1. Introduction
In recent decades, hydrologic extremes such as floods and droughts have caused more public attention because they have been occurring more frequently and in greater severity worldwide (Easterling et al. 2000; Costa and Soares 2009; Wang et al. 2014; Chen et al. 2015; Elewa et al. 2016; Zilli et al. 2017). As Earth warms, a higher temperature likely means that more precipitation will fall over shorter time intervals, thus increasing the frequency and severity of extreme storm events, which could incur significant damage and severe hardship to our society and natural systems (Gao et al. 2017; Zhang et al. 2011). Conversely, because of global warming impacts, droughts in semiarid/arid regions of Africa have worsened in recent years (Gan et al. 2016). A brief review of some related studies is presented below.
Climatic extremes can have devastating impacts on our societies (Hales et al. 2003; Mass et al. 2011). For example, Canada has experienced severe floods that resulted in billions of dollars of damage, such as the flood events of Calgary and Toronto in 2013 which, as the respective worst natural disaster of Alberta and Ontario, are also ranked the first and the third largest natural insured disasters in Canada, respectively (Milrad et al. 2015; Wang et al. 2014). In southern Alberta, Canada, Gizaw and Gan (2016) projected an overall increase in its future extreme precipitation in the mid- and late twenty-first century, while Kunkel et al. (1999) detected increasing trends in extreme precipitation of 1–7-day duration over 1951–93 in Canada. Meanwhile, the increase in the frequency or intensity of precipitation extremes has also been observed in southern China (Zhai et al. 2005), Japan (Duan et al. 2015), Denmark (Gregersen et al. 2013), Sweden (Gregersen et al. 2015), the United States (Dhakal and Tharu 2018; Huang et al. 2017), and Brazil (Zilli et al. 2017).
Flooding may cause an outbreak of cholera, typhoid, and diarrheal disease because of environmental pollution resulting from floodwater mixed with human and animal waste. On the other hand, droughts that reduce the amount of water available for sanitation can increase the risk of diseases such as malaria and dengue fever (Hales et al. 2003). Other than damage and hardships caused by floods and famines by droughts, extreme climate events can also have significant impacts on human health. Li et al. (2018) have shown that human perceived temperature or apparent temperature (AP) has increased faster than air temperature over land, and the summer increase in AP-based thermal discomfort is expected to outpace the winter decrease in AP-based thermal discomfort.
Global warming could increase occurrences of precipitation extremes (Allan and Soden 2008) since according to the Clausius–Clapeyron equation, the water-holding capacity of the atmosphere will increase at about 7% °C−1 in temperature. According to the Synthesis Report of the IPCC Fifth Assessment Report, the surface temperature is projected to increase over the twenty-first century under all representative concentration pathway (RCP) emission scenarios of IPCC (2013), and extreme precipitation events are projected to become more intensive and frequent in many regions across the world (IPCC 2014). Located in high-latitude areas, warming and extreme precipitation in Canada are expected to be more pronounced (Lemmen and Warren 2004; Bush et al. 2014; Fischer and Knutti 2016). From 1948 to 2012, air temperature has increased in most parts of Canada, with the largest warming in winter and spring, and precipitation has also increased, especially in northern Canada (Jiang et al. 2015; Vincent et al. 2015).
Among various climate indices used for research in extreme climate, 27 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI; http://etccdi.pacificclimate.org/) have been relatively popular, for they are designed to provide scientifically robust measures of the characteristics of precipitation and temperature, the two most important daily climate variables (Zhang et al. 2011); for example, Miao et al. (2015) and Jiang et al. (2017) used ETCCDI indices to study changing behaviors of precipitation extremes in China.
Past studies on the extreme precipitation in Canada have either used probability distributions such as the generalized extreme value (GEV) distribution (Simonovic et al. 2016; Tan and Gan 2017), or they have only focused on extreme precipitation of some parts of Canada (Benyahya et al. 2014; Wang et al. 2015). Even though large-scale climate patterns have been teleconnected to precipitation in some parts of Canada (e.g., Gan et al. 2007), their influences on precipitation extremes have not received much attention and thus require further investigation (Xi et al. 2018; Zhang et al. 2001). Using the Pacific decadal oscillation (PDO), Pacific–North American (PNA), and Atlantic Oscillation (AO) teleconnection patterns as covariates, Asong et al. (2016) built a generalized linear model (GLM) to model seasonal precipitation, temperature, and their extremes in the Canadian Prairies (CP). Based on the Bayesian spatiotemporal quantile (BSTQR) model, Tan et al. (2018a) examined effects of large-scale climate patterns on Canadian winter precipitation at different quantile levels. They found that the teleconnections of Canadian winter precipitation to climate patterns are stronger at higher than at medium quantiles, implying that large-scale climate patterns likely exert stronger influence on precipitation extremes.
The primary objectives of this study are 1) to analyze spatiotemporal changes of historical extreme precipitation over Canada using 10 extreme precipitation indices and 2) to find the influence of large-scale climate patterns on precipitation extremes of Canada. The results of this study will provide a more comprehensive understanding of precipitation extremes and their observed changes in Canada. The datasets are described in section 2, the research methodology in section 3, results and discussion in section 4, and conclusions in section 5.
2. Datasets
a. Precipitation time series
Among 464 stations of daily precipitation data given in the second generation of the Adjusted Daily Precipitation (ACP2) dataset for Canada (Mekis and Vincent 2011), 164 stations across Canada (Fig. 1) that met the following requirements were selected in this study. The station should have data over the 1950–2012 period, and it should have no more than two consecutive years of missing values. ACP2 is part of the Adjusted and Homogenized Canadian Climate Dataset (AHCCD), and it is the most homogeneous long-term observed daily precipitation data currently available for Canada (Tan and Gan 2017). Among the 164 stations selected, only a few stations are located in northern Canada. More details about the adjustments and quality control of this dataset have been extensively discussed by Mekis and Vincent (2011).
Study area and meteorological stations. Abbreviations: Alberta (AB), Saskatchewan (SK), Manitoba (MB), Newfoundland and Labrador (NL), Prince Edward Island (PE), Nova Scotia (NS), Northwest Territories (NT), Nunavut (NU), Ontario (ON), New Brunswick (NB), Yukon Territory (YT), British Columbia (BC), Quebec (QC).
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Study area and meteorological stations. Abbreviations: Alberta (AB), Saskatchewan (SK), Manitoba (MB), Newfoundland and Labrador (NL), Prince Edward Island (PE), Nova Scotia (NS), Northwest Territories (NT), Nunavut (NU), Ontario (ON), New Brunswick (NB), Yukon Territory (YT), British Columbia (BC), Quebec (QC).
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Study area and meteorological stations. Abbreviations: Alberta (AB), Saskatchewan (SK), Manitoba (MB), Newfoundland and Labrador (NL), Prince Edward Island (PE), Nova Scotia (NS), Northwest Territories (NT), Nunavut (NU), Ontario (ON), New Brunswick (NB), Yukon Territory (YT), British Columbia (BC), Quebec (QC).
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
b. Extreme precipitation indices
Out of 27 indices recommended by ETCCDI, we have adopted 10 extreme indices grouped into two types to analyze extreme precipitation data of Canada and make the results comparable internationally (Fu et al. 2015; Lovino et al. 2018; Wang et al. 2013): 1) precipitation amount or intensity, which includes PRCPTOT, SDII, R95p, R99p, Rx1day, and Rx5day, and 2) number of days exceeding certain thresholds in rainfall depth, which are CDD, CWD, R10mm, and R20mm. The definitions and units of these indices are described in Table 1.
Definitions of extreme precipitation indices.
c. Climate indices
We have selected certain large-scale climate patterns that have contributed to the precipitation variability over Canada (Coulibaly 2006), such as the North Atlantic Oscillation (NAO), PNA, North Pacific Gyre Oscillation (NPGO), PDO, and El Niño–Southern Oscillation (ENSO). ENSO likely has the most significant interannual climate variability and influence on the climate of Northern Hemisphere (Rasmusson and Wallace 1983), including Africa (e.g., Ntale and Gan 2004). The Multivariate ENSO Index (MEI) is selected to represent ENSO.
The PDO is the leading principal component (PC1) of North Pacific (poleward of 20°N) monthly sea surface temperature anomalies since 1900 (Mantua and Hare 2002). The warm and cold phases of PDO have an influence on the precipitation of North America. For example, winter precipitation in the western United States (Brown and Comrie 2004) and western Canada has been found to be affected by the PDO (Gan et al. 2007). The PNA depicts a quadripole of 500-hPa geopotential height anomalies, with opposite anomalies centered over Hawaii and central Canada, and with similar signals south of the Aleutian Islands and over the southeastern United States (https://ncdc.noaa.gov/teleconnections/pna/). It is one of the most significant modes of low-frequency variability in the extratropics of the Northern Hemisphere throughout the year except June and July.
The NAO represents the climate variability from the East Coast of the United States to Siberia and from the Arctic to the subtropical Atlantic (Hurrell et al. 2001). It is characterized by changes in surface pressure, and it is one of the dominant and prevailing modes of atmospheric behavior in the North Atlantic (Hurrell et al. 2001; Coulibaly 2006). The NPGO is the second dominant mode of sea surface height (SSH) variability in the northeast Pacific correlated with fluctuations of salinity, nutrients, and chlorophyll in the California Current and Gulf of Alaska. It indicates changes in the intensity of central and eastern North Pacific gyre circulations, and it is driven by upwelling and horizontal advection (Di Lorenzo et al. 2008).
The MEI is the first unrotated principal component of six atmosphere–ocean variables over the tropical Pacific: sea level pressure, zonal and meridional components of the surface wind, sea surface temperature, surface air temperature, and total cloudiness fraction of the sky over the tropical Pacific. It gives a more comprehensive description of ENSO events than the traditional ENSO indices, the Niño-3 and Southern Oscillation index (SOI; Wolter and Timlin 1993, 1998, 2011).
d. CAPE and specific humidity
Convective available potential energy (CAPE), a proxy for weather conditions amenable for the occurrence of extreme precipitation events, is the vertical integral of parcel buoyancy between the level of free convection and the level of neutral buoyancy (Ye et al. 1998). CAPE (J kg−1) has been widely used to measure the onset of convection given that high CAPE values represent favorable conditions for the occurrence of severe convective storms and tornado events (Brooks et al. 2007; Dong et al. 2018; Kishtawal et al. 2010; Monkam 2002; Seeley and Romps 2015). We used monthly CAPE data of version 2 of the Twentieth Century Reanalysis (20CR) of the National Oceanic and Atmospheric Administration (https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2.html), which is an international project aimed at producing a high-quality global atmospheric circulation dataset (Compo et al. 2011). The dataset covers from 1871 to 2012 and is available at 2° spatial resolution. Comparisons with satellite data and other reanalysis data show that 20CR is generally of high quality (Compo et al. 2011). Using the CAPE data from 20CR, Krichak et al. (2015) found that heavy precipitating events are associated with an intense intrusion of humid tropical air and the presence of high CAPE values in the Mediterranean region. Further, given that precipitation is also related to air temperature and humidity, monthly temperature and specific humidity were also analyzed in this study.
3. Research methodology
a. Trend analysis
The nonparametric Mann–Kendall (MK) trend test (Mann 1945; Kendall 1955) recommended by the World Meteorological Organization (WMO) was used for the trend analysis of the precipitation data. The null hypothesis H0 is that the data are independent and randomly distributed, while the alternative hypothesis H1 is that a monotonic trend exists (Song et al. 2015). The MK method has been widely used in the trend analysis of hydrologic and climate data (Shadmani et al. 2012; Frazier and Giambelluca 2016; Pedron et al. 2017). However, the presence of autocorrelation in a time series can affect the detection of trends in the time series. Hamed and Rao (1998) proposed subtracting a nonparametric trend estimator from the original time series to account for the autocorrelation, and a detailed calculation procedure was given by Daufresne et al. (2009). The modified MK test was then applied to the time series of all indices listed in Table 1, and trend magnitudes were estimated using the nonparametric Sen’s slope estimator (Sen 1968).
b. Field significance and false discovery rate
Statistically, an individual test performed at a given significance level α has an α chance of falsely rejecting the null hypothesis. When conducting multiple tests simultaneously on data that are spatially correlated, it is necessary to adjust α to avoid falsely rejecting a large number of the null hypotheses (Ventura et al. 2004). In view of a large number of stations selected in the study, it is necessary to consider the field significance statistically in relation to multiple tests. Here the false discovery rate (FDR) method which controls the expected percentage of falsely rejected null hypotheses is employed to identify the significance of tests conducted in this study (Benjamini and Hochberg 1995). We applied the FDR method by using the “p.adjust” function in the R language (R Core Team 2017).
c. Principal component analysis
Principal component analysis (PCA) is a statistical method to reduce the dimensionality of a multivariate time series to several orthogonal principal components (PCs) that explain a large percentage of the variability in the time series (Jolliffe 2002). For the R95p and R99p indices analyzed in this study, the first two leading PCs account for more than 30% of the total variance. Following Cioffi et al. (2015) and for brevity, the leading PC1s are used for subsequent analysis of the time series of R95p and R99p.
d. Wavelet analysis
The wavelet transform is an effective tool designed to transform a time series into time and frequency domains simultaneously, revealing temporal and frequency changes of the dominant oscillations of the time series (Torrence and Compo 1998). Compared to the traditional Fourier transform, the wavelet transform is well known for its ability to analyze nonstationary time series (Cazelles et al. 2008), which is particularly useful for climate series that exhibit nonstationary behaviors (Chang et al. 2015; Tan and Gan 2017).
Another important application of the wavelet transform is the wavelet transform coherence (WTC) between two time series, defined as the square of their cross-spectrum normalized by the power spectrum of each time series, which gives us their cross-correlation (between 0 and 1) as a function of frequency (Torrence and Compo 1998). Wavelet analysis has been extensively used in climate research (Gan et al. 2007; Mwale et al. 2009; Jiang et al. 2014; Okonkwo 2014). Herein we chose the Morlet wavelet as the mother wavelet because it finds a delicate balance between time and frequency localizations (Grinsted et al. 2004). It should be noted that since we are dealing with finite-length time series, zeros are padded at the ends of the time series to reduce the edge effects (Torrence and Compo 1998), and so the wavelet power beyond the cone of influence (COI) should be explained with caution (Gan et al. 2007).
4. Results and discussion
a. Changes in extreme precipitation amount/intensity
To present overall changes to extreme precipitation of Canada in 1950–2012, Fig. 2 shows the average annual time series of Canada for all indices, while Table 2 shows the number of individual stations detected with positive or negative trends for the indices analyzed in this study. Overall, in these indices, positive trends dominate over negative trends, which means that extreme precipitation over Canada shows more increasing than decreasing trends in the study period.
Annual series of extreme precipitation indices from 1950 to 2012. The red line is the linear trend, the blue dashed line is the mean, and S is the trend per decade by Sen’s slope.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Annual series of extreme precipitation indices from 1950 to 2012. The red line is the linear trend, the blue dashed line is the mean, and S is the trend per decade by Sen’s slope.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Annual series of extreme precipitation indices from 1950 to 2012. The red line is the linear trend, the blue dashed line is the mean, and S is the trend per decade by Sen’s slope.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Trends per decade and number of stations showing positive or negative trends of extreme precipitation indices during 1950–2012. Annual trends that are statistically significant at the 0.05 level are in bold.
Over 1950–2012, both the average time series of RX1day (Fig. 2a) and RX5day (Fig. 2b) of Canada show statistically significant increasing trends at 0.34 and 0.90 mm decade−1, respectively. Figures 2c and 2d show that the average time series of R95p and R99p also show significant positive trends at 3.13 and 1.52 mm decade−1, respectively. However, for individual stations, R99p trends ranged from −8.77 to 21.06 mm decade−1 (Table 2). As for the average time series of PRCPTOT, the trend of 9.44 mm decade−1 is statistically significant, while the average SDII time series of Canada has a nonsignificant negative trend.
There are more stations showing positive than negative trends for RX1day and RX5day (respectively 102 and 107 vs 60 and 55), but only about 10% of positive trends are statistically significant, which agrees with Shephard et al. (2014), who also reported a general lack of significant trend signals. By contrast, no station has shown a significant negative trend in either index. A comparable number of stations show positive and negative trends in R95p and R99p (respectively 108 and 103 vs 54 and 60), but for R99p only two stations show statistically significant positive trends. Additionally, a comparable number of stations show positive and negative trends in SDII (88 vs 76), but more positive than negative trends are statistically significant (20 vs 9).
Spatial patterns of these results are presented in Fig. 3. Generally, stations with a positive extreme precipitation amount/intensity trend were located along the southern border of Canada while trends that are more negative are found in the central CP: British Columbia (BC), Alberta (AB), Saskatchewan (SK), and Manitoba (MB). Meanwhile, positive trends dominate the northern part of Canada.
Spatial patterns of trends for extreme precipitation amount/intensity indices from 1950 to 2012. The abbreviations are P, positive trend; SP, significant positive trend; N, negative trend; SN, significant negative trend; and NT, no trend.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial patterns of trends for extreme precipitation amount/intensity indices from 1950 to 2012. The abbreviations are P, positive trend; SP, significant positive trend; N, negative trend; SN, significant negative trend; and NT, no trend.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial patterns of trends for extreme precipitation amount/intensity indices from 1950 to 2012. The abbreviations are P, positive trend; SP, significant positive trend; N, negative trend; SN, significant negative trend; and NT, no trend.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
b. Changes in number of days with extreme precipitation
About twice as many stations show positive than negative trends in R10mm and R20mm (number of days ≥ 10 and 20 mm day−1 of precipitation, respectively), and more positive trends are statistically significant especially for R10mm (27 vs 9). Therefore, out of 164 stations of R10mm data of Canada analyzed, the average trend estimated was about 0.27 days decade−1. For R20mm, a comparable number of negative and positive trends are significant, which over 1950–2012 range from −1.71 to 1.32 days decade−1. It is noted that more stations show negative trends in consecutive dry days (CDD) than in consecutive wet days (CWD) (101 vs 50), but the reverse number of positive trends between CDD and CWD (62 vs 114). However, most of the detected trends are not statistically significant except for positive trends in CWD. The trend magnitude for the average CDD time series (−0.33 days decade−1) is much higher than that of the average CWD time series (0.08 days decade−1). The overall results obtained for the above 10 indices show that from 1950 to 2012, Canada has generally become wetter, which is what we would expect from increasing atmospheric moisture as the global climate has become warmer since the mid-twentieth century.
Figure 4 shows spatial distributions of trends for extreme precipitation indices, R10mm, R20mm, CWD, and CDD across Canada, of which the first three indices display analogous spatial distributions with the indices Rx1day, Rx5day, R95p, R99p, SDII, and PRCPTOP discussed in section 4a. However, CDD displays a different spatial distribution: negative trends dominated in the north and a mixed pattern in the south, which again shows that Canada had generally become wetter since the 1950s, especially in the north.
Spatial patterns of trends for extreme precipitation day indices from 1950 to 2012. The abbreviations are as in Fig. 3.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial patterns of trends for extreme precipitation day indices from 1950 to 2012. The abbreviations are as in Fig. 3.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial patterns of trends for extreme precipitation day indices from 1950 to 2012. The abbreviations are as in Fig. 3.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
We have detected decreasing trends in the annual SDII and CDD over Canada and more increasing trends in days with heavy precipitation (R10mm) in 1950–2003, similar to the results of Vincent and Mekis (2006). One consequence of this change could be an increased frequency and severity of flash floods (Limsakul and Singhruck 2016). From analyzing changes in global precipitation extremes using ETCCDI indices, Alexander et al. (2006) also found a widespread and significant increase in precipitation extremes. Wang et al. (2015) detected decreasing heavy rainfall intensities in eastern and southern Ontario, which is similar to the decreasing SDII of southern Ontario in Fig. 3f. However, using a regional climate model, Wang et al. (2014) projected both the intensity and frequency of extreme rainfall of Ontario would likely increase in the future. Even though hydrologic extremes are generally expected to become more severe in North America (Kunkel 2003), locally possible changes to future extreme precipitation remain uncertain because of many possible factors involved, such as moisture availability, thermodynamic instability, effects of large-scale atmospheric circulations, terrain features, and others.
Spatially, these extreme precipitation indices exhibit a mixture of increasing and decreasing trends across Canada in 1950–2012, which is consistent with the relatively low spatial coherence of extreme precipitation shown by Vincent and Mekis (2006). However, other than CDD, all indices exhibit more increasing than decreasing trends in southern Canada, except in the central CP, where decreasing trends are more dominant. Zhang et al. (2001) found that although the temporal distribution of the number of heavy precipitation events in Canada is spatially coherent, it varies considerably between seasons and regions. Tan and Gan (2017), who analyzed the nonstationarity of heavy precipitation events of Canada, concluded that stations with increasing trends are mostly located in the southwest and in Quebec while more decreasing trends are detected in the CP.
c. Relationship between extreme precipitation indices and annual precipitation
To examine if extreme precipitation indices are good indicators of the annual precipitation, their relationships are estimated using the Spearman’s rank correlation rho (Spearman 1904). As Table 3 shows, all indices are significantly correlated to the annual precipitation, especially for R95p, PRCPTOT, R10mm, and R20mm, whose Spearman rank correlation coefficients exceeded 0.80. CDD is the only index negatively correlated to the annual precipitation, which is expected because CDD represents the number of consecutive dry days. The result demonstrates that these 10 extreme precipitation indices can adequately reflect changes in the annual precipitation of Canada.
Correlation coefficients between extreme precipitation indices and annual precipitation. AP stands for annual precipitation. Coefficients that are statistically significant at the 0.05 level are in bold.
d. Probability distribution functions
To investigate the temporal variation in extreme precipitation further, all indices were divided into three 20-yr subperiods: 1950–70, 1971–91, and 1992–2012 (Fig. 5). Based on results obtained from the Kolmogorov–Smirnov test, it is noted that distributions of RX5day, PRCPTOT, R10mm, CDD, and CWD of subperiod 1 are statistically different from that of subperiod 3, suggesting a shift in these extreme precipitation indices. Generally, all indices except CDD tend to shift to the right from subperiods 1 to 2 and to 3, which demonstrates that probabilistically, extreme precipitation of Canada has been increasing in severity and frequency since the mid-twentieth century. For example, R10mm was less than 32 days in 1950–70, but it exceeded 32 days in 1971–91 and 1992–2012.
Annual probability density functions for extreme precipitation indices from 1950 to 2012 for three time periods: 1950–70, 1971–91, and 1992–2012.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Annual probability density functions for extreme precipitation indices from 1950 to 2012 for three time periods: 1950–70, 1971–91, and 1992–2012.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Annual probability density functions for extreme precipitation indices from 1950 to 2012 for three time periods: 1950–70, 1971–91, and 1992–2012.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Moreover, from probability distribution functions (PDFs) of RX1day, RX5day, R99p, PRCPTOT, and R10mm, it seems that the probability of occurrence of moderate extreme precipitation events has decreased since the 1950s. For example, the probability of getting RX1day at 46 mm was about 35% in 1950–70, but the probability decreased to just over 10% in 1992–2012. In contrast, the probability of getting moderate CDD has increased in the second and third subperiods, while CWD has shifted so that the duration of CWD tends to increase compared to the past. Overall, it seems that extreme precipitation of Canada will tend to occur more frequently in the future, probably leading to more flooding events.
e. Relationship between R95p/R99p and climate indices
1) Wavelet analysis of R95p and R99p
Given the vast landmass and various climatic zones of Canada, we have divided Canada into three regions, eastern, central, and western Canada. A similar regionalization approach has been adopted to analyze synoptic circulation patterns related to heavy precipitation (Tan and Gan 2017) and seasonal precipitation (Coulibaly 2006) in Canada. Figure 6 shows the wavelet power spectra of PC1 for R95p and R99p of western, central, and eastern Canada. The thick black contours represent statistically significant power at the 95% significance level against red noise, and the white sag line is the COI, outside of which results may be affected by edge effects of zero paddings.
Wavelet power spectra of PC1 for R95p and R99p in eastern, central, and western Canada.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Wavelet power spectra of PC1 for R95p and R99p in eastern, central, and western Canada.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Wavelet power spectra of PC1 for R95p and R99p in eastern, central, and western Canada.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
In general, R95p and R99p showed somewhat similar oscillation patterns in the same regions. However, each region exhibited different oscillation patterns for R95p and R99p. As shown in Figs. 6a and 6b, the power spectrum plot of western R95p reveals two distinct bands at 4–8-yr and 2–4-yr periodicities from 1960 to 1975 and in the 1980s, respectively. Two bands of 2–6-yr and 12–16-yr periodicities are found in the 1980s for the power spectrum plot of western R99p. In contrast, an 8–16-yr band and a 1–4-yr band dominate the central R95p and R99p power spectrum plots in 1950–70 and the 2000s, respectively. A small band of 1 year in the 1980s, two bands of 2–4-yr and 6–8-yr periodicities in 2000s are found in eastern R95p and R99p power spectrum plots, respectively.
In investigating heavy precipitation of Canada for 1900–98, Zhang et al. (2001) also found decadal oscillation as a dominant feature in precipitation extremes, while wavelet analysis of R95p and R99p indices in Fig. 6 show a combination of significant interannual and decadal oscillations that appeared and disappeared over 1950–2012 in eastern, central, and western Canada without any consistent pattern.
2) Wavelet transform coherence
The WTC plots between extreme precipitation indices (PC1 of R95p and R99p) and selected climate indices are shown in Figs. 7–9. The arrows indicate the phase difference: arrows pointing to the right (left) mean that two time series are in phase (antiphase) while arrows pointing up (down) mean that one time series leads (lags) the other by 90°. Analogous to Fig. 6, WTC between R95p and climate indices are similar to WTC between R99p and climate anomalies in the same region, and so we only present results of R99p. The coherence spectrum plots between the MEI and R99p show statistically significant power at 2–4-yr bands in some years over western Canada (Fig. 7a) and at 2–6-yr bands over eastern Canada, respectively. After 1970s, there was a consistently strong coherence at 8–16-yr bands between R99p and MEI in western and eastern Canada, although parts of the bands are outside the COI. For central Canada, the power of the wavelet coherence between MEI and R99p (Fig. 7c) was relatively weak, which is expected because of the blocking effect of the Canadian Rockies.
Wavelet coherence between the MEI/PDO and R99p for eastern, central, and western Canada.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Wavelet coherence between the MEI/PDO and R99p for eastern, central, and western Canada.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Wavelet coherence between the MEI/PDO and R99p for eastern, central, and western Canada.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
As in Fig. 7, but for the PNA and NAO.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
As in Fig. 7, but for the PNA and NAO.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
As in Fig. 7, but for the PNA and NAO.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
As in Fig. 7, but for the NPGO.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
As in Fig. 7, but for the NPGO.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
As in Fig. 7, but for the NPGO.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
There was a strong 8–12-yr wavelet coherence since the 1980s between PDO and R99p in western Canada (Fig. 7b), but a weak coherence between PDO and R99p in eastern and central Canada. There was a high 2–8-yr coherence between the PNA and R99p from the 1950s to 1990s in western Canada (Fig. 8a) and a strong 8–14-yr coherence between the PNA and R99p during the 1950s and after 1995 in eastern Canada (Fig. 8e). For central Canada, other than a strong 1–4-yr coherence that appeared since the 1990s, the PNA did not seem to have much effect on R99p. Figure 8b shows that there is scattered strong interannual (1–4-yr) coherence between the NAO and R99p in western Canada and a significant coherence band of 6–8-yr periodicity in central Canada. The band of high 12–16-yr coherence mainly occurred after the 1990s, but it is outside the COI of the wavelet coherence plot (Fig. 8f). The wavelet coherence between the NPGO and R99p was strong in western Canada, of interannual 2–8-yr cycle and 8–16-yr cycle for R99p (Fig. 9a). Some scattered coherence between the NPGO and R99p of 4–6-yr cycle can be found in central Canada, and a strong 8–16-yr coherence from the 1990s is detected in eastern Canada (Fig. 9c).
From a nonstationary analysis of the frequency and intensity of heavy precipitation over Canada using a GEV distribution, Tan and Gan (2017) found a strong nonstationary relationship between heavy precipitation and large-scale climate patterns. For example, annual maximum daily precipitation in southwestern coastal regions and the southern CP tend to be larger in El Niño than in La Niña years. In our study, results of the WTC analysis further show that extreme precipitation of western Canada based on R95p/R99p indices are strongly correlated with the MEI, PDO, PNA, and NPGO. For extreme precipitation of central Canada, the NAO and NPGO exerted more influence than other climate anomalies, while for eastern Canada, extreme precipitation primarily comes under the influence of the MEI, PNA, and NPGO. Jiang et al. (2014) also found the seasonal precipitation of Alberta (central Canada) to be strongly influenced by ENSO, PDO, and NPGO, while Gan et al. (2007) showed the influence of ENSO and PDO on the winter precipitation of southwestern Canada.
3) Relations of atmospheric circulations with climate patterns and anomalous precipitation
Oceanic and atmospheric circulation patterns are potential drivers behind climate extremes worldwide through their impacts on sea surface temperatures, surface winds, and sea level pressure (Limsakul and Singhruck 2016; Xi et al. 2018). Using storm back-trajectory analyses, Tan et al. (2018b) showed that extreme precipitation events in southwestern Canada are generally associated with the atmospheric river over the North Pacific, while moisture pathways for central and eastern Canada follow the westerlies in the midlatitudes coming from the North Pacific Ocean or the northern polar jet stream over high-latitude regions.
Using boosted regression tree analysis, Theobald et al. (2018) demonstrated that teleconnections do not act in isolation, and their complex interactions with synoptic atmospheric circulation can affect precipitation variability. Furthermore, Tan et al. (2018c) applied the self-organizing map (SOM) algorithm on vertically integrated water vapor transport (IVT) data to analyze large-scale meteorological patterns (LSMPs) associated with precipitation extremes for summer and fall in Canada. During summer, the occurrence of LSMPs associated with ENSO in western Canada is greater than that in eastern Canada (Tan et al. 2018c), as shown by more extensive statistically significant wavelet coherence between the MEI and R99p in Fig. 7a than that in Fig. 7e. In addition, Tan et al. (2018c) further show that LSMPs associated with more frequent occurrence of extreme precipitation over western Canada are more likely to occur during positive phases of ENSO, as shown by the coherence phase in Fig. 7a, implying that ENSO has large influence on certain LSMP patterns identified by Tan et al. (2018c). Similarly, the occurrence of LSMPs associated with the NAO in western Canada is larger than that in eastern Canada (Tan et al. 2018c), which again agrees with the more extensive wavelet coherence of NAO–R99p in Fig. 8b than that in Fig. 8f. Additionally, LSMPs patterns associated with less frequent summer extreme precipitation events over western Canada tend to occur during the negative phase of the NAO, which has also been shown by the coherence phase in Fig. 8b, indicating that the NAO also has significant influence on certain LSMPs patterns they have identified.
Furthermore, it has been shown that regional positive (negative) precipitation anomalies are generally associated with midtropospheric convergence (divergence), located to the right (left) of the ridge axis and left (right) of the trough. Extremely large IVT values over western Canada are related to the Aleutian low and Gulf cyclone, which force moisture from the North Pacific to western Canada; however, it is not necessarily associated with positive precipitation anomalies or frequent extreme precipitation events, because intensive moisture fluxes sometimes just pass over a region without precipitating. In comparison, anomalously low IVT values are associated with more frequent extreme precipitation events, partly caused by an extremely low ground surface temperature that facilitates the moisture flux to precipitate (Tan et al. 2018c).
f. Correlation between extreme precipitation and seasonal CAPE, specific humidity, and temperature
Table 4 shows that extreme precipitation tends to occur more frequently in summer and winter for eastern Canada, in summer for central Canada, and in summer and winter for western Canada. To investigate the possible impacts of CAPE, specific humidity, and temperature on extreme precipitation of Canada, Fig. 10 shows the spatial distribution of trends in seasonal CAPE, specific humidity, and surface temperature, respectively.
Months from January (1) to December (12) are ranked in terms of the number of extreme precipitation events that occurred in each month.
Spatial distribution of the seasonal CAPE, specific humidity, and surface temperature trend from 1985 to 2012. The abbreviations are as in Fig. 3.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial distribution of the seasonal CAPE, specific humidity, and surface temperature trend from 1985 to 2012. The abbreviations are as in Fig. 3.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial distribution of the seasonal CAPE, specific humidity, and surface temperature trend from 1985 to 2012. The abbreviations are as in Fig. 3.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
In summer, both CAPE and specific humidity show increasing trends in central and eastern Canada, but decreasing trends along the southern border and in parts of northern Canada in 1950–2012. For seasonal surface temperature, negative trends were widespread in central Canada while positive trends dominated northern and southern Canada. In winter, there had been more increasing trends in CAPE in central and southeastern Canada, and more increasing trends in specific humidity over central and western Canada. However, widespread increasing trends in the winter surface temperature were detected across Canada except for the easternmost part.
Using the Spearman rank correlation, the influence of CAPE, specific humidity, and surface temperature on extreme precipitation (Rx1day) is further investigated (Fig. 11). Given that CAPE values are much higher in summer than in other seasons, the correlation analysis is only conducted for the summer. Figures 11a and 11b show that summer CAPE and specific humidity values are positively correlated with extreme precipitation in southern Canada, respectively. The annual cycle of CAPE generally reaches its maximum during summer in the Northern Hemisphere (Riemann-Campe et al. 2009), and summer precipitation is often of convective origin, for which latent heat release is the primary source of energy driving the upward air motions (Guichard et al. 2004; Lenderink and Van Meijgaard 2008). Apparently, increasing trends of extreme precipitation detected in this region are at least partly attributed to increasing CAPE and specific humidity in the summer. Our results agree with Lepore et al. (2015), who investigated the dependency of rainfall extremes on temperature and CAPE in the United States. They found that rainfall intensity quantiles are related to CAPE by a power-law relationship. In Turkey, Lolis and Türkeş (2016) also found that the occurrence of extreme precipitation events in summer is linked to low upper-air temperatures and high static instability (through CAPE) associated with the summer heating of land and upper-air disturbances. Similarly, Murugavel et al. (2012) also found that increasing CAPE over India compensates the weakening of monsoon circulation and is responsible for the increase in the frequency of extreme events over the 1984–2008 period. From simulating the future regional climate of the Great Lakes using a regional climate model called the Weather Research and Forecasting (WRF) Model, d’Orgeville et al. (2014) projected the future moisture availability and the rainfall of the Great Lakes basin to increase because of climate change. Besides, extreme rainfall is projected to increase more than the mean annual rainfall because extreme rainfall is projected to originate from the midtroposphere, where warming is projected to be higher than surface warming.
Spatial distribution of the Spearman rank correlation between extreme precipitation and (a) summer CAPE, (b) summer specific humidity, (c) winter specific humidity, (d) summer temperature, and (e) winter temperature.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial distribution of the Spearman rank correlation between extreme precipitation and (a) summer CAPE, (b) summer specific humidity, (c) winter specific humidity, (d) summer temperature, and (e) winter temperature.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
Spatial distribution of the Spearman rank correlation between extreme precipitation and (a) summer CAPE, (b) summer specific humidity, (c) winter specific humidity, (d) summer temperature, and (e) winter temperature.
Citation: Journal of Hydrometeorology 20, 2; 10.1175/JHM-D-18-0004.1
For the CP, negative correlations between extreme precipitation and specific humidity (Fig. 11c), and between extreme precipitation and temperature (Fig. 11e), have been detected in winter. This means that winter extreme precipitation has decreased even though both specific humidity and surface temperature have increased under climate warming. Zhang et al. (2001) detected both positive and negative trends in summer heavy rainfall, but negative trends in winter heavy snowfall in central CP, respectively. Vincent and Mekis (2006) confirmed that annual total snowfall in southern Canada has decreased significantly in the second half of the twentieth century, and annual maximum snow depth in Canada has also decreased (Kunkel et al. 2016; Vincent et al. 2015), which is likely related to climate warming impacts. Wang and Zhang (2008) used statistically downscaled principal components of sea level pressure and specific humidity as covariates to a GEV to derive winter maximum daily precipitation over North America. From projected changes in covariates obtained from climate change simulations of the CCCma Coupled Global Climate Model, version 3.1 (CGCM3.1) forced by the IPCC Special Report Emissions Scenario A2, they projected the maximum daily precipitation over the CP to decrease. This is because higher humidity values in the CP are associated with smaller GEV location parameters, which means that higher humidity is associated with large-scale circulations unfavorable for precipitation in the CP, where circulations exert a stronger influence over humidity. Additionally, climate models projected a smaller decrease in the intensity of the daily snowfall extreme than in the mean snowfall over many terrestrial regions of Northern Hemisphere due to global warming (O’Gorman 2014). This could be related to the theory of rain–snow phase transition that snowfall extremes occur in a range near the optimal temperature that is insensitive to warming (Dai 2008). These together may contribute to the negative correlation with specific humidity and the general decline of extreme precipitation in the central CP.
From simulations of the Canadian Regional Climate Model, Mladjic et al. (2011) projected that 1–7-day precipitation extremes of 20-, 50-, and 100-yr return levels will increase over most of Canada, especially for northern regions, which would have severe implications for managing water resources of Canada such as sewer systems, flood control, and water storage systems. However, precipitation extremes of some regions in southern Canada (mainly the central CP) are projected to decrease. Results of Mladjic et al. (2011) and Zhou et al. (2018) generally agree with our results based on historical precipitation data of Canada. In addition, Emori and Brown (2005) separated the dynamic (atmospheric motion) change and atmospheric moisture content (thermodynamic) change of extreme precipitation changes projected in six climate model experiments. Their climate model results consistently show that over the subtropics, the thermodynamic change for extreme precipitation is an overall increase as a result of increased atmospheric moisture, while for mean precipitation the thermodynamic change is small or it decreases. Tandon et al. (2018) also noted that large-scale upward motion of air during extreme precipitation events could be related to changes in vertical stability in subtropical areas, or changes in the seasonal mean circulation near the equator. Therefore, while changes to the summer extreme precipitation could be attributed to the thermodynamic impact, winter extreme precipitation changes are more complicated and are likely attributed to the combined thermodynamic and dynamic effects (Ban et al. 2018; Tandon et al. 2018; Zhou et al. 2018).
5. Summary and conclusions
Because of effects of climate warming, hydrologic extremes such as droughts and floods have occurred more frequently and in greater severity globally in recent decades, resulting in severe environmental and societal impacts (Beniston and Stephenson 2004). This study analyzed monotonic trends of 10 extreme precipitation indices and possible relationships between R95p/R99p and several climate patterns over Canada. Statistically significant trends have been detected for all indices except for SDII and R20mm. Except for CDD, most indices analyzed exhibit positive trends, which demonstrate that increasing extreme precipitation events have occurred in Canada over 1950–2012, constituting a growing risk for urban settlements and infrastructure losses (Lovino et al. 2018). In addition, probability distribution functions of these indices plotted over three subsequent subperiods further confirm this conclusion. Spatially, even though these extreme precipitation indices exhibit a mixture of increasing and decreasing trends, positive trends dominate over negative trends across the southern and northern part of Canada, except for CDD. While in the central CP, more decreasing trends were detected.
From wavelet analysis and wavelet transform coherence plots, extreme precipitation of western Canada was strongly teleconnected to the MEI, PDO, PNA, and NPGO, that of central Canada was highly associated with the NAO and NPGO, and for eastern Canada by the MEI, PNA, and NPGO. However, teleconnections do not act in isolation, and their complex interactions with synoptic atmospheric circulation can affect precipitation variability. For example, LSMPs related to more frequent extreme precipitation in western Canada tend to occur during the positive phase of the PNA, ENSO, and the PDO.
Detected increasing trends in extreme precipitation in southern Canada are related to the increasing CAPE and specific humidity in summer, while decreasing extreme precipitation over central CP is associated with the decreasing snowfall and the influence of circulation in winter. It is noted that while changes to the summer extreme precipitation could be explained by the thermodynamic impact, winter extreme precipitation changes are more complicated and are likely attributed to the combined thermodynamic and dynamic effects.
In addition to the influence of large-scale climate patterns, land reclamation, urbanization, and other human activities can also cause changes in climate extremes (Xi et al. 2018). For example, as terrestrial evapotranspiration is the major moisture source for many extreme precipitation events in Canada (Tan et al. 2018b), urbanization and reclamation that change the land surface could also have impacts on extreme precipitation events. Therefore, a comprehensive analysis of the influence of climatic and nonclimatic factors on precipitation extremes will require further research.
Acknowledgments
This project was partly funded by the Floodnet, a Strategic Network of Natural Science and Engineering Research Council (NSERC) of Canada. The first author was also partly funded by the Chinese Scholarship Council of China and by the University of Alberta. CAPE, specific humidity, and surface temperature data were taken from the 20th Century Reanalysis (V2) of National Oceanic and Atmospheric Administration (https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2.html). PNA and NAO were collected from the NOAA Climate Prediction Center (CPC), PDO from the Joint Institute for the Study of the Atmosphere and Ocean (JISAO), NPGO from http://www.o3d.org/npgo/index.html, and NOI from the Pacific Fisheries Environmental Laboratory (PFEL). For ENSO, the MEI data were taken from the NOAA Earth System Research Laboratory (http://www.esrl.noaa.gov/psd/enso/mei/#ref_w).
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