Articles | Volume 14, issue 10
https://doi.org/10.5194/tc-14-3565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-3565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Oct 2020
Research article |
| 29 Oct 2020
| 29 Oct 2020
Systematic bias of Tibetan Plateau snow cover in subseasonal-to-seasonal models
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Shuzhen Hu
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Pang-Chi Hsu
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Weidong Guo
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Jiangfeng Wei
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
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The Tibetan Plateau region exerts a considerable influence on regional climate, yet the snowpack over that region is poorly represented in both climate and forecast models due a large precipitation and snowfall bias. We evaluate the snowpack in state-of-the-art atmospheric reanalyses against in situ observations and satellite remote sensing products. Improved snow initialisation through better use of snow observations in reanalyses may improve medium-range to seasonal weather forecasts.
Dušan Materić, Elke Ludewig, Kangming Xu, Thomas Röckmann, and Rupert Holzinger
The Cryosphere, 13, 297–307, https://doi.org/10.5194/tc-13-297-2019, https://doi.org/10.5194/tc-13-297-2019, 2019
Niels Souverijns, Alexandra Gossart, Stef Lhermitte, Irina V. Gorodetskaya, Jacopo Grazioli, Alexis Berne, Claudio Duran-Alarcon, Brice Boudevillain, Christophe Genthon, Claudio Scarchilli, and Nicole P. M. van Lipzig
The Cryosphere, 12, 3775–3789, https://doi.org/10.5194/tc-12-3775-2018, https://doi.org/10.5194/tc-12-3775-2018, 2018
Short summary
Short summary
Snowfall observations over Antarctica are scarce and currently limited to information from the CloudSat satellite. Here, a first evaluation of the CloudSat snowfall record is performed using observations of ground-based precipitation radars. Results indicate an accurate representation of the snowfall climatology over Antarctica, despite the low overpass frequency of the satellite, outperforming state-of-the-art model estimates. Individual snowfall events are however not well represented.
Cited articles
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B.,
Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model:
Verification from Field Site to Terrestrial Water Storage and Impact in the
Integrated Forecast System, J. Hydrometeorol., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1, 2009.
Bamzai, A. S. and Shukla, J.: Relation between Eurasian Snow Cover, Snow
Depth, and the Indian Summer Monsoon: An Observational Study, J. Climate,
12, 3117–3132, https://doi.org/10.1175/1520-0442(1999)012<3117:RBESCS>2.0.CO;2, 1999.
Barnett, T. P., Dümenil, L., Schlese, U., Roeckner, E., and Latif, M.:
The effect of Eurasian snow cover on regional and global climate variations,
J. Atmos. Sci., 46, 661–686,
https://doi.org/10.1175/1520-0469(1989)046<0661:TEOESC>2.0.CO;2, 1989.
Chen, L. and Frauenfeld, O. W.: A comprehensive evaluation of precipitation
simulations over China based on CMIP5 multimodel ensemble projections, J.
Geophys. Res.-Atmos., 119, 5767–5786, https://doi.org/10.1002/2013JD021190,
2014.
Chen, X. N., Long, D., Hong, Y., Liang, S. L., and Hou, A. Z.: Observed
radiative cooling over the Tibetan Plateau for the past three decades driven
by snow cover-induced surface albedo anomaly, J. Geophys. Res.-Atmos., 122,
6170–6185, https://doi.org/10.1002/2017jd026652, 2017.
Clark, M. P. and Serreze, M. C.: Effects of variations in east Asian snow
cover on modulating atmospheric circulation over the north pacific ocean, J.
Climate, 13, 3700–3710, https://doi.org/10.1175/1520-0442(2000)013<3700:eoviea>2.0.co;2, 2000.
Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C.
S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B.,
Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.:
The Community Climate System Model version 3 (CCSM3), J. Climate, 19,
2122–2143, https://doi.org/10.1175/jcli3761.1, 2006.
de Andrade, F. M., Coelho, C. A. S., and Cavalcanti, I. F. A.: Global
precipitation hindcast quality assessment of the Subseasonal to Seasonal
(S2S) prediction project models, Clim. Dynam., 52, 5451–5475,
https://doi.org/10.1007/s00382-018-4457-z, 2019.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Koehler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Deutscher Wetterdienst: Global Precipitation Analysis Products of the Global Precipitation Climatology Centre, available at: https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, last access: October 2020.
Dirmeyer, P. A., Gentine, P., Ek, M. B., and Balsamo, G.: Chapter 8 – Land
Surface Processes Relevant to Sub-seasonal to Seasonal (S2S) Prediction, in:
Sub-Seasonal to Seasonal Prediction, edited by: Robertson, A. W. and
Vitart, F., Elsevier, 165–181, 2019.
Diro, G. T. and Lin, H.: Subseasonal Forecast Skill of Snow Water
Equivalent and Its Link with Temperature in Selected SubX Models, Weather
Forecast., 35, 273–284, https://doi.org/10.1175/WAF-D-19-0074.1, 2020.
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M. A., Beljaars, A.,
Schär, C., and Elder, K.: An Improved Snow Scheme for the ECMWF Land
Surface Model: Description and Offline Validation, J. Hydrometeorol., 11,
899–916, https://doi.org/10.1175/2010JHM1249.1, 2010.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851,
https://doi.org/10.1029/2002JD003296, 2003.
European Centre for Medium-Range Weather Forecasts: S2S datasets, available at: https://apps.ecmwf.int/datasets/, last access: October 2020a.
European Centre for Medium-Range Weather Forecasts: ERA-Interim data, available at: https://apps.ecmwf.int/datasets/, last access: October 2020b.
Fayad, A., Gascoin, S., Faour, G., López-Moreno, J. I., Drapeau, L.,
Page, M. L., and Escadafal, R.: Snow hydrology in Mediterranean mountain
regions: A review, J. Hydrol., 551, 374–396,
https://doi.org/10.1016/j.jhydrol.2017.05.063, 2017.
Helfrich, S. R., McNamara, D., Ramsay, B. H., Baldwin, T., and Kasheta, T.:
Enhancements to, and forthcoming developments in the Interactive Multisensor
Snow and Ice Mapping System (IMS), Hydrol. Process., 21, 1576–1586,
https://doi.org/10.1002/hyp.6720, 2007.
Henderson, G. R., Peings, Y., Furtado, J. C., and Kushner, P. J.:
Snow–atmosphere coupling in the Northern Hemisphere, Nat. Clim. Change, 8,
954–963, https://doi.org/10.1038/s41558-018-0295-6, 2018.
Hong, S.-Y. and Lim, J.-O. J.: The WRF Single-Moment 6-Class Microphysics
Scheme (WSM6), Asia-Pac., J. Atmos. Sci., 42, 129–151, 2006.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, https://doi.org/10.1175/mwr3199.1, 2006.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman,
K. P., Hong, Y., and Stocker, E. F., and Wolff, D. B.: The TRMM multisatellite
precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/jhm560.1, 2007.
Immerzeel, W. W., Droogers, P., de Jong, S. M., and Bierkens, M. F. P.:
Large-scale monitoring of snow cover and runoff simulation in Himalayan
river basins using remote sensing, Remote Sens. Environ., 113, 40–49,
https://doi.org/10.1016/j.rse.2008.08.010, 2009.
Jeelani, G., Feddema, J. J., van der Veen, C. J., and Stearns, L.: Role of
snow and glacier melt in controlling river hydrology in Liddar watershed
(western Himalaya) under current and future climate, Water Resour. Res., 48,
W12508, https://doi.org/10.1029/2011WR011590, 2012.
Jeong, J. H., Linderholm, H. W., Woo, S. H., Folland, C., Kim, B. M., Kim,
S. J., and Chen, D. L.: Impacts of Snow Initialization on Subseasonal
Forecasts of Surface Air Temperature for the Cold Season, J. Climate, 26,
1956–1972, https://doi.org/10.1175/jcli-d-12-00159.1, 2013.
Kain, J. S.: The Kain-Fritsch convective parameterization: An update, J.
Appl. Meteorol., 43, 170–181,
https://doi.org/10.1175/1520-0450(2004)043<0170:tkcpau>2.0.co;2, 2004.
Kolstad, E. W.: Causal Pathways for Temperature Predictability from Snow
Depth, J. Climate, 30, 9651–9663, https://doi.org/10.1175/JCLI-D-17-0280.1,
2017.
Koren, V., Schaake, J., Mitchell, K., Duan, Q. Y., Chen, F., and Baker, J.
M.: A parameterization of snowpack and frozen ground intended for NCEP
weather and climate models, J. Geophys. Res.-Atmos., 104, 19569–19585,
https://doi.org/10.1029/1999JD900232, 1999.
Li, F., Orsolini, Y. J., Keenlyside, N., Shen, M. L., Counillon, F., and
Wang, Y. G.: Impact of Snow Initialization in Subseasonal-to-Seasonal Winter
Forecasts With the Norwegian Climate Prediction Model, J. Geophys. Res.-Atmos., 124,
10033–10048, https://doi.org/10.1029/2019JD030903, 2019.
Li, W., Guo, W., Hsu, P.-C., and Xue, Y.: Influence of the Madden–Julian
oscillation on Tibetan Plateau snow cover at the intraseasonal time-scale,
Sci. Rep., 6, 30456, https://doi.org/10.1038/srep30456, 2016.
Li, W., Guo, W., Qiu, B., Xue, Y., Hsu, P.-C., and Wei, J.: Influence of
Tibetan Plateau snow cover on East Asian atmospheric circulation at
medium-range time scales, Nat. Commun., 9, 4243,
https://doi.org/10.1038/s41467-018-06762-5, 2018.
Li, W., Chen, J., Li, L., Chen, H., Liu, B., Xu, C.-Y., and Li, X.:
Evaluation and Bias Correction of S2S Precipitation for Hydrological
Extremes, J. Hydrometeorol., 20, 1887–1906,
https://doi.org/10.1175/JHM-D-19-0042.1, 2019.
Li, W., Qiu, B., Guo, W., Zhu, Z., and Hsu, P.-C.: Intraseasonal variability
of Tibetan Plateau snow cover, Int. J. Climatol., 40, 3451–3466,
https://doi.org/10.1002/joc.6407, 2020a.
Li, W., Qiu, B., Guo, W., and Hsu, P.-C.: Rapid response of the East Asian
trough to Tibetan Plateau snow cover, Int. J. Climatol.,
https://doi.org/10.1002/joc.6618, in press, 2020b.
Lin, H. and Wu, Z. W.: Contribution of the Autumn Tibetan Plateau Snow
Cover to Seasonal Prediction of North American Winter Temperature, J.
Climate, 24, 2801–2813, https://doi.org/10.1175/2010jcli3889.1, 2011.
Lin, P., Wei, J., Yang, Z. L., Zhang, Y., and Zhang, K.: Snow data
assimilation-constrained land initialization improves seasonal temperature
prediction, Geophys. Res. Lett., 43, 11423–411432,
https://doi.org/10.1002/2016GL070966, 2016.
Mariotti, A., Ruti, P. M., and Rixen, M.: Progress in subseasonal to
seasonal prediction through a joint weather and climate community effort,
npj Clim. Atmos. Sci., 1, 4, https://doi.org/10.1038/s41612-018-0014-z,
2018.
NASA Goddard Earth Sciences Data and Information Services Center: TRMM (TMPA/3B43) Rainfall Estimate L3 1 month 0.25 degree x 0.25 degree V7, available at: https://disc.gsfc.nasa.gov, last access: October 2020.
National Center for Atmospheric Research: NCEP FNL data, available at: https://rda.ucar.edu/datasets/ds083.2/, last access: October 2020.
National Geophysical Data Center: ETOPO1 1 Arc-Minute Global Relief Model, https://doi.org/10.7289/V5C8276M, last access: October 2020.
National Snow and Ice Data Center: IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1, available at: https://nsidc.org/data/G02156, last access: October 2020.
Orsolini, Y., Wegmann, M., Dutra, E., Liu, B., Balsamo, G., Yang, K., de Rosnay, P., Zhu, C., Wang, W., Senan, R., and Arduini, G.: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations, The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, 2019.
Orsolini, Y. J., Senan, R., Balsamo, G., Doblas-Reyes, F. J., Vitart, F.,
Weisheimer, A., Carrasco, A., and Benestad, R. E.: Impact of snow
initialization on sub-seasonal forecasts, Clim. Dynam., 41, 1969–1982,
https://doi.org/10.1007/s00382-013-1782-0, 2013.
Robertson, A. W., Kumar, A., Peña, M., and Vitart, F.: Improving and
Promoting Subseasonal to Seasonal Prediction, B. Am. Meteorol. Soc., 96,
ES49–ES53, https://doi.org/10.1175/BAMS-D-14-00139.1, 2014.
Schmitt Quedi, E. and Mainardi Fan, F.: Sub seasonal streamflow forecast
assessment at large-scale basins, J. Hydrol., 584, 124635,
https://doi.org/10.1016/j.jhydrol.2020.124635, 2020.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B.,
and Ziese, M.: GPCC full data reanalysis version 6.0 at 0.5: monthly
land-surface precipitation from rain-gauges built on GTS-based and historic
data, GPCC Data Rep., https://doi.org/10.5676/DWD_GPCC/FD_M_V7_100, 2011.
Senan, R., Orsolini, Y. J., Weisheimer, A., Vitart, F., Balsamo, G.,
Stockdale, T. N., Dutra, E., Doblas-Reyes, F. J., and Basang, D.: Impact of
springtime Himalayan-Tibetan Plateau snowpack on the onset of the Indian
summer monsoon in coupled seasonal forecasts, Clim. Dynam., 47, 2709–2725,
https://doi.org/10.1007/s00382-016-2993-y, 2016.
Song, L. and Wu, R.: Intraseasonal Snow Cover Variations Over Western
Siberia and Associated Atmospheric Processes, J. Geophys. Res.-Atmos., 124,
8994–9010, https://doi.org/10.1029/2019JD030479, 2019.
Song, L., Wu, R. G., and An, L.: Different Sources of 10-to 30-day
Intraseasonal Variations of Autumn Snow over Western and Eastern Tibetan
Plateau, Geophys. Res. Lett., 46, 9118–9125,
https://doi.org/10.1029/2019gl083852, 2019.
Su, F., Duan, X., Chen, D., Hao, Z., and Cuo, L.: Evaluation of the Global
Climate Models in the CMIP5 over the Tibetan Plateau, J. Climate, 26,
3187–3208, https://doi.org/10.1175/JCLI-D-12-00321.1, 2013.
Suriano, Z. J. and Leathers, D. J.: Great Lakes Basin Snow-Cover Ablation
and Synoptic-Scale Circulation, J. Appl. Meteorol. Clim., 57,
1497–1510, https://doi.org/10.1175/jamc-d-17-0297.1, 2018.
UCAR/NCAR/CISL/TDD: The NCAR Command Language (Version 6.6.2), https://doi.org/10.5065/D6WD3XH5, last access: October 2020.
University Corporation for Atmospheric Research: WRF Users Page, available at: https://www2.mmm.ucar.edu/wrf/users/download/get_source.html, last access: October 2020.
Vitart, F.: Madden–Julian Oscillation prediction and teleconnections in the
S2S database, Q. J. Roy. Meteor. Soc., 143, 2210–2220,
https://doi.org/10.1002/qj.3079, 2017.
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,
Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H.,
Hodgson, J., Kang, H. S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi,
P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean,
P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M.,
Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F.,
Waliser, D., Woolnough, S., Wu, T., Won, D. J., Xiao, H., Zaripov, R., and
Zhang, L.: The Subseasonal to Seasonal (S2S) Prediction Project Database,
B. Am. Meteorol. Soc., 98, 163–173,
https://doi.org/10.1175/BAMS-D-16-0017.1, 2016.
Wang, C., Yang, K., Li, Y., Wu, D., and Bo, Y.: Impacts of Spatiotemporal
Anomalies of Tibetan Plateau Snow Cover on Summer Precipitation in Eastern
China, J. Climate, 30, 885–903, https://doi.org/10.1175/JCLI-D-16-0041.1,
2017.
Wang, T., Peng, S., Ottle, C., and Ciais, P.: Spring snow cover deficit
controlled by intraseasonal variability of the surface energy fluxes,
Environ. Res. Lett., 10, 024018,
https://doi.org/10.1088/1748-9326/10/2/024018, 2015.
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J. T., Lazo, J. K.,
Kumar, A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V.,
Bharwani, S., MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B.,
Graham, R., Kjellström, E., Becker, E., Pegion, K. V., Holbrook, N. J.,
McEvoy, D., Depledge, M., Perkins-Kirkpatrick, S., Brown, T. J., Street, R.,
Jones, L., Remenyi, T. A., Hodgson-Johnston, I., Buontempo, C., Lamb, R.,
Meinke, H., Arheimer, B., and Zebiak, S. E.: Potential applications of
subseasonal-to-seasonal (S2S) predictions, Meteorol. Appl., 24, 315–325,
https://doi.org/10.1002/met.1654, 2017.
Wu, R. G. and Kirtman, B. P.: Observed relationship of spring and summer
East Asian rainfall with winter and spring Eurasian snow, J. Climate, 20,
1285–1304, https://doi.org/10.1175/jcli4068.1, 2007.
Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., Zhang, Y., Zhang,
L., Li, J., Wu, F., Liu, Y., Zhang, F., Shi, X., Chu, M., Zhang, J., Fang,
Y., Wang, F., Lu, Y., Liu, X., Wei, M., Liu, Q., Zhou, W., Dong, M., Zhao,
Q., Ji, J., Li, L., and Zhou, M.: An overview of BCC climate system model
development and application for climate change studies, J. Meteorolog. Res.,
28, 34–56, https://doi.org/10.1007/s13351-014-3041-7, 2014.
Wu, T. and Wu, G.: An empirical formula to compute snow cover fraction in
GCMs, Adv. Atmos. Sci., 21, 529–535, https://doi.org/10.1007/BF02915720,
2004.
Wu, T. W. and Qian, Z. A.: The relation between the Tibetan winter snow and
the Asian summer monsoon and rainfall: An observational investigation, J.
Climate, 16, 2038–2051, https://doi.org/10.1175/1520-0442(2003)016<2038:trbttw>2.0.co;2, 2003.
Wulff, C. O. and Domeisen, D. I. V.: Higher Subseasonal Predictability of
Extreme Hot European Summer Temperatures as Compared to Average Summers,
Geophys. Res. Lett., 46, 11520–11529, https://doi.org/10.1029/2019GL084314,
2019.
Xiao, Z. X. and Duan, A. M.: Impacts of Tibetan Plateau Snow Cover on the
Interannual Variability of the East Asian Summer Monsoon, J. Climate, 29,
8495–8514, https://doi.org/10.1175/jcli-d-16-0029.1, 2016.
Yang, J., Jiang, L., Ménard, C. B., Luojus, K., Lemmetyinen, J., and
Pulliainen, J.: Evaluation of snow products over the Tibetan Plateau,
Hydrol. Process., 29, 3247–3260, https://doi.org/10.1002/hyp.10427, 2015.
Yang, J., Zhu, T., Gao, M., Lin, H., Wang, B., and Bao, Q.: Late-July
Barrier for Subseasonal Forecast of Summer Daily Maximum Temperature Over
Yangtze River Basin, Geophys. Res. Lett., 45, 12610–612615,
https://doi.org/10.1029/2018GL080963, 2018.
You, Q., Wu, T., Shen, L., Pepin, N., Zhang, L., Jiang, Z., Wu, Z., Kang,
S., and AghaKouchak, A.: Review of snow cover variation over the Tibetan
Plateau and its influence on the broad climate system, Earth Sci. Rev., 201,
103043, https://doi.org/10.1016/j.earscirev.2019.103043, 2020.
Zhang, F., Ren, H., Miao, L., Lei, Y., and Duan, M.: Simulation of Daily
Precipitation from CMIP5 in the Qinghai-Tibet Plateau, SOLA, 15, 68–74,
https://doi.org/10.2151/sola.2019-014, 2019.
Zhang, G., Xie, H., Yao, T., Liang, T., and Kang, S.: Snow cover dynamics of
four lake basins over Tibetan Plateau using time series MODIS data
(2001–2010), Water Resour. Res., 48, W10529,
https://doi.org/10.1029/2012wr011971, 2012.
Zhang, L. L., Su, F. G., Yang, D. Q., Hao, Z. C., and Tong, K.: Discharge
regime and simulation for the upstream of major rivers over Tibetan Plateau,
J. Geophys. Res.-Atmos., 118, 8500–8518,
https://doi.org/10.1002/jgrd.50665, 2013.
Zhang, T. J.: Influence of the seasonal snow cover on the ground thermal
regime: An overview, Rev. Geophys., 43, RG4002,
https://doi.org/10.1029/2004rg000157, 2005.
Zhang, Y. and Li, J.: Impact of moisture divergence on systematic errors in
precipitation around the Tibetan Plateau in a general circulation model,
Clim. Dynam., 47, 2923–2934, https://doi.org/10.1007/s00382-016-3005-y, 2016.
Zhang, Y., Zou, T., and Xue, Y.: An Arctic-Tibetan Connection on Subseasonal
to Seasonal Time Scale, Geophys. Res. Lett., 46, 2790–2799,
https://doi.org/10.1029/2018GL081476, 2019.
Short summary
Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan Plateau snow cover (TPSC) is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau. This study conducted a multimodel comparison of the TPSC prediction skill to learn about their performance in capturing TPSC variability. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Systematic biases of TPSC were found.
Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan...
