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Monitoring trends in free tropospheric humidity using operational satellites

Study on using operational satellites to monitor trends in upper tropospheric humidity, addressing calibration stability and sampling biases to improve climate data accuracy. The research highlights the importance of continuous records and the challenges in data adjustment.

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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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