Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-135-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-135-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Yilun Han
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Yong Wang
CORRESPONDING AUTHOR
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Wenqi Sun
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Jianbo Deng
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Hunan Institute of Meteorological Sciences, Changsha, 410118, China
Daoming Wei
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Ying Kong
College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000,
China
Bin Wang
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy
of Sciences, Beijing, 100029, China
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Cited articles
Beaudoing, H., Rodell, M., and NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/LWTYSMP3VM5Z, 2020.
CESM Software Engineering Group: CESM User’s Guide (CESM1.2 Release Series User’s Guide), https://www2.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code, last access: 21 December 2022.
Chakraborty, T. and Lee, X.: Land Cover Regulates the Spatial Variability of
Temperature Response to the Direct Radiative Effect of Aerosols, Geophys.
Res. Lett., 46, 8995–9003, https://doi.org/10.1029/2019gl083812, 2019.
Dai, A.: Precipitation Characteristics in Eighteen Coupled Climate Models,
J. Climate, 19, 4605–4630, https://doi.org/10.1175/JCLI3884.1, 2006.
Duveiller, G., Forzieri, G., Robertson, E., Li, W., Georgievski, G., Lawrence, P., Wiltshire, A., Ciais, P., Pongratz, J., Sitch, S., Arneth, A., and Cescatti, A.: Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations, Earth Syst. Sci. Data, 10, 1265–1279, https://doi.org/10.5194/essd-10-1265-2018, 2018.
Findell, K. L., Gentine, P., Lintner, B. R., and Kerr, C.: Probability of
afternoon precipitation in eastern United States and Mexico enhanced by high
evaporation, Nat. Geosci., 4, 434–439, https://doi.org/10.1038/ngeo1174, 2011.
Forzieri, G., Duveiller, G., Georgievski, G., Li, W., Robertson, E., Kautz,
M., Lawrence, P., Garcia San Martin, L., Anthoni, P., Ciais, P., Pongratz,
J., Sitch, S., Wiltshire, A., Arneth, A., and Cescatti, A.: Evaluating the
Interplay Between Biophysical Processes and Leaf Area Changes in Land
Surface Models, J. Adv. Model Earth Sy., 10, 1102–1126,
https://doi.org/10.1002/2018MS001284, 2018.
Forzieri, G., Miralles, D. G., Ciais, P., Alkama, R., Ryu, Y., Duveiller,
G., Zhang, K., Robertson, E., Kautz, M., Martens, B., Jiang, C., Arneth, A.,
Georgievski, G., Li, W., Ceccherini, G., Anthoni, P., Lawrence, P.,
Wiltshire, A., Pongratz, J., Piao, S., Sitch, S., Goll, D. S., Arora, V. K.,
Lienert, S., Lombardozzi, D., Kato, E., Nabel, J. E. M. S., Tian, H.,
Friedlingstein, P., and Cescatti, A.: Increased control of vegetation on
global terrestrial energy fluxes, Nat. Clim. Change, 10, 356–362,
https://doi.org/10.1038/s41558-020-0717-0, 2020.
Gao, Y., Leung, L. R., Zhao, C., and Hagos, S.: Sensitivity of U.S. summer
precipitation to model resolution and convective parameterizations across
gray zone resolutions, J. Geophys. Res.-Atmos., 122, 2714–2733,
https://doi.org/10.1002/2016jd025896, 2017.
Gelaro, R., McCarty, W., Suarez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy,
L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da
Silva, A., Gu, W., Kim, G. K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Global Modeling and Assimilation Office (GMAO): MERRA-2 instM_3d_asm_Np: 3d,Monthly mean,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/2E096JV59PK7, 2015a.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_flx_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/0JRLVL8YV2Y4, 2015b.
Hao, D., Bisht, G., Gu, Y., Lee, W.-L., Liou, K.-N., and Leung, L. R.: A parameterization of sub-grid topographical effects on solar radiation in the E3SM Land Model (version 1.0): implementation and evaluation over the Tibetan Plateau, Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, 2021.
Hao, D., Bisht, G., Huang, M., Ma, P. L., Tesfa, T., Lee, W. L., Gu, Y., and
Leung, L. R.: Impacts of Sub-Grid Topographic Representations on Surface
Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study
in Sierra Nevada, J. Adv. Model Earth Sy., 14, 4, https://doi.org/10.1029/2021ms002862, 2022.
Hao, Z., Hao, F., Xia, Y., Singh, V. P., Hong, Y., Shen, X., and Ouyang, W.:
A Statistical Method for Categorical Drought Prediction Based on NLDAS-2, J.
Appl. Meteorol. Clim., 55, 1049–1061, https://doi.org/10.1175/jamc-d-15-0200.1, 2016.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci. Data,
7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Hartmann, D. L.: Global Physical Climatology, Academic Press, San Diego, America, 1994.
Hinkelman, L. M.: The Global Radiative Energy Budget in MERRA and MERRA-2:
Evaluation with Respect to CERES EBAF Data, J. Climate, 32, 1973–1994,
https://doi.org/10.1175/jcli-d-18-0445.1, 2019.
Huffman, G. J., Dan, B., Bolvin, A., Hsu, K., Joyce, R., and Xie, P.: Integrated Multi-satellitE Retrievals for GPM (IMERG), version 4.4. NASA's Precipitation Processing Center, ftp://arthurhou.pps.eosdis.nasa.gov/gpmdata/ (last access: 21 December 2022), 2014.
Jiménez, C., Prigent, C., Mueller, B., Seneviratne, S. I., McCabe, M.
F., Wood, E. F., Rossow, W. B., Balsamo, G., Betts, A. K., Dirmeyer, P. A.,
Fisher, J. B., Jung, M., Kanamitsu, M., Reichle, R. H., Reichstein, M.,
Rodell, M., Sheffield, J., Tu, K., and Wang, K.: Global intercomparison of
12 land surface heat flux estimates, J. Geophys. Res., 116, D02102,
https://doi.org/10.1029/2010jd014545, 2011.
Klein, S. A. and Hartmann, D. L.: The Seasonal Cycle of Low Stratiform
Clouds, J. Climate, 6, 1587–1606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993.
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen, P.: A
coupled data assimilation system for climate reanalysis, Q. J. Roy. Meteor.
Soc., 142, 65–78, https://doi.org/10.1002/qj.2629, 2016.
Lee, J. M., Zhang, Y., and Klein, S. A.: The effect of land surface
heterogeneity and background wind on shallow cumulus clouds and the
transition to deeper convection, J. Atmos. Sci., 76, 401–419,
https://doi.org/10.1175/JAS-D-18-0196.1, 2019.
Lee, W. L., Liou, K. N., Wang, C. c., Gu, Y., Hsu, H. H., and Li, J. L. F.:
Impact of 3-D Radiation-Topography Interactions on Surface Temperature and
Energy Budget Over the Tibetan Plateau in Winter, J. Geophys. Res.-Atmos.,
124, 1537–1549, https://doi.org/10.1029/2018jd029592, 2019.
Lee, X., Goulden, M. L., Hollinger, D. Y., Barr, A., Black, T. A., Bohrer,
G., Bracho, R., Drake, B., Goldstein, A., Gu, L., Katul, G., Kolb, T., Law,
B. E., Margolis, H., Meyers, T., Monson, R., Munger, W., Oren, R., Paw, U.
K., Richardson, A. D., Schmid, H. P., Staebler, R., Wofsy, S., and Zhao, L.:
Observed increase in local cooling effect of deforestation at higher
latitudes, Nature, 479, 384–387, https://doi.org/10.1038/nature10588, 2011.
Liu, S., Chen, M., and Zhuang, Q.: Aerosol effects on global land surface
energy fluxes during 2003–2010, Geophys. Res. Lett., 41, 7875–7881,
https://doi.org/10.1002/2014gl061640, 2014.
Liu, S., Liu, X., Yu, L., Wang, Y., Zhang, G. J., Gong, P., Huang, W., Wang,
B., Yang, M., and Cheng, Y.: Climate response to introduction of the ESA CCI
land cover data to the NCAR CESM, Clim. Dynam., 56, 4109–4127,
https://doi.org/10.1007/s00382-021-05690-3, 2021.
Liu, S., Wang, Y., Zhang, G. J., Wei, L., Wang, B., and Yu, L.: Contrasting
influences of biogeophysical and biogeochemical impacts of historical land
use on global economic inequality, Nat. Commun., 13, 2479,
https://doi.org/10.1038/s41467-022-30145-6, 2022.
Loeb, N. G., Kato, S., Loukachine, K., Manalo-Smith, N., and Doelling, D.
R.: Angular Distribution Models for Top-of-Atmosphere Radiative Flux
Estimation from the Clouds and the Earth's Radiant Energy System Instrument
on the Terra Satellite. Part II: Validation, J. Atmos. Ocean Tech., 24,
564–584, https://doi.org/10.1175/jtech1983.1, 2007.
Loeb, N. G., Lyman, J. M., Johnson, G. C., Allan, R. P., Doelling, D. R.,
Wong, T., Soden, B. J., and Stephens, G. L.: Observed changes in
top-of-the-atmosphere radiation and upper-ocean heating consistent within
uncertainty, Nat. Geosci., 5, 110–113, https://doi.org/10.1038/ngeo1375, 2012.
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G.,
Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's
Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31, 895–918,
https://doi.org/10.1175/jcli-d-17-0208.1, 2018.
Lothon, M., Campistron, B., Chong, M., Couvreux, F., Guichard, F., Rio, C.,
and Williams, E.: Life Cycle of a Mesoscale Circular Gust Front Observed by
a C-Band Doppler Radar in West Africa, Mon. Weather Rev., 139, 1370–1388,
https://doi.org/10.1175/2010MWR3480.1, 2011.
Lun, Y., Liu, L., Cheng, L., Li, X., Li, H., and Xu, Z.: Assessment of GCMs
simulation performance for precipitation and temperature from CMIP5 to CMIP6
over the Tibetan Plateau, Int. J. Climatol., 41, 3994–4018,
https://doi.org/10.1002/joc.7055, 2021.
Ma, J., Wang, H., and Fan, K.: Dynamic downscaling of summer precipitation
prediction over China in 1998 using WRF and CCSM4, Adv. Atmos. Sci., 32,
577–584, https://doi.org/10.1007/s00376-014-4143-y, 2015.
Mehran, A., AghaKouchak, A., and Phillips, T. J.: Evaluation of CMIP5
continental precipitation simulations relative to satellite-based
gauge-adjusted observations, J. Geophys. Res.-Atmos., 119, 1695–1707,
https://doi.org/10.1002/2013jd021152, 2014.
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.:
Land-atmospheric feedbacks during droughts and heatwaves: state of the
science and current challenges, Ann. N. Y. Acad. Sci., 1436, 19–35,
https://doi.org/10.1111/nyas.13912, 2019.
Mueller, B. and Seneviratne, S. I.: Systematic land climate and
evapotranspiration biases in CMIP5 simulations, Geophys. Res. Lett., 41,
128–134, https://doi.org/10.1002/2013GL058055, 2014.
Na, Y., Fu, Q., and Kodama, C.: Precipitation Probability and Its Future
Changes From a Global Cloud-Resolving Model and CMIP6 Simulations, J.
Geophys. Res.-Atmos., 125, e2019JD031926, https://doi.org/10.1029/2019jd031926, 2020.
Novick, K. A., Biederman, J. A., Desai, A. R., Litvak, M. E., Moore, D. J.
P., Scott, R. L., and Torn, M. S.: The AmeriFlux network: A coalition of the
willing, Agr. Forest Meteorol., 249, 444–456,
https://doi.org/10.1016/j.agrformet.2017.10.009, 2018.
O'Brien, T. A., Collins, W. D., Kashinath, K., Rübel, O., Byna, S., Gu,
J., Krishnan, H., and Ullrich, P. A.: Resolution dependence of precipitation
statistical fidelity in hindcast simulations, J. Adv. Model Earth Sy., 8,
976–990, https://doi.org/10.1002/2016ms000671, 2016.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,
Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A.,
Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque,
J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S.,
Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., and Zeng, X.:
Technical description of version 4.0 of the Community Land Model (CLM), NCAR
Technical Note NCAR/TN-478+STR, 257 pp., 2010.
Osborn, T. J., Jones, P. D., and Joshi, M.: Recent United Kingdom and global
temperature variations, Weather, 72, 323–329, https://doi.org/10.1002/wea.3174, 2017.
Pielke, R. A.: Influence of the spatial distribution of vegetation and soils
on the prediction of cumulus Convective rainfall, Rev. Geophys., 39,
151–177, https://doi.org/10.1029/1999rg000072, 2001.
Pitman, A. J.: The evolution of, and revolution in, land surface schemes
designed for climate models, Int. J. Climatol., 23, 479–510,
https://doi.org/10.1002/joc.893, 2003.
Rieck, M., Hohenegger, C., and van Heerwaarden, C. C.: The Influence of Land
Surface Heterogeneities on Cloud Size Development, Mon. Weather Rev., 142,
3830–3846, https://doi.org/10.1175/mwr-d-13-00354.1, 2014.
Rochetin, N., Couvreux, F., and Guichard, F.: Morphology of breeze
circulations induced by surface flux heterogeneities and their impact on
convection initiation, Q. J. Roy. Meteor. Soc., 143, 463–478,
https://doi.org/10.1002/qj.2935, 2017.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Roc., 85, 381–394,
https://doi.org/10.1175/bams-85-3-381, 2004.
Rotenberg, E. and Yakir, D.: Contribution of semi-arid forests to the
climate system, Science, 327, 451–454, https://doi.org/10.1126/science.1179998, 2010.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R.,
Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker,
E.: The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/jcli-d-12-00823.1, 2014.
Stone, D. A., Risser, M. D., Angélil, O. M., Wehner, M. F., Cholia, S.,
Keen, N., Krishnan, H., O'Brien, T. A., and Collins, W. D.: A basis set for
exploration of sensitivity to prescribed ocean conditions for estimating
human contributions to extreme weather in CAM5.1-1degree, Weather and
Climate Extremes, 19, 10–19, https://doi.org/10.1016/j.wace.2017.12.003, 2018.
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.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K. L.: A
Review of Global Precipitation Data Sets: Data Sources, Estimation, and
Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017rg000574, 2018.
Sun, W., Li, L., and Wang, B.: Reducing the biases in shortwave cloud
radiative forcing in tropical and subtropical regions from the perspective
of boundary layer processes, Sci. China Earth Sci., 59, 1427–1439,
https://doi.org/10.1007/s11430-016-5290-z, 2016.
Sun, W., Wang, B., Wang, Y., Zhang, G. J., Han, Y., Wang, X., and Yang, M.:
Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the
Atmosphere Improves Boreal Summer Land Precipitation Simulation with the
NCAR CESM1.2, Geophys. Res. Lett., 48, e2020GL090715, https://doi.org/10.1029/2020gl090715, 2021.
Tang, Y., Wen, X., Sun, X., and Wang, H.: Interannual variation of the Bowen
ratio in a subtropical coniferous plantation in southeast China, 2003–2012,
PLoS One, 9, e88267, https://doi.org/10.1371/journal.pone.0088267, 2014.
Taylor, C. M., Parker, D. J., and Harris, P. P.: An observational case study
of mesoscale atmospheric circulations induced by soil moisture, Geophys.
Res. Lett., 34, L15801, https://doi.org/10.1029/2007gl030572, 2007.
Waliser, D. E., Moncrieff, M. W., Burridge, D., Fink, A. H., and Yuter, S.:
The “year” of tropical convection (May 2008–April 2010): Climate
variability and weather highlights, B. Am. Meteorol. Soc., 93, 1189–1218,
https://doi.org/10.1175/2011bams3095.1, 2012.
Wang, Y., Zhang, G. J., and Craig, G. C.: Stochastic convective
parameterization improving the simulation of tropical precipitation
variability in the NCAR CAM5, Geophys. Res. Lett., 43, 6612-6619,
https://doi.org/10.1002/2016gl069818, 2016.
Wang, Y., Zhang, G. J., and He, Y. J.: Simulation of Precipitation Extremes
Using a Stochastic Convective Parameterization in the NCAR CAM5 Under
Different Resolutions, J. Geophys. Res.-Atmos., 122, 12875–12891,
https://doi.org/10.1002/2017jd026901, 2017.
Wang, Y., Zhang, G. J., and Jiang, Y.: Linking Stochasticity of Convection
to Large-Scale Vertical Velocity to Improve Indian Summer Monsoon Simulation
in the NCAR CAM5, J. Climate, 31, 6985–7002, https://doi.org/10.1175/jcli-d-17-0785.1,
2018.
Wang, Y., Xia, W., Liu, X., Xie, S., Lin, W., Tang, Q., Ma, H.-Y., Jiang,
Y., Wang, B., and Zhang, G. J.: Disproportionate control on aerosol burden
by light rain, Nat. Geosci., 14, 72–76, https://doi.org/10.1038/s41561-020-00675-z,
2021a.
Wang, Y., Zhang, G. J., Xie, S., Lin, W., Craig, G. C., Tang, Q., and Ma, H.-Y.: Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model, Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, 2021b.
Wei, L., Wang, Y., Liu, S., Zhang, G. J., and Wang, B.: Distinct roles of
land cover in regulating spatial variabilities of temperature responses to
radiative effects of aerosols and clouds, Environ. Res. Lett., 16, 124070,
https://doi.org/10.1088/1748-9326/ac3f04, 2021.
Xia, Y., Hao, Z., Shi, C., Li, Y., Meng, J., Xu, T., Wu, X., and Zhang, B.:
Regional and Global Land Data Assimilation Systems: Innovations, Challenges,
and Prospects, J. Meteorol. Res.-Prc., 33, 159–189,
https://doi.org/10.1007/s13351-019-8172-4, 2019.
Yin, M., Wang, Y., and Sun, W.: Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2, Zenodo [data set], https://doi.org/10.5281/zenodo.6606418, 2022.
Yu, R., Li, J., Zhang, Y., and Chen, H.: Improvement of rainfall simulation
on the steep edge of the Tibetan Plateau by using a finite-difference
transport scheme in CAM5, Clim. Dynam., 45, 2937–2948,
https://doi.org/10.1007/s00382-015-2515-3, 2015.
Yue, S., Yang, K., Lu, H., Zhou, X., Chen, D., and Guo, W.: Representation
of Stony Surface-Atmosphere Interactions in WRF Reduces Cold and Wet Biases
for the Southern Tibetan Plateau, J. Geophys. Res.-Atmos., 126, e2021JD035291,
https://doi.org/10.1029/2021jd035291, 2021.
Zaitchik, B. F., Rodell, M., and Olivera, F.: Evaluation of the Global Land
Data Assimilation System using global river discharge data and a
source-to-sink routing scheme, Water Resour. Res., 46, W06507,
https://doi.org/10.1029/2009wr007811, 2010.
Zhou, X., Yang, K., Beljaars, A., Li, H., Lin, C., Huang, B., and Wang, Y.:
Dynamical impact of parameterized turbulent orographic form drag on the
simulation of winter precipitation over the western Tibetan Plateau, Clim.
Dynam., 53, 707–720, https://doi.org/10.1007/s00382-019-04628-0, 2019.
Zhou, X., Yang, K., Ouyang, L., Wang, Y., Jiang, Y., Li, X., Chen, D., 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.
Zhu, Y.-Y. and Yang, S.: Evaluation of CMIP6 for historical temperature and
precipitation over the Tibetan Plateau and its comparison with CMIP5, Adv.
Clim. Change Res., 11, 239–251, https://doi.org/10.1016/j.accre.2020.08.001, 2020.
Short summary
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the...