Seasonal Prediction of Growing Season Start of Warm

Seasonal Prediction of Growing Season Start of Warm-season
Crops across Canada with Sea Surface Temperature
and Snow Cover
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
To be submitted to Journal of Applied Meteorology and Climatology
2010−10−13
Corresponding author: Dr. Hai Lin, MRD/ASTD, Environment Canada, 2121
route Trans-canadienne, Dorval, Québec H9P 1J3, Canada. Email:
hai.lin@ec.gc.ca
Abstract
Growing season start of warm-season crops (GSSWC) is one of the principal
agro-meteorological indicators in Canada and its seasonal prediction is crucial for the
agriculture sector to identify risks and opportunities in advance. Based on
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 which are dominated by two distinct leading modes.
The first mode accounts for 20.2% of the total GSSWC variances and features a
mono-sign pattern with the maximum anomalies in central-southern Canada. It
indicates that warm-season crops in most Canadian areas usually experience a
consistent early or late growing season start while those in central-southern Canada
have the most pronounced inter-annual variations. The second mode explains 10.8%
of the total variances and bears a zonal seesaw pattern in general, accompanied by a
prominent anomalies covering the West Coast of Canada and anomalies with a reverse
sign prevailing central-eastern Canada. Therefore, a strong second mode year
represents an early GSSWC in western Canada and a late GSSWC in the rest region.
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 Ocean in 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 Ocean provide
1
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. Based on these predictors of snow cover
anomalies and SSTAs in prior April, an empirical model is established for predicting
the two principal components (PCs) of the GSSWC across Canada. Hindcast 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 99% confidence level. Since all these predictors can be readily monitored
in real time, this empirical model provides a new prediction tool for
agro-meteorological events across Canada.
2
1. Introduction
How to improve seasonal prediction skill of agro-meteorological conditions
in Canada is becoming an urgent issue and receiving fervent research interests during
the past decades. Under a global warming background, climate in Canada is
experiencing a dramatic 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 0.9°C 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 agro-meteorological conditions. In
light of this, a useful prediction of year-to-year variations of agro-meteorological
conditions in Canada can not only benefit Canadian agriculture but also enhance
Canadian preparedness and adaptation to global climate change. Unfortunately,
research on seasonal prediction of agro-meteorological conditions in Canada is
relatively few up to now. This motivates us to conduct this work.
Agro-meteorological 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 warm-season crops (GSSWC)
is a principal one. Warm-season crops include bean, corn, pea, potato, and soybean,
etc. It was found that warm-season crops in most Canadian areas will not start
growing until daily mean surface air temperature (Ts) exceeds 10°C for 10
consecutive days. Therefore, seasonal prediction of the GSSWC is practically an issue
of predicting when a period of 10 consecutive days emerges in a year with mean Ts
3
reaching 10°C.
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),
etc. (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 inter-annual variations of seasonal
mean Ts (winter in particular) in Canada are greatly influenced by the surrounding
ocean. For example, the El Niño and Southern Oscillation (ENSO) is a primary
predictability source for inter-annual 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 tele-connections related to tropical diabatic forcing (e.g., Horel
and Wallace 1981; Trenberth 1990; Lin and Derome 2004; Lin et al. 2005). Besides
ENSO, SSTAs in Indian Ocean may also contribute to seasonal prediction of winter
Ts over North America (e.g., Wu et al. 2009a; Lin and Wu 2010)..
As the ocean accounts for only a portion of Ts variability (Ting et al. 1996;
Hurrell 1996), the substantial land mass 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 1995; Wang et al.
2010). For instance, Foster et al. (1983) investigated the relationships between snow
4
cover and temperature over North America and Eurasia. Barnett et al. (1987)
discussed the effect of Eurasian snow cover on global climate. Lin and Wu (2010)
revealed that the prior autumn snow cover anomalies over Tibetan Plateau can sustain
through the ensuing winter and exert profound influences on winter Ts over Canada.
Although lots of studies have been conducted on inter-annual variations of Ts
and its seasonal prediction, nevertheless, most of them focused on boreal winter
season and few 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?
The outline of this study is as following. Section 2 introduces the datasets and
methodology used in this study. Section 3 suggests that the GSSWC in most Canadian
areas begins in May-June and is 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. Hindcast is performed for the
1972−2007 period. The last section summarizes major findings and discusses some
outstanding issues.
5
2. Data and methodology
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 European Centre for Medium-Range
Weather Forecasts (ECMWF) 40-year reanalysis data (ERA-40; Uppala et al. 2005)
and the ERA-interim reanalysis data; 3) the Met Office Hadley Centre's SST datasets
gridded at 1.0° × 1.0° resolution (Rayner et al. 2003); 4) the Northern Hemisphere
snow
cover
data
gridded
at
2.0°
×
2.0°
resolution,
obtained
from
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 exposure, location, instrumentation,
observer, and observing procedures. The period of our analysis 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 2010). To maintain temporal
homogeneity, the 2002−2008 ERA-interim data were adjusted by removing the
climatological difference between the ERA-40 and ERA-interim data.
The GSSWC is defined as the beginning date of 10 consecutive days with
their daily mean Ts reaching 10°C. The beginning date is represented by the number
of days with reference to January 1st. For example, if the beginning date is January 2nd,
6
the GSSWC value will be 2. Missing data in GSSWC are replaced by the climatology
at this station. Stations with more than 10% of missing GSSWC (namely, 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 GSSWC across Canada
Figure 2 presents the climatology of the GSSWC across Canada. A
prominent feature is that the GSSWC value 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 May 1st (120 days), although most Canadian regions will not see warm-season
crops start growing until the end of June (around 180 days). Therefore, the period
May-June (MJ) is the essential growing start season for Canadian warm-season crops.
The seasonal prediction of GSSWC in the following section will focus on the MJ
period.
Figure 3 displays the two leading modes of 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,
7
which accounts for 10.8% of the total variance (Fig. 3c), is not separable from the rest
high modes. Nevertheless, the agro-meteorological meaning of the first two modes is
examined here.
The first mode basically shows a mono-sign pattern with maximum loading
located in the central-southern Canada, its amplitude decreasing northwestward and
northeastward (Fig. 3a). A positive phase of the first mode corresponds to an early
GSSWC in Canada, while a negative phase of the first mode corresponds to a late
GSSWC. The central-southern Canada has the most significant year-to-year variations.
PC1 is primarily dominated by interannual variability (Fig. 3b). 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
1957−1975 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 the global warming, because after 1980
when the dramatic global warming happens, the PC1 do 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 the western Canada and the rest 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 is corresponding 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
8
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 twenty seven years.
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 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
large-scale circulations associated with these two modes. Figure 4 shows MJ surface
circulation anomalies regressed to the two leading PCs along with the climatology.
One prominent feature of the atmospheric circulations near 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 well
resembles 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) center
occupying the entire northeastern Pacific (contours in Fig. 4b) with significant
anti-cyclonic 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
9
reflects a stronger than normal and northward shifted Hawaiian high pressure system.
Meanwhile, a negative SLP center controls the middle latitude western North Atlantic,
which is corresponding to a weaker than normal North Atlantic high pressure system.
Anomalous easterly surface winds over Mid-Atlantic may carry warmer and moister
air to Canada. These are favorable for a warmer than normal MJ in Canada.
For a strong second mode MJ, a zonal seesaw Ts pattern prevails 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 center associated with
anti-cyclonic 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 mid-troposphere circulation anomalies regressed to
the two leading modes along with the climatology. For a strong first mode MJ, the NA
continent is basically controlled by positive geo-potential height (H) anomalies at 500
hPa centered over central-southern Canada (Fig. 5b), which is primarily above the
surface warming center (Fig. 4b). This positive H anomaly extends eastward across
the middle latitude North Atlantic, with another center over the eastern Atlantic. The
high pressure system in the middle-high troposphere over Canada (Fig. 5b), which
may lead to clear sky and increased solar radiation, favors a warmer than normal MJ
10
in Canada..A salient negative H anomaly center and a positive center occupy the
Aleutian region and the central North Pacific, respectively. Two negative H anomaly
centers are located over the West and East Coast of U.S., expanding towards Pacific
and Atlantic, respectively. 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 towards the central NA continent.
This suppresses cold air mass activities in Canada. The negative H anomalies over
mid-western Atlantic imply a weakened North Atlantic subtropical high.
For s strong second mode MJ, a tremendous positive H center prevails over
central-eastern NA continent, while a negative H center controls the northwest of
Canada and Alaska. An anomalous positive H belt extends from central North Pacific
to the West Coast of the U.S. This pattern tends to strengthen the cold air mass
activities over western Canada and weaken those over central-eastern Canada and the
U.S.
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, their 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
11
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 towards 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 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 relationship. 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 prior
April (Figs. 6 and 7). For a strong first mode year, reduced snow cover anomalies
12
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
in 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 prior April through MJ, which makes
them predictors for the two distinct modes of GSSWC in Canada.
6. Seasonal prediction
To verify how well the above predictors contribute to the seasonal prediction
of GSSWC, an empirical seasonal prediction model is established using a linear
regression method for the period of 1972−2007 (see Eqs. (1) and (2)).
y1 = a10 + a11 i x11 + a12 i x12 + a13 i x13
y2 = a20 + a21 i x21 + a22 i x22 + a23 i x23 + a24 i x24
(1)
(2)
where Eqs. 1 and 2 are for PC1 and PC2, respectively. In Eq. (1), x11 denotes the
April normalized snow cover averaged in (40°−63°N, 125°−65°W), whereas x12 and
x13 refer to April normalized SSTAs averaged in the Indian Ocean (20°S−10°N,
50°−85°E), and the North Pacific ((10°−20°N, 180°−140°W) plus (37°−49°N,
160°E−150°W)), respectively (boxes in Fig. 7a). All these predictors show an intimate
linkage with PC1 (Fig. 10a), with their correlation coefficients being −0.57, 0.34, and
13
−0.48, respectively, all reaching 95% confidence level. In Eq. (2), x21 denotes the
April normalized snow cover averaged in (45°−53°N, 125°−105°W), whereas x22 ,
x23 and x24 refer to April normalized SSTAs averaged in the Indian Ocean
(5°S−5°N, 60°−100°E), the North Pacific (45°−60°N, 160°−120°W), and the tropical
eastern Pacific (15°S−20°N, 160°−120°W), respectively (boxes in Fig. 7b). All these
predictors show an intimate linkage with PC2 (Fig. 10b), with their correlation
coefficients being 0.41, −0.45, −0.61, and −0.49, respectively (beyond 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 following: 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 data and the remainder as a training set for performing regression
can prevent overfitting or wasting data. For the two leading PCs, 25% of the whole
hindcast period (36 years) equals to 9 years. That’s 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,
14
exceeding 99% 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 99% confidence level.
Therefore, the empirical method shows a promising hindcast skill. Since all these
predictors can be readily monitored in real time, this empirical model provides a new
prediction tool for the agro-climatic events in Canada.
7. Conclusion and discussion
Seasonal prediction of agro-climatic conditions in Canada is of central
importance for the Canadian agriculture 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, one of the principal agro-climatic
indicators in Canada (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 which 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 mono-sign pattern with the maximum anomalies in central-southern Canada.
It indicates that warm-season crops in most Canadian areas usually experience a
consistent early or late growing season start while those in central-southern Canada
have the most pronounced inter-annual variations. The second mode explains 10.8%
of the total variances and bears a zonal seesaw pattern in general, accompanied by
15
prominent anomalies covering the west coast of Canada and anomalies with reverse
sign prevailing central-eastern Canada. Therefore, a strong second mode year
represents an early GSSWC in western Canada and a late GSSWC in the rest 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 North Pacific and Indian Ocean in prior April. For the
second mode, the preceding April snow cover anomalies over western North America
and SSTAs in equatorial-eastern Pacific, North Pacific and equatorial Indian Ocean
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
impact on the ensuing MJ large-scale atmospheric circulations. In April of a high PC1
year, large areas of reduced NA snow cover in April persists through MJ (Figs. 6a and
8a) and the lowest layers of the atmosphere are warmed due to 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 tri-pole SSTA pattern in North Pacific
(Fig. 9a) may favor a stronger than normal and northward shifted Hawaiian high
pressure system and an enhanced Aleutian low pressure system. In a low PC1 year,
the situation is just opposite. For a high (low) PC2 year, an excessive (reduced) snow
16
cover in western Canada cooled (warmed) the lowest layers of the atmosphere due to
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)
center with anti-cyclonic (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 well interpret the main features of the
corresponding large-scale atmospheric circulations.
Based on these predictors of snow cover anomalies and SSTAs in prior April,
we establish an empirical model to predict the PC1 and PC2 of the GSSWC. Hindcast
is performed for the 1972−2007 period with a leaving-nine-out cross-validation
strategy 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
99% confidence level. Since all these predictors can be readily monitored in real time,
this empirical model provides a new prediction tool for agro-meteorological events
across Canada.
This seasonal prediction model assumes that the two distinct modes are
stable on inter-annual time scales. If the two distinct modes changes with time, i.e.,
inter-decadal changes, the predictors and the prediction scenario may also change,
correspondingly. In addition, how can the SSTAs in Indian Ocean 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 question. The hypotheses
17
concerning the origins of the first leading mode call for further numerical and
theoretical studies.
Acknowledgements. 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.
18
References
Barnett, T. P., L. Dumenil, U. Schlese, and Roechner, 1987: The effect of Eurasian
snow cover on global climate. Science, 239, 504-507.
Blockeel, H., and J. Struyf, 2002: Efficient algorithms for decision tree crossvalidation. J. Machine Learning Res., 3, 621−650.
Charney, J. G., and J. Shukla, 1981: Predictability of monsoons, in Monsoon
Dynamics, edited by J. Lighthill and R. P. Pearce, Cambridge Univ. Press, New
York, pp. 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, 1995: Inter-annual variability of wintertime snow
cover across the Northern Hemisphere. Climate Dyn., 12, 21−35.
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.
Qian, B., X. Zhang, K. Chen, Y. Feng, and T. O’Brien, 2010: Observed long-term
trends for Agro-climatic conditions in Canada. J. Appl. Meteor. Climatol., 49,
19
604−618.
Lin, H., and J. Derome, 2004: Nonlinearity of extratropical response to tropical
forcing. J. Climate, 17, 2597−2608.
Lin, H., 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.
Lin, H., and Z. W. Wu, 2010: Contribution of the autumn Tibetan Plateau snow cover
to seasonal prediction of North American winter temperature. J. Climate,
submitted.
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.
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, 10.1029/2002JD002670.
Robinson, D. A., K. F. Dewey, 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
20
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.
Shabbar, A. and M. Khandekar 1996: The impact of El Niño-Southern Oscillation on
the temperature field over Canada. Atmosphere-Ocean, 34, 401−416.
Shabbar, A. 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.
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., P. W. Kallberg, A. J. Simmons, et al., 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 centure. Atmos.-Ocean, 44,
177−193.
21
Vincent, L. A., X. Zhang, B. R. Bonsal, and W. D. Hogg, 2002: Homogenization of
daily temperatures over Canada. J. Climate, 15, 1322−1334.
Wang, B., R. Wu, and X. Fu, 2000: Pacific-East Asia teleconnection: How does
ENSO affect East Asian climate? J. Climate, 13, 1517−1536.
Wang, B., 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.
Wu, Z. W., B. Wang, J. Li and F.-F. Jin, 2009b: An emperical 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.
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Figure Captions
FIG. 1 Distribution of 210 surface air temperature (Ts) gauge stations across Canada.
FIG. 2 Climatological growing season start for warm-season crops (GSSWC) (color
shadings in unit of day). The GSSWC refers to the day numbers with reference to
January 1st.
FIG. 3 Upper panels: (a) spatial pattern (color shadings in unit of day) and (b) the
corresponding principal component (PC) of the first EOF mode of the GSSWC.
Lower panels (c) and (d): same as in (a) and (b) but for the second mode. The
numbers in the brackets indicate fractional variance of the EOF modes.
FIG. 4 May-June (MJ) (a) Climatology in sea level pressure (SLP; contours in unit of
hPa), Ts (color shadings in unit of °C), and 925 hPa winds (vectors in unit of m/s) and
their anomalies regressed to (b) PC1 and (c) PC2.
FIG. 5 Same as Fig. 4 except for 500 hPa geopotential height (H; contours in unit of
10 gpm), and winds (vectors, in unit of m/s).
FIG. 6 Correlation coefficients between North American snow cover in prior April and
(a) PC1, (b) PC2. The interval of contours is 0.1. The shaded regions represent
23
correlation coefficients exceeding 95% confidence level.
FIG. 7 Correlation coefficients between sea surface temperature anomalies (SSTAs) in
prior April and (a) PC1, (b) PC2. The interval of contours is 0.1. The shaded regions
represent correlation coefficients exceeding 95% confidence level.
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.
FIG. 9 MJ composite differences in SST between high and low PCs (high minus low)
years. (a) PC1; (b) PC2. A high (low) PC year is measured by one standard deviation.
The interval of contours is 0.3°C. The shaded regions exceed 95% confidence level.
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 brackets. Note
that NP, IO, NA, and TP refer to North Pacific, Indian Ocean, North America, and
tropical Pacific, respectively.
FIG. 11 Comparison of the observed (black curves) and the hindcast (red curves) PCs
made by the empirical seasonal prediction model. (a) PC1, (b) PC2. The correlation
coefficients between the observation and the hindcast are indicated in the brackets.
24
FIG. 1 Distribution of 210 surface air temperature (Ts) gauge stations across Canada.
25
FIG. 2 Climatological growing season start for warm-season crops (GSSWC) (color
shadings in unit of day). The GSSWC refers to the day numbers with reference to
January 1st.
26
FIG. 3 Upper panels: (a) spatial pattern (color shadings in unit of day) and (b) the
corresponding principal component (PC) of the first EOF mode of the GSSWC.
Lower panels (c) and (d): same as in (a) and (b) but for the second mode. The
numbers in the brackets indicate fractional variance of the EOF modes.
27
FIG. 4 May-June (MJ) (a) Climatology in sea level pressure (SLP; contours in unit of
hPa), Ts (color shadings in unit of °C), and 925 hPa winds (vectors in unit of m/s) and
their anomalies regressed to (b) PC1 and (c) PC2.
28
FIG. 5 Same as Fig. 4 except for 500 hPa geopotential height (H; contours in unit of
10 gpm), and winds (vectors, in unit of m/s).
29
FIG. 6 Correlation coefficients between North American snow cover in prior April and
(a) PC1, (b) PC2. The interval of contours is 0.1. The shaded regions represent
correlation coefficients exceeding 95% confidence level.
30
FIG. 7 Correlation coefficients between sea surface temperature anomalies (SSTAs) in
prior April and (a) PC1, (b) PC2. The interval of contours is 0.1. The shaded regions
represent correlation coefficients exceeding 95% confidence level.
31
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.
32
FIG. 9 MJ composite differences in SST between high and low PCs (high minus low)
years. (a) PC1; (b) PC2. A high (low) PC year is measured by one standard deviation.
The interval of contours is 0.3°C. The shaded regions exceed 95% confidence level.
33
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 brackets. Note
that NP, IO, NA, and TP refer to North Pacific, Indian Ocean, North America, and
tropical Pacific, respectively.
34
FIG. 11 Comparison of the observed (black curves) and the hindcast (red curves) PCs
made by the empirical seasonal prediction model. (a) PC1, (b) PC2. The correlation
coefficients between the observation and the hindcast are indicated in the brackets.
35