Time series of monthly,
seasonal and annual mean air temperature, precipitation, snow cover duration
and specific runoff of rivers in Estonia are analysed for detecting of trends
and regime shifts during 1951–2015. Trend analysis is realised using the
Mann–Kendall test and regime shifts are detected with the Rodionov test
(sequential
Different methods of trend analysis are usually applied for the detection of
climate changes. For example, using regression analysis climate warming in
the Baltic Sea region including Estonia is estimated to be faster than the
increase in the global mean temperature (Jaagus, 2006; BACC, 2008, 2015).
Annual mean air temperature has increased by 0.11 K decade
The North Atlantic Oscillation (NAO) strongly affects surface temperatures in the Northern Hemisphere with patterns often reported to be similar to the global warming trend (e.g. Hurrell, 1995; Rodwell et al., 1999). Also, several studies show the evidence for ongoing intensification of the global water cycle, including increasing river runoff (Labat et al., 2004; Huntington, 2006). However, it is known that trend analysis, especially of linear trends, has certain limitations. Linear regression analysis disregards the internal variability in the time series. At the same time, those internal alterations provide a lot of important information on climate changes.
In addition to linear trends, there could also be abrupt changes, i.e. jumps or breaks, which divide a time series into parts with different statistical properties, called regime shifts. Initially, the term regime shift was used in marine ecology and was inspired by changes in climate of the North Pacific (Kerr, 1992) and salmon production around 1977 (Mantua et al., 1997; Rodionov, 2004). This means a large, abrupt and persistent change in the structure and function of a (natural) system (Biggs et al., 2009). In oceanography and climatology, a regime shift is characterised by an abrupt transition from one quasi-steady climatic state to another, and its transition period is much shorter than the lengths of the individual periods of each climatic state. The semantics and the essence of regime shifts are described in detail by Overland et al. (2008). There are several concepts of the definition of regimes (Rodionov, 2005; Overland et al., 2008). Here we used only the concept of displacement, i.e. the inspection of time series over relatively short periods where there can be sequential multi-year intervals with mean values in each interval that are statistically different, relative to their within-regime variance.
As there are several definitions and methods to detect regime shifts, there are also numerous regime shifts in climate variables detected by different authors. For example, Swanson and Tsonis (2009) identified climatic regime shifts in global mean temperature in 1910–1920, 1938–1945, 1976–1981 and 2001–2002. A regime shift in the North Atlantic was observed in the 1920s and 1930s (Drinkwater, 2006). However, a number of studies have detected regime shifts in many parameters in late 1980s, especially since 1989. For example, analysing wintertime sea-surface temperatures over the Northern Hemisphere during the 20th century, six regime shifts were detected, one of them being in 1988/1989 (Yasunaka and Hanawa, 2002). An abrupt warming over the Northern Hemisphere in the late 1980s has been examined by many authors (Rodionov and Overland, 2005; Tsunoda et al., 2008; Lo and Hsu, 2010; Kim et al., 2015). Oceanographic, climatological and biological time series from the North Pacific and the Bering Sea revealed regime shifts in 1977, 1989 and 1998 (Overland et al., 2008). The shift of 1989 in that region was also analysed by Hare and Mantua (2000). Regime shifts in the late 1980s have also been detected in the Arctic sea-level pressure (Walsh et al., 1996), intensification of upper air polar vortex (Tanaka et al., 1996) and a decadal-scale atmospheric circulation (Watanabe and Nitta, 1999). This change is explained by a northward expansion of the Hadley cell, a poleward expansion and intensification of the Ferrel cell, and a collapse of the polar cell (Kim et al., 2015). A thorough analysis of the regime shifts in the 1980s and their global impact on the biosphere was conducted by Reid et al. (2016).
There is also much evidence of regime shifts in the North Atlantic and European region in late 1980s. For example, a regime shift on this period was reported from the Mediterranean Sea based on ecological, hydrological and climatic variables (Conversi et al., 2010). In a review article on regime shifts in marine ecosystems, many studies are referred to where the shift since 1989 in the North Atlantic was found (DeYoung et al., 2004). Using long time series of the winter Baltic climate index (WIBIX) based on monthly values of the first principal component of winter anomalies (January–March) of the NAO index, sea-level anomalies at Landsort (Sweden) and maximum Baltic ice cover, Hagen and Feistel (2005) determined the alternation of two climate regimes over the Baltic Sea. The last period of mild winters started in 1988 (Hagen and Feistel, 2005). In marine ecosystems of the Baltic Sea a regime shift was observed in the late 1980s. Here, the phytoplankton biomass increased, the composition of phyto- and zooplankton communities changed conspicuously and the growing season was extended (Alheit et al., 2005; Möllmann et al., 2009; Dippner et al., 2012).
As the Baltic Sea is situated in the transition area of Atlantic marine and Eurasian continental climate systems and influenced by air masses from Arctic to subtropical origin, the climatic conditions here are characterised by high spatio-temporal variations (BACC, 2008; Rutgersson et al., 2014). Thus, the Baltic Sea region poses a great challenge for the detection of trends and regime shifts in the climatic, hydrological and ecological variables.
Location map of Estonia with meteorological and hydrological stations, as well as annual mean specific runoff of studied river basins.
Estonia is located in the central part of the Baltic Sea region, at the
eastern coast of the sea (Fig. 1). As in the whole of northern Europe, air
temperature has increased in Estonia during the period of instrumental
meteorological observations (Jaagus, 1998). The highest warming was detected
during the second half of the 20th century, when statistically
significant trends were detected for annual and spring mean temperature (Jaagus,
2006). Trend values for annual temperature were 0.2–0.3 K decade
Several studies have shown regime shifts in climate variables over the Gulf of Finland and in Estonia. An increase in zonal circulation starting in 1987 was detected in February (Keevallik and Soomere, 2008). A similar shift at the end of the 1980s was found in the time series of cumulative wind stress calculated for December–January using data from Utö (Elken et al., 2014). Dynamics of wave properties of the Baltic Sea were analysed by Soomere and Räämet (2014), who simulated these using the WAM model. These dynamics were related to annual mean components of air flow of the adjusted geostrophic wind. A shift in simultaneity in annual mean wave length between eight stations in the eastern coast of the Baltic Sea was detected starting in 1988 (Soomere et al., 2015). This was related to the observed shifts in the annual mean zonal and meridional components of airflow of the adjusted geostrophic wind. During the period 1981–2010 an upward shift in average airflow speed over the Gulf of Finland area in January was detected in 1988 and a downward return shift in 1994 (Keevallik and Soomere, 2014). Based on the data of two stations (Vilsandi and Tiirikoja, Estonia), an abrupt increase was observed in the zonal component of average air flow from January to March, in air temperature for January and February, and in precipitation for February (Keevallik, 2011). However, the precipitation regime in the eastern Baltic region can be characterised by a very high spatial and temporal variability and thus there are few reliable trends in time series of precipitation (Jaagus et al., 2010, 2016).
Long-term fluctuations in climatic parameters are reflected in the dynamics of river runoff that has relatively short residence time. River runoff in Estonia is characterised by a significant seasonal and interannual variability. It is mostly caused by precipitation variations and snow conditions during the winter season and the melting period in spring. The runoff regime is also affected by hydrographical and hydrogeological characteristics, in which we can find relatively large differences in Estonian catchment areas. Variability and trends in river discharge in the Baltic countries have been analysed by many authors (Järvet, 1998; Reihan et al., 2006, 2007, 2012; Kriaučiuniene et al., 2012). These authors found that increasing trends in river runoff were characteristic only in the winter season during the 20th century. At the same time, runoff fluctuations with a period of ca. 30 years are dominant in the time series for Estonia. Using the water balance model WATBAL, changes in river runoff in Estonia were modelled for the case of continuous climate warming (Jaagus et al., 1998). The results indicated an increase in runoff in winter and decrease in spring, which could lead to drought conditions in the end of spring and beginning of summer.
The objective of this study is a joint analysis of changes, i.e. trends and regime shifts in main climatic (temperature, precipitation, snow cover duration) and hydrologic (river runoff) variables and in indices of large-scale atmospheric circulation to detect coherent changes. The study period 1951–2015 was chosen as it is the period when the highest number of stations with continuous time series is available in Estonia. The main idea and novelty of this study in comparison with the previous ones was to realise an integrated analysis of trends and regime shifts in time series of many inter-related and indicative variables of the climate system starting from the indices of the large-scale atmospheric circulation and ending with specific runoff of rivers.
Annual, seasonal and monthly mean air temperature and precipitation at 12 stations in Estonia (Fig. 1) for 1951–2015 have been used for the analysis. No significant relocations of the measuring sites have taken place at these stations, with the exception of two stations. In 1980, the Tallinn station was moved from Ülemiste (airport) to Harku, ca. 12 km to the west. Thereby, the local placement on the limestone plateau several kilometres from the sea coast remained nearly the same. In 1997 the Tartu station was moved from Ülenurme to Tõravere, 15 km to the south-west. Analysis of time series did not reveal inhomogeneities due to these displacements (Sits and Post, 2006; Keevallik and Vint, 2012). Adding of the wetting correction to the measured precipitation at all stations since 1966 was the only important source of inhomogeneity that is not eliminated from the time series. The wetting correction takes about 10 % of monthly precipitation, which is difficult to detect. In addition, time series of snow cover duration at five stations are analysed to better describe winter weather conditions.
For generalisation of the results, time series of spatial mean temperature and precipitation were also analysed. Mean temperature for Estonia was calculated by averaging the values of the 12 stations. Spatial mean precipitation for Estonia was calculated by averaging the gridded values, which were interpolated using all available precipitation data (Jaagus, 1992).
The list of hydrological stations used in this study,
their catchment areas and characteristics of annual mean specific runoff
(L s
We used specific runoff of rivers (in litres per second per square kilometre) because it allows comparing observed data from river basins of different size and assessing results of statistical analyses. Monthly, seasonal and annual specific runoff values in the period 1951–2015 have been used from all 21 stations with various catchment areas, hydrological and hydrogeological conditions where the long series of measurements is available in Estonia (Fig. 1). Seasons were defined by 3 months as usual in climatological studies: spring (MAM), summer (JJA), autumn (SON) and winter (DJF). Hydrological parameters of these river basins are presented in Table 1.
Large-scale atmospheric circulation is an important factor influencing on local weather conditions. In this study, changes in circulation are analysed using annual, seasonal and monthly values of the Arctic Oscillation (AO) index, several North Atlantic Oscillation indices and teleconnection indices provided by the NOAA Climate Prediction Center. The AO index reflects the intensity of the circumpolar air vortex (Thompson and Wallace, 1998). North Atlantic Oscillation (NAO) describes the intensity of westerlies over the Atlantic/European sector (Hurrell, 1995). We have used several NAO indices: NAOG using sea-level pressure (SLP) data from Gibraltar and Stykkishólmur/Reykjavík (Jones et al., 1997), NAOL using data from Lisbon and Reykjavík/Stykkishólmur (Hurrell, 1995) and NAOPC, which are calculated using principal component analysis (PCA) of SLP fields (Hurrell and Deser, 2009).
Teleconnection patterns have been defined as a result of PCA of SLP fields
over the Northern Hemisphere (Barnston and Livezey, 1987). Here we use only
these teleconnection patterns, which represent atmospheric circulation over
the Baltic Sea region. Monthly values are presented and updated by the NOAA
Climate Prediction Centre
(
The Mann–Kendall test is used to detect trends in time series (Mann, 1945;
Kendall, 1975). The reason for selecting the test is that the consistency
with normal (Gaussian) distribution in the case of climatic and hydrological
parameters is usually not fulfilled. The trend slope is calculated using
Sen's method (Sen, 1968). Trends are expressed in changes per decade. They
are considered statistically significant at the
Correlation coefficients are used for expressing the strength and direction
of statistical relationship between time series of different climatic and
hydrological variables. As the length of the time series in this case is
65 years,
then the critical value of statistical significance at the
The STARS (sequential
The output of the test is a year when a regime shift occurs (or years of shifts if the time series contains multiple statistically significant shifts), i.e. the first year of the new level. The shift is expressed in RSI, which represents a relative magnitude of the shift. The higher the value of RSI the more abrupt and statistically reliable the shift is. However, RSI does not present the shift in terms, for example, of centigrade or millimetres of precipitation. Thus, one output of the test is the difference between the weighted means, which is used as a shift value for describing the magnitude of the shift.
One of the peculiarities of the STARS method is that it tends to find regime shifts in the end of time series. This characteristic of the method has been considered as an advantage that allows processing “data in real time, signalling the emergence of a potential shift and measuring changing confidence in the evidence for a shift as new data arrive” (Rodionov and Overland, 2005). Also, from a formal point of view, those RSI shifts are statistically reliable. However, it is understandable that in reality those shifts in the last years are meaningless and do not represent any substantial changes. Thus, we have ignored shifts that occurred in last 5 years of the analysed time series.
Trend values of monthly, annual and seasonal indices of
large-scale atmospheric circulation. Statistically significant trends on
As large-scale atmospheric circulation has close relationships with air temperature and precipitation in Estonia (Jaagus, 2006), circulation indices were analysed as the main factors inducing climate variability. Statistically significant trends in the indices of large-scale atmospheric circulation are comparatively rare (Table 2). The most remarkable feature is the significant increase in different NAO indices in winter. It means that negative NAO indices have become to occur less and positive anomalies more during the study period indicating the tendency of intensification of westerly circulation over the Atlantic/European sector. Increasing trends in the NAO indices were observed in January, February and March, while decreasing trends were found in June and October. The AO index has a statistically significant increasing trend only in March, November, spring and annually. It is interesting that the EA index can be characterised by an increasing tendency throughout a year. The EAWR, SCA and POL teleconnection indices have some, mostly decreasing, trends in single months (Table 2).
Years of statistically significant upward (
The AO index and NAO indices for winter demonstrate a statistically significant positive regime shift since the winter of 1988/1989 (Table 3), reflecting an abrupt intensification of the westerly circulation. The upward shift year 1989 was detected for the following indices: AO – February, winter and annual time series; NAOL – February; NAOG – winter; NAOPC – February, March, winter, spring and annually; and NAOT – February, winter. In the case of the NAOT the shift year was 1988. Return shifts for the NAO indices appeared mostly in February since 2004. A negative regime shift was found for the summer NAO index starting in 2007. The EA teleconnection index has significant upward regime shifts in many months and in all seasons except autumn (Table 3) corresponding to their increasing trends (Table 2) but at different years: from 1970 to 2009. Other teleconnection indices had few regime shifts (not shown in Table 3).
Trend values, shift years and shift values of spatially
averaged monthly, annual and seasonal mean air temperature and precipitation
in Estonia during 1951–2015. Statistically significant trends on
Results of the Mann–Kendall test show a large warming in Estonia during the
period 1951–2015. Annual mean air temperature has significantly increased at
the studied stations by 2.0–2.5 K for the whole period or by 0.3–0.4 K decade
Upward regime shifts are typical for air temperature (Table 4). Winter mean temperature has abruptly increased since 1988/1989 by 1.9–2.7 K at different stations. The highest shift value was recorded in Tartu. Generally, the trends and regime shifts at the coastal stations in the western Estonia are weaker than in the inland stations of eastern Estonia. For example, the changes by trend in annual, winter and spring temperature during the 65 years were in coastal stations (Vilsandi: 2.2, 2.4 and 3.0; Ristna: 2.0, 2.1 and 2.5; Tallinn: 1.9, 2.4 and 3.2, respectively) and in inland stations of eastern Estonia (Võru: 2.4, 2.4 and 3.4; Tartu: 2.5, 2.9 and 3.7; Tiirikoja: 2.3, 3.1 and 3.7 K, respectively). The values of regime shift in winter mean temperature since 1989 were the following: Vilsandi: 1.9; Ristna: 1.9; Tallinn: 2.2; Võru: 2.4; Tartu: 2.7; and Tiirikoja 2.7 K.
A similar upward shift of even higher magnitude was found for monthly mean temperature in February, while some stations in southern Estonia had a downward shift starting in 2005. Stations in the continental part of Estonia had a downward shift of monthly mean temperature in January by 2.3–2.7 K starting in 1966 following the upward shift by 4.1–4.7 K from 1988. Statistically significant regime shifts in March and April temperature were found in two different years. In March, an upward shift by 3.2–3.9 K was revealed starting 1966 in the eastern half of the country and by 1.9–2.3 K from 1989 in the coastal region. In April, the shift to higher temperature by 1.5–2.1 K occurred in 1989 in the southern regions and the western coast, while in northern Estonia it happened starting in 1999 by 1.7–1.9 K. The time series of spatial mean temperature in Estonia has a shift in March starting in 1966 and in April from 1989 (Table 4).
Regime shifts in air temperature for the warm half-year were detected more than 10 years later than for the cold half-year. Mean temperature in July had a jump by 2.0–2.4 K starting in 2001 (1999 in the island stations Vilsandi and Kihnu), in August the shift began in 2002 (1996 in Vilsandi and Kihnu) by 1.3–1.7 K, and in September it began in 2004 by 1.5–1.7 K. Mean temperature in spring has upward regime shifts in different years – starting in 1966, 1982 and 1989. Summer temperature shifted beginning in 1999, 2001 or 2010 at different stations and autumn temperature shifted from 2005 onwards. Annual mean temperature had a statistically significant increase by 1.2–1.5 K in 1988 (or 1989).
Interannual fluctuations in time series of air temperature are statistically related to the variations in the indices of large-scale atmospheric circulation. The indices describing the intensity of westerly circulation (AO and NAO indices) are highly correlated with winter temperature. The correlation coefficient is usually 0.6–0.7 or even higher. For example, correlations between winter mean temperature in Tartu and Ristna with the AO index in winter were 0.710 and 0.755, and with NAOPC being 0.765 and 0.800, respectively. Higher correlation was clearly seen in the coastal zone.
The warming in winter is closely related to the decrease in snow cover
duration. The trend values were 3–4 days decade
Trend values, years of regime shifts and corresponding
shift values of snow cover duration. Statistically significant trends at
Time series of snow cover duration at all studied stations in the continental part of Estonia have a statistically significant negative shift by 16–20 days since the winter of 1988/89. An exception was detected in Vilsandi, which is located on the westernmost island under the direct influence of the Baltic Sea. A downward shift in snow cover duration by 31.9 days was observed in Vilsandi starting in 1988/1989, followed by an upward shift by 32.4 days detected beginning in 2009/2010.
Correlation between winter mean temperature and snow cover duration is lower
(
Precipitation has even higher spatial and temporal variability than snow
cover duration. Generally, correlations between precipitation and other
climatic parameters are comparatively weaker. Trend values for stations are
quite different. There are no months or seasons when a statistically
significant trend was observed in all stations. Increasing trends in monthly
precipitation up to 5 mm decade
Due to the extremely high temporal variability, regime shifts in time series of precipitation are not so clearly expressed as in the case of temperature (Table 4). Several significant shifts up and down have been detected, which express, primarily, multi-annual fluctuations of precipitation and less abrupt climatic changes. Upward shifts by more than even 100 mm for annual precipitation were detected in many stations during 1977–1986. These shifts reflect the start of the period of higher precipitation starting in 1977. Similar shifts for winter precipitation were observed later, starting with 1980/1981 and 1981/1982 in three stations and ending with 1988/1989 and 1989/1990 in 4 of 12 stations. Positive regime shifts for monthly precipitation were revealed in all stations in the 1980s during the cold season from November to March. In many cases they were followed by return shifts in 2003 (February) and 1996 (March). Downward shifts in September precipitation were detected in many stations mostly beginning in 1998. Significant upward shifts since 1966 in spring (seven stations) and autumn precipitation (two stations) can be explained by the added wetting correction to every measured precipitation event.
Trend values, years of regime shifts and corresponding
shift values of annual mean specific runoff. Statistically significant
trends on
The trend analysis of specific runoff of rivers demonstrated mostly an
increase in annual values, while a statistically significant trend was detected
only in five stations (Table 6). At the same time, increasing trends were
revealed at all stations (except Vasknarva, which is naturally regulated by
Lake Peipsi) in the winter season and in the first 3 months of the year (Table 6).
Trend values of specific runoff at single stations were quite different but
mostly between 0.4 and 0.9 L s
During the other months few significant trends were detected. In southern
Estonia (Õhne, Väike-Emajõgi and Piigaste rivers) runoff also increased in June and in Võhandu river (Räpina station) in
July. They are related to the increase in precipitation in June. Mostly
negative changes are typical for spring. In April, which is the most common
month for spring flood to occur, the specific runoff has decreased by up to
1 L s
Relationship between climatic variables and runoff in Estonia is well
exposed, particularly in winter. Higher temperature, precipitation and lower
snow cover duration in winter are related to higher winter runoff.
Correlations between temperature and runoff in January, February and March
are up to 0.7; those between precipitation and runoff are up to 0.6; and those between snow
cover duration and runoff are up to
Time series of annual mean runoff describe long-term alternation of wet and dry periods in Estonia (Table 6). A downward shift in 1963 or 1964 followed by an upward shift starting in 1978 or 1981 mark the dry period in the middle of the whole study period. Some stations and some months show decreasing runoff again in the beginning of the 21st century.
Trend values of mean monthly, annual and seasonal specific
runoff averaged over the 21 stations in Estonia. Statistically significant
trends at the
Regime shifts in winter temperature in Estonia have caused shifts in specific runoff of rivers (Table 7). Warmer winters are naturally related to higher runoff in winter and earlier maximum in spring after the snowmelt that is typical for Estonia. Usually it has been observed in April but during the last decades it has shifted to March. As a consequence, runoff in March has a positive shift, which causes the negative shift in April. In 14 of 21 stations, upward regime shifts in runoff appeared starting in the winters of 1988/1989 or 1987/1988 (Fig. 2). The same shift was also present in January, February and March. The consistent upward regime shifts in specific runoff appeared in 19 of 21 stations beginning March 1989, which has been the most prominent change despite the high variability in hydrological and hydrogeological conditions in watersheds analysed in this study. The increase in runoff in March is closely related to the decrease in April starting in 1989 with the return shift in the end of the time series beginning 2009 or 2010 (Fig. 3). During the other months there were practically no significant regime shifts in specific runoff.
Regime shifts in winter (DJF) specific runoff of rivers in 14 stations where a statistically significant shift was detected in 1987/1988 or 1988/1989.
It is rather natural that the elements of the climate system – air temperature, precipitation, snow cover, river runoff and indices of the large-scale atmospheric circulation – have close relationships. Changes in some parameters will induce corresponding changes in other parameters. We analysed correlations between them, similarities in their trends and regime shifts in Estonia. This kind of local-scale integrated analysis of trends and regime shifts in time series of many inter-related and indicative variables of the climate system starting is innovative, enabling detection of general regularities of a long-term environmental change.
Time series of specific runoff (L s
The character of the large-scale atmospheric circulation is the main factor causing a very high interannual variability in weather conditions in the region. The highest role of circulation can be observed during the cold season when the role of solar radiation is negligible due to the high latitude of the study region. The Atlantic Ocean is virtually the only source of warm air for northern Europe in winter. Winter weather conditions in Estonia are largely determined by the intensity of westerly circulation over the Atlantic/European sector causing the advection of comparatively warm and moist air from the ocean. In the case of weakening of the westerlies, the influence of the ocean decreases and weather is formed under the influence of a cold and dry continental air mass.
The AO and NAO indices are appropriate variables for describing the intensity of westerlies. They have high correlations with winter climatic and hydrological parameters in Estonia. First of all, they determine temperature conditions in winter. Snow cover duration is directly dependent on temperature, while precipitation has more indirect relationships. Higher temperature in winter is caused by the cyclonic weather situation. It is related to cloudy, windy and rainy (snowy) conditions with higher precipitation. We detected increasing trends and upward regime shifts in AO and NAO indices in winter months and for the winter season as a whole. This reflects the intensification of the westerly circulation that abruptly occurred beginning in the winter of 1988/1989.
Changes in large-scale atmospheric circulation, trends and regime shifts have naturally caused changes in other parameters. For example, the intensification of westerly circulation in winter during 1951–2015 induced a significant increase in winter and spring temperature as well as winter precipitation and river runoff and a decrease in snow cover duration. It is expressed by linear trends and even better by coherent regime shifts starting in the late 1980s. The shift year 1989 was found for the majority of time series analysed in this study. This result is in good concordance with the previous investigations in the same region (Keevallik and Soomere, 2008; 2014; Elken et al., 2014; Soomere et al., 2015).
The results of the trend analysis confirm the fact that climate warming in
the Baltic Sea region has been faster than the global mean (BACC, 2015).
Trend values of 0.3–0.4
In the majority of cases with significant trends, a
regime shift was also detected. This allows assuming that the climate change is not a
monotonic process but consists of abrupt changes. Several return shifts, i.e. shifts
of an opposite direction, were also found. In these cases the initial regime
was more or less re-established. The shift value of about 2
It is important to emphasise the differences between the coastal zone and the continental part of Estonia. The winter warming has been higher in the continent and lower near the coast of the Baltic Sea. This can be explained by the thermal inertia of the sea and by the fact that, in the case of stronger westerly circulation, the advection of a mild and moist maritime air mass into the continental parts of Estonia causes more substantial change in weather conditions than in the coastal zone. At the same time correlation between the circulation indices and mean temperature is much higher at the coast.
Snow cover duration has a highly negative correlation with air temperature
in winter. Therefore, the decrease by 3–4 days decade
Various results of the analysis of trends and regime shifts for precipitation can be explained by its extremely high spatial and temporal variability. Mostly increasing trends appeared in the case of winter months. It is natural because higher winter temperature in Estonia is related to cyclonic weather conditions that are also explained by higher cloudiness, wind speed and precipitation. The results of the trend analysis are in line with the previous study. The only exception is November, when precipitation significantly increased during the longer period (Table 4). Regime shifts in precipitation are not coherent in the stations. They reflect long-term fluctuations. For example, the start of a rainy period in the end of the 1970s and in the beginning of the 1980s is characterised by an upward shift in annual precipitation detected in many stations. Similar regime shifts were also found for the three Baltic countries (Jaagus et al., 2016).
Annual mean runoff has not revealed any strong trends or shifts in spite of the increased precipitation and temperature. However, there are clear seasonal changes found in several earlier studies and confirmed in the current one. Specific runoff has significantly increased for the winter season and in all winter months separately from December to March. The highest increase occurred in western Estonia. As the most important change in the hydrological regime of rivers, the maximum spring runoff has moved from May and April to March due to milder winters and earlier snowmelt. It could be considered as a logical consequence of climate warming that is projected in Estonia for the end of this century (Jaagus et al., 1998; Jaagus and Mändla, 2014).
Despite the comparatively small territory, quite obvious spatial differences were found in regime shifts in Estonia. The Vasknarva station on the Narva River reflects runoff fluctuations over a much wider area than the territory of Estonia buffered by Lake Peipsi. Therefore, they are not well comparable with shifts in other stations. Much less statistically significant regime shifts were detected in specific runoff in northern Estonia (Jägala, Valgejõgi, Kunda and Purtse rivers) than in the other parts of Estonia. Upward shifts were revealed there only in March beginning in 1989. Rivers in northern Estonia are also characterised by strong decreasing trends in specific runoff in May.
The second group consists of Vihterpalu, Leivajõgi and Keila rivers in
north-western Estonia, where shifts were also detected in January, February
and winter as a whole. Rivers in western Estonia (Kasari, Pärnu,
Lõve) have the strongest regime shifts. The Kasari and Lõve rivers have
extremely high shift values, which exceed 11 L s
One peculiarity of the STARS method (Rodionov test) is the fact that it does not give reliable results for the end of the time series. Therefore, using the updated time series permits us to confirm that the regime shifts at the end of the 1980s have really taken place.
The main result of this study is the detection of coherent regime shifts in
many climatic and hydrological parameters in Estonia that mainly occurred
starting from the winter of 1988/89. This significant change was caused by an abrupt
intensification of westerlies described by the AO and NAO indices, which has
brought a bigger amount of warm air from the North Atlantic to the Baltic Sea
region. As a consequence, winter air temperature has increased significantly
and the duration of snow cover has decreased. Due to the thermal inertia
mild winters are followed by early and warmer springs. The warming trend has
been mentioned throughout the year, but it is the highest in winter and spring.
The shift value of about 2
Precipitation can be described by a moderate increase observed during the cold season (from November to March) and June, as well as by various regime shifts. Annual precipitation has shift years starting in 1977 at some stations and winter precipitation beginning at the end of the 1980s.
Changes in climatic parameters were reflected in specific runoff of rivers. Winter runoff has increased significantly and especially in March. The runoff maximum caused by snowmelt has shifted from April to March and runoff in April has decreased. There were few changes in specific runoff during the warm half-year.
All meteorological and hydrological data from Estonia used
in this study have been provided by the Estonian Weather Service. We have no
permission to make them publicly accessible. The data on large-scale
atmospheric circulation have been obtained from the following web pages:
The authors declare that they have no conflict of interest.
This article is part of the special issue “Multiple drivers for Earth system changes in the Baltic Sea region”. It is a result of the 1st Baltic Earth Conference, Nida, Lithuania, 13–17 June 2016.
This study was done using the financial support of the European Regional Development Fund (project EstKliima of the Environmental Protection and Technology Programme no. 3.2.0802.11-0043), the EU JPI WATER project IMDROFLOOD and the institutional research grant IUT2-16 of the Estonian Research Council. Edited by: Anna Rutgersson Reviewed by: two anonymous referees