* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Effects of Climate Change on Human Health
Effects of global warming on human health wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate sensitivity wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Solar radiation management wikipedia , lookup
Climate change in the United States wikipedia , lookup
Global warming wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Future sea level wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
North Report wikipedia , lookup
Climate change feedback wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Global warming hiatus wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Effects of global warming wikipedia , lookup
General circulation model wikipedia , lookup
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 Email: [email protected] ABSTRACT The United Kingdom Meteorological Office Hadley Centre’s Regional Climate Modelling system, PRECIS (Providing REgional Climates for Impact Studies) was used to run climate change model simulations for the South East Asia (SEA) region. The 50km resolution PRECIS simulation was driven by the 150km resolution HadAM3P GCM data. The boundary data from HadAM3P was supplied by the Hadley Centre, which is the results of their simulation using sea surface temperature as boundary conditions at the sea points from their coupled oceanatmosphere GCM, namely HadCM3. PRECIS was then used to downscale to 50km regional simulations for the baseline and future climate simulations using the IPCC’s SRES lower emission (B2) and higher emission (A2) scenarios with sulphur cycle included. The baseline simulation period is 1961-1990 and the simulation experiments period is 2071-2100. The precipitation and temperature observation data (CRU20) from the Climate Research Unit, University of East Anglia, United Kingdom is used to validate the model performance in simulating the current climate. The PRECIS captures important regional information on seasonal rainfall, which is missing in its parent GCM simulation. The basic parameters analysed to study the future climate change occurring during 2071-2100 are the mean surface (1.5 meters) temperature and precipitation. The analysis is comprised of both annual mean and seasonal mean for December-January-February (DJF), March-April-May (MAM), June-July-August (JJA) and September-October-November (SON). To detect the possible atmospheric circulation change during the monsoon periods (DJF and JJA) in the future climate we analyzed the mean 850hPa wind for the lower emission scenario (B2). Overall, both the lower emission (B2) and the higher emission (A2) scenarios produce an increase in the annual mean temperature over SEA during 2071-2100. However, the A2 scenario shows the larger increase in temperature. The land area show higher annual mean warming compared to maritime regions. The SEA land surface annual mean warming is in the range of 1.5-3.0 °C for B2 and 3.0-4.5 °C for A2. Meanwhile the SEA maritime surface annual mean warming is 0.5-1.5 °C for B2 and 2.0-3.5 °C for A2. Both the B2 and A2 scenarios show the highest increase in mean annual temperature to occur over western Sumatra, southern Borneo and central Thailand. The A2 simulation projects a spatial difference by about 0.5°C between the maritime SEA and western Pacific region. However, this spatial difference in warming is not observed with the B2 simulation. At a more localized scale, over Malaysia, the annual mean surface temperature increase is lowest for northern Peninsular and highest for central Peninsular and Sabah. Seasonally the rate of warming varies with the greatest change occurring during the summer months (JJA) for both B2 and A2, with the most significant warming to occur over western Sumatra and southern Borneo. The precipitation change has greater spatial variation compared to the temperature change. Overall, both scenarios show a positive anomaly of mean annual precipitation over continental SEA while a negative anomaly over maritime SEA region. Seasonally, continental SEA will experience the highest positive anomaly during the inter-monsoon months of SON, but maritime SEA will experience the highest negative anomaly during the other inter-monsoon months of MAM. SEA, at large will experience a deficit in mean annual precipitation for both A2 and B2 scenarios, with B2 giving the larger deficit. Locally, northern Peninsular Malaysia will experience positive mean annual precipitation anomaly while southern Peninsular Malaysia will experiences negative mean annual precipitation anomaly by both scenarios. Sabah and Sarawak, overall, will experience a deficit in annual mean precipitation. In terms of mean seasonal precipitation, SEA in general will experience a negative anomaly during all the seasons with B2 bringing more deficit in precipitation. The same pattern of the above precipitation deficit is true during the winter months (DJF) for the whole area of Malaysia. Northern Peninsular Malaysia will experience a positive mean seasonal precipitation anomaly during the summer months (JJA) and inter-monsoon months (SON) but a negative anomaly during the winter months (DJF) and inter-monsoon months (MAM). Southern Peninsular Malaysia will experience a negative mean seasonal precipitation anomaly during winter (DJF), inter-monsoon (MAM) and summer (JJA) but a positive anomaly during the other inter-monsoon (SON). Sabah will experience the largest deficit in seasonal mean precipitation during the winter months (DJF) and inter-monsoon months (MAM), while Sarawak will experience the largest deficit in precipitation only during winter (DJF). The analysis of 850hPa level wind anomaly during the winter months (DJF) for the period 2071–2100 compared to the baseline period of 1961–1990 for B2 show a significant weakening of the easterly over the western Pacific region by 2.0 to 3.5 m/s. This may indicate a weakening of the northeast monsoon experienced in the SEA region. In the summer (JJA), over the Indian Ocean, west of Sumatra, the anomalous easterly component winds with a magnitude of 1.5 to 2.5 m/s will tend to enhance the positive Indian Ocean Dipole (IOD) phenomenon.