1. Variability of Atmospheric Moisture During the Boreal Spring in West Africa Roberto J. Mera 1, Arlene Laing 2, and Fred H.M. Semazzi 1 1 Dept. of Marine, Earth and Atmospheric Sciences, North Carolina State University 2 National Center for Atmospheric Research AMS Hurricanes & Tropical Meteorology Conference May11th, 2010 NCAR-MMMclimlab.meas.ncsu.edu

2. Outline Motivation – Societal impacts Boreal Spring – Conceptual Model – Sources of Moisture Data & Methods Dynamics – Short-term events – Convectively-coupled Equatorial Waves Regional Model Simulations Conclusions, Future Work & Acknowledgements 2climlab.meas.ncsu.edu

3. Our Motivation Prediction of Monsoon rainfall Agriculture African Easterly Waves Public health: Meningitis Outbreaks 3 Why is the moisture during the boreal spring important? climlab.meas.ncsu.edu

4. The Application Meningitis is a serious infectious disease affecting 21 countries 300 million people at risk 700,000 cases in the past 10 years 10-50 % case fatality rates 4 Based on Molesworth (2002) and references therein Source: AP climlab.meas.ncsu.edu

5. Meningitis-Climate link Outbreaks coincide with dry, dusty conditions over the Sahel due to the Harmattan winds Largest correlation occurs between humidity and disease outbreaks (Molesworth et al., 2003)‏ Disease occurrence drops dramatically with the onset of humidity JanuaryJuly SHL Harmattan Moisture 5 2 Contrasting months climlab.meas.ncsu.edu

6. A Case study: Kano, Nigeria 2009 6 District in Epidemic District in Alert Number of cases / 100000 per week Source: Multi-disease Control Center, Ouagadougou, Burkina-Faso, World Health Organization 40% Threshold based on Besancenot (1998) climlab.meas.ncsu.edu

7. What happened in Kano? Short-term events Kano 7 New moisture regime climlab.meas.ncsu.edu

8. Simplified Conceptual Model of the Boreal Spring 8 Equatorial Waves climlab.meas.ncsu.edu

9. Orographic Features climlab.meas.ncsu.edu9

10. Sources of Moisture We employ a parcel back-trajectory analysis utilizing u and v wind components from the NCEP/NCAR reanalysis for 2000-2008 The end points surface is set at 925mb *NCEP: National Centers for Environmental Prediction *NCAR: National Center for Atmospheric Research DRY MOIST 10climlab.meas.ncsu.edu

11. Data & Methods Parcel Back-trajectory analysis & Synoptic Environment – NCEP/NCAR u & v wind components – NCEP/NCAR geopotential, total column precipitable water (mm) Space-time filtering – Television Infrared Observation Satellite–National Oceanic and Atmospheric Administration ( TIROS–NOAA), twice-daily outgoing longwave radiation (OLR), resolution of 2.5 (Gruber and Krueger 1974) Modeling – WRF-ARW Version 3.0 season simulations 2006 & 2009 11climlab.meas.ncsu.edu

12. Model Customization Trial-and-error approach for customization during the AMMA period of 2006 26 Physics parameter ensemble tests for WRF2, 19 for WRF3, numerous domain setups and relaxation zones KF Cumulus physics, YSU PBL, Noah Land Surface Model, CAM Radiation Domains 12climlab.meas.ncsu.edu

13. 13 Short-term events propagate westward climlab.meas.ncsu.edu

14. Synoptic Situation mid-May, 2009 event Shaded: Precipitable Water Contoured: 925mb Geopotential Vectors: 925mb winds SHL Westward-propagating moist event 14climlab.meas.ncsu.edu

15. Are these events early-season African Easterly Waves? What is their climatology (frequency per season)? What synoptic factors are involved? – Saharan Heat Low – Mid-Latitude Systems – Sea surface temperatures (SSTs) – Convectively-coupled Equatorial Waves 15 Diagnosing short-term Events climlab.meas.ncsu.edu

16. Two Contrasting years: 2006 & 2009 2006 (AMMA, late monsoon), 2009 (Meningitis reports, real-time forecasts) Unfiltered daily NCEP OLR 16 2009 April May climlab.meas.ncsu.edu MJO

17. 15N has a cleaner signal of the mid-May event 20062009 April June 17climlab.meas.ncsu.edu

18. MJO Kelvin? Unfiltered analysis: OLR & precipitable water climlab.meas.ncsu.edu18 time

19. Contrasting years: 2006 & 2009 19climlab.meas.ncsu.edu Less activity in 2006 2006 2009

20. Kelvin Filtered T B Variance climlab.meas.ncsu.edu20

21. Seasonally, 2006 & 2009 did not differ much April had important differences climlab.meas.ncsu.edu21

22. Correlations: 2000-2009 KTb VarTD-Type Var.Sahel-EastSahel-West KTb Var10.6437990.1511280.622085 TD-Type0.64379910.4273110.560129 climlab.meas.ncsu.edu22 Sahel East Sahel West

23. Advantages of Dynamical Downscaling WRF at 30km resolutionNCEP/NCAR Reanalysis at 2.5° Ghana 23climlab.meas.ncsu.edu

24. Kano 24climlab.meas.ncsu.edu

25. AIRS, 1:30 AM LST WRF, 00z 12z is slightly different, closer to obs 25climlab.meas.ncsu.edu

26. TRMM (daily) April 29 – May 5 Week average WRF (daily) Week average Increasing precip (Chad) 26climlab.meas.ncsu.edu

27. April 11 May 1 20W 20EMay 20 May 1 20W 20E April 11 TRMM Hovmoller, April 11 – May 20, 20W-20E, at 10N Westward propagation WRF Hovmoller, April 11 – May 20, 20W-20E, At 10N 27climlab.meas.ncsu.edu

28. 28climlab.meas.ncsu.edu time WRF captures intraseasonal events

29. Concluding Remarks & Future Work Convectively-coupled Equatorial Waves constitute an important source of variability for Sahelian moisture during the boreal spring TD-Type systems modulate the moisture in the atmosphere TD-Type systems tend to occur with higher Kelvin wave variance Filtering of WRF simulations, spectral nudging climlab.meas.ncsu.edu29

30. Updates on our work: http://climlab.meas.ncsu.edu/googleucar http://climlab.meas.ncsu.edu/googleucar twitter.com/climlab Facebook.com/climlab 30climlab.meas.ncsu.edu

31. Acknowledgements Special thanks: Arlene Laing, George Kiladis, Stefan Tulich, Fred Semazzi, Anantha Aiyyer, Liang Xie, Matt Norman Google/UCAR team: Mary Hayden, Abraham Hodgson, Thomas Hopson, Benjamin Lamptey, Jeff Lazo, Raj Pandya, Jennie Rice, Madeleine Thomson, Sylwia Trazka, Tom Warner, Tom Yoksas 31climlab.meas.ncsu.edu

32. Questions? 32climlab.meas.ncsu.edu