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and re-emit it from the high, cold cloud tops, leading to low OLR. These points form the outliers at high x and high y . Given the physical realism of these outliers, it is reasonable to exclude them from the regression analysis. Hence, the 5% of the data points that lie farthest from the regression lines are labeled with a blue circle in Fig. A1b . The regression was performed on the remaining 95% of the data, and the new regression line is shown by the green dashed line in Fig. A1b . This
and re-emit it from the high, cold cloud tops, leading to low OLR. These points form the outliers at high x and high y . Given the physical realism of these outliers, it is reasonable to exclude them from the regression analysis. Hence, the 5% of the data points that lie farthest from the regression lines are labeled with a blue circle in Fig. A1b . The regression was performed on the remaining 95% of the data, and the new regression line is shown by the green dashed line in Fig. A1b . This
datasets against them. Appendix A provides a detailed description of each variable in DYNAMO surface meteorology and flux dataset from the Research Vessel (R/V) Roger Revelle . The methods used for isolating and compositing equatorial waves and the MJO are described in section 3 , with more details in appendix B . In section 4 we present observed time series of daily and subdaily variability from DYNAMO and TOGA COARE, and the analysis of 27 years of the daily time–longitude structure air
datasets against them. Appendix A provides a detailed description of each variable in DYNAMO surface meteorology and flux dataset from the Research Vessel (R/V) Roger Revelle . The methods used for isolating and compositing equatorial waves and the MJO are described in section 3 , with more details in appendix B . In section 4 we present observed time series of daily and subdaily variability from DYNAMO and TOGA COARE, and the analysis of 27 years of the daily time–longitude structure air
generate pressure anomalies in the ML, it must be related to negative density anomalies in the atmosphere. On 3-times-daily running averaged SST, we regress temperature and virtual temperature anomalies from DYNAMO radiosondes ( Yoneyama et al. 2013 ; Johnson and Ciesielski 2013 ) released from the R/V Revelle in October–November 2011 at the equator, 80°E ( Fig. 2 ). Radiosondes sampled about every 3 h. Virtual temperature anomalies of about 0.2°C, half the strength of daily low-pass-filtered SST
generate pressure anomalies in the ML, it must be related to negative density anomalies in the atmosphere. On 3-times-daily running averaged SST, we regress temperature and virtual temperature anomalies from DYNAMO radiosondes ( Yoneyama et al. 2013 ; Johnson and Ciesielski 2013 ) released from the R/V Revelle in October–November 2011 at the equator, 80°E ( Fig. 2 ). Radiosondes sampled about every 3 h. Virtual temperature anomalies of about 0.2°C, half the strength of daily low-pass-filtered SST
