Recent Change—Atmosphere

  • Martin Stendel
  • Else van den Besselaar
  • Abdel Hannachi
  • Elizabeth C. Kent
  • Christiana Lefebvre
  • Frederik Schenk
  • Gerard van der Schrier
  • Tim Woollings
Open Access
Chapter
Part of the Regional Climate Studies book series (REGCLIMATE)

Abstract

This chapter examines past and present studies of variability and changes in atmospheric variables within the North Sea region over the instrumental period; roughly the past 200 years. The variables addressed are large-scale circulation, pressure and wind, surface air temperature, precipitation and radiative properties (clouds, solar radiation, and sunshine duration). Temperature has increased everywhere in the North Sea region, especially in spring and in the north. Precipitation has increased in the north and decreased in the south. There has been a north-eastward shift in storm tracks, which agrees with climate model projections. Due to large internal variability, it is not clear which aspects of the observed changes are due to anthropogenic activities and which are internally forced, and long-term trends are difficult to deduce. The number of deep cyclones seems to have increased (but not the total number of cyclones). The persistence of circulation types seems to have increased over the past century, with ‘more extreme’ extreme events. Changes in extreme weather events, however, are difficult to assess due to changes in instrumentation, station relocations, and problems with digitisation. Without thorough quality control digitised datasets may be useless or even counterproductive. Reanalyses are useful as long as biases introduced by inhomogeneities are properly addressed. It is unclear to what extent circulation over the North Sea region is controlled by distant factors, especially changes in Arctic sea ice.

2.1 Introduction

Situated in northern central Europe, the North Sea exhibits large climate variability with inflow of a wide range of air masses from arctic to subtropical. For this reason, it is difficult to differentiate between natural and externally forced variability, despite large amounts of historical data. This chapter examines past and present studies of variability and changes in atmospheric variables over the instrumental period; roughly the last 200 years. Research areas lacking consensus in the scientific community are highlighted to stimulate further research.

The main driver of atmospheric variability in the North Sea region is the North Atlantic Oscillation (NAO). Despite its apparent long-term irregularity, the NAO exhibits extended periods of positive or negative index values. No consensus exists with respect to the size of the fraction of interannual NAO variance that cannot be explained by random forcing and is therefore probably influenced by external forcing. Slowly varying natural factors with an effect on European climate, such as the Atlantic Multidecadal Oscillation (AMO), may superimpose long-term trends on atmospheric variability and so be difficult to distinguish from the anthropogenic climate change signal.

The source of atmospheric and surface data influences the results obtained, even in the comparatively data-rich North Sea region. Based on reanalysis data, several studies find positive trends in storm activity over the North Sea region and a northeast shift in storm tracks over the past few decades. However, studies based on direct or indirect historical records of long-term variations in pressure, wind or wind-related proxies, mostly do not identify robust long-term trends. This counter-intuitive result is explained by uncertainties in the long-term historical wind and atmospheric pressure observations, and additional uncertainties arising from the lack of quality control when digitising old data as well as potential biases in the reanalyses due to the fact that the underlying amount of available data is not constant in time. Nevertheless, the northeast shift in storm tracks appears to be a new phenomenon. In contrast, the increase in wind speed and storminess in the latter half of the 20th century does not seem to be unprecedented within the context of historical observations. There are indications of an increase in the number of deep cyclones (but not in the total number of cyclones). There are also indications that the persistence of circulation types has increased over the past century.

Temperatures have increased both over land and over the North Sea. There is a distinct signal in the number of frost days and the number of summer days. While there is a clear winter and spring warming signal over the Baltic Sea region, this is not as clear for the North Sea region. As expected, the variability in marine temperatures on seasonal timescales is less than for the land temperatures.

Precipitation over land and, but to a lesser extent, over sea is positively correlated with the NAO, and on longer time scales with the AMO. There are indications of an increase in precipitation in the north of the region and a decrease in the south, in agreement with the north-eastward shift in the storm tracks. There are also indications that extreme precipitation events have become more extreme and that return periods have decreased.

From the few datasets available on radiative properties, it may be concluded that there are non-negligible trends together with potential uncertainties and land-sea inhomogeneities which make it difficult to assess these quantities in detail.

Climate change in the North Sea region cannot be investigated in isolation. In particular, what the relation is between changes in the Arctic cryosphere and trends in storminess, number of cyclones, persistence of circulation anomalies and extreme events further south, is an open research question. As analyses of the latter often rely on small datasets covering relatively short time scales, it is difficult to draw statistically significant conclusions. It is therefore essential to make available the large amount of data from past decades that have not yet been digitised. However, it is essential to thoroughly quality-check the data.

2.2 Large-Scale Circulation

2.2.1 Circulation Over the North Sea Region in a Climatological Perspective

From a climatological perspective, the North Sea region is characterised by strong ocean-atmosphere interactions, especially during winter, compared to other regions at similar latitudes (Furevik and Nilsen 2005). These interactions involve transfer of momentum, moisture and various trace gases, mainly carbon dioxide (Takahashi et al. 2002). In addition to the recent warming trend (Delworth and Knutson 2000; Johannessen et al. 2004), the North Sea and nearby regions witnessed climate change during the early 20th century, which was large in comparison to similar latitudes elsewhere (von Storch and Reichardt 1997; Gönnert 2003).

Atmospheric circulation in the European/North Atlantic region plays an important role in the regional climate of the North Sea and surrounding land areas (Hurrell 1995; Slonosky et al. 2000, 2001). It is mainly described by the NAO (e.g. Hurrell et al. 2003) which is an expression of the zonality of the atmospheric flow. The North Sea region is controlled by two large-scale quasi-stationary atmospheric patterns, the Icelandic Low (IL) and the Azores High (AH) plus a thermally driven system over Eurasia with high pressure in winter and low pressure in summer. The dominant flow is therefore westerly, although any other wind direction is also frequently observed, and one of the main factors controlling air-sea interactions in the North Sea region is wind stress. Large-scale processes also constitute one of the main driving mechanisms responsible for the connection between local processes and global change. It is therefore important to pay attention to the recent changes in large-scale flow directly affecting the North Sea region.

The remainder of Sect. 2.2 reviews the status of the large-scale atmospheric variability affecting the North Sea region by focusing on the major teleconnection patterns and their effect on the jet stream. The NAO can be seen as the European expression of a larger-scale phenomenon, the Arctic Oscillation (AO). The relationship between the NAO and AO is briefly discussed in the following section, while a general description of the NAO and its properties is given in Annex 1. The NAO varies on a wide range of time scales from days to decades, reflecting interactions with surface conditions including sea-surface temperature (SST) and sea ice. These changes translate into changes in pressure and winds (Sect. 2.3), temperature (Sect. 2.4) and precipitation (Sect. 2.5) and also affect other variables, like sunshine (Sect. 2.6).

2.2.2 NAO and AO

The strength of the westerlies and the eddy-driven jet stream over the North Atlantic and western Europe are controlled by various factors including the pressure difference between the IL and AH as the main centres of action of the NAO (Wanner et al. 2001; Hurrell et al. 2003; Budikova 2009). The NAO and its changes can be understood as signals in the surface pressure field of jet stream variation (Hurrell and Deser 2009) and, as such, are often referred to as the regional expression of the AO, which describes sea-level pressure variations between the Arctic and northern hemisphere lower latitudes (Budikova 2012) or, in other words, variability in the strength of the polar vortex. The AO, also termed the Northern Annular Mode (NAM), was first identified by Lorenz (1951) and named by Thompson and Wallace (1998). Its positive phase is characterised by low surface pressure in the Arctic and a generally zonal (west to east) jet stream, thus keeping cold air in the Arctic. When the AO index is negative, there is high pressure in the Arctic and a stronger meridional (north to south or vice versa) component of the jet stream so that cold air can extend to lower latitudes. With respect to the North Sea region, the state of the AO controls the westerly flow and the storm track.

The AO is strongly correlated with the NAO, and the latter can be viewed as the signature of the former over the North Atlantic region. Therefore, the discussion below and in Annex 1 focuses on the NAO. Note, however, that there is extensive literature on the relationship between the NAO and AO, including studies on the robustness and consistency of one versus the other (Ambaum et al. 2001). Even though the AO and NAO are strongly correlated, their relationship is not linear (Kravtsov et al. 2006; de Viron et al. 2013). A fully three-dimensional picture discussing the connection between the AO and stratospheric circulation anomalies is provided by, for example, Ripesi et al. (2012).

2.2.3 Temporal and Spatial Changes in the NAO

Given the importance of the NAO in the North Atlantic and European climate, substantial efforts have been made to understand its variability in order to gain insight into its potential predictability. The NAO index varies on a wide range of time scales ranging from days to decades. As Fig. A1.1 in Annex 1 shows, the long-term behaviour of the NAO is irregular, and there is large interannual and interdecadal variability, reflecting the interaction with the surface, including SST and sea ice. Focusing on the 20th century, a period with predominantly positive NAO index values prevailed in the 1920s, followed by mainly negative values in the 1960s. Since then, a positive trend has been observed, which means more zonal circulation with mild and wet winters and increased storminess in central and northern Europe, including the North Sea region (e.g. Hurrell et al. 2003). This was especially the case in the early 1990s, raising claims that this behaviour was ‘due to anthropogenic climate change’. After the mid-1990s, however, there was a tendency towards more negative NAO index values, in other words a more meridional circulation and according to Jones et al. (1997), Slonosky et al. (2000, 2001) and Moberg et al. (2006), the strongly positive NAO phase in the early 1990s should be seen as an element of multi-decadal variation comparable to that at the start of the 20th century rather than as a trend towards more positive NAO values. It should also be noted that the winter of 2010/2011 had one of the most negative NAO indices in the record (Jung et al. 2011; Pinto and Raible 2012).

Intraseasonal variability in the NAO can be reasonably well described by a Markov process or first-order autoregressive (AR1) model (Feldstein 2000), although the observed skewness of the NAO index (Woollings et al. 2010; Hannachi et al. 2012) or the particularly enhanced persistence of the negative phase (Barnes and Hartmann 2010) are not captured very well. Nonlinear Rossby wave breaking mechanisms have been proposed to explain the intraseasonal variability in the NAO (e.g. Benedict et al. 2004; Franzke et al. 2004; Woollings et al. 2008). Interannual variability in the winter mean NAO index is also found to be linked to the intraseasonal transitions between the positive and negative phases of the NAO pattern (Luo et al. 2012).

Several studies have attempted to quantify the fraction of interannual NAO variance that can be explained by ‘climate noise’, namely by random sampling of the intraseasonal variations (Feldstein 2000; Keeley et al. 2009; Franzke and Woollings 2011). As the NAO exhibits no preferred periods on the interannual and longer timescale (Hurrell and Deser 2009), the results of these studies differ widely, meaning as yet no consensus on the fraction of interannual NAO variability that cannot be explained by such noise and which is thus likely to be forced externally (Stephenson et al. 2000; Feldstein 2002; Rennert and Wallace 2009). Several external forcing mechanisms have been proposed, including bottom boundary conditions of local SST (Rodwell et al. 1999; Marshall et al. 2001) and sea ice (Strong and Magnusdottir 2011), volcanoes (Fischer et al. 2007), solar activity (Shindell et al. 2001; Spangehl et al. 2010; Ineson et al. 2011), and even stratospheric influence (Blessing et al. 2005; Scaife et al. 2005), including the quasi-biennial oscillation (Marshall and Scaife 2009) and stratospheric water vapour trends (Joshi et al. 2006). Remote SST forcing of the NAO originating from as far as the Indian Ocean was proposed by Hoerling et al. (2001) and Kucharski et al. (2006), while Cassou (2008) proposed an influence of the Madden-Julian Oscillation, but no consensus has been reached.

The positive trend in the NAO from the 1960s to the 1990s has been a particular focus of interest, and it has been a concern that many climate models have been unable to simulate the observed trends (Gillett 2005). Even though some models simulate quite large natural variability (Selten et al. 2004; Semenov et al. 2008), they still fall short of the strongest observed 30-year trend (Scaife et al. 2009). While Gillett et al. (2003) suggested that anthropogenic forcing could have contributed to the positive NAO trend there is no agreement on this, and concerns over the ability of models to represent NAO variability mean that attribution attempts should be treated with caution. The downturn in the NAO since the mid-1990s has brought its relation to climate change under further doubt (Cohen and Barlow 2005).

The NAO pattern is not entirely stationary, neither geographically nor with respect to season (Fig. 2.1). Although the amplitude is greatest and the explained variance highest in winter, the NAO is present all year round with varying strength and position. In particular, there is a westward shift of the southern centre of action in spring and an eastward displacement of both centres in autumn. Portis et al. (2001) developed a seasonally and geographically varying ‘mobile’ NAO, which is obtained from sea-level pressure data by taking into account the migration of the AH nodal points. Other techniques also exist, such as optimally interpolated patterns, trend empirical orthogonal functions (EOFs; Hannachi 2007a, 2008) and cluster analysis (Cheng and Wallace 1993; Hannachi 2007b, 2010).
Fig. 2.1

Leading EOF of seasonal mean sea-level pressure (SLP) anomalies over the North Atlantic (20°–0°N, 90°W–40°E for the period 1948–2014. The percentage of explained variance is given above each panel

Regression of the NAO on near-surface winds (Fig. 2.2) illustrates the well-known fact that the positive NAO phase is accompanied by stronger than average westerlies in the mid-latitudes right across the North Sea region into Scandinavia (Hurrell and Deser 2009), thus contributing to enhanced precipitation in the northern mid-latitudes.
Fig. 2.2

Wind speed and direction associated with a 1 standard deviation change in the NAO index. The index is obtained from an EOF analysis of NCEP/NCAR sea-level pressure data (1958–2006) over the North Atlantic sector. Colour scale: m s−1, unit vectors: 1 m s−1 (Hurrell and Deser 2009)

2.2.4 Other Modes of Variability

The NAO is essentially a signal of variations in the Atlantic eddy-driven jet stream, but one pattern is not sufficient to fully describe the jet variability. The eddy-driven jet stream variability and regimes can, in fact, be described by at least two modes of variability—the NAO and the East Atlantic Pattern (EA; Wallace and Gutzler 1981; Woollings et al. 2010; Hannachi et al. 2012). The latter is defined as the second prominent mode of atmospheric low frequency variability over the North Atlantic. It appears throughout the year, but is more prominent in winter. Comprising a north-south dipole of anomalies, the EA pattern1 resembles the NAO, but with anomaly centres displaced south-eastward and thus is sometimes interpreted as a ‘southward shifted’ NAO (e.g. Barnston and Livezey 1987). The positive phase of the pattern is associated with wetter-than-average conditions over northern Europe and Scandinavia. The EA pattern has particularly strong fluctuations in the low frequency component showing a consistent positive trend over recent decades. However, this is indicative of trends over the Mediterranean region rather than the North Sea region (Woollings and Blackburn 2012). Trouet et al. (2012) introduced a ‘summer NAO’ pattern with a blocking high over the British Isles and a low over south-eastern Europe in its positive phase which is most pronounced on interannual timescales and resembles the EA.

A third pattern that has some impact on the North Sea and Scandinavia is the Scandinavian pattern1 (Wallace and Gutzler 1981), characterised primarily by a centre over Scandinavia and weaker centres of opposite polarity over western Europe. The positive phase of the Scandinavian pattern is frequently linked to the occurrence of atmospheric blocking over northern Europe (Barriopedro et al. 2006) and is therefore characterised by below-average precipitation in this region, as well as by large interannual and decadal variability over the past 50 years (Croci-Maspoli et al. 2007; Tyrlis and Hoskins 2008), which may be related to longer term variability of blocking across the Atlantic Ocean (Häkkinen et al. 2011). Before the 1980s the positive phase was dominant, this was followed by a negative phase between 1980 and 2000. The pattern amplitude has weakened over the past decade compared to the earlier part of the record.

Finally, on longer timescales, atmospheric conditions in the North Sea region are significantly influenced by the Atlantic Multidecadal Oscillation (AMO—more correctly termed Atlantic Multidecadal Variability, as there is no temporal regularity), which describes basin-wide variations in the temperature of the North Atlantic (Knight et al. 2005a, b) on the order of decades. These are particularly important in summer (Sutton and Hodson 2005) when the atmospheric response resembles a pattern termed the ‘summer NAO’ (Folland et al. 2009; Ionita et al. 2012b), see also Fig. 2.1. Warm periods were observed prior to 1880, between 1930 and 1965 and after 1995, and cool periods between 1900 and 1930 and between 1965 and 1995.

2.2.5 Summary

The NAO is the dominant mode of near-surface pressure variability over the North Atlantic and Europe, including the North Sea region. Amplitude and explained variance are largest in winter, but the NAO impacts the North Sea region throughout the year. Despite its apparent long-term irregularity, the NAO exhibits extended periods of positive or negative index values. It is therefore important to quantify the fraction of interannual NAO variance that cannot be explained by random forcing and so is likely to be influenced by external forcing. There is no consensus on the size of this fraction, or on the possible external forcing mechanisms.

2.3 Atmospheric Pressure and Wind

A typical characteristic of the climatology of the North Sea region is the large variability in meteorological variables on multiple time scales. The strong increase in wave height and storminess between the 1970s and the 1990s over the North Sea and North Atlantic (Carter and Draper 1988; Hogben 1994) raised public concern about a roughening wind climate and speculations about whether global warming might have an impact on storminess (Schmidt and von Storch 1993). With the availability of many more observations and gridded reanalysis data sets, detailed studies have significantly improved understanding of the atmospheric circulation and related winds over the North Atlantic and North Sea.

Owing to the large climate variability, results regarding changes or trends in the wind climate are strongly dependent on the period and region considered (Feser et al. 2015b). Through the strong link to large-scale atmospheric variability over the North Atlantic, conclusions about changes over the North Sea region are best understood in a wider spatial context. The following sections summarise studies on variations and trends in pressure and wind for recent decades and place these in the context of studies about changes over the last roughly 200 years.

2.3.1 Atmospheric Circulation and Wind Since Around 1950

The period since about 1950 is relatively well covered by observational data. The beginning of the satellite period in 1979 led to further substantial improvements in global data coverage, especially over the oceans and in data-sparse regions. Although in situ wind observations allow direct analysis of this variable, in particular over the sea (e.g. International Comprehensive Ocean-Atmosphere Data Set, ICOADS; Woodruff et al. 2011), the information is often predominantly local and inhomogeneities make the straightforward use of these data difficult, even for recent decades (Annex 1). Examples include an increase in roughness length over time due to growing vegetation or building activities, inhomogeneous wind data over the German Bight from 1952 onwards (Lindenberg et al. 2012) or ‘atmospheric stilling’ in continental surface wind speeds due to widespread changes in land use (Vautard et al. 2010).

Most studies therefore do not use direct wind observations, but instead rely on reanalysis products such as NCEP/NCAR (from 1948 onwards; Kalnay et al. 1996; Kistler et al. 2001), ERA40 (from 1958 onwards; Uppala et al. 2005) or, more recently, ERA-Interim, starting in 1979 (Dee et al. 2011) and the 20th Century Reanalysis 20CR (from 1871 onwards; Compo et al. 2011) and other reanalysis products, see Electronic (E-)Supplement Sect. S2.2. Making use of all available observations, a frozen scheme for the data assimilation of observations into state-of-the-art climate models is used to minimise inhomogeneities caused by changes in the observational record over time. However, studies indicate that these inhomogeneities cannot be fully eliminated (see E-Supplement S2). In addition, systematic differences between the underlying forecast models, such as due to their different spatial resolutions (Trigo 2006; Raible et al. 2008) and differences in detection and tracking algorithms (Xia et al. 2012) may affect cyclone statistics (for example changes in their intensity, number and position). Apart from these differences and inhomogeneities, the number of detected cyclones and their intensities show very high correlations between reanalyses (Weisse et al. 2005; Raible et al. 2008).

Three recent studies cover a continental-scale area. Franke (2009) manually counted the number of strong low pressure systems (central pressure below 950 hPa) over the North Atlantic (north of 30°N) from weather maps of the Maritime Department of the German Weather Service (‘Seewetteramt’), see Fig. 2.3, which shows generally weak activity prior to 1988 and enhanced activity for the following decade, followed by a decrease to 2006.
Fig. 2.3

Number of low pressure systems on the North Atlantic with a core pressure of 950 hPa or below, 1956/57–2015/16 (after Franke 2009, updated)

Since then, the number of deep cyclones has again increased despite the predominantly negative NAO (Fig. A1.1 in Annex 1), and the maximum value with 18 such cyclones was observed in 2013/2014. Despite large decadal variations, there is still a positive trend in the number of deep cyclones over the last six decades, which is consistent with results based on NCEP reanalyses since 1958 over the northern North Atlantic Ocean (Lehmann et al. 2011). Using an analogue-based field reconstruction of daily pressure fields over central to northern Europe (Schenk and Zorita 2012), the increase in deep lows over the region might be unprecedented since 1850 (Schenk 2015). Barredo (2010) investigated adjusted storm losses in the period 1970–2008 on a European scale but did not find any trends despite a roughened wind climate. Based on the CoastDat2 reanalysis (Geyer 2014) and using E-OBS pressure data (van den Besselaar et al. 2011), von Storch et al. (2014) did not find robust evidence for supporting claims that the intensity of the two strong storms in late 2013 would be beyond historical occurrences and that the recent clustering of storms should be related to anthropogenic influence.

2.3.1.1 North Sea Region

Different studies based on reanalyses confirm the strong increase in wind speeds and wave heights observed over the North Sea region since the 1970s. Covering the period since about 1950, a positive trend is visible in annual storm activity in the NCEP, ERA40 and 20CR datasets, although the most recent decade shows a decrease in wind speed (e.g. Matulla et al. 2007; Donat et al. 2011) with no notable trend in mean wind speed for the period as a whole (1948–2014) over the North Sea (Fig. 2.4).
Fig. 2.4

Time series of mean seasonal wind speed derived from NCEP/NCAR reanalysis over the North Sea (blue) and the NAO index (black) for winter (DJFM) 1948–2014 recalculated and updated as in Siegismund and Schrum (2001), by F. Schenk. The positive trend (red linear fit) from this study has ended due to more average wind conditions in the last decade (blue linear fit). Smoothed lines are shown to highlight decadal-scale variations (11-year Hamming window)

Siegismund and Schrum (2001) analysed decadal changes in wind forcing over the North Sea based on the NCEP reanalysis for the period 1958–1997 (Fig. 2.4). Over the 40 years, mean annual wind speeds increased by 10 %, mainly due to an increase in autumn and winter (ONDJ) after the 1960s and in late winter (FM) since the mid-1980s, but no trend was found for summer. Increased wind speeds are accompanied by an increase in WSW wind directions in autumn and winter (ONDJ) over the last three decades compared to the first (1958–1967). The enhanced mean winter wind speeds agree with an increase in the mean winter NAO index with a correlation of 0.69 for the year-to-year variations (Fig. 2.4). An update of the graphic for 1948–2014 shows a return to average wind conditions in the last decade with no notable trend remaining. The updated correlation with the NAO is 0.73.

Weisse et al. (2005) compared an NCEP-driven regional climate simulation (50 km resolution) with wind speeds from marine stations and found relatively good agreement. For the period 1958–2001, they found increasing storminess over most marine areas north of 45°N with a small, but significant positive trend over the North Sea and Norwegian Sea. The relative increase in storm frequency is largest over the southern North Sea across Denmark towards the Baltic Sea (1–2 % per year). The number of storms was lowest during the 1970s (with some notable exceptions, in particular the ‘Capella’ storm in January 1976) and peaked around 1990–1995. However, since then, a decrease in storm frequency has been observed which is confirmed by other studies (e.g. Matulla et al. 2007). Based on a high-resolution model hindcast forced by NCEP reanalyses for the storm season (November to March) 1958–2002, simulated storm-related sea-level variations confirm a significant positive trend for the Frisian and Danish coast (Weisse and Plüß 2006) while insignificant changes in mean and 90th percentile water levels are found for the UK, the Dutch coast and the German Bight. However, Weisse and Plüß also noted that positive trends in observations are higher than those in the NCEP-driven hindcast (see Chap. 3).

In contrast to the strong increase in wind speed in the NCEP reanalysis, Smits et al. (2005) found no increase in geostrophic wind speeds and even a decrease in homogenised wind observations for inland stations in the Netherlands for the period 1962–2002, while coastal stations show an increase consistent with NCEP. Smits et al. (2005) claimed that this is due to inconsistencies in the NCEP data, but it might also be that the ‘atmospheric stilling’ postulated by Vautard et al. (2010; see E-Supplement Sect. S2.1) can explain these differences.

2.3.1.2 Northern North Atlantic Region

As variations in atmospheric circulation and the wind climate over the North Sea show a high co-variability with large-scale variations and cyclonic activity over the North Atlantic, the majority of studies focus on the whole Euro-Atlantic region rather than on the North Sea alone. These studies show that the increase in wind speed or storminess is related to a general intensification of storm tracks over the North Atlantic north of about 55°N.

Based on NCEP reanalyses, Chang and Fu (2002) found a significant increase of about 30 % in decadal mean winter (DJF) storm track intensity for the period 1948–1998, with values about 30 % higher in the 1990s than during the late 1960s and early 1970s. The strengthening of storm track intensity is most pronounced over the North Atlantic albeit the trend compared to the few available conventional measurements seems to be overestimated in the NCEP reanalysis (Chang and Fu 2002; Harnik and Chang 2003; see E-Supplement Sect. S2.2). Geng and Sugi (2001) also found a significant increase in the number of North Atlantic cyclones and a significant intensifying trend for the cyclone central pressure gradient.

Similar results were found by Raible et al. (2008) based on ERA40 and NCEP for the period 1958–2001. The authors highlighted seasonal differences and reported a slight increase in number and a significant increase in intensity in winter (DJF) for the northern North Atlantic including the North Sea region (55°N–70°N, 45°W–15°E), a negative tendency in summer (JJA) and a non-significant increase in autumn (SON). The intensification in winter is stronger in NCEP than in ERA40 and also includes spring (MAM) in agreement with a similar increase in the number of deep lows (<980 hPa) in both seasons (Lehmann et al. 2011). The number of deep lows shows a minimum in the 1970s, followed by a strong increase.

The general enhancement in winter storm track intensity is accompanied by a northward shift in the storm track of 2–5° (depending on the data set) for NCEP (1948–1997; Chang and Fu 2002), ERA15 (1979–1997; Sickmoeller et al. 2000) and ERA40 (1958–2001; Wang et al. 2006), in agreement with a shift and intensification of deep lows (<980 hPa) towards the NE over the North Atlantic in the period 1948–2008 (Lehmann et al. 2011).

2.3.1.3 Southern North Atlantic Region

The general increase in the number of deep cyclones and storminess over the northern North Atlantic and North Sea is accompanied by partly opposing tendencies for the mid-latitudes south of 55–60°N (Gulev et al. 2001), suggesting a general northward shift in the cyclone tracks, consistent with findings of McCabe et al. (2001).

The north-south contrast in the sign of trends was also confirmed by Trigo (2006) who applied an objective detection and tracking algorithm to NCEP and ERA40 for winter (DJFM) 1958–2000 to produce a storm-track database for different stages in the cyclone lifecycle over the North Atlantic and Europe (20°–70°N; 85°W–70°E). On a seasonal basis, the trend is generally positive at higher latitudes (mostly due to an increased frequency of moderate and intense storms) and negative in the subtropical belt. Wang et al. (2006) and Raible et al. (2008) also drew similar conclusions. All these results consistently show a northward shift in mean storm track position since about 1950 (Feser et al. 2015a).

2.3.2 Regional Variations in Pressure and Wind Since Around 1800

Observed changes in cyclone characteristics and winds over the last 40–60 years pose the question as to whether these changes merely reflect (multi-)decadal variations or whether they reflect long-term change. This section summarises current knowledge about the historical evolution of pressure and wind in the last 200 years over the Euro-Atlantic region.

Information about long-term variations in pressure and wind rely on multiple direct and indirect observations. They provide qualitative to semi-quantitative historical descriptions (the latter quite far back in time), such as storm surge-related damage on the Dutch coast since the 15th century (de Kraker 1999) or daily weather diaries like those at the observatory of Armagh (Ireland) since 1798 (Hickey 2003). Direct measurements such as surge levels at Liverpool since 1768 (Woodworth and Blackman 2002) and Cuxhaven since 1843 (Dangendorf et al. 2014) or wind records in the Dublin region since 1715 (Sweeney 2000) also provide important information on variations in the storm climate. The difficulty with these observations is that they often represent local conditions and so often exhibit inhomogeneities to an unknown extent.

As recommended by WASA Group (1998), most studies use pressure observations (which are more homogeneous over time than wind measurements) to derive wind and storm indices (e.g. WASA Group 1998; Klein Tank et al. 2002). The usefulness of typically-used pressure-based indices has recently been re-assessed and confirmed. Even single-station pressure indices such as strong pressure changes over 6- or 24-h periods or the annual number of deep lows provide useful information about long-term variations in the wind and storm climate (Krueger and von Storch 2011). The information content to describe long-term variations in the statistics of pressure and wind is even higher for indices of geostrophic wind speeds calculated from triangles of daily pressure observations (Krueger and von Storch 2012). The correlation of geostrophic wind speeds calculated from station triplets with real model wind speeds is especially high over open terrain and sea areas (i.e. regions that often lack conventional observations). Although pressure-based indices provide only an indirect link to real wind speeds or storminess, they can be considered a valid approach for assessing long-term statistics of pressure and wind (Krueger and von Storch 2011, 2012).

As historical information on pressure and wind mostly relates to regional or local scale rather than gridded fields, the studies presented in the following sections are discussed by region. Figure 2.5 provides an overview of potential long-term trends in the wind and storm climate.
Fig. 2.5

Long-term trends for storminess over the Euro-Atlantic region based on different historical and observational sources (Feser et al. 2015a). Red, blue and green colours indicate positive, negative or no trend, respectively

2.3.2.1 North Atlantic and Iceland

The region north of around 55–60°N is of special interest regarding changes in the intensity or position of the main storm tracks. Historical information here is limited to the Shetland, Orkney and Faroe Islands as well as Iceland. The longest pressure-based wind index, the annual mean of absolute pressure changes over 24 h, suggests a significant positive trend over Iceland in the period 1823–2006 (Hanna et al. 2008), but no robust trend exists over the Norwegian Sea (since 1833) or the North Sea (since 1874). The latter is consistent with the result of Schmith et al. (1998) who found no significant trend for absolute daily pressure tendencies for stations around the NE Atlantic for winter 1871–1997. Analysis of high annual geostrophic wind speed percentiles over the NE Atlantic also indicates no significant change since the late 19th century (WASA Group 1998; Alexandersson et al. 2000; Matulla et al. 2007; Wang et al. 2009a, 2011). For the shorter period 1923–2008, positive trends exist over the northern NE Atlantic for spring (Wang et al. 2009a) which is in agreement with the intensification and northeast shift in cyclone activity in the last 60 years.

A positive trend also exists for the annual frequency of zonal weather types in the winter half-year 1881–1992 (Schiesser et al. 1997). As this weather type (Großwetterlagen) classification (Baur 1937; Hess and Brezowsky 1952, 1977; Hoy et al. 2012) relies on historical weather maps over the North Atlantic and Europe, the trend should be viewed with caution due to an improvement over time in detecting smaller lows (E-Supplement S2).

2.3.2.2 British Isles

There are many historical wind and wind-related documents and records for Great Britain and Ireland. Although robust trend estimates have not been undertaken, available information suggests large multi-decadal variations but no overall long-term trends (e.g. Sweeney 2000 for the number of storms per decade from historical reports of the Dublin region 1715–1999). For the shorter period 1903–1999, however, adjusted wind observations do show a decrease in the decadal number of storms exceeding 50 knots (25.7 m s−1). The record of storms from a daily weather diary of Armagh (Ireland) 1798–1999 (Hickey 2003) also indicates similarly large variations to those of recent decades, although observer bias reduces reliability over time. Anemometer readings at the station show no obvious trend in the number of gale days per year for the period 1883–1999.

For the Irish Sea, tide gauge records at Liverpool provide an indirect estimate of long-term variations in storms. These show a negative tendency for the annual maximum surge at high water for the period 1768–1999 (Woodworth and Blackman 2002) but no trend in the annual maximum high water level or the surges at annual maximum high water for this period. For shorter periods, no trends are found for maximum monthly wind speed observations for the Irish Sea 1929–2002 (Ciavola et al. 2011) while Esteves et al. (2011) found a weak but significant negative trend for monthly mean wind speeds at the Bidston observatory on the northern Irish Sea coast over the same period.

For southern England, Hammond (1990) used different stations to calculate an annual windiness index taking the annual average of monthly mean wind speeds for the Boscombe Down area. For the period 1881–1989, the annual windiness index does not show any long-term trend. For the end of the 17th and the first half of the 18th century (Late Maunder Minimum), wind indices were derived from ship logbooks for the Øresund region (Frydendahl et al. 1992) and the English Channel (Wheeler et al. 2009). Ship logbooks offer a unique source of information about past wind climates, as discussed for example by Küttel et al. (2009). Wind indices based on these logbooks suggest a generally stationary wind climate with large decadal variations. For the period 1920–2004, the 1930s show another period of more severe storms for the British Isles based on extreme three-hourly pressure changes (Allan et al. 2009).

2.3.2.3 North Sea Region

Limited historical information about dike repair costs for northern Flanders indicates no obvious visible long-term trend for the period 1488–1609 (de Kraker 1999). However, storm-related damage does appear to reflect similarly large multi-decadal variations as for storm observations over recent decades. An update of the historical index using water level observations at Flushing at the mouth of the Western Scheldt estuary for 1848–1990 shows a notable increase in spring tides in the 1990s which at least partly reflects the 30 cm rise in sea level over this period.

Other datasets do not indicate long-term trends in storm intensity. Surge information for the Netherlands shows a decrease in storm frequency over the period 1890–2008 (Ciavola et al. 2011). Also, Cusack (2012) found a weak negative tendency for the decadal running mean of the annual number of damaging storms and a related storm loss index calculated from homogenised wind observations of the Netherlands for 1910–2010, but did find large decadal variations for stormy conditions in the 1920s and 1990s. Storm intensity estimates derived from wind, wave and surge observations from Belgium for the period 1925–2007 show no trend (Hossen and Akhter 2015).

An analysis of geostrophic wind speeds for the German Bight shows no robust trends for the period 1876–1990 (Schmidt and von Storch 1993). But when the record is extended to include the period up to 2012 (Fig. 2.6) a tendency for decreasing wind speed in the upper percentiles becomes visible, corroborating direct wind, surge and wave observations from Belgium and the Netherlands, and findings by Rosenhagen et al. (2011). Analogue-based storminess shows a good correlation with the German Bight index and indicates no long-term trend since 1850 (Schenk 2015). Wang et al. (2011) found significant negative trends over the North Sea and surrounding land areas for the 99th percentile of geostrophic wind in summer, but no robust trends in other seasons. Direct wind observations from Skagen in northern Denmark also suggest decreasing overall storminess for the period 1860–2012 with extremely high storminess prior to 1875 (Clemmensen et al. 2014).
Fig. 2.6

High annual percentiles of geostrophic wind speeds over the German Bight after Schmidt and von Storch (1993) updated and reproduced for 1879–2012. Running 11-year means and linear trends are displayed to highlight long-term variations (data by G. Rosenhagen, figure by F. Schenk)

2.3.2.4 Northern Alps and Central Europe

Although not directly linked to the North Sea wind climate, observations from further south are also useful to help understand variations in large-scale atmospheric circulation and give indications about a northward displacement in storm tracks. For Vienna, the number of gale days (above 8 Bft, 17.2 m s−1) show a clear decrease for the period 1872–1992 (Matulla et al. 2007), however based on non-homogenised observations. This is corroborated by significant negative trends in the number of days exceeding 7, 8 and 9 Bft (>13.9, 17.2 and 20.8 m s−1, respectively) in northern Switzerland for the period 1894–1994 (Schiesser et al. 1997). The duration of strong winds (>7 Bft) also shows a negative trend for Zürich for 1871–1991 except in winter (Brönnimann et al. 2012). Negative trends are also found for central Europe (Matulla et al. 2007; Wang et al. 2011). In contrast, Stucki et al. (2014) found no clear trends, but large interdecadal variability, over Switzerland.

2.3.3 Trends in the 20th Century Reanalysis Since 1871

As shown in Fig. 2.5, the majority of studies using observational storm proxies find no robust trends, some even a negative tendency, for the wind and storm climate in historical pressure and wind observations. In contrast, 20CR (Compo et al. 2011) suggests significant upward trends for storminess over data-sparse regions like the NE Atlantic (Donat et al. 2011) while Bett et al. (2013) did not find a clear trend over Europe. Closer inspection reveals that the agreement of 20CR and wind observations over land like Zürich is reasonable (Brönnimann et al. 2012), but there are discrepancies over sea (Krueger et al. 2013; Schenk 2015). This is because 20CR, like other reanalyses, assimilates all available pressure observations at a given time step which leads to a strong increase in assimilated land pressure observations (and to a lesser extent also sea pressure observations) over time. Following Krueger et al. (2013), inconsistencies between 20CR and pressure-based storm indices over data-sparse regions increase back in time as the number of assimilated stations by 20CR, mainly over sea areas, decreases. Spurious pressure trends in data-sparse regions, identified in the NCEP/NCAR reanalysis (Hines et al. 2000) might also affect 20CR (E-Supplement Sect. S2.3).

2.3.4 Summary

Different studies mainly based on reanalysis data show positive trends in storm activity over the NE Atlantic and North Sea together with a northeast shift in the position of storm tracks over the last 40–60 years. This is also reflected in a roughened wind and wave climate, although a return to average conditions beginning at the end of the 20th century has clearly reduced trends from earlier publications. As summarised in Fig. 2.5 direct or indirect historical records of long-term variations in pressure, wind or wind-related proxies mostly show no robust long-term trends for the last 100 years or more. Large decadal variations seem to dominate for centuries.

While the increase in wind speeds and storminess in the latter half of the 20th century does not seem unprecedented in the context of historical observations, the northeast shift in storm tracks in this period may be a new phenomenon. The long-term decrease north of the Alps mainly results from a less stormy period during the 1990s compared to the North Sea and North Atlantic while the period at the end of the 19th century is comparably windy. The less stormy 1990s further south are consistent with the northeast shift in storm tracks and the decrease in winter cyclone activity in the mid-latitudes. This northeast shift together with the trend pattern of decreasing cyclone activity for southern mid-latitudes and increasing trends north of 55–60°N after around 1950 seems consistent with scenario simulations to 2100 under increasing greenhouse gas concentrations (e.g. Ulbrich et al. 2009; Feser et al. 2015a; see Chap. 5). This corroborates the findings by Wang et al. (2009b) that combined anthropogenic and natural forcing had a detectable influence on this pattern of atmospheric circulation, storminess and ocean wave heights during boreal winter 1955–2004 while an analysis for the first half of the 20th century is less likely to be dominated by external forcing.

Uncertainties remain not only for long historical wind and pressure observations (e.g. Lindenberg et al. 2012; Wang et al. 2014), but also for 20CR that to a large extent relies on these observations (Brönnimann et al. 2013; Krueger et al. 2013; Dangendorf et al. 2014; Schenk 2015). These are discussed in detail in E-Supplement S2. As a better understanding of long-term variations versus trends, and their link to atmospheric circulation is crucial for any regional climate change analysis, data rescue initiatives and digitisation initiatives such as data.rescue@home (www.data-rescue-at-home.org) or oldWeather (www.oldweather.org) are essential for further improvements towards the homogenisation of observations and reanalyses prior to about 1950. Such data can then be used in reanalysis projects such as ACRE (Atmospheric Circulation Reconstructions over the Earth; www.met-acre.org). However, in the light of problems apparently introduced into the WASA dataset during the digitisation step (see E-Supplement Sect. S2.3), it is also essential to thoroughly quality-check this type of data.

2.4 Surface Air Temperature

Despite the large variability in temperature, the warming trend of recent decades is strong enough to be discernible in local temperature observations, and it is larger than the warming trend simulated by state-of-the-art climate models. The principal drivers for this ‘excess warming’ appear to be changes in atmospheric circulation, mainly in winter and spring, and feedbacks involving soil moisture and cloud cover, mainly in summer and autumn (Van Oldenborgh et al. 2009).

The data sources for near-surface air temperature are different over land and sea. Terrestrial measurements are made at fixed locations, with typically standardised installations (WMO 2010) and at a reference height of 2 m (e.g. Klein Tank et al. 2002). In contrast, marine air temperature observations are typically made aboard moving ships (ICOADS; Woodruff et al. 2011), adjusted to a common reference height of 10 m (necessary because the typical observation height has increased by about 20 m over the period of record; Kent et al. 2013). Only a few fixed station measurements exist, such as on oil platforms. Observations of marine air temperature from ships are affected by daytime heating biases, and to avoid these problems datasets (for example from the Hadley Centre) are constructed using night-time observations only. Alternatively, both day and night observations, with adjustments for daytime heating following Berry et al. (2004), can be used. The North Sea region is relatively well sampled, but observations are sparse in the 19th Century and, more recently, during the Second World War.

2.4.1 Terrestrial Surface Air Temperature

The first decade of the 21st century was characterised by some extreme seasons. The hot summer of 2003 was probably unprecedented for at least 500 years in western Europe (Luterbacher et al. 2004), but was even surpassed in extremity by the East European summer of 2010 (Barriopedro et al. 2011). Summer and autumn 2006 and winter 2006/2007 were also exceptionally warm (Luterbacher et al. 2007; Cattiaux et al. 2009). On the other hand, winter 2010/2011 had a very negative NAO index (see Sect. 2.2), but was much warmer than comparable winters with a similarly negative NAO index (Cattiaux et al. 2010). Figure 2.7 shows time series of annually averaged land air temperature for the North Sea region, defined here as the area between 48°N and 62°N and 6°W and 10°E, for various data sets. The graphic shows 2014 to be unprecedentedly warm, even though none of the four seasons was the warmest on record (winter ranks 2nd after 2006/2007, spring 3rd after 2007 and 2011, summer 14th and autumn 2nd after 2006), and the previous maximum from 2011 was exceeded by almost 0.5°C. The datasets used are the CRUTEM4v (from UEA/CRU; Jones et al. 2012), GHCN-M version 3 (NOAA/NCDC, Peterson and Vose 1997; Jones and Moberg 2003), GISTEMP (NASA/GISS; Hansen et al. 2010) and BerkeleyEarth (http://berkeleyearth.org/), which have been subject to a homogeneity adjustment, supplemented by the E-OBS daily dataset version 10.0 (Haylock et al. 2008). To compare the different datasets, the grids of the global datasets are regridded to match that of the E-OBS grid (0.5° × 0.5°; van der Schrier et al. 2013).
Fig. 2.7

Land-based annual mean air temperatures averaged over the North Sea region (48°–62°N, 6°W–10°E) with respect to the 1961–1990 climatology as calculated by E-OBS (red), CRUTEM4v (green), GHCN-M (blue), GISTEMP (purple) and the Berkeley Earth dataset (light blue)

The similarity between these estimates of temperature over the North Sea region is evident, with only minor differences in trend values (Table 2.1). Over the period 1980–2010, the trend in annual averaged daily mean temperature is approximately 0.38 °C decade−1. Trend values are based on a linear least-square approximation to the data. Table 2.1 also gives temperature change for the whole of Europe (30°–75°N, 12°W–45°E plus Iceland, based on E-OBS), the northern hemisphere land and the global land area, both based on CRUTEM4. For 1980–2010, the warming trend in the North Sea region is smaller than that of Europe as a whole, but larger than the average over the northern hemisphere and global land areas.
Table 2.1

Linear temperature trends (°C decade−1) over 1950–2010 and 1980–2010 for the North Sea region for CRUTEM4v, GHCN-D, GISTEMP, BerkeleyEarth and E-OBS

 

CRUTEM4v

GHCN-D

GISTEMP

Berkeley Earth

E-OBS

Europe

NH land

Global land

1950–2010

0.210

0.174

0.228

0.157

0.204

0.179

0.199

0.172

1980–2010

0.383

0.389

0.389

0.353

0.408

0.414

0.337

0.267

The last three columns give trends for Europe (based on E-OBS), the northern hemisphere land and global land temperatures (based on CRUTEM4), respectively. Numbers in bold indicate that the trend is statistically significant at the 5 % level based on a t-test accounting for the reduced degrees of freedom due to autocorrelation (von Storch and Zwiers 1999)

In all datasets the period from the early 1990s onwards is warmest. Figure 2.8 highlights annual temperatures of the past few decades, averaged over the North Sea region and relative to the 1961–1990 climatology, based on the E-OBS dataset. The grey bars in Fig. 2.8 indicate the estimated uncertainties which take into account both errors introduced by spatial interpolation over areas without observations, by inhomogeneities in the temperature data that result from station relocations or instrument changes etc., and by urbanisation, as documented by van der Schrier et al. (2013) and Chrysanthou et al. (2014). The uncertainties indicate that although it is not possible to be 100 % certain about the ranking of individual years, the positive overall trend since the 1980s is very pronounced and 2014 stands out, even taking the uncertainties into account.
Fig. 2.8

Annual averages for land-based air temperature over the North Sea region with respect to the 1961–1990 climatology as calculated by the E-OBS dataset. The uncertainty estimate for the E-OBS data is included as grey boxes

Ionita et al. (2012b) examined the connection between diurnal temperature range (DTR) and atmospheric circulation. They found that modes of interannual winter DTR variability are strongly related to the NAO and, to a lesser extent, the AMO, whereas in summer DTR variability is mainly influenced by a blocking pattern over Europe.

2.4.2 Number of Frost Days and Summer Days

According to Della-Marta et al. (2007), the length of western European heat waves has doubled since 1880 and Europe’s climate has seen more warm extremes. This is illustrated in Fig. 2.9 which shows the difference in the annual number of frost days (minimum temperature <0 °C) and summer days (maximum temperature ≥25 °C) between 1981–2010 and 1951–1980 (based on E-OBS data). The change in these indices is not spatially consistent (in contrast to the increase in annual averaged temperature—not shown). All differences are statistically significant at the 5 % level using a one-sided Student t-test. The figure shows that the number of frost days has declined almost everywhere, with the strongest decreases found in the northern and eastern parts of the domain. The number of summer days has also increased almost everywhere, with the smallest increases in Scotland, northern England and Scandinavia and the largest in northern France.
Fig. 2.9

Difference between the 1981–2010 and 1951–1980 climatological values of the annual number of frost days (left, daily minimum temperature <0 °C) and summer days (right, daily maximum temperature ≥25 °C). Grid squares with missing data or where the difference did not pass the 95 % significance level using a Student t-test, are white. Calculations based on E-OBS data

2.4.3 Night Marine Air Temperature

As for land-based temperatures, the night marine air temperature (NMAT) also increased over the period 1856–2010 (Fig. 2.10). Two datasets were used, an uninterpolated 5° monthly mean dataset for 1880–2010 (HadNMAT2; Kent et al. 2013) and an interpolated (using a large-scale reconstruction technique; Rayner et al. 2003) 5° monthly mean dataset for 1856–2001. Differences between these datasets are larger than for land temperatures, especially around 1900 and during and just after the Second World War. In the latter period, sampling is sparse and non-standard observing practices necessitated adjustments to the observations (Kent et al. 2013). After about 1950, agreement improves. Linear trends in air temperature, adjusted for day-time heating biases (Berry and Kent 2009) show similar values.
Fig. 2.10

Annual average night marine air temperature anomalies (°C) for the region 50°–60°N, 5°W–10°E from 5° monthly mean datasets: HadNMAT2 (black) and HadMAT1 (red)

Figure 2.10 also indicates that for marine air temperature the values in the most recent decade are likely to be the warmest on record, although uncertainty is large in the early part of the record due to sparse sampling.

Seasonal time series of the marine air temperature data sets show broadly similar variability to land-based temperatures (Fig. 2.11), but with a smaller amplitude. Very recently, the differences again increase, but this seems to be due to sparse observations and changes in the marine observing system (Kent et al. 2007, 2013).
Fig. 2.11

Seasonal mean night marine air temperature (°C) from NOCv2.0 (19702010), HadNMAT2 (19502010) and HadMAT1 (19502001). HadNMAT2 and HadMAT1 were averaged to the same 1° grid as NOCv2.0 and masked to the NOCv2.0 land mask

2.4.4 Comparison of Land and Marine Air Temperatures

A comparison of land and marine temperatures (Fig. 2.12) shows general agreement. The lower plot in each panel depicts three estimates of the land-marine air temperature difference over the North Sea region based on E-OBS data for the land component and three different marine air temperature datasets: the NOCv2.0 dataset (Berry and Kent 2009, 2011), the HadNMAT2 dataset and the HadMAT1 dataset. Due to the much larger heat capacity of water, the difference series between the land and marine air temperature shows a residual positive trend over the last few decades of the record.
Fig. 2.12

Land-based air temperature over the North Sea region for winter (upper left), spring (upper right), summer (lower left) and autumn (lower right) based on the CRU TS 3.10 (Harris et al. 2014; red) and E-OBS (black) datasets. The uncertainty estimate for the E-OBS data is included as grey boxes. The lower plots show the difference between land-based air temperature and marine air temperature for this region, for three marine air temperature datasets

2.4.5 Summary

There is generally good agreement between the different temperature data sets over the oceans and over land. While temperatures have clearly increased over land, the NMAT shows there has also been an increase over the North Sea, even though the variability on seasonal timescales is smaller than for the land temperatures. Furthermore, due to the large heat capacity of water it takes much longer to warm the ocean than the land. In addition, heat is transported away from the surface into deeper waters, where it cannot be directly measured. Thus, it would be expected that the land warms faster than the sea as long as the radiative forcing is positive. This is exactly the situation being observed, and according to the datasets, the imbalance is up to several tenths of a degree.

2.5 Precipitation

2.5.1 Precipitation Over Land in the North Sea Region

In a warmer climate, the atmospheric water vapour content is likely to rise due to the increase in saturation water vapour pressure with air temperature, as described by the Clausius-Clapeyron relation, and to result in an intensification of rainfall (Held and Soden 2006; O’Gorman and Schneider 2009). Evidence of higher amounts and more extreme precipitation has already been reported (e.g. Groisman et al. 2005; Moberg et al. 2006; Donat et al. 2013; Hartmann et al. 2013). Even though floods are a recurring event in Europe, attempts have been made to link increased flood risk to changes in the frequency of atmospheric blocking events (Lavers et al. 2012) or to anthropogenic climate change (Pall et al. 2011).

In a global study, Donat et al. (2013) showed a weak increase in the number of days exceeding 10 mm of precipitation (R10 mm) over the northern parts of Europe (but statistically significant only over eastern Europe at the 96 % level). Over the Iberian Peninsula, a non-significant decrease in this metric is observed. A non-significant increase in the contribution of extreme precipitation events to the total precipitation amount (R95pTOT) is also observed over Great Britain and Scandinavia.

The European Climate Assessment and Dataset (ECA&D, Klein Tank et al. 2002) is a collection of daily station observations of 12 elements (of which five are gridded) and contains (as of March 2014) data from nearly 8000 stations across Europe and the Mediterranean. The station time series are updated on a regular basis using data provided by the national meteorological and hydrological services (NMHSs), universities or, before updates from these institutions are available, synoptic messages from the Global Telecommunication System (GTS). ECA&D receives time series from 61 data providers for 62 countries (as of March 2014).

Figure 2.13 compares precipitation for three stations with data since the beginning of the 20th century; Cambridge (UK), Stromsfoss Sluse (Norway) and De Bilt (Netherlands). The time series show strong interannual and decadal variability. A general upward trend is visible in the Dutch and Norwegian time series with trends of 14.52 mm decade−1 in the annual data over the 1901–2014 period for De Bilt and 28.81 mm decade−1 over the 1901–1950 period for Stromsfoss Sluse. Both trends are (just) statistically significant at the 5 % level following a t-test accounting for autocorrelation in the time series (von Storch and Zwiers 1999). A long-term trend in the UK time series is less pronounced. The Norwegian time series exhibits an enhanced trend since the mid-1990s, especially in summer. A weak drying trend since the 1990s, although not unprecedented, is visible in the UK series. It is also clear that, under certain circumstances, the entire area is influenced by high pressure for extended periods (e.g. in 1921) such that the whole North Sea area remains very dry.
Fig. 2.13

Annual, winter and summer precipitation series for three stations from the ECA&D dataset; De Bilt (Netherlands, top left), Stromsfoss Sluse (Norway, top right) and Cambridge (UK, lower left). A low-pass filter is applied for the black curves

Trends in annual land precipitation are positive almost everywhere over the North Sea region for the period 1951–2012 (Fig. 2.14, top panel). The greatest increase in precipitation is observed in winter (Fig. 2.14, lower left), especially along the west coast of Norway, over southern Sweden, parts of Scotland and the Netherlands and Belgium. Further inland, trends are much smaller and statistically non-significant almost everywhere. In summer, there is no evidence of increasing precipitation trends along the coast of western Norway, while the contrast between trends in coastal regions and more inland regions of the European mainland increases considerably as the latter show negative trends in summer (Fig. 2.14, lower right).
Fig. 2.14

Linear least-squares fit trends in annual (top), winter (lower left) and summer (lower right) precipitation over the period 1951–2012 in mm decade−1. Blue circles denote a trend towards wetter conditions, while orange and red circles denote a trend towards drier conditions, both significant at the 5 % level (p ≤ 0.05). Black circles fail to be significant at the 25 % level (p ≤ 0.25) and are added to the figure to illustrate areas without any significant trend. Source ECA&D

Winter and spring in northern Europe (defined as the land area north of 48°N) show an overall decreasing trend in return periods of extreme precipitation (van den Besselaar et al. 2013), which is indicative of increasing precipitation extremes. The trend is most pronounced in the 5-day precipitation amount in northern Europe during spring. The 5-day amount which is statistically a 20-year event over the 1951–1970 period becomes an approximately 8-year event in the 1991–2010 period.

For annual 5-day and 10-day precipitation amounts in the UK, Fowler and Kilsby (2003) found significant decadal-level changes in many regions. For the 10-day precipitation amount, the 50-year event during 1961–1990 became an 8-, 11- and 25-year event in eastern, southern and northern Scotland, respectively, during the 1990s. In northern England the average return period has also halved.

Cortesi et al. (2012) analysed the precipitation concentration index, which is a measure of the amount of precipitation on a day with precipitation. The north-western coast of Europe shows relatively low values for this index (i.e. evenly distributed precipitation) compared to more Mediterranean climate types. No clear spatial pattern was detected in the trends in the index.

Groisman et al. (2005) studied total precipitation and frequency of intense precipitation in several regions of the world, including Fennoscandia. They found a significant increase in the annual totals and in the frequency of very heavy annual and summer precipitation events, where ‘very heavy’ precipitation events are defined by counting the upper 0.3 % of daily rainfall events (relating to a daily event that occurs once every 3–5 years).

There is temporal variability in trends when studying precipitation indices of extremes. For example, the trend in precipitation fraction due to very wet days, related to the 95th percentile in daily sums (R95pTOT), shows a different picture when the trends are determined over the period 1951–1978 compared to 1979–2012 (Fig. 2.15). Along the coasts of south-eastern England and the Netherlands, there is no trend apparent for the period 1951–1978, while the period 1979–2012 has an increasing trend for several stations in these areas.
Fig. 2.15

Linear trends in the precipitation fraction due to very wet days (R95pTOT) in winter over the periods 1951–1978 (left) and 1979–2012 (right). Blue circles denote a trend towards wetter conditions, while orange and red circles denote a trend towards drier conditions, both significant at the 5 % level (p ≤ 0.05). Black circles fail to be significant at the 25 % level (p ≤ 0.25) and are added to the figure to illustrate areas without any significant trend. Source ECA&D

Care should be taken if the precipitation fraction exceeding the 95th percentile (R95pTOT) is determined over a climatological period of several decades, since extremes may have increased disproportionally and thus the shape of the distribution may have changed. For example, an index S95pTOT, using the Weibull shape parameter instead of an explicit estimate of the 95th percentile, can be used (Leander et al. 2014). Northern Europe shows a (significant) increase in R95pTOT, but this is far less pronounced for S95pTOT. Since R95pTOT cannot distinguish between a shift in the median of the probability distribution for precipitation and a change in only the tail of the distribution, trends are generally ‘more negative’ for S95pTOT, especially over southern Scandinavia, the Netherlands, Germany and the UK.

Zolina et al. (2009) introduced a new index for R95pTOT, making use of a gamma distribution for wet day precipitation amounts and the associated theoretical distribution of the fractional contribution of the wettest days to the seasonal or annual total. The trend results for their new index are similar to R95pTOT.

Another way of analysing changes in precipitation is by counting the number of wet days. An example of this is the index CWD (maximum number of consecutive wet days, here defined as the number of days with precipitation ≥1 mm). Trends in the station records for the period 1951–2012 are shown in Fig. 2.16, which indicates that most of the stations in the North Sea region show a slight increasing trend in the annual number of consecutive wet days. A similar map for trends in the maximum number of consecutive dry days (CDD) does not indicate a coherent change in the region. The majority of stations show trend values that do not meet even the 25 % significance level.
Fig. 2.16

Linear trend in annual maximum number of consecutive wet days (CWD) over the period 1951–2012. Blue circles denote a trend towards wetter conditions, while orange and red circles denote a trend towards drier conditions, both significant at the 5 % level (p ≤ 0.05). Black circles fail to be significant at the 25 % level (p ≤ 0.25) and are added to the figure to illustrate areas without any significant trend. Source ECA&D

An example of interaction between North Sea waters and coastal climate was documented by Lenderink et al. (2009) for a month with extreme precipitation in the coastal region of the Netherlands (August 2006), where precipitation amounts were four times higher than the climatological average. Preceded by an extremely warm July (see Sect. 2.4) with very high sea surface temperatures in the North Sea at the end of July, favourable atmospheric flow conditions transported large amounts of moisture onto land, producing excessive rainfall in an area less than 50 km from the coastline. This phenomenon seems to be a robust finding since the positive trend in the difference between coastal and inland precipitation observed in the Netherlands is not sensitive to the period analysed.

2.5.2 Precipitation Over the North Sea

Only limited information is available for precipitation over oceans in general and the North Sea in particular. Almost no in situ measurements exist, which means it is necessary to rely on satellite observations using passive microwave detectors. HOAPS (Hamburg Ocean-Atmosphere Parameters and Fluxes) is one such dataset (Andersson et al. 2010, 2011). This covers the period 1988–2008 and is the only generally available satellite-based dataset for which fields of precipitation and evaporation over the oceans are consistently derived (Andersson et al. 2011). Over land, the dataset is gauge-based. Figure 2.17 shows the geographical distribution of annual average precipitation for the North Sea region (Fennig et al. 2012). Over most of the North Sea, the mean annual precipitation is between 600 and 800 mm, although values below 600 mm are also found off the east coast of England. Most coastal regions receive more than 800 mm, and in some mountainous regions (Scotland, Norway) more than 2000 mm are observed. While land stations in the south of the region have most rain in winter with a weak secondary summer maximum, further north and generally over the sea, there is only a maximum in winter, and May and June are the driest months.
Fig. 2.17

Precipitation over the North Sea area. Ocean: HOAPS dataset (Fennig et al. 2012), land: gauge-based. Annual sum (left), monthly sum for January (centre), and monthly sum for May (right) for the period 1988–2008. All data in mm

Table 2.2 shows annual precipitation totals over the central North Sea region (54°–58°N, 1.5°–5.5°E) from the few available datasets.
Table 2.2

Estimates of annual average precipitation over the North Sea region from different reanalyses and satellite-based datasets

Dataset

1979–2001

1988–2008

Source

HOAPS

643

Andersson et al. (2010)

ERA-Interim

812

800

Simmons et al. (2010) and Berrisford et al. (2009)

ERA40

691

 

Uppala et al. (2005)

Coastdat2 (cDII.00)

853

861

Geyer (2014)

NCEP-CFSR

966

1000

Saha et al. (2010)

MERRA

754

772

Rienecker et al. (2011)

All units in mm

There are considerable differences between the various estimates of precipitation, even in reasonably data-rich regions like the North Sea. There are also large differences between periods, highlighting the problems in deriving trends in precipitation (Bengtsson et al. 2004). Precipitation in reanalyses depends on the moisture flux divergence, a rather weakly constrained quantity, which in turn does not depend on direct observations, but on the assimilation of satellite radiances (see e.g. Lorenz and Kunstmann 2012).

2.5.3 Summary

An assessment of temporal variability shows that precipitation over land and, but somewhat weaker, over sea is positively correlated with the NAO. Winters with strong positive NAO anomalies show distinct peaks in precipitation and amounts up to twice the average over the North Sea region (Andersson et al. 2010). On longer time scales, drought conditions over central Europe are also connected to the AMO (Atlantic Multidecadal Oscillation; Ionita et al. 2012a). Generally speaking, precipitation is more variable than temperature, and agreement between datasets is less. Nevertheless, there are indications of an increase in precipitation to the north of the North Sea region and a decrease to the south, in agreement with the projected north-eastward shift in the storm tracks. In many regions, there are also indications that extreme precipitation events have become more extreme and that return periods have decreased.

2.6 Radiative Properties

Meteorological observations aboard ships usually do not include measurements of sunshine duration and radiation. As a result there are few data available, and these are mainly from isolated field campaigns on research vessels. In contrast, cloud parameters are often observed routinely, although the quality of observations varies widely. The following discussion of clouds, solar radiation and sunshine duration therefore relies mainly on studies concerning a wider area, but these data should also be valid for the North Sea region.

2.6.1 Clouds

Clouds have a significant impact on the Earth’s radiation budget. They affect incoming solar shortwave (SW) radiation (by reflecting this back to space) as well as outgoing thermal longwave (LW) radiation (by reducing its emission to space). The difference between the actual radiative flux and that under clear sky conditions is referred to as cloud radiative forcing (CRF). The largest contribution to LW CRF is made by high clouds, whereas the largest contribution to SW CRF is from optically thick clouds due to their higher albedo compared to the clear sky surface albedo. Thus, variations in cloudiness are of great interest in relation to rising global temperatures. The Extended Edited Cloud Report Archive (EECRA; Warren et al. 1986, 1988, 2006) consists of quality controlled climatologies of total cloud cover and cloud type amounts over land and ocean, respectively, based on surface synoptic cloud observations.

Few analyses of changes in cloud cover exist, and even less for the North Sea region. A decrease has been observed in global high cloud cover over almost all land regions since 1971 and most ocean regions since 1952. Norris (2008) analysed global mean time series from gridded surface observations of low-, mid- and upper-level clouds as well as total cloud cover and satellite cloud observations over land and ocean based on EECRA, other surface synoptic cloud reports from land since 1971, the ship-based ICOADS which includes observations since 1952, and satellite observations available from July 1983 in the International Satellite Cloud Climatology Project (ISCCP). Norris (2008) found inconsistencies for the overlapping period of in situ and satellite data except for high clouds.

Over Europe, variability in total winter cloud cover is strongly connected to the NAO. Because the NAO was undergoing a positive trend during a study by Warren et al. (2006), there is a strong positive trend in total cloud cover over Norway at this time (Fig. 2.18). No clear trend is visible further south in the North Sea region (Thompson et al. 2000; Hense and Glowienka-Hense 2008).
Fig. 2.18

Linear trends in total cloud cover in percent per decade for 2.5° × 2.5° boxes in Europe and North Africa in winter (DJF) for the period 1971–1996. The size of each dot indicates the magnitude of the trend (Warren et al. 2006)

Warren et al. (2006) also analysed cloud types observed at European land stations in relation to the NAO/AO signal in winter for the period 1971–1996. Not surprisingly, the strongest correlation was for nimbostratus (Fig. 2.19). Across the western parts of Europe correlations are negative, but high positive correlations exist in the northern part of the North Sea region from northern Scotland to Norway.
Fig. 2.19

Correlation of nimbostratus anomalies with the Arctic Oscillation index, for 2.5° × 2.5° boxes in Europe and North Africa in winter (DJF) for the period 1971–1996. The size of each dot indicates the magnitude of the correlation coefficient (Warren et al. 2006)

2.6.2 Solar Radiation

Time series of measured solar radiation data at various sites around the globe show decreasing irradiances on the order of 6–9 W m−2 (corresponding to a decline of 4–6 % over 30 years) after the mid-1950s (‘global dimming’; Gilgen et al. 1998; Stanhill and Cohen 2001; Liepert and Tegen 2002) and mainly over land, and subsequent increases since the mid-1980s (‘global brightening’; Wild et al. 2005; Norris and Wild 2007) which cannot be explained by variations in solar irradiance or cloudiness alone (Wild 2009), but are largely due to marked changes in the amount of anthropogenic aerosol particles after the Second World War (Stanhill and Cohen 2001; Liepert and Tegen 2002; Streets et al. 2006; Norris and Wild 2007). Since the 1980s, air-quality regulations have led to a decline in air pollution, as can be seen from time series of optical depth (Mishchenko et al. 2007; Ruckstuhl et al. 2008). More recent studies (e.g. Granier et al. 2011; Lee et al. 2013; Myhre et al. 2013; Shindell et al. 2013) corroborate these findings.

For Europe, Norris and Wild (2007) examined changes in SW downward radiation and total cloud cover to distinguish the effects of cloud variability from long-term aerosol influences in the period 1971–2002 (Fig. 2.20). Their ‘cloud cover radiative effect’ (CCRE), defined as the radiative effects of changes in cloud cover, is derived from daytime synoptic surface observations and ISCCP data, subtracted from the downward radiation obtained from the Global Energy Balance Archive (GEBA). The resulting time series comprises variations in clear-sky solar flux as well as radiative effects of changes in cloud albedo that are not linearly correlated to the cloud cover. They found a high correlation (r = 0.88) between global radiation anomalies and the estimated cloud cover radiative effect on monthly and sub-decadal timescales, but the time series of differences show dimming and brightening as well as low frequency trends with minima related to the volcanic eruptions of El Chichón (Mexico) and Pinatubo (Philippines). Decreasing trends for the period 1971–1986 and then increasing trends for 1987–2002 are found for coastal areas of the North Sea region (not shown), but these are mostly not statistically significant (Ruckstuhl et al. 2008, Ruckstuhl and Norris 2009).
Fig. 2.20

Time series of monthly anomalies averaged over grid boxes covering most of Europe with a a 1-2-1 filter and b a 61-point 5-year Lanczos low-pass filter for GEBA global radiation flux (upper, black), ISCCP all-sky downward SW radiation flux (upper, red), SW cloud cover radiation effect (CCRE) estimated from synoptic reports of total cloud cover (middle, blue), SW CCRE estimated from ISCCP total cloud cover amount (middle, red), residual anomalies after removing synoptic-estimated SW CCRE from GEBA global radiation (lower, blue) and residual anomalies after removing ISCCP-estimated SW CCRE from GEBA global radiation (lower, red). Dashed values indicate where less than 75 % of the grid boxes contributed to the GEBA time series. Small vertical bars denote 95 % confidence intervals for June and December anomalies, and vertical dashed lines mark the start and end times for trend calculations (Norris and Wild 2007)

Aerosol particles influence the radiation budget and hence air temperature in two ways. The direct aerosol radiative effect refers to clear-sky cases, when solar radiation is directly scattered (mainly by sulphate) or absorbed (mainly by black carbon), while the indirect aerosol effect enhances cloud albedo via an increase in the number of aerosol particles that act as condensation nuclei creating smaller droplets and prolonging cloud lifetime due to a decrease in droplet size and less precipitation loss.

For the period 1981–2005, Ruckstuhl et al. (2008) estimated the direct and indirect aerosol effects by determining the SW downward radiation for cloud-free and cloudy conditions from eight sites in northern Germany. Excluding the sunny year 2003, the net LW forcing under cloud-free skies is 0.84 W m−2 decade−1 (range: 0.49–1.20), whereas the SW net forcing from changes in cloudiness is 0.56 W m−2 decade−1 (range: −0.91 to 2.00), resulting in a total cloud forcing of 0.16 W m−2 decade−1 (range: −0.26 to 0.57). Thus, the direct aerosol effect has a much larger impact on climate forcing than the indirect aerosol and other cloud effects.

Philipona et al. (2009) found an increase in LW downward radiation over Germany, based on observations for 1981–2005, due to rising temperature and humidity and to the increase in greenhouse gas concentration, but found no effect of changes in cloudiness (Fig. 2.21). The total net LW radiation (the difference between incoming and outgoing radiation at the surface) depends on temperature and absolute humidity, which itself is dependent on temperature.
Fig. 2.21

Radiation budget and surface forcing. Annual mean values (W m−2) for the individual components of the surface radiation budget for eight stations in northern Germany: SW net radiation (SNR), LW downward radiation (LDR), total absorbed radiation (TAR), LW upward radiation (LUR) and total net radiation (TNR) from 1981 to 2005 (missing 2003 data). Downward fluxes are positive and upward fluxes negative. Trends in W m−2 decade−1 with the 95 % confidence interval in brackets. TNR, the balance between downward and upward fluxes at the surface representing the energy available for sensible and latent energy fluxes increases primarily due to the increase of water vapour in the atmosphere (Philipona et al. 2009)

For northern Germany, the LW forcing due to greenhouse gases including water vapour resulted in 0.95 W m−2 decade−1 (range: 0.26–1.64), while the part due to water vapour feedback alone is 0.60 W m−2 decade−1 (range: 0.16–1.04) (Philipona et al. 2009). Thus, the total SW forcing is three times larger than the LW forcing from rising atmospheric levels of anthropogenic greenhouse gases.

2.6.3 Sunshine Duration

Operational measurements of sunshine duration started at most weather stations in the 1930s or 1940s, mainly using the Campbell-Stokes heliograph. However, over recent decades, new electronic–optical equipment has increasingly been used, causing data quality issues and thus consistency problems within the time series of sunshine duration data (Augter 2013). As the existing sunshine duration database for the open sea is insufficient for climate analyses, the results presented in this chapter are based on coastal or island stations only. Sunshine duration depends on three factors: daylength (which is a function of latitude and season), amount of daytime clouds, and atmospheric opacity. Cloudiness and opacity are influenced by meteorological conditions and the latter also by aerosol concentration, which can be very different over land and sea. For Germany, Schönwiese and Janoschitz (2005) analysed changes in sunshine duration for the periods 1951–2000 and 1971–2000 and found no obvious trends. It is not clear whether the increase in global radiation is related to an increase in sunshine duration.

2.6.4 Summary

From the few available datasets on radiative properties, it may be concluded that there are non-negligible trends together with potential uncertainties and land-sea inhomogeneities which make it difficult to assess these quantities in detail.

2.7 Summary and Open Questions

It is not obvious how atmospheric circulation has changed in the North Sea region over the last roughly 200 years. Further research is therefore necessary to understand climate change versus climate variability. One open research question is the extent to which circulation over the North Sea region is controlled by distant factors. In particular, whether there is a link between changes in the Arctic cryosphere and atmospheric circulation further south, including over the North Sea region. Overland and Wang (2010) highlighted a connection between the recent decrease in Arctic sea ice and cold winters in several areas of Europe. With the ongoing decline in sea ice in the Arctic, any such effect on circulation patterns would be important for climate in the North Sea region. Rahmstorf et al. (2015) proposed a proxy-based connection between the observed cooling in the North Atlantic south of Greenland and a weakening of the Atlantic Meridional Overturning Circulation (AMOC) partly due to increased melting of the Greenland Ice Sheet and subsequent freshening of the surface waters. Changes in the strength of the AMOC, however, are still debated and Zhang (2008) and more recently, Tett et al. (2014) have stated that its strength has actually increased.

Owing to large internal variability, it is unclear which part of the observed atmospheric changes is due to anthropogenic activities and which is internally forced. Slowly varying natural factors with an effect on European climate, such as the AMO (Petoukhov and Semenov 2010), may superimpose long-term trends and therefore be difficult to distinguish from the anthropogenic climate change signal.

There are signs of an increase in the number of deep cyclones (but not in the total number of cyclones). There are also indications that the persistence of circulation types has increased over the last century or so (Della-Marta et al. 2007). It is an open question whether this is also related to the decline in Arctic sea ice.

Another open question is whether there have been changes in extreme weather events. However, most studies rely on small datasets covering relatively short time periods, which makes it is difficult to draw statistically significant conclusions. As short time series and a lack of homogeneous data make it impossible to obtain reliable trend estimates, it is important to make available and homogenise the large number of data from past decades that have not yet been digitised. However, shown by the case of the erroneous pressure digitisations in the WASA dataset (see E-Supplement Sect. S2.3), it is essential for data to be thoroughly quality-checked. Experience from the WASA data suggests that this step requires human expertise and cannot be fully automated. On the other hand, further reanalyses, which may be considered a ‘best-possible’ time-space interpolator for observed data, can be useful as long as any bias that is potentially introduced through new instruments, station relocations etc. is properly addressed. The same is true for existing reanalyses, as it is unclear how homogeneous reanalyses can be that rely only on surface observations such as 20CR (Compo et al. 2011).

Temperature has increased in the North Sea region, and there is a clear signal in the annual number of frost days or summer days. While there is a clear winter and spring warming signal over the Baltic Sea region (Rutgersson et al. 2014), this is not as clear for the North Sea region. For precipitation, it is difficult to deduce long-term trends; however, there are indications of longer precipitation periods and ‘more extreme’ extreme events.

Other quantities, such as clouds, radiation or sunshine duration, are difficult to judge owing to a general lack of data.

Footnotes

Supplementary material

314670_1_En_2_MOESM1_ESM.pdf (671 kb)
Supplementary material 1 (PDF 711 kb)

References

  1. Alexandersson H, Tuomenvirta H, Schmith T, Iden K (2000) Trends of storms in NW Europe derived from an updated pressure data set. Clim Res 14:71–73Google Scholar
  2. Allan R, Tett S, Alexander L (2009) Fluctuations in autumn-winter severe storms over the British Isles: 1920 to present. Int J Climatol 29:357–371Google Scholar
  3. Ambaum MHP, Hoskins BJ, Stephenson DB (2001) Arctic Oscillation or North Atlantic Oscillation? J Clim 14:3495–3507Google Scholar
  4. Andersson A, Bakan S, Graßl H (2010) Satellite derived precipitation and freshwater flux variability and its dependence on the North Atlantic Oscillation. Tellus A 62:453–468Google Scholar
  5. Andersson A, Klepp C, Bakan S, Graßl H, Schulz J (2011) Evaluation of HOAPS-3 ocean freshwater flux components. J Appl Meteor Climatol 50:379–398Google Scholar
  6. Augter G (2013) Vergleich der Referenzmessungen des Deutschen Wetterdienstes mit automatisch gewonnenen Messwerten [Comparison of reference measurements of the German Weather Service with automatically obtained measurements]. Berichte des Deutschen Wetterdienstes 238Google Scholar
  7. Barnes EA, Hartmann DL (2010) Dynamical feedbacks and the persistence of the NAO. J Atmos Sci 67:851–865Google Scholar
  8. Barnston AG, Livezey RE (1987) Classification, seasonality and persistence of low frequency atmospheric circulation patterns. Mon Wea Rev 115:1083–1126Google Scholar
  9. Barredo JI (2010) No upward trend in normalised windstorm losses in Europe: 1970–2008. Nat Hazard Earth Syst Sci 10:97–104Google Scholar
  10. Barriopedro D, Garcìa-Herrera R, Lupo AR, Hernández E (2006) A climatology of northern hemisphere blocking. J Climate 19:1042–1063Google Scholar
  11. Barriopedro D, Fischer EM, Luterbacher J, Trigo RM, Garcia-Herrera R (2011) The hot summer of 2010: Redrawing the temperature record map of Europe. Science 332:220–224Google Scholar
  12. Baur F (1937) Einführung in die Großwetterforschung [Introduction to Grosswetter research; in German]. Verlag BG Teubner, LeipzigGoogle Scholar
  13. Benedict JJ, Lee S, Feldstein SB (2004) Synoptic view of the North Atlantic Oscillation. J Atmos Sci 61:121–144Google Scholar
  14. Bengtsson L, Hagemann S, Hodges KI (2004) Can climate trends be calculated from reanalysis data? J Geophys Res 109:D11111 doi:10.1029/2004JD004536
  15. Berrisford P, Dee DK, Fuentes M, Kållberg P, Kobayashi S, Uppala S (2009). The ERA-Interim archive. ERA-40 Report Series No. 1. European Centre for Medium-Range Weather Forecasts, UKGoogle Scholar
  16. Berry DI, Kent EC (2009) A new air-sea interaction gridded dataset from ICOADS with uncertainty estimates. Bull Am Met Soc 90:645–656Google Scholar
  17. Berry DI, Kent EC (2011) Air-sea fluxes from ICOADS: The construction of a new gridded dataset with uncertainty estimates. Int J Climatol 31:987–1001Google Scholar
  18. Berry DI, Kent EC, Taylor PK (2004) An analytical model of heating errors in marine air temperatures from ships. J Atmos Ocean Technol 21:1198–1215Google Scholar
  19. Bett PE, Thornton HE, Clark RT (2013) European wind variability over 140 yr. Adv Sci Res 10:51–58Google Scholar
  20. Blessing S, Fraedrich K, Junge M, Kunz T, Linkheit F (2005) Daily North Atlantic Oscillation (NAO) index: statistics and its stratospheric polar vortex dependence. Meteorol Z 14:763–769Google Scholar
  21. Brönnimann S, Martius O, von Waldow H, Welker C, Luterbacher J, Compo G, Sardeshmukh PD, Usbeck T (2012) Extreme winds at northern mid-latitudes since 1871. Meteorol Z 21:13–27Google Scholar
  22. Brönnimann S, Martius O, Franke J, Stickler A, Auchmann R (2013) Historical weather extremes in the “Twentieth Century Reanalysis”. In: Brönnimann S, Martius O (eds) (2013) Weather Extremes During the Past 140 Years. Geographica Bernensia G89:7–17Google Scholar
  23. Budikova D (2009) Role of Arctic sea ice in global atmospheric circulation: A review. Global Plan Change 68:149–163Google Scholar
  24. Budikova D (2012) Northern Hemisphere climate variability: Character, forcing mechanisms, and significance of the North Atlantic/Arctic Oscillation. Geogr Compass 6:401–422Google Scholar
  25. Carter DJ, Draper L (1988) Has the north-east Atlantic become rougher? Nature 332:494Google Scholar
  26. Cassou C (2008) Intraseasonal interaction between the Madden-Julian Oscillation and the North Atlantic Oscillation. Nature 455:523–527Google Scholar
  27. Cattiaux J, Vautard R, Yiou P (2009) Origins of the extremely warm European fall of 2006. Geophys Res Lett 36:L06713 doi:10.1029/2009GL037339
  28. Cattiaux J, Vautard R, Cassou C, Yiou P, Masson-Delmotte V, Codron F (2010) Winter 2010 in Europe: A cold extreme in a warming climate. Geophys Res Lett 37:L20704 doi:10.1029/2010GL044613
  29. Chang EK, Fu Y (2002) Interdecadal variations in Northern Hemisphere winter storm track intensity. J Climate 15:642–658Google Scholar
  30. Cheng X, Wallace JM (1993) Cluster analysis of the Northern Hemisphere wintertime 500 hPa height field: Spatial patterns. J Atmos Sci 50:2674–2696Google Scholar
  31. Chrysanthou A, van der Schrier G, van den Besselaar EJM, Klein Tank AMG, Brandsma T (2014) The effects of urbanization on the rise of the European temperature since 1960. Geophys Res Lett 41:7716–7722 doi:10.1002/2014GL061154
  32. Ciavola P, Ferreira O, Haerens P, van Koningsveld M, Armaroli C, Lequeux Q (2011) Storm impacts along European coastlines. Part 1: The joint effort of the MICORE and ConHaz Projects. Environ Sci Policy 14:912–923Google Scholar
  33. Clemmensen LB, Hansen KWT, Kroon A (2014) Storminess variation at Skagen, northern Denmark since AD 1860: Relations to climate change and implications for coastal dunes. Aeolian Res 15:101–112Google Scholar
  34. Cohen J, Barlow M (2005) The NAO, the AO, and global warming: How closely related? J Climate 18:4498–4513Google Scholar
  35. Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin, X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P, Brönnimann S, Brunet M, Crouthamel RI, Grant AN, Groisman PY, Jones PD, Kruk M, Kruger AC, Marshall GJ, Maugeri M, Mok HY, Nordli Ø, Ross TF, Trigo RM, Wang XL, Woodruff SD, Worley SJ (2011) The twentieth century reanalysis project. Q J Roy Met Soc 137:1–28Google Scholar
  36. Cortesi N, Gonzalez-Hidalgo JC, Brunetti M, Martin-Vide J (2012) Daily precipitation concentration across Europe 1971–2010. Nat Hazards Earth Syst Sci 12:2799–2810Google Scholar
  37. Croci-Maspoli M, Schwierz C, Davies H (2007) Atmospheric blocking: Space-time links to the NAO and PNA. Clim Dyn 29:713–725Google Scholar
  38. Cusack S (2012) A 101 year record of windstorms in the Netherlands. Clim Change 116:693–704Google Scholar
  39. Dangendorf S, Müller-Navarra S, Jensen J, Schenk F, Wahl T, Weisse R (2014) North Sea storminess from a novel storm surge record since AD 1843. J Climate 27:3582–3595Google Scholar
  40. de Kraker A (1999) A method to assess the impact of high tides, storms and storm surges as vital elements in climatic history - the case of stormy weather and dikes in the northern part of Flanders, 1488 to 1609. Clim Change 43:287–302Google Scholar
  41. de Viron O, Dickey JO, Ghil M (2013) Global models of climate variability. Geophys Res Lett 40:1832–1837Google Scholar
  42. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay,Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart J Roy Met Soc 137:553–597Google Scholar
  43. Della-Marta PM, Haylock MR, Luterbacher J, Wanner H (2007) Doubled length of western European summer heat waves since 1880. J Geophys Res 112:D15103 doi:10.1029/2007JD008510
  44. Delworth TL, Knutson TR (2000) Simulation of early 20th century global warming. Science 287:2246–2250Google Scholar
  45. Donat MG, Renggli D, Wild S, Alexander LV, Leckebusch GC, Ulbrich U (2011) Reanalysis suggests long-term upward trends in European storminess since 1871. Geophys Res Lett 38:L14703 doi:10.1029/2011GL047995
  46. Donat MG, Alexander LV, Yang H, Durre I, Vose R, Dunn RJH, Willett KM, Aguilar E, Brunet M, Caesar J, Hewitson B, Jack C, Klein Tank AMG, Kruger AC, Marengo J, Peterson TC, Renom M, Oria Rojas C, Rusticucci M, Salinger J, Elrayah AS, Sekele SS, Srivastava AK, Trewin B, Villarroel C, Vincent LA, Zhai P, Zhang X, Kitching S (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. J Geophys Res 118:2098–2118Google Scholar
  47. Esteves LS, Williams JJ, Brown JM (2011) Looking for evidence of climate change impacts in the eastern Irish Sea. Nat Hazards and Earth System Sci 11:1641–1656Google Scholar
  48. Feldstein SB (2000) Teleconnection and ENSO: The timescale, power spectra, and climate noise properties. J Climate 13:4430–4440Google Scholar
  49. Feldstein SB (2002) The recent trend and variance increase of the annular mode. J Climate 15:88–94Google Scholar
  50. Fennig K, Andersson A, Bakan S, Klepp C, Schroeder M (2012) Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data - HOAPS 3.2 - Monthly means / 6-hourly composites. Satellite Application Facility on Climate Monitoring. doi:10.5676/EUM_SAF_CM/HOAPS/V001
  51. Feser F, Barcikowska M, Krueger O, Schenk F, Weisse R, Xia L (2015a) Storminess over the North Atlantic and Northwestern Europe – A Review. Q J Roy Met Soc. 141:350–382Google Scholar
  52. Feser F, Barcikowska M, Haeseler S, Lefebvre C, Schubert-Frisius M, Stendel M, von Storch H, Zahn M (2015b) Hurricane Gonzalo and its extratropical transition to a strong European storm. In: Explaining Extreme Events of 2014 from a Climate Perspective. Bull Amer Met Soc 96:S51–S55Google Scholar
  53. Fischer EM, Luterbacher J, Zorita E, Tett SFB, Casty C, Wanner H (2007) European climate response to tropical volcanic eruptions over the last half millennium. Geophys Res Lett 34:L05707 doi:10.1029/2006GL027992
  54. Folland CK, Knight J, Linderholm HW, Fereday D, Ineson S, Hurrell JW (2009) The summer North Atlantic Oscillation: Past, present, and future. J Climate 22:1082–1103Google Scholar
  55. Fowler H, Kilsby C (2003) A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000. Int J Climatol 23:1313–1334Google Scholar
  56. Franke R (2009) Die nordatlantischen Orkantiefs seit 1956. Naturwissenschaftliche Rundschau 62:349–356Google Scholar
  57. Franzke C, Woollings T (2011) On the persistence and predictability properties of North Atlantic climate variability. J Climate 24:466–472Google Scholar
  58. Franzke C, Lee S, Franzke SB (2004) Is the North Atlantic Oscillation a breaking wave? J Atmos Sci 61:145–160Google Scholar
  59. Frydendahl K, Frich P, Hansen C (1992) Danish weather observations 1685–1715. Danish Met Inst Tech Rep 92-3Google Scholar
  60. Furevik T, Nilsen JEØ (2005) Large-scale atmospheric circulation variability and its impacts on the Nordic Seas ocean climate - a review. In Drange H, Dokken T, Furevik T, Gerdes R, Berger W (eds.) The Nordic Seas: An Integrated Perspective. Geophys Mon Ser 158.Google Scholar
  61. Geng Q, Sugi M (2001) Variability of the North Atlantic cyclone activity in winter analyzed from NCEP-NCAR reanalysis data. J Climate 14:3863–3873Google Scholar
  62. Geyer B (2014) High-resolution atmospheric reconstruction for Europe 1948–2012: coastDat2. Earth Syst Sci Data 6:147–164Google Scholar
  63. Gilgen H, Wild M, Ohmura A (1998) Means and trends of shortwave irradiance at the surface estimated from Global Energy Balance Archive data. J Climate 11:2042–2061Google Scholar
  64. Gillett NP (2005) Climate modelling: Northern Hemisphere circulation. Nature 437:496Google Scholar
  65. Gillett NP, Zwiers FW, Weaver AJ, Stott PA (2003) Detection of human influence on sea-level pressure. Nature 422:292–294Google Scholar
  66. Gönnert G (2003) Sturmfluten und Windstau in der Deutschen Bucht, Charakter, Veränderungen und Maximalwerte im 20. Jahrhundert. Die Küste 67Google Scholar
  67. Granier C, Bessagnet B, Bond T, d’Angiola A, van der Gon HD, Frost GJ, Heil A, Kaiser JW, Kinne S, Klimont Z, Kloster S, Lamarque J-F, Liousse C, Masui T, Meleux F, Mieville A, Ohara T, Raut J-C, Riahi K, Schultz MG, Smith SJ, Thompson A, van Aardenne J, van der Werf GR, van Vuuren DP (2011) Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period. Clim Change 109:163–190Google Scholar
  68. Groisman P, Knight R, Easterling D, Karl T, Hegerl G, Razuvaev V (2005) Trends in intense precipitation in the climate record. J Climate 18:1326–1350Google Scholar
  69. Gulev SK, Zolina O, Grigoriev S (2001) Extratropical cyclone variability in the Northern Hemisphere winter from the NCEP/NCAR reanalysis data. Clim Dyn 17:795–809Google Scholar
  70. Häkkinen S, Rhines PB, Worthen DL (2011) Atmospheric blocking and Atlantic multidecadal ocean variability. Science 334:655–659Google Scholar
  71. Hammond JM (1990). Storm in a teacup or winds of change? Weather 45:443–448Google Scholar
  72. Hanna E, Cappelen J, Allan R, Jónsson T, LeBlancq F, Lillington T, Hickey K (2008) New insights into North European and North Atlantic surface pressure variability, storminess, and related climatic change since 1830. J Climate 21:6739–6766Google Scholar
  73. Hannachi A (2007a): Pattern hunting in climate: a new method for trends in gridded climate data. Int J Climatol 27:1–15Google Scholar
  74. Hannachi A (2007b) Tropospheric planetary wave dynamics and mixture modeling: Two preferred regimes and a regime shift. J Atmos Sci 64:3521–3541Google Scholar
  75. Hannachi A (2008) A new set of orthogonal patterns in weather and climate: Optimally interpolated patterns. J Climate 21:6724–6738Google Scholar
  76. Hannachi A (2010) On the origin of planetary-scale extratropical circulation regimes. J Atmos Sci 67:1382–1401Google Scholar
  77. Hannachi A, Woollings T Fraedrich K (2012) The North Atlantic jet stream: a look at preferred positions, paths and transitions. Q J Roy Met Soc 138:862–877Google Scholar
  78. Hansen, J, Ruedy R, Sato M, Lo K (2010) Global surface temperature change. Rev Geophys 48:RG4004 doi:10.1029/2010RG000345
  79. Harnik N, Chang EK (2003) Storm track variations as seen in radiosonde observations and reanalysis data. J Climate 16:480–495Google Scholar
  80. Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations - the CRU TS 3.10 dataset. Int J Climatol 34:623–642Google Scholar
  81. Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Brönnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM (2013) Observations: atmosphere and surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Cambridge University PressGoogle Scholar
  82. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation for 1950–2006. J Geophys Res 113:D20119 doi:10.1029/2008JD10201
  83. Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Climate 19:5686–5699Google Scholar
  84. Hense A, Glowienka-Hense R (2008) Auswirkungen der Nordatlantischen Oszillation. [Climate impact of the North Atlantic Oscillation; in German]. Promet 34:89–94Google Scholar
  85. Hess P, Brezowsky H (1952) Katalog der Großwetterlagen Europas [Catalogue of European Grosswetterlagen; in German]. Ber Dt Wetterd in der US-Zone 33:39Google Scholar
  86. Hess P, Brezowsky H (1977) Katalog der Großwetterlagen Europas 1881–1976 (3. verb. u. erg. Aufl.), Ber. Dt. Wetterd. 15(113) Selbstverlag des Deutschen Wetterdienstes, Offenbach am Main, GermanyGoogle Scholar
  87. Hickey KR (2003) The storminess record from Armagh Observatory, Northern Ireland, 1796–1999. Weather 58:28–35Google Scholar
  88. Hines KM, Bromwich DH, Marshall G J (2000) Artificial surface pressure trends in the NCEP–NCAR Reanalysis over the Southern Ocean and Antarctica. J Climate 13:3940–3952Google Scholar
  89. Hoerling MP, Hurrell JW, Xu T (2001) Tropical origins for recent North Atlantic climate change. Science 292:90–92Google Scholar
  90. Hogben N (1994) Increases in wave heights over the North Atlantic: A review of the evidence and some implications for the naval architect. Trans R Inst Naval Arch 5:93–101Google Scholar
  91. Hossen MA, Akhter F (2015) Study of the wind speed, rainfall and storm surges for the Scheldt Estuary in Belgium. Int J Sci Tech Res 4:130–134Google Scholar
  92. Hoy A, Sepp M, Matschullat J (2012) Atmospheric circulation variability in Europe and northern Asia (1901 to 2010). Theor Appl Climatol 113:105–126Google Scholar
  93. Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation, regional temperatures and precipitation. Science 269:676–679Google Scholar
  94. Hurrell JW, Deser C (2009) North Atlantic climate variability: The role of the North Atlantic Oscillation. J Mar Sys 78:28–41Google Scholar
  95. Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (2003) An overview of the North Atlantic Oscillation. In: The North Atlantic Oscillation: Climatic significance and environmental impact. Geoph Monog Series 134:1–36Google Scholar
  96. Ineson S, Scaife AA, Knight JR, Manners JC, Dunstone NJ, Gray LJ, Haigh JD (2011) Solar forcing of winter climate variability in the northern hemisphere. Nature Geosci 4:753–757Google Scholar
  97. Ionita M, Lohmann G, Rimbu N, Chelcea S, Dima M (2012a) Interannual to decadal summer drought variability over Europe and its relationship to global sea surface temperature. Clim Dyn 38:363–377Google Scholar
  98. Ionita M, Lohmann G, Rimbu N, Scholz P (2012b) Dominant modes of diurnal temperature range variability over Europe and their relationship with large-scale atmospheric circulation and sea surface temperature anomaly patterns. J Geophys Res 117:D15111, doi:10.1029/2011JD016669
  99. Johannessen OM, Bengtsson L, Miles MW, Kuzmina SI, Semenov VA, Alekseev GV, Nagurnyi AP, Zakharov VF, Bobylev LP, Pettersson LH, Hasselmann K, Cattle HP (2004) Arctic climate change: Observed and modelled temperature and sea-ice variability. Tellus A 56:328–341Google Scholar
  100. Jones PD, Moberg A (2003) Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. J Climate 16:206–223Google Scholar
  101. Jones PD, Jonsson T, Wheeler D (1997) Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and south-west Iceland. Int J Climatol 17:1433–1450Google Scholar
  102. Jones, PD, Lister DH, Osborn TJ, Harpham C, Salmon M, Morice CP (2012) Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010. J Geophys Res 117:D05127 doi:10.1029/2011JD017139
  103. Joshi MM, Charlton AJ, Scaife AA (2006) On the influence of stratospheric water vapor changes on the tropospheric circulation. Geophys Res Lett 33:L09806. doi:10.1029/2006GL025983
  104. Jung T, Vitart F, Ferranti L, Morcrette JJ (2011) Origin and predictability of the extreme negative NAO winter of 2009/10. Geophys Res Lett 38:L07701 doi:10.1029/2011GL046786
  105. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bull Am Met Soc 77:437–471Google Scholar
  106. Keeley SPE, Sutton RT, Shaffrey, LC (2009) Does the North Atlantic Oscillation show unusual persistence on intraseasonal timescales? Geophys Res Lett 36:L22706. doi:10.1029/2009GL040367
  107. Kent, EC, Woodruff SD, Berry DI (2007) Metadata from WMO Publication No. 47 and an Assessment of Voluntary Observing Ships Observation Heights in ICOADS. J Atmos Ocean Tech 24:214–234Google Scholar
  108. Kent EC, Rayner NA, Berry DI, Saunby M, Moat BI, Kennedy JJ, Parker DE (2013) Global analysis of night marine air temperature and its uncertainty since 1880, the HadNMAT2 Dataset. J Geophys Res 118:1281–1298Google Scholar
  109. Kistler R, Collins W, Saha S, White G, Woollen J, Kalnay E, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP-NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation. Bull Am Met Soc 82:247–267Google Scholar
  110. Klein Tank AMG, Wijngaard JB, Können GP, Böhm R, Demarée G, Gocheva A, Mileta M, Pashiardis S, Hejkrlik L, Kern-Hansen C, Heino R, Bessemoulin P, Müller-Westermeier G, Tzanakou M, Szalai S, Pálsdóttir T, Fitzgerald D, Rubin S, Capaldo M, Maugeri M, Leitass A, Bukantis A, Aberfeld R, van Engelen AFV, Forland E, Mietus M, Coelho F, MaresC, Razuvaev V, Nieplova E, Cegnar T, Antonio López J, Dahlström B, Moberg A, Kirchhofer W, Ceylan A, Pachaliuk O., Alexander LV, Petrovic P (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int J Climatol 22:1441–1453Google Scholar
  111. Knight JR, Folland CK, Scaife AA (2005a) Climate impacts of the Atlantic Multidecadal Oscillation. Geophys Res Lett 33:L17706. doi:10.1029/2006GL026242
  112. Knight JR, Allan RJ, Folland CK, Vellinga M, Mann ME (2005b) A signature of persistent natural thermohaline circulation cycles in observed climate. Geophys Res Lett 32:L20708. doi:10.1029/2005GL024233
  113. Kravtsov S, Robertson AW, Ghil M (2006) Multiple regimes and low‒frequency oscillations in the Northern Hemisphere’s zonal-mean flow. J Atmos Sci 63:840–860Google Scholar
  114. Krueger O, von Storch H (2011) Evaluation of an air pressure‐based proxy for storm activity. J Climate 24:2612–2619Google Scholar
  115. Krueger O, von Storch H (2012) The informational value of pressure-based single-station proxies for storm activity. J Atmos Ocean Technol 29:569–580Google Scholar
  116. Krueger O, Schenk F, Feser F, Weisse R (2013) Inconsistencies between long-term trends in storminess derived from the 20CR reanalysis and observations. J Climate 26:868–874Google Scholar
  117. Kucharski F, Molteni F, Bracco A (2006) Decadal interactions between the western tropical Pacific and the North Atlantic Oscillation. Clim Dyn 26:79–91Google Scholar
  118. Küttel M, Xoplaki E, Gallego D, Luterbacher J, García-Herrera R, Allan R, Barriendos M, Jones PD, Wheeler D, Wanner H (2009) The importance of ship log data: reconstructing North Atlantic, European and Mediterranean sea level pressure fields back to 1750. Clim Dyn 34:1115–1128Google Scholar
  119. Lavers DA, Villarini G, Allan RP, Wood EF, Wade AJ (2012) The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation. J Geophys Res 117:D20106. doi:10.1029/2012JD018027
  120. Leander R, Buishand TA, Klein Tank AMG (2014) An alternative index for the contribution of precipitation on very wet days to the total precipitation. J Climate 27:1365–1378Google Scholar
  121. Lee YH, Lamarque J-F, Flanner MG, Jiao C, Shindell DT, Berntsen T, Bisiaux MM, Cao J, Collins WJ, Curran M, Edwards R, Faluvegi G, Ghan S, Horowitz LW, McConnell JR, Ming J, Myhre G, Nagashima T, Naik V, Rumbold SDT, Skeie RB, Sudo K, Takemura T, Thevenon F, Xu B, Yoon J-H (2013) Evaluation of preindustrial to present-day black carbon and its albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Atmos Chem Phys 13:2607–2634Google Scholar
  122. Lehmann A, Getzlaff K, Harlaß J (2011) Detailed assessment of climate variability in the Baltic Sea area for the period 1958 to 2009. Clim Res 46:185–196Google Scholar
  123. Lenderink G, van Meijgaard E, Selten F (2009) Intense coastal rainfall in the Netherlands in response to high sea surface temperatures: analysis of the event of August 2006 from the perspective of a changing climate. Clim Dyn 32:19–33Google Scholar
  124. Liepert B, Tegen I (2002) Multidecadal solar radiation trends in the United States and Germany and direct tropospheric aerosol forcing. J Geophys Res 107:L02806. doi:10.1029/2001JD000760
  125. Lindenberg J, Mengelkamp HT, Rosenhagen G (2012) Representativity of near surface wind measurements from coastal stations at the German Bight. Meteorol Z 21:99–106Google Scholar
  126. Lorenz E (1951) Seasonal and irregular variations of the Northern Hemisphere sea-level pressure profile. J. Meteor 8:52–59Google Scholar
  127. Lorenz C, Kunstmann H (2012) The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis. J Hydromet 13:1397–1420Google Scholar
  128. Luo D, Cha J, Feldstein SB (2012) Weather regime transitions and the interannual variability of the North Atlantic Oscillation. Part I: A likely connection. J Atmos Sci 69:2329–2346Google Scholar
  129. Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H (2004) European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303:1499–1503Google Scholar
  130. Luterbacher J, Liniger, MA, Menzel A, Estrella N, Della-Marta PM, Pfister C, Rutishauser T, Xoplaki E (2007) Exceptional European warmth of autumn 2006 and winter 2007: Historical context, the underlying dynamics, and its phenological impacts. Geophys Res Lett 34:L12704. doi:10.1029/2007GL029951
  131. Marshall AG, Scaife AA (2009) Impact of the QBO on surface winter climate. J Geophys Res 114:D18110. doi:10.1029/2009JD011737
  132. Marshall J, Johnson H, Goodman J (2001) A study of the interaction of the North Atlantic Oscillation with the ocean circulation. J Climate 14:1399–1421Google Scholar
  133. Matulla C, Schöner W, Alexandersson H, von Storch H, Wang X (2007) European storminess: late nineteenth century to present. Clim Dyn 31:125–130Google Scholar
  134. McCabe GJ, Clark MP, Serreze MC (2001) Trends in Northern Hemisphere surface cyclone frequency and intensity. J Climate 14:2763–2768Google Scholar
  135. Mishchenko MI, Geogdzhayev IV, Rossow WB, Cairns B, Carlson BE, Lacis AA, Liu L, Travis LD (2007) Long-term satellite record reveals likely recent aerosol trend. Science 315:1543Google Scholar
  136. Moberg A, Jones PD, Lister D, Walther A, Brunet M, Jacobeit J, Alexander LV, Della-Marta PM, Luterbacher J, Yiou P, Chen D, Klein Tank AMG, Saladié O, Sigró J, Aguilar E, Alexandersson H, Almarza C,Auer I, Barriendos M, Begert M, Bergström H, Böhm R, Butler C. J. Caesar J, Drebs A, Founda D, Gerstengarbe F-W, Micela G, Tolasz R, Tuomenvirta H, Werner PC, Linderholm,H, Philipp A, Wanner H, Xoplaki E (2006) Indices for daily temperature and precipitation extremes in Europe analyzed for the period 1901–2000. J Geophys Res 111:2156–2202Google Scholar
  137. Myhre G, Samset BH, Schulz M, Balkanski Y, Bauer S, Berntsen TK, Bian H, Bellouin N, Chin M, Diehl T, Easter RC, Feichter J, Ghan SJ, Hauglustaine D, Iversen T, Kinne S, Kirkevåg A, Lamarque J-F, Lin G, Liu X, Lund MT, Luo G, Ma X, van Noije T, Penner JE, Rasch PJ, Ruiz A, Seland Ø, Skeie RB, Stier P, Takemura T, Tsigaridis K, Wang P, Wang Z, Xu L, Yu H, Yu F, Yoon J-H, Zhang K, Zhang H, Zhou C (2013) Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. Atmos Chem Phys 13:1853–1877Google Scholar
  138. Norris JR (2008) Observed interdecadal changes in cloudiness: real or spurious. In: Brönnimann S, Luterbacher J, Ewen T, Diaz HF, Stolarski RS, Neu U (eds) Climate Variability and Extremes During the Past 100 Years. SpringerGoogle Scholar
  139. Norris JR, Wild M (2007) Trends in aerosol radiative effects over Europe inferred from observed cloud cover, solar “dimming” and solar “brightening”. J Geophys Res 112:D08214 doi:10.1029/2006JD007794
  140. O’Gorman PA, Schneider T (2009) The physical basis for increases in precipitation extremes in simulations of 21st century climate change. Proc Natl Acad Sci USA 106:14773–14777Google Scholar
  141. Overland JE, Wang M (2010) Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice. Tellus A 62:1–9Google Scholar
  142. Pall P, Stone DA, Stott PA, Nozawa T, Hilberts AGJ, Lohmann D, Allen MR (2011) Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470:382–385Google Scholar
  143. Peterson TC, Vose RS (1997) An overview of the global historical climatology network temperature data base. Bull Am Met Soc 78:2837–2849Google Scholar
  144. Petoukhov V, Semenov VA (2010) A link between reduced Barents-Kara sea ice and cold winter extremes over northern continents. J Geophys Res 115:D21111 doi:10.1029/2009JD013568
  145. Philipona R, Behrens K, Ruckstuhl C (2009) How declining aerosols and rising greenhouse gases forced rapid warming in Europe since the 1980s. Geophys Res Lett 36:L02806 doi:10.1029/2008GL036350
  146. Pinto JG, Raible CC (2012) Past and recent changes in the North Atlantic oscillation. Clim Change 3:79–90Google Scholar
  147. Portis DH, Walsh JE, El-Hamly M, Lamb PJ (2001) Seasonality of the North Atlantic Oscillation. J Climate 14:2069-2078Google Scholar
  148. Rahmstorf S, Box JE, Feulner G, Mann ME, Robinson A, Rutherford S, Schaffernicht EJ (2015) Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nature Clim Change 5:475–480Google Scholar
  149. Raible C, Della-Marta PM, Schwierz C, Blender R (2008) Northern Hemisphere extratropical cyclones: a comparison of detection and tracking methods and different reanalyses. Mon Wea Rev 136:880–897Google Scholar
  150. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407 doi:10.1029/2002JD002670
  151. Rennert KJ, Wallace JM (2009) Cross-frequency coupling, skewness, and blocking in the Northern Hemisphere winter circulation. J Climate 22:5650–5666Google Scholar
  152. Rienecker MR, Suarez MJ, Gelaro R, Todling R, Julio Bacmeister EL, Bosilovich MG, Schubert SD, Takacs L, Kim G-K, Bloom S, Chen J, Collins D, Conaty A, da Silva A, Gu W, Joiner J, Koster RD, Lucchesi R, Molod A, Owens T, Pawson S, Pegion P, Redder CR, Reichle R, Robertson FR, Ruddick AG, Sienkiewicz M, Woollen J (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Climate 24:3624–3648Google Scholar
  153. Ripesi O, Ciciulla F, Maimone F, Pelino V (2012) The February 2010 Arctic Oscillation Index and its stratospheric connection. Q J Roy Met Soc 138:1961–1969Google Scholar
  154. Rodwell MJ, Rowell DP, Folland CK (1999) Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature 398:320–323Google Scholar
  155. Rosenhagen G, Schatzmann M, Schrön A (2011) Das Klima der Metropolregion auf Grundlage meteorologischer Messungen und Beobachtungen. In: v Storch H, Claussen M (eds) Klimabericht für die Metropolregion HamburgGoogle Scholar
  156. Ruckstuhl C, Norris JR (2009) How do aerosol histories affect solar “dimming” and “brightening” over Europe? IPCC-AR4 models versus observations. J Geophys Res 114:D00D04 doi:10.1029/2008JD011066
  157. Ruckstuhl C, Philipona R, Behrens K, Coen MC, Dürr B, Heimo A, Mätzler C, Nyeki S, Ohmura A, Vuilleumier L, Weller M, Wehrli C, Zelenka A (2008) Aerosol and cloud effects on solar brightening and recent rapid warming. Geophys Res Lett 35:L12708 doi:10.1029/2008GL034228
  158. Rutgersson A, Jaagus J, Schenk F, Stendel M (2014) Observed changes and variability of atmospheric parameters in the Baltic Sea region during the last 200 years. Clim Res 61:177–190Google Scholar
  159. Saha S, Moorthi S, Pan H-L, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu H, Stokes D, Grumbine R, Gayno G, Wang J, Hou Y-T, Chuang H-Y, Juang H-MH, Sela J, Iredell M, Treadon R, Kleist D, van Delst P, Keyser D, Derber J, Ek M, Meng J, Wei H, Yang R, Lord S, van den Dool H, Kumar A, Wang W, Long C, Chelliah M, Xue Y, Huang B, Schemm J-K, Ebisuzaki W, Lin R, Xie P, Chen M, Zhou S, Higgins W, Zou C-Z, Liu Q, Chen Y, Han Y, Cucurull L, Reynolds RW, Rutledge G, Goldberg M (2010) The NCEP climate forecast system reanalysis. Bull Amer Meteor Soc 91:1015–1057Google Scholar
  160. Scaife AA, Knight JR, Vallis G, Folland CK (2005) A stratospheric influence on the winter NAO and north Atlantic surface climate. Geophys Res Lett 32:L18715 doi:10.1029/2005GL023226
  161. Scaife AA, Kucharski F, Folland CK, Kinter J, Brönnimann S, Fereday D, Fischer AM, Grainger S, Jin EK, Kang IS, Knight JR, Kusunoki S, Lau NC, Nath MJ, Nakaegawa T, Pegion P, Schubert S, Sporyshev P, Syktus J, Yoon JH, Zeng N, Zhou T (2009) The CLIVAR C20C project: selected twentieth century climate events. Clim Dyn 33:603–614Google Scholar
  162. Schenk F (2015) The analog-method as statistical upscaling tool for meteorological field reconstructions over Northern Europe since 1850. Dissertation Univ HamburgGoogle Scholar
  163. Schenk F, Zorita E (2012) Reconstruction of high resolution atmospheric fields for Northern Europe using analog-upscaling. Clim Past 8:1681–1703Google Scholar
  164. Schiesser HH, Pfister C, Bader J (1997) Winter storms in Switzerland north of the Alps 1864/1865–1993/1994. Theor Appl Climatol 58:1–19Google Scholar
  165. Schmidt H, von Storch H (1993) German Bight storms analysed. Nature 365:791Google Scholar
  166. Schmith T, Kaas E, Li TS (1998) Northeast Atlantic winter storminess 1875-1995 re-analysed. Clim Dyn 14:529–536Google Scholar
  167. Schönwiese CD, Janoschitz R (2005) Klima-Trendatlas Deutschland, 1901-2000. Berichte Inst Atmosph Umwelt Univ Frankfurt 4Google Scholar
  168. Selten FM, Branstator GW, Dijkstra HA, Kliphuis M (2004) Tropical origins for recent and future Northern Hemisphere climate change. Geophys Res Lett 31:L21205 doi:10.1029/2004GL020739
  169. Semenov VA, Latif M, Jungclaus JH, Park W (2008) Is the observed NAO variability during the instrumental record unusual? Geophys Res Lett 35:L11701 doi:10.1029/2008GL033273
  170. Shindell DT, Schmidt GA, Mann ME, Rind D, Waple A (2001) Solar forcing of regional climate change during the Maunder minimum. Science 294:2149–2152Google Scholar
  171. Shindell DT, Lamarque J-F, Schulz M, Flanner M, Jiao C, Chin M, Young PJ, Lee Y.H, Rotstayn L, Mahowald N, Milly G, Faluvegi G, Balkanski Y, Collins WJ, Conley AJ, Dalsoren S, Easter R, Ghan S, Horowitz L, Liu X, Myhre G, Nagashima T, Naik V, Rumbold ST, Skeie R, Sudo K, Szopa S, Takemura T, Voulgarakis A, Yoon J-H, Lo F (2013) Radiative forcing in the ACCMIP historical and future climate simulations. Atmos Chem Phys 13:2939–2974Google Scholar
  172. Sickmoeller M, Blender R, Fraedrich K (2000) Observed winter cyclone tracks in the Northern Hemisphere in re-analysed ECMWF data. Q J Roy Met Soc 126:591–620Google Scholar
  173. Siegismund F, Schrum C (2001) Decadal changes in the wind forcing over the North Sea. Clim Res 18:39–45Google Scholar
  174. Simmons AJ, Willett KM, Jones PD, Thorne PW, Dee DP (2010) Low-frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets. J Geophys Res 115:D01110 doi:10.1029/2009JD012442
  175. Slonosky VC, Jones PD, Davies TD (2000) Variability of the surface atmospheric circulation over Europe, 1774–1995. Int J Climatol 20:1875–1897Google Scholar
  176. Slonosky VC, Jones PD, Davies TD (2001) Atmospheric circulation and surface temperature in Europe from the 18th century to 1995. Int J Climatol 21:63–75Google Scholar
  177. Smits A, Klein Tank AM, Können GP (2005) Trends in storminess over the Netherlands, 1962–2002. Int J Climatol 25:1331–1344Google Scholar
  178. Spangehl T, Cubasch U, Raible CC, Schimanke S, Korper J, Hofer D (2010) Transient climate simulations from the Maunder minimum to present day: role of the stratosphere. J Geophys Res 115:D00110 doi:10.1029/2009JD012358
  179. Stanhill G, Cohen S (2001) Global dimming: A review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural consequences. Agric for Meteorol 107:255–278Google Scholar
  180. Stephenson DB, Pavan V, Bojariu R (2000) Is the North Atlantic Oscillation a random walk? Int J Climatol 20:1–18Google Scholar
  181. Streets DG, Wu Y, Chin M (2006) Two-decadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophys Res Lett 33:L15806 doi:10.1029/2006GL026471
  182. Strong C, Magnusdottir G (2011) Dependence of NAO variability on coupling with sea ice. Clim Dyn 36:1681–1689Google Scholar
  183. Stucki P, Brönnimann S, Martius O, Welker C, Imhof M, von Wattenwyl N, Philipp N (2014) A catalogue of high-impact windstorms in Switzerland since 1859. Nat Haz Earth Syst Sci Discuss 2:3821–3862Google Scholar
  184. Sutton RT, Hodson DLR (2005) Atlantic Ocean forcing of North American and European summer climate. Science 309:115–118Google Scholar
  185. Sweeney J (2000) A three-century storm climatology for Dublin 1715–2000. Irish Geogr 33:1–14Google Scholar
  186. Takahashi T, Sutherland SC, Sweeney C, Poisson A, Metzl N, Tilbrook B, Bates N, Wanninkhof R, Feely RA, Sabine C (2002) Global sea-air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep Sea Res II 49:1601–1622Google Scholar
  187. Tett SFB, Sherwin TJ, Shravat A, Browne O (2014) How much has the North Atlantic Ocean overturning circulation changed in the last 50 years? J Climate 27:6325–6342Google Scholar
  188. Thompson DWJ, Wallace JM (1998) The Arctic oscillation signature in the wintertime geopotential height and temperature fields. Geophys Res Lett 25:1297–1300Google Scholar
  189. Thompson DWJ, Wallace JM, Hegerl GC (2000) Annual modes in the extratropical circulation: Part II: Trends. J Climate 13:1018–1036Google Scholar
  190. Trigo IF (2006) Climatology and interannual variability of storm-tracks in the Euro-Atlantic sector: a comparison between ERA-40 and NCEP/NCAR reanalyses. Clim Dyn 26:127–143Google Scholar
  191. Trouet V, Panayotov MP, Ivanova A, Frank D (2012) A pan-European summer teleconnection mode recorded by a new temperature reconstruction from the northeastern Mediterranean (ad 1768–2008). The Holocene 22:887–898Google Scholar
  192. Tyrlis E, Hoskins BJ (2008) Aspects of a Northern Hemisphere atmospheric blocking climatology. J Atmos Sci 65:1638–1652Google Scholar
  193. Ulbrich U, Leckebusch GC, Pinto JG (2009) Extra-tropical cyclones in the present and future climate: a review. Theor Appl Climatol 96:117–131Google Scholar
  194. Uppala SM, Kållberg PW, Simmons AJ, Andrae U, Da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Beljaars ACM, Van De Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf J-F, Morcrette J-J, Rayner NA, Saunders RW, Simon P, Sterl A, KE Trenberth, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J ROY METEOR SOC 131, Issue 612, 2961–3012. J (2005) The ERA-40 re-analysis. Q J Roy Met Soc 131:2961–3012Google Scholar
  195. van den Besselaar EJM, Haylock MR, Klein Tank AMG, van der Schrier G (2011) A European daily high-resolution observational gridded data set of sea level pressure. J Geophys Res 116:D11110 doi:10.1029/2010JD015468
  196. van den Besselaar EJM, Klein Tank AMG, Buishand TA (2013) Trends in European precipitation extremes over 1951–2010. Int J Climatol 33:2682–2689Google Scholar
  197. van der Schrier G, van den Besselaar, EJM, Klein Tank AMG,Verver G (2013) Monitoring European averaged temperature based on the E-OBS gridded dataset. J Geophys Res 118:5120–5135Google Scholar
  198. van Oldenborgh GJ, Drijfhout SS, van Ulden A, Haarsma R, Sterl A, Severijns C, Hazeleger W, Dijkstra H (2009) Western Europe is warming much faster than expected. Clim Past 5:1–12Google Scholar
  199. Vautard RJ, Yiou P, Thépaut J-N, Ciais P (2010) Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat Geosci 3:756–761Google Scholar
  200. von Storch H, Reichardt H (1997) A scenario of storm surge statistics for the German Bight at the expected time of doubled atmospheric carbon dioxide concentration. J Climate 10:2653–2662Google Scholar
  201. von Storch H, Zwiers FW (1999) Statistical analysis in climate research. Cambridge University PressGoogle Scholar
  202. von Storch H, Feser F, Haeseler S, Lefebvre C, Stendel M (2014) A violent mid-latitude storm in Northern Germany and Denmark, 28 October 2013. Special Supplement to the Bull Am Met Soc 95:76–78Google Scholar
  203. Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon Wea Rev 109:784–812Google Scholar
  204. Wang XL, Swail VR, Zwiers FW (2006) Climatology and changes of extratropical cyclone activity: comparison of ERA40 with NCEP-NCAR reanalysis for 1958-2001. J Climate 19:3145–3166Google Scholar
  205. Wang XL, Zwiers F, Swail V, Feng Y (2009a) Trends and variability of storminess in the Northeast Atlantic region, 1874–2007. Clim Dyn 33:1179–1195Google Scholar
  206. Wang XL, Swail V, Zwiers F, Zhang X, Feng Y (2009b) Detection of external influence on trends of atmospheric storminess and northern oceans wave heights. Clim Dyn 32:189–203Google Scholar
  207. Wang XL, Wan H, Zwiers FW, Swail VR, Compo GP, Allan RJ, Vose RS, Jourdain S, Yin X (2011) Trends and low-frequency variability of storminess over western Europe, 1878–2007. Clim Dyn 37:2355–2371Google Scholar
  208. Wang XL, Feng Y, Compo GP, Zwiers FW, Allan RJ, Swail VR, Sardeshmukh PD (2014) Is the storminess in the Twentieth Century Reanalysis really inconsistent with observations? A reply to the comment by Krueger at al. (2013b). Clim Dyn 42:1113–1125Google Scholar
  209. Wanner H, Brönnimann S, Casty C, Gyalistras D, Luterbacher J, Schmutz C, Stephenson DB, Xoplaki E (2001) North Atlantic Oscillation - concepts and studies. Surv Geophys 22:321–381Google Scholar
  210. Warren SG, Hahn CJ, London J, Chervin RM, Jenne RL (1986) Global distribution of total cloud cover and cloud type amounts over land. NCAR Tech. Note TN-273 + STRGoogle Scholar
  211. Warren SG, Hahn CJ, London J, Chervin RM, Jenne RL (1988) Global distribution of total cloud cover and cloud type amounts over the ocean. NCAR Tech. Note TN-317 + STRGoogle Scholar
  212. Warren SG, Eastman RM, Hahn CJ (2006) A survey of changes in cloud cover and cloud types over land from surface observations, 1971-96. J Climate 20:717–738Google Scholar
  213. WASA Group (1998) Changing waves and storms in the Northeast Atlantic? Bull Am Met Soc 79:741–760Google Scholar
  214. Weisse R, Plüß A (2006) Storm-related sea level variations along the North Sea coast as simulated by a high-resolution model 1958–2002. Ocean Dyn 56:16–25Google Scholar
  215. Weisse R, von Storch H, Feser F (2005) Northeast Atlantic and North Sea storminess as simulated by a regional climate model during 1958–2001 and comparison with observations. J Climate 18:465–479Google Scholar
  216. Wheeler D, Garcia-Herrera R, Wilkinson CW, Ward C (2009) Atmospheric circulation and storminess derived from Royal Navy logbooks: 1685 to 1750. Clim Change 110:257–280Google Scholar
  217. Wild M (2009) Global dimming and brightening: A review. J Geophys Res 114:D00D16 doi:10.1029/2008JD011470
  218. Wild M, Gilgen H, Roesch A, Ohmura A, Long CN, Dutton EG, Forgan B, Kallis A, Russak V, Tsvetkov A (2005) From dimming to brightening: Decadal changes in solar radiation at Earth’s surface. Science 308:847–850Google Scholar
  219. WMO (2010) WMO Guide to Meteorological Instruments and Methods of Observation, World Meteorological Organisation Publication No. 8, WMO, GenevaGoogle Scholar
  220. Woodruff SD, Worley SJ, Lubker SJ, Ji Z, Freeman JE, Berry DI, Brohan P, Kent EC, Reynolds RW, Smith SR, Wilkinson C (2011) ICOADS Release 2.5 and Data Characteristics. Int J Climatol 31:951–967Google Scholar
  221. Woodworth PL, Blackman DL (2002) Changes in extreme high waters at Liverpool since 1768. Int J Climatol 22:697–714Google Scholar
  222. Woollings TJ, Blackburn M (2012) The North Atlantic jet stream under climate change and its relation to the NAO and EA patterns. J Climate 25:886–902Google Scholar
  223. Woollings TJ, Hoskins BJ, Blackburn M, Berrisford P (2008) A New Rossby wave-breaking interpretation of the North Atlantic oscillation. J Atmos Sci 65:609–626Google Scholar
  224. Woollings TJ, Hannachi A, Hoskins B, Turner A (2010) A regime view of the North Atlantic Oscillation and its response anthropogenic forcing. J Climate 23:1291–1307Google Scholar
  225. Xia L, Zahn M, Hodges KI, Feser F (2012) A comparison of two identification and tracking methods for polar lows. Tellus A 64:17196Google Scholar
  226. Zhang R (2008) Coherent surface-subsurface fingerprint of the Atlantic meridional overturning circulation. Geophys Res Lett 35:L20705 doi:10.1029/2008GL035463
  227. Zolina O, Simmer C, Belyaev K, Kapala A, Gulev S (2009) Improving estimates of heavy and extreme precipitation using daily records from European rain gauges. J Hydromet 10:701–716Google Scholar

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Authors and Affiliations

  • Martin Stendel
    • 1
  • Else van den Besselaar
    • 2
  • Abdel Hannachi
    • 3
  • Elizabeth C. Kent
    • 4
  • Christiana Lefebvre
    • 5
  • Frederik Schenk
    • 6
  • Gerard van der Schrier
    • 2
  • Tim Woollings
    • 7
  1. 1.Department for Arctic and ClimateDanish Meteorological Institute (DMI)CopenhagenDenmark
  2. 2.Royal Netherlands Meteorological Institute (KNMI)De BiltThe Netherlands
  3. 3.Department of MeteorologyStockholm UniversityStockholmSweden
  4. 4.National Oceanography CentreSouthamptonUK
  5. 5.German Meteorological Service (DWD)HamburgGermany
  6. 6.Bolin Centre for Climate ResearchUniversity of StockholmStockholmSweden
  7. 7.Atmospheric Physics, Clarendon LaboratoryUniversity of OxfordOxfordUK

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