A Synoptic–Dynamic Model of Subseasonal Atmospheric Variability

2007 ◽  
Vol 135 (2) ◽  
pp. 449-474 ◽  
Author(s):  
Klaus Weickmann ◽  
Edward Berry
Keyword(s):  
Dynamic Model ◽  
Time Scales ◽  
Life Cycles ◽  
Extreme Weather Events ◽  
Recurrence Time ◽  
Frictional Torque ◽  
Multiple Time ◽  
Fast Time ◽  
Tropical Forcing ◽  
Time Space

Abstract A global synoptic–dynamic model (GSDM) of subseasonal variability is proposed to provide a framework for real-time weather–climate monitoring and to assist with the preparation of medium-range (e.g., week 1–3) predictions. The GSDM is used with a regional focus over North America during northern winter. A case study introduces the time scales of the GSDM and illustrates two circulation transitions related to eastward-moving wave energy signals and their connection to remote tropical forcing. Global and zonal atmospheric angular momentum (AAM) is used to help define the synoptic evolution of the GSDM components and to link regional synoptic variations with physical processes like the global mountain and frictional torque. The core of the GSDM consists of four stages based on the Madden–Julian oscillation (MJO) recurrence time. Additionally, extratropical behaviors including teleconnection patterns, baroclinic life cycles, and ∼monthly oscillations provide intermediate and fast time scales that are combined with the quasi-oscillatory (30–70 day) MJO to define multiple time-/space-scale linear relationships. A unique feature of the GSDM is its focus on global and regional circulation transitions and the related extreme weather events during periods of large global AAM tendency.

scholarly journals Direct Determination of Reduced Models of a Class of Singularly Perturbed Nonlinear Systems on Three Time Scales in a Bond Graph Approach

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 104
Author(s):  
Gerardo Ayala-Jaimes ◽  
Gilberto Gonzalez-Avalos ◽  
Noe Barrera Gallegos ◽  
Aaron Padilla Garcia ◽  
Juancarlos Mendez-B
Keyword(s):  
Nonlinear Systems ◽  
Dynamic Model ◽  
Time Scales ◽  
Bond Graph ◽  
Singularly Perturbed ◽  
Fast Time ◽  
State Variables ◽  
Slow Dynamics ◽  
Reduced Models ◽  
Bond Graph Modeling

One of the most important features in the analysis of the singular perturbation methods is the reduction of models. Likewise, the bond graph methodology in dynamic system modeling has been widely used. In this paper, the bond graph modeling of nonlinear systems with singular perturbations is presented. The class of nonlinear systems is the product of state variables on three time scales (fast, medium, and slow). Through this paper, the symmetry of mathematical modeling and graphical modeling can be established. A main characteristic of the bond graph is the application of causality to its elements. When an integral causality is assigned to the storage elements that determine the state variables, the dynamic model is obtained. If the storage elements of the fast dynamics have a derivative causality and the storage elements of the medium and slow dynamics an integral causality is assigned, a reduced model is obtained, which consists of a dynamic model for the medium and slow time scales and a stationary model of the fast time scale. By applying derivative causality to the storage elements of the fast and medium dynamics and an integral causality to the storage elements of the slow dynamics, the quasi-steady-state model for the slow dynamics is obtained and stationary models for the fast and medium dynamics are defined. The exact and reduced models of singularly perturbed systems can be interpreted as another symmetry in the development of this paper. Finally, the proposed methodology was applied to a system with three time scales in a bond graph approach, and simulation results are shown in order to indicate the effectiveness of the proposed methodology.

scholarly journals COMPUTATIONAL MODELING OF DYNAMICAL SYSTEMS

2005 ◽  
Vol 15 (03) ◽  
pp. 471-481 ◽  
Author(s):  
JOHAN JANSSON ◽  
CLAES JOHNSON ◽  
ANDERS LOGG
Keyword(s):  
Dynamical System ◽  
Dynamical Systems ◽  
Computational Modeling ◽  
Time Scales ◽  
Time Scale ◽  
Short Note ◽  
Multiple Time Scales ◽  
Time Interval ◽  
Multiple Time ◽  
Fast Time

In this short note, we discuss the basic approach to computational modeling of dynamical systems. If a dynamical system contains multiple time scales, ranging from very fast to slow, computational solution of the dynamical system can be very costly. By resolving the fast time scales in a short time simulation, a model for the effect of the small time scale variation on large time scales can be determined, making solution possible on a long time interval. This process of computational modeling can be completely automated. Two examples are presented, including a simple model problem oscillating at a time scale of 10–9 computed over the time interval [0,100], and a lattice consisting of large and small point masses.

SOME REMARKS ON THE MULTIPLE TIME SCALES OF DIFFUSION IN MINERALS AND MELTS

2018 ◽  
Author(s):  
Yan Liang ◽  
◽  
Daniele J. Cherniak ◽  
Chenguang Sun
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time

scholarly journals Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

2019 ◽  
Vol 11 (4) ◽  
pp. 1163 ◽  
Author(s):  
Melissa Bedinger ◽  
Lindsay Beevers ◽  
Lila Collet ◽  
Annie Visser
Keyword(s):  
Climate Change ◽  
Human Nature ◽  
Climate Change Adaptation ◽  
Time Scales ◽  
Spatial Scales ◽  
Local Context ◽  
Multiple Time Scales ◽  
Future Research ◽  
Multiple Time ◽  
Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

scholarly journals A multistate model incorporating estimation of excess hazards and multiple time scales

2021 ◽  
Vol 40 (9) ◽  
pp. 2139-2154
Author(s):  
Caroline E. Weibull ◽  
Paul C. Lambert ◽  
Sandra Eloranta ◽  
Therese M. L. Andersson ◽  
Paul W. Dickman ◽  
...  
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Multistate Model

scholarly journals Slow and Fast Time Scales in Cardiomyocyte Beating

2019 ◽  
Vol 116 (3) ◽  
pp. 162a
Author(s):  
Ohad Cohen ◽  
Samuel Safran
Keyword(s):  
Time Scales ◽  
Fast Time

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

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