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Climate Modeling LaboratoryMEASNC State University Predictability of the Moisture Regime Associated with the Pre-onset of Sahelian Rainfall Roberto J.

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Presentation on theme: "Climate Modeling LaboratoryMEASNC State University Predictability of the Moisture Regime Associated with the Pre-onset of Sahelian Rainfall Roberto J."— Presentation transcript:

1 Climate Modeling LaboratoryMEASNC State University Predictability of the Moisture Regime Associated with the Pre-onset of Sahelian Rainfall Roberto J. Mera* and Fred H.M. Semazzi Marine, Earth and Atmospheric Sciences North Carolina State University AMS Annual Meeting January 19th, 2010

2 Climate Modeling LaboratoryMEASNC State University A broader context: Predictability of the Moisture Regime During the Boreal Spring in West Africa and its Implications on Meningitis Mitigation

3 Climate Modeling LaboratoryMEASNC State University Our Motivation Why is the moisture regime important? Prediction of Monsoon rainfall Agriculture African Easterly Waves Public health: Meningitis Outbreaks

4 Climate Modeling LaboratoryMEASNC State University Outline The Application – Background – Current efforts Our Study – Relevant Variables – Sources of Moisture – Importance of Downscaling – Intraseasonal prediction

5 Climate Modeling LaboratoryMEASNC State University 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

6 Climate Modeling LaboratoryMEASNC State University 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

7 Climate Modeling LaboratoryMEASNC State University Current Efforts UCAR, in conjunction with IRI, NCSU, Navrongo (Ghana) Health Research Center are working on a prototype Earth-gauging system integrating weather and health data to manage meningitis The latest research was presented at the World Health Organization (WHO) Meningitis Environmental Risk Information Technologies (MERIT) project meeting in Niamey, Niger in 2009

8 Climate Modeling LaboratoryMEASNC State University Google/UCAR Project at NCSU Recent work for the MERIT project has been aimed at constructing a decision tree on “action threshold” for vaccine implementation This decision tree, however, does not include climate information at this point

9 Climate Modeling LaboratoryMEASNC State University Google/UCAR Project at NCSU Our aim is to address the climate factors pertinent to meningitis mitigation at the appropriate time scales We present preliminary results on seasonal and intraseasonal scales

10 Climate Modeling LaboratoryMEASNC State University Our Study: Predictability of Atmospheric Moisture What are the variables important for the prediction of the moisture regime? What are the sources of moisture in West Africa during the Boreal Spring? Can regional climate models forecast moisture transport dynamics?

11 Climate Modeling LaboratoryMEASNC State University The Variables Variables related to the West Africa monsoon have the highest correlation with meningitis We use relative humidity (RH) as an indicator in our study From Yaka et al (2008)

12 Climate Modeling LaboratoryMEASNC State University 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

13 Climate Modeling LaboratoryMEASNC State University Three different time periods to analyze conditions: early spring (P1, Jan 27 – Feb 15), mid-spring (P2, Apr 15 – May 4) & late spring (P3 Jun 11-30) We use Relative Humidity derived from NCEP/NCAR reanalysis at 40% as a divider between dry and moist conditions P1P2P3 April 20, 2000

14 Climate Modeling LaboratoryMEASNC State University Source Regions P1 P3 P2

15 Climate Modeling LaboratoryMEASNC State University Implications Role of large scale forcing: NAO, ENSO Other factors: Sea Surface Temperature anomalies (SSTA) in the Gulf of Guinea and Northeast Tropical Atlantic Global teleconnections

16 Climate Modeling LaboratoryMEASNC State University Analysis of added value of dynamical downscaling We use the Weather Research and Forecasting WRF Model as both a predictive and analytical tool for High resolution reanalysis Real-time forecasts Sensitivity experiments Comparison with large-scale models Dynamical Downscaling Scale of relevance

17 Climate Modeling LaboratoryMEASNC State University Advantages of Dynamical Downscaling WRF at 30km resolutionNCEP/NCAR Reanalysis at 2.5° Ghana

18 Climate Modeling LaboratoryMEASNC State University Attack rates and reanalysis

19 Climate Modeling LaboratoryMEASNC State University Advantages of Dynamical Downscaling Kano

20 Climate Modeling LaboratoryMEASNC State University Intraseasonal Variability Kano Short-term events

21 Climate Modeling LaboratoryMEASNC State University WRF captures intraseasonal events (westwatd- propagating disturbances) Domains

22 Climate Modeling LaboratoryMEASNC State University Implications Short-term phenomena: Westward-propagating systems Dynamics of Saharan Heat Low Incursions from mid-latitudes SST anomalies Diurnal variability Walker circulation

23 Climate Modeling LaboratoryMEASNC State University Summary Back ‐ trajectory analysis has determined that the sources of air mass during the onset of higher moisture into the region are highly variable in horizontal and vertical scales. Further attention needs to be directed towards the variance of large scale patterns that dictate the state of the atmosphere in these source regions. WRF can be used as a tool to diagnose the moisture regime preceding the West African Monsoon for health efforts in the region

24 Climate Modeling LaboratoryMEASNC State University Future Work Data assimilation of satellite and in-situ information for further analysis of the meningitis- climate interface Integration of WRF into meningitis prediction Simulations using spectral nudging to retain large scale information from the air mass source regions Stratification of predictive model skill for different modes of variability

25 Climate Modeling LaboratoryMEASNC State University Communication Updates on our work: http://climlab.meas.ncsu.edu/googleucar http://climlab.meas.ncsu.edu/googleucar twitter.com/climlab

26 Climate Modeling LaboratoryMEASNC State University Acknowledgements

27 Climate Modeling LaboratoryMEASNC State University Questions?


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