Relationships between Atmospheric Circulation and
Snowmaking Conditions at Beech Mountain, NC
Alex McMahon
GHY 4620 Perry
Introduction
Since its introduction, artificial snowmaking technology has become a vital asset to ski
resorts worldwide. The technology has allowed for the opening of new ski areas that couldn’t
exist without its usage and reduced the dependency on natural snowfall at others. Since the
ability to produce snow is so critical at ski resorts, the ability to predict when snowmaking can
occur is useful to ski results for a multitude of reasons. Snowmaking can use a significant portion
of a locale’s available water and electricity. The ability to predict snowmaking conditions would
be useful to ski area management and government officials alike to budget the usage of those
resources. In addition, snowmaking forecasts could allow for greater efficiency in the
management of snowmaking, more accurate predictions in opening and closing dates, more snow
on the ground at ski resorts, and the potential for more tourism revenue to be brought into the
area. The relationship between both temperature and humidity must be known in order to
quantify snowmaking conditions. Snowmaking machines force small droplets of water through a
nozzle which must refreeze through evaporative cooling to produce snow. Drier air allows
evaporative cooling to occur more effectively, so wet bulb temperature is used as the industry
standard metric.
Snowmaking may also become increasingly important for ski tourism in coming decades
as global temperatures continue to increase. Research performed in the European Alps has
already found decreases in annual natural snow cover duration, increasing reliance on artificial
snow at ski areas. However, the increase in global mean temperatures has complex impacts on
atmospheric circulation, and it is possible that relationships between temperature and humidity
will change, complicating future projections of snowmaking conditions.
The goal of this paper is to discover to what extent snowmaking conditions can be forecast at
Beech Mountain, North Carolina, on multiple scales of atmospheric circulation. The first
objective is to look at relationships between synoptic types and snowmaking conditions with the
goal of being able to predict short-term snowmaking conditions based on synoptic atmospheric
conditions influencing the area. Second, teleconnections between phases and combinations
between global circulation patterns, synoptic conditions, and snowmaking conditions on Beech
Mountain will be explored to search for longer range seasonal indicators of snowmaking
conditions. Specifically, the AO and ENSO will be looked at in detail. The viability of making
short term and seasonal forecasts for snowmaking conditions will also be considered based on
these interactions. In addition, this study will look at the future outlook of snowmaking
conditions with regard to global climate change, specifically how changing atmospheric
circulation patterns will impact snowmaking at Beech Mountain.
Location and Background
The primary focus area of this study is Beech Mountain, NC. The mountain is located in
the Blue Ridge province of the southern Appalachians and is the location of Beech Mountain Ski
Resort. The Blue Ride is subject to profound microclimatic effects, with steep slopes and sharp
elevational contrasts, and an elevation range of fewer than 2,000 feet to 6,683 feet at the peak of
Mount Mitchell. At an elevation of 5,500’, the Beech Mountain MET station sits above the peaks
of two local ski areas: Sugar Mountain Ski Resort (5,236’), and Appalachian Ski Mountain
(3,999’). It is also worth noting that Beech Mountain and Appalachian Ski Mountain’s ski areas
face the north, while Sugar Mountain’s is situated on an east facing slope. The difference in
positioning could have some bearing on microclimatic differences between ski areas. The
primary source of data for this study comes from Beech Mountain’s MET station, which has
been collecting temperature, moisture, and wind observations since February 2008, and is within
12 miles of both other local ski resorts.
Current literature already outlines various aspects of this study, but none consider the
possibility of linking snowmaking conditions to atmospheric circulation on the synoptic and
hemispheric scales for the purposed of forecasting. In a study focused on the boundary
conditions for artificial snow production in the Austrian Alps, boundaries were set using
specifications of snow guns (Olefs et al., 2010). Studies previous to theirs were in disagreement
of the threshold condition for artificial snow production. Based on their calculations, they
defined the boundary condition for snow production to be a wet bulb temperature of -2°C, which
was also used as the snowmaking threshold in this study. After defining a threshold, a
classification scheme was required. (Sheridan, 2002) defines a spatial synoptic classification
scheme based on air mass types, and divides them according to origin and moisture content. The
article also compares frequencies between air mass types and the NAO and PNA using z-scores.
Sheridan’s SSC classification settings are too broad to fully define the synoptic settings affecting
Beech Mountain with regard to snowmaking conditions. Additionally, Sheridan’s definition of
the z-score formula is specific to the goals of that study and isn’t useful to this one. However, the
article does lay important groundwork in developing the methodology to link synoptic settings to
hemispheric scale circulation patterns.
A plethora of literature connecting the synoptic and hemispheric scale exists, and several
articles look at teleconnections between atmospheric circulation patterns and winter weather in
the northern hemisphere. Specifically, the AO is frequently mentioned as an indicator of extreme
weather events and long term climate patterns in extratropical regions. (Wallace, 2000), goes into
depth about the AO’s behavior and potential for cold air outbreaks during its negative phase. In
(Wu et al., 2006), nonlinear teleconnections between the AO and winter climate in North
America are defined, further strengthening the AO as a dominant driver of winter climate in the
region. The strong evidence linking the AO to winter weather makes it important as a
hemispheric circulation pattern to be correlated with synoptic settings at Beech Mountain. While
the AO is important, other atmospheric modes like the ENSO can have significant impacts on
winter weather in the US. (Patten et al., 2003) discusses impacts of ENSO phase on snowfall
frequencies in the US. While the article is not directly related to snowmaking conditions,
snowfall frequencies could be an indicator of both air temperature and humidity. Natural
snowfall is also an important factor to consider in determining ski slope conditions; ENSO is an
important mode to compare to Beech Mountain’s synoptic settings. In addition to AO and
ENSO, autumnal Siberian snowcover advance has been linked to AO anomaly and temperature
anomalies in the U.S. (Mote & Kutney, 2012), and could be another important long range
indicator of snowmaking conditions. Mote & Kutney also found correlations between smaller
geographic regions and regions of the U.S., and that these teleconnections between regions occur
during specific winter months.
A number of articles also shed some light on the future outlook of snowmaking
conditions. (Rixen et al., 2011) looks at impacts of climate change will have on winter tourism
areas in the Alps. In the study, the authors used a snow depth value of 30cm to define a skiable
surface, and looked at decreases in snow depth between the present and 2050. In their study area,
the number of days with skiable surface declined by 5-10 days by 2030, and by 10-20 days by
2050. Even high elevation ski areas that currently have over 150 skiable days were projected to
have as little as 100 skiable days in 2050. Rixen et al. also point out the increased strain on
municipal water resources that will affect ski areas. The authors cite (Agrawala, 2007), another
study on climate change in the Alps, which also looked at impacts on low elevation ski areas.
Agrawala found that low elevation resorts in Europe will be heavily impacted compared to the
Alps; for example, a 1°C increase in temperatures at German ski resorts would reduce the
reliability of natural snowcover on the slopes by 60%. Finally, Rixen et al. discuss the
microclimatic effects at each individual ski area, noting that shaded slopes in cool microclimates
at low elevations might be better suited for the production of snow in projected warming
scenarios. According to the IPCC, mean global temperatures are expected to rise anywhere from
1.8°C to 4°C by 2090-2099 from the 1980-1999 global average. The IPCC estimates are based
on a number of global scenarios with different rates of population growth, resource usage, and
emissions (IPCC, 2007). As stated by Rixen, snowmaking conditions vary on much smaller
scales, but the IPCC scenarios could still be useful as a rough outline to look into the availability
of future snowmaking hours at a local and regional scale. However, other studies provide insight
into more specific processes that could have an influence in the future of snowmaking conditions
that could be used in conjunction with IPCC scenarios. In a study of the Chacaltaya glacier in
Bolivia, (Francou et al., 2003) notes that glacial ablation occurs more heavily in the presence of
higher humidity due to the favoring of melting over ablation. This also holds true for artificial
snowpack on ski slopes, and is an important component for future projections. However the
IPCC doesn’t define humidity projections in their warming scenarios and adjustments to
temperature will be made assuming no change in humidity.
Data and Methods
Hourly values of dry bulb, wet bulb, wind speed, wind direction, and relative humidity
for all hours between December 2008 and November 2012 were acquired from the Beech
Mountain MET station. Hours in which the wet bulb temperature was above 0° C were filtered
out, and the remaining data was used to calculate hours in which snowmaking was possible per
month over the period of record. A synoptic classification scheme was developed based on wind
speed and direction to further divide the data. Hours in which wind speeds were less than 10 mph
were classified as periods of high pressure. Hours with wind speeds over 10mph and wind
direction between 0 ° and 180° were classified as periods of cold air damming. The remaining
hours in which wind speeds were over 10mph were classified as upsloping events: events with
wind direction between 270° and 360° were classified as northwest flow, and events with wind
direction between 180° and 270° were classified as southwest flow events. Classification criteria
are also listed in table 1. Additionally, relative humidity, dry bulb temperature, and average wind
speed were calculated per synoptic type. A number of hours in the record had gaps in wind speed
and directional data, particularly during December 2009, and could not be classified. However,
the temperature data from these hours were still included where applicable. All data between
August 7th 2010 and December 5th 2010 were also missing, and the entire period was excluded
from the study. Threshold conditions for snowmaking were set at a wet bulb value of -2° C,
roughly 28.4° Fahrenheit. However a wet bulb threshold of 0°C was also set. A snow quality
threshold was set a wet bulb value of -6.66°C (20°F) to separate especially cold and dry events
from warmer ones. Thresholds were ranked from 1-3 in order from highest to lowest wet bulb
temperature, and referred to as t1-t3.
Following classification, average values of dry bulb temperature, relative humidity, and
average wind speed were calculated for each wet bulb threshold per synoptic type. Frequencies
of synoptic types were then compared against the bimonthly MEI index and monthly AO index.
Since the MEI index is bimonthly, it was compared against the MET data twice, once for each
bimonthly value that occurred partially during the month it was compared to. For example, in the
month of December, both the November/December and December/January MEI index values
were compared against December MET data separately from each other. The comparisons using
the earlier time period are referred to as ENSO1, and comparisons using the later time period are
referred to as ENSO2. Correlations between synoptic types and global circulation types were
also compared using a 2-tailed Pearson’s R correlation test. Correlations were computed at all
three wet bulb thresholds. IPCC AR4 warming scenario data were also utilized to adjust hourly
dry bulb temperature values to get a general view of future air temperature trends on the
mountain. With a relative humidity of 20%, snowmaking can take place up to approximately 3°
C. Using this number, the maximum hours of snowmaking possible per warming scenario were
calculated using dry bulb values. Maximum snowmaking hours were also calculated using a
second threshold of 6.35° C, the maximum temperature on record that occurred below t2. In
addition, a third dry bulb temperature of -0.5°C was computed based on the average relative
humidity of the study period, and was also included.
Results
Frequencies and Correlations
Over the 31,459 hour study period, 8,361 hours had a wet bulb value of 0°C or less. (<t1)
At a monthly scale, temperatures were below T1 during 72% of January months, 69% of
February, 37% of March, 17% of April, 3% of May, 15% of October, 35% of November, and
74% of December. 72% of DJF hours were below t1, while 27% MA hours and 25% ON hours
were below t1, hinting at the possibility of slightly more snowmaking hours during spring
months than fall months. 1,548 hours were included in t1 in which dry bulb was greater than
0°C. Roughly a fourth of these hours were under t2, indicating a large number of warm and dry
snowmaking hours. These hours were split mostly among NWF, CAD, and HP periods.
However, given that CAD events make up a much smaller portion of t2 hours, it is important to
note the relative frequency of warm dry snowmaking hours during periods of CAD. Out of t1
hours, high pressure and northwest flow dominated, making up 38% and 47% of t1 hours
respectively. Trends were similar at t2: HP made up 40.33% of t2 hours while NWF made up
47.3% of them. This trend changes at t3, in which NWF makes up 57% of the entire threshold,
while HP makes up 23.9%. Periods of NWF were also the coldest and wettest at t1, with an
average Td of -2°C and a mean RH of 82%. A full list of summary statistics is listed in table 2a2d.
Colder wet bulb values tended to be associated with a negative AO phase; table 3 shows synoptic
types compared with wet bulb temperatures. At t1, 62.3% of all hours occurred during AO-.
65.9% of HP hours, 57.3% of CAD hours, 62% of NWF hours, and 48.6% of SWF hours
occurred with AO-. At t2, 65.7% of records were during AO-. In addition, frequencies of every
synoptic type increased with AO-. 68.8% of HP hours, 59.2% of CAD hours, 64.8% of NWF
hours and 52.4% of SWF events occurred with AO- at the threshold. At t3, 71.4% of the record
occurred during the AO- phase. 75.8% of HP, 69.4% of CAD hours, 71.4% of NWF hours, and
47.4% of SWF hours occurred with AO- at the threshold. Pearson’s R was used to check the
correlation and significance values of these patterns, and found a lack of significance in the
relations between AO and CAD/SWF. At T1, CAD exhibited an R value of .059, while SWF had
an R value of.116. At t2, the trends remained weak, with CAD and SWF R values of .086 and
.023 respectively. At t3, CAD had an R value of.039, and SWF had an R value of -.29 with a
significance value of 0. By Contrast, moderate R-values existed between AO and HP/NWF. At
t1, the values were .274 and .348. At t2, they decreased to .241 and .308, and decreased to .183
and .165 at t3. The general downward trend in R value reflects the filtering out of wet bulb
values at every temperature threshold and the decreasing amount of data available to correlate.
This was especially true with CAD and SWF, whose frequencies were relatively low, especially
at t3. However this doesn’t explain the moderate negative correlation between SWF and AO at
t3, in which only 96 hours of record were available for correlation. A full listing of correlation
values are available in tables 4a-4c.
As compared to AO, ENSO values are generally less important to snowmaking conditions,
however, some correlation still existed in some instances. In all cases, the differences between
ENSO1 and ENSO2 were marginal. In most cases, the number of snowmaking hours between
the two settings were within 2% of each other. At t1 and t2, most snowmaking hours took place
during the negative ENSO phase. For ENSO 1, ENSO- made up 69.3% of HP hours, 57.5% of
CAD hours, 65.8% of NWF hours, and 82.5% of SWF hours at t1. Again, these numbers were
remarkably similar at ENSO2, with ENSO- making up 70.8%, 57.9%, 63.6%, and 79.5% of HP,
CAD, NWF, and SWF hours respectively. At t2, AO- made up 71.8% of HP hours, 62.6% of
CAD hours, 66.7% of NWF hours, and 81.1% of SWF hours using ENSO1. Again, results were
very similar between ENSO1 and ENSO2 at the t2 threshold. At t3, respective values of HP,
CAD, NWF, and SWF made up 73.4%, 63.3%, 72% and 77.9% of ENSO- hours.
Overall, trends show an increasing number of hours taking place during ENSO- between t1 and
t3 with the exception of SWF, which decreased by 1.5% from t1 to t2 using ENSO1, but almost
no change occurred using ENSO2. Pearson correlation values showed a general weaker trend
between both ENSO1 and ENSO2 and snowmaking hours than with AO, despite their frequent
occurrence with ENSO-. At t1, ENSO1 had an R value of 0.091, and ENSO2’s R value was .105.
At t2, the values dropped to 0.071 and .086, and rose at t3 to 0.132 and 0.147. Significance
values were well within 95% confidence at all three thresholds, despite the weak relationships.
At all three thresholds, SWF had very low R values, none of which were significant within 95%
confidence. Interestingly, t3 CAD events had R values of 0.283 and 0.263 with ENSO1 and
ENSO2, but had values below 0.1 at both t1 and t2, showing the possible value of ENSO as a
predictor of CAD during particularly cold and dry events.
IPCC Warming Scenarios
Over the study period, the Beech Mountain met station recorded 5,783 hours with a dry
bulb temperature of 3°C or lower, representing the maximum number of hours in which snow
could be made with a relative humidity of 20%. 5,905 hours had a dry bulb temperature of
6.39°C or lower, the highest dry bulb temperature recorded in which the wet bulb was below t2.
The relative humidity during this period was 3.7%. The overall average relative humidity value
over the study period was 78%, which corresponds to a maximum dry bulb value of -0.5°C at t2.
5,143 hours were recorded over the study period which met this threshold. Using the IPCC
warming scenarios, the maximum number of snowmaking hours assuming these two dry bulb
values is significantly reduced. Depending on the IPCC scenario, snowmaking hours using the
3°C value are reduced to values between 5,573 and 4,990 hours. Using the 6.39°C value, the
maximum hours are reduced to values between 5,855 and 5,743 hours. At the -0.5°C,
snowmaking hours would be reduced to values between 4,510 and 3,327 over the study period.
Averaging every IPCC scenario using the 3°C/20%RH snowmaking threshold, 493 hours of
snowmaking would be lost. Using the 6.39°C/3.7%RH value, 84 hours would be lost. At 0.5°C/78%RH, 1220 hours would be lost. The IPCC scenarios are available in Table 5, and table
6 shows a full list of results.
Summary and Conclusions
The methodology introduced in this paper has revealed useful summary statistics
regarding the occurrences of snowmaking conditions as compared to synoptic settings and larger
scale hemispheric modes. In addition, some insight has been provided into future projections of
snowmaking conditions. However, several major limitations were present, due to both limitations
and data and the need for methodology beyond the scope of this study.
Using estimates of frequencies provides some insight into the chances of snowmaking
hours occurring in a given period, and in that regard, this study does provide a climatology of
snowmaking conditions. However, many more questions are raised using this approach. For
instance, we know that most snowmaking hours occur during negative phases of both AO and
ENSO, however, how does one predict when snowmaking can occur during positive phase AO
and ENSO? On a synoptic scale, northwest flow can occur under a variety of conditions, but
under which ones can snowmaking occur? To provide a more robust answer to these questions, a
detailed understanding of regional and microclimatic atmospheric response to synoptic processes
and hemispheric modes is required. Ideally, study period hours would be divided by synoptic
events which would then be classified in a more specific manner than used in this study. A fuller
understanding of the atmospheric processes in which snowmaking could occur would be
generated. In addition, findings that did occur could be more accurately described with a more
detailed classification, especially connections that were found in the process of this study.
In addition to limitations instilled by methodology, the study was also significantly limited by
the data available. The period of record was relatively short, (1427 days) greatly limiting the
number of synoptic events used in the study. The short period also increases the vulnerability of
the data to anomalous events. This occurred in both the winter of 2010-2011 as well as the winter
of 2011-2012, in which one winter was anomalously cold and one was anomalously warm,
reducing the probability that the numbers computed in this study reflect long term averages. The
length of the study also has impacts on ties between synoptic settings associated with snowfall
and the atmospheric modes they are compared to. Both the MEI and AO indices are limited in
length, and have potential to skew data towards one phase of the mode. In this study, 36
bimonthly values of ENSO were negative, whereas only 22 were positive. The same occurred
with AO, in which 34 values were negative and 25 were positive during the study period. The
usage of the Pearson Correlation may have corrected this to some extent, which may explain the
disparities between frequencies and R-values computed for comparisons between synoptic types
and hemispheric modes. The short period of record might also explain the anomalous
correlations between some R-values, such as the correlation between SWF and ENSO at t1, as
well as SWF and AO at t3, both of which were contrary to the overall trends seen in other
synoptic types. However, the potential for a legitimate correlation exists as well, again pointing
towards limitations brought upon this study by its methodology. What this short study period
does provide is a climatology of snowmaking for the last 4 years at Beech Mountain, which
could still be useful, especially due to recent departures from long term trends.
A final limitation to this study was in the inclusion of maximum snowmaking hours using IPCC
scenarios. First, the IPCC scenarios represent changes to global mean temperatures, but the
impact on the study area could be significantly different. It is likely that actual warming will fall
somewhere into the range proposed by the warming scenarios, however it could also fall well
outside of the range, including cooling in the study area. Such general numbers little in the way
of specifics to ski area management and government officials to aid them in preparing for future
snowmaking conditions. However, it does them provide them with general guidelines of what to
expect. In addition, the lack of availability of data related to atmospheric moisture was
significantly limiting, as it only allowed for the maximum number of snowmaking hours that
could occur, not an actual estimate using wet bulb values. Another major caveat of using dry
bulb values is they assume constant values of humidity. According to literature cited in Location
and Background, values of dry bulb temperature and atmospheric moisture will both be subject
to change with warming.
Despite the limitations of this study, an important groundwork has been laid to greater
understanding of the predictability of snowmaking conditions. Even with the short study period,
data compiled here could be useful in the construction of winter snowmaking forecasts, not
unlike the methodology used in some long term seasonal snowfall forecasts that compare
snowfall amounts to phases in atmospheric modes. In addition, analysis of synoptic types could
be a useful short to medium range probabilistic indicator of snowmaking conditions. However,
these forecasts could be greatly improved with more research.
References
Agrawala S. 2007. Climate Change in the European Alps—Adapting
Winter Tourism and Natural Hazards Management. OECD Publishing: London
Francou B et al. 2003. Tropical climate change recorded by a glacier in thecentral Andes during
the last decades of the twentieth century: Chacaltaya, Bolivia, 16_S. Journal of Geophysical
Research. 108: X1-X12. DOI :10.1029/2002JD002959, 2003
IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group
I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon,
S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)].
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Mote TL, Kutney ER. 2012. Regions of Eurasian Snow cover and Associations with North
American Winter Temperatures. International Journal of Climatology. 32: 1164-1177. DOI:
10.1002/joc.2341
Olefs M, Fischer A, Lang J. 2010. Boundary Conditions for Artificial Snow Production in the
Austrian Alps. Journal of Applied Meteorology and Climatology. 49: 1096-1113. DOI:
10.1175/2010JAMC2251.1
Patten JM, Smith SR, O’Brien JJ. 2003. Impacts of ENSO on Snowfall Frequencies in the United
States. Weather and Forecasting. 18: 965-980. DOI: 10.1175/15200434(2003)018<0965:IOEOSF>2.0.C0;2
Rixen C et al. 2011. Winter Tourism and Climate Change in the Alps: An Assessment of
Resource Consumption, Snow Reliability, and Future Snowmaking Potential. Mountain
Research and Development. 31(3): 229-236. DOI: 10.1659/MRD-JOURNAL-D-10-00112.1
Sheridan SC. 2002. North American Weather Type Frequency and Teleconnection Indices.
International Journal of Climatology. 23: 27–45. DOI: 10.1002/joc.863
Wallace JM. 2000. Cited 2012. On the Arctic and Antarctic Oscillations.
http://www.jisao.washington.edu/wallace/ncar_notes/
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American Winter Climate. Climate Dynamics. 26: 865-879. DOI: 10.1007/s00382-006-0118-8.
Figures and Tables
Classification
Criteria
Wind
Direction
Wind Speed
High Pressure
<10 mph
Cold Air Damming
>10mph
Northwest Flow
>10mph
Southwest Flow
>10mph
Table 1
Total
Snowmaking
Hours
Mean Dry
bulb, °C
t1
t2
t3
t1
t2
HP
2945
2063
741
HP
-3.23
-5.19
CAD
749
500
49
CAD
-1.04
-2.06
NWF
3684
3047
1754
NWF
-6.15
-7.53
SWF
395
296
95
SWF
-1.53
-3.16
2a
2b
Mean Wind
Speed,
average 1
Hour
Mean
%RH
t1
t2
t3
t1
t2
HP
6.48
6.40
6.52
HP
76.05
78.04
CAD
17.02
17.52
15.33
CAD
66.46
64.40
NWF
15.46
15.59
16.01
NWF
82.70
82.66
SWF
12.68
12.78
17.02
SWF
52.38
54.06
2c
2d
Hours per
Circulation
Type: t1
Hours per
Circulation
Type: t2
HP
CAD
NWF
SWF
HP
CAD
N
AO+
34.11%
42.72%
38.03%
51.39%
AO+
31.17%
40.83%
AO-
65.89%
57.28%
61.97%
48.61%
AO-
68.83%
59.17%
ENSO+1
30.66%
42.46%
34.23%
17.47%
ENSO+1
28.21%
37.40%
ENSO+2
29.22%
42.11%
36.39%
20.46%
ENSO+2
26.82%
35.91%
ENSO-1
69.34%
57.54%
65.77%
82.53%
ENSO-1
71.79%
62.60%
ENSO-2
70.78%
57.89%
63.61%
79.54%
ENSO-2
73.18%
64.09%
Table 3a
3b
Hours per
Circulation
Type: t3
HP
CAD
NWF
SWF
AO+
24.22%
30.61%
28.56%
52.63%
AO-
75.78%
69.39%
71.44%
47.37%
ENSO+1
26.05%
36.73%
28.05%
22.11%
ENSO+2
26.49%
38.78%
30.76%
22.11%
ENSO-1
73.95%
63.27%
71.95%
77.89%
ENSO-2
73.51%
61.22%
69.24%
77.89%
3c
Pearson
Correlation R
values
t1
ALL
PHP
CAD
NWF
AO
0.275
0.274
0.059
0.348
ENSO1
0.091
0.133
0.183
0.093
ENSO2
0.105
0.145
0.185
0.12
Table 4a
t2
ALL
PHP
CAD
NWF
AO
0.246
0.241
0.086
0.302
ENSO1
0.071
0.111
0.097
0.094
ENSO2
0.086
0.123
0.073
0.125
4b
t3
ALL
PHP
CAD
NWF
AO
0.152
0.183
0.039
0.165
ENSO1`
0.132
0.162
0.283
0.139
ENSO2
0.147
0.202
0.263
0.142
4c
IPCC Scenarios: °C increase at 2090-2099 relative to 1980-1999)
Case
Likely
Range
1.10.60
2.9
1.41.80
2.9
1.42.40
3.8
1.72.80
4.4
2.03.40
5.4
2.44.00
6.4
Best Estimate
B1
A1T
B2
A1B
A2
A1FI
Table 5
Maximum
Snowmaking
Hours: IPCC
scenarios
and Dry
Bulb/RH
Current
B1
A1T/B2
A1B
A2
3°C/20%RH
5796
5573
5444
5331
5175
6.39°C/3.7%RH
5905
5876
5855
5833
5796
-0.5°C/78%RH
5143
4510
4170
3972
3634