Articles | Volume 18, issue 10
https://doi.org/10.5194/tc-18-4589-2024
© Author(s) 2024. 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-18-4589-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Oct 2024
Research article |
| 08 Oct 2024
| 08 Oct 2024
Simulating lake ice phenology using a coupled atmosphere–lake model at Nam Co, a typical deep alpine lake on the Tibetan Plateau
State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Xiaogang Ma
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
Zhu La
School of Ecology and Environment, Tibet University, Lhasa 850000, China
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
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Cited articles
Brown, L. C. and Duguay, C. R.: The response and role of ice cover in lake-climate interactions, Prog. Phys. Geog., 34, 671–704, 2010.
Brown, L. C. and Duguay, C. R.: A comparison of simulated and measured lake ice thickness using a Shallow Water Ice Profiler, Hydrol. Process., 25, 2932–2941, 2011.
Charusombat, U., Fujisaki-Manome, A., Gronewold, A. D., Lofgren, B. M., Anderson, E. J., Blanken, P. D., Spence, C., Lenters, J. D., Xiao, C., Fitzpatrick, L. E., and Cutrell, G.: Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes, Hydrol. Earth Syst. Sci., 22, 5559–5578, https://doi.org/10.5194/hess-22-5559-2018, 2018.
Dai, Y., Wei, N., Huang, A., Zhu, S., Shangguan, W., Yuan, H., Zhang, S., and Liu, S.: The lake scheme of the Common Land Model and its performance evaluation, Chinese Sci. Bull., 63, 3002–3021, https://doi.org/10.1360/N972018-00609, 2018a.
Dai, Y., Wang, L., Yao, T., Li, X., Zhu, L., and Zhang, X.: Observed and Simulated Lake Effect Precipitation Over the Tibetan Plateau: An Initial Study at Nam Co Lake, J. Geophys. Res.-Atmos., 123, 6746-6759, https://doi.org/10.1029/2018jd028330, 2018b.
Deacu, D., Fortin, V., Klyszejko, E., Spence, C., and Blanken, P.D.: Predicting the Net Basin Supply to the Great Lakes with a Hydrometeorological Model, J. Hydrometeorol., 13, 1739–1759, 2012.
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., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, 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.
Dudhia, J.: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:Nsocod>2.0.Co;2, 1989.
Efremova, T. V. and Pal'shin, N. I.: Ice Phenomena Terms on the Water Bodies of Northwestern Russia, Russ. Meteorol. Hydro+, 36, 559–565, https://doi.org/10.3103/S1068373911080085, 2011.
Fujisaki-Manome, A., Anderson, E.J., Kessler, J.A., Chu, P.Y., Wang, J., and Gronewold, A.D.: Simulating Impacts of Precipitation on Ice Cover and Surface Water Temperature Across Large Lakes, J. Geophys. Res.-Oceans, 125, e2019JC015950, https://doi.org/10.1029/2019JC01595, 2020.
Gao, Y. H., Xu, J. W. and Chen, D. L.: Evaluation of WRF Mesoscale Climate Simulations over the Tibetan Plateau during 1979–2011, J. Climate, 28, 2823–2841, https://doi.org/10.1175/JCLI-D-14-00300.1, 2015.
Gu, H. P., Jin, J. M., Wu, Y. H., Ek, M. B., and Subin, Z. M.: Calibration and validation of lake surface temperature simulations with the coupled WRF-CLake model, Climatic Change, 129, 471–483, https://doi.org/10.1007/s10584-013-0978-y, 2015.
Guo, Y. H., Zhang, Y. S., Ma, N., Song, H. T., and Gao H. F.: Quantifying Surface Energy Fluxes and Evaporation over a Significant Expanding Endorheic Lake in the Central Tibetan Plateau, J. Meteorol. Soc. Jpn., 94, 453–465, https://doi.org/10.2151/jmsj.2016-023, 2016.
Huang, A., Lazhu, Wang, J., Dai, Y., Yang, K., Wei, N., Wen, L., Wu, Y., Zhu, X., Zhang, X., and Cai, S.: Evaluating and Improving the Performance of Three 1-D Lake Models in a Large Deep Lake of the Central Tibetan Plateau, J. Geophys. Res.-Atmos., 124, 3143–3167, https://doi.org/10.1029/2018jd029610, 2019.
Huang, W., Cheng, B., Zhang, J., Zhang, Z., Vihma, T., Li, Z., and Niu, F.: Modeling experiments on seasonal lake ice mass and energy balance in the Qinghai–Tibet Plateau: a case study, Hydrol. Earth Syst. Sci., 23, 2173–2186, https://doi.org/10.5194/hess-23-2173-2019, 2019.
Huang, W., Zhao, W., Zhang, C., Leppäranta, M., Li, Z., Li, R., and Lin, Z.: Sunlight penetration dominates the thermal regime and energetics of a shallow ice-covered lake in arid climate, The Cryosphere, 16, 1793–1806, https://doi.org/10.5194/tc-16-1793-2022, 2022.
Janjić, Z.: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP mesoscale model, National Centers for Environmental Prediction Office, Tech. Rep., 437, https://repository.library.noaa.gov/view/noaa/11409 (last access: 26 September 2024), 2001.
Kirillin, G., Leppäranta, M., Terzhevik, A., Granin, N., Bernhardt, J., Engelhardt, C., Efremova, T., Palshin, N., Sherstyankin, P., Zdorovennova, G., and Zdorovennov, R.: Physics of seasonally ice-covered lakes: a review, Aquat. Sci., 74, 659–682, https://doi.org/10.1007/s00027-012-0279-y, 2012.
Kirillin, G. B., Shatwell, T., and Wen, L. J.: Ice-Covered Lakes of Tibetan Plateau as Solar Heat Collectors, Geophys. Res. Lett., 48, e2021GL093429, https://doi.org/10.1029/2021GL093429, 2021.
La, Z., Yang, K., Wang, J. B., Lei, Y. B., Chen, Y. Y., Zhu, L. P., Ding, B. H., and Qin, J.: Quantifying evaporation and its decadal change for Lake Nam Co, central Tibetan Plateau, J. Geophys. Res.-Atmos., 121, 7578–7591, https://doi.org/10.1002/2015jd024523, 2016.
La, Z., Yang, K., Qin, J., Hou, J. Z., Lei, Y. N., Wang, J. B., Huang, A. N., Chen, Y. Y., Ding, B. H., and Li, X.: A Strict Validation of MODIS Lake Surface Water Temperature on the Tibetan Plateau, Remote Sens.-Basel, 14, 5454, https://doi.org/10.545410.3390/rs14215454, 2022.
Lazhu, Yang, K., Hou, J. Z., Wang, J. B., Lei, Y. B., Zhu, L. P., Chen, Y. Y., Wang, M. D., and He, X. G.: A new finding on the prevalence of rapid water warming during lake ice melting on the Tibetan Plateau, Sci. Bull., 66, 2358–2361, https://doi.org/10.1016/j.scib.2021.07.022, 2021.
Lei, Y. B., Yao, T. D., Bird, B. W., Yang, K., Zhai, J. Q., and Sheng, Y. W.: Coherent lake growth on the central Tibetan Plateau since the 1970s: Characterization and attribution, J. Hydrol., 483, 61–67, https://doi.org/10.1016/j.jhydrol.2013.01.003, 2013.
Lei, Y. B., Yang, K., Wang, B., Sheng, Y. W., Bird, B. W., Zhang, G. Q., and Tian, L. D.: Response of inland lake dynamics over the Tibetan Plateau to climate change, Climatic Change, 125, 281–290, https://doi.org/10.1007/s10584-014-1175-3, 2014.
Lei, Y. B., Yao, T. D., Yang, K., Bird, B. W., Tian, L., Zhang, X. , Wang, W., Xiang, Y., Dai, Y., Lazhu, Zhou, J., and Wang, L.: An integrated investigation of lake storage and water level changes in the Paiku Co basin, central Himalayas, J. Hydrol., 562, 599–608, https://doi.org/10.1016/j.jhydrol.2018.05.040, 2018.
Li, X. Y., Ma, Y. J., Huang, Y. M., Hu, X., Wu, X. C., Wang, P., Li, G. Y., Zhang, S. Y., Wu, H. W., Jiang, Z. Y., Cui, B. L., and Liu, L.: Evaporation and surface energy budget over the largest high-altitude saline lake on the Qinghai-Tibet Plateau, J. Geophys. Res.-Atmos., 121, 10470–10485, https://doi.org/10.1002/2016jd025027, 2016.
Li, Z. G., Ao, Y. H., Lyu, S., Lang, J. H., Wen, L. J., Stepanenko, V., Meng, X. H., and Zhao, L.: Investigation of the ice surface albedo in the Tibetan Plateau lakes based on the field observation and MODIS products, J. Glaciol., 64, 506–516, https://doi.org/10.1017/jog.2018.35, 2018.
Li, Z. G., Lyu, S. H., Wen, L. J., Zhao, L., Ao, Y. H., and Meng X. H.: Study of freeze-thaw cycle and key radiation transfer parameters in a Tibetan Plateau lake using LAKE2.0 model and field observations, J. Glaciol., 67, 91–106, https://doi.org/10.1017/jog.2020.87, 2021.
Ma, X. G., Yang, K., La, Z., Lu, H., Jiang, Y. Z., Zhou, X., Yao, X. N., and Li, X.: Importance of Parameterizing Lake Surface and Internal Thermal Processes in WRF for Simulating Freeze Onset of an Alpine Deep Lake, J. Geophys. Res.-Atmos., 127, e2022JD036759, https://doi.org/10.1029/2022JD036759, 2022.
Mellor, G. L. and Yamada, T.: Hierarchy of Turbulence Closure Models for Planetary Boundary-Layers, J. Atmos. Sci., 31, 1791–1806, https://doi.org/10.1175/1520-0469(1974)031<1791:Ahotcm>2.0.Co;2, 1974.
Mironov, D.: Parameterization of Lakes in Numerical Weather Prediction, Description of a Lake Model.Rep, https://doi.org/10.5676/DWD_pub/nwv/cosmo-tr_11, 2008.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97jd00237, 1997.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010jd015139, 2011.
Oleson, K., Lawrence, D., Bonan, G. B., Flanner, M., Kluzek, E., Lawrence, P., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, C., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., and Zeng, X.: Technical Description of version 4.0 of the Community Land Model (CLM), No. NCAR/TN-478+STR, University Corporation for Atmospheric Research, https://doi.org/10.5065/D6FB50WZ, 2010.
Skamarock, W. C., Klemp, J. B., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A description of the advanced research WRF model version 3 Rep., National Center for Atmospheric Research, National Center for Atmospheric Research, Boulder, CO, USA, 145, https://doi.org/10.13140/RG.2.1.2310.6645, 2008.
Steppeler, J., Doms, G., Schättler, U., Bitzer, H., Gassmann, A., Damrath, U., and Gregoric, G.: Meso-gamma scale forecasts using the nonhydrostatic model LM, Meteorol. Atmos. Phys., 82, 75–96, 2003.
Su, F. G., Duan, X. L., Chen, D. L., Hao, Z. C., 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.
Su, D. S., Wen, L. J., Gao, X. Q., Lepparanta, M., Song, X. Y., Shi, Q. Q., and Kirillin, G.: Effects of the Largest Lake of the Tibetan Plateau on the Regional Climate, J. Geophys. Res.-Atmos., 125, e2020JD033396 https://doi.org/10.1029/2020JD033396, 2020.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008mwr2387.1, 2008.
Wan, Z., Hook, S., and Hulley, G.: MYD11C3 MODIS/Aqua Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MYD11C3.006, 2015.
Wang, B. B., Ma, Y. M., Wang, Y., Su, Z. B., and Ma, W.: Significant differences exist in lake-atmosphere interactions and the evaporation rates of high-elevation small and large lakes, J. Hydrol., 573, 220–234, https://doi.org/10.1016/j.jhydrol.2019.03.066, 2019.
Wang, J. B.: Water temperature observation data at Nam Co Lake in Tibet (2011–2014), edited by National Tibetan Plateau Data Center, National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Hydro.tpdc.270332, 2020.
Wang, J. B., Zhu, L. P., Daut, G., Ju, J. T., Lin, X., Wang, Y., and Zhen, X. L.:, Investigation of bathymetry and water quality of Lake Nam Co, the largest lake on the central Tibetan Plateau, China, Limnology, 10, 149–158, https://doi.org/10.1007/s10201-009-0266-8, 2009.
Wang, J. B., Huang, L., Ju, J. T., Daut, G., Wang, Y., Ma, Q. F., Zhu, L. P., Haberzettl, T., Baade, J., andMausbacher, R.: Spatial and temporal variations in water temperature in a high-altitude deep dimictic mountain lake (Nam Co), central Tibetan Plateau, J. Great Lakes Res., 45, 212–223, https://doi.org/10.1016/j.jglr.2018.12.005, 2019.
Wang, M., Wen, L., Li, Z., Leppäranta, M., Stepanenko, V., Zhao, Y., Niu, R., Yang, L., and Kirillin, G.: Mechanisms and effects of under-ice warming water in Ngoring Lake of Qinghai–Tibet Plateau, The Cryosphere, 16, 3635–3648, https://doi.org/10.5194/tc-16-3635-2022, 2022.
Wen, L. J., Lyu, S. H., Kirillin, G., Li, Z. G., and Zhao L: Air-lake boundary layer and performance of a simple lake parameterization scheme over the Tibetan highlands, Tellus A, 68, 31091, https://doi.org/10.3402/tellusa.v68.31091, 2016.
White, B., Austin, J. and Matsumoto, K.: A three-dimensional model of Lake Superior with ice and biogeochemistry, J. Great Lakes Res., 38, 61–71, 2012.
Wu, Y., Huang, A. N., Yang, B., Dong, G. T., Wen, L. J., Lazhu, Zhang, Z., Fu, Z., Zhu, X., Zhang, X., and Cai, S.: Numerical study on the climatic effect of the lake clusters over Tibetan Plateau in summer, Clim. Dynam., 53, 5215–5236, https://doi.org/10.1007/s00382-019-04856-4, 2019.
Wu, Y., Huang, A. N., Lu, Y. Y., Lazhu, Yang, X. Y., Qiu, B., Zhang, Z. Q., and Zhang, X. D.: Numerical Study of the Thermal Structure and Circulation in a Large anal Deep Dimictic Lake Over Tibetan Plateau, J. Geophys. Res.-Oceans., 126, e2021JC017517, https://doi.org/10.1029/2021JC017517, 2021.
Wu, Y., Huang, A.N., Lu, Y. Y., Fujisaki-Manome, A., Zhang, Z. Q., Dai, X. L., and Wang, Y.: Application of a Three-Dimensional Coupled Hydrodynamic-Ice Model to Assess Spatiotemporal Variations in Ice Cover and Underlying Mechanisms in Lake Nam Co, Tibetan Plateau, 2007–2017, J. Geophys. Res.-Atmos., 128, e2023JD038844, https://doi.org/10.1029/2023JD038844, 2023.
Xie, Z., Hu, Z., Ma, Y., Sun, G., Gu, L., Liu, S., Wang, Y., Zheng, H., and Ma, W.: Modeling Blowing Snow Over the Tibetan Plateau With the Community Land Model: Method and Preliminary Evaluation, J. Geophys. Res.-Atmos., 124, 9332–9355, https://doi.org/10.1029/2019JD030684, 2019.
Xu, X. D., Lu, C. G., Shi, X. H., and Gao, S. T.: World water tower: An atmospheric perspective, Geophys. Res. Lett., 35, L20815, https://doi.org/10.1029/2008gl035867, 2008.
Yang, X. Y., Wen, J., Huang, A. N., Lu, Y. Q., Meng, X. H., Zhao, Y., Wang, Y. R., and Meng, L. X.: Short-Term Climatic Effect of Gyaring and Ngoring Lakes in the Yellow River Source Area, China, Front Earth Sci., 9, 770757, https://doi.org/10.3389/feart.2021.770757, 2022.
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y. L.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010jd015140, 2011.
Yao, X. N., Yang, K., Zhou, X., Wang, Y., Lazhu, Chen, Y. Y., and Lu, H: Surface friction contrast between water body and land enhances precipitation downwind of a large lake in Tibet, Clim. Dynam., 56, 2113–2126, https://doi.org/10.1007/s00382-020-05575-x, 2021.
Yao, X. N., Yang, K., Letu, H., Zhou, X, Wang, Y., Ma, X., Lu, H., and La, Z.: Observation and Process Understanding of Typical Cloud Holes Above Lakes Over the Tibetan Plateau, J. Geophys. Res.-Atmos., 128, e2023JD038617, https://doi.org/10.1029/2023JD038617, 2023.
Zhang, B., Wu, Y., Zhu, L., Wang, J., Li, J., and Chen, D.: Estimation and trend detection of water storage at Nam Co Lake, central Tibetan Plateau, J. Hydrol., 405, 161–170, https://doi.org/10.1016/j.jhydrol.2011.05.018, 2011.
Zhang, G., Yao, T., Xie, H., Qin, J., Ye, Q., Dai, Y., and Guo, R.: Estimating surface temperature changes of lakes in the Tibetan Plateau using MODIS LST data, J. Geophys. Res.-Atmos., 119, 8552–8567, https://doi.org/10.1002/2014jd021615, 2014.
Zhang, G., Luo, W., Chen, W., and Zheng, G.: A robust but variable lake expansion on the Tibetan Plateau, Sci. Bull., 64, 1306–1309, https://doi.org/10.1016/j.scib.2019.07.018, 2019.
Zhang, G., Yao, T., Xie, H., Yang, K., Zhu, L., Shum, C. K., Bolch, T., Yi, S., Allen, S., Jiang, L., Chen, W., and Ke, C.: Response of Tibetan Plateau lakes to climate change: Trends, patterns, and mechanisms, Earth-Sci. Rev., 208, 103269, https://doi.org/10.1016/j.earscirev.2020.103269, 2020.
Zhao, Z. Z., Huang, A. N., Ma, W. Q., Wu, Y., Wen, L. J., Lazhu, and Gu, C. L.: Effects of Lake Nam Co and Surrounding Terrain on Extreme Precipitation Over Nam Co Basin, Tibetan Plateau: A Case Study, J. Geophys. Res.-Atmos., 127, e2021JD036190, https://doi.org/10.1029/2021JD036190, 2022.
Zhou, X.: Simulating lake ice phenology using a coupled atmosphere-lake model at lake Nam Co, V1, Institute of Tibetan Plateau Research Chinese Academy of Sciences [data set], https://doi.org/10.17632/bpjcbdkj4c.1, 2023.
Zhou, X., Beljaars, A., Wang, Y., Huang, B., Lin, C., Chen, Y., and Wu, H.: Evaluation of WRF Simulations With Different Selections of Subgrid Orographic Drag Over the Tibetan Plateau, J. Geophys. Res.-Atmos., 122, 9759–9772, https://doi.org/10.1002/2017jd027212, 2017.
Zhou, X., Yang, K., Ouyang, L., Wang, Y., Jiang, Y. Z., Li, X., Chen, D. L., and Prein, A.: Added value of kilometer-scale modeling over the third pole region: a CORDEX-CPTP pilot study, Clim. Dynam., 57, 1673–1687, https://doi.org/10.1007/s00382-021-05653-8, 2021.
Zhou, X., Lazhu, Yao, X., and Wang, B.: Understanding two key processes associated with alpine lake ice phenology using a coupled atmosphere-lake model, J. Hydrol., 46, 101334, https://doi.org/10.1016/j.ejrh.2023.101334, 2023.
Short summary
The simulation of the ice phenology of Nam Co by WRF is investigated. Compared with the default model, improving the key lake schemes, such as water surface roughness length for heat fluxes and the shortwave radiation transfer for lake ice, can better simulate the lake ice phenology. The still existing errors in the spatial patterns of lake ice phenology imply that challenges still exist in modelling key lake and non-lake physics such as grid-scale water circulation and snow-related processes.
The simulation of the ice phenology of Nam Co by WRF is investigated. Compared with the default...
