The Long-Term Course of the Annual Total Sunshine Duration in Europe and Changes in the Phases of the Thermohaline Circulation in the North Atlantic (1901–2018)

The long-term course of the annual total sunshine duration (SD) in Europe shows variability in the occurrence of successive long-term brightening and dimming (e.g. Brázdil et al. 1994, Sanchez-Lorenzo et al. 2008, 2009, Manara et al. 2015). Until now, long-term variability in the course of the SD over Europe was most often explained as a result of changes in the concentration of aerosols of natural and/or anthropogenic origin (e.g. Liepert 2002, Norris, Wild 2007, Ruckstuhl, Norris 2009, Vetter, Wechsung 2015), and less often as the influence of atmospheric circulation (e.g. Sanchez-Lorenzo et al. 2008, Stjern et al. 2009).

The relatively close relationship between changes in the annual SD in Central Europe and long-term changes in mid-tropospheric circulation was pointed out by Marsz et al. (2022). According to their research, changes in the structure of the mid-tropospheric macrocirculation processes that create circulation epochs (Girs, Kondratovich 1978) are controlled by changes in the intensity of the surface component of the thermohaline circulation in the North Atlantic (North Atlantic Thermohaline Circulation [NA THC]; Meridional Overturning Circulation [AMOC]). Therefore, the annual total SD in Central Europe change in line with the changes in NA THC phases. Previous studies have shown that in the years 1951–2018, positive NA THC phases corresponded to the periods of increased SD, while negative phases corresponded to the periods of decreased SD (Marsz et al. 2022).

To determine the nature of the variability in the SD and to explain its causes, the analysis of long-term components seems to be the most important. The aim of this study is to identify common features of the long-term course of the annual total SD at the European stations with the longest measurement series and to explain the relationships between the variability in the intensity of thermohaline circulation in the North Atlantic (NA THC) over a period of over 100 years (1901–2018) and SD. This issue was described for a shorter period (1951–2015) in the work of Marsz et al. (2022), and this article attempts to answer the following questions:

In view of the large diversity in the date of the beginning of the measurements of SD at individual stations and the number of measurement breaks, it was assumed that the analysed time series must cover a uniform period from 1901 to 2018, and the number of shortages in measurement series during this period cannot exceed 1 annual value. In this analysis, it was decided not to consider heliographic sequences from high mountain stations because of the specific SD forming over them, especially in winter months (Urban et al. 2018, Bartoszek et al. 2021). As a result of a detailed survey of SD measurement data at European stations, 13 stations were found (Table 1). The few missing annual values were calculated by completing monthly values (De Bilt) or by calculating the annual value as an average of the year preceding and the year following the year with no data (Potsdam, Zagreb).

The data used to analyse the relationship between the spatial distribution of annual SST values in the North Atlantic and the annual SD in Europe and to calculate the value of the DG_3L index, characterising the NA THC intensity, is the set of monthly SST values derived from the ERSST v.3b set (NOAA Extended Reconstructed Sea Surface Temperature; Smith et al. 2008). This set is based on SST measurements in situ, performed from research vessels and buoys compiled in the ICOADS R2.5 database (The International Comprehensive Ocean-Atmosphere Data Set). It is a global set with a spatial resolution of 2° × 2° and a monthly temporal resolution. The monthly SST averages in this set are considered reliable since 1880 and highly reliable since 1950 (Smith et al. 2008). The data were downloaded from NOAA/OAR/ESRL PSL, Boulder, CO, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v3.html.

The index marked with the acronym DG_3L (the Delta of Gulf Stream, 3 years) characterises the relative amount of heat transferred with the transport of water by NA THC from the tropical North Atlantic region to the north. A detailed description of the physical foundations of this index and its structure is explained in the works of Marsz (2015) and Wrzesiński et al. (2019). The value of the DG_3L index is a standardised anomaly, which indicates the extent to which the flux of the heat transported by THC in the North Atlantic is greater or less than the 1901–2000 average.

In the course of the DG_3L index, next to the strong inter-annual variability (Fig. 1), very strong long-term (multi-decadal) variability is marked, creating clear periods in which the value of this index is, on average, lower or higher than zero. This corresponds to the periods of weakening and strengthening of the surface intensity of the NA THC component, in which the northward flow of heat is below the average for the century and above this average. This makes it possible to separate out long-term negative and positive phases in the course of NA THC. The boundaries of the phases are determined by the transition of the DG_3L index value through zero.

Fig. 1.

It should be noted that the course of the DG_3L index in the last positive phase of NA THC (1989–2018) differs significantly from the course in the earlier phases; from the beginning of this phase, its values never drop below zero, and in its course, there is a positive trend, strongly increasing since 2011. In the period under consideration, 1901–2018, there were four distinct phases in the course of NA THC, differing in length, mean values of the index and their variability ranges (Table 2), with the first two-part negative NA THC phase beginning earlier than in 1901 (in 1873) and the last warm phase (1989–…) continuing until today.

Additionally, the work uses time series of the geopotential height of the isobaric surface of 500 hPa (h500) and similar series of pressure at sea level (SLP) with monthly resolution from selected grids. The annual values of h500 and SLP for individual grids were calculated as simple arithmetic means of monthly values in a given calendar year. Both types of data come from National Centers for Environmental / National Center for Atmospheric Research (NCEP/NCAR) Reanalysis (Kalnay et al. 1996) and were downloaded from NOAA/OAR/ESRL PSL, Boulder, CO, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html. The period covered by these data is shorter (1949–2018) than the basic analysis period in this work.

The time series of the annual total SD from the years 1901–2018 from 13 stations is given in Table 1. From the values of all series, the area average series of the annual total SD was calculated as arithmetic means. This series is marked as SD_13S (Sunshine Duration, 13 stations).

The courses of all variables (series of the SD at individual stations), except for one case (Copenhagen & Zagreb), are significantly correlated with each other, the vast majority of which variables are highly significant (p <0.001). This justifies analysing the set using the factor analysis method. It makes it possible to explain the total stock of variance contained in the set of the annual total SD at the analysed stations and, as a result of further analyses, to explain the activity of which factors this variance is related to. For the factor analysis, raw series of the annual total SD at individual stations, counted in hours, were adopted, without transforming them into anomalies, without standardisation, and without smoothing them using any filters.

The results of the factor analysis revealed the existence in the set of three principal components (PC) of eigenvalues greater than 1.0, which are statistically significant according to the Kaiser criterion. Similarly, the scree test (Hill, Lewicki 2007) indicated that the first three factors (PC) sufficiently explain the variance of the analysed set of variables. The selected three factors together explain 73.59% of the total variance of the set, of which:

The analysis of the relationships between the time series of factor values of each PC (eigenvectors) and the series of SD at individual stations made it possible to explain the physical meaning of individual distinguished PC.

The SD at all stations shows highly significant (p ≪0.000) positive correlations with 1 eigenvector (PC1). This vector presents standardised anomalies of the average annual SD from the 13 analysed stations in individual years. The correlation coefficient (r) between PC1 and the course of SD_13S is 0.999. The relationship is as follows:

SD13S=1696.9(±0.6)+124.5(±0.6)×PC1. $${\rm{S}}{{\rm{D}}_{13{\rm{S}}}} = 1696.9( \pm 0.6) + 124.5( \pm 0.6) \times {\rm{PC}}1.$$

This almost completely (adj.R² = 0.997) explains the variability in SD_13S in the area under consideration. The variability included in the courses of SD_13S and PC1 is identical, and the two courses only differ in the scaling of the y-axis (Fig. 2).

Fig. 2.

This allows us to interpret that PC1, explaining more than half of the total variance of the SD set at all analysed stations, presents a common variability occurring in their series. Thus, the same percentage of the total variance (53.78%) of the annual SD must be represented by area-average (SD_13S).

The results of the PC analysis allow for the conclusion that in the considered set of European stations with very different locations, more than half of the common variability occurs in their course of SD. The second and third eigenvectors, explaining about 20% of the variance, show the features of regional differentiation in the SD in the area of the considered part of Europe.

For the analysis of the relationships between the variability in the area-average annual SD in Europe (SD_13S) and the variability in the NA THC intensity (DG_3L), standard statistical procedures were used, mainly linear correlation analysis and regression analysis. The statistical significance of certain relationships was tested by means of tests appropriate for a given procedure (tests of differences between averages, t and F tests, etc.).

The analysis of relationships between SD_13S with SST in the North Atlantic was performed, correlating the annual SST series in nodal points (grids) with a spatial resolution of 10° x 10°λ counted every 10° (70°N, 10°E; 70°N, 000°; 60°N, 000°; 60°N, 10°W;…). Between the values of the correlation coefficients at nodal points, an interpolation using the ordinary kriging method was carried out, resulting in a spatial distribution (map) of isocorrelates, spanning on the surface of the North Atlantic between 20°N and 70°N and between 80°W and 10°E.

The presented results of the analyses clearly show that there are relationships between the thermal condition of the North Atlantic and the average annual total SD over Europe, which can be consistently traced over 118 years (1901–2018). This makes it possible to conclude that these relationships are stable, and their existence recorded in the years 1951–2018 (Marsz et al. 2022) was not the only case of their occurrence.

In periods when the NA THC phase is positive and SST increases (the state of the warm North Atlantic in the western part of tropical and subtropical waters and in the temperate and subpolar zones in its NE part), the SD over Europe becomes higher than average, that is a period of brightening occurs. In periods when the NA THC phase is negative and SST decreases (the state of the cool North Atlantic in the same waters), the SD over Europe drops below its average values, which corresponds to the periods of dimming. Long-term changes in SST in tropical and subtropical waters in the western part of the North Atlantic, in the vicinity of 30–40°N, 60–50°W, have the strongest impact on changes in the SD in Europe.

As a result, the following question arises: What is the mechanism of the teleconnection-like relationship between the variable NA THC intensity and the associated multi-decadal variability in SST (Fig. 7) and SD over Europe?

In the work of Marsz et al. (2022), it has been shown that the influence of changes in THC NA intensity on the SD is realised by controlling the mid-tropospheric circulation (level 500 hPa) in the Atlantic-Eurasian circulation sector. A special role in controlling the variability in the mid-tropospheric circulation is played by the variability in the meridional gradients of the ocean surface temperature, which is strongly dependent on the spatial distribution of heat resources in the waters and, therefore, to a large extent, on the spatial distribution of the SST at low latitudes.

Depending on whether the heat resources in the waters of the tropical and subtropical zones are larger or smaller than the average, and thus on the increases or decreases in the meridional gradients of the SST, changes occur in the wavenumber of long waves travelling in this circulation sector and in the average position of the upper ridges and upper troughs over the eastern North Atlantic and Eurasia, to approximately 100–130°E.

In periods when heat resources in tropical and subtropical waters are greater than the average, the frequency of long waves with wavenumber 4, which are an equivalent of macrotype W according to the Wangengejm–Girs classification (Wangengejm 1952, Girs 1964), increases in this circulation sector. What is characteristic for this macrotype is the occurrence of an extensive upper trough over the western and central North Atlantic with its axis at ~40–35° W, and over the eastern North Atlantic and Western Europe of a stretched upper ridge of a not very large amplitude, the axis of which is located along a longitude of ~5–10°E.

During periods when ocean heat transport to the north weakens and the ocean heat resources are lower than average, the frequency of long waves with wavenumber 5 increases. The equivalent of waves of this length are macrotypes E and C according to the Wangengejm–Girs classification. In the average long-term course, the frequency of macrotype E definitely dominates over the frequency of macrotype C (~2:1). With the decrease in the annual frequency of macrotype W, this leads to a significant increase in the frequency of macrotype E in such periods. With the occurrence of macrotype E over the eastern part of the North Atlantic and Western Europe, there is a strongly extended upper trough reaching the southern shores of the Mediterranean Sea with an axis located at a longitude of ~5–10°E, and an equally strongly marked upper ridge over Eastern Europe (with an axis at ~40–50°E), reaching latitudes of 75–80°N.

Depending on the changes in the frequency of macrotypes W and E in relation to their long-term average frequency, the SD over European stations changes; along with the increase in the frequency of macrotype W, the frequency of macrotype E must decrease, and the SD over Europe increases.

The annual total SD over a given area are strongly related to changes in the average annual geopotential height over that area. The reason for the occurrence of such relationships is relatively simple, but their explanation should be related to the processes of the synoptic scale. The occurrence over a given area of the upper trough or upper ridge is recorded as changes in height h500. The h500 area above the upper ridge lies higher than the upper trough, and thus, the average annual height of the geopotential above a given area depends on the frequency of passage of the upper ridges and upper troughs over it. The more the cases of upper ridges occurring over a given area during the year, the higher the average annual isobaric area of 500 hPa. Thus, the increase in the annual height h500 over Western Europe signals an increase in the frequency of macrotype W this year and a decrease in the frequency of macrotype E.

To the east of the axis of the upper ridge, in the lower troposphere, lower anticyclonic systems (Fortak 1971) are formed, in which anticyclonic types of intra-mass weather with reduced layer cloud cover predominate, especially at the low and the middle level. Weather of this kind is conducive to an increase in the SD. This is particularly important in the warm half-year, when the day is long; the more often anticyclonic weather occurs, the higher the total annual SD becomes.

The increase in the frequency of anticyclonic weather over an area reduces the percentage of the time of the year in which cyclonic weather occurs above it. Amongst cyclonic weathers, the frontal weather, which is characterised by the presence of extensive zones of low and medium layer cloud cover strongly limiting SD, represents a significant percentage.

Thus, with changes in the height of the geopotential above Western and Central Europe and the corresponding changes in the frequency of macrotypes W and E during the year, it is not so much the value of the total cloud cover (N) as the cloud cover structure that must change. With an increased frequency of macrotype W, the frequency of anticyclonic weather increases and the share of stratiform clouds (Altostratus, Nimbostratus, Stratus), typical for frontal weather, in the cloud cover structure decreases, and the share of convective clouds (Cumulus, Cumulonimbus) and possibly high clouds (Cirrus, Cirrocumulus, Cirrostratus) increases.

Such a diagram of changes in the cloud cover structure, showing connections with changes in the NA THC phases and circulation epochs, is confirmed by the results of research on cloud cover changes carried out in Poland. Analysing the course of the cloud cover in Krakow in the years 1906–2000, Matuszko and Węglarczyk (2018) found a decrease in the frequency of Nimbostratus and Stratus clouds in the last four decades of that period (1961–2000). Żmudzka (2007), who found that a decrease in the frequency of low- and medium-level stratiform clouds is accompanied by an increase in the frequency of high-level clouds and also pointed to such changes in the cloud cover structure over Poland in the years 1966–2000. The results of the research by Żmudzka (2007) also indicate quite a clear linking of changes in cloud cover to the boundaries of circulation epochs. Similarly, analysing the trends of changes in cloud types in the period 1951–2000 in Łódź, Wibig (2008) detected the occurrence of negative trends in the number of occurrences of Stratus and Nimbostratus clouds and, in the course of the number of occurrences of Altostratus clouds, the occurrence of a negative sub-trend in the years ~1980–2000. A recent detailed study by Matuszko et al. (2022) of the changes in the cloud cover over Poland, covering the period 1971–2020, indicates a clear increase in the SD during that period and a simultaneous decrease in cloud cover created by Stratus, Nimbostratus, and Altostratus clouds. On an annual scale, this decrease is statistically significant. In the cloud cover structure, the stratiform clouds of the low and medium levels are replaced by vertical clouds (Cumulus, Cumulonimbus), the positive annual trend of which is also statistically significant, and the value of this trend is close to the value of the negative trend of stratiform clouds.

These works cover, in their time interval, the transition from circulation epochs E+C and E to epoch W and, in relation to the NA THC phases, the final period of the positive NA THC phase (1927–1963), the period of the negative NA THC phase (1964–1988), and the last, positive NA THC phase (1989–.…) which has lasted to date. Thus, changes in the nature of cloud cover during the transition from the negative NA THC phase (1964–1988) to the positive NA THC phase, in which a strong positive trend is marked (after 1988; Fig. 1), should show trends with exactly these signs. Since the macrotypes of the mid-tropospheric circulation according to the Wangengejm–Girs classification are nothing other than long waves (Rossby waves) with a specific, characteristic location of the upper ridges and upper troughs, the change in the frequency of macrotypes in individual NA THC phases or circulation epochs entails changes in the SD.

The following examples illustrating the relationships discussed previously do not cover the entire period under consideration (1901–2018), but only the years 1949–2018, as the data about height h500 from the National Centers for Environmental / National Center for Atmospheric Research (NCEP/NCAR) Reanalysis begin with the year 1949. The European stations, the data of which were used to calculate the SD_13S variable, lie approximately between 45 and 55°N and 5°W and 20°E. From the taken values of h500, 3 latitudinal profiles were created for 45°, 50° and 55°N across Europe (from 10°W to 60°E), with a horizontal resolution of 5°λ. The values of the total annual SD from the considered stations (variable SD_13S) were correlated with h500 in each of the points of these profiles (Fig. 8).

Fig. 8.

The distribution of correlation coefficients between h500 and SD_13S, as shown in Figure 8, indicates the presence of strong and highly significant correlations between SD and geopotential height, where the local maxima of the curves are, obviously, located at longitudes corresponding to the location of the set of 13 stations (~5–15°E). However, it should be noted that regardless of latitude, these maxima remain in the same longitude range.

The meridional SST gradient between 30°N and 60°N at 40°W (dSST_[40W]) describes the annual temperature difference between tropical waters and subpolar waters in the middle of the North Atlantic (dSST_[40W] = SST [30°N, 40°W] – SST [60°N, 40°W]). The warmer the waters in the tropics (30°N) than the temperature of the waters in the North Atlantic subpolar zone (60°N), the greater the gradient. It is on the value of this gradient that the meridian temperature gradient in the central troposphere (on average ~45°N) depends, which determines the velocity of the zonal wind and the stability conditions for long waves (Fortak 1971).

The course of the correlation coefficients between dSST_[40W] and h500, as shown in Figure 8, reproduces the waveform in the geopotential area between 10°W and 60°E. The maximum value of the correlation coefficient curve is between 10°E (at 50° and 55°E) and 20°E (at 45°N), which indicates that the axis of the upper ridge is located, on average, on these longitudes. At a latitude of 50°N, the axis of the upper ridge is located at 10°E, while the axis of the upper trough of the same wave is located at 55°E. This indicates that the wavelength is 90°, that is the wavenumber of the presented wave is 4. This allows us to identify the long wave presented in Figure 8, as macrotype W according to the Wangengejm–Girs classification.

A positive correlation coefficient between the meridional SST gradient and h500 indicates that as the value of the meridional SST gradient increases in the middle parts of the North Atlantic, and thus along with the increase in SST in the Atlantic tropics, the frequency of macrotype W increases, and with it, as well as the height of the geopotential at the level of 500 hPa over Europe, with a maximum longitude of 10–20°E. Thus, the annual SD over Europe also increases, and the consistency of the maxima of the strength of SD relationships with h500 and the maxima of the strength of relationships of h500 with dSST_[40W] is not accidental.

A similar analysis of the correlations between h500 on the latitudinal profile across Europe and variables DG_3L and SST_ST (Fig. 9), limited to a latitude of 50°N, also reveals the wave course of the relationship, with a clearly defined maximum of its strength at 30°E. The same, positive sign of the correlation coefficient is preserved, clearly indicating that with the increase in the value of DG_3L and SST_ST, the height of the geopotential increases. The maximum values of the correlation coefficients are slightly higher here than in the case of the h500 correlation with the meridional SST gradient at 40°W, but it seems important to indicate that in the zone between ~5°E and ~45°E (within ~2,860 km), this relationship is highly significant. Thus, as NA THC intensity increases and SST increases in the tropics and subtropics in the western North Atlantic, there will be an increase, albeit to a different extent, in the height of the geopotential and SD over virtually all of Europe.

Fig. 9.

The asymmetry of the slope of the curves with respect to the maximum point can be considered a certain peculiarity of the relationship presented in Figure 9, that is the increasing r values greater than r = 0.4 extend approximately from 5°E to 30°E, while the decreasing from 30°E to 45°E. This shows that with the same changes in SST and DG_3L index, the average increase in height h500 (and SD) over Western Europe and Central Europe (west of 30°E) is greater than that east of 30°E, where height h500 and SD decrease approximately twice as fast.

The presented dependencies explain that the occurrence of long-term variability in the SD over Europe, manifested in the occurrence of successive phases of dimming and brightening, can be explained without resorting to changes in the concentration of volcanic and anthropogenic aerosols in the atmosphere. Over the course of 118 years, the course of changes in the annual SD over Europe shows consistency in time with the changes in the thermal condition of the North Atlantic, which are controlled by the surface component of the thermohaline circulation. This consistency of changes in time of both amounts can be relatively easily explained by outlining the chain of processes successively influencing the course of the next process:

However, the interrelationships in this chain of processes are so strong that the last element of this chain (the effect – changes in SD) shows a significant correlation with the first element of this chain, which is the primary cause that triggers the cycle of changes (changes of NA THC phases).

The NA THC is widely regarded as a factor regulating the course of climatic processes in the vicinity of the North Atlantic (e.g. Dong, Sutton 2003, Sutton, Hodson 2005, 2007, Alexander et al. 2014, Jackson et al. 2015), in the northern hemisphere (e.g. Knight et al. 2005, Semenov et al. 2010, Wyatt et al. 2012, Buckley, Marshall 2015), and quite often also as a factor regulating climate change on a global scale (e.g. Chylek et al. 2014, 2016, Lyu, Yu 2017). In such a situation, the same factor as the Atlantic THC also regulates one of the most important climatic elements for Europe’s heat balance, which is the annual total SD.

However, the ultimate primary cause of changes in the SD over Europe, whether these are changes in NA THC or the long-term changes in the concentration of aerosols in the atmosphere, mentioned at the beginning of this article, is somewhat debatable. The problem is whether the long-term variability in the thermal condition of the North Atlantic is the result of natural variability resulting from the internal dynamics of the climate system, or is the result of changes in the concentration of aerosols in the atmosphere.

A number of climatologists assume that long-term changes in the SST in the North Atlantic, including changes in AMO, occur as a result of changes in the concentration of aerosols in the atmosphere, which themselves or in cooperation with cloud cover changing in step with changes in aerosol concentrations change the inflow of solar radiation to the ocean’s surface (e.g. Mann, Emanuel 2006, Booth et al. 2012, Birkel et al. 2018, Qin et al. 2020). Recently, Mann et al. (2020, 2021), based on the results of modelling, have put forward the thesis that the long-term variability in the thermal condition of the North Atlantic, manifested in the occurrence of AMO, is only the effect of forcing by the variability in the concentrations of volcanic and anthropogenic aerosols, and that the participation of the internal dynamics of the system in the formation of the long-term variability in SST, as such, does not exist. Adopting this point of view, the described changes in the SD would have their primary cause in the changes in the concentration of aerosols, regardless of the share of the ocean and atmospheric circulation in the formation of their variability.

A polemic with the views of Mann and Emanuel (2006) was undertaken by Zhang (2007) who presented arguments clearly pointing to AMOC as the main cause of the long-term variability in the thermal condition of the North Atlantic Ocean. He showed the existence of long-term anti-correlations between SST anomalies in the tropical North Atlantic and water temperature anomalies at a depth of 400 m, as well as a shift in time between the course of the SST anomalies between the tropical and subpolar North Atlantic (Fig. 1A and 1C in the cited work by Zhang). Both of these phenomena are physically impossible to explain by the effect of changes in the amount of radiation reaching the ocean’s surface, and thus regulating changes in SST by changes in the concentrations of aerosols, while clearly indicating the action of the ocean circulation in their formation (AMOC). AMO and AMOC are manifestations of the action of the NA THC, which, according to the commonly accepted opinion, is the result of the natural, internal variability in the system (e.g. Trenberth, Shea 2006, Grossmann, Klotzbach 2009, Knight 2009, Hartmann 2016, Zhang et al. 2019). In view of the appearance of further studies presenting the results of modelling, indicating changes in aerosol concentration as the cause of long-term variability in SST in the North Atlantic, a number of studies have appeared pointing to the lack of justification for such results and the inconsistency of the modelling results with the materials from in situ observations. The most important of them, specifically devoted to the role of aerosols in the formation of the long-term variability in the thermal condition of the North Atlantic, was the work of Zhang et al. (2013), in which a number of pieces of evidence were presented to indicate that there is no basis for formulating this type of hypothesis. In Mann et al. (2020), neither the work of Zhang (2007) nor the work of Zhang et al. (2013) was referred to, ignoring the arguments presented there that indicate the functioning of internal variability and its role in the formation of long-term changes in the thermal condition of the North Atlantic. Also, Stanhill et al. (2014) and Dübal and Vahrenholt (2021) indicated that the role of changes in aerosol concentration in shaping changes in SD is not at all obvious. Similarly, Veretenenko and Ogurtsov (2016) showed that changes in cloud cover created by low clouds in moderate latitudes are closely related to changes in the frequency of cyclones and their frontal cloud zones.

The mechanisms of THC NA functioning are known and indicate that they are the result of the internal variability in the ocean–atmosphere system. SST changes occur not only because of changes in the amount of radiation reaching the ocean’s surface but also due to the meridonal oceanic heat transport.

Adopting this point of view, it is the long-term changes in the thermal condition of the North Atlantic, and not the changes in the concentration of aerosols, that would be the primary cause of the long-term changes in the SD over Europe presented here.