Adverse-Weather Trends in the Canadian Arctic

John M. Hanesiak Centre for Earth Observation Science, University of Manitoba, Winnipeg, Manitoba, Canada

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Xiaolan L. Wang Climate Research Branch, Meteorological Service of Canada, Downsview, Ontario, Canada

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Abstract

This study provides an assessment of changes in the occurrence frequency of four types of adverse-weather (freezing precipitation, blowing snow, fog, and low ceilings) and no-weather (i.e., no precipitation or visibility obscuration) events as observed at 15 Canadian Arctic stations of good hourly weather observations for 1953–2004. The frequency time series were subjected to a homogenization procedure prior to a logistic regression–based trend analysis.

The results show that the frequency of freezing precipitation has increased almost everywhere across the Canadian Arctic since 1953. Rising air temperature in the region has probably resulted in more times that the temperature is suitable for freezing precipitation. On the contrary, the frequency of blowing snow occurrence has decreased significantly in the Canadian Arctic. The decline is most significant in spring. Changes in fog and low ceiling (LC) occurrences have similar patterns and are most (least) significant in summer (autumn). Decreases were identified for both types of events in the eastern region in all seasons. In the southwest, however, the fog frequency has increased significantly in all seasons, while the LC frequency has decreased significantly in spring and summer. The regional mean rate of change in the frequency of the four types of adverse weather was estimated to be 7%–13% per decade.

The frequency of no-weather events has also decreased significantly at most of the 15 sites. The decrease is most significant and extensive in autumn. Comparison with the adverse-weather trends above indicates that the decline in no-weather occurrence (i.e., increase in weather occurrence) is not the result of an increase in blowing snow or fog occurrence; it is largely the result of the increasing frequency of freezing precipitation and, most likely, other types of precipitation as well. This is consistent with the reported increases in precipitation amount and more frequent cyclone activity in the lower Canadian Arctic.

Corresponding author address: Dr. John Hanesiak, Centre for Earth Observation Science, Room 212 Isbister Bldg., University of Manitoba, Winnipeg MB R3T 2N2, Canada. Email: john_hanesiak@umanitoba.ca

Abstract

This study provides an assessment of changes in the occurrence frequency of four types of adverse-weather (freezing precipitation, blowing snow, fog, and low ceilings) and no-weather (i.e., no precipitation or visibility obscuration) events as observed at 15 Canadian Arctic stations of good hourly weather observations for 1953–2004. The frequency time series were subjected to a homogenization procedure prior to a logistic regression–based trend analysis.

The results show that the frequency of freezing precipitation has increased almost everywhere across the Canadian Arctic since 1953. Rising air temperature in the region has probably resulted in more times that the temperature is suitable for freezing precipitation. On the contrary, the frequency of blowing snow occurrence has decreased significantly in the Canadian Arctic. The decline is most significant in spring. Changes in fog and low ceiling (LC) occurrences have similar patterns and are most (least) significant in summer (autumn). Decreases were identified for both types of events in the eastern region in all seasons. In the southwest, however, the fog frequency has increased significantly in all seasons, while the LC frequency has decreased significantly in spring and summer. The regional mean rate of change in the frequency of the four types of adverse weather was estimated to be 7%–13% per decade.

The frequency of no-weather events has also decreased significantly at most of the 15 sites. The decrease is most significant and extensive in autumn. Comparison with the adverse-weather trends above indicates that the decline in no-weather occurrence (i.e., increase in weather occurrence) is not the result of an increase in blowing snow or fog occurrence; it is largely the result of the increasing frequency of freezing precipitation and, most likely, other types of precipitation as well. This is consistent with the reported increases in precipitation amount and more frequent cyclone activity in the lower Canadian Arctic.

Corresponding author address: Dr. John Hanesiak, Centre for Earth Observation Science, Room 212 Isbister Bldg., University of Manitoba, Winnipeg MB R3T 2N2, Canada. Email: john_hanesiak@umanitoba.ca

1. Introduction

Adverse weather in the Canadian Arctic can have extreme effects on its inhabitants, including species mortality, travel and transportation, hunting, economic losses, and recreation. The frequency and intensity of adverse-weather events with present and future climate variability is also of concern [see Serreze et al. (2000) for a good overview of current observational high-latitude evidence for recent climate change]. Adverse impacts can be associated with various types of weather, such as extreme temperatures, precipitation amount and type, wind, and visibility restrictions, to name a few.

There have been several studies that include historical extreme Arctic temperature and precipitation trends (e.g., Tuomenvirta et al. 2000; Zhang et al. 2000; Bonsal et al. 2001; Przybylak 2000a, b, 2002; Stone et al. 2000; Shabbar and Bonsal 2003), as well as sea level pressure and cyclone activity changes (e.g., Zhang et al. 2004; Wang et al. 2004; McCabe et al. 2001; Walsh et al. 1996; Serreze et al. 1997). All of these studies point to significant climatic variations and trends in related climate variables. However, there is limited trend information in other adverse-weather events, such as freezing precipitation (FZ), blowing snow (BS), fog, and low ceiling (LC) conditions, particularly in the Arctic. It is the goal of this study to assess the observed changes in the frequency of these four types of adverse weather in the Canadian Arctic. The definition of each of these adverse-weather types is described below, together with a brief overview of their scientific and societal significance.

In this study, FZ includes freezing rain and freezing drizzle, which are by definition “drops of which freeze on impact with the ground or with other objects at or near the Earth’s surface” (Environment Canada 1990, EC90 hereafter). Drizzle is defined as “fine drops of water (diameter less than 0.5 mm)” (EC90). Ice pellets are not considered freezing precipitation, but, rather, are frozen precipitation (EC90), and, hence, are not included in this study. Freezing precipitation can have significant impacts on local human activities as well as animals. It often puts transportation (especially surface transportation) in jeopardy. The mortality of young animals (e.g., seals and polar bear) increases dramatically in freezing precipitation events, particularly in spring (I. Sterling 2003, personal communication). Thus, any FZ event can be considered as adverse weather. There are very limited studies on freezing precipitation in the Arctic because only in situ data can provide such information. Previous studies that outline the occurrence frequency of freezing precipitation over wide areas by season include Maxwell (1980, 1982), Canadian Hydrographic Service (1970, CHS70 hereafter), and McKay and Thompson (1969). The climatology of freezing precipitation occurrence over Canada has been shown and discussed in Stuart and Isaac (1999) and Stuart (1994). More recently, Cortinas et al. (2004) also analyzed freezing precipitation across the United States and Canada. However, these studies did not include trend analysis.

According to the Manual of Surface Weather Observations (MANOBS; EC90), BS events are considered “an obstruction to vision other than precipitation,” and are defined as being “snow particles raised by the wind to sufficient heights above the ground to reduce the horizontal visibility at eye level (1.8 m) to 6 miles or less.” This study did not focus on blizzard trends because the definition of a blizzard includes other criteria that accompany blowing snow in many cases (see, e.g., Lawson 2003). Blowing snow is a significant problem in terms of impact (transportation and loss of life) and our ability to forecast these events (e.g., Baggaley and Hanesiak 2005). The central Canadian Arctic archipelago is a prominent, well-known corridor for blowing snow (Phillips 1990), and more than half of the reports with low visibility (<6 miles) in the Arctic winter are caused by blowing snow (Fraser 1964), making this adverse-weather event a prominent phenomena. Typically, more than 80% of all Arctic BS reports are associated with a visibility of less than three statute miles, and, of that population, 30%–40% occur with a less than 1/2 mile visibility (Hanesiak et al., 2003). There are a few climatological studies on blowing snow (e.g., Fraser 1964; Maxwell 1981; Lawson 1987; Phillips 1990; King and Turner 1997; Pomeroy and Goodison 1997), but none of them has examined trends in BS events.

Fog is considered an “obstruction to vision other than precipitation,” and is reported as an official event if it extends to at least 2 m above ground level and reduces visibility to less than 5/8 mile (i.e., 1 km; EC90; note that ice fog is not considered in this study). Fog is adverse with respect to visibility. Its presence can be problematic to aviation and transport (especially marine transport). Fog in the Arctic can form as a result of radiational cooling, advection, and strong vertical heat and moisture fluxes (i.e., steam fog); however, the majority of Arctic fogs develop from advective processes (e.g., Rae 1951; CHS70). Fog becomes frequent when strong temperature contrasts exist between the surface and atmosphere under weaker wind regimes. Spatially based Arctic climatologies of fog exist (e.g., Rae 1951; CHS70; Berry and Lawford 1977; Phillips 1990; Maxwell 1980; Przybylak 2003; Hanesiak et al. 2003) and show its highly variable seasonal occurrence and strong local influences (terrain and proximity to open water), with higher frequencies in coastal regions.

Ceiling is “usually used with reference to the base of a layer aloft,” and is defined as the lesser of (a) the height above ground of the base of the lowest layer (cloud) aloft, at which the summation of (cloud) opacity is 6/10 or more of the whole sky, or (b) the vertical visibility in a surface-based layer that completely obscures the whole sky (EC90). Any ceiling lower than 1000 ft (∼305 m) restricts many smaller aircraft from flying because instrument flight rules (IFR) conditions apply. Thus, any hour that ceiling height is lower than 1000 ft is defined as an occurrence of an LC event, which is an adverse-weather event for aviation that occurs frequently in the Arctic. The LC definition inherently includes obscurations to visibility other than low cloud (e.g., blowing snow, dense fog, etc.); thus, it is a broad indicator of adverse weather for aviation. To our knowledge, there is no peer-reviewed literature documenting the climatology of LC occurrence, although there are some studies of cloud in the Arctic using in situ or satellite observations (e.g., Maxwell 1980; CHS70; Przybylak 2003; Milewska 2004; Barry et al. 1987; Schweiger and Key 1992; Key and Barry 1989; Wang and Key 2003).

The objective of this study is to examine whether or not there are statistically significant trends in the occurrence frequency of the four types of adverse-weather and no-weather (i.e., no precipitation or visibility obscuration) events in the Canadian Arctic during the last half-century, using in situ hourly weather observations.

The remainder of this paper is arranged as follows. The dataset used in this study, as well as the homogeneity test and trend analysis methods, are described in section 2. The climatological annual cycle of occurrence frequency of each type of event under investigation is briefly discussed in section 3. The results of the trend analysis are presented in section 4, with a summary and discussion in section 5.

2. Data and methods

a. Data

This study is based on human observations of hourly weather at 15 stations across the Canadian Arctic (all north of 60°N, except Churchill, Manitoba, which is at 58.73°N; see Fig. 1 and Table 1). This selection of sites includes all stations of long-term in situ hourly weather observations (nearly complete records for at least 30 yr) in the Canadian Arctic; no other stations in the region have such good, long records of hourly observations.

The data series that is analyzed in this study consists of monthly occurrence counts (or frequency, equivalently) of the four types of adverse-weather (i.e., BS, FZ, fog, and LC) and no-weather events. The definition of each type of these events is described in section 1, above, which is also the same as the official definition in MANOBS (EC90). Any hourly report with FZ occurrence (exclusively or mixed with other weather) is counted as an FZ event, and this same counting rule applies to the BS and fog events. The LC event is counted as such for any hourly report of ceiling heights that are below 1000 ft. An hourly report with no precipitation or visibility obscuration occurrence is counted as a no-weather event.

Having defined the events of interest, the monthly counts and occurrence frequencies of each type of event were calculated from hourly weather observations. Table 1 lists the periods of hourly observations at the 15 Canadian Arctic stations that are used in this study, which date back to as early as January 1953, and cover up to July 2004. The data are nearly complete over the selected period, with the exception that some stations had one or more short periods of missing observations (e.g., Cambridge Bay, Nunavut, had no nighttime observations from May 1995 to April 1999). This, and many other causes (e.g., station relocation, or instrument/observer changes), might induce artificial step changes in the time series of occurrence counts or frequency, which could have a significant impact on the estimate of trend, and, hence, may need to be removed prior to the trend analysis (to be addressed in section 2b). Discussion of other error sources for these observations follows below.

The hourly observations of current weather and ceiling heights are mainly subjective human observations. Potential issues with these subjective observations include observer error and inconsistencies among the different observers, observational errors resulting from low visibility or darkness, and so on. Although observers are highly trained for current weather observations, inconsistencies can still arise among them, especially for freezing precipitation that is mixed with other types of precipitation. The estimation of ceiling heights near 1000 ft can pose some errors among observations (daylight and darkness) during periods in which ceilometers or pilot balloons (pibals) were not available. Ceilometers also have some degree of error, although it is much smaller (∼3–10 m) compared to human estimation (C. Keenan, Vaisala, Inc., 2004, personal communication). Pibals were regularly used in the Arctic; thus, ceiling-height errors are considered not to be problematic, although ceilometer relocation or -type change could cause a mean shift (discontinuity) in the data time series (to be addressed in section 2b). Inherent visibility errors that are associated with blowing snow and fog are also considered to be relatively small (near 10%) because all of the stations used special distance markers, and, in more recent times, a combination of distance markers and visibility instruments. Quantifying these biases and any other errors in the observations would be very difficult and was not attempted in any great detail in this study. However, all significant artificial step changes were removed prior to the trend analysis, in order to obtain a more realistic estimate of trend. The technique for removing artificial step changes is described in the next subsection.

b. Data homogenization and trend analysis

Because an hourly observation of any type of the above-defined events is either “yes, it occurs,” or “no, it does not occur,” the occurrence counts of any type of these events over a period (say, a month/season) have binomial distributions. It is necessary to transform the count data so that conventional regression models can be used to estimate trends in time series of counts or frequency. For computational convenience and better estimates of the regression parameters, we used the following empirical logistic transformation (McCullagh and Nelder 1989):
i1520-0442-18-16-3140-e1
where s(t) is the occurrence count of a type of event in month t of m(t) observations [so f(t) = s(t)/m(t) is the occurrence frequency], and ε(t) denotes a zero mean white-noise process. Here, η(t) is called the empirical log odds (referred to as log odds hereafter). The essence of the transformation is to map the unit interval of frequency f (t) ∈ [0, 1], or nonnegative counts s(t) ∈ {0, 1, . . . , m(t)}, onto the whole real line (−, +) to ensure compatibility with the linear model on the right-hand side of Eq. (1), above. The transformation provides a better approximation to normality than does the frequency f (t). It does not change the sign or direction of the trends, but the linear trend in the time series of log odds corresponds to an exponential trend in the frequency time series, as shown in Fig. 2. The exponential trend is nearly linear over the period of around 50 yr of observation (cf. Fig. 2a). The use of log odds or frequency also greatly diminishes the effect of missing observations (if any). Note that the trend analysis on counts of event occurrence implicitly takes missing observations as being a nonoccurrence of an event, and, hence, could bias the estimate of the trend.
To obtain a realistic estimate of a climatic trend, it is necessary to ensure the homogeneity of the related climate data time series first. In this study, the two-phase regression model (Wang 2003)
i1520-0442-18-16-3140-e2
was used to detect a possible step change (sudden jump/drop) at time tc in time series η(t) for the period from N1 to N2 (1 ≤ N1 < N2 ≤ N). The position of change point (tc) and its statistical significance were determined by comparing the sum of squared errors (SSEs) of model (2) with that of the null model (1) above (note that it is assumed in the null model that the time series has no step changes, i.e., is homogeneous). The comparison was done for each and every trial value of tc ∈ {N1 + Nmin, N1 + Nmin + 1, . . . , N2Nmin}, where Nmin is a selected minimum length of segment. The time tc that is associated with the maximum reduction in the SSE of model (2) (among all trial values of tc) and the statistically significant improvement [over the null model (1)] in the fit of model (2) to the data is chosen as a possible change point. This process was repeated to check possible change points in each segment resulting from previously detected change points, until all statistically significant change points were identified, or the segments were too short to be divided further (i.e., each segment has fewer than 2 × Nmin data points). Then, metadata (station history and inspection reports) were used to check the veracity of the step changes that were detected using the above statistical approach. The spatial consistency of the trend was also used as another indicator to check the veracity of the step changes. For example, if two nearby stations were found to have significant trends of opposite signs, we visualized both time series again and revised the lists of change points, if applicable and necessary. At the same time, we kept in mind that inconsistent trends can exist at nearby stations as a result of local effects. Readers are referred to Wang and Feng (2004) and Wang (2003) for more details of the data homogenization technique/procedure and the relevant statistical test.

After detection of all of the artificial step changes in a time series under investigation, we fit the data series with the two-phase regression model (2), or, more precisely, the k-phase regression model (where k is a positive integer, and k > 2 if there is more than one step change detected), to estimate the intercept values μk and the trend β. Note that step changes are reflected as differences in the intercept values of time series segments, and that the size of the kth step change is ck = μk+1μk. The sample mean k of the kth segment t ∈ {Nk + 1, Nk + 2, . . . , Nk+1} and the relevant intercept estimate μ̂k has the following relationship: k = μ̂k + β̂(Nk+1 + Nk + 1)/2. Thus, the regression parameter estimation procedure is as follows: (i) calculate and subtract each segment’s sample mean k = (ΣNk+1t=Nk+1ηt)/(Nk+1Nk) from the time series ηt, which results in a new time series that has no mean shifts and, yet, has the same trend as the original time series ηt; (ii) estimate the trend β using the new time series derived in (i); (iii) use the estimated trend β̂ and the sample mean k to calculate the intercept estimate μ̂k [i.e., μ̂k = kβ̂(Nk+1 + Nk + 1)/2]. In other words, the estimate of the trend is not affected by the artificial step changes because these changes have been removed through subtracting the sample means of segments from the time series.

A few examples showing the effects of artificial step changes on the estimate of the trend are illustrated in Fig. 3, which clearly shows that trends that are estimated from the relevant raw data series [i.e., using the null model (1), without taking into account the artificial step changes] can be very misleading.

The Student’s t test was carried out to check whether or not the slope (trend) β is statistically different from zero, that is, to assess the statistical significance level of the estimated trend (von Storch and Zwiers 2001).

The estimate of the trend and its significance was carried out for each type of event at each of the 15 stations, separately. To estimate the overall trend, we first used monthly log odds for all 12 months consecutively (with the long-term mean annual cycle removed from the monthly time series to reduce the data dependence between two consecutive months). Then, we also estimated trends using seasonal averages of monthly log odds for each season separately, to investigate the seasonality of trends. The four seasons were defined as winter [January–February–March (JFM)], spring [April–May–June (AMJ)], summer [July–August–September (JAS)], and autumn [October–November–December (OND)]. Note that the estimate of the trend in each season is subject to a larger sampling variability because of the much smaller sample size (note that the sample size of the monthly time series is 12 times as large as that of the seasonal time series). As a result, changes that are identified from the seasonal time series might have a lower spatial consistency than those that are identified from the relevant monthly time series.

The estimated trend line in the time series of log odds can be easily converted to an exponential trend curve in the corresponding occurrence count/frequency time series. Because the latter is nearly linear over the period studied (cf. Fig. 2a), the average rate of change over the whole period can be derived and reexpressed in percentages of the long-term mean occurrence count/frequency. This was done for the Canadian Arctic as a whole, or for areas of stations that are identified with the same sign of trend, to illustrate the regional mean magnitude of change. The results are discussed in section 4.

3. Climatological annual cycle of frequency

To characterize the climatology and seasonality of the five types of weather events under investigation, long-term mean monthly occurrence frequencies were calculated for each type of event and are shown in Figs. 4 –7. These are discussed subsequently in the rest of this section.

As shown in Fig. 4, FZ events are most often seen in the Canadian Arctic in September–November. In the northeastern part of the region, another peak of FZ occurrence is also seen in May–June. The FZ frequency is below 2.5% at all 15 stations in all seasons. The FZ event is very infrequent at Iqaluit, Nunavut, Watson Lake, Yukon, and Whitehorse, Yukon, with the frequency being below 0.5% (Fig. 4).

The annual cycle of BS occurrence is quite different from that of the freezing precipitation occurrence. The BS occurrence is generally much more frequent than that of FZ, especially in the northeastern part of the region. As shown in Fig. 5, blowing snow events in the Canadian Arctic mostly occurs in the cold season from October to April; their occurrence is very infrequent during June–August. The BS occurrence is much more frequent in the northeastern part of the region than in the southwest (southwest of Cambridge Bay). During its peak season, the long-term mean frequency ranges from 6% to 25% in the northeast and below 3.5% in the southwest. It is below 2% at Yellowknife, Northwest Territories, Hay River, Northwest Territories, Fort Smith, Northwest Territories, and Fort Simpson, Northwest Territories, and below 0.4% at Watson Lake and Whitehorse (all as a result of a combination of terrain and boreal forest).

As shown in Fig. 6, the fog occurrence has an annual cycle that is similar to that of the LC occurrence at the same site. But the LC occurrence is generally more frequent than the fog occurrence, as expected (i.e., by definition, fog should never be greater than LC). As the fog and LC lines converge (or equal each other), this implies that fog is the main cause for the LC event, whereas, if they diverge, IFR cloud and/or other weather must also be responsible for creating LC events (e.g., blowing snow and snow). In the extreme northeastern part of the region (Resolute, Nunavut, Hall Beach, Nunavut, and Iqaluit), the occurrence of fog and LC events peaks during the warm season from May to September. In the southwestern part of the Canadian Arctic, it peaks in October–November. Both fog and LC events show two peaks of occurrence, in September–October and May–June, at the following stations: Coral Harbour, Nunavut, Cambridge Bay, Baker Lake, Nunavut, Churchill, and Inuvik, Northwest Territories. Among the four seasons, winter (January–March) has the lowest frequency of fog and LC occurrence at almost all stations (except at Watson Lake and Whitehorse). Among the 15 stations, the fog and LC events are most frequent at Resolute, and are least frequent at Iqaluit, Norman Wells, Northwest Territories, Watson Lake, and Whitehorse (Fig. 6).

In the Canadian Arctic, as shown in Fig. 7, the occurrence of the no-weather event peaks during the warm season from April to September, with frequency ranging from 60% to 90%. Figure 7 also indicates that the occurrence of weather (i.e., precipitation or visibility obscuration) is most frequent during November–January (it peaks in January at most stations).

4. Changes over the past half-century

In this section, the direction or sign of change over the last half-century is presented, together with the statistical significance (1 − p), or the p values. The spatial patterns of change in the occurrence frequency of each type of event in question are shown in Figs. 8 –12, with three selected significance levels (p ≥ 0.95, 0.80 ≤ p < 0.95, and p < 0.80). Hereafter, trends that are identified with p ≥ 0.95 are said to be of “statistical significance” or “(statistically) significant,” and with 0.80 ≤ p < 0.95, of “marginal significance” or “marginally significant.” Each figure shows the overall trend as estimated from the monthly time series (12 data points per year), and the seasonal trends as estimated from time series of seasonal mean log odds.

Generally, the Canadian Arctic has experienced significantly more frequent FZ events. As shown in Fig. 8a, an upward trend was identified from the monthly time series at all stations, except Iqaluit. The regional mean rate of increase (averaged over all 15 stations) is about 7% decade−1 (or 8% decade−1 when excluding Iqaluit) and is statistically highly significant (p = 0.999). The increase is most significant in spring and least significant in winter (see Figs. 8b–d). In winter, the increase is mainly seen in the south-central part of the region (being significant only at Yellowknife, Hay River, and Baker Lake), while a significant downward trend was identified at three eastern stations (Coral Harbour, Iqaluit, and Hall Beach; see Fig. 8b). However, freezing precipitation at these eastern locations is climatologically very infrequent in winter. No data are presented for summer because of the very infrequent FZ occurrences in this season. The trends that are associated with the FZ occurrence frequency could be the result of a number of factors (e.g., changes in temperatures or local open water source, major storm-track shifts, among others). In particular, the patterns of change in the frequency of FZ occurrence have substantial similarity to the patterns of change in surface air temperature (see Figs. 10–11 in Zhang et al. 2000). The areas of increasing temperature correspond well to the areas of increasing freezing precipitation occurrence. It is possible that the temperature in the Canadian Arctic has been increasing in such a manner that there are more times that the temperature is suitable for freezing precipitation occurence. However, more studies need to be done in this area.

On the contrary, the trend in BS occurrence in the Canadian Arctic is dominantly negative (Fig. 9). From the monthly time series, the downward trend was found at 10 of the 15 stations, and it is statistically significant at 7 locations (Fig. 9a). The downward trend is most significant at the eastern stations (Hall Beach, Iqaluit, Coral Harbour, and Churchill) and in the Northwest Territories (Norman Wells, Yellowknife, Hay River, and Fort Smith). At these locations, a downward trend was identified in all seasons of BS occurrence (note that the BS event rarely occur during summer; hence, no data are shown for this season here). Whitehorse was identified to have experienced significantly less frequent blowing snow in winter and marginally more frequent blowing snow in spring (Figs. 9b–c). Although an upward trend was identified from the monthly time series at four stations, none of them is statistically significant (Fig. 9a). With the exception of Watson Lake, this insignificant upward trend was identified in only one of the seasons of BS occurrence, namely, in winter at Inuvik (Fig. 9b), in spring at Baker Lake (Fig. 9c), and in autumn at Resolute (Fig. 9d). At Watson Lake, the upward trend was identified for all seasons and is of marginal significance (p = 0.907) in spring. Averaged over all the 15 stations, the rate of decline is about 13% per decade (p ≈ 1.000). It is not known which blowing snow processes are responsible for the decline in the frequency of BS occurrence, because changes in wind speed/direction, snow amount/state, and temperature and surface characteristics can all play a role in the BS occurrence, and changes in any one of these factors can lead to changes in the BS frequency.

As shown in Fig. 10, in the past half-century, the occurrence of fog has become more frequent in the southwestern Canadian Arctic and less frequent in the rest of the region. The upward trend was identified at the southwestern stations (including Whitehorse, Watson Lake, Fort Simpson, Yellowknife, Hay River, and Fort Smith) in all seasons other than autumn (Figs. 10b–e); it is most significant in summer and least significant in autumn. However, the downward trend in the northeast is a little more significant in spring than in the other seasons (Figs. 10b–e). The downward trend was identified at the eastern stations (Hall Beach, Iqaluit, Coral Harbour, Baker Lake, and Churchill) in all seasons other than autumn. In addition, little consistency was found in trends that were estimated for the northwestern stations. A downward trend was found at Inuvik in all seasons other than winter, and was significant in spring and summer. A significant upward trend was found for Norman Wells in summer, with a downward trend in the other seasons. Cambridge Bay was found to have no significant trends, while Resolute was identified to have changes of marginal significance in winter and spring. The regional mean rate of change is about 13% decade−1 (p ≈ 1.000) for all stations with a positive trend shown in Fig. 10a, and about 7% decade−1 (p = 0.991) for those with a negative trend. The observed changes can be associated with many different changing processes, such as open water sources for coastal stations, temperature and humidity regimes, light winds, stability regimes, large-scale circulation patterns, etc. Therefore, it is difficult to explain the observed trends and would require considerable effort.

The patterns of change in the LC occurrence have some similarity to those in the fog occurrence, which are commonly characterized by a downward trend in the eastern Canadian Arctic (Figs. 10 –11). This is not surprising, because a low ceiling condition can be the result of the existence of fog. However, the LC occurrence has more significant downward trend and much less notable upward trend than does fog occurrence, which indicates that changes in the LC occurrence are not all the results of changes in fog occurrence. In fact, the LC occurrence has become less frequent at most of the Canadian Arctic stations, as shown in Fig. 11. This downward trend is least significant in autumn (see Fig. 11e) but is most extensive and significant in summer, although it was identified for all seasons at the eastern stations (Hall Beach, Iqaluit, Coral Harbour, Baker Lake, and Churchill; see Figs. 11b–e). At most of the southwestern stations (Yukon and the Northwest Territories), the trend is also dominantly negative (or downward), especially in spring and summer. The regional mean rate of decrease that is averaged over the 15 stations is about 7% decade−1 (or 10% decade−1 if averaged over the 11 stations of negative trend shown in Fig. 11a) and is highly significant (p ≈ 1.000). Hay River is the only station at which an upward trend was identified in all four seasons, with it being significant in spring and summer (Figs. 11b–e). In addition, a significant upward trend was only found at Fort Smith and Yellowknife in winter (Fig. 11b) and at Resolute in summer (Fig. 11d), although an insignificant positive trend was found to dominate in the southwestern part of the region in winter (Fig. 11b). Milewska (2004) reported that only two Arctic stations (Yellowknife and Hay River) have experienced statistically significant (at the 5% level) increasing trends in annual cloud amount during the period of 1953–2002, which is quite different from our LC trends. Note that no homogenization procedure was used in Milewska (2004), although a lengthy discussion about the potential data problems is provided. However, as shown in Fig. 3, artificial step changes in ceiling-height observations are numerous and their impact on trend estimates are too significant to ignore, which was also found to be true for cloud amount observations (not shown here; further analysis is ongoing).

The occurrence frequency of the no-weather event can be considered to be a potentially important climate change indicator. It was found to be decreasing at most stations across the Canadian Arctic (Fig. 12). From the monthly time series, an upward trend was found only at two stations: it has only marginal significance at Whitehorse and is insignificant at Watson Lake (Fig. 11a). Averaged over all of the 15 stations, the occurrence frequency of the no-weather event has decreased significantly (p = 0.971), at a rate of about 1% decade−1 (or 2% decade−1 when excluding the 2 stations of positive trend shown in Fig. 12a; note that this relatively lower rate is the result of a high climatological frequency, which is much higher than that of the adverse-weather events; cf. Figs. 4 –7). The decrease is most significant and extensive in autumn (significant at 11 of the 15 stations), and is least significant in spring (Figs. 12b–e). The increase mainly happened in winter and spring at Whitehorse, but also occurred in winter and autumn at Watson Lake. Additionally, an upward trend was identified to be of statistical significance at Resolute, Inuvik, and Normal Wells in spring (Fig. 12c), and at Iqaluit and Baker Lake in summer (Fig. 12d).

5. Summary and conclusions

Using the logistic regression technique, we have estimated changes in the occurrence frequency of the four adverse-weather events and the no-weather event observed in the Canadian Arctic over the past half-century.

It was found that the freezing precipitation (FZ) occurrence has become more frequent almost everywhere across the Canadian Arctic. A significant downward trend was identified only at the three eastern Arctic stations in winter. However, freezing precipitation at these eastern sites is extremely infrequent during this time of year. The trends that are associated with the FZ occurrence frequency could also be the result of a number of factors (e.g., temperature or local open water source changes, major storm-track shifts). In particular, the areas of increasing temperature (see Figs. 10–11 in Zhang et al. 2000) correspond well to the areas of increasing freezing precipitation occurrence, which suggests that the temperature in the Canadian Arctic has probably been increasing in such a manner that there are more times that the temperature is suitable for freezing precipitation to occur. Higuchi et al. (2000) found that three out of four past major freezing rain events in south-central Canada since 1958 were associated with the positive phase of the North Atlantic Oscillation (NAO). Their analysis also indicates an apparent connection between the positive phase of NAO and the northern Quebec high pressure system, which is an essential synoptic feature of a major freezing rain occurrence over south-central Canada. The possible cause of changes shown here will be investigated in future studies.

In contrast to the FZ trend, the blowing snow (BS) occurrence has become significantly less frequent at most of the Canadian Arctic stations. The decline is most significant in spring. An upward trend was only identified with marginal significance at the two Yukon stations (Whitehorse and Watson Lake) in spring. Changes in wind speed/direction, snow amount/state, temperature, and surface characteristics can all play a role in the observed decline in blowing snow events. The majority of major BS events in the Arctic stem from long-lasting (several days) surface pressure gradient situations (e.g., between high and low surface pressure centers) without concurrent precipitation (Hanesiak et al. 2003). With increases in cyclonic activity in the northern latitudes (Zhang et al. 2004; Wang et al. 2004; McCabe et al. 2001) disrupting these long-lasting pressure gradient flows, it is possible that there may be less frequent long-lasting pressure gradient flows that arise. This will be the subject of future study.

The frequency of fog occurrence has changed significantly across the Canadian Arctic with a somewhat consistent spatial pattern in most seasons, which is characterized by increases in the southwest and decreases in the east. There are many reasons that would contribute to the fog trends and a detailed analysis would take considerable effort.

The low ceiling (LC) event has also become less frequent at most stations in the Canadian Arctic. The decline in LC occurrence is most significant in summer, and least significant in autumn. It was identified in the eastern region in all seasons. Significant increases in LC occurrence were only identified at Yellowknife and Fort Smith in winter, at Hay River in spring and summer, and at Resolute in summer. The patterns of change in LC occurrence are similar to those in fog occurrence, especially in the eastern Canadian Arctic. This is because a LC condition may be the result of fog occurrence. However, the increases in fog occurrence in the southwest do not translate into more frequent LC events. This indicates that changes in LC occurrence may be partly, and only partly, the result of changes in fog occurrence.

The occurrence frequency of the no-weather event has also decreased at the majority of the Canadian Arctic stations. The downward trend is most significant and extensive in autumn. Significant increases were identified only at Whitehorse and Watson Lake in winter, at Resolute, Inuvik, and Norman Wells in spring, and at Iqaluit and Coral Harbour in summer. Because the no-weather event is defined as “no precipitation or visibility obscuration” occurrence, its less frequent occurrence indicates more frequent “precipitation or visibility obscuration.” However, the results of our analysis show less, rather than more, frequent blowing snow or fog occurrence, although it does show more frequent freezing precipitation occurrence. This indicates that the decline in no-weather occurrence is not the result of an increase in blowing snow or fog occurrence; it is largely the result of the increasing frequency of freezing precipitation and, most likely, other types of precipitation as well. Zhang et al. (2000) reported significant increases in precipitation amount in the Canadian Arctic, which is particularly strong in all seasons, except summer (it is relatively weaker in summer). These are consistent with the more frequent cyclone activity that has been reported for the lower Canadian Arctic (McCabe et al. 2001; Wang et al. 2004; Zhang et al. 2004), because more frequent cyclone activity is associated with more frequent weather (particularly precipitation) events and, hence, less frequent no-weather events.

In conclusion, this paper has presented an up-to-date trend analysis of adverse-weather and no-weather events in the Canadian Arctic for all locations of good long-term records of in situ weather observations. Most importantly, the trends that are identified in this study are physically in agreement with trends that are identified in previous studies from different climate variables. It adds value to the body of literature identifying short-term climate trends in various parts of the world. Future analysis will explore the relationship of these changes to changes in atmospheric circulation regimes.

Acknowledgments

The authors are very grateful to Baoling Wang for his great help with the metadata investigation and data homogenization. The Natural Sciences and Engineering Research Council of Canada (NSERC) is acknowledged for providing a discovery grant to J. Hanesiak for his part of this research. Special thanks also go to Edward Hudson (Prairie and Arctic Storm Prediction Centre) and John Iacozza (CEOS, University of Manitoba) for providing useful comments over the course of this work, and to the three anonymous reviewers for their helpful comments during the review process.

REFERENCES

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    • Search Google Scholar
    • Export Citation
  • Barry, R. G., R. G. Crane, A. Schweiger, and J. Newell, 1987: Arctic cloudiness in spring from satellite imagery. J. Climatol., 7 , 423451.

    • Search Google Scholar
    • Export Citation
  • Berry, M. O., and R. G. Lawford, 1977: Low-temperature fog in the Northwest Territories. Meteorological Service of Canada Tech. Memo. 850, 27 pp.

  • Bonsal, B., X. Zhang, L. A. Vincent, and W. D. Hogg, 2001: Characteristics of daily extreme temperatures over Canada. J. Climate, 14 , 19591976.

    • Search Google Scholar
    • Export Citation
  • Canadian Hydrographic Service, 1970: Climate of the Canadian Arctic. Meteorological Branch, Department of Transport, Department of Energy, Mines and Resources, 71 pp.

  • Cortinas Jr., J. V., B. C. Bernstein, C. C. Robbins, and J. W. Strapp, 2004: An analysis of freezing rain, freezing drizzle, and ice pellets across the United States and Canada: 1976–90. Wea. Forecasting, 19 , 377390.

    • Search Google Scholar
    • Export Citation
  • Environment Canada, 1990: Manual of Surface Weather Observations (MANOBS). User’s manual. [Available from Meteorological Service of Canada, 4905 Dufferin St, Downsview, ON M3H 574, Canada.].

  • Fraser, W. C., 1964: A study of winds and blowing snow in the Canadian Arctic. Canadian Department of Transport, Meteorological Branch, 31 pp.

  • Hanesiak, J. M., T. Fisico, C. Rindahl, and E. Carriere, 2003: Climatology of adverse-weather events in the Canadian Arctic. Faculty of Environment, University of Manitoba, Centre for Earth Observation Science Tech. Rep. CEOS-Tech-2003-2, 3540 pp.

  • Higuchi, K., C. W. Yuen, and A. Shabbar, 2000: Ice Storm ’98 in Southcentral Canada and Northeastern United States: A climatological perspective. Theor. Appl. Climatol., 66 , 6179.

    • Search Google Scholar
    • Export Citation
  • Key, J., and R. G. Barry, 1989: Cloud cover analysis with Arctic AVHRR data 1. Cloud detection. J. Geophys. Res., 94 , D15,. 1852118535.

    • Search Google Scholar
    • Export Citation
  • King, J. C., and J. Turner, 1997: Antarctic Meteorology and Climatology. Cambridge Press, 409 pp.

  • Lawson, B., 1987: The climatology of blizzards in western Canada 1953–1986. Meteorological Service of Canada Central Region Rep. CAES 88–1, 109 pp.

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  • Maxwell, J. B., 1980: The Climate of the Canadian Arctic Islands and Adjacent Waters. Vol. I. Environment Canada, Minister of Supply and Services Canada, 531 pp.

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  • McCabe, G. J., M. P. Clark, and M. C. Serreze, 2001: Trends in Northern Hemisphere surface cyclone frequency and intensity. J. Climate, 14 , 27632768.

    • Search Google Scholar
    • Export Citation
  • McCullagh, P., and J. A. Nelder, 1989: Generalized Linear Models. 2d ed. Chapman & Hall, 511 pp.

  • McKay, G. A., and H. A. Thompson, 1969: Estimating the hazard of ice accretion in Canada from climatological data. J. Appl. Meteor., 8 , 927935.

    • Search Google Scholar
    • Export Citation
  • Milewska, E. J., 2004: Baseline cloudiness trends in Canada 1953–2002. Atmos.–Ocean, 42 , 267280.

  • Phillips, D. W., 1990: The Climates of Canada. Environment Canada, 176 pp.

  • Pomeroy, J. W., and B. E. Goodison, 1997: Winter and snow. Surface Climates of Canada, W. G. Bailey, T. R. Oke, and W. R. Rouse, Eds., McGill Queens Press, 68–100.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2000a: Temporal and spatial variations of surface air temperature over the period of instrumental observations in the Arctic. Int. J. Climatol., 20 , 587614.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2000b: Diurnal temperature range in the Arctic and its relation to hemispheric and Arctic circulation patterns. Int. J. Climatol., 20 , 231253.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2002: Variability of total and solid precipitation in the Canadian Arctic from 1950 to 1995. Int. J. Climatol., 22 , 395420.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2003: The Climate of the Arctic. Kluer Academic, 270 pp.

  • Rae, R. W., 1951: Climate of the Canadian Arctic Archipelago. Meteorological Service of Canada, 90 pp.

  • Schweiger, A. J., and J. Key, 1992: Arctic cloudiness: Comparison of ISCCP-C2 and Nimbus-7 satellite-derived cloud products with a surface-based cloud climatology. J. Climate, 5 , 15141527.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., F. Carse, R. G. Barry, and J. C. Rogers, 1997: Icelandic low cyclone activity: Climatological features, linkages with the NAO, and relationships with recent changes in the Northern Hemisphere circulation. J. Climate, 10 , 453464.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and Coauthors, 2000: Observational evidence of recent change in the northern high-latitude environment. Climate Change, 46 , 159207.

    • Search Google Scholar
    • Export Citation
  • Shabbar, A., and B. Bonsal, 2003: An assessment of changes in winter cold and warm spells over Canada. Nat. Hazards, 29 , 173188.

  • Stone, D. A., A. J. Weaver, and F. W. Zwiers, 2000: Trends in Canadian precipitation intensity. Atmos.–Ocean, 38 , 321347.

  • Stuart, R. A., 1994: Freezing precipitation in Canada: 1961–1990—Maps of occurrence frequency and duration. Weather Research House Rep., 306 pp.

  • Stuart, R. A., and G. A. Isaac, 1999: Freezing precipitation in Canada. Atmos.–Ocean, 37 , 87102.

  • Tuomenvirta, H., H. Alexandersson, A. Drebs, P. Frich, and P. O. Oyvind, 2000: Trends in Nordic and Arctic temperature extremes and ranges. J. Climate, 13 , 977990.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., and F. W. Zwiers, 2001: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Walsh, J. E., W. L. Chapman, and T. L. Shy, 1996: Recent decrease of sea level pressure in the central Arctic. J. Climate, 9 , 480486.

    • Search Google Scholar
    • Export Citation
  • Wang, X., and J. R. Key, 2003: Recent trends in Arctic surface, cloud and radiation properties from space. Science, 299 , 17251728.

  • Wang, X. L., 2003: Comments on “Detection of undocumented changepoints: A revision of the two-phase regression model”. J. Climate, 16 , 33833385.

    • Search Google Scholar
    • Export Citation
  • Wang, X. L., and Y. Feng, cited. 2004: RHTest user manual. [Available online at http://cccma.seos.uvic.ca/ETCCDMI/RHTest/RHTestUserManual.doc.].

  • Wang, X. L., V. R. Swail, and F. W. Zwiers, 2004: Changes in extra-tropical storm tracks and cyclone activities as derived from two global reanalyses and the Canadian CGCM2 projections of future climate. Preprints, Eighth Int. Workshop on Wave Hindcasting and Forecasting, North Shore, HI, U.S. Army Engineer Research and Development and Environment Canada.

  • Zhang, X., L. A. Vincent, W. D. Hogg, and A. Niitsoo, 2000: Temperature and precipitation trends in Canada during the 20th century. Atmos.–Ocean, 38 , 395429.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda, 2004: Climatology and interannual variability of arctic cyclone activity: 1948–2002. J. Climate, 17 , 23002317.

    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Names and locations of the 15 stations of long-term hourly weather observations that were used in this study.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 2.
Fig. 2.

Time series of monthly occurrence (a) frequencies (adjusted for the step changes detected) and (b) log odds of the no-weather event observed at Cambridge Bay. The exponential trend curve in the frequency time series is equivalent to the solid trend line in the log-odds time series. The dashed line is the trend line estimated from the raw data (without taking into account the step changes shown).

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 3.
Fig. 3.

Time series of monthly log odds of LC events observed at the indicated station. The dashed line is the trend line estimated from the raw data (without taking into account the step changes shown).

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 4.
Fig. 4.

Long-term mean monthly occurrence frequencies (%) of FZ events at the indicated stations.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 5.
Fig. 5.

Long-term mean monthly occurrence frequencies (%) of BS events at the indicated stations.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 6.
Fig. 6.

Long-term mean monthly occurrence frequencies (%) of LC and fog events at the indicated stations.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 7.
Fig. 7.

Long-term mean monthly occurrence frequencies (%) of the no-weather (i.e., no precipitation or visibility obscuration) events at the indicated stations.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 8.
Fig. 8.

Statistical significance of change in the occurrence frequency of FZ events in all and each of the indicated seasons. The large, medium, and small dots indicate changes of at least 5%, 5%–20%, and lower than 20% significance (i.e., p ≥ 0.95, 0.80 ≤ p < 0.95, p < 0.80), respectively. Orange dots superimposed by a plus sign indicate positive changes (i.e., increased frequency), and blue dots, negative changes. Open circles indicate zero trends.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 9.
Fig. 9.

The same as in Fig. 8, but for BS events.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 10.
Fig. 10.

The same as in Fig. 8, but for fog events.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 11.
Fig. 11.

The same as in Fig. 8, but for the LC events.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Fig. 12.
Fig. 12.

The same as in Fig. 8, but for no-weather events.

Citation: Journal of Climate 18, 16; 10.1175/JCLI3505.1

Table 1.

List of stations and periods of data used in this study.

Table 1.
Save
  • Baggaley, D. G., and J. M. Hanesiak, 2005: An empirical blowing snow forecast technique for the Canadian Arctic and Prairie Provinces. Wea. Forecasting, 20 , 5162.

    • Search Google Scholar
    • Export Citation
  • Barry, R. G., R. G. Crane, A. Schweiger, and J. Newell, 1987: Arctic cloudiness in spring from satellite imagery. J. Climatol., 7 , 423451.

    • Search Google Scholar
    • Export Citation
  • Berry, M. O., and R. G. Lawford, 1977: Low-temperature fog in the Northwest Territories. Meteorological Service of Canada Tech. Memo. 850, 27 pp.

  • Bonsal, B., X. Zhang, L. A. Vincent, and W. D. Hogg, 2001: Characteristics of daily extreme temperatures over Canada. J. Climate, 14 , 19591976.

    • Search Google Scholar
    • Export Citation
  • Canadian Hydrographic Service, 1970: Climate of the Canadian Arctic. Meteorological Branch, Department of Transport, Department of Energy, Mines and Resources, 71 pp.

  • Cortinas Jr., J. V., B. C. Bernstein, C. C. Robbins, and J. W. Strapp, 2004: An analysis of freezing rain, freezing drizzle, and ice pellets across the United States and Canada: 1976–90. Wea. Forecasting, 19 , 377390.

    • Search Google Scholar
    • Export Citation
  • Environment Canada, 1990: Manual of Surface Weather Observations (MANOBS). User’s manual. [Available from Meteorological Service of Canada, 4905 Dufferin St, Downsview, ON M3H 574, Canada.].

  • Fraser, W. C., 1964: A study of winds and blowing snow in the Canadian Arctic. Canadian Department of Transport, Meteorological Branch, 31 pp.

  • Hanesiak, J. M., T. Fisico, C. Rindahl, and E. Carriere, 2003: Climatology of adverse-weather events in the Canadian Arctic. Faculty of Environment, University of Manitoba, Centre for Earth Observation Science Tech. Rep. CEOS-Tech-2003-2, 3540 pp.

  • Higuchi, K., C. W. Yuen, and A. Shabbar, 2000: Ice Storm ’98 in Southcentral Canada and Northeastern United States: A climatological perspective. Theor. Appl. Climatol., 66 , 6179.

    • Search Google Scholar
    • Export Citation
  • Key, J., and R. G. Barry, 1989: Cloud cover analysis with Arctic AVHRR data 1. Cloud detection. J. Geophys. Res., 94 , D15,. 1852118535.

    • Search Google Scholar
    • Export Citation
  • King, J. C., and J. Turner, 1997: Antarctic Meteorology and Climatology. Cambridge Press, 409 pp.

  • Lawson, B., 1987: The climatology of blizzards in western Canada 1953–1986. Meteorological Service of Canada Central Region Rep. CAES 88–1, 109 pp.

  • Lawson, B., 2003: Trends in blizzards at selected locations on the Canadian Prairies. Nat. Hazards, 29 , 123138.

  • Maxwell, J. B., 1980: The Climate of the Canadian Arctic Islands and Adjacent Waters. Vol. I. Environment Canada, Minister of Supply and Services Canada, 531 pp.

  • Maxwell, J. B., 1981: Climatic regions of the Canadian Arctic Islands. Arctic, 34 , 225240.

  • Maxwell, J. B., 1982: The Climate of the Canadian Arctic Islands and Adjacent Waters. Vol. II. Environment Canada, Minister of Supply and Services Canada, 589 pp.

  • McCabe, G. J., M. P. Clark, and M. C. Serreze, 2001: Trends in Northern Hemisphere surface cyclone frequency and intensity. J. Climate, 14 , 27632768.

    • Search Google Scholar
    • Export Citation
  • McCullagh, P., and J. A. Nelder, 1989: Generalized Linear Models. 2d ed. Chapman & Hall, 511 pp.

  • McKay, G. A., and H. A. Thompson, 1969: Estimating the hazard of ice accretion in Canada from climatological data. J. Appl. Meteor., 8 , 927935.

    • Search Google Scholar
    • Export Citation
  • Milewska, E. J., 2004: Baseline cloudiness trends in Canada 1953–2002. Atmos.–Ocean, 42 , 267280.

  • Phillips, D. W., 1990: The Climates of Canada. Environment Canada, 176 pp.

  • Pomeroy, J. W., and B. E. Goodison, 1997: Winter and snow. Surface Climates of Canada, W. G. Bailey, T. R. Oke, and W. R. Rouse, Eds., McGill Queens Press, 68–100.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2000a: Temporal and spatial variations of surface air temperature over the period of instrumental observations in the Arctic. Int. J. Climatol., 20 , 587614.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2000b: Diurnal temperature range in the Arctic and its relation to hemispheric and Arctic circulation patterns. Int. J. Climatol., 20 , 231253.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2002: Variability of total and solid precipitation in the Canadian Arctic from 1950 to 1995. Int. J. Climatol., 22 , 395420.

    • Search Google Scholar
    • Export Citation
  • Przybylak, R., 2003: The Climate of the Arctic. Kluer Academic, 270 pp.

  • Rae, R. W., 1951: Climate of the Canadian Arctic Archipelago. Meteorological Service of Canada, 90 pp.

  • Schweiger, A. J., and J. Key, 1992: Arctic cloudiness: Comparison of ISCCP-C2 and Nimbus-7 satellite-derived cloud products with a surface-based cloud climatology. J. Climate, 5 , 15141527.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., F. Carse, R. G. Barry, and J. C. Rogers, 1997: Icelandic low cyclone activity: Climatological features, linkages with the NAO, and relationships with recent changes in the Northern Hemisphere circulation. J. Climate, 10 , 453464.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and Coauthors, 2000: Observational evidence of recent change in the northern high-latitude environment. Climate Change, 46 , 159207.

    • Search Google Scholar
    • Export Citation
  • Shabbar, A., and B. Bonsal, 2003: An assessment of changes in winter cold and warm spells over Canada. Nat. Hazards, 29 , 173188.

  • Stone, D. A., A. J. Weaver, and F. W. Zwiers, 2000: Trends in Canadian precipitation intensity. Atmos.–Ocean, 38 , 321347.

  • Stuart, R. A., 1994: Freezing precipitation in Canada: 1961–1990—Maps of occurrence frequency and duration. Weather Research House Rep., 306 pp.

  • Stuart, R. A., and G. A. Isaac, 1999: Freezing precipitation in Canada. Atmos.–Ocean, 37 , 87102.

  • Tuomenvirta, H., H. Alexandersson, A. Drebs, P. Frich, and P. O. Oyvind, 2000: Trends in Nordic and Arctic temperature extremes and ranges. J. Climate, 13 , 977990.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., and F. W. Zwiers, 2001: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Walsh, J. E., W. L. Chapman, and T. L. Shy, 1996: Recent decrease of sea level pressure in the central Arctic. J. Climate, 9 , 480486.

    • Search Google Scholar
    • Export Citation
  • Wang, X., and J. R. Key, 2003: Recent trends in Arctic surface, cloud and radiation properties from space. Science, 299 , 17251728.

  • Wang, X. L., 2003: Comments on “Detection of undocumented changepoints: A revision of the two-phase regression model”. J. Climate, 16 , 33833385.

    • Search Google Scholar
    • Export Citation
  • Wang, X. L., and Y. Feng, cited. 2004: RHTest user manual. [Available online at http://cccma.seos.uvic.ca/ETCCDMI/RHTest/RHTestUserManual.doc.].

  • Wang, X. L., V. R. Swail, and F. W. Zwiers, 2004: Changes in extra-tropical storm tracks and cyclone activities as derived from two global reanalyses and the Canadian CGCM2 projections of future climate. Preprints, Eighth Int. Workshop on Wave Hindcasting and Forecasting, North Shore, HI, U.S. Army Engineer Research and Development and Environment Canada.

  • Zhang, X., L. A. Vincent, W. D. Hogg, and A. Niitsoo, 2000: Temperature and precipitation trends in Canada during the 20th century. Atmos.–Ocean, 38 , 395429.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda, 2004: Climatology and interannual variability of arctic cyclone activity: 1948–2002. J. Climate, 17 , 23002317.

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

    Names and locations of the 15 stations of long-term hourly weather observations that were used in this study.

  • Fig. 2.

    Time series of monthly occurrence (a) frequencies (adjusted for the step changes detected) and (b) log odds of the no-weather event observed at Cambridge Bay. The exponential trend curve in the frequency time series is equivalent to the solid trend line in the log-odds time series. The dashed line is the trend line estimated from the raw data (without taking into account the step changes shown).

  • Fig. 3.

    Time series of monthly log odds of LC events observed at the indicated station. The dashed line is the trend line estimated from the raw data (without taking into account the step changes shown).

  • Fig. 4.

    Long-term mean monthly occurrence frequencies (%) of FZ events at the indicated stations.

  • Fig. 5.

    Long-term mean monthly occurrence frequencies (%) of BS events at the indicated stations.

  • Fig. 6.

    Long-term mean monthly occurrence frequencies (%) of LC and fog events at the indicated stations.

  • Fig. 7.

    Long-term mean monthly occurrence frequencies (%) of the no-weather (i.e., no precipitation or visibility obscuration) events at the indicated stations.

  • Fig. 8.

    Statistical significance of change in the occurrence frequency of FZ events in all and each of the indicated seasons. The large, medium, and small dots indicate changes of at least 5%, 5%–20%, and lower than 20% significance (i.e., p ≥ 0.95, 0.80 ≤ p < 0.95, p < 0.80), respectively. Orange dots superimposed by a plus sign indicate positive changes (i.e., increased frequency), and blue dots, negative changes. Open circles indicate zero trends.

  • Fig. 9.

    The same as in Fig. 8, but for BS events.

  • Fig. 10.

    The same as in Fig. 8, but for fog events.

  • Fig. 11.

    The same as in Fig. 8, but for the LC events.

  • Fig. 12.

    The same as in Fig. 8, but for no-weather events.

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