Influence of seasonal pressure patterns on
temporal variability of vegetation activity in
central Siberia
SERGIO M. VICENTE-SERRANO, MANUELA GRIPPA, NICOLAS
DELBART, THUY LE TOAN and LAURENT KERGOAT
Centre d'Etudes Spatiales de la Biosphère (CESBIO), 18 avenue. Edouard Belin,
bpi 2801, 31401 Toulouse cedex 9, France
Abstract. This paper analyses the spatial distribution of the inter-annual variability of
vegetation activity in central Siberia and its relationship with atmospheric circulation
variability. We used NOAA-AVHRR NDVI series from Pathfinder Land Data Set at 1
degree of spatial resolution, and we calculated the annual vegetation activity in each pixel
(aNDVI) from 1982 to 2001. Principal Component Analysis was used to determine the
general spatial patterns of inter-annual variability of vegetation activity. We identified
three main modes, which explain more than 50 % of total variance, each corresponding to
a large region. By means of surface pressure grids we analysed the main patterns of the
seasonal atmospheric circulation in the study area: its variability was summarised by
means of a few circulation modes and the patterns differ significantly between winter,
spring and summer. However, a pattern with a North-South dipole structure represents the
general spatial pattern of atmospheric circulation.
We investigated the effect of seasonal atmospheric circulation patterns on the inter-annual
variation of vegetation activity. In general, the strongest relationships between the
atmospheric circulation variability, climate and the aNDVI variability were found in areas
where the climatic characteristics are more limiting for the vegetation development, such
as the northern regions. This may be explained by the fact that in these areas the
variability of atmospheric circulation modes determines summer temperatures, which
have a direct impact on vegetation activity.
Key Words. Vegetation activity, NDVI, Atmospheric Circulation, Teleconnections,
Principal Component Analysis, Climate – vegetation relationships, Siberia.
1. Introduction
Several studies have provided evidence that climate is an important constraint to vegetation
activity and its temporal variability (Schultz and Halpert 1993; Nemani et al. 2003). In cold
regions and in areas where moisture availability is sufficient for vegetation to develop, the
main constraint is temperature (Braswell et al. 1997; Suzuki et al. 2000 and 2001; Gong and
Ho 2003).
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At present, some serious questions arise about the possible consequences of climate change
for the distribution of vegetation types and biomass production. The influence of climate on
vegetation needs to be better observed and understood in order to develop possible strategies
to mitigate the negative consequences of climate change. On a global scale there is evidence
of recent climate changes in terms of temperature increase (Houghton et al. 2001), increase of
extreme events (Easterling et al. 2000) and changes in the main atmospheric circulation
modes (Hurrell 1995; Trenberth 1997). The effects of these changes on vegetation have been
observed in large areas of the Northern Hemisphere mainly as an increase of vegetation
activity (Slayback et al. 2003; Lucht et al. 2002; Zhou et al. 2003) and an earlier onset of
greening (Myneni et al. 1998).
Different studies have analysed the influence of precipitation, snow cover and temperature on
vegetation dynamics and biomass at high latitudes (Nemani et al. 2003; Dye and Tucker
2003). Nevertheless few papers have investigated the influence of atmospheric circulation on
vegetation (i.e. Gong and Ho 2003; Buermann et al. 2003). This may be important in order to
better understand the effects of climate change on ecosystems.
Most analyses of the influence of atmospheric circulation on vegetation activity have been
focused on the impact of El Niño-Southern Oscillation (ENSO) (e.g., Kogan 2000; SalinasZavala et al. 2002; Eastman and Fulk 1993), which is one of the main sources of atmospheric
circulation variability on a global scale (Philander 1990; New et al. 2001). Kogan and Wei
(2000) analysed the response of global vegetation to ENSO and showed that this atmospheric
mode mainly affects tropical areas and some regions of the Southern Hemisphere. However,
Buerman et al. (2003) also indicated some ENSO influence in some areas of the Northern
Hemisphere.
In the Northern Hemisphere, although ENSO affects atmospheric circulation, precipitation
and temperature (Rogers 1984; Fraedrich 1994; Ropelewski and Halpert 1987), other
2
atmospheric modes dominate climate variability (Hurrell and van Loon 1997; Thompson et al.
2002). These modes are summarised by means of teleconnection indices such as the Arctic
Oscillation (Thompson and Wallace 1998), the North Atlantic Oscillation (Hurrell 1995) and
other atmospheric configurations (Wallace and Gutzler 1981).
Although the influence of Northern Hemisphere atmospheric circulation patterns on
precipitation and temperature are well known, few analyses have been done on the role of
atmospheric circulation in the variability of vegetation activity (i.e. Vicente-Serrano and
Heredia 2004; Wang 2003; Wang and You 2004; Gong and Hu 2003). Gong and Shi (2003)
indicated that 57.2% of the temporal variability of vegetation cover in the Northern
Hemisphere is explained by the variability of atmospheric circulation, and vegetation activity
trends in some areas can be explained by trends in the principal modes of atmospheric
circulation.
Studies on smaller spatial scales are even scarcer. In particular the highest latitudes of the
Northern Hemisphere, where the influence of the atmospheric circulation pattern on
vegetation may be very important, have been not analysed. These regions, where the natural
vegetation has not been modified by human activities, are especially sensitive to climate
change due to the limiting climatic conditions (Brovkin et al. 2003; Esper and Schweingruber
2004). They are also important carbon reservoirs (Baldocchi et al. 2000; Bird et al. 2002). For
these reasons, it is crucial to understand how atmospheric variability affects vegetation
functioning in these areas.
2. Study area
This study was carried out in central Siberia, at longitudes between 55°E and 115°E and
latitudes between 50°N and 80°N (Figure 1). This area extends from west of the Ural
Mountains to east of Lake Baikal, and it includes the Ob and Yenissei River basins. The Ob
3
River basin in the west is characterised by flat terrain where wetlands are abundant. The
highest elevated areas (about 2500m) are found around Lake Baikal and in the central
southern areas; an extended mountainous region with an elevation of about 1000m is also to
be found at about 60°N and 90°E.
Climate in this area is extremely continental. Mean annual temperatures go from about 0°C in
the southwest (with a monthly mean temperature of about -20°C in January and about 22°C in
July) to about –20°C in the northeast (with a monthly mean temperature of about -40°C in
January and about 5°C in July). Average annual precipitation is highest in the west (for
maximum values of about 600 mm) and generally decreases from west to east, reaching
minimum values in the northeast (200 mm or less) (Wang and Cho 1997). Winter is
characterised by wetter conditions in the west central region (influenced by the moisture of
Mediterranean air masses) and dryer conditions in the east, especially east of the Yenissei
River. Nevertheless the inter-annual variability of climate in these regions is very high (Wang
and Cho 1997; Ye 2001). Winter precipitation falls as snow, the inter-annual variability of
which is significantly influenced by atmospheric circulation and the ocean (Clark et al. 1999;
Ye 2000). The north-eastern areas are characterised by the presence of permafrost conditions
(Malevsky-Malevich et al. 2001) but also by an important inter-annual variability of
precipitation (Frauenfeld et al. 2004).
Figure 1 shows the spatial distribution of land cover (De Fries et al. 1998). There is a clear
organisation of vegetation in latitudinal belts created by the climatic characteristics (Suzuki et
al. 2000; Zhang et al. 2004). Steppe and crops are dominant in the south-western areas
characterised by higher temperatures and low precipitation. Different bands of forests are
observed at latitudes about 55°N- 67°N. The forest types differ depending on climatic
characteristics, mainly temperatures, which determine their phenological cycles (Zhang et al.
4
2004). North of the forest belts are tundra areas, where the activity of vegetation, composed of
mosses and lichens, is limited to the summer months, when snow cover disappears.
3. Methods
3.1. Estimation of the spatial and temporal variability of vegetation from remote sensing
Optical remote sensing has been widely used to analyse vegetation activity (Nicholson et al.
1990; Ringrose and Matheson 1991; Kogan 2001). Different satellite sensors provide spectral
data of the terrestrial surface in different spatial and temporal resolutions. The principles for
vegetation monitoring using this kind of sensor are based on the response of vegetation to
radiation in the visible and near-infrared regions of the electromagnetic spectrum. Visible
radiation is mainly absorbed by vegetation while near-infrared radiation is primarily reflected
(Knipling 1970). These properties have made it possible to create vegetation indexes that have
been used to derive the vegetation type and cover and to monitor the dynamics of vegetation
activity (Kogan 2001). The most widely used among these indexes is the Normalized
Difference Vegetation Index (NDVI) (Tucker 1979), which is calculated as: NDVI = (infrared
– red)/(infrared + red).
Different NDVI global data series are available from AVHRR data (PAL and GIMMS NDVI)
(Justice and Townshend 1994, James and Kalluri 1994). The PAL-NDVI database
(http://daac.gsfc.nasa.gov) provides data for every ten days from 1981 to 2000. The
homogenisation of the series has been checked with good results (Smith et al. 1997,
Kaufmann et al. 2000). Although some errors in the computation of solar zenith angles (SZA)
have been found (NASA 2004), Kaufmann et al. (2000) concluded that the PAL-NDVI data
set temporal homogeneity is not affected by SZA changes. Shabanov et al. (2002) also
provided evidence that the NDVI trends recorded in some regions of the world are not related
to errors in the data process, and Gong and Ho (2003) indicated that the NDVI-climate
5
coupling modes are stable and detectable even when there are errors in the original NDVI data
set.
Here we used the PAL-NDVI data set at 1° of spatial resolution and at temporal resolution of
ten days from 1982 to 2001. From this data set we calculated the vegetation activity for each
year adapting the method used by Zhang et al. (2003) to determine phenological dates from
the MODIS vegetation index. This method fits logistic functions y(t) to the PAL NDVI annual
time series in each pixel:
y (t ) 
1 e
c
d
(a  bt )
where c+d is the maximum NDVI value and c the minimum value each year. One such
function fits the ascending part of the NDVI cycle, and another one fits the descending part.
This method permits the elimination of perturbations due to noise.
To avoid snow effect at the early and late stages of the NDVI cycle, the cumulated (aNDVI)
is taken as the sum of the two functions between the dates of maximum curvature on the fitted
functions. Consequently, the NDVI cumulated in this way relates the two quantities that are
important for this study: the duration of the activity season and the intensity of this activity,
denoted by the maximum NDVI value.
For each pixel of 1° of spatial resolution we obtained a time series of 20 years that we used to
investigate the inter-annual variability of vegetation activity in central Siberia.
3.2. Study of the spatial and temporal variability of vegetation activity by Principal
Component Analysis
To summarise the inter-annual vegetation activity and identify the main modes of vegetation
evolution we used Principal Component Analysis (PCA). The application of PCA to different
time series in a specific area can be performed in spatial (S) or temporal (T) modes (Jollife
1990; Serrano et al., 1999). The T-mode, where the time observations are the variables and
6
the pixels the cases, permits the identification of the general spatial patterns of climatic
variables. In contrast, the S-mode is used to determine the main patterns of the time series
evolution. In remote sensing PCA has usually been applied to summarise spectral data
(Richards 1993). However, it has also been used in the T-mode to classify land uses and land
cover (Townshend 1984) and in the S-mode to analyse vegetation variability or land use
changes (Townshend et al. 1985; Fung and LeDrew 1987; Eastman and Fulk 1993).
In this paper we used PCA in S-mode from the aNDVI time series of each pixel to identify the
main modes of the variability of vegetation activity. Since in the study area the vegetation
cover is highly variable in space, we standardised the original aNDVI values to avoid
differences caused by environmental conditions or by vegetation type and coverage. The
standardised series of NDVI have been widely used to detect spatio-temporal anomalies in
vegetation activity (Gutman and Ignatov 1995; Maseli et al. 1992; Hartmam et al. 2003).
Peters et al. (2002) showed that long series of NDVI are close to a normal distribution and
that the standardisation procedure is a good approach with which to normalise vegetation
series in time and space. Consequently, previous to PCA, each series of 1° was standardised
according to its mean and standard deviation.
Finally, the components obtained from the PCA were rotated to redistribute the final
explained variance. The rotation procedure makes it possible to obtain a clearer separation of
components that maintain their orthogonality (Hair et al. 1998). We used the Varimax rotation
(Kaiser 1958), which is the most widely applied option because it produces more stable and
physically robust patterns (Richman 1986; Ye 2002; Kadioglu 2000; Serrano et al. 1999).
The land cover map by De Fries et al. (1998) (Figure 1) was used to calculate the percentage
of each land cover that corresponds to the representative areas of each component according
to significant REOF (Rotated Empirical Function Values).
7
3.3. Analysis of the seasonal atmospheric circulation variability in Siberia using surface
pressure data
The atmospheric circulation was characterised using a database of monthly grids of surface
pressure provided by NCEP-NCAR (http://dss.ucar.edu/datasets/ds010.1/) between 1982 and
2001 at a resolution of 5°. We also considered pressure fields located east and west of the
study area to obtain non-local circulation patterns, because atmospheric circulation in central
Russia usually has a large spatial scale (Clark et al. 1999; Aizen et al. 2001).
The chosen database has been widely tested (Trenberth and Paolino 1980; Basnett and Parker
1997) and used to obtain atmospheric circulation patterns in the Northern Hemisphere
(Linderson 2001; Spellman 2000). We applied PCA analysis in T-mode to calculate the
general circulation modes in Russia. Such an analysis had been done by Barnston and Livezey
(1987) who identified the main teleconnection patterns in the Northern Hemisphere. However,
the atmospheric modes obtained for the whole Northern Hemisphere may be too general to
identify the influence of atmospheric circulation on vegetation activity in northern areas.
For this reason we used an intermediate spatial scale (between synoptic weather types and
general hemispheric patterns). We worked with standardised seasonal series of surface
pressure values to identify the principal modes of atmospheric variability (Trigo and Palutikof
2001). In the study area the climate has a high seasonality (Fukutomi et al. 2003) and the
atmospheric patterns that control it may differ significantly from season to season (Aizen et
al. 2001; Ye 2001 and 2002). As a consequence, we adopted a seasonal time scale separating
winter (December, January and February), spring (March, April and May) and summer (June,
July and August). We did not take into account the autumn because in the northern regions the
senescence of vegetation occurs very early and during the greatest part of autumn and the
whole winter period the vegetation is not active.
8
3.4. Analysis of the relationships between seasonal atmospheric modes and inter-annual
variability of vegetation activity
To determine the influence of seasonal atmospheric circulation on spatio-temporal differences
of aNDVI values, we used an approach similar to that of Gong and Shi (2003) to investigate
the influence of teleconnection patterns on spring NDVI in the Northern Hemisphere. We
used the Rotated Empirical Orthogonal Functions (REOFs) series from the seasonal
atmospheric circulation modes to create empirical models by means of linear stepwise
regression analysis (Hair et al. 1998). A statistical criterion was used to include different
regional circulation modes in the empirical models following the stepwise regression analysis
procedure. The method does not consider a priori knowledge about climate-vegetation
relationships and only takes into account the statistical relation between variables. The
selection of the atmospheric circulation modes to be included in the models is based on
correlations between the REOFs of regional circulation patterns (independent variables) and
the Principal Components (PCs) obtained from aNDVI time series (dependent variables). Due
to the nature of the Principal Components, which are non-correlated, we had no problems
related to collinearity (Hair et al. 1998). The ordinary least-squares technique was used to
estimate the coefficients of each regional circulation mode. The role and the sign of each
seasonal atmospheric circulation pattern on the vegetation patterns were determined by means
of the standardised beta coefficients of the regression.
To physically explain the influence of the seasonal atmospheric modes on the spatial and
temporal variability of aNDVI, we analysed the influence of the seasonal atmospheric
patterns, included in the explanatory models of aNDVI, on seasonal precipitation and
temperature. For this purpose we used monthly climate grids from the Climate Research Unit
of
the
University
of
East
Anglia
resampled
to
1°
of
spatial
resolution
(http://www.cru.uea.ac.uk/~timm/grid/CRU_TS_2_0.html, Mitchell et al. 2003).
9
Finally, we used different global and hemispheric atmospheric circulation modes to infer the
connection between general and regional atmospheric circulation and investigate the links
with the inter-annual variability of vegetation activity. For this purpose the general
atmospheric circulation indices of the Northern Hemisphere were used. They were obtained
from
the
Climate
Prediction
www.cpc.ncep.noaa.gov/data/teledoc/telecontents.html).
Center
The
indices
used
(USA,
were
the
Polar/Eurasian pattern (POL), the North Atlantic Oscillation (NAO), the Scandinavian pattern
(SCA), the East/Atlantic/West Russia pattern (EA/WR), the North Pacific pattern (NP), the
East Atlantic pattern (EA), the West Pacific pattern (WP), the East Pacific pattern (EP) and
the Pacific/North American pattern (PNA). Several works have described these indices, their
dynamics and their climate impact over the last few decades (i.e., Mo and Livezey 1986;
Hurrell 1995; Hurrell and van Loon 1997). We also took into account two global indices of
atmospheric
circulation:
The
Arctic
Oscillation
(AO,
http://www.cpc.ncep.noaa.gov/products/precip/Cwlink/daily_ao_index/ao_index.html,
Thomson and Wallace 1998) and the Southern Oscillation Index (obtained from the Climate
Research Unit of the University of East Anglia, http://www.cru.uea.ac.uk/cru/data/soi.htm,
Ropelewski and Jones 1987).
4. Results
4.1. Spatio-temporal variability of inter-annual vegetation activity in central Siberia
Figure 2 shows the 11 rotated temporal components obtained from PCA applied to aNDVI
series. Figure 3 shows the spatial patterns of REOFs. The first component represents the
temporal evolution of aNDVI values in south-eastern areas, the second individuates a large
area in the central-western region and the third component locates northern areas. The other
components individuate smaller areas.
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Figure 4 shows the percentage of variance explained by each component. There is an
important decrease in the percentage of variance from component 3 to 4. The three first
components explain 48.5% of the total inter-annual variability of aNDVI and these were
retained for subsequent analysis. The other components, which were not retained, represent
more local behaviour.
In the areas represented by the first three components there is an increase of vegetation
activity between 1982 and 2001. Table 1 shows the trends for the series of the first three
components calculated by means of the Non-parametric Rho-Spearman test (Siegel and
Castelan 1988). The slope of least-square adjustment is also shown to quantify the magnitude
of the change. In all cases there is an increase, indicated by positive values for both the slope
and the Rho test, although the magnitude of the change is not large.
4.2. Relationships between temporal patterns of aNDVI and topography and vegetation cover
In terms of terrain elevation, component 1 represents mainly mountainous areas with an
average elevation of 602.8 m but with an important range of 3960 m between the lowest and
highest sectors. Component 2 represents plains and areas of slight elevation with a mean
altitude of 157.3 m. The areas represented by component 3 have a mean elevation of 291 m.
Table 2 indicates the percentage of land cover type in areas represented by each component
with REOF values > 0.45 (p < 0.05). The time series of components 1 and 2 represent the
temporal evolution of aNDVI mainly in forested areas (74%) with similar forest types
(evergreen and needleaf). Component 3 represents mainly tundra areas.
4.3. Seasonal atmospheric circulation patterns in northern Eurasia areas
Table 3 shows the results of PCA on surface pressure grids from 1982 to 2001, and the
number of circulation patterns obtained for each season. In winter, we obtain six patterns of
atmospheric circulation that explain 84.4% of the total spatial variability. Seven components
11
are obtained in spring (82.9 % of variance) and in summer (79.8 % of variance). In general,
the atmospheric circulation variability is summarised by a few seasonal modes that represent
an important percentage of total variance. Figure 5 shows the winter circulation modes. Mode
1 indicates a latitudinal gradient from low pressures in the north to high pressures in the south.
This atmospheric circulation mode is indicative of zonal circulation and dominant flows from
the east. Mode 2 shows a similar pattern but the low pressures are displaced to the north.
Mode 3 shows a different configuration, where the circulation is meridian with dominant high
pressures in the west and low pressures in the east, with dominant flows from the southeast.
Mode 4 shows a configuration of low pressures in the central region of Siberia and high
pressures in Beringia. Mode 5 shows a clearly dominant meridian circulation with low
pressures in the east and west extremes of Russia and high pressures in the central areas. This
indicates dominant flows from the south in central Siberia. Finally, mode 6 also shows
dominant meridian circulation, but the direction and intensity of the flows in central Siberia
are more widely distributed than for mode 5.
Figure 6 shows the spring atmospheric circulation modes. Spring mode 1 also shows a
dominant zonal circulation, but, unlike winter mode 1, low pressures in the north and highpressures in the south are observed, which implies dominant flows from the west. Patterns 2,
4, 6 and 7 indicate dominant meridian circulation and cyclonic, north or west dominant
advective flows in central Siberia. Modes 3 and 5 have a quasi-zonal circulation with
dominant low pressures in the south and high pressures in the north.
Figure 7 shows the most important circulation modes observed in the summer. Mode 1
indicates high pressures in Arctic areas and low pressures in the south, similar to winter mode
1, although in summer mode the pressure gradient is more important. The other atmospheric
configurations show a higher spatial complexity with different centres of low or high
pressures.
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4.4. Influence of seasonal atmospheric circulation modes on the inter-annual variability of
aNDVI in central Siberia
Table 4 reports the results of the empirical models created to explain the inter-annual
variability of aNDVI using atmospheric circulation modes in the areas represented by the
three main components. Table 5 reports the variables included in each model, the standardised
beta coefficients and the partial correlation. For component 1, only 24% of the total variance
of aNDVI inter-annual variability is explained by atmospheric circulation. Only one
atmospheric circulation mode is included in the model by means of the stepwise regression
procedure (summer mode 5). Nevertheless, the correlation of this atmospheric pattern and the
temporal evolution of aNDVI values in the areas represented by component 1 is significant (R
= -0.49, p < 0.05).
The model for component 2 (west-central areas) includes three variables: two atmospheric
summer modes (2 and 7) and the winter mode 5. In this case a high percentage of the variance
of the inter-annual variability of vegetation activity is explained by these three modes of
atmospheric circulation (63 %). Summer mode 7 has the main role in the model (partial
correlation = 0.65).
The model for component 3 also explains a high percentage of the total variance (53%) and it
includes 2 atmospheric modes (summer mode 1 and winter mode 4). Summer mode 1 is the
most important with a partial correlation of 0.68.
Figure 8 shows the temporal evolution of the three main components of vegetation activity
(continuous lines) and the models obtained from atmospheric circulation (dashed lines).
Model 1 shows a high uncertainty but the generally positive trend of this component is also
reproduced by the model, with less variability than the aNDVI data.
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For components 2 and 3 it is clear that the temporal evolution of the models obtained from
atmospheric circulation has a strong similarity to the real aNDVI evolution and only in 1989
does model 3 show a significant difference from the aNDVI data.
The influence on temperature of the seasonal atmospheric circulation patterns, included in the
explanatory models discussed above, is illustrated in figures 9, 10 and 11. Figure 9 shows the
spatial distribution of the correlation (R) between REOF series of summer atmospheric
circulation mode 5 and the summer mean temperature in the study area. This mode produces
positive thermal anomalies in the central region. However, there is a negative correlation with
the summer temperature in the south-eastern sector, due to cold flows from the north. The
aNDVI evolution in this area coincides with the evolution of component 1. In these high
altitude areas the main climatic factor limiting vegetation development is temperature.
Summer mode 5 produces a temperature decrease in southern areas that might limit vegetation
activity and explain the negative correlation between this atmospheric mode and aNDVI
evolution.
For component 2, three atmospheric circulation variables are included in the model (summer
modes 2 and 7 and winter mode 5). Figure 10 shows the correlation between these
atmospheric modes and the climatic parameters. Summer mode 2 has a negative impact on the
summer temperature over large regions in the centre of the study area, where the temporal
evolution of vegetation is represented by component 2. Summer mode 7 also has a positive
effect on temperature in areas represented by component 2. The influence of winter mode 5
on the climate parameters is less clear, and it does not clearly affect temperature in the
western areas of central Siberia. Nevertheless, we have found a positive correlation between
this atmospheric mode and winter precipitation, hence snowfall.
In areas in which the aNDVI temporal evolution is represented by component 3, the model
created by atmospheric circulation patterns included summer mode 1 and winter mode 4. The
14
spatial distribution of the correlation between these atmospheric modes and the climatic
variables is shown in figure 11. Summer mode 1 affects temperature positively in the northern
areas: a very high correlation is found in areas represented by component 3 of aNDVI
evolution. Since temperature is the main constraint to vegetation activity in this region, higher
values of this climatic parameter enhance vegetation development. Moreover, although
summer mode 1 plays the main role in explaining the aNDVI variability in northern areas,
winter atmospheric circulation also has a significant impact. We have found a negative
correlation between winter mode 4 and the winter mean temperature in these areas (Figure 11,
2). It is difficult to explain the relationship between this winter atmospheric mode and
vegetation activity based on temperature. However, winter mode 4 affects the precipitation
variability in the northern regions (Figure 11, 3). The correlation between this atmospheric
mode and precipitation in these areas is negative and highly significant.
4.5. Relationship between seasonal atmospheric circulation modes in Siberia and the main
hemispherical and global teleconnection indexes
Finally, we analysed the relationships between the evolution of global and hemispherical
teleconnection modes and regional circulation patterns obtained from PCA in Russia between
1982 and 2001. The purpose was to understand whether aNDVI evolution is linked to general
atmospheric circulation in the Northern Hemisphere. Table 6 shows the correlation between
the regional seasonal patterns obtained in the study area and the general atmospheric
circulation modes in the Northern Hemisphere (average of monthly teleconnection indexes
over the season). In winter the atmospheric circulation modes are closely related to the main
atmospheric circulation patterns of the Northern Hemisphere.
Winter mode 1 has high
correlation with the Polar (POL) teleconnection (r = -0.82) and mode 3 is related to the North
Atlantic Oscillation (r = 0.76). Winter mode 5, included in the aNDVI model for component
15
2, is significantly related to ENSO and winter mode 4 (included in the aNDVI model for
component 3) is related to WP pattern.
In spring there is also a high level of correlation with the atmospheric circulation modes of the
Northern Hemisphere. In summer, the season in which the number of atmospheric circulation
modes affecting aNDVI variability is higher, the regional patterns correlate poorly to the
teleconnection indexes: only mode 1 and 3 are significantly related to the AO index and the
correlation values are not very high. In summary, the relationship between the atmospheric
circulation modes that significantly influence the inter-annual variability of vegetation activity
in central Siberia and the general teleconnection patterns of the Northern Hemisphere is weak.
Discussion and conclusions
In this paper we have analysed the influence of the inter-annual variability of seasonal
atmospheric circulation patterns on vegetation activity in central Siberia. We found that the
spatial patterns of vegetation activity variability are quite complex. Three main temporal
patterns were identified, which represent the temporal evolution in three large regions (SE, W
and N). The temporal evolution of aNDVI in the first 2 PC series is not related to differences
in land-cover since components 1 and 2 represent forests of the same type (mainly evergreen
needleaf forest). Only in tundra areas representing component 3 does land cover determine
aNDVI evolution. The temporal series of aNDVI, representative of the three main areas, show
a positive increase in vegetation activity between 1982 and 2001, in agreement with other
results on the NDVI trends in the northern Eurasian areas (Shabanov et al. 2002; Bogaert et
al. 2002; Slayback et al. 2003). We have found the highest increase in the northern areas of
tundra.
It is difficult to establish the connections between climatic and vegetation dynamics.
Nevertheless we have observed a significant impact of atmospheric circulation on inter-annual
16
variations of aNDVI. We have summarised the seasonal atmospheric variability in Siberia by
means of a few circulation modes. It is interesting to note that the patterns are significantly
different for winter, spring and summer, although the general surface pressure mode during
the three seasons is indicative of the northern annular mode structure reported by Thompson
and Wallace (1998).
The effect of these atmospheric circulation patterns on the variability of vegetation activity
differs spatially. To explain the aNDVI evolution by atmospheric circulation in areas
represented by component 1, we have shown that summer mode 5 affects temperatures
negatively in these areas, and this can have a negative influence on vegetation activity. These
regions are mainly mountainous, and temperature is the main constraint to vegetation
development. However, a high percentage of the total variance of vegetation activity in these
areas is not explained by atmospheric circulation. In fact, the model for component 1 explains
only 24% of the total variance. Nevertheless, this model shows a positive trend similar to that
observed in the aNDVI series.
The explanation of component 2 (western areas) by means of atmospheric circulation modes
is more reliable. However, the influence of summer modes 2 and 7 and winter mode 5 on this
component is difficult to establish in terms of precipitation and temperature. In these central
regions an important connection between winter and summer precipitation and Atlantic and
Pacific Ocean surface temperatures has been identified (Ye 2000, 2001 and 2002). For this
reason, it is difficult to exclusively associate the dynamics of climate to regional atmospheric
circulation. Moisture and temperature of air mass may play a more important role than the
spatial distribution and intensity of pressure patterns.
In tundra areas, characterised by more extreme climatic conditions, aNDVI variability is
closely related to atmospheric circulation. We have shown that summer mode 1 significantly
affects the inter-annual variability of vegetation activity in these regions. We found a high
17
positive correlation between summer mode 1 and the summer temperatures. There is also a
negative correlation between winter mode 5 and precipitation. Since in these areas winter
precipitation is solid, the relationship of this atmospheric circulation mode with vegetation
activity is indirect, through the winter snowpack.
We have provided evidence that shows that atmospheric circulation in Siberia has a certain
relationship with the general teleconnection patterns of the Northern Hemisphere;
nevertheless, the seasonal variations are important. In winter and spring the atmospheric
circulation modes observed are significantly related to general teleconnection indexes, but in
summer the relationship is not significant. This confirms that there is more local atmospheric
circulation during summer and has important implications for identifying the relations
between climate and vegetation activity.
It may explain why other studies using
teleconnection indexes in relation to vegetation activity have not found significant
correlations for the northern Siberian areas (Buermann et al. 2003). The vegetation activity of
these regions is recorded mainly in summer and during this season the climate (mainly
temperature), which determines vegetation activity, is mainly related to regional circulation.
In conclusion, the relationship between the variability of atmospheric circulation, climatic
conditions and vegetation activity is clearer in areas where climatic conditions are more
limiting, as in the northern regions. In more southern areas the climatic conditions are less
restrictive and other factors and feedback mechanisms ( such as climate, soil, human activities
and vegetation) can affect vegetation activity variability.
Acknowledgements
The authors wish to acknowledge financial support from the Siberia II project (5th
Framework Program of the European Commission).
18
References
Aizen EM, Aizen VB, Melack JM, Nakamura T, Ohta T. 2001. Precipitation and atmospheric
circulation patterns at mid-latitudes of Asia. International Journal of Climatology 21:
535-556.
Baldocchi DF, Kelliher M, Black TA, Jarvis P. 2000. Climate and vegetation controls on
boreal zone energy exchange. Global Change Biology 6: 69-83.
Barnston AG, Livezey RE. 1987. Classification, seasonality and persistence of low-frecuency
atmospheric circulation patterns. Monthly Weather Review 115: 1083-1126.
Basnett TA, Parker DE. 1997. Development of the global mean sea level pressure data set
GMSLP2. Climate Research technical Note 79, Hadley Centre, Meteorological office,
Bracknell.
Bird MI, Santruckova H, Arneth A, Grigoriev S, Gleixner G, Kalaschnikov YN, Lloyd J,
Schulze ED. 2002. Soil carbon inventories and carbon-13 on a latitude transect in
Siberia. Tellus Series B-Chemical and Physical Meteorology 54: 631-641.
Bogaert J, Zhou L, Tucker CJ, Myneni RB, Ceulemans R. 2002. Evidence for a persistent and
extensive greening trend in Eurasia inferred from satellite vegetation index data.
Journal of Geophysical Research. 107: NO. D11, 10.1029/2001JD001075.
Braswell BH, Schimel DS, Linder E, Moore III B. 1997. The response of global terrestrial
ecosystems to inter-annual temperature variability. Science 278: 870-872.
Brovkin V, Levis S, Loutre M, Crucifix M, Claussen M, Ganopolski A, Kubatzki C,
Petoukhov V. 2003. Analysis of the Climate-Vegetation System in the Northern High
Latitudes. Climatic Change 57: 119-138
Buermann W, Anderson B, Tucker CJ, Dickinson RE, Lucht W, Potter CS, Myneni RB, 2003.
Inter-annual covariability in Northern hemisphere air temperatures and greenness
associated with El Niño-Southern Oscillation and the Arctic Oscillation. Journal of
Geophysical Research 108, 4396, doi : 10.1029/2002JD002630.
Clark MP, Serreze MC, Robinson DA. 1999. Atmospheric controls on Eurasian snow extent.
International Journal of Climatology 19: 27-40.
DeFries RS, Hansen M, Townshend JRG, Sohlberg R. 1998. Global land cover classifications
at 8km spatial resolution: the use of training data derived from Landsat imagery in
decision tree classifiers. International Journal of Remote Sensing 19: 3141-3168.
19
Dye GD, Tucker CJ. 2003. Seasonality and trends of snow cover, vegetation index and
temperature in northern Eurasia. Geophysical Research Letters 30: 1-4.
Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Hearns LO. 2000. Climate
extremes: observations, modelling and impacts. Science 289: 2068-2074.
Eastman JR, Fulk MA. 1993. Long sequence time series evolution using standardized
principal component analysis. Photogrammetric Engineering and Remote Sensing 53:
1649-1658.
Esper J, Schweingruber FH. 2004.: Large-scale treeline changes recorded in Siberia
Geophysical Research Letters 31: Art. No. L06202
Fraedrich K. 1994. An ENSO impact on Europe?. Tellus 46A: 541-552.
Frauenfeld OW, Zhang TJ, Barry RG, Gilichinsky D. 2004. Interdecadal changes in seasonal
freeze and thaw depths in Russia. Journal of Geophysical Research-Atmospheres 109:
Art. No. D05101.
Fukutomi Y, Igarashi H, Masuda K, Yasunari T. 2003. Inter-annual variability of summer
water balance components in three major river basins of northern Eurasia. Journal of
Hydrometeorology 4: 283-296.
Fung T, LeDrew E. 1987. Applications of principal components in change detection.
Photogrammetric Engineering and Remote Sensing 53: 1649-1658.
Gong DY, Ho CH. 2003. detection of large-scale climate signals in spring vegetation index
(normalized difference vegetation index) over the Northern Hemisphere. Journal of
Geophysical Research 108: 4498, doi: 10.1029/2002JD002300.
Gong D, Shi P. 2003. Northern hemispheric NDVI variations associated with large-scale
climate indices in spring. International Journal of Remote Sensing 24: 2559-2566.
Gutman G, Ignatov A. 1995. Global land monitoring from AVHRR: potential and limitations.
International Journal of Remote Sensing 16: 2301-2309.
Hair JF, Anderson RE, Tatham RL, Black WC. 1998. Multivariate data Analysis. Prentice
Hall, New York.
Hartmam T, DiBella C, Oricchio P. 2003. Assessment of the possible drought impact on farm
production in the SE of the province of Buenos Aires, Argentina. ISPR Journal of
Photogrammetry & Remote Sensing 57: 281-288.
Houghton JT, Ding Y, Giggs D, Noguet M, van del Linden P, Dai X, Maskell A, Johnson CA.
2001. Climate Change 2001: The scientific Basis. Eds. Cambridge University Press.
Cambridge.
20
Hurrell J. 1995. Decadal trends in North Atlantic Oscillation and relationship to regional
temperature and precipitation. Science 269: 676-679.
Hurrell J, Van Loon H. 1997. Decadal variations in climate associated with the North Atlantic
Oscillation. Climatic Change 36: 301-326.
James ME, Kalluri SNV. 1994. The Pathfinder AVHRR land data set: an improved coarse
resolution data set for terrestrial monitoring. International Journal of Remote Sensing
15: 3347-3363.
Jollife IT. 1990. Principal component analysis: a beginner’s guide. Part I: Introduction and
application. Weather 45: 375-382.
Justice CO, Townshend JR. 1994. Data sets for global remote sensing: lessons learnt.
International Journal of Remote Sensing 15: 3621-3639.
Kadioglu M. 2000. Regional variability of seasonal precipitation over Turkey. International
Journal of Climatology 20: 1743-1760.
Kaiser HE. 1958. The varimax criterion for analytic rotation in factor analysis. Psikometrica,
23: 187-200.
Kaufmann RK, Zhou L, Shabanov NV, Myneni RB, Tucker CJ. 2000. Effect of orbital drift
and sensor changes on the time series of AVHRR vegetation index data. IEEE
Transactions in Geoscience and Remote Sensing 38: 2584-2597.
Knipling EB. 1970. Physical and physiological basis for the reflectance of visible and nearinfrared radiation from vegetation. Remote Sensing of Environment 1: 155-159.
Kogan FN. 2000. Satellite-observed sensitivity of world land ecosystems to El Niño/La Niña.
Remote Sensing of Environment 74: 445-462.
Kogan FN, Wei G. 2000. Using AVHRR data for detection of El Niño/La Niña impacts on
land ecosystems. Advances in Space Research 26: 1165-1168.
Kogan FN, 2001. Operational space technology for global vegetation assessment. Remote
Sensing of Environment 82: 1949-1964.
Linderson M. 2001. Objective classification of atmospheric circulation over southern
Scandinavia. International Journal of Climatology 21: 155-169.
Lucht W, Prentice IC, Myneni RB, Sitch S, Friedlingstein P, Cramer W, Bousquet P,
Buermann W, Smith B. 2002. Climatic control of the high-latitude vegetation greening
trend and Pinatubo effect. Science 296: 1687-1689.
Malevsky-Malevich SP, Molkentin EK, Nadyozhina ED, Shklyarevich OB. 2001. Numerical
simulation of permafrost parameters distribution in Russia. Cold Regions Science and
Technology 32: 1-11
21
Maseli F, Conese C, Petkov L, Gilabert MA, 1992. Use of NOAA-AVHRR NDVI data for
environmental monitoring and crop forecating in the Sahel. Preliminary results.
International Journal of Remote Sensing 13: 2743-2749.
Mitchell TD, Carter TR, Jones PD, Hulme M, New M. 2003. A comprehensive set of highresolution grids of monthly climate for Europe and the globe : the observed record
(1901-2000) and 16 scenarios (2001-2100). Journal of Climate, submitted.
Mo KC, Livezey RE. 1986. Tropical-Extratropical Geopotential Height teleconnections
during the northern hemisphere winter. Monthly Weather Review 114: 2488-2515.
Myneni RB, Tucker CJ, Asrar G, Keeling CD. 1998. Inter-annual variations in satellite-sensed
vegetation index data from 1981 to 1991. Journal of Geophysical Research 103: 61456160.
NASA. 2004. Pathfinder AVHRR Land Data. http://daac.gsfc.nasa.gov
Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, Myneni RB,
Running SW. 2003. Climate-driven increases in global terrestrial net primary
production from 1982 to 1999. Science 300: 1560-1563.
Nicholson SE, Davenport ML, Malo AR. 1990. A comparison of the vegetation response to
rainfall in the Sahel and east Africa, using normalized difference vegetation index
from NOAA-AVHRR. Climatic Change 17: 209-241.
New M, Todd M, Hulme M, Jones P. 2001. Precipitation measurements and trends in the
twentieth century. International Journal of Climatology 21: 1899-1922.
Peters AJ, Walter-Shea EA, Viña A, Hayes M, Svoboda MD. 2002. Drought monitoring with
NDVI-based standardized vegetation index. Photogrammetric Engineering and
Remote Sensing 68: 71-75.
Philander SG. 1990. El Niño, La Niña and the Southern Oscillation Academic Press: San
Diego.
Richards JA. 1993. Remote sensing digital analysis: an introduction. Springer-Verlag. NewYork.
Richman MB. 1986. Rotation of Principal Components. Journal of Climatology 6: 29-35.
Rogers JC. 1984. The association between the north Atlantic Oscillation and the southern
oscillation in the northern hemisphere. Monthly Weather Review 112: 1199-2015.
Ropelewski CF, Jones PD. 1987. An extension of the Tahiti-Darwin Southern Oscillation
Index. Monthly Weather Review 115: 2161-2165.
Ropelewski CF, Halpert MS. 1987. Precipitation patterns associated with the high phase of
the Southern Oscillation. Journal of Climate 2: 268-284.
22
Ringrose S, Matheson W. 1991. A Landsat analysis of range conditions in the Botswana
Kalahari drought. International Journal of Remote Sensing 12: 1023-1051.
Salinas-Zavala CA, Douglas AV, Díaz MF. 2002. Inter-annual variability of NDVI in
northwest Mexico. Associated climatic mechanisms and ecological implications.
Remote Sensing of Environment 82: 417-430.
Schultz PA, Halpert MS. 1993. Global correlation of temperature, NDVI and precipitation.
Advances in Space Research 13: 277-280.
Serrano A, García JA, Mateos VL, Cancillo ML, Garrido J. 1999. Monthly modes of variation
of precipitation over the Iberian peninsula. Journal of Climate 12: 2894-2919.
Siegel S, Castelan NJ. 1988. Nonparametric statistics for the behavioral sciences. Mc-GrawHill, Inc. New York.
Shabanov NV, Zhou L, Knyazikhin Y, Myneni RB, Tucker CJ. 2002. Analysis of inter-annual
changes in northern vegetation activity observed in AVHRR data from 1981 to 1994.
IEEE Transactions on Geoscience and Remote Rensing 40: 115-130.
Slayback DA, Pinzon JE, Los SO, Tucker CJ. 2003. Northern hemisphere photosynthetic
trends 1982-99. Global Change Biology 9: 1-15.
Smith PM, Kalluri SNV, Prince SD, DeFries RS. 1997. The NOAA/NASA pathfinder
AVHRR 8-km land data set. Photogrammetric Engineering and Remote Sensing 63:
12-31.
Spellman G. 2000. The use of an index-based regression model for precipitation analysis on
the Iberian peninsula. Theoretical and Applied Climatology 66: 229-239.
Suzuki R, Tanaka S, Yasunari T. 2000. Relationships between meridional profiles of satellitederived vegetation index (NDVI) and climate over Siberia. International Journal of
Climatology 20: 955-967.
Suzuki R, Nomaki T, Yamasuri T. 2001. Spatial distribution and its seasonality of satellitederived vegetation index (NDVI) and climate in Siberia. International Journal of
Climatology 21: 1321-1335.
Thompson DWJ, Wallace JM. 1998. The Artic Oscillation signature in the wintertime
geopotential height and temperature fields. Geophysical Research Letters 25: 12971300.
Thompson DW, Baldwin MP, Wallace JM. 2002. Stratospheric Connection to Northern
Hemisphere Wintertime Weather: Implications for Prediction. Journal of Climate 15:
1421–1428.
23
Townshend JRG. 1984. Agricultural land-cover discrimination using thematic mapper
spectral bands. International Journal of Remote Sensing 5: 681-698.
Townshend JRG, Goff TE, Tucker CJ. 1985. Multitemporal dimensionality of images of
normalized difference vegetation index at continental scales. IEEE Transactions in
Geoscience and Remote Sensing 6: 888-895.
Trenberth KE. 1997. The definition of El Niño. Bulletin of the American Meteorological
Society 78: 2771-2777.
Trenberth KE, Paolino DA. 1980. The northern hemisphere sea-level pressure data set: trends,
errors and discontinuities. Monthly Weather Review 108: 855-872.
Trigo RM, Palutikof JP. 2001. Precipitation scenarios over Iberia. A comparison between
direct GCM output and different downscaling techniques. Journal of Climate 14:
4422-4446.
Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring
vegetation. Remote Sensing of Environment 8: 127-150.
Vicente-Serrano SM, Heredia A. 2004. NAO influence on NDVI trends in the Iberian
Peninsula (1982-2000). International Journal of Remote Sensing 25: 2871-2879.
Wallace JM, Gutzler DS. 1981. Teleconnections in the geopotential height field during the
Northern Hemisphere winter. Monthly Weather Review 109: 784-812.
Wang JXL, Cho HR. 1997. Spatial-temporal structures of trend and oscillatory variabilities of
precipitation over Northern Eurasia. Journal of Climate 10: 2285-2298.
Wang GL. 2003. Reassessing the impact of North Atlantic Oscillation in the sub-Saharan
vegetation productivity. Global Change Biology 9: 493-499.
Wang G, You L. 2004. delayed impact of the North Atlantic Oscillation on biosphere
productivity
in
Asia.
Geophysical
Research
Letters
31:
L12210,
doi :
10.1029/2004GL019766.
Ye H. 2000. Decadal variability of Russian winter snow accumulation and its associations
with Atlantic sea surface temperature anomalies. International Journal of Climatology
20: 1709-1728.
Ye H. 2001. Characteristic of winter precipitation variation over northern central Eurasia and
their connections to sea surface temperatures over the Atlantic and Pacific oceans.
Journal of Climate 14: 3140-3155.
Ye H. 2002. Observed regional and climatological associations between spring and summer
precipitation over northern central Eurasia. Water Resources Research 32: 1317, doi :
10.1029/2001WR001060.
24
Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A. 2003.
Monitoring vegetation phenology using MODIS, Remote Sensing of Environment 84:
471-475.
Zhang X, Friedi MA, Schaaf CB, Strahler AH. 2004. Climate controls on vegetation
phenological patterns in northern mid- and high latitudes inferred from MODIS data.
Global Change Biology 10: 1133-1145.
Zhou L, Kaufmann RK, Tian Y, Myneni RB, Tucker CJ. 2003. Relation between inter-annual
variations in satellite measures of northern forest greenness and climate between 1982
and 1999. Journal of Geophysical Research. 108:
25
Figure 1: Location of the study area and land-cover map. Obtained from University of
Maryland (http://www.geog.umd.edu/landcover/8km-map.html), De Fries et al. (1998
26
3
3
2
2
1
1
COMPONENT2
COMPONENT1
0
0
1
2
1
2
3
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
3
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
2
2
1
1
0
COMPONENT4
COMPONENT3
0
1
2
3
4
1
2
3
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
2
4
1
3
2
0
COMPONENT6
COMPONENT5
1
1
2
3
0
1
2
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
3
3
2
2
1
1
COMPONENT8
COMPONENT7
0
0
1
2
1
2
3
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
2
3
2
1
1
0
COMPONENT10
COMPONENT9
0
1
2
1
2
3
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
3
2
y
e
a
rv
s
C
1
COMPONENT1
1
0
1
2
1
9
8
01
9
8
21
9
8
41
9
8
61
9
8
81
9
9
01
9
9
21
9
9
41
9
9
61
9
9
82
0
0
02
0
0
2
Figure 2: Temporal evolution of rotated components
27
Figure 3: Spatial distribution of the 11 REOF obtained from Principal Component Analysis.
Values higher than 0.45 (p < 0.05) are statistically significant.
28
Figure 4: Total variance explained by the different components
29
Components Rho Slope (increase/year)
1 0.29
0.04
2 0.13
0.02
3 0.37
0.04
Table 1: Trend analysis of the series of the first three components
30
COMPONENT 1
COMPONENT 2
COMPONENT 3
LAND COVER
1
2
3
4
5
6
7
8
9
0.26 0.19 0.06 0.17 0.06 0.10 0.10 0.00 0.06
0.32 0.10 0.11 0.14 0.08 0.05 0.07 0.00 0.12
0.01 0.13 0.00 0.00 0.14 0.01 0.00 0.02 0.69
Table 2: Proportion of land cover types in the different components (areas selected according
to REOF > 0.45, p < 0.05). 1: Evergreen Needleleaf Forests, 2: Deciduous Needleleaf Forests,
3: Deciduous Broadleaf Forests, 4: Mixed Forests, 5: Woodlands, 6: Grasses (Steppe), 7:
Croplands, 8: Bare, 9: Mosses and Lichens
31
Winter
Component % of variance % accumulated
1
21.4
21.4
2
15.0
36.3
3
14.5
50.8
4
12.4
63.2
5
11.1
74.4
6
10.1
84.4
Spring
Component % of variance % accumulated
1
20.2
20.2
2
15.3
35.4
3
11.8
47.2
4
10.3
57.5
5
9.8
67.2
6
8.5
75.7
7
7.2
82.9
Summer
Component % of variance % accumulated
1
18.2
18.2
2
11.8
30.0
3
11.8
41.9
4
10.3
52.2
5
9.5
61.7
6
9.4
71.1
7
8.7
79.8
Table 3: Seasonal atmospheric patterns obtained from PCA
32
Figure 5: Main atmospheric circulation modes during winter season (1982-2001)
33
Figure 6: Main atmospheric circulation modes during spring season (1982-2001)
34
Figure 7: Main atmospheric circulation modes during summer season (1982-2001)
35
COMPONENT 1
COMPONENT 2
COMPONENT 3
R
0.49
0.8
0.73
R2
0.24
0.63
0.53
R2 Corrected
0.2
0.57
0.48
Table 4: Results of the regression models used to explain temporal evolution of aNDVI by
atmospheric circulation
36
Mode 5 (summer)
Mode 2 (summer)
Mode 7 (summer)
Mode 5 (winter)
Mode 1 (summer)
Mode 4 (winter)
Beta
Partial
Component 1
-0.49 -0.49
Component 2
0.39
0.53
0.52
0.65
-0.39 -0.53
Component 3
0.63
0.68
0.46
0.55
Table 5: Standardised beta coefficient and partial correlation of the atmospheric circulation
variables for each regression model
37
Figure 8: Comparison among time series of the different components and the models obtained
from atmospheric circulation (dashed lines).
38
Figure 9: Spatial distribution of correlation between summer atmospheric mode 5 and summer
mean temperature
39
Figure 10: Spatial distribution of correlation between: 1- summer atmospheric mode 2 and
summer mean temperature, 2- summer atmospheric mode 7 and winter mean temperature, 3winter atmospheric mode 5 and winter precipitation
40
Figure 11: Spatial distribution of correlation between: 1- summer atmospheric mode 1 and
summer mean temperature, 2- winter atmospheric mode 4 and winter mean temperature, 3winter atmospheric mode 4 and winter precipitation
41
TELECONNECTION Correlation
WINTER
Component 1
Component 2
Component 3
Component 4
Component 5
Component 6
SPRING
Component 1
Component 2
Component 3
Component 4
Component 5
Component 6
Component 7
POL
-0.82**
NAO
WP
SOI
PNA
0.76**
-0.47*
-0.61**
-0.57**
AO
EA/WR
NP
NP
0.70**
0.49*
-0.55*
-0.58**
SCA
0.60**
TELECONNECTION Correlation
SUMMER
Component 1
Component 2
Component 3
Component 4
Component 5
Component 6
Component 7
AO
-0.50*
AO
-0.61**
Table 6: Correlation between the seasonal atmospheric circulation patterns obtained from
pressure grids and seasonal teleconnection patterns (average of monthly values)
42