GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus GmbHGöttingen, Germany10.5194/gmd-7-3037-2014Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation modelRäisänenP.petri.raisanen@fmi.fihttps://orcid.org/0000-0003-4466-213XLuomarantaA.JärvinenH.https://orcid.org/0000-0003-1879-6804TakalaM.JylhäK.https://orcid.org/0000-0003-0853-4747BulyginaO. N.LuojusK.https://orcid.org/0000-0002-4066-6005RiiheläA.https://orcid.org/0000-0001-6581-8792LaaksonenA.KoskinenJ.PulliainenJ.Finnish Meteorological Institute, Helsinki, FinlandDepartment of Physics, University of Helsinki, Helsinki, FinlandAll-Russian Research Institute of Hydrometeorological Information,
World Data Centre, Obninsk, Russian Federation (RIHMI-WDC), RussiaDepartment of Physics, University of Eastern Finland, Kuopio, FinlandFinnish Geodetic Institute, Masala, FinlandP. Räisänen (petri.raisanen@fmi.fi)18December2014763037305719March20145June20147November201424November2014This 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://www.geosci-model-dev.net/7/3037/2014/gmd-7-3037-2014.htmlThe full text article is available as a PDF file from https://www.geosci-model-dev.net/7/3037/2014/gmd-7-3037-2014.pdf
The timing of springtime end of snowmelt (snow-off date) in
northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation
model (GCM) is
evaluated through comparison with a snow-off date data set based on
space-borne microwave radiometer measurements and with Russian snow
course data. ECHAM5 reproduces well the observed gross geographical
pattern of snow-off dates, with earliest snow-off (in March) in the
Baltic region and latest snow-off (in June) in the Taymyr Peninsula
and in northeastern parts of the Russian Far East. The primary
biases are (1) a delayed snow-off in southeastern Siberia
(associated with too low springtime temperature and too high surface
albedo, in part due to insufficient shielding by canopy); and (2) an
early bias in the western and northern parts of northern
Eurasia. Several sensitivity experiments were conducted, where
biases in simulated atmospheric circulation were corrected through
nudging and/or the treatment of surface albedo was modified. While
this alleviated some of the model biases in snow-off dates, 2 m
temperature and surface albedo, especially the early bias in
snow-off in the western parts of northern Eurasia proved very
robust and was actually larger in the nudged runs.
A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to
the treatment of the surface energy budget. On one hand, the surface
temperature Ts is not computed separately for the
snow-covered and snow-free parts of the grid cells, which prevents
Ts from rising above 0 ∘C before all snow
has vanished. Consequently, too much of the surface net radiation is
consumed in melting snow and too little in heating the air. On the
other hand, ECHAM5 does not include a canopy layer. Thus, while the
albedo reduction due to canopy is accounted for, the shielding of
snow on ground by the overlying canopy is not considered, which
leaves too much solar radiation available for melting snow.
Introduction
Snow cover is one of the most important elements in the climate and hydrology
of the Northern Hemisphere. Large areas of the Eurasian and North American
continents are covered by seasonal snow. The varying snow cover affects
directly the surface energy balance by interfering with the energy storage,
net radiation and fluxes of sensible and latent heat. A significant positive
feedback mechanism of the snow, albedo and solar radiation amplifies the
climatic effects related to the snow cover: decreasing snow cover reduces the
surface albedo and increases the amount of absorbed solar radiation at the
surface, leading to increased melting and further reduction in the snow
cover. The snow–albedo feedback (SAF) is largest when changes in snow cover area
are linked with substantial changes in regional albedo . This
coincides with the maximum influence of snow cover on surface net radiation
in spring, typically in April and May, when strong solar radiation and snow
cover co-exist . Snow cover also serves as a fresh water
reservoir, thus regulating run-off in winter and spring, and influencing soil
moisture content. Typically, delayed snowmelt can increase spring and summer
soil moisture content which can further contribute to cooler and wetter
weather conditions even after the snowmelt , and conversely
for early snowmelt .
The key climatic role of snow cover has prompted a wide range of
observational and modelling studies on the topic. These include several
intercomparisons of snow conditions simulated by atmospheric and fully
coupled general circulation models (GCMs) with observational data
. Most
recently, evaluated the snow cover simulated by models
participating in Phase 5 of the Coupled Model Intercomparison Project
(CMIP5). In terms of the multi-model average, the models reproduced the
observed snow cover extent very well, with a slight tendency toward too late
snowmelt in Eurasia and too early snowmelt in northern North America.
However, there was still substantial inter-model dispersion around the
multi-model average. Moreover, the results highlighted two issues already
found in earlier intercomparison studies. First, the interannual variability
in Northern Hemisphere snow cover extent was underestimated by almost all
models, which was already noted by in an analysis of
Atmospheric Model Intercomparison Project, phase 1 (AMIP1) models. Second,
the models underestimated considerably the observed negative trend in snow
cover in spring (for years 1979–2005), which is similar to the findings of
for CMIP3 models. further demonstrated,
for a subset of eight CMIP5 models, that the models failed to capture the
rapid decline in Northern Hemisphere late spring (May–June) snow cover
observed in 2008–2012.
Regarding the reasons for biases in modelled snow conditions, the
intercomparison studies have, in general, not been very conclusive. Most
attention has been paid to biases in simulated air temperature
and total precipitation or snowfall
. further suggested that the
exclusion of subgrid-scale treatments for terrain and land cover contributed
to overestimated ablation rate of snow in spring over North America in AMIP2
models.
Multi-model intercomparisons have also demonstrated that the strength of
SAF varies substantially among both CMIP3
and CMIP5 models . There is
a strong correspondence between the SAF evaluated based on transient climate
change experiments and based on the seasonal cycle. Model results for the
seasonal SAF fall on both sides of the corresponding observational estimates
. The simulated SAF is strongly influenced by
the climatological surface albedo of snow-covered land, which shows
a surprisingly large spread even among the CMIP5 models. Presumably, this is
related to how vegetation masking of snow-covered land is treated
.
The focus of the current work is narrower than in the multi-model
intercomparisons discussed above, which, however, allows for more in-depth
analysis. We look in detail at the performance of a single model, the ECHAM5
atmospheric GCM , in simulating the timing of
snowmelt in spring in northern Eurasia, north of latitude 55∘ N.
Specifically, we focus on the average timing of the end of the snowmelt
season (i.e. the snow-off date; the day when all snow accumulated during the
winter has vanished). Snow-off dates simulated by ECHAM5 are compared with
snow-off dates derived from two observational data sets: first, a satellite
data set based on data from passive multichannel microwave radiometers
, and second, Russian in situ snow course measurements
. The geographical focus on northern Eurasia is motivated
by the vast area of the continent, which makes Eurasian snow conditions
important for understanding the planetary climate as a whole.
The performance of a slightly earlier version of ECHAM5 in simulating
the Northern Hemisphere snow depth, snow-covered area and surface
albedo was assessed by . By using snow products based
on visible and microwave remote-sensing data, they found that ECHAM5
reproduces the amplitude and phase of the annual snow depth cycle
quite precisely – however, with a slight overestimation of the snow
depth in late winter and spring over Eurasia. The present work builds
on but goes deeper in analysing the regional details
and causes underlying the biases in modelled snow-off dates. Thus,
while it is shown that in ECHAM5 simulations, snow-off tends to occur
too late in the eastern part of northern Eurasia (especially
southeastern Siberia) and too early in the western and northern parts,
the most fundamental issue is that snow-off occurs at
lower-than-observed air temperatures. The likely main reason for this
are simplifications inherent to the model's surface energy budget
calculation in the presence of partial snow cover and in the treatment
of forest canopy. This highlights the need to consider carefully the
treatment of the surface energy budget in the models, in addition to
the fidelity of simulated temperature and precipitation fields.
The rest of this paper is organized as follows. First, in
Sect. we introduce the ECHAM5 model and the
experiments conducted. In Sect. , the observational
data sets used in this work are described.
Section addresses the non-trivial issue of the
definition of snow-off dates. Results are reported in
Sect. , both for the default version of ECHAM5 and
for sensitivity experiments, in which biases in simulated atmospheric
circulation were corrected through nudging and/or the treatment of
surface albedo was modified. The reasons underlying the biases in
modelled snow-off dates are further discussed in
Sect. , followed by conclusions in
Sect. .
Model and experimentsModel description
Version 5.4 of the ECHAM5 atmospheric general circulation model
was used. The dynamical part of ECHAM5 is
formulated in spherical harmonics, while physical parameterizations are
computed in grid point space. The simulations reported were conducted at
horizontal resolution T63 (corresponding to a grid spacing of 1.875∘)
with 31 layers in the vertical and model top at 10 hPa.
A semi-implicit time integration scheme is used for model dynamics with
a time step of 12 min. Model physical parameterizations
are invoked at every time step, except for radiation, which is computed once
in 2 hours.
The snow scheme in ECHAM5 is relatively simple: the snow water equivalent
(SWE; kgm-2) is a prognostic quantity, but changes in snow
density or grain size are not considered. In the presence of snow, the top of
the snow layer is treated as the top of the soil model. For snow-free and
snow-covered land alike, the surface temperature is determined through the
surface energy balance, while the thermal diffusion equation is used to
calculate the soil (or snow) temperature profile. Five layers within the
topmost 10 m are considered, with thicknesses of 0.065, 0.254, 0.913,
2.902 and 5.700 m, respectively. For snow-free land, spatially
varying volumetric heat capacity and thermal diffusivity are prescribed for
five soil types according to the FAO soil map . For
snow-covered land the procedure is the same except that the thermal
properties are modified. For example, if snow fills the top soil layer
completely, and the second layer partially, the thermal properties of snow
are used for the top layer while a mass-weighted mixture of soil and snow
properties is used for the second layer. A constant snow density of
330 kgm-3 is assumed in this procedure.
The ECHAM5 snow scheme considers both SWE intercepted by the canopy
and SWE on the ground . The budget
equation for snow on the ground accounts for snowfall through the
canopy, sublimation/deposition, melting, and unloading of snow
from the canopy due to wind. The snowmelt rate M is computed from
the surface energy budget equation:
CL∂Ts∂t=Rnet+H+LE+G-M,
where CL is the heat capacity of the surface layer,
Ts the surface temperature, Rnet the surface
net radiation, H the sensible heat flux, LE the latent heat flux,
and G the ground heat flux (all defined positive when the surface
layer gains energy). A preliminary estimate for Ts at the
next time step (T∗) is obtained by considering everything else
but snowmelt (M=0). If T∗ exceeds the melting point (T∗>T0=0∘C), the snowmelt rate is inferred from the
condition that the heat consumed in melting snow restores
Ts to T0:
M=CLLfT∗-T0Δt,
where Lf is the latent heat of fusion and Δt the model
time step.
The snow cover fraction on the ground (SCF) is diagnosed following
:
SCF=0.95tanh100hsn1000hsn1000hsn+0.15σz+ϵ,
where hsn is SWE expressed in metres of liquid water, σz
(m) is the subgrid-scale standard deviation of surface elevation and
ϵ is a small number used to avoid division by zero for totally flat
and snow-free grid cells.
The parameterized grid-mean surface albedo depends on the specified
background albedo, the fractional forest area of the grid cell, the snow
cover on the canopy, the snow cover on the ground, and a specified snow
albedo. While a complete description of the parameterization can be found in
, two details are mentioned here to provide a background
for the sensitivity tests in Sect. . First, the albedo of snow
on land (αsn) depends on the surface temperature
Ts according to
αsn=αsn, min+αsn, max-αsn, minf(Ts),
where
f(Ts)=minmaxT0-TsT0-Td,0,1
and αsn, min=0.3, αsn, max=0.8,
T0=0∘C and Td=-5∘C. Second, the albedo of
snow-covered forests is parameterized according to
αfor=SVFαg+(1-SVF)αcan,
where αg is the ground albedo (αg=αsn if the ground is completely snow covered),
αcan is the albedo of the canopy (0.2 for completely
snow-covered canopy) and the sky view factor (SVF) depends on the leaf-area
index (LAI):
SVF=e-LAI.
Experiments
A total of six ECHAM5 experiments were conducted. All experiments were run
for years 1978–2006, and years 1979–2006 were used for analysis of the
results. Note that the years 2008–2012 during which a rapid reduction in
Northern Hemisphere May–June snow cover has been observed
fall outside this period. All simulations used observed sea surface
temperatures (SSTs) and sea ice , and some of them used nudging
fields and/or observed albedo fields that likewise included “real”
year-to-year variations (see below). The concentrations of well-mixed
greenhouse gases were held constant following AMIP II guidelines
, at 348 ppmv for CO2, 1650 ppbv for
CH4, 306 ppbv for N2O, 280 pptv for CFC-11,
and 484 pptv for CFC-12. For aerosols, a climatological distribution
was assumed . The distribution of ozone, vegetation area and
LAI followed a prescribed climatological seasonal cycle.
Three of the experiments (REF, ALB1 and ALB2) were run in an ordinary climate
simulation mode. In the remaining three experiments (REF_NDG, ALB1_NDG and
ALB2_NDG), four model fields were nudged towards ERA-Interim reanalysis data
: vorticity (relaxation timescale 6 h), divergence
(48 h), atmospheric temperature (24 h) and logarithm of
surface pressure (24 h). Nudging acts to minimize the errors in
simulated atmospheric circulation, which is one of the possible causes for
differences between simulated and observed snow-off dates.
REF and REF_NDG
The reference experiment (REF) and the corresponding nudged experiment
(REF_NDG) used the default version of ECHAM5.4. To evaluate the impact of
model internal variability on the results, three runs were conducted for the
REF experiment. The runs were started from different initial dates (1, 2 and
3 January 1978, respectively), which is sufficient for ensuring that within
a few weeks, the weather conditions in the three runs become essentially
independent of each other. Where not otherwise stated, the mean value of
these three runs is reported. REF_NDG, as well as ALB1, ALB1_NDG, ALB2 and
ALB2_NDG consist of a single run for years 1978–2006.
ALB1 and ALB1_NDG
Surface albedo influences strongly the energy available for melting
snow in spring. In an attempt to eliminate errors in surface albedo,
in the experiments ALB1 and ALB1_NDG the model's albedo field
over continents was replaced by prescribed surface albedos based on
observations. Monthly mean albedos in the CLARA-SAL data set derived
from AVHRR satellite data were applied. Since
this data set starts from year 1982, for years 1978–1981 the average
annual cycle of CLARA-SAL albedo for years 1982–2006 was
employed. While this approach is instructive for diagnostic purposes,
it has the major weakness that the albedo is independent of simulated
land-surface properties, including snow cover.
ALB2 and ALB2_NDG
In an attempt to reduce biases in ECHAM5's surface albedo field
while keeping it interactive, experiments ALB2 and ALB2_NDG were
conducted. Two modifications were implemented in ECHAM5's
surface albedo parameterization. First, for snow-covered forests, the
sky-view factor in Eq. () was replaced by
SVF=e-LAI+SAI.
Here, the stem area index (SAI) assumes a constant value of 2 for all
forest types, following the Biosphere–Atmosphere Transfer Scheme
. This modification was motivated by
, who noted that ECHAM5 overestimates the total
surface albedo in eastern Siberia in the dormancy season of deciduous
needleleaf trees, and ascribed this problem to the fact that the
shadowing of the ground below the canopy by stems and branches is
neglected. Second, the value of αsn, min in
Eq. () was increased from 0.3 to 0.6. This was
motivated by the findings of and ,
who note that for ECHAM5's snow albedo parameterization, and also for
ECHAM4 for which αsn, min=0.4, snow albedo decreases
too early and too fast during snowmelt.
Observational data
Seven observational data sets were used in the present work. First,
a snow-off date data set based on remote sensing of snow with
space-borne microwave radiometer measurements was
used for evaluating snow-off dates in the ECHAM5 simulations. The
Eurasian region is well suited for remote sensing of snowmelt for two
reasons. First, temperatures in much of the Eurasian region are very
low in winter-time, which leads to the formation of a dry snow
pack. Second, as tundra is the predominant surface type, the snow
conditions are relatively homogeneous over extended areas in the absence
of e.g. mountain regions with a complicated topography. These
properties are profitable for microwave instruments that measure
highly contrasting surface brightness temperatures for dry vs. melting
snow related to the progression of spring.
The remote-sensing data set utilized measurements by the Scanning Multichannel
Microwave Radiometer SMMR; onboard Nimbus 7 for years
1978–1987 and measurements by the Special Sensor Microwave/Imager (SSM/I)
onboard the Defence Meteorological Satellite Program
(DMSP) satellites D-11 and D-13 for years 1988–2007. A time series
thresholding algorithm based on the brightness temperature difference
between vertically polarized radiances around 37 and 19 GHz was used
to determine the snow-off date for each year (see for
details). The snow-off dates (given as day-of-year from 1 to 180) are
provided at a nominal resolution of 25km×25km.
The snow-off date estimates in the microwave data set were calibrated
against the INTAS-SCCONE observations of
snow depth and snowmelt flag at Eurasian, mostly Russian, weather
stations. Specifically, for the calibration data, the snow-off date
was defined as the last event during spring when the station snow
status flag changed from “snow depth is correct” to “temporary
melting” or “continuous melting”, both of which refer to a situation
in which there is no snow left at the station. Thus, in principle, the
microwave data set is targeted at presenting the final snow-off date at each
station. This is discussed further in Sect. .
Second, snow course measurements made in Russia (or the former Soviet
Union) were used for evaluating both the simulated snow-off dates and
the seasonal cycle of SWE. These data were
acquired from the Russian Hydrometeorological Centre;
http://meteo.ru/english/climate/snow1.php.
The “routine snow surveys” data set contains data from 517
meteorological stations (288 within the region considered here), for
which either open-terrain
The term “open-terrain snow courses” is
used here instead of the term “field snow courses” used in
. These refer to non-forested snow courses
in general, some of which are above (or north of) the treeline.
or forest snow course measurements (or both) have
been performed. These are a subset of the 958 stations considered in
.
The SWE was measured at 100 m intervals along the forest snow courses, which
had a total length of 1 km, and at 200 m intervals along the
open-terrain snow courses with a total length of 2 km. Typically,
measurements are provided at 10-day intervals in winter and 5-day intervals
in spring (starting from March or April). The data availability varies,
however, and not all stations provide data throughout the period 1979–2006
considered here. To compare with ECHAM5, the SWE values were regridded to the
T63 grid, by averaging the SWE values over the stations if several stations
existed in a grid cell. The procedure for estimating the snow-off date from
the snow course data is described in the Appendix. We include in our analysis
those grid cells for which the snow-off date could be determined for at least
5 years during 1979–2006.
Third, for surface albedo, we employ the monthly mean version of the
CLARA-SAL data set , which is based on a homogenized AVHRR
radiance time series. These data provide black-sky albedo values from
January 1982 onwards. The data, originally given at a 0.25∘×0.25∘ resolution, were averaged to the T63 grid for comparison with
modelled values, and for use as input for the ALB1 and ALB1_NDG experiments
(Sect. ).
Fourth, for snow cover fraction, we use version 2.0 of the snow extent (SE)
data set created in the European Space Agency's (ESA) Data User Element
project GlobSnow . The GlobSnow SE is based on data
acquired by the ERS-2/ATRS-2 and Envisat/AATSR satellite instruments, and is
provided on a 0.01∘×0.01∘ grid. Here, monthly mean data
averaged to the T63 grid are used. The years for which there is springtime
snow cover data both for GlobSnow and the current ECHAM5 experiments are
1997–2006, but 2002 was discarded due to issues with data quantity and
quality. While longer-term snow cover data sets exist ,
GlobSnow was selected for the present study because its retrieval algorithm
was specifically designed to enable accurate snow mapping also in forests,
which cover a large part of northern Eurasia.
Fifth, information on forest cover from the GlobCover 2009 data set
is used, along with the GlobSnow snow cover data,
to aid the interpretation of the differences between modelled and observed
albedo fields.
Sixth, for 2 m air temperature, Climate Research Unit (CRU) land surface air
temperature data, version 3 CRUTEM3; is employed.
Seventh, daily measurements of snow depth and diurnal-mean temperature
conducted at the Finnish Meteorological Institute Arctic Research Centre at
Sodankylä (67.37∘ N, 26.63∘ E, 179 ma.s.l.) in
January–June 1979–2006 are employed for a detailed comparison with ECHAM5
experiments in Sect. . The Sodankylä site belongs to the
northern boreal forest zone with the snow type of taiga, which is typical of
most of northern Eurasia.
Definition of snow-off date
Snow-off date is evaluated in ECHAM5 based on daily mean SWE values. There
are several possible methods for defining the snow-off date, the most obvious
ones being (1) the first snow-off date (i.e. the first day with zero SWE
after a winter's SWE maximum) and (2) the final snow-off date (i.e. the day
following the last day with SWE>0 in spring). In some cases, the
first and final snow-off dates differ substantially. As an example,
Fig. shows the time series of SWE for spring 1988 for
a grid point in western Russia (60.6∘ N, 39.4∘ E) in one of
the REF runs. The first snow-off date is day 99 (8 April), but three separate
short periods with snow occur after it, the final snow-off date being day 129
(8 May).
In this paper, we use the first snow-off date for ECHAM5 because it is a more
robust indicator of model behaviour than the final snow-off date. The first
snow-off date represents an integral measure of how much snow accumulates
during the winter and how fast it melts in the spring. In contrast, when the
final snow-off date differs from the first snow-off date, it is, in essence,
determined by the last occurrence of solid or mixed-phase precipitation in
spring. This makes the final snow-off date much more sensitive to day-to-day
weather patterns in spring than the first snow-off date.
Time series of snow water equivalent (kgm-2) in
days 0–150 of year 1988 for a grid cell in western Russia
(60.6∘ N, 39.4∘ E) for one of the ECHAM5 runs
included in the REF experiment (SWE plotted in a square root scale
for a better viewing of small values). The grey horizontal lines
correspond to SWE values of 100,
10, 1 and 0.1 kgm-2.
The four arrows at days 99 (8 April), 110 (19 April), 121 (30 April)
and 129 (8 May) indicate possible snow-off days (first day with
SWE=0 after a period with SWE>0). The first snow-off
day is employed in this paper for comparison with observational
data.
Even when setting aside potential issues related to spatial and temporal
resolution, the definition of snow-off date in ECHAM5 results is not fully
compatible with how the snow-off date is derived from the microwave satellite
data. As noted in Sect. , the satellite snow-off date
represents, in principle, the final snow-off date rather than the first
snow-off date; that is, it can be affected by secondary periods of snow after
the first snow-off date. Nevertheless, the use of final snow-off date in
ECHAM5 for comparison with the satellite data would be problematic. The
secondary periods of snow after the first snow-off date in ECHAM5 are often
short and the values of SWE very low (e.g.
SWE∼0.1kgm-2 for the last two periods of snow in
Fig. ) so it is unclear whether they would really be
detected by the satellite algorithm. Thus, we opt to use the first snow-off
date for ECHAM5, but acknowledge that this may contribute towards an early
bias in snow-off dates when compared with the satellite data.
In the comparisons with the snow course data, the snow-off date in ECHAM5 is
evaluated as the first snow-off date, but using SWE for only those days for
which snow course measurements are available (i.e. every 5th or 10th day).
This is fully consistent with how the snow-off date is derived from the snow
course data (see the Appendix).
The relationship between time-mean snow-off dates based on
the snow course data and the satellite retrievals. The satellite
snow-off dates were averaged to the T63 horizontal resolution and
screened according to the availability of snow course data. Only
those grid cells for which the snow-off date in the snow course data
could be determined for at least 5 years during 1979–2006 are
included. The data points are colour-coded according to longitude.
The solid diagonal line indicates equal snow-off dates for the two
data sets, while the dashed diagonals correspond to a difference of
±10 days.
Figure compares time-average snow-off dates
derived from the snow course data and the satellite data, for each ECHAM5
grid cell separately. While these estimates are, of course, strongly
correlated (r= 0.775), there is an appreciable scatter among them. For
some grid cells, the difference between satellite and snow-survey snow-off
dates is more negative than -10 days, and for many more grid cells
(especially in Siberia, in particular between 100 and 120∘ E) more
positive than 10 days. The mean difference between the satellite and snow
survey snow-off dates is 5.1 days, while the rms difference is 12.2 days. The
positive mean difference is, in principle, consistent with the notion that
the satellite snow-off date may be in some cases influenced by secondary
periods of snow after the first snow-off date; however, the substantial
scatter indicates that there must be other factors at play. Unravelling the
causes of these differences falls beyond the scope of this paper. Rather, we
focus on what can be concluded from the model behaviour, given the
observational uncertainty.
Mean snow-off date in years 1979–2006 based on (a)
the satellite retrievals and (b) the REF experiment. Unit:
day of year (Julian day). Snow-off dates of 90, 120 and 150
corresponding approximately to the beginning of April, May and June
are indicated with black lines. (c) The difference in the
average snow-off date between the REF experiment and the satellite
retrievals. For computing the difference, the satellite snow-off
dates were averaged to the model grid. (d) The standard
deviation (σn-1) in 28-year mean snow-off date among the
n=3 differently initialized runs included in the REF experiment.
(e) The standard deviation of yearly snow-off dates in the
satellite retrievals (for snow-off dates averaged to the model grid), and
(f) in the REF experiment (computed
first separately for the three runs in REF and then averaged).
ResultsReference experiment REFSnow-off timing
The geographical distribution of the mean snow-off date during the period
1979–2006 in the satellite retrievals is shown in Fig. a.
In general, springtime snow-off progresses gradually from the southwestern
parts of the domain towards the northern and eastern parts. Earliest snow-off
occurs in the Baltic Sea area (around 20∘ E), before day 90 (end of
March). An area of rather early snow-off dates can also be found in eastern
Siberia where around the latitude 60∘ N snow melts right after day
120 (beginning of May). Snow melts latest in the Taymyr Peninsula (around
75∘ N, 100∘ E), after day 170 (about 20 June). Snow also
persists until June in parts of Russian Far East (east of 160∘ E).
In addition to the general southwest-to-northeast gradient, some orographic
effects can be detected. In the Ural Mountains (60∘ E) and in the
Scandinavian (about 20∘ E) and Verkhoyansk (130∘ E)
mountain ranges, snow melts later than in the surrounding regions, by up to
30 days in the Ural region. Although mountainous areas are problematic to
handle in algorithms based on microwave radiometer data , these features are expected on physical grounds: colder
temperatures and orographically enhanced precipitation favour later snowmelt.
The REF experiment (Fig. b) reproduces well the general
pattern of snow-off dates seen in the satellite data, the snow-off being
latest in the Taymyr Peninsula (between days 150 and 160) and earliest in the
Baltic Sea region (around day 80). However, in most of northern Eurasia, snow melts earlier in the model results than in the satellite retrievals
(Fig. c). The difference to the satellite retrievals is
mainly 5–20 days but locally exceeds 20 days in northern Europe. In
contrast, in eastern Siberia and in some far eastern parts of Russia, snow melts locally over 10 days later in REF than in the satellite data. The
orographic effects seen in Fig. a are absent in the model
results, presumably because the model resolution (T63) is too coarse for
describing them.
Figure d displays the standard deviation in the 28-year
mean (1979–2006) snow-off date among the three runs included in the REF
experiment. For most of northern Eurasia, the standard deviation is less than
2 days, with larger values mainly confined to the southwestern part of the
domain and the Scandinavian coastline. In general, the standard deviation is
much smaller than the respective differences between REF and the satellite
data. This provides a justification for including only a single model run in
the sensitivity experiments. Finally, Fig. e and f show
the interannual standard deviation of snow-off dates for the satellite
retrievals averaged to the model grid and for the REF simulation,
respectively. Overall, the magnitude and the geographic pattern of the
standard deviation are similar for the model results and for the
observations, typical values ranging from 5–6 days in central and eastern
Siberia to ∼20 days in the Baltic Sea region. Naturally, there are some
differences in the details, such as, for example, a smaller standard
deviation of snow-off dates in REF than in the satellite data set in western
Siberia.
The difference in the average snow-off date for years
1979–2006 between the REF experiment and Russian snow course data
for (a) all snow courses, (b) open-terrain snow courses,
and (c) forest snow courses. Only those grid cells with
snow-off data for at least 5 years are included.
Figure a compares the snow-off dates in the REF experiment
with those derived from the snow course data. The general tendency towards
too early snow-off dates in the west (about 30–90∘ E) and too late
snow-off dates in the east in REF as compared with the snow course data is in
qualitative agreement with the corresponding comparison with satellite data
(Fig. c). However, the positive differences in the east,
indicating delayed snow-off in ECHAM5, are more widespread and more
pronounced than those in Fig. c, exceeding 20 days at some
locations. Figure b and c show a similar comparison as
Fig. a, but separately for open-terrain and forest snow
courses. It is seen that particularly in the west, the model snow-off dates
are rather close to those derived from the open-terrain snow courses, the
differences being only slightly negative, and in some cases slightly
positive. In contrast, a comparison with the forest snow courses west of
90∘ E shows a persistent negative bias, indicating too early snowmelt in the model. The more negative differences for the forest snow courses
than for the open-terrain courses indicate that snow tends to persist longer
in forests than on open ground. For those grid cells (located mainly in
western Russia) that have both forest and open-terrain courses, the snow
clearance occurs on average 10.5 days later for the forest courses. In
ECHAM5, however, neither snow-off dates nor SWE values are defined separately
for the forested and non-forested parts of a grid cell.
The later snow-off for forests is consistent with the findings of
for locations with cold winters
(December–January–February (DJF) mean temperatures colder than
-6 ∘C, which applies to most of northern Eurasia). However, the
opposite behaviour (earlier snow-off in forests than on open ground) was
observed in climates with warm winters (DJF mean
temperature >-1 ∘C). In general, several factors influence the
relative timing of snow-off in forests and on open ground
e.g.. During the accumulation season, the
interception and subsequent sublimation of canopy snow reduces accumulation
of snow in forests, while wind-blown snow from open areas may be deposited
around forest edges, thus increasing the snow depth. In spring, less solar
radiation is available for melting the snow under a forest canopy than on
open ground, but increased downwelling long-wave radiation may partly
compensate for this.
Differences in 2 m air temperature [K] for years 1979–2006
between the REF experiment and the CRUTEM3 data set for the months of
March, April, May and June.
(a, c, e, g) Differences in surface albedo for years
1982–2006 between the REF experiment and the CLARA-SAL data set for the
months of March, April, May, and June.
(b, d, f, h) Corresponding differences in snow cover fraction for years 1997–2006
(excluding 2002) between the REF experiment and the GlobSnow data set.
The coloured contours (magenta = 0.1; orange = 0.5; blue = 0.8; and
violet = 0.9) indicate the snow cover fraction in REF.
Other snow-related quantities
To set the stage for further discussion, 2 m air temperature (T2),
surface albedo, SCF and SWE are
considered. Figure shows a comparison of T2 in REF
and in the CRU data for the extended spring season (March through
June). A cold bias prevails through most of the spring and peaks at
-7 K in southeastern Siberia in April. Positive temperature
biases occur in the Taymyr region (throughout the spring) and in the
Russian Far East (mainly in March and April).
The left half of Fig. displays a comparison of surface
albedo in the REF experiment with the CLARA-SAL data set. Two pronounced
biases appear. First, in agreement with , a positive bias
prevails in the central and eastern parts of Siberia for much of the spring,
especially in March and April. Second, a negative albedo bias occurs in the
northernmost parts of northern Eurasia (especially in the Taymyr region) in
May and June, and in northern Fennoscandia especially in April. Some
understanding of the albedo biases can be gained by considering the snow
cover fraction along with forest fraction and LAI.
The right half of Fig. shows monthly mean SCF differences
between the REF simulation and the GlobSnow data set for the years 1997–2006,
excluding 2002. Although this period is shorter than the period 1982–2006
used for the albedo comparison, the REF vs. CLARA-SAL albedo differences for
these two periods are very similar, with monthly spatial correlations of
0.98–0.99. Interestingly, ECHAM5 underestimates SCF compared to GlobSnow
almost throughout northern Eurasia, with the exception of parts of
southeast Siberia in May, where snow-off is delayed in the REF simulation.
During March and April, the GlobSnow SCF is very high (0.99–1) through much
of the central and northern parts of northern Eurasia. For ECHAM5, SCF is
typically 0.90–0.95 in non-mountainous regions, but locally only
≈ 0.75–0.8 in the Verkhoyansk range in eastern Siberia, where SWE
is relatively low (60–80 kgm-2) and subgrid orographic
variability is fairly large, σz≈250m (see
Eq. ). The largest negative SCF differences to GlobSnow
occur, however, in the snowmelt season, in April and May in the western parts
of northern Eurasia and in June in the Taymyr peninsula, consistent with the
too early snow-off in these regions. The small negative SCF biases that
appear in June in southern and western parts of northern Eurasia in
Fig. h are, however, artifacts related to clouds
misinterpreted as snow in the GlobSnow data set.
The impact of SCF biases on surface albedo is best discernible in tundra
(i.e. forest-free) regions (see Fig. a, b for the
distribution of forests). In particular, the strong negative albedo bias in
June in the Taymyr peninsula in Fig. g is related to
insufficient snow cover in the REF simulation. The negative albedo bias in
the northernmost parts of Fennoscandia and Russia in May can also be partly
ascribed to underestimated SCF. However, especially in the Taymyr peninsula,
the albedo bias (≈-0.24, averaged over land grid points north of
72.5∘ N) is larger than the SCF bias (≈-0.12). Very likely,
this is related to the unrealistically low value (0.3) assumed for the albedo
of “warm” snow (Ts≥0∘C).
(a) Forest fraction in the GlobCover 2009 land cover map
(computed as the sum of land cover classes 40, 50, 60, 70, 90 and 100).
(b) Forest fraction assumed in the ECHAM5 simulations.
(c) Leaf area index assumed in the ECHAM5 simulations, averaged over
March and April.
Mean annual cycle of SWE according to the snow course
measurements (solid line), in the REF experiment (dashed line) and
in the REF_NDG experiment (dotted line) for (a) all snow
courses for the whole northern Eurasian domain, (b) for
western Russia (55–70∘ N, 30–70∘ E) and
(c) for eastern Siberia (55–70∘ N,
100–140∘ E). (d–f) as (a–c) but
including only open-terrain snow courses. (g–i) as
(a–c) but including only forest snow courses. Only those
ECHAM5 grid cells with snow course data are included in the
domain-mean values. For clarity, results for the ALB1, ALB2,
ALB1_NDG and ALB2_NDG experiments are not shown. In general,
albedo changes had little effect on SWE, except for the snowmelt
season.
The positive albedo bias that prevails in central and eastern Siberia (and to
a lesser extent, in parts of western Russia) in March and April is related to
the treatment of forests. Indeed, the regions with most pronounced positive
albedo bias are associated with a high forest fraction (locally higher than
0.9) in the GlobCover 2009 data set (Fig. a). In ECHAM5, the
forest fraction is somewhat smaller, typically ≈ 0.5–0.6. This
difference should be interpreted with caution, however, as the dominant
GlobCover land cover class in forested parts of Siberia is “open
needle-leaved deciduous or evergreen forest”, which has a canopy coverage of
15–40 % when viewed from directly above. The reason why the albedo bias is
especially large in central and eastern Siberia is related to the LAI. There,
the LAI in ECHAM5 is very low in the dormancy season of deciduous needleleaf
trees, including March and April (Fig. c). When only the
leaves (and not the stems and branches) are considered in the computation of
the sky-view factor (Eq. ), this results in very little shading
of the snow surface by the forest. Therefore, as previously discussed by
, the albedo is overestimated substantially.
Figure shows the average annual cycle of SWE in the REF
experiment and in the snow course measurements, for the entire northern
Eurasia and for two subregions denoted as western Russia (55–70∘ N,
30–70∘ E) and eastern Siberia (55–70∘ N,
100–140∘ E). Note that grid cells without snow course data are not
included in the averages, and therefore, for example, the average over the
entire northern Eurasia gives more weight to the western and southern parts
of the region than its eastern and northern parts, especially when
considering open-terrain snow courses. With this caveat in mind, we note that
the domain-mean annual cycle of SWE over the entire northern Eurasia in REF
agrees well with the snow course data, although the maximum is slightly
higher and occurs 5–10 days earlier than observed (Fig. a).
There are, however, regional differences. For western Russia
(Fig. b), the simulated maximum SWE is very close to that
observed, but SWE starts to decrease earlier than observed in the spring, in
agreement with the too early snow-off days in Figs. c and
a. In contrast, for eastern Siberia, the REF experiment
overestimates substantially the accumulation of snow during winter
(Fig. c), and the timing of maximum SWE and snowmelt is
delayed, which is again consistent with Fig. a.
When considering the open-terrain snow courses only, the simulated SWE
maximum is higher than observed for all three regions
(Fig. d–f), and the overestimate is especially
pronounced for eastern Siberia. In contrast, when compared with the
forest snow courses, the simulated maximum SWE is slightly too low for
the entire northern Eurasia (Fig. g) and for western
Russia (Fig. h) and only moderately overestimated for
eastern Siberia (Fig. i).
The more positive ECHAM5 vs. observation differences for open-terrain
than forest snow courses suggest that in reality
(but not in ECHAM5), more snow accumulates in forests than on open
ground. We verified that this also holds true when the comparison is
restricted to grid cells and years with both forest and open-terrain
observations. It is worth noting that often the opposite has been reported
(though mainly for sites at lower latitudes): less accumulation in
forests due to sublimation of intercepted snow or due to
midwinter melt induced by the larger downwelling long-wave flux in forests
.
The delayed snow-off in the REF experiment in central and eastern
Siberia is physically consistent with the low-temperature bias and
high-albedo bias in spring. On one hand, overestimated surface albedo
keeps the absorbed solar radiation low, which favours cold
temperatures and delays the onset of snowmelt. On the other hand,
delayed snowmelt provides a positive feedback by keeping the albedo
high. Furthermore, too large accumulation of snow in winter
contributes to the delayed snow-off in eastern Siberia
(Fig. c). Similarly, underestimated albedo and
overestimated T2 in spring in the Taymyr region are consistent with
the snow vanishing too early. For western Russia, however, the main
reason for the earlier than observed snow-off dates
(Figs. c and a) seems to be that
at least in a domain-average sense, snowmelt starts somewhat too
early (Fig. b). Intriguingly, this occurs in spite of
a slightly negative temperature bias in spring (Fig. ).
Sensitivity experiments
The sensitivity experiments show that both nudging and changes in the
treatment of surface albedo have substantial impacts on the snow-off date
simulated by ECHAM5 (Fig. ). Nudging makes snow-off occur
earlier in most of northern Eurasia, with largest effect (over 15 days) in
southeastern Siberia and locally in Fennoscandia. The earlier snow-off in
REF_NDG is both due to higher temperatures (as discussed below) and due to
slightly reduced snowfall in eastern Siberia, as reflected in the seasonal
cycle of SWE in Fig. c, f and i. However, in the Taymyr region,
snow-off is delayed by more than 5 days in REF_NDG as compared with REF
(Fig. a). Use of observed (CLARA-SAL) albedo in ALB1
likewise makes the snowmelt earlier in southeastern Siberia and later in the
Taymyr region, with larger impact in the latter (ALB1–REF differences of
≈-5 days and ≈15 days, respectively;
Fig. b). In general, snow-off is delayed somewhat in the
northern parts of northern Eurasia, and also in central Russia. For the ALB2
experiment with changed albedo parameterization, snow-off occurs up to 5 days
earlier in southeastern Siberia than in REF (Fig. c). This
is very similar to the ALB1 experiment, and results from the modification of
the sky-view factor in the calculation of surface albedo in forested regions.
However, due to the increase of the albedo of “warm” snow (Ts≥0∘C) from 0.3 to 0.6, snow-off is delayed in the
northeastern parts of the Russian Far East and particularly in the
Taymyr region, locally by 5–10 days. This response is qualitatively similar
but somewhat weaker than that in ALB1. Finally, when nudging is combined with
changed treatment of albedo (ALB1_NDG and ALB2_NDG;
Fig. c and e), the earlier snow-off in southeastern
Siberia and delayed snow-off in the Taymyr region become even more
pronounced. In southeastern Siberia, the difference to REF reaches locally
-20 days.
Figures and compare the
snow-off dates in all ECHAM5 experiments with the snow-off dates
derived from microwave satellite data and Russian snow course data,
respectively. In spite of the inter-experiment differences noted
above, all free-running (i.e. non-nudged) simulations show the same
basic pattern of differences compared to the satellite data
(Fig. ): too early snow-off dates in the west,
along with regions of delayed snow-off in eastern parts of northern
Eurasia. The ALB1 and ALB2 experiments show some improvement in
southeastern Siberia, where the positive bias in snow-off date is
reduced but not eliminated. Furthermore, the negative bias in the
Taymyr region is reduced in the ALB2 experiment with changed snow
albedo parameterization, and turned into a slight positive bias in
ALB1, which uses observation-based CLARA-SAL albedo data.
Differences in average snow-off date between the five
sensitivity experiments (REF_NDG, ALB1, ALB1_NDG, ALB2 and
ALB2_NDG) and the REF experiment.
Differences in average snow-off date between the six ECHAM5
experiments and the satellite retrievals.
Differences in average snow-off date between the six ECHAM5
experiments and the Russian snow course data. Both open-terrain and forest
snow courses are included in the comparison.
Differences in April–May mean 2 m air temperature between
ECHAM5 and the CRUTEM3 data set for the (a) REF,
(b) REF_NDG, (c) ALB1, (d) ALB1_NDG,
(e) ALB2 and (f) ALB2_NDG experiments. The
contours in (b–f) indicate the difference from the REF
experiment (contour interval 1 K; zero contour omitted).
Differences in April–May mean albedo between ECHAM5 and the
CLARA-SAL data set for the (a) REF, (b) REF_NDG,
(c) ALB2 and (d) ALB2_NDG experiments. In ALB1
and ALB1_NDG (not shown) the albedo values are, by construction,
identical to the CLARA-SAL data.
Nudging eliminates entirely the positive bias in snow-off date in
southeastern Siberia as compared with the satellite data. As
a consequence, the REF_NDG experiment features an early bias
throughout northern Eurasia (Fig. b), with largest
biases in the west. Likewise, for the nudged simulations with albedo
changes (ALB1_NDG and ALB2_NDG), snow-off generally occurs earlier
than in the satellite data, the most notable exception being that for
ALB1_NDG, near-zero or even positive differences (i.e. delayed
snow-off) appear in the Taymyr region.
It should be recalled that the early bias in snow-off dates compared with the
satellite data may be, in part, an artifact related to differences in the
definition of snow-off time between the ECHAM5 simulations and the satellite
data (Sect. ). Indeed, when compared with the snow course
data (Fig. ), all free-running simulations feature delayed
snow-off in eastern Siberia and in the Russian Far East. The differences
between REF, ALB1 and ALB2 are rather small in comparison with their biases
with respect to the snow course data. Even for the nudged simulations
(REF_NDG, ALB1_NDG, and ALB2_NDG), positive differences indicating delayed
snow-off prevail for many measurement stations in eastern Siberia and in the
Russian Far East, although slightly negative differences occur for some
stations. In the western parts of northern Eurasia, however, all simulations
feature negative biases, snow-off occurring 10–20 days earlier than in the
snow course data for many stations in western Russia. The negative biases
are, in general, slightly larger for the nudged simulations, especially in
the westernmost parts of Russia. Furthermore, as noted in
Sect. for the REF experiment, the negative biases are
especially pronounced when compared with forest snow courses.
The changes in snow-off timing are influenced by, and they feed back on,
simulated 2 m air temperature (Fig. ) and surface
albedo (Fig. ) in the sensitivity experiments. For
brevity, only mean values over the months of April and May are
shown. All experiments feature a cold bias in southeastern Siberia,
which amounts down to -7 K in REF
(Fig. a). Consistent with the earlier snowmelt
(Fig. ), this bias is reduced in ALB1
(Fig. c) and ALB2 (Fig. e), and
especially in the nudged experiments (Fig. b, d and f).
A slight negative temperature bias (≈-2 to -1K)
prevails in large parts of western and central Russia, and this
feature varies only slightly between the experiments. Positive
temperature biases are seen in all experiments in the Taymyr region
and in parts of the Russian Far East.
Figure displays surface albedo differences from the
CLARA-SAL data for the REF, REF_NDG, ALB2 and ALB2_NDG experiments (for
ALB1 and ALB1_NDG, the differences are zero by construction). It is seen
that the high-albedo bias in southeastern Siberia is reduced substantially in
both REF_NDG and ALB2, and it is eliminated completely in ALB2_NDG. In the
case of ALB2 and ALB2_NDG, the modified computation of the sky-view factor
in the albedo parameterization for forested regions contributes to this. For
REF_NDG, however, the change in surface albedo stems entirely from changes
in meteorological conditions, the reduced negative temperature bias
(Fig. b) leading to both lower snow albedo and reduced snow
cover. However, all four experiments show some common biases, most distinctly
an underestimation of albedo compared to the CLARA-SAL data in the northern
parts of northern Eurasia and in the Russian Far East. Interestingly, the use
of a higher value for the albedo of “warm” snow (0.6 instead of 0.3 when
Ts≥0∘C) in the ALB2 and ALB2_NDG experiments
reduces somewhat the negative bias in the Taymyr region but does not
eliminate it. A negative SCF bias likely contributes to the remaining albedo
bias, the average difference to GlobSnow data in the Taymyr peninsula being
ΔSCF≈-0.08 both in April and May. However, it still
appears that snow albedo is underestimated in May, which suggests that even
the value of 0.6 is too low at least in this region.
Discussion
The analysis of the sensitivity experiments in Sect.
showed that nudging and changes in the treatment of surface albedo in the
presence of snow alleviated some of the model biases in snow-off dates, 2 m
temperature and surface albedo. Nevertheless, many of the biases seen in
Figs. – are quite similar for all
experiments. Regarding the timing of springtime snow-off, the results are
somewhat ambiguous for the eastern parts of northern Eurasia, due to large
differences between observational snow-off date estimates from satellite and
snow course data, and hence in the resulting model biases. For western
Russia, however, comparisons with the satellite data and the snow course data
indicate unanimously that snow-off occurs too early in ECHAM5 for all
experiments, with only moderate variations due to nudging or changes in the
treatment of surface albedo (Figs. and
). Moreover, surprisingly, the too early snow-off
co-occurs with a slight negative temperature bias in the snow-melt season
(Fig. ).
To shed more light on the seemingly contradictory biases in temperature and
snow-off dates, a detailed comparison of ECHAM5 results with observations at
Sodankylä in Finnish Lapland is presented. The black line in
Fig. a displays the mean seasonal cycle of snow depth
measured at Sodankylä in 1979–2006, for days of year 1–165 (i.e. from
1 January until 14 June). The other curves show the corresponding seasonal
cycle of SWE for four ECHAM5 experiments (REF, REF_NDG, ALB1 and ALB2).
While there is no one-to-one correspondence between snow depth and SWE, due
to variations in snow density, it is clear from
Fig. a that in three of the four ECHAM5 experiments (REF,
REF_NDG and ALB2), snowmelt occurs earlier than in the observations, by
roughly 10–15 days. This is consistent with Fig. c, which
indicates that in the Finnish Lapland, snow-off in the REF experiment occurs
∼15 days earlier than in the satellite data. The exception is that in
the experiment ALB1, which prescribes surface albedo from the AVHRR-based
CLARA-SAL data set, the timing of snowmelt coincides well with the
observations.
Comparison of ECHAM5 simulations with observations at
Sodankylä (67.37∘ N, 26.63∘ E).
(a) Mean seasonal cycle of observed snow depth (black line, scale on the
left) and modelled SWE (four curves for different ECHAM5
experiments, scale on the right) in 1979–2006. (b) Mean
seasonal cycle of 2 m air temperature. (c) Mean 2 m air
temperature composited with respect to the snow-off date, “day 0”
representing the first completely snow-free day. The ECHAM5 results
are taken from the grid point nearest to the Sodankylä site
(68.08∘ N, 26.25∘ E).
Figure b shows a comparison for the seasonal cycle of 2 m
air temperature. From mid-March (day 75) onwards, all ECHAM5 simulations
underestimate the average T2 systematically. The average underestimate in
the primary snowmelt season (mid-April to mid-May; days 105–135), is
≈1.8 K for REF, REF_NDG and ALB2, and ≈3.5 K for ALB1.
Thus the Sodankylä site represents a case where snowmelt (and snow-off)
occurs earlier in ECHAM5 than in the observations, in spite of a negative
temperature bias in the snowmelt season.
The problems with representing correctly the relationship between snowmelt
timing and temperature become even more obvious, when the temperature data
are composited with respect to the snow-off date. Thus, for each year in
1979–2006, the snow-off date (“day 0”) was defined as the first day after
the winter's snow maximum completely without snow (in ECHAM5) or with snow
depth equal to zero in the morning (in the observations), and the average
T2 was computed for each day in the range from 45 days before snow-off to
15 days after snow-off (Fig. c). Note specifically that as
“day 0” represents the first completely
snow-free day, snow actually vanishes sometimes during “day -1”, and
“day -2” is (generally) the last day with snow persisting throughout the
day.
It is clear from Fig. c that ECHAM5 substantially
underestimates T2 in the snowmelt season. Strikingly, this depends quite
little on the experimental details such as nudging or changed treatment of
surface albedo. The negative bias in T2 culminates just before snow-off,
being ≈-7 K on “day -2”. Furthermore, it is noted that in ECHAM5,
the average T2 reaches 0 ∘C as late as “day -1”, during which the
snow vanishes in the model. In the observations, the average T2 reaches
0 ∘C already on “day -20”, and climbs to 7 ∘C by “day -1”.
It is further seen that in ECHAM5, there is a substantial jump in temperature
from “day -2” (the last day with snow throughout the day) to “day 0” (the
first completely snow-free day), 2.9–3.9 ∘C depending on the
experiment, whereas the observed change is only 1.0 ∘C. A similar
composite analysis of temperature with respect to snow-off date was repeated
for ECHAM5 for the entire northern Eurasia, and it confirmed that the
behaviour seen in Fig. is quite universal. In particular,
throughout the region, the average T2 stayed below 0 ∘C until and
including “day -2” (not shown).
The likely main reason for the fact that T2 simulated by ECHAM5 stays
close to 0 ∘C in the snowmelt season is that the surface energy
budget (and hence surface temperature) is not computed separately for the
snow-free and snow-covered parts of the grid cell. Rather, while snow cover
fraction is taken into account in defining grid-mean properties like surface
albedo and roughness length, a single snow-covered energy balance computation
is performed (Eq. ).
As explained in Sect. , the amount of snowmelt is determined
from the condition that, when the surface temperature Ts would
rise above 0 ∘C without considering snowmelt, the heat consumed in
melting snow restores Ts to 0 ∘C (Eq. ).
Here, Ts refers to the grid-mean surface temperature, not the
temperature of melting snow. Therefore, as long as there is any snow left in
the grid cell, Ts is not allowed to rise above 0 ∘C,
irrespective of the snow cover fraction. Naturally, this acts to suppress the
sensible heat flux (or even makes it negative), so 2 m air temperature
cannot rise much above 0 ∘C either. In reality, in a region with
partial (patchy) snow cover, surface temperature is kept to zero only in the
patches of melting snow. In the snow-free patches, Ts, and
consequently, T2, can rise substantially above 0 ∘C. Furthermore,
local temperature advection from snow-free to snow-covered patches and
subsidence associated with a “snow breeze” circulation can increase T2
over the latter e.g..
In summary, the use of a single surface energy budget computation leads to
a misrepresentation of grid-mean surface fluxes in the presence of fractional
snow cover : too much energy is spent in melting snow, and
too little in warming the air and the ground. Consequently, T2 stays too
low in the snowmelt season (Fig. c). This likely explains
why ECHAM5 features a persistent cold bias in springtime T2 even in
regions where snow-off occurs earlier than observed
(Figs. –).
In addition, there is another factor related to the treatment of surface
energy budget that may contribute to the too early snow-off: ECHAM5 does not
include a canopy layer. In ECHAM5, forests influence the energy budget
through changing the surface albedo and roughness length, but, for example,
the shading of the surface by the canopy is not considered. Since forests
reduce the surface albedo in the presence of snow (or more precisely, the
combined albedo of the surface and the canopy) in ECHAM5, this implies that
the amount of solar radiation available for snowmelt at ground is increased
in forests. In reality, the opposite happens, which acts to delay springtime
snowmelt in forests relative to non-forested areas . This
may explain why, in comparison with the snow course data, ECHAM5's tendency
toward too early snow-off is more pronounced for forest than open-terrain
measurements (Fig. b–c).
Recently, found that, while there is still substantial
intermodel dispersion among the CMIP5 models, on average the springtime snowmelt is slightly delayed in northern Eurasia. Taken at face value, the
default version of ECHAM5 agrees with this result for the eastern parts of
northern Eurasia, while in the west, snow vanishes too early
(Figs. and ). However, such regional
features are not discussed in , and moreover, a rigorous
comparison with their results is difficult due to the different data sets and
analysis methods used e.g.used only monthly data. An
interesting question for further research is how well the CMIP5 models are
able to represent the relationship between springtime temperature and
snow-off timing. In particular, is the problem of snowmelt occurring at too
cold grid-mean temperatures, as demonstrated in the current study, an
exception or the rule for the CMIP5 models? A priori, we would expect some of
the models to behave better (or at least differently) than ECHAM5. A prime
example is the CLM4 land-surface model employed in the
Community Earth System Model (CESM) , which addresses all
the main limitations of ECHAM5 identified in this work: the energy budget
computation is separated for the snow-covered and snow-free parts of a grid
cell, the computation of radiative fluxes at the snow surface accounts for
the shading by the overlying forest canopy, and the snow albedo computation
is more rigorous, based on radiative transfer modelling and a prognostic
effective radius of snow grains. The CLASS land surface scheme
used in the CanCM4 climate model also
separates the energy budgets for snow-covered and snow-free land.
Conclusions
In the present work, we have evaluated the timing of springtime snow-off in
northern Eurasia in the ECHAM5 (version 5.4) atmospheric GCM. Simulated
snow-off dates were compared with a snow-off date data set based on
space-borne microwave radiometer measurements and with Russian snow course
data. The primary conclusions are as follows:
In general, the default version of ECHAM5 reproduces well the
observed geographic pattern of snow-off dates, with earliest snowmelt (snow disappearing in March) in the Baltic region, and latest
snowmelt (in June) in the Taymyr region and parts of the Russian
Far East. However, compared to the satellite data, snow-off occurs
too early in the western parts of northern Eurasia, and also in the
northernmost regions like the Taymyr peninsula, with largest
differences (locally over 20 days) in northern Europe. In
contrast, in southeastern Siberia and in some far eastern parts of
Russia, snow melts locally over 10 days later than in the satellite
data. Comparison with the Russian snow course data confirms the
pattern of too early snow-off in the west and too late snow-off in
the east, although the former is slightly less pronounced, and the
latter more pronounced, than in the corresponding comparison with
the satellite data set.
The later than observed snow-off in southeastern Siberia is
associated both with overestimated snow accumulation during winter
and a springtime cold bias, which exceeds -6 K in
April. The latter is, in part, related to an overestimation of
surface albedo, which arises from insufficient shadowing of
the snow surface by the canopy in ECHAM5 in the dormancy season of
deciduous needleleaf trees. In contrast, surface albedo is
underestimated in late spring especially in the Taymyr region,
both due to underestimated snow cover and because an unrealistically
low albedo (0.3) is assumed for “warm” snow
(Ts≥0∘C). This promotes too early
snow-off in this region.
Several sensitivity experiments were conducted, where biases in
simulated atmospheric circulation were corrected through nudging
and/or the treatment of surface albedo was modified. Both nudging
and surface albedo modifications alleviated some of the model biases
in snow-off dates, 2 m temperature (T2) and surface albedo. In
particular, it proved possible to reduce substantially the biases in
snow-off date in southeastern Siberia and in the Taymyr region. In
contrast, the early bias in snow-off in the western parts of
northern Eurasia was not reduced appreciably in any of the
experiments; rather it was slightly increased by
nudging. Furthermore, surprisingly, this early bias in snow-off was
accompanied by a slight negative bias (≈-2 to
-1K) in springtime T2, both for the default version of
ECHAM5 and for the sensitivity experiments.
The combination of a too early snow-off with a cold springtime
temperature bias implies that temperature stays too low in the snowmelt season. In fact, as long as there is any snow left on the
ground, the daily mean T2 simulated by ECHAM5 rarely rises above
0 ∘C. In contrast, as demonstrated for the
Sodankylä site in Finnish Lapland, the observed daily mean T2
typically climbs several degrees above 0 ∘C before
all snow has vanished.
The likely main reason for the fact that T2 in ECHAM5 stays
close to 0 ∘C in the snowmelt season is that the
surface energy budget (and hence the surface temperature
Ts) is not computed separately for the snow-free and
snow-covered parts of the grid cell. Thus, even if the diagnosed
snow cover fraction is well below 1, the grid-mean Ts is
not allowed to rise above 0 ∘C. This acts to suppress
the sensible heat flux (or even makes it negative), so T2 cannot
rise much above 0 ∘C either, and leaves too large
a fraction of the grid-mean surface net radiation to be consumed in
melting snow.
There is another factor related to the treatment of surface
energy budget, which also likely contributes to the too early
snow-off: ECHAM5 does not include a canopy layer. Thus, in
particular, the shielding of snow on ground by the overlying canopy
is not accounted for, which leaves too much solar radiation
available for melting snow. This may explain why the early bias of
snow-off in ECHAM5 in western Russia is especially pronounced when
compared with snow course measurements made in forests.
Overall, the present study highlights the fact that snow-off timing in an
atmospheric GCM depends on the simulation of a number of processes:
large-scale circulation and temperature (which mainly determine the snowfall
during winter), computation of snow properties on ground, treatment of
surface albedo, and in general, the surface energy budget (which plays a key
role for snowmelt). In such a situation, as often in climate modelling,
compensating errors are likely, so that improving any single process in the
model may either improve or deteriorate the agreement with observations. An
example of this is that for ECHAM5, the general tendency towards too early
snow-off becomes clearer when biases in atmospheric circulation and
temperature are corrected by nudging. This exposes more clearly the problems
related to the treatment of surface energy budget, especially in the presence
of partial snow cover and forests. Beyond that, an obvious area for further
development would be the snow scheme itself, which is rather simplistic in
ECHAM5. Only the SWE and snow temperature are computed, with no consideration
of snow density and snow grain size. Furthermore, the temperature dependent
snow albedo scheme in ECHAM5 is very simple and, as demonstrated in this and
previous work, to some extent unrealistic.
Finally, according to our preliminary tests, snowmelt also occurs at too low
(grid-mean) temperatures in the Max Planck Institute's newest atmospheric
GCM, ECHAM6 . Like ECHAM5, ECHAM6 does not define separately
the surface temperature for the snow-free and snow-covered parts of a grid
cell. It is an intriguing question to which extent this issue pertains to
other global and regional climate models.
Determination of snow-off dates based on Russian snow course data
In the Russian snow course data , SWE measurements are
typically provided at 10-day intervals in winter and 5-day intervals in
spring (starting from March or April). A major issue in defining the snow-off
date based on these data is, however, that in the absence of snow, SWE
measurements are generally not reported. Thus one cannot always be sure
whether missing data indicate that there is no snow left to be measured, or
that the measurement was not performed for some other reason. To define the
snowmelt date, we adopted the following procedure.
The observation date with maximum SWE (dmax) for the
winter was located.
The part of the SWE time series after dmax was
studied, and cases were sought in which a valid measurement of SWE
was followed by missing data, with the corresponding dates denoted
by dmiss-1 and dmiss.
In such cases, it was assessed whether the missing data could
plausibly indicate the absence of snow. For this end, we evaluated
the statistics of SWE changes between two observation times (either
5 or 10 days apart from each other) within 1 month
of the date in question, considering all years for which the station
reported data. If the change in SWE from dmiss-1 to
dmiss required for all snow to melt by the time
dmiss (i.e. ΔSWE_required=-SWEmiss-1) was within two standard deviations
(σΔSWE) of the mean value
(ΔSWE‾) of SWE changes for the time of the
year, that is
ΔSWE_required≥ΔSWE‾-2σΔSWE,
it was assumed that the missing SWE value at day dmiss
indicates the absence of snow (SWEmiss=0).
If the missing value was deemed to be zero, all subsequent
missing values were also interpreted as zero, until (possibly)
a positive SWE value was found.
After the SWE time series was corrected as outlined above, the
snow-off date was determined. Data for three observation days were
used: the first observation day (dzero) with corrected
SWE=0 after the winter's SWE maximum (dmax), and the 2
observation days preceding it with SWE>0 (denoted as
dm2 and dm1, with SWEs of SWEm2 and
SWEm1, respectively). If linear extrapolation based on
the values SWEm2 and SWEm1 suggested all
snow to have melted before dzero, the snow-off date was
computed as
dsnow-off=dm1+(dm1-dm2)SWEm1SWEm2-SWEm1,
otherwise, it was assumed that
dsnow-off=dzero.
Finally, if the SWE reached values higher than
20 kgm-2 after the determined snow-off date, the case
was considered suspicious; thus this winter's data for this snow
course were ignored in further analysis. Cases in which the above
algorithm failed to find a snow-off date were likewise ignored in
the subsequent analysis.
Clearly, the above algorithm involves some arbitrary choices (especially the
criterion of two standard deviations in Eq. () and the limit of
20 kgm-2 in step 6 of the algorithm). However, a number of
sensitivity tests were conducted regarding the choice of these parameters,
and it was found that the statistics of model vs. observation differences
were largely insensitive to them. For example, changing the criterion of two
standard deviations in Eq. () to either one or three standard
deviations changed the average model vs. observation difference in snow-off
dates by less than 1 day.
Lastly but importantly, to compare ECHAM5's snow-off dates with the
snow course data as consistently as possible, the simulated SWE time
series were first subsampled according to the availability of the snow
course data (i.e. including only the days with measurements), and the
snow-off dates for ECHAM5 were then determined according to the
algorithm outlined above. For comparison with satellite data, however,
the simulated snow-off dates were derived from the complete time
series of daily mean SWE.
Acknowledgements
This research was supported by the Academy of Finland (project
numbers 116109, 140915 and 254195). The Russian Hydrometeorological
Centre and the Climatic Research Unit, University of East Anglia,
respectively, are acknowledged for making available the snow course
data and the 2 m temperature data used in this study. Sebastian
Rast (Max Planck Institute for Meteorology, Hamburg, Germany) is
thanked for producing the ERA-Interim files for nudged ECHAM5 runs.
Jaakko Ikonen (FMI) is thanked for help with the GlobCover data.
Finally, thanks are due to Richard Essery and an anonymous reviewer
for their helpful comments on the paper. Edited by: D. Roche
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