1. Trends & Variability of Liquid Water Clouds from Eighteen Years of Microwave Satellite Data: Initial Results 6 July 2006 Chris O’Dell & Ralf Bennartz University of Wisconsin-Madison

2. Talk Outline Motivation Description of sensors & retrieval product Mean climatology & comparison with ERA40, ISCCP Diurnal cycle Long-term trends?

3. Motivation for a cloud liquid water path (LWP) climatology Anthropogenic trends in cloud properties are possible, due both to global warming and aerosol effects. A robust LWP climatology can serve as a benchmark for global climate models. The 18-year passive microwave record contains a robust and independent measure of liquid clouds.

4. Previous successes from passive microwaves: Increases in lower tropospheric temperature, decreases in stratospheric temperatures from Microwave Sounding Unit (Mears et al. 2003, Christy et al. 2003, Vinnikov & Grody 2003) Increases in global (especially northern hemisphere) water vapor path (Trenberth et al., 2005)

5. Microwave sensors measuring LWP Sensor - PlatformDates UsedAscending Overpass Time SSM/I – F081/1988 – 12/199106:14 SSM/I – F1012/1990 – 11/199707:38 – 10:26 SSM/I – F1112/1991 – 5/200005:00 – 07:38 SSM/I – F135/1995 – 12/200505:39 – 06:33 SSM/I – F145/1997 – 12/200208:49 – 08:16 TMI - TRMM12/1997 – 12/2005N/A (equatorial orbit) SSM/I – F1512/1999 – 12/200509:33 – 08:42 AMSRE - Aqua6/2002 – 12/200513:30 All instruments are conical scanners, with footprints ~ 40 km

6. Calibration/Retrieval All satellites have been intercalibrated – the radiances are consistent from one satellite to the next (RSS, unpublished!) All satellites use the same, modern retrieval algorithm to simultaneously retrieve LWP, water vapor path, and surface wind speed. Probably better than older algorithms which often used 2 channels to retrieve a given quantity, algorithms tended to be regression- based, and tended to retrieve different quantities independently.

7. Existing LWP Climatologies Disagree Annually-Averaged LWP [g/m 2 ] * Both climatologies use SSM/I data, but employ different retrieval techniques.

8. Over-Ocean Physical Retrieval Methodology Model: T 19V T 22V T 37V T 37H WVP LWP Wind Speed Wind Direction Sfc Rain Rate Atm. Optical Properties Surface Properties Radiative Transfer Model Compare SST Climatology or measured SST Measurements: T 19V T 22V T 37V T 37H Modify parameters Wentz,F.J. "A well calibrated ocean algorithm for special sensor microwave/imager," Journal of Geophysical Research, 1997.

9. LWP Agreement between sensors is good No global trend with simple average!

10. Processing Scheme At Remote Sensing Systems Retrieved LWP binned daily onto a 0.25º grid (1440x720) for both morning & evening overpasses Even pixels with heavy rain retrieve LWP (but not water vapor or surface winds) At Wisconsin Quantities further binned to 2.5º grid, monthly average for each sensor & local overpass time. Monthly diurnal cycle fits made for each pixel (average of all years). Diurnally-corrected monthly means calculated for each pixel. Seasonal & annual LWP trends calculated for each pixel.

11. MAM Mean LWP

12. Does microwave LWP agree with ERA40?

13. Problem in Sc regions!

14. Does microwave LWP agree with ISCCP*? Problems at higher latitudes Ice? * ISCCP D3 water path (WP)

15. Diurnal Cycle Fitting Goal is to make a diurnally-corrected LWP climatology Previous work with TRMM only retrieved diurnal cycle for tropics. Possible midlatitude diurnal cycle? For each 2.5º pixel & month, fit local time versus LWP to this function: (  corresponds to 24 hours) Use resultant fits to correct each monthly binned observation.

16. LWP Diurnal Cycle Strength Wood et al., Geophysical Research Letters, 29 (23), 2002 F13 F11 F14 F15 SSM/I TRMM-TMI

17. Liquid Water Path [kg/m 2 ] Local Time [hours] Normalized Diurnal Amplitude

18. Liquid Water Path [kg/m 2 ] Local Time [hours]

19. Liquid Water Path [kg/m 2 ] Local Time [hours]

20. Liquid Water Path [kg/m 2 ] Local Time [hours]

21. Liquid Water Path [kg/m 2 ] Local Time [hours]

22. Liquid Water Path [kg/m 2 ] Local Time [hours]

23. Robust Regional Trends in LWP?

24. Local trends – tropical western pacific

25. Local trends – northern midlatitudes

26. Local trends – southern mid-high latitudes

27. Local trends – Arctic ocean

28. Conclusions  Existing passive microwave observatinos appear to provide a stable, long-term record for climate studies of liquid clouds.  ERA40’s cloud parameterization seems to poorly characterize LWP seasonal and interannual variability in the subtropical high stratocumulus regions.  The diurnal cycle of LWP has been well- characterized in most ocean locations, and is generally in agreement with previous studies.  Initial studies of LWP trends are promising, with hints of regional trends (especially in the northern high latitudes), but no significant long-term global trend.

29. “To Do List”  Principle component analysis - may reveal interesting patterns of variability or problems with the data set.  Further investigation of the derived diurnal cycles – how constant are they from year-to-year? How well do they compare with CA & precip diurnal cycles?  More sophisticated statistical analyses of LWP trends…(hint to audience for guidance)  Make the complete LWP climatology available on the web.

30. LWP Trends from ERA40 Scale now twice as large

31. Can El Nino explain any trends?

32. LWP Trend w/o Nino3.4

33. Can El Nino explain any trends? Original Trend

34. Amplitude of the Seasonal Cycle

35. Phase of the Seasonal Cycle Jan Jul Apr Dec Oct

36. Diurnal Cycle Strength from TRMM Wood et al., Geophysical Research Letters, 29 (23), 2002

37. Are the SSM/I’s sufficiently intercalibrated to measure the LWP diurnal cycle? AMSR-E

38. Zonal Mean Trends

39. Microwave Local Time of Diurnal Maximum