Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coher- ence for examining relationships in time frequency space be- tween two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relation- ships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (http://www.pol.ac.uk/home/research/waveletcoherence/). As we are interested in extracting low s/n ratio signals in time series we discuss only CWT in this paper. While CWT is a common tool for analyzing localized intermittent os- cillations in a time series, it is very often desirable to ex- amine two time series together that may be expected to be linked in some way. In particular, to examine whether re- gions in time frequency space with large common power have a consistent phase relationship and therefore are sug- gestive of causality between the time series. Many geophys- ical time series are not Normally distributed and we suggest methods of applying the CWT to such time series. From two CWTs we construct the Cross Wavelet Transform (XWT) which will expose their common power and relative phase in time-frequency space. We will further define a measure of Wavelet Coherence (WTC) between two CWT, which can find significant coherence even though the common power is low, and show how confidence levels against red noise back- grounds are calculated. We will present the basic CWT theory before we move on to XWT and WTC. New developments such as quanti- fying the phase relationship and calculating the WTC sig- nificance level will be treated more fully. When using the methods on time series it is important to have solid mecha- nistic foundations on which to base any relationships found, and we caution against using the methods in a "scatter-gun" approach (particularly if the time series probability density functions are modified). To illustrate how the various meth- ods are used we apply them to two data sets from meteo- rology and glaciology. Finally, we will provide links to a MatLab software package.

/pdf/application-of-the-cross-wavelet-transform-and-wavelet-3aorulqrv4.pdf

Application of the cross wavelet transform and wavelet coherence to geophysical time series

[1] Atmospheric gravity waves have been a subject of intense research activity in recent years because of their myriad effects and their major contributions to atmospheric circulation, structure, and variability. Apart from occasionally strong lower-atmospheric effects, the major wave influences occur in the middle atmosphere, between ∼ 10 and 110 km altitudes because of decreasing density and increasing wave amplitudes with altitude. Theoretical, numerical, and observational studies have advanced our understanding of gravity waves on many fronts since the review by Fritts [1984a]; the present review will focus on these more recent contributions. Progress includes a better appreciation of gravity wave sources and characteristics, the evolution of the gravity wave spectrum with altitude and with variations of wind and stability, the character and implications of observed climatologies, and the wave interaction and instability processes that constrain wave amplitudes and spectral shape. Recent studies have also expanded dramatically our understanding of gravity wave influences on the large-scale circulation and the thermal and constituent structures of the middle atmosphere. These advances have led to a number of parameterizations of gravity wave effects which are enabling ever more realistic descriptions of gravity wave forcing in large-scale models. There remain, nevertheless, a number of areas in which further progress is needed in refining our understanding of and our ability to describe and predict gravity wave influences in the middle atmosphere. Our view of these unknowns and needs is also offered.

/pdf/gravity-wave-dynamics-and-effects-in-the-middle-atmosphere-4huexaq7y8.pdf

Gravity wave dynamics and effects in the middle atmosphere

/pdf/climate-models-and-their-evaluation-3l7t3uekup.pdf

Climate models and their evaluation

Climate models have continued to be developed and improved since the AR4, and many models have been extended into Earth System models by including the representation of biogeochemical cycles important to climate change. These models allow for policy-relevant calculations such as the carbon dioxide (CO2) emissions compatible with a specified climate stabilization target. In addition, the range of climate variables and processes that have been evaluated has greatly expanded, and differences between models and observations are increasingly quantified using ‘performance metrics’. In this chapter, model evaluation covers simulation of the mean climate, of historical climate change, of variability on multiple time scales and of regional modes of variability. This evaluation is based on recent internationally coordinated model experiments, including simulations of historic and paleo climate, specialized experiments designed to provide insight into key climate processes and feedbacks and regional climate downscaling. Figure 9.44 provides an overview of model capabilities as assessed in this chapter, including improvements, or lack thereof, relative to models assessed in the AR4. The chapter concludes with an assessment of recent work connecting model performance to the detection and attribution of climate change as well as to future projections.

/pdf/evaluation-of-climate-models-21sy7e310w.pdf

Evaluation of climate models

Observations show that large variations in the strength of the stratospheric circulation, appearing first above ∼50 kilometers, descend to the lowermost stratosphere and are followed by anomalous tropospheric weather regimes During the 60 days after the onset of these events, average surface pressure maps resemble closely the Arctic Oscillation pattern These stratospheric events also precede shifts in the probability distributions of extreme values of the Arctic and North Atlantic Oscillations, the location of storm tracks, and the local likelihood of mid-latitude storms Our observations suggest that these stratospheric harbingers may be used as a predictor of tropospheric weather regimes

https://science.sciencemag.org/content/sci/294/5542/581.full.pdf

Stratospheric harbingers of anomalous weather regimes.

The quasi-biennial oscillation (QBO) dominates the variability of the equatorial stratosphere (∼16–50 km) and is easily seen as downward propagating easterly and westerly wind regimes, with a variable period averaging approximately 28 months. From a fluid dynamical perspective, the QBO is a fascinating example of a coherent, oscillating mean flow that is driven by propagating waves with periods unrelated to that of the resulting oscillation. Although the QBO is a tropical phenomenon, it affects the stratospheric flow from pole to pole by modulating the effects of extratropical waves. Indeed, study of the QBO is inseparable from the study of atmospheric wave motions that drive it and are modulated by it. The QBO affects variability in the mesosphere near 85 km by selectively filtering waves that propagate upward through the equatorial stratosphere, and may also affect the strength of Atlantic hurricanes. The effects of the QBO are not confined to atmospheric dynamics. Chemical constituents, such as ozone, water vapor, and methane, are affected by circulation changes induced by the QBO. There are also substantial QBO signals in many of the shorter-lived chemical constituents. Through modulation of extratropical wave propagation, the QBO has an effect on the breakdown of the wintertime stratospheric polar vortices and the severity of high-latitude ozone depletion. The polar vortex in the stratosphere affects surface weather patterns, providing a mechanism for the QBO to have an effect at the Earth's surface. As more data sources (e.g., wind and temperature measurements from both ground-based systems and satellites) become available, the effects of the QBO can be more precisely assessed. This review covers the current state of knowledge of the tropical QBO, its extratropical dynamical effects, chemical constituent transport, and effects of the QBO in the troposphere (∼0–16 km) and mesosphere (∼50–100 km). It is intended to provide a broad overview of the QBO and its effects to researchers outside the field, as well as a source of information and references for specialists. The history of research on the QBO is discussed only briefly, and the reader is referred to several historical review papers. The basic theory of the QBO is summarized, and tutorial references are provided.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/1999RG000073

J. S. Kinnersley

Papers

The quasi-biennial oscillation