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Using PRECIS to simulate climate change scenarios for Bangladesh

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Title: Using PRECIS to simulate climate change scenarios for Bangladesh


1
Using PRECIS to simulate climate change scenarios
for Bangladesh
Short course for NGO and International
Organization staff International Centre for
Climate Change and Development (ICCCAD), at
Independent University, Bangladesh
  • A.K.M. Saiful Islam

Associate Professor, IWFM Coordinator, Climate
Change Study Cell
Bangladesh University of Engineer and Technology
(BUET)
2
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

3
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4
Climate Models
  • Climate models are computer-based simulations
    that use mathematical formulas to re-create the
    chemical and physical processes that drive
    Earths 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.

5
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.

6
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7
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.
8
Heart of Climate Model
Conservation of momentum
Conservation of mass
Conservation of energy
9
Complexity of GCM
10
Hardware Behind the Climate Model
  • Geophysical Fluid Dynamics Laboratory

11
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.

12
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.

13
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14
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.

15
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.

16
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.

17
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

18
GCM output described in the 2007 IPCC Fourth
Assessment Report (SRES scenarios), multilayer
mean
Models Scenarios Variables
BCCCM1BCCRBCM2CCCMACGCM3_1-T47CCCMACGCM3_1-T63CNRMCM3CONSECHO-GCSIROMK3GFDLCM2GFDLCM2_1INMCM3IPSLCM4LASGFGOALS-G1_0MPIMECHAM5MRICGCM2_3_2NASAGISS-AOMNASAGISS-EHNASAGISS-ERNCARCCSM3NCARPCMNIESMIROC3_2-HINIESMIROC3_2-MEDUKMOHADCM3UKMOHADGEM1 1PTO2X1PTO4X20C3MCOMMITPICTLSRA1BSRA2SRB1 specific humidityprecipitation fluxair pressure at sea levelnet upward shortwave flux in airair temperatureair temperature daily maxair temperature daily mineastward windnorthward wind
19
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

20
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

21
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

22
Arctic Sea Ice Prediction using community climate
system model
Arctic Sea Ice in 2040
Arctic Sea Ice in 2000
23
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.

24
Prediction of Temperature increase
25
Prediction of Sea level rise
26
Regional details of Climate Change
27
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 500300
    km

28
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29
RCM can simulate cyclones and hurricanes
30
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.

31
Domain used in PRECIS experiment
32
Topography of Experiment Domain
Simulation Domain 88 x 88 Resolution 0.44
degree
Zoom over Bangladesh
33
Predicted Change of Mean Temperature (0C) using
A1B
Baseline 2000
2050
2090
34
Predicting Maximum Temperature using A2 Scenarios
Output of PRECIS model using SRES A2 scenario
35
Predicting Minimum Temperature using A2 Scenarios
Output of PRECIS model using SRES A2 scenario
36
Change of Mean Rainfall (mm/d) using A1B Scenarios
Baseline 2000
2050
2090
37
Predicting Rainfall using A2 Scenarios
Output of PRECIS model using SRES A2 scenario
38
Change of mean climatic variables of Bangladesh
using A1B Scenarios
Temperate (0C)
Rainfall (mm/d)
39
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
40
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
41
Trends of Temperature of Bangladesh (1947-2007)
Max. Temp. 0.63 0C/100 year
Min. Temp. 1.37 0C/100 year
42
Spatial Distribution of Trends of Temperature
(1947-2007)
Maximum Temperature Maximum increase 0.0581 at
Shitakunda Minimum increase -0.026 at Rangpur
Minimum Temperature Maximum increase 0.0404 at
Bogra Minimum increase -0.023 at Tangail
43
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.

44
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.
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