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Advances in Atmospheric Sciences

, Volume 36, Issue 4, pp 378–396 | Cite as

Interannual Salinity Variability in the Tropical Pacific in CMIP5 Simulations

  • Hai Zhi
  • Rong-Hua ZhangEmail author
  • Pengfei Lin
  • Peng Yu
Original Paper
  • 6 Downloads

Abstract

Salinity variability and its causes in the tropical Pacific are analyzed using observations, reanalysis products and model simulations. The mixed-layer salinity (MLS) budget analyses from observations and reanalysis products indicate that its interannual evolution is closely related to ENSO and is predominantly governed by surface forcing and surface advection in the western-central equatorial Pacific. It is found that the observed MLS tendency leads Ni˜no3.4 by about 12 months due to the effect of negative freshwater flux (evaporation minus precipitation). These observation-based analyses are used to evaluate the corresponding simulation using GFDL-ESM2M. It is evident that the model can simulate the spatiotemporal variations of MLS with some discrepancies compared to observations. In the warm pool of the equatorial Pacific the MLS tendency in the model is sensitive to ocean dynamics, however model biases cause the tendency to be underestimated. In particular, the freshwater flux is overestimated while the ocean surface zonal current and vertical velocity at the base of the mixed layer are underestimated. Due to model biases in representing the related physics, the effects of surface forcing on the simulated MLS budget are overestimated and those of subsurface and surface advection are relatively weak. Due to weaker surface advection and subsurface forcing than observed, the simulated compensations for surface forcing are suppressed, and the simulated MLS tendency that leads Ni˜no3.4 by 8–10 months, which is shorter than the observed lead time. These results are useful for the interpretation of observational analyses and other model simulations in the tropical Pacific.

Key words

mixed-layer salinity salt budget interannual variability tropical Pacific model simulation 

摘 要

利用观测, 再分析数据和模式模拟, 分析了热带太平洋盐度变化及其原因. 从观测和再分析的混合层盐度(MLS)的收支分析表明, 其年际演化与ENSO演变密切相关. 热带太平洋的盐度的变化主要受赤道西太平洋表层强迫和表面平流的控制. 研究发现, 由于负淡水通量(蒸发-降水)的影响, 观测的MLS变化趋势超前Niño3.4提前约12个月. 同时挑选GFDL-ESM2M作为CMIP5的代表, 利用基于观测的分析结果来评估相应的模拟. 很明显, 模式可以模拟MLS的时空变化, 但与观测结果有一定的差异. 在赤道太平洋暖池中, 模式的MLS趋势对海洋动力过程非常敏感, 相关物理场的模拟偏差会导致MLS趋势被低估, 特别GFDL-ESM2M对淡水通量的模拟过高, 而表面纬向流和混合层底部垂直速度的估计过低都会导致盐度的收支的偏差. 该模式高估了表面强迫对模拟MLS收支的影响, 但低估了次表层混合和表层平流对盐度收支的的影响相. 由于模拟的表面平流和次表层混合比观测到的弱, 模拟的和淡水通量相关的表层强迫的补偿被抑制, 模拟的MLS趋势只超前Niño3.4提前8–10个月, 比观测到的提前时间短. 这些结果对于解释热带太平洋的观测分析和评估气候模式模拟很有益处.

关键词

混合层盐度 盐度收支 年际变率 热带太平洋 数值模拟 

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Notes

Acknowledgements

The authors wish to thank the anonymous reviewers for their numerous comments that helped improve the original manuscript. This study was supported by the National Natural Science Foundation of China (Grant Nos. 41690122, 41690120 and 41475101), the NSFC–Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401), the NSFC Innovative Group Grant (Project No. 41421005), and Taishan Scholarship.

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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Hai Zhi
    • 1
  • Rong-Hua Zhang
    • 2
    • 3
    • 4
    Email author
  • Pengfei Lin
    • 4
    • 5
  • Peng Yu
    • 6
  1. 1.College of Atmospheric SciencesNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  3. 3.Laboratory for Ocean and Climate DynamicsQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP)Chinese Academy of SciencesBeijingChina
  6. 6.The Cooperative Institute for Climate and Satellites (CICS)University of MarylandCollege ParkUSA

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