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Introduction to Climate change Study Cell by k966Xd

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									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



           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)
         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 1995. 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
Models              Scenarios   Variables
BCC:CM1             1PTO2X      specific humidity
BCCR:BCM2           1PTO4X
CCCMA:CGCM3_1-T47   20C3M       precipitation flux
CCCMA:CGCM3_1-T63   COMMIT      air pressure at sea level
CNRM:CM3            PICTL
CONS:ECHO-G         SRA1B       net upward shortwave flux in air
CSIRO:MK3           SRA2        air temperature
GFDL:CM2            SRB1
GFDL:CM2_1
                                air temperature daily max
INM:CM3                         air temperature daily min
IPSL:CM4                        eastward wind
LASG:FGOALS-G1_0
MPIM:ECHAM5                     northward wind
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
         List of GCM – Page 1
• BCC-CM1
   – AgencyBeijing Climate Center, National Climate
     Center, China Meteorological Administration, No.46,
     S.Road, Zhongguancun Str., Beijing 100081, 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    Arctic Sea Ice in
2000                 2040
      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 2100.
• Predictions for every
  hour. Needs more than
  100 GB free space.
Domain used in PRECIS experiment
Topography of Experiment Domain




Simulation Domain = 88 x 88
                              Zoom over Bangladesh
Resolution = 0.44 degree
 Predicted Change of Mean
Temperature (0C) using A1B
       Baseline = 2000




2050                     2090
Predicting Maximum Temperature
       using A2 Scenarios




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




      [Output of PRECIS model using SRES A2 scenario]
Change of Mean Rainfall (mm/d)
    using A1B Scenarios
           Baseline = 2000




    2050                     2090
Predicting Rainfall using A2
         Scenarios




     [Output of PRECIS model using SRES A2 scenario]
Change of mean climatic variables of
 Bangladesh using A1B Scenarios
      Temperate (0C)   Rainfall (mm/d)
         Monthly Average Rainfall (mm/d)
  Month     1990    2000    2010    2020    2030    2040    2050    2060    2070    2080    2090

January      2.61   0.34     0.03    0.03    0.42    0.99    1.24    0.21    0.12    1.66    1.02

February     0.61   0.55     1.38    1.01    1.24    1.88    0.45    1.10    0.53    1.61    0.76

March        2.42   1.02     4.82    3.04    1.87    3.07    0.99    3.62    2.84    1.27    3.59

April        5.84   1.38    11.46    5.99    2.82    7.84   11.41    6.60    8.39    8.74    3.66

May         10.03   5.59    10.36    6.42   11.92   18.16   33.47   16.53   29.47   11.29   11.96

June        17.06    7.90   14.79   13.59   10.84   21.48   12.87   12.93    7.24   10.04   11.70

July         7.20   9.07     7.97    8.13    7.32   11.26    5.62   10.26   10.31    6.33    9.98

August       7.39   5.46     5.11    3.92    9.79    6.67    7.46   13.60   10.65    9.13    9.59

September    4.49    6.71    5.47    7.83    7.51    8.82   10.29   10.80   10.52    8.18    7.48

October      5.68   1.48     4.16    2.76    6.16    3.11    1.89    3.94    2.55    8.84    7.58

November     0.14   0.16     0.41    0.91    0.03    0.73    0.08    1.91    0.27    1.23    0.51

December     0.14   0.06     0.10    0.26    0.06    0.18    1.09    0.04    0.13    0.32    0.03
 Monthly Average Temperature (0C)
   Month    1990    2000     2010     2020     2030     2040     2050     2060     2070     2080     2090

January     14.74    15.08    14.63    15.94    15.66    17.66    19.52    16.49    17.68    21.55    20.88

February    14.27    21.18    20.18    22.36    20.61    20.65    23.14    25.37    24.50    23.00    23.32

March       24.25    26.34    25.68    25.66    28.82    26.70    29.23    29.04    29.71    28.53    28.84

April       27.95    32.36    29.10    31.28    34.07    31.96    31.29    32.64    32.81    31.53    34.52

May         29.51    32.11    32.16    33.17    31.97    32.37    29.31    32.00    32.59    33.88    35.62

June        29.18    31.42    30.66    31.44    30.82    31.56    31.94    31.18    37.24    34.80    35.07

July        28.59    28.23    28.88    28.99    29.35    30.28    30.58    30.45    31.03    31.76    30.44

August      28.19    28.24    29.06    29.65    28.62    30.34    30.26    29.31    30.12    29.93    30.09

September   28.02    27.29    28.65    28.11    28.58    30.72    29.07    29.79    30.72    29.01    29.87

October     25.24    25.21    27.10    27.29    26.14    28.48    28.22    29.25    29.72    27.82    29.09

November    19.44    20.20    21.03    20.52    21.06    23.21    22.64    22.04    23.76    25.52    26.30

December    14.48    17.37    17.86    18.53    16.24    18.85    19.99    18.26    19.36    20.90    20.80
                                                  Trends of Temperature of
                                                  Bangladesh (1947-2007)

              Max. Temp. = 0.63 0C/100 year                                                                                                                        Min. Temp. = 1.37 0C/100 year

                                          Trends of Maximum Temperature                                                                                                                             Trends of Minimum Temperature
31.4                                                                                                                                                       22
31.2                                                       y = 0.0063x + 17.855                                                                           21.8                                                    y = 0.0137x - 6.0268
 31                                                                                                                                                       21.6
30.8                                                                                                                                                      21.4
30.6                                                                                                                                                      21.2
30.4                                                                                                                                                       21
30.2                                                                                                                                                      20.8
 30                                                                                                                                                       20.6
29.8                                                                                                                                                      20.4
29.6                                                                                                                                                      20.2
29.4                                                                                                                                                       20
       1948
              1951
                     1954
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                                   1960
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                                                                                                         1990
                                                                                                                1993
                                                                                                                       1996
                                                                                                                              1999
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                                                                                                                                            2005
                                                                                                                                                   2008




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                                                                                                                                                                                                                                                                                               2002
                                                                                                                                                                                                                                                                                                      2005
                                                                                                                                                                                                                                                                                                             2008
         Spatial Distribution of Trends of
           Temperature (1947-2007)




Maximum Temperature                         Minimum Temperature
   Maximum increase: 0.0581 at Shitakunda      Maximum increase: 0.0404 at Bogra
   Minimum increase: -0.026 at Rangpur         Minimum increase: -0.023 at Tangail
                  Conclusions
 Analysis of the historic data (1948-2007) shows that
  daily maximum and minimum temperature has been
  increased with a rate of 0.63 0C and 1.37 0C per 100
  years respectively.

 PRECIS simulation for Bangladesh using A1B climate
  change scenarios showed that mean temperature will be
  increased at a constant rate 40C per 100 year from the
  base line year 2000.

 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
http://teacher.buet.ac.bd/diriwfm/climate/
Thank you

								
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