Seasonal Prediction of Air Temperature Associated with the Growing

AUGUST 2011
WU ET AL.
1637
Seasonal Prediction of Air Temperature Associated with the Growing-Season
Start of Warm-Season Crops across Canada
ZHIWEI WU AND HAI LIN
Meteorological Research Division, Environment Canada, Dorval, Québec, Canada
TED O’BRIEN
National Service Office—Agriculture, Environment Canada, Regina, Saskatchewan, Canada
(Manuscript received 26 October 2010, in final form 7 April 2011)
ABSTRACT
Seasonal prediction of growing-season start of warm-season crops (GSSWC) is an important task for the
agriculture sector to identify risks and opportunities in advance. On the basis of observational daily surface air
temperature at 210 stations across Canada, this study found that the GSSWC in most Canadian areas begins
during May–June and exhibits significant year-to-year variations that are dominated by two distinct leading
empirical orthogonal function modes. The first mode accounts for 20.2% of the total GSSWC variances and
features a monosign pattern with the maximum anomalies in central-southern Canada. It indicates that warmseason crops in most Canadian areas usually experience a consistent early or late growing-season start and
those in central-southern Canada have the most pronounced interannual variations. The second mode explains 10.8% of the total variances and bears a zonal seesaw pattern in general, accompanied by prominent
anomalies covering the west coast of Canada and anomalies with a reverse sign prevailing in central-eastern
Canada. Therefore, a strong second-mode year represents an early GSSWC in western Canada and a late
GSSWC in the rest of the regions. The predictability sources for the two distinct leading modes show considerable differences. The first mode is closely linked with the North American continental-scale snow cover
anomalies and sea surface temperature anomalies (SSTAs) in the North Pacific and Indian Oceans in the prior
April. For the second mode, the preceding April snow cover anomalies over western North America and
SSTAs in the equatorial-eastern Pacific, North Pacific, and equatorial Indian Oceans provide precursory
conditions. These snow cover anomalies and SSTAs sustain from April through May–June, influence the
large-scale atmospheric circulation anomalies during the crops’ growing-start season, and contribute to the
occurrence of the two leading modes of the GSSWC across Canada. On the basis of these predictors of snow
cover anomalies and SSTAs in the prior April, an empirical model is established for predicting the two
principal components (PCs) of the GSSWC across Canada. Hindcasting is performed for the 1972–2007
period with a leaving-nine-out cross-validation strategy and shows a statistically significant prediction skill.
The correlation coefficient between the observation and the hindcast is 0.54 for PC1 and 0.48 for PC2, both
exceeding the 95% confidence level. Because all of these predictors can be readily monitored in real time, this
empirical model provides a new prediction tool for agrometeorological events across Canada.
1. Introduction
How to improve seasonal prediction skill of agrometeorological conditions in Canada is becoming an urgent issue and has been receiving fervent research interest
during the past decades. Under a global-warming background, climate in Canada is experiencing a dramatic
Corresponding author address: Dr. Hai Lin, MRD/ASTD, Environment Canada, 2121 Route Trans-Canadienne, Dorval, QC
H9P 1J3, Canada.
E-mail: [email protected]
DOI: 10.1175/2011JAMC2676.1
change (e.g., Zhang et al. 2000; Shabbar and Bonsal 2003;
Vincent and Mekis 2006). For example, the average increase in annual mean temperatures in southern Canada
is 0.98C since 1895 and winter and spring are warming
more than summer and autumn (Vincent and Mekis 2006;
Qian et al. 2010). These changes are inevitably modifying Canadian agrometeorological conditions. In light
of this, a useful prediction of year-to-year variations of
agrometeorological conditions in Canada will not only
benefit Canadian agriculture but will also enhance Canadian preparedness and adaptation to global climate
change. The amount of research on seasonal prediction
1638
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
FIG. 1. Distribution of 210 Ts gauge stations across Canada.
of agrometeorological conditions in Canada has unfortunately been relatively small up to now. This motivates us to conduct this work.
Agrometeorological conditions include quite a few
aspects, and several indices have been proposed to measure their variations (e.g., Vincent and Mekis 2006; Qian
et al. 2010). Among them, growing-season start of warmseason crops (GSSWC) is a principal one. Warm-season
crops include bean, corn, pea, soybean, and so on. It was
found that warm-season crops in most Canadian areas will
not start growing until daily mean surface air temperature (Ts) exceeds 108C for 10 consecutive days. Therefore,
seasonal prediction of the Ts associated with the GSSWC
is practically an issue of predicting when a period of 10
consecutive days with mean Ts reaching 108C emerges in
a year.
It has long been recognized that the physical basis
of seasonal prediction of climate events lies in coupled
mechanisms between atmosphere and low boundary forcing anomalies such as snow cover and sea surface temperature anomalies (SSTAs) (e.g., Charney and Shukla
1981; Shukla 1998) because the atmosphere, on its own,
lacks the mechanisms to generate predictable variations
beyond two weeks (Lorenz 1963). Previous studies revealed that the interannual variations of seasonal mean
Ts (winter in particular) in Canada are greatly influenced by the surrounding ocean. For example, El Niño–
Southern Oscillation (ENSO) is a primary predictability
source for interannual variations of the winter climate (e.g.,
Ropelewski and Halpert 1986; Hurrell 1996; Shabbar
and Khandekar 1996; Shabbar and Barnston 1996; Wang
et al. 2000). The influence of ENSO extends to Canada
through atmospheric teleconnections related to tropical
diabatic forcing (e.g., Horel and Wallace 1981; Trenberth
1990; Lin and Derome 2004; Lin et al. 2005). Besides
VOLUME 50
ENSO, SSTAs in the Indian Ocean may also contribute
to seasonal prediction of winter Ts over North America
(e.g., Wu et al. 2009a; Lin and Wu 2011).
Because the ocean accounts for only a portion of Ts
variability (Ting et al. 1996; Hurrell 1996), the substantial
landmass is a viable candidate for at least amplifying climate anomalies. Snow cover is the most variable land
surface condition in both time and space and exerts profound influences on winter Ts variations in the Northern
Hemisphere (Robinson et al. 1993; Cohen 1994; Gutzler
and Rosen 1992; Wang et al. 2010). For instance, Foster
et al. (1983) investigated the relationships between snow
cover and temperature over North America and Eurasia.
Barnett et al. (1988) discussed the effect of Eurasian snow
cover on global climate. Lin and Wu (2011) revealed that
the prior autumn snow cover anomalies over the Tibetan
Plateau can sustain through the ensuing winter and exert
profound influences on winter Ts over Canada.
Although many studies have been conducted on interannual variations of Ts and its seasonal prediction,
most of them focused on boreal winter season and few
focused on Ts in transitional seasons (late spring–early
summer in particular). The latter is directly connected
with the GSSWC across Canada. In this study, we attempt
to answer the following questions: What are the major
features of the GSSWC across Canada? How does the
GSSWC link to the large-scale atmospheric circulations
and the low boundary forcing (SSTAs and snow cover
anomalies)? What are the predictors for the GSSWC if it
is predictable, and how do they contribute to seasonal
prediction of the GSSWC from the Ts perspective?
The outline of this study is as follows. Section 2 introduces the datasets and method used in this study. Section
3 suggests that the air temperature conditions favorable
for the GSSWC in most Canadian areas begin in May–
June and are dominated by two distinct leading modes.
Section 4 presents the large-scale three-dimensional circulation features associated with the two distinct modes
and predictability sources for them. In section 5, an empirical model is established to predict the principal components (PCs) of the two distinct modes based on the prior
April snow cover anomalies and SSTAs. Hindcasting is
performed for the 1972–2007 period. The last section summarizes major findings and discusses some outstanding
issues.
2. Data and method
The main datasets employed in this study include 1) the
homogenized Canadian historical daily Ts at 210 relatively
evenly distributed stations across Canada (Vincent et al.
2002; see Fig. 1); 2) the 40-yr European Centre for MediumRange Weather Forecasts Re-Analysis (ERA-40; Uppala
AUGUST 2011
WU ET AL.
1639
FIG. 2. Climatological GSSWC (gray shading; days). The GSSWC refers to the day numbers
with reference to 1 Jan (yearday).
et al. 2005) and the ERA-Interim reanalysis data; 3)
the Met Office Hadley Centre’s SST datasets gridded
at 1.08 3 1.08 resolution (Rayner et al. 2003); 4) the
Northern Hemisphere snow cover data gridded at
2.08 3 2.08 resolution, obtained online (http://www.
cpc.ncep.noaa.gov/data/snow/).
The daily Ts data at 210 stations have been adjusted to
account for inhomogeneities caused by changes in site
FIG. 3. (a) Spatial pattern (color shading; days) and (b) the corresponding PC of the first EOF mode of the GSSWC.
(c),(d) As in (a) and (b), but for the second mode. The numbers in the parentheses indicate fractional variance of the
EOF modes.
1640
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
VOLUME 50
FIG. 4. For MJ: (a) climatological values in SLP (contours; hPa), Ts (color shading; 8C), and
925-hPa winds (vectors; m s21) and their anomalies regressed to (b) PC1 and (c) PC2.
exposure, location, instrumentation, observer, and observing procedures. The period of our analysis on the
GSSWC covers from 1957 through 2007. To get a longer
time length covering the period from 1957 through 2008,
the ERA-40 and ERA-Interim data are combined together. We use the ERA-40 data for the period 1957–2001
and extend the data from 2002 through 2008 by using
ERA-Interim data (Wang et al. 2010; Lin and Wu 2011).
To maintain temporal homogeneity, the 2002–08 ERAInterim data were adjusted by removing the climatological
difference between the ERA-40 and ERA-Interim data.
Because this study focuses on seasonal prediction of the
GSSWC from the Ts perspective, the GSSWC is defined as
the beginning date of 10 consecutive days with their daily
AUGUST 2011
WU ET AL.
FIG. 5. As in Fig. 4, but for 500-hPa geopotential height H (contours; 0.1 3 gpm), and winds
(vectors; m s21).
1641
1642
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
FIG. 6. Correlation coefficients between North American snow
cover in the prior April and GSSWC (a) PC1 and (b) PC2. The
interval of contours is 0.1. The shaded regions represent correlation
coefficients that exceed the 95% confidence level.
mean Ts reaching 108C. The beginning date is represented
by the number of days with reference to 1 January. For
example, if the beginning date is 2 January, the GSSWC
value will be 2. Missing data in GSSWC are replaced by the
climatological value at this station. Stations with more than
10% of missing GSSWC (viz., five observations in this
study) are excluded from the analysis. To derive the leading modes, we performed an empirical orthogonal function
(EOF) analysis on the GSSWC. The EOF analysis was
carried out by constructing an area-weighted covariance
matrix.
3. Major features of the GSSWC across Canada
Figure 2 presents the climatological GSSWC across
Canada. A prominent feature is that the GSSWC value
VOLUME 50
increases with latitude. It indicates that the warm-season
crops in high-latitude regions start growing later than those
in low-latitude regions. This is consistent with seasonal
alternation of the solar radiation. The earliest growing start
date of the warm-season crops climatologically begins
around 1 May (120 days). The period May–June (MJ) is the
essential growing start season for Canadian warm-season
crops. The seasonal prediction of the GSSWC in the following section will focus on the MJ period.
Figure 3 displays the two leading modes of the GSSWC.
The first mode accounts for 20.2% of the total variance
(Fig. 3a). According to the rule given by North et al.
(1982), the first mode is statistically distinguished from the
rest of the eigenvectors in terms of the sampling error bars
(not shown). The second mode, which accounts for 10.8%
of the total variance (Fig. 3c), is not separable from the rest
of the high modes. Nevertheless, the agrometeorological
meaning of the first two modes is examined here.
The first mode basically shows a monosign pattern with
maximum loading located in the central-southern Canada,
its amplitude decreasing northwestward and northeastward (Fig. 3a). The central-southern Canada has the most
significant year-to-year variations. PC1 is primarily dominated by interannual variability (Fig. 3b). In this study,
a high (low) PC1 year refers to an early (late) growingseason start year across Canada. An interesting phenomenon is that most of the years before 1975 have a negative
PC1; namely, the warm crops in Canada are more likely to
have a late growing-season start during the 1957–75 period
than they do after 1975. This leads to a decreasing tendency in GSSWC, which is basically consistent with the
result from Qian et al. (2010). It is still not clear whether
this phenomenon is due to global warming, because after
1980 when the dramatic global warming happens PC1 does
not exhibit a significantly increasing tendency in its positive phases as expected.
The prominent feature of the EOF2 mode is a zonal
dipole pattern with anomalies of opposite signs in western
Canada and the rest of the region except the area east of
Hudson Bay (Fig. 3c). The extreme-value centers are located along the west coast of Canada. A high (or low) PC2
year corresponds to a late-west–early-east (early-west–
late-east) GSSWC pattern over most Canadian areas.
PC2 is also dominated by interannual variability, and its
amplitude has increased considerably since 1980, with
negative phases in particular (Fig. 3d). It indicates that
the early-west–late-east GSSWC patterns are more pronounced than the late-west–early-east GSSWC patterns
in the latest 27 years, which is basically consistent with the
results in Zhang et al. (2000).
The distinct spatial–temporal structures of the two
leading modes imply that they may have different physical
origins and predictability sources. In the next section, we
AUGUST 2011
WU ET AL.
1643
FIG. 7. Correlation coefficients between SSTAs in the prior April and (a) PC1 and (b) PC2.
The interval of contours is 0.1. The shaded regions represent correlation coefficients that
exceed the 95% confidence level.
will examine the large-scale circulation anomalies accompanied by the two leading modes and their predictability
sources.
4. Circulation anomalies and predictability sources
To better understand the linkage between the two
distinct modes of the GSSWC and their predictability
sources, first we need to examine the simultaneous largescale circulations associated with these two modes. Figure 4
shows MJ surface circulation anomalies regressed to the
two leading PCs along with the climatological values. One
prominent feature of the atmospheric circulations near the
surface in a high-PC1 MJ is an anomalous Ts warming
area prevailing over the North American (NA) continent
and centered in central-southern Canada (shadings in Fig.
4b). The Ts pattern resembles well the spatial pattern of
the EOF1 mode (Fig. 3a). It indicates an early GSSWC in
Canada is often accompanied by a warmer-than-normal
MJ, and vice versa. Another prominent feature is one gigantic positive sea level pressure (SLP) anomaly center
occupying the entire northeastern Pacific Ocean (contours
in Fig. 4b) with significant anticyclonic wind anomalies
at 925 hPa (vectors in Fig. 4b). It is located slightly to the
north of the climatological Hawaiian high pressure system
(Fig. 4a). This pattern reflects a stronger-than-normal
and northward-shifted Hawaiian high pressure system.
Meanwhile, a negative SLP anomaly center controls the
midlatitude western North Atlantic Ocean, which is
corresponding to a weaker-than-normal North Atlantic
high pressure system. These are favorable for a warmerthan-normal MJ in Canada.
For a strong second-mode MJ, a zonal seesaw Ts pattern
prevails over the NA continent (shadings in Fig. 4c), with
warm Ts anomalies over central-eastern Canada and
cold anomalies over western Canada. This pattern also
resembles the spatial pattern of the EOF2 mode (Fig. 3c).
One large positive SLP anomaly center associated with
anticyclonic surface wind anomalies occupies the Aleutian
region, which implies a weaker-than-normal Aleutian low
pressure system. Northerly surface wind anomalies prevail
in the northeastern Pacific that advect cooler and drier air
southward from the north, which decreases Ts over the
northeastern Pacific–western Canada in a high-PC2 MJ.
In a low-PC2 MJ, the situation tends to be opposite.
Figure 5 compares MJ midtroposphere circulation
anomalies regressed to the two leading modes along
with the climatological values. For a strong first-mode
MJ, the NA continent is basically controlled by positive
geopotential height H anomalies at 500 hPa centered
1644
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
FIG. 8. North American MJ snow cover anomalies regressed to
(a) PC1 and (b) PC2. The interval of contours is 2%. The shaded
regions exceed 95% confidence level.
over central-southern Canada (Fig. 5b), which is primarily
above the surface warming center (Fig. 4b). This positive
H anomaly extends eastward across the midlatitude
North Atlantic, with another center over the eastern
Atlantic. The high pressure system in the mid–high troposphere over Canada (Fig. 5b) may lead to clear sky
and increased solar radiation and consequently favor a
warmer-than-normal MJ in Canada. A salient negative H
anomaly center and a positive anomaly center occupy the
Aleutian region and the central North Pacific, respectively. Two negative H anomaly centers are located
over the west and east coasts of the United States, expanding toward the Pacific and Atlantic, respectively.
VOLUME 50
Because the climatological ridge over the northeastern
Pacific and the west coast of the NA continent tilts
southeastward from Alaska to the Rocky Mountains
(Fig. 5a), the negative H anomalies over the west coast
of North America imply an eastward shift of the high
ridge toward the central NA continent. This suppresses
synoptic eddy activities in Canada. The negative H
anomalies over the midwestern Atlantic imply a weakened North Atlantic subtropical high.
For a strong second-mode MJ, a tremendous positive
H anomaly center prevails over the central-eastern NA
continent while a negative H anomaly center controls
the northwest of Canada and Alaska. An anomalous
positive H belt extends from the central North Pacific to
the west coast of the United States. This pattern tends to
strengthen the synoptic eddy activities over western
Canada and weaken those over central-eastern Canada
and the United States.
The above circulation anomalies associated with the
two distinct modes are likely intimately coupled with the
anomalous low boundary conditions such as snow cover
and SST; namely, they are potential predictability sources.
Figure 6 presents the correlation map between the two
PCs and the prior April snow cover over North America.
In April of a high-PC1 year, large areas of significantly
negative correlations cover most NA continental areas
(Fig. 6a). It indicates that a reduced (excessive) NA snow
cover in April signifies precursory conditions for an early
(late) GSSWC in Canada. Meanwhile, with respect to
SST, negative correlations are observed in the North
Pacific and positive correlations in the Indian Ocean
basin (Fig. 7a), which means that a colder-than-normal
North Pacific and a warmer-than-normal Indian Ocean
provide preceding signals for an early GSSWC in Canada,
and vice versa.
In April of a high-PC2 year, positive correlations with
the April snow cover are observed in western Canada,
expanding northwestward toward Alaska (Fig. 6b), which
means that a high-PC2 mode of GSSWC in Canada is
usually preceded by an excessive snow cover in western
Canada in April. The anomalously negative SST correlation areas are basically located in the equatorial eastern
Pacific, the northeastern Pacific adjacent to the west coast
of North America, and the tropical Indian Ocean (Fig.
7b). The signal in the Pacific is reminiscent of a La Niña
SSTA. It implies that a colder-than-normal SST in the
above-mentioned ocean areas in April are often prior to
a high-PC2 mode of GSSWC in Canada.
It is known that the atmosphere responds to an
anomalous low boundary forcing within about two
weeks, even for a remote response; thus on the seasonal
time scale the interaction between the atmosphere and
low boundary forcing can be regarded as a simultaneous
AUGUST 2011
1645
WU ET AL.
FIG. 9. The MJ composite differences in SST between high-PC and low-PC (high minus low)
years for (a) PC1 and (b) PC2. A high-PC (low PC) year is measured by 1 standard deviation.
The interval of contours is 0.38C. The shaded regions exceed the 95% confidence level.
relationship (e.g., Charney and Shukla 1981; Wu et al.
2009b). Can these prior April low boundary forcing
anomalies associated with the two distinct modes persist
through the MJ period? Figures 8 and 9 display snow
cover anomalies and SSTAs in MJ associated with the
two distinct modes. The major feature of the two figures
is that both the snow cover and the SST in MJ bear
a similar anomaly pattern with their correlation maps in
the prior April (Figs. 6 and 7). For a strong first-mode
year, reduced snow cover anomalies appear in large
areas of the NA continent during MJ (Fig. 8a), whereas
colder-than-normal SSTAs emerge in the North Pacific
and warmer SSTAs in the Indian Ocean basin (Fig. 9a).
For a strong second mode, excessive snow cover anomalies are basically located in western Canada, expanding
to Alaska (Fig. 8b), and colder SSTAs are in the equatorial eastern Pacific, northeastern Pacific adjacent to
the west coast of North America, and the tropical Indian
Ocean (Fig. 9b). These anomalous patterns basically
resemble their correlation patterns (Figs. 6 and 7). It
manifests that these low boundary forcing anomalies can
persist from the prior April through MJ, which makes
them predictors for the two distinct modes of GSSWC in
Canada.
5. Seasonal prediction
To verify how well the above predictors contribute
to the seasonal prediction of the GSSWC, an empirical
seasonal prediction model is established using a linearregression method for the period of 1972–2007 [see Eqs.
(1) and (2)]:
y1 5 a10 1 a11 x11 1 a12 x12 1 a13 x13 and
(1)
y2 5 a20 1 a21 x21 1 a22 x22 1 a23 x23 1 a24 x24 ,
(2)
where Eqs. (1) and (2) are for PC1 and PC2, respectively. In Eq. (1), x11 denotes the April normalized
snow cover averaged over 408–638N, 1258–658W and x12
and x13 refer to April normalized SSTAs averaged in the
Indian Ocean (208S–108N, 508–858E) and the North
Pacific (108–208N, 1808–1408W plus 378–498N, 1608E–
1508W), respectively (boxes in Fig. 7a). All of these
predictors show an intimate linkage with PC1 (Fig. 10a),
with their correlation coefficients being 20.57, 0.34, and
20.48, respectively, all reaching the 95% confidence
level. In Eq. (2), x21 denotes the April normalized snow
cover averaged over 458–538N, 1258–1058W and x22, x23,
1646
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
VOLUME 50
FIG. 10. Time series of April predictors for (a) PC1 and (b) PC2. The correlation coefficients
between the PCs and their predictors are indicated in the parentheses. Note that NP, IO, NA,
and TP refer to the North Pacific, the Indian Ocean, North America, and the tropical Pacific,
respectively.
and x24 refer to April normalized SSTAs averaged in the
Indian Ocean (58S–58N, 608–1008E), the North Pacific
(458–608N, 1608–1208W), and the tropical eastern Pacific
(158S–208N, 1608–1208W), respectively (boxes in Fig.
7b). All of these predictors show an intimate linkage
with PC2 (Fig. 10b), with their correlation coefficients
being 0.41, 20.45, 20.61, and 20.49, respectively (beyond the 95% confidence level).
The cross-validation method is performed to hindcast
PC1 and PC2 for the 1972–2007 period (Michaelsen 1987;
Wu et al. 2009b). To warrant a robust hindcast, we choose
a leaving-nine-out strategy (Blockeel and Struyf 2002). The
relevant procedures are as follows: The cross-validation
method systematically deletes nine years from the period
1972–2007, derives a forecast model from the remaining
years, and tests it on the deleted cases. Note that the choice
of ‘‘leaving nine out’’ is not random. Blockeel and Struyf
(2002) suggested that randomly choosing 20%–30% of the
data to be in a test dataset and the remainder as a training
set for performing regression can prevent overfitting or
wasting of data. For the two leading PCs, 25% of the whole
hindcast period (36 yr) is equal to 9 yr. That is why we
choose a leaving-nine-out strategy.
The cross-validated estimates of PCs are shown in
Fig. 11. For PC1, the correlation coefficient between the
observation (black line in Fig. 11a) and the cross-validated
estimates of the empirical scenario (red line in Fig. 11a)
reaches 0.54, exceeding the 95% confidence level. For
PC2, the correlation coefficient between the observation
(black line in Fig. 11b) and the cross-validated estimates
of the empirical scenario (red line in Fig. 11b) reaches
0.48, also exceeding the 95% confidence level. Therefore, the empirical method shows a promising hindcast
skill. Because all of these predictors can be readily monitored in real time, this empirical model provides a new
prediction tool for agroclimatic events in Canada.
6. Conclusions and discussion
Seasonal prediction of agroclimatic conditions in Canada
is of central importance for the Canadian agricultural sector to identify risks and opportunities in advance and has
become a focal issue under a global-warming background.
This paper focuses on seasonal prediction of the GSSWC
from the Ts perspective (Qian et al. 2010). Based on observational daily Ts data at 210 stations across Canada
(Vincent et al. 2002), we find that the GSSWC in most
Canadian areas climatologically begins in May–June and
exhibits significant year-to-year variations that are dominated by two distinct leading modes (North et al. 1982).
The first mode accounts for 20.2% of the total GSSWC
variances and features a monosign pattern with the
AUGUST 2011
WU ET AL.
1647
FIG. 11. Comparison of the observed (solid curves) and the hindcast (dot–dashed curves) PCs
made by the empirical seasonal prediction model for (a) PC1 and (b) PC2. The correlation
coefficients between the observation and the hindcast are indicated in the parentheses.
maximum anomalies in central-southern Canada. It indicates that warm-season crops in most Canadian areas
usually experience a consistent early or late growingseason start while those in central-southern Canada have
the most pronounced interannual variations. The second
mode explains 10.8% of the total variances and bears a
zonal seesaw pattern in general, accompanied by prominent anomalies covering the west coast of Canada and
anomalies with reverse sign prevailing in central-eastern
Canada. Therefore, a strong second-mode year represents
an early GSSWC in western Canada and a late GSSWC in
the rest of the region, and vice versa. Seasonal prediction
of the two leading modes is essential for seasonal prediction of the GSSWC across Canada.
The predictability sources for the two distinct modes are
also examined. The first mode is intimately connected with
the North American continental-scale snow cover anomalies and SSTAs in the North Pacific and Indian Oceans in
the prior April. For the second mode, the preceding April
snow cover anomalies over western North America and
SSTAs in the equatorial-eastern Pacific, North Pacific, and
equatorial Indian Oceans provide precursory conditions.
These low boundary forcing anomalies can persist from
April through MJ.
The question arises as to how the April low boundary
forcing anomalies affect the ensuing MJ large-scale atmospheric circulations. In April of a high-PC1 year,
large areas of reduced NA snow cover in April persist
through MJ (Figs. 6a and 8a) and the lowest layers of the
atmosphere are warmed because of the low albedo of
snow cover (e.g., Foster et al. 1983). Warming at the bottom of the atmospheric column produces convergent flow,
weakening high pressure in the region and causing it to
expand (contours in Fig. 4b). Meanwhile, the tripole SSTA
pattern in the North Pacific (Fig. 9a) may favor a strongerthan-normal and northward-shifted Hawaiian high pressure system and an enhanced Aleutian low pressure system.
In a low-PC1 year, the situation is just the opposite. For
a high-PC2 (low PC2) year, an excessive (reduced) snow
cover in western Canada cooled (warmed) the lowest layers
of the atmosphere because of the high (low) albedo of snow
cover and induced positive (negative) SLP anomalies over
the local region (contours in Fig. 4c). The tremendous H
positive (negative) anomaly center with anticyclonic (cyclonic) wind anomalies (Fig. 5c) indicates a Rossby wave
response to the La Niña–like (El Niño–like) SSTAs (Fig.
9b) (Hoskins and Karoly 1981; Sardeshmukh and Hoskins
1988). Thus, the snow cover anomalies and SSTAs associated with the two leading modes can interpret well the
main features of the corresponding large-scale atmospheric circulations.
On the basis of these predictors of snow cover
anomalies and SSTAs in the prior April, we establish an
empirical model to predict the PC1 and PC2 of the
GSSWC. Hindcasting is performed for the 1972–2007
period with a leaving-nine-out cross-validation strategy
1648
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
and shows a significant prediction skill. The correlation
coefficient between the observation and the hindcast is
0.54 for PC1 and 0.48 for PC2, both exceeding the 95%
confidence level. In this study we have focused on seasonal prediction of the two leading modes of the GSSWC,
which explain about 31% of the seasonal mean variance
of the GSSWC variability over Canada. Because the EOF
was done over a large area covering the whole country,
the percentage of variance explained refers to an average
of the whole analysis area. For local regions such as the
anomaly centers over southern and western Canada, the
variance explained by these two modes would be much
larger.
Here, we used the April snow cover and SST to establish this empirical seasonal prediction model for the
Canadian GSSWC. By using the latest signals of the
predictors, we want to show the best prediction skill of
this model. Because the snow cover and SST have a
longer memory than 1 month (e.g., Wu et al. 2009b; Lin
and Wu 2011), this indicates that the empirical model
can use the earlier snow cover and SST signals to do
1-month-lead or 2-month-lead prediction. Because all of
these predictors can be readily monitored in real time,
this empirical model provides a new prediction tool for
agrometeorological events across Canada.
This seasonal prediction model assumes that the two
distinct modes are stable on interannual time scales. For
predictions on a time scale of a decade or longer, the
predictability sources and relevant physical processes
are likely to be different from those with the interannual
variability. If the two distinct modes change with time—
that is, interdecadal changes—then the predictors and the
prediction scenario may also change correspondingly. In
addition, how can the SSTAs in the Indian Ocean have
an impact on circulation anomalies associated with the
two distinct modes of the GSSWC and what kind of
physical processes are involved? These are still open
questions. The hypotheses concerning the origins of the
first leading mode call for further numerical and theoretical studies.
Acknowledgments. Zhiwei Wu is supported by the
Sustainable Agriculture Environment Systems (SAGES)
research initiative of Agriculture and Afri-Food Canada
through the Natural Sciences and Engineering Research
Council of Canada (NSERC) Fellowship Program.
REFERENCES
Barnett, T. P., L. Dumenil, U. Schlese, and E. Roeckner, 1988: The
effect of Eurasian snow cover on global climate. Science, 239,
504–507.
Blockeel, H., and J. Struyf, 2002: Efficient algorithms for decision
tree cross-validation. J. Mach. Learn. Res., 3, 621–650.
VOLUME 50
Charney, J. G., and J. Shukla, 1981: Predictability of monsoons.
Monsoon Dynamics, J. Lighthill and R. P. Pearce, Eds.,
Cambridge University Press, 99–109.
Cohen, J., 1994: Snow cover and climate. Weather, 59, 150–156.
Foster, J., M. Owe, and A. Rango, 1983: Snow cover and temperature relationships in North America and Eurasia. J. Climate
Appl. Meteor., 22, 460–469.
Gutzler, D. S., and R. D. Rosen, 1992: Interannual variability of
wintertime snow cover across the Northern Hemisphere.
J. Climate, 5, 1441–1447.
Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric
phenomena associated with the Southern Oscillation. Mon.
Wea. Rev., 109, 813–829.
Hoskins, B. J., and D. J. Karoly, 1981: The steady linear response of
a spherical atmosphere to thermal and orographic forcing.
J. Atmos. Sci., 38, 1179–1196.
Hurrell, J. W., 1996: Influence of variations in extratropical wintertime teleconnections on Northern Hemisphere temperature. Geophys. Res. Lett., 23, 665–668.
Lin, H., and J. Derome, 2004: Nonlinearity of extratropical response to tropical forcing. J. Climate, 17, 2597–2608.
——, and Z. W. Wu, 2011: Contribution of the autumn Tibetan
Plateau snow cover to seasonal prediction of North American
winter temperature. J. Climate, 24, 2801–2813.
——, J. Derome, and G. Brunet, 2005: Tropical Pacific link to the
two dominant patterns of atmospheric variability. Geophys.
Res. Lett., 32, L03801, doi:10.1029/2004GL021495.
Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci.,
20, 130–141.
Michaelsen, J., 1987: Cross-validation in statistical climate forecast
model. J. Climate Appl. Meteor., 26, 1589–1600.
North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982:
Sampling errors in the estimation of empirical orthogonal
functions. Mon. Wea. Rev., 110, 699–706.
Qian, B., X. Zhang, K. Chen, Y. Feng, and T. O’Brien, 2010: Observed long-term trends for agroclimatic conditions in Canada.
J. Appl. Meteor. Climatol., 49, 604–618.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V.
Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan,
2003: Global analyses of sea surface temperature, sea ice,
and night marine air temperature since the late nineteenth
century. J. Geophys. Res., 108, D14407, doi:10.1029/
2002JD002670.
Robinson, D. A., K. F. Dewey, and R. R. Heim, 1993: Global snow
cover monitoring: An update. Bull. Amer. Meteor. Soc., 74,
1689–1696.
Ropelewski, C. F., and M. S. Halpert, 1986: North American
precipitation and temperature patterns associated with the
El Niño/Southern Oscillation (ENSO). Mon. Wea. Rev.,
114, 2352–2362.
Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of
global rotational flow by steady idealized tropical divergence.
J. Atmos. Sci., 45, 1228–1251.
Shabbar, A., and A. G. Barnston, 1996: Skill of seasonal climate
forecasts in Canada using canonical correlation analysis. Mon.
Wea. Rev., 124, 2370–2385.
——, and M. Khandekar, 1996: The impact of El Niño–Southern
Oscillation on the temperature field over Canada. Atmos.–
Ocean, 34, 401–416.
——, and B. Bonsal, 2003: An assessment of changes in winter cold
and warm spells over Canada. Nat. Hazards, 29, 173–188.
Shukla, J., 1998: Predictability in the midst of chaos: A scientific
basis for climate forecasting. Science, 282, 728–731.
AUGUST 2011
WU ET AL.
Ting, M. F., M. P. Hoerling, T. Y. Xu, and A. Kumar, 1996:
Northern Hemisphere teleconnection patterns during extreme phases of the zonal-mean circulation. J. Climate, 9,
2614–2633.
Trenberth, K. E., 1990: Recent observed inter-decadal climate
change in the Northern Hemisphere. Bull. Amer. Meteor. Soc.,
71, 988–993.
Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis.
Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/
qj.04.176.
Vincent, L. A., and E. Mekis, 2006: Changes in daily and extreme
temperature and precipitation indices for Canada over the
twentieth century. Atmos.–Ocean, 44, 177–193.
——, X. Zhang, B. R. Bonsal, and W. D. Hogg, 2002: Homogenization of daily temperatures over Canada. J. Climate, 15,
1322–1334.
1649
Wang, B., R. Wu, and X. Fu, 2000: Pacific–east Asia teleconnection: How does ENSO affect east Asian climate?
J. Climate, 13, 1517–1536.
——, Z. W. Wu, J. Liu, C.-P. Chang, J. Li, and T.-J. Zhou, 2010: Another look at climate variations of the east Asian winter monsoon:
Northern and southern modes. J. Climate, 23, 1495–1512.
Wu, Z. W., J. Li, B. Wang, and X. Liu, 2009a: Can the Southern
Hemisphere annular mode affect China winter monsoon?
J. Geophys. Res., 114, D11107, doi:10.1029/2008JD011501.
——, B. Wang, J. Li, and F.-F. Jin, 2009b: An empirical seasonal
prediction model of the east Asian summer monsoon using
ENSO and NAO. J. Geophys. Res., 114, D18120, doi:10.1029/
2009JD011733.
Zhang, X., L. A. Vincent, W. D. Hogg, and A. Niitsoo, 2000:
Temperature and precipitation trends in Canada during the
20th century. Atmos.–Ocean, 38, 395–429.