Mountain Snowpack SnowMIP, an intercomparison of snow models: first results. P. Etchevers· 1 , E. MartinI, R. Brown 2 , C. Fierz 3 ,Yo Lejeune 1 E. Bazile 4 , A. Boone 4 , Y.-J. Dais, R. Esserl, A. Fernandez 7 , Y. Gusev 8 , R. Jordan 9 , V. Koren 10 ,E. Kowalczyk ll , R. D. Pyles l2 , A. Schlosser 13 , A. B.Shmakin I4 , T. G. Smirnova 1S , 16 ? ·17 18 U. Strasser ,D. Verseghy-, T. Yamazaki ,Z.-L. Yang lCentre d'Etudes de la Neige, CNRM Meteo-France, Grenoble, France 2Canadian Meteorological Service, Dorval, Qc, Canada 3Swiss Federal Institutefor Snow and Avalanche Research (SLF), Fliielastrasse 11, CH-7260 Davos Dorf, Switzerland 4Meteo-France, Toulouse, France, 5Institute of Atmospheric Physics; Chinese Academy of Sciences, Beijing, China, 6Hadley Centre, Met. Office, Bracknell, Berks. UK., 7Instituto Nacional de Meteorologia, Madrid, Spain, 8Laboratory of Soil Water Physics, Institute of Water Problems, Russian Academy of Sciences, Moscow, Russia, 9CRREL, Cold Regions Research and Engineering Laboratory,Hanover, US.A., loNOAAINWSIOHIIHRL, Silver Spring, MD 20910 US.A., 11 CSIRO Atmospheric Research, Aspendale, Australia, LCooperative Institute for Research in the Environmental Sciences, Boulder, US.A., 13COLA/IGES, Calverton, US.A., 14La boratory t... Climatology, Institute of Geography, Russian Academy of Sciences, Moscow, Russia, 1 Forecast Systems Laboratory, Boulder, USA, 16Institute ofHydromechanics and Water Resources Management; ETH-Honggerberg, Ziirich, Switzerland, 17 Frontier Observational Research System for Global Change, Tohoku University, Sendai, Japan, 18 Dept of Hydrology and Water Resources, The University of Arizona, Tucson, US.A. Abstract: Many snow models are now used for various applications such as hydrology, global circulation models, snow monitoring, snow physics research and avalanche forecasting. The degree of complexity of these models is highly variable, from simple index methods to multi-layer models simulating the snow cover stratigraphy and texture. The main objective of the intercomparison project SnowMJP (Snow Model Intercomparison Project) is to identify key processes for each application. Four sites have been selected for the representativeness of their snowpack and the quality of the collected data. 26 models have participated in intercomparison by simulating the snowpack with the observed meteorological parameters. The validation of the simulation consists in comparing the results with snow pack observations. In a first step, the analysis focuses on the snow water simulation (compared with weekly snow pits). In particular, the snow water equivalent (SWE) maximum and the snow cover duration are two interesting features to consider, because they allow the estimation of the models abilities in terms of simulating the accumulation and melting periods. Keywords: snow simulation, model intercomparison, SNOWMIP. - 1. Introduction In the last thirty years, many snow models have been developed and have been used for various • Corresponding author address: Pierre Etchevers, Meteo-France/CEN, 1441, rue de la Piscine, F- 38400 Saint Martin d'Heres, France. E-mail : nierre_etcheversiGlmeteo_fr 353 applications such as hydrology, global circulation models, snow monitoring, snow physics ani avalanche forecasting as well. The degree of complexity of these models is highly variable, from simple index methods to multi-layer models simulating the snow cover stratigraphy and texture. The complexity is determined by multiple
Transcript
Mountain Snowpack
SnowMIP, an intercomparison of snow models: first results.
P. Etchevers· 1, E. MartinI, R. Brown2
, C. Fierz3 ,Yo Lejeune1
E. Bazile4, A. Boone4
, Y.-J. Dais, R. Esserl, A. Fernandez7, Y. Gusev8
, R. Jordan9, V.
Koren10 ,E. Kowalczykll, R. D. Pyles l2
, A. Schlosser13, A. B.ShmakinI4
, T. G. Smirnova1S,
16 ? ·17 18U. Strasser ,D. Verseghy-, T. Yamazaki ,Z.-L. Yang
lCentre d'Etudes de la Neige, CNRM Meteo-France, Grenoble, France2Canadian Meteorological Service, Dorval, Qc, Canada
3Swiss Federal Institutefor Snow and Avalanche Research (SLF), Fliielastrasse 11, CH-7260Davos Dorf, Switzerland
4Meteo-France, Toulouse, France, 5Institute ofAtmospheric Physics; Chinese Academy ofSciences, Beijing, China, 6Hadley Centre, Met. Office, Bracknell, Berks. UK., 7InstitutoNacional de Meteorologia, Madrid, Spain, 8Laboratory ofSoil Water Physics, Institute ofWater Problems, Russian Academy of Sciences, Moscow, Russia, 9CRREL, Cold RegionsResearch and Engineering Laboratory,Hanover, US.A., loNOAAINWSIOHIIHRL, SilverSpring, MD 20910 US.A., 11CSIRO Atmospheric Research, Aspendale, Australia,LCooperative Institute for Research in the Environmental Sciences, Boulder, US.A.,13COLA/IGES, Calverton, US.A., 14Laboratory t... Climatology, Institute of Geography,Russian Academy ofSciences, Moscow, Russia, 1 Forecast Systems Laboratory, Boulder,USA, 16Institute ofHydromechanics and Water Resources Management; ETH-Honggerberg,Ziirich, Switzerland, 17Frontier Observational Research System for Global Change, TohokuUniversity, Sendai, Japan, 18Dept ofHydrology and Water Resources, The University ofArizona, Tucson, US.A.
Abstract: Many snow models are now used for various applications such as hydrology, global circulationmodels, snow monitoring, snow physics research and avalanche forecasting. The degree of complexity of thesemodels is highly variable, from simple index methods to multi-layer models simulating the snow coverstratigraphy and texture. The main objective of the intercomparison project SnowMJP (Snow ModelIntercomparison Project) is to identify key processes for each application. Four sites have been selected for therepresentativeness of their snowpack and the quality of the collected data. 26 models have participated inintercomparison by simulating the snowpack with the observed meteorological parameters. The validation of thesimulation consists in comparing the results with snow pack observations. In a first step, the analysis focuses onthe snow water simulation (compared with weekly snow pits). In particular, the snow water equivalent (SWE)maximum and the snow cover duration are two interesting features to consider, because they allow theestimation of the models abilities in terms of simulating the accumulation and melting periods.
Keywords: snow simulation, model intercomparison, SNOWMIP.
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1. Introduction
In the last thirty years, many snow models havebeen developed and have been used for various
• Corresponding author address: Pierre Etchevers,Meteo-France/CEN, 1441, rue de la Piscine, F38400 Saint Martin d'Heres, France. E-mail :nierre_etcheversiGlmeteo_fr
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applications such as hydrology, global circulationmodels, snow monitoring, snow physics aniavalanche forecasting as well. The degree ofcomplexity of these models is highly variable, fromsimple index methods to multi-layer modelssimulating the snow cover stratigraphy and texture.The complexity is determined by multiple
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constraints: the nwneric power of the computer, theavailability of complete datasets, the simulatedphysical processes and the model application. Up tonow, snow-cover models have only been subjectedto a few comparisons as stand-alone models (e. g.Jin et al., 1999, Essery et al., 1999, Schlosser et al.,2000, Boone and Etchevers, 2001). Thesecomparisons generally concern some models ofvarious complexity which are tested for 1 or 2 sites.These studies established that processes internal tothe snow cover are important for improvedperformance and understanding in most of the citedapplications. It also appears that the modelperformance is very dependent on its finalapplication. In some cases, a very simple model ismore relevant than a sophisticated one (for instance,when the input data sets are poor).Following these studies, it appears that a moregeneral comparison of snow models is needed.Indeed, until now comparisons were limited to afew models and sites. Very few works concern thecomparison of models of similar complexity andthe results for various sites.Hence, the main objectives of the intercomparisonproject SnowMIP (for Snow ModelIntercomparison Project) are:
• to define a common method to compare alarge variety of models,
• to estimate the impact of the differentphysical parametrisations,
• to identify the key processes for eachapplication.
It is anticipated that the comparison of detailed andsimple models will be of great interest for thedesign of future GCM snow parametrizations andsimple snow melt models.
Four sites have been selected and data from thesesites have been assembled (section 2). Using themeteorological data, a large nwnber of snowmodels have been invited to simulate the snowpackfor these sites (section 3). Then, the results havebeen analysed and compared to validation data. Afirst anaysis is presented , concerning the snowwater equivalent (SWE), and the snow coverduration (section 4).
2. The data sets
2.1 The sites
As shown in table 1, four different sites have beenchosen for the comparison following two criteria:the site representativeness of a typical seasonal
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snowpack and the availability of complete data setsfor the input data (meteorological parameters) andthe validation data (snow pack measurements).
Name Reference Latllon Elev. Season(m) number
Col de CDP 45.30oN 1340 2Porte (France) 5.77°EGoose Bay GSB 53.32°N 46 15(Canada) 60.42°WSleepers SLR 44.5°N 552 1River (USA) 72.17°WWeissfluhjoch WFJ 46.83°N 2540 I(Switzerland) 9.81°E
Table 1 : The four sites usedfor the snow modesintercomparison.
The Col de Porte, located in the French Alps, is amiddle elevation site. The air temperature is notvery cold, even in mid-winter (the monthly averageis close to O°C) and the precipitation amount is high(about 150 kg ni per month). Rainfall and snowmelting can occur during the entire winter and themaximum extent of the snowpack is generallymeasured in the first days of March. The typicalduration of the snow cover is 6 months. Goose Bayis located close to the Labrador river, in easternCanada. The elevation is very close to the sea level,the air temperature is very low in winter (-16.4°C inJanuary), and the site is very windy and humid(perhumid high boreal climate). The snow coverduration is comparable to Col de Porte : thesnowpack reaches a maximwn of 1.2 m at thebeginning of March. Sleepers River is a midmountain site located in the in the north-westernpart of the Appalacian mountains (Vermont, USA).The monthly averaged temperature is low «-5°C)during winter and the maximum snow depth isabout 1 m. Endly, Weissfluhjoch is the mostmountainous site, since it lies at an altitude of 2500m in the Swiss Alps. Air temperature is comparableto Sleepers River (low during all the winter), butthe snow falls are more significant (maximwn snowdepth generally higher than 2 m). The 4 sitesrepresent quite different climatic and snowcharacteristics, which allow the testing of the snowmodels in different configurations : for example,the contrast is high between Weissfluhjoch, wherethe large snowpack stays cool and dry during thewhole winter, and Col de Porte, where the mediwnsnowpack can melt between two snow falls and isoften partly or completely wet.
2.2 The input data
The data used as input are standard meteorologicalparameters collected every hour: incoming shortwave and long wave radiation, air temperature andhumidity, wind speed and precipitation (amountand phase). The precipitation phase (solid or liquid)is not measured and each data provider hasdetermined it by using other measurements like airtemperature, snow depth sensors or different raingauge types. For SLR and WFJ, only the airtemperature was available for calculating theprecipitation phase.
2.3 The validation data
Data used for the validation have been collectedfrom the sites by the center or the laboratorymanaging the sites. The data consist of the surfacecharacteristics, the internal state and the fluxexchanges which govern the snowpack (table 2).The snow depth, snow water equivalent and snowbottom runoff allow an estimation of the massbalance of the snow pack. The surface albedo andsnow temperature are useful for determining theaccuracy of the energy budget simulation. Thesnow pits are specific measurements of the internalstate of the snowpack and they are done following astandard international procedure. They are usefulfor a precise analysis of the snowpack stratigraphy(vertical profiles of temperature, liquid watercontent, density, grains, ... ).
Frequency CDP GSB SLR WFJSnow water W x x x xequivalentSnow depth H x x x x
(exceptGSB: D)
Snow H x x xbottom run-offSurface H x xalbedoSurface H x xtemperatureSWE W x x x xSnow pits W x x
Table 2 : The validation data availablefor the four sites.TheFequency is hourly (H) for automatic measurementsand daily (D) or weekly (W) for manual measurements.
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Mountain Snowpack
3. The experiments
The simulations are "stand-alone" simulation,meaning that the meteorological parameters areprescribed every hour and the models calculate thesnowpack evolution. The validation data have notbeen diffused and no calibration was possible.
Four experiments have been proposed in order totest the sensitivity of the models. The referenceexperiment (REF) consists in the standardsimulation of the snowpack. In the albedo (ALB)experiment, the models use a prescribed constantalbedo equal to 0.7. This experiment focuses on thealbedo role, which is critical during the meltingperiod. Albedo is a complex property of thesnowpack depending on the micro-properties ofsnow, which are generally not calculated by thesnow models. It is generally estimated by empiricalfOITIlulae linking the snow albedo with the snowage, the grain type, melting and/or density. TheALB experiment should permit a betterunderstanding of the albedo role and bring into lightthe possible feedbacks in the models. The longwave radiation (LWR) experiment deals with theimpact of the measurement uncertainties on thesimulation quality. As the surface energy budget isvery sensitive to the incoming long wave radiation,an arbitrary error of 20 W m-2 is added to theprescribed incoming flux (corresponding to theorder of the measurement error magnitude of theLW radiation). The third sensitivity experimentconcerns the new snow density (NSD). Thisparameter may have an important role, as it affectsthe surface energy budget (through the fresh snowtheITIlal conductivity). As it is never automaticallymeasured, all the models estimate it from othermeteorological parameters. By using a constantdensity of 100 kg ni3 for the fresh snow, one canestimate the models sensitivity to the fresh snowdensity. Lastly, the models have the possibility totest their sensitivity to the heat flux from the soil.This flux is generally constant during the winter,except at the beginning of the season (when it isstronger , because the soil has accumulated a largeheat content during summer) and at the end (whenis it very low, because the melt water penetrates inthe soil). Thus, the models can be run using aconstant prescribed soil flux during the wholeseason (between 0 and 7 W ni2
, depending on thesite), or they can explicitly simulate the heatexchanges between snow and ground.
As the vegetation does not exist (as in WFJ) or isonly short grass (other sites), the interactions
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between snow and vegetation are not simulated bythe participant models for these experiments. In thesame way, the sites are not submitted to significantsnow drift (low wind and/or dense snow) and nosimulation of this process is done.
4. The results
26 snow models have provided results from all or apart of the experiments, depending on the relevanceof the sensitivity studies for themselves. Thesemodels included a more or less sophisticateddescription of physical snow ·processes (anoverview of the general characteristics of theparticipating models can be found in Essery andYang, 2001). Three of them are running in twomodes: with or without an explicit resolution of thesoiVsnow heat exchange. The results presentedwithin this paper only correspond to the referenceexperiment (REF).
4.1 Snow water equivalent
As the majority of the models try to estimate aparticular aspect of the snowpack (snow waterequivalent, snow surface temperature, ...), the snowdensity is generally roughly derived from othercharacteristics (age, snow melt) and the simulatedsnow depth is not always accurate. Thus, it seemsto be more relevant to compare the model results tothe snow water equivalent (SWE), even if theobservational (snow pits) frequency is lower(weekly) and if each snow pit is not done exactly inthe same place as the previous one. For each modelfor each site, figure 1 shows the root mean squareerror RMS, calculated using the snow pits of eachsite (19 and 22 for the CDP seasons, 20 for SLR, 22for WFJ). For GSB, the results are not shown
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because the snow pits are not done at the sameplace as the snow gauge. Thus, as the precipitationamount and the observed snow water equivalent arenot consistent, the model results do not fit well withthe observation. In order to compare theperformance for each site, the RMS is normalizedby SWE",ax, the maximum SWE observed duringthe season (390 and 402 kg m-2 for the both seasonsin CDP, 238 kg m-2 in SLR, 834 kg ni2 in WFJ):
RMSRMSnoml = SWE
ma.x
Two sites (CDP9697 and WFJ) seem to be wellsimulated by a large majority of models: for 90% ofthem the RMSnorm is lower than 0,26 and for 50%of them the RMSnorm is even lower than 0,14. Thetwo seasons are characterized by two distinctphases (accumulation, then melting), and no meltoccurs during the winter. The models well simulatethe accumulation phase and differences appearduring the melting period (meting rate, beginning ofmelting, end of snow cover). Comparatively,CDP9798 seems to be more difficult to simulatebecause melting occurs as early as February, the20th
. 60% of the models well simulate the snowpackevolution (RMSnonn <0,16), whereas the othermodels simulate snowmelt which is too fast. SLRis the least well simulated site (RMSnonn <0.2 foronly 26% of the models). The majority of themodels overestimate the observed snowpack duringthe whole accumulation period, when the airtemperature is very low (no melting). The simulatedsnowpack increases faster than the snow pits, whichcould be due to the uncertainty on the precipitationphase (calculated by only using air temperature).
Figure 1 : RMS ofthe simulated SWE for each model on the different sites
4.2 Maximum snow water equivalent
The maximum snow water equivalent (value anddate) indicates if the snow accumulation period iswell simulated and if the snow melt occurs at theright time. The table 3 indicates the standartddeviations of the maximum snow water equivalent(SWEmax)and the date of the SWEmax calculated bythe models. The observed values have beenestimated from the weekly snow pits for each site.Thus, the accuracy of the estimated SWEmax date islimited by the measurement frequency (weekly) andby the spatial variability of the snow cover betweentwo snow pits.
Table 3 : Standard deviation of the SWEmax and of datewhere SWEmax is reached (calculatedfor all ofthe modelstogether by comparison with observations). Some modelshave been excluded because their results are too far ji-omthe observations.
The values of <JSWEmax, the standard deviation of themaximum SWE, correspond to the analysis of thewhole season: compared to the maximum SWEitself, <JSWEmax is low for WFJ, moderate for CDPand high for SLR. (Jdate_SWEmax, the standarddeviation of the date where SWE is maximum, is aminimum at WFJ (5.8 days), where theaccumulation and melting periods are distinct. ForSLR and CDP9798, it is higher (about two weeks),because melting and accumulation can occursimultaneously. The highest value of (Jdate_SWEmax isreached on the site CDP9697 (38.2 days), wheretwo SWE peaks are observed (at the beginning ofDecember and of February). All of the models wellreproduce the first one, which is due to large snowfall events. At the end of December, theatmospheric conditions allow a limited melting,which is overestimated by several models (group 1of the figure 2). For these models, the maximumSWE corresponds to the first peak. The models ofthe group 2 well calculate the melting and themaximum SWE is correctly determinated, if oneconsiders the uncertainty on the observed date dueto spatial variability between two snowpits. Thegroup 3 simulates a maximum SWE at the endMarch, with a value a bit overestimated comparedto the observation.
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Maximum SWE for CDP9697
21/03/1997
•
01/03/1997
Group 3
09/02/1997
Observed
31/12/1996
1
11/12/1996 20/01/1997
Date
Figure 2 : Maximum SWE simulated by the models (horizontal coordinate: date, vertical coordinate: value). Eachdiamond corresponds to a model, the trian~le to the SWE observations.
700
600
-~E 500C)~-W 4003:(f)
E 300::JE)(
200CIJE
100
0
21/11/1996
4.3 Snow cover duration
The snow cover duration is a particularly importantfeature of the snowpack because it has a majorimpact on the surface energy budgets. For instance,the surface energy fluxes are strongly governed bythe surface temperature, which is limited to 273.16K if snow is present. Moreover, the snow coverlimits evaporation from ground. Thus, the presenceof snow influences at the same time the local
atmospheric circulation and the watershed waterresources.On average, the snow cover duration isunderestimated at CDP9798 (-10.5 days) andoverestimated at SLR (17.3 days). The RMS of thesnow cover duration is roughly the same for allsites (16 to 23 days), which indicates that thisparameter does not allow to the classification of themodel ability to simulate the local snow cover(table 4).
Table 4: Snow cover duration error (SCDE, in days, calculated by using the snow depthobservations) : average for all models, RMS, fraction ofaccurate models (SCDE <1 week) andnumber ofexcluded models for the calculation.
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If one considers the fraction of the most precisemodels, the best simulated site is CDP9798, wherethe snow cover duration error is lower than 1 weekfor 65% of the models. About one third of themodels reach an equivalent score for CDP9697 andWFJ9293, due to different melting rates in spring.Only 8% of the models calculate a correct snowcover duration in SLR, which is coherent with theoverestimation of the snowpack mass balancealready mentioned above. 15 % of the models
Mountain Snowpack
calculate a correct snow cover duration in GSB, onaverage for 15 seasons.For the 5 sites, the RMS on the snow cover durationis 17.4 days in average for all models (figure 3). Itis lower than 2 weeks for 35% of the models (9,6days for the best one). If one excludes the SLR site(where the snowpack is overestimated by a greatmajority of models), the fraction of models with aRMS lower than 2 weeks reaches 54% (5.8 days thefor the best one).
Snow cover duration error
60
40
20
III>-cu 0
"...0.. -20CD,gE -40;:,Z
-60
-80
-100
~~~!1
~'~R-~ .J~.. '~ lJ ~1~
J III I hn' J 1.1 ~If m ~
I~ H
1• CDP9697 iEm CDP9798 IEli! SLR9697
IDWFJ9293fiI GSB6983 I
!Models
Figure 3 : Snow cover duration error for each model and for the different sites.
5. Conclusion
The results of 26 snow models have been comparedto validation data for the different experiment sites.Some models show a good ability to correctlysimulate the snow pack features for all of the sites,whereas other models are more adapted toparticular conditions. The WFJ site is the bestsimulated site, because the accumulation andmelting periods are distinct. SLR is the mostdifficult site (the snowpack is overestimated bymost of the models), which is probably due tovague precipitation phase. Between these two
extremes, the two CDP seasons are moderately wellsimulated because accumulation and meltingperiods are mixed. The SWE evolution for theseason CDP9697 is generally better estimated, butthe snow cover duration is better capturated bymodels for the season CDP9798. The next step ofthe analysis will focus on the explanation of themodel differences. The energy budget will beexamined and compared to validation data such asalbedo or snow surface temperature. The sensibilityexperiments will be used to bring into light thecomplex feedbacks, the role of the parametrisationsand the impact of scheme complexity. 1bis moredetailed study will then try to classifY the models
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following their characteristics and theirapplications.
References
Boone, A and Etchevers P., 2001. Anintercomparison of three snow schemesof varying complexity coupled to thesame land-surface model: Local scaleevaluation at an Alpine site. Journal ofHydrometeorology, 2,374--394.
Essery R, Martin E., Douville H., Femadez A,Bron E., 1998. A comparison of foursnow models using observations from analpine site. Climate Dynamics, 15, 583593.
Essery R and Yang Z. -L., 200 LAn overview ofmodels participating in the snow modelintercomparison project (SNOWMIP).Proceedings of the SnowMIP Workshop,11 July 2001, 8th Scientific Assembly oflAMAS, Innsbruck
Jin, 1., Gao X, Yang Z. L., et aI., 1999.Comparative analyses of physically basedsnowmelt models for climate simulations.J. of Climate,12 (8), 2643-2657 part 2.
Schlosser C.A, Slater AG., Robock A, PitmanAJ., Vinnikov K.Y., Henderson-SellersA, Speranskaya N.A, Mitchell K.,Boone A, Braden H., Chen F., Cox P., deRosnay P., Desborough C.E., DickinsonRE., Dai Y.J., Duan Q., Entin 1.,Etchevers P., Gedney N., Gusev Y.,Habets F., Kim J., Koren V., KowalczykE., Nasonova O.N., Noilhan J., SchaakeJ., Schmakin AB., Smirnova T.G.,Verseghy D., Wetzel P., Xue Y. andYang Z.L., 2000: Simulation of a borealgrassland hydrology at Valdai, Russia:Pn.,PS Phase 2(d). Monthly WeatherReview: Vol. 128, No.2, pp. 301-321.
