Peicai Yang

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.

The nonlinear response of stratospheric ozone to NOx and ClOx perturbations

A mathematical analysis of the stratospheric photochemical system (gas phase reactions only) shows the existence of potential catastrophe mechanisms which could produce a dramatic reduction in the ozone concentration near 25 km altitude. The conditions leading to such catastrophe correspond to a relatively modest (i.e., factor 2–2.5) increase in the stratospheric source of reactive nitrogen (NOx). For a tenfold increase in the source of reactive chlorine (ClOx), the ozone system exhibits a large amplitude oscillatory behavior with a period of several tens of years. Transport processes could, however, damp these dramatic changes resulting from the nonlinear nature of the stratospheric chemical system.

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.

Retrievals of over-ocean precipitable water from SSM/I by SOM network model

An SOM network model was developed for retrievals of the oceanic total precipitable water (PW) from the SSM/I, by using the SSM/I and radiosonde observation data set provided by the NASDA (Japan). The model was first trained by 5/6 of the data, and the other data were used to test the retrieval ability of the model. The retrieval results showed that the SOM network model was better than the routine operational algorithm.

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.

SOM-network-based algorithm for retrieving over-ocean precipitable water from the SSM/I measurements

Based on the nearly coincided and collocated SSM/I and radiosonde data provided by NASDA (Japan), a self-organizing map (SOM) network-based model is developed for retrieving the oceanic total precipitable water (PW) from the SSM/I brightness temperature measurements. The model was firstly trained with the 5/6th of the data and then tested by the rest data. Comparisons of retrieved results with other algorithms show that the SOM model is significantly better than the classical statistically-based algorithms, especially in the low PW regime. The consuming time for PW retrievals with the SOM model is also acceptable in the operational applications.

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.