Anastasios A. Tsonis

What Do Networks Have to Do with Climate

The study of networks has recently exploded into a major research tool in many areas of science. The discovery of “small world” and scale-free networks has led to many new insights about the collective behavior of a large number of interacting agents and complex systems. Here we introduce the basic ideas behind networks, as well as some initial applications of networks to the climate system. Our results suggest that the climate system exhibits aspects of small-world networks as well as scale-free networks, with supernodes corresponding to major teleconnection patterns. This result suggests that the organization of teleconnections may play a role in the stability of the climate system. In addition, preliminary work suggests that temporal changes in the networks architecture may be used to identify signatures of global change. These and other applications suggest that networks provide a new tool for investigating and reconstructing climate dynamics from both models and observations.

Functional box-counting and multiple elliptical dimensions in rain.

Many physical systems that have interacting structures that span wide ranges in size involve substantial scale invariant (or scaling) subranges. In these regimes, the large and small scales are related by an operation that involves only the scale ratio. The system has no intrinsic characteristic size. In the atmosphere gravity causes differential stratification, so that the scale change involves new elliptical dimensions (del). Fields that are extremely variable, such as rain, involve multiple scaling and dimensions that characterize the increasingly intense regions. Elliptical dimensional sampling and functional box-counting have been used to analyze radar rain data to obtain both the multiple dimensions of the rain field and the estimate del = 2.22 � 0.07.

Nonlinear Prediction, Chaos, and Noise

Abstract We present a brief overview of some new methodologies for making predictions on time-series data. These ideas stern from two rapidly growing fields: nonlinear dynamics (choas) theory and parallel distributed processing. Examples are presented that show the usefulness of such methods in making short-term predictions. It is suggested that such methodologies are capable of distinguishing between chaos and noise. Implications of these ideas and methods in the study of weather and climate are discussed.

Estimating the Dimension of Weather and Climate Attractors: Important Issues about the Procedure and Interpretation

Abstract When the reconstruction of attractors from observables is sought, the Grassberger-Procaccia algorithm for estimating the correlation dimension is often used. An overview of recent developments concerning data requirements and algorithm performance is presented within. In view of these developments the significance of previously estimated dimensions of weather and climate attractors is discussed.

Periodicity in DNA coding sequences: implications in gene evolution.

In this paper we have employed Fourier analysis of DNA coding and non-coding sequences in an attempt to identify possible patterns in gene sequences. It was found that while intronic sequences show a rather random pattern, coding sequences show periodicities and in particular a periodicity of 3. We were able to reconstruct such patterns by assuming a gene having one codon occurring in about 40% of the sequence. This could indicate that the predominant presence of codons all starting from the same base could confer the observed periodicities. Indeed, it was found that proteins do obey this rule. Implications of this finding in gene evolution are discussed.

Chaos, Strange Attractors, and Weather

Abstract Some of the basic principles of the theory of dynamical systems are presented, introducing the reader to the concepts of chaos theory and strange attractors and their implications in meteorology. New numerical techniques to analyze weather data according to the above theory are also presented.

Long-term natural variability and 20th century climate change

Global mean temperature at the Earths surface responds both to externally imposed forcings, such as those arising from anthropogenic greenhouse gases, as well as to natural modes of variability internal to the climate system. Variability associated with these latter processes, generally referred to as natural long-term climate variability, arises primarily from changes in oceanic circulation. Here we present a technique that objectively identifies the component of inter-decadal global mean surface temperature attributable to natural long-term climate variability. Removal of that hidden variability from the actual observed global mean surface temperature record delineates the externally forced climate signal, which is monotonic, accelerating warming during the 20th century.

On the Role of Atmospheric Teleconnections in Climate

Abstract In a recent application of networks to 500-hPa data, it was found that supernodes in the network correspond to major teleconnection. More specifically, in the Northern Hemisphere a set of supernodes coincides with the North Atlantic Oscillation (NAO) and another set is located in the area where the Pacific–North American (PNA) and the tropical Northern Hemisphere (TNH) patterns are found. It was subsequently suggested that the presence of atmospheric teleconnections make climate more stable and more efficient in transferring information. Here this hypothesis is tested by examining the topology of the complete network as well as of the networks without teleconnections. It is found that indeed without teleconnections the network becomes less stable and less efficient in transferring information. It was also found that the pattern chiefly responsible for this mechanism in the extratropics is the NAO. The other patterns are simply a linear response of the activity in the tropics and their role in thi...

Long-Range Correlations in the Extratropical Atmospheric Circulation: Origins and Implications

Abstract The atmospheric general circulation often enters into regimes that cause weather anomalies (departures from an average state) to persist over areas of the globe. By considering 500-hPa measurements the authors demonstrate the existence of scale invariance in the variability of extratropical atmospheric circulation anomalies over the whole range of timescales resolved by the available data, from a week to a decade. This scale invariance indicates an absence of characteristic timescales and the presence of positive long-range correlations, meaning that if an anomaly of a particular sign exists in the past it will most likely continue to exist in the future. Moreover, this scale invariance indicates that the dynamics of small scales are connected to the dynamics of large scales via a simple power law. A consequence of this finding is that the memory of the system is not confined only to large scales but extends to small scales as well. By investigating the hemispheric structure of 500-hPa fields ove...

A characteristic time scale in the global temperature record

Using modern time series analysis we discover a characteristic time scale in the global temperature record. This time scale corresponds to about 20 months and separates processes that promote a trend in the past from processes that reverse this tendency. This characteristic scale has important implications one of which might be that the El Nino/La Nina cycle may act as a mechanism countering the tendency of shorter time scale events to organize a positive or a negative temperature trend.