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Monitoring trends in free tropospheric humidity using operational satellites

Monitoring trends in free tropospheric humidity using operational satellites. Eui-Seok Chung Brian Soden University of Miami. Publications: Chung et al., 2012a: Diagnosing climate feedbacks in coupled ocean atmosphere models, Surv. of Geophys ., doi: 10.1007/s10712-012-9187.

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Monitoring trends in free tropospheric humidity using operational satellites

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  1. Monitoring trends in free tropospheric humidity using operational satellites Eui-Seok Chung Brian Soden University of Miami Publications: Chung et al., 2012a: Diagnosing climate feedbacks in coupled ocean atmosphere models, Surv. of Geophys., doi: 10.1007/s10712-012-9187. Chung et al., 2012b, Monitoring long-term variations in upper and mid-tropospheric water vapor from satellite observations, submitted J. Climate.

  2. How long would it take to empirically constrain climate sensitivity? Use CMIP3/CMIP5 models to investigate limits imposed by climate system … What if we had the perfect CLARREO …

  3. Most of uncertainty is due to clouds Utility of a continuous record diminishes after 5 years Chung et al. 2012a

  4. Chung, et al. 2012a: Diagnosing climate feedbacks in coupled ocean atmosphere models, Surv. of Geophys., doi: 10.1007/s10712-012-9187 Satellite Duration For periods < 20 years the changes are dominated by natural variability (ENSO) which has different feedbacks than climate change RMSE of Climate Feedback (W/m2/K) Length of Record (years) (period between satellites) • Minimum duration of satellite ~5 years. • Error decreases linearly with length of record at ~0.1 W/m2/K per decade (or ~0.25 K/decade ECS)

  5. What if there is no CLARREO?

  6. Trends in Upper Tropospheric RH: Reanalyses vs. Models Chung et al. (2012b)

  7. Decadal Variations in UTH from AMSU-B AMSU-B derived Upper Tropospheric RH from NOAA-15 Chung et al. (2012b)

  8. Decadal Variations in UTH from AMSU-B • Differences due to: • Diurnal sampling • Calibration

  9. Change in diurnal sampling due to orbital drift NOAA-15 and NOAA-16 have largest changes in observation times

  10. Differencing of ASC and DSC Orbits Separating ASC and DSC tracks highlights diurnal sampling drift

  11. Estimated Sampling Drift: ERA-Interim Simulations Differences between satellite observed time and true diurnal mean Tb

  12. Distribution of Diurnal Sampling Errors • Sampling biases are largest over land convective regions • Sampling bias evolves in time

  13. Orbital Drift Adjustment • Use 6 hourly ERA-Interim to compute the anomaly of the satellite observation time (t) relative to full diurnal mean <d> for each location and day. dTb (x,y,t) = Tb (x,y,t) – <Tb (x,y,d)> • Each adjustment is independent of any prior or subsequent adjustment  not aliasing spurious trends into data set. • After adjustment, ASC and DSC orbits should have same value.

  14. Intercalibrated and Diurnal-Adjusted Tb183.31+/-1

  15. Intercalibrated and Diurnal-Adjusted Tb183.31+/-1 Before After Chung et al. (2012b)

  16. Intersatellite Differences and Calibration Stability Differences between satellite highlight calibration stability

  17. Intercalibrated and Diurnal-Adjusted Tb183.31+/-1 Before Even after adjustments, problems remain ... After

  18. Unadjusted NOAA-15 Linear Trends Tb183.31+/-1

  19. Adjusted Linear Trends Tb183.31+/-1

  20. Intercalibrated and Diurnal-Adjusted Tb183.31+/-3

  21. Intercalibrated and Diurnal-Adjusted Tb183.31+/-3

  22. Conclusions • End of HIRS/2 record in 2004 requires new data set for monitoring UTWV trends • Microwave radiances from AMSU-B and METOP provide multiple overlapping records for monitoring these trends • AMSU-B records .have a substantial drift in orbital crossing times which requires adjustment • After adjusting for changes in diurnal sampling times, intercalibration drifts become very apparent (especially for N-15 and N-16). • Operational sensors can definitely be improved for climate purposes, but there are limits and redundancy is critical.

  23. T6.7 Emission Levels of O2 and H2O • As UT mixing ratio increases, • T6.7 emission level must increase • T2 emission level remains fixed • (O2 is not changing) T2 • So T2-T6.7 must diverge

  24. Upper Tropospheric Water Vapor from Microwave (183 GHz) Radiances Microwave offers similar information but with better sampling and less cloud effects

  25. MSU T2 - HIRS T12 Anomalies 0.4 K divergence • Emission levels of T2-T6.7 diverge over past decade by ~0.4 K • T6.7 with fixed mixing ratio shows no divergence Soden et al. 2004

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