• Applequist, S., , G. E. Gahrs, , R. L. Pfeffer, , and X-F. Niu, 2002: Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecasting, 17 , 783799.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., , J. W. Lee, , and J. P. Craven, 2003: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 67–68 , 7394.

    • Search Google Scholar
    • Export Citation
  • Cao, Z., 2008: Severe hail frequency over Ontario, Canada: Recent trend and variability. Geophys. Res. Lett., 35 , L14803. doi:10.1029/2008GL034888.

    • Search Google Scholar
    • Export Citation
  • Cao, Z., , P. Pellerin, , and H. Ritchie, 2004: Verification of mesoscale modeling for the severe rainfall event over southern Ontario in May 2000. Geophys. Res. Lett., 31 , L23108. doi:10.1029/2004GL020547.

    • Search Google Scholar
    • Export Citation
  • Diffenbaugh, N. S., , J. S. Pal, , R. J. Trapp, , and F. Giorgi, 2005: Fine-scale processes regulate the response of extreme events to global climate change. Proc. Natl. Acad. Sci. USA, 102 , 1577415778.

    • Search Google Scholar
    • Export Citation
  • Fritsch, J. M., , and K. F. Heideman, 1989: Some characteristics of the limited-area fine-mesh (LFM) model quantitative precipitation forecasts (QPF) during the 1982 and 1983 warm seasons. Wea. Forecasting, 4 , 173185.

    • Search Google Scholar
    • Export Citation
  • Groisman, P. Ya, , R. W. Knight, , D. R. Easterling, , T. R. Karl, , G. C. Hegerl, , and V. N. Razuvaev, 2005: Trends in intense precipitation in the climate record. J. Climate, 18 , 13261350.

    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., , F. W. Zwiers, , P. A. Stott, , and V. V. Kharin, 2004: Detectability of anthropogenic changes in annual temperature and precipitation extremes. J. Climate, 17 , 36833700.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

  • Karl, T. R., , and R. W. Knight, 1998: Secular trends of precipitation amount, frequency, and intensity in the USA. Bull. Amer. Meteor. Soc., 79 , 231241.

    • Search Google Scholar
    • Export Citation
  • Kendall, M. G., 1975: Rank Correlation Methods. Charles Griffin, 202 pp.

  • Kling, G. W., and Coauthors, 2003: Confronting Climate Change in the Great Lakes Region: Impacts on our Communities and Ecosystems. Union of Concerned Scientists/Ecological Society of America, 104 pp.

    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., 2003: North American trends in extreme precipitation. Nat. Hazards, 29 , 291305.

  • Lemmen, D. S., , F. J. Warren, , J. Lacroix, , and E. Bush, 2008: From Impacts to Adaptation: Canada in a Changing Climate 2007. Government of Canada, 448 pp.

    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Nonparametric test against trend. Econometrica, 13 , 245259.

  • Meehl, G. A., , J. M. Arblaster, , and C. Tebaldi, 2005: Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys. Res. Lett., 32 , L18719. doi:10.1029/2005GL023680.

    • Search Google Scholar
    • Export Citation
  • Olson, D. A., , N. W. Junker, , and B. Korty, 1995: Evaluation of 33 years of quantitative precipitation forecasting at the NMC. Wea. Forecasting, 10 , 498511.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., and Coauthors, 2007: Identification of human-induced changes in atmospheric moisture content. Proc. Natl. Acad. Sci. USA, 104 , 1524815253.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., , N. S. Diffenbaugh, , H. E. Brooks, , M. E. Baldwin, , E. D. Robinson, , and J. S. Pal, 2007a: Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing. Proc. Natl. Acad. Sci. USA, 104 , 1971919723.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., , B. A. Halvorson, , and N. S. Diffenbaugh, 2007b: Telescoping, multimodel approaches to evaluate extreme convective weather under future climates. J. Geophys. Res., 112 , D20109. doi:10.1029/2006JD008345.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1999: Conceptual framework for changes of extremes of the hydrological cycle with climate change. Climatic Change, 42 , 327339.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., 1995: Misuses of statistical analysis in climate research. Analysis of Climate Variability Applications of Statistical Techniques, H. von Storch and A. Navarra, Eds., Springer, 11–26.

    • Search Google Scholar
    • Export Citation
  • Yin, J. H., 2005: A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett., 32 , L18701. doi:10.1029/2005GL023684.

    • Search Google Scholar
    • Export Citation
  • Yue, S., , and C. Y. Wang, 2002: Regional streamflow trend detection with consideration of both temporal and spatial correlation. Int. J. Climatol., 22 , 933946.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) Time series of summer severe-rainfall event number (1979–2002) over Ontario. (b) As in (a) but after removing the AR(1) process, also included is the trend.

  • View in gallery

    Composite mean (1979–2002) of Ontario summer precipitation rate (mm day−1) based on the NCEP reanalysis.

  • View in gallery

    Composite difference of (a) geopotential height (1000 hPa) anomaly (m) and (b) precipitable water anomaly (kg m−2) between the 10 high-event years and the 10 low-event years.

  • View in gallery

    As in Fig. 3, but for (a) SAT anomaly (°C) and (b) 1000–500-hPa thickness anomaly (m).

  • View in gallery

    Spatial distribution of correlation coefficients (a) between summer severe-rainfall event numbers (1979–2002) and summer 1000–500-hPa thickness (m), and (b) between summer severe-rainfall event numbers (1979–2002) and summer precipitable water (kg m−2). The shaded areas indicate a statistical significance level of >95%.

  • View in gallery

    Summer precipitable water trend (kg m−2 yr−1) over the period of 1979–2002. The shaded areas indicate a statistical significance level of >95%.

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Summer Severe-Rainfall Frequency Trend and Variability over Ontario, Canada

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  • 1 Meteorological Service of Canada, Environment Canada, Toronto, Ontario, Canada
  • 2 Science and Technology Branch, Environment Canada, Toronto, Ontario, Canada
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Abstract

During the last two decades (1979–2002), there has been an ever-increasing frequency of summer severe-rainfall events over Ontario, Canada. This observed upward trend is robust as demonstrated through the Mann–Kendall test with consideration of removing a lag-1 autoregressive process. It is shown through composite analyses using the NCEP reanalysis data that in the presence of warming conditions the summer severe-rainfall events occur more frequently over Ontario, especially under atmospheric conditions with stronger low-level cyclonic circulations and more precipitable water. Further analyses indicate that over north and central Ontario the summer severe-rainfall frequency is linked with a positive trend of precipitable water whereas over central and south Ontario there is a strong interannual response of summer severe-rainfall frequency to the changes in precipitable water through the variations of air temperature.

Corresponding author address: Dr. Zuohao Cao, Meteorological Service of Canada, 4905 Dufferin St., Toronto, ON M3H 5T4, Canada. Email: zuohao.cao@ec.gc.ca

Abstract

During the last two decades (1979–2002), there has been an ever-increasing frequency of summer severe-rainfall events over Ontario, Canada. This observed upward trend is robust as demonstrated through the Mann–Kendall test with consideration of removing a lag-1 autoregressive process. It is shown through composite analyses using the NCEP reanalysis data that in the presence of warming conditions the summer severe-rainfall events occur more frequently over Ontario, especially under atmospheric conditions with stronger low-level cyclonic circulations and more precipitable water. Further analyses indicate that over north and central Ontario the summer severe-rainfall frequency is linked with a positive trend of precipitable water whereas over central and south Ontario there is a strong interannual response of summer severe-rainfall frequency to the changes in precipitable water through the variations of air temperature.

Corresponding author address: Dr. Zuohao Cao, Meteorological Service of Canada, 4905 Dufferin St., Toronto, ON M3H 5T4, Canada. Email: zuohao.cao@ec.gc.ca

1. Introduction

Observations over the last two decades indicate that there is a substantial increasing trend of summer (June–August, referred to as JJA hereinafter) severe-rainfall events over Ontario, Canada (Fig. 1a). The summer rainfall has significant impacts on Canadian society and economy, especially in highly populated areas such as the Great Lakes regions (e.g., Cao et al. 2004). These ever-increasing summer severe-rainfall events are characterized by high impact but low predictability. The accuracy and skill in summer precipitation forecasts are very low (Olson et al. 1995; Applequist et al. 2002), because during the warm season the accuracy and skill levels reach the lowest at the time of year when the area of severe precipitation associated with convective and/or small-scale weather systems is the largest (Fritsch and Heideman 1989; Olson et al. 1995). Because summer severe rainfall involves convective and/or small-scale phenomena and it is difficult to detect and predict in terms of its amount, location, and timing, an alternative approach needs to be developed to anticipate large-scale environments that are favorable for summer severe-rainfall development. Hence, it is important to gain experience through analysis of the summer severe-rainfall climatological behavior and its association with large-scale environments, even though summer rainfall frequency data sometimes have a relatively short climatological period. In this work, we demonstrate the existence of a trend for summer severe-rainfall frequency over Ontario and examine how large-scale climate conditions are linked with the observed variability in summer severe-rainfall frequency over Ontario.

The data and methods used in this study are briefly described in sections 2 and 3. The results obtained from trend and variability analyses for summer severe-rainfall frequency over Ontario are presented in section 4. The discussion and conclusions are given in sections 5 and 6, respectively.

2. Data

The quality-controlled data for summer severe-rainfall frequency over Ontario are obtained from the Ontario Storm Prediction Centre (OSPC) of Environment Canada [refer to Cao (2008)]. The quality-control process involves many checks and verification using different observational data performed by a meteorologist at OSPC. Ontario is geographically located between Hudson Bay and the Great Lakes (Fig. 2). To the north of 46°N, Ontario’s west–east area extent is from 96° to 79°W, and to the south of 46°N, its eastern boundary is extended to approximately 74°W. Ontario is a unique climatic region that ranges from humid continental in the south to subarctic in the north. The major influence on the regional climate is the large bodies of water in the south (the Great Lakes) and north (Hudson Bay). These bodies of water influence the climate with abundant moisture, which leads to a large amount of rainfall (see Fig. 2). The summer severe-rainfall frequency dataset contains numbers of summer severe-rainfall events over 24 yr (1979–2002); this dataset is currently available only through 2002. The summer severe-rainfall events are identified using the OSPC Weather Event Verification Decision Tree. For a given rain event to be considered to be severe, which is usually associated with flooding, it needs to meet either of the following criteria: 1) duration less than 3 h and precipitation rate equal to or greater than 50 mm h−1 or 2) duration greater than 3 h and precipitation rate equal to or greater than 50 mm 12 h−1. The reanalysis data are obtained from the National Centers for Environmental Prediction (NCEP). The NCEP data are available from 1948 to 2009. These analyses are archived with a horizontal resolution of 2.5° latitude × 2.5° longitude, and 17 constant pressure levels at 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa (Kalnay et al. 1996). The period of 1979–2002 is examined for details.

3. Methods

To detect a linear trend of summer severe-rainfall frequency over Ontario, a widely used nonparametric Mann–Kendall (MK) (Mann 1945; Kendall 1975) statistical test is employed. Under the null hypothesis H0 that a sample of data {Xi, i = 1, 2, … , n} is independent and identically distributed, the MK test statistic S is defined as
i1558-8432-48-9-1955-e1
where
i1558-8432-48-9-1955-e2
Mann (1945) and Kendall (1975) showed that when n ≥ 8 the statistic S is approximately normally distributed with the mean and the variance as follows:
i1558-8432-48-9-1955-e3
i1558-8432-48-9-1955-e4
where m is the number of tied (i.e., equal values) groups and ti is the number of data points in the ith tied group. Under the null hypothesis, the standardized MK statistic Z follows the standard normal distribution with a mean of 0 and variance of 1:
i1558-8432-48-9-1955-e5
If |Z| > Z1−α/2, a trend is statistically significant at a level of 1 − α/2.

Because the existence of positive serial correlation in a time series increases the probability of detection of a significant trend by the MK test, von Storch (1995) suggested removing the AR(1) (a lag-1 autoregressive process) from the time series through a prewhitening procedure. However, this prewhitening also removes a portion of the trend as demonstrated by Yue and Wang (2002). To detect a trend properly, we use a trend-free prewhitening approach (e.g., Cao 2008; Yue and Wang 2002) prior to applying the MK test so that the true trend is preserved and is no longer influenced by the effects of autocorrelation.

In this work, a composite analysis method is also used to examine the relationship between the summer rainfall frequency and its large-scale atmospheric environments.

4. Results

a. Summer severe-rainfall frequency trend

The method described in section 3 has been employed to detect a possible trend of summer severe-rainfall frequency over Ontario. As shown in Table 1, the computed Z statistic is greater than Z1−α/2 at different significance levels. It is demonstrated that the upward trend of Ontario summer severe-rainfall frequency is at least significant at the level of 99%. After removing the AR(1) process, we have plotted this upward trend of summer severe-rainfall frequency in Fig. 1b. Based on our calculation, the trend is about 11 events per decade with a statistical significance level of greater than 99%. This detected trend is robust and is not sensitive to the low-value data points in the beginning of the time series (e.g., years 1981, 1982, and 1983) and the high values in the end (e.g., years 1993 and 2002). After removing these five years of data points from the original time series, we have redone the statistical test with the new time series and found that the upward trend is still statistically significant at a level greater than 99%.

b. Summer severe-rainfall frequency variability

As shown in Fig. 1, there are a number of years with positive and negative anomalies of summer rainfall events. The 10 strongest positive-anomaly years are 1989, 1980, 1998, 1995, 2000, 1994, 1993, 2001, 1999, and 2002, referred to as high-event years, whereas the 10 strongest negative-anomaly years are 1981, 1992, 1983, 1979, 1982, 1991, 1990, 1984, 1985, and 1986, referred to as low-event years.

To understand how the summer rainfall frequency is linked with large-scale atmospheric conditions, we consider a simple composite of summer anomalies for geopotential height (1000 hPa), precipitable water, surface air temperature, and 1000–500-hPa thickness during the 10 high-event and low-event years. As shown in Fig. 3a, the differences of geopotential height (1000 hPa) anomalies between the 10 high-event years and the 10 low-event years are negative, with a magnitude mainly from −0.5 to −5 m. This indicates that the high-event years are associated with more anomalous cyclonic circulations than are the low-event years. As displayed in Fig. 3b, composite differences of precipitable water anomaly (kg m−2) between the 10 high-event years and the 10 low-event years are positive over Ontario. The difference of precipitable water anomaly is about 0.4–1.8 kg m−2. This suggests that there is more precipitable water available during the 10 high-event years than during the 10 low-event years.

Because moisture held in the atmosphere is closely associated with air temperature, we plot a spatial distribution of composite differences of surface air temperature (SAT) anomaly (°C) between the 10 high-event years and the 10 low-event years (Fig. 4a). Over Ontario, the difference of SAT anomaly is about 0.2°–0.6°C, indicating that the high-event years are linked to the warmer SAT whereas the low-event years are associated with the cooler SAT. Furthermore, it is interesting to examine whether more (fewer) summer severe-rainfall events are related to warmer (colder) mean low-level air temperature. According to the thermal wind relationship, the mean low-level (between 1000 and 500 hPa) air temperature is represented by 1000–500-hPa thickness. We perform a composite analysis of difference in thickness anomalies (1000–500 hPa) between the 10 high-event years and the 10 low-event years. As displayed in Fig. 4b, the magnitude of the anomaly difference over the Ontario region is about 10–17 m. This further confirms that the high-event years are connected to the warmer temperature environments whereas the low-event years are associated with the cooler temperature scenarios over Ontario. It is therefore suggested that the warming condition is favorable for more severe-rainfall event occurrence.

As shown in Fig. 1a, there is substantial interannual variability in Ontario summer severe-rainfall event frequency over the last two decades. To see whether the interannual variability of the summer severe-rainfall frequency is linked to air temperature over Ontario, we plot a spatial distribution of correlation coefficients between summer severe-rainfall event numbers and mean low-level air temperature, represented by the 1000–500-hPa thickness (Fig. 5a). Over central and south Ontario, the correlation coefficients vary from 0.35 to 0.5, which exceeds the requirement for a statistical significance level greater than 95%. The correlation coefficient of 0.5 is observed over southern Ontario. This indicates a response of summer severe-rainfall frequency to the variations of air temperature. When compared with Fig. 5b, it is found that the significant precipitable water correlation with summer severe-rainfall events (from 0.35 to 0.6) occurs over almost the whole Ontario region. This indicates a strong response of summer severe-rainfall frequency to the changes in precipitable water through the variations of air temperature at least over central and south Ontario.

5. Discussion

Increases in intense precipitation events have been observed over large spatial scales such as half of the land area of the globe (Groisman et al. 2005) and North America (Kunkel 2003). Analyses have shown that in the United States these increase trends are mainly due to increases in the frequency of days with precipitation for all categories of precipitation amount and in the intensity of the extremely heavy precipitation events (Karl and Knight 1998). However, at regional and local scales, these changes and variabilities still remain to be explored, such as severe summer rainfall frequency over highly populated Great Lakes regions of Canada. The upward trend of severe summer rainfall frequency identified in this work provides such an example at a regional scale. The summer severe-rainfall event occurrence over Ontario is associated with low-level cyclonic circulations and precipitable water. This finding is consistent with the one obtained from the nine-member general circulation model (GCM) ensemble simulation (Meehl et al. 2005). Furthermore, we have found that precipitable water has positive trends over most of the Ontario region at significance levels of at least 95% (Fig. 6). This result is in agreement with the one obtained from the 12 GCM ensemble means (Santer et al. 2007).

Because finescale processes are critical for accurate assessment of regional- and local-scale vulnerability to climate changes (e.g., Diffenbaugh et al. 2005), it is interesting to examine whether other parameters for severe weather, such as convective available potential energy (CAPE) and wind shear (e.g., Brooks et al. 2003; Trapp et al. 2007a), show any trends. Brooks et al. (2003) used 3-yr (1997–99) of NCEP reanalysis data to analyze on how many days per year the values of CAPE exceed 2000 J kg−1 over parts of North America and Europe. Trapp et al. (2007a) performed composite analyses using GCM and regional climate model (RCM) outputs and showed that summer CAPE is higher over the period of 2072–99 than during the period of 1962–89 whereas summer wind shear is mostly lower over the period of 2072–99 than during the period of 1962–89. In this study, we have carried out trend analyses for the summer CAPE and wind shear (e.g., between 1000 and 400 hPa) and found that the trends for the summer CAPE and wind shear over the Ontario region during the past 24 yr (1979–2002) are not statistically significant at, say, the 95% level.

By general consensus, global warming leads to increases in temperatures and evaporation, which in turn enhance the atmospheric moisture content and precipitation (e.g., Trenberth 1999). Its effects on regional- and local-scale precipitation frequency and intensity, however, remain unknown (e.g., Hegerl et al. 2004; Trapp et al. 2007b; Yin 2005), mainly because of uncertainties in GCM and RCM projections of future climate changes in terms of the location, timing, and magnitude of regional and local precipitation. For example, by 2100, heavy-rainfall-event frequency (during 24-h and 7-day periods) over Ontario projected by the third climate configuration of the Met Office Unified Model (HadCM3) is more than double with respect to the 1900–2000 mean, although little change in projected summer precipitation amount is expected in Ontario (Kling et al. 2003). On the other hand, the future climates in Ontario projected by the seven GCMs indicate that for the south subregion summer and autumn precipitation decreases by up to 10% by 2050 (Lemmen et al. 2008). Because of these uncertainties, observation-based climate analyses can provide tools for verification of GCM and/or RCM simulations of past/current climates and for guidance of their future climate projections.

6. Conclusions

Summer severe-rainfall frequency over Ontario is investigated in this study. The frequency dataset has a relatively short climatological period (24 yr). It is found for the first time that over the last two decades the summer severe-rainfall frequency exhibited an increasing trend over Ontario. The robustness of this upward trend is established through the MK statistical test with consideration of removing the AR(1) process. The connection between the Ontario summer severe-rainfall frequency variability and large-scale atmospheric conditions is also examined. It is demonstrated through the composite analysis that the high-event years are linked with stronger low-level cyclonic circulations and more precipitable water than the low-event years. It is found that the high-event years are associated with warmer low-level air temperature than the low-event years. It is therefore suggested that the warming condition is favorable for the ever-increasing summer severe-rainfall events occurring in Ontario. Based on our analyses, we have also found that Ontario summer severe-rainfall frequency is linked with a positive trend of precipitable water over north and central Ontario and that there is a strong interannual response of summer severe-rainfall frequency to the changes in precipitable water through the variations of air temperature, at least over central and south Ontario.

Acknowledgments

The authors thank the Ontario Storm Prediction Center of Environment Canada for providing the data of summer severe-rainfall frequency over Ontario and three anonymous reviewers for their valuable comments and suggestions.

REFERENCES

  • Applequist, S., , G. E. Gahrs, , R. L. Pfeffer, , and X-F. Niu, 2002: Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecasting, 17 , 783799.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., , J. W. Lee, , and J. P. Craven, 2003: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 67–68 , 7394.

    • Search Google Scholar
    • Export Citation
  • Cao, Z., 2008: Severe hail frequency over Ontario, Canada: Recent trend and variability. Geophys. Res. Lett., 35 , L14803. doi:10.1029/2008GL034888.

    • Search Google Scholar
    • Export Citation
  • Cao, Z., , P. Pellerin, , and H. Ritchie, 2004: Verification of mesoscale modeling for the severe rainfall event over southern Ontario in May 2000. Geophys. Res. Lett., 31 , L23108. doi:10.1029/2004GL020547.

    • Search Google Scholar
    • Export Citation
  • Diffenbaugh, N. S., , J. S. Pal, , R. J. Trapp, , and F. Giorgi, 2005: Fine-scale processes regulate the response of extreme events to global climate change. Proc. Natl. Acad. Sci. USA, 102 , 1577415778.

    • Search Google Scholar
    • Export Citation
  • Fritsch, J. M., , and K. F. Heideman, 1989: Some characteristics of the limited-area fine-mesh (LFM) model quantitative precipitation forecasts (QPF) during the 1982 and 1983 warm seasons. Wea. Forecasting, 4 , 173185.

    • Search Google Scholar
    • Export Citation
  • Groisman, P. Ya, , R. W. Knight, , D. R. Easterling, , T. R. Karl, , G. C. Hegerl, , and V. N. Razuvaev, 2005: Trends in intense precipitation in the climate record. J. Climate, 18 , 13261350.

    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., , F. W. Zwiers, , P. A. Stott, , and V. V. Kharin, 2004: Detectability of anthropogenic changes in annual temperature and precipitation extremes. J. Climate, 17 , 36833700.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

  • Karl, T. R., , and R. W. Knight, 1998: Secular trends of precipitation amount, frequency, and intensity in the USA. Bull. Amer. Meteor. Soc., 79 , 231241.

    • Search Google Scholar
    • Export Citation
  • Kendall, M. G., 1975: Rank Correlation Methods. Charles Griffin, 202 pp.

  • Kling, G. W., and Coauthors, 2003: Confronting Climate Change in the Great Lakes Region: Impacts on our Communities and Ecosystems. Union of Concerned Scientists/Ecological Society of America, 104 pp.

    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., 2003: North American trends in extreme precipitation. Nat. Hazards, 29 , 291305.

  • Lemmen, D. S., , F. J. Warren, , J. Lacroix, , and E. Bush, 2008: From Impacts to Adaptation: Canada in a Changing Climate 2007. Government of Canada, 448 pp.

    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Nonparametric test against trend. Econometrica, 13 , 245259.

  • Meehl, G. A., , J. M. Arblaster, , and C. Tebaldi, 2005: Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys. Res. Lett., 32 , L18719. doi:10.1029/2005GL023680.

    • Search Google Scholar
    • Export Citation
  • Olson, D. A., , N. W. Junker, , and B. Korty, 1995: Evaluation of 33 years of quantitative precipitation forecasting at the NMC. Wea. Forecasting, 10 , 498511.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., and Coauthors, 2007: Identification of human-induced changes in atmospheric moisture content. Proc. Natl. Acad. Sci. USA, 104 , 1524815253.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., , N. S. Diffenbaugh, , H. E. Brooks, , M. E. Baldwin, , E. D. Robinson, , and J. S. Pal, 2007a: Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing. Proc. Natl. Acad. Sci. USA, 104 , 1971919723.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., , B. A. Halvorson, , and N. S. Diffenbaugh, 2007b: Telescoping, multimodel approaches to evaluate extreme convective weather under future climates. J. Geophys. Res., 112 , D20109. doi:10.1029/2006JD008345.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1999: Conceptual framework for changes of extremes of the hydrological cycle with climate change. Climatic Change, 42 , 327339.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., 1995: Misuses of statistical analysis in climate research. Analysis of Climate Variability Applications of Statistical Techniques, H. von Storch and A. Navarra, Eds., Springer, 11–26.

    • Search Google Scholar
    • Export Citation
  • Yin, J. H., 2005: A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett., 32 , L18701. doi:10.1029/2005GL023684.

    • Search Google Scholar
    • Export Citation
  • Yue, S., , and C. Y. Wang, 2002: Regional streamflow trend detection with consideration of both temporal and spatial correlation. Int. J. Climatol., 22 , 933946.

    • Search Google Scholar
    • Export Citation
Fig. 1.
Fig. 1.

(a) Time series of summer severe-rainfall event number (1979–2002) over Ontario. (b) As in (a) but after removing the AR(1) process, also included is the trend.

Citation: Journal of Applied Meteorology and Climatology 48, 9; 10.1175/2009JAMC2055.1

Fig. 2.
Fig. 2.

Composite mean (1979–2002) of Ontario summer precipitation rate (mm day−1) based on the NCEP reanalysis.

Citation: Journal of Applied Meteorology and Climatology 48, 9; 10.1175/2009JAMC2055.1

Fig. 3.
Fig. 3.

Composite difference of (a) geopotential height (1000 hPa) anomaly (m) and (b) precipitable water anomaly (kg m−2) between the 10 high-event years and the 10 low-event years.

Citation: Journal of Applied Meteorology and Climatology 48, 9; 10.1175/2009JAMC2055.1

Fig. 4.
Fig. 4.

As in Fig. 3, but for (a) SAT anomaly (°C) and (b) 1000–500-hPa thickness anomaly (m).

Citation: Journal of Applied Meteorology and Climatology 48, 9; 10.1175/2009JAMC2055.1

Fig. 5.
Fig. 5.

Spatial distribution of correlation coefficients (a) between summer severe-rainfall event numbers (1979–2002) and summer 1000–500-hPa thickness (m), and (b) between summer severe-rainfall event numbers (1979–2002) and summer precipitable water (kg m−2). The shaded areas indicate a statistical significance level of >95%.

Citation: Journal of Applied Meteorology and Climatology 48, 9; 10.1175/2009JAMC2055.1

Fig. 6.
Fig. 6.

Summer precipitable water trend (kg m−2 yr−1) over the period of 1979–2002. The shaded areas indicate a statistical significance level of >95%.

Citation: Journal of Applied Meteorology and Climatology 48, 9; 10.1175/2009JAMC2055.1

Table 1.

The MK test for trend detection of Ontario summer severe-rainfall frequency. The Z statistic is a calculated Z value based on Eq. (5), and Z1−α/2 is the Z value at a statistical significance level of 1 − α/2.

Table 1.
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