Atmosphere-Ocean
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Canadian In Situ Snow Cover Trends for 1955–
2017 Including an Assessment of the Impact of
Automation
R. D. Brown, C. Smith, C. Derksen & L. Mudryk
To cite this article: R. D. Brown, C. Smith, C. Derksen & L. Mudryk (2021) Canadian In Situ
Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation,
Atmosphere-Ocean, 59:2, 77-92, DOI: 10.1080/07055900.2021.1911781
To link to this article: https://doi.org/10.1080/07055900.2021.1911781
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Environment and Climate Change Canada.
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Taylor & Francis Group.
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Canadian In Situ Snow Cover Trends for 1955–2017 Including
an Assessment of the Impact of Automation
R. D. Brown
1*
, C. Smith
2
, C. Derksen
3
, and L. Mudryk
3
1
Climate Processes Section, Climate Research Division, Environment and Climate Change
Canada, Montréal, Quebec, Canada
2
Climate Processes Section, Climate Research Division, Environment and Climate Change
Canada, Saskatoon, Saskatchewan, Canada
3
Climate Processes Section, Climate Research Division, Environment and Climate Change
Canada, Downsview, Ontario, Canada
[Original manuscript received 25 September 2020; accepted 4 March 2021]
Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing
network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by a
rapid decline in manual observations (loss of over 800 manual observing sites between 1995 and 2017) and an
increasing number of automated stations equipped with sonic snow depth sensors. In 2017 these accounted for
approximately 45% of the network and more than 80% of the snow depth–observing network north of latitude
55°N. Automated stations are characterized by more frequent missing and anomalous data than manual ruler
observations, particularly at Arctic sites. A comparison of closely located automated sonic and manual ruler
observations showed similar numbers of days with snow cover but the sonic sensors detected significantly
lower snow depths. For time series analysis of annual snow cover variables, the systematic difference between
ruler and sonic snow depth can be removed using a common 2003–2016 reference period to compute snow
cover anomalies. The updated trend results are broadly similar to previously published assessments showing
long-term decreases in annual snow cover duration (SCD) and snow depth over most of Canada, with the
largest decreases observed in spring snow cover and seasonal maximum snow depth (SDmax). Significant declines
in SCD and SDmax of −1.7 (±1.1) days decade-1 and −1.8 cm (±0.8) cm decade−1 were observed in the Canada–
averaged series over the 1955–2017 period. These trends mainly reflect snow cover conditions over southern
Canada where the observing network is concentrated and where there are significant negative correlations
between snow cover and winter air temperature. Declining numbers of stations reporting snow depth, issues
with sonic sensor data quality, and systematic differences between ruler and sonic sensor measurements are
major challenges for continued climate monitoring with the current ECCC snow depth–observing network.
ABSTRACT
[Traduit par la rédaction] Les tendances de la couverture neigeuse au Canada sur la période allant de
1955 à 2017 pour le réseau d’observation quotidienne de l’épaisseur de la neige d’Environnement et Changement
climatique Canada (ECCC) sont présentées sur la base d’un ensemble de données historiques quotidiennes sur
l’épaisseur de la neige in situ dont la qualité a été contrôlée. La période suivant 1995 environ se caractérise
par un déclin rapide des observations manuelles (perte de plus de 800 sites d’observation manuelle entre 1995
et 2017) et un nombre croissant de stations automatisées équipées de capteurs soniques d’épaisseur de neige.
En 2017, celles-ci représentaient environ 45% du réseau et plus de 80% du réseau d’observation de l’épaisseur
de la neige au nord de la latitude de 55° nord. Les stations automatisées se caractérisent par des données manquantes et anormales plus fréquentes que les observations manuelles à la règle, notamment sur les sites arctiques.
Une comparaison entre des observations automatisées obtenues par capteurs soniques et des observations manuelles obtenues à la règle a montré un nombre similaire de jours avec une couverture neigeuse, mais les capteurs
soniques ont détecté des épaisseurs de neige significativement inférieures. Pour l’analyse des séries chronologiques des variables annuelles de la couverture neigeuse, la différence systématique entre la règle et le capteur
sonique peut être supprimée en utilisant la période de référence commune de 2003 à 2016 pour calculer les
anomalies de la couverture neigeuse. Les résultats actualisés des tendances sont largement similaires aux évaluations publiées précédemment qui montrent des diminutions à long terme de la durée annuelle de la couverture
neigeuse (DAC) et de l’épaisseur de la neige sur la majeure partie du Canada, les diminutions les plus importantes
RÉSUMÉ
*Corresponding author’s email: rdbrown@videotron.ca
© 2021 Copyright of the Crown in Canada. Environment and Climate Change Canada. Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
ATMOSPHERE-OCEAN 59 (2) 2021, 77–92 https://doi.org/10.1080/07055900.2021.1911781
Canadian Meteorological and Oceanographic Society
78 / R. D. Brown et al.
étant observées dans la couverture neigeuse printanière et l’épaisseur maximale saisonnière de la neige (Emax).
Des diminutions significatives de DAC et Emax de -1,7 (±1,1) jour décade−1 et de -1,8 (±0,8) cm décade−1 ont été
observées dans la série moyenne du Canada sur la période allant de 1955 à 2017. Ces tendances reflètent principalement les conditions de couverture neigeuse dans le sud du Canada, où le réseau d’observation est concentré
et où il existe des corrélations négatives importantes entre la couverture neigeuse et la température de l’air en
hiver. Le nombre décroissant de stations rapportant l’épaisseur de la neige, les problèmes de qualité des
données du capteur sonique et les différences systématiques entre les mesures de la règle et du capteur
sonique sont des défis majeurs pour la surveillance continue du climat avec le réseau actuel d’observation de
l’épaisseur de la neige d’ECCC.
KEYWORDS
snow depth; Canada; ruler; sonic sensor; trends
1 Introduction
The depth of snow on the ground is an essential climate variable (GCOS, 2016) because of its widespread importance for
applications, such as initialization of weather forecast models
(Brasnett, 1999; de Rosnay et al., 2015), climate monitoring
(Brown & Braaten, 1998; Vincent et al., 2015), estimation
of design ground snow loads (Hong & Ye, 2014; Newark
et al., 1989), impacts on ground heat transfer and permafrost
(Goodrich, 1982; O’Neill & Burn, 2017), and multiple interactions and feedbacks in biophysical systems (Callaghan
et al., 2011; Jones et al., 2000). In the International Classification for Seasonal Snow on the Ground (Fierz et al., 2009)
snow depth is defined as the total height of the snowpack
(i.e., the vertical distance from the ground to the snow
surface) and is denoted here by the symbol SD with units of
centimetres.
Environment and Climate Change Canada’s (ECCC) daily
SD network, which reached a peak of over 1600 active
stations during the 1981–1994 period, represents the
primary in situ SD observing network for Canada and was
the basis of two previous assessments of Canadian snow
cover trends (Brown & Braaten, 1998; Vincent et al., 2015).
Other sources of SD information in Canada include manual
snow courses (Brown et al., 2019), a number of stations
across Quebec operated by the Ministère de l’Environnement
et de la Lutte contre les changements climatiques (MELCC)
whose data are not currently shared with ECCC, and the Community Collaborative Rain, Hail & Snow (CoCoRaHs)
network of approximately 115 stations that has reported
daily observations since 2011 (https://www.cocorahs.org/
Canada.aspx). The analysis provided here is based solely on
the ECCC in situ SD network because these data are in the
public domain, have the largest spatial coverage, and
contain the longest time series for analyzing snow cover
variability and change.
The overarching purpose of this paper is to provide updated
information on snow cover trends in Canada from surface
measurements based on a recent effort to update the Canadian
Historical Snow Depth Dataset, which was released in CDROM format in 2000 (MSC, unpublished manuscript,
2000). This latest update covers snow seasons 2000–2001 to
2016–2017, a period characterized by a rapid decline in the
manual observing network and increasing automation of
observations (Fig. 1). This presents a number of challenges
for climate monitoring, which requires internally consistent
measurement series over the past three to five decades to
reach valid conclusions about variability and trends in Canadian snow cover. These challenges include the following:
.
a decline of approximately 50% in the number of sites
measuring daily SD since 1995
. an increasing proportion of stations in the SD-monitoring
network with automated sonic SD sensors (45% of SD
network automated as of 2017)
. little quality control of SD observations in the ECCC
climate archive
. incomplete or inaccessible metadata on the SD-observing
methods at stations
For these reasons, recent assessments of snow trends over
Canada have focused on the complete spatial coverage
offered by satellite and model-derived datasets (Derksen
et al., 2019; Mudryk et al., 2018). Still, robust trends from
surface networks are highly desirable as a complement to satellite and model-derived datasets, and it is recognized that
surface networks provide essential observations for ingestion
in state-of-the-art satellite algorithms (e.g., Pulliainen et al.,
2020) and data assimilation products, such as the fifth generation atmospheric reanalysis from the European Centre for
Medium-range Weather Forecasts (ERA5; Hersbach et al.,
2020). Hence, it is important to understand the impact of
changing measurement technology and data management
standards on surface-based snow observations.
We elaborate further on these challenges in the following
sections that represent the main objectives of this paper: (i)
documenting the history of the ECCC SD measurement programme (Section 2); (ii) evaluating automated SD observations with adjacent manual ruler observations (Section 3);
(iii) assessing the influence of changing measurement
methods on snow cover trends (Section 4); and (iv) analysis
of in situ snow cover trends over Canada (Section 5). A
final discussion and recommendations for ECCC SD monitoring is provided in Section 6. A detailed description of the
work carried out to update and perform quality control on
the ECCC historical daily SD data for the 2000–2001 to
2016–2017 period is provided in the supplemental material
(supplemental material can be accessed at http:/dx.doi.org/
10.1080/07055900.2021/1911781), along with a description
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Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation / 79
Fig. 1
Temporal evolution of the number of stations in Canada reporting a
non-zero snow depth in February by measurement method.
of the methodology used to develop reference snow cover
series for the trend analysis (Section S3).
2 History of ECCC snow depth observations
The earliest recorded SD observation in the ECCC historical
archives was made at Chaplin, Saskatchewan, in 1883. Prior
to 1941, SD observations were typically only made at the
end of the month and at a relatively small number of sites.
Regular daily ruler measurements of SD began in 1941 at
principal observing stations (Potter, 1965), with spatial coverage mainly confined to southern Canada until the mid-1950s
when the synoptic station network expanded into the Arctic.
The majority of observing stations are located near airports
and populated areas, and measurements are typically made
over exposed, grassy areas that may not represent the prevailing terrain or land cover (Neumann et al., 2006). The manual
SD network (henceforth referred to as “ruler”) underwent a
major expansion after 1980 when daily SD observations
were included in the volunteer climate station network
(Brown & Braaten, 1998) but has experienced a consistent
decline since the mid-1990s as a result of the closure of
stations and curtailment of manual SD-observing programmes. This has not been compensated for by a commensurate increase in the number of automated stations equipped
with Campbell Scientific SR50 or SR50A sonic SD sensors
(Fig. 1; henceforth referred to as “SR50”). Over the 2000–
2017 period, the ECCC in situ SD-monitoring network lost
an average of 17 SD-monitoring sites per year, for a total
loss of over 300 observing sites.
Determining the SD measurement method (ruler or SR50)
was a non-trivial task because ECCC does not archive the
measurement method along with the observations. Station
metadata are fragmented and were still undergoing ingestion
into a new metadata repository when this paper was written.
Observing method information for many stations was available in the Station Information System discontinued in
2018. This was supplemented with information on the
current sensors deployed at automated weather stations to
construct a daily measurement type array (0 = ruler, 1 =
SR50) for use with the updated daily SD dataset. Typically,
automated stations are assigned a new station ID, which
helps maintain separation of the ruler and SR50 measurement
streams. Thirty-six stations, however, were identified with
different measurement methods under the same station ID.
Figure 2 shows the spatial distribution of ruler and SR50
stations in the ECCC SD network with complete data in
snow season 2016–2017 and highlights the greater reliance
on automated sensors over northern regions of Canada (e.g.,
over 80% of the Canadian surface SD-observing network
north of 55°N was equipped with sonic sensors). The
network is concentrated across the densely populated
regions of southern Canada, with network density dropping
rapidly north of about 55°N with an important low elevation
bias in mountainous regions (Figs S1 and S2). The spatial
and temporal coverage is insufficient to provide a complete
picture of snow cover trends across Canada, but it does
provide information for the most populated regions and at
the community level across northern Canada. Efforts are currently underway to apply spatial modelling approaches
(McKenney et al., 2011) to generate five-day average interpolated SD fields across Canada at approximately 1 km
resolution.
The protocol for manual SD observations is described in
the Canadian Manual of Climate Observations (MANCLIM,
2012) and the Canadian Manual of Surface Weather Observations (MANOBS, 2013) and has remained essentially
unchanged since 1941. Observers are instructed to record
the average of a series of measurements, avoid snowdrifts,
and ensure that the total depth, including any layers of ice,
is measured (Potter, 1965). Observations are made in the
early morning, around 0800 local time, and are attributed to
the current climatological day starting at 0600 UTC . For
patchy snow cover conditions, observers are instructed to
use their best judgment and to record the depth as “trace”
when the ground is “essentially bare” (MANCLIM, 2012).
These instructions are essentially the same as those issued
Fig. 2 Spatial distribution of ECCC ruler (blue) and SR50 (red) stations
with complete daily snow depth data during the 2016–2017 snow
season. SR50 refers to stations equipped with either SR50 or
SR50A sonic sensors.
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80 / R. D. Brown et al.
by the US National Weather Service (NWS, 2013). Measurements of SD were recorded in whole inches prior to 1975 and
to the nearest centimetre after that date (Brown & Braaten,
1998). The unit change can introduce a discontinuity for variables, such as snow cover duration, that are defined by an SD
threshold, which is why a 2 cm threshold is used here to define
snow cover days, following the recommendation of Brown
and Goodison (1996). The manual measurement process
includes subjective selection of the number and location of
measurement points, and there is anecdotal evidence that
under patchy snow conditions observers tend to avoid including snow-free areas in the average, which would introduce a
positive bias compared with measurements made at fixed
locations. According to Goodison et al. (1981, p. 192), the
observer’s judgment and the availability of multiple measurements should ensure accurate observations. We are unaware
of any systematic comparisons in Canada of standard
manual ruler SD observations with other measurement
methods, such as fixed stakes or sonic sensors.
The addition of sonic SD sensors to ECCC automated
weather stations was championed by Dr. Barry Goodison of
the Hydrometeorology Division of the Canadian Climate
Centre in the early 1980s. The prototype was based on a commercially available ultrasonic ranging kit from Polaroid Corporation that provided an economical means of accurately
measuring target distance from the travel time of the return
acoustic signal (Goodison et al., 1985). Prototype development began in 1982, with field testing during the 1982–
1983 winter season. The sensor was simple, relatively
inexpensive, and licensed to Campbell Scientific Canada for
commercial manufacture in 1987. The instrument went
through several iterations during the late 1980s and early
1990s and officially went into service for ECCC in 1993 as
the Campbell Scientific Sonic Ranger (SR50). It was later
sold as the SR50-45 to indicate certification for an air temperature range from −45°C to +45°C. The updated, smaller
SR50A replaced the SR50 and SR50-45 in March 2007 and
is available with a heater option for locations where rime
ice is a problem (SR50AH) and with an integrated temperature sensor (SR50AT). The SR50A has a 30° beam width
compared with 22° for the SR50, which means it samples a
slightly larger area of snow—a beam diameter of 1.07 m
versus 0.78 m, respectively, at a height of 2 m above the
surface (ECCC, unpublished manuscript, 2016). Figure 3
shows a typical sonic sensor configuration above a fixed artificial target that stops the growth of vegetation that affects the
signal and provides a stable flat surface for reflecting the sonic
pulse when there is no snow under the sensor.
At ECCC stations, the SR50 obtains an SD estimate every
15 min based on a burst of measurements made every 5 s over
the last 5 min of each 15-minute period. Until 2017, the
average of the 5 s values was used to determine the 15 min
depth, with the last 15 min value of the hour used as the official report for the hour (ECCC, unpublished manuscript,
2015). The calculation of a median value (rather than an
average) was recommended by Campbell Scientific as being
Fig. 3
SR50 sonic sensor configuration at Bratt’s Lake, Saskatchewan. The
plastic surface targets under the sensors were typically installed after
about 2010 to avoid false snow depth values from growing vegetation (reproduced by permission of ECCC).
more stable (less affected by outliers in the high frequency
measurements) and replaced the average value in 2017. The
hourly observation corresponding to 1200 UTC is used for
the daily SD value, which corresponds closely to the time
of manual SD observations.
The velocity of sound in air varies strongly with temperature, and for the SR50 prototype, a correction was applied
during post processing (see Nitu et al., 2018, pp. 430–432,
for a detailed discussion and derivation of the correction
equations). Prior to the 1984–1985 winter season, the SD
sensors were modified to include an on-board microcontroller
and integral temperature sensor, which improved the root
mean squared error (RMSE) to less than 2 cm compared
with ruler SD measurements made under the sensor (Goodison et al., 1988). Similar RMSE values were reported by Brazenec (2005) and Ryan et al. (2008) from evaluations of the
SR50 at several sites across the United States. Factors affecting the accuracy of the acoustic sensor measurements include
the nature of the snow surface (very low density and rotten
snow tend to be associated with higher uncertainties), the distance of the sensor above the surface, the accuracy of the air
temperature measurement used for the speed of sound correction, the occurrence of precipitation at the time of the
measurement, and the potential zero-drift related to ground
heave or subsidence, or growing vegetation in the instrument
field of view (Nitu et al., 2018). Other factors that can
affect the performance of the SR50 include vibrations of the
instrument because of high winds, riming and misalignment
with the surface (i.e., if not directly pointing vertically
to the surface), and disturbance of the underlying snow or
instrument by animals or humans (S. Déry, personal communication, 2020).
Early evaluations of the SR50 indicated that it underestimated SD slightly (by less than 2 cm) compared with
co-located ruler SD observations (Goodison et al., 1985).
Goodison et al. (1988) flagged anomalous measurements
occurring during periods of snowfall and/or drifting or
blowing snow events and noted that these can be readily
quality controlled using snowfall precipitation data and previous SD observations. The current data logger configuration
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for the SR50 performs a rate-of-change limit to ensure each
sample is within 2.5 cm of a running 1-minute average. If
the sample exceeds this limit, it is capped to ±2.5 cm of the
1-minute average (ECCC, unpublished manuscript, 2016).
The SR50 measurement output includes an internally generated data quality assessment (QA) code to flag when the
sensor is unable to obtain a “good measurement.” A detailed
evaluation of these codes by Nitu et al. (2018) concluded there
was no clear relationship between the sensor internal QA code
and environmental conditions that could guide users for
quality control although they did note a decrease in the frequency of good measurement codes during mixed or heavy
precipitation events, as well as during periods of rapid melt.
These QA codes are not currently archived by ECCC.
In the early 2000s, ECCC proposed to address quality
control and spatial sampling issues with the implementation of a “triple configuration” of three sonic sensors.
The logic behind the triple configuration is that three
measurements provide improved quality control by rejecting sensor values that fail internal QA checks, while
three spatially distributed sensors would provide a more
representative estimate of the landscape mean SD. The
triple configuration is the current standard for ECCC automated stations and is implemented at most sites in the
automated station network. Although some testing was
carried out, observational advantages of the triple configuration have not yet been realized because it could not be
determined how to most effectively combine the information from the three sensors into a single reported
depth value. The current procedure only uses observations
from sensor #1 for the official observation (Mekis, unpublished manuscript, 2020), and the information from the
other sensors is not currently available in the ECCC
climate data archive. Efforts are currently underway to
re-examine the advantages and utility of multiple SD
sensor measurements in the ECCC automated network.
3 Comparison of manual ruler and SR50 observations
The manual ruler SD observations made in Canada are
fundamentally different from an SR50 measurement: the
latter is a fixed measurement generally of the highest
point in the sensor’s field of view (1 m2), while the
former is an average of a subjectively selected series of
ruler measurements that attempts to provide a representative estimate of the SD in the area of the measurement.
Neumann et al. (2006) found that single, fixed-point
measurements of SD from SR50s were unable to represent
the average SD measured by multi-point snow surveys
over a range of sites, even for relatively uniform snow
covers.
Most of the published evaluations of the performance of the
SR50 used ruler measurements of SD made underneath or
very close to the sensor. The only published study we are
aware of in which ultrasonic sensors were compared with
nearby stations reporting manual SD observations was the
evaluation of ultrasonic sensors reported in Brazenec (2005)
for a single snow season (2004–2005). Observations of SD
from SR50 and Judd sonic sensors were compared with
nearby standard manual SD observations that followed
NWS observing guidelines at seven sites from different
snow-climate regions. Root mean squared difference
(RMSD) values for the seven sites ranged from 2 to 11 cm
with an average of 5 cm. This was only slightly higher than
the RMSD (3.5 cm) from a manual ruler measurement made
close to the SR50 sensors. The relative spread (RMSD
divided by maximum SD) revealed an exponential relationship (Fig. S3a), indicating that the relative uncertainty in
SR50 observations was highest for shallow snow cover. At
all sites, the SR50 measurements were lower than ruler
measurements (average difference of −2.2 cm, similar to the
bias reported by Goodison et al., 1985), with evidence that
the negative difference increased with increasing SD (Fig.
S3b).
The observation that the relative spread between ruler and
SR50 observations is higher for shallow snowpacks is not surprising: in windy environments, such as the Arctic and Prairies where the snowpack is shallow and subject to frequent
drifting and scouring, the two observing methods can be
expected to have quite different values. This issue was
flagged early on in the deployment of SR50-equipped autostations by B. Brasnett (personal communication, 2003) from the
Canadian Meteorological Centre (CMC) who noted frequent
rejection of SR50 shallow SD observations that differed significantly from the first guess field of the CMC operational
SD analysis. In windy environments, the requirements of
the World Meteorological Organization (WMO) for the
siting of meteorological instruments in open areas (WMO,
2008) and the current ECCC practice of using a single sonic
sensor for reporting depth increases the likelihood it will
differ from the average of a series of distributed ruler observations. Another issue flagged by the CMC SD analysis was
non-zero SD observations during the period after snow had
melted resulting from vegetation growth in the sensor field
of view, a problem with sensors mounted over grass surfaces.
This effect has been minimized since about 2011 with the
installation of a maintenance-free plastic target that serves
to create a vegetation barrier under the sensor (see Fig. 3).
The documentation of the station SD measurement method
investigated in this study allowed a comparison of standard
manual ruler and SR50 observations to be carried out at 92
sites across Canada over the 2000–2017 period where the
metadata indicated measurements were made in close proximity (defined as <1 km separation distance and <100 m
elevation difference). The sites were well distributed across
Canada, with elevations ranging from 4 to 1190 m. Two
key snow cover metrics were evaluated: annual snow cover
duration (SCD, the number of days in a snow season with
SD ≥ 2 cm) and annual maximum SD (SDmax, the
maximum SD in a snow season). Buchmann et al. (2021)
found that these two metrics were the most robust with
respect to local influences and changes in the environment
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Fig. 4 (a) Comparison of SCD from manual ruler observations versus observations from nearby SR50s over the 2000–2017 period excluding zero pairs. The
dashed line is the least squares best fit, and the 1:1 line is shown as a solid black line. (b) As in (a) but for annual maximum snow depth, SDmax.
or measurement procedures. Annual values were only used if
there were less than 20 missing daily snow depths in a snow
season. Gap-filled SD values within 14 days of an observation
were included in the computation of SCD and SDmax (see
Section S2.3 for details of the gap-filling methodology).
This is less restrictive than the seven days recommended by
Brown and Braaten (1998) but was found to greatly increase
the amount of data available for analysis. Approximately 500
co-located ruler and SR50 observation pairs with non-zero
annual SCD values were obtained for analysis.
Because one of the main objectives of this study was to
assess the impact of a shift from ruler to SR50 SD observations, ruler observations are plotted on the x-axis, and
differences are computed as SR50 observations minus ruler
observations. The scatterplot of the SCD comparison
(Fig. 4a) shows a close linear agreement between the two
measurement methods (r2 = 0.96, n = 500), with a slope
close to one (least squares slope and 95% confidence interval
= 0.98 ± 0.017). The RMSD for SCD was 17.7 days (19.3%
of ruler mean SCD), with SR50s observing, on average, 4.0
days less snow cover than standard ruler observations, not
statistically significant at the 0.05 level. Similar close agreement was found for SCD values computed over the first and
second halves of the snow season (not shown), which provides some evidence that manual observations are not
unduly biased during the spring period when patchy snow
cover is more likely to be encountered.
The SDmax comparison (Fig. 4b) shows considerably more
scatter than the SCD comparison (r2 = 0.72, n = 523) with a
slope significantly less than one (slope and 95% confidence
interval = 0.80 ± 0.04). The SR50s observed, on average,
5.5 cm less snow than ruler observations, significant at the
0.05 level. The RMSD was 15.2 cm (51.3% of ruler mean
SDmax), with the average SDmax difference increasing
with depth (Fig. 5) in agreement with the results of Brazenec
(2005). There is no evidence in Fig. 5 of any snow depth
dependencies in the SCD difference, which likely reflects
the rapid melting of snow in the spring period (i.e., positive
depth anomalies are rapidly removed in the spring because
most of the available energy goes into snowmelt; Gray &
Landine, 1988), which is further enhanced by sensible heat
advection when snow cover becomes patchy (Liston, 1995).
Analysis of the seasonal distribution of depth differences
between ruler and SR50 observations with monthly mean
SDs (not shown) revealed that the difference between ruler
and SR50 observations increased over the snow accumulation
period, consistent with the depth dependency seen in Fig. 5.
Fig. 5
Average difference (SR50 minus ruler) in SDmax and SCD between
closely located ruler and SR50 locations by ruler-observed SDmax
classes. The dashed lines are ±1 standard deviation.
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4 Reducing the influence of changing measurement
methods on snow cover series
Do the different observing characteristics of ruler versus
SR50 SD observations need to be taken into account when
snow cover trends are calculated over the period with mixed
measurement methods? This question was investigated
using regionally averaged series of SCD and SDmax over
the Prairie (PRA) region (49°–54°N, 95°–115°W) of
Canada, which has a relatively homogeneous terrain and
land cover and a relatively even spread of stations with
ruler and SR50 SD observations (Fig. 2). The regional
average approach reduces noise from individual stations and
provides relatively stable time series for evaluating the
impact of a changing measurement method.
Regionally averaged snow cover series were computed separately for ruler and SR50 stations using raw values to document differences and were computed as anomalies with
respect to a 2003–2016 common reference period to
examine the effectiveness of anomalies at removing any systematic differences (Fig. 6). At least 10 years of data were
Fig. 6
required to compute anomalies, with stations given equal
weighting in computing the regional average. There were,
on average, 74 ruler stations in the PRA region during the
2003–2016 reference period, as well as 22 SR50 stations.
Annual series were compared over the 2004–2017 period
when both data clusters were observed to provide approximately complete spatial coverage of the defined region. The
ruler and SR50 series were significantly (0.05 level) correlated over the 13 years (r2 = 0.69 for SCD, r2 = 0.65 for
SDmax) with the SR50 series underestimating ruler SCD
and SDmax data by 20.9 days and 12.9 cm, respectively.
However, both observing methods provide similar representations of the interannual variability, which means that converting station series to anomalies with respect to a recent
common reference period is an effective strategy for
removing the systematic difference in measurement
methods (Fig. 6).
Snow cover series for trend analysis were obtained by combining ruler and SR50 anomaly series following the three
steps described in detail in Section S3. First, complete
Regionally averaged SCD and SDmax series for the PRA region. (a) and (c) Series from raw station values. (b) and (d) Anomaly series for a common
2003–2016 reference period.
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annual snow cover series at ruler and SR50 sites were
obtained by interpolating missing values from nearby
(within 100 km) ruler or SR50 stations. This takes advantage
of the fact that annual snow cover variables, such as SCD and
SDmax, are significantly correlated over distances of several
hundred kilometres (Fig. S3.1). Second, the station series
were converted to anomalies with respect to a common
2003–2016 reference period to remove the systematic difference observed between SR50 and ruler snow cover series.
Third, the ruler and SR50 station anomaly series were
jointly interpolated to a 190.5 km polar stereographic grid
over Canada creating approximately 175 series with complete
data for snow seasons 1955–1956 to 2016–2017. The blending of multiple station anomalies in the grid value average
has the effect of joining station series and reduces the
impact of potential inhomogeneities from individual stations,
which is analogous to the production of reference series (LiJuan & Zhong-Wei, 2012). Brown and Braaten (1998) investigated the homogeneity of Canadian daily SD observations
and were unable to find evidence of systematic inhomogeneities except at two urban locations with evidence of urban
warming. This is consistent with the findings of Buchmann
et al. (2021) that snow cover indicators, such as SCD and
SDmax, are relatively insensitive to local influence and
station shifts. A change point analysis (standard two-sample
t-test for difference in means) provided no evidence of significant inhomogeneities in the reference time series during the
period since 2000 when SR50 observations accounted for an
increasing fraction of the surface SD observations (Fig. S3.2).
5 Trend analysis results
The following snow cover variables were investigated in the
trend analysis across Canada to provide a comprehensive
assessment of key characteristics of snow cover timing, duration, and accumulation:
first and last dates of any snow on the ground excluding
trace amounts (JDfirst, JDlast), and the snow season
length (SSL) defined by JDlast–JDfirst
. the number of days with snow cover (depth ≥ 2 cm) in the
first and second halves of the August–July snow year
(SCDfall, SCDspr), as well as snow year (SCD)
. annual maximum SD (SDmax) and the corresponding date
(JDmax)
.
The method proposed by Zhang et al. (2000) was used
to estimate trend and trend significance, where trend magnitude is based on the slope estimator of Sen (1968) and
statistical significance from the nonparametric Kendall’s
τ-test (Kendall, 1955). The influence of serial correlation
is taken into account by removing the lag-1 autocorrelation
from the series prior to computing the trend. This is the
same method used by Vincent et al. (2015) in a previous
assessment of in situ snow cover trends in Canada. The
analysis was carried out for two periods: 1955–2017
(minimum 50 years of data) to assess longer-term trends
during the period when daily SD reporting was carried
out at synoptic stations across Canada, and the more
recent 1981–2017 (minimum 30 years of data) when the
SD reporting network was expanded to include volunteer
climate stations. Spatial plots of snow cover trends for
the 1955–2017 period and 1981–2017 period are presented
in (Figs 7–9) and are summarized in Table 1.
a Snow season variables (JDfirst, JDlast, SSL)
The snow season variables (Fig. 7) show little evidence of
long-term significant trends, which can be explained by
their co-dependence on synoptic scale weather events (e.g.,
first and last snowfall-producing storms), which have a
lower signal-to-noise ratio for large scale anthropogenic
warming (Bindoff et al., 2013). The more recent 1981–2017
period shows more evidence of a Canada-wide response of
a shorter SSL that appears to be mainly driven by a later
start to the snow season, with corresponding widespread
reductions in SCD during the first half of the snow season
(SCDfall in Fig. 8). This is contrary to a general trend to
earlier snow onset across Canada reported by Allchin and
Déry (2019) based on the Climate Data Record of Northern
Hemisphere Snow Cover Extent product produced by the
National Oceanic and Atmospheric Administration
(NOAA–CDR; Estilow et al., 2015), further confirming the
conclusions of Brown and Derksen (2013), Mudryk et al.
(2017), and Hori et al. (2017) that the NOAA–CDR product
contains an artificial increasing trend in the snow onset
period related to changing data streams and methods of analysis. The median grid point trends in JDfirst and SSL were 1.96
and −1.98 days decade−1, respectively, with JDfirst having
the largest fraction of grid points with locally significant
trends (15.5%) for all the snow cover variables investigated
in the 1981–2017 period (Table 1b). These results are consistent with the multi-dataset analysis of Mudryk et al. (2018)
who found that the largest significant decreases in snow
cover fraction over Canada from 1981 to 2015 occurred in
October to December in response to stronger fall warming
during that period. The mainly decreasing trend in SSL contrasts with increasing SSL trends over Eurasia reported by
Ye and Ellison (2003), but their results were for an earlier
period (1937–1994) with less influence from anthropogenic
warming.
b Snow cover duration variables (SCDfall, SCDspr, SCD)
The snow cover duration variables (Fig. 8) show more evidence of long-term decreases, with 31.6% of grid points
displaying locally significant decreases in annual SCD
over the 1955–2017 period. The median trend in SCD
was −1.54 days decade−1, with an average trend of
−3.90 days decade−1 for grid points with significant negative trends (Table 1a). Decreasing SCD was more marked
in the spring period with 27.1% of points showing significant decreases and a median trend of −0.88 days decade−1
compared with 14.3% and -0.57 days decade−1 for the fall
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Fig. 7
Spatial plots of grid point trends for snow season variables for the 1955–2017 period (top row) and the 1981–2017 period (bottom row). The area of the
bubbles is proportional to the trend (see legend for scale), with units of days decade−1. The more prominent colours signify locally significant (0.05 level)
trends.
Fig. 8 As in Fig. 7, but for snow cover duration variables.
period. The more significant trend response for SCD is
consistent with the sensitivity analysis of Brown and
Mote (2009) who showed that annual SCD had the
highest signal-to-noise ratio for an imposed climate
warming; SCD trends for the more recent 1981–2017
period show less evidence of widespread significant
change due to the relatively stronger influence of interannual variability in the shorter period (Table 1b). As noted
previously, the main changes are seen in the first half
of the snow season with 12.4% of points exhibiting
locally significant decreasing trends, with a median trend
of −1.68 days decade−1. The marked lack of spring
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Fig. 9
As in Fig. 7, but for annual maximum snow depth (SDmax) and date of annual maximum (JDmax). Units are cm decade−1 for SDmax and days decade−1
for JDmax.
T ABLE 1.
(a) Summary of trend analysis for the 1955–2017 period, for series with a minimum of 50 years data (minyrs = 50). The units for variables are days
except SDmax (cm). A significance level of 0.05 is applied to determine grid point series with locally significant trends, and asterisks indicate results
that are estimated to be field significant following Wilks (2019). (b) As in (a) but for the 1981–2017 period and minyrs = 30.
Variable
(a) 1955–2017
JDfirst
JDlast
SCDfall
SCDspr
SCD
SSL
SDmax
JDmax
(b) 1981–2017
JDfirst
JDlast
SCDfall
SCDspr
SCD
SSL
SDmax
JDmax
# Grid Points
Median Trend (per decade)
% Points with Significant Increase
% Points with Significant Decrease
170
170
175
177
171
170
171
170
−0.01
−0.14
−0.57
−0.88
−1.54
−0.36
−1.31
−0.78
4.7
2.9
1.1
2.3
1.2
5.3
3.5
2.4
4.1
12.9*
14.3*
27.1*
31.6*
11.8*
29.8*
11.8*
181
181
185
184
181
181
181
181
1.96
0.35
−1.68
0.24
−1.32
−1.98
−0.58
0.46
15.5*
4.4
0.5
3.3
1.7
2.8
5.5
2.8
0.6
2.8
12.4*
2.2
7.7*
8.3*
8.3*
1.7
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SCD decreases over the 1981–2017 period is linked to
recent air temperature trends and is discussed further in
Section 5.5.
c Snow depth variables (SDmax, JDmax)
The observed significant long-term decreases in SCD are
accompanied by decreases in SDmax over much of Canada
(Fig. 9), primarily over southern Canada. This latitudinal
difference is consistent with climate model simulations that
show little change or increases in depth over higher latitudes
in response to increased winter precipitation (Brown & Mote,
2009; Meredith et al., 2019; Räisänen, 2008). Both significant
decreases in winter precipitation over much of western and
southern central Canada and a lower fraction of precipitation
falling as snow from winter warming contribute to the decline
in SDmax (Vincent et al., 2015). For the 1955–2017 period
(Table 1a), the median SDmax trend is −1.31 cm decade−1,
with an average trend of -4.80 cm decade−1 at points with
locally significant negative trends. There were fewer significant changes in JDmax than in SDmax, with most significant
trends associated with an earlier JDmax. These points were
mainly located over northern and western Canada and are
consistent with studies reporting earlier snowmelt over extensive areas of western North America (Dettinger & Cayan,
1995; Fritze et al., 2011; McCabe & Clark, 2005) that has
been attributed to anthropogenic climate change (Barnett
et al., 2008; Fyfe et al., 2017; Najafi et al., 2017). Trends in
SDmax and JDmax in the 1981–2017 period were spatially
heterogeneous (Fig. 9) but with some evidence of more widespread significant declines in SDmax (Table 1b).
Several points in the southern interior of British Columbia
stand out as having contrasting trends in SCD and SDmax.
Inspection of time series at these points showed evidence of
a regime shift around 1980 to a period with increasing SD
that has some support from annual maximum snow water
equivalent (SWE) trends presented in Mudryk et al. (2018).
The increases in SDmax observed over the Maritimes is consistent with previously published trend results and was attributed to increasing winter snowfall by Brown and Braaten (1998).
d Snow season snow-free periods (SSL minus SCD)
An investigation of trends in snow season snow-free periods
was performed to address recent findings published in Ma
et al. (2020), which showed that snow-free periods during the
snow season (referred to as “snow-free breaks” by Ma et al.)
exhibited significant decreasing trends at many stations in
China. Analysis of Canadian station trends in SSL minus
SCD showed no evidence of this phenomenon, with 28% of
the 92 stations with 50 years of data in the 1955–2017 period
exhibiting significant increases (larger snow-free gaps) and
no stations exhibiting significant decreases. The stations with
significant increases in snow season snow-free periods were
distributed over all regions of Canada (not shown) and are consistent with the observed widespread trends of declining
SDmax, unlike China where Ma et al. (2020) document
increasing SDmax at many stations north of 40°N.
e Snow cover air temperature linkages
Air temperature is a major driver of snow cover variability
and change, and most of southern Canada, where the observing network is concentrated (centroid located at 52.6°N,
-95.4°W), is a zone where both SCD and SDmax are projected
to decline in response to the combined influence of warming
in generating a shorter snow season and a reduction in the proportion of precipitation falling as snow (Mudryk et al., 2018).
The close coupling between SCD and SDmax is highlighted
in Fig. 10, which presents anomaly series of SCD and
SDmax averaged over all grid points, along with the scatterplot of the detrended series. Both series exhibit significant
decreasing trends over the 1955–2017 period of −1.7 (±1.1)
days decade−1 and −1.8 cm (±0.8) cm decade−1, respectively,
with the detrended series significantly correlated (r2 = 0.60).
Both SCD and SDmax exhibit significant (0.05 level) negative correlations with winter (November–March) air temperatures averaged over southern Canada (50°–55°N) for the
1955–2015 period from the Karl et al. (2015) gridded
dataset (r2 = 0.50 and 0.40, respectively). Both series follow
the regional winter air temperature series relatively closely
over the period from 1955 (Fig. S4) but are noticeably
decoupled from air temperature from approximately 1985 to
1995, which coincides with a period of relatively cool
autumn air temperatures that drove contrasting responses in
fall and spring snow cover duration (Fig. 11). Figure 11
also highlights the lack of spring warming over southern
regions of Canada since the mid-1980s, a contributing
factor to the smaller decreases observed in recent decades,
as well as the warming of fall temperatures during the
1990s that contributed to later starts to the snow season
over the 1981–2017 period observed in Figs 7 and 8.
A notable feature of Fig. 10 is the rapid decrease in SCD and
SDmax that occurred during the 1970s, which accounts for a
large fraction of the change observed from 1955 to 2017. For
example, if the Canada–averaged series are split in half,
the average change in SCD over the 1955–1985 period is
−2.9 days decade−1 compared with −0.9 days decade−1 for
the 1986–2017 period. The contrast is even greater for
SDmax (−3.0 cm decade−1 and −0.2 cm decade−1, respectively). The rapid decline in snow cover during the 1970s and
early 1980s was previously documented in Brown and
Braaten (1998) and coincides with a period of rapid spring
warming and a shift in the atmospheric circulation over the
North Pacific in 1976 (Trenberth, 1990), as well as a shift to
more positive modes of the North Atlantic Oscillation around
1980 associated with a reduction in the number of winter
storms and lower snow depths over eastern Canada (Brown,
2010; Wang et al., 2006).
6 Discussion and conclusions
The automation of SD measurements is a rational response to
declining budgets and network modernization that offers the
potential for increasing network coverage in data sparse
areas and providing potential all-weather quantitative data
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Fig. 10 Regionally averaged anomaly series for annual (a) SCD and (b) SDmax for all grid points in Canada, and (c) the scatterplot of the detrended series. The
smoothed thick black lines in (a) and (b) are the result of applying a 9-term binomial filter to the regionally averaged values and the thin black lines
indicate the 95% confidence interval. Anomalies are computed with respect to a 2003–2016 reference period.
for time intervals of one hour or less that meet WMO requirements. The technology deployed across Canada (SR50 and
SR50A sensors) is reliable and robust, with an accuracy of
± 2 cm compared with a co-located ruler observation.
However, a single sonic sensor observation differs significantly from the standard manual ruler observations carried
out at ECCC climate stations since the 1950s and creates a
discontinuity in historical snow cover series derived from
manual ruler observations. The sonic sensors also contain
additional sources of error and noise that have not been corrected in the ECCC climate archive and which reduce confidence in the use of these data, especially for Arctic sites
subject to frequent blowing snow conditions. In this study,
we were able to isolate and correct over 2000 anomalous
SD values, but sonic sensor data at some sites, particularly
in the Arctic, exhibit behaviour that differs noticeably from
historical manual observations (e.g., strong day-to-day variability and/or extended periods with a very shallow SD of 1–
2 cm). These may well reflect local effects from blowing
snow and wind scour, which are impossible to verify, and
introduce discontinuities into the historical SD record. The
development of gridded historical SD products based on
data assimilation methods (e.g., Leisenring & Moradkhani,
2011; Magnusson et al., 2017; Slater & Clark, 2006) may
be a practical way to address some of the different characteristics and uncertainties associated with manual and sonic
sensor SD observations.
The rapid decline in the number of stations reporting SD
since 1995 and the increasing fraction of stations with
automated sonic sensors are real concerns for continued use
of the network for monitoring in situ snow cover changes in
Canada. Satellite data offer some potential for filling the
gaps but have important limitations. The NOAA–CDR
product (Estilow et al., 2015), the longest satellite snow
cover record over the northern hemisphere, has weekly
snow cover presence or absence data from the early 1970s
but has been shown to be inconsistent with other products,
particularly in the snow onset period (Brown & Derksen,
2013; Hori et al., 2017; Mudryk et al., 2017). The new
GlobSnow 3.0 Passive microwave product (Pulliainen et al.,
2020) has corrected the regional SWE underestimates of versions 1 and 2, but only covers the period since 1979 and does
not provide SWE in mountainous regions. Reanalysis-driven
snow cover simulations (e.g., Brun et al., 2013) typically only
have coverage from approximately 1980 and are subject to
potential sources of inhomogeneity from changes in sources
of assimilated data (for example, snow mass from ERA5 as
shown in Mortimer et al., 2020). Importantly, the quality
and sustainability of satellite and model-derived datasets
must consider the ongoing availability and quality of
surface observing networks because state-of-the-art satellite
retrieval algorithms (Pulliainen et al., 2020) and the most
recent generation of reanalysis products (Hersbach et al.,
2020) both rely on the assimilation of point SD
measurements.
Accurate, reliable, and consistent surface observations
are a keystone for a series of overlapping needs within
and outside ECCC that include initialization of weather
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Fig. 11
(a) Fall and spring Canada–averaged anomalies of snow cover duration and (b) air temperature from the Karl et al. (2015) dataset. Air temperatures are
land area averages between 50° and 55°N, with fall and spring seasons defined as October–December and March–May, respectively. Series are smoothed
with a 9-term binomial filter.
forecast models, climate monitoring, assessment of snow
process and climate models, validation of satellite snow
retrievals, climate attribution studies, and a wide range of
applications, such as ecological studies and ground snow
loads. Several recommendations can be made from the
results of this study that would help ECCC better meet
these needs. First, an urgent effort is needed to improve
the quality of the sonic sensor data archived by ECCC.
Fully utilizing the triple sensor configuration is an important first step (i.e., archiving and making available the
hourly observations from all three sensors), along with
ensuring that the siting of the three sensors is optimized
to provide representative estimates of the snow cover in
the vicinity of the observing site. This is especially important in exposed, windy environments with strong spatial
variability in snow cover. Second, more effort is needed
to carry out quality control of the observations included
in the ECCC climate archive; this includes additional
data consistency checks (the observations from the three
sensors would greatly facilitate this process) and the
archiving of key metadata, such as measurement method
history, along with the data so that users are aware of
the type of data they are working with. Third, site information should be readily available including photographs
showing the location of the sensors under a variety of
snow cover conditions. Finally, there is a need to identify
(and protect) stations with long-term, consistent manual SD
measurements. These stations represent the reference for
documenting change, and their importance for this and
other needs cannot be overstated.
The trend results showing significant declines of −1.7
(±1.1) days decade−1 and -1.8 (±0.8) cm decade-1 in SCD
and SDmax, respectively, across much of Canada over the
1955–2017 period were for the most part consistent with
Vincent et al. (2015) but with a smaller fraction of data
points exhibiting locally significant long-term decreases.
Vincent et al. (2015) computed trends over the 1950–2012
period; including the 1950–1954 data in the trend analysis
was found to increase the number of points with significant
decreases. However, we chose to exclude these data
because the number of stations observing daily SD was markedly lower across Canada in the period prior to 1955. The
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trend results are also consistent with the snow cover change
summary provided in the recent Canada’s Changing
Climate Report (Derksen et al., 2019) outlining a general
decrease in SCD and SD over most of Canada, with the
largest decreases observed in spring snow cover and
maximum snow depths. Winter air temperatures were
observed to exert a strong control on both SCD and SD
over southern Canada, and further warming of this region
will drive declines in snow depth and snow cover based on
the observed air temperature sensitivities. Finally, the important role of natural variability in observed trends was highlighted by the rapid decrease in snow cover over southern
Canada in the 1970s. This was an important contributor to
the observed change since 1955 and was likely a response
to anthropogenic warming reinforced by atmospheric circulation changes, similar to recent rapid warming events observed
over Labrador (Way & Viau, 2015).
Columbia), Lucie Vincent (ECCC), Robert Way (Queens
University), Christoph Marty (WSL Institute for Snow and
Avalanche Research SLF), Barry Goodison, and Brian Day,
as well as an anonymous external reviewer. Thanks also go
to Mike Brady (ECCC) for his assistance in plotting the
station locations and trend results. The Ouranos Consortium
is acknowledged for providing office space and computing
facilities and support for R. Brown. A final acknowledgement
to all the current and former ECCC weather observers for
their diligence in providing a high-quality data resource for
the use of all Canadians and the international snow
community.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplemental material
Acknowledgements
Dan McKenney and Pia Papadopol (NRCan) are acknowledged for providing the interpolated daily climate data for
the 3111 stations included in the dataset update. The filling
of missing data would not have been possible without this
important contribution. Barry Goodison (ECCC retired) and
Brian Day (Campbell Scientific) are acknowledged for providing information on the historical development of the
SR50A sonic snow depth sensor used at ECCC weather
stations. Éva Mekis (ECCC) is acknowledged for her invaluable assistance in obtaining information on observing programmes within ECCC and for providing a number of
internal ECCC documents describing the SR50 measurement
procedures. Rick Fleetwood (ECCC) is also gratefully
acknowledged for providing information and data from the
Canadian CoCoRaHS project. The authors are very grateful
for helpful feedback and comments on the manuscript provided by Stephen Déry (University of Northern British
Supplemental data for this article can be accessed at https://
doi.org/10.1080/07055900.2021.1911781. This also includes
descriptions of the unpublished ECCC documents mentioned
in the text.
Data access
The datasets developed for this study are archived and documented on the Government of Canada Open Data portal at
http:/dx.doi.org/10.18164/e75562d9-625c-4dd8-9481682d50adf2d7.
ORCID
R. D. Brown http://orcid.org/0000-0001-7196-2686
C. Smith http://orcid.org/0000-0002-6552-1486
C. Derksen http://orcid.org/0000-0001-6821-5479
L. Mudryk http://orcid.org/0000-0001-6381-4288
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