## 1. Introduction

[2] The problems of trend estimation and change point detection in climatic series have received substantial attention in recent years. Key issues include the magnitudes and directions of underlying trends (defined here as long-term changes in statistical properties; see section 1.1 of*Chandler and Scott* [2011]for a justification of this definition), and the existence (or otherwise) of abrupt shifts in the mean background state. The detection of abrupt discontinuities is an important step in characterizing climatic trends, because records may contain non-climatic artifacts due, for example, to undocumented changes in recording practice, instrumentation and station location: failure to account for such artifacts can lead to biased estimates of trends [e.g.,*Yang et al.*, 2006; *Menne et al.*, 2009; *Fall et al.*, 2011]. Unfortunately it is also an extremely challenging statistical problem when the times of these potential discontinuities are unknown, since the theory underpinning almost all standard statistical test procedures breaks down in this case [*Lund and Reeves*, 2002]. Many techniques have been proposed for addressing the problem including *t*-tests [e.g.,*Staudt et al.*, 2007; *Marengo and Camargo*, 2008], Mann-Whitney and Pettitt tests [e.g.,*Mauget*, 2003; *Fealy and Sweeney*, 2005; *Li et al.*, 2005; *Yu et al.*, 2006], linear and piecewise linear regression [e.g., *Tomé and Miranda*, 2004; *Portnyagin et al.*, 2006; *Su et al.*, 2006], cumulative sum analysis [e.g., *Fealy and Sweeney*, 2005; *Levin*, 2011], hierarchical Bayesian change point analysis [e.g., *Tu et al.*, 2009]; Markov chain Monte Carlo methods [e.g., *Elsner et al.*, 2004; *Zhao and Chu*, 2006], reversible jump Markov chain Monte Carlo [e.g., *Zhao and Chu*, 2010], and nonparametric regression [e.g., *Bates et al.*, 2010].

[3] While there are many procedures in use, the purpose of this paper is to motivate wider use of flexible regression techniques. In particular, we highlight the availability of methods that allow for both smooth trends and abrupt changes, and that enable discrimination between the two. These techniques can give different (and more credible) results from those of classical parametric regression and change point methods. Of course, we do not claim a monopoly on the use of flexible statistical techniques; other relevant work with a similar goal includes *Grégoire and Hamrouni* [2002] and *Gijbels and Goderniaux* [2004]. We do, however, aim to motivate the use of techniques that are intuitive, flexible and for which user-friendly software is freely available so that implementation by non-statisticians is straightforward.

[4] We consider three case studies: the winter (December–March) North Atlantic Oscillation (NAO) index series for the period 1864–2010 (i.e., the *Hurrell* [1995] NAO index); an annual mean relative humidity series sourced from the NCEP/NAR Reanalysis 1 project for the period 1948–2010 [*Kalnay et al.*, 1996]; and a seasonal (June to October) series of typhoon counts in the vicinity of Taiwan for the period 1970–2006 [*Tu et al.*, 2009]. In the next section we briefly describe the methods used. Analyses of the winter NAO index, relative humidity and the typhoon count series are described in sections 3 to 5, respectively. Finally, a discussion and our findings are given in section 6.