Journal of Tropical Oceanography ›› 2019, Vol. 38 ›› Issue (5): 52-67.doi: 10.11978/2019002CSTR: 32234.14.2019002
• Marine Meteorology • Previous Articles Next Articles
Deficiency of CMIP5 models in simulating changes of Pacific Walker circulation in recent three decades: the role of Sea Surface Temperature *
Shuheng LIN1,2,Yuping GUAN1,2,4(
),Banglin ZHANG3
- 1. State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, Institute of Tropical and Marine Meteorology, Guangzhou 510640, China
4. Zhuhai Joint Innovative Center for Climate-Environment-Ecosystem, Zhuhai 519078, China
-
Received:2019-01-01Revised:2019-02-27Online:2019-09-20Published:2019-10-09 -
Contact:Yuping GUAN E-mail:guan@scsio.ac.cn -
Supported by:National Key Research and Development Plan Project(2018YFC1506903);National Natural Science Key Fund Project(41830538);Independent project of State Key Laboratory of Tropical Oceanography(South China Sea Institute of Oceanology, Chinese Academy of Sciences)(LTOZZ1802)
CLC Number:
- P732.64
Cite this article
Shuheng LIN,Yuping GUAN,Banglin ZHANG. Deficiency of CMIP5 models in simulating changes of Pacific Walker circulation in recent three decades: the role of Sea Surface Temperature *[J].Journal of Tropical Oceanography, 2019, 38(5): 52-67.
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Table 1
Reanalysis datasets used in this study and their detailed information"
| 数据 | 来源 | 同化方法 | 参考文献 | 时间跨度 | 空间分辨率 | |
|---|---|---|---|---|---|---|
| 水平 | 垂直 | |||||
| 20CR | NOAA-CIRES | 3D-Var | 1871—现在 | 2°×2° | 24层 | |
| ERA-Interim | ECMWF | 4D-Var | 1979—现在 | 1.5°×1.5° | 37层 | |
| JRA-55 | JMA | 4D-Var | 1958—现在 | 1.25°×1.25° | 37层 | |
| MERRA | NASA | 3D-Var | 1979—现在 | 2/3°×1/2° | 42层 | |
| NCEP-1 | NCEP-NCAR | 3D-Var | 1948—现在 | 2.5°×2.5° | 17层 | |
| NCEP-2 | NCEP-DOE | 3D-Var | 1979—现在 | 2.5°×2.5° | 17层 | |
Table 1
Tab. 2
Details of 18 CMIP5 models used in this study (Resolutions are given in terms of latitude×longitude grid points)"
| 序号 | 模式 | 分辨率/(纬度×经度) | 所属机构 |
|---|---|---|---|
| 1 | ACCESS1.0 | 144×192 | 联邦科学与工业研究组织——气象局(澳大利亚) |
| 2 | ACCESS1.3 | 144×192 | 联邦科学与工业研究组织——气象局(澳大利亚) |
| 3 | BCC-CSM1.1 | 64×128 | 北京气候中心——中国气象局(中国) |
| 4 | BCC-CSM1.1(m) | 160×320 | 北京气候中心——中国气象局(中国) |
| 5 | CCSM4 | 192×288 | 美国国家大气研究中心(美国) |
| 6 | CMCC-CM | 240×480 | 意大利欧洲-梅迪特拉内奥气候研究中心(意大利) |
| 7 | CNRM-CM5 | 128×156 | 法国气象局气候变化研究中心(法国) |
| 8 | CSIRO Mk3.6.0 | 96×192 | 澳大利亚联邦科学和工业研究, 昆士兰气候变化卓越中心(澳大利亚) |
| 9 | GFDL CM3 | 90×144 | 美国国家海洋和大气管理局地球流体实验室(美国) |
| 10 | INM-CM4.0 | 120×180 | 俄罗斯数值数学研究所(俄罗斯) |
| 11 | IPSL-CM5A-LR | 96×96 | 皮埃尔-西蒙-拉普拉斯研究所(法国) |
| 12 | IPSL-CM5A-MR | 143×144 | 皮埃尔-西蒙-拉普拉斯研究所(法国) |
| 13 | IPSL-CM5B-LR | 96×96 | 皮埃尔-西蒙-拉普拉斯研究所(法国) |
| 14 | MIROC5 | 128×256 | 日本气候系统研究中心, 国家环境研究所和前沿气候变化研究中心(日本) |
| 15 | MPI-ESM-LR | 96×192 | 马克斯普朗克气象研究所(德国) |
| 16 | MPI-ESM-MR | 96×192 | 马克斯普朗克气象研究所(德国) |
| 17 | MRI-CGCM3 | 160×320 | 日本气象研究所(日本) |
| 18 | NorESM1-M | 96×144 | 挪威气候中心(挪威) |
Tab. 2
Fig. 1
Linear trends of PWC intensity over the period from 1979 to 2008. (a) AMIP stimulations and six reanalysis datasets; (b) CMIP5 and six reanalysis datasets. Error bars denote standard errors of linear trends. The labels of x-axis are the names of reanalysis data (corresponding to Tab. 1) and CMIP5 models (corresponding to Tab. 2). Positive linear trends of CMIP5 and AMIP simulations satisfying 90% confidence level are highlighted in red and blue, respectively. Linear trends of ensemble mean of reanalysis and models are marked by red triangle and blue dot, respectively"
Fig. 1
Fig. 2
Long-term mean (1979-2008) of zonal mass stream function ψ along the equatorial Pacific (5oS—5oN) (a~c, shading and contour; units: ×109 kg·s-1) and long-term mean (1979-2008) of SST in the tropical Pacific Ocean (d~f, shading and contour; units: oC). (a) Ensemble mean of six reanalysis datasets; (b) ensemble mean of AMIP simulations; (c) ensemble mean of CMIP5 simulations; (d) ERSST; (e) HadISST; (f) ensemble mean CMIP5 simulations"
Fig. 2
Fig. 3
Taylor diagrams of the climatology of tropical Pacific Walker circulation and SST in 18 CMIP5 models. (a) Tropical Pacific Walker circulation; (b) SST. The REF in (a) is ensemble mean of the six reanalysis datasets and that in (b) is mean of ERSST and HadISST datasets. The radial coordinate is the standard deviation of model results divided by standard deviation of the observations. The angular coordinate is weighted pattern correlation coefficient between model results and observations. The closer the angular and polar values are to 1 (i.e., REF), the closer the spatial distribution of the simulated field is to the observation"
Fig. 3
Fig. 4
Climatology (contour, units: ×109 kg·s-1) and linear trend (shading, units: ×109 kg·s-1·decade-1) of zonal mass stream function ψ over 1979-2008. (a) Ensemble mean of six reanalysis datasets (ENS); (b) ensemble mean of 18 AMIP stimulations; (c) CMIP5 (+PWCI) models; (d) CMIP5 (-PWCI) models; (e) AMIP (+CMIP5) stimulations; (f) AMIP (-CMIP5) stimulations. Stippling indicates the trend is statistically significant at the level 95%. Black thick lines at the bottom indicate the Maritime and South American Continent"
Fig. 4
Fig. 5
Linear trends of SLP (shading, units: hPa·decade-1), wind at 1000 hPa (vector, units: m·s-1·decade-1) and precipitation (contour, units: mm·d-1·decade-1) during 1979-2008. (a) observation; (b) ensemble mean of 18 AMIP stimulations; (c) CMIP5 (+PWCI) models; (d) CMIP5 (-PWCI) models; (e) AMIP (+CMIP5) stimulations; (f) AMIP (-CMIP5) stimulations. Wind and SLP data are from the ENS and precipitation is from the GPCP in (a). Green solid contour denotes the trend of precipitation is positive, and purple denotes the negative trend. Vectors are plotted only for regions with surface zonal wind trends that are statistically significant at the 95% level. Stippling indicates the trend of SLP is statistically significant at the 95% level"
Fig. 5
Fig. 6
Scatterplot of linear trends of SST gradient [$\Delta \text{SST}$, regional mean over (80o—160oW, 5oS—5oN) minus that over (80o—160oE, 5oS—5oN)] and PWC intensity during 1979-2008. The black line denotes the least-squares linear fit of the trend based on 18 CMIP5 models. Black dot denotes the trend of PWC intensity and $\Delta \text{SST}$ in ensemble mean of CMIP5 models, and red dot denotes the trend in the observations"
Fig. 6
Fig. 7
Linear trend of annual-mean SST (shading, units: K·decade-1) during 1979-2008. (a) ERSST; (b) HadISST; (c) CMIP5 (+PWCI) models; (d) CMIP5 (-PWCI) models. Stippling indicates the trend is statistically significant at the 95% level"
Fig. 7
Fig. 8
Time series of the annual anomalies of SST gradient and PWCI. (a) CMIP5 (+PWCI) models and observation; (b) CMIP5 (-PWCI) models and observation; (c) CMIP5 (+PWCI) models, AMIP (+CMIP5) stimulations and observations; (d) CMIP5 (-PWCI) models, AMIP (-CMIP5) stimulations and observation. The CMIP5 (+PWCI) models include GFDL-CM3, IPSL-CM5A-MR, INMCM4, CSIRO-Mk3-6-0 mode, and CMIP5 (-PWCI) model include MPI-ESM-MR and MIROC5. See Tab. 2 for more details. Red line denotes reversed Niño3.4 index derived from HadISST dataset. ENS denotes ensemble mean of six reanalysis datasets, and observed SST is derived from the mean of ERSST.V4b and HadISST"
Fig. 8
Fig. 9
Time series of the nine-year smooth anomalies of SST gradient and PWCI. (a) CMIP5 (+PWCI) models and observation; (b) CMIP5 (-PWCI) models and observation; (c) CMIP5 (+PWCI) models, AMIP (+CMIP5) stimulations and observations; (d) CMIP5 (-PWCI) models, AMIP (-CMIP5) stimulations and observation. The CMIP5 (+PWCI) models include GFDL-CM3, IPSL-CM5A-MR, INMCM4, CSIRO-Mk3-6-0 mode, and CMIP5 (-PWCI) model include MPI-ESM-MR and MIROC5. See Tab. 2 for more details. Red line denotes reversed IPO index derived from HadISST dataset. ENS denotes ensemble mean of six reanalysis datasets, and observed SST is derived from the mean of ERSST.V4b and HadISST"
Fig. 9
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