Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

Sicheng He State Key Laboratory of Earth Surface Process and Resource Ecology, and Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs and Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing, China

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Jing Yang State Key Laboratory of Earth Surface Process and Resource Ecology, and Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs and Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing, China

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Qing Bao Institute of Atmospheric Physics, Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG), Chinese Academy of Sciences, Beijing, China

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Lei Wang Institute of Atmospheric Physics, Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG), Chinese Academy of Sciences, and University of Chinese Academy of Science, Beijing, China

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Bin Wang Department of Atmospheric Sciences, University of Hawai‘i at Mānoa, Honolulu, Hawaii

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Abstract

Realistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

© 2018 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: Jing Yang, yangjing@bnu.edu.cn

Abstract

Realistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

© 2018 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: Jing Yang, yangjing@bnu.edu.cn
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  • Accadia, C., S. Mariani, M. Casaioli, A. Lavagnini, and A. Speranza, 2003: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Wea. Forecasting, 18, 918932, https://doi.org/10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashouri, H., K.-L. Hsu, S. Sorooshian, D. K. Braithwaite, K. R. Knapp, L. D. Cecil, B. R. Nelson, and O. P. Prat, 2015: PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Amer. Meteor. Soc., 96, 6983, https://doi.org/10.1175/BAMS-D-13-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, G., and G. J. Zhang, 2017: Role of vertical structure of convective heating in MJO simulation in NCAR CAM5.3. J. Climate, 30, 74237439, https://doi.org/10.1175/JCLI-D-16-0913.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. C., E. J. Kendon, H. J. Fowler, S. Blenkinsop, C. A. T. Ferro, and D. B. Stephenson, 2013: Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation? Climate Dyn., 41, 14751495, https://doi.org/10.1007/s00382-012-1568-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, H., and J. Sun, 2017: Contribution of human influence to increased daily precipitation extremes over China. Geophys. Res. Lett., 44, 24362444, https://doi.org/10.1002/2016GL072439.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, https://doi.org/10.1175/JCLI3884.1.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deng, X., A. Ye, Y. Mao, Y. Lang, and J. Xu, 2015: TRMM precipitation evaluation for inland of China. Shuiwen, 35, 4754, 61.

  • Ding, Y., 1994: Monsoons over China. Adv. Atmos. Sci., 11, 252–252, https://doi.org/10.1007/BF02666553.

  • Easterling, D. R., J. L. Evans, P. Ya. Groisman, T. R. Karl, K. E. Kunkel, and P. Ambenje, 2000: Observed variability and trends in extreme climate events: A brief review. Bull. Amer. Meteor. Soc., 81, 417425, https://doi.org/10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emori, S., A. Hasegawa, T. Suzuki, and K. Dairaku, 2005: Validation, parameterization dependence, and future projection of daily precipitation simulated with a high-resolution atmospheric GCM. Geophys. Res. Lett., 32, L06708, https://doi.org/10.1029/2004GL022306.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, X., Y. Xu, Z. Zhao, J. S. Pal, and F. Giorgi, 2006: On the role of resolution and topography in the simulation of East Asia precipitation. Theor. Appl. Climatol., 86, 173185, https://doi.org/10.1007/s00704-005-0214-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haarsma, R. J., and Coauthors, 2016: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev., 9, 41854208, https://doi.org/10.5194/gmd-9-4185-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., and Coauthors, 2010: Estimates of tropical diabatic heating profiles: Commonalities and uncertainties. J. Climate, 23, 542558, https://doi.org/10.1175/2009JCLI3025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herold, N., L. V. Alexander, M. G. Donat, S. Contractor, and A. Becker, 2016: How much does it rain over land? Geophys. Res. Lett., 43, 341348, https://doi.org/10.1002/2015GL066615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herold, N., A. Behrangi, and L. V. Alexander, 2017: Large uncertainties in observed daily precipitation extremes over land. J. Geophys. Res. Atmos., 122, 668681, https://doi.org/10.1002/2016JD025842.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., and J. D. Neelin, 2009: Moisture vertical structure, column water vapor, and tropical deep convection. J. Atmos. Sci., 66, 16651683, https://doi.org/10.1175/2008JAS2806.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, A., Y. Zhang, and J. Zhu, 2009: Effects of the physical process ensemble technique on simulation of summer precipitation over China. Acta Meteor. Sin., 23, 713724.

    • Search Google Scholar
    • Export Citation
  • Huang, D.-Q., J. Zhu, Y.-C. Zhang, and A.-N. Huang, 2013: Uncertainties on the simulated summer precipitation over eastern China from the CMIP5 models. J. Geophys. Res., 118, 90359047, https://doi.org/10.1002/jgrd.50695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, D.-Q., P. Yan, J. Zhu, Y. Zhang, X. Kuang, and J. Cheng, 2017: Uncertainty of global summer precipitation in the CMIP5 models: A comparison between high-resolution and low-resolution models. Theor. Appl. Climatol., 132, 5569, https://doi.org/10.1007/s00704-017-2078-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, https://doi.org/10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, Z., W. Li, J. Xu, and L. Li, 2015: Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. J. Climate, 28, 86038619, https://doi.org/10.1175/JCLI-D-15-0099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., and Coauthors, 2014: Process-oriented MJO simulation diagnostic: Moisture sensitivity of simulated convection. J. Climate, 27, 53795395, https://doi.org/10.1175/JCLI-D-13-00497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kimoto, M., N. Yasutomi, C. Yokoyama, and S. Emori, 2005: Projected changes in precipitation characteristics around Japan under the global warming. SOLA, 1, 8588, https://doi.org/10.2151/sola.2005-023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84–87, https://doi.org/10.1038/nature16467.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., X. Jia, J. Ling, W. Zhou, and C. Zhang, 2009: Sensitivity of MJO simulations to diabatic heating profiles. Climate Dyn., 32, 167187, https://doi.org/10.1007/s00382-008-0455-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., and B. Wang, 2017: Predictability of summer extreme precipitation days over eastern China. Climate Dyn., https://doi.org/10.1007/s00382-017-3848-x, in press.

    • Search Google Scholar
    • Export Citation
  • Li, L., W. Li, T. Ballard, G. Sun, and M. Jeuland, 2016: CMIP5 model simulations of Ethiopian Kiremt-season precipitation: Current climate and future changes. Climate Dyn., 46, 28832895, https://doi.org/10.1007/s00382-015-2737-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J., B. Mapes, M. Zhang, and M. Newman, 2004: Stratiform precipitation, vertical heating profiles, and the Madden–Julian oscillation. J. Atmos. Sci., 61, 296309, https://doi.org/10.1175/1520-0469(2004)061<0296:SPVHPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, S.-J., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307, https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, S.-J., and R. B. Rood, 1996: Multidimensional flux-form semi-Lagrangian transport schemes. Mon. Wea. Rev., 124, 20462070, https://doi.org/10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., M. Zhao, Y. Ming, J.-C. Golaz, L. J. Donner, S. A. Klein, V. Ramaswamy, and S. Xie, 2013: Precipitation partitioning, tropical clouds, and intraseasonal variability in GFDL AM2. J. Climate, 26, 54535466, https://doi.org/10.1175/JCLI-D-12-00442.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., and C. Zhang, 2013: Diabatic heating profiles in recent global reanalyses. J. Climate, 26, 33073325, https://doi.org/10.1175/JCLI-D-12-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., and Z. Ren, 2005: Progress in quality control of surface meteorological data. Mater. Sci. Technol., 33, 199203.

  • Ma, S., T. Zhou, A. Dai, and Z. Han, 2015: Observed changes in the distributions of daily precipitation frequency and amount over China from 1960 to 2013. J. Climate, 28, 69606978, https://doi.org/10.1175/JCLI-D-15-0011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matsumoto, J., and K. Takahashi, 1999: Regional differences of daily rainfall characteristics in East Asian summer monsoon season. Geogr. Rev. Japan, 72B, 193201, https://doi.org/10.4157/grj1984b.72.193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2000: An introduction to trends in extreme weather and climate events: Observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bull. Amer. Meteor. Soc., 81, 413416, https://doi.org/10.1175/1520-0477(2000)081<0413:AITTIE>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miao, C., H. Ashouri, K.-L. Hsu, S. Sorooshian, and Q. Duan, 2015: Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation over China. J. Hydrometeor., 16, 13871396, https://doi.org/10.1175/JHM-D-14-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murata, A., and M. Ueno, 2005: The vertical profile of entrainment rate simulated by a cloud-resolving model and application to a cumulus parameterization. J. Meteor. Soc. Japan, 83, 745770, https://doi.org/10.2151/jmsj.83.745.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murray, V., and K. L. Ebi, 2012: IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX). J. Epidemiol. Community Health, 66, 759760, https://doi.org/10.1136/jech-2012-201045.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ou, T., D. Chen, H. W. Linderholm, and J.-H. Jeong, 2013: Evaluation of global climate models in simulating extreme precipitation in China. Tellus, 65A, 19799, https://doi.org/10.3402/tellusa.v65i0.19799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and D. L. Hartmann, 2014: Two modes of change of the distribution of rain. J. Climate, 27, 83578371, https://doi.org/10.1175/JCLI-D-14-00182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Song, X., G. J. Zhang, and J.-L. F. Li, 2012: Evaluation of microphysics parameterization for convective clouds in the NCAR Community Atmosphere Model CAM5. J. Climate, 25, 85688590, https://doi.org/10.1175/JCLI-D-11-00563.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and K. J. Wilson, 1980: The response of a deep cumulus convection model to changes in radiative heating. J. Atmos. Sci., 37, 421434, https://doi.org/10.1175/1520-0469(1980)037<0421:TROADC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, S., and L. Chen, 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, C.-P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.

  • Tokioka, T., K. Yamazaki, A. Kitoh, and T. Ose, 1988: The Equatorial 30–60 day oscillation and the Arakawa-Schubert penetrative cumulus parameterization. J. Meteor. Soc. Japan, 66, 883901, https://doi.org/10.2151/jmsj1965.66.6_883.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toreti, A., and Coauthors, 2013: Projections of global changes in precipitation extremes from Coupled Model Intercomparison Project Phase 5 models. Geophys. Res. Lett., 40, 48874892, https://doi.org/10.1002/grl.50940.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., J. Liu, J. Yang, T. Zhou, and Z. Wu, 2009: Distinct principal modes of early and late summer rainfall anomalies in East Asia. J. Climate, 22, 38643875, https://doi.org/10.1175/2009JCLI2850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., G. J. Zhang, and Y.-J. He, 2017: Simulation of precipitation extremes using a stochastic convective parameterization in the NCAR CAM5 under different resolutions. J. Geophys. Res. Atmos., 122, 12 87512 891, https://doi.org/10.1002/2017JD026901.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., and L. Zhou, 2005: Observed trends in extreme precipitation in China during 1961–2001 and the associated changes in large-scale circulation. Geophys. Res. Lett., 32, L09707, https://doi.org/10.1029/2005GL022574; Corrigendum, 32, L17708, https://doi.org/10.1029/2005GL023769.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willmott, C. J., C. M. Rowe, and W. D. Philpot, 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Amer. Cartogr., 12, 516, https://doi.org/10.1559/152304085783914686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, J., and X.-J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets (in Chinese). Chin. J. Geophys., 56, 11021111.

    • Search Google Scholar
    • Export Citation
  • Yang, J., Q. Bao, B. Wang, D.-Y. Gong, H. He, and M.-N. Gao, 2014: Distinct quasi-biweekly features of the subtropical East Asian monsoon during early and late summers. Climate Dyn., 42, 14691486, https://doi.org/10.1007/s00382-013-1728-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yatagai, A., O. Arakawa, K. Kamiguchi, H. Kawamoto, M. I. Nodzu, and A. Hamada, 2009: A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges. SOLA, 5, 137140, https://doi.org/10.2151/sola.2009-035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc., 93, 14011415, https://doi.org/10.1175/BAMS-D-11-00122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, P., X. Zhang, H. Wan, and X. Pan, 2005: Trends in total precipitation and frequency of daily precipitation extremes over China. J. Climate, 18, 10961108, https://doi.org/10.1175/JCLI-3318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and H. Chen, 2016: Comparing CAM5 and superparameterized CAM5 simulations of summer precipitation characteristics over continental East Asia: Mean state, frequency–intensity relationship, diurnal cycle, and influencing factors. J. Climate, 29, 10671089, https://doi.org/10.1175/JCLI-D-15-0342.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., H. Chen, and R. Yu, 2014: Simulations of stratus clouds over eastern China in CAM5: Sensitivity to horizontal resolution. J. Climate, 27, 70337052, https://doi.org/10.1175/JCLI-D-13-00732.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, C.-Y., Y. Wang, X.-Y. Zhou, Y. Cui, Y.-L. Liu, D.-M. Shi, H.-M. Yu, and Y.-Y. Liu, 2013: Changes in climatic factors and extreme climate events in northeast China during 1961–2010. Adv. Climate Change Res., 4, 92102, https://doi.org/10.3724/SP.J.1248.2013.092.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, L., and Coauthors, 2015: Global energy and water balance: Characteristics from finite-volume atmospheric model of the IAP/LASG (FAMIL1). J. Adv. Model. Earth Syst., 7, 120, https://doi.org/10.1002/2014MS000349.

    • Crossref
    • Search Google Scholar
    • Export Citation
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