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 warm-season 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.
