Nonlinear Processes in Geophysics最新文献_第10页

Characteristics of intrinsic non-stationarity and its effect on eddy-covariance measurements of CO₂ fluxes 本征非平稳性特征及其对CO2通量涡旋协方差测量的影响

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-03-24 DOI: 10.5194/npg-29-123-2022

Lei Liu, Yu Shi, F. Hu

引用次数: 2

Ensemble Riemannian data assimilation: towards large-scale dynamical systems 集成黎曼数据同化：面向大尺度动力系统

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-02-18 DOI: 10.5194/npg-29-77-2022

S. Tamang, A. Ebtehaj, P. V. van Leeuwen, Gilad Lerman, E. Foufoula‐Georgiou

引用次数: 2

An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model 通过同化古海平面资料到冰川均衡调整模式来约束地幔粘度的方法

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-02-17 DOI: 10.5194/npg-29-53-2022

R. Schachtschneider, J. Saynisch‐Wagner, V. Klemann, M. Bagge, Maik Thomas

{"title":"An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model","authors":"R. Schachtschneider, J. Saynisch‐Wagner, V. Klemann, M. Bagge, Maik Thomas","doi":"10.5194/npg-29-53-2022","DOIUrl":"https://doi.org/10.5194/npg-29-53-2022","url":null,"abstract":"Abstract. Glacial isostatic adjustment is largely governed by the rheological\u0000properties of the Earth's mantle. Large mass redistributions in the\u0000ocean–cryosphere system and the subsequent response of the\u0000viscoelastic Earth have led to dramatic sea level changes in the\u0000past. This process is ongoing, and in order to understand and predict\u0000current and future sea level changes, the knowledge of mantle\u0000properties such as viscosity is essential. In this study, we present a\u0000method to obtain estimates of mantle viscosities by the assimilation of\u0000relative sea level rates of change into a viscoelastic model of the\u0000lithosphere and mantle. We set up a particle filter with probabilistic\u0000resampling. In an identical twin experiment, we show that mantle\u0000viscosities can be recovered in a glacial isostatic adjustment model\u0000of a simple three-layer Earth structure consisting of an elastic\u0000lithosphere and two mantle layers of different viscosity. We\u0000investigate the ensemble behaviour on different parameters in the following three set-ups: (1) global observations data set since last glacial maximum\u0000with different ensemble initialisations and observation uncertainties,\u0000(2) regional observations from Fennoscandia or Laurentide/Greenland\u0000only, and (3) limiting the observation period to 10 ka until the\u0000present. We show that the recovery is successful in all cases if the\u0000target parameter values are properly sampled by the initial ensemble\u0000probability distribution. This even includes cases in which the target\u0000viscosity values are located far in the tail of the initial ensemble\u0000probability distribution. Experiments show that the method is\u0000successful if enough near-field observations are available. This makes\u0000it work best for a period after substantial deglaciation until the present\u0000when the number of sea level indicators is relatively high.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46254183","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}

引用次数: 1

Direct Bayesian model reduction of smaller scale convective activity conditioned on large-scale dynamics 大尺度动力学条件下小尺度对流活动的直接贝叶斯模型约简

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-02-16 DOI: 10.5194/npg-29-37-2022

R. Polzin, A. Müller, H. Rust, P. Névir, P. Koltai

引用次数: 1

How many modes are needed to predict climate bifurcations? Lessons from an experiment 预测气候分叉需要多少种模式？实验的经验教训

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-02-07 DOI: 10.5194/npg-29-17-2022

B. Dubrulle, F. Daviaud, D. Faranda, L. Marié, B. Saint-Michel

{"title":"How many modes are needed to predict climate bifurcations? Lessons from an experiment","authors":"B. Dubrulle, F. Daviaud, D. Faranda, L. Marié, B. Saint-Michel","doi":"10.5194/npg-29-17-2022","DOIUrl":"https://doi.org/10.5194/npg-29-17-2022","url":null,"abstract":"Abstract. According to everyone's experience, predicting the weather reliably over more than 8 d seems an impossible task for our best weather agencies. At the same time, politicians and citizens are asking scientists for climate projections several decades into the future to guide economic and environmental policies, especially regarding the maximum admissible emissions of CO2. To what extent is this request scientifically admissible? In this review we will investigate this question, focusing on the topic of predictions of transitions between metastable states of the atmospheric or oceanic circulations. Two relevant examples are the switching between zonal and blocked atmospheric circulation at mid-latitudes and the alternation of El Niño and La Niña phases in the Pacific Ocean. The main issue is whether present climate models, which necessarily have a finite resolution and a smaller number of degrees of freedom than the actual terrestrial system, are able to reproduce such spontaneous or forced transitions. To do so, we will draw an analogy between climate observations and results obtained in our group on a laboratory-scale, turbulent, von Kármán flow in which spontaneous transitions between different states of the circulation take place. We will detail the analogy, investigate the nature of the transitions and the number of degrees of freedom that characterize the latter, and discuss the effect of reducing the number of degrees of freedom in such systems. We will also discuss the role of fluctuations and their origin and stress the importance of describing very small scales to capture fluctuations of correct intensity and scale.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47706510","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}

引用次数: 7

Predicting Sea Surface Temperatures with Coupled Reservoir Computers 用耦合油藏计算机预测海面温度

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-02-02 DOI: 10.5194/npg-2022-4

Benjamin Walleshauser, E. Bollt

引用次数: 5

Climate Bifurcations in a Schwarzschild Equation Model of the Arctic Atmosphere 北极大气史瓦西方程模式的气候分岔

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-01-19 DOI: 10.5194/npg-2022-2

Kolja L. Kypke, W. Langford, G. Lewis, Allan R. Willms

引用次数: 0

The Empirical Adaptive Wavelet Decomposition (EAWD): An adaptive decomposition for the variability analysis of observation time series in atmospheric science 经验自适应小波分解（EAWD）：一种用于大气科学观测时间序列变异性分析的自适应分解

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-01-07 DOI: 10.5194/npg-2021-37

O. Delage, T. Portafaix, H. Bencherif, A. Bourdier, Emma Lagracie

{"title":"The Empirical Adaptive Wavelet Decomposition (EAWD): An adaptive decomposition for the variability analysis of observation time series in atmospheric science","authors":"O. Delage, T. Portafaix, H. Bencherif, A. Bourdier, Emma Lagracie","doi":"10.5194/npg-2021-37","DOIUrl":"https://doi.org/10.5194/npg-2021-37","url":null,"abstract":"Abstract. Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several time and space scales. As a consequence, measurements time series have often characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different time-scales. The variability analysis of a time series consists in decomposing it into several mode of variability, each mode representing the fluctuations of the original time series at a specific time-scale. Such a decomposition enables to obtain a time-frequency representation of the original time series and turns out to be very useful to estimate the dimensionality of the underlying dynamics. Decomposition techniques very well suited to non-linear and non-stationary time series have recently been developed in the literature. Among the most widely used of these technics are the empirical mode decomposition (EMD) and the empirical wavelet transformation (EWT). The purpose of this paper is to present a new adaptive filtering method that combines the advantages of the EMD and EWT technics, while remaining close to the dynamics of the original signal made of atmospheric observations, which means reconstructing as close as possible to the original time series, while preserving its variability at different time scales.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44185571","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}

引用次数: 1

Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta 数据匮乏的河口三角洲复合洪水预测的流体动力学和机器学习集成模型

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2022-01-04 DOI: 10.5194/npg-2021-36

J. Sampurno, Valentin Vallaeys, Randy Ardianto, E. Hanert

{"title":"Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta","authors":"J. Sampurno, Valentin Vallaeys, Randy Ardianto, E. Hanert","doi":"10.5194/npg-2021-36","DOIUrl":"https://doi.org/10.5194/npg-2021-36","url":null,"abstract":"Abstract. Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41617389","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}

引用次数: 6

Brief communication: Lower-bound estimates for residence time of energy in the atmospheres of Venus, Mars and Titan 简短交流：能量在金星、火星和泰坦大气层中停留时间的下限估计

IF 2.2 4区地球科学

Nonlinear Processes in Geophysics Pub Date : 2021-11-03 DOI: 10.5194/npg-28-627-2021

J. Pelegrina, C. Osácar, A. Fernández-Pacheco

引用次数: 0