ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-11-71-201957 years (1960–2017) of snow and meteorological observations from a
mid-altitude mountain site (Col de Porte, France, 1325 m of
altitude)Col de Porte snow and meteorological dataLejeuneYvesDumontMariemarie.dumont@meteo.frcol_de_porte@meteo.frhttps://orcid.org/0000-0002-4002-5873PanelJean-MichelLafaysseMatthieuLapalusPhilippeLe GacErwanLesaffreBernardMorinSamuelhttps://orcid.org/0000-0002-1781-687XUniv. Grenoble Alpes, Université de Toulouse, Météo-France,
Grenoble, France, CNRS, CNRM, Centre d'Etudes de la Neige,
Grenoble, FranceMarie Dumont (marie.dumont@meteo.fr, col_de_porte@meteo.fr)11January2019111718820July201815August20187November201821November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/11/71/2019/essd-11-71-2019.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/11/71/2019/essd-11-71-2019.pdf
In this paper, we introduce and provide access to daily (1960–2017) and
hourly (1993–2017) datasets of snow and meteorological data measured at the
Col de Porte site, 1325 m a.s.l., Chartreuse, France. Site metadata and
ancillary measurements such as soil properties and masks of the incident
solar radiation are also provided. Weekly snow profiles are made available
from September 1993 to March 2018. A detailed study of the uncertainties
originating from both measurement errors and spatial variability within the
measurement site is provided for several variables. We show that the
estimates of the ratio of diffuse-to-total shortwave broadband irradiance is
affected by an uncertainty of ±0.21 (no unit). The estimated root mean
square deviation, which mainly represents spatial variability, is ±10 cm for snow depth, ±25 kg m-2 for the water equivalent of
snow cover (SWE), and ±1 K for soil temperature (±0.4 K during
the snow season). The daily dataset can be used to quantify the effect of
climate change at this site, with a decrease of the mean snow depth
(1 December to 30 April) of 39 cm from the 1960–1990 period to the
1990–2017 period (40 % of the mean snow depth for 1960–1990) and an
increase in temperature of +0.90 K for the same periods. Finally, we show
that the daily and hourly datasets are useful and appropriate for driving and
evaluating a snowpack model over such a long period. The data are placed on
the repository of the Observatoire des Sciences de l'Univers de Grenoble
(OSUG) data centre: 10.17178/CRYOBSCLIM.CDP.2018.
Introduction
The Col de Porte (CDP) site is a mid-elevation meadow site located at 1325 m
altitude (45.30∘ N, 5.77∘ E) in the Chartreuse mountain range.
This observation site has been operated since 1959 in collaboration with
several academic and non-academic partners
(https://www.umr-cnrm.fr/spip.php?rubrique218; last access: 3 December 2018).
Daily measurements of snow depth, air temperature, and precipitation amount
have been performed since 1960. Hourly measurements of meteorological and snow
variables required to run and evaluate detailed snowpack model such as Crocus
started in 1987 and have been almost continuous
during the snow season since the snow season of 1993–1994. Measured data are
manually and automatically checked and corrected using the measurements of
several sensors and meteorological analyses (SAFRAN, ) if
required, thus ensuring the quality and continuity of the dataset.
Such a dataset provides a unique framework to drive and evaluate snowpack
models over a long period. Indeed demonstrated that the
evaluation of snowpack models can be misleading if performed over only a few
snow seasons. In recent years, such datasets with varying levels of detail
have been made public for several snow sites e.g. and
have motivated the publication of a special issue in Earth System Science Data to gather openly available detailed meteorological and
hydrological observational archives from long-term research catchments in
well-instrumented mountain regions around the world, such as the Col du Lac
Blanc dataset . This
initiative arises from a GEWEX Hydroclimatology Panel cross-cut project,
INARCH (available at: http://www.usask.ca/inarch, last access: 3 December 2018), the International Network for
Alpine Research Catchment Hydrology.
CDP is part of several observation networks at the local level (Observatoire
des Sciences de l'Univers de Grenoble, OSUG) and at the national
scale (Observation pour l'Experimentation et la Recherche en
Environnement CryObsClim and Systèmes d'Observation et
d'Expérimentation au long terme pour la Recherche en
Environnement des glaciers, GlacioClim) and contributes to OZCAR
(Observatoires de la Zone Critique: Applications et Recherches), one of the
French components of the ILTER European Research Infrastructure
(International Long-term Ecological Research Networks,
). It is also a reference station of the World
Meteorological Organization (WMO) Global Cryosphere Watch CryoNet network and
of the INARCH network. CDP snow and meteorological observations have been
selected as an indicator of climate change effects at medium elevation by the
National Climate Change Observatory
(ONERC, available at: https://www.ecologique-solidaire.gouv.fr/impacts-du-changement-climatique-montagne-et-glaciers,
last access: 3 December 2018).
The CDP dataset has been used as driving and evaluation data in several snow
model intercomparison projects: SnowMIP and
ESM-SnowMIP . CDP is also an ideal place for specific snow-related measurement campaigns, e.g. the WMO Solid Precipitation
Intercomparison Experiment
(SPICE,
available at: http://www.wmo.int/pages/prog/www/IMOP/intercomparisons/SPICE/SPICE.html, last access: 3 December 2018),
measurement of the spectral reflectance of snow , snow surface roughness , and snow in forested
areas .
Picture of the site taken on 10 March 2014 from the south barrier,
looking towards north.
The objectives of the present paper are (i) to extend the hourly dataset
published in from 1993–2011 to 1993–2017, (ii) to provide
a daily dataset over the 1960–2017 period, and (iii) to provide estimates of
the uncertainties of several variables due to both spatial variability within
the observation site and measurement uncertainties. The paper first
describes the site and the dataset. The second section is dedicated to
providing estimates of measurement uncertainties and spatial variability
within the site, and the last section describes some examples of the use of
this dataset.
Data description
The Col de Porte site (Fig. ) is a grassy
meadow surrounded by mainly coniferous (spruces) and some lobed-leaf trees.
All the instruments are located within an area of 40×50 m2
(Fig. , Tables , , ).
The height of the trees ranges from 10 to 40 m. Note that all datasets are
provided in Universal Time Coordinated (UTC).
Schematic view of the experimental sites with sensor locations. The
sensors indicated in yellow are for meteorological variables. The sensors
indicated in red are not used anymore as of 2018, and those in blue
correspond to snow measurements. Areas 23 and 24 correspond to soil
temperature and humidity measurements. The correspondence between numbering
and sensors is indicated in Tables , , and
. The three dark blue asterisks correspond to the three
hemispherical webcam locations. The dedicated experimental area has been used
for specific experiments, e.g. and .
Radiation masks
Surrounding trees and topography mask part of the shortwave radiation. Masks
were measured at location 31 (Fig. ) (corresponding to the
measurements of the incoming shortwave radiation, see Fig.
and Table ) with 5∘ resolution in azimuth for two
dates: July 1998 (using a theodolite) and June 2018 (using a compass and a
clinometer). Masks are provided as a csv file
(https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.
SolarMask); they contain three values for each azimuth that correspond to
lower elevation, upper elevation, and occultation percentage (pocc,
visually estimated), defined as follows (Fig. ). Below the
lower mask elevation, there is no direct radiation. Above the upper mask
elevation, 100 % of the direct radiation is available, and between the two,
only 100-pocc % of the direct radiation is available. These masks
are applied for the calculation of the direct and diffuse shortwave incoming
radiation as explained in Sect. . The discrepancies between
the two masks are most likely due to changes of the vegetation (growing and
major tree cutting in 1999, see ).
Masks measured at location 31 (Fig. ) on July 1998
(a) and on June 2018 (b). Upper and lower mask elevations
are represented by the coloured areas. Elevations are given in degrees, the
centre is 60∘ elevation.
Soil and vegetation properties
Soil properties were measured close to location 33 (Fig. ) on
29 September 2008, close to location 24 (Fig. ) on 2 October
2012, and close to location 30 on 18 October 2017.
On 29 September 2008, the soil properties were measured over the first metre
as illustrated by Fig. . The layering of the soil was
estimated visually and is provided in Table . The soil
properties (particle size analysis, organic matter, nitrogen, and carbon
total content) were also analysed down to 87 cm depth. The dataset is
provided as a csv file (soil_properties_2008.csv). On 2 October 2012, the
same analysis was conducted over the first 30 cm of soil at
location 24 (Fig. ) along with measurements of the dry soil
density. The dataset is provided as a csv file (soil_properties_2012.csv).
The two csv files are available as
10.17178/CRYOBSCLIM.CDP.2018.Soil.
Soil profile of 1 m depth performed close to
location 33 (Fig. ) on 29 September 2008. The visual
characterization provided in Table can be seen on this
picture.
On 18 October 2017, the soil densities were analysed for the first 30 cm. At
that time, the dry soil density was 1100±67 kg m-3 without
considering the vegetation. The wet soil density was 1475±59 kg m-3.
These values are the mean and standard deviation of two measurements
over 0–10 cm depth and two measurements at 20–30 cm depth close to
location 30 (Fig. ). No significant differences between the
two sampling depths were observed. On the same day, the vegetation's (grass)
dry and wet mass were measured on a 50 by 50 cm surface at the same location.
The measurements result in a value of 1.92 kg m-2 for wet mass and 1.54 kg m-2
for dry mass. The height of the grass (roughly 5 cm) during the
time of the measurements can be considered typical for late autumn. Note that
the grass is frequently cut during summer. These measured soil and vegetation
properties can be useful for constraining soil and vegetation schemes, which
are often coupled with snowpack models .
Visual characterization of the soil layers
corresponding to Fig. on 29 September 2008.
TopBottomVisualdepth (cm)depth (cm)texture05organic soil with grass roots518organic soil without roots1847clay and sand4770grey clay and sand7087grey clay87100pebbles and grey clay, no samplingMeteorological hourly data, 1993-2017
The meteorological hourly dataset over 1993–2017 is an extension of the
meteorological dataset provided in , in which an extensive
description of the dataset is available. Below, only changes that happened
after 2011 and additional details not provided in are
reported.
The dataset is provided as a continuous hourly dataset since 1993 so that it
can be easily used to drive snowpack models. The partitioning of the dataset
between in situ data and the output of the meteorological analysis
and downscaling tool SAFRAN is the same as in
Fig. 4 of . For years 2011 to 2015, in situ data
are restricted to the period of 20 October of one year to 10 June of the next
year. Summer in situ data are thus missing (calibration of the
sensors during summer) from 1993 to 2015. Starting on 10 June 2015, all data
are in situ year-round except for very short periods with
observation issues. An in situ flag is provided together with the
meteorological data (value=1 for in situ data).
Table provides an update of the type of sensors used for
meteorological measurements with respect to Table 1 in .
The dataset is provided in netCDF format
(https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.
MetInsitu)
in the standard format for SURFEX surface model meteorological inputs
. The atmospheric pressure value corresponds
to the mean climatological value at CDP.
Overview of the sensors used to gather the
hourly meteorological data between 1993 and 2017 at Col de Porte,
France.
The locations refer to Fig. .
∗ Height adjusted manually above snow surface
(≈ weekly). ⋄ The sensors were installed for the WMO SPICE
project and are used in this study only to complement the dataset if a
problem exists for the reference sensor. c Amount processed in non-real-time (filtered
values).
Shortwave incoming radiation
The meteorological dataset provides both total and diffuse
incoming broadband radiation at location 31 (Fig. ). The
diffuse shortwave radiation is not measured but calculated from total
shortwave and longwave incident radiation and air temperature as described in
the following.
The first step of the procedure is to compute a cloudiness value, η (no
unit, between 0 for clear sky and 1 for fully overcast), from measured air
temperature Tair (K), longwave radiation LWdown (W m-2), and
specific humidity using Eqs. () and () from
and :
LWdown=1.05εσTair4,ε=0.58+0.9k(η)+0.06eair(1-k(η)),k(η)=(0.09+0.2η)η2,
where σ is the Stefan–Boltzmann constant, and eair is the water
vapour partial pressure calculated from measured Tair and relative
humidity, expressed in hectopascals. The correction factor 1.05 in Eq.()
accounts for the additional longwave radiation that is
reaching the sensor due to the presence of surrounding trees. Equation ()
solution does not necessarily range between 0 and 1; η
must be bounded between 0 and 1 when solving the equation.
The calculated value of η is then used to partition the total measured
shortwave radiation into direct and diffuse fractions using the radiative
transfer model from and the measured mask described in Sect. .
An additional shortwave radiation sensor (Delta-T SPN1 – heated) was installed
at location 5 (Fig. ) in September 2016 (9.5 m above ground)
and measures both diffuse and total shortwave radiation over the 400–2700 nm range.
A comparison between these measured and calculated direct and diffuse
distributions is provided in Sect. .
Longwave incident radiation
The sensor for incident longwave radiation was replaced in October 2015 by a
Kipp & Zonen CGR4 sensor (location 30, Fig. ).
Figure displays the comparison of the measured incident longwave
radiation with simulated longwave radiation from SAFRAN based on monthly
averages. It shows that the deviation between SAFRAN and the measurements
displays two large breaks in October 2015 and in autumn 2010 (corresponding
to another sensor replacement, Table ). Based on the
hypothesis that the newest sensor can be used as a reference because it was
fully calibrated at the Physikalisch-Meteorologisches Observatorium (Davos,
Switzerland) outside and inside with a blackbody, the dataset was corrected
as follows: -10 W m-2 from 1993 to November 2010 and +10 W m-2 from
November 2010 to November 2015. Since SAFRAN is the only available reference
and does not account for local conditions, e.g. cloudiness, due to its coarse
spatial resolution, it is unfortunately not possible currently to investigate this instrumental bias
with more temporal refinement. This correction,
although spanning the uncertainty values provided by the manufacturer, is of
large significance for snowpack modelling, considering the high sensitivity of
the snowpack to processes governed by this variable (e.g. ). Using the Crocus snowpack
model with or without the corrections leads to a shift in the melt-out date
ranging between 5 and 10 days.
Monthly average of the difference between measured downward longwave
(LW) and SAFRAN estimates. The two vertical black lines indicate the sensor
changes (cf. Table ). The blue lines correspond to the raw
time series and the green one to the corrected time series.
Precipitation
Precipitation data are handled according to . Precipitation
data are manually partitioned between liquid and solid phases using all
relevant sources of data at the site, namely snow depth, surface albedo,
surface and air temperatures, and differences between heated and non-heated
rain gauges (locations 1 and 9, Fig. ). The precipitation
values provided in the dataset are based on the reference gauge (GEONOR) at
location 20 (Fig. ). Other OTT and GEONOR gauges are used to
complement the reference sensor measurements. Hourly solid precipitation
measurements are corrected for undercatch depending on temperature and wind
speed, as described in . From 2013 to 2017, the wind
measurement used for the correction was the one placed at
location 18 (Fig. ) instead of location
15 (Fig. ) since the ultrasonic sensor at location 18 (Fig. ) is
more accurate than the wind sensor at location 15 (Fig. ).
Note that locations 15 and 18 are very close, i.e. a few metres, so that the
wind speed values are not significantly different between the two locations.
Snow and soil data, 1993–2017
The hourly evaluation dataset over 1993–2017 is an extension of the
evaluation dataset provided in . An extensive description
of the dataset is available in the latter study. Below, only changes that
happened after 2011 and additional details not provided in
are reported. The hourly dataset is provided as a netCDF file
(https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.
HourlySnow).
Within this dataset, the soil temperature, soil humidity, and settling disk
temperature are raw measurements (uncorrected).
Table provides an update of the type of sensors used for
evaluation measurements with respect to Table 2 in .
Example of the snow profile measured on 13 January 2001, visualized using
Niviz software.
Starting in October 2010, the snow depth at location
32 (Fig. ) has been measured with a Dimetix laser ranger. The field of view is a few
millimetres in diameter and the accuracy provided by the manufacturer is ±1.5 mm.
Since October 2010, the snow depth measurement provided in the dataset
(reference snow depth) is the measurement of the Dimetix laser ranger. Data
from the other snow depth sensors and precipitation amounts are used to
correct the laser data from small artefacts.
The surface temperature reference values contained in the dataset mainly
originate from the Kipp & Zonen upward pyrgeometer
(location 25, Fig. , same sensor as
location 30, Fig. and Table ). Since
September 2010, these data have been complemented by the other surface temperature
sensors with a conical field of view shown in Table . The
reference surface temperature is bounded to 273.15 K when snow is present on
the ground.
Overview of the sensors used to gather the hourly
and daily snow and soil data between 1993 and 2017 at Col de Porte, France.
Note that outgoing shortwave and longwave radiation is measured using
instruments similar to the corresponding incoming radiation, described in
Table . Also note that snow surface temperature can be
derived from the outgoing longwave radiation sensor in addition to the
sensors presented here. The locations refer to Fig. .
a Sensor including shielding for ground-originating neutrons (reduced data scatter).
b Height adjusted manually above snow surface (≈ weekly).
c Progressive migration from mercury to solid state
electric contact. ⋄ The sensors have been installed for the WMO SPICE
project and are used in this study only to complement the dataset if a
problem exists for the reference sensor.
New sensors for soil temperature and humidity have been installed in October
2012 at several depths (-0.05, -0.1, -0.2, -0.3 m) at
location 23 (Fig. ) close (roughly 2 m) to
location 24 (Fig. ), where the older soil temperature sensors
were located. In total, for location 23 (Fig. ), three probes are
placed at 10 cm depth, roughly 10 cm away from each other. In the
following, they are referred as s1_loc23_10, s2_loc23_10, and s3_loc23_10.
At 20 cm depth, there are only two probes roughly 10 cm away from each other
that are referred as s1_loc23_20 and s2_loc23_20.
The differences between the measurements at these two locations are discussed
in Sect. . It must be underlined that the soil humidity
measurements show that the soil is almost always saturated by liquid water
when snow is present. This characteristic may not be typical for mountain
slopes e.g. and may be difficult to reproduce with
usual soil models.
Description of the daily dataset between 1960 and
2017 at Col de Porte, France. The locations refer to Fig. .
VariableLocationSensorPeriod of operationUnitDescriptionTmin12PT100... → 1993KMin temp. between 00:00 (day D) and 24:00 (day D)12cf. Table 1993 → ...KMin temp. between 06:00 (day D-1) and 06:00 (day D)Tmax12PT100... → 1993KMax temp. between 00:00 (day D) and 24:00 (day D)12cf. Table 1993 → ...KMax temp. between 06:00 (day D) and 06:00 (day D+1)snow_depth_autoclose to 33automatic sensor... → 1977–1978mSnow depth 06:00 (day D)33BEN ultrasonic depth gauge1978–1979 → 1999–2000mSnow depth 06:00 (day D)33–6cf. Table 1993 → ...mSnow depth 06:00 (day D)snow_depth_pithatchedmanual1963–1964 → 7 Feb 1996mIrregular frequencyhatchedmanual8 Feb 1996 → ...mWeeklysnow_depth_pit_northhatchedmanual2001–2002 →...mWeeklysnow_depth_pit_southhatchedmanual2001–2002 →...mWeeklyswe_auto16cf. Table 2001–2002 →...kg m-2Daily (not available for 2015–2016)swe_pithatchedmanual1963–1964 → 7 Feb 1996kg m-2Irregular frequency, SWE core 38.5 and 25. cm2hatchedmanual8 Feb 1996 → ...kg m-2Weekly, SWE core 100 cm2swe_pit_northhatchedmanual2001–2002 → ...kg m-2Weekly, SWE core 100 cm2swe_pit_southhatchedmanual2001–2002 → ...kg m-2Weekly, SWE core 100 cm2total_precipitation9cf. Table 1960–1961 → 2004–2005kg m-2Daily sum of precipitation not corrected for undercatch06:00 (day D) to 06:00 (day D+1)raina20cf. Table 1993–1994 → ...kg m-2Daily sum of corrected liquid precipitation06:00 (day D) to 06:00 (day D+1)snowa20cf. Table 1993–1994 → ...kg m-2Daily sum of corrected solid precipitation06:00 (day D) to 06:00 (day D+1)height of new snow33 and 27calculated from snow depthwhole recordcmDaily sum of new snowmeasurement and settlement disks06:00 (day D) to 06:00 (day D+1)albedo_daily26 and 31cf. Table 2005–2006 → ...NARatio of the daily sums of reflected and incidentshortwave radiationalbedo_daily_flag26 and 31NA2005–2006 → ...NANumber of hourly measurementsused to calculate daily albedo
a Note that rain and snow variables are provided
only when in situ measurements are available (i.e. in situ
flag of Table – see also Fig. 4 in
).
The measurements of the vertical profile of snowpack properties as described
in are also provided in caaml format (version 6) according
to the International Association of Cryospheric Sciences (IACS) standard
(http://caaml.org/Schemas/SnowProfileIACS/v6.0.3/index.html, last
access: 3 December 2018). They can be visualized using Niviz software
(https://niviz.org/, last access: 3 December 2018). An example is
displayed in Fig. for 13 January 2001. These profiles are
available on a weekly basis from September 1993 to March 2018
(https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.
SnowProfile).
1960–2017 data
Table describes the daily dataset that combines snow and
meteorological measurements. The dataset is provided in netCDF format
(https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.
MetSnowDaily).
Variable names correspond to the names listed in Table . Within
this daily dataset, the total precipitation dataset is not corrected for
undercatch, contrary to rain and snow datasets (starting in September 1993).
The total precipitation dataset is also not measured by the same sensor used
for the rain and snow datasets (cf. Table ). The total
precipitation dataset is measured with a PG-2000 sensor, for which the
undercatch plays a minor role compared to the GEONOR due to the collecting
surface area being 10 times larger (Table ). In addition,
the total precipitation time series may be qualified as inhomogeneous in time
due to the various changes in precipitation gauges. The daily SWE (water
equivalent of snow cover) automatic measurements (location 16,
Fig. , Table ) are discarded for snow season
2015–2016 due to a disfunction of the sensor. Also note that the daily
albedo data are uncorrected for local snow surface slope.
Comparison of different broadband diffuse-to-total shortwave
radiation ratios, r. (a) Difference in ratio estimated with the mask
measured in June 2018 and in June 1998 at location 25 (Fig. ).
Statistics are calculated during daylight from 1 September 2015 to
30 June 2017, excluding July and August for each year. (b) Difference in ratio
estimated with the 2017 mask (measured at location 5, Fig. ,
21 October 2017) and the measured ratio at location 5 (Fig. ).
Statistics are calculated during daylight from 1 September 2016 to
30 June 2017.
The hourly meteorological dataset that contains the whole SAFRAN reanalysis
at Col de Porte for the period of 1960–2017 is provided
in order to drive the snowpack simulation over the whole period. The dataset
is provided in netCDF format
(https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.
MetSafran),
which is the standard format for SURFEX meteorological inputs
. The solar mask measured in 1998
(Fig. ) is accounted for in this dataset.
Spatial variability and measurement uncertainties
The dataset presented in this study is, like any observation dataset,
affected by different sources of uncertainties. Regardless of whether these
data are used for model evaluation or process studies, characterizing their
associated uncertainties is essential for proper use of the data. The
uncertainties of the dataset may come from measurement uncertainties
(including instrumental and environmental uncertainties) but also from the
spatial variability of the variables within the measurement plot.
A lower bound of the uncertainty for each variable can be estimated from the
information provided by the sensor manufacturer. Some variables are measured
at different locations within the field sites and by different sensors. This
provides a better insight of the uncertainty associated with both sources for
each variable. already provided a first estimate of the
uncertainties associated with snow depth, the water equivalent of snow cover,
bulk density, broadband albedo, soil temperature, and snow surface
temperature. In this section, we extend the period and the number of points
used for the uncertainty evaluations for snow depth, the water equivalent of snow
cover, and soil temperature, for which several measurements are available over
a sufficiently long period. We also provide uncertainty assessments of the
direct and diffuse incident shortwave radiation estimates (cf. Sect.
for the calculation of the estimates). Note that an update on
the uncertainties for snow surface temperature and broadband albedo is not
provided in this study (lack of a sufficient number of sensors), though their
uncertainty estimates are crucial for snow model evaluation. In this respect,
we recommend the use of uncertainty values provided in
for these two variables.
Comparison of snow depth measurements at different locations. The
variable href corresponds to location 33 (Fig. ).
(a) Difference in measured snow depth between the ultrasound sensor
placed at the Nivose 1 location (Fig. ) and the reference snow depth (locations
32–33, Fig. ) in blue. The differences between the measured
snow depth at location 6 (Fig. ) and the reference snow
depth are in red. The differences are calculated from the snow season
2009–2010 to the snow season 2015–2016 using only data from 20 September to
10 June. Data where both locations indicate 0 snow depth are excluded from
the statistics. (b) Difference in measured snow depth between the
manual snow depth measurements at the snow pit field location
(Fig. ) and the automatic reference snow depth (location
33, Fig. ) in blue, the manual snow depth measurement in the
south snow pit field and the reference in grey, and the north snow pit field
and the reference in red. Difference values are calculated over the
1960–2017 period for the pit value and over the 2001–2017 period for the
north and south pits. Data where both locations indicate 0 snow depth are
excluded from the statistics. Corresponding statistics are provided in
Table .
Statistics of the comparisons between the different
snow depth measurements represented in Fig. .
A first source of uncertainty in the calculation of the distribution of the
measured broadband shortwave radiation into diffuse and direct radiation
originates from the uncertainties of the mask used for the calculation (cf.
Sect. , Fig. ). Using the methodology explained
in Sect. , we estimate the direct and diffuse shortwave
incoming radiation based on the mask from 1998 and the mask from 2018 for two
snow seasons (1 September to 30 June): 2015–2016 and 2016–2017. The mean
difference (mask measured in 2018 minus mask measured in 1998) and root mean
square deviation (RMSD) computed between diffuse components (over non-zero
values only) are -1.30 and 10.1 W m-2. The mean difference
and RMSD for the diffuse-to-total ratio are -0.02 and 0.10, respectively. The
histogram of differences is provided in Fig. a.
Comparison of SWE measurements at different locations. (a) Difference
in measured SWE between the manual measurement in the snow pit
field (Fig. ) and the automatic reference SWE
(SWEref, location 16, Fig. in blue). The
differences are calculated over the period of 2001–2017 (no reference data for 2015–2016
snow season). Data where both locations indicate 0 SWE are excluded from the
statistics. Note that the manual measurements from the south and north snow
pit fields are used for the SWE sensor (location 16, Fig. ) calibration.
(b) Difference in manually measured SWE between the south snow pit field and
the main snow pit field locations in blue, the north and south snow pit field locations in green, and the north snow pit field and the main snow pit field in red.
Differences are calculated over the 2001–2017 period. Data where both
locations indicate 0 SWE are excluded from the statistics. Numerical values
are provided in Table .
The accuracy of the methodology described in Sect. has also
been evaluated using the measurements of total and diffuse radiation from
location 5 (Fig. ) (at 10 m above ground) and the mask
measured in October 2017 at the same location. The comparison is done from
1 September 2016 to 30 June 2017 during daylight (i.e. if the total measured
shortwave is larger than 4 W m-2). The mean difference between the
estimated and simulated diffuse component is -15.26 W m-2 (RMSD:
53 W m-2). The mean difference and RMSD computed for the diffuse-to-total
ratio are -0.08 and 0.21, respectively. The histogram of differences is
provided in Fig. b. This shows that the estimation of the
diffuse radiation has a slightly negative bias and that this uncertainty has
to be taken into account for applications such as radiative balance
calculation, for which the direct and diffuse distribution has a significant
impact. It also shows that the methodology applied to partition the direct
and diffuse components has a larger impact on the uncertainty than the change
in solar masks shown in Fig. .
Statistics of the comparisons between the different
SWE measurements represented in Fig. .
Figure compares the snow depth reference value mostly
measured at locations 32 and 33 (Fig. ), href, with
several other measurements of snow depth: in panel (a) with respect to
automatic snow depth measurements at Nivose 1 and location 6 (Fig. ) and in panel (b) with respect to manual snow depth measurement in
snow pit fields (main, north, and south; blue hatched areas in Fig. ).
For panel (a), the comparison is done over the 2009–2016
period, and any blank or inconsistent measurement period in the Nivose 1
(or mast) sensor was discarded from the comparison. For panel (b), the
comparison with the main snow pit field is done over 1960–2017 and for the
south and north pits over 2001–2017. For each sensor, the number of points
used to calculate the statistics are in Table .
Comparison between the different soil temperature measurements at
10 cm (a, b) and 20 cm (c, d) depths. Panels (a) and (c) compare
the new sensors (three probes) at location 23 (Fig. ) at
10 cm underground and two probes at 20 cm underground. Panels b and d compare the average values of the new
sensors (location 23, Fig. ) to the old ones
(location 24, Fig. ). Statistics are calculated from
December 2015 to July 2017. Summer (b and d, in red) corresponds to the
periods between 20 June 2016 and 10 October 2016 as well as 20 June 2017 and 31 July 2017.
The rest of the dates correspond to the snow season (b and d, in blue).
Numerical values are provided in Table .
Figure a and Table show that the three automatic
measurements exhibit deviations lower than 1.3 cm and an RMSD lower
than 4 cm. Higher discrepancies are found between the automatic reference
measurements and the manual measurements (Fig. b), with the mean
deviation reaching almost 13 cm and RMSD 13 cm. These higher difference
values might be attributed to the local slope, aspect, and small topographic
features within the three snow pit field areas and to the higher measurement
uncertainty associated with manual measurements. Extreme difference values
correspond to the end of the snow season when the snow cover is patchy.
During the 2014–2015 snow season, installed an automatic
scanning laser 1 metre close to location 6 (Fig. ) that scanned
an area of 100–200 m2. During this snow season, the laser measurements
indicated a spatial variability of the snow depth within the footprint that
can reach 7–10 cm (RMSD). Thus, we recommend the use of a ±10 cm
uncertainty value for snow depth in any evaluation to represent the spatial
variability within the site, comparable to the values used in
.
Statistics of the comparisons between the different
soil temperature measurements represented in Fig. .
Evolution of mean snow depth, air temperature, and total
precipitation over 1960–2017. The mean and total values are calculated over
the period of 1 December to 30 April for each snow season. The black lines
are 15-year moving means. Figure adapted from
.
Water equivalent of snow cover
Figure and Table compare the SWE automatic
measurements at location 16 (Fig. ) with the manual
measurements from the main snow pit field (panel a) and the three locations
for manual SWE measurements (panel b). The statistics are calculated over the
2001–2017 period. It must be underlined that the automatic SWE sensor is
calibrated using the manual measurements at the south and north snow pit fields.
The average of the annual maximum value of SWEref during this
period is 389±104 kg m-2.
Figure and Table show that the mean difference
between the automatic and manual measurements in the main snow pit field
reaches -17 kg m-2 with an RMSD of almost 25 kg m-2. The comparison
between the three locations of manual measurements displays an RMSD reaching
25 kg m-2, i.e. 8.6 % of average peak SWE values. This value is
consistent with the spatial variability of snow depth and can probably be
used as an estimate of the uncertainty associated with the SWE dataset, both
due to measurement errors and spatial variability.
Link to the dataset repository.
DatasetPeriodFormatRepositorySolar maskJul 1998 and Jun 2018csv10.17178/CRYOBSCLIM.CDP.2018.SolarMaskSoil properties29 Sep 2008 and 2 Oct 2012csv10.17178/CRYOBSCLIM.CDP.2018.SoilHourly in situ meteorological data1 Aug 1993 to 31 Jul 2017netCDF10.17178/CRYOBSCLIM.CDP.2018.MetInsituHourly SAFRAN meteorological data1 Aug 1960 to 31 Jul 2017netCDF10.17178/CRYOBSCLIM.CDP.2018.MetSafranDaily snow and meteorological data1 Aug 1960 to 31 Jul 2017netCDF10.17178/CRYOBSCLIM.CDP.2018.MetSnowDailyHourly snow data1 Aug 1960 to 31 Jul 2017netCDF10.17178/CRYOBSCLIM.CDP.2018.HourlySnowSnow profilesSep 1993 to Mar 2018caaml10.17178/CRYOBSCLIM.CDP.2018.SnowProfileSoil temperature
Figure and Table compare the different soil
temperature measurements at 10 and 20 cm depths for
locations 23 and 24 (Fig. ). The left panels in
Fig. display the statistics of the different temperature probes at
location 23 (Fig. ), which are spaced by roughly 10 cm
(s1_loc23_10, s2_loc23_10, and s3_loc23_10 for 10 cm depths and s1_loc23_20 and
s2_loc23_20 for 20 cm depths). It indicates that the RMSD between the three probes is
lower than 0.25 K (Table ). The right panels in Fig.
compare locations 24 (Fig. ) (old sensors) and
23 (new sensors, mean) for two periods: summer (20 June to 10 October) and
the snow season (11 October to 19 June). During the snow season, the two
locations show a small mean deviation of -0.11 K and an RMSD of 0.42 K, while
during summer the mean deviation is roughly -1.06 K, leading to an RMSD of
1.10 K (Table ). Note that these two locations are spaced by only a
few metres (see Fig. ). The temperature difference between
the two sensors may be attributed to differences in soil properties, local
topography, and shading. The larger differences in summer may be due to (i) larger
heterogeneity in soil wetness and (ii) the absence of the snow cover
that spatially tempers the surface temperature signal in winter.
From these observations, a lower bound of the uncertainty of the soil
temperature measurements (spatial variability and measurements errors) is
roughly 1.10 K during summer, roughly 0.42 K during the snow season, and a
little higher than 0.5 K averaged over the whole year.
Data useTemperature, snow depth, and precipitation since 1960
Figure displays the evolution of mean snow depth, air
temperature, and total precipitation from 1 December to 30 April of each snow
season for the whole period of the dataset (December 1960–April 2017). This
figure shows an example of a direct use of the dataset to study the past
evolution of winter conditions at Col de Porte. It demonstrates that the
decrease in mean snow depth between 1960–1990 and 1990–2017 is 39 cm (40 %
of the mean snow depth for 1960–1990), while the air temperature has
increased by 0.90 ∘C over the same period, and while the total
precipitation does not exhibit a significant trend. This indicates that at
this site, the reduction of the snow cover is mainly due to the increase in
temperature and its consequences (e.g. higher snow and rain limit during
precipitation and higher melt rates). These long time series contribute to
placing long-term climate change impact studies on mountain snow conditions
in the context of past changes .
Snow model evaluation
This dataset has been widely used to drive and evaluate snow models (e.g.
). A list of the studies using the CDP dataset is
available at http://www.umr-cnrm.fr/spip.php?article533 (last access: 3 Decemebr 2018).
The database presented and described in this article is
available for download at 10.17178/CRYOBSCLIM.CDP.2018. Table provides the links to the different
datasets.
Conclusions
This paper describes and provides access to the daily snow and
meteorological dataset measured at the Col de Porte site, 1325 m a.s.l.,
Chartreuse, France, for the period of 1960–2017. The hourly dataset of snow and
meteorological observations for the period of 1993–2017 is made available along
with weekly snow profiles from September 1993 to March 2018, soil properties,
and solar radiation masks. Based on measurements at several locations within
the measurement field, we estimated the uncertainties and spatial variability
of the ratio between solar diffuse and total irradiance, snow depth, the water
equivalent of snow cover, and soil temperature. The data are placed on the
repository of the Observatoire des Sciences de l'Univers de
Grenoble (OSUG) data centre:
http://doi.osug.fr/public/CRYOBSCLIM_CDP/CRYOBSCLIM.CDP.2018.html
(last access: 3 December 2018).
YL and JMP endorse the responsibility of the
experimental site and of the instruments. MD led the consolidation of
the dataset and wrote this manuscript together with all co-authors. EL
and JMP ensure the instruments were properly working.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Hydrometeorological
data from mountain and alpine research catchments”. It is not associated with a
conference.
Acknowledgements
Many thanks are expressed to the people of CNRM and CEN who have assisted in the
collection, collation, and archiving of this unique dataset. We thank, in
particular, É. Pougatch, Y. Danielou, and J.-L. Dumas for their crucial work
on the database. Generating and distributing this dataset directly benefitted
from the LABEX OSUG@2020 (ANR10 LABX56). The authors are also thankful to
EDF-DTG, ONF, SOERE CryObsClim, GCW, and the IDEX Univ. Grenoble Alpes Cross
Disciplinary Project Trajectories. The authors also thank Arnaud Foulquier
(LECA) for his help with soil and vegetation property measurements and
Laurent Bourges (OSUG) for the establishment of the data repository and the
allocation of dedicated DOIs. The authors are grateful to R. Essery, C.
Fierz, and one anonymous referee for their useful comments on the manuscript, and to
M. Bavay and C. Fierz for their help with niViz and caaml formats.
Edited by: Danny Marks
Reviewed by: Charles Fierz, Richard L. H. Essery, and one anonymous referee
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