Download The Character of Climate Change in Ligatne and

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
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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
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*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
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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 p0,05)
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
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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
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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
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
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%.
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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
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Let RRij be the daily precipitation amount for day i pf the period j. Then counted is the number of days
where RRij1 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)
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