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Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al.

Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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Page 1: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations

Yuk Yung, Hui Su, Katie, Hazel et al.

Page 2: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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Introduction

• AIRS observations since 2004 provide a unique opportunity to examine decade-long simultaneous cloud and water vapor variabilities.

• These datasets serve as important references to evaluate climate models.

• We examine AIRS retrieved high cloud fraction and corresponding humidity distributions in the upper troposphere.

• CAM5 simulations driven by observed SST from 2004 to 2013 are analyzed.

Page 3: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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Climatological Cloud Fraction from AIRS and CAM5

• (left column) AIRS high, middle and low cloud fraction climatology is shown in Kahn et al. (2014). Explain the definitions of three cloud types.

• (right column) CAM5 corresponding cloud fraction spatial maps.

• This study focuses on interannual variability of clouds.

• EOF analyses are performed on the de-seasonalized monthly anomalies (3-month running mean is applied to the time series).

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Explained Variance of EOF modes

• Here, it can be interesting to show the explained variances of leading EOF modes, from EOF1 to EOF6 (or lower) for both AIRS and CAM5 simulations, for high, middle and low clouds.

• The EOF1 in the AIRS data is quite dominant, explaining about 25% of the variance, while the EOF1 in CAM5 explains only 15% of the variance.

• The EOF2 in the AIRS data explains ~6% of variance, while the EOF2 in CAM5 explains ~8% of the variance.

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EOF1

Page 6: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

EOF 1 (high): the Canonical ENSO Signal

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Page 7: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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EOF 1 (middle): the Canonical ENSO Signal

Page 8: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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EOF 1 (low): the Canonical ENSO Signal

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Comparing CAM5 with AIRS

Page 10: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

High Clouds—AIRS and CAM5 (EOF 1)

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Page 11: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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Middle Clouds—AIRS and CAM5 (EOF 1)

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Low Clouds—AIRS and CAM5 (EOF 1)

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EOF2

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EOF 2 (high): the ENSO Modoki

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EOF 2 (middle): the ENSO Modoki

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EOF 2 (low): the ENSO Modoki

Page 17: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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Comparing CAM5 with AIRS

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High Clouds—AIRS and CAM5 (EOF 2)

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Middle Clouds—AIRS and CAM5 (EOF 2)

Page 20: Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al

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Clouds—AIRS and CAM5 (Low, EOF 2)

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Conclusions

• The most dominant mode (EOF 1) of clouds observed by AIRS shows a consistent pattern with the canonical ENSO for all three cloud types.

• CAM5 captures well the EOF 1 of high clouds, but its leading modes of middle and low clouds are not as well-simulated as the high clouds.

• The second dominant mode of clouds has a weak resemblance to the ENSO Modoki.

• CAM5 has a difficulty in reproducing the EOF 2 of all three types of clouds.