Evaluation of Soil Moisture in CMIP6 Simulations

Liang Qiao aDepartment of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai, China
bState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Zhiyan Zuo aDepartment of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai, China
cCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Dong Xiao bState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Abstract

This study employs multiple reanalysis datasets to evaluate the global shallow and deep soil moisture in Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The multimodel ensemble mean produces generally reasonable simulations for overall climatology, wet and dry centers, and annual peaks in the melt season at mid- to high latitudes and the rainy season at low latitudes. The simulation capability for shallow soil moisture depends on the relationship between soil moisture and the difference between precipitation and evaporation (P − E). Although most models produce effective simulations in regions where soil moisture is significantly related to the P − E (e.g., Europe, low-latitude Asia, and the Southern Hemisphere), considerable discrepancies between simulated conditions and reanalysis data occur at high elevations and latitudes (e.g., Siberia and the Tibetan Plateau), where cold-season processes play a driving role in soil moisture variability. These discrepancies reflect the lack of information concerning the thaw of snow and frozen ground in the reanalyzed data and the inability of models to simulate these processes. The models also perform poorly in areas of extreme aridity. On a global scale, the majority of models provide consistent and capable simulations owing to the minimal variability in deep soil moisture and limited observational information in reanalysis data. Models with higher spatial resolution do not exhibit closer agreement with the reanalysis data, indicating that spatial resolution is not the first limiting factor for CMIP6 soil moisture simulations.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhiyan Zuo, zuozhy@fudan.edu.cn

Abstract

This study employs multiple reanalysis datasets to evaluate the global shallow and deep soil moisture in Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The multimodel ensemble mean produces generally reasonable simulations for overall climatology, wet and dry centers, and annual peaks in the melt season at mid- to high latitudes and the rainy season at low latitudes. The simulation capability for shallow soil moisture depends on the relationship between soil moisture and the difference between precipitation and evaporation (P − E). Although most models produce effective simulations in regions where soil moisture is significantly related to the P − E (e.g., Europe, low-latitude Asia, and the Southern Hemisphere), considerable discrepancies between simulated conditions and reanalysis data occur at high elevations and latitudes (e.g., Siberia and the Tibetan Plateau), where cold-season processes play a driving role in soil moisture variability. These discrepancies reflect the lack of information concerning the thaw of snow and frozen ground in the reanalyzed data and the inability of models to simulate these processes. The models also perform poorly in areas of extreme aridity. On a global scale, the majority of models provide consistent and capable simulations owing to the minimal variability in deep soil moisture and limited observational information in reanalysis data. Models with higher spatial resolution do not exhibit closer agreement with the reanalysis data, indicating that spatial resolution is not the first limiting factor for CMIP6 soil moisture simulations.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhiyan Zuo, zuozhy@fudan.edu.cn
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  • Albergel, C., P. de Rosnay, G. Balsamo, L. Isaksen, and J. Muñoz-Sabater, 2012: Soil moisture analyses at ECMWF: Evaluation using global ground-based in situ observations. J. Hydrometeor., 13, 14421460, https://doi.org/10.1175/JHM-D-11-0107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, A., J. Sheffield, and P. C. D. Milly, 2017: Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett., 44, 236244, https://doi.org/10.1002/2016GL071921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bi, H., J. Ma, W. Zheng, and J. Zeng, 2016: Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau. J. Geophys. Res. Atmos., 121, 26582678, https://doi.org/10.1002/2015JD024131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., and J. Chern, 2006: Simulation of water sources and precipitation recycling for the MacKenzie, Mississippi, and Amazon River basins. J. Hydrometeor., 7, 312329, https://doi.org/10.1175/JHM501.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brubaker, K. L., D. Entekhabi, and P. Eagleson, 1993: Estimation of continental precipitation recycling. J. Climate, 6, 10771089, https://doi.org/10.1175/1520-0442(1993)006<1077:EOCPR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, J., G. Wang, Y. Gao, and Y. Wang, 2014: The influence of seasonal snow on soil thermal and water dynamics under different vegetation covers in a permafrost region. J. Mt. Sci., 11, 727745, https://doi.org/10.1007/s11629-013-2893-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., K. Yang, J. Qin, L. Zhao, W. Tang, and M. Han, 2013: Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J. Geophys. Res. Atmos., 118, 44664475, https://doi.org/10.1002/jgrd.50301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, M., L. Zhong, Y. Ma, M. Zou, N. Ge, X. Wang, and Y. Hu, 2019: A study on the assessment of multi-source satellite soil moisture products and reanalysis data for the Tibetan Plateau. Remote Sens., 11, 1196, https://doi.org/10.3390/rs11101196.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, M. P., and Coauthors, 2015: Improving the representation of hydrologic processes in Earth system models. Water Resour. Res., 51, 59295956, https://doi.org/10.1002/2015WR017096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, M., K. AchutaRao, K. Ashok, S. Bhandari, A. Mitra, S. Prakash, R. Srivastava, and A. Turner, 2013: Observational challenges in evaluating climate models. Nat. Climate Change, 3, 940941, https://doi.org/10.1038/nclimate2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T., and S. Manabe, 1988: The influence of potential evaporation on the variabilities of simulated soil wetness and climate. J. Climate, 1, 523547, https://doi.org/10.1175/1520-0442(1988)001<0523:TIOPEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deng, M., and Coauthors, 2020: Responses of soil moisture to regional climate change over the Three Rivers Source Region on the Tibetan Plateau. Int. J. Climatol., 40, 24032417, https://doi.org/10.1002/joc.6341.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Douville, H., 2002: Influence of soil moisture on the Asian and African monsoons. Part II: Interannual variability. J. Climate, 15, 701720, https://doi.org/10.1175/1520-0442(2002)015<0701:IOSMOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Douville, H., F. Chauvin, and H. Broqua, 2001: Influence of soil moisture on the Asian and African monsoons. Part I: Mean monsoon and daily precipitation. J. Climate, 14, 23812403, https://doi.org/10.1175/1520-0442(2001)014<2381:IOSMOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eltahir, E., and R. Bras, 1996: Precipitation recycling. Rev. Geophys., 34, 367378, https://doi.org/10.1029/96RG01927.

  • Eyring, V., S. Bony, G. Meehl, C. Senior, B. Stevens, R. Stouffer, and K. Taylor, 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findell, K., and E. Eltahir, 2003: Atmospheric controls on soil moisture–boundary layer interactions. Part II: Feedbacks within the continental United States. J. Hydrometeor., 4, 570583, https://doi.org/10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E., S. Seneviratne, P. Vidale, D. Lüthi, and C. Schär, 2007: Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J. Climate, 20, 50815099, https://doi.org/10.1175/JCLI4288.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T., and S. Quiring, 2014: In situ soil moisture coupled with extreme temperatures: A study based on the Oklahoma Mesonet. Geophys. Res. Lett., 41, 47274734, https://doi.org/10.1002/2014GL060949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, S., S. Nie, Y. Luo, and X. Chen, 2020: Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data. J. Geogr. Sci., 30, 1836, https://doi.org/10.1007/s11442-020-1712-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007JD008972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greve, P., B. Orlowsky, B. Mueller, J. Sheffield, M. Reichstein, and S. I. Seneviratne, 2014: Global assessment of trends in wetting and drying over land. Nat. Geosci., 7, 716721, https://doi.org/10.1038/ngeo2247.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagemann, S., and T. Stacke, 2015: Impact of the soil hydrology scheme on simulated soil moisture memory. Climate Dyn., 44, 17311750, https://doi.org/10.1007/s00382-014-2221-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hauser, M., R. Orth, and S. I. Seneviratne, 2016: Role of soil moisture versus recent climate change for the 2010 heat wave in western Russia. Geophys. Res. Lett., 43, 28192826, https://doi.org/10.1002/2016GL068036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Hirschi, M., and Coauthors, 2011: Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nat. Geosci., 4, 1721, https://doi.org/10.1038/ngeo1032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jamison, N., and S. Kravtsov, 2010: Decadal variations of North Atlantic sea surface temperature in observations and CMIP3 simulations. J. Climate, 23, 46194636, https://doi.org/10.1175/2010JCLI3598.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and M. J. Suarez, 2001: Soil moisture memory in climate models. J. Hydrometeor., 2, 558570, https://doi.org/10.1175/1525-7541(2001)002<0558:SMMICM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and M. J. Puma, 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335, https://doi.org/10.1175/2009JCLI2832.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, H., A. Robock, and M. Wild, 2007: Evaluation of Intergovernmental Panel on Climate Change Fourth Assessment soil moisture simulations for the second half of the twentieth century. J. Geophys. Res., 112, D05108, https://doi.org/10.1029/2006JD007455.

    • Search Google Scholar
    • Export Citation
  • Li, R., C. Wang, and D. Wu, 2016: Changes in precipitation recycling over arid regions in the Northern Hemisphere. Theor. Appl. Climatol., 131, 489502, https://doi.org/10.1007/s00704-016-1978-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, L., R. Zhang, and Z. Zuo, 2014: Intercomparison of spring soil moisture among multiple reanalysis data sets over eastern China. J. Geophys. Res. Atmos., 119, 5464, https://doi.org/10.1002/2013JD020940.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, L., R. Zhang, and Z. Zuo, 2017: Effect of spring precipitation on summer precipitation in eastern China: Role of soil moisture. J. Climate, 30, 91839194, https://doi.org/10.1175/JCLI-D-17-0028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, C., M. Kanamitsu, J. O. Roads, W. Ebisuzaki, K. E. Mitchell, and D. Lohmann, 2005: Evaluation of soil moisture in the NCEP–NCAR and NCEP–DOE global reanalyses. J. Hydrometeor., 6, 391408, https://doi.org/10.1175/JHM427.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, L., and Coauthors, 2003: Effects of frozen soil on soil temperature, spring infiltration, and runoff: Results from the PILPS 2(d) experiment at Valdai, Russia. J. Hydrometeor., 4, 334351, https://doi.org/10.1175/1525-7541(2003)4<334:EOFSOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahfouf, J. F., 1991: Analysis of soil moisture from near-surface parameters: A feasibility study. J. Appl. Meteor., 30, 15341547, https://doi.org/10.1175/1520-0450(1991)030<1534:AOSMFN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menard, C. B., and Coauthors, 2021: Scientific and human errors in a snow model intercomparison. Bull. Amer. Meteor. Soc., 102, E61E79, https://doi.org/10.1175/BAMS-D-19-0329.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, B., and S. I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys. Res. Lett., 41, 128134, https://doi.org/10.1002/2013GL058055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakaegawa, T., 2017: Statistical evaluation of future soil moisture changes in East Asia projected in a CMIP5 multi-model ensemble. Hydrol. Res. Lett., 11, 3743, https://doi.org/10.3178/hrl.11.37.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orth, R., and S. I. Seneviratne, 2012: Analysis of soil moisture memory from observations in Europe. J. Geophys. Res., 117, D15115, https://doi.org/10.1029/2011JD017366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orth, R., and S. I. Seneviratne, 2014: Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe. Climate Dyn., 43, 34033418, https://doi.org/10.1007/s00382-014-2112-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, J., J. Niesel, A. Loew, S. Zhang, and J. Wang, 2015: Evaluation of satellite and reanalysis soil moisture products over southwest China using ground-based measurements. Remote Sens., 7, 15 72915 747, https://doi.org/10.3390/rs71115729.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., C. D. Peters-Lidard, S. V. Kumar, C. Alonge, and W. Tao, 2009: A modeling and observational framework for diagnosing local land–atmosphere coupling on diurnal time scales. J. Hydrometeor., 10, 577599, https://doi.org/10.1175/2009JHM1066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., and Coauthors, 2006: Soil moisture memory in AGCM simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) data. J. Hydrometeor., 7, 10901112, https://doi.org/10.1175/JHM533.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., C. Thierry, L. Edouard, H. Martin, B. Eric, L. Irene, O. Boris, and J. Adriaan, 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth Sci. Rev., 99, 125161, https://doi.org/10.1016/j.earscirev.2010.02.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, E., and Coauthors, 2019: Impact of soil moisture initialization on boreal summer subseasonal forecasts: Mid-latitude surface air temperature and heat wave events. Climate Dyn., 52, 16951709, https://doi.org/10.1007/s00382-018-4221-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shinoda, M., 2001: Climate memory of snow mass as soil moisture over central Eurasia. J. Geophys. Res., 106, 33 39333 403, https://doi.org/10.1029/2001JD000525.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, https://doi.org/10.1029/2000JD900719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van den Hurk, B., H. Kim, and G. Krinner, 2016: LS3MIP (v1.0) contribution to CMIP6: The Land Surface, Snow and Soil Moisture Model Intercomparison Project—Aims, setup and expected outcome. Geosci. Model Dev., 9, 28092832, https://doi.org/10.5194/gmd-9-2809-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whan, K., J. Zscheischler, R. Orth, M. Shongwe, M. Rahimi, and E. O. Asare, and S. I. Seneviratne, 2015: Impact of soil moisture on extreme maximum temperatures in Europe. Wea. Climate Extremes, 9, 5767, https://doi.org/10.1016/j.wace.2015.05.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., and R. E. Dickinson, 2004: Time scales of layered soil moisture memory in the context of land–atmosphere interaction. J. Climate, 17, 27522764, https://doi.org/10.1175/1520-0442(2004)017<2752:TSOLSM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., M. A. Geller, and R. E. Dickinson, 2002: The response of soil moisture to long-term variability of precipitation. J. Hydrometeor., 3, 604613, https://doi.org/10.1175/1525-7541(2002)003<0604:TROSMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Z., H. Feng, H. He, J. Zhou, and Y. Zhang, 2021: Evaluation of soil moisture climatology and anomaly components derived from ERA5-land and GLDAS-2.1 in China. Water Resour. Manage., 35, 629643, https://doi.org/10.1007/s11269-020-02743-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeh, T. C., R. T. Wetherald, and S. Manabe, 1984: The effect of soil moisture on the short-term climate and hydrology change—A numerical experiment. Mon. Wea. Rev., 112, 474490, https://doi.org/10.1175/1520-0493(1984)112<0474:TEOSMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, S., and S. M. Quiring, 2017: Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations. Hydrol. Earth Syst. Sci., 21, 22032218, https://doi.org/10.5194/hess-21-2203-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zampieri, M., E. Serpetzoglou, E. N. Anagnostou, E. I. Nikolopoulos, and A. Papadopoulos, 2012: Improving the representation of river–groundwater interactions in land surface modeling at the regional scale: Observational evidence and parameterization applied in the Community Land Model. J. Hydrol., 420–421, 7286, https://doi.org/10.1016/j.jhydrol.2011.11.041.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Li, X. Rong, Y. Xin, J. Li, H. Chen, J. Su, and L. Hua, 2018: An assessment of CAMS-CSM in simulating land–atmosphere heat and water exchanges. J. Meteor. Res., 32, 862880, https://doi.org/10.1007/s13351-018-8055-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., Z. Yang, L. Wu, and K. Yang, 2019: Summer high temperature extremes over northeastern China predicted by spring soil moisture. Sci. Rep., 9, 12577, https://doi.org/10.1038/s41598-019-49053-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Q., S. Wang, F. Yang, P. Yue, T. Yao, and W. Wang, 2015: Characteristics of dew formation and distribution, and its contribution to the surface water budget in a semi-arid region in China. Bound.-Layer Meteor., 154, 317331, https://doi.org/10.1007/s10546-014-9971-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, R., and Z. Zuo, 2011: Impact of spring soil moisture on surface energy balance and summer monsoon circulation over East Asia and precipitation in east China. J. Climate, 24, 33093322, https://doi.org/10.1175/2011JCLI4084.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, R., L. Liu, and Z. Zuo, 2016: Variation of soil moisture over China and their influences on Chinese climate. Chin. J. Nat., 38, 313319.

    • Search Google Scholar
    • Export Citation
  • Zhao, M., H. Zhang, and I. Dharssi, 2019: On the soil moisture memory and influence on coupled seasonal forecasts over Australia. Climate Dyn., 52, 70857109, https://doi.org/10.1007/s00382-018-4566-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, J., Z. Zuo, X. Rong, and J. Wen, 2020: Role of May surface temperature over eastern China in East Asian summer monsoon circulation and precipitation. Int. J. Climatol., 40 (4), 114, https://doi.org/10.1002/joc.6588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, T., L. Zou, and X. Chen, 2019: Commentary on the Coupled Model Intercomparison Project phase 6 (CMIP6). Climate Change Res., 15, 445456, https://doi.org/10.12006/j.issn.1673-1719.2019.193.

    • Search Google Scholar
    • Export Citation
  • Zuo, Z., and R. Zhang, 2007: The spring soil moisture and the summer rainfall in eastern China. Chin. Sci. Bull., 52, 33103312, https://doi.org/10.1007/s11434-007-0442-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zuo, Z., and R. Zhang, 2016: Influence of soil moisture in eastern China on the East Asian summer monsoon. Adv. Atmos. Sci., 33, 151163, https://doi.org/10.1007/s00376-015-5024-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
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