TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-11-1625-2017Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate modelsTerzagoSilvias.terzago@isac.cnr.itvon HardenbergJosthttps://orcid.org/0000-0002-5312-8070PalazziElisahttps://orcid.org/0000-0003-1683-5267ProvenzaleAntonellohttps://orcid.org/0000-0003-0882-5261Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, Turin, ItalyInstitute of Geosciences and Earth Resources, National Research Council of Italy, Via Moruzzi 1, Pisa, ItalySilvia Terzago (s.terzago@isac.cnr.it)10July2017114162516455December201626January201723May201724May2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/11/1625/2017/tc-11-1625-2017.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/11/1625/2017/tc-11-1625-2017.pdf
The estimate of the current and future conditions of snow resources
in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly
representing snow processes and snow–climate interactions. At the moment, the
development of such tools is hampered by the sparseness of station-based
reference observations. In past decades passive microwave remote
sensing and reanalysis products have mainly been used to infer information on the
snow water equivalent distribution. However, the investigation has usually
been limited to flat terrains as the reliability of these products in
mountain areas is poorly characterized.
This work considers the available snow water equivalent data sets from remote
sensing and from reanalyses for the greater Alpine region (GAR), and explores
their ability to provide a coherent view of the snow water equivalent
distribution and climatology in this area. Further we analyse the simulations
from the latest-generation regional and global climate models (RCMs, GCMs),
participating in the Coordinated Regional Climate Downscaling Experiment over
the European domain (EURO-CORDEX) and in the Fifth Coupled Model
Intercomparison Project (CMIP5) respectively. We evaluate their reliability
in reproducing the main drivers of snow processes – near-surface air
temperature and precipitation – against the observational data set EOBS, and
compare the snow water equivalent climatology with the remote sensing and
reanalysis data sets previously considered. We critically discuss the model
limitations in the historical period and we explore their potential in
providing reliable future projections.
The results of the analysis show that the time-averaged spatial distribution of snow water
equivalent and the amplitude of its annual cycle are reproduced quite
differently by the different remote sensing and reanalysis data sets, which in
fact exhibit a large spread around the ensemble mean. We find that GCMs at
spatial resolutions equal to or finer than 1.25∘ longitude are in closer
agreement with the ensemble mean of satellite and reanalysis products in
terms of root mean square error and standard deviation than lower-resolution
GCMs. The set of regional climate models from the EURO-CORDEX ensemble
provides estimates of snow water equivalent at 0.11∘ resolution that
are locally much larger than those indicated by the gridded data sets, and
only in a few cases are these differences smoothed out when snow water
equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven
RCM simulations show an annual snow cycle that is comparable in amplitude to
those provided by the reference data sets, while GCM-driven RCMs present a
large positive bias. RCMs and higher-resolution GCM simulations are used to
provide an estimate of the snow reduction expected by the mid-21st century (RCP
8.5 scenario) compared to the historical climatology, with the main purpose
of highlighting the limits of our current knowledge and the need for
developing more reliable snow simulations.
Introduction
The increase in surface temperatures has relevant
consequences for high-elevation regions, where snow is a dominant climatic
feature . The shift of the
0 ∘C isotherm to higher elevations results in a decrease in the
solid-to-total precipitation ratio in mid- and low-altitude mountain areas,
where temperatures are currently close to the melting point
. In addition, higher
temperatures may result in earlier snowmelt and shortening of the snow cover
duration. Finally, snow cover and its local-scale variability affect climate
at larger scales through the snow-albedo feedback .
Changes in mountain snowpack are expected to have implications on water
availability, in particular on the timing of the seasonal run-off, likely
characterized in the future by earlier spring or even winter discharge and
reduced flows in summer and autumn , and on the timing of the groundwater recharge. Similarly,
changes in the seasonality, amount and duration of snow cover can have significant impacts on mountain economies for winter tourism
and on mountain ecosystems, including
high-altitude vegetation and the population dynamics of
animal species that depend on snow resources .
For these reasons, reliable regional estimates of current and future expected
changes in snow cover are essential to develop adaptation and management
strategies. Detailed studies on the recent and projected impacts of global
warming in snow-dominated regions are necessary to inform future management
of water resources and to
preserve essential ecosystem services for millions of people living in
downstream areas. For such applications, the uncertainties associated with
the future snow projections must be carefully estimated and the reliability
of the model results should be assessed.
In order to evaluate state-of-the-art global and regional climate models
(GCMs, RCMs) and their future projections, as well as to improve the
representation of snow processes in such models, reliable data sets are required, possibly
at high spatial resolution and representing the local climate characteristics
in orographically complex areas. However, the density of
surface stations measuring snow is currently insufficient to develop a
global, reliable gridded snow water equivalent data set based on in situ
measurements, thus calling for the use of alternative sources of information
on snow depth and mass, derived from remote sensing observations and
reanalyses .
Satellite measurements have been shown to provide a reliable picture of the
global snow cover extent at a spatial resolution of a few hundred metres
, while the estimations of snow depth and snow water
equivalent from satellite are typically calculated at spatial scales of 25 km
and are more challenging see also
Sect. . Global reanalyses provide snow water
equivalent fields at horizontal resolutions that are comparable
(∼ 30 km in the zonal direction) or coarser than satellite products.
Some reanalyses, such as ERA-Interim and NCEP-CFSR
, assimilate surface snow depth measurements and satellite
snow cover extent while others, such as MERRA and 20CR
, are not constrained by snow measurements and thus rely on the
capability of their land-surface model component to estimate snow fields.
Overall, one must be aware of the very different meanings of “high
resolution” in remote sensing studies, where spatial resolution can be of a
few metres, and in climate modelling and/or gridded data sets, where the
highest spatial resolutions that can be usually achieved are of the order of
a few kilometres.
To date, a few studies have investigated the accuracy of satellite-based and
reanalysis snow water equivalent (SNW) data sets against available
observations, and very little is known about their performance in mountain
areas. , for example, compared the long-term global snow
water equivalent climatology provided by the National Snow and Ice Data
Center NSIDC-SNW,, derived from passive microwave instruments,
to the ERA40 reanalysis and to the output of the global
climate model HadCM3 . The largest differences
between the three data sets were found for the Himalayas and for the west
coast of North America, likely owing to heterogeneity of the subgrid
topography. Globally, the GCM and the reanalysis were found to be in better
agreement with each other than with the satellite product. The GCM and
reanalysis fields displayed a similar climatological annual cycle in the
Northern Hemisphere, a thick snow depth over Eurasia and a thin one over
Siberia, while the satellite data indicated a thin snowpack in Eurasia and a
thick one in Siberia, overestimating snow depth with respect to the available
ground observations. Another recent study by widened the
analysis of by investigating additional SNW global
data sets derived from satellite and surface measurements
GlobSnow,, from reanalyses (ERA-Interim/Land and
MERRA) and from land-surface models driven by meteorological forcing. The
spread among these products was found to be lowest in midlatitude boreal regions while their temporal
correlation was highest, likely owing to the fact
that snow cover is generally ubiquitous during the cold season and the
atmospheric circulation (midlatitude winter cyclones) is well reproduced in
the models. The largest spread was found in the Arctic and alpine regions, where
reanalyses are poorly constrained by surface observations and the uncertainty
in the meteorological forcing is higher. Alpine regions are characterized by
additional complexity due to steep elevation gradients and subgrid
surface heterogeneities that are difficult to represent in land-surface
models.
The present work is devoted to reviewing the available snow data sets and
quantitatively assessing the uncertainties in the estimation of the snow water
equivalent in alpine environment. First, we expand the study by
by including additional global SNW gridded data sets
obtained from remote sensing and reanalyses, and we explore how these
data sets represent the snow climatology over the greater Alpine region (GAR).
Based on this analysis, we critically discuss the performance of
state-of-the-art SNW products in an orographically complex area and we
provide an estimate of the inter-data set spread in the Alps. These results
are used as a reference for evaluating the state-of-the-art climate models
participating in the two major coordinated global and regional climate
modelling experiments: the 5th Coupled Model Intercomparison Project
CMIP5,, providing global simulations at spatial
resolution on the order of 100 km, and the Coordinated Regional Climate
Downscaling Experiment over the European domain
EURO-CORDEX,, providing regional simulations up to
12 km spatial resolution. For each model, we assess its ability to represent
(i) the main drivers of snow processes, i.e. surface air temperature and
precipitation, compared to the observational data set EOBS and (ii) the snow
water equivalent climatology compared to the ensemble mean of the satellite
and reanalysis data sets.
At the present state of affairs, i.e. without sufficient knowledge of real
surface snow conditions, it is not possible to make any statement on the
reliability of future snow water equivalent projections at mountain range
scale. In this study, without attempting to assess how snow resources will
evolve in the future, we show how model uncertainty and spread found in
the historical period project into the future to (i) assess the overall
agreement in relative snow changes (i.e. changes relative to each model's
historical
climatology) and (ii) discuss the differences in the amplitude of the
relative snow changes projected for the mid-21st century under a high-range
emission scenario (RCP 8.5) by coarse- (CMIP5) and fine-scale (EURO-CORDEX)
models.
The paper is organized as follows: Sect. introduces the data sets
used for the analysis; Sect. describes the area of study,
discusses the representation of orography in the current generation regional
and global climate models and summarizes the methodology employed for the
data processing; Sect. reports the results in terms of
(i) snowpack distribution in remote sensing products, reanalyses and climate
model simulations over the greater Alpine region during the last decades,
(ii) inter-data set spread in the representation of the annual cycle of snow
water equivalent and (iii) inter-data set spread in the representation of the
snow changes expected by the mid-21st century in the RCP8.5 scenario.
Sections and 6 provide a general discussion of the
results in relation to other studies and conclude the paper.
Data setsRemote sensing products
Satellite sensors can provide a reliable picture of the snow cover extent,
but the estimation of the snow water equivalent is more challenging.
Passive microwave methods are based on the difference in brightness
temperatures in two microwave channels, typically corresponding to
frequencies of 18 and 36 GHz. These methods are unable to detect very thin
snow layers i.e. less thick than 15 mm, and suffer
from saturation above ∼ 250 mm SNW . Snow
estimates from satellite are also affected by metamorphism of snow grains and
snowmelt: large, plate-like crystals increase the scattering of radiation
from the surface, and a shallow but dense snowpack can be misinterpreted as a
thick one. Owing to its high emissivity, liquid water, either within the
snowpack or at the air–snow interface, overwhelms the scattering by the snow
cover and can cause an underestimation of the snow thickness. Additionally,
melt–refreeze processes during the melt season can cause spurious snow peak
values . The horizontal resolution of satellite brightness
temperature measurements makes the snow estimates extremely challenging in
complex terrain owing to the heterogeneity of snow properties at subgrid
scale. An eloquent example is the European Space Agency GlobSnow product in
which the alpine regions are masked out because of intrinsic poorer
performance and limited possibility to validate the snow estimates with
surface observations .
Notwithstanding these limitations, satellite products are commonly used to
evaluate SNW as they offer a global view of snowpack characteristics for
several decades. In the present study we consider the following satellite
products available for our study area:
Global Monthly EASE-Grid Snow Water Equivalent Climatology provided by the National Snow and
Ice Data Center (NSIDC-SNW): this data set includes global, monthly satellite-derived snow water equivalent data from November 1978 through May 2007 at 25 km resolution
(Equal-Area Scalable Earth Grid, EASE-Grid). The snow water equivalent is derived from a Scanning Multichannel Microwave Radiometer (SMMR) and selected Special Sensor Microwave/Imagers
(SSM/I).
AMSR-E/Aqua Monthly L3 Global Snow Water Equivalent (level-3) monthly data from the Advanced Microwave Scanning Radiometer –
Earth Observing System (AMSR-E) instrument on the NASA Earth Observing System (EOS) Aqua satellite. This data set contains SNW data and quality assurance flags mapped to 25 km EASE-Grids from 2002 to 2011.
NSIDC-SNW data have been evaluated over Russia using snow
observations from the period 1979–2000 for March only, showing an average
12 mm bias, which means a bias of 10 % or less if the mean SNW is 120 mm
or higher . The evaluation of the AMSR-E SNW daily product in
complex topography (Mackenzie River basin, Canada) against in situ snow depth
observations showed similar results, a mean absolute error ranging from
12 mm in the early winter season to 50 mm in the late winter season
. The differences among the two satellite products over the
Alpine region in terms of average snow water equivalent during the overlapping
period have been analysed and discussed in Sect. ,
Fig. f.
Reanalyses
A clear advantage of reanalysis products over observation-based data is that
they provide global, physically consistent estimates of all atmospheric and
land-surface fields of interest, mostly constrained by observations. The
reliability of reanalyses is related to the density of the assimilated
observations; thus it depends on the location, the time period and the
variable considered. Reanalysis products, for example, are known to be poorly
constrained by surface measurements in mountain areas where their uncertainty
is larger than in other regions. Precipitation is treated differently in
different reanalyses: in some cases it is a prognostic variable, i.e. it is
generated by the atmospheric general circulation model and it is not
constrained by observations i.e. MERRA reanalysis,;
in other cases it is a prescribed forcing derived from global precipitation
data sets (as in the case of CFSR and ERA-Interim/Land reanalyses). The
reanalysis products considered in the present study are as follows:
Climate Forecast System Reanalysis CFSR, by the National Center for Environmental Prediction
(NCEP) is a global, high-resolution, coupled atmosphere–ocean–land-surface–sea-ice system reanalysis covering the period
1979–2009 and providing, among other variables, SNW fields at horizontal resolution of 0.3125∘ (∼ 38 km at the Equator).
CFSR uses two sets of observed global precipitation analyses as precipitation forcing, namely CMAP (a 5-day mean precipitation data set at 2.5∘
latitude-longitude grid) and CPC (daily gauge analysis at 0.5 degree lat–lon over land). CFSR snow fields are simulated by the land-surface model
Noah and constrained by the CFSR snow analysis. The snow analysis is based on the SNODEP model ,
which integrates surface observations, SSM/I-based detection algorithms and the NESDIS IMS Northern Hemisphere snow cover,
based on in situ and satellite data . Snow analyses
are used to limit the upper and lower boundaries of Noah fields, which cannot
be more than twice as large or less than half of the value provided by the
analysis.
Modern Era-Retrospective analysis for Research and Applications
MERRA, by the National Aeronautics and Space
Administration (NASA) is a global atmospheric reanalysis generated through the
Goddard Earth Observing System Model (GEOS-5) atmospheric general circulation
model and an atmospheric data assimilation system. MERRA covers the time
period from 1979 to the present and uses a grid of 1/2∘
latitude and 2/3∘ longitude with 72 vertical levels. Its
land-surface model, Catchment , includes an intermediate
complexity snow scheme with up to three snow layers describing snow
accumulation, melting, refreezing and compaction in response to
meteorological forcings .
ERA-Interim/Land reanalysis by the European Centre for Medium-Range Weather
Forecasts (ECMWF) is a global reanalysis of land-surface parameters at
∼ 80 km spatial resolution covering the period 1979–2010
. ERA-Interim/Land is the result of offline simulations
performed with the improved land-surface model HTESSEL ,
which was forced by the meteorological fields from ERA-Interim and
precipitation adjustments based on GPCP v2.1. ERA-Interim/Land rescales
ERA-Interim precipitation estimates on the Global Precipitation Climatology
Project (GPCP) data to remove possible biases and introduce a constraint to the
observations at a monthly timescale . In fact, in the
Alps ERA-Interim/Land has been found to reduce the dry bias present in
ERA-Interim (see Fig. S1 of the supplementary material). At large scales, the
correction on snowfall has been found to be small, owing to an overall good
representation in the original ERA-Interim reanalysis . In
ERA-Interim/Land snow density and snow depth are not constrained by data
assimilation owing to limited availability of surface observations. In this
way the accuracy of these variables relies purely on the capability of the
HTESSEL land-surface model to correctly reproduce the real fields.
ERA-Interim/Land has been proven to provide good quality land snow mass
analyses, owing mainly to the improvements in the single layer snow scheme,
with enhanced parameterizations of snow density and revised formulations for
the subgrid snow cover fraction and snow albedo .
20th Century Reanalysis version 2 20CR v2, by the NOAA
Earth System Research Laboratory (ESRL) Physical Sciences Division and the
University of Colorado CIRES Climate Diagnostics Center provides a
synoptic-observation-based estimate of global tropospheric variability
spanning the time period from 1871 to 2008. It is derived using only surface
pressure observations and prescribing monthly SST and sea-ice distributions
as boundary conditions for the atmosphere . SNW fields are
available at a spatial resolution of ∼1.875∘ (∼ 200 km
in the zonal direction).
Global climate models
Global climate models (GCMs) are the main tools available to explore climate
processes and feedbacks at global scales, and to make projections for future
climate change scenarios. Owing to coarse-grid limitations, current GCMs
resolve explicitly only the main snow processes while the snow physics at
subgrid scale is parameterized. In such conditions, the snow schemes used in
GCMs are strongly simplified: they often treat snowpack as a single-layer
over the ground surface and small-scale processes such as the refreezing of
melted water within the snowpack and snow metamorphism are not properly taken
into account .
Thanks to the availability of increasing computing resources it has been
possible to run models at finer and finer spatial resolutions, thus
permitting a more accurate representation of the topography in orographically
complex areas . Increased spatial resolution
implies a more detailed view of the atmospheric forcings relevant for the
mountain snowpack dynamics, i.e. altitudinal temperature gradients,
precipitation distribution and phase, downward radiation, and the important
physical processes could be better represented. As an example, the
variable-resolution Laboratoire Meteorologie Dynamique (LMD) global climate
model has been successfully employed to test the impact of the horizontal
resolution on the representation of the monsoon over southern Asia
. They showed that the enhanced-resolution simulation at
about 35 km greatly improves the representation of circulation features, the
monsoon flow and the precipitation patterns with respect to the standard
resolution model.
In the present study we consider the global climate models included in the
CMIP5 archive (http://www.cmip-pcmdi.llnl.gov/cmip5), available in
January 2015, which provide the SNW variable at monthly resolution
(Table ) during both the historical period (1850–2005) and the
projection period (2006–2100) under the Representative Concentration
Pathways scenario RCP8.5 . We consider the ensemble member
r1i1p1 for all models except for EC-Earth for which the
SNW data were not stored in the CMIP5 archive and for which we used the
ensemble member r8i1p1. The spatial resolution varies from model to model in
a range from 0.75 to 3.75∘ longitude (∼ 80 to 400 km in the
zonal direction; see Table ).
Snow water equivalent data sets, including remote sensing products,
reanalyses and CMIP5 global climate models used in this study. For each of
these we report the land-surface model (LSM, when it applies), the
spatial/spectral horizontal resolution and the relevant references. CMIP5
models with horizontal resolution equal to or finer than 1.25∘ longitude
are highlighted in bold.
ModelInstitutionLSMRes. [∘lon]/Sp.ResReferenceNSIDC-SNWNational Snow and Ice Data Center–25 kmAMSR-ENational Snow and Ice Data Center–25 kmCFSRUS National Centers for Environmental PredictionNoah0.3125MERRAUS National Aeronautics and Space AdministrationCatchment LSM0.67ERA-Interim/LandEuropean Centre for Medium-Range Weather ForecastsHTESSEL0.720th Century ReanalysisNOAA Earth System Research LaboratoryNoah1.875CMCC-CMEuro-Mediterranean Centre for Climate ChangeECHAM50.75/T159EC-EarthEC-Earth ConsortiumHTESSEL1.125/T159BCC-CSM1-1-MBeijing Climate Center, ChinaBCC_AVIM1.01.125/T106MRI-CGCM3Meteorological Research Institute, JapanHAL1.125/T159CESM1-BGCNational Center for Atmospheric ResearchCLM41.25CESM1-CAM5National Center for Atmospheric ResearchCLM41.25CESM1-FASTCHEMNational Center for Atmospheric ResearchCLM41.25CCSM4National Center for Atmospheric ResearchCLM41.25CNRM-CM5Centre National de Recherches MétéorologiquesISBA1.4/T127ACCESS1-0CSIRO/BOM, AustraliaMOSES21.875/N96ACCESS1-3CSIRO/BOM, AustraliaCABLE1.01.875/N96CMCC-CMSEuro-Mediterranean Centre for Climate ChangeECHAM51.875/T63CSIRO-Mk3-6-0CSIRO, AustraliaMOSES II1.875/T63HadGEM2-AOMet Office Hadley CentreMOSES II1.875/N96HadGEM2-CCMet Office Hadley CentreMOSES II1.875/N96HadGEM2-ESMet Office Hadley CentreMOSES II1.875/N96MPI-ESM-LRMax Planck Institute for MeteorologyJSBACH1.875/T63MPI-ESM-MRMax Planck Institute for MeteorologyJSBACH1.875/T63MPI-ESM-PMax Planck Institute for MeteorologyJSBACH1.875/T63INM-CM4Institute for Numerical MathematicsINM2.0CESM1-WACCMNational Center for Atmospheric ResearchCAM2.5GFDL-CM3NOAA Geophysical Fluid Dynamics LaboratoryLM32.5GFDL-ESM2GNOAA Geophysical Fluid Dynamics LaboratoryLM32.5GFDL-ESM2MNOAA Geophysical Fluid Dynamics LaboratoryLM32.5GFDL-CM2p1NOAA Geophysical Fluid Dynamics LaboratoryLM22.5GISS-E2-H-CCNASA Goddard Institute for Space StudiesGISS LSM2.5GISS-E2-HNASA Goddard Institute for Space StudiesGISS LSM2.5GISS-E2-R-CCNASA Goddard Institute for Space StudiesGISS LSM2.5GISS-E2-RNASA Goddard Institute for Space StudiesGISS LSM2.5NorESM1-MENorwegian Climate CentreCLM42.5NorESM1-MNorwegian Climate CentreCLM42.5BNU-ESMBeijing Normal University, ChinaBNU-CoLM32.8125/T42*CanESM2Canadian Centre for Climate Modelling and AnalysisCLASS2.8125/T63FGOALS-g2LASG/CESS, ChinaCLM32.8125FIO-ESMThe First Institute of Oceanography, ChinaCLM3.52.8125/T42HadCM3Met Office Hadley CentreMOSES I3.75/N48
* Reference is
http://esg.bnu.edu.cn/BNU_ESM_webs/htmls/index.html.
EURO-CORDEX regional climate models providing ERA-Interim-driven
runs for the snow water equivalent variable at 0.11∘ spatial
resolution considered in this study. For each of model we also report the
land-surface model (LSM), the number of available GCM-driven runs and the
reference.
ModelInstitutionLSMEnsembleReferencemembersCCLM4-8-17CLM CommunityTerra-ML4ALADIN53Centre National de Recherches MétéorologiquesISBA–HIRHAM5Danish Meteorological Institute1RACMO22ERoyal Netherlands Meteorological InstituteHTESSEL2REMO2009Climate Service Center1Regional climate models
Dynamical downscaling of global climate models and reanalyses through
regional models can potentially provide valuable information on the mountain
cryosphere. Regional climate models are currently run at horizontal
resolutions ranging from 50 km up to a few kilometres, allowing for a more refined
representation of mountain topography and altitudinal gradients with respect
to global models. Similarly to GCMs, RCM snow schemes are strongly
simplified with respect to dedicated snowpack models , so
their main added value is to reproduce snow processes in high-elevation
areas, which are simply not represented in coarse grid GCMs.
In this work we consider all the RCMs participating in the EURO-CORDEX
regional climate model experiment and providing the
snow water equivalent variable at monthly resolution at the finest
available spatial resolution, i.e. 0.11∘ (Table ). We
evaluate the ERA-Interim-driven runs, available for five models at the time we
downloaded the data set in October 2016, in order to assess the RCM bias when
the RCM is driven by a realistic atmospheric forcing. Three models show
non-reliable trends (characterized by continuous snow accumulation and no
melting) in a limited number of pixels – possibly areas masked as glaciers.
As this feature introduces an error in the surface water budget and hampers
the calculation of SNW spatial averages over the GAR, we retained only two
RCMs out of the five to further investigate the historical and future
simulations under the RCP 8.5 scenario (see Sect. 4.1.3 for
details). Specifically one, the COSMO Climate version of Local Model
CCLM, provides simulations driven by several different
GCMs (namely EC-Earth, CNRM-CM5, HadGEM2-ES and MPI-ESM-LR), and thus it
can be used to investigate the uncertainty in the snow estimate coming from the
large-scale driver. The other, REMO2009, provides simulations driven by the
MPI-ESM-LR global climate model.
Observational data sets of air temperature and precipitation
The ability of climate models to properly reproduce snow water equivalent
depends on the accuracy of their surface snow schemes and on the reliability
of the atmospheric fields forcing the snow schemes. Near-surface air
temperature (TAS) and precipitation (PR) climatologies provided by the
reanalyses and the climate models considered in this study are validated
against two gridded observational data sets. Along the line of previous
studies we consider the daily gridded EOBS data set
version 13, at 0.25∘ resolution, based on the
European Climate Assessment and Data set station measurements.
In addition to this established and widely used reference, a second
observational data set specifically developed for the Alpine region, HISTALP
, is analysed for comparison. HISTALP provides
monthly temperature and precipitation fields at 0.08∘ spatial
resolution, and is based on surface measurements. Owing to its higher spatial
resolution, HISTALP can explore such variables in finer detail with respect
to EOBS.
Domain and methods
The study domain is the greater Alpine region ,
extending in the range 4–19∘ E, 43–49∘ N
(Fig. a). The complex orography of the area and the heterogeneous
pattern of steep slopes and valleys hamper the representation of
climate features from both an observational and a modelling point
of view. As an example, Fig. b points out how the topography is
represented in the 1 km GLOBE digital elevation model , in the
CORDEX ERA-Interim-driven regional climate models and in the CMIP5 global
climate models, in terms of median, 5th and 95th percentiles of the distribution of
elevation. The median elevation is well reproduced by all models while the
lowest and highest elevations are progressively cut out as the spatial
resolution of the model coarsens. While RCMs are closer to the expected values, global
climate models, including those with the finest spatial resolution, do not properly take
into account elevations above 1500 m a.s.l. in the GAR. This
limitation has to be considered when analysing GCM outputs over mountain
areas since the world reproduced by the global models has a smooth orography
and simplified physical processes.
(a) Orography of the greater Alpine region
(4–19∘ E; 43–49∘ N) as in the GLOBE 1 km digital
elevation model (DEM). (b) The 95th (dash-dotted), 50th (dashed) and 5th (dash-dotted) percentiles of the
elevation distribution in the DEM
compared to the corresponding values obtained from the CORDEX and CMIP5 model
orographies. RCM and GCM models are ordered along the x-axis from finest to
coarsest spatial resolution. RCMs and GCMs are separated by a vertical
dashed line.
In this paper we explore the degree of agreement (i) among the reference
data sets illustrated in Sect. and , (ii) of the
CORDEX and CMIP5 models compared to the ensemble mean of the reference
data sets and (iii) between the different climate model ensembles, by inspecting
the December to April (DJFMA) mean TAS, PR and SNW climatologies.
The model performance with respect to the reference snow water equivalent
data sets is quantified using Taylor diagrams, which provide a concise
statistical summary of how well patterns match a given reference in terms of
their linear correlation (R), root mean square difference (RMSE), and ratio of
their variances (NSD) . In order to compare point by point
data sets built on different coordinate reference systems and with different
spatial resolutions, all data sets are reprojected onto a common grid. The
ERA-Interim/Land 0.7∘ longitude grid is chosen because of its
intermediate resolution between global and regional climate models. Global
climate models are also evaluated at their own resolution, comparing each
model to remote sensing products and reanalyses upscaled at the climate model
grid. This second approach allows the impact of the horizontal
resolution on the performance of coarse-scale climate models to be reduced. Spatial
interpolations are performed via conservative remapping ,
using the Climate Data Operators software .
Assessments of the SNW characteristics at the scale of the mountain range
(Figs. and ) are obtained by spatially averaging the
snow water equivalent over all areas above 1000 m a.s.l. in the GAR. To
take into account the mismatch between the model topography and the real one,
we use the data sets at their native resolution and weight the values by the
fraction of each grid cell at elevation above 1000 m a.s.l as provided by
the 1 km GLOBE digital elevation model; then the weighted
values are spatially averaged over the domain of interest, the greater Alpine region. This procedure can be used to compare data sets characterized by very
different spatial resolutions without introducing uncertainties due to
regridding see alsofor further details.
ResultsThe spatial distribution of snow water equivalent in gridded data setsSNW in satellite products and reanalyses
We first illustrate the spatial distribution of snow water equivalent in the
satellite products and the reanalyses, hereafter referred to as the
reference data sets, and we evaluate the differences among the
reanalyses in relation to possible biases in the meteorological forcing.
Figure shows the multiannual mean (1980–2005) of near-surface air temperature (TAS), precipitation (PR) and SNW averaged (or accumulated
in the case of PR) over the months from December to April. In order to
facilitate the comparison we present the differences (or percent biases) with
respect to a given data set, namely EOBS for TAS and PR and NSIDC-SNW for SNW, since
it is available for a longer period (1980–2005) than the other satellite
product, AMSR-E (2003–2011). All data sets
are conservatively remapped onto a regular 0.25∘ resolution grid.
Biases are calculated over the period 1980–2005 except for AMSR-E, for which
the period of overlap with the reference data set is 2003–2007.
Multiannual mean (1980–2005) of the DJFMA average (a) air
temperature, (b) total precipitation from EOBS observational
data sets and (c) snow water equivalent from NSIDC-SNW. Panels
from (d) to (r) represent the bias of HISTALP, AMSR-E and
reanalyses with respect to EOBS and NSIDC-SNW data sets respectively.
Compared to EOBS, the alternative observational, high-resolution climatology
from HISTALP (Fig. 2d–e) presents a similar temperature distribution, drier
conditions at high elevations and wetter conditions at low elevations. This
comparison is reported to highlight the fact that uncertainties are larger in
precipitation than in temperature estimates, especially in mountain areas,
and also observational data sets can exhibit biases with respect to each
other.
Focusing on the snow water equivalent distribution, the NSIDC-SNW
climatology (Fig. c) shows maximum values of about
50 kg m-2 over the western Alps and 70 kg m-2 over the eastern
Alps. If we consider the other satellite and reanalysis products we obtain a
rather heterogeneous picture. AMSR-E (Fig. f) presents higher values in the western
Alps and lower values in the eastern Alps compared to the NSIDC-SNW.
CFSR (Fig. g–i) shows TAS and PR patterns that are similar to
EOBS over the Alpine ridge and a SNW distribution that is similar to NSIDC-SNW.
The similarity in the SNW range of variability is probably due to the
fact that both products integrate the Special
Sensor Microwave Imager (SSM/I) data but to different extents. NSIDC-SNW is
specifically derived from the Special Sensor Microwave Imager (SSM/I) data. The
CFSR snow output is mainly based on the Noah land-surface model first guess,
and a daily snow analysis based on several inputs, including
the Special Sensor Microwave Imager (SSM/I) data, is used to constrain the
model first guess . The CFSR snow depth/SNW is
limited in the upper and lower boundaries by the snow analysis (it cannot be
larger than twice and lower than half the snow analysis) but the temporal
evolution of snow depth and SNW is determined by the Noah model. As a
consequence, the two SNW data sets lie in similar ranges of variability, but
except for this feature they can be considered independent.
The MERRA Reanalysis (Fig. j–l) shows a thicker snowpack with
respect to NSIDC-SNW, especially over the western Alps, as
well as AMSR-E. The MERRA behaviour can be explained by a cold bias over that
area, partly compensated by drier conditions over the Alpine peaks.
ERA-Interim/Land (Fig. m–o) shows the largest SNW values, with
peaks exceeding NSIDC-SNW values by more than 200 kg m-2. The SNW bias is
not directly explainable in terms of biases in temperature and precipitation,
which indeed go towards the opposite direction (warmer and slightly drier
with respect to EOBS). This result suggests that ERA-Interim/Land high SNW
values can be attributed to the snow scheme in use.
20CR (Fig. p–r) shows the lowest SNW values. Owing to its coarse
spatial resolution, 20CR presents a warm and dry bias at high elevations and
a cold and wet bias at low elevations, which in turn result in low snow
accumulation and shallow snowpack over the mountain range. These simplified
patterns can presumably be ascribed to an excessively smooth orography and
highlight the limitations of the 20CR reanalysis in the representation of
snow processes in mountain areas.
This analysis provides a quite heterogeneous picture of SNW and, despite the
considerations on the biases of the drivers, it is not possible with current knowledge to ultimately define which product is closest to
reality over the full GAR domain. For further analysis we disregard the
20CR reanalysis owing to its poor performance in this orographically complex
region and the AMSR-E satellite product for its short period of
availability. We consider as reference the mean of the other four
data sets, i.e. NSIDC-SNW, CFSR, MERRA and ERA-Interim/Land reanalyses.
This multi-reference mean (MRM) is calculated after
conservatively remapping all the data sets to the 0.7∘ longitude
ERA-Interim/Land grid.
SNW in global climate models
Here we discuss in detail the DJFMA TAS, PR and SNW climatologies provided by
CMIP5 global climate models with spatial resolution equal to or finer than
1.25∘ (Fig. ); coarser resolution GCMs are discussed
further in Sect .
DJFMA (first column) air temperature, (second column) total
precipitation and (third column) snow water equivalent biases of the CMIP5
global climate models with spatial resolution equal to or finer than
1.25∘ longitude with respect to the EOBS and NSIDC-SNW climatologies
reported in Fig. a, b, c.
CMIP5 model biases with respect to EOBS and NSIDC-SNW references
(Fig. a–c) are shown Fig. . The comparison period is
1980–2005. Of the four CESM-family models, namely CESM1-CAM5, CESM1-BGC,
CESM1-FASTCHEM and CCSM4, three models present very similar climatologies so here
we consider only one of them, CESM1-BGC, which is taken to be representative
of CESM1-FASTCHEM and CCSM4 (see Fig. S2 in the Supplement and
Sect. for further details).
GCMs with spatial resolution equal to or finer than 1.25∘ show snow amounts which
are comparable to those of the reference data sets over the greater Alpine region. Compared to NSIDC-SNW, the models CMCC-CM, EC-Earth and, to a smaller
extent, MRI-CGCM3 and CESM1-CAM5, show thicker snowpack at the
northern slope of the Alps and in Switzerland. A common feature of all
data sets is a shallower snowpack over the eastern Alps, at the border between
Italy and Austria. This spatial pattern, characterized by an east–west
gradient, with shallower snowpack in the eastern Alps and thicker snowpack in
the western Alps, more closely resembles that provided by the AMSR-E satellite
product rather than that provided by NSIDC-SNW.
BCC-CSM1-1-M and CESM1-BGC show shallower snowpacks than NSIDC-SNW,
and higher temperatures with respect to the observational data sets. In these
cases the warm bias in the model can explain a less abundant snowpack.
From this analysis the precipitation bias over the Alpine ridge between the different high-resolution GCMs seems
to be comparable. In fact, GCMs generally
tend to a slight underestimation of winter precipitation at the ridges and to
an overestimation at lower altitudes. This uniform behaviour in the
precipitation pattern suggests that temperature can be the leading cause of biases in the estimation of surface snow water equivalent.
SNW in regional climate models
Figure shows the biases of ERA-Interim-driven regional climate
model DJFMA TAS as well as PR and SNW climatologies with respect to the EOBS and NSIDC-SNW references,
all averaged over the common period 1990–2005.
All RCMs show SNW amounts several hundreds of kg m-2 larger than any
other reference data set (Fig. ) at the mountain ridge and lower
values at low elevations. Extremely high values (shown in black) are
not reliable as they correspond to areas of continuous snow accumulation and
no melting, possibly areas masked as glaciers in the models. Such grid points
show artificially high erroneous, positive trends and they have to be
discarded from the analysis. Despite these details, RCM snow estimates are
much higher than those provided by the reference data sets, and these high
values can be related to the fine representation of the orography that
allows, in principle, for lower temperatures in high mountain areas that are not
represented in coarse-scale reanalyses, for increased solid precipitation and
longer snowpack duration.
In some cases the large SNW values in RCMs can be partly explained by cold
biases (RACMO22E, ALADIN53) or wet biases (HIRHAM5) with respect to the
observations. In other cases (CCLM4-8-17), despite remarkable biases in some
parts of the domain, the atmospheric forcings in correspondence of the
mountain ridge are in better agreement with observations and they do not show
relevant deviations from the reference climatologies, so the differences have
to be attributed to the snow scheme in use and/or to the finer representation
of the topography.
From the analysis of RCMs we can conclude that higher spatial resolution
allows areas of snow accumulation to be better separated and, consequently, to
reproduce higher snow maxima in correspondence of mountain peaks.
For the CCLM4-8-17 and REMO2009 models, which display no issues in the snow
accumulation trends, we also investigated the GCM-driven simulations
(Table ). GCM-driven CCLM4-8-17 climatologies have a stronger
negative temperature bias (CNRM-CM5, EC-Earth, HadGEM2-ES) and/or stronger
positive precipitation biases (CNRM-CM5, MPI-ESM-LR) with respect to the
ERA-Interim-driven runs (Fig. S3). These features result in thicker snow water
equivalent. In the case of MPI-ESM-LR-driven REMO2009 the temperature bias is
comparable while the precipitation bias is larger than for the ERA-Interim-driven runs.
In conclusion, GCM-driven RCM simulations tend to inherit the
biases already present in the driver GCM and to reflect them in SNW fields.
As in Fig. but for the CORDEX ERA-Interim-driven RCM
simulations, averaged over the period 1990–2005.
Global view of SNW products
In this section we provide a comprehensive view of all the previously
considered SNW gridded data sets. The similarity of the SNW climatologies is
quantified using the metrics of Taylor diagrams .
Figure a compares the spatial distribution of the DJFMA snow
water equivalent, averaged over the period 1980–2005, for the
multi-reference mean (MRM, mean of the four reference data sets CFRS, MERRA,
ERA-Interim/Land and NSIDC-SNW) to which all other data sets are
compared; the multi-model mean (MMM), mean of all 36 CMIP5 models; the
multi-model mean of the CMIP5 models with spatial resolution equal to or finer than
1.25∘MMM-HiRes, as in; the individual
reference data sets; and the individual regional and global climate models.
Taylor diagrams of the multiannual mean (1980–2005) of the DJFMA
average snow water equivalent as described by climate models against the
multi-reference mean (MRM): (a) all data sets are projected onto the
same reference grid at 0.7∘lon; (b) the climate models are
kept at their original resolution and the reference data sets are remapped
onto the grid of each model. Points included in the rectangles correspond to
models highlighted with ** in the legend.
First we compare data sets built on different coordinate reference systems and
with different spatial resolutions by reprojecting all remote sensing
products, reanalyses and climate model outputs onto a common grid,
specifically the ERA-Interim/Land 0.7∘ longitude grid.
Figure a provides an evaluation of the individual data sets with
respect to the multi-reference mean, all resampled on the same 0.7∘
grid. Reference data sets are generally highly correlated with the MRM (R>0.85 for all data sets except the coarsest 20CR). This feature is related to
the dependence of the snow water equivalent on topography; i.e. these data sets
represent larger SNW values at higher altitudes. Satellite products and the
CFSR reanalysis are very close to each other, with lower variance with
respect to the MRM. The MERRA reanalysis is close to the MRM, with comparable
standard deviation and small RMSE. The ERA-Interim/Land and 20CR reanalyses
show opposing behaviours in terms of normalized standard deviation, i.e. very
high and very low respectively. ERA-Interim/Land has a wider statistical
dispersion of SNW values and higher SNW peaks, clearly reflected in
Fig. o, while 20CR has a narrow range of SNW values and a smooth
SNW pattern (Fig. r).
Of the two RCMs considered, REMO2009 is in better agreement with the MRM in
terms of RMSE and NSD. CCLM4-8-17 has a large normalized standard deviation,
which is
comparable to that found in ERA-Interim/Land. All GCM-driven simulations show
higher variance with respect to the corresponding ERA-Interim-driven runs.
For GCMs, an important feature emerging from this analysis is that, on
average, the ensemble mean of the high-resolution models performs better in
terms of standard deviation, root mean square difference and pattern
correlation, with respect to the ensemble mean of all CMIP5 GCMs. This result
highlights the importance of the horizontal resolution in simulating snowpack
spatial patterns .
An alternative approach has been devised to provide a fair comparison of the
GCMs. Each GCM is compared to the MRM after having conservatively remapped
each reference data set onto the individual GCM grid, so that the reference
is reshaped each time according to the model resolution. This approach allows
for a fair evaluation of each GCM on its own grid, regardless of its
resolution. For the sake of clarity, we present the results relative to this
approach by separately plotting the models with resolutions equal to or
finer and coarser than 1.25∘ (Fig. 5b). The
clustering based on spatial resolution reveals that coarse resolution models
generally have very high or very low standard deviation (please note that the
CNRM-CM5 model lays outside the range of the plot). In such circumstances the
ensemble mean of the models is the result of compensating extreme behaviours,
and it should be considered with caution. On the contrary, individual high-resolution GCMs are generally closer to the MRM and do not exhibit extreme
features, constituting a more homogeneous ensemble.
Figure provides information on the similarity of SNW
climatologies and, indirectly, qualitative information on the degree of
interdependency of the models belonging to the same “family”. For example,
among the previously mentioned four CESM-family models, namely CESM1-CAM5,
CESM1-BGC, CESM1-FASTCHEM and CCSM4, three models show a high degree of
similarity (Figure b). In the calculation of the MMM-HiRes,
in order to limit the bias related to the interdependency of the models, out
of these three similar models we retained only one, CESM1-BGC. In the
following we will use the term “high-resolution GCMs” to indicate only the following six models: CMCC-CM,
EC-Earth, MRI-CGCM3, BCC-CSM1-1-M, CESM1-BGC and CESM1-CAM5. These models are
further analysed in the following sections. The interdependency of lower-resolution GCMs is not clearly detectable from the Taylor diagram and it is
not investigated further as these models are not the main focus of the paper,
owing to their overall poor performance in the representation of SNW.
Annual cycle of snow water equivalent
In Fig. a–b we show the annual cycle of snow water equivalent as
represented by the reference data sets and by the high-resolution GCMs. The
monthly SNW at elevations higher than 1000 m a.s.l is spatially averaged
over the greater Alpine region and temporally averaged over the common period
1980–2005 (see Sect. for details).
(a) Annual cycle of snow water equivalent in the reference
data sets and (b) in CMIP5 high-resolution GCMs (spatial averages
over areas above 1000 m a.s.l., temporal averages over the historical period
1980–2005). (c) Annual cycle in ERA-Interim-driven and GCM-driven
regional climate model simulations, calculated over the period 1990–2005, in
comparison to reference data sets and GCM simulations.
The annual cycle in the reference data sets displays a unimodal distribution,
with the maximum occurring in different months from January to March for
different data sets. The spread in the reference data sets is quite large,
ranging from about 40 kg m-2 SNW peak in January in the NSIDC-SNW
satellite product to 150 kg m-2 SNW peak in March in ERA-Interim/Land.
These two products have the most extreme behaviour. NSIDC-SNW and CFSR show a very
similar annual cycle (and comparable spatial patterns), while MERRA presents
intermediate values between these two and ERA-Interim/Land. The MRM peaks in
February, at about 75 kg m-2. The spread among the high-resolution
GCMs is also rather large, as it is for the reference data sets. Snow water
equivalent maximum values range from 3 kg m-2 according to
BCC-CSM1-1-M to about 90 kg m-2 according to EC-Earth. CESM1-BGC and
BCC-CSM-1-1-M show very shallow SNW (few kg m-2) throughout the year
and a much shorter snow season, owing to a large positive bias in air
temperature (Fig. 3g, m). CMCC-CM and EC-Earth display
above-average values, with EC-Earth reproducing a snow cycle similar to
ERA-Interim/Land but with lower amplitude. The similarity between EC-Earth
and ERA-Interim/Land is likely related to the fact that they use the same land-surface model, HTESSEL .
As in the case of the MRM, the MMM-HiRes peaks in February but with lower SNW values of approximately
50 kg m-2. With respect to the reference ensemble mean, the GCM
ensemble mean tends to underestimate SNW throughout the snow season.
An important outcome of this analysis is that the reference data sets exhibit
a large spread in the Alps. As a consequence, any assessment based on the use
of individual data sets within this ensemble and within this region should be
taken with extreme caution.
Figure c shows a synthetic view of the SNW annual cycle as in the
RCM simulations compared to the reference data sets and to GCMs. ERA-Interim-driven simulations provide similar results to the reference data sets. In
particular the ERA-Interim-REMO2009 annual cycle is close to the ensemble
mean of the reference data sets and the ERA-Interim-CCLM4-8-17 annual cycle is
close to that provided by ERA-Interim/Land. Relatively larger snow water
equivalent values by the CCLM4-8-17 model can be related to wetter conditions
(Fig. a, b) which probably result in larger snow accumulation.
GCM-driven simulations overestimate the SNW annual cycle in comparison to
their ERA-Interim-driven counterparts. REMO2009, when driven by MPI-ESM-LR
GCM, provides SNW values close to the maximum values found in reference
data sets, and CCLM-4-8-17, irrespective of the driving GCM, shows notably
thicker snowpack than any reference data sets and/or GCM. The snow peak is
about three times higher than the reference ensemble mean, up to almost twice the
ERA-Interim-driven value, and it is shifted later in the snow season. Such
an outcome reflects the biases inherent in the driving GCMs, which result in
large errors in SNW estimates.
An important hint of this analysis is that despite the large differences in
horizontal resolutions, the reference data sets, selected high-resolution
GCMs and the ERA-Interim-driven RCMs provide comparable results in terms of
SNW when the quantities are spatially averaged over the Alpine domain.
Unfortunately the uncertainty on the SNW annual cycle as represented by these
data sets is large, and conclusive statements on the accuracy of these SNW
estimates require a reliable ground truth to validate the model results.
Future changes in the annual cycle of SNW
Figure a shows the projected annual cycle of snow water
equivalent for the mid-21st century (2040–2065) in the RCP8.5 scenario compared
to the historical annual cycle (1980–2005), according to the high-resolution
CMIP5 models. Both the ensemble mean and the spread of GCMs are shown. The
SNW peak is expected to reduce by more than 50 % in the future, with
respect to the historical multi-model mean. The uncertainty on the amplitude
of the snow peak is, however, very large and the value depends upon the
selected GCM. The spread in the percent changes of SNW according to the
various models (Fig. b) reveals the degree of inter-model
consistency. The largest uncertainty is found in summer months, i.e. when
snow cover persists only at high altitudes, and it can be very shallow.
EC-Earth shows a smaller reduction while all the other models predict almost
complete snow loss, on average, over the Alpine region (not shown). The
lowest reduction is found in December, when the projected decrease ranges
between -20 and -70 % depending on the model.
For comparison we also analyse the projected changes in the annual SNW cycle
according to the REMO2009 model and to the CCLM4-8-17 model driven by
different GCMs (Fig. b). Interestingly, the percent SNW reduction
according to RCMs, although still remarkable, is lower compared to CMIP5
GCMs, especially in the spring season. From February to April the percent SNW
change reported by RCMs lies outside the range of variability of CMIP5
models. The robustness of this result should be verified by considering a
larger RCM ensemble, as soon as additional RCM simulations become
available. Figure b also shows the influence of the driving GCM
on SNW change. The spread among the different RCM simulations allows
for an evaluation of the impact of the uncertainty due to the drivers of the snow
changes, and its amplitude stresses the importance of performing ensemble
analyses.
(a) Annual cycle of snow water equivalent expected by the mid-21st century in the RCP8.5 scenario compared to the baseline 1980–2005, as
provided by the high-resolution CMIP5 models. (b) Percent change in
snow water equivalent (2040–2065 average with respect to the baseline
1980–2005) as in the high-resolution CMIP5 GCMs (box plot) and RCM
simulations.
Discussion
We tested the agreement and the uncertainties of the main snow water
equivalent data sets, including remote sensing products, reanalyses, global
and regional climate models, in reproducing the spatial pattern and the
annual cycle of snow over the greater Alpine region. The spatial and temporal
distribution of SNW is the result of the complex interaction of temperature,
precipitation, solar radiation, wind and local geographical features. In
mountain areas, in particular, meteo-climatic variables are characterized by
high spatial variability depending, among other factors, on elevation, slope,
aspect and exposure to wind. The grid resolution of the remote sensing,
reanalysis and climate model products is clearly insufficient to properly
represent the spatial variability of snow water equivalent at small scales
and at specific locations. For this reason, this study is aimed at analysing
this ensemble of largely used data sets for regional assessment and
quantifying their consistency and degree of agreement in reproducing the
average snow conditions at their own resolution.
The reference data sets provide very different pictures of the multiannual
mean DJFMA snow water equivalent in the greater Alpine region. The
satellite-derived data sets and CFSR compare better with each other than with
the other products. The two satellite products are based on similar
algorithms but rely on different radiometer observations, and AMSR-E doubles
the spatial resolution of SMMR and SSM/I. NSIDC-SNW and CFSR are likely more
similar to each other because CFSR integrates snow analyses based on the same
SSM/I observations used by the snow algorithm employed in NSIDC-SNW .
It is worth stressing that CFSR is, of all the reanalyses considered in this
study, the only one based on atmospheric–ocean–sea-ice coupling. It has the
highest horizontal resolution and, as ERA-Interim/Land, it is driven by
observed rather than by forecasted precipitation fields. Interestingly, the
analysis system used in CFSR for the atmosphere is similar to the one used in
MERRA and although they use almost the same input data they
have rather dissimilar snow water equivalent climatologies. MERRA shows a
snow distribution comparable to ERA-Interim/Land, likely because they
assimilate observations from the same sources and they are run at similar
horizontal resolutions. MERRA compares better to the MRM in terms of
normalized standard deviation and RMSE, while ERA-Interim/Land displays
higher snow values in agreement with the results obtained at the Northern
Hemispheric scale and over the Hindu-Kush Karakoram Himalaya region
. The ERA-Interim/Land and 20CR reanalyses show opposing
behaviour, i.e. very high and very low spatial variability respectively. In
particular the 20CR snow water equivalent fields are extremely smooth with
respect to all other data sets. This behaviour has been related to a strong
warm bias in air temperature corresponding to the Alpine ridge.
The documented wide range of uncertainty has to be taken into account when
using these snow data sets. Some discrepancies can be explained by possible
biases in the drivers of snow processes, the use of different land-surface
models, different snow schemes and different data assimilation methods, as
discussed above. Additional weak points of these products are (i) their low
spatial resolution with respect to what would be required to represent
snowpack processes in mountain environments and (ii) the limited or null
constraint by surface snow depth or snow water equivalent observations at
high elevations (i.e. no snow assimilation). At the global scale, the spread
over mountain regions has been estimated to be several times larger than over
non-mountainous midlatitude regions . Reducing this gap
through improvements in the horizontal resolution and enhanced assimilation
of surface data will open new perspectives for a more reliable representation
of snow resources in mountain regions at regional to global scales. Efforts
have already been spent to provide reliable atmospheric fields to
land-surface and snow schemes, for example improving precipitation in CFSR
and ERA-Interim/Land. Further inclusion of a better resolved topography
allows for a more realistic representation of snow processes and could
mitigate the issue of upscaling surface measurements at the model grid in the
assimilation process.
GCMs have evident limitations in representing the distribution of altitudes
in the greater Alpine region, with the most resolved models underestimating
the 95th percentile of the distribution by 500–800 m. GCMs do not take into
proper account elevations above 1500–2000 m a.s.l. which are simply
not represented in most models (see also Fig. S4 for further details on the
elevation ranges represented in each data set). On the other hand, the
analysis of the CMIP5 GCMs reveals that models with spatial resolution finer
or equal to 1.25∘ are in better agreement with the ensemble mean of
the reference data sets than the whole GCM ensemble. Compared to low-resolution models, the high-resolution models form a more homogeneous cluster
with no extreme behaviour and a higher score (lower RMSE and relative standard
deviation closer to one). Provided that high-resolution GCMs have different
characteristics and different land-surface model components
(Table ), their better performance is likely due to the
(relatively) finer spatial resolution. This analysis clearly indicates the
added value of snow simulations at higher horizontal resolution, even for the
typical resolutions of GCMs.
The EURO-CORDEX regional downscaling experiment further elucidates how
horizontal resolution can affect the representation of the snow processes in
mountain areas. The results from the currently available simulations at
0.11∘ resolution (five ERA-Interim-driven models) show a much thicker
average snowpack over the alpine ridge and shallower snowpack at low
elevations with respect to the reference data set. This behaviour, related to
the finer-resolution RCM, is sometimes smoothed out when snow water equivalent
is spatially averaged over the Alpine domain. At the regional scale, the annual
cycle represented by ERA-Interim-driven RCMs results comparable to those
found in the reference data sets and in GCMs. Important deviations from the
reference data sets arise in GCM-driven RCM simulations, owing to the biases
inherent in the GCM forcing.
The influence of the single model bias with respect to the reference has been
minimized by analysing the future change in snow water equivalent with
respect to the historical mean, i.e. by considering anomalies. GCM
projections agree in showing a strong reduction of snow resources by the mid-21st
century in the RCP 8.5 scenario, especially in the spring season. The
uncertainties on the amplitude of the snow water equivalent change are large,
but the signal is coherent across all models.
Future RCM projections show weaker snow reductions with respect to the
coarser-scale high-resolution GCMs, especially in spring, when
future snow projections appear particularly uncertain. While a few regional
models can have limited representativeness of the whole EURO-CORDEX ensemble
and a larger set of simulations has to be considered as soon as they become
available, this analysis highlights the large discrepancy among the
considered data sets over the historical period and calls for a reference
observation-based product that could reliably represent the ground truth.
Conclusions
This study shows that the spatial and temporal distribution of snow water
equivalent in the greater Alpine region (one of the most measured mountain
regions in the world) is quite uncertain. The main available gridded snow
water equivalent data sets are derived from remote sensing observations and
reanalyses but they have never been properly validated in mountain regions
owing to the limited availability of in situ snow observations. In this work,
we compared such data sets to highlight the degree of agreement in the mean
climatologies, to quantify their spread and assess the uncertainties
associated with snow estimates. These data sets provide very different
pictures of the snow spatial distribution and seasonal cycle. Of course,
mountain regions have non-optimal conditions to test these coarse-grid
data sets, as surface heterogeneity at subgrid scale is difficult to
represent, both for remote sensing and reanalysis data. This argument enforces
the evidence that we currently lack proper information on snowpack
distribution at mountain range scale. Knowledge of the long-term variability
of the snowpack at high spatial resolution and at mountain
range scale is limited but necessary for climate studies, for
calibrating/validating models, for data assimilation in the reanalysis
products and for assessing seasonal water resources. In our opinion,
improving the open availability and the exchange of in situ snow observations
and developing gridded snow data sets representative of the ground truth in
mountain regions is a priority for advancing cryospheric/hydrologic
research in mountain environments.
A second method of improving snow estimates in mountain areas in both
reanalyses and climate models is to pursue high-resolution simulations to
allow for a better representation of the main drivers of the snow processes,
i.e. temperature and precipitation patterns and their dependence on
elevation. An increased horizontal resolution, and thus a more accurate
representation of topography, allows for a better description of the spatial
distribution and phase of precipitation and of altitudinal temperature
gradients. New insights on this topic are expected by the High RESolution
Model Intercomparison Project , the CMIP6-endorsed
coordinated experiment that will provide an ensemble of GCM runs at spatial
resolutions significantly finer than the current generation CMIP5 models.
A further goal is the refinement of the representation of snowpack processes,
which at the moment are drastically simplified, in global climate and earth
system models (ESMs). This issue is being addressed by the ESM-SnowMIP
initiative see also
http://www.climate-cryosphere.org/activities/targeted/esm-snowmip
through coordinated experiments to evaluate snow modules of large-scale
climate models and quantify the required complexity to be represented in
ESMs.
The present study contributes to these challenges by providing a picture
of the main available snow products and measuring the related uncertainties
in the Alpine environment. The relative assessment of the capability of
satellite-based products, reanalyses, RCMs and GCMs in reproducing snowpack
features provides important information to both model developers and to the
community of users, allowing critical factors in the model
components to be identified and raising awareness of the strengths and limitations of the available
products.
All the data sets used in this study are publicly accessible
and were downloaded from the following websites: CMIP5 and CORDEX model
simulations, https://esgf-data.dkrz.de/projects/esgf-dkrz/; NSIDC Global Snow
Water Equivalent climatology and AMSR-E products, https://nsidc.org/;
CFSR reanalysis, https://rda.ucar.edu/; MERRA reanalysis,
https://mirador.gsfc.nasa.gov/; ERA-Interim/Land reanalysis,
http://apps.ecmwf.int; EOBS, http://www.ecad.eu; HISTALP,
http://www.zamg.ac.at/histalp/.
The Supplement related to this article is available online at https://doi.org/10.5194/tc-11-1625-2017-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
This work has received funding from the European Union's Horizon 2020
research and innovation programme under Grant Agreements No. 641762
(ECOPOTENTIAL), No. 641816 (CRESCENDO) and No. 641727 (PRIMAVERA). This work was also supported by
the Italian project of Interest NextData of the Italian Ministry for
Education, University and Research. We acknowledge the World Climate Research
Programme's Working Group on Coupled Modelling and Working Group on Regional
Climate, which are responsible for CMIP5 and CORDEX, and we thank the climate
modelling groups (listed in Tables and ) for
producing and making available their model output. For CMIP the U.S.
Department of Energy's Program for Climate Model Diagnosis and
Intercomparison provides coordinating support and led development of software
infrastructure in partnership with the Global Organization for Earth System
Science Portals. We also acknowledge the EOBS data set from the EU-FP6
project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data
providers in the ECA&D project (http://www.ecad.eu). We finally thank
the two referees for their valuable comments which allowed us to significantly
improve the paper.Edited by: Xavier Fettweis
Reviewed by: Yves Cornet and one anonymous referee
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