Indian Indian Indian Indian Ocean Ocean Ocean Ocean variability variability variability variability in in in in the the the the CMIP5 CMIP5 CMIP5 CMIP5 multi-model multi-model multi-model multi-model ensemble: ensemble: ensemble: ensemble: The The The The basin basin basin basin mode mode mode mode Yan DU * State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China Shang-Ping XIE International Pacific Research Center and Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii; Physical Oceanography Laboratory, Ocean University of China, Qingdao, China; Scripps Institution of Oceanography, UC San Diego, La Jolla, California Ya-Li YANG State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China Xiao-Tong ZHENG Physical Oceanography Laboratory and Key Laboratory of Ocean-Atmosphere Interaction and Climate in Universities of Shandong, Ocean University of China, Qingdao, China Lin LIU First Institute of Oceanography, State Oceanic Administration, China Gang HUANG Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China (Submitted to Journal of Climate, September 16, 2012; Revised January 31, 2013, and March 12, 2013; accepted March 15, 2013) * Corresponding author: Yan DU, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, 164 West Xingang Road, Guangzhou 510301, China. E-mail: [email protected]
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State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, ChineseAcademy of Sciences, Guangzhou, China
Shang-Ping XIE
International Pacific Research Center and Department of Meteorology, University of Hawaii atManoa, Honolulu, Hawaii; Physical Oceanography Laboratory, Ocean University of China,Qingdao, China; Scripps Institution of Oceanography, UC San Diego, La Jolla, California
Ya-Li YANG
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, ChineseAcademy of Sciences, Guangzhou, China
Xiao-Tong ZHENG
Physical Oceanography Laboratory and Key Laboratory of Ocean-Atmosphere Interaction andClimate in Universities of Shandong, Ocean University of China, Qingdao, China
Lin LIU
First Institute of Oceanography, State Oceanic Administration, China
Gang HUANG
Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute ofAtmospheric Physics, Chinese Academy of Sciences, Beijing, China
(Submitted to Journal of Climate, September 16, 2012; Revised January 31, 2013, and March 12,2013; accepted March 15, 2013)
* Corresponding author: Yan DU, State Key Laboratory of Tropical Oceanography, South ChinaSea Institute of Oceanology, 164 West Xingang Road, Guangzhou 510301, China. E-mail:
We wish to thank Y. Wu of LTO/SCSIO for helps in downloading and processing CMIP5 data. Weacknowledge the WCRP Working Group on Coupled Modelling, which is responsible for CMIP5,the climate modeling groups (listed in Table 1 of this paper) for producing and making availabletheir model output, and thank the U.S. Department of Energy's Program for Climate ModelDiagnosis and Intercomparison (PCMDI) for providing coordinating support and organizing theanalysis activity in partnership with the Global Organization for Earth System Science Portals. Thesatellite merged SSH data, SODA reanalysis, and ERSST were obtained from APDRC, Universityof Hawaii (http://apdrc.soest.hawaii.edu), HadISST and HadSST3 from the Met Office HadleyCentre (http://www.metoffice.gov.uk). This work is supported by MoST (2012CB955603,2010CB950302), CAS (XDA05090404), NSFC (41176024, 41106010), LTO (LTOZZ1202), NSF,and JAMSTEC.
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TableTableTableTable 1111. The WCRP CMIP5 models used in this study. In the following figures, the ensemble meanof all models marks MME (multi-model ensemble) composite.
FigFigFigFig.... 1111 First EOF modes of tropical Indian Ocean SST variability (°C; second mode forCSIRO-Mk3.6 and INMCM4). Percentage explains variance contribution in each model. Patterncorrelation coefficient between ERSST and CMIP5 SST EOF modes is included in the lower rightcorner of each panel. MME presents all models ensemble mean.
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FigFigFigFig.... 2222 Standard deviation of PC1 (PC2 in CSIRO-Mk3.6 and INMCM4) as a function of calendarmonth. The interval of 95% significant confidence level across the models on a t test is marked inMME.
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FigFigFigFig.... 3333 Standard deviation (shading, °C) of SSTa along equator, averaged in 5ºS-5ºN, as a functionof longitude and calendar month, superimposed with the seasonal cycle (contour, °C).
FigFigFigFig.... 4444 Relationship of IOB and ENSO: a) Scatter diagram of standard deviations (STDs) of the TIOSST (20°S-20°N, 40°-100°E) and Niño 3.4 SST (5°S-5°N, 170°W-120°W), and b) Taylor diagramof IOB simulation. In b), the horizontal and vertical axises denote the STD of IOB. The IOB STD isnormalized by the ratio of the STD of the Niño 3.4 SST index in observations (averaged of ERSSTand HadSST3) to each model. The outer arc is the maximum correlation between TIO SST andNiño 3.4 NDJ(0/1) index. The green arcs denote RMS difference between the normalized IOB STDand Niño 3.4 SST STD.
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FigFigFigFig.... 5555 Correlation of NDJ(0/1) Niño3.4 index with Niño3.4 SST (5°S-5°N, 170°W-120°W, blacksolid line), TIO SST (20°S-20°N, 40°E-100°E, green dashed line), NIO SST (0°-20°N, 40°E-100°E,blue dashed line) based on the (a) ERSST, (b) HadSST3, (c) all model composite. Correlation ofNiño3.4 NDJ(0/1) SST index with (d) Niño3.4 SST (5°S-5°N,170°W-120°W), (e) TIO SST(20°S-20°N, 40°E-100°E), (f) NIO SST (0°-20°N, 40°E-100°E). Dotted black line denotesstatistical significance at the 95% confidence level on a t test. Figs d-f show in two panels (e.g. d1,d2) to provide better visibility.
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FigFigFigFig.... 6666 Regression upon the NDJ(0/1) Niño-3.4 index for SON(0): SST (color shading, °C), surfacewind (vector, m/s), and precipitation (contour, CI=0.6mm/day) over 90% confidence level on a ttest.
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FigFigFigFig.... 7777 Regression upon the NDJ(0/1) Niño-3.4 index for DJF(0/1): SST (color shading, °C), surfacewind (vector, m/s), and precipitation (contour, CI=0.6mm/day) over 90% confidence level on a t test.
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FigFigFigFig.... 8888 Longitude-time section of correlation with the NDJ(0/1) Niño3.4 index: SSH (color shading),and precipitation (contour, CI=0.2, from 0.4) averaged in 8°S-12°S.
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FigFigFigFig.... 9999 Longitude-time sections averaged in 8°S-12°S: a, b) SST (color shading, ºC) and SSH (whitecontour line, CI=2cm), expressed as regression upon the NDJ(0/1) Niño3.4 index, and SSHseasonal climatology (black contour, CI=2cm), c, d) precipitation (color shading, mm/d), expressedas regression upon the NDJ(0/1) Niño3.4 index, and precipitation seasonal climatology (blackcontour, CI=0.9mm/d).
FigFigFigFig.... 11110000 The correlation between SSHa and SSTa averaged in SWIO (15°S-5°S, 55°E-75°E) duringMAM season for 20 models. Dark gray bars in the right present 20 models ensemble mean andobservations. Numbers indicate the models listed in Table 1. Dashed line denotes statisticalsignificance at the 95% confidence level on a t test. The interval of 95% confidence level acrossthe models is marked in MME, which is calculated on the Fisher transformation of the correlation(Fisher, 1921).
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FigFigFigFig.... 11111111 Regression upon the NDJ(0/1) Niño-3.4 index for MAM(1): SST (color shading, °C),surface wind (vector, m/s), sea level height (red contour, CI=2cm), and precipitation (black contour,CI=0.6mm/day) over 90% confidence level on a t test.
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FigFigFigFig.... 11112222 Scatter diagram of the relationship between zonal wind anomaly and SSTa of SIO (0°-15°S,50°E-100°E) minus NIO (0°-15°N, 50°E-100°E) in MAM(1). Error bars give ranges of onestandard deviation.
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FigFigFigFig.... 11113333 Regression upon the NDJ(0/1) Niño-3.4 index for JJA(1): SST (color shading, °C), surfacewind (vector, m/s), precipitation (red contour, CI=0.3mm/day), and SLP (black contour, CI=0.1hPa)over 90% confidence level on a t test.
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FigFigFigFig.... 11114444 a) Scatter diagram of the relationship between Qatm/zonal wind anomaly (W/m2 and m/s,respectively) and SSTa (°C) in NIO (0°-15°N, 50°E-100°E) in AMJ(1). b) Correlation betweenQatm/zonal wind anomaly and SSTa in NIO ( 0°-15°N, 50°E-100°E) in AMJ(1) for 20 models,respectively. c) same as b), except for regression, and Qatm regression on SSTa is divided 10 for abetter vision. Note Qatm is defined as the atmospheric forcing component in the latent heat flux,due to atmospheric adjustments in wind speed, relative humidity, and surface air-sea temperaturedifference, defined as the latent heat flux minus the Newtonian cooling term (Du et al. 2009).Numbers indicate the models listed in Table 1. Dashed line denotes statistical significance at the95% confidence level for r(Qatm, SST) and r(u, SST) on a t test. In b) and c), the interval of 95%confidence level across the models is marked in MME, which is calculated on the Fishertransformation of the correlation (Fisher, 1921).
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FigFigFigFig.... 11115555 Regressions upon NDJ(0/1) Niño3.4 index as a function of calendar month and latitude forSST (color shading, °C) averaged over TIO (40°E-100°E), surface wind (vectors, m/s) averagedover 40°E-80°E and SLP averaged over 40°E-80°E (contour, CI= 0.2 hPa ) over 90% confidencelevel on a t test.
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FigFigFigFig.... 11116666 21-yr running standard deviation (STD) of Niño3.4 NDJ (red line) and TIO MJJ SSTindices (blue line).
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FigFigFigFig.... 11117777 21-yr running correlation with the NDJ(0/1) Niño3.4 index: TIO (color shading) andNiño3.4 SST (contours, light grey, dark grey and black line show 0.5, 0.9 and 0.975, respectively).
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FigFigFigFig.... 11118888 Scatter diagram: a) correlation between NDJ(0/1) Niño3.4 with JJA(1) IOB indices [r(IOB,Niño)] against standard deviation of NDJ(0/1) Niño3.4 index [STD(Niño)]; b) STD(Niño) duringdecades when r(IOB, Niño) reaches the maximum against that during decades when r(IOB,Niño)reaches the minimum (analysis covers 1870-2005). The standard deviation and correlation arecalculated in 21-yr running windows. Error bars give ranges of one standard deviation.
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Fig.Fig.Fig.Fig. 19191919 Regression upon the NDJ(0/1) Niño-3.4 index for DJF(0/1), MAM(1) and JJA(1): SST(color shading, °C), surface wind (vector, m/s), precipitation [black contour in DJF(0) and MAM(1),red contour in JJA(1), CI=0.3mm/d], sea level height [red contour in MAM(1), CI=2cm], and SLP[black contour in JJA(1), CI=0.1hPa] over 90% confidence level on a t test in periods 1926-1955,1956-1975, and 1976-2005 for GFDL-ESM2G.