TCThe CryosphereTCThe Cryosphere1994-0424Copernicus GmbHGöttingen, Germany10.5194/tc-9-2417-2015Inconsistency in precipitation measurements across the Alaska–Yukon borderScaffL.YangD.LiY.yanping.li@usask.caMekisE.https://orcid.org/0000-0002-2011-2400Global Institute for Water Security and School of Environment and Sustainability, University of
Saskatchewan,
Saskatoon, Saskatchewan, CanadaNational Hydrology Research Center, Environment and Climate Change Canada, Saskatoon, Saskatchewan, CanadaClimate Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, Ontario, CanadaY. Li (yanping.li@usask.ca)21December201596241724288June201516July201516November20156December2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/9/2417/2015/tc-9-2417-2015.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/9/2417/2015/tc-9-2417-2015.pdf
This study quantifies the inconsistency in gauge precipitation observations
across the border of Alaska and Yukon. It analyses the precipitation
measurements by the national standard gauges (National Weather Service (NWS) 8 in. gauge and Nipher
gauge) and the bias-corrected data to account for wind effect on the gauge
catch, wetting loss and trace events. The bias corrections show a
significant amount of errors in the gauge records due to the windy and cold
environment in the northern areas of Alaska and Yukon. Monthly corrections
increase solid precipitation by 136 % in January and 20 % for July at the
Barter Island in Alaska, and about 31 % for January and 4 % for July at
the Yukon stations. Regression analyses of the monthly precipitation data
show a stronger correlation for the warm months (mainly rainfall) than for
cold month (mainly snowfall) between the station pairs, and small changes in
the precipitation relationship due to the bias corrections. Double mass
curves also indicate changes in the cumulative precipitation over the study
periods. This change leads to a smaller and inverted precipitation gradient
across the border, representing a significant modification in the
precipitation pattern over the northern region. Overall, this study
discovers significant inconsistency in the precipitation measurements across
the USA–Canada border. This discontinuity is greater for snowfall than
for rainfall, as gauge snowfall observations have large errors in windy and
cold conditions. This result will certainly impact regional, particularly
cross-border, climate and hydrology investigations.
Study areas and locations of selected climate stations, and photos
of the national standard gauges, NWS 8 in. gauge (left) and the Nipher snow
gauge (right), respectively, for the USA and Canada.
Introduction
It is known that discontinuities in precipitation measurements may exist
across the national boundaries because of the different instruments and
observation methods used (Nitu and
Wong, 2010; Sanderson, 1975; Sevruk and Klemm, 1989; Yang et al., 2001). For
instance, the National Weather Service (NWS) 8 in. gauge is used for
precipitation measurements in the United States (USA), and the Nipher snow
gauge has been used in Canada for decades. Different instruments have also
been used in various observational networks within the same country. In the
synoptic network, the Type-B rain gauge and Nipher gauge are the standard
manual instruments for rain and snow observations in Canada
(Mekis and Vincent, 2011; Metcalfe and
Goodison, 1993), and recently the Geonor automatic gauges have been
installed.
Instruments also change over time at most operational networks, resulting in
significant breaks in data records. It has been realized that combination of
regional precipitation records from different sources may result in
inhomogeneous precipitation time series and can lead to incorrect spatial
interpretations (Yang et al., 2005). Efforts have been
reported to examine the precipitation discontinuity within a country
(Groisman and Easterling, 1994; Sanderson, 1975).
Leeper et al. (2015) found that the US Cooperative Observer Program (COOP) Network
stations reported slightly more
precipitation overall (1.5 %), with network differences varying seasonally.
The COOP gauges were sensitive to wind biases, particularly over winter when
COOP observed (10 %) less precipitation than the US Climate Reference
Network (USCRN). Conversely, wetting and evaporation losses, which dominate
in summer, were sources of bias for USCRN. Mekis and
Brown (2010) developed adjustment method to link the Nipher gauge and ruler
snowfall measurements over Canada. Yang and Simonenko (2013)
compared the measurements among six Russian Tretyakov gauges at the Valdai
experimental station and reported differences of less than 5–6 % for
the study period. These results are useful to determine the homogeneity of
precipitation data collected by a standard gauge within the national and
regional networks.
Many studies show that the national standard gauges, including the Canadian
Nipher and US 8 in. gauges, under-measure precipitation, especially for
snowfall (Goodison, 1981; Goodison et al., 1998; Yang et al., 1995, 1998a, 1999). Compatibility
analysis of precipitation measurements by various national gauges suggests
little difference (less than 5 %) for rainfall observations, but a
significant discrepancy (up to 110 %) for snowfall measurements
(Yang et al., 2001). For instance, the
experimental data from Valdai show that the US 8 in. gauge at Valdai
systematically measured 30–50 % less snow and mixed precipitation than the
Canadian Nipher gauge (Yang et al., 2001). This
difference in national gauge catch has introduced a significant
discontinuity in precipitation records across the USA–Canada border,
particularly in windy and cold regions. Differences in the snow measurements
across the USA–Canada border have also been noticed in other studies as
problematic for producing gridded products and for developing precipitation input for
basin hydrological investigations (Šeparović et
al., 2013; Zhao et al., 2010).
Station information and climate summary.
IDCountryStation nameLocation Data MeasurementAnnual means WMOperiod deviceLat (∘)Lon (∘)Altitude (m)StartEndPrecipitation gaugePrecipitationMissingMin.Max.Wind(mm)precipitationtemp.temp.speeddata (%)(∘C)(∘C)(m s-1)700860USABarter IS WSO AP70.13-143.631119781988US 8 in. unshielded1550.3-27.14.64.0719690CAKomakuk Beach ARPT69.58-140.18719781988Nipher Type-B gauge191.82.9-27.57.43.9719680CAShingle Point ARPT68.95-137.214919781988Nipher Type-B gauge3026-26.610.63.4701975USAEagle64.78-141.1626820062013US 8 in. unshielded2470.2-22.715.50.9719660CADawson Airport64.05-139.1336920062013Nipher Type-B gauge2580.6-25.815.91
Although Yang et al. (2001) compared the
relative catch of many national standard gauges, little has been done to
address the inconsistency of precipitation records across the national
borders. This is an important issue, since most regional precipitation data
and products have been compiled and derived from the combination of various
data sources, assuming these data and observations were compatible across
the borders and among the national observational networks.
Simpson et al. (2005) studied
temperature and precipitation distributions over the state of Alaska (AK) and
west Yukon (YK), and documented precipitation increase from north to south. They
also report differences in mean monthly precipitation across the
Alaska–Yukon border, i.e. about 5–15 mm in central-east Alaska and 15–40 mm
in central-west Yukon. Jones and Fahl (1994) found a weak
gradient in annual precipitation across the AK–YK border, including the
headwaters of the Yukon River. Other studies also discuss precipitation
distribution and changes over the Arctic regions
(Legates and Willmott, 1990; Serreze and Hurst, 2000; Yang et al., 2005).
The objective of this work is to examine the inconsistency in precipitation
measurements across the border between Alaska and Yukon. We analyse both
gauge-measured and bias-corrected monthly precipitation data at several
climate stations across the border, and quantify the changes in
precipitation amounts and patterns due to the bias corrections. We also
calculate the precipitation gradients across the border and discuss
precipitation distribution for the warm and cold seasons. The methods and
results of this study are useful for cold-region climate and hydrology
investigations and applications.
Study area, data and methods
The study areas include the northern and central regions of Alaska and
Yukon. We choose five climate stations across the Yukon–Alaska border,
which use the national standard gauges (NWS 8 in. gauge and the Canadian
Nipher gauge) for precipitation observations (Fig. 1). These stations can
be classified into two groups. The first group, three stations about 150 km apart,
is the northern region along the coast of the Beaufort Sea, with the Barter
Island station in Alaska and Komakuk and Shingle Point stations in Yukon.
The second group is in the central part of the region: the Eagle station in
Alaska and Dawson station in Yukon, about 130 km apart.
The three northern stations selected for this study are located north of the
Brooks Range. The approximate distances to the mountain edge are 100 km for
the Barter Island station, 90 km for Shingle Point station, and 150 km for
the Komakuk station. Both stations in Yukon are along the shoreline, and the
station in Alaska is an island site, very close to the coastline. The
altitudes of the stations range from 7 to 49 m a.s.l. According to
Manson and Solomon (2007), the summer storm tracks
are usually from the northwest, coming from the open water in the Beaufort
Sea, and are the greatest contributor to annual precipitation. The storms are
obstructed by the Brooks Range once moving inland. The weather patterns in
the surrounding of the stations might be affected by the mountains, but the
stations are not separated by the Brooks Range. Given this setting, it is
expected to see little impact of mountain range on the precipitation process
and distribution along the relatively flat coastline.
These stations have been operated by the NWS and Environment Canada (EC)
since the early 1970s. The observations have been done according to the
national standards of the USA and Canada. The detailed information for these
stations is given in Table 1, such as the location; period of measurement
used for this work; instrument types for precipitation observations; and a
climate summary for yearly temperature, precipitation, and wind speed.
Yang et al. (2005) have developed a bias-corrected daily
precipitation data set for the northern regions above 45∘ N. The
source data are acquired from the National Centers for Environmental
Information (NCEI), i.e. a global daily surface data archive for over 8000
stations around the world (https://www.ncdc.noaa.gov/data-access/quick-links#ghcn). To focus on
the high-latitude regions, a subset of the global daily data, about 4000
stations located north of 45∘ N with data records longer than 20 years
during 1973–2003, has been created. Yang et al. (2005) applied a consistent procedure derived from the WMO Solid
Precipitation Intercomparison (SPICE; Goodison et al., 1998),
using wind speed, temperature, and precipitation as inputs
(Yang et al., 1998b, 2005). They quantify the
precipitation gauge measurement biases for the wind-induced undercatch,
wetting losses, and trace amount of precipitation. For the US stations, wind
data from the standard height were reduced to the gauge level of the NWS
8 in. gauge (standard height is 1 m). Wind speeds and directions were measured at
the Canadian climatic network; the same approach was applied to estimate the
wind speed at the gauge height (standard height is 2 m) on precipitation
days. The corrections were done only for those stations with wind
observations. Unfortunately there are many stations in the USA without wind
information, and this is a challenge to gauge bias corrections.
Monthly mean precipitation at three stations during 1977–1988 (upper
panels) and corresponding monthly mean wind speed and air temperature
(bottom panels). Shadows represent the 95 % confidence interval for the
temperature and wind speed. The percentages above the bars represent the
missing data for the corresponding time step. The bold percentage is the
monthly mean, and the one in the parentheses is the maximum missing value in
the study period.
This study uses the updated (until 2013) monthly precipitation, temperature
and wind speed data from Yang et al. (2005) for the
selected AK and YK stations (Table 1). The selected data periods range from
7 to 10 years for the stations, which is considered long enough to examine
precipitation patterns in these regions. Missing records affect regional
climate data analyses. In this study, a threshold of 0 ∘C of
monthly temperature has been used to determine the cold and warm months for
snow and rain. Mixed precipitation has not been classified separately. The
frequency of missing values was calculated when the bias correction was made
in Yang et al. (2005). Any month with fewer than 20 days (∼ 30 %) of measurements is excluded from data
analysis. Statistical methods to compare the measured and corrected monthly
and yearly precipitation data across the selected border station pairs is
used to analyse these data. It also carries out regression analysis on
monthly precipitation records and calculates the cumulative precipitation
amounts to derive the double mass curves (DMCs) over the study period. The
DMC is a useful tool to evaluate the consistency of
observation records over space and time (Searcy and Hardison,
1960). Some typical issues of observations that DMCs can identify include
changes in the station location and instruments or sensors. A reference
station is needed for DMC analyses. In this study, the DMC has been applied
without a reference station to mainly detect any shifts between the observed
and corrected precipitation. Through the data analyses and comparisons with
other studies, we document the spatial and temporal variations of bias
corrections across the border stations. We also determine the precipitation
gradients across the border and examine the changes, due to the
bias corrections of the US and Canadian gauge data, in precipitation
distributions on both seasonal and yearly timescales.
Results
Based on the analyses of the measured precipitation (Pm) and corrected
precipitation (Pc) data, this section presents the results on the bias
corrections of monthly and yearly precipitation for each station, regression
and correlation of monthly precipitation data between the stations, and
cumulative precipitation via the double mass curves for the warm (monthly
temperature > 0 ∘C) and cold seasons (monthly
temperature < 0 ∘C).
Monthly data and corrections
The monthly mean precipitation and bias corrections are illustrated in
Fig. 2 for the northern group during the corresponding observation period
(Table 1). In Fig. 2, the missing data percentages are also presented for
each month. Barter Island had the lowest percentages of missing data, about
2 % as a maximum monthly mean in December. The mean missing percentages
for the Komakuk station was about 5 % (in May), with the maximum month in
July 1984 (16 %). For Shingle Point, the mean missing values were 11 %
for both April and May, with the maximum (26 %) in April 1979. Given the
small percentages of missing records, its impact is insignificant on monthly
mean and yearly precipitation calculations. Figure 2 shows that annual
precipitation cycle was centred on August, with an approximate maximum
Pm around 40 to 80 mm between August and September. This maximum was
coincident with the monthly mean maximum temperature in the area (around
10 ∘C).
For the Barter Island station in AK, the corrections were variable through
the months. The monthly corrections increased the Pm amount by 3–31 mm
for snow to 4–9 mm for rain. The relative increases were 59–136 % for snow
and 20–41 % for rain, with a monthly mean of 9 mm (or 76 %). The
relative changes were usually large for months with low Pm and small
for months with high precipitation. In other words, the monthly correction
amounts did not always match the percentage changes; i.e. a small
correction in a dry month can have a large percentage change.
It is important to note that gauge measurements at Barter showed the maximum
precipitation in August, but the peak shifted to October due to the
corrections; i.e. the mean monthly Pc in October were 98 % (about
32 mm) more than the Pm (Fig. 2). Closer examination of the monthly
precipitation time series for Barter Island (Fig. 3) indicated that, for
most of the years, October was the most significant contributor to the total
annual (23 % for Pm and 22 % for Pc). However, there were some
years in the study period with the maximum Pm in other months; for
example, the highest Pm in 1982 was in September, as documented by
Yang et al. (1998b). Climate data and analyses showed
the highest wind speed (4.5 m s-1) and cold temperature (about
-9 ∘C) for October, indicating higher undercatch by the US
standard gauge for snowfall. On the other hand, the wind speed showed the
minimum values in July and August (3.3 m s-1), coincident with the highest
temperatures (4.6 and 4 ∘C) (Fig. 2). Due to the
combination of warm temperatures and low wind speeds, the corrections for
summer months were the lowest at this station (20–27 %).
Monthly precipitation records at the Barter station during
1978–1988. The months with more than 50 mm (black line) are labelled.
For the Komakuk Beach station in Yukon, the corrections increased the
precipitation by 0.7–5.5 mm (or 14–34 %) for snow and 1–2.6 mm
(4–10 %) for rain, with a total monthly mean change of 2.6 mm (14 %)
(Fig. 2). The monthly maximum precipitation was in August, i.e. 48 and
50 mm, respectively, for the Pm and Pc. The monthly minimum
precipitation was in March, i.e. Pm= 4.2 mm and Pc= 5 mm.
For this station, the extremes remained in the same month after the bias
corrections. The wind speed had the minimum value in August (3.1 m s-1) and
September (3.2 m s-1), and maximum in December (4.3 m s-1) and January (4.7 m s-1). The temperatures
were highest in July (6.9 ∘C) and August (5.8 ∘C), and lowest in February and March (-25 ∘C). Given this climate
condition, the corrections were lower in the summer months (mean of 6 %)
and higher in winter (mean of 23 %).
The monthly corrections for the Shingle Point station in Yukon ranged from
1–7.6 mm (3–15 %) for rain to 1–8.2 mm (14–28 %) for snow, with
the monthly mean correction of 4.2 mm (14 %). The maximum precipitation
was in August, about 73–76 mm (or 20 % of the annual total) (Fig. 2). The
minimum precipitation was in March, with 9.8 mm for Pm and 11 mm for
Pc. The monthly wind speeds were generally higher in winter and lower
in summer, with the maximum in February (4 m s-1) and minimum in May (2.7 m s-1).
The temperatures had a common annual cycle with the maximum in July
(11 ∘C) and the minimum in February (-24.3 ∘C).
Because of the higher wind speeds and cold temperatures in the cold months,
the corrections were greater for the winter season.
Comparison of the catch ratio of snowfall as a function of wind
speed at gauge height for the Alter-shielded or unshielded NWS 8 in.
standard gauge and the Canadian Nipher snow gauge. DFIR is the Double Fence
Intercomparison Reference (Yang et al., 1998)
Monthly mean precipitation at two stations during 2006–2013 (upper
panels) and corresponding monthly mean wind speed and air temperature
(bottom panels). Shadows represent the 95 % confidence interval for the
temperature and wind speed. The percentages above the bars represent the
missing data for the corresponding time step. The bold percentage is the
monthly mean, and the one in the parentheses is the maximum missing value in
the study period.
It was necessary to compare the correction result across the border in order
to quantify the effect of biases in gauge observations on precipitation
analyses, such as precipitation distribution and seasonal patterns. The mean
snowfall corrections were about 96 % for Barter Island in Alaska and
around 22 % for both Shingle Point and Komakuk stations in Yukon; while
the rainfall corrections were approximately 32 % for Barter and 7 % for
the two Yukon stations. Bias corrections also demonstrated a clear shift in
the maximum precipitation timing for the Barter Island, but no change for
the Yukon stations. This remarkable contrast across the border was caused
mainly by the difference in gauge types and their catch efficiency. Many
experimental studies have shown that the Canadian Nipher snow gauge catches
more snowfall relative to the US gauge (Goodison
et al., 1998; Yang et al., 1998b). For instance, the mean catch ratios for
snowfall were about 40 and 85 % for 4 m s-1 wind speed, respectively,
for the NWS 8 in. unshielded and Nipher gauges (Fig. 4) (Yang et al., 1998b).
For the central group, the maximum and minimum Pm were in July and
March for the Eagle station, respectively (Fig. 5). The corrections did not modify the
timings of maximum and minimum amounts; they remained in July for the
maximum (Pm= 67 mm and Pc= 70 mm) and in March for the minimum
(Pm= 3 mm and Pc= 4 mm) precipitation. The correction increased
the precipitation by 0.6–1.8 mm (8–22 %) for snow and 1–3 mm
(5–10 %) for rain, with a monthly mean correction of 1.7 mm (12 %).
The annual temperature cycle for Eagle showed warmer temperatures relative
to the northern station, with the maximum of 16.2 ∘C and above
0 ∘C during April to mid-October. Eagle had lower wind speeds
around 1 m s-1 (Fig. 5).
Annual precipitations during 1978–1988 for the three stations in the
northern group across the border. The percentages above the bars represent
the missing data for the corresponding year.
Mean annual (1978–1988) measured and corrected precipitation for
cold (T < 0 ∘C) and warm (T > 0 ∘C)
months. The percentages are the changes from measured to corrected
precipitation. The approximate horizontal distance between the stations is
displayed at the bottom.
For Dawson station, precipitation was more homogeneous throughout months,
varying from 10 mm in October to 50 mm in June. Another
relative maximum occurs in January with Pm= 38 mm (Fig. 5). The
precipitation correction was small and fluctuated from 0.3 to 1 mm (or
2–4 %) for snow and 0.4–1.3 mm (3–4 %) for rain. This small
correction was due to the lower undercatch correction for the Nipher gauge,
besides the warmer temperatures and lighter winds. The temperature annual
amplitude was between 16 ∘C in July and -25 ∘C in
January, with temperatures above 0 ∘C from April to September.
Wind speeds showed a clear annual cycle with the maximum in May (1.6 m s-1)
and lighter winds in winter months, with the minimum in January (0.4 m s-1).
The temperature and wind conditions were similar between the Eagle and
Dawson stations, with mean temperature around 1 ∘C and wind speed
of 1 m s-1. The missing data percentages were also similar for Eagle and Dawson
stations: less than 3 % for most months, with the maximum of 10 % in May
2006 for Eagle and 20 % in September 2009 for Dawson. The bias corrections
were quite different, with the mean corrections of 16 % for snow and 7 %
for rain at Eagle, and about 2 and 3 % for both rain and snow at
Dawson. Overall, the correction was 4 times greater at Eagle than that at
Dawson. This discrepancy reflects again the catch difference between the US
and Canadian standard gauges.
In order to understand the effect of precipitation bias corrections on
regional climate around the AK–YK border, it was useful to examine and
compare the temperature and precipitation features between the northern and
central regions. The monthly mean temperature threshold of 0 ∘C
did not occur exactly at the same time among the two groups; the warm months
(above 0 ∘C) were between June and September in the north group
and between April and September in the central group. Although both regions
had similar mean minimum temperatures, around -24 ∘C and
-27 ∘C, the maximum temperature was considerably lower in the
north part, with the average of 8 ∘C in the north group vs.
16 ∘C for the central region. Additionally the monthly mean wind
speed was higher for the northern region, 4 vs. 1 m s-1. Therefore,
because of the colder temperatures and higher winds in the northern region,
the bias corrections were higher in the north relative to the central
region.
Yearly data and corrections
The annual Pm and Pc time series for 11 years during 1978–1988 in
the northern group is presented in Fig. 6. There were almost no missing
data for the whole period, except 3 % for 1978. At the Barter Island
station in Alaska, the yearly Pm ranged from 114 to 211 mm, with
a long-term mean of 155 mm. The mean annual corrections ranged from 67 to 138 mm,
with a long-term mean of 101 mm (or 65 %). The Pc records varied
from 181 to 343 mm. The maximum precipitation was in 1985 for both Pm
and Pc (211 and 343 mm, respectively). The minimum precipitation was
in 1983 for the Pm and Pc (114 and 181 mm,
respectively).
For Komakuk Beach station in Yukon, the Pm ranged from 103 to 306 mm,
with the missing data between 0 and 7 % among the years. The bias
corrections increased the precipitation by 13 to 45 mm (or 8–19 %). The
long-term mean was about 194 mm for Pm and 220 mm with the corrections.
The maximum precipitation occurred in 1981: 306 and 347 mm for Pm
and Pc, respectively. The minimum precipitation was in 1988 for both
the Pm and Pc: 103 and 123 mm, respectively.
For Shingle Point station in Yukon, yearly Pm varied from 126 to 551 mm
and the Pc ranged from 138 to 638 mm. The mean annual total
precipitation was about 302 mm for Pm and 341 mm after the corrections
(change of 13 %). The high and low extreme years were 1981 (Pm= 551 mm,
Pc= 638 mm) and 1988 (Pm= 126 mm, Pc= 138 mm).
Shingle station had missing data from 2 % in 1983 to 10 % in 1979.
Figure 7 displays the mean annual precipitation in cold and warm seasons for
the northern group. The gauge measurements showed annual values from 155 mm
at Barter Island and 194 mm at Komakuk to 302 mm at Shingle Point, i.e. a
strong precipitation increase from the west to the east, particularly
between Komakuk Beach and Shingle Point. However, the corrected data
(Pc) showed a different pattern (Fig. 7), i.e. higher precipitation
at Barter than Komakuk, so the gradient across the border changed the sign
and magnitude. This change was caused mainly by the high correction at the
Barter station, particularly for snowfall data during the cold months (Fig. 2).
For the central group, the annual results are shown for 8 years (2006–2013)
in Fig. 8. The Pm ranged from 66 to 391 mm at Eagle, and the bias
corrections were 5–27 mm, correspondingly, which on average increase the
total precipitation by 7 %. At Dawson, the Pm ranged from 158
to 333 mm, and the adjustments were from 4 to 10 mm, with an average
increase in yearly precipitation by 3 %. The gauge data showed a slight
increase (12 mm) of mean precipitation from west to east, i.e. slightly
higher P in Yukon relative to Alaska. This result is consistent with other
studies (Simpson
et al., 2002, 2005). The corrected data, on the other hand, suggest a
smaller gradient (1 mm) across the border (Fig. 9). This change was mainly
due to the higher corrections for the US 8 in. gauge at Eagle.
Annual precipitations during 2006–2013 for two stations in the
central part of the AK–YK border. The percentages above the bars represent
the missing data for the corresponding year.
Mean annual (2006–2013) measured and corrected precipitation for
cold (T< 0 ∘C) and warm (T> 0 ∘C)
months. The percentages are the change from measured to corrected
precipitation. The approximate horizontal distance between the stations is
displayed at the bottom.
Similar to the monthly results, the northern stations exhibited higher
yearly corrections for snowfall and rainfall measurements relative to the
central group. This was because of higher winds in the northern stations,
i.e. yearly mean wind speeds of 3.8 m s-1 in the north group and 1 m s-1 in the
central group. This windy and snowy environment in the north produced higher
wind loss for the snowfall measurements by the gauges, which were the largest
errors in precipitation records in the high latitudes
(Benning and Yang, 2005; Yang and
Ohata, 2001; Yang et al., 1998b). It is important to note that gauge-measured
and bias-corrected data showed different pattern in seasonal and
yearly precipitation in the northern region. In other words, bias
corrections of gauge measurements alter the precipitation gradient in the
northern areas; this change was mainly due to the difference in the catch
efficiency between the US and Canadian standard gauges. The corrections for
the US gauge snow measurements were much higher than the Canadian gauge,
particularly in the cold and windy coastal regions.
Regression analysis of monthly data
The scatter plots of corresponding monthly precipitation for the two
stations across the border and between the two Yukon stations in Canada are
illustrated in Fig. 10. For the cold season (Fig. 10a), the gauge data
showed more snowfall at Barter for most years. Regression analysis suggested
a weak relationship, with R2= 0.34. The corrected data showed a
similar relationship, but a shift in the regression line, indicating a
greater precipitation difference over the cold season across the border. For
the warm season (Fig. 10b), the gauge data showed higher precipitation at
the Komakuk station, and the regression suggested a much stronger
relationship. The corrected data revealed a closer relationship between
these two stations, proposing a smaller gradient for the warm months.
Scatter plots between station pairs for the measured and
corrected precipitation (mm). The red colour shows warm months and the blue
represents the cold months. (a) and (b) – Barter and Komakuk comparison across
the border; the highest corrected values for Barter (AK) are labelled with
the date to compare with Fig. 4c and d – Komakuk and Shingle Point
comparison within Canada. (e) and (f) – Eagle vs. Dawson across the border for
the central group.
The scatter plot between the two stations in the Yukon Territory showed
higher precipitation at Shingle Point for both cold and warm seasons. It
also gave another point of view about the effect of the correction in this
area. Relative to the cold months (Fig. 10c), the corrections were
smaller for the warm months (Fig. 10d), and correlation improved
(R2= 0.72–0.76). However, the relationship did not change much in both
cases between the measured and corrected data. This was because of the very small
amount of corrections for the lower wind conditions and higher catch
efficiency of the Canadian Nipher gauge.
For the central group, the scatter plot between Eagle and Dawson stations
illustrated a clear difference in precipitation amount for the cold and warm
months (Fig. 10e–f). The cold months showed more precipitation at Dawson,
particularly for the wettest events, while Eagle did not show any comparable
amount. The correlation was weak and insignificant (R2= 0.13). The
shift in the fit line between measured and corrected data was also very
small. The warm months showed low precipitation at Dawson: a different
pattern from the cold months. The regression was better, R2= 0.59
with a smaller shift due to the corrections.
Overall, we obtained consistent results among the Alaska and Yukon stations.
The correlations were higher in warm months (R2= 0.58 to 0.76) and
lower for the cold season (R2 between 0.13 and 0.52). This result may
suggest that the rainfall was more homogeneous over the regions in summer,
and greater difficulty and errors in snowfall measurements during the cold
months.
Double mass curves between station pairs. The red colour shows the
warm months, and blue represents the cold months. The top and the central
plots compare the stations for the northern group, and the bottom one is the
central station comparison across the border.
Cumulative precipitation via DMCs
The DMC plot for Barter Island and Komakuk Beach showed more Pm at
Komakuk than Barter (Fig. 11a). The bias corrections led to a shift of
the relationship with a significant increase in the total precipitation
amount at Barter. Relatively, the total cumulative precipitation for Barter
Island increased by 65 % after the correction and by 14 % at Komakuk.
The difference between the two stations at the last cumulative point
(December 1988) is 426 mm for Pm and 393 mm for Pc. This shift
represented a modification in the precipitation difference between these
stations, i.e. a change in the gradient's direction (Fig. 7).
The comparison of cumulative precipitation values between Shingle Point and
Komakuk, both in Yukon, is illustrated in Fig. 11b. Shingle Point showed
more cumulative precipitation at the end of the period (Pm= 3322 mm
vs. Pm= 2115 mm for Komakuk). Although the relationship was more
homogeneous between these stations, there was a break in the records around
1300 mm for Komakuk, maybe associated with changes in instruments or
sensors. Examination of the station history and information revealed an
anemometer issue around the critical time that was fixed by August 1980.
This may affect wind data and thus the corrected precipitation values. Both
stations showed increases in total cumulative precipitation by 13 %.
The central stations showed a greater amount of Pm in Dawson (2065 mm)
than in Eagle (1973 mm) over the study period. Bias corrections changed the
total precipitation by 3 and 7 % for Dawson and Eagle, respectively,
resulting in a shift in the DMC (Fig. 11c), particularly for the last
period of time, to 2123 mm in Dawson and to 2116 mm in Eagle. This shift
also represented a slightly smaller precipitation difference between the two
stations. During the 8 years, the cumulative difference decreased from 92
to 7.3 mm.
In summary, the DMC for measured and corrected precipitation showed that the
main change was due to the difference in their corrections (Fig. 11); the
north stations showed a greater change compared with the central group. The
Pc showed in all the cases a smaller precipitation difference between
the two countries. This smaller difference led to a decrease in the
precipitation gradient across the border. This result implies that existing
precipitation climate maps and information derived from gauge measurement
without bias corrections may overestimate the precipitation gradient in
these regions. This overestimation will affect regional climate and
hydrology analyses.
Summary and discussion
This study documents and quantifies the inconsistency in precipitation
measurements in the northern and central regions of Alaska/Yukon, with a
focus on station pairs across USA–Canada border. The monthly bias corrections
show large errors in the gauge records due to the windy and cold environment
in the northern areas of Alaska and Yukon. The corrections for gauge
undercatch increase the snowfall by 136 % in January for Barter Island
station in Alaska. For the Yukon stations, the increase is about 31 % in
January and 4 % in July. These represent an annual mean loss of 81 mm
(101 %) in snowfall and 20 mm (29 %) of rain at Barter, while at Shingle
Point and Komakuk Beach in Yukon the corrections are, on average, about 25 mm (21 %)
for snow and 8 mm (6 %) for rain. For Eagle (AK) and Dawson
(YK) stations in the central region, the bias corrections are small. The
monthly corrections range from 2 to 22 % in winter and from 3 to
10 % in summer months.
On the annual scale, Barter Island station in AK shows a yearly mean
correction of around 65 %, 5 times greater than the correction at Shingle
Point and Komakuk Beach (13 and 14 %) in Canada. In the central
region, Eagle station shows an increase by 7 %, meanwhile for Dawson the
increase is only 3 %. Thus, the bias correction for Alaska is twice that
of the Yukon stations. Relative to the northern region, these
corrections are small mainly due to warmer temperatures and lower winds in
the central region. These results clearly demonstrate that bias corrections
may affect the spatial distribution of precipitation across the border.
Regression analyses of the monthly data show small changes in the
relationship due to the bias corrections. The most evident change in the
regression is between Barter Island and Komakuk Beach for both warm and cold
seasons. The rest of the scatter plots, for Komakuk Beach–Shingle Point
and Eagle–Dawson, do not show any appreciable change as the result of the
bias corrections. There is a stronger precipitation correlation for the warm
months (mainly rainfall) than for the cold month (mainly snowfall) for all
the station pairs. The cold months seem to have greater precipitation
variability across the regions.
The double mass curve analyses demonstrate a significant change in the
precipitation accumulation and difference between the two stations across
the AK–YK border for the northern region, little changes for the two
stations in Yukon, and a smaller change in the central group. These changes,
caused by gauge catch efficiency, alter the precipitation difference,
resulting in a smaller and inverted precipitation gradient across the border
in the northern region. The DMC is a useful tool for
evaluating the consistency of observation records over space and time
(Searcy and Hardison, 1960). Although in this work the DMC has
not been constructed against a reference station, the results clearly show
some breaks on the slope and gaps in the curves, indicating changes in
precipitation relationship across the border that could be caused by any of
the two stations. This information provides the timing when significant
changes occurred in the precipitation regime. Detailed metadata and
information for the stations/networks are necessary to understand the
changes in precipitation observations and to improve the homogenization of
the precipitation records over the high latitudes.
This study shows similar monthly Pm across the north border region and
higher Pm in Yukon than Alaska over the central region. This result is
similar to other studies (Serreze and Hurst, 2000; Simpson et al., 2005). After the bias corrections,
precipitation patterns across the border changed, i.e. higher precipitation
in Barter than Komakuk, or, in other words, an inverted gradient across the
borderline. Over the central region, the measured mean annual precipitation
is slightly higher in Yukon than Alaska, which is also consistent with
Simpson et al. (2002, 2005). Our results suggest that the gradient between the
central pair of stations becomes smaller after the bias correction. This
discrepancy should be taken into account when using the precipitation data
across the national borders for regional climate and hydrology
investigations.
Missing data may affect regional precipitation analyses. In this study, we
calculated the missing data percentages for all stations during the
corresponding study periods and set up a threshold of 30 % to exclude
those months with higher missing values from monthly precipitation
calculations. We compared the precipitation amounts with and without the
application of the threshold. The results do not show any significant
changes in the differences of gauge-measured annual mean precipitation
across the border, although this filter affected annual precipitation in
certain years. For instance, the northern station pair (Barter and Komakuk
stations) has missing value of 32 % in July 1987. Calculations of yearly
precipitation for 1987 with and without this month show 16 and 10 %
difference at Komakuk and Barter Island stations, respectively. Over the
study period of 11 years, the annual mean bias correction percentages remain
the same (65 % in Barter and 13 % in Komakuk) with or without the
missing months. The mean annual decrease in bias correction amounts after
the consideration of missing data is about 1–3 % in the northern region.
This analysis suggests that the effect of missing data for our study is not
significant, particularly with the application of a 30 % missing threshold.
More efforts are needed to further examine the issues of missing records in
climate analyses.
Classification of precipitation types is the first step for the bias
corrections of gauge records. It is also important for climate change
analyses over the cold regions.
Leeper et al. (2015), in comparison of USCRN with the COOP station network
precipitation measurements, averaged the USCRN hourly temperature data
during precipitation periods into an event mean and used it to group
precipitation events into warm (mean temperature > 5 ∘C),
near-freezing (mean temperature between 0 and 5 ∘C), and freezing (mean
temperature < 0 ∘C) conditions. Yang et al. (2005)
used the daily mean air temperature to estimate precipitation types (snow,
mixed, and rain) when this information was not available for the northern
regions. In this study, monthly mean temperatures have been used to
determine the warm months (mainly for rain) and cold months (mainly for
snow). Mixed precipitation has not been classified separately. This approach
is reasonable for our analysis to focus on the inconsistency in the monthly
and yearly Pm records across the border. Data collections and analyses
on shorter timescales, such as daily or hourly steps, are expected to
produce better results, since temperatures vary throughout the days in a
month, particularly in the spring and fall seasons. Automatic sensors will
also be important to decide precipitation types at the operational and
research networks.
The bias-corrected precipitation data set developed by
Yang et al. (2005) has been used for this analysis. The
corrections have been done systematically on a daily timescale that affects
the daily Pm time series. This analysis focuses on the results of
monthly and yearly precipitation data and quantifies the changes in
precipitation pattern across the AK–YK border. Careful analyses of available
daily measured Pm and corrected Pc data are necessary, since in
the northern regions with low precipitation in winter the bias corrections
can easily increase the daily Pm by a factor of up to 4–5
(Benning and Yang, 2005; Kane and
Stuefer, 2015; Yang et al., 1998b, 2005). This means that extreme
precipitation events have been very likely and seriously underestimated by
using the gauge records without any bias corrections. The consequence is
certainly significant for climate regime and change investigations. To fill
this knowledge gap, our efforts are underway to examine the daily
corrections, particularly on the windy and heavy-precipitation days, and to
document the possible underestimation of precipitation extremes over the
large northern regions.
Automation of the meteorological observation networks and instruments has
been a trend over the past few decades around the world, including both
developed and developing nations. There is a large variety of automatic
gauges currently used for precipitation measurements at the national
networks (Nitu and Wong, 2010). These gauges differ in the
measuring system, orifice area, capacity, sensitivity, and configuration.
The variation in automatic gauges is much greater relative to the manual
standard gauges (Goodison et al., 1998; Sevruk and
Klemm, 1989). As demonstrated by Yang et al. (2001) and this study, the use of different instruments and configurations
significantly affect the accuracy and consistency of regional precipitation
data. Fortunately, the Geonor gauge has recently been chosen and used at
both the US Climate Reference Network and the Surface Weather and
Climate Network (SWCN) in Canada. This may reduce the inconsistency in
precipitation measurements across the USA–Canada border, although the double
and single Alter windshields have been installed with the Geonor gauges in
the USA and Canada, respectively.
Finally, it is important to emphasize that automatic gauges also
significantly under-catch snowfall (Wolff et al., 2015), and bias
corrections are necessary in order to obtain reliable precipitation data for
the cold regions and seasons. The WMO SPICE project aims to examine the
performance of automatic gauges and instruments for snowfall observations in
various climate conditions. It has tested many different automatic gauges,
including the Geonor gauge, at more than 20 field sites around the globe
(Nitu et al., 2012; Rasmussen et al., 2012; Wolff et al., 2015). The results of this
project will be very useful to improve precipitation data quality and
regional climate analyses, including the border regions between the USA and
Canada.
Acknowledgements
The authors gratefully acknowledge the support from the Global Institute of
Water Security at the University of Saskatchewan and Environment Canada.
Edited by: M. Wolff
ReferencesBenning, J. and Yang, D.: Adjustment of Daily Precipitation Data at Barrow
and Nome Alaska for 1995–2001, Arct. Antarct. Alp. Res., 37, 276–283,
10.1657/1523-0430(2005)037[0276:AODPDA]2.0.CO;2, 2005.Goodison, B. E.: Compatibility of Canadian snowfall and snow cover data,
Water Resour. Res., 17, 893–900, 10.1029/WR017i004p00893, 1981.
Goodison, B. E., Louie, P. Y. T., and Yang, D.: WMO solid precipitation measurement
intercomparison, WMO/TD 872, World Meteorological Organization, Geneva, Switzerland, 1998.Groisman, P. Y. and Easterling, D. R.: Variability and Trends of Total
Precipitation and Snowfall over the United States and Canada, J. Climate, 7,
184–205, 10.1175/1520-0442(1994)007<0184:VATOTP>2.0.CO;2, 1994.
Jones, S. H. and Fahl, C. B.: Magnitude and Frequency of Floods in Alaska
and Conterminous Basins of Canada, Water-Resources Investigations Report 93-4179, U.S. Geological Survey,
Anchorage, Alaska, 1994.Kane, D. L. and Stuefer, S. L.: Reflecting on the status of precipitation
data collection in Alaska: a case study, Hydrol. Res., 46, 478–493,
10.2166/nh.2014.023, 2015.
Leeper, R. D., Rennie, J., and Palecki, M. A.: Observational Perspectives
from US Climate Reference Network (USCRN) and Cooperative Observer Program
(COOP) Network: Temperature and Precipitation Comparison., J. Atmos. Ocean. Tech., 32, 703–721, 2015.Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability in
gauge-corrected, global precipitation, Int. J. Climatol., 10, 111–127,
10.1002/joc.3370100202, 1990.Manson, G. K. and Solomon, S. M.: Past and future forcing of Beaufort Sea
coastal change, Atmos. Ocean, 45, 107–122, 10.3137/ao.450204,
2007.Mekis, É. and Brown, R.: Derivation of an adjustment factor map for the
estimation of the water equivalent of snowfall from ruler measurements in
Canada, Atmos. Ocean, 48, 284–293, 10.3137/AO1104.2010, 2010.Mekis, É. and Vincent, L. A.: An Overview of the Second Generation
Adjusted Daily Precipitation Dataset for Trend Analysis in Canada,
Atmos. Ocean, 49, 163–177, 10.1080/07055900.2011.583910, 2011.
Metcalfe, J. R. and Goodison, B. E.: Correction of Canadian winter
precipitation data, in Proc. 8th Symp. on Meteorological Observations and
Instrumentation, Amer. Meteor. Soc., Anaheim, CA, 338–343, USA, 1993.
Nitu, R. and Wong, K.: CIMO Survey on national summaries of methods and instruments for
solid precipitation measurement at automatic weather stations,
World Meteorological Organization (WMO), Geneva, Switzerland, 2010.
Nitu, R., Rasmunssen, R., Baker, B., Lanzinger, E., Joe, P., Yang, D.,
Smith, C., Roulet, Y. A., Goodison, B., Liang, H., Sabatini, F.,
Kochendorfer, J., Wolff, M., Hendrikx, J., Vuerich, E., Lanza, L., Aulamo,
O., and Vuglinsky, V.: WMO intercomparison of instruments and methods for the measurement
of solid precipitation and snow on the ground: organization of the experiment, World Meteorological Organization (WMO), Brussels, Belgium, 2012.Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S.,
Fischer, A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D.,
Smith, C., Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How well are we
measuring snow? The NOAA/FAA/NCAR Winter Precipitation Test Bed,
B. Am. Meteorol. Soc., 93, 811–829, 10.1175/BAMS-D-11-00052.1, 2012.
Sanderson, M.: Notes and correspondence: A comparison of Canadian and United
States Standard Methods of Measuring Precipitation, J. Appl. Meteorol., 14,
1197–1199, 1975.
Searcy, J. and Hardison, C.: Double-Mass Curves, United States Department of
the Interior, Washington DC, USA, 1960.Šeparović, L., Alexandru, A., Laprise, R., Martynov, A., Sushama,
L., Winger, K., Tete, K., and Valin, M.: Present climate and climate change
over North America as simulated by the fifth-generation Canadian regional
climate model, Clim. Dynam., 41, 3167–3201, 10.1007/s00382-013-1737-5, 2013.Serreze, M. C. and Hurst, C. M.: Representation of mean Arctic precipitation
from NCEP-NCAR and ERA reanalyses, J. Climate, 13, 182–201,
10.1175/1520-0442(2000)013<0182:ROMAPF>2.0.CO;2,
2000.Sevruk, B. and Klemm, S.: Types of standard precipitation gauges, in
Proceedings of International Workshop on Precipitation Measurement,
WMO/IAHS/ETH, vol. 227236, St. Moritz, Switzerland, 1989. Simpson, J. J., Hufford, G. L., Fleming, M. D., Berg, J. S., and Ashton, J.
B.: Long-term climate patterns in Alaskan surface temperature and
precipitation and their biological consequences, IEEE T. Geosci. Remote, 40, 1164–1184, 10.1109/TGRS.2002.1010902,
2002.
Simpson, J. J., Hufford, G. L., Daly, C., Berg, J. S., and Fleming, M. D.:
Comparing maps of mean monthly surface temperature and precipitation for
Alaska and adjacent areas of Canada produced by two different methods,
Arctic, 58, 137–161, 2005.Wolff, M. A., Isaksen, K., Petersen-Øverleir, A., Ødemark, K., Reitan,
T., and Brækkan, R.: Derivation of a new continuous adjustment function for correcting
wind-induced loss of solid precipitation: results of a Norwegian field study,
Hydrol. Earth Syst. Sci., 19, 951–967, 10.5194/hess-19-951-2015, 2015.Yang, D. and Ohata, T.: A Bias-Corrected Siberian Regional Precipitation
Climatology, J. Hydrometeorol., 2, 122–139,
10.1175/1525-7541(2001)002<0122:ABCSRP>2.0.CO;2, 2001.Yang, D. and Simonenko, A.: Comparison of Winter Precipitation Measurements
by Six Tretyakov Gauges at the Valdai Experimental Site, Atmos. Ocean,
52, 39–53, 10.1080/07055900.2013.865156, 2013.Yang, D., Goodison, B. E., Metcalfe, J. R., Golubev, V. S., Elomaa, E.,
Gunther, T., Bates, R., Pangburn, T., Hanson, C. L., Emerson, D., Copaciu,
V., and Milkovic, J.: Accuracy of tretyakov precipitation gauge: Result of
WMO intercomparison, Hydrol. Process., 9, 877–895,
10.1002/hyp.3360090805, 1995.Yang, D., Goodison, B. E., Metcalfe, J. R., Golubev, V. S., Bates, R.,
Pangburn, T., and Hanson, C. L.: Accuracy of NWS 8′′ standard nonrecording
precipitation gauge: Results and application of WMO intercomparison, J. Atmos. Ocean. Tech., 15, 54–68,
10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2,
1998a.Yang, D., Goodison, B. E., Ishida, S., and Benson, C. S.: Adjustment of daily
precipitation data at 10 climate stations in Alaska: Application of World
Meteorological Organization intercomparison results, Water Resour. Res.,
34, 241–256, 10.1029/97WR02681, 1998b.Yang, D., Ishida, S., Goodison, B. E. and Gunther, T.: Bias correction of
daily precipitation measurements for Greenland, J. Geophys. Res., 104,
6171, 10.1029/1998JD200110, 1999.Yang, D., Goodison, B., Metcalfe, J., Louie, P., Elomaa, E., Hanson, C.,
Golubev, V., Gunther, T., Milkovic, J., and Lapin, M.: Compatibility
evaluation of national precipitation gage measurements, J. Geophys. Res.,
106, 1481–1491, 10.1029/2000JD900612, 2001.Yang, D., Kane, D., Zhang, Z., Legates, D. and Goodison, B.: Bias
corrections of long-term (1973–2004) daily precipitation data over the
northern regions, Geophys. Res. Lett., 32, L19501,
10.1029/2005GL024057, 2005.Zhao, K., Stadnyk, T., Koenig, K., and Crawford, J.: Better Precipitation
Product over the Red River Basin, B.Sc. thesis, University of Manitoba,
Winnipeg, Manitoba, Canada, available at:
http://watflood.ce.umanitoba.ca/Publication.html (last access: 18
December 2015), 2010.