JSCDA Summer Colloquium 2015

James Taylor

Cooperative Institute for Research in the Atmosphere

Outline of talk

Current Role

Background

Future

Internship in DA at CIRA

PhD in Meteorology - Overview

Future plans in DA

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

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

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

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)

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

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-QContours = Q (column int. heating rate)

Results - Dynamical Response to heating profiles

Blue = convergenceRed = 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

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

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

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

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

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