WFM 6311: Climate Change Risk Management

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam

WFM 6311: Climate Change Risk Management

Akm Saiful Islam

Lecture-4: Module- 3Regional Climate Change Modeling

December, 2009

Institute of Water and Flood Management (IWFM)Bangladesh University of Engineering and Technology (BUET)


Module-3

Prediction of climate change Global and regional climate change predictions Dynamic and static downscaling for impact

study. Uncertainty of predictions


Regional Climate Change Modeling


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.


PRECIS

PRECIS, developed by Hadley Center's, UK, is a regional climate modeling system.

A regional climate model (RCM) is a dynamic downscaling tool that adds fine scale (high resolution) information to the large-scale projections of a global general circulation model (GCM).

This makes for a more accurate representation of many surface features, such as complex mountain topographies and coastlines. RCMs are full climate models, and as such are physically based.


Domain used in PRECIS experiment


Topography of Experiment Domain

Zoom over BangladeshSimulation Domain = 88 x 88 Resolution = 0.44 degree


Predicted Change of Mean Temperature (0C) using A1B

2050 2090

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

2050 2090

Baseline = 2000


Predicting Rainfall using A2 Scenarios



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

May10.0

3 5.59 10.36 6.42 11.92 18.16 33.47 16.53 29.47 11.29 11.96

June17.0

6 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

January14.7

4 15.08 14.63 15.94 15.66 17.66 19.52 16.49 17.68 21.55 20.88

February14.2

7 21.18 20.18 22.36 20.61 20.65 23.14 25.37 24.50 23.00 23.32

March24.2

5 26.34 25.68 25.66 28.82 26.70 29.23 29.04 29.71 28.53 28.84

April27.9

5 32.36 29.10 31.28 34.07 31.96 31.29 32.64 32.81 31.53 34.52

May29.5

1 32.11 32.16 33.17 31.97 32.37 29.31 32.00 32.59 33.88 35.62

June29.1

8 31.42 30.66 31.44 30.82 31.56 31.94 31.18 37.24 34.80 35.07

July28.5

9 28.23 28.88 28.99 29.35 30.28 30.58 30.45 31.03 31.76 30.44

August28.1

9 28.24 29.06 29.65 28.62 30.34 30.26 29.31 30.12 29.93 30.09

September28.0

2 27.29 28.65 28.11 28.58 30.72 29.07 29.79 30.72 29.01 29.87

October25.2

4 25.21 27.10 27.29 26.14 28.48 28.22 29.25 29.72 27.82 29.09

November19.4

4 20.20 21.03 20.52 21.06 23.21 22.64 22.04 23.76 25.52 26.30

December14.4

8 17.37 17.86 18.53 16.24 18.85 19.99 18.26 19.36 20.90 20.80


Summary 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.

WFM 6311: Climate Change Risk Management

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