THE ROLE OF ATMOSPHERIC CIRCULATION PATTERNS
IN AGROCLIMATE VARIABILITY IN FINLAND, 1961–2011
MASOUD IRANNEZHAD1,2 , DELIANG CHEN3 and BJØRN KLØVE1
1
Water Resources and Environmental Engineering Research Group, Faculty of Technology,
University of Oulu, Oulu, Finland
2
Remote Sensing and Water Resources Lab, Department of Civil and Environmental Engineering,
Portland State University, Portland, Oregon, USA
3
Regional Climate Group, Department of Earth Sciences, University of Gothenburg,
Gothenburg, Sweden
Irannezhad, M., Chen, D. and Kløve, B., 2016. The role of
atmospheric circulation patterns in agroclimate variability in
Finland, 1961–2011. Geografiska Annaler: Series A, Physical
Geography, xx, 1–15. DOI:10.1111/geoa.12137
ABSTRACT. This study evaluates interannual variations and
trends in growing season daily temperature sum and daily
precipitation sum in Finland during 1961–2011, and their
connections to well known atmospheric circulation patterns.
Changes in summer (June–August) climate partially explain
changes in growing season daily temperature sum and daily
precipitation sum over Finland, which naturally decreased
from south to north. On a national scale, growing season
warmed and became wetter during 1961–2011, as growing
season daily temperature sum and daily precipitation sum
significantly (p < 0.05) increased by 5.01 ± 3.17°C year–1
and 1.39 ± 0.91 mm year–1 , respectively. The East Atlantic
pattern was the most influential atmospheric circulation
pattern for variations in growing season daily temperature
sum (rho = 0.40) across Finland and the East Atlantic/West
Russia pattern was most influential for growing season daily
precipitation sum variability (rho = –0.54). There were
significant (p < 0.05) increasing trends in growing season
daily temperature sum and daily precipitation sum throughout
Finland during 1961–2011. Increased growing season daily
temperature sum was mainly observed in northern, central,
western, eastern and coastal areas of south-western Finland.
This warming was positively associated with the East
Atlantic pattern in the north, centre and south, but negatively
associated with the East Atlantic/West Russia pattern in
eastern Finland. Increased GSP mostly occurred in southern,
eastern, western, central, northern and north-western Finland.
These wetting trends were positively correlated with the East
Atlantic pattern in the north and negatively correlated with the
Polar pattern in the south and the East Atlantic/West Russia
pattern in the east, west, centre and north-east of Finland.
The overall agroclimatic year-to-year variability in Finland
between 1961 and 2011 was mostly linked to variations in
the East Atlantic and East Atlantic/West Russia patterns.
Key words: agroclimate variability, trend analysis, atmospheric circulation patterns, growing season, Finland
© 2016 Swedish Society for Anthropology and Geography
DOI:10.1111/geoa.12137
Introduction
Climate change, one of the most important
challenges facing humanity, has already started to
influence crop production in different agricultural
regions around the world (e.g. Hatfield 2010, 2013;
Lobell et al. 2011). Studies show that climate
change is potentially threatening established farming practices relying on empirically tested calendar
dates or historical climate (e.g. Wolfe 2013; Takle
et al. 2014). At the same time, new opportunities
for agricultural improvements have also been
reported (e.g. Linderholm 2006; Peltonen-Sainio
et al. 2009a, 2011). The positive effects of climate
change on agricultural productivity (e.g. increased
atmospheric CO2 , enhancement of soil moisture
recharge and longer growing season) may be
partially or completely offset by the negative
impacts of temperature warming (e.g. shorter grainfill period and higher evapotranspiration rates) and
changes in precipitation pattern (e.g. declines in
water stored in reservoirs and more frequent heavy
rainfall increasing crop losses) (Adams et al. 1990;
Lobell et al. 2011; Wolfe 2013). However, such
effects of climate change on agriculture differ
between regions (e.g. Fischer et al. 2005; Lobell
et al. 2008). Therefore, progress in understanding
regional climate change can play a key role
in sustainable agronomic decisions, agricultural
production and food security (e.g. Lobell and Burke
2008).
In northerly agricultural regions, including
Finland, climate change is primarily manifesting
itself as lengthening growing season (GS) resulted
from earlier start and later end days (e.g.
Linderholm 2006). In recent years, therefore,
increased attention has been paid to changes in GS
1
MASOUD IRANNEZHAD ET AL.
parameters (start, end and length) (e.g. Klein Tank
et al. 2002; Linderholm 2006; Linderholm et al.
2008), which describe only one suitable feature
for plant growth, namely GS duration (Carter
1998). However, agricultural production remains
highly dependent on other agroclimatic conditions,
including growing season daily temperature sum
(GST) and daily precipitation sum (GSP), which
influence crop suitability and soil moisture
situations (Fischer et al. 2005; Peltonen-Sainio
et al. 2011). In fact, extended GS duration does
not tell much about climate suitability for plant
growth. In high-latitude regions, the earlier start
of the GS, combined with high surface ozone
concentration, can indeed have some negative
effects on vegetation activity (e.g. Karlsson et al.
2007). Agroclimatic conditions (GST and GSP)
play important roles in controlling both the quantity
and quality of harvested crops and consequently
in determination of crop yields for a given
agricultural region (e.g. Carter 1998; Lobell et al.
2007; Peltonen-Sainio et al. 2009a, 2009b, 2011).
Hence, understanding changes in past agroclimatic
conditions is a prerequisite in developing data
support for agricultural adaptation policies and thus
improving agricultural climate adaptation work.
Many studies have reported that regional
climate variability, mainly in terms of temperature
and precipitation, is predominantly controlled
by atmospheric circulation patterns (ACPs), e.g.
the Atlantic Oscillation (AO) (Dayan and Lamb
2005; Dore 2005; Bartolini et al. 2009; Jaagus
2009). For Finland, Irannezhad et al. (2014,
2015a, 2015c) identified significant relationships
between climate conditions (precipitation and
temperature) and the well known ACPs influencing
the Northern Hemisphere climate. In general,
ACPs are recurring, persistent and large modes
of pressure anomalies describing the power of
main airflow across a wide geographical area
(Hurrell 1995; Chen and Chen 2003). Glantz et al.
(2009) comprehensively reviews large-scale ACPs
and their natural effects on climate variability in
different parts of the world. Understanding and
establishing such effects of ACPs on variations
in agroclimatic conditions (GST and GSP) (e.g.
Bonsal et al. 1999; Jones et al. 2002) improves
knowledge on plant growing processes (e.g.
Stenseth et al. 2002; Menzel 2003) and also enables
agricultural producers to adjust their decisions to
enable crop yields to increase (Legler et al. 1999;
Hill et al. 2000; Chen et al. 2002).
Our previous studies show increasingly wetter
and warmer conditions in Finland over the last
2
century (Irannezhad et al. 2014, 2015a, 2015c).
Likewise, our recent study (Irannezhad and Kløve
2015) indicates that such climatic changes have
already caused GS in Finland to become several
days longer, which may potentially provide more
favourable agroclimatic conditions (GST and
GSP) in the country. Despite the importance of
such agroclimatic changes in plant growth and
development in Finland, only a few studies have
focused on GST and GSP over the country, e.g.
Carter (1998), Kaukoranta and Hakala (2008),
Ylhäisi et al. (2010) and Ruosteenoja et al. (2011).
However, none of these studies has analysed the
influence of ACPs on variations in GST and GSP
in Finland. Hence, a comprehensive study on
interannual variations and trends in agroclimatic
conditions in Finland, in terms of GST and GSP,
and their relationships with different ACPs is well
motivated.
The present study is focused on relationships
between the overall agroclimatic conditions (GST
and GSP) in Finland and ACPs during 1961–2011.
Specific objectives were to differentiate agroclimate conditions (GST and GSP) from summer
(June–August) temperature and precipitation; to
identify long-term trends in GST and GSP and their
spatial distribution; and to measure correlations
between GST and GSP and predominant ACPs.
These issues are important for many sectors of
society in Finland, particularly agriculture and
ecosystems, concerning adaption to recent and
future climate change.
Materials and methods
Study area and data used
Finland extends over about 1320 km in the north–
south direction in northern Europe (Fig. 1a) and
is generally categorised as lying in the boreal
or temperate climate zone (Castro et al. 2007;
Chen and Chen 2013). Mean annual temperature in
Finland during 1961–2011 (hereafter ‘base value’)
was 1.8°C (Mikkonen et al. 2014) and mean
annual precipitation was 601 mm (Irannezhad et al.
2014). Summer in Finland is naturally moderate
(Dfb) over a small area on the south-west coast
of the country and short (Dfc) across most other
parts (e.g. Peel et al. 2007; Chen and Chen 2013).
For Finland as a whole, the base value for GS
length, defined as the period with mean daily
temperature permanently greater than 5°C (e.g.
Carter 1998; Grimenes and Nissen 2004) was about
116 days, from 25 May to 17 Sep. (Irannezhad and
Kløve 2015). The longest GS was approximately
© 2016 Swedish Society for Anthropology and Geography
ATMOSPHERIC CIRCULATION PATTERNS IN FINLAND, 1961–2011
Fig. 1. (a) Study area, (b) spatial distribution map of long-term average growing season length (GSL) in Finland during 1961–2011
(base value; compiled based on Irannezhad and Kløve 2015) and (c) different regions of Finland defined for this study.
146 days (11 May to 4 Oct. 2011), while the
shortest was about 98 days (4 Jun. to 10 Sep.
1977) (Irannezhad and Kløve 2015). The base value
of GS length in Finland (Fig. 1b) was longest
(range 140–158 days) in south-west coastal areas
(Fig. 1c), naturally decreasing to the shortest range
(25–55 days) in the north of the country (Fig. 1b);
see Irannezhad and Kløve (2015) for more details.
Based on the GS definition given above, in the
present study we calculated GST as the sum of
daily mean temperature and GSP as the sum of
daily precipitation during the GS.
Daily mean temperature and precipitation station
datasets were spatially interpolated onto 3829
regular grid squares (10 × 10 km2 ) throughout
Finland in the period 1961–2011 (Fig. 2a) by
PaITuli-Spatial Data for Research and Teaching
(the data are distributed by the CSC IT Centre for
Science Ltd through their website1 ). To generate
these gridded datasets, the Finnish Meteorological
Institute used daily mean temperature measurements at 100–200 stations (Fig. 2b, d) and daily
precipitation records at 400–600 stations (Fig. 2c, e)
scattered fairly evenly across Finland as input
1
http://www.csc.fi/english
© 2016 Swedish Society for Anthropology and Geography
to a spatial model (Henttonen 1991) based on
the kriging spatial interpolation technique (Ripley
1981); see Venäläinen et al. (2005) for more
details. The gridded daily temperature dataset
has previously been used by Venäläinen and
Heikinheimo (2002), Vajda and Venäläinen (2003),
Vajda (2007), Tietäväinen et al. (2010), Irannezhad
et al. (2015a) and Irannezhad and Kløve (2015).
The precipitation records have also been used in
previous studies in terms of monthly time scale,
e.g. by Ylhäisi et al. (2010) and Aalto et al.
(2013). These gridded daily mean temperature and
precipitation datasets were chosen for use in this
study to visually display the spatial patterns in agroclimate variability in Finland in relation to ACPs.
Table 1 summarises the main information about
influential ACPs for climate variability over the
Northern Hemisphere used by this study. These are
the North Atlantic Oscillation (NAO), the Arctic
Oscillation (AO), the East Atlantic/West Russia
(EA/WR), the East Atlantic (EA), the Scandinavian
(SCA) and the Polar/Eurasian (POL) patterns.
For more details, see Irannezhad et al. (2014,
2015a). Standardised monthly values of these
ACPs were obtained from the website of the
Climate Prediction Center (CPC) at the National
3
MASOUD IRANNEZHAD ET AL.
Fig. 2. (a) Regular grid points (10 × 10 km2 ) covering daily temperature and precipitation datasets for Finland obtained from PaiTuli,
(b) daily temperature measurement stations in Finland, (c) daily precipitation measurement stations in Finland, and (d, e) temporal
variations in the number of daily temperature and precipitation measurement stations in Finland during 1961–2011, respectively.
Oceanic and Atmospheric Administration.2 For
every year between 1961 and 2011, we calculated
the average of these standardised monthly values
over the period April–October during the year as
the corresponding ACP datasets for both GST and
GSP.
Statistical analyses
Statistically significant (p < 0.05) trends in GST
and GSP were determined using the Mann–
Kendall non-parametric test (Mann 1945; Kendall
1975). The Sen method (Sen 1968) was applied
to calculate the slope of detected significant
trends, while the 95% confidence intervals for the
estimated slopes were calculated to acknowledge
uncertainties (Helsel and Hirsch 1992; Drápela and
Drápelova 2011). The Spearman rank correlation
2
http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml
4
(rho) was used to measure relationships between
agroclimatic conditions (GST and GSP) in Finland
and ACPs during 1961–2011. However, in the event
of autocorrelation in time series, the trend-free prewhitening method (Yue et al. 2002) was used to
detect significant trends and the residual bootstrap
method (Park and Lee 2001) with 5000 independent
replications to estimate the standard deviation of the
rho values. All these statistical methods have been
widely used in previous studies evaluating climate
variability and trends (e.g. Tabari et al. 2012; Dai
et al. 2015; Irannezhad and Kløve 2015; Irannezhad
et al. 2015b, 2016a, 2016b).
Results and discussion
Growing season temperature and precipitation as
agroclimate in Finland
On national scale of Finland, both GST and
GSP were strongly correlated with summer
© 2016 Swedish Society for Anthropology and Geography
ATMOSPHERIC CIRCULATION PATTERNS IN FINLAND, 1961–2011
Table 1. Summary of the six well known atmospheric circulation patterns over the Northern Hemisphere considered in this study.
Atmospheric
circulation
pattern
Centre/s of circulation
Natural signature over northern Europe
in positive phase
North Atlantic
Oscillation
Ponta Delagada (Azores) and
Stykkisholmur (Iceland)
Strong westerly circulation bringing
warmer and wetter weather than
normal
Arctic Oscillation
A dipole between the adjacent zonal
ring centred along 45° N and the
polar cap area
Low pressure in the Arctic and high
pressure at mid-latitudes, leading to
warmer and wetter weather than
normal
East Atlantic/West
Russia
Western Europe, north-west Europe and
Portugal in spring and autumn,
Caspian Sea in winter and Russia
Northerly and north-westerly
circulation across the Baltic Sea,
resulting in milder and drier weather
than normal
East Atlantic
North–south dipoles across the North
Atlantic
Intensive westerly circulation, causing
the weather to be warmer and wetter
than normal
Scandinavia
West of Europe, Mongolia and
Scandinavia
High pressure over Scandinavia,
bringing milder and drier weather
than normal
Polar/Eurasia
North Pole, Europe and north-eastern
China
Strong polar vortex resulting in milder
and drier weather than normal
a
Reference
Barnston and
Livezey (1987)
Jones et al. (1997)
Irannezhad et al.
(2014, 2015a)
Thompson and
Wallace (1998)
CPCa
Irannezhad et al.
(2014, 2015a)
Barnston and
Livezey (1987)
Lim and Kim
(2013)
Irannezhad et al.
(2014, 2015a)
Barnston and
Livezey (1987)
CPCa
Irannezhad et al.
(2014, 2015a)
Barnston and
Livezey (1987)
Bueh and
Nakamura
(2007)
Irannezhad et al.
(2014, 2015a)
CPCa
Irannezhad et al.
(2014, 2015a)
http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml
(June–August) season temperature (R2 = 0.72)
and precipitation (R2 = 0.71), respectively. Such
relationships is expected as the average GS length
of 116 days from 25 May to 17 Sep. (Irannezhad and
Kløve 2015) is fairly similar to the climatological
summer season (92 days from 1 Jun. to 31 Aug.)
in Finland. Spatial distribution of GST showed
highest range of correlations (R2 = 0.60–0.76)
with summer temperature over central and northern
Finland (Fig. 3a), where GS length (75–95 days;
Fig. 1b) is almost the same as the summer
duration (92 days covering June–August). The weak
relationships (R2 = 0.20–0.40) between GST and
summer temperature were found in the south and
west of Finland (Fig. 3a) with longer GS (125–
158 days) than the summer duration (Fig. 1b).
On the other hand, GSP was strongly correlated
(R2 = 0.50–0.70) with summer precipitation over
most of north-eastern Finland (Fig. 3b) with shorter
© 2016 Swedish Society for Anthropology and Geography
GS length (55–75 days; Fig. 1b) than the summer
duration. GSP was also in moderate relationships
(R2 = 0.30–0.50) with the summer precipitation
in northern, central and western Finland (Fig. 3b),
where GS length shows a high range of variability
(55–140 days) (Fig. 1b). The weakest correlations
(R2 = 0.10–0.20) between GSP and summer
precipitation, but statistically significant, were seen
over southern and eastern Finland (Fig. 3b), where
GS length generally ranged from 140 to 158 days
(Fig. 1b). These spatial analyses reveal differences
between summer and GS climates across the parts
of Finland with essentially shorter and longer GS
than summer season, particularly in southern and
western Finland. Hence, this study considered GST
and GSP in Finland as agroclimatic conditions
physiologically influencing natural ecosystems,
which may not be fully explained by summer
climate as GS has already become longer.
5
MASOUD IRANNEZHAD ET AL.
Fig. 3. Spatial distribution maps of the coefficient of determinations (R2 ) between growing season and summer (a) temperature sum
and (b) precipitation sum in Finland, 1961–2011. All R2 values ranging from 0.10 to 0.76 are statistically significant (p < 0.05).
Agrocliamte change in Finland (1961–2011)
Interannual variability and long-term trends in
Finnish GST and GSP. For the whole of Finland,
the warmest GS was observed in 2011, with GST
of 1992.0°C, and the coldest GS in 1987, with GST
of 1211.3°C (Fig. 4a). The base value of GST in
Finland for the period 1961–2011 was 1538.0°C.
The trend analysis reveals that GST increased by
5.01±3.17°C year–1 (p < 0.05) on a national scale
in Finland during the period 1961–2011 (Fig. 4a).
The spatial distribution of GST base values over
Finland is displayed in Fig. 5a. The warmest GST,
1950–2200°C, was observed in south-west coastal
regions and GST naturally decreased toward the
north of the country, where the coldest value was
between 250 and 950°C (Fig. 5a). During the
study period (1961–2011), GST showed significant
(p<0.05) warming trends, ranging from 2.5 to
10.0°C year–1 , mostly over south-western coastal
areas and eastern, western, central and northern
Finland (Fig. 6a).
In the period 1961–2011, the wettest GS on a
national scale in Finland occurred in 2011, with
GSP of 365.7 mm, and the driest GS was in
1969, with GSP of 128.4 mm (Fig. 4c). The base
value of GSP for Finland was 223.2 mm (Fig. 4c).
6
The Mann–Kendall non-parametric test reveals a
statistically significant increasing trend (1.39±0.91
mm year–1 ; p < 0.05) in GSP over Finland during
1961–2011 (Fig. 4c). The base value of GSP varied
markedly throughout Finland (Fig. 5b). The driest
GS, with GSP ranging from 60.0 to 90.0 mm,
occurred in the most north-western part of Finland,
while the wettest, with GSP between 270.0 and
300.0 mm, was observed in south-west coastal areas
(Fig. 5b). All the significant (p < 0.05) trends
detected in GSP were positive, with an increase of
1.0–3.0 mm year–1 observed mainly in the northwest, north, centre, east, south and west of Finland
(Fig. 6a).
Along with GS lengthening in Finland, as
reported previously by Irannezhad and Kløve
(2015), the present study indicates that the GS
has generally become warmer and wetter over the
country in recent decades. Spatial analyses of GST
and GSP base values revealed that GS was warmest
and wettest in south-west coastal parts of Finland,
accompanied by the longest GS (Irannezhad and
Kløve 2015). According to latitude, both GST and
GSP naturally changed to the coldest and driest
range in the north of the country with the shortest
GS (Irannezhad and Kløve 2015). Carter (1998)
© 2016 Swedish Society for Anthropology and Geography
ATMOSPHERIC CIRCULATION PATTERNS IN FINLAND, 1961–2011
Fig. 4. Time series with trend line and the most significant associated atmospheric circulation pattern for (a) GST and (c) GSP on a
national scale in Finland, 1961–2011; and scatter plot of Spearman rank correlation between agroclimatic conditions and the most
significant associated atmospheric circulation pattern for (b) GST and EA and (d) GSP and EA/WR.
also concluded that during 1961–1995, the GS was
warmer than during 1891–1925 at Helsinki, Turku
and Kajaani stations in Finland. Moreover, warmer
GS in Finland during recent decades was noted
by Kaukoranta and Hakala (2008). Regarding GSP,
Ylhäisi et al. (2010) concluded precipitation sums
in May–September in the north-east of Finland has
significantly (p < 0.05) increased by 4.8 mm per
decade during 1908–2008. However, they reported
no clear trends in GSP over the north-east and southwest of Finland during 1961–2000. In contrast,
the present study found significant (p < 0.05)
increasing trends in 1961–2011 for GSP in the area
between Kajaani and Kuopio in eastern Finland
(referred to as NE Finland by Ylhäisi et al. 2010),
while there were non-significant increasing trends
(p > 0.05) in the area delineated by Turku-Tampere
and Hämeenlinna in south-west Finland (referred
to as SW Finland by Ylhäisi et al. 2010). Such
dissimilarities may arise from the use of different
data processing approaches (grid by grid here
compared with average of some grids in the area in
their study), GS definitions (the period with daily
mean temperature persistently over 5°C compared
with May–September during a year) and study
period (1961–2011 compared with 1908–2008 and
1961–2000).
© 2016 Swedish Society for Anthropology and Geography
Implications of warmer and wetter GS. The
Nordic countries, including Finland, have particular
growing conditions that are markedly exceptional
and challenging compared with those in other parts
of the continent (Trnka et al. 2011; Peltonen-Sainio
and Niemi 2012). Climate change has already
mitigated some of the conditions that typically limit
northern agricultural systems. It is generally
expected that warmer and wetter GS will increase
agriculture production, but crop responses to such
agroclimatic conditions differ from one region to
another (Peltonen-Sainio et al. 2010, 2011). As
temperature along with sunlight strongly controls
crop growth and development, the warmer GS may
provide improved conditions for agriculture in high
latitude regions where the GS is short (Linderholm
2006; Peltonen-Sainio and Rajala 2007; PeltonenSainio et al. 2016a, 2016c). On the other hand,
the wetter GS primarily alters crop growth,
yield and quality, and also many agricultural
activities like tillage, sowing, crop protection and
harvesting (Peltonen-Sainio et al. 2010, 2011,
2016b). However, due to climate change, more
common warmer and wetter conditions in the
future call for developing macro-strategies to adjust
cropping structures, adopt proper technologies and
improve adaptive infrastructure (IPCC 2013).
7
MASOUD IRANNEZHAD ET AL.
Fig. 5. Spatial distribution maps of long-term average (base value) (a) GST and (b) GSP in Finland, 1961–2011.
In general, cool GS temperature in Finland
provides favourable conditions for increased yields
(Hakala et al. 2012), while it may expose the
risk of delayed harvests, crop quality and yield
losses, failures in harvesting and higher energy
expenses required for seed drying (Peltonen-Sainio
et al. 2014). Warmer GS improves development of
crops in Finland by enabling harvesting in desired
conditions, while negatively influences crop quality
and yield (e.g. size reduction of cereal grains) in
the country (Rötter et al. 2011; Peltonen-Sainio
and Jauhainen 2014). Previous studies indicate that
the warmer GS makes the spring-sown cereals in
Finland prone to losses by shifting sowing and
timing of phenophases, particularly during early
GS (e.g. Peltonen-Sainio et al. 2011; Rötter et al.
2011; Peltonen-Sainio and Jauhainen 2014). In
Finland, the negative effects of warmer GS on the
critical phases of forage crops growth over eastern
and northern production regions generally decline
biomass, intensify lignification process of forage
crops, limit regrowth after cutting, and impact
the quality and digestibility of pasture, hay and
silage (Bertrand et al. 2008). Such impacts on
forage crops may cause great economic losses for
8
animal farms and increase direct and indirect health
risks (Gauly et al. 2013). Besides, the warmer GS
may increase the risk of pest raid and disease prevalence, particularly during early GS over east-central
and southern Finland in agreement with observed
pest migration (Bale et al. 2002; Hakala et al. 2011).
Such early arrivals of pest would enhance aphids
and severity of damage as the risk is at the highest
rate during the seedling stage of spring cereals. For
example, in 2010, 2013 and 2014, the migration
and outbreaks of Plutella xylostella during early GS
resulted in severe damage to oilseed crops in Finland during the warm season (Peltonen-Sainio et al.
2016a). In contrast to all these disadvantages experienced by the most common grown crops in Finland, the warmer GS may favour several crops, like
faba beans (Vicia faba L.) and peas (Pisum sativum
L.) (Stoddard et al. 2009; Peltonen-Sainio et al.
2011), and also encourages cultivation of more robust oilseed rape (Brassica napus L.) over the country (Peltonen-Sainio et al. 2009c). Yet, the warmer
GS is identified as one of the most harmful impacts
of climate change on high latitude agriculture.
Field crop production in Finland is mainly
rain fed. From an agronomic point of view, the
© 2016 Swedish Society for Anthropology and Geography
ATMOSPHERIC CIRCULATION PATTERNS IN FINLAND, 1961–2011
Fig. 6. Spatial distribution maps of trends for (a) GST and (b) GSP in Finland, 1961–2011. Stippling indicates areas where the trends
are statistically insignificant (p>0.05).
distribution of precipitation during GS is often
opposite to the requirements of the crops (PeltonenSainio et al. 2014). The early half of GS (late
spring to early summer) is generally linked with the
drought episodes, while more frequent precipitation
with high accumulated amounts are common in
the latter part of GS (Peltonen-Sainio et al. 2011).
Less precipitation in the early GS is particularly
associated with crop development and growth at
the most important phases of yield determination,
thereby resulting in more frequent yield losses in
Finland. For example, for spring barley (Hordeum
vulgare L.), the yield losses were generally between
7% and 17% over different regions of Finland
during a 30-year period, with the highest rate
in south-western coastal areas and the lowest in
north-eastern parts (Peltonen-Sainio et al. 2011).
Hence, wetter conditions during GS, especially in
its early half, would be favourable for crop yield in
Finland. However, filed operations during short GS
in Finland may suffer from such wetter early GS,
which causes late soil drying for sowing. Wetter
late GS increases nutrient leaching and erosion
due to a long period of bare soil in the autumn
(Børgeson and Olesen 2011; Øygarden et al. 2014)
and results in flooding and lodging (Peltonen-Sainio
et al. 2014) because of high soil moisture content
(Spoor et al. 2003). Hence, impacts of wetter GS
on crop production in Finland are also dependent
on soil conditions such as type, compaction and
management (Turunen et al. 2015).
© 2016 Swedish Society for Anthropology and Geography
Table 2. Spearman rank correlation (rho) between agroclimatic
conditions [growing season temperature (GST) and precipitation
(GSP)] on national scale in Finland and atmospheric circulation
patterns. Significant correlations (p < 0.05) given in bold.
Atmospheric
circulation
pattern
Agroclimatic
conditions
Value
GST
GSP
North Atlantic
Oscillation
East Atlantic
rho
p
–0.04
0.81
–0.17
0.23
rho
p
0.40
0.00
0.30
0.03
East Atlantic/West
Russia
Scandinavia
rho
p
–0.35
0.01
–0.54
0.00
rho
p
0.02
0.93
–0.28
0.04
Polar
rho
p
0.05
0.75
–0.26
0.07
Arctic Oscillation
rho
p
0.20
0.16
–0.04
0.80
Role of atmospheric circulation patterns
On the national scale, GST and GSP showed
the strongest significant relationships with the EA
(rho = 0.40) and the EA/WR (rho = –0.54) patterns
(Fig. 4), respectively, during 1961–2011 (Table 2).
In terms of spatial analysis, the EA pattern has
a significant (p < 0.05) positive effect on GST
in western, upper eastern, central and northern
Finland (rho = 0.30–0.60). However, the EA/WR
9
MASOUD IRANNEZHAD ET AL.
Fig. 7. Spatial distribution maps of Spearman’s rank correlations (first raw) with the most influential atmospheric circulation patterns
(second raw) for (a, c) GST and (b, d) GSP in Finland, 1961–2011. Stippling indicates areas where the correlations are statistically
insignificant (p>0.05).
pattern negatively influences GST over the east and
south of the country, with rho = –0.70 to –0.30
(Fig. 7a, c). For GSP, the strongest negative
correlations (rho = –0.45 to –0.70, p < 0.05)
were found with the EA/WR pattern, mainly for
eastern, western, central, northern and upper parts
of southern Finland and with the POL pattern for
the south of the country (Fig. 7b, d). Conversely,
10
the most significant positive relationships with
variations in GSP, with rho ranging from 0.20
to 0.45 (p < 0.05), were observed with the EA
pattern over northern Finland (Fig. 7b, d). Hence,
the general signatures of the EA and EA/WR
patterns as the most significant ACPs controlling
agroclimate variability in Finland are discussed
below.
© 2016 Swedish Society for Anthropology and Geography
ATMOSPHERIC CIRCULATION PATTERNS IN FINLAND, 1961–2011
Based on the normalised 500 hPa geopotential
height anomalies at four different pressure centres,
Wallace and Gutzler (1981) primarily defined the
EA pattern. Two centres with high pressure are
located in the south-western Canary Islands and
between the Caspian and Black Seas, while two
other centres with low pressure are in the west of
the British Isles and central Serbia (Sáenz et al.
2001; Panagiotopoulos et al. 2002).3 This pattern
describes westerly circulation from east Canada
towards the centre and south of Europe. The EA
positive phase results in above-average pressures
from the west to the east of the Atlantic and
below-average pressures over the west of Ireland.
The positive anomalies (above average) of pressure
across the subtropics during the EA’s positive
phase bring warm air towards Europe, particularly
in summer. In warm months, the northerly wind
over Finland prevailing during the negative phase
of the EA results in below-average temperature
and precipitation, while the positive EA phase
accompanied by dominant anomalous winds from
the south brings warm, humid air to the country
(Irannezhad et al. 2014, 2015a). Such positive
relationships were confirmed by the significant
positive correlations observed in the present study
between agroclimatic conditions (GSP and GST),
which mainly cover the summer season in Finland,
and the EA pattern.
In general, the EA/WR pattern has a strong
control over the climate conditions in Eurasia
during the year. This pattern defines the meridional
circulation over Finland, usually weakening the
effects of westerly airflow (Krichak and Alpert
2005). The positive phase of the EA/WR pattern
describes anomalous northwesterly and northerly
circulation, transporting positive anomalies of
atmospheric pressure towards Europe and northern
China. Its negative phase refers to pressure
anomalies over central parts of the North Atlantic
and northern areas of the Caspian Sea, accompanied
by dominant anomalous southerly and southeasterly
winds (Krichak and Alpert 2005). Hence, the
positive EA/WR mode is complemented by colder
and drier weather than normal (below-average
temperature and precipitation) over large parts of
western Russia, north-eastern Africa and the Arctic
region, but by warmer and wetter weather (aboveaverage temperature and precipitation) in eastern
Asia (e.g. Barnston and Livezey 1987; Lim and Kim
2013). The present study revealed how these well
3
http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml
© 2016 Swedish Society for Anthropology and Geography
known effects of the EA/WR pattern on temperature
and precipitation over northern Europe can be
seen in agroclimatic (GST and GSP) variability in
Finland. However, the effects of the EA/WR pattern
on climate variability in Europe during the year,
particularly temperature and precipitation at high
latitudes, require more investigation.
Conclusions
This study used a gridded daily mean temperature
and precipitation dataset during the period 1961–
2011 for the whole of Finland to analyse
interannual variations and long-term trends in
overall agroclimatic conditions in terms of GST
and GSP and their relationships with various
ACPs. This study confirms that summer climate
may partially explain the variability and trends
of the GST and GSP. On the national scale,
GST and GSP both increased significantly (p <
0.05), by 5.01±3.17 (°C year–1 ) and 1.39±0.91
(mm year–1 ) during 1961–2011, respectively.
Variations in GST and GSP throughout Finland
were positively associated with the EA pattern
(rhoGST = 0.4 and rhoGSP = 0.30) and negatively
associated with the EA/WR pattern (rhoGST = –
0.35 and rhoGSP = –0.54).
In terms of the spatial pattern, GST and GSP
naturally increased from the north to the south of
Finland. GST warming was observed for all parts
of the country except the north-eastern border, the
centre and some areas in the south and west. The
significant GST increase in the north, centre and
west was positively associated with the EA pattern,
while in the east it was negatively associated with
the EA/WR pattern (p < 0.05). GSP also showed
increases in most of northern, eastern, western
and upper central Finland. In these areas, GSP
was significantly associated with the variability
in the EA/WR and EA patterns. In general, the
overall agroclimatic conditions became warmer and
wetter during the period 1961–2011, and were
significantly dependent upon variations in the EA
and EA/WR patterns.
Acknowledgements
The authors gratefully acknowledge the Finnish
Cultural Foundation and Maa- ja Vesitekniikan
Tuki r.y. for funding this research. We also thank
the CSC_IT Centre for Science Ltd for providing
gridded daily mean temperature and precipitation
datasets for Finland and the Climate Prediction
Center at the National Oceanic and Atmospheric
Administration of the United States for making
11
MASOUD IRANNEZHAD ET AL.
available online the standardised monthly values
of ACPs. Support from Swedish BECC, MERGE
and VR is also acknowledged.
Masoud Irannezhad and Bjørn Kløve, Water Resources
and Environmental Engineering Research Group, Faculty of
Technology, University of Oulu, 90014 Oulu, Finland
Email: [email protected], [email protected]
Deliang Chen, Regional Climate Group, Department of Earth
Sciences, University of Gothenburg, PO Box 460, 405 30
Gothenburg, Sweden Email: [email protected]
References
Aalto, J., Pirinen, P., Heikkinen, J. and Venalainen, A., 2013.
Spatial interpolation of monthly climate data for Finland:
comparing the performance of kriging and generalized
additive models. Theoretical and Applied Climatology,
112 (1–2), 99–111.
Adams, R.M., Rosenzweig, C., Peart, R.M., Richie, J.T.,
McCarl, B.A., Glyer, J.D., Curry, R.B., Jones, J.W., Boote,
K.J. and Allen, L.H. Jr, 1990. Global climate change and
US agriculture. Nature, 345, 219–224.
Bale, J.S., Masters, G.J., Hodkinson, I.D., Awmack, C.,
Bezemer, T.M., Brown, V.K., Butterfield, J., Buse, A.,
Coulson, J.C, Farrar, J., Good, J.E.G., Harrington, R.,
Hareley, S., Jones, T.H., Lindroth, R.L., Press, M.C.,
Symrnioudis, I., Watt, A.D. and Whittaker, J.B. 2002.
Herbivory in global climate change research: direct
effects of rising temperature on insect herbivores. Global
Change Biology, 8, 1–16.
Barnston, A.G. and Livezey, R.E., 1987. Classification, seasonality and persistence of low-frequency atmospheric
circulation patterns. Monthly Weather Review, 115, 1083–
1126.
Bartolini, E., Claps, P. and D’Odorico, P., 2009. Interannual
variability of winter precipitation in the European Alps:
relations with the North Atlantic Oscillation. Hydrology
and Earth System Sciences, 13, 17–25.
Bertrand, A., Tremblay, G.F., Pelletier, S., Castonguay, Y. and
Bélanger, G., 2008. Yield and nutritive value of timothy as
affected by temperature, photoperiod and time of harvest.
Grass and Forage Science, 63, 421–432.
Bonsal, B.R., Zhang, X. and Hogg, W.D., 1999. Canadian
Prairie growing season precipitation variability and
associated atmospheric circulation. Climate Research,
11, 191–208.
Børgeson, C. and Olesen, J.E., 2011. A probabilistic
assessment of climate change impacts on yield and
nitrogen leaching from winter wheat in Denmark.
Natural Hazards and Earth System Sciences, 11, 2541–
2553.
Bueh, C. and Nakamura, H., 2007. Scandinavian pattern
and its climatic impact. Quarterly Journal of the Royal
Meteorological Society, 133, 2117–2131.
Carter, T., 1998. Changes in the thermal growing season in
Nordic countries during the past century and prospects
for the future. Agriculture and Food Science in Finland,
7, 161–179.
Castro, M., Gallardo, C., Jylhä, K. and Tuomenvrita, H., 2007.
The use of a climate-type classification for assessing
12
climate change effects in Europe from an ensemble of
regional climate models. Climatic Change, 81, 329–341.
Chen, C-C., McCarl, B. and Hill, H., 2002. Agricultural value
of ENSO information under alternative phase definition.
Climatic Change, 54, 305–325.
Chen, D. and Chen, H.W., 2013. Using the Köppen
classification to quantify climate variations and change:
an example for 1901–2010. Environmental Development,
6, 69–79.
Chen, D. and Chen, Y., 2003. Association between winter
temperature in China and upper air circulation over East
Asia revealed by canonical correlation analysis. Global
and Planetary Change, 37, 315–325.
Dai, S., Shulski, M.D., Hubbard, K.G. and Takle, E.S., 2015.
A spatiotemporal analysis of Midwest US temperature
and precipitation trends during the growing season from
1980 to 2013. International Journal of Climate Change.
doi:10.1002/joc.4354
Dayan, U. and Lamb, D., 2005. Global and synoptic-scale
weather patterns controlling wet atmospheric deposition
over central Europe. Atmospheric Environment, 39, 521–
533.
Dore, M.H.I., 2005. Climate change and changes in global
precipitation patterns: what do we know? Environment
International, 31, 1167–1181.
Drápela, K. and Drápelova, I., 2011. Application of MannKendall test and the Sen’s slope estimates for trend
detection in deposition data from Bily Křiž. Beskydy
Mts., the Czech Republic. 1997–2010. Beskydy, 4 (2),
133–146.
Fischer, G., Shah, M., Tubiello, F.N. and van Velhuizen, H., 2005. Socio-economic and climate change
impacts on agriculture: an integrated assessment,
1990–2080. Philosophical Transactions B, 360, 2067–
2083.
Gauly, M., Bollwein, H., Breves, G., Brügemann, K.,
Dänicke, S., Das, G., Demeler, J., Hansen, H., Isselstein,
J., König, S., Lohölter, M., Martinsohn, M., Meyer,
U., Potthoff, M., Stinshoff, H. and Wrenzycki, C.,
2013. Future consequences and challenges for dairy
cow production systems arising from climate change in
Central Europe – a review. Animal, 7, 843–859.
Glantz, M.H., Katz, R.W. and Nicholls, N. (eds), 2009.
Teleconnections Linking Worldwide Climate Anomalies:
Scientific Basis and Societal Impact. Cambridge
University Press, New York.
Grimenes, A.A. and Nissen, Ø., 2004. Mathematical
modelling of the annual temperature wave based on
monthly mean temperatures, and comparison between
local climate trends at seven Norwegian stations.
Theoretical and Applied Climatology, 78, 229–246.
Hakala, K., Hannukkala, A., Huusela-Veistola, E., Jalli, M.
and Peltonen-Sainio, P., 2011. Pests and diseases in a
changing climate: a major challenge for Finnish crop
production. Agricultural and Food Science, 20, 3–14.
Hakala, K., Jauhiainen, L., Himanen, S., Rötter, R., Salo, T.
and Kahiluoto, H., 2012. Sensitivity of barley varieties to
weather in Finland. Journal of Agricultural Science, 150,
145–160.
Hatfield, J.L., 2010. Climate impacts on agriculture in the
United States: the value of past observations. In: Hillel, D.
and Rosenzwieg, C. (eds), Handbook of Climate Change
© 2016 Swedish Society for Anthropology and Geography
ATMOSPHERIC CIRCULATION PATTERNS IN FINLAND, 1961–2011
and Agroecosystems: Impact, Adaptation and Mitigation.
Imperial College Press, London. 239–253.
Hatfield, J.L., 2013. North American perspectives on
potential climate change and agricultural responses. In:
Hillel, D. and Rosenzweig, C. (eds), Handbook of Climate
Change and Agroecosystems. Mainland Press, Singapore.
33–55.
Helsel, D.R. and Hirsch, R.M., 1992. Statistical methods in
water resources: Amsterdam, Elsevier Science Publishers, Studies in Environmental Science, 49, 522 p.
Henttonen, H., 1991. Kriging in interpolating July mean
temperatures and precipitation sums. Reports from
the Department of Statistics, University of Jyväskylä,
12.
Hill, H.S., Park, J., Mjelde, J.W., Rosenthal, W., Love, H.A.
and Fuller, S.W., 2000. Comparing the value of Southern
Oscillation Index based climate forecast methods for
Canadian and U.S. wheat producers. Agricultural and
Forest Meteorology, 100, 261–272.
Hurrell, J.W., 1995. Decadal trends in the North Atlantic
Oscillation and relationship to regional temperature and
precipitation. Science, 269, 676–679.
IPCC, 2013. Summary for policymakers. In: Stocker,
T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen,
S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and
Midgley, P.M. (eds), Climate Change 2013: The Physical
Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge University Press,
Cambridge, 28 p.
Irannezhad, M. and Kløve, B., 2015. Do atmospheric
teleconnection patterns explain variations and trends
in thermal growing season parameters in Finland?
International Journal of Climatology, 35 (15), 6419–
6430.
Irannezhad, M., Chen, D. and Kløve, B., 2015a. Interannual
variations and trends in surface air temperature in Finland
in relation to atmospheric circulation patterns, 1961–
2011. International Journal of Climatology, 35 (10),
3078–3092.
Irannezhad, M., Marttila, H., Chen, D. and Kløve,
B., 2016a. Century-long variability and trends in
daily precipitation characteristics at three Finnish
stations. Advances in Climate Change Research.
doi:10.1016/j.accre.2016.04.004.
Irannezhad, M., Marttila, H. and Kløve, B., 2014. Longterm variations and trends in precipitation in Finland.
International Journal of Climatology, 34 (10), 3139–
3153.
Irannezhad, M., Ronkanen, A-K. and Kløve, B., 2015b.
Effects of climate variability and change on snowpack
hydrological processes in Finland. Cold Regions Science
and Technology, 118, 14–29.
Irannezhad, M., Ronkanen, A-K. and Kløve, B., 2016b.
Wintertime climate factor controlling snow resource
decline in Finland. International Journal of Climatology,
36, 110–131.
Irannezhad, M., Torabi Haghighi, A., Chen, D. and Kløve,
B., 2015c. Variability in dryness and wetness in
central Finland and the role of teleconnection patterns.
Theoretical and Applied Climatology, 122 (3), 471–486.
© 2016 Swedish Society for Anthropology and Geography
Jaagus, J., 2009. Regionalization of the precipitation pattern
in the Baltic Sea drainage basin and its dependence on
large-scale atmospheric circulation. Boreal Environment
Research, 14, 31–44.
Jones, P.D., Briffa, K.R., Osborn, T.J., Moberg, A. and
Bergström, H., 2002. Relationships between circulations
strength and the variability of growing-season and coldseason climate in northern and central Europe. The
Holocene, 12 (6), 6343–6356.
Jones, P.D., Jónsson, T. and Wheeler, D., 1997. Extension to
the North Atlantic Oscillation using early instrumental
pressure observations from Gibraltar and South-West
Iceland. International Journal of Climatology, 17, 1433–
1450.
Karlsson, P.E., Tang, L., Sundberg, J., Chen, D., Lindskog,
A. and Pleijel, H., 2007. Increasing risk for negative
ozone impacts on vegetation in northern Sweden.
Environmental Pollution, 150, 96–106.
Kaukoranta, T. and Hakala, K., 2008. Impact of spring
warming on sowing times of cereal, potato and sugar
beet in Finland. Agriculture and Food Science, 17, 165–
176.
Kendall, M.G., 1975. Rank Correlation Methods. Griffin,
London.
Klein Tank, A.M.G., Wijngaard, J.B., Können, G.P., Böhm,
R., Demarée, G., Gocheva, A. et al., 2002. Daily dataset
of 20th century surface air temperature and precipitation
series for the European climate assessment. International
Journal of Climatology, 22, 1441–1453.
Krichak, S.O. and Alpert, P., 2005. Decadal trends in the
East Atlantic–West Russia pattern and Mediterranean
precipitation. International Journal of Climatology, 25,
183–192.
Legler, D.M., Bryant, K.J. and O’Brien, J.J., 1999. Impact
of ENSO related climate anomalies on crop yields in the
US. Climatic Change, 42, 351–375.
Lim, Y-K. and Kim, H-D., 2013. Impact of the dominant
large-scale teleconnections on winter temperature
variability over East Asia. Journal of Geophysical
Research: Atmosphere, 118, 7835–7848.
Linderholm, H.W., 2006. Growing season changes in the last
century. Agricultural and Forest Meteorology, 137, 1–
14.
Linderholm, H.W., Walther, A. and Chen, D., 2008.
Twentieth-century trends in the thermal growing season
in the Greater Baltic Area. Climatic Change, 87, 405–
419.
Lobell, D.B. and Burke, M.B., 2008. Why are agricultural
impacts of climate change so uncertain? The important
of temperature relative to precipitation. Environmental
Research Letter, 3, 1–8.
Lobell, D.B., Burke, M.B., Tebaldi, C., Mastrandrea, M.D.,
Falcon, W.P. and Naylor, R.L., 2008. Prioritizing climate
change adaptation needs for food security in 2030.
Science, 319, 607–610.
Lobell, D.B., Cahill, K.N. and Field, C.B., 2007. Historical
effects of temperature and precipitation on California
crop yields. Climatic Change, 81, 187–203.
Lobell, D.B., Schlenker, W. and Costa-Robberts, J., 2011.
Climate trends and global crop production since 1980.
Science, 333, 616–620.
13
MASOUD IRANNEZHAD ET AL.
Mann, H.B., 1945. Nonparametric tests against trend.
Econometrica, 13, 245–259.
Menzel, A., 2003. Phenological anomalies in Germany and
their relation to air temperature and NAO. Climatic
Change, 57, 243–263.
Mikkonen, S., Laine, M., Mäkelä, H.M., Gregow, H.,
Tuomenvirta, H., Lahtinen, M. and Laaksonen, A., 2014.
Trends in the average temperature in Finland, 1847–2013.
Stochastic and Environment Research and Risk Analysis,
29, 1521–1529.
Øygarden, L., Deelstra, J., Lagzdins, A., Bechmann, M.,
Greipsland, I., Kyllmar, K., Povilaitis, A. and Iital,
A., 2014. Climate change and the potential effects on
runoff and nitrogen losses in the Nordic-Baltic region.
Agriculture, Ecosystems and Environment, 198, 114–
126.
Panagiotopoulos, F., Shahgedanova, M. and Stephen, D.B.,
2002. A review of Northern Hemisphere wintertime
teleconnection patterns. Journal de Physique, IV(1), 27–
47.
Park, E. and Lee, Y.J., 2001. Estimates of standard
deviation of Spearman’s rank correlation coefficients with
dependent observations. Communications in Statistics Simulation and Computation C, 30 (1), 129–142.
Peel, M.C., Finlayson, B.L. and McMahon, T.A., 2007.
Update world map of the Köppen-Geiger climate
classification. Hydrology and Earth System Sciences, 11,
1633–1644.
Peltonen-Sainio, P. and Jauhiainen, L., 2014. Lessons from
the past in weather variability: sowing to ripening
dynamics and yield penalties for northern agriculture in
1970–2012. Regional Environmental Change, 14, 1505–
1516.
Peltonen-Sanio, P. and Niemi, J.K., 2012. Protein crop
production at the northern margin of farming: to boost,
or not to boost. Agriculture and Food Science, 21, 370–
383.
Peltonen-Sainio, P. and Rajala, A., 2007. Duration of
vegetative and generative development phases in oat
cultivars released since 1921. Field Crops Research, 101,
72–79.
Peltonen-Sainio, P., Jauhiainen, L. and Hakala, K.,
2011. Crop responses to temperature and precipitation
according to long-term multi-location trials at highlatitude conditions. Journal of Agricultural Science, 149,
49–62.
Peltonen-Sainio, P., Jauhiainen, L., Hakala, K. and Ojanen,
H., 2009a. Climate change and prolongation of growing
season: changes in regional potential for field crop
production in Finland. Agriculture and Food Science 18,
171-190.
Peltonen-Sainio, P., Jauhiainen, L. and Laurila, I.P., 2009b.
Cereal yield trends in northern European conditions:
changes in yield potential and its realization. Field Crops
Research, 110, 85–90.
Peltonen-Sainio, P., Jauhiainen, L. and Venäläinen, A., 2009c.
Comparing regional risks in producing turnip rape and
oilseed rape – today in light of long-term datasets. Acta
Agriculturae Scandinavica, B Soil and Plant Science, 59,
118–128.
Peltonen-Sainio, P., Jauhiainen, L., Trnka, M., Olesen, J.E.,
Calanca, P., Eckersten, H. et al., 2010. Coincidence of
14
variation in yield and climate in Europe. Agriculture,
Ecosystems and Environment, 139, 483–489.
Peltonen-Sainio, P., Pirinen, P., Mäkelä, H.M., Hyvärinen,
O., Huussela-Veistola, E., Ojanen, H. and Venäläinen, A.,
2016a. Spatial and temporal variation in weather events
critical for boreal agriculture: I. Elevated temperatures.
Agricultural and Food Science, 25, 44–56.
Peltonen-Sainio, P., Pirinen, P., Mäkelä, H.M., Ojanen, H. and
Venäläinen, A., 2016b. Spatial and temporal variation
in weather events critical for boreal agriculture: II.
Precipitation. Agricultural and Food Science, 25, 57–
70.
Peltonen-Sainio, P., Rajala, A., Känkänen, H. and Hakala, K.,
2014. Improving farming systems in northern European
conditions. In: Sadras, V.O. and Calderini, D. (eds), Crop
Physiology: Applications for Genetic Improvement and
Agronomy, updated edition. Elsevier, Amsterdam, 65–
91.
Peltonen-Sainio, P., Venäläinen, A., Mäkelä, H.M., Pirinen,
P., Laapas, M., Jauhiainen, L., Kaseva, J., Ojanen, H.,
Korhonen, P., Huusela-Veistola, E., Jalli, M., Hakala, K.,
Kaukoranta, T. and Virkajärvi, P., 2016c. Harmfulness
of weather events and adaptive capacity of farmers at
high latitudes of Europe. Climate Research, 67, 221–240.
doi:10.3354/cr01378
Ripley, B.D., 1981. Spatial Statistic. Wiley, New York.
Rötter, R.P., Palosuo, T., Pirttioja, N.K., Dubrovsky, M.,
Salo, T., Ristolainen, A., Fronzek, S., Aikasalo, R.,
Trnka, M. and Carter, T.R., 2011. What would happen to
barley production in Finland if global warming exceeded
4°C? A model-based assessment. European Journal of
Agronomy, 35, 205–214.
Ruosteenoja, K., Räisänen, J. and Pirinen, P., 2011. Projected
changes in thermal seasons and the growing season in
Finland. International Journal of Climatology, 31, 1473–
1487.
Sáenz, J., Rodrı́guez-Puebla, C., Fernández, J. and Zubillaga,
J., 2001. Interpretation of interannual winter temperature
variations over southwestern Europe. Journal of
Geophysical Research, 106 (D18), 20641–20651.
Sen, P.K., 1968. Estimates of the regression coefficient based
on Kendall’s tau. International Journal of American
Statistical Association, 63, 1379–1389.
Spoor, G., Tijink, F.G.J. and Weisskopf, P., 2003.
Subsoil compaction: risk, avoidance, identification and
alleviation. Soil & Tillage Research, 73, 175–182.
Stenseth, N.C., Mysterud, A., Ottersen, G., Hurrell, J.W.,
Chan, K-S. and Lima, M., 2002. Ecological effects of
climate fluctuations. Science, 297, 1292–1296.
Stoddard, F., Hovinen, S., Kontturi, M., Lindström, K. and
Nykänen, A., 2009. Legumes in Finnish Agriculture:
history, present status and future prospects. Agricultural
and Food Science, 18, 191–205.
Tabari, H., Hosseinzadeh Talaee, P., Ezani, A. and Shifteh
Some’e, B., 2012. Shift changes and monotonic trends in
autocorrelated temperature series over Iran. Theoretical
and Applied Climatology, 109, 95–108.
Takle, E.S., Anderson, C., Andresen, J., Angel, J.R., Elmore,
R., Gramig, B., Guinan, P., Hilberg, S., Kluck, D., Massey,
R., Niyogi, D., Schneider, J., Shulski, M., Todey, D. and
Widhalm, M., 2014. Climate forecasts for corn producer
decision-making. Earth Interactions, 18, 1–8.
© 2016 Swedish Society for Anthropology and Geography
ATMOSPHERIC CIRCULATION PATTERNS IN FINLAND, 1961–2011
Thompson, D.W.J. and Wallace, J.M., 1998. The Arctic
Oscillation signature in the wintertime geopotential
height and temperature fields. Geophysical Research
Letter, 25, 1297–1300.
Tietäväinen, H., Tuomenvirta, H. and Venäläinen, A., 2010.
Annual and seasonal mean temperatures in Finland
during the last 160 years based on gridded temperature
data. International Journal of Climatology, 30, 2247–
2256.
Trnka, M., Olesen, J.E., Kersebaum, K.C., Skjelvåg, A.O.,
Eitzinger, J., Seguin, B. et al., 2011. Agroclimatic
conditions in Europe under climate change. Global
Change Biology, 17, 2298–2318.
Turunen, M., Warsta, L., Paasonen-Kivekäs, M., Nurminen,
J., Alakukku, L., Myllys, M. and Koivusalo, H., 2015.
Effects of terrain slope on long-term and seasonal
water balances in clayey, subsurface drained agricultural
fields in high latitude conditions. Agricultural Water
Management, 150, 139–151.
Vajda, A., 2007. Spatial variation of climate and the impact
of disturbances on local climate and forest recovery
in northern Finland. Finnish Meteorological Institute
Contributions, 64.
Vajda, A. and Venäläinen, A., 2003. The influence of natural
conditions on the spatial variations of climate in Lapland,
northern Finland. International Journal of Climatology,
23, 1011–1022.
© 2016 Swedish Society for Anthropology and Geography
Venäläinen, A. and Heikinheimo, M., 2002. Meteorological
data for agricultural applications. Physics and Chemistry
of the Earth, Parts A/B/C, 27 (23–24), 1045–
1050.
Venäläinen, A., Tuomenvirta, H., Pirinen, P. and Drebs,
A., 2005. A basic Finnish climate dada set 1961–2000
– description and illustrations. Finnish Meteorological
Institute, Reports No. 2005:5, 27.
Wallace, J.M. and Gutzler, D.S., 1981. Teleconnection in the
geopotential height field during the Northern Hemisphere
winter. Monthly Weather Review, 109, 784–812.
Wolfe, D.W., 2013. Contributions to climate change solutions
from the agronomy perspective. In: Hillel, D. and
Rosenzweig, C. (eds), Handbook of Climate Change and
Agroecosystems. Mainland Press, Singapore, 11–29.
Ylhäisi, J.S., Tietäväinen, H., Peltonen-Sainio, P., Venäläinen,
A., Eklund, J., Räisänen, J. and Jylhä, K., 2010. Growing
season precipitation in Finland under recent and projected
climate. Natural Hazards and Earth System Sciences, 10,
1563–1574.
Yue, S., Pilon, P., Phinney, R. and Cavadias, G., 2002. The
influence of autocorrelation on the ability to detect trend
in hydrological series. Hydrological Processes, 16, 1807–
1829.
Manuscript received 25 Dec., 2015; revised and accepted 7
Jun., 2016
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