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International Seminar On Climate Variability, Change and Extreme Weather Events

Regional Climate Change over Southeast Asia Region. Mohan Kumar Sammathuria, Ling Leong Kwok & Wan Azli Wan Hassan Malaysian Meteorological Department Ministry of Science, Technology & Innovation, Malaysia. International Seminar On Climate Variability, Change and Extreme Weather Events

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International Seminar On Climate Variability, Change and Extreme Weather Events

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  1. Regional Climate Change over Southeast Asia Region Mohan Kumar Sammathuria, Ling Leong Kwok & Wan Azli Wan Hassan Malaysian Meteorological Department Ministry of Science, Technology & Innovation, Malaysia International Seminar On Climate Variability, Change and Extreme Weather Events 26-27 February 2008, Bangi, MALAYSIA

  2. SCOPE • Introduction • Present Climate (1961-1990) • Future Climate (2071-2100) • Mean Temp (annual & seasonal) Anomaly • Mean Precip (annual & seasonal) Anomaly • Seasonal Mean Wind Anomaly • Concluding Remarks

  3. GCMs to Regional Adaptive Responses: Modelling Path PRECIS 50 km

  4. Projected climate change depend on illustrative scenarios (storylines) of greenhouse gases emissions: Special Report on Emission Scenarios (SRES) Based on different plausible pathways of future: • development of the world • population growth and consumption patterns • standards and life style of living • energy consumption & energy sources (e.g. fossil fuel usage) • technology change • land use change

  5. Four Marker IPCC’s SRES Future Emission Scenarios A qualitative description of the SRES scenarios

  6. The driving model HadCM3 has predict climate change (global temperature rise) arising from each of the four IPCC’s SRES future emissions scenarios ~5.0oC ~2.0oC IPCC AR4 B1: 1.8oC (1.1-2.9) B2: 2.4oC (1.4-3.8) A2: 3.4oC (2.0-5.4) A1FI: 4.0oC (2.4-6.4)

  7. PRECIS • Providing REgional Climates for Impact Studies • High-resolution limited area model driven at its lateral and sea-surface boundaries by output from HadCM • PRECIS runs on Linux PC (horizontal resolutions: 50 x 50 & 25 x 25 km). • Needs data for the selected domain on lateral boundary conditions (LBC) from the driving GCM (e.g., HadCM3/ HadAM3) and the associated ancillary files (e.g., sea surface temp, vegetation, topography, etc). • Hadley Centre, UK has been providing PRECIS as well as the driving data to several regional groups. • Baseline (1961-90), A2 & B2 scenarios (2071-2100). Reanalysis-driven runs provide comprehensive regional data sets representing current conditions, which can assist model evaluation as well as assessment of vulnerability to current climate variability. • Ensembles to estimate model-related uncertainties.

  8. Orography Resolution PRECIS resolution 0.44° x 0.44° HadCM3 resolution 2.5° x 3.75°

  9. PRECIS Runs at MMD • LBCs derived from HadAM3P. HadCM3 provided SST as boundary conditions for HadAM3P. • A2 & B2 scenarios runs of PRECIS performed consecutively on a PC. • PRECIS runs on Linux PC (horizontal resolutions: 0.44° x 0.44°) • The LBCs have a length of 31 years, and are available for Baseline (1961-90), A2 & B2 scenarios (2071-2100), with the sulphur cycle. • The basic parameters analyzed are the mean surface (1.5 m) temp and total precip. • The precip & temp obs data (CRU20, 1961-90) is used to validate model performance in simulating current climate. • The analysis comprised of both annual mean and seasonal mean for DJF, MAM, JJA and SON. • To detect possible atmospheric circulation change during monsoon periods (DJF & JJA) in future climate, the seasonal mean 850 hPa wind for the lower emission scenario (B2) was analysed.

  10. PRECIS captures important regional information on summer monsoon rainfall missing in its parent GCM simulations

  11. PRECIS performs reasonably well too on winter monsoon rainfall compared to its parent GCM simulations

  12. PRECIS Simulations of Present Climate (1961-1990) Mean Annual Cycles of SEA Rainfall and Temperature

  13. PRECIS Simulations of Future Climate (2071-2100) Mean Annual Cycles of SEA Rainfall and Temperature

  14. Mean Annual Temp Anomaly Continental –– larger +ve anomaly (A2, 3.0-4.5 °C; B2, 1.5-3.0 °C) Maritime –– smaller +ve anomaly (A2, 2.0-3.5 °C; B2, 0.5-1.5 °C) Larger anomaly over SCS vs western Pacific in A2 N-E P. Malaysia –– Smaller +ve anomaly c-S P. Malaysia, Sabah & Sarawak –– Larger +ve anomaly

  15. (A2-Baseline) Mean Seasonal Temperature Anomaly DJF MAM JJA SON

  16. (B2-Baseline) Mean Seasonal Temperature Anomaly DJF MAM JJA SON

  17. Mean Annual Precip Anomaly Precip deficit over maritime SEA -12% -7% Northern P. Malaysia (A2, 17%; B2, 6%) Sarawak (A2, 5%; B2, -8%) Southern P. Malaysia (A2, -3%; B2, -20%) Sabah (A2, -15%; B2, -18%)

  18. A2 (%) B2 (%) DJF -5 -21 MAM -21 -24 JJA -8 -13 SON 1 -9 SEA Mean Seasonal Precip Anomaly Larger deficit in B2 Deficit in most seasons

  19. MAM -21% (A2-Baseline) Mean Precip (%) DJF -5% JJA -8% SON +1%

  20. (B2-Baseline) Mean Precip (%) DJF -21% MAM -24% JJA -13% SON -9%

  21. Reg DJF MAM JJA SON ANNUAL A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) PM -15 -31 -9 -18 14 -1 19 9 5 -7 SBH -34 -35 -36 -32 2 -3 2 -9 -15 -18 SRWK -14 -25 9 -2 18 1 11 -4 5 -8 NPM -17 -25 1 -11 38 21 27 18 17 6 EPM -7 -30 -17 -24 17 4 20 9 6 -7 CPM -29 -39 -5 -10 12 5 16 14 1 -5 SPM -19 -37 -6 -20 -7 -25 15 -1 -3 -20 SEA -5 -21 -21 -24 -8 -13 1 -9 -7 -12 Mean Seasonal Precip Anomaly Malaysia – NEGATIVE ANOMALY mean precip in DJF

  22. Reg DJF MAM JJA SON ANNUAL A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) PM -15 -31 -9 -18 14 -1 19 9 5 -7 SBH -34 -35 -36 -32 2 -3 2 -9 -15 -18 SRWK -14 -25 9 -2 18 1 11 -4 5 -8 NPM -17 -25 1 -11 38 21 27 18 17 6 EPM -7 -30 -17 -24 17 4 20 9 6 -7 CPM -29 -39 -5 -10 12 5 16 14 1 -5 SPM -19 -37 -6 -20 -7 -25 15 -1 -3 -20 SEA -5 -21 -21 -24 -8 -13 1 -9 -7 -12 Mean Seasonal Precip Anomaly Northern P. Malaysia – POSITIVE ANOMALY mean precip in JJA & SON, deficit in DJF & MAM

  23. Reg DJF MAM JJA SON ANNUAL A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) PM -15 -31 -9 -18 14 -1 19 9 5 -7 SBH -34 -35 -36 -32 2 -3 2 -9 -15 -18 SRWK -14 -25 9 -2 18 1 11 -4 5 -8 NPM -17 -25 1 -11 38 21 27 18 17 6 EPM -7 -30 -17 -24 17 4 20 9 6 -7 CPM -29 -39 -5 -10 12 5 16 14 1 -5 SPM -19 -37 -6 -20 -7 -25 15 -1 -3 -20 SEA -5 -21 -21 -24 -8 -13 1 -9 -7 -12 Mean Seasonal Precip Anomaly Southern P. Malaysia – deficit mean precip in DJF, MAM & JJA, +ve anomaly in SON

  24. Reg DJF MAM JJA SON ANNUAL A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) A2 (%) B2 (%) PM -15 -31 -9 -18 14 -1 19 9 5 -7 SBH -34 -35 -36 -32 2 -3 2 -9 -15 -18 SRWK -14 -25 9 -2 18 1 11 -4 5 -8 NPM -17 -25 1 -11 38 21 27 18 17 6 EPM -7 -30 -17 -24 17 4 20 9 6 -7 CPM -29 -39 -5 -10 12 5 16 14 1 -5 SPM -19 -37 -6 -20 -7 -25 15 -1 -3 -20 SEA -5 -21 -21 -24 -8 -13 1 -9 -7 -12 Mean Seasonal Precip Anomaly Sabah – largest deficit in DJF & MAM Sarawak – DJF only

  25. Mean Seasonal 850 hPa Wind Anomaly (DJF) Baseline Anomaly Weakening easterly (2.0-3.5 m/s) Rainfall Anomaly

  26. Mean Seasonal 850 hPa Wind Anomaly (JJA) Baseline Anomaly Anomalous easterly comp. (1.5-2.5 m/s) Rainfall Anomaly

  27. Concluding Remarks • PRECIS was found able to capture important regional information on seasonal rainfall which is missing in GCM simulation • Both A2 & B2 scenarios show an increase in the annual mean temp over SEA during 2071-2100, with A2 shows larger increase in temp • The SEA land surface annual mean warming is in the range of 1.5-3.0 °C with B2 and 3.0-4.5 °C with A2 • The SEA maritime surface annual mean warming is 0.5-1.5 °C with B2 and 2.0-3.5 °C with A2

  28. Concluding Remarks (cont.) • Both scenarios show a +ve anomaly of mean annual precip over SEA continent while a -ve anomaly over maritime region • SEA, at large will experience a deficit in mean annual precipitation for both A2 and B2 scenarios, with B2 giving the larger deficit • Weakening of the easterly during the winter months (DJF) over the western Pacific region in B2 scenarios indicates a weakening of the NE monsoon in SEA region • In summer (JJA), the anomalous easterly component winds over the Indian Oceanwill tend to enhance the +ve IOD phenomenon

  29. Note: Multi-Model IPCC AR4 Uncertainty Ranges B1: 1.8oC (1.1 - 2.9) A1T: 2.4oC (1.4 - 3.8) B2: 2.4oC (1.4 - 3.8) A1B: 2.8oC (1.7 - 4.4) A2: 3.4oC (2.0 - 5.4) A1F1: 4.0oC (2.4 - 6.4) Concluding Remarks (cont.) This is our preliminary results. More works are needed to obtain credible climate change scenarios with better certainty….. IPCC’s AR4 employed multi-model means of surface warming for the SRES marker scenarios. Numbers indicate the number of models which have been run for a given scenario. The gray bars at right indicate the best estimate (solid line within each bar) and the likely range assessed for the SRES marker scenarios. RCM (e.g. PRECIS), too, should be driven by multi-model in order to know the uncertainty range of climate change

  30. Thank You

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