This review assesses storm studies over the North Atlantic and northwestern Europe regarding the occurrence of potential long‐term trends. Based on a systematic review of available articles, trends are classified according to different geographical regions, datasets, and time periods. Articles that used measurement and proxy data, reanalyses, regional and global climate model data on past and future trends are evaluated for changes in storm climate. The most important result is that trends in storm activity depend critically on the time period analysed. An increase in storm numbers is evident for the reanalyses period for the most recent decades, whereas most long‐term studies show merely decadal variability for the last 100–150 years. Storm trends derived from reanalyses data and climate model data for the past are mostly limited to the last four to six decades. The majority of these studies find increasing storm activity north of about 55–60° N over the North Atlantic with a negative tendency southward. This increase from about the 1970s until the mid‐1990s is also mirrored by long‐term proxies and the North Atlantic Oscillation and constitutes a part of their decadal variability. Studies based on proxy and measurement data or model studies over the North Atlantic for the past which cover more than 100 years show large decadal variations and either no trend or a decrease in storm numbers. Future scenarios until about the year 2100 indicate mostly an increase in winter storm intensity over the North Atlantic and western Europe. However, future trends in total storm numbers are quite heterogeneous and depend on the model generation used.

Global atmospheric reanalyses have become a common tool for both the validation of climate models and diagnostic studies, such as assessing climate variability and long-term trends. Presently, the 20th Century Reanalysis (20CR), which assimilates only surface pressure reports, sea-ice, and sea surface temperature distributions, represents the longest global reanalysis dataset available covering the period from 1871 to the present. Currently, the 20CR dataset is extensively used for the assessment of climate variability and trends. Here, we compare the variability and long-term trends in Northeast Atlantic storminess derived from 20CR and from observations. A well established storm index derived from pressure observations over a relatively densely monitored marine area is used. It is found that both, variability and long-term trends derived from 20CR and from observations, are inconsistent. In particular, both time series show opposing trends during the first half of the 20th century. Only for the more recent periods both storm indices share a similar behavior. While the variability and long-term trend derived from the observations are supported by a number of independent data and analyses, the behavior shown by 20CR is quite different, indicating substantial inhomogeneities in the reanalysis most likely caused by the increasing number of observations assimilated into 20CR over time. The latter makes 20CR likely unsuitable for the identification of trends in storminess in the earlier part of the record at least over the Northeast Atlantic. Our results imply and reconfirm previous findings that care is needed in general, when global reanalyses are used to assess long-term changes.

A linear statistical model relating the nocturnal urban heat island (UHI) intensity of Hamburg with meteorological conditions is constructed from observations taken by the German Meteorological Service (DWD). To find the appropriate predictors the relationship between different meteorological variables and the UHI of Hamburg is analyzed. Results and physical plausibility suggest that cloud cover, wind speed and relative humidity are the relevant variables and can be used to construct a statistical model. The parameters for the statistical model are determined with the generalized least square method. With the help of the statistical model up to 42% of the UHI variance can be explained. The statistical model is then used to statistically downscale results from climate runs of the regional climate models (RCM) REMO and CLM. Both RCMs were driven with the A1B SRES emission scenario runs of the global climate model ECHAM5/MPI-OM. The resulting values for the future UHI are analyzed with respect to monthly averages and the frequency distribution. Results show that changes in the UHI are different for the different months. Significant change (decrease of UHI) in the results of both RCMs and for both realizations of the A1B scenario can be found for April in at the middle and the end of the century and in December at the end of the century. For the summer months which are most relevant to the development of adaption strategies the results differ between the RCMs. REMO results show no significant changes for the summer, while analyses of CLM suggest significant increase in July and August. The frequency distribution of the summer UHI shows no significant changes for REMO and only in one realization of CLM a significant increase in moderate and strong UHI days can be found for the end of the century.

Geostrophic wind speeds calculated from mean sea level pressure readings are used to derive time series of northeast Atlantic storminess. The technique of geostrophic wind speed triangles provides relatively homogeneous long-term storm activity data and is thus suited for statistical analyses. This study makes use of historical air pressure data available from the International Surface Pressure Databank (ISPD) complemented with data from the Danish and Norwegian Meteorological Institutes. For the first time, the time series of northeast Atlantic storminess is extended until the most recent year available, that is, 2016. A multidecadal increasing trend in storm activity starting in the mid-1960s and lasting until the 1990s, whose high storminess levels are comparable to those found in the late nineteenth century, initiated debate over whether this would already be a sign of climate change. This study confirms that long-term storminess levels have returned to average values in recent years and that the multidecadal increase is part of an extended interdecadal oscillation. In addition, new storm activity uncertainty estimates were developed and novel insights into the connection with the North Atlantic Oscillation (NAO) are provided.

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Yearly percentiles of geostrophic wind speeds serve as a widely used proxy for assessing past storm activity. Here, daily geostrophic wind speeds are derived from a geographical triangle of surface air pressure measurements and are used to build yearly frequency distributions. It is commonly believed, however unproven, that the variation of the statistics of strong geostrophic wind speeds describes the variation of statistics of ground-level wind speeds. This study evaluates this approach by examining the correlation between specific annual (seasonal) percentiles of geostrophic and of area-maximum surface wind speeds to determine whether the two distributions are linearly linked in general.The analyses rely on bootstrap and binomial hypothesis testing as well as on analysis of variance. Such investigations require long, homogeneous, and physically consistent data. Because such data are barely existent, regional climate model-generated wind and surface air pressure fields in a fine spatial and temporal resolution are used. The chosen regional climate model is the spectrally nudged and NCEP-driven regional model (REMO) that covers Europe and the North Atlantic. Required distributions are determined from diagnostic 10-m and geostrophic wind speed, which is calculated from model air pressure at sea level.Obtained results show that the variation of strong geostrophic wind speed statistics describes the variation of ground-level wind speed statistics. Annual and seasonal quantiles of geostrophic wind speed and groundlevel wind speed are positively linearly related. The influence of low-pass filtering is also considered and found to decrease the quality of the linear link. Moreover, several factors are examined that affect the description of storminess through geostrophic wind speed statistics. Geostrophic wind from sea triangles reflects storm activity better than geostrophic wind from land triangles. Smaller triangles lead to a better description of storminess than bigger triangles.

Changes in intensity, frequency, and location of temperature extreme events are a focus for many studies that often rely on simulations from climate models to assess changes in temperature extremes. Given the use of climate models for attributing such events to human and natural influences and for projecting future changes, an assessment of the capability of climate models to properly simulate the mechanisms associated with temperature extreme events is necessary. In this study, known mechanisms and relevant meteorological variables are explored in a composite analysis to identify and quantify a climatology of synoptic weather patterns related to hot and cold seasonal temperature extreme events over Central Europe. The analysis is based on extremes that recur once or several times per season for better sampling. Weather patterns from a selection of CMIP5 models are compared with patterns derived from the ERA interim reanalysis. The results indicate that climate models simulate mechanisms associated with temperature extreme events reasonably well, in particular circulation-based mechanisms. The amplitude and average length of events is assessed, where in some cases significant deviations from ERA interim are found. In three cases, the models have on average significantly more days per season with extreme events than ERA interim. Quantitative analyses of physical links between extreme temperature and circulation, relative humidity, and radiation reveal that the strength of the link between the temperature and the variables does not vary greatly from model to model and ERA interim.

Decadal climate prediction is a challenging aspect of climate research. It has been and will be tackled by various modeling groups. This study proposes a simple empirical forecasting system for the near-surface temperature that can be used as a benchmark for climate predictions obtained from atmosphere–ocean GCMs (AOGCMs). It is assumed that the temperature time series can be decomposed into components related to external forcing and internal variability. The considered external forcing consists of the atmospheric CO2 concentration. Separation of the two components is achieved by using the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) twentieth-century integrations. Temperature anomalies due to changing external forcing are described by a linear regression onto the forcing. The future evolution of the external forcing that is needed for predictions is approximated by a linear extrapolation of the forcing prior to the initial time. Temperature anomalies owing to the internal variability are described by an autoregressive model. An evaluation of hindcast experiments shows that the empirical model has a cross-validated correlation skill of 0.84 and a cross-validated rms error of 0.12 K in hindcasting global-mean temperature anomalies 10 years ahead.