Blizzards in the Upper Midwest
Copyright Lawrence Burkett 2015, minor rev. 2019
Annual Blizzard Frequency and Trends
Annual blizzard frequency in the Upper Midwest: 1) varied considerably from year to year from 1980-2013; and, 2) exhibited no meaningful long-term trend from 1980-2013 as indicated in Table 1 and Figure 2. Considering that annual blizzard frequency in the Upper Midwest varied considerably from year to year suggests that using annual averages or probabilities to describe blizzard frequency as was done by prior authors is perhaps rather inappropriate since averages and probabilities in this case give little clue as to what should be expected from year to year (Black 1971; Schwartz and Schmidlin 2002). Nevertheless, it is clear that no years endured 10 or more blizzards. Annual blizzard frequency exhibited no meaningful trend from 1980-2013, which disagrees with Schwartz and Schmidlin (2002) in particular who identified a linear increase in blizzard frequency, albeit for the entire conterminous United States from 1959-2000.
Monthly Blizzard Frequency and Trends
Monthly blizzard frequency showed a favorable result in terms of blizzard frequency with respect to time as indicated in Figure 3. Similar to Schwartz and Schmidlin (2002), it is clear that certain months of the year are more or less favorable for blizzards. Recalling that most snow-producing cyclones that generated blizzards from December-February in the north-central conterminous United States where Alberta lows (Black 1971), suggest that most blizzards in the Upper Midwest probably resulted from circulations embedded in intense cyclones that originated in Alberta and tracked southeast through the Upper Midwest. Intense Colorado lows (Black 1971) were probably more often associated with blizzards in the Upper Midwest in other months as they tracked northeast through the Upper Midwest. The polynomial fit to the monthly blizzard frequency histogram in Figure 3 does offer reliable guidance about the time of year blizzards occur although it fails to capture the subtle differences in frequency from month to month.
Blizzard Duration and Area Impacted
Blizzards ranged in duration from three to 48 hours. On average, most blizzards generally last between roughly 6 and 18 hours. Due to differences in reporting efficacy in Storm Data however in terms of start and end times, longer-term trends in blizzard duration where not constructed. When blizzards occurred, most were typically on the order of about 50,000 km2 in size, or roughly between one-quarter to one-half of the Upper Midwest. The largest area impacted by a blizzard covered the entirety of the Upper Midwest while the smallest covered a single county. Combining typical durations on the order of several hours, and areas impacted on the order of several tens of thousands of kilometers, supports the discovery that blizzards do indeed have mesoscale aspects as suggest by Branick (1997).
County Blizzard Frequencies
Considering the order of magnitude difference in blizzard frequency in the Upper Midwest from west to east, as indicated in Figure 4, irrespective of snow, my premise is that the presence of forest cover or lack thereof might lend insight into explaining differences in county blizzard frequencies. Further, if blizzards can be generalized as a severe wind phenomenon resulting from the passage of intense, snow-producing cyclones as they were in Black (1971), and markedly mesoscale as indicated in Branick (1997), then the efforts of Coutts and Grace (1995) who authored a book titled Wind and Trees which reviews much of the seminal research done on the interaction between wind and trees might lend new insight into explaining blizzard frequency on such scales.
Exploring Forest Cover Premise
To explore my premise that forest cover might be able to explain the differences in blizzard frequency observed across the Upper Midwest from west to east, I obtained percent forest canopy cover data from the United States Department of Agriculture’s United States Forest Service, in Forest Resources of the United States, 2007. The forest cover data in Forest Resources of the United States, 2007 was made available as a choropleth map with each county in the conterminous Unites States classified as integers ranging from 1 to 7 depending on the percent forest canopy cover normalized by county area. The purpose of normalizing forest cover data is that individual trees which make up forests are considerably smaller in scale in terms of length versus counties. As an extension of my methods, I chose to modify the forest cover data classes by combining the two lowest classes so the first class includes county forest cover of less than 10%. Following the same method I used to generate Figure 4, Figure 5 shows a map created using ArcMap 10.2 to indicate county forest cover.
Figure 5. Percent forest covers from 2007 for counties in the Upper Midwest are shown using a percent classification scheme inherited from the United States Forest Service. Sources: ESRI; United States Forest Service, 2007.
Examining Forest Cover Premise
By comparing Figures 4 and 5 under the assumption that forest cover has not changed appreciably from 2007, it appears that blizzard frequency is indeed related to percent forest cover. Counties with high blizzard frequencies tend to overlap with counties with low percent forest cover. To determine the strength of the relationship between blizzard frequency and percent forest cover, I regarded blizzard frequency and percent forest cover as statistical parameters and calculated a Pearson product-moment correlation coefficient relating the two. Statistically speaking, I regarded correlation values between -0.75 and +0.75 as weak, and correlations less than -0.75 or greater than +0.75 as strong. In my analysis, I obtained a correlation of -0.91 relating blizzard frequency to percent forest cover. A Pearson correlation of -0.91 indicates that counties with little forest cover – like Pembina, Walsh, Grand Forks, Traill, and Cass counties – show the greatest number of blizzards. Hence, blizzard frequency is strongly related to percent forest cover as indicated in Table 4.
Table 4. Pearson correlations: blizzard frequency, forest cover, and winter storm frequency. Sources: Storm Data, 1980-2013; United States Forest Service, 2007.
Par. 1 Par. 2 Corr. Regard
Bliz. 80-13 Per. For. - 0.91 Strong
Win. 00-13 Per. For. - 0.49 Weak
Bliz. 00-13 Per. For. - 0.90 Strong
Win. 00-13 Bliz. 00-13 +0.46 Weak
To exclude the possibility that the strong relationship obtained between blizzard frequency and percent forest cover is: 1) shared among other similar, comparable types of winter weather hazards such as winter storms (Branick 1997); and, 2) whether there is a spatial reporting bias in Storm Data, I identified the frequency of winter storms in Storm Data from August 1, 2000 through July 31, 2010 (winter seasons from 2000 to 2009), in “Character of Storm” by assigning a 1 for occurrence or a 0 for non-occurrence for counties impacted in my spreadsheet. Identical to how I treated blizzard reports in Storm Data, winter storms spanning several days were regarded as one continuous winter storm while days with two or more winter storms separated by more than three hours were regarded as separate winter storms by assigning a two or greater for those dates. By restricting my sample of blizzards to align with my new winter storm data set in my spreadsheet, I obtained a new, temporally smaller sample period that represents approximately one-third of the original data in terms of time (i.e. 1980-2013), and calculated three more Pearson product-moment correlation coefficients relating: 1) winter storm frequency from 2000-2009 to percent forest cover; 2) winter storm frequency from 2000-2009 to blizzard frequency from 2000-2009; and, 3) blizzard frequency from 2000 – 2009 to percent forest cover to ensure that blizzard frequency maintained a strong correlation with percent forest cover over the temporally smaller sample period chosen from 2000-2009.
To test the strength of the three new relationships, I once again regarded correlations between -0.75 and +0.75 as weak, and correlations less than -0.75 or greater than +0.75 as strong. In my review, I obtained: 1) a weak correlation of -0.49 relating winter storm frequency from 2000-2009 to percentage forest cover; 2) a weak correlation of +0.46 relating winter storm frequency from 2000-2009 to blizzard frequency from 2000-2009; and, 3) a strong correlation of -0.90 relating blizzard frequency from 2000-2009 to percent forest cover as summarized in Table 4.