The SST–Wind Causal Relationship during the Development of the IOD in Observations and Model Simulations
<p>Evolution of the SSTA field (shading, unit: °C) and wind anomalies (vectors) during IOD development (from occurrence to disappearance) obtained by compositing 12 IOD events listed in <a href="#remotesensing-14-01064-t001" class="html-table">Table 1</a> during the period from 1948 to 2009, (<b>a</b>–<b>l</b>) represent SSTA and wind anomalies pattern from March to February of the next year, respectively. Hachured regions indicate the areas where the SST anomalies are statistically significant, and only statistically significant wind anomalies are plotted. Statistical significance is tested by evaluating the difference between the composite mean and climatological mean at the 95% confidence level.</p> "> Figure 2
<p>Second singular vectors of (<b>a</b>) SSTA (unit: °C), (<b>b</b>) UA (unit: m/s) and (<b>c</b>) VA (unit: m/s) based on SVD analysis. The black boxes in panel (<b>a</b>) denote the western and eastern poles of the IOD. The green boxes in panels (<b>b</b>,<b>c</b>) denote the regions used to calculate the equatorial zonal wind index and Sumatra meridional wind index. The second singular vector accounts for 7.8% of the total variance. The SVD is jointly conducted by SSTA with UA and VA.</p> "> Figure 3
<p>Lead–lag correlation coefficients of the DMI (<b>a</b>), eastern-pole SSTA (<b>b</b>) and western-pole SSTA (<b>c</b>) with the ZWI (blue lines) and SMWI (red lines). The yellow dotted lines indicate the 95% confidence level.</p> "> Figure 4
<p>Monthly STDs of the eastern-pole SSTA (unit: °C, light blue), western-pole SSTA (unit: °C, blue), ZWI (unit: m/s, gray), and SMWI (unit: m/s, red). The red stars represent the maximum STD of each index at the IOD maturity stage (August–December).</p> "> Figure 5
<p>Spatial distributions of the lead–lag correlation coefficients of the ZWI index against the SSTA (<b>a</b>–<b>c</b>), and that of SMWI index against SSTA (<b>d</b>–<b>f</b>). The lead time in each subpanel indicates the lead–lag time (month); a negative (positive) value indicates the SSTA leading (lagging) the wind index, and 0 indicates both correlated simultaneously.</p> "> Figure 6
<p>STDs of the DMI (unit: °C), SMWI (unit: m/s) and ZWI (unit: m/s) in the four groups of experiments.</p> "> Figure 7
<p>SSTA dipole mode obtained via SVD analysis and the corresponding UA and VA modes in the CPL4(1°) (<b>a</b>–<b>c</b>), CPL4(2°) (<b>d</b>–<b>f</b>), CPL5(1°) (<b>g</b>–<b>i</b>), and CPL5(2°) (<b>j</b>–<b>l</b>) experiments, where the variance accounted for by each dipole mode is 21%, 64%, 77% and 66% of the total variance, respectively. The unit of SSTA pattern is °C; the unit of UA and VA pattern is m/s.</p> "> Figure 8
<p>Lead time-longitude diagrams of the lead–lag correlation coefficients between the ZWI and SSTA (top row) and between the SMWI and SSTA (bottom row). The correlation is the same as that depicted in <a href="#remotesensing-14-01064-f005" class="html-fig">Figure 5</a> but for the meridional mean in the equatorial region (5°N°–5°S). (<b>a</b>,<b>f</b>) show the observations, whereas (<b>b</b>,<b>g</b>), (<b>c</b>,<b>h</b>), (<b>d</b>,<b>i</b>) and (<b>e</b>,<b>j</b>) show the results of the CPL4(1°), CPL4(2°), CPL5(1°), and CPL5(2°) experiments, respectively. The <span class="html-italic">y</span>-axis indicates the lead–lag time (month) of the SSTA relative to the wind index where a negative (positive) value indicates the SSTA leading (lagging) the wind index, and 0 indicates the SST synchronously varying with the wind.</p> "> Figure 9
<p>(<b>a</b>) Monthly STDs of the eastern-pole SSTA (°C), (<b>b</b>) western-pole SSTA (°C), (<b>c</b>) SMWI (m/s) and (<b>d</b>) ZWI (m/s) in the observations (light blue) and four experiments, with the CPL4(1°) in blue, CPL4(2°) in gray, CPL5(1°) in red, and CPL5(2°) in green, respectively. The red stars indicate the maximum STD of each index during the IOD evolution.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experiment
2.2. Observational Data
2.3. Methods
3. Observed Evolution of the IOD
3.1. Statistical Analysis of the SST–Wind Causal Relationship
3.2. Case Analysis of the SST–Wind Causal Relationship
4. Model-Simulated Evolution of the IOD
4.1. IOD Intensity
4.2. SST–Wind Relationship in Coupled Experiments
4.3. Simulated SST–Wind Relationships during the Individual IOD Events
5. Summary and Discussion
5.1. Conclusions
5.2. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Experiment | Atmospheric Physics Model | Resolution (Atmosphere) | Resolution (Ocean) | Time Period | ||
---|---|---|---|---|---|---|
Horizontal | Vertical | Horizontal | Vertical | |||
CPL4(2°) | CAM4 | 1.9° × 2.5° | 26 | gx1v6 | 60 | 50 years |
CPL4(1°) | CAM4 | 0.9° × 1.25° | 26 | gx1v6 | 60 | 50 years |
CPL5(2°) | CAM5 | 1.9° × 2.5° | 30 | gx1v6 | 60 | 50 years |
CPL5(1°) | CAM5 | 0.9° × 1.25° | 30 | gx1v6 | 60 | 50 years |
Year | EIO SST Month | WIO SST Month | SMWI Month | ZWI Month | EIO SST STD(°C) | WIO SST STD(°C) | EIO SST → WIND → WIO SST | IOD Intensity |
---|---|---|---|---|---|---|---|---|
1961 | May | May | July | May | 2.7 | 1.1 | No | 3.0 × STD |
1963 | Last December | August | Feb | Feb | 2.1 | 1.3 | Yes | 2.0 × STD |
1967 | May | August | June | July | 2.2 | 0.4 | Yes | 1.3 × STD |
1972 | March | April | July | July | 1.3 | 2.6 | No | 2.7 × STD |
1982 | Last December | December | July | August | 1.6 | 1.4 | Yes | 1.9 × STD |
1987 | April | March | June | June | 0.1 | 2.4 | No | 1.5 × STD |
1994 | March | August | March | June | 3.2 | 0.6 | Yes | 2.5 × STD |
1997 | May | October | May | June | 2.9 | 2.1 | Yes | 3.3 × STD |
2006 | June | September | July | July | 2.1 | 0.9 | Yes | 1.7 × STD |
2015 | June | March | May | July | 0.3 | 2.3 | No | 1.4 × STD |
2018 | April | July | May | July | 1.6 | 1.0 | Yes | 1.8 × STD |
2019 | May | August | July | July | 2.8 | 2.1 | Yes | 2.7 × STD |
EIO SSTA and ZWI | EIO SSTA and SMWI | WIO SSTA and ZWI | WIO SSTA and SMWI | |||||
---|---|---|---|---|---|---|---|---|
Lead Time | Correlation | Lead Time | Correlation | LEAD TIME | Correlation | Lead Time | Correlation | |
OBS | −2 | 0.35 | −2 | −0.33 | 1 | −0.44 | 1 | 0.40 |
CPL4(1°) | −1 | 0.61 | −2 | −0.56 | 1 | −0.63 | 2 | 0.51 |
CPL4(2°) | −1 | 0.72 | −2 | −0.59 | 1 | −0.70 | 1 | 0.66 |
CPL5(1°) | −1 | 0.71 | −2 | −0.69 | 1 | −0.87 | 1 | 0.74 |
CPL5(2°) | −2 | 0.72 | −3 | −0.67 | 1 | −0.82 | 1 | 0.73 |
Experiment | CPL4(1°) | CPL4(2°) | CPL5(1°) | CPL5(2°) | Total | ||||
---|---|---|---|---|---|---|---|---|---|
Number | Proportion | Number | Proportion | Number | Proportion | Number | Proportion | ||
DMI > 1.5 × STD | 7 | 3/7 | 7 | 3/7 | 9 | 6/9 | 9 | 7/9 | 19/32 (59%) |
DMI < 1.5 × STD | 4 | 2/4 | 6 | 1/6 | 6 | 2/6 | 0 | 5/16 (31%) | |
Total | 11 | 5 (45%) | 13 | 4 (31%) | 15 | 8 (53%) | 9 | 7 (78%) |
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Xiao, Y.; Tang, Y.; Tan, X.; Wu, Y.; Yao, Z. The SST–Wind Causal Relationship during the Development of the IOD in Observations and Model Simulations. Remote Sens. 2022, 14, 1064. https://doi.org/10.3390/rs14051064
Xiao Y, Tang Y, Tan X, Wu Y, Yao Z. The SST–Wind Causal Relationship during the Development of the IOD in Observations and Model Simulations. Remote Sensing. 2022; 14(5):1064. https://doi.org/10.3390/rs14051064
Chicago/Turabian StyleXiao, Yao, Youmin Tang, Xiaoxiao Tan, Yanling Wu, and Zhixiong Yao. 2022. "The SST–Wind Causal Relationship during the Development of the IOD in Observations and Model Simulations" Remote Sensing 14, no. 5: 1064. https://doi.org/10.3390/rs14051064