post-processing daily precipitation sums in complex terrain, they outperform reference machine learning methods at most of the stations considered. Additionally, the method is highly robust in relation to changes in data size and works well even when less than 100 observations are available for training.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"53 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-15DOI: 10.5194/egusphere-2023-2168
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 in the underlying true trajectory. We demonstrate that this sensitivity is consistent with and helps explain the errors in the approximate Lyapunov vectors from the estimated trajectory of the filter. Using the idea of principal angles, we demonstrate that the Oseledets' subspaces defined by the LVs computed from the approximate trajectory are less sensitive than the individual vectors.
{"title":"Computation of covariant lyapunov vectors using data assimilation","authors":"Shashank Kumar Roy, Amit Apte","doi":"10.5194/egusphere-2023-2168","DOIUrl":"https://doi.org/10.5194/egusphere-2023-2168","url":null,"abstract":"<strong>Abstract.</strong> 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 in the underlying true trajectory. We demonstrate that this sensitivity is consistent with and helps explain the errors in the approximate Lyapunov vectors from the estimated trajectory of the filter. Using the idea of principal angles, we demonstrate that the Oseledets' subspaces defined by the LVs computed from the approximate trajectory are less sensitive than the individual vectors.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"39 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 shear given in Forryan et al. (2013), the profiles of kinetic energy dissipation rate are calculated, to be in reasonable agreement with the experimental data. This confirms the importance of including potential energy into realistic models of subsurface turbulence.
摘要。将作者早先提出的分层湍流理论应用于海洋上层的资料，证实了小尺度湍流在准平稳水平上，即使在大的理查德森数梯度下，由于动能和势能之间的交换，也可以被放大和支持。利用Forryan et al.(2013)给出的Brunt-Väisälä频率和垂直电流剪切的平均廓线，计算出了动能耗散率廓线，与实验数据较为吻合。这证实了在真实的地下湍流模型中包含势能的重要性。
{"title":"Evolution of small-scale turbulence at large Richardson numbers","authors":"Lev Ostrovsky, Irina Soustova, Yuliya Troitskaya, Daria Gladskikh","doi":"10.5194/npg-2023-22","DOIUrl":"https://doi.org/10.5194/npg-2023-22","url":null,"abstract":"<strong>Abstract.</strong> 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 shear given in Forryan et al. (2013), the profiles of kinetic energy dissipation rate are calculated, to be in reasonable agreement with the experimental data. This confirms the importance of including potential energy into realistic models of subsurface turbulence.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"81 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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, especially concerning the climate crisis and its consequences. Here we briefly review the mechanisms of classical tipping phenomena and investigate rate-dependent tipping phenomena which occur in non-autonomous systems characterized by multiple timescales in more detail. We focus on the mechanism of rate-induced tipping caused by basin boundary crossings. We unravel the mechanism of this transition and analyze, in particular, the role of such basin boundary crossings in non-autonomous systems when a parameter drift induces a saddle-node bifurcation in which new attractors and saddle points emerge, including their basins of attraction. Furthermore, we study the detectability of those bifurcations by monitoring single trajectories in state space and find that depending on the rate of environmental parameter drift, such saddle-node bifurcations might be masked or hidden, and they can only be detected if a critical rate of environmental drift is crossed. This analysis reveals that unstable states of saddle type are the organizing centers of the global dynamics in non-autonomous multistable systems and as such need much more attention in future studies.
{"title":"Rate-induced tipping in ecosystems and climate: the role of unstable states, basin boundaries and transient dynamics","authors":"Ulrike Feudel","doi":"10.5194/npg-30-481-2023","DOIUrl":"https://doi.org/10.5194/npg-30-481-2023","url":null,"abstract":"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, especially concerning the climate crisis and its consequences. Here we briefly review the mechanisms of classical tipping phenomena and investigate rate-dependent tipping phenomena which occur in non-autonomous systems characterized by multiple timescales in more detail. We focus on the mechanism of rate-induced tipping caused by basin boundary crossings. We unravel the mechanism of this transition and analyze, in particular, the role of such basin boundary crossings in non-autonomous systems when a parameter drift induces a saddle-node bifurcation in which new attractors and saddle points emerge, including their basins of attraction. Furthermore, we study the detectability of those bifurcations by monitoring single trajectories in state space and find that depending on the rate of environmental parameter drift, such saddle-node bifurcations might be masked or hidden, and they can only be detected if a critical rate of environmental drift is crossed. This analysis reveals that unstable states of saddle type are the organizing centers of the global dynamics in non-autonomous multistable systems and as such need much more attention in future studies.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"39 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 land–atmosphere DA experiments. We explore various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves the SM analysis and forecasts and mitigates a warm bias in the lower troposphere where a dry SM bias exists. On the other hand, updating SM by assimilating atmospheric observations has detrimental impacts due to spurious error correlations between the atmospheric observations and land model variables. We also find that assimilating SM by strongly coupled DA is beneficial in the Sahel and equatorial Africa from May to October. These regions are characterized by seasonal variations in the precipitation patterns and benefit from updates in the atmospheric variables through SM DA during periods of increased precipitation. Additionally, these regions coincide with those identified in the previous studies, where a global initialization of SM would enhance the prediction skill of seasonal precipitation.
{"title":"Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model","authors":"Kenta Kurosawa, Shunji Kotsuki, Takemasa Miyoshi","doi":"10.5194/npg-30-457-2023","DOIUrl":"https://doi.org/10.5194/npg-30-457-2023","url":null,"abstract":"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 land–atmosphere DA experiments. We explore various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves the SM analysis and forecasts and mitigates a warm bias in the lower troposphere where a dry SM bias exists. On the other hand, updating SM by assimilating atmospheric observations has detrimental impacts due to spurious error correlations between the atmospheric observations and land model variables. We also find that assimilating SM by strongly coupled DA is beneficial in the Sahel and equatorial Africa from May to October. These regions are characterized by seasonal variations in the precipitation patterns and benefit from updates in the atmospheric variables through SM DA during periods of increased precipitation. Additionally, these regions coincide with those identified in the previous studies, where a global initialization of SM would enhance the prediction skill of seasonal precipitation.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"60 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135461141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 rich area, island of Milos, Greece, and (ii) southern Scotland due to the occurrence of a significantly high electrical conductivity anomaly under crust and upper mantle, extending from the Midland Valley across the Southern Uplands into northern England. Even though the fact that many models provide a good fit in a large predefined search space, specific models do not fit well. As a result, we used a Bayesian statistical technique to construct and assess the posterior probability density function (PDF) rather than picking the global model based on the lowest misfit error. The study proceeds using a 68.27 % confidence interval for selecting a region where the PDF is more prevalent to estimate the mean model which is more accurate and close to the true model. For illustration, correlation matrices show a significant relationship among layer parameters. The findings indicate that wPSOGSA is less sensitive to model parameters and produces more stable and reliable results with the least uncertainty in the model, compatible with existing borehole samples. Furthermore, the present methods resolve two additional geologically significant layers, one highly conductive (less than 1.0 Ωm) and another resistive (300.0 Ωm), over the island of Milos, Greece, characterized by alluvium and volcanic deposits, respectively, as corroborated by borehole stratigraphy.
{"title":"The joint application of a metaheuristic algorithm and a Bayesian statistics approach for uncertainty and stability assessment of nonlinear magnetotelluric data","authors":"Kuldeep Sarkar, Upendra K. Singh","doi":"10.5194/npg-30-435-2023","DOIUrl":"https://doi.org/10.5194/npg-30-435-2023","url":null,"abstract":"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 rich area, island of Milos, Greece, and (ii) southern Scotland due to the occurrence of a significantly high electrical conductivity anomaly under crust and upper mantle, extending from the Midland Valley across the Southern Uplands into northern England. Even though the fact that many models provide a good fit in a large predefined search space, specific models do not fit well. As a result, we used a Bayesian statistical technique to construct and assess the posterior probability density function (PDF) rather than picking the global model based on the lowest misfit error. The study proceeds using a 68.27 % confidence interval for selecting a region where the PDF is more prevalent to estimate the mean model which is more accurate and close to the true model. For illustration, correlation matrices show a significant relationship among layer parameters. The findings indicate that wPSOGSA is less sensitive to model parameters and produces more stable and reliable results with the least uncertainty in the model, compatible with existing borehole samples. Furthermore, the present methods resolve two additional geologically significant layers, one highly conductive (less than 1.0 Ωm) and another resistive (300.0 Ωm), over the island of Milos, Greece, characterized by alluvium and volcanic deposits, respectively, as corroborated by borehole stratigraphy.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136294172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 of climate change has led more recently to two major developments in the climate sciences: (i) the Intergovernmental Panel on Climate Change's successive Assessment Reports and (ii) an increasing understanding of the interplay between natural climate variability and anthropogenically driven climate change. Both of these developments have benefited from remarkable technological advances in computing resources, relating throughput as well as storage, and in observational capabilities, regarding both platforms and instruments. Starting with the early contributions of nonlinear dynamics to the climate sciences, we review here the more recent contributions of (a) the theory of non-autonomous and random dynamical systems to an understanding of the interplay between natural variability and anthropogenic climate change and (b) the role of algebraic topology in shedding additional light on this interplay. The review is thus a trip leading from the applications of classical bifurcation theory to multiple possible climates to the tipping points associated with transitions from one type of climatic behavior to another in the presence of time-dependent forcing, deterministic as well as stochastic.
{"title":"Review article: Dynamical systems, algebraic topology and the climate sciences","authors":"Michael Ghil, Denisse Sciamarella","doi":"10.5194/npg-30-399-2023","DOIUrl":"https://doi.org/10.5194/npg-30-399-2023","url":null,"abstract":"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 of climate change has led more recently to two major developments in the climate sciences: (i) the Intergovernmental Panel on Climate Change's successive Assessment Reports and (ii) an increasing understanding of the interplay between natural climate variability and anthropogenically driven climate change. Both of these developments have benefited from remarkable technological advances in computing resources, relating throughput as well as storage, and in observational capabilities, regarding both platforms and instruments. Starting with the early contributions of nonlinear dynamics to the climate sciences, we review here the more recent contributions of (a) the theory of non-autonomous and random dynamical systems to an understanding of the interplay between natural variability and anthropogenic climate change and (b) the role of algebraic topology in shedding additional light on this interplay. The review is thus a trip leading from the applications of classical bifurcation theory to multiple possible climates to the tipping points associated with transitions from one type of climatic behavior to another in the presence of time-dependent forcing, deterministic as well as stochastic.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134976278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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