This paper presents a new technique of combining the driving force of a time series obtained using the slow feature analysis (SFA) approach, then introducing the driving force into a predictive model to predict nonstationary time series. It could be considered to be a data-driven attempt to make progress in predicting nonstationary climatic time series and in better understanding the climate causality research from observed climate data.

It has long been known that verification of a forecast against the sequence of analyses used to produce those forecasts can under-estimate the magnitude of forecast errors. Here we show that under certain conditions the verification of a short-range forecast against a perturbed analysis coming from an ensemble data assimilation scheme can give the same root-mean-square error as verification against the truth. This means that a perturbed analysis can be used as a reliable proxy for the truth.

Highly energetic mode-2 nonlinear internal waves were found to be generated at several sites near the northern Heng-Chun (western) Ridge south of Taiwan. The local environment however was highly dissipative, and the energy budget suggests that little wave energy escapes the ridge to contribute to the large transbasin waves that have been previously observed in other studies.

Terrestrial water storage (TWS) plays a key role in global water and energy cycles. This work applies complex climate networks to analyzing spatial patterns in TWS. A comparative analysis is conducted using a remotely sensed (GRACE) and a model-generated TWS data set. Our results reveal hotspots of TWS anomalies around the global land surfaces. Prospects are offered on using network connectivity as constraints to further improve current global land surface models.

The present work, to the best of our knowledge, is the first DNS study reporting temporal (long-term) statistics of intermittent turbulence (a.k.a. bursting events) in stably stratified flows. The present paper not only accurately reproduces recently published key spatial statistics of intermittent turbulence but also reports some intriguing features, e.g., the coexistence of internal waves and intermittent turbulence.

A 531-day wobble (531 dW) signal is clearly detected with a mean amplitude of about 7 mas after applying the ensemble empirical mode decomposition (EEMD) to the 1962-2013 polar motion (PM) time series. This signal is also detected in the two longest available superconducting gravimeter (SG) records. Synthetic tests are carried out to explain why the 531 dW signal can only be observed in recent 30-year PM time series after using EEMD.

A new type of ensemble Kalman filter for data assimilation is developed, based on fast Fourier transform and wavelet transform. The method can work with minimal computational resources. We develop variants for several general types of observations, give a rigorous proof that the method improves the approximation of the state covariance, and present computational experiments showing that the new technique works reliably with very small ensembles and is stable over multiple analysis cycles.