5. climatic trends in estonia during the period of instrumental ...

EE9800004 5. CLIMATIC TRENDS IN ESTONIA DURING THE PERIOD OF INSTRUMENTAL OBSERVATION AND CLIMATE SCENARIOS

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5. CLIMATIC TRENDS IN ESTONIA DURING THE PERIOD OF INSTRUMENTAL OBSERVATIONS AND CLIMATE SCENARIOS Jaak Jaagus University of Tartu, Institute of Geography, 46 Vanemuise Str., EE2400 Tartu

Introduction Estonia is located on the eastern coast of the Baltic Sea between 57°30'N and 59°40'N. It represents a transition zone from the maritime climate type to the continental one. In spite of its comparatively small territory (45 215 km"), climatic differences in Estonia are significant, especially during the colder half of the year. For example, mean air temperature in January varies from -2.5°C on the western coast of the Island of Saaremaa up to -7.5°C in the coldest places in east Estonia. Mean duration of snow cover is between 75 and 130 days. Furthermore, temporal variability of meteorological values has been very high in Estonia. Weather conditions depend directly on cyclonic activity in the Northern Atlantic. Long-term fluctuations in its intensity reflect on meteorological anomalies. Based on high variability of weather conditions, Estonia can be expected to be a sensitive region for possible climate change. The aim of this study is to establish trends and long-term periodical fluctuations in meteorological time series in Estonia, and to develop climate change scenarios for the estimation of climate change tendencies caused by the increase in the concentration of greenhouse gases in the atmosphere.

Data The first regular meteorological observations in Estonia date back to the late 18th century. The longest continuous time series are available since the early 19th century. The majority of stations at that time were located on the seashore at lighthouses. In 1865, the Meteorological Observatory of the University of Tartu was founded. Since that year, high quality measurements, such as for precipitation, have been made. In this study, time series of five meteorological values - air pressure, sunshine duration, air temperature, precipitation and snow cover- are analyzed. Owing to the statistical peculiarities, each value needs an individual approach. A very high spatial variability is characteristic of precipitation and snow cover data. They are measured in a great number of stations. In this study, time series of spatial mean values of annual and seasonal precipitation and snow cover duration (e.g., number of days with snow cover during a winter) were analyzed. Territorial averaging of precipitation data was made by spatial interpolation into grid cells for the period 1866-1994 (Jaagus, 1992).

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5. CLIMATIC TRENDS IN ESTONIA DURING THE PERIOD OF INSTRUMENTAL OBSERVATIONS AND CLIMATE SCENARIOS

Spatial mean values of snow cover duration in Estonia were found by means of a geographical information system. IDRISI raster images were created to represent snow cover fields. The mean value of all raster elements on the terrestrial part of Estonia was calculated for 124-year time series of winters (1891/1892-1994/1995) where snow cover was observed. Territorial variability of the other meteorological characteristics was much lower. Therefore, time series of single stations were studied. Four stations in different climatic regions in Estonia with the longest observation periods were chosen to describe trends and fluctuations of air temperature. Tallinn represents northern Estonia and has the longest observation period-18281994. Tartu (1866-1994) is located in the continental part of Estonia in the south-east, Parnu (1842-1994) is situated on the coast of the Gulf of Riga in the south-west, and Vilsandi (18651994) resides on a small island with the most maritime climate, near the western coast of the Island of Saaremaa. Sunshine duration and air pressure were studied only in Tartu during the periods 1901-1994 and 1881-1994, correspondingly. The absence of observations has replaced by values calculated on the basis of data measured at neighbouring stations. Time series both of annual and of seasonal values were analyzed. Fluctuations in intensity of cyclonic activity occur, first of all, in seasonal patterns. Seasonal values were calculated for a period of three months. For example, spring means the period from March to May and summer from June to August. The problem of homogeneity in meteorological time series is of great importance. Typically,, they are not entirely homogenuous. Over a long time, locations of stations, surroundings of observation places, times of measurements, gauge types, and measuring techniques have changed. Possible inhomogeneities were studied and taken into account in the analysis of the results. The standard climatic period 1961-1990 was used as a baseline period for developing climate change scenarios. The baseline scenario was developed as a complex of mean values of monthly air temperature, precipitation and solar radiation measured at meteorological stations in Estonia and averaged over the baseline period. Mean temperature was calculated for 25 stations. Spatial mean totals of precipitation were found by averaging values interpolated into grid points by use of data recorded at more than a hundred precipitation stations. Solar radiation was measured only in Toravere.

Trends and periodicities By linear trend analysis trends were determined, and autocorrelation and spectral analyses were used to estimate periodical fluctuations in meteorological time series in Estonia. In some cases, significant trends and periodicities were found; in other cases they were not. It is important to emphasize that periodical fluctuations of meteorological times series are not exactly cyclic, but are more like quasi-periodical. It means that the alternation of maximum and minimum values is clearly distinguishable but the time interval between them varies.

Air pressure is a less variable meteorological 'alue, but it has a great significance. Air pressure expresses the character of the entire complex of meteorological conditions: the prevailing low or

5. CLIMATIC TRENDS IN ESTONIA DURING THE PERIOD OF INSTRUMENTAL OBSERVATION AND CLIMATE SCENARIOS

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high pressure. Figure 1 represents the time series of annual mean air pressure in Tartu. A series of five-year moving means and a linear trend line were added to the figure. The weak increasing trend is not statistically significant. Noticeable fluctuations of air pressure were observed. In the 1960s, air pressure was the highest, while during the 1920s and 1980s it was the lowest. The long-term fluctuations were determined by the frequency of different types of atmospheric circulation. Lower pressure corresponded to the domination of zonal circulation and to intense cyclonic activity. Our analysis of the time series of seasonal mean air pressure gave interesting results. The increasing trend of air pressure in spring (March-May) and in summer (June-August) was clearly distinguishable and statistically truthful. At the same time, the decrease of air pressure was observed in autumn (September-November) and in winter (December-February). Such kinds of opposite trends allow one to conclude that the climate change in Estonia has progressed toward a more maritime climate type. High pressure during the warmer half of the year and low pressure during the colder half of the year is peculiar to a maritime climate, while the opposite fluctuations are characteristic of a continental one.

Fig. 1. Mean annual air pressure in Tartu. The time series of annual sunshine duration in Tartu is somewhat similar to that of air pressure (Fig.2). A weak increasing tendency and significant deviations were observed. The most sunny years were the 1940s and the 1960s, and the most cloudy were the 1920s, the 1950s and the 1980s. Sunshine duration depends directly on cloudiness. Cloudiness was determined by atmospheric circulation and air pressure.

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1200 1901

1911

1921

1931

1941

1951

1961

1971

1981

1991

Fig. 2. Annual total of sunshine duration in Tartu. Mean air temperature is the most important meteorological value for detecting climate change. There is a very high correlation (more than 0.95) between the times series of annual mean air temperature at the four stations. Therefore, data measured in Tallinn (Fig.3) can represent the whole country quite well. Annual mean air temperature has increased by 0.7-1.0°C at every station in Estonia since the middle of the last century.

o

1828

1848

1868

1888

1908

1928

1948

1968

1988

Fig. 3. Mean annual air temperature in Tallinn. The temporal course of air temperature is coherent with large-scale temperature changes in the Northern Hemisphere. A significant increasing trend existed over the period from the middle of the last century up to the 1930s. Then followed a stable period. But the last decade was the warmest also in Estonia. The warming was observed in spring (by 1.9°C), autumn (by 0.3°C) and


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winter (by 1.4°C) but not in summer. Remarkable periodicities in air temperature series were not determined. Air temperature in winter had the highest variations. It determined annual mean values. The time series of spatial mean annual precipitation has an increasing trend. It is mainly caused by the improvement of gauges and measuring techniques. The corrected time series (Fig.4) was characterized only by a small increase (by 27 mm). A reliable trend was detected only for autumn and winter precipitation. By spectral analysis, obvious periodical fluctuations of precipitation were detected with periods of 50-60, 25-30, and 5-7 years. The most important periods of high precipitation were observed in 1866-1873, 1923-1935, and 1978-1990. These maxima appeared in annual and in autumn totals of precipitation. There were weaker secondary maxima between them in 1897-1906 and 1952-1962. They corresponded to a periodicity of 25-30 years. During summer season, only the period of 5-7 years was determined. In case of spring rainfall, another periodicity (22-23, 45-47 years) was observed. Winter precipitation series was entirely inhomogenuous and was not analyzed.

850 750

k

E 650 550 450

1866

Ail

A

i I

III I

V!'

Hrj I 1886

1

1906

1926

1946

ll I

1

r \ 966

1986

Fig. 4. Spatial mean annual precipitation in Estonia. The analysis above has shown that the winter period has the highest variability of weather conditions. Snow cover duration is the best characteristic to describe winter weather. Figure 5 illustrates variations of spatial mean snow cover duration in Estonia. The time series has a decreasing trend. During the 104 years, mean number of days with snow cover has changed by 16 days - from 121 to 105. The first half of the period, until the 1930s, is characterized by a very significant trend, while, during the latter half, the trend is very weak. The decrease of snow cover duration over the recent ten years has been rapid again. Figure 5 demonstrates significant periodical fluctuations over 13-18 years. Periods of mild winters with a lower duration of snow cover were observed during the following periods: 1929/1930-1938/1939, 1948/19491951/1952, 1958/1959-1960/1961, 1971/1972-1974/1975, and 1988/1989-1991/1992.

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1892 1902 1912 1922 1932 1942 1952 1962 1972 1982 1992

Fig. 5. Spatial mean duration of snow cover in Estonia.

Climate change scenarios Global climate warming due to the increase in greenhouse gas concentrations in the atmosphere has been the main research topic in climatology over the recent decades. Possible climate changes all around the world can be examined on the basis of general circulation model (GCM) outputs. They are the best sources for developing climate change scenarios (Robock et al., 1993). Results of five GCM runs were used in the current study (Table 1). These outputs were combined with the baseline climate data to produce climate change scenarios that could serve as inputs for vulnerability assessment (Carter et al., 1992; U.S. Country Studies Program, 1994). Table 1. General circulation models used in this study GCM developed by Goddard Institute of Space Studies Geophysical Fluid Dynamics Laboratory Canadian Climate Centre United Kingdom Meteorological Office

Abbrevation

Date of model run

Model resolution (latitude x longitude)

Vertical levels

MeandT for2xC0,

GISS

1982

7.83°x10.0°

9

4.2°

GFD3 CCCM

1989 1989

2.22°x3.75° 3.75°x3.75°

9 10

4.0° 3.5°

UK89

1989

2.50x3.75°

11

3.5°

GF01

1991

4.44°x7.50°

9

4.0°

Geophysical Fluid Dynamics Laboratory transient

Two types of GCM output were used in this study. The first type, lxCO2, should correspond to the baseline climate, and the second one, 2xCC>2, to the climate in the case of a double carbon dioxide concentration. The outputs consisted of monthly mean air temperature, precipitation and solar radiation. The selection of GCMs for developing climate change scenarios was made by means of comparing 1 xCO2 model output data with the baseline climate scenario.


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GCM outputs in lxCO2 scenarios for air temperature are quite similar within the continental part of Estonia and sligthly different in the West Estonian Archipelago. Figures 6 and 7 show the mean annual curve of monthly mean air temperature at two widely separated stations according to the baseline scenario and the GCM-based lxCO2 scenarios. Voru is located in the most continental part in south-east Estonia and Sorve - on the westernmost island.

• Baseline -GISS GFD3 CCCM _ _#_ _ GF01

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Fig. 6. lxCO2 air temperature scenarios for Voru.

• Baseline -GISS

Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

Fig. 7. lxCO2 air temperature scenarios for Sorve. UK89 output data were excluded from Fig.6 because the values were excessively low. In the continental part of Estonia, the model indicates below -20°C in winter. Summer maximum values are below +15°C. Such kinds of data are typical of the climate in Siberia at the same latitudes. The rest of the GCMs also tend to underestimate air temperature in winter. Only CCCM output indicates a

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higher temperature. In summer, overestimates (GF01, CCCM) as well as underestimates (UK89, GISS) occur. Among the single GCMs, the best results were obtained by CCCM, especially during spring and autumn when the other models markedly underestimated air temperature. CCCM overestimates winter temperatures, and in the continental Estonia also summer temperatures. GISS model results are satisfactory, too. They are the best in winter time. Air temperatures in spring and in autumn are obviously underestimated. GFD3 was the third suitable model for describing the annual curve of air temperature. Generally, it underestimates temperature except in the summer time. This model suits better for west Estonia, presenting quite well the influence of the Baltic Sea on air temperature. The GF01 transient model underestimates temperature in winter and overestimates it in summer. UK89 underestimates current climatic conditions during the whole year, particularly in the region of the continental type of climate. In terms of precipitation, the deviation of GCM results from the baseline data was substantially higher. Unfortunately, most of the GCMs highly overestimate precipitation during the colder half of the year, and underestimate it in summer, in July and August. Consequently, the models do not describe correctly annual curve of precipitation in Estonia. Precipitation measurements are representative only for the small area around a gauge. Therefore, it is not reasonable to use data from single stations. GCM results were compared with spatial mean data for the territory of Estonia (Fig. 8). The UK89 model gave the best results, expressing more or less correctly the annual curve of precipitation, although usually underestimating precipitation totals.

- Baseline -GISS ID

E

Jul

Sep

Nov

Fig. 8. lxCO2 precipitation scenarios for Estonia. Correlations between the baseline scenario and IXCO2 scenarios of the rest of the GCMs are uniformly insufficient. CCCM overestimates precipitation by the biggest amount. Only summer rainfall is within realistic limits. GISS, GFD3 and GF01 results are evenly high in winter and low in summer. It is ridiculous that, according to GFD3 model, the driest month in Estonia should be August, although, in fact, it is the richest in rainfall.


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GCM results were compared with the solar radiation data measured in TSravere station (58.3°N, 26.5°E). They are in good agreement (Fig.9). The highest correlation was obtained using the GISS model. It overestimates solar radiation in summer and underestimates in winter. GF01 results are also good but the model tends to overestimate solar radiation in summer. CCCM permanently overestimates and UK89 strongly underestimates it. A comparison of GCM lxCO2 results with the baseline scenarios indicated significant differences. No one GCM describes current climatic conditions in Estonia with sufficient adequacy. The UK89 model results seem to correspond to the much more severe climate of higher latitudes. At the same time, CCCM results are typical of the warmer climate of lower latitudes. Taking into consideration all the three parameters, the GISS model seems to have the lowest total deviation. GFD3 and GF01 results are rather similar and have a number of disadvantages. All the GCMs available, are more or less of the same kind, and each model describes one meteorological value as being better than another. Consequently, they can be used for developing climate change scenarios in Estonia. Only the UK89 model is not suitable, because of excessively low estimates of winter temperature. GCM-based climate change scenarios were developed according to the baseline scenarios, adding the difference between 2xCC>2 and lxCC>2 scenarios (in the case of air temperature) or multiplying by the ratio between the 2xCO2 and 1 xCO2 scenarios (in the case of precipitation and solar radiation). These adjustment statistics for Estonia are given in Tables 2-5. Annual curves of monthly mean air temperatures by use of different 2xCO2 scenarios for Voru and S5rve are shown in Figs. 10 and 11. All the scenarios show an increase in temperature by approximately 3-6°C. As a rule, warming is expected to be higher during the colder half of the year. The CCCM and GFD3 models indicate a stronger increase and the GISS model a smaller increase in summer.

-Baseline -GISS CM

Jan

Mar

May

Jul

Fig. 9. lxCO2 solar radiation scenarios for T5ravere.

Nov


44

Table 2. Adjustment statistics for the difference between 2XCO 2 and current (1XCO2) in Estonia generated by the GISS model Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Year

6.5

5.6

4.8

4.2

3.8

1.5

1.2

1.0

3.4

3.6

5.2

5.5

3.9

1.18

1.37

1.35

1.48

1.45

1.07

1.88

1.43

1.27

1.32

1.28

1.13

1.35

Temper. Prec. ratio

Table 3. Adjustment statistics for the difference between 2XCO2 and current (1XCO2) in Estonia generated by the CCCM model Temper Prec. ratio

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Year

6.5

6.4

5.8

4.9

3.1

3.0

3.1

3.3

2.9

3.3

2.9

3.9

4.1

1.12

1.33

1.39

1.42

1.09

1.00

0.79

1.20

0.97

1.14

1.29

1.39

1.18

Table 4. Adjustment statistics for the difference between 2XCO2 and current (IXCO2) in Estonia generated by the GFDL30 model Temper. Prec. ratio

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Year

4.9

4.0

5.8

6.5

5.4

4.2

5.0

4.0

2.9

3.8

7.0

6.9

5.0

0.96

1.17

1.08

1.50

1.41

1.04

1.00

0.95

1.38

1.25

1.36

1.30

1.20

Table 5. Transient scenario, based on adjustment statistics from the GFDL transient general circulation model and daily weather data from 1961-1990

January February March April May June July August September October November December Annual

Fourth decade(2000) Temperatur Precip. ratio 0.98 1.6 0.98 2.3 2.9 1.9 0.8 -0.7 •0.6 0.5 1.2 2.8 1.1 1.2 1.2

1.19 0.82 1.14 1.27 0.74 1.01 1.12 1.07 0.88 1.17 1.03

Seventh decade (2030) Temperatur Precip. ratio 1.16 3.6 1.05 2.9 3.9 1.25 0.82 3.1 1.05 3.0 0.91 1.5 2.1 0.76 0.93 2.5 2.9 1.19 1.11 3.3 0.97 3.3 5.7 1.25 1.04 3.2

Tenth decade (2070) Temperatur Precip. ratio 6.4 1.23 3.0 1.16 1.15 4.4 3.7 0.91 3.5 1.03 2.6 1.52 3.6 1.15 4.1 1.10 1.35 4.2 4.8 1.28 4.7 1.11 1.44 6.9 4.3

1.20


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•Baseline -GISS GFD3 CCCM _ _*_ _ GF01

Jul

Sep

Nov

Fig. 10. 2xCO2 air temperature scenarios for Voru.

• Baseline -GISS

Jan

Mar

May

Nov

Fig. 11. 2xCC>2 air temperature scenarios for S5rve. Climate change scenarios for precipitation are highly variable (Fig. 12). In most cases, an increase in precipitation is expected. In some cases, it could be many times greater. The UK89 and GISS model results show the highest increase and CCCM and GFD3 the lowest one. Global circulation models indicate comparatively small changes in solar radiation in the case of a doubling concentration of greenhouse gases. Usually, 2xCO2 scenarios show solar radiation slightly smaller than in the baseline scenario (Fig. 13). As a conclusion, the data of climate change scenarios for doubled CO2 concentration are presented in Tables 6-9.

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• Baseline -GISS

Jan

Mar

May

Jul

Nov

Fig. 12. 2xCC>2 precipitation scenarios for Estonia.

•Baseline -GISS

Jan

Mar

May

Nov

Jul

Fig. 13. 2xCC>2 solar radiation scenarios for Toravere. Table 6. Climate change scenarios for air temperature in Voru Scenario Baseline GISS GFD3 CCCM GF01

Jan •7.1

Feb

Mar

•6.2

•2.0

•0.5

•0.2

•2.5

•2.3

•0.6

0.8 •3.3

•0.6

2.7 3.5 4.7 2.5

Apr 4,6 8.9 11.0 9.9 8.5

May 11.5 15.4 16.7 14.2 14.8

Jun 15.6 16.8 19.6 18.3 18.0

Jul 16.9 18.0 21.8 19.9 20.4

Aug 15.7 16.8 19.7 19.0 19.8

Sep 10.9 14.5 13.6 13.7 15.2

Oct 6.0 9.5 9.7 9.2 10.8

Nov 0.5 6.0 7.4 3.5 5.3

Dec •4.2

1.2 2.6 -0.9 2.8

Year 5.2 9.1 10.1 9.3 9.5

In addition to GCM-based climate change scenarios, incremental scenarios are also used in vulnerability assessment. They provide a wide range of potential regional climate changes in


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temperature and precipitation. In this study, a great number of incremental scenarios were used. Increases in air temperature by 2, 4 and 6°C have been combined with no change, and with ±10 and ±20 % changes in precipitation. Table 7. Climate change scenarios for air temperature in Sorve Scenario Baseline GISS GFD3 CCCM GF01 UK89

Jan -2.6 3.3 2.1 1.1 3.6 4.1

Feb

Mar

•3.4

•1.3

1.9 0.8 0.4 0.0 3.1

3.2 4.1 2.5 3.2 5.8

Apr 2.7 6.6 8.9 6.6 6.1 10.0

May 8.3 11.8 13.8 12.0 11.5 14.2

Jun 13.6 15.9 18.2 17.1 15.9 18.3

Jul 16.0 17.5 20.9 19.5 19.6 20.5

Aug 16.0 16.8 19.7 19.5 20.2 20.4

Sep 12.5 15.6 15.6 15.8 16.3 16.5

Oct 8.4 11.8 12.2 11.7 13.2 13.6

Nov 3.9 8.6 10.2 7.0 8.4 9.2

Dec 0.2 5.6 6.7 3.6 6.7 6.1

Year 6.2 9.9 11.1 9.7 10.4 11.8

Dec 54 61 70 76 79 97

Year 663 904

Dec

Year 111 106 108 107 103 107

Table 8. Climate change scenarios for spatial mean precipitation in Estonia Scenario Baseline GISS GFD3 CCCM GF01 UK89

Jan 4-1 48 39 46 52 90

Feb 30 41 35 41 35 62

Mar 33 45 36 46 37 61

Apr 36 53 54 50 32 65

May 42 60 58 46 42 45

Jun 56 61 59 55 86 74

Jul 79 149 78 63 88 124

Aug 83 116 78 100 93 90

Sep 77 97 104 75 103 92

Oct 67 89 82 77 86 97

Nov 65 84

Sep 105 99 103 104 97 106

Oct 48 47 45 47 41 39

Nov 18 16 11

86 84 71 83

779 759 804 980

Table 9. Climate change scenarios for solar radiation in T5ravere Scenario Baseline GISS GFD3 CCCM GF01 UK69

Jan 18 15 19 16 16 14

Feb 47 40 4-7 41 43 39

Mar 105 98 94 98 99 79

Apr 151 146 118 139 136 140

May 205 203 191 199 189 217

Jun 239 232 256 237 220 239

Jul 219 204 228 214 204 204

Aug 169 157 176 163 168 186

16 16 15

11 10 8 10 9 9

Conclusions 1. Weather conditions in Estonia are quite variable. Long-term periodical fluctuations have been observed in meteorological values. At the same time, the climate change during the last 100-150 years is marked. As a general tendency, the climate has become more maritime. Air pressure is characterized by an increasing trend in spring and summer, and by a decreasing trend in autumn and winter. Mean air temperature has increased, particularly over the colder half of the year. Precipitation area totals have risen, most of all in autumn and winter. Snow cover duration has decreased significantly. 2. GCM-based climate change scenarios expect a general increase in air temperature in Estonia with warming in winter more significant than that in summer. Moreover, they indicate an increase

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in precipitation, but the results of the individual models are quite variable. The transient scenario shows that the main increase in precipitation will not occur during next decades, but only at the end of the transient period, around 2070. It can be stated that observed tendencies of climate change in Estonia concur with expected changes caused by global warming. 3. According to the long-term fluctuations of meteorological values in Estonia, changes different from general trends can take place during the next decade. An increase in mean air pressure, sunshine duration and snow cover duration, as well as a decrease in mean air temperature and precipitation is expected in the following years.

References Carter, T. R., Parry, M. L., Nishioka, S. & Harasawa, H. (1992). Preliminary guidelines for assessing impacts of climate change. Environmental Change Unit, Oxford, United Kingdom, and Center for Global Environmental Research, Tsukuba, Japan, 28 pp. Jaagus, J. (1992). Periodicity of precipitation in Estonia. Estonia. Man and Nature. Tallinn, pp. 4353. Robock, A., Turco, R. P., Harwell, M. A., Ackermann, T. P., Andressen, R., Chang, H. S. & Sivakumar, M. (1993). Use of general circulation model output in the creation of climate change scenarios for impact analysis. Climatic Change, 23, pp. 293-335. U.S. Country Studies Program. (1994). Guidance for vulnerability and adaptation assessments. Version 1.0, Washington D.C., U.S.A.