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An analysis of climate change tendencies based on empirical observation data over Ligatne and Straupe areas Changing climate and its impact to environment and various human activities is an important topic world-wide. A special emphasis has been put to investigate the main climate extremes bound to enormous losses of human life and make significant influence to different sectors of human activities (energy sector, agriculture etc.). Empirical observations allow identifying the key regularities and changes in the climate system. The longest climatic observation data series are of special significance to obtain precise information on the climate system state, its development and changes. The climate change is obviously global issue; nevertheless, its local manifestation depends on regional processes and features very much. The present work is dedicated to analyze long-term air temperature, precipitation and snow cover data changes in Ligatne and Straupe local area. Long-term data series from Priekuļi weather station have been used for these purposes. Basic quality and homogeneity control was undertaken for all of the series The Mann-Kendall (MK) non-parametric test is used to detect monotone trends of time series. The test allows toidentify a significance of data variations and the trend’s linearity or non-linearity. Changes in data series were considered statistically significant (p=0,05), as corresponds to the MK test statistical values more than 1,65 (significant positive change) and less than – 1,65 (significant negative change). Regression analysis method has been used to assess the magnitude of the changes. An ensemble climate change indices, derived from daily temperature and precipitation data, describing changes of the mean and extremes, were computed and analyzed (Annex 1). Variations in precipitation amount Precipitation totals Changes and fluctuations in precipitation regime significantly correspond to climate change processes and make influence to hydropower production and agriculture as well. Overall, annual precipitation amount for period 1921-2008 has not a significant tendency to increase or decrease. There is no statistically significant increase in precipitation amount during the cold half of the year (November - March). At the same time a decreasing tendency (Fig.1) of total precipitation amount is observed in warm period (April - October). An investigation of changes in monthly precipitation series provides a more detailed overview of the timing of significant changes in annual precipitation. Precipitation has a pronounced tendency to increase in December, January, February, with a significant decrease in September. The total changes in the amount of precipitation were considered by using slope of linear regression. The study detected precipitation increase by 54 mm (or 50%) in winter. The decreasing patterns have been found for summer and autumn seasons, and Climate change analysis - Target Area Latvia specific, 13/11/2009 1 these are indicative of a decreasing contribution of summer and autumn precipitation to the annual total. The tendencies in precipitation changes in Priekuļi is consistent with the tendencies found in the whole territory of Latvia A 1200 1100 1000 P, mm 900 800 700 600 500 19 21 19 26 19 31 19 36 19 41 19 46 19 51 19 56 19 61 19 66 19 71 19 76 19 81 19 86 19 91 19 96 20 01 20 06 400 B 400 y = 0.7117x + 163.78 R2 = 0.1211 350 300 P, mm 250 200 150 100 50 19 21 19 26 19 31 19 36 19 41 19 46 19 51 19 56 19 61 19 66 19 71 19 76 19 81 19 86 19 91 19 96 20 01 20 06 0 Climate change analysis - Target Area Latvia specific, 13/11/2009 2 C 1000 900 800 P, mm 700 600 500 400 300 200 100 19 21 19 26 19 31 19 36 19 41 19 46 19 51 19 56 19 61 19 66 19 71 19 76 19 81 19 86 19 91 19 96 20 01 20 06 0 Figure 1. Precipitation totals with 5-year smoothed data and trend line for cold period, Priekuļi. A – annual, B - cold (XI-III) period, C - warm (IV-X) period Precipitation extremes Heavy precipitation that has fallen within a short time period may bring flooding events that causes great losses to agriculture, transport and national economy in general. The total number of days with daily precipitation exceeding 1 mm (wet days) has increased significantly in winter for the Līgatne and Straupe parishes territory. Wet day’s index for the winter season over the period 1921-2008 have increased by 14 days and this value is statistically significant (Tab. 3). The heavy precipitation days (>10 mm) show a positive trend for cold season and a strong interdecadal variability. For very heavy precipitation days (>20 mm) no clear long-term trend, but interannual and also interdecadal variability was high. Overall significant positive trends in 1-day and 5-day maximum precipitation during the period 1921-2008 were found only for winter season. Table 1 Mann-Kendall test values for number of precipitation indices, Priekuļi (1921.-2008.) Statistically significant values (p 0,05) are in bold Precipitation warm cold winter spring summer autumn index period period Wet days 0,3 -0,9 0,1 -1,4 4,0 4,8 Heavy precipitation days Highest 1-day precipitation amount Highest 5-day precipitation amount year 2,8 * * * * -0,5 3,8 1,4 4,7 1,7 0,4 -1,4 0,4 2,2 0,1 3,6 0,8 0,9 -0,1 0,3 1,6 0,6 Climate change analysis - Target Area Latvia specific, 13/11/2009 3 *Values were not calculated The analysis showed that days of precipitation exceeding the 99th percentile (extremely wet days) have been observed at least 1-2 times per year, both in the cold and the warm period of the year; in some years 3-4 times per year. With this, no significant tendency in the changes was identified The Priekuļi station showed a significant positive trend in the time series of very wet days number in the cold period. In the warm period of the year, periodical variations in the number of days of very wet precipitation occurred and no significant trend was found. The number of moderate wet days over the period 1921-2008 was positive only for cold season. Both precipitation totals and extreme precipitation indices showed a well pronounced positive tendency in the cold period of the year, particularly in winter. Climate change analysis - Target Area Latvia specific, 13/11/2009 4 Variations in the mean and extreme ambient air temperatures Mean, maximum and minimum temperature Long-term air temperature can be considered one of the most important indicators of climate change. Table 2 provides generalized tendencies in mean (TG), mean maximum (TX) and mean minimum (TN) air temperatures, their levels and significance. Table 2 Long-term trends of mean (TG), mean maximum (TX) and mean minimum (TN) air temperature (in bold p0,05) T – Manna-Kendall test statistic I – magnitude of changes in temperature (0C) Month Season Year I II III IV V VI VII VIII IX X XI XII WINTER SPRING SUMMER AUTUMN YEAR TG TX TN T I T I T I 2,4 1,7 2,6 2,9 1,2 2,1 0,8 1,6 0,2 1,4 0,1 1,9 2,5 3,8 2,3 0,9 3,6 3,1 2,6 2,7 2,4 0,9 1,6 0,6 0,8 0,1 1,0 0,2 1,6 2,4 2,0 1,0 0,4 1,6 2,4 1,9 3,6 3,8 2,2 2,4 0,9 1,9 0,5 1,8 0,1 2,0 3,0 5,2 2,6 0,9 5,0 3,1 2,7 3,3 3,7 1,9 2,1 0,7 1,3 0,1 1,3 0,1 1,6 1,9 3,0 1,4 0,5 2,0 2,3 1,8 2,5 2,8 1,2 2,7 1,1 2,2 0,8 1,7 0,1 1,9 2,7 3,3 3,1 1,1 3,9 3,5 3,0 3,1 2,0 0,7 1,7 0,5 0,8 0,4 1,2 0,2 1,9 2,8 1,9 1,0 0,6 1,6 In general, the station Priekuļi reported increased mean, mean maximum and mean minimum air temperatures throughout the year, with marked rising in monthly mean temperature in winter and spring. The statistically significant rise in mean air temperatures in summer and autumn was less pronounced than in winter and spring. The analysis of mutual relations of monthly mean and annual mean temperatures testified to a close correlation between the annual air temperature and the temperature in winter (correlation coefficient, 0,75) and spring (0,76). This correlation has not greatly changed over more than 80 years thus testifying to a strong effect of the variation and level of air temperatures of these seasons on the annual mean temperature. In Climate change analysis - Target Area Latvia specific, 13/11/2009 5 progressively increasing air temperature over seasons and throughout the year, pronounced variations in the air temperature have become obvious. The mean maximum temperature has increased more rapidly in spring (MarchMay) and summer (June, August) while the minimum temperature has increased more rapidly in winter season. The character of changes of air temperature in general is similar to other meteorological observation stations in Latvia. Growing degree days and frost days The growing degree days index and frost days index are the indicators of impact of climate change on agricultural activities. All in all, the value of the growing degree-days has increased by 2700C over the 88-years observation period (Fig. 2). A close correlation has been found between the growing degree-days and the mean air temperatures in April throughout October (correlation coefficient, 0.90). This supports the fact that in rising temperature in future, the increase in growing degree-days values is expectable. 3300 y = 3.1204x + 2296.5 R2 = 0.1597 growing degree days, o C 3100 2900 2700 2500 2300 2100 1900 2005 2001 1997 1993 1989 1985 1981 1977 1973 1969 1965 1961 1957 1953 1949 1945 1941 1937 1933 1929 1925 1921 1700 Figure 2. Long-term changes in growing degree- days in Priekuļi with 5-year smoothed data and trend line Climate change analysis - Target Area Latvia specific, 13/11/2009 6 230 y = -0.2477x + 153.7 R2 = 0.0943 210 190 days 170 150 130 110 90 70 2005 2001 1997 1993 1989 1985 1981 1977 1973 1969 1965 1961 1957 1953 1949 1945 1941 1937 1933 1929 1925 1921 50 Figure 3. Long-term changes in days with minimum temperature below 0oC in Priekuļi with 5-year smoothed data and trend line In the period 1921-2008, the number of frost days has greatly decreased by 20 days (Fig. 3). Heating degree days Heating degree-days is among the climate change indices that well characterize the consumption of energy necessary for heating buildings in the cold period of the year. The pattern of long-term changes in heating degree-days has shown a significant fall in the sum of degree days for the 88 years observation period (Fig.4). According to the linear regression equation, heating degree-days have decreased by 5520C (13%). As with the sum of temperatures of the growing degree days, the value of heating degreedays highly correlates (correlation coefficient, 0,8) with the mean temperature in the period October throughout March. 5500 y = -6.272x + 4503.5 R2 = 0.1708 heating degree days, o C 5000 4500 4000 3500 Climate change analysis - Target Area Latvia specific, 13/11/2009 2005 2001 1997 1993 1989 1985 1981 1977 1973 1969 1965 1961 1957 1953 1949 1945 1941 1937 1933 1929 1925 1921 3000 7 Figure 4. Long-term changes in heating degree- days in Priekuļi. Long-temp changes of snow cover Snow cover conditions are a sensitive and reliable indicator of climate. Snow cover affects the Earth’s surface energy, influences ecosystems (soil and air temperature, soil water recharge) and biochemical cycles of elements. The snow cover parameters are of importance for hydrological regime during the period of maximal discharge of accumulated atmospheric precipitation. Snow cover season can directly influence the vegetation growing season length. The rise of air temperature in winter and spring caused the snow storage to decline even with increased winter precipitation. The maximum water equivalent, number of days with snow and length of period with persistent snow cover have decreased during period 1945-2008. The linear regression analysis shows that within period 1944-2008 the duration of persistent snow cover in Priekuli weather station has decreased by 23 days. (Fig. 5) and the number of days with snow cover has decreased by 13 days. For the period 1950-2008 the maximum snow water equivalent in the Līgatne and Straupe area has decreased by 50%. 180 y = -0.3612x + 117.82 R2 = 0.0697 160 140 days 120 100 80 60 20 07 20 04 20 01 19 98 19 95 19 92 19 89 19 86 19 83 19 80 19 77 19 74 19 71 19 68 19 65 19 62 19 59 19 56 19 53 19 50 19 47 19 44 40 Figure 5. Long-term changes in the duration of snow cover (days) in Priekuļi CONCLUSIONS Long-term observations have shown significant changes in the air temperature, precipitation and snow in Ligatne and Straupe areas. According to data of the station Priekuļi: Climate change analysis - Target Area Latvia specific, 13/11/2009 8 Annual mean air temperature has increased by 1,60 C; mean winter temperature by 2,40 C; mean spring temperature by 2,00C; mean summer temperature by 1,00 C; mean autumn temperature by 0,40 C. Most rapid increase features mean maximum temperature. In the period from 1920-ties, the mean maximum temperature has increased more rapidly in spring and summer while the minimum temperature has increased more rapidly in winter season. Seasonality indices have changed for the period 1921-2008: a) increased values of growing degree days by 270o C; b) decreased number of frost days by 20 days; c) reduced heating degree-days by 552oC. Unlike air temperature, precipitation has shown cyclic fluctuations over the long-term period with a general increase of 50% in winter. The decreasing patterns have been found for summer and autumn seasons, and these are indicative of a decreasing contribution of summer and autumn precipitation to the annual total. Overall significant positive trends in precipitation extremes found only for cold season. The rise of air temperature in winter and spring cause the snow storage to decline in the Līgatne and Straupe area even with increased winter precipitation. Within period 1944-2008 the duration of persistent snow cover has decreased by 23 days, the total number of days with snow cover has decreased by 13 days and the maximum snow water equivalent has decreased by 50%. Climate change analysis - Target Area Latvia specific, 13/11/2009 9 Annex 1 The air temperature and precipitation indices TG – mean of daily mean temperature (0C) I TG j TGij / I i 1 where TGj – the mean of daily mean temperature in period j TGij – the mean temperature at day i for period j I – number of observations TN – Mean of daily minimum temperature (0C) I TN j TN ij / I i 1 where TNj – mean of daily minimum temperature in period j TNij – the minimum temperature at day i for period j I – number of observations TX – Mean of daily maximum temperature (0C) I TX j TX ij / I i 1 where TXj – mean of daily maximum temperature in period j TXij – the maximum temperature at day i for perod j I – number of observations GD4 – Growing degree days (sum of TG>50C) (0C) I GD4 j (TGij TG ij 5 0 C ) i 1 where GD4j – growing degree days TGij – the daily mean temperature at day i of period j FD – Frost days (TN<00C) (days) Let TNij be the daily minimum temperature at day i of period j. Then counted is the number of days where TNij < 00C HD17 – Heating degree days (sum of 170C-TG) (0C) I HD17 j (17 0 C TGij ) i 1 where HD17j – heat degree days in period j TGij – the mean temperature at day i for period j RR – Precipitation sum (mm) I RR j RRij i 1 where RRj – precipitation sums in period j RRij – the daily precipitation amount for day i of period j RR1 – Wet days (RR 1mm) (days) Climate change analysis - Target Area Latvia specific, 13/11/2009 10 Let RRij be the daily precipitation amount for day i pf the period j. Then counted is the number of days where RRij1 mm R10mm – Heavy precipitation days (precipitation 10 mm) (days) Let RRij be the daily precipitation amount for day i of period j. Then counted is the number of days where RRij 10mm) R20mm – Very heavy precipitation days (precipitation 20 mm) (days) Let RRij be the daily precipitation amount for day i of period j. Then counted is the number of days where RRij 20mm. RX1 – Highest 1-day precipitation amount (mm) Let RRij be the daily precipitation amount for day i of period j. Then maximum 1-day values for period j are: RX1dayj=max (RRij) RX5 – Highest 5-day precipitation amount (mm) Let RRkj be the daily precipitation amount for the 5-day interval k of period j where k is defined by the last day. Then maximum 5-day values for period j are: RX5dayj=max (RRij) Climate change analysis - Target Area Latvia specific, 13/11/2009 11