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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
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
– 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
List of GCM – Page 1
• BCC-CM1
– AgencyBeijing Climate Center, National Climate
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),
NASA, and NOAA
• Model NIES-MIROC3_2-MED
– CCSR/NIES/FRCGC, Japan
– 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
• PRECIS regional climate
modeling is now running
in Climate change study
cell at IWFM,BUET.
• Uses LBC data from
• 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
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
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

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
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008

1948
1951
1954
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
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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