scholarly journals An improved Grassberger-Procaccia algorithm for analysis of climate system complexity

2017 ◽  
Author(s):  
Chongli Di ◽  
Tiejun Wang ◽  
Xiaohua Yang ◽  
Siliang Li
Keyword(s):  
Air Temperature ◽  
Chaotic Systems ◽  
Climate System ◽  
New Method ◽  
Consensus Algorithm ◽  
Haihe River Basin ◽  
Mountainous Area ◽  
System Complexity ◽  
Plain Area ◽  
Computing Correlation

Abstract. Understanding the complexity of natural systems, such as climate systems, is critical for various research and application purposes. A range of techniques have been developed to quantify system complexity, among which Grassberger-Procaccia (G-P) algorithm has been mostly used. However, the use of this method is still not adaptive and relies heavily on subjective criteria. To this end, an improved G-P algorithm was proposed, which integrated the normal-based K-means clustering technique and Random Sample Consensus algorithm (RANSAC) for computing correlation dimensions. To test its effectiveness for computing correlation dimensions, the proposed algorithm was compared with traditional methods using the classical Lorenz and Henon chaotic systems. The results revealed that the new method outperformed traditional algorithms in computing correlation dimensions for both chaotic systems, demonstrating the improvement made by the new method. Based on the new algorithm, the complexity of precipitation and air temperature in the Haihe River Basin (HRB) in northeast China was further evaluated. The results showed that there existed considerable regional differences in the complexity of both climatic variables across the HRB. Specifically, precipitation was shown to become progressively more complex from the mountainous area in the northwest to the plain area in the southeast; whereas, the complexity of air temperature exhibited an opposite trend with less complexity in the plain area. Overall, the spatial patterns of the complexity of precipitation and air temperature reflected the influence of the dominant climate system in the region.

scholarly journals Technical note: An improved Grassberger–Procaccia algorithm for analysis of climate system complexity

2018 ◽  
Vol 22 (10) ◽  
pp. 5069-5079 ◽  
Author(s):  
Chongli Di ◽  
Tiejun Wang ◽  
Xiaohua Yang ◽  
Siliang Li
Keyword(s):  
Air Temperature ◽  
Chaotic Systems ◽  
Technical Note ◽  
Climate System ◽  
New Method ◽  
Mountainous Area ◽  
System Complexity ◽  
Natural Systems ◽  
Plain Area ◽  
Computing Correlation

Abstract. Understanding the complexity of natural systems, such as climate systems, is critical for various research and application purposes. A range of techniques have been developed to quantify system complexity, among which the Grassberger–Procaccia (G-P) algorithm has been used the most. However, the use of this method is still not adaptive and the choice of scaling regions relies heavily on subjective criteria. To this end, an improved G-P algorithm was proposed, which integrated the normal-based K-means clustering technique and random sample consensus (RANSAC) algorithm for computing correlation dimensions. To test its effectiveness for computing correlation dimensions, the proposed algorithm was compared with traditional methods using the classical Lorenz and Henon chaotic systems. The results revealed that the new method outperformed traditional algorithms in computing correlation dimensions for both chaotic systems, demonstrating the improvement made by the new method. Based on the new algorithm, the complexity of precipitation, and air temperature in the Hai River basin (HRB) in northeastern China was further evaluated. The results showed that there existed considerable regional differences in the complexity of both climatic variables across the HRB. Specifically, precipitation was shown to become progressively more complex from the mountainous area in the northwest to the plain area in the southeast, whereas the complexity of air temperature exhibited an opposite trend, with less complexity in the plain area. Overall, the spatial patterns of the complexity of precipitation and air temperature reflected the influence of the dominant climate system in the region.

scholarly journals Technical note: Towards a continuous classification of climate using bivariate colour mapping

2011 ◽  
Vol 15 (10) ◽  
pp. 3071-3075 ◽  
Author(s):  
A. J. Teuling
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Technical Note ◽  
Climate System ◽  
Global Distribution

Abstract. Climate is often defined in terms of discrete classes. Here I use bivariate colour mapping to show that the global distribution of Köppen-Geiger climate classes can largely be reproduced by combining the simple means of two key states of the climate system (i.e. air temperature and relative humidity). This allows for a classification that is not only continuous in space, but can be applied at and transferred between timescales ranging from days to decades.

scholarly journals A new method of synchronization between two different chaotic systems

2006 ◽  
Vol 55 (11) ◽  
pp. 5681
Author(s):  
Li Shuang ◽  
Xu Wei ◽  
Li Rui-Hong ◽  
Li Yu-Peng
Keyword(s):  
Chaotic Systems ◽  
New Method ◽  
Different Chaotic Systems

scholarly journals Quantification of Information Exchange in Idealized and Climate System Applications

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1094
Author(s):  
Praveen Kumar Pothapakula ◽  
Cristina Primo ◽  
Bodo Ahrens
Keyword(s):  
Nonlinear Systems ◽  
Sample Size ◽  
Information Flow ◽  
Air Temperature ◽  
Information Exchange ◽  
Nearest Neighbor ◽  
Climate System ◽  
Transfer Entropy ◽  
K Nearest Neighbor ◽  
Lorenz 96

Often in climate system studies, linear and symmetric statistical measures are applied to quantify interactions among subsystems or variables. However, they do not allow identification of the driving and responding subsystems. Therefore, in this study, we aimed to apply asymmetric measures from information theory: the axiomatically proposed transfer entropy and the first principle-based information flow to detect and quantify climate interactions. As their estimations are challenging, we initially tested nonparametric estimators like transfer entropy (TE)-binning, TE-kernel, and TE k-nearest neighbor and parametric estimators like TE-linear and information flow (IF)-linear with idealized two-dimensional test cases along with their sensitivity on sample size. Thereafter, we experimentally applied these methods to the Lorenz-96 model and to two real climate phenomena, i.e., (1) the Indo-Pacific Ocean coupling and (2) North Atlantic Oscillation (NAO)–European air temperature coupling. As expected, the linear estimators work for linear systems but fail for strongly nonlinear systems. The TE-kernel and TE k-nearest neighbor estimators are reliable for linear and nonlinear systems. Nevertheless, the nonparametric methods are sensitive to parameter selection and sample size. Thus, this work proposes a composite use of the TE-kernel and TE k-nearest neighbor estimators along with parameter testing for consistent results. The revealed information exchange in Lorenz-96 is dominated by the slow subsystem component. For real climate phenomena, expected bidirectional information exchange between the Indian and Pacific SSTs was detected. Furthermore, expected information exchange from NAO to European air temperature was detected, but also unexpected reversal information exchange. The latter might hint to a hidden process driving both the NAO and European temperatures. Hence, the limitations, availability of time series length and the system at hand must be taken into account before drawing any conclusions from TE and IF-linear estimations.

scholarly journals Performance Comparison of Geoid Refinement between XGM2016 and EGM2008 Based on the KTH and RCR Methods: Jilin Province, China

2020 ◽  
Vol 12 (2) ◽  
pp. 324
Author(s):  
Qiong Wu ◽  
Hongyao Wang ◽  
Bin Wang ◽  
Shengbo Chen ◽  
Hongqing Li
Keyword(s):  
Gravity Field ◽  
Mean Square Error ◽  
Gravity Data ◽  
Jilin Province ◽  
Mountainous Area ◽  
Mean Square ◽  
Geoid Model ◽  
Plain Area ◽  
Gravity Field Models ◽  
Global Mean

The selection of an appropriate global gravity field model and refinement method can effectively improve the accuracy of the refined regional geoid model in a certain research area. We analyzed the accuracy of Experimental Geopotential Model (XGM2016) based on the GPS-leveling data and the modification parameters of the global mean square errors in the KTH geoid refinement in Jilin Province, China. The regional geoid was refined based on Earth Gravitational Model (EGM2008) and XGM2016 using both the Helmert condensation method with an RCR procedure and the KTH method. A comparison of the original two gravity field models to the GPS-leveling benchmarks showed that the accuracies of XGM2016 and EGM2008 in the plain area of Jilin Province are similar with a standard deviation (STD) of 5.8 cm, whereas the accuracy of EGM2008 in the high mountainous area is 1.4 cm better than that of XGM2016, which may be attributed to its low resolution. The modification parameters between the two gravity field models showed that the coefficient error of XGM2016 model was lower than that of EGM2008 for the same degree of expansion. The different modification limits and integral radii may produce a centimeter level difference in global mean square error, while the influence of the truncation error caused by the integral was at the millimeter level. The terrestrial gravity data error accounted for the majority of the global mean square error. The optimal least squares modification obtained the minimum global mean square error, and the global mean square error calculated based on XGM2016 model was reduced by about 1~3 cm compared with EGM2008. The refined geoid based on the two gravity field models indicated that both KTH and RCR method can effectively improve the STD of the geoid model from about six to three centimeters. The refined accuracy of EGM2008 model using RCR and KTH methods is slightly better than that of XGM2016 model in the plain and high mountain areas after seven-parameter fitting. EGM2008 based on the KTH method was the most precise at ± 2.0 cm in the plain area and ± 2.4 cm in the mountainous area. Generally, for the refined geoid based on the two Earth gravity models, KTH produced results similar to RCR in the plain area, and had relatively better performance for the mountainous area where terrestrial gravity data is sparse and unevenly distributed.

scholarly journals Impact of Subsurface Temperature Variability on Surface Air Temperature Variability: An AGCM Study

2008 ◽  
Vol 9 (4) ◽  
pp. 804-815 ◽  
Author(s):  
Sarith P. P. Mahanama ◽  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Max J. Suarez
Keyword(s):  
Surface Temperature ◽  
Soil Temperature ◽  
Air Temperature ◽  
Seasonal Cycle ◽  
Surface Air Temperature ◽  
Temperature Variability ◽  
Climate System ◽  
Boreal Summer ◽  
Temperature Anomalies ◽  
Subsurface Soil

Abstract Anomalous atmospheric conditions can lead to surface temperature anomalies, which in turn can lead to temperature anomalies in the subsurface soil. The subsurface soil temperature (and the associated ground heat content) has significant memory—the dissipation of a temperature anomaly may take weeks to months—and thus subsurface soil temperature may contribute to the low-frequency variability of energy and water variables elsewhere in the system. The memory may even provide some skill to subseasonal and seasonal forecasts. This study uses three long-term AGCM experiments to isolate the contribution of subsurface soil temperature variability to variability elsewhere in the climate system. The first experiment consists of a standard ensemble of Atmospheric Model Intercomparison Project (AMIP)-type simulations in which the subsurface soil temperature variable is allowed to interact with the rest of the system. In the second experiment, the coupling of the subsurface soil temperature to the rest of the climate system is disabled; that is, at each grid cell, the local climatological seasonal cycle of subsurface soil temperature (as determined from the first experiment) is prescribed. Finally, a climatological seasonal cycle of sea surface temperature (SST) is prescribed in the third experiment. Together, the three experiments allow the isolation of the contributions of variable SSTs, interactive subsurface soil temperature, and chaotic atmospheric dynamics to meteorological variability. The results show that allowing an interactive subsurface soil temperature does, indeed, significantly increase surface air temperature variability and memory in most regions. In many regions, however, the impact is negligible, particularly during boreal summer.

A New Method Based on Lyapunov Exponent to Determine the Threshold of Chaotic Systems

2014 ◽  
Vol 511-512 ◽  
pp. 329-333
Author(s):  
Yang Liu ◽  
Xin Chun Zhang
Keyword(s):  
Lyapunov Exponent ◽  
Chaotic Systems ◽  
Sampling Period ◽  
Qr Decomposition ◽  
Sampling Time ◽  
Duffing Equation ◽  
Experimental Results ◽  
New Method ◽  
Critical Threshold ◽  
Periodic Signals

The Duffing equation has been widely used to detect weak periodic signals. The transition threshold is the key to detect. Unfortunately, there is no effective method to determine the critical threshold. To solve this problem, a new method based on the QR decomposition to calculate Lyapunov exponent is presented. The accuracy of the algorithm will gradually improve with the sampling time increasing or the sampling period decreasing. The experimental results show that the method can determine the interval which contains the threshold, and detect the mutation acts of chaotic systems.

PROJECTIVE SYNCHRONIZATION OF UNIDENTICAL CHAOTIC SYSTEMS BASED ON STABILITY CRITERION

2006 ◽  
Vol 16 (04) ◽  
pp. 1049-1056 ◽  
Author(s):  
HONGJIE YU ◽  
JIANHUA PENG ◽  
YANZHU LIU
Keyword(s):  
Linear Systems ◽  
Stability Criterion ◽  
Chaotic Systems ◽  
Three Dimensional ◽  
New Method ◽  
Projective Synchronization ◽  
Partially Linear ◽  
Higher Dimensional ◽  
The Lorenz System ◽  
The Stability

A new method of projective synchronization of unidentical chaotic systems is proposed in this letter. This method is based on the stability criterion of linear systems. The response of two unidentical chaotic systems can synchronize up to any desired scaling factor by a suitable separation of the systems. The new method of projective synchronization is suitable not only for the three-dimensional coupled partially linear systems, but also for higher dimensional even hyperchaotic systems. The simplicity and effectiveness of the proposed method are illustrated by the Lorenz system, the four-dimensional partially linear system, the four-dimensional hyperchaotic Rösser system and Chua's circuit system as four numerical examples.

scholarly journals BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model

2021 ◽  
Vol 14 (5) ◽  
pp. 2977-3006
Author(s):  
Tongwen Wu ◽  
Rucong Yu ◽  
Yixiong Lu ◽  
Weihua Jie ◽  
Yongjie Fang ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Cyclones ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Climate System ◽  
System Model ◽  
Quasi Biennial Oscillation ◽  
Climate System Model ◽  
Medium Resolution ◽  
The Tropics

Abstract. BCC-CSM2-HR is a high-resolution version of the Beijing Climate Center (BCC) Climate System Model (T266 in the atmosphere and 1/4∘ latitude × 1/4∘ longitude in the ocean). Its development is on the basis of the medium-resolution version BCC-CSM2-MR (T106 in the atmosphere and 1∘ latitude × 1∘ longitude in the ocean) which is the baseline for BCC participation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study documents the high-resolution model, highlights major improvements in the representation of atmospheric dynamical core and physical processes. BCC-CSM2-HR is evaluated for historical climate simulations from 1950 to 2014, performed under CMIP6-prescribed historical forcing, in comparison with its previous medium-resolution version BCC-CSM2-MR. Observed global warming trends of surface air temperature from 1950 to 2014 are well captured by both BCC-CSM2-MR and BCC-CSM2-HR. Present-day basic atmospheric mean states during the period from 1995 to 2014 are then evaluated at global scale, followed by an assessment on climate variabilities in the tropics including the tropical cyclones (TCs), the El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and the quasi-biennial oscillation (QBO) in the stratosphere. It is shown that BCC-CSM2-HR represents the global energy balance well and can realistically reproduce the main patterns of atmospheric temperature and wind, precipitation, land surface air temperature, and sea surface temperature (SST). It also improves the spatial patterns of sea ice and associated seasonal variations in both hemispheres. The bias of the double intertropical convergence zone (ITCZ), obvious in BCC-CSM2-MR, almost disappears in BCC-CSM2-HR. TC activity in the tropics is increased with resolution enhanced. The cycle of ENSO, the eastward propagative feature and convection intensity of MJO, and the downward propagation of QBO in BCC-CSM2-HR are all in a better agreement with observations than their counterparts in BCC-CSM2-MR. Some imperfections are, however, noted in BCC-CSM2-HR, such as the excessive cloudiness in the eastern basin of the tropical Pacific with cold SST biases and the insufficient number of tropical cyclones in the North Atlantic.

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