Geophysical Research Letters
Supporting Information for
Artificial amplification of warming trends
across the mountains of the western United States
Jared W. Oyler1,2*, Solomon Z. Dobrowski3, Ashley P. Ballantyne2,3, Anna E. Klene4, Steven W.
Running1
1Numerical Terradynamic
Simulation Group, Department of Ecosystem and Conservation Sciences, University of
Montana, 32 Campus Drive, Missoula, MT 59812, USA, 2Montana Climate Office, Montana Forest and Conservation
Experiment Station, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA, 3College of Forestry and
Conservation, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA, 4Department of Geography,
University of Montana, Stone Hall 208, Missoula, MT, 59812, USA
Contents of this file
Text S1 to S4
Figures S1 to S7
Additional Supporting Information (Files uploaded separately)
Caption for Table S1 (GEMS2014GL062803_ts01.xls)
Introduction
Supporting information includes additional details on missing observation infilling (Text S1),
the SNOTEL network (Text S2), the SNOTEL sensor bias (Text S3), gridded climate products
(Text S4), supplementary Figures S1 to S7, and a spreadsheet of SNOTEL station changes in
Utah (Table S1).
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Text S1. Missing Observation Infilling
Out of 254 496 SNOTEL station-months used in the analysis, 2 439 were missing
(0.96%). The median number of missing monthly observations at a station was 1 for both Tmin
and Tmax and there was no spatial clustering in missing values. Although this was an overall
small number of missing values, we required each SNOTEL station to be serially complete for
the analysis. To infill missing observations at a target station, we built and used models of the
relationship between the target station’s observations, and neighboring non-missing station
observations and atmospheric reanalysis fields. Details of the infilling method can be found in
Oyler et al. [2014]. One major assumption of the method is that the relationship between a
target station and neighboring stations and atmospheric reanalysis fields is stationary through
time. Given the severity of the inhomogeneities in the SNOTEL record (Fig. 2), this assumption
did not hold. For infilling missing non-homogenized Tmin observations, we found that the
mean monthly bias between estimated and observed values for non-missing months had a
systematic switch from +0.15°C at the beginning of the record to −0.15°C at the end of record.
Therefore, we used station-specific generalized additive models (GAMs) to model a station’s
systematic infill bias as a smooth function of time and subsequently remove it from the infilled
values. For consistency, we removed any time-dependent bias for both Tmin and Tmax and
non-homogenized and homogenized values. Nonetheless, removing the time-dependent bias
from the infilled missing observations had very little effect on the composite western US
SNOTEL trends (<= ±0.001°C decade−1) since missing values only represented 0.96% of all
station-months. To also assess general infill model performance, we performed a cross
validation at the SNOTEL stations with complete records (n=194) where we randomly set 2
observations to missing in each month (24 total), built the infill model, and repeated the
process 10 times at each station. The cross validation mean absolute error (MAE) for infilled
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non-homogenized values was 0.38°C (bias = +0.00°C) for Tmin and 0.45°C (bias = +0.00°C) for
Tmax. Similarly, cross validation MAE for infilled homogenized values was 0.34°C (bias =
+0.00°C) for Tmin and 0.41°C (bias = −0.00°C) for Tmax.
Text S2. SNOTEL Network Details
Initially established in the 1970s for collecting water supply-related observations in the
mountains of the western US [Julander et al., 2007], the SNOTEL network provides the bulk of
continuous mountain climate observations available for the region. The network currently has
over 700 stations in the western US and is managed by the United States Department of
Agriculture (USDA) National Resources Conservation Service (NRCS). Standard station
observations now include temperature, precipitation, snow water equivalent and snow depth.
Like most station networks, SNOTEL was never intended for detailed long-term climate
analyses.
This paragraph summarizes the history of SNOTEL temperature observations as
documented by Julander et al. [2007]. Temperature observations mainly started to come online
in the 1980s (Fig. S1). Initially, temperature sensors were poorly and inconsistently sited with
many sensors mounted on dark brown storage sheds, likely subjecting them to longwave
radiation influences. Starting in the mid-1990s and up through the mid-2000s, a field campaign
was conducted to better standardize siting and instrumentation. During the field campaign,
sensors were moved to standard meteorological towers with changes in height, sensor type,
and radiation shields. Changes at a specific site were not necessarily conducted on the same
date. Except for a subset of stations in Utah (Table S1), metadata for the SNOTEL changes is
inconsistent with many records only on paper file and difficult to access [Julander et al., 2007].
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Text S3. SNOTEL Sensor Bias
Out of the numerous station changes that occurred at the SNOTEL sites, the transition
to a new sensor type was likely the main driver of the systematic changes in observed
temperature. Applied across the entire SNOTEL network during the standardization
campaigns, the new sensor was installed to better capture extremely cold temperatures
[Julander et al., 2007]. At 4 stations in Idaho, the new and old sensor where co-located for
various time periods from 1999-2001. These co-located observations suggested that the new
sensor was warm biased relative to the old and that the bias magnitude increased at colder
temperatures [Julander et al., 2007]. To see if these observed biases were consistent with the
detected network-wide inhomogeneities, we obtained the co-located observations courtesy of
Phil Morrisey, hydrologist, USDA NRCS. Co-located observations were reported at 3-hour
intervals at Prairie (43.51°N, 115.57°W, 1463 m, 4 489 observations), Howell Canyon (42.32°N,
113.62°W, 2432 m, 4 244 observations), and Trinity Mountain (43.63°N, 115.44°W, 2368 m, 2
123 observations), and for daily Tmin and Tmax at Mountain Meadows (45.70°N, 115.23°W,
1939 m, 546 observations). A GAM fit of the sensor bias as a smooth function of temperature
suggests that the bias is strongly temperature-dependent with colder temperatures positively
biased and warmer temperatures negatively biased (Fig. S4a). Even though there are less colocated observations at warmer temperatures and at cold and warm extremes (Fig. S4a), the
model still likely provides a good preliminary estimate of the temperature-dependency of the
bias. If we apply the model to all Tmin and Tmax observations at the 482 SNOTEL stations used
in the analysis, the average estimated sensor bias for Tmin remains positive throughout the
year (winter = +1.72°C, summer = +0.83°C), but the estimated average Tmax bias switches from
positive in winter (+1.28°C) to negative in summer (0.61°C, Fig. S4b). As discussed in the main
text, these results are consistent with the differences in seasonal trends between SNOTEL and
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USHCN (Fig. S3). They not only provide strong evidence that the sensor change was likely the
main driver of the detected SNOTEL inhomogeneities, but also an explanation for why annual
Tmax trends were not as substantially affected by the station changes. Since the sensor was
installed at different years in each state with possible differences in sensor version, future work
should look to make additional network-wide co-located observations across a wide range of
temperatures to confirm and improve the sensor bias model. Such a model can likely provide a
more accurate method for homogenization compared to traditional statistical homogenization
if the date of the sensor change at a station is known.
Text S4. Gridded Climate Product Details
In the gridded dataset comparison, we used monthly minimum temperature from the
800 m TopoWx product [Oyler et al., 2014], the 1 km Daymet product [Thornton et al., 1997;
Thornton et al., 2014], and the 4 km PRISM product [Daly et al., 2008; PRISM Climate Group,
2014]. All three products use point-source weather station observations and gridded predictors
to statistically model the influence of various topoclimatic factors on temperature. TopoWx is
available from 1948-2012, Daymet from 1980-2013 and PRISM from 1895-present. The
increasing use of these products resides in the fact that their higher resolution interpolated
daily and monthly temperature grids better match the scales of local topoclimatic factors and
related environmental processes than the relatively coarse outputs of global interpolated
products and climate models, and atmospheric reanalyses. All three datasets input SNOTEL
observations, but TopoWx is the only dataset to apply the PHA homogenization procedure to
its input station data [Oyler et al., 2014]. For the dataset comparison, we downsampled the
higher resolution TopoWx and Daymet grids to match the 4 km PRISM grid. Additionally, to
focus the analysis on trend differences between the datasets and not absolute temperature
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differences, we rescaled the TopoWx and Daymet temperatures to match the 1981-2010 PRISM
normals, the most widely used dataset of the three.
While all three datasets statistically model how topoclimatic factors influence
temperature, they differ slightly in how they interpolate temperature through time. To
interpolate a daily or monthly temperature anomaly to a prediction point, PRISM takes a
weighted average of surrounding observed station anomalies. Each neighboring station is
weighted by distance and physiographic similarity to the prediction point. Thus, since SNOTEL
forms the bulk of higher elevation stations in the western US, PRISM propagates any SNOTEL
temporal patterns to the higher elevations (Fig. 3 and Fig. S6). In contrast, Daymet and TopoWx
use a regression approach to model a daily anomaly as a function of elevation and other
topoclimatic factors. The Daymet regression function not only propagates the SNOTEL
inhomogeneity to higher elevations, but also extrapolates and enhances it. (Fig. 3 and Fig. S6).
This degree of enhancement is not seen in TopoWx due to homogenization of the SNOTEL
observations. However, since homogenization is likely not effective at removing all SNOTEL
inhomogeneities due to seasonal dependencies in the SNOTEL sensor bias (Text S3, Fig. S4),
any remaining inhomogeneities in the TopoWx input station data will still be extrapolated
during interpolation. For instance, the SNOTEL Tmin inhomogeneity, while much subdued in
TopoWx, is still evident within smaller spatial extents (Fig. S7).
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Figure S1: Number of SNOTEL stations observing temperature in the conterminous western
US from 1981 to 2012.
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Figure S2: 1991-2012 composite SNOTEL and USHCN annual temperature trends and
associated standard error by state and for the entire western US. (a) Minimum temperature. (b)
Maximum temperature. Red and blue colored cells represent statistically significant (p ≤ 0.05)
positive and negative trends when accounting for temporal autocorrelation. SNOTEL-H
represents SNOTEL observations that have been homogenized. SNOTEL Minus USHCN and
SNOTEL-H Minus USHCN are trends in anomaly differences.
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Figure S3: Average 1991-2012 monthly trends in SNOTEL minus USHCN anomaly differences
for both original and homogenized SNOTEL observations. A positive (negative) trend implies
that SNOTEL showed greater (less) warming than USHCN. Error bars are the interquartile
range. (a) Minimum (Tmin) and (b) maximum (Tmax) average monthly trends for entire
western US. (c) Tmin and (d) Tmax average monthly trends for Northern Rockies of Montana.
(e) Tmin and (f) Tmax average monthly trends for Southern Rockies of Colorado.
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Figure S4: New SNOTEL sensor bias as a function of temperature. (a) New sensor bias relative
to the old as measured by 3-hour and daily minimum and maximum co-located sensor
observations (n=11 392) at 4 stations in Idaho over 1999-2001: Prairie (43.51°N, 115.57°W, 1463
m, 4 489 observations), Howell Canyon (42.32°N, 113.62°W, 2432 m, 4 244 observations),
Trinity Mountain (43.63°N, 115.44°W, 2368 m, 2 123 observations), and Mountain Meadows
(45.70°N, 115.23°W, 1939 m, 546 observations). Red line is a smooth GAM fit of the sensor bias
as a function of temperature. Data courtesy of Phil Morrisey, hydrologist, USDA NRCS. (b)
Estimated average sensor bias when model in (a) is applied to all Tmin and Tmax observations
in winter (DJF) and summer (JJA) at SNOTEL stations in Fig. 1 (n=432). Dots are the average and
colored bars are the interquartile range.
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Figure S5: Percentage of total SNOTEL changepoints detected by the homogenization
procedures by year. Bar color represents the average magnitude of detected changepoints in
each year. Changepoints are from SNOTEL stations in Fig. 1. (a) Minimum (Tmin) and (b)
maximum (Tmax) changepoints for entire western US. (c) Tmin and (d) Tmax changepoints for
Northern Rockies of Montana. (e) Tmin and (f) Tmax changepoints for Southern Rockies of
Colorado.
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Figure S6: Average differences in 1991-2012 annual minimum temperature anomalies between
gridded temperature datasets and neighboring USHCN stations by elevation band. Gray lines
and gray shaded areas are the means and interquartile ranges for original, unhomogenized
SNOTEL minus USHCN differences from Fig. 2. (a) Homogenized TopoWx dataset, (b) PRISM,
and (c) Daymet annual anomaly differences for entire western US. (d) Homogenized TopoWx
dataset, (e) PRISM, and (f) Daymet annual anomaly differences for Northern Rockies of
Montana. (g) Homogenized TopoWx dataset, (h) PRISM, and (i) Daymet annual anomaly
differences for Southern Rockies of Colorado.
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Figure S7: Same as Fig. S6 except only for the homogenized TopoWx dataset in a one-degree
by one-degree region in the Northern Rockies of Montana (46 to 47° N, 113 to 114° W)
Table S1. Documented SNOTEL station changes in Utah (GEMS2014GL062803_ts01.xls). Data
courtesy of Randall Julander, USDA NRCS.
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