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This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).
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Geosci. Model Dev., 11, 5027–5049, 2018
https://doi.org/10.5194/gmd-11-5027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ESM-SnowMIP: assessing snow models and quantifying
snow-related climate feedbacks
Gerhard Krinner1, Chris Derksen2, Richard Essery3, Mark Flanner4, Stefan Hagemann5, Martyn Clark6, Alex Hall7,
Helmut Rott8, Claire Brutel-Vuilmet1, Hyungjun Kim9, Cécile B. Ménard3, Lawrence Mudryk2, Chad Thackeray7,
Libo Wang2, Gabriele Arduini10, Gianpaolo Balsamo10, Paul Bartlett2, Julia Boike11,12, Aaron Boone13,
Frédérique Chéruy14, Jeanne Colin13, Matthias Cuntz15, Yongjiu Dai16, Bertrand Decharme13, Jeff Derry17,
Agnès Ducharne18, Emanuel Dutra19, Xing Fang20, Charles Fierz21, Josephine Ghattas22, Yeugeniy Gusev23,
Vanessa Haverd24, Anna Kontu25, Matthieu Lafaysse26 , Rachel Law27, Dave Lawrence28, Weiping Li29,
Thomas Marke30, Danny Marks31 , Martin Ménégoz1, Olga Nasonova23, Tomoko Nitta9, Masashi Niwano32,
John Pomeroy20, Mark S. Raleigh33, Gerd Schaedler34 , Vladimir Semenov35, Tanya G. Smirnova33, Tobias Stacke36,
Ulrich Strasser30, Sean Svenson35 , Dmitry Turkov37, Tao Wang38, Nander Wever21,39,40, Hua Yuan16, Wenyan Zhou29,
and Dan Zhu41
1CNRS, Université Grenoble Alpes, Institut de Géosciences de l’Environnement (IGE), 38000 Grenoble, France
2Climate Research Division, Environment and Climate Change, Toronto, Canada
3School of GeoSciences, University of Edinburgh, Edinburgh EH9 3FF, UK
4Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor Michigan 48109, USA
5Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
6Hydrometeorological Applications Program, Research Applications Laboratory, National Center for Atmospheric
Research, Boulder, USA
7Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
8ENVEO – Environmental Earth Observation IT GmbH, Innsbruck , Austria
9Institute of Industrial Science, the University of Tokyo, Tokyo, Japan
10European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
11Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
12Department of Geography, Humboldt University of Berlin, Berlin, Germany
13CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
14LMD-IPSL, Centre National de la Recherche Scientifique, Université Pierre et Marie-Curie, Ecole Normale Supérieure,
Ecole Polytechnique, Paris, France
15INRA, Université de Lorraine, AgroParisTech, UMR Silva, 54000 Nancy, France
16School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
17Center for Snow and Avalanche Studies, Siverton, CO, USA
18Sorbonne Universités, UMR 7619 METIS, UPMC/CNRS/EPHE, Paris, France
19Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
20Centre for Hydrology, University of Saskatchewan, Saskatoon, Canada
21WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
22Institut Pierre Simon Laplace (IPSL), UPMC, 75252 Paris, France
23Institute of Water Problems, Russian Academy of Sciences, Moscow, Russia
24CSIRO Oceans and Atmosphere, Canberra, ACT, Australia
25Space and Earth Observation Centre, Finnish Meteorological Institute, 99600 Sodankylä, Finland
26Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France
27CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia
28National Center for Atmospheric Research, Boulder, CO, USA
29National Climate Center, China Meteorological Administration, Beijing, China
30Department of Geography, University of Innsbruck, Innsbruck, Austria
Published by Copernicus Publications on behalf of the European Geosciences Union.
5028 G. Krinner et al.: ESM-SnowMIP
31USDA Agricultural Research Service, Boise, ID, USA
32Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
33Cooperative Institute for Research in Environmental Science/Earth System Research Laboratory, NOAA, Boulder, CO,
USA
34Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
35A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia
36Max-Planck-Institut für Meteorologie, Hamburg, Germany
37Institute of Geography, Russian Academy of Sciences, Moscow, Russia
38Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing, China
39Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado, USA
40Laboratory of Cryospheric Sciences, School of Architecture and Civil Engineering, Ecole Polytechnique Fédérale
de Lausanne, Lausanne, Switzerland
41Laboratoire des Sciences du Climat et de l’Environnement, CEA, CNRS, UVSQ, Gif Sur Yvette, France
Correspondence: Gerhard Krinner (gerhard.krinner@cnrs.fr)
Received: 25 June 2018 – Discussion started: 30 July 2018
Revised: 14 November 2018 – Accepted: 19 November 2018 – Published: 10 December 2018
Abstract. This paper describes ESM-SnowMIP, an interna-
tional coordinated modelling effort to evaluate current snow
schemes, including snow schemes that are included in Earth
system models, in a wide variety of settings against local and
global observations. The project aims to identify crucial pro-
cesses and characteristics that need to be improved in snow
models in the context of local- and global-scale modelling.
A further objective of ESM-SnowMIP is to better quantify
snow-related feedbacks in the Earth system. Although it is
not part of the sixth phase of the Coupled Model Intercom-
parison Project (CMIP6), ESM-SnowMIP is tightly linked to
the CMIP6-endorsed Land Surface, Snow and Soil Moisture
Model Intercomparison (LS3MIP).
1 Introduction
Snow is a crucial cryospheric component of the climate sys-
tem. It perennially covers the large continental ice sheets al-
most entirely and the Earth’s sea ice and a large fraction of
the Northern Hemisphere ice-free terrestrial areas seasonally.
Due to its particular physical properties, snow plays multi-
ple roles in the Earth system. Its high albedo gives rise to
the positive snow albedo feedback (Flanner et al., 2011; Qu
and Hall, 2014) that amplifies global climate variations and is
thought to be a driver of the observed Arctic amplification of
the current global warming (Bony et al., 2006; e.g. Chapin III
et al., 2005; Pithan and Mauritsen, 2014; Serreze and Barry,
2011) and the observed amplification of global warming at
high altitudes (Palazzi et al., 2017; Pepin et al., 2015). Simi-
larly, snow acts as a “fast climate switch” on shorter (weekly
and seasonal) timescales, with observed strong coupling be-
tween temperature and snow cover (Betts et al., 2014) on re-
gional scales. Insulation of underlying soil in winter strongly
influences the soil temperature regime and thus the thermal
state of permafrost and its carbon balance (Cook et al., 2008;
Gouttevin et al., 2012; e.g. Groffman et al., 2001; Park et al.,
2015; Vavrus, 2007; Zhang, 2005). In addition, snow influ-
ences the ecosystem carbon balance by protecting low veg-
etation in winter from frost damage (Sturm et al., 2001) and
by conditioning the springtime onset of the growing season
(Pulliainen et al., 2017). Furthermore, substantial impacts of
the presence of snow cover on the atmospheric circulation
have been found (Cohen et al., 2012; e.g. Vernekar et al.,
2010; Xu and Dirmeyer, 2011); in general, the coupling be-
tween snow and atmosphere is strongest during the melting
season, and the effect of snow was found to last well into
the snow-free season because of its delayed effect on soil hu-
midity (Xu and Dirmeyer, 2011). Linked to its effect on soil
humidity, snow has an obvious and profound impact on water
availability in snow-dominated regions (Barnett et al., 2005),
and large potential economic impacts of snowpack decrease
in a warming climate can be expected regionally (e.g. Fyfe et
al., 2017; Sturm et al., 2017).
Observed global and (predominantly northern) hemi-
spheric trends of snow cover extent and duration over the
recent decades are consistently negative and linked to the ob-
served warming trends (Brown and Mote, 2009; Derksen and
Brown, 2012; e.g. Déry and Brown, 2007). Observed snow
mass follows similar trends (Cohen et al., 2012; Räisänen,
2008), except in high northern latitudes where warming leads
to higher moisture availability (Mote et al., 2018; Räisä-
nen, 2008). Global and hemispheric seasonal snow mass, ex-
tent, and snow cover duration are consistently projected to
decrease with ongoing warming (e.g. Brutel-Vuilmet et al.,
Geosci. Model Dev., 11, 5027–5049, 2018 www.geosci-model-dev.net/11/5027/2018/
G. Krinner et al.: ESM-SnowMIP 5029
2013; Collins et al., 2013; Mudryk et al., 2017; Thackeray et
al., 2016).
The snow modules included in global coupled climate
models which are used for producing these projections come
in varying degrees of complexity, from very simple slab mod-
els with prescribed physical properties of snow to more so-
phisticated multilayer models that represent processes such
as prognostic albedo, snowpack compaction, liquid water
percolation, snow interception, and unloading by vegetation,
microstructure in more or less detail. For example, widely
varying treatments of the vegetation masking of snow in for-
est areas are suspected to be a major reason for large inter-
model variations of the intensity of the snow albedo feedback
(Qu and Hall, 2014; Thackeray et al., 2018). Besides snow
modules included in the land surface parameterization pack-
ages of large coupled Earth system models (ESMs), there is
a large number of other snow models for a range of appli-
cations, with their degree of complexity depending on the
intended applications (e.g. Magnusson et al., 2015).
Particularly in very cold conditions, it is clear that some
important physical processes affecting snow are not captured
even by the most detailed physically based snow models, for
example the appearance of inverted density gradients due to
water vapour fluxes (Domine et al., 2016; Gouttevin et al.,
2018). A further important and rarely represented process is
wind-blown snow and its sublimation (Pomeroy and Jones,
1996). In addition to these physical processes, accurate rep-
resentation of vegetation distribution and parameters is also
found to be critical for realistic simulation of surface albedo
for snow-covered forests (Essery, 2013; Loranty et al., 2014;
Wang et al., 2016). Interestingly, however, the behaviour of
many snow models can be emulated with multi-physics mod-
els (Essery, 2015; Lafaysse et al., 2017). This suggests that
in spite of their large variety, “many of them draw on a small
number of process parameterizations combined in different
configurations and using different parameter values” (Essery
et al., 2013). This gives reason to hope that some persistent
problems of snow models, and some snow-related problems
in ESMs, could in fact be tackled fairly easily, sometimes
simply by careful parameter choices. In ESMs, these prob-
lems include, for example, the representation of snow mask-
ing by vegetation (Essery, 2013; Thackeray et al., 2015), the
thermal (Cook et al., 2008; Gouttevin et al., 2012; Slater et
al., 2017) and radiative (Flanner et al., 2011; Thackeray et
al., 2015) properties of the snowpack, and a resulting per-
sistent large uncertainty concerning the emergent strength of
the planetary snow albedo feedback (Flanner et al., 2011; Qu
and Hall, 2014). However, it is also clear that even if there is
room for improvement in the snow schemes currently used in
ESMs, the current knowledge of snow physics also maintains
an irreducible uncertainty in snow modelling, even in the
most detailed snowpack models currently available (Lafaysse
et al., 2017). Additional uncertainty in snow modelling of-
ten comes from imperfect meteorological driving data (e.g.
Raleigh et al., 2015; Schlögl et al., 2016).
Several model intercomparison exercises focusing on
snow, or at least regions that are heavily influenced by the
seasonal presence of snow, have been carried out in the
past, notably the Programme for Intercomparison of Land-
Surface Parameterization Schemes (PILPS) Phase 2d (Slater
et al., 2001) and Phase 2e (Bowling et al., 2003) as well as
SnowMIP Phase 1 (Etchevers et al., 2004) and Phase 2 (Es-
sery et al., 2009; Rutter et al., 2009). These intercomparisons
have highlighted some common problems of snow models
such as masking of viewable snow by trees and internal pro-
cesses affecting snow physical properties, particularly during
the melting season, leading to potentially large errors in the
simulated date of disappearance of the seasonal snow cover.
These previous intercomparison exercises have been carried
out at small scales, and one important conclusion of the most
recent one of these, SnowMIP Phase 2, was that the challenge
of evaluating snow models at larger scales, on which they are
often applied, needed to be tackled (Essery et al., 2009).
The purpose of this paper is to present a new, already on-
going initiative aiming at evaluating a large range of snow
models both at local and large scales, including, but not lim-
ited to, land surface models that are part of the ESMs con-
tributing to the current sixth phase of CMIP (Eyring et al.,
2016). The initiative presented here, called ESM-SnowMIP,
is an extension of LS3MIP (van den Hurk et al., 2016) in-
cluding site-scale evaluation and process studies as well as
complementary, snow-specific, large-scale (global) simula-
tions and analyses.
The overall objectives and rationale of ESM-SnowMIP are
presented in the following section. Section 3 describes the
planned experiments and analysis strategy, presenting some
initial results from the site-scale reference simulations. The
discussion in Sect. 4 concerns the expected outcome and im-
pacts of ESM-SnowMIP as well as possible future exten-
sions.
2 Objectives and rationale
Common conclusions emerging from previous snow model
intercomparisons in PILPS and SnowMIP (Essery et al.,
2009; Nijssen et al., 2003; Slater et al., 2001) are that there
are large differences between models and that these differ-
ences are largest at warmer sites, in warmer winters, and
during spring snowmelt. Little insight has been gained into
how to reduce this model uncertainty, but it is precisely in the
warmer regions that current snow cover is most “at risk” from
climate warming (Nolin and Daly, 2006) and where most
confidence in projections is required. There has also been a
lack of connection between intercomparisons at site scales
for which detailed analyses of snow processes are possible
and intercomparisons at global scales for which projections
of changes in snow cover are required.
The first objective of ESM-SnowMIP is to assess the cur-
rent state of the art of snow models on spatial scales rang-
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5030 G. Krinner et al.: ESM-SnowMIP
ing from the site scale to global scales. On the site scale, the
inclusion of newly developed multi-physics ensemble mod-
els (Essery, 2015; Lafaysse et al., 2017) will allow highly
controlled experiments to determine the influence of model
structure on model performance in snow simulations. The
availability of longer-term high-quality observations at a
larger range of sites than in previous intercomparison exer-
cises provides the opportunity for a more comprehensive as-
sessment of the current modelling capacity in different cli-
mate settings (see Sect. 3.1). Similarly, on the global scale,
a wealth of new Northern Hemisphere datasets based on ad-
vanced remote-sensing techniques and land surface models
driven by reanalysis allows for more meaningful evaluations
than has been possible in the past (see Sect. 3.2.1).
In this respect, one particular motivation of ESM-
SnowMIP is to take advantage of the multi-model CMIP6
setting and at the same time to make particular snow mod-
elling and observational expertise available to climate mod-
elling groups that in the past have not focused their atten-
tion on the representation of snow in their coupled mod-
els. CMIP6 provides the opportunity to evaluate the rep-
resentation of the historical evolution of seasonal snow in
global simulations with varying degrees of freedom, ranging,
for a given model, from global coupled ocean–atmosphere
simulations to atmosphere-only (AMIP) climate simulations
with prescribed oceanic boundary conditions (Gates, 1992),
to land-surface-only simulations (LMIP) forced by obser-
vationally based meteorological data (van den Hurk et al.,
2016). Combining the evaluation of these global-scale sim-
ulations with the detailed process-based assessment at the
site scale provides an opportunity for substantial progress
in the representation of snow, particularly in Earth system
models that have not been evaluated in detail with respect
to their snow parameterizations. Concerning this first ma-
jor objective of model evaluation and improvement, we aim
(1) at identifying the optimum degree of complexity required
and sufficient in global models to simulate snow-related pro-
cesses satisfyingly on large scales, (2) at identifying previ-
ously unrecognized weaknesses in these models, and (3) at
identifying feasible ways to correct these by including rele-
vant processes and setting model parameters judiciously. Be-
sides the site simulations using the reference model set-up,
additional simulations at the same scale are planned to iden-
tify the role of specific processes or snow properties such
as snow albedo and thermal conductivity. It is hoped that
the conjunction of global simulations with long site simu-
lations, including sensitivity tests with simplified parameter-
izations (e.g. fixed albedo), and the systematic comparison
with multi-physics models will provide insights into the op-
timum degree of complexity for the intended applications of
the various model types. Specifically, beyond the minimum
number of vertical levels required, we aim at getting a bet-
ter idea about how finely the time evolution of fundamental
physical properties of the snowpack (albedo, density, con-
ductivity, liquid water content) and interactions with vege-
tation need to be represented, particularly in climate mod-
els, in order to correctly simulate the most climate-relevant
snow-related variables (snow fraction, thermal insulation of
the underlying soil).
The second major objective of ESM-SnowMIP is to
better quantify snow-related global climate feedbacks.
In LS3MIP, simulations extending the Global Land–
Atmosphere Coupling Experiment–CMIP (GLACE–CMIP)
approach (Seneviratne et al., 2013) are planned to quantify
the combined land surface feedbacks involving snow and
soil moisture on interannual timescales and in the context
of projected future climate change. Complementary coordi-
nated simulations in ESM-SnowMIP, described in the rele-
vant section of this paper, aim at separating the effect of snow
from that of soil moisture in order to quantify each of these
independently. To this end, diagnoses of snow shortwave ra-
diative forcing as simulated by the participating ESMs are
carried out. Snow shortwave radiative forcing is a metric of
the radiative effect of snow cover within the climate system
(Flanner et al., 2011). ESM-SnowMIP is also complementary
to the Polar Amplification Model Intercomparison Project
(PAMIP: Smith et al., 2018), which aims at investigating the
climate feedbacks related to sea ice changes.
ESM-SnowMIP is part of the World Climate Re-
search Programme (WCRP) Grand Challenge “Melting Ice
and Global Consequences” (https://www.wcrp-climate.org/
grand-challenges/grand-challenges-overview, last access:
3 December 2018). As such, it is intended to ensure progress
in the understanding of snow-related processes and feed-
backs in the global climate and their depiction in global cli-
mate models in the context of ongoing global changes, which
are characterized by a decrease in the extent and mass of the
global cryosphere.
3 Experimental design and analysis strategy
As in CMIP6, experiments in ESM-SnowMIP are tiered. Tier
1 simulations are mandatory for all participating groups, pro-
vided their model structure is adapted to the specific exper-
iment; exceptions are snow models that are not part of an
ESM and which therefore do not participate in the coupled
experiments, even those that are labelled as Tier 1. The num-
ber of groups participating in Tier 2 simulations will neces-
sarily be lower than or equal to those participating in Tier 1
experiments; we anticipate that not all proposed Tier 2 exper-
iments will necessarily attract a sufficient number of partic-
ipating groups for a meaningful multi-model analysis to be
possible.
Global snow simulations are subject to uncertainty in
the meteorological data used to drive models (whether pro-
vided by bias-corrected reanalyses as in LS3MIP offline land
model experiments or by coupling with atmospheric models
as in CMIP6); global products providing vegetation and soil
characteristics for model parameters are often contradictory,
Geosci. Model Dev., 11, 5027–5049, 2018 www.geosci-model-dev.net/11/5027/2018/
G. Krinner et al.: ESM-SnowMIP 5031
and global observations of snow properties for evaluation of
models (e.g. for snow density and thermal conductivity) are
limited. To gain more insight into the behaviour of models
in coupled and uncoupled global snow simulations, ESM-
SnowMIP includes experiments using high-quality driving
and evaluation data from well-instrumented reference sites.
Only climate models will be able to perform the global
coupled simulations required for CMIP6, and their land sur-
face models will carry out global uncoupled simulations
driven with a prescribed meteorological forcing. However,
all models – both those that are coupled to an atmospheric
model and those that are not (i.e. stand-alone snow models)
– can perform the local uncoupled reference site simulations
for ESM-SnowMIP at much lower computational expense.
Models that have already completed the first round of refer-
ence site simulations, listed in Table 1, include land surface
schemes (LSS) of CMIP6 models, stand-alone snow physics
models, hydrological models, and multi-physics ensemble
models.
The experiments proposed in ESM-SnowMIP, with links
to relevant LS3MIP reference simulations where appropri-
ate, are listed in Table 2 and described in detail in this
section. First, we describe single-point experiments, part of
which have already been carried out and are currently be-
ing analysed. Then, the spatially distributed simulations are
described. These latter simulations will be carried out by a
subset of the models participating in ESM-SnowMIP, namely
global land surface models (LSM) that are also components
of an ESM. Finally, we describe planned experiments with
land–atmosphere coupling.
3.1 Local scale
3.1.1 Overview of the sites, models, forcing, and
evaluation data
These single-point experiments are enabled by the consid-
erable efforts of organizations maintaining the sites to com-
pile, quality control, gap fill, and publish their data. Even
these reference sites cannot provide all of the input data
required by the most sophisticated snow physics models,
such as shortwave radiation partitioned into direct and dif-
fuse components or aerosol deposition fluxes. Details on the
sites used in a first round of ESM-SnowMIP reference site
simulations that have already been completed are given in
Table 3, and temperature and snowfall statistics are shown in
Fig. 1 (the forcing data provide separate rainfall and snow-
fall rates). Alpine, Arctic, boreal, and maritime sites have
been included in the first round of simulations, and a sec-
ond round will introduce tundra and glacier sites. The chal-
lenges of maintaining unattended hydrometeorological mea-
surements in cold and snowy environments and a require-
ment for multiple years of data limit the number of possible
sites, but the range of sites and the numbers of years simu-
Figure 1. Winter (DJF) temperatures and annual snowfall averaged
over the forcing data periods at the ESM-SnowMIP reference sites
(see Table 3).
lated in ESM-SnowMIP far exceed those in similar experi-
ments for SnowMIP and PILPS2d.
3.1.2 Tier 1: reference site simulations (Ref-Site)
Measurements of snow water equivalent (SWE) and depth,
and thus also bulk snow density, are available for all of the
reference sites. Several sites also have albedo, surface tem-
perature, and soil temperature measurements. As examples,
Figs. 2 and 3 show measurements and simulations at Col de
Porte, which has mild and wet winters, and Sodankylä, which
is cold and dry. Observations of SWE, snow depth, surface
albedo, and soil temperature are within the model spread and
close to the model ensemble means. At the warmer site, the
simulations of SWE and depth spread out rapidly as snow
accumulates, but most of the soil temperature simulations re-
main within a relatively narrow range. Some models have
rather low albedos, leading to early melt, and other models
melt the snow too late. Apart from a few outliers, SWE sim-
ulations at the colder site remain tightly bunched until the
spring, but there is a wide spread in winter soil temperature
simulations. Some models maintain soil temperatures under
snow close to 0 C, whereas other models are much too cold.
For both sites, there is a strong reduction in model tempera-
ture spread as soils cool in autumn before the onset of snow
cover and soil freezing.
With several observed variables available for comparison
with model outputs and several metrics that can be used
for measuring the match between models and observations,
there are many ways in which the reference site simulations
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5032 G. Krinner et al.: ESM-SnowMIP
Table 1. Models performing ESM-SnowMIP reference site simulations.
Model Type Reference
BCC_AVIM LSS in BCC-ESM Based on Oleson et al. (2004)
CABLE LSS in ACCESS Cuntz and Haverd (2018)
CRHM hydrological model Pomeroy et al. (2012)
CLASS LSS in CanESM Bartlett et al. (2006)
CLM5 LSS in CESM Lawrence et al. (2018)
CoLM LSS in BNU-ESM and CAS-ESM Dai et al. (2003)
Crocus snow physics model Vionnet et al. (2012)
EC-Earth LSS in EC-Earth Dutra et al. (2010)
ESCIMO snow surface energy balance model Marke et al. (2016)
ESCROC multi-physics snow model (35-member ensemble) Lafaysse et al. (2017)
FSM multi-physics snow model (32-member ensemble) Essery (2015)
HTESSEL LSS of ECMWF operational forecasting system Dutra et al. (2012)
HTESSELML LSS of ECMWF forecasting system (research) Dutra et al. (2012)
ISBA-ES LSS in CNRM-CM Decharme et al. (2016)
ISBA-MEB LSS in CNRM-CM Boone and Etchevers (2001)
JSBACH LSS in MPI-ESM Roeckner et al. (2003)
JSBACH3_PF LSS in MPI-ESM Ekici et al. (2014)
JULES LSS in UKESM Best et al. (2011)
MATSIRO LSS in MIROC Tatebe et al. (2018)
MOSES LSS in HadCM3 Cox et al. (1999)
ORCHIDEE-E LSS in IPSL-CM Wang et al. (2013)
ORCHIDEE-I LSS in IPSL-CM Wang et al. (2013)
RUC LSS in NOAA/NCEP operational forecasting systems Smirnova et al. (2016)
SMAP snow physics model Niwano et al. (2014)
SNOWPACK snow physics model Lehning et al. (1999)
SPONSOR hydrological model Turkov and Sokratov (2016)
SWAP LSS Gusev and Nasonova (2003)
VEG3D soil and vegetation model Braun and Schädler (2005)
Figure 2. Measurements (red lines), simulations (black lines), and averages of simulations (blue lines) of SWE, snow depth, albedo, and soil
temperature at 20 cm of depth for Col de Porte averaged over October 1994 to September 2014.
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G. Krinner et al.: ESM-SnowMIP 5033
Table 2. Proposed ESM-SnowMIP simulations. Configurations are as follows. LND 1-D: site-scale 1-D (column); LND: global land simulations with LSMs; LND-ATM-OC: coupled
land–atmosphere–ocean simulations.
Experiment name Tier Experiment description and de-
sign
Configuration Start and end No. years per run Ens. size No. years total Science question and/or gap ad-
dressed with this experiment
Possible synergies with
other runs
Ref-Site 1 Site reference simulations LND 1-D Variable Evaluate snow model on site
scale
LS3MIP LMIP-H
FA-Site 2 Site simulations, prescribed
constant snow albedo
LND 1-D Variable Evaluate effect of snow albedo
variations
Ref-Site
NS-Site 2 Site simulations, prescribed
neutral exchange coefficient
LND 1-D Variable Quantify effect of melt-induced
near-surface temperature inver-
sions
Ref-Site
NI-Site 2 Site simulations, no soil insula-
tion
LND 1-D Variable Diagnose snow soil insulation
effect
Ref-Site
LSF-down-scaled-Site 2 Site simulations, downscaled
forcing
LND 1-D Variable Evaluate impact of downscaled
gridded forcing in complex to-
pography
Ref-Site
SWE-LSM 1 Prescribed observed snow wa-
ter equivalent
LND 1980–2014 35 1 35 Evaluate link between snow
mass and snow fraction
Land-Hist (LS3MIP)
FA-LSM 2 Land-only simulation, pre-
scribed constant snow albedo
LND 1980–2014 35 1 35 Evaluate effect of snow albedo
variations
Land-Hist (LS3MIP)
NI-LSM 2 Land-only simulation, no soil
insulation
LND 1850–2014 165 1 165 Diagnose snow soil insulation
effect
Land-Hist (LS3MIP)
FLC-LSM 2 Land-only simulation, pre-
scribed common land cover
LND 1980–2014 35 1 35 Diagnose effect of varying pre-
scribed land covers
Land-Hist (LS3MIP)
SnowMIP-rmLC 1 (2) Prescribed snow conditions 30-
year running mean
LND-ATM-OC 1980–2100 121 1 ( +4) 121 (+484) Diagnose snow–climate feed-
back including ocean response
CMIP6 historical,
Scenario-MIP, LFMIP-
rmLC
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5034 G. Krinner et al.: ESM-SnowMIP
Table 3. ESM-SnowMIP reference sites with abbreviations used in Fig. 4.
Site Site (short) Latitude Longitude Elevation Period Type Reference
BERMSOld Aspen, Canada oas 53.63N 106.20W 600 m 1997–2010 Boreal Bartlett et al. (2006)
BERMS Old Black Spruce, Canada obs 53.99N 105.12W 629 m 1997–2010 Boreal Bartlett et al. (2006)
BERMS Old Jack Pine, Canada ojp 53.92N 104.69W 579 m 1997–2010 Boreal Bartlett et al. (2006)
Col de Porte, France cdp 45.30N 5.77E 1325 m 1994–2014 Alpine Lejeune et al. (2018)
Reynolds Mountain East, USA rme 43.06N 116.75W 2060 m 1988–2008 Alpine Reba et al. (2011)
Sapporo, Japan sap 43.08N 141.34E 15 m 2005–2015 Maritime Niwano et al. (2012)
Senator Beck, USA snb 37.91N 107.73W 3714 m 2005–2015 Alpine Landry et al. (2014)
Sodankylä, Finland sod 67.37N 26.63E 179 m 2007–2014 Arctic Essery et al. (2016)
Swamp Angel, USA swa 37.91N 107.71W 3371 m 2005–2015 Alpine Landry et al. (2014)
Weissfluhjoch, Switzerland wfj 46.83N 9.81E 2540 m 1996–2016 Alpine WSL (2017)
BERMS: Boreal Ecosystem Research and Monitoring Sites.
Figure 3. As Fig. 2, but for October 2007 to September 2014 at Sodankylä (albedo measurements are not available for the snow surface).
could be evaluated and ranked. Figure 4 shows one exam-
ple, in which root mean squared errors in simulated SWE
have been calculated for each model at each site and normal-
ized by the standard deviation of measured SWE at the given
site for comparison between sites. A value greater than 1 for
this metric shows that a model fits variations in the observa-
tions no better than the average of all observations. Ranking
models according to their average error for all sites shows
that a couple of models perform well and a couple perform
poorly at all sites, but most models perform well at some
sites and poorly at others. Many models have larger errors
for the forested sites than the open sites and larger errors for
warmer sites than colder sites. The ensemble mean of the
models has lower errors than the majority of the individual
models at most of the sites. The only individual models to
have normalized errors less than 1 for all sites are the Cro-
cus snow physics model and the HTESSEL and SWAP land
surface schemes, which have very different complexity. This
illustrates that model complexity per se does not explain the
spread in performance observed here. There will be a thor-
ough evaluation of model performance in a forthcoming pa-
per.
3.1.3 Tier 2 site simulations
Snow mass balance is influenced by radiated, advected, and
conducted heat fluxes in the energy balance. Soil temperature
is influenced by snow depth, thermal conductivity, amount
of meltwater refreezing, and refreezing depth (van Kampen-
hout et al., 2017). To investigate how these influences differ
between models, additional experiments are proposed with
prescribed snow albedo, with prescribed aerodynamic pa-
rameters, and with the thermal insulation of snow removed.
These experiments have not started yet, but pilot studies have
been conducted using version 2 of the Factorial Snow Model
(FSM, Essery, 2015); this is a multi-physics model designed
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G. Krinner et al.: ESM-SnowMIP 5035
Figure 4. Normalized root mean square SWE errors for the 26 non-ensemble models in Table 1 returning a single simulation for each
site (white symbols) and the 26-model ensemble mean (black symbols), with simulations identified by circles at open sites and triangles at
forested sites. (a) Models ranked according to their average error for all sites. (b) Errors for all models at each site (for abbreviations see
Fig. 4).
to run ensembles of simulations producing a range of model
behaviours by using alternative parameterizations for snow
albedo, thermal conductivity, compaction, liquid water stor-
age, and coupling with the atmosphere. The pilot studies us-
ing FSM allow for the validation of the intended experiment
set-up, provide a benchmark for model spread, and facilitate
the interpretation of the results.
Fixed snow albedo (FA-Site). Seasonal and subseasonal
variations of snow albedo are substantial and strongly in-
fluence the energy balance of the snowpack. This is partic-
ularly important during the melting season when complex
processes within the snowpack lead to strong and rapid varia-
tions of albedo. Positive albedo feedbacks strongly influence
melt timing. Snowmelt timing is a critical climatic metric
that is often incorrectly simulated by climate and dedicated
snow models, but it is difficult to untangle the effects of the
simulation of snow albedo from other processes because of
the strong feedbacks involved (Mudryk et al., 2017; Qu and
Hall, 2014). An experiment in which snow albedo is fixed to
0.7, which is a typical snow pre-melt albedo (Harding and
Pomeroy, 1996; Melloh et al., 2002; Wang et al., 2016), will
enable evaluation of the effect of seasonal snow albedo vari-
ations and biases.
Differences between an ensemble of fixed snow albedo
simulations with FSM and reference simulations for Col de
Porte are shown in Fig. 5. Ensemble members differ widely
in their responses to the removal of snow albedo feedbacks.
Fixing the snow albedo prevents it from decreasing as the
snow melts and delays the snowmelt. Extending the duration
of snow cover as a result delays warming of the soil in spring,
leading to large temperature differences when snow remains
in a fixed albedo simulation but has disappeared in the corre-
sponding reference simulation.
No suppression of fluxes in stable surface layers over snow
(NS-Site). Models generally calculate turbulent heat fluxes in
the atmospheric boundary layer using exchange coefficients
that only depend on surface roughness and wind speed in
neutral conditions but are reduced in stable conditions by a
factor depending on a Richardson number or an Obukhov
length. The strength of this decoupling between the atmo-
sphere and the surface is a major source of uncertainty in
climate and snow models (Schlögl et al., 2017) and will in-
fluence how strongly snowmelt responds to warming of the
atmosphere. The coupling strength in a model will be quan-
tified by an additional experiment in which exchange coeffi-
cients are kept fixed at neutral values.
FSM only has a single option for stability adjustment of
the surface exchange coefficient, but ensemble members still
respond differently to switching this option off, as seen in
Fig. 6. Snow-free soil temperatures would also be influenced,
so the fix is only applied when snow is on the ground. With
heat transfers predominantly being downwards from the at-
mosphere to snow, fixing the exchange coefficient increases
the heat transfer and warms the snow surface, often leading
to decreases in snow albedo and earlier melt. The soil warms
rapidly in spring when the snow melts, but winter soil tem-
peratures can also be increased relative to the reference sim-
ulation despite the decrease in insulating snow depth because
of the increased heat flux from the atmosphere.
No thermal insulation by snow (NI-Site). The low ther-
mal conductivity of snow has a major climatic control on
the temperature of underlying soils and heat fluxes to the at-
mosphere that is spatially and temporally variable and often
not well represented in climate models (Koven et al., 2013).
This insulating effect might be quantified by an experiment
in which snow takes a very high (effectively infinite) thermal
conductivity, while its other properties (albedo, latent heat of
melting, etc.) are kept unchanged. In practice, the numerical
scheme of a model might become unstable for high thermal
conductivities and another solution might be envisaged, such
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5036 G. Krinner et al.: ESM-SnowMIP
Figure 5. Differences between the 32 FSM ensemble members in fixed albedo and reference simulations for Col de Porte averaged over
October 1994 to September 2014.
Figure 6. As Fig. 5, but for differences between 16 FSM ensemble members with fixed surface exchange coefficients and 16 with variable
coefficients.
as resetting the temperature or the net heat flux at the soil–
snow interface to that calculated at the snow surface.
In a pilot study, FSM simulations were found to be numer-
ically stable with a fixed 50 W m1K1thermal conductivity
for snow, which is much higher than a typical range of 0.05
to 0.5 W m1K1(Sturm et al., 1997). This value of thermal
conductivity led to vanishing temperature gradients across
the snowpack and was therefore deemed high enough with-
out compromising numerical stability. Results are shown in
Fig. 7. Without the insulating effect of snow, the soil freezes
even in the relatively mild winters at Col de Porte. Building
up a cold reservoir in the soil over winter has a secondary
effect of delaying snowmelt in spring. Even without the in-
sulating effect, the high albedo of snow and energy required
for snowmelt reduce the amount of energy to warm the soil,
leading to a second trough in soil temperature differences be-
tween high thermal conductivity and reference simulations.
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G. Krinner et al.: ESM-SnowMIP 5037
Figure 7. As Fig. 5, but for differences between the 32 FSM ensemble members in simulations without thermal insulation by snow and
reference simulations.
Downscaled large-scale forcing (LSF-downscaled-Site).
Most of the mid-latitude ESM-SnowMIP reference sites
were established for snow research in mountainous regions
and are at higher elevations than much of the surrounding ter-
rain. Meteorological variables in large-scale forcing datasets,
such as the GSWP3 (Global Soil Water Project Phase 3)
meteorological forcing data provided at 0.5spatial resolu-
tion for LS3MIP (Kim et al., 2017), would therefore be ex-
pected to be biased relative to in situ measurements at the
sites even if they were perfect on the grid scale. Col de
Porte, for example, is located at an elevation of 1325m in
the French Chartreuse Mountains but lies within a 0.5grid
cell with an average elevation of 870 m. Figure 8a shows that
an FSM simulation for winter 2009–2010 at Col de Porte
with GSWP3 driving data gives almost no snow accumula-
tion; this is because temperature on the grid scale, because of
the lower mean altitude, is higher than at Col de Porte, while
total precipitation is lower and snowfall is much lower. Site
and grid elevations for Sodankylä, in contrast, only differ by
40 m because this site is situated in a flat area. The large-scale
simulation shown in Fig. 8b is not so strongly influenced by
driving data biases.
Downscaling is commonly required when using regional
climate predictions in hydrological impact studies. An ESM-
SnowMIP experiment with altitude-adjusted large-scale driv-
ing data will be helpful in using reference site observations
and simulations to evaluate the performance of models in
large-scale simulations. Simply removing average altitude-
related errors in the GSWP3 driving data, with no attempt
to adjust variations on interannual and shorter timescales,
greatly improves the SWE simulation for Col de Porte, re-
placing a massive underestimate with a slight overestimate
(Fig. 8a). For Sodankylä, removing the smaller long-term
driving data biases has very little effect on the SWE simu-
lation (Fig. 8b).
We expect the Tier 2 site simulations with the individual
models to essentially align with the FSM results presented
here. However, we also expect a varying degree of sensitiv-
ity of the various models to the different model parameter
and set-up changes, which will allow for the identification of
unique priorities for the development of each of the partici-
pating models.
3.2 Global scale
3.2.1 Large-scale observational data
Observation-based estimates of SWE and snow cover frac-
tion (SCF) are required for the evaluation of historical mean
and model spread from ESM-, AMIP-, and LMIP- type
simulations, as well as prescribed historical SWE and SCF
as required for specific experiments. For this purpose, we
will employ a blended dataset of snow products from ERA-
Interim/Land (Balsamo et al., 2015), MERRA (Rienecker et
al., 2011), ERA-Crocus (Brun et al., 2013), ESA-GlobSnow
(Takala et al., 2011), and GLDAS-2 (Rodell et al., 2004).
These SWE datasets were assessed previously for temporal
and spatial consistency by Mudryk et al. (2015), who pro-
posed a climatology derived from a combination of these five
datasets.
The rationale for using a blended suite of snow products is
threefold. First, it provides a measure of historical observa-
tional uncertainty given by the range of estimates from indi-
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5038 G. Krinner et al.: ESM-SnowMIP
Figure 8. Simulations with a single FSM ensemble member and in situ driving data (dashed black lines), large-scale GSWP3 driving data
(solid black lines), or bias-corrected GSWP3 driving data (blue lines) compared with in situ SWE measurements (red lines) for 2009–2010
at (a) Col de Porte and (b) Sodankylä.
Figure 9. Daily median and spread (5th–95th percentile) among the
five snow products listed above for the 1981–2010 period.
vidual analyses. This is demonstrated in Fig. 9, which shows
the daily median and spread (5th–95th percentile) among the
five snow products listed above for the 1981–2010 seasonal
SWE climatology. For a given day, statistics are calculated
from the pooled distribution of data across the 30-year pe-
riod and across all five datasets. As analysed in Mudryk et
al. (2015), the range across the snow products likely results
from differences in the snow schemes within the land sur-
face models, differences in precipitation and temperature in
the forcing meteorology (from various reanalyses), and the
impact of satellite and weather station measurements (used
in GlobSnow and ERA-Brown). Still, the illustrated spread
is a useful proxy for observation-based uncertainty (which
cannot be determined when a single product is applied for
evaluation) and may be used to evaluate the corresponding
level of agreement from LS3MIP and ESM-SnowMIP simu-
lations over a similar historical period.
A second reason to use a suite of analyses for MIP evalua-
tion is that the bias and error of individual datasets vary with
geographical location and datasets that perform well in some
regions may perform more poorly in others. The lack of a
clear “best” dataset provides minimal reason to favour one
analysis over another. In fact, it has been explicitly demon-
strated that combinations of products have both lower bias
and RMSE than individual products when evaluated over do-
mains with in situ data (Schwaizer et al., 2016). For this rea-
son, a blended combination of snow products will be used for
time-varying prescribed SWE simulations (see Sect. 3.2.2)
and could also be used for evaluation of SWE output from
simulations which are observationally constrained by non-
snow-related variables.
Finally, the use of SWE analyses also allows for the defini-
tion of a mutually consistent set of snow cover extent (SCE)
data by choosing a seasonal and climatological SWE thresh-
old above which a grid cell is considered snow-covered. The
spread of total NH SCE estimated from a range of thresholds
between 0 and 10 mm is large (Fig. 10a; light shading) with
much of the uncertainty related to very low SWE thresholds
(< 2 mm), for which reanalysis-based SWE persists on the
land surface for physically unrealistic amounts of time. Op-
timization based on satellite-based observations of climato-
logical SCE has identified 5 mm as a reasonable choice of
threshold (Fig. 10a; dark shading) for deriving SCE from
SWE (Robinson et al., 2014).
3.2.2 Global land-only simulations
The global land-only simulations planned in ESM-SnowMIP
build on the reference historical land simulation (Land-
Hist) currently carried out in the framework of the LS3MIP
project. The aim of this 1850–2014 simulation, using
GSWP3 meteorological forcing, is to provide a land-only
simulation carried out with the land surface modules used in
the CMIP6 ESMs at the same resolution as used in the cou-
pled model, allowing for the separate evaluation of the land
surface components of these models and potentially attribut-
ing sources of coupled model biases to the individual coupled
model components. The global land simulations planned in
ESM-SnowMIP share the model set-up with this Land-Hist
simulation to optimize complementarity.
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G. Krinner et al.: ESM-SnowMIP 5039
Figure 10. (a) Daily median and spread (5th–95th percentile) among the five snow products for SCE calculated using a 5mm SWE threshold
(solid curve and dark shading) and spread calculated using a range of thresholds between 0 and 10 mm (light shading). (b) January and May
SCE for four choices of thresholds.
Tier 1: prescribed observed snow water equivalent (SWE-
LSM). The relationship between grid-scale snow water equiv-
alent (SWE), fractional snow cover, and hence surface albedo
is complicated and very diverse solutions are presently im-
plemented in coupled climate models. Here we propose a
prescribed SWE experiment to identify LSM biases that are
linked to the parameterization of surface albedo as a func-
tion of the snow cover fraction, which in turn is very often
a diagnostic function of the SWE which is prescribed in this
experiment. The aim is to evaluate the simulated grid-scale
albedo in these simulations against satellite-based observa-
tions of surface albedo.
Simulated grid-scale surface albedo in the presence of
snow can depend explicitly on subgrid-scale topography, pa-
rameterized patchiness, vegetation cover, snow albedo, and
other factors. The vegetation cover dependence includes ex-
plicitly simulated masking of vegetation by snow or vice
versa. In particular, the albedo effect of transient snow
load on trees after snowfall with subsequent unloading due
to wind and melting, which is sometimes represented in
current-generation ESM snow modules, should not be off-
set by too simple a prescription of observed SWE. It should
therefore be left up to the modelling groups to decide exactly
how SWE is prescribed in their models. However, the model
SWE should satisfy the condition that the weekly average
SWE in the model is close to the observed value (by less
than 10 %). This can, for example, be obtained by a Newto-
nian relaxation of SWE to the weekly average with a time
constant of a few days. Other state variables of the snow
module (e.g. snow internal temperature, water content, snow
grain size, etc.) will have to be adapted accordingly; again,
given the diversity of snow modules, it is impossible to define
here exactly how this needs to be done in general. Note that
these considerations also apply for the land-only simulations
of LS3MIP in which soil wetness and SWE are to be pre-
scribed. In cases of snow modules for which an unequivocal
relationship ties surface albedo to SWE, it might be sufficient
to run only the albedo scheme with prescribed SWE as input.
While a number of snow products are available to serve
as prescribed SWE, we recommend the Mudryk et al. (2015)
combined climatology (see Sect. 3.2.1) for ESM-SnowMIP
simulations. Because biases in individual products are com-
pensated for through averaging, this dataset represents an
improved reference for model evaluation compared to any
single-component dataset (Sospedra-Alfonso et al., 2016),
just like a climate model ensemble mean is often preferred
over a single member.
The simulated surface albedo will be compared to sur-
face albedo as derived from satellite observations (MODIS,
Schaaf et al., 2002; APP-x, Wang and Key, 2005; Glob-
Albedo, Lewis et al., 2012). In particular, the change in
the quality of the simulated surface albedo compared to
the “free” LS3MIP Land-Hist simulation and the historical
CMIP6 simulation will be evaluated in order to infer the part
of surface albedo errors linked to a biased snow mass bal-
ance.
Tier 2: fixed snow albedo (FA-LSM). This is a spatially dis-
tributed version of the FA-Site simulation described above.
It consists of prescribing snow albedo to a fixed value of
0.7. The aim of the experiment is, similar to that of the FA-
Site simulation, to enable evaluation of the effect of seasonal
snow albedo variations and biases in LSMs, although the
model response will depend very much on how snow mask-
ing by vegetation is parameterized.
Simulated snow water equivalent (SWE), fractional snow
cover, and vegetation masking will still influence the grid-
point average surface albedo. The simulation period is 1980–
2014, as in the Land-Hist and SWE-LSM simulations. If pos-
sible, the fixed snow albedo value should also be used over
ice sheets. Correct prescription of snow albedo can be ver-
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5040 G. Krinner et al.: ESM-SnowMIP
ified by checking grid-scale average surface albedo in ar-
eas with deep snow cover and low vegetation. In addition,
the effect of vegetation masking on surface albedo in snow-
covered areas will be isolated, since the snow–vegetation pa-
rameterizations will vary between models, but snow albedo
will remain fixed.
This simulation is tightly linked to the LS3MIP Land-Hist
offline reference simulation. In synergy with the site sim-
ulation with prescribed snow albedo (FA-Site), comparison
with the same period in the reference simulation allows for
the evaluation of the effect of snow albedo in terms of tim-
ing of snowmelt, winter season surface temperature, and en-
ergy flux partitioning as a source of model biases. In addition,
the effect of vegetation masking on surface albedo in snow-
covered areas will be isolated, since the snow–vegetation pa-
rameterizations will vary between models, but snow albedo
will remain fixed. Again, as in FA-Site, a basic metric to eval-
uate the effect of prescribed snow albedo will be the dura-
tion of snow cover (in particular melt onset) in this experi-
ment compared to the reference simulation and observation.
Required observations therefore concern snow cover season-
ality, in particular snowmelt dates (to be obtained from the
ensemble of large-scale SCE data), and general climate vari-
ables such as surface air temperature.
Tier 2: no thermal insulation by snow (NI-LSM). We pro-
pose a global LSM simulation with “infinite” snow conduc-
tivity (that is, no effective thermal insulation by snow) as a
global extension of the site-scale experiment NI-Site. Again,
this simulation will otherwise have an identical set-up as the
relevant reference simulation Land-Hist. However, the mod-
els might need to be spun up for a substantial number of years
in this set-up in order to achieve thermal equilibrium at the
lowest soil levels.
In this global setting, simulated potential permafrost extent
(that is, the permafrost extent in equilibrium with the pre-
scribed climate and model set-up, also often termed “near-
surface permafrost”; e.g. Lawrence et al., 2008) will be di-
agnosed from the thermal state of the lowermost soil layer
in the simulations. It will be compared to the correspond-
ing output of the Land-Hist reference simulation and GTN-
P observations (Biskaborn et al., 2015). Required reference
data are soil temperature measurements and observations and
analyses of surface energy fluxes at all seasons in areas with
seasonal snow cover. Again, in the multi-model context, we
expect a relationship between the sensitivity of the simulated
potential permafrost extent to thermal insulation by soil and
diagnosed errors of the simulated near-surface permafrost ex-
tent, which we hope will be useful to identify ways for model
improvement.
Tier 2: fixed land cover (FLC-LSM). Previous studies show
that inaccurate representation of vegetation distribution and
parameters in LSMs may result in large biases in simulated
surface albedo for snow-covered forests (Essery, 2013; Wang
et al., 2016). Most current LSMs represent vegetation from
a set of plant functional types (PFTs), which are usually de-
rived from global land cover datasets (Bonan et al., 2002;
Poulter et al., 2015). There are large differences among PFTs
used in LSMs, which may result from the differences in the
land cover datasets, the cross-walking tables used to map
land cover datasets into PFTs represented in LSMs, or un-
certainties in dynamic PFT simulations (Hartley et al., 2017;
Poulter et al., 2011). In order to separate biases due to dif-
ferences in vegetation distribution from those due to physi-
cal processes in LSMs, we propose an experiment in which
models derive their PFTs from the same land cover dataset
and using the same cross-walking table.
Several global land cover datasets are available with spa-
tial resolutions ranging from 300 m to 1km (Bontemps et al.,
2012). The newly released European Space Agency (ESA)
Climate Change Initiative (CCI) land cover datasets are de-
veloped specifically to address the needs of the climate mod-
elling community (Poulter et al., 2015). The CCI maps in-
clude 22 level-1 classes and 15 level-2 subclasses based on
the United Nations Land Cover Classification System, which
was identified as a suitable thematic legend and compati-
ble with the PFT concept of most LSMs (Bontemps et al.,
2012). While most previous land cover datasets are for a sin-
gle year, the CCI datasets are available from 1992–2015 at
300 m resolution (ESA, 2017). The finer spatial resolution
of 300 m (versus 1 km) makes it inherently superior for land
cover mapping in heterogeneous landscapes for which dif-
ferent datasets tend to disagree (Fritz et al., 2011; Herold et
al., 2008). In addition, a cross-walking table to convert the
categorical land cover classes to the fractional area of PFTs
was provided with the CCI datasets (Poulter et al., 2015). We
thus suggest the use of the CCI land cover dataset for the year
2000 as the common land cover dataset from which to derive
PFTs. Different models usually have their own unique set of
PFTs. For cases in which both the phenology type and the
associated climate zone are considered, the Köppen–Geiger
climate classification can be used as in Poulter et al. (2015).
The simulation period of 1980–2014 matches the LS3MIP
Land-Hist offline reference simulations. A comparison of the
surface albedo with the reference simulation will isolate the
impact of PFT distribution on surface albedo and associ-
ated feedbacks in snow-covered areas. Since the PFTs are
from the same land cover data in the participating models,
the differences in surface albedo among the models will re-
veal differences in snow–vegetation interactions and other
vegetation-related parameterizations (e.g. leaf area index)
used in the models.
3.2.3 Global simulation with land–atmosphere
coupling: SnowMIP-rmLC
ESM-SnowMIP proposes one coupled Tier 1 experiment,
which serves the purpose of quantifying snow-related feed-
backs in the global climate system on interannual timescales.
It is designed to separate the effects of snow from the effects
of snow and soil humidity, the combined effect being ad-
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G. Krinner et al.: ESM-SnowMIP 5041
dressed by the LS3MIP Tier 1 coupled experiment LFMIP-
rmLC. This LS3MIP experiment uses 30-year running mean
land conditions (snow and soil humidity), as simulated in
a reference transient climate change experiment, and pre-
scribes these in a second simulation. In these runs, snow and
soil moisture feedbacks on decadal and shorter timescales
are muted. Comparing the LFMIP-rmLC simulation to the
appropriate projection used for prescribing the land surface
conditions allows for the identification of these feedbacks.
In the context of a transient run, additional diagnoses of geo-
graphic shifts of land–atmosphere coupling hotspots (Senevi-
ratne et al., 2006) and changes in potential predictability re-
lated to land surface (Dirmeyer et al., 2013) can be carried
out. The SnowMIP-rmLC will isolate the effects of snow–
atmosphere coupling by prescribing only the soil humidity
state from the reference simulation, not the entire surface
state as in LFMIP-rmLC.
For the SnowMIP-rmLC experiment, the LFMIP-rmLC
experiment set-up is modified such that only the climatologi-
cal soil humidity is prescribed. Contrary to the LFMIP-rmLC
experiment, snow is allowed to evolve freely. The rationale
behind this choice is that in this way, our simulation set-
up is more similar to previous GLACE–CMIP experiments
(Seneviratne et al., 2013), while the effect of snow can be
deduced from the difference between these two simulations.
Because of internal variability in the climate system, a five-
member ensemble simulation would be ideal, but this is ex-
pensive. Similar to the LFMIP-rmLC set-up, we propose the
first ensemble member as Tier 1 and suggest four other en-
semble members as Tier 2 (see Table 2). The simulation pe-
riod is the same as in LFMIP-rmLC, i.e. 1980–2100. Cor-
rect prescription of snow can be verified easily by comparing
the simulated SWE for an individual year with the simulated
climatological (1980–2014) SWE of the free projection. It
should be very close.
The SnowMIP-rmLC experiments will allow for the eval-
uation of the effect of snow feedbacks on interannual to
decadal timescales as well as on the centennial climate
change signal (since even by the end of the 21st century, the
1980–2014 average snow conditions will be used).
The simulation will be analysed in parallel to the LFMIP-
rmLC simulations, following very closely the methodologies
of Seneviratne et al. (2013). Required observations are snow
cover seasonality, in particular snowmelt dates, and general
climate variables such as surface air temperature, and circu-
lation patterns.
3.2.4 Snow shortwave radiative effect diagnosis
Another useful measure of the impact of snow on climate
is the snow shortwave radiative effect (SSRE) (e.g. Flanner
et al., 2011; Perket et al., 2014; Singh et al., 2015). For the
purposes outlined here, SSRE is the instantaneous change
in surface absorbed solar energy flux caused by the pres-
ence of terrestrial snow. The diagnosis of SSRE provides a
precise, overarching measure of the snow-induced perturba-
tion to solar absorption in each model, integrating over the
variable influences of vegetation masking, snow grain size,
snow cover fraction, soot content, and other factors. SSRE
is also a useful measure for climate feedback analysis and
has a direct analogue in the widely used “cloud radiative ef-
fect”. To enable us to calculate and analyse inter-model dif-
ferences of SSRE and their causes, participating modelling
groups are requested to provide specific gridded output (see
below) from their LS3MIP Land-Hist and Land-Future sim-
ulations, as well as from the ESM-SnowMIP FA-LSM and
SWE-LSM simulations. Ideally, these output fields should
also be provided for one or more of the coupled atmosphere–
ocean simulations, preferably from the historical reference
run.
SSRE can be calculated in a land surface model through
the following procedures.
1. Conducting an additional surface albedo calculation at
each model time step with zero snow. This implies set-
ting to zero the mass of snow on ground, mass of snow
in vegetation canopy, and snow cover fraction, but only
for the purpose of this diagnostic albedo calculation. It
should have no effect on the prognostic snow simula-
tion.
2. Calculating net and reflected surface solar energy fluxes
at each model time step using the diagnostic albedo
from (1) and using the same surface downwelling (in-
cident) flux that would otherwise be used to calculate
solar heating.
3. Archiving the diagnostic calculations from (1) and (2) at
the same frequency as other model output (e.g. daily or
monthly).
The following gridded fields should be provided from the
model: (1) net surface shortwave irradiance calculated with-
out snow (rss_ nosno); and (2) mean shortwave surface
albedo calculated without snow (albs_nosno).
Net surface solar energy flux in the absence of snow can
then be differenced from that calculated with snow (output
by default) to provide the SSRE.
Although the no-snow albedo fields are not strictly needed
for the calculation of SSRE, they will complement standard
albedo output from the model to facilitate convenient evalu-
ation and the derivation of hypothetical SSRE from different
(e.g. clear-sky) surface downwelling irradiance fields. De-
pending on the spectral resolution of solar energy in each
model, it would also be useful to provide the visible and
near-infrared partitions of the following: (1) net surface visi-
ble (0.2–0.7 µm) irradiance calculated without snow; (2) net
surface near-IR (0.7–5.0 µm) irradiance calculated without
snow; (3) mean visible surface albedo calculated without
snow; and (4) mean near-IR surface albedo calculated with-
out snow.
www.geosci-model-dev.net/11/5027/2018/ Geosci. Model Dev., 11, 5027–5049, 2018
5042 G. Krinner et al.: ESM-SnowMIP
3.3 Timeline, data request, and data availability
The first set of reference site simulations (Ref-Site) have al-
ready been carried out and are currently being analysed. The
additional site simulations will be carried out in the near fu-
ture. In 2018 and 2019, the global climate modelling com-
munity will be heavily involved in CMIP6. To decrease peak
workload on the modelling groups while at the same time
optimizing synergies with the CMIP6 activities and in par-
ticular with LS3MIP, it was decided to launch the ESM-
SnowMIP simulations after the main CMIP6 activities. This,
however, has the disadvantage that ESM-SnowMIP global
simulation results will not be available for analysis in time
for the sixth IPCC assessment report.
For these global land-only and coupled simulations, the
request for output variables is identical to the LS3MIP
data request (https://www.earthsystemcog.org/projects/wip/
CMIP6DataRequest, last access: 3 December 2018) for the
respective reference simulations indicated in Table 2, and the
model set-up will be very similar, as described in the preced-
ing sections of this paper. This further keeps the additional
workload for the ESM-SnowMIP coupled simulations to a
minimum.
The ESM-SnowMIP site simulation output is sufficiently
small to be easily handled via an ftp server at one of the
participating institutes (see the dedicated website at https:
//www.geos.ed.ac.uk/~ressery/ESM-SnowMIP.html, last ac-
cess: 3 December 2018). Gridded Northern Hemisphere
SWE data are freely available from the National Snow and
Ice Data Center (http://nsidc.org/data/NSIDC-0668, last ac-
cess: 3 December 2018). Large-scale meteorological forc-
ing (GSWP3) for the distributed simulations, also used in
LS3MIP, will be made available via the ESGF at https:
//esgf-node.llnl.gov/search/input4mips/ (last access: 3 De-
cember 2018). To optimize synergies with LS3MIP, ESM-
SnowMIP will seek endorsement by the WCRP Working
Group on Coupled Modelling, which oversees CMIP. This
will allow ESM-SnowMIP output to be handled via the Earth
System Grid Federation (https://esgf.llnl.gov/, last access:
3 December 2018), i.e. the same infrastructures as the rel-
evant CMIP6 simulations these simulations are to be com-
pared with.
4 Discussion
4.1 Expected outcome and impact of ESM-SnowMIP
The parameterization of “cold” land surface processes has
received varying degrees of attention by climate modelling
groups. In the framework of CMIP6, there is now a specific
type of numeric experiment specifically designed for evalu-
ating the land surface components of the current-generation
Earth system models. It is envisaged that these so-called
LMIP exercises could become a central component of future
CMIP editions as part of the so-called DECK core experi-
ments (Eyring et al., 2016), along with separate evaluations
of the atmospheric and ocean components.
The specific effort aiming at evaluating and improving the
representation of snow in ESMs and in more specific dedi-
cated snow models relates to this broader context. It is hoped
that ESM-SnowMIP, in conjunction with LS3MIP, will pro-
vide a clear determination of the current state of the art
of snow modelling in the different participating communi-
ties and will spur knowledge transfer between these partially
disjointed scientific communities. Snow modules in current-
generation ESMs show a wide spread in their degree of com-
plexity. We expect that ESM groups who have not devoted
particular effort to evaluating and improving their snow mod-
ules in the past will be able to benefit from a clear strategy
for priorities of first-order snow module enhancements iden-
tified within ESM-SnowMIP. For groups that have already
put substantial effort into testing and continuously adapting
their ESM snow modules or specific snow models, ESM-
SnowMIP will be an opportunity to assess past and determine
future priorities for model enhancement.
The intended assessment of snow-related feedbacks on in-
terannual and longer timescales, such as a multi-model eval-
uation of the snow shortwave radiative effect, will hopefully
help better constrain the global climate response to anthro-
pogenic forcing and better understand regional responses, in-
cluding the amplification of global warming at high northern
latitudes.
4.2 Possible actions for future follow-up projects
Recent work on tundra snow (Domine et al., 2016) has high-
lighted the importance and specificity of snow metamorphic
processes under very cold conditions, specifically in the pres-
ence of strong vertical temperature gradients. While wind
compaction in the absence of shelter by higher vegetation
can increase snow density (Sturm et al., 2001) and hence
snow conductivity, depth hoar formation induced by strong
vertical temperature gradients within the snowpack (Derksen
et al., 2009, 2014) can substantially reduce the conductiv-
ity (Domine et al., 2016). In ESM-SnowMIP, an effort will
be made to include snow observation sites from tundra en-
vironments in the near future (e.g. Boike et al., 2018). How-
ever, it is clear that in future extensions of ESM-SnowMIP,
snow on sea ice and on the polar ice sheets should also be-
come a focus of attention. The physical properties of snow on
sea ice are linked to low accumulation rates and strong ver-
tical temperature gradients due to bottom heat flux, its spa-
tial heterogeneity, its peculiar evolution in summer leading to
melt ponds on sea ice due to inhibited drainage of meltwater,
and the presence of salt. These are specificities that are, to
our knowledge, often not taken into account in Earth system
models; however, snow on sea ice obviously concerns the sea
ice module of the coupled models and thus a slightly different
community than that mobilized in this first version of ESM-
Geosci. Model Dev., 11, 5027–5049, 2018 www.geosci-model-dev.net/11/5027/2018/
G. Krinner et al.: ESM-SnowMIP 5043
SnowMIP. Assessments of snow on sea ice usually focus on,
and are usually limited to, snow mass and height (Blanchard-
Wrigglesworth et al., 2015; e.g. Hezel et al., 2012). However,
an extension of the ESM-SnowMIP approach based on com-
bined small- and large-scale evaluation of snow models will
necessarily be conditioned by the availability of high-quality
observations. In this context, the MOSAIC international Arc-
tic drift expedition (https://www.mosaic-expedition.org/, last
access: 3 December 2018) is projected to provide valuable
new observations.
A further obvious follow-up of ESM-SnowMIP could
tackle snow in extreme polar environments such as Dome C
on the East Antarctic Plateau or the Greenland Summit; long-
term meteorological observations have already been used at
these locations for testing stand-alone snow models (Car-
magnola et al., 2013; Libois et al., 2015). Perennial snow
cover evolving under such extreme conditions would sub-
stantially broaden the range of snow types evaluated within
our framework. This would provide stringent tests for snow
models designed to simulate snow types that are extreme
but far from rare, as the interior regions of the continental
polar ice sheets make up an essential part of the perennial
cryosphere.
ESM-SnowMIP combines model evaluation at local and
global scales. While snow, and particularly the timing and
intensity of its melt season, does have important effects on
basin-scale hydrology (Barnhart et al., 2016; e.g. Berghuijs
et al., 2014; Fyfe et al., 2017), basin-scale processes exert
a less dominant control on snow as such in terms of physi-
cal properties. Therefore, intermediate scales, such as those
addressed in the Rhône-Aggregation Land Surface Scheme
Intercomparison Project (Boone et al., 2004), are bridged in
the current phase of ESM-SnowMIP. However, terrain con-
figuration and vegetation distribution (that is, geographical
characteristics at intermediate (basin) scales) have an obvi-
ous and important effect on the snow cover, in particular on
snow cover fraction. In the current phase of ESM-SnowMIP,
such links are implicitly addressed through the assessment of
snow cover fraction in the prescribed SWE experiment SWE-
LSM. In future phases, basin-scale properties, processes, and
characteristics might be addressed more explicitly. A fur-
ther aspect involving unresolved scales that could be tack-
led in future extensions of ESM-SnowMIP is the high spatial
variability of impurities deposited on snow surfaces, given
the known strong impact of this deposition on snow albedo
(Flanner et al., 2007) and model errors that can be induced
by not taking this effect into account (e.g. Clark et al., 2015).
Data availability. Site forcing data are available through https:
//www.geos.ed.ac.uk/~ressery/ESM-SnowMIP/netcdf.zip (last ac-
cess: 7 December 2018). The Weissfluhjoch data are accessible via
https://doi.org/10.16904/16 (WSL, 2017). A paper describing the
site-scale input data is currently in preparation.
Author contributions. GK, CD, RE, MF, SH, MC, AH, and HR ini-
tially designed the experimental design. GK, CD, RE, CBM, LM
and LW led the initial writing of different parts of the paper. RE and
CBM led the analysis of the site simulations. All other participants
contributed to the final experimental design, the site simulations and
the projected analysis strategy.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. This project was supported by the World
Climate Research Programme’s Climate and Cryosphere (CliC)
core project. We acknowledge the World Climate Research
Programme’s Working Group on Coupled Modelling, which is
responsible for CMIP. For CMIP the U.S. Department of Energy’s
Program for Climate Model Diagnosis and Intercomparison
provides coordinating support and led the development of software
infrastructure in partnership with the Global Organization for Earth
System Science Portals. Analysis of ESM-SnowMIP reference
site simulations is supported by NERC grant NE/P011926/1.
Gerhard Krinner, Chris Derksen, and Richard Essery acknowledge
support by the ESA in the framework of the ESA CCI snow project.
Simulations by the SWAP model were supported by the Russian
Science Foundation (grant 16-17-10039; Yeugeniy M. Gusev is the
recipient). Simulations by the SPONSOR model were supported
by the Russian Foundation for Basic Research (grant 18-05-60216;
Vladimir A. Semenov is the recipient). The work of Gabriele Ar-
duini leading to this paper was funded through the APPLICATE
project. APPLICATE has received funding from the European
Union’s Horizon 2020 research and innovation programme under
grant agreement 727862. Nander Wever was supported by the
Swiss National Science Foundation (project 200021E-160667).
IGE and CNRM/CEN are part of LabEX OSUG@2020 (ANR10
LABX56). Stefan Hagemann and Tobias Stacke were partially
supported by funding from the European Union within the Horizon
2020 project CRESCENDO (grant no. 641816). Hyungjun Kim
acknowledges support by a Grant-in-Aid for Specially Promoted
Research 16H06291 from JSPS. Alex Hall was supported by the
U.S. National Science Foundation under grant number 1543268:
“Reducing Uncertainty Surrounding Climate Change Using
Emergent Constraints”. The authors sincerely thank Eric Brun-
Barrière for his leading role during the initial planning stages of
ESM-SnowMIP.
Edited by: Jeremy Fyke
Reviewed by: two anonymous referees
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... A variety of forest structure indicators have been used to reveal the impact of forests on snow processes, such as forest cover (Varhola et al., 2010;Pomeroy et al., 2012;Varhola and Coops, 2013), canopy cover (Pomeroy et al., 2002), leaf area index (Gelfan et al., 2004;Woods et al., 2006;Rutter et al., 2009;Lendzioch et al., 2019), and canopy closure (Broxton et al., 2021). In the meantime, as technology continues to progress, various methods have been applied comprehensively, such as forest snow sampling survey (Watson et al., 2006;Parajuli et al., 2020), snow model simulation (Pomeroy et al., 2007;Rutter et al., 2009;Krinner et al., 2018;Napoly et al., 2020), statistical modeling (López-Moreno and Nogués-Bravo, 2006), snow remote sensing (Zhang et al., 2010;Frei et al., 2012;Hojatimalekshah et al., 2021), LiDAR technology (Harpold et al., 2014;Broxton et al., 2021;Russell et al., 2021), UAV remote sensing (Lendzioch et al., 2019), and delayed photography (Parajka et al., 2012;Dong and Menzel, 2017). When studying forest snow process in specific areas, the most appropriate method needs to be selected and balanced, as each method has certain advantages and limitations. ...
... Snow model method has the problems of a coarse resolution and the simplification of the interaction processes between forest and snow (Broxton et al., 2021), restricting the ability of existing models to quantify effects of the forest canopy structure changes on snow processes. For example, Rutter et al. (2009) andKrinner et al. (2018) only focused on the accurate simulation of snow processes in specific forests and in adjacent open areas when conducting SnowMIP2 and ESM-SnowMIP studies. In addition, snow remote sensing products cannot be widely used to complex forest areas because of their high cost and limited snow capture accuracy (Rutter et al., 2009;Steele et al., 2017;Jacobs et al., 2021). ...
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Forest canopy closure affects snow processes by changing the redistribution of snowfall, snow interception, accumulation, sublimation, and melt. However, how the forest closure impacts snow processes at different periods has not been well explored. We conducted 3-year measurements of snow density and depth and carried out snow process calculations (i.e., interception, sublimation, and snowmelt) from 2018 to 2021 in four mixed forests with different canopy closures and an open site in the Changbai Mountains, northeast China. We found that the snow density of the five study sites varied greatly (0.14–0.45 g/cm3). The snow depth (SD) at four mixed forests sites was smaller than that of the nearby open site. The SD decreased as the forest canopy closure increased. Additionally, the forest interception effect increased with the canopy closure and decreased as the snowfall intensity increased. The total interception efficiency of the four mixed forests in normal snow years changed from 34% to 73% and increased with forest canopy closure. The averaged sublimation rate (S s ) and snowmelt rate (S r ) of the four mixed forests varied during different periods of snow process. The S s was 0.1–0.4 mm/day during the accumulation period and 0.2–1.0 mm/day during the ablation period, and the S r was 1.5–10.5 mm/day during the ablation period. There was a good correlation between S s , or S r , and canopy closure, but interannual variation was observed in the correlation. The mean values of the effect of the four mixed forests on understory SWE (snow water equivalent) over the 3 years ranged from −45% to −65%. Moreover, the impact effect was correlated with the forest canopy closure and enhanced with the canopy closure. This study provided more scientific information for studies of snow cover response to forest management.
... Models show that afforestation has the greatest impact during the snowmelt season, and there is good agreement between the models in simulating the impacts on snow cover. This is consistent with other international studies assessing the ability of climate and land surface models to simulate snow cover (Essery et al., 2009;Mudryk et al., 2020;Krinner et al., 2018). However, there is less agreement in the magnitude of changes during the snowmelt season when afforestation impacts are greatest. ...
... However, there is less agreement in the magnitude of changes during the snowmelt season when afforestation impacts are greatest. Simulating vegetation-snow interactions during snowmelt is a known challenge for models (Krinner et al., 2018). The models also showed good agreement in simulating the impact of afforestation on the onset of the snowmelt season, although there was disagreement on the magnitude of change. ...
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Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe.
... The same type of study was then conducted over forest areas and results from the SnowMIP2 experiment (Essery et al., 2009) showed that many land surface models represent a sufficient range of processes that can be calibrated to well reproduce the mass bal-ance of forest snowpack while simultaneously providing reasonable estimates of albedo and canopy temperatures that are essential for simulating the surface energy balance. More recently, an intercomparison of current ESM models has been conducted (Krinner et al., 2018) in an attempt to systematically integrate into future Coupled Model Intercomparison Project (CMIP)-type exercises an evaluation of snow models in order to improve them. They showed that there is a large dispersion in the complexity of the snow schemes, thus pointing to the interest in improving the simplest as well as the most advanced parameterizations. ...
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The Antarctic plateau, characterized by cold and dry weather conditions with very little precipitation, is mostly covered by snow at the surface. This paper describes an intercomparison of snow models, of varying complexity, used for numerical weather prediction or academic research. The results of offline numerical simulations, carried out during 15 d in 2009, on a single site on the Antarctic plateau, show that the simplest models are able to reproduce the surface temperature as well as the most complex models provided that their surface parameters are well chosen. Furthermore , it is shown that the diversity of the surface parameters of the models strongly impacts the numerical simulations, in particular the temporal variability of the surface temperature and the components of the surface energy balance. The models tend to overestimate the surface temperature by 2-5 K at night and underestimate it by 2 K during the day. The observed and simulated turbulent latent heat fluxes are small, of the order of a few W m −2 , with a tendency to underestimate , while the sensible heat fluxes are in general too intense at night as well as during the day. The surface temperature errors are consistent with too large a magnitude of sensible heat fluxes during the day and night. Finally, it is shown that the most complex multilayer models are able to reproduce well the propagation of the daily diurnal wave, and that the snow temperature profiles in the snowpack are very close to the measurements carried out on site.
... mountains. Yet, their ability to represent snow cover variability over large areas is inherently limited by large errors in their meteorological forcings (Raleigh et al., 2015) and uncertain physical parameterizations (Essery et al., 2013;Krinner et al., 2018). In addition, explicitly accounting for processes such as wind drift and snow-vegetation interaction is not yet affordable at large scales. ...
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The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterizations, and unresolved terrain features. In situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large-scale modeling errors by means of data assimilation. In this work, we assimilate HS observations from an in situ network of 295 stations covering the French Alps, Pyrenees, and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialized snow cover modeling framework, we investigate whether such observations can be used to correct neighboring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modeling, based on a particle filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localization strategies to assimilate snow observations. These approaches are evaluated in a leave-one-out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that an intermediate localization radius of 35–50 km yields a slightly lower root mean square error (RMSE), and a better spread–skill than the strategy of assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modeling errors are the largest, e.g., the Haute-Ariège, Andorra, and the extreme southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses and, therefore, snow simulations. In situ HS observations thus show an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.
... as part of various snow model intercomparison projects (e.g., Brown et al., 2006;Krinner et al., 2018;Rutter et al., 2009). Recent MESH/ CLASS revisions include an updated fresh snow density parameterization (Brown et al., 2006), a revised snow ageing algorithm Brown et al., 2006), a new parameterization for snow canopy interception and unloading after Hedstrom and Pomeroy (1998), as well as an adjusted formulation for the albedo of snow-covered forest canopies (Bartlett & Verseghy, 2015). ...
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Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources but are challenged in cold regions. Ground‐based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper discusses the scientific and technical challenges of the large‐scale modelling of cold region systems and reports recent modelling developments, focussing on MESH, the Canadian community hydrological land surface scheme. New cold region process representations include improved blowing snow transport and sublimation, lateral land‐surface flow, prairie pothole pond storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi‐stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision‐support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km2). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. These illustrate the current capabilities and limitations of cold region modelling, and the extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology. This article is protected by copyright. All rights reserved.
... The RUC LSM is an LSM option for the WRF (Weather Research and Forecasting) community model (Skamarock et al. 2019) through which it has been widely used in many WRF applications other than the NOAA HRRR/RAP models. RUC LSM has been evaluated in many soil-and snow-model intercomparisons, including ESM-SnowMIP (Krinner et al. 2018), and found to be an effective snow model compared to other international snow models (Menard et al. 2021) despite its intentional design choosing simplicity where possible. Fixed and surface fields (e.g., land use and albedo) are prescribed largely through MODIS datasets (Smirnova et al. 2016), with real-timevarying VIIRS-based greenness vegetation fraction, and use of the Beijing Normal University (BNU) soil dataset (Dy and Fung 2016). ...
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Initialization methods are needed for geophysical components of earth-system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range earth-system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land-atmosphere data assimilation (SCDA), producing balanced initial conditions across the land-atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere vs. land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land/snow-atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA towards SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (e.g., aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land-surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels) and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6-12h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000-900 hPa layer corresponding roughly to the boundary layer.
... The impact of blowing snow ice particles (BIPs) has been studied thoroughly, either using observations collected in mountainous regions (e.g., Lloyd et al., 2015;Beck et al., 2018;Lowenthal et al., 2019), remote sensing (e.g., Rogers and Vali, 1987;Vali et al., 2012;Geerts et al., 2015), or detailed snow-cover models (e.g., Lehning et al., 2006;Krinner et al., 2018) coupled with atmospheric models (e.g., Vionnet et al., 2014;Sharma et al., 2021). The extent to which BIPs can affect ICNCs in MPCs remains poorly understood. ...
Article
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Observations of orographic mixed-phase clouds (MPCs) have long shown that measured ice crystal number concentrations (ICNCs) can exceed the concentration of ice nucleating particles by orders of magnitude. Additionally, model simulations of alpine clouds are frequently found to underestimate the amount of ice compared with observations. Surface-based blowing snow, hoar frost, and secondary ice production processes have been suggested as potential causes, but their relative importance and persistence remains highly uncertain. Here we study ice production mechanisms in wintertime orographic MPCs observed during the Cloud and Aerosol Characterization Experiment (CLACE) 2014 campaign at the Jungfraujoch site in the Swiss Alps with the Weather Research and Forecasting model (WRF). Simulations suggest that droplet shattering is not a significant source of ice crystals at this specific location, but breakups upon collisions between ice particles are quite active, elevating the predicted ICNCs by up to 3 orders of magnitude, which is consistent with observations. The initiation of the ice–ice collisional breakup mechanism is primarily associated with the occurrence of seeder–feeder events from higher precipitating cloud layers. The enhanced aggregation of snowflakes is found to drive secondary ice formation in the simulated clouds, the role of which is strengthened when the large hydrometeors interact with the primary ice crystals formed in the feeder cloud. Including a constant source of cloud ice crystals from blowing snow, through the action of the breakup mechanism, can episodically enhance ICNCs. Increases in secondary ice fragment generation can be counterbalanced by enhanced orographic precipitation, which seems to prevent explosive multiplication and cloud dissipation. These findings highlight the importance of secondary ice and seeding mechanisms – primarily falling ice from above and, to a lesser degree, blowing ice from the surface – which frequently enhance primary ice and determine the phase state and properties of MPCs.
Article
Mountain seasonal snow cover is undergoing major changes due to global climate change. Assessments of future snow cover usually rely on physically based models, and often include post-processed meteorology. Alternatively, we here propose a direct statistical adjustment of snow cover fraction from regional climate models by using long-term remote-sensing observations. We compared different bias-adjustment routines (delta change, quantile mapping, and quantile delta mapping) and explored a downscaling based on historical observations for the Greater Alpine Region in Europe. All bias-adjustment methods account for systematic biases, for example due to topographic smoothing, and reduce model spread in future projections. The trend-preserving methods delta change and quantile delta mapping were found to be more suitable for snow cover fraction than quantile mapping. Averaged over the study region and whole year, snow cover fraction decreases from 12.5 % in 2001–2020 to 10.4 % (8.9 %, 11.5 %; model spread) in 2071–2100 under RCP2.6 (representative concentration pathway), and to 6.4 % (4.1 %, 7.8 %) under RCP8.5 (bias-adjusted estimates from quantile delta mapping). In addition, changes strongly depended on season and elevation. The comparison of the statistical downscaling to a high-resolution physically based model yields similar results for the elevation range covered by the climate models, but different elevation gradients of change above and below. Downscaling showed overall potential but requires further research. Since climate model and remote-sensing observations are available globally, the proposed methods are potentially widely applicable but are limited to snow cover fraction.
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In Earth system models, terrestrial snow is usually modelled by the land surface component. In most cases, these snow models have been developed with an emphasis on seasonal snow. Questions about future sea level rise, however, prompt the need for a realistic representation of perennial snow, as snow processes play a key role in the mass balance of glaciers and ice sheets. Here we enhance realism of modelled polar snow in the Community Land Model (CLM), the land component of the Community Earth System Model (CESM), by implementing 1) new parametrizations for fresh snow density, destructive metamorphism and compaction by overburden pressure, 2) by allowing for deeper snow packs, and 3) by introducing drifting snow compaction, with a focus on the ice sheet interior. Comparison with Greenlandic and Antarctic snow density observations show that the new physics improve model skill in predicting firn and near-surface density in the absence of melt. Moreover, compensating biases are removed and spurious subsurface melt rates at ice sheets are eliminated. The deeper snow pack enhances refreezing and allows for deeper percolation, raising ice temperatures up to 15°C above the skin temperature.