Analysis and Modeling of Climate Change A.K.M. Saiful Islam Associate Professor, IWFM Coordinator, Climate Change Study Cell Bangladesh University of Engineer and Technology (BUET) Training Course on Facing the Challenges of Climate Change: Issues, Impacts and adaptation Strategies for Bangladesh with focus on Water and Waste Management” Organized by International Training Network (ITN) Centre, BUET

Presentation Outline Overview of the Climate System Modeling of Climate Change General Circulation Model (GCM) IPCC SRES Scenarios Regional Climate Model (RCM) Climatic Modeling at BUET

Climate Models Climate models are computer-based simulations that use mathematical formulas to re-create the chemical and physical processes that drive Earth’s climate. To “run” a model, scientists divide the planet into a 3-dimensional grid, apply the basic equations, and evaluate the results. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points. Climate models use quantitative methods to simulate the interactions of the atmosphere, oceans, land surface, and ice.

General Circulation Model (GCM) General Circulation Models (GCMs) are a class of computer- driven models for weather forecasting, understanding climate and projecting climate change, where they are commonly called Global Climate Models. Three dimensional GCM's discretise the equations for fluid motion and energy transfer and integrate these forward in time. They also contain parameterizations for processes - such as convection - that occur on scales too small to be resolved directly. Atmospheric GCMs (AGCMs) model the atmosphere and impose sea surface temperatures. Coupled atmosphere- ocean GCMs (AOGCMs, e.g. HadCM3, EdGCM, GFDL CM2.X, ARPEGE-Climate) combine the two models.

GCM typical horizontal resolution of between 250 and 600 km, 10 to 20 vertical layers in the atmosphere and sometimes as many as 30 layers in the oceans.

Heart of Climate Model

Complexity of GCM

Hardware Behind the Climate Model Geophysical Fluid Dynamics Laboratory

Special Report on Emissions Scenarios (SRES) The Special Report on Emissions Scenarios (SRES) was a report prepared by the Intergovernmental Panel on Climate Change (IPCC) for the Third Assessment Report (TAR) in 2001, on future emission scenarios to be used for driving global circulation models to develop climate change scenarios. It was used to replace the IS92 scenarios used for the IPCC Second Assessment Report of The SRES Scenarios were also used for the Fourth Assessment Report (AR4) in 2007.

SERS Emission Scenarios A1 - a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Three sub groups: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B). A2 - A very heterogeneous world. The underlying theme is that of strengthening regional cultural identities, with an emphasis on family values and local traditions, high population growth, and less concern for rapid economic development. B1 - a convergent world with the same global population, that peaks in mid-century and declines thereafter, as in the A1 storyline. B2 - a world in which the emphasis is on local solutions to economic, social and environmental sustainability.

A1 The A1 scenarios are of a more integrated world. The A1 family of scenarios is characterized by: –Rapid economic growth. –A global population that reaches 9 billion in 2050 and then gradually declines. –The quick spread of new and efficient technologies. –A convergent world - income and way of life converge between regions. Extensive social and cultural interactions worldwide. There are subsets to the A1 family based on their technological emphasis: –A1FI - An emphasis on fossil-fuels. –A1B - A balanced emphasis on all energy sources. –A1T - Emphasis on non-fossil energy sources.

A2 The A2 scenarios are of a more divided world. The A2 family of scenarios is characterized by: –A world of independently operating, self-reliant nations. –Continuously increasing population. –Regionally oriented economic development. –Slower and more fragmented technological changes and improvements to per capita income.

B1 The B1 scenarios are of a world more integrated, and more ecologically friendly. The B1 scenarios are characterized by: –Rapid economic growth as in A1, but with rapid changes towards a service and information economy. –Population rising to 9 billion in 2050 and then declining as in A1. –Reductions in material intensity and the introduction of clean and resource efficient technologies. –An emphasis on global solutions to economic, social and environmental stability.

B2 The B2 scenarios are of a world more divided, but more ecologically friendly. The B2 scenarios are characterized by: –Continuously increasing population, but at a slower rate than in A2. –Emphasis on local rather than global solutions to economic, social and environmental stability. –Intermediate levels of economic development. –Less rapid and more fragmented technological change than in A1 and B1

GCM output described in the 2007 IPCC Fourth Assessment Report (SRES scenarios), multilayer mean ModelsScenariosVariables BCC:CM1 BCCR:BCM2 CCCMA:CGCM3_1-T47 CCCMA:CGCM3_1-T63 CNRM:CM3 CONS:ECHO-G CSIRO:MK3 GFDL:CM2 GFDL:CM2_1 INM:CM3 IPSL:CM4 LASG:FGOALS-G1_0 MPIM:ECHAM5 MRI:CGCM2_3_2 NASA:GISS-AOM NASA:GISS-EH NASA:GISS-ER NCAR:CCSM3 NCAR:PCM NIES:MIROC3_2-HI NIES:MIROC3_2-MED UKMO:HADCM3 UKMO:HADGEM1 1PTO2X 1PTO4X 20C3M COMMIT PICTL SRA1B SRA2 SRB1  specific humidity  precipitation flux  air pressure at sea level  net upward shortwave flux in air  air temperature  air temperature daily max  air temperature daily min  eastward wind  northward wind

List of GCM – Page 1 BCC-CM1 –AgencyBeijing Climate Center, National Climate Center, China Meteorological Administration, No.46, S.Road, Zhongguancun Str., Beijing , China BCCR –Bjerknes Centre for Climate Research (BCCR), Univ. of Bergen, Norway CGCM3 –Canadian Centre for Climate Modelling and Analysis (CCCma) CNRM-CM3 –Centre National de Recherches Meteorologiques, Meteo France, France

List of GCM– Page 2 CONS-ECHO-G –Meteorological Institute of the University of Bonn (Germany), Institute of KMA (Korea), and Model and Data Group. CSIRO, Australia INMCM3.0 –Institute of Numerical Mathematics, Russian Academy of Science, Russia. GFDL –Geophysical Fluid Dynamics Laboratory, NOAA NASA-GISS-AOM –NASA Goddard Institute for Space Studies (NASA/GISS), USA

List of GCM – Page 3 MRI-CGCM2_3_2 –Meteorological Research Institute, Japan Meteorological Agency, Japan NCAR-PCM –National Center for Atmospheric Research (NCAR), NSF (a primary sponsor), DOE (a primary sponsor), NASA, and NOAA Model NIES-MIROC3_2-MED –CCSR/NIES/FRCGC, Japan UKMO-HADCM3 –Hadley Centre for Climate Prediction and Research, Met Office, United Kingdom

Arctic Sea Ice Prediction using community climate system model Arctic Sea Ice in 2040 Arctic Sea Ice in 2000

Prediction of Global Warming Figure shows the distribution of warming during the late 21st century predicted by the HadCM3 climate model. The average warming predicted by this model is 3.0 °C.

Prediction of Temperature increase

Prediction of Sea level rise

Regional details of Climate Change

Regional Climate modeling An RCM is a tool to add small-scale detailed information of future climate change to the large-scale projections of a GCM. RCMs are full climate models and as such are physically based and represent most or all of the processes, interactions and feedbacks between the climate system components that are represented in GCMs. They take coarse resolution information from a GCM and then develop temporally and spatially fine-scale information consistent with this using their higher resolution representation of the climate system. The typical resolution of an RCM is about 50 km in the horizontal and GCMs are typically 500~300 km

RCM can simulate cyclones and hurricanes

Regional Climate change modeling in Bangladesh PRECIS regional climate modeling is now running in Climate change study cell at IWFM,BUET. Uses LBC data from GCM (e.g. HadCM3). LBC data available for baseline, A2, B2, A1B scenarios up to Predictions for every hour. Needs more than 100 GB free space.

Domain used in PRECIS experiment

Topography of Experiment Domain Zoom over Bangladesh Simulation Domain = 88 x 88 Resolution = 0.44 degree

Predicted Change of Mean Temperature ( 0 C) using A1B Baseline = 2000

Predicting Maximum Temperature using A2 Scenarios [Output of PRECIS model using SRES A2 scenario]

Predicting Minimum Temperature using A2 Scenarios

Change of Mean Rainfall (mm/d) using A1B Scenarios Baseline = 2000

Predicting Rainfall using A2 Scenarios [Output of PRECIS model using SRES A2 scenario]

Change of mean climatic variables of Bangladesh using A1B Scenarios Temperate ( 0 C)Rainfall (mm/d)

Monthly Average Rainfall (mm/d) Month January February March April May June July August September October November December

Monthly Average Temperature ( 0 C) Month January February March April May June July August September October November December

Trends of Temperature of Bangladesh ( ) Max. Temp. = C/100 year Min. Temp. = C/100 year

Maximum Temperature Maximum increase: at Shitakunda Minimum increase: at Rangpur Minimum Temperature Maximum increase: at Bogra Minimum increase: at Tangail Spatial Distribution of Trends of Temperature ( )

Conclusions  Analysis of the historic data ( ) shows that daily maximum and minimum temperature has been increased with a rate of C and C per 100 years respectively.  PRECIS simulation for Bangladesh using A1B climate change scenarios showed that mean temperature will be increased at a constant rate 4 0 C per 100 year from the base line year  On the other hand, mean rainfall will be increased by 4mm/d in 2050 and then decreased by 2.5mm/d in 2100 from base line year 2000.

Recommendations In future, Climate change predictions will be generated in more finer spatial scale(~25km). PRECIS model will be simulated with other Boundary condition data such as ECHAM5 using A1B scenarios. Results will be compared with other regional climate models such as RegCM3 etc.

Climate Change Study Cell, BUET

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