Geli Wang

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...

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature

Significance Here we use newly available methods to examine the dynamical association between cosmic rays (CR) and global temperature (GT) in the 20th-century observational record. We find no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend; however, on short interannual timescales, we find a significant, although modest, causal effect of CR on short-term, year-to-year variability in GT. Thus, although CR clearly do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales, providing another interesting piece of the puzzle in our understanding of factors influencing climate variability. As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR) [Ney ER (1959) Nature 183:451–452]. Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research [Duplissy J, et al. (2010) Atmos Chem Phys 10:1635–1647; Kirkby J, et al. (2011) Nature 476(7361):429–433] and elsewhere [Svensmark H, Pedersen JOP, Marsh ND, Enghoff MB, Uggerhøj UI (2007) Proc R Soc A 463:385–396; Enghoff MB, Pedersen JOP, Uggerhoj UI, Paling SM, Svensmark H (2011) Geophys Res Lett 38:L09805], demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we find no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. However, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.

The prediction of non-stationary climate series based on empirical mode decomposition

This paper proposes a new approach which we refer to as “segregated prediction” to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method (EMD), which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.

An approach to improving the dynamical extended-range (monthly) prediction

Focusing on common and significant forecast errors—the zonal mean errors in the numerical prediction model, this report proposes an approach to improving the dynamical extended-range (monthly) prediction. Firstly, the monthly pentad-mean nonlinear dynamical regional prediction model of the zonal-mean height based on a large number of historical data is constituted by employing the reconstruction phase space theory and the spatio-temporal series predictive method. The zonal height thus produced is transformed to its counterpart in the numerical model and further used to revise the numerical model prediction during the integration process. In this way, the two different kinds of prediction are combined. The forecasting experimenal results show that the above hybrid approach not only reduces the systematical error of the numerical model, but also improves the forecast of the non-axisymmetric components due to the wave-flow interaction.

Extracting the driving force from ozone data using slow feature analysis

Slow feature analysis (SFA) is a recommended technique for extracting slowly varying features from a quickly varying signal. In this work, we apply SFA to total ozone data from Arosa, Switzerland. The results show that the signal of volcanic eruptions can be found in the driving force, and wavelet analysis of this driving force shows that there are two main dominant scales, which may be connected with the effect of climate mode such as North Atlantic Oscillation (NAO) and solar activity. The findings of this study represent a contribution to our understanding of the causality from observed climate data.

Identification of the driving forces of climate change using the longest instrumental temperature record

The identification of causal effects is a fundamental problem in climate change research. Here, a new perspective on climate change causality is presented using the central England temperature (CET) dataset, the longest instrumental temperature record, and a combination of slow feature analysis and wavelet analysis. The driving forces of climate change were investigated and the results showed two independent degrees of freedom —a 3.36-year cycle and a 22.6-year cycle, which seem to be connected to the El Niño–Southern Oscillation cycle and the Hale sunspot cycle, respectively. Moreover, these driving forces were modulated in amplitude by signals with millennial timescales.

On the nonlinear response of lower stratospheric ozone to external forces—the inclusion of BrOx and radiation processes

In this paper the bromine family and radiative effects are considered in an updated box model under the framework of ozone-temperature feedback, in order to further analyze the possible behavior of atmospheric ozone in the lower mid-latitude stratosphere. Results show that this updated photochemical system can present several different solutions, within a certain domain of parameters, with fixed-point and periodic states appearing in turn. The temperature feedback effect introduced in this box model has not changed the topology of the ozone system. This result presents nonlinear characteristics of the ozone system, and possible trends in the stratospheric atmosphere between complex chemistry and radiation processes.

The Prediction of Nonstationary Climate Series by Incorporating External Forces

Almost all climate time series have some degree of nonstationarity due to external forces of the observed system. Therefore, these external forces should be taken into account when reconstructing the climate dynamics. This paper presents a novel technique in predicting nonstationary time series. The main difference of this new technique from some previous methods is that it incorporates the driving forces in the prediction model. To appraise its effectiveness, some prediction experiments were carried out using the data generated from some known classical dynamical models and climate data. Experimental results indicate that this technique is able to improve the prediction skill effectively.