GIGeoscientific Instrumentation, Methods and Data SystemsGIGeosci. Instrum. Method. Data Syst.2193-0864Copernicus PublicationsGöttingen, Germany10.5194/gi-5-219-2016A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland)EsseryRichardrichard.essery@ed.ac.ukKontuAnnahttps://orcid.org/0000-0001-6880-6260LemmetyinenJuhahttps://orcid.org/0000-0003-4434-9696DumontMariehttps://orcid.org/0000-0002-4002-5873MénardCécile B.School of GeoSciences, University of Edinburgh, Edinburgh EH9 3FE, UKArctic Research Unit, Finnish Meteorological Institute, 99600 Sodankylä, FinlandFinnish Meteorological Institute, 00101 Helsinki, FinlandMétéo-France-CNRS, CNRM-GAME UMR3589, CEN, Grenoble 38000, FranceCORES Science and Engineering Ltd, Edinburgh, UKRichard Essery (richard.essery@ed.ac.uk)16June2016512192277December201519January201612April201631May2016This 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://gi.copernicus.org/articles/5/219/2016/gi-5-219-2016.htmlThe full text article is available as a PDF file from https://gi.copernicus.org/articles/5/219/2016/gi-5-219-2016.pdf
Datasets derived from measurements at Sodankylä, Finland, for driving and
evaluating snow models are presented. This is the first time that such
complete datasets have been made available for a site in the Arctic. The
continuous October 2007–September 2014 driving data comprise all of the
meteorological variables required as inputs for physically based snow models
at hourly intervals: incoming solar and longwave radiation, snowfall and
rainfall rates, air temperature, humidity, wind speed and atmospheric
pressure. Two versions of the driving data are provided: one using radiation
and wind speed measurements made above the height of the trees around the
clearing where the evaluation data were measured and one with adjustments for
the influence of the trees on conditions close to the ground. The available
evaluation data include automatic and manual measurements of bulk snow depth
and snow water equivalent, and profiles of snow temperature, snow density and
soil temperature. A physically based snow model is driven and evaluated with
the datasets to illustrate their utility. Shading by trees is found to extend
the duration of both modelled and observed snow cover on the ground by
several days a year.
Introduction
Many studies have used meteorological data to drive snow models and
meteorological or hydrological data to evaluate model performance at
instrumented sites. These studies have often only used limited periods of
driving data (e.g. two winters for several sites in ) or
limited evaluation data (e.g. infrequent manual measurements of snow mass in
). Recently, valuable datasets have been published with
multiple years of driving data and multiple sources of evaluation data for
several snow research sites: Reynolds Mountain East in the Owyhee Mountains
of Idaho , Col de Porte in the Chartreuse Mountains of France
, the Senator Beck Basin in the San Juan Mountains of Colorado
, Snoqualmie Pass in the Cascade Range of Washington
and Weissflujoch in the Plessur Alps of Switzerland
. All of these are high-elevation, mid-latitude sites; there has
been a lack of comparable datasets that could be used for evaluating snow
models at high latitudes.
Instruments and missing data for meteorological driving variables
between 1 October 2007 and 30 September 2014.
(a) The location of FMI-ARC (dot), 90 km north of the
Arctic Cirle (dashed line) in Finland. (b) Orthophotograph of the
FMI-ARC site, showing the locations of the automatic weather station (AWS),
the radiometer tower (rad) and the intensive observation area (IOA).
(c) The automatic weather station, with the radiometer tower in the
background. (d) The radiometer tower. (e) The IOA, showing
the locations of the ultrasonic depth gauge (UDG), the snow temperature
profile and the snow pit for manual measurements.
Hourly time series of shortwave radiation, longwave radiation, air
temperature, relative humidity, wind speed and pressure. Longwave radiation
data points in red are from reanalyses.
Snow models operating on energy balance principles form components of land
surface models that are used to provide energy and moisture flux boundary
conditions for the atmosphere in numerical weather prediction and climate
models, but they can also be driven with measured meteorological data. The
typical input data required are downwelling shortwave and longwave radiation
fluxes, precipitation rate, air temperature, humidity, wind speed and
atmospheric pressure. All of these variables can be measured with low-power
instruments, but all are challenging to measure in cold and snowy
environments where instruments can be covered by snow or ice and access for
maintenance may be difficult. Model-driving data have to be continuous, so
gap filling is required if instrument or power failures occur. Data time steps
have to be somewhat shorter than a day (often 30 min or 1 h) if
situations in which snow melts during the day and refreezes at night are to
be explicitly represented.
This paper presents model-driving and evaluation datasets collated from
measurements made at the Finnish Meteorological Institute's Arctic Research
Centre (FMI-ARC) over the 7-year period starting on 1 October 2007.
Descriptions are given of the site, instrumentation, gap filling used to
construct a continuous driving dataset and adjustments of above-canopy
measurements to allow for influences of shading by trees in below-canopy
conditions. Comparisons of model simulations with evaluation data are
presented as an illustration of data use and as a quality-control check on
the data.
Site
FMI-ARC (67.368∘ N, 26.633∘ E, 179 m above sea level,
Fig. 1a) is collocated with the Sodankylä Geophysical Observatory beside
the Kitinen River, 90 km north of the Arctic Circle and 7 km southeast of the
town of Sodankylä in northern Finland. Snow typically lies from October
until May; in daily records between 1951 and 2000, the annual maximum snow
depth had a median of 83 cm, an interquartile range of 21 cm and a range
from 62 cm (1954) to 119 cm (2000). Soil frost depths can reach over 2 m
, and air temperatures can fall below -30 ∘C in
winter, but the sun only remains entirely below the horizon for a few days in
December. Continuous meteorological measurements have been made at or near
this site since 1908 . Current instrumentation includes an
automatic weather station (AWS) and an upper-air sounding station (World
Meteorological Organization index number 02836) which transmit data on the
Global Telecommunication System for use by numerical weather prediction
centres. In addition to regular measurement programmes, the Sodankylä area
has been used in many remote-sensing missions and field campaigns, including
the Nordic Snow Radar Experiment (NoSREx), the Snow Reflectance Transition
Experiment (SnoRTEx) and the Solid Precipitation Intercomparison
Experiment (SPICE).
Figure 1b is an aerial orthophotograph of the site. The area around FMI-ARC
is level and forested, predominantly with pine trees about 15 m tall, but
many measurements are made in clearings or in a large wetland area to the
east of the site. Driving data for this paper are taken from an automatic
weather station (Fig. 1c) and a radiometer tower (Fig. 1d) 30 m apart, with
instruments that are calibrated annually. Evaluation data are taken from an
intensive observation area (IOA) (Fig. 1e) that was established 590 m to the south
of the weather station for NoSREx . A list of many other
observations not discussed in this paper and contact information can be found
at http://litdb.fmi.fi/index.php.
Driving data and gap filling
All of the meteorological variables necessary for model driving are measured
by the AWS and the radiometer tower at FMI-ARC with the instruments and at
the heights listed in Table 1; note that radiation and wind measurements are
made at heights above the forest canopy. The radiometers are ventilated,
the anemometer is heated to reduce problems with freezing or snow
accumulation and instruments are cleaned after every snowfall or at least
three times a week. Temperature and humidity sensors are naturally ventilated
inside a Stevenson screen. Precipitation is measured using an optical sensor
and two weighing gauges which give similar total amounts; data from the
optical sensor are used here. There is no nearby wind speed measurement that
could be used for gauge correction, but wind speeds are generally low, and
measured snowfall has been adjusted to match snow accumulation on the ground
as described below. FMI-ARC is staffed 5 days a week, and automatic error
checking can identify instrument problems immediately. For the 7-year period
collated here, fewer than 1 % of hourly data (visible as red points in Fig. 2)
are missing for any variable with the exception of longwave
radiation; the longest period of missing data is a 52-day gap in the longwave
radiation measurements from 10 September to 31 October 2011 because of a
faulty power supply. The archived driving data files include a flag that
records which data were missing and had to be filled for each hour.
Measurements from the AWS and the radiometer tower are used for driving data
whenever they are available, but gaps have to be filled to form a complete
driving dataset. Gaps of 4 h or shorter are filled by linear interpolation.
For shortwave radiation, air temperature, humidity and wind speed, longer
gaps are filled with data from nearby instruments. No alternative longwave
radiometer was operating at FMI-ARC for the full period, so longwave gaps are
filled using ERA-Interim reanalyses . Longwave radiation
fluxes in ERA-Interim are produced by short-range forecasts that can be
expected to be accurate if the analysed vertical profiles of temperature and
humidity in the atmosphere are accurate, although errors may be larger in
cloudy conditions . Data from both the surface synoptic
station and the upper-air station at Sodankylä are available for
assimilation in reanalyses, and ERA-Interim compares well with the in situ
measurements; the longwave radiation measurements and forecasts have a
correlation coefficient of 0.88 and a root mean square difference of
26.2 W m-2 after removal of a 5.1 W m-2 bias for periods when
both are available (a scatter plot is included as the Supplement). Direct
measurements of longwave radiation are rarely available for cold regions, and
snow models are known to be sensitive to longwave driving data
; having near-continuous longwave measurements is therefore
a distinct advantage of the FMI-ARC site.
Seven-year series of gap-filled hourly data are shown in Fig. 2 for all of
the driving variables apart from precipitation. Measuring solid precipitation
is particularly challenging, and uncertainties in snowfall inputs are a major
source of uncertainty in snow model outputs . Total
precipitation is usually measured but has to be partitioned into snow and
rain for mass balance calculations, either in the driving data or by the
model. This is usually done by selecting a threshold or function of air
temperature or wet-bulb temperature discriminating between rain and snow
. Figure 3a shows the annual average snowfall
partitioned from total precipitation for Sodankylä with varying temperature
or wet-bulb temperature thresholds; the snowfall is not very sensitive to the
choice of temperature or wet-bulb temperature as a predictor because humidity
is usually high during precipitation, but it is sensitive to the choice of
threshold because a significant amount of precipitation falls at temperatures
close to 0 ∘C. With precipitation classified as snow for
temperatures lower than 2 ∘C, Fig. 3b shows that the cumulated
amount of snowfall is less than the maximum observed snow water equivalent
(SWE) on the ground in most winters but slightly greater in 2010–2011 and
2012–2013. Because the site is cold and little melting of snow occurs in
autumn or winter, the cumulated snowfall should be close to the amount of
snow on the ground at points that are not affected by canopy interception or
wind redistribution. Snowfall data are therefore scaled by the factors
required to match the maximum measured SWE each winter (Table 2); cumulated
snowfall then also matches the rate of accumulation on the ground quite well,
as shown in Fig. 3b.
Scaling factors required to match measured snowfall to measured snow
accumulation
(a) Average annual snowfall derived from total
precipitation with varying temperature (solid line) or wet-bulb temperature
(dashed line) thresholds. (b) SWE on the ground from manual
observations up to the maximum each winter (black dots), cumulated snowfall
up to the date of maximum SWE (white dots) and snowfall scaled to the annual
maxima (black lines).
The snow measurement points in the IOA (Fig. 2e) are not directly beneath
trees, so snow accumulation there will not be greatly affected by canopy
interception, but they are shaded from direct solar radiation by nearby
trees. The presence of the trees will also increase the incoming longwave
radiation and decrease the wind speed near the ground relative to more open
locations. Measurements above the forest canopy height do not take these
influences into account. To allow the use of snow models without
representations of forest canopies, radiation fluxes and wind speed are
adjusted in a modified driving dataset. From the hemispherical image of the
canopy at the IOA in Fig. 4a, the sky view fraction is estimated as fv =
0.8 and a transmissivity τ for direct solar radiation is calculated by
determining the fraction of each hour for which the sun would be blocked by
the canopy. Modified solar radiation is given by
SW′=fvSWdif+τ(SW-SWdif),
where SW and SWdif are the measured incoming global and diffuse solar
radiation . Longwave radiation is modified by assuming that the
canopy temperature can be approximated by the air temperature
so that
LW′=fvLW+(1-fv)σT4,
where LW is the measured incoming longwave radiation, σ=5.67×10-8 W m-2 K-4 is the Stefan–Boltzmann constant and T is the
air temperature in kelvin. The resulting decreases in solar radiation and
increases in longwave radiation are shown in Fig. 4b. Solar and longwave
radiation will both be underestimated by these modifications close to tree
trunks at the sun-lit northern edge of the IOA clearing where the snow is
observed to melt first.
(a) A hemispherical photograph taken close to the IOA snow depth
sensor in August 2011, showing the track of the sun (grey lines) on the first
days of February, March, April and May. (b) Measured above-canopy and
modified below-canopy daily solar (blue points) and longwave (red points)
radiation.
An anemometer installed temporarily at 2 m height above the ground close to
the IOA for 7 days in March 2012 recorded an average wind speed that was
35 % of the wind speed at 22 m height (equal to the ratio given by a
logarithmic wind profile with a roughness length of 0.55 m). This ratio is
used to scale the wind speed in the modified driving dataset. There is no
permanently installed anemometer below the canopy height at the IOA, so the
wind adjustment is highly uncertain. Because the wind is rarely strong enough
to move snow in the IOA and snowmelt is dominated by radiation in spring,
however, it is expected that models will not be highly sensitive to the wind
adjustment.
Evaluation data
Physically based snow models may include snow temperature, mass, density,
liquid water content and grain size in layers as state variables. Predicted
fluxes will include reflected shortwave radiation, emitted longwave
radiation, sensible and latent heat exchanges with the atmosphere, and
conducted heat flux and drainage of water at the base of the snowpack. Snow
properties that have to be predicted include albedo and thermal conductivity.
Measurements of any state variable, flux or property may be used as
evaluation data for models, and the measurements need not be continuous;
measured and modelled variables can be compared at whatever times for which
measurements are available.
FMI-ARC data that will be used in the model evaluation below are listed in
Table 3. Again, many more measurements are made in the IOA in addition to
those discussed here, including snow grain size, hardness, wetness,
microwave brightness temperatures and soil moisture. The microstructure of
snow samples taken during special experiments has been measured in great
detail by X-ray-computed tomography . Outgoing radiative and
turbulent flux measurements are made above the canopy height at FMI-ARC, so
they would be most useful for evaluating models that include vegetation
canopies.
Snow depth and SWE are measured in the IOA both manually about once a week
and many times daily with automatic instruments. These measurements are
compared in Fig. 5. The output of the experimental SWE sensor, which works by
measuring the attenuation of gamma radiation from a source beneath the snow,
is noisy but tracks the manual measurements well after calibration and
averaging. Snow accumulation varies spatially. Figure 6 compares the snow
depth in the IOA for the winter of 2012–2013 with snow depths measured in
the forest beside the IOA and 900 m to the northeast on the wetland. The
snow was deepest throughout the winter and melted latest in the IOA. Some
snow is intercepted by the forest canopy as it falls and can sublimate,
reducing the depth of snow on the forest floor. Wind can remove and compact
snow in the open wetland area, again reducing the snow depth. Differences in
snow accumulation and melt rates lead to differences in the persistence of
snow cover at different sites; the measured snow depth fell to zero on 3 May
2013 on the wetland, 6 May in the forest and 13 May in the IOA. Photographs
of the IOA in Fig. 7 show small-scale variations in cover as the snow melts.
Bare patches first appear around the bases of trees, and the snow lies
longest at the shady side of the clearing.
Evaluation data from the IOA.
VariableInstrumentSnow depthCampbell Scientific SR50Manual samplingSnow water equivalentAstrock Gamma Water InstrumentManual samplingSnow density profilesToikka Snow Fork sampling at 10 cm height increments from 09/10/2009Manual sampling at 5 cm height increments from 07/12/2009Snow temperature profilesCampbell Scientific 107-L at 10 cm height increments from 06/09/2011Manual sampling at 10 cm height incrementsSoil temperature profilesDecagon Devices 5TE at 5, 10, 20, 40 and 80 cm depths from 06/09/2011
Measured snow depth and SWE from manual measurements (dots) and
automatic instruments (lines). Daily averages of the automatic SWE
measurements are used to reduce noise.
Snow depths measured in the IOA (black line), in the forest (green
line) and on the wetland (blue line) for the winter of 2012–2013.
Snow melting in the IOA at noon on (a) 1 May and
(b) 13 May 2013.
Snow temperatures are measured continuously by an array of thermistors
supported on a stick that becomes buried in the snow
(http://litdb.fmi.fi/ioa0007_data.php) and intermittently by inserting
a stem thermometer into the snow face when pits are dug. Both methods are
subject to biases; it has been observed that the thermistor stick interferes
with the accumulation of snow and can form a depression up to 30 cm deep in
the snow surface, and digging a snow pit brings air into contact with snow
beneath the surface. Density is measured by weighing 250 or 500 cm3 snow
samples cut from snow pits and also by a dielectric method
that relates density and wetness to the measured permittivity of snow
. The dielectric method generally gives lower densities than
gravimetric sampling of snow at Sodankylä.
Model results
Preliminary versions of the driving and evaluation datasets were used in a
study with the Joint UK Land Environment Simulator (JULES) land surface model by . The above-canopy
and modified driving datasets are used here to drive Crocus
, which is a detailed multi-layer snowpack model originally
developed for avalanche forecasting in the French mountains. Although
physically based, some of the processes in Crocus have been parametrized
using experimental results from the mid-latitude site at Col de Porte
(45.3∘ N, 5.8∘ E, 1325 m a.s.l.), which is much warmer
than Sodankylä in winter and has heavier snowfall.
Crocus simulations with the above-canopy (red lines) and
below-canopy (blue lines) driving datasets, compared with measurements (black
dots and lines) of snow depth, SWE and soil temperature at 10 cm depth. For
clarity, only manual measurements of snow depth and SWE are shown.
Figure 8 compares Crocus simulations driven by the above-canopy and
below-canopy datasets with measurements of snow depth, SWE and soil
temperature. Simulated snow depths are generally close to the measurements
but are sometimes overestimated after snowfall because of Crocus predicting
densities for fresh snow that are lower than observed at Sodankylä.
Simulated SWE follows the measurements during the accumulation periods, which
is to be expected because of the lack of mid-winter melt and the scaling of
the snowfall in the driving data to the SWE measurements. Snowmelt starts at
about the right time each spring in the simulations but then proceeds faster
than observed. The modified driving data reduce melt rates; simulations with
the above-canopy driving data remove the snow on average 13 days earlier than
the snow disappearance dates inferred from the ultrasonic depth gauge at the
IOA, but simulations with the modified below-canopy driving data remove the
snow on average only 6 days earlier than observed. As shown by Fig. 7, the
dates of snow disappearance can differ by 2 weeks even over short distances
in reality; this spatial variability is not represented by a one-dimensional
model such as Crocus. Simulated soil temperatures have cold spikes that are
greater than observed at the start of some winters but then remain close to
0 ∘C once the snowpack has become established. Measured soil
temperatures also show a strong influence of insulation by snow but can fall
a couple of degrees lower than the simulations in late winter.
The frequent snow pit measurements in the IOA and the multi-layer outputs of
Crocus give a large amount of data for comparison. Profiles of temperature
and density for 140 snow pits dug between 7 December 2009 and 14 May 2014 are
plotted in the Supplement, but the evolution of the snowpack over the winter of
2012–2013 alone is shown in Fig. 9. Snow pits were dug once a week, usually
on Tuesday but sometimes on Wednesday or Thursday, for the 28 weeks between
31 October 2012 and 7 May 2013. Simulations and measurements both show
temperatures remaining close to 0 ∘C at the base of the snowpack
with periods of much colder temperatures in snow layers close to the surface.
The snow then rapidly warms and becomes wet and isothermal at 0 ∘C
when melt begins in spring. Density generally increases with depth in the
snowpack and with time, again increasing rapidly once the snow becomes wet.
Profiles of snow temperature and density from Crocus simulations
(background colours) and snow pit measurements (coloured dots) for the winter
of 2012–2013. Dotted lines show the measured snow depth.
Scatter plots of Crocus simulations and manual snow pit measurements
of snow temperature and density for the winter of 2012–2013.
Quantitative comparisons between simulated and measured profiles of snow
properties are complicated by differences in simulated and measured snow
depths. Simply making scatter plots (Fig. 10) of variables at the same
heights above the ground and at the same times shows strong correlations of
0.80 between simulated and measured snow temperatures and 0.74 for densities.
The simulated temperatures tend to be higher than observed for the warmer
temperatures found near the base of the snowpack.
Conclusions
Data from the FMI Arctic Research Centre at Sodankylä have been used to
construct datasets that will allow driving of snow models for multiple years
and evaluation of model outputs against multiple types of observations. There
are some gaps in the data, but the availability of additional instruments and
high-quality atmospheric reanalyses give confidence in the filling of gaps to
provide continuous driving data. The utility of the datasets has been
demonstrated by driving the Crocus snow model and evaluating its outputs
against snow depth, SWE, snow density, snow temperature and soil temperature
measurements. The physical basis of the model allows it to perform well in an
Arctic environment very different to the mid-latitude mountain environments
for which it was first developed. It is intended that Sodankylä will be
used as a reference site in an upcoming evaluation of snow simulations in
Earth System models
(http://www.climate-cryosphere.org/activities/targeted/esm-snowmip).
Under the open-data policy of the Finnish Ministry of Transport and
Communications, FMI is committed to the long-term upkeep and public
distribution of its data; the datasets used in this paper can be downloaded
from the FMI litdb archive at http://litdb.fmi.fi/ESMSnowMIP.php.
The Supplement related to this article is available online at doi:10.5194/gi-5-219-2016-supplement.
Acknowledgements
The staff at FMI-ARC are thanked for data collection and maintenance of
instruments. Collection of evaluation data in the IOA was supported by ESA
ESTEC contract 22671/09/NL/JA/ef. Visits to FMI-ARC by the first author were
supported by NERC grant NE/H008187/1 and ESA ESTEC contract 23103/09/NL/JC.
Samuel Morin and Matthieu Lafaysse assisted with the Crocus simulations. The
orthophotograph in Fig. 1b was supplied by the Finnish Geospatial Research
Institute. We are grateful to Charles Fierz and Mark Raleigh for their
helpful comments in reviewing this paper.Edited by: J. Pulliainen
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