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            <note type="commentary">ISI Document Delivery No.: AG4GK Times Cited: 0 Cited Reference Count: 84 Cited References: ANDERSON EA, 1976, 19 NWS NOAA OFF HYDR Bartlett PA, 2006, ATMOS OCEAN, V44, P207, DOI 10.3137/ao.440301 BEST M, 2009, JULES TECHNICAL DOCU Best MJ, 2011, GEOSCI MODEL DEV, V4, P677, DOI 10.5194/gmd-4-677-2011 BOHREN CF, 1979, COLD REG SCI TECHNOL, V1, P47, DOI 10.1016/0165-232X(79)90018-1 Boone A, 2006, REMOTE SENS ENVIRON, V101, P482, DOI 10.1016/j.rse.2006.01.014 Brun E, 2013, J HYDROMETEOROL, V14, P203, DOI 10.1175/JHM-D-12-012.1 BULIGINA ON, 2013, DATASET ROUTINE SNOW Bulygina ON, 2011, ENVIRON RES LETT, V6, DOI 10.1088/1748-9326/6/4/045204 Burke EJ, 2013, CLIM DYNAM, V41, P1025, DOI 10.1007/s00382-012-1648-x Christensen JH, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P847 Collins WD, 2006, J CLIMATE, V19, P2122, DOI 10.1175/JCLI3761.1 Cox P, 2007, SCIENCE, V317, P207, DOI 10.1126/science.1145956 Cramer W, 2001, GLOBAL CHANGE BIOL, V7, P357, DOI 10.1046/j.1365-2486.2001.00383.x Dankers R, 2011, CRYOSPHERE, V5, P773, DOI 10.5194/tc-5-773-2011 Davis RE, 1997, J GEOPHYS RES-ATMOS, V102, P29389, DOI 10.1029/97JD01335 Derksen C, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL053387 Dery SJ, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001251 Essery R, 2003, GLOBAL PLANET CHANGE, V38, P161, DOI 10.1016/S0921-8181(03)00026-2 Flanner MG, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008003 Foster JL, 1997, REMOTE SENS ENVIRON, V62, P132, DOI 10.1016/S0034-4257(97)00085-0 Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1 *GCOS, 2004, 1219 GCOS WMO GCOS, 2010, IMPL PLAN GLOB OBS S Gelfan AN, 2004, J HYDROMETEOROL, V5, P785, DOI 10.1175/1525-7541(2004)005&lt;0785:MFCIOS&gt;2.0.CO;2 Gerten D, 2004, J HYDROL, V286, P249, DOI 10.1016/j.jhydrol.2003.09.029 GIBSON JK, 1999, ERA DESCRIPTION ECMW GROISMAN PY, 1994, SCIENCE, V263, P198, DOI 10.1126/science.263.5144.198 Hancock S, 2014, J CLIMATE, V27, P624, DOI 10.1175/JCLI-D-13-00382.1 Hancock S, 2013, REMOTE SENS ENVIRON, V128, P107, DOI 10.1016/j.rse.2012.10.004 Hansen M., 2003, EARTH INTERACT, V7, P1, DOI DOI 10.1175/1087-3562 Harding RJ, 1996, J CLIMATE, V9, P2778, DOI 10.1175/1520-0442(1996)009&lt;2778:TEBOTW&gt;2.0.CO;2 HARDING RJ, 2011, WATER GLOBAL CHANGE Hood E, 1999, HYDROL PROCESS, V13, P1781, DOI 10.1002/(SICI)1099-1085(199909)13:12/13&lt;1781::AID-HYP860&gt;3.0.CO;2-C Judson A, 2000, B AM METEOROL SOC, V81, P1577, DOI 10.1175/1520-0477(2000)081&lt;1577:DOFFSI&gt;2.3.CO;2 Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077&lt;0437:TNYRP&gt;2.0.CO;2 Kanamitsu M, 2002, B AM METEOROL SOC, V83, P1631, DOI [10.1175/Bams-83-11-1631, 10.1175/BAMS-83-11-1631] KANTZAS E, 2012, MODEL ENERGY FLOWS P KOURAEV AV, 2010, MONTHLY 5 DAY FIELDS KRENKE A, 2004, FORMER SOVIET UNION Lawrence DM, 2008, J GEOPHYS RES-EARTH, V113, DOI 10.1029/2007JF000883 Lawrence DM, 2011, J ADV MODEL EARTH SY, V3, DOI 10.1029/2011MS000045 Lawrence DM, 2008, CLIM DYNAM, V30, P145, DOI 10.1007/s00382-007-0278-1 Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689 Ling F, 2004, COLD REG SCI TECHNOL, V38, P1, DOI 10.1016/S0165-232X(03)00057-0 Liston GE, 2006, J HYDROMETEOROL, V7, P1259, DOI 10.1175/JHM548.1 Lundberg A, 2001, THEOR APPL CLIMATOL, V70, P117, DOI 10.1007/s007040170010 LUOJUS K, 2011, GEOPH RES ABSTR, V13 McGuire AD, 1995, J BIOGEOGR, V22, P785, DOI 10.2307/2845980 Mitchell TD, 2005, INT J CLIMATOL, V25, P693, DOI 10.1002/joc.1181 Mognard NM, 2002, ANN GLACIOL-SER, V34, P15 Molotch NP, 2007, HYDROL PROCESS, V21, P1567, DOI 10.1002/hyp.6719 *NCDC, 2013, GLOB SURF SUMM DAT V Noilhan J, 1996, GLOBAL PLANET CHANGE, V13, P145, DOI 10.1016/0921-8181(95)00043-7 Oelke C, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002722 Osterkamp TE, 2007, J GEOPHYS RES-EARTH, V112, DOI 10.1029/2006JF000578 Perovich DK, 2007, J GLACIOL, V53, P201, DOI 10.3189/172756507782202919 Piao SL, 2009, NATURE, V458, P1009, DOI 10.1038/nature07944 Polyakov IV, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL011111 Pomeroy JW, 1999, HYDROL PROCESS, V13, P2963, DOI 10.1002/(SICI)1099-1085(19991230)13:18&lt;2963::AID-HYP11&gt;3.0.CO;2-9 Pomeroy JW, 1998, HYDROL PROCESS, V12, P2317, DOI 10.1002/(SICI)1099-1085(199812)12:15&lt;2317::AID-HYP799&gt;3.0.CO;2-X Press W. H., 1992, NUMERICAL RECIPES FO Pulliainen J, 2006, REMOTE SENS ENVIRON, V101, P257, DOI 10.1016/j.rse.2006.01.002 RAZUVAEV VN, 2008, ORNLCDIAC56 US DEP E, DOI DOI 10.3334/CDIAC/CLI.NDP040 Reba ML, 2012, HYDROL PROCESS, V26, P3699, DOI 10.1002/hyp.8372 Roebber PJ, 2003, WEATHER FORECAST, V18, P264, DOI 10.1175/1520-0434(2003)018&lt;0264:ISFBDS&gt;2.0.CO;2 Saito K, 2012, POLAR SCI, V6, P79, DOI 10.1016/j.polar.2012.02.003 Serreze MC, 2000, CLIMATIC CHANGE, V46, P159, DOI 10.1023/A:1005504031923 Serreze MC, 2006, CLIMATIC CHANGE, V76, P241, DOI 10.1007/s10584-005-9017-y Stroeve J, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029703 Stroeve JC, 2012, CLIMATIC CHANGE, V110, P1005, DOI 10.1007/s10584-011-0101-1 Sturm M, 2001, NATURE, V411, P546, DOI 10.1038/35079180 STURM M, 1992, ARCTIC ALPINE RES, V24, P145, DOI 10.2307/1551534 Sturm M, 1997, J GLACIOL, V43, P26 Takala M, 2011, REMOTE SENS ENVIRON, V115, P3517, DOI 10.1016/j.rse.2011.08.014 Tarnocai C, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003327 VERNEKAR AD, 1995, J CLIMATE, V8, P248, DOI 10.1175/1520-0442(1995)008&lt;0248:TEOESC&gt;2.0.CO;2 VERSEGHY DL, 1993, INT J CLIMATOL, V13, P347, DOI 10.1002/joc.3370130402 Wania R, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003413 Wania R, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003412 Woodward FI, 2004, BIOL REV, V79, P643, DOI 10.1017/S1464793103006419 WOODWARD FI, 1995, GLOBAL BIOGEOCHEM CY, V9, P471, DOI 10.1029/95GB02432 Yang J, 2010, HYDROL EARTH SYST SC, V14, P1063, DOI 10.5194/hess-14-1063-2010 Zhang YS, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005902 Kantzas, E. Quegan, S. Lomas, M. Zakharova, E. Zakharova, Elena/N-7731-2013 Zakharova, Elena/0000-0002-2962-1439 EU [242446, 295068] This study was supported by the EU FP7 project Monarch-A (grant no. 242446), Monitoring and assessing regional climate change in high latitudes and the Arctic, and the EU FP7 project EuRuCAS (grant no. 295068), European-Russian Centre for cooperation in the Arctic and sub-Arctic environmental and climate research. 0 COPERNICUS GESELLSCHAFT MBH GOTTINGEN CRYOSPHERE</note>
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                <title xml:lang="en">Evaluation of the snow regime in dynamic vegetation land surface models using field measurements</title>
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                <term xml:lang="en">CLIMATE-CHANGE</term>
                <term xml:lang="en">WATER EQUIVALENT</term>
                <term xml:lang="en">EDDY COVARIANCE</term>
                <term xml:lang="en">RADIOMETER DATA</term>
                <term xml:lang="en">ENERGY-BALANCE</term>
                <term xml:lang="en">BOREAL FOREST</term>
                <term xml:lang="en">BLOWING SNOW</term>
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              <p>An increasing number of studies have demonstrated significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled Earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both system processes and its initial state. This study focuses on snow-related variables and makes extensive use of a historical data set (1966-1996) of field snow measurements acquired across the extent of the former Soviet Union to evaluate a range of simulated snow metrics produced by several land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific failings in simulating snowpack properties such as magnitude, inter-annual variability, timings of snow water equivalent and evolution of snow density. We develop novel and model-independent methodologies that use the field snow measurements to extract the values of fresh snow density and snowpack sublimation, and exploit them to assess model outputs. By directly forcing the surface heat exchange formulation of a land surface model with field data on snow depth and snow density, we evaluate how inaccuracies in simulating snow metrics affect soil temperature, thaw depth and soil carbon decomposition. We also show how field data can be assimilated into models using optimization techniques in order to identify model defects and improve model performance.</p>
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