Summer regional rainfall over southern Ontario and its associations with outgoing longwave radiation and moisture convergence

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1 Meteorol. Appl. 12, (2005) doi: /s x Summer regional rainfall over southern Ontario and its associations with outgoing longwave radiation and moisture convergence Zhenhao Bao 1*, Hengchun Ye 2 & Peter A. Taylor 1 1 Department of Earth and Atmosphere Science, York University, 4700 Keele Street, Toronto Ont., M3J 1P3 Canada 2 Department of Geography and Urban Analysis, California State University, Los Angeles, California , USA Hye2@calstatela.edu Daily rainfall records at 50 sites across southern Ontario are used to investigate the association of summer regional rainfall with outgoing longwave radiation (OLR) and moisture convergence, from 1991 to The results suggest that OLR values correspond well to heavy rainfall amounts when rain occurs over more than 90% of the study region. The average regional daily precipitation of 12.1 mm/day corresponds to W/m 2 of OLR values in the study region. Also, OLR is more significantly associated with the regional daily rainfall amount than moisture convergence in the atmospheric layers between 1000 hpa and 300 hpa although both are associated with precipitation. The study suggests that when a low OLR departure centres over Ontario, the region tends to have extremely heavy rainfall and flooding. The results of this study indicate that OLR is a useful tool for satellite detection of extremely heavy rainfall and possible flash flooding during the summer season over southern Ontario. Introduction The increased accessibility of satellite remote sensing databases has initiated many studies to evaluate the application of these databases in meteorological and climatological research (e.g. Troup & Streten 1972; Schiffer & Rossow 1985; Rossow & Grader 1993; Weare 1994; Xie & Arkin 1996; Janowiak & Xie 1999; Lucas et al. 2001). Outgoing longwave radiation (OLR), measuring the amount of energy emitted to space by the earth atmosphere system in the µm wavelength band, has been recognised as an important index in energy budget applications. When combined with cloud-age and cloud-top temperature, OLR is used to describe precipitation intensity and atmospheric circulation (Chelliah & Arkin 1992; Ramusson & Arkin 1993). In general, OLR, rainfall and moisture convergence are all used to indicate the intensity of convection in analysing large-scale (>500 km) deep convective activity (Murakami 1980; Morrissey 1986). Studies of tropical convective rainfall suggest that OLR is a very good index for measuring precipitation intensity and have been used to derive global precipitation data sets when combined with ground Climate Research Branch, Meteorological Service of Canada, 4905 Dufferin Street, Downsview, Ontario, M3H 5T4 Canada stations and other satellite observations ( Janowiak & Arkin 1991; Arkin & Xie 1994; Morrissey 1986). Wu (1991) used an index of clear OLR minus cloudy OLR at the top of the earth-atmosphere system to correlate global precipitation from the FGGE (First GARP Global Experiment) database. Richards & Arkin (1981) used a linear relationship between precipitation and OLR anomalies to estimate precipitation in the GATE (Global Atmospheric Research Program s Atlantic Tropical Experiment) database. Xie & Arkin (1998) analysed the relationship between OLR and summer and winter precipitation at the global scale. They found that over mid-latitude land areas (40 60 N and S), correlations exhibit seasonal variations similar to, but larger in amplitude than, those observed over subtropical land areas. Liebmann et al. (1998) have pointed out that observed rainfall is best correlated with OLR on day time scales over the Amazon Basin. In general, convective rainfall in North America is associated with meso-scale systems developed under favourable large-scale circulation situations such as moisture convergence and surface frontal processes (e.g. Maddox et al. 1979, 1995; Laing & Fritsh 2000). During the summer season, the intensity of large-scale atmospheric circulation is weakened, and thermodynamic convection effects are strengthened (Whittaker & Horn 1981); thus 161

2 Zhenhao Bao, Hengchun Ye & Peter A. Taylor convective activity becomes an important mechanism for heavy rainfall. Strong convective weather in summer is associated with a large variance in the heights of convective cloud tops that are directly reflected in OLR values (Richards & Arkin 1981; Arkin & Xie 1994; Xie & Arkin 1998). Southern Ontario is a peninsula surrounded by large inland lakes on three sides and experiences strong convective processes in addition to the frequent passage of fronts and different air masses (Burrows et al. 2002). Especially during summer, the lake land topography increases the intensity and frequency of convective weather, and flooding is a frequent event (Clodman & Chisholm 1994; King et al. 1996; Burrows et al. 2002). Flooding not only causes damage to properties but also pollutes water and leads to disease outbreaks (Auld et al. 2001). Based on studies by Murphy (2001), who classified 42 severe rainstorms in Ontario, there are five major synoptic types that bring flood threats to southern Ontario during the summer season: (1) the cutoff low: a completely displaced (cut off) low pressure in the upper atmosphere that moves independently of the westerly wind and sometimes remains stationary for days (for example, 31 July 2000); (2) the frontal: occurs along or south of a west (northwest-) to east- (southeast-) oriented warm or quasi-stationary front and covers large areas (for example, 11 June and 9 July 2000); (3) the overrunning: rain falls well north of a west-toeast warm front with a moderate to strong thermal gradient and covers a medium to large area (for example, 26 June 1996); (4) the Maddox synoptic: occurs east or southeast of a slow-moving northeast-to-southwest cold front and covers a large area (for example, 10 September 2000); and (5) the wave on front: heavy rain occurs along a southwest northeast oriented slow-moving cold front ahead of the wave and covers a moderate large area (for example, June 2000). All five of these types are likely to be enhanced by lake-breeze fronts (Murphy 2001). A recent report by Klaassen et al. (2003) on heavy rainfall during spring and summer 2000 in Ontario suggests that low density distribution of quality gauge stations and inaccuracies in gauge or tipping bucket measurements under extreme rainfall events (Bruce & Clark 1966) result in inadequate information for flood prediction. Meanwhile, radar-based precipitation estimates tend to underestimate heavy rainfall (Klaassen et al. 2003). They suggested that more information on high intensity heavy rainfall is needed in order to better predict summer floods over the region. In this study, 162 we explore the use of OLR values to predict heavy rainfall events over southern Ontario by examining their association with summer rainfall. The relationship is also compared with those associated with atmospheric moisture convergence over the same region. Data and methods Daily summer ( June August) rainfall data at 50 cooperative and national weather service rain gauge sites across southern Ontario during 1991 and 2000 were obtained from the Meteorological Service of Canada (MSC) (Figure 1). The daily rainfall is the 24-hour accumulation ending in the early morning. The data have been quality-controlled (Klaassen et al. 2003) and there are no missing values at these 50 selected stations. The OLR, specific humidity, and wind field (U and V vectors) at eight standard levels (1000 hpa, 925 hpa, 850 hpa, 700 hpa, 600 hpa, 500 hpa, 400 hpa and 300 hpa) were obtained from the reanalysis database available from the Climate Diagnostics Center (CDC) ( (Liebmann & Smith 1996; Kalnay et al. 1996). A rainfall coverage index was developed based on the percentage of stations at which rainfall was observed. We choose this index due to the fact that most floodrelated heavy rainfall is caused by the systems that cover a relatively large area (Murphy 2001). Also, the individual rainfall amount varies greatly from one station to another due to the complex topography and the characteristics of the storm system. Therefore, it is difficult to find a single threshold to represent the severity of storms for all the stations in the study region at the daily time scale. The classification of the rainfall area coverage index was established based on 10% increments of 10 20%, 20 30%, 30 40%, 40 50%, 50 60%, 60 70%, 70 80%, 80 90% and %. The average rainfall for each category was derived by averaging daily rainfall amounts at all 50 sites over southern Ontario. The average rainfall was then correlated to the average daily OLR and moisture convergences over the study region of 42.5 N 45 N, 77.5 W 82.5 W for each category. In addition, for heavy rainfall days, singleday events were separated from the two- and threeday continuous rainfall events in order to show any differences in the relationships that may be associated with different atmospheric mechanisms. The single-day events have higher precipitation intensities in general. This may be associated with enhanced lake breeze activity or may be associated with enhanced local strong convective activities related to meso-scale weather systems. The two- and three-day events are more likely to be associated with large synoptic-scale systems (Blackmon 1976; Blackmon & Lee 1984). Composite maps of OLR corresponding to the 10 highest and

3 Summer rainfall over southern Ontario Figure 1. Station distribution over southern Ontario. 10 lowest rainfall days were produced to reveal the magnitude of OLR anomalies and their locations. Also, the Student s t test was used to examine the rainfall anomalies associated with the 10 highest and lowest OLR days. Atmospheric moisture flux convergence was calculated by Q = h p1 p2 q V dp g = p1 p2 ( h q V dp ) g where g is acceleration due to gravity (9.8 ms 2 ), and p1 = 1000 hpa is bottom layer and p2 = 700 hpa is the top level for lower atmospheres, p2 = 500 hpa for middle atmosphere, and p2 = 300 hpa for upper atmosphere; q is specific humidity and V is wind vector. Results The results of correlations between daily rainfall and OLR for each rainfall coverage category are listed in Table 1. It shows that daily regional rainfall with % area coverage is most closely related to OLR with a correlation coefficient of (significant at the 99% confidence level), while correlation seems to be random in other categories. This category of large area coverage rainfall is also associated with the highest rainfall intensity and lowest OLR values: an average of 12.1 mm rainfall and W/m 2 OLR averaged (1) Table 1. Rainfall categories and the correlation coefficients between daily rainfall amount and OLR values. Rain Avg. Avg. R-cover event precipitation OLR rate no. (mm) (W/m 2 ) Correlation 10 20% % % % % % % % % over the study area of 42.5 N 45 N, 77.5 W 82.5 W. Thus, we will concentrate only on rainfall events in this >90% area coverage category ( 45 stations receiving precipitation) in southern Ontario for the remainder of the analyses. Table 2 lists the dates of the 99 rainfall events that occurred in the >90% area coverage category for the period of , 32 and 25 of these rain events occurred in June, July and August, respectively. The maximum regional daily precipitation of 35.8 mm occurred on 24 June 2000, a documented widespread flood day in southern Ontario (Klaassen et al. 2003). Table 3 shows the t-test results of daily rainfall based on the highest and lowest 10 OLR values. During the 163

4 Zhenhao Bao, Hengchun Ye & Peter A. Taylor Table 2. Calendars of the 99 rainfall cases used in this study. Dates in bold are the multiple-day events. Year June (date) July (date) August (date) , 15 4, 7, 29 2, , 18 8, 12 14, 17, 31 2, 10, 18, , 8 9, 14, 19 20, 25 14, 18, 29 15, , 23 25, , 13, 20, , 27 13, , 6 7, 10 11, , 18, , , 8 12, 15, , , 14, 24, , 8 9, 31 7, 10, , 20 21, 24, , 9, 14, , 22 Table 3. Mean daily rainfall corresponding to the 10 highest and 10 lowest OLR values and t-test results. Extreme cases The other cases Rainfall t Cases Mean rainfall (n = 10) OLR (W/m 2 ) Mean rainfall (n = 89) OLR (W/m 2 ) Student s-t Highest 10 OLR 9.1 mm mm Lowest 10 OLR 19.5 mm mm Table 4. Correlation coefficients between daily rainfall, OLR and moisture convergence at three atmospheric thickness layers. Type I represents the single-day events and Type II represents the two- and three-day events. Month/season Type I Type II Sample no hpa OLR and moist con Rainfall and moist con hpa OLR and moist con Rainfall and moist con hpa OLR and moist con Rainfall and moist con OLR and rainfall is significant at 95% confidence level, 1 is significant at 99% confidence level. 10 highest OLR days (248.0 W/m 2 ), the mean daily rainfall is 9.1 mm, about 3.0 mm below average and the departure is significant at the 90% confidence level. During the 10 lowest OLR cases (180.7 W/m 2 ), the mean daily rainfall is 19.5 mm, about 7.4 mm above average and is significant at the 99% confidence level. It is apparent that rainfall departures are much greater and more significant on extremely low OLR days than on high OLR days. The results of correlation analyses between daily rainfall, OLR and moisture convergence are shown in Table 4. Rainfall amount is most significantly associated with OLR in all types: single-day events (Type I), twoand three-day events (Type II), and all days. Moisture convergence in the troposphere between the 1000 hpa and the 300 hpa layer is significantly correlated with 164 rainfall only on Type I and all days, although it is significantly associated with OLR in all types. Also, the significance level of correlation between rainfall and OLR is higher in Type I (above 99% level) than Type II (above 95% level). This may suggest two things: (1) relationships to OLR are better when rainfall is heavier, and/or (2) OLR is a better indicator for enhanced local and short-lived convective activities than synoptic-scale events at a multi-day time scale. In either case, OLR seems to be useful for establishing relationships with severe thunderstorms and associated flash floods. The composite maps of OLR corresponding to the 10 highest and 10 lowest rainfall days are shown in Figure 2. Their corresponding dates are listed in Table 5. For the 10 highest rainfall cases, a low OLR of 190 W/m 2 is centred on Ontario, and is significantly lower (at a 95% confidence level) than OLR values averaged over the rest of the rainy days in the >90% area category (Figure 2a). In the 10 lowest rainfall cases, the OLR value decreases towards northeastern Canada and the Great Lakes region and has a higher value of 230 W/m 2 (Figure 2b). Figure 3 shows composites of average moisture divergence in the hpa layer. A strong area of convergence ( kg/m 2 s) is centred over the southwestern part of the study region during the 10 highest rain days (Figure 3a). The area generally corresponds to the low OLR areas with its position slightly shifted to the southwest (lower than 210 W/m 2 ) in Figure 2a. For the 10 lowest rain days, a weaker moisture convergence centre is located to the east of the study region. These two composite results suggest that extremely high rainfalls correspond better to both OLR and moisture convergence in geographical locations than do low rainfall days.

5 Summer rainfall over southern Ontario Figure 2. Composite maps of OLR corresponding to (a) the 10 highest, and (b) the 10 lowest daily rainfall cases in the heavy rainfall (>90% coverage) category. Shaded areas are statistically significant at the 99% confidence level. Table 5. The 10 highest and 10 lowest rainfall cases in the heavy rainfall category during the summers of Highest precipitation Regional rain Date (mm) cover (%) , 24 June , 27 Aug , 11 June , 29 July , 20 June , 15 Aug , 18 July , 20 Aug , 24 June , 3 Aug Lowest precipitation Regional rain Date (mm) cover (%) , 7 June , 25 June , 14 July , 10 Aug , 9 July , 8 July , 21 June , 6 July , 17 Aug , 30 Aug Although monthly precipitation totals over midlatitude areas associated with large-scale systems have better correlations with OLR in general (Xie & Arkin 1998), regional precipitation at a daily time scale may be different. It is true that large-scale systems are usually associated with lower OLR values and that OLR values are significantly lower in Type II (multiple days) than Type I (single day) precipitation. This is mostly due to the coarse spatial resolution of OLR values (2.5 latitude by 2.5 longitude) and the fact that regional averages of OLR tend to smooth out the smaller-scale activities. So, even though Type I has a higher daily precipitation amount (although not statistically significantly higher), the regional averaged OLR values are yet statistically significantly higher than Type II (at the 95% significance level, not shown). However, the correlation between daily precipitation and OLR seems to be a little stronger for Type I for the study period. Summary and conclusions This study examines the associations of summer daily rainfall with OLR and moisture convergence at different atmospheric thickness layers using 50 station records over southern Ontario. In the heavy summer rainfall category (rainfall area coverage is >90%), daily rainfall amount is statistically significantly associated with OLR and moisture convergence at atmospheric layers between 1000 hpa and 300 hpa. The association with OLR is stronger than with moisture convergence for all types of rainfall (single day or multiple day) events. Rainfall departures are significantly larger when OLR values are extremely low compared with those during higher OLR values. Similarly, OLR departures are larger during extremely high rainfall days compared with those of low rainfall days. For the 10 extremely heavy rainfall days, the two days of 11 June 2000 (frontal synoptic type) and 24 June 2000 (wave on front synoptic type) are the documented flooding days over many cities in southern Ontario, while 21 June 2000 belonging to the lowest 10 heavy rainfall days is not associated with flooding (Klaassen et al. 2003). Although no published documents have listed flood events in other years of the study period except for 2003, the authors are confident that these top 10 extremely heavy rainfall days are all associated 165

6 Zhenhao Bao, Hengchun Ye & Peter A. Taylor Figure 3. As Figure 2 but for moisture convergence in hpa. with flooding in certain places considering their high precipitation amounts and large area coverage. The composite for OLR during these extremely heavy rainfall events shows that low OLR values are centred over Ontario as opposed to shifted outside Ontario. This gives a good signal for predicting severe rainfall associated with flooding. The results from this study suggest that OLR can be incorporated into the heavy rainfall and flood prediction system. They suggest that OLR can be very useful for detecting heavy summer rainfall or flash flooding regardless of the type of synoptic weather conditions in middle- and high-latitude regions. Acknowledgements We are grateful to Dr William Burrows of the Meteorological Service of Canada (MSC) for providing us with the daily rainfall database. The OLR data are provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, USA, available at noaa.gov/. We also would like to thank Dr Xuebin Zhang for providing us with flood-related documents in Canada. Finally, we appreciate the insightful comments from two anonymous reviewers that improved the quality of the paper. This research is supported by the ELBOW 2001 project, funded by the Canadian Foundation for Climate and Atmosphere Science. References Arkin, P. A. & Xie, P. (1994) The global precipitation climatology project: first algorithm inter-comparison project. Bull. Am. Meteorol. Soc. 75(3): Auld, H., Klaassen, J. & Geast, M. (2001) Report on an Assessment of the Historical Significance of Rainfalls in the 166 Walkerton Area during May, Atmospheric Science Division, Meteorological Service of Canada, Ontario Region, Environmental Canada. 66pp. Blackmon, M. L. (1976) A climatological spectral study of the 500 mb geopotential height of the Northern Hemisphere. J. Atmos. Sci. 33: Blackmon, M. L. & Lee, Y.-H. (1984) Horizontal structure of 500 mb height fluctuations with long, intermediate and short time scales. J. Atmos. Sci. 41: Bruce, J. P. & Clark, R. H. (1966) Introduction to Hydrometeorology. Toronto: Pergamon Press. Burrows, W. R., King, P., Lewis, P. J., Kochtubajda, B., Snyder B. & Turcotte, V. (2002) Lightning occurrence patterns over Canada and adjacent United States from lightning detection network observations. Atmos. Ocean. 40: Chelliah, M. & Arkin, P. A. (1992) Large-scale interannual variability of outgoing longwave radiation anomalies over the global tropics. J. Climate 5: Clodman, S. & Chisholm, W. (1994) High lightning flash density storms in the southern Great Lakes region. National Weather Digest 19: Janowiak, J. E. & Arkin, P. A. (1991) Rainfall variations in the tropics during , as estimated from observations of cloud-top temperature. J. Geophys. Res. 96: Janowiak, J. E. & Xie, P. (1999) CAMS-OPI: a global satelliterain gauge merged product for real-time precipitation monitoring applications. J. Climate 12: Kalnay, E. & co-authors (1996) The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77: King, P., Leduc, M. J. & Murphy, B. P. (1996) The climatology of tornadoes in southern Ontario: possible effects of lake breezes. In: Proc. 18th AMS Conference on Severe Local Storms, February (1996), San Francisco CA, pp Klaassen, J., Ford, P., Auld, H., Li, G. & Li, Q. (2003) Ontario Heavy Rainfall Study for Spring and Summer, A report prepared for: Ontario conservation authorities and Ontario Ministry of Natural Resources. Meteorological Service of Canada-Ontario Region, Environment Canada. 212pp.

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