1. JSCDA Summer Colloquium 2015 James Taylor Cooperative Institute for Research in the Atmosphere

2. Outline of talk Current Role Background Future Internship in DA at CIRA PhD in Meteorology - Overview Future plans in DA

3. My background PhD in Meteorology 2010 -2014 – University of Reading MSc Geophysical Hazards 2007-2008 – University College London BSc Physical Geography and Geology 2001-2004 – Brighton, UK Past Experience Education Catastrophe Risk Analyst at Risk Management Solutions (RMS) in London - Understanding physical processes hurricanes, earthquakes, landslides, volcanoes - Using hurricane tracking data/info from NHC/JTWC to analyse risk of hurricane landfall (mainly US) to Insurers/Reinsurers Field Geologist in Mali, West Africa – Gold Mining Company

4. PhD in Meteorology (University of Reading) Thesis: The Dynamical Response to Vertical Diabatic Heating Structures in the Tropics Investigate the large-scale steady-state response to heating over the Maritime Continent using heating datasets derived from TRMM latent heating algorithms (TRAIN, CSH) and reanalyses (ECMWF ERA-I, CFS-R, MERRA) – Reading IGCM model Investigate the dynamical response to vertical heating structures associated with the Madden Julian Oscillation (MJO), with implications for moisture convergence 1) 2) 3)Investigate the role of the vertical heating structures associated with the MJO on the atmospheric energetics

5. Brief Introduction to Madden Julian Oscillation (MJO) Madden and Julian (1972) MJO - Dominant mode of intraseasonal variability in the tropics (Madden and Julian 1972) Eastward propagating wave of tropical deep convective rainfall anomalies near the equator over warm pool region (60°E-180; 10°N-10°S) Globally propagating with period of oscillation of 30-60 days Region of deep convection termed the “active phase”, flanked to the east and west by anomalously dry regions called the “suppressed phase” Both phases are linked by overturning zonal circulations that extends through the depth of the troposphere

6. MJO Precipiation Anomalies Convective signal first observed over EEIO, matures over Martime Contient and W Pacific, decays over C Pacific – 8 phases Important influences in TC’s, diurnal cycle, Asian monsoon, ENSO, extratropical influence – important to forecast Current GCMs have limited ability to simulate the MJO (≈2 weeks)-challenge Improvements when changing convective parameterization schemes – moisture mode theory etc.. Fundamental underlying physics /mechanisms not fully understood – many theories Madden Julian Oscillation (MJO)

7. Observational studies and numerical modelling studies suggest westward vertical tilt to heating What is the role of this vertical tilt in heating on the dynamics of the MJO? Could it indicate an important mechanism for propagating the MJO eastwards through the warm pool region? Use MJO heating structures from TRMM LH algorithms (CSH, TRAIN) and reanalysis datasets (ERA, MERRA, CFS) Jiang et al (2011) Vertical Diabatic Heating Structure of the MJO. Mon. Wea. Rev. Madden Julian Oscillation (MJO) Heating Structure

8. For each product, designed a set of numerical model simulations to understand the role of the MJO heating structure and specially the vertical tilt Compared MJO simulations where heating structure was that associated with the MJO (with tilt) vs heating structure was that of climatology (no tilt) Calculated vertically integrated moisture convergence (MC) – how does tilt change MC? Climatological heating structure (no tilt) MJO heating structure (with tilt) Setup and Results Shift in MC – surplus moisture convergence (blue shading) relative to heating rate ahead of MJO heating– indicates preconditioning for convection Active phase Suppressed phase Shading MC anomalies=MC-Q Contours = Q (column int. heating rate)

9. Results - Dynamical Response to heating profiles Blue = convergence Red = divergence Shading = divergence (x10 s -1 ) Contours = heating rate (K day -1 ) Longitude-pressure profiles (5°N-5°S) of divergence at Phase 3 of MJO cycle Low level convergence – max located higher and extends further eastwards Prominent shallow convection ahead of main convection driving stronger low level convergence Climatological MJO

10. EOF analysis of MJO heating structures EOF1 and EOF2 describe 90%+ of variance anomalous heating structures associated with MJO EOF1 – stratiform heating structure – found to lag climatological heating structure by ~8-15º EOF2 – mid-level congestus – leads climatological structure by ~15-25º Removed climatological mean heating structure

11. New Idealised MJO simulations using combination of climatological heating structure and EOFs EOF1 (stratiform) found to be responsible for large change to low-level convergence +EOF1 (no lag) +EOF1 (with lag) +EOF1+EOF2 (with lead and lag) EOF2 (congestus) strengthens low-level convergence ahead of heating Climatological heating structure (control simulation) 4 new idealised MJO simulations where the vertical structure is fixed temporally and spatially MJO represented by simple sine wave of heating through warm pool region

12. Summary Both stratiform and congestus heating structures important in changing low level convergence structure and shift in moisture convergence (~1 day shift) Surplus MC ahead of heating, indicating a preconditioning of the atmosphere prior to the onset of convection Suggests better represention of stratiform heating and shallow heating in GCMs is important for improved simulation of MJO Suggests vertical structure and westward tilt could play important role in MJO propagation through warm pool region Lagged correlations of MC, averaged over warm pool +EOF1+EOF2 MJO vs climatological

13. NOAA award Theorectical and practical training in DA techniques GSI, WRF/HWRF models GSI Single Obs Experiment Current Role - Data Assimilation training at CIRA Complete Internship in March 2016 DA Research Project

14. Data Assimilation training at CIRA and beyond… Combine skills in numerical modelling, tropical meteorology and data assimilation Work with HWRF/GSI system Improving hurricane track forecast, intensity through data assimilation Thanks! Possible Future Plans