Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialised techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology, meteorology, atmospheric science, and oceanography.

The ''spectral nudging'' method imposes time-variable large-scale atmospheric states on a regional atmospheric model. It is based on the idea that regional-scale climate statistics are conditioned by the interplay between continental-scale atmospheric conditions and such regional features as marginal seas and mountain ranges. Following this ''downscaling'' idea, the regional model is forced to satisfy not only boundary conditions, possibly in a boundary sponge region, but also large-scale flow conditions inside the integration area.In the present paper the performance of spectral nudging in an extended climate simulation is examined. Its success in keeping the simulated state close to the driving state at larger scales, while generating smaller-scale features is demonstrated, and it is also shown that the standard boundary forcing technique in current use allows the regional model to develop internal states conflicting with the large-scale state. It is concluded that spectral nudging may be seen as a suboptimal and indirect data assimilation technique.

The derivation of local scale information from integrations of coarse-resolution general circulation models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In this paper a relatively simple analog method is described and applied for downscaling purposes. According to this method the large-scale circulation simulated by a GCM is associated with the local variables observed simultaneously with the most similar large-scale circulation pattern in a pool of historical observations. The similarity of the large-scale circulation patterns is defined in terms of their coordinates in the space spanned by the leading observed empirical orthogonal functions.The method can be checked by replicating the evolution of the local variables in an independent period. Its performance for monthly and daily winter rainfall in the Iberian Peninsula is compared to more complicated techniques, each belonging to one of the broad families of existing statistical downscaling techniques: a method based on canonical correlation analysis, as representative of linear methods; a method based on classification and regression trees, as representative of a weather generator based on classification methods; and a neural network, as an example of deterministic nonlinear methods.It is found in these applications that the analog method performs in general as well as the more complicated methods, and it can be applied to both normally and nonnormally distributed local variables. Furthermore, it produces the right level of variability of the local variable and preserves the spatial covariance between local variables. On the other hand linear multivariate methods offer a clearer physical interpretation that supports more strongly its validity in an altered climate. Classification and neural networks are generally more complicated methods and do not directly offer a physical interpretation.

Abstract. This paper discusses the state of European research in historical climatology. This field of science and an overview of its development are described in detail. Special attention is given to the documentary evidence used for data sources, including its drawbacks and advantages. Further, methods and significant results of historical-climatological research, mainly achieved since 1990, are presented. The main focus concentrates on data, methods, definitions of the "Medieval Warm Period" and the "Little Ice Age", synoptic interpretation of past climates, climatic anomalies and natural disasters, and the vulnerability of economies and societies to climate as well as images and social representations of past weather and climate. The potential of historical climatology for climate modelling research is discussed briefly. Research perspectives in historical climatology are formulated with reference to data, methods, interdisciplinarity and impacts.

Extreme weather and climate‐related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human‐induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23–41. doi: 10.1002/wcc.380For further resources related to this article, please visit the WIREs website.