1 Climate Ensemble Simulations and Projections for Vietnam using PRECIS Model Presented by Hiep Van Nguyen Main contributors: Mai Van Khiem, Tran Thuc, Nguyen Van Thang, Hoang Duc Cuong IMHEN, Vietnam Grace Redmond, David Hein, Met Office Hadley Centre, UK Kevin Hodges The University of Reading, UK

Outlines Experiment design for VN downscaling Experiment design for VN downscaling Data Data Model verification Model verification Future projections of TCs Future projections of TCs Summary Summary

 25x 25 km resolution  19 vertical levels  5 ensemble members The Hadley Centre (UK) regional modelling system PRECIS + Providing REgional Climates for Impacts Studies + Can be run on Linux desktop – useful in countries with limited computing capacity. + Run over Vietnam ( ) with scenario A1B forcing by 5 different HadCM3 runs Experiment design for VN downscaling

HadCM3Q0– The standard model run HadCM3Q0– The standard model run HadCM3Q3– A model run with smaller temperature changes HadCM3Q3– A model run with smaller temperature changes HadCM3Q13– A model run show larger temperature changes HadCM3Q13– A model run show larger temperature changes HadCM3Q10– A model run that gives the driest projections HadCM3Q10– A model run that gives the driest projections HadCM3Q11– A model run that gives the wettest projections HadCM3Q11– A model run that gives the wettest projections Member nameDriving GCMSimulation period Validation period Q0HadCM3Q Q3HadCM3Q Q10HadCM3Q Q11HadCM3Q Q13HadCM3Q ERA-INTERA-INTERIM Experiment design for VN downscaling

Gridded data 61 Meteorological stations over seven climatic zones Station data Observed data

 Spatial patterns of circulation, rainfall and temperature  Annual cycles of rainfall and temperature  Variability of rainfall  Extremes analysis Validation method X x + Temp simulation at stations: nearest grid point (elevation correction with lapse rate o C/100m )

PRECIS simulations with reanalysis (ERA-interim ) forcing Temperature Model reproduces the geographic patterns of surface air temp reasonably well, low temp to the north and higher to the south. Winter: DJF Summer: JJA ObsSimulationObsSimulation

Annual cycle of temperature PRECIS simulations with reanalysis (ERA-interim ) forcing

Summer (JJA) precipitation mm/day Captured minimum rainfall in central Vietnam CRU Obs Simulation APH Obs

Winter (DJF) precipitation Captured maximum rainfall in central Vietnam mm/day CRU Obs Simulation APH Obs

Precipitation bias (model-APH) %

Annual cycle of precipitation

PRECIS Ensemble Simulations forcing by 5 HadCM3 runs

Temperature: summer (JJA) OBS Q0 Q3 Q10 Q11 Q13 34 o C14 o C  The model reproduces the geographical patterns of temperature realistically  Have a stronger east- west temperature gradient in comparison with OBS

Temperature: Winter (DJF)  Winter mean temperature is also well captured by the models  Cold bias in the north and central provinces OBS Q0 Q3 Q10 Q11 Q13 30 o C12 o C

Temperature bias (model-CRU) o C Q0 Q3 Q10 Q11 Q13 Q0 Q3 Q10 Q11 Q13 DJF JJA

Annual cycle of precipitation

Summer (JJA) 850hpa- Wind + Summer monsoon flow pattern is well produced, + Stronger than OBS Obs

Winter (DJF) 850hpa- Wind Winter monsoon is well produced both pattern and strength

Simulate ensemble members: track density Summer (JJAS) Reasonable summer/winter spatial distribution Winter (OND)

Validation: annual cycle of TC number Q3 appears to have reasonably comparable annual cycle All other members underestimate, July cyclones particularly low. OBS Q0 Q3Q10 Q11Q13

Track density ( Number of TCs per year per ~10 6 km 2 ) Track density ( Number of TCs per year per ~10 6 km 2 ) Consistent decrease in most areas, except Q0, increase in SE of domain. Future changes: minus

Mean strength Mean strength Overall increases, except Q10. Overall: Number of TCs tends to decrease while intensity tends to increase + The domain may not capture well TC genesis? + Over estimate summer wind speed  increase TC intensity?? Future changes: minus

Summary The PRECIS model + Capture the present climate reasonably well + Systematically underestimate temperature. + Overestimates precipitation about 94% and 30% for DJF and JJA. + Show good simulations on monsoon flow patterns, however, summer wind speed is overestimated. + Future projections by PRECIS show number of TCs tends to decrease while intensity tends to increase

Main reference: 1. Khiem et. al., 2012: VALUATION OF DYNAMICALLY DOWNSCALED ENSEMBLE CLIMATE SIMULATIONS FOR VIETNAM, International journal of climatology (Accepted) Acknowledgements We would like to thank IMHEN, UNDP, UK Met Office, CSIRO for supporting this work This work is supported under projects 1.“Technical support in development of climate change scenarios in Vietnam” funded by the UNDP 2.“High-resolution Climate Projections for Vietnam” funded by CSIRO, Australia

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Experiment design for VN downscaling Criteria for GCM selection Validation Selected models should represent Asian summer monsoon (position, timing, magnitude), and associated rainfall and temperature Future Magnitude of response: greatest/least regional/local warming, greatest/least magnitude of change in precipitation Characteristics of response Tendency in changing wet-season precipitation (increases and decreases) Spatial patterns of precipitation response over south- east Asia Response of the monsoon circulation

© Crown copyright Met Office Relative vorticity – units of s-1 Describes the rotation of a fluid and may be considered as the ‘circulation per unit area at a point.’ In NH (SH) cyclones are positive (negative) vorticity anomalies. Relative vorticity at a point = z-component of the horizontal wind velocity field (in relation to earth's surface) No vorticitySome vorticity

© Crown copyright Met Office The TRACK program Written and maintained by Kevin Hodges, University of Reading, UK (can be applied to meteorological and oceanographic data) A General Method for Tracking Analysis and its Application to Meterological Data, 1994, K. I. Hodges, Monthly Weather Review., V122, TRACK identifies suitable features through a time sequence, based on thresholds set by the user. These features are then tracked through the time sequence to produce feature trajectories These trajectories are then analysed to produce statistical diagnostic fields: - track density, mean intensity, genesis density, lysis density, mean lifetime.