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2016年论文

l  Abraham, J.*, R. Cowley, L. Cheng, 2016: Quantification of the effect of water temperature on the fall rate of
     eXpendable BathyThermographs, Journal of Atmospheric and Oceanic Technology, 33(6), 1271-1284,
     doi: 10.1175/JTECH-D-15-0216.1.

l   Bueh Cholaw, Li Yan, Lin Dawei, et al., 2016: Interannual variability of summer rainfall over the northern part of
     China and the related circulation features. J. Meteor. Res., 30(5), 615630, doi: 10.1007/s13351-016-5111-5

l   Chen, J., Y. Liu, M. Zhang, and Y. Peng (2016), New understanding and quantification of the regime
      dependence of aerosol-cloud interaction for studying aerosol indirect effects, Geophys. Res. Lett., 43,
      1780–1787, doi:10.1002/2016GL067683.

l   Cheng L. *, K. Trenberth, M. D. Palmer, J. Zhu, J. Abraham, 2016: Observed and simulated full-depth ocean
       heat content changes for 1970-2005, Ocean Science, 12, 925-935, doi:10.5194/os-12-925-2016.

l   Cheng L. and J. Zhu*, 2016, Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean
      subsurface temperature variation, Journal of Climate, 29(15), 5393–5416, doi: 10.1175/JCLI-D-15-0730.1.

l   Cheng L.*, John Abraham, Gustavo Goni, Timothy Boyer, Susan Wijffels, Rebecca Cowley, Viktor Gouretski,
      Franco Reseghetti, Shoichi Kizu, Shenfu Dong, Francis Bringas, Marlos Goes, Loïc Houpert, Janet
      Sprintall, Jiang Zhu, 2016: XBT Science: assessment of instrumental biases and errors, Bulletin of the
      American Meteorological Society, 97, 924-933, doi: http://dx.doi.org/10.1175/BAMS-D-15-00031.1.

l   Chenglai Wu,Zhaohui Lin,Juanxiong He,Minghua Zhang,Xiaohong Liu,Renjian Zhang,Hunter Brown.2016:
      A process-oriented evaluation of dust emission parameterizations in CESM: Simulation of a typical severe
      dust storm in East Asia. Journal of Advances in Modeling Earth Systems DOI: 10.1002/2016MS000723

l   Ding, R.-Q., J.-P. Li*, F. Zheng, J. Feng, and D.-Q. Liu, 2016: Estimating the limit of decadal-scale climate
      predictability using observational data. Clim. Dyn., 46(5), 1563-1580, doi: 10.1007/s00382-015-2662-6.

l   Dong X., 2016: Influences of the Pacific Decadal Oscillation on the East Asian Summer Monsoon in Non-

ENSO Years. Atmospheric Science Letters, 17(1), 115-120, doi: 10.1002/asl.634.

l   Dong X., R. Lin, J. Zhu, Z. Lu. 2016: Evaluation of Ocean Data Assimilation in CAS-ESM-C:
      Constraining the SST Field. Advances in Atmospheric Sciences, 33(7), 795-807.

l   Dong, X.,and F. Xue*, 2016The phase transition of the Pacific decadal oscillation and decadal variation of
      the East Asian summer monsoon in the twentieth century. Advances in Atmospheric Sciences, 33(3),
      330-338.

l   Du Juan, J. Zhu, F. Fang, C.C. Pain, and I.M. Navon, 2016: Ensemble data assimilation applied to an adaptive
      mesh ocean model. International Journal for Numerical Methods in Fluids, Published online in Wiley Online
      Library (wileyonlinelibrary.com). DOI: 10.1002/fld.4247.

l   Fu, S.-M. (傅慎明), H.-J. Wang, J.-H. Sun, and Y.-C. Zhang, 2016: Energy budgets on the interactions between
      the mean and eddy flows during a persistent heavy rainfall event over the Yangtze River Valley in summer
      2010. J. Meteor. Res., doi: 10.1007/s13351- 016-5121-3.

l   Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I. C., Rabin, S. S., Archibald, S., Mouillot, F.,
      Arnold, S. R., Artaxo, P., Bachelet, D., Ciais, P., Forrest, M., Friedlingstein, P., Hickler, T., Kaplan, J. O.,
      Kloster, S., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Meyn, A., Sitch, S., Spessa, A., van
      der Werf, G. R., Voulgarakis, A., and Yue, C.: The status and challenge of global fire modelling,
      Biogeosciences, 13, 3359-3375, doi:10.5194/bg-13-3359-2016, 2016.

l   Jiang, D., Y. Sui, and X. Lang, Timing and associated climate change of a 2 °C global warming, International
      Journal of Climatology, 2016, 36(14), 4512
4522, doi: 10.1002/joc.4647.

l   Jiang, D., Z. Tian, and X. Lang, Reliability of climate models for China through the IPCC Third to Fifth
      Assessment Reports, International Journal of Climatology, 2016, 36(3), 1114
1133, doi: 10.1002/joc.4406.

l   Jia-Wen ZHU & Xiao-Dong ZENG (2016) Influences of the interannual variability of vegetation LAI on surface
      temperature, Atmospheric and Oceanic Science Letters, 9:4, 292-297, DOI:
      10.1080/16742834.2016.1189800

l   Jiang-Bo JIN, Qing-Cun ZENG, Hai-Long LIU, Lin WU. (2016) The impacts of different surface boundary
      conditions for sea surface salinity on simulation in an OGCM. Atmos. Sci. Lett., 9(6): 465-470.

l   Jiang-Bo JIN, Qing-Cun ZENG, Hai-Long LIU & Lin WU. (2016)  Freshening biases in the freshwater flux of
      CORE data, Atmos. Sci. Lett., 9(5): 361-365.

l   Karl-Heinz Wyrwoll, Junhong Wei,Zhaohui Lin ,Yaping Shao ,Feng HeCold surges and dust events:
      Establishing the link between the East
Asian Winter Monsoon and the Chinese loess recordquaternary
      Science Reviews
149(2016)102-108.

l   Li,Jing, Xichen Li, Barbara E. Carlson, Ralph A. Kahn, Andrew A. Lacis, Oleg Dubovik, and Teruyuki
      Nakajima. "Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective
      assessment of current AERONET locations." Journal of Geophysical Research: Atmospheres (2016).

l   Li,Xichen, Shang-Ping Xie, Sarah T. Gille, and Changhyun Yoo. "Atlantic-induced pan-tropical climate change
      over the past three decades." Nature Climate Change (2016).

l   LIN Zhao-Hui, YU Zheng, ZHANG He, WU Chenglai2016:Quantifying the attribution of model bias in
      simulating the summer hot days in China with IAP AGCM4.1. Atmospheric And Oceanic science Letters,9(6),
      436-442.

l   Liu R.-X., J.-H. Sun, J. Wei, and S.-M. Fu (傅慎明), 2016: Classification of persistent heavy rainfall events over
      South China and associated moisture source analysis. J. Meteor. Res., 30(5): 678-693, doi: 
      10.1007/s13351-016-6042-x.

l   Lu Zeting, Lijing Cheng, Jiang Zhu, Renping Lin. 2016: The complementary role of SMOS sea surface salinity
      observations for estimating global ocean salinity state. Journal of Geophysical Research: Oceans, 121,
      3672–3691.

l   Meixia Lv Zhenchun HaoZhaohui Lin2016: Reservoir Operation with Feedback in a Coupled Land
      Surface and Hydrologic Model: A Case Study of the Huai River Basin, China. JOURNAL OF THE AMERICAN
      WATER RESOURCES ASSOCIATION.52
1),168-183.

l   Ping Liu, Qin Zhang, Chidong Zhang, Yuejian Zhu, Marat Khairoutdinov, Hye-Mi Kim, Courtney Schumacher,
      and Minghua Zhang, 2016: A Revised Real-Time Multivariate MJO Index. Mon. Wea. Rev., 144, 627–642. doi:
      http://dx.doi.org/10.1175/MWR-D-15-0237.1

l   Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Li, F., Mangeon, S., Yue, C.,
      Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T.,
      Voulgarakis, A., Prentice, I. C., Sitch, S., Kaplan, J. O., Harrison, S., and Arneth, A.: The Fire Modeling
      Intercomparison Project (FireMIP), phase 1: Experimental and analytical protocols, Geosci. Model Dev.
      Discuss., doi:10.5194/gmd-2016-237, 2016.

l   Smirnov, O., Zhang, M., Xiao, T. et al. 2016: The relative importance of climate change and population growth
      for exposure to future extreme droughts. Climatic Change (2016) 138: 41. doi:10.1007/s10584-016-1716-z

l   Song, X., X. D. Zeng, J.W. Zhu, and P. Shao, 2016: Development of an establishment scheme for a dynamic
      global vegetation model. Adv. Atmos. Sci., 33, 829–840, doi: 10.1007/s00376-016-5284-y.

l   Song, X., X.-D. Zeng, and F. Li, 2016: Evaluation of the individual allocation scheme and its impacts in a
      Dynamic Global Vegetation Model. Atmos. Oceanic Sci. Lett. 9, 38-44, doi:10.3878/AOSL20150050.

l   Song, X., Zeng, X., Zhu, J., Shao, P.: Development of an establishment scheme for the Dynamic Global
      Vegetation Model, Adv. Atmo. Sci., 2016, 33(7):829-840.

l   Tian, X.,Feng X. B, Zhang H.Q. Zhang B. and Han R, 2016. An enhanced ensemble-basedmethod for
      computing CNOPs using an efficient localization implementation scheme and a two-step optimization
      strategy: formulation and preliminary tests. Q. J. R. Meteorol. Soc. 142: 1007–1016, January 2016 B DOI:
      10.1002/qj.2703

l   Tian, X., and Feng X. B., 2016. A nonlinear least-squares-based ensemble method with a penalty strategy for
      computing the conditional nonlinear optimal perturbations. Q. J. R. Meteorol. Soc. 141: 000–000, April 2016
      B DOI:10.1002/qj.2946

l   Trenberth K*, K., J. Fasullo, K. v. Schuckmann, L. Cheng, Insights into Earth’s energy imbalance from multiple
      sources. Journal of Climate, 29, 7495-7505. doi:10.1175/JCLI-D-16-0339.

l   WANG K., Z. H. Lin, J. LING, Y. YU, C. L. WU, 2016: MJO potential predictability and predictive skill in IAP
      AGCM 4.1. Atmospheric And Oceanic science Letters, 9(5), 1–6.

l   Xiang Song, Xiaodong Zeng, and Fang Li, 2016: Evaluation of the individual allocation scheme and its impacts
      in a dynamic global vegetation model, Atmos. Oceanic Sci. Lett., 9(1): 38-44.

l   Xue, F., and F. Fan, 2016: Anomalous western Pacific subtropical high during late summer in weak La
      Niña years: Contrast between 1981 and 2013. Adv. Atmos. Sci., 33(12), 1351-1360, doi:
      10.1007/s00376-016-5281-1.

l   Y.-C. Zhang, J.-H. Sun, and S.-M. Fu (傅慎明), 2016: Main energy paths and energy cascade processes of the
      two types of persistent heavy rainfall events over the YangtzeRiver–Huaihe River Basin. Adv. Atmos.
      Sci., 10.1007/s00376-016-6117-8.

l   Yu, H., Zhang, M., Lin, W. and Zhang, X. (2016), Cloud transitions: comparison of temporal variation in the
      southeastern Pacific with the spatial variation in the northeastern Pacific at low latitudes. Int. J. Climatol..
      doi:10.1002/joc.4889

l   Yunying Li and Minghua Zhang, 2016: Cumulus over the Tibetan Plateau in the Summer Based on
      CloudSat–CALIPSO Data. J. Climate, 29, 1219–1230. doi: http://dx.doi.org/10.1175/JCLI-D-15-0492.1

l   Zheng, F.*, and J. Zhu, 2016: Improved ensemble-mean forecast skills of ENSO events by a zero-mean
      stochastic error model of an intermediate coupled model. Clim. Dyn., 47, 3901–3915, doi:
      10.1007/s00382-016-3048-0.

l   Zhi, H., R.-H. Zhang*, F. Zheng, and coauthors 2016: Assessment of interannual sea surface salinity variability
      and its effects on the barrier layer in the equatorial Pacific using the BNU-ESM. Adv. Atmos. Sci., 33(3),
      339-351, doi: 10.1007/s00376-015-5163-y.

l   Zhou, G., H. Zhang, F. Zhang, et al., 2016: Studies on High-Resolution Atmospheric and Oceanic General
      Circulation Models. In Development and Evaluation of High Resolution Climate System Models, Springer
      Singapore, 49-103, doi: 10.1007/978-981-10-0033-1_2.

l   Zhou, W.*, M.-Y., Chen, W., Zhuang, F.-H., Xu, F. Zheng, T.-W., Wu, and X., Wang, 2016: Evaluation of the
      tropical variability from the Beijing climate center’s real-time operational global ocean data assimilation
      system. Adv. Atmos. Sci., 33(2), 208-220, doi: 10.1007/s00376-015-4282-9.

l   黄鑫,布和朝鲁,谢作威等,2016:春季影响中国北方地区的蒙古气旋及其背景环流[J].大气科学, 40 (3): 489503.

l   李娟,曾晓东,陈红,等,2016,强度尺度分解方法在气候温度场检验中的应用,大气科学,406),
  
1117-1126

l   李妍,布和朝鲁,林大伟,谢作威, 2016: 内蒙古夏季降水变率的优势模态及其环流特征,大气科学, 40 (4): 756-776
     doi:10.3878/j.issn.1006-9895.1509.15187

l   廖宏, 任小波, 葛全胜, 严中伟, 林朝晖, 周天军, 2016: 气候变暖及其对二氧化碳浓度敏感性的新认识——中国科学
   院战略性先导科技专项“应对气候变化的碳收支认证及相关问题”之气候敏感性任务群研究进展
, 中国科学院院
   刊
, 31(1), 134-141.

l   林大伟,布和朝鲁,谢作威, 2016: 夏季中国华北与印度降水之间的关联及其成因分析[J].大气科学, 40(1): 201-214,
      doi:10.3878/j.issn.1006-9895.1503.14339.

l   彭京备*, 布和朝鲁, 陈红, 郎咸梅, 马洁华, 郑飞. 2016年夏季-2017年春季全国气候趋势展望. 中国科学院院刊,
      2016, 31(7), 830
834

l   彭京备, 刘舸, 孙淑清. 2016. 2013 年我国南方持续性高温天气及副热带高压异常维持的成因分析 [J]. 大气科学,
      40 (5): 897−906.

l   王天一,迟学斌,张贺,郝卉群. 2016. 地球系统模式 CAS-ESM 在“元”上的性能评估. 科研信息化技术与应用,
      7(1): 59-66.

l   张洪芹、田向军*,张承明,非线性集合四维变分同化方法NLS-4DVar之局地化改进,2016,中国海洋大学学报,
   
46(10):010-015.

l   赵俊杰,薛峰*,林万涛等,2016 El Niño对东亚夏季风和夏季降水季节内变化的影响. 气候与环境研究,
  
216),678686.

l   郑飞, 朱江*, 张荣华, 彭京备. 2015年超级厄尔尼诺事件的成功预报. 中国科学院院刊, 2016, 31(2), 251257.