1. Three-Dimensional Water Vapor and Cloud Variations Associated with the MJO during Northern Hemisphere Winter By: David S. Myers and Duane E. Waliser Presented by: Emily M. Riley 24 April 2007

2. Outline Motivation Data and Methodology Digression to examine weighting function Results Summary

3. Motivation Improve GCMs simulation of the MJO How? Characterize typical co-evolution of the moisture, rainfall, and cloud field associated with various phases of the MJO Why? Most studies focus on moisture through the interaction of dynamics with convection Understand role of hydrological cycle with tropical dynamics, particularly within intraseasonal variations (e.g. the MJO)

4. Data Moisture profiles from TOVS Pathfinder-A High-Resolution IR Sounder 2 (HRIS2) Microwave Sounding Unit (MSU) Stratospheric Sounding Unit (SSU) Resolution: 5-day avgs 1° X 1° Standard pressure heights

5. Data Clouds from ISCCP 2.5° X 2.5° July 1983 - February 1994 Precipitation from CMAP 1979 - 1999

6. Methodology TOVS soundings calculate relative & specific humidity Precipitation based indexing scheme: EOF trickery with filtered rainfall data from CMAP averaged from 10°N - 10°S to capture MJO events Leading EOF explains 39% of the intraseasonal variance, which apparently is good…

7. A picture shows it better! 46 events were identified Signal confined to E. Hemisphere Noticeable disruption around Maritime Continent Leading EOF of bandpassed, equatorially averaged rainfall in NH winter. Contour interval is 0.5 mm per day

8. Great, but how does TOVS measure moisture? Recall Temperature profiles and the weighting function (Petty, 8.3.2) Consider a satellite able to measure radiant intensities for a series of closely spaced wavelengths, located on the edge of a strong absorption line for constituent X. Each channel measures thermal emission from a different level. Wavelengths closer to the center of the absorption band will peak at higher heights Intensity of emission is determined by temperature within layer

9. Ideal Weighting Function

10. TOVS Weighting Function

11. Results: How good is the TOVS data? Specific Humidity Radiosonde DataSpecific Humidity TOVS Data Surface dry bias. Also found in comparison with ECMWF data. In all, captures essential moisture features well

12. Results: Evolution of composite MJO humidity variations 500-mb composite specific humidity anomalies

13. Results: Evolution of composite MJO humidity variations surface 700-mb 500-mb 300-mb

14. Results: Equatorially Avg. composite moisture analysis

15. Results: Vertical profile of Equatorially averaged moisture during the MJO life cycle

16. Results: Timing between precipitation and moisture anomalies Surface level moisture leads precipitation 90°E upper level moisture lags precipitation 150°E upper level moisture and precip. practically in phase for both dry and wet phases 90°W upper and lower level moisture variations are roughly out of phase 90°E 150°E 90°W

17. Results: Timing between precipitation and total cloud fraction anomalies Cloud fraction and precipitation lag near-surface moisture While precipitation and moisture are in phase in time, they are not in phase in space. Higher cloudiness at trailing edge, despite lower precipitation rate.

18. Results: Equatorially avg. ISCCP cloud fraction by cloud-top heights Over Indian Ocean transition from moist to dry phase appears to be quicker than dry to moist Western Hemi. is dominated by low cloud amounts, consistent with low moisture Low-top clouds vulnerable to biasing

19. Summary Clear westward tilt with height of the moisture maximum across the Indian Ocean, which become almost vertical in W. Pacific Near surface (upper-level) moisture leads (lags) precipitation Higher cloudiness at the western (trailing) edges of precip. Maximum Dry anomalies dominate over moist anomalies