An appropriate general circulation model (GCM) to investigate climate change impact

34 Int. J. Hydrology Science and Technology, Vol. 2, No. 1, 2012

Copyright © 2012 Inderscience Enterprises Ltd.

An appropriate general circulation model (GCM) to investigate climate change impact

Mohammad Reza Farzaneh* Department of Water Engineering, Tarbiat Modarres University, Tehran, 14115-111, Iran E-mail: [email protected] *Corresponding author

Saeid Eslamian Department of Water Engineering, Collage of Agriculture, Isfahan University of Technology, Isfahan, 8415683111, Iran E-mail: [email protected]

S. Zahra Samadi Hydro-Environmental Research Centre, Cardiff School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF24 3AA, UK E-mail: [email protected]

Aboulfazl Akbarpour Department of Water Engineering, Birjand University, Birjand, 97175/615, Iran E-mail: [email protected]

Abstract: There is a wide agreement in the international scientific society that climate change will modify climatic variables. Atmosphere-Ocean General Circulation Model (AOGCM) is an appropriate model to produce climatic scenarios. The first step to climate change studies is currently using the most appropriate AOGCM output. In this study, AOGCM’s for a time period of 1961–1990 in Shahrekord synoptic station are therefore compared and finally the best AOGCM is presented. The employed models in the paper include CCSR, CGCM2, CSIRO and HADCM3 which was mentioned in the Third Assessment Reports of IPCC. The results display that the HADCM3 output has more appropriate correlation than the other models comparing with the observed data of the station. According to the evaluation of climatic scenarios, climate change has the unfavourable impacts on hydrology and water resources of the basin, thus these results are suitable ones of the most achievements to study climate change effects on stream flow changes and variability in Northern Karoon region, Iran.

An appropriate GCM to investigate climate change impact 35

Keywords: climate change; general circulation model; GCM; HADCM3; CCSR; CGCM2; CSIRO; Shahrekord; Northern Karoon.

Reference to this paper should be made as follows: Farzaneh, M.R., Eslamian, S., Samadi, S.Z. and Akbarpour, A. (2012) ‘An appropriate general circulation model (GCM) to investigate climate change impact’, Int. J. Hydrology Science and Technology, Vol. 2, No. 1, pp.34–47.

Biographical notes: Mohammad Reza Farzaneh is currently a PhD student of Water Resources in Tarbiat Modares University, Tehran, Iran. Advising two finished and four understudy Master theses (dissertation) in the field of climate change impacts on water resources; 34 under-review and published papers in conferences and journals; collaboration in national and international projects such as study of climate change impact on African water resources; teaching GIS, hydrology, groundwater, and water resources; and registration of four inventions are some of his scientific experiences.

Saeid Eslamian received his PhD from University of New South Wales, Australia with Professor David Ilgrim. He was a Visiting Professor in Princeton University, USA and ETH Zurich, Switzerland. He is currently an Associate Professor of Hydrology in Isfahan University of Technology. He is Founder and Chief Editor of Journal of Flood Engineering and International Journal of Hydrology Science and Technology. He has published more than 200 publications mainly in statistical and environmental hydrology and hydrometeorology. He is recently appointed as the Chief Editor for three-volume 2012 Handbook of Hydrologic Engineering by Tylor and Francis Group (CRC Press).

S. Zahra Samadi holds a Doctorate in Watershed Engineering and Science in Iran and studied for her Masters at the University of Tehran. Currently, she is working in Cardiff University, UK on a project entitled ‘Uncertainty analysis of downscaling models on flooding and water resource management in welsh catchments, UK’. She was also involved in research studies including: statistical downscaling models of climate variables, uncertainty analysis of hydrological/climate models, GIS-based hydrological modelling and river engineering modelling.

Aboulfazl Akbarpour was accepted in Water Engineering from Ferdowsi University of Mashhad of Iran in 1993. He obtained his Master in Civil Engineering in Ferdowsi University of Mashhad in 1996 and PhD in 2005. Currently, he is an Assistant Professor of Water Engineering Department, University of Birjand.

1 Introduction

In recent decades, the quick development of industrial activities following increasing greenhouse gases causes climatic imbalance on the earth which names climate change phenomenon. Negative effects of climate change, in the future, can be more intensive since nations only want to develop rapidly disregarding environmental aspects. Researches show that this phenomenon can affect different fields including water resources, agriculture, environment, hygiene, industry and economy negatively. Because using GCM outputs considers the most valid tool, many researches have been applied in

36 M.R. Farzaneh et al.

the use of these models and various emission scenarios to investigate impact of climate change.

Wibly and Harris (2006) invented a comprehensive method for evaluating climate change impacts and coupling regional climate models (CSIRO, CGCM2, HadCM3 and two emission scenarios including A2 and B2) to water resources (CACHMOD) and water quality (INCA) in a definite framework. In the research by Calanca (2007), based on A2 emission scenario, they investigated climate change impacts on probability and severity of drought. The study by Blenkinsop and Fowler (2007), examined functions of six RCMs and four GCMs and assessed the average rainfall and drought statistics for 1961–1990 in the UK using two indices of drought severity, based on monthly rainfalls. The researchers estimated the uncertainty ranges in the future for A2 circulation scenario from 2071 to 2100. They concluded that RCMs are able to generate annually local cycle of rainfall in the UK, but could not generate the observed frequency of the events especially droughts in the study area.

Kirono et al. (2011) simulated the drought characteristics utilising different GCMs based on increasing greenhouse gases. They used 14 GCMs, the forth report of IPCC under various scenarios, and achieved the required meteorological from parameters to calculate Reconnaissance Drought Index (RDI) for evaluating the drought changes in 12 regions of Australia. The results indicated an increase in the area affected by drought magnitude and frequency. Also, after the year 2030, more changes than before were observed and they mentioned that the prediction uncertainty goes a long time. They concluded that rainfall will decrease by about 3% to 5% and temperature An appropriate general circulation model (GCM) to investigate climate change impact will increase by 1°C and evapotranspiration will increase by 2% to 6% by the year of 2030.

It is considered that the different researchers analysed their studies in various conditions. Some of them using one model and one circulation scenario and some other researchers considered various situations due to differences of emission scenarios, general and regional models. A key point in review of these models is lack of a fixed trend in final output due to different models and even in some studies, different models had completely reversed the results. This problem will cause to add an uncertainty source to the study. Because there are many studies relating to the uncertainty in climate change investigations, adding other sources makes less accuracy in the study. For this reason, a methodology in present study is employed to analyse the uncertainty of downscaled data simulated by various AOGCMs for historical data to introduce the best model or models for the studied regions.

The main objective of this paper is to compare GCMs models and present the most skilled GCM for future climate change impact assessment in the study area. The analysis aims at providing a comprehensive comparison of GCMs models at Shahrekord climate station. In order to accomplish this objective, the best GCM selected according to correlation analysis and comparison of some function standards (like R², RMSE, MAE). The ultimate goal of this research is to introduce an appropriate GCM model for future estimation of meteorological variables corresponding to a given scenario of future climate so the candidate GCM variables will use as a basis for hydrological impact assessments.


2 Methodology

2.1 Study area

Behasht-Abad watershed is located in the north and north eastern of Northern Karoon region between 50°23´ and 51°25´ longitude in east, 32°1´ and 32°34´ latitude in north. The area is 3,860 km2 including about 27% northern watershed area of Karoon River. Two main branches of Behesht-Abad are Kiar and Junaghan. 79% of the region is located between 2,000 and 2,500 metres altitude. The lowest and highest elevations are the outlet reaching 1,660 metres and Saldaran Mountain reaching 3620 metres, respectively. Behesht-Abad has the lowest slope among the watersheds in Northern Karoon containing 44% of the terrains is lower than 10% slope and 32% lower than 5% slope. The average slope is estimated 27%. Although watershed area involves 27% of Northern Karoon total area, it does not receive more than 18% of total rainfall in the region. The average rainfall is approximately 471 mm including 245 mm in winter, 89 mm in spring, 5 mm in summer and 132 mm in autumn of the total rainfall in the watershed, snow portion is 55%. The annual average isothermal lines are mainly between 8.5 and 13.5, in the flats 10 and 12.5 centigrade degree. As the results of AOGCM models are the most credible tool in climate change studies and the selection of appropriate model is essential for this field of study, the purpose of this study is to present the best model selection method and study the difference of the results achieved from different AOGCM models. For this aim, Shahrekord synoptic station is selected among all available stations located in under study region because of its long time rainfall and temperature available data.

Figure 1 Digital elevation model (Dem) of Behesht-Abad watershed (in Northern Karoon) and Shahrekord synoptic station


Table 1 Characteristics of available models on DDC section at IPCC website IPCC

C

CSR

N

CAR

G

FDL-

R30

CAO

GC

M2

CSI

RO

Had

CM

3 EC

HAM

4

Loca

l acc

urac

y (d

egre

e)

(leng

th ×

wid

th)

5.6

× 5.

6 4.

5 ×

7.5

4.5

× 7.

5 3.

7 ×

3.7

3.2

× 5.

6 2.

5 ×

3.75

2.

8 ×

2.8

Loca

l acc

urac

y (d

egre

e)

(wid

th ×

leng

th)

2.8

× 2.

8 1

× 1

4.5

× 3.

75

1.8

× 1.

8 3.

2 ×

5.6

2.5

× 3.

75

2.8

× 2.

8

Sim

ulat

ion

perio

d of

gr

eenh

ouse

gas

es a

nd

susp

ende

d in

gred

ient

in p

ast

CO

2: 18

90–1

989

SO4:

189

0–19

89

CO

2: 19

01–1

989

SO4:

1901

–198

9 C

O2:

1766

–198

9 SO

4: 19

01–1

989

CO

2: 19

00–1

989

SO4:

1860

–198

9 C

O2:

1860

–198

9 SO

4: 18

60–1

989

CO

2: 18

60–1

989

SO4:

1860

–198

9 C

O2:

1860

–198

9 SO

4: 18

60–1

989

Sim

ulat

ion

dura

tion

(yea

r)

Con

trol p

erio

d 21

0 SR

ES

scen

ario

s 18

90–2

100

Con

trol p

erio

d 13

6 SR

ES

scen

ario

s 19

80–2

099

Con

trol p

erio

d 1,

000

SRES

sc

enar

ios

1961

–210

0

Con

trol p

erio

d 20

0 SR

ES

scen

ario

s 19

00–2

100

Con

trol p

erio

d 21

9 SR

ES

scen

ario

s 19

61–2

100

Con

trol p

erio

d 24

0 SR

ES

scen

ario

s 19

50–2

099

Con

trol p

erio

d 24

0 SR

ES

scen

ario

s 19

90–2

000

Sim

ulat

ed sc

enar

ios S

RES

A

1, A

1FI,

A1T

, A

2, B

1, B

2 A

2, B

2, A

1B

A2,

B2

A2,

B2

A1,

A2,

B1,

B2

A2,

A2b

, A2c

, B2

A2,

B2

Ref

eren

ce

Noz

awa

et a

l. (2

001)

M

eehl

et a

l. (2

001)

K

nuts

on e

t al.

(199

9)

Flat

o an

d B

oer

(200

1)

Gor

don

and

O’F

arre

ll (1

997)

G

ordo

n et

al.

(200

0)

Sten

del e

t al.

(200

0)

Sour

ce:

IPC

C-T

GC

IA (1

999)


2.2 Making climatic scenarios

There are several methods to create climatic scenarios. In this paper, Atmosphere and Ocean General Circulation Models (AOGCMs) is used and the fundamental climatic processes including atmosphere, ocean, ground surface, ice surface and biosphere are simulated by secondary models separately and then all these models couple to simulate the earth climate, and finally AOGCM was developed. IPCC makes a committee gathering outputs of AOGCMs called Data Distribution Center (DDC) in 1998. One of the main aims for making this committee was gathering the AOGCMs output used in the climate change studies. The outputs of the models under various emission scenarios were placed in DDC section on the IPCC website. Horizontal local accuracy in GCMs on the earth surface is prevalently 250 km and the vertical one is 1 km while, vertical and horizontal local accuracy in oceans respectively are 200–400 km and 125–250 km. In all of these models, the climatic parameters are simulated under various scenarios for future. The characteristics of AOGCMs are displayed in Table 1.

2.3 Data extract for the studied stations from AOGCM output

The files relating to the simulated climatic parameters by AOGCMs in different periods are available at IPCC website. The files contain the time series of climatic parameters of calculated cells encircling earth globe simulated by these models. Therefore, considering local situation of the under study, time series of celled climatic parameters places in this condition is extractable. Because of long process in extract, AOGCM-RDP AOGCM-retrieve data programme is used which is able to extract the time series after entering local coordination of the situation and required statistical time period too. In addition, the program enables the users to achieve climatic parameters time series of the studied stations under control run of AOGCMs. Figure 2 shows partially AOGCM-RDP programme. Figure 2 Sample page of AOGCM-RDP program for extracting regional climatic parameters

(see online version for colours)


Regarding what been mentioned, in this study Shahrekord synoptic station is selected because of its suitable data duration (1961–1990). Since historic period is chosen from 1961 to 1990, only four models including CSIRO, CCSR, CGCM2 and HADCM3 performing simulations in the duration are investigated.

2.4 Function criteria

It is necessary to find some criteria in the study to assess model functions and perform the result comparison. The root mean square error (RMSE), R2 and mean absolute error (MAE) are selected and used.

( ) ( )2

2 1

1 n

p p o om

Xp Xo

X Xn

Rμ μ

σ σ=

⎡ ⎤− −⎢ ⎥

⎢ ⎥= ⎢ ⎥×⎣ ⎦

∑

( )21

n

p om

X XRMSE

n=

−

=∑

1

n

p om

X XMAE

n=

−

=∑

Where, X is the value, µ is the average value, σ is the standard deviation and n is the number of values. Indices p and o are the observed and downscaled values, respectively. R2 demonstrates the linear correlation between two group values mentioned and its quantity is in the range of 0–1. The closer quantity to one explains the stronger correlation between two values. R2 cannot be the only criterion for recognising correlation because there are a lot of cases that those downscaled values appropriately can simulate the behaviour patterns of downscaled values, while there is a deep difference between them. Thus, it is essential to use other criteria showing the difference too. In the study, RMSE and MAE as two criteria besides R2 are used. Both of them have an X-dimension unity that the lower quantity means the stronger correlation and there is not any threshold for them (Samadi et al., 2009).

2.5 High resolution downscaled data

Considering existence of difference in observed values, the downscaled values using different models will indicate the different results. In final stage, the models will be chosen which their parameters are close to the observed values, then accordance among the models in estimating of the future and comparing with historical data is done. Therefore, the A2 emission scenario that notices the most critical status in estimate of the parameters will be the reference. In this stage, the high resolution data generated by Tyndell Center for Climate Change research is used. This downscaling method is similar to the proposed method by New et al. (2000).


3 Results and discussions

In this paper, the best AOGCM model proposed for research with capturing and comparing the observed average temperature and rainfall data in historical 30-year duration (1961–1990) with the simulated ones by AOGCMs. The observed and model data by the various AOGCMs are shown in Tables 2 and 3. Table 2 The 30-year average rainfall data and simulated ones by AOGCMs in Shahrekord

station in mm

Month Observed CSIRO CGCM2 CCSR HadCM3

Jan 60.93 10.99 25.96 30.80 42.96 Feb 50.33 19.35 32.43 43.48 47.08 Mar 56.93 26.10 70.38 53.54 44.71 Apr 42.81 14.09 94.64 62.79 36.98 May 14.58 5.96 65.06 27.47 22.67 Jun 0.66 3.12 25.30 22.97 7.65 Jul 1.84 2.96 1.30 15.69 3.01 Aug 0.48 8.27 0.56 5.84 1.72 Sep 0.00 5.60 0.23 2.61 3.32 Oct 8.72 2.89 2.91 3.22 21.18 Nov 32.85 4.10 34.48 34.74 42.04 Dec 59.93 5.00 30.23 33.04 44.37

Table 3 Thirty-year average temperature data and simulated ones by AOGCMs in Shahrekord station in Centigrade

Month Observed CSIRO CGCM2 CCSR HADCM3

Jan –1.70 9.28 –4.47 0.42 –0.20 Feb 1.11 13.05 –1.93 4.20 1.51 Mar 6.32 19.25 3.78 8.76 4.87 Apr 11.30 25.56 9.71 13.81 8.82 May 16.24 30.42 14.89 18.94 13.77 Jun 21.11 34.08 22.44 23.49 20..22 Jul 24.22 36.80 27.09 25.22 23.38 Aug 23.32 36.14 26.42 23.63 22.40 Sep 19.02 30.79 20.15 19.08 16.97 Oct 13.39 23.07 11.22 13.59 10.90 Nov 7.62 15.60 4.29 7.55 4.20 Dec 2.06 9.81 –1.53 1.45 0.47

To examine and compare the AOGCM simulation results with the observed data in Shahrekord station, temperature and rainfall graphs have been extracted. Figures 3 and 4 exhibit the observed and modelled rainfall and temperature data, respectively.


Figure 3 Comparing 30-year average rainfall data with the simulated ones from 1961 to 1990

Figure 4 Comparing 30-year average temperature data with the simulated ones from 1961 to 1990

The analysis of temperature parameter indicated a low precision in CSIRO model and the best precision in output of HADCM3, CCSR and CGCM2, respectively. The rainfall parameter has a similar trend.

In this stage, for quantifying the results, the function criteria are done. Utilising the indices of RMSE, RSQ and MAE shows the model precisions in estimation of the parameters. The results are presented in Table 4.

Error percentages of rainfall and temperature parameters are presented in Figures 5 and 6, respectively. The HADCM3 model has the lowest error in estimate of the parameters in all of the months and comparing with the other models, HADCM3 has positively significant difference in rainfall estimates. Also, the results of CSIRO and CGCM2 have the appropriate precisions.

Considering function criteria results (Table 4) and error percentage of various models (Figures 5 and 6), concludes the HADCM3 model outputs have the appreciate correlation and a lower error than the other models in this research. Figures 4 and 5 display the differences between 30-year average data simulated by the HADCM3 model and observed data in Shahrekord synoptic station.


Table 4 Function criteria of various AOGCMs comparing with the observed values

Rainfall Temperature GCMs

RMSE RSQ MAE RMSE RSQ MAE CSIRO 7.71 0.33 5.89 11.84 0.96 11.66 CCSR 28.94 0.60 24.00 1.85 0.98 1.46 HADCM3 26.85 0.94 22.46 1.91 0.98 1.71 CGCM2 39.37 0.31 27.94 2.54 0.99 2.40

Figure 5 Error percentages of the simulated rainfall data by various AOGCMs in Shahrekord station

Figure 6 Error percentages of the simulated temperature data by various AOGCMs in Shahrekord station


In order to investigate the models for future parameter prediction, at first the downscaled parameters are collected from CRU institute and the changes of these parameters are compared in three future periods under various models. Secondly, the A2 scenario is chosen as the most critical status and then regarding the used methods by CRU institute, they are the same for all of the models, the differences of climatic parameter changes are because of the AOGCM models. The final results are demonstrated in Figures 7 and 8.

Figure 7 Average changes of monthly temperature in three periods of the future compared with the past period for the AOGCM models


Figure 8 Total changes of monthly rainfalls in three periods of the future compared with the past period for the AOGCM models

In this section, using the results of the first section, the HADCM3 model is chosen as the reference. As the Figures 7, 8 and Table 5 show, the models have differences in the parameter estimations. The differences are being more and more in the near future (2011–2039) and in the middle future (2040–2069); and the most differences are in the far future (2070–2099). Generally, CGCM2 has a lower difference and the CSIRO model has more differences than this model. The maximum difference is 0.83 centigrade in temperature parameter and 6.73 millimetre in rainfall parameter for the future period in


the estimations related to CSIRO and HADCM3, respectively. The results show that the CCSR model is the second proper model, however, its reliability is less comparing to HadCM3 especially in rainfall modelling. CSIRO and CGCM2 have the least correlation to daily rainfall because the climate sensitivity of them (particularly CSRIO) is at the upper end of the range of model sensitivities. Table 5 Average of annual changes in CGCM2 and CSIRO estimations compared with

HADCM3 estimation

Precipitation (mm) Temperature (°C)

CGCM CSIRO CGCM CSIRO

2020s 1.79 2.24 0.16 0.25

2050s 3.43 4.13 0.33 0.48

2080s 5.72 6.73 0.57 0.83

4 Conclusions

The present study is an attempt to find the best AOGCM model in downscaling climatic parameters for Northern Karoon, Iran.

HadCM3 presented the best prediction of climate variability and change, as similar results presented by Wibly and Harris (2006) and they presented most of the climate change modelling by HadCM3 in different regions. Also, these study’s results are similar to Cook et al. (2008) outputs, which show that HadCM3 is more accurate in capturing of climate variable changes compared with the other GCMs.

The main conclusions are presented as follows: The HadCM3 model as a selective model for downscaling temperature and rainfall

climatic parameters in Northern Karoon region is chosen and could be introduced for other nearby stations.

Temperature predicted data have a better correlation with the observed in comparison to rainfall data. It means that temperature comparing with the rainfall has a less variability than the latter and could be assumed as a normal distributed parameter. The reason for decreasing correlation in the rainfall data is that many factors are affective on the rainfall parameter as a discrete distributed parameter in a watershed. Much research is needed to find a solution for increasing the rainfall correlation in developing the climate change models in the future.

Historical data accuracy has an important role in climate change studies. Otherwise, the simulation of climatic impacts will have more uncertainty compare to GCM data.

Uncertainty source is usually existed in the future period estimations because of the AOGCM model calculations and whereas these differences are being more and more with passing time, the maximum differences will be in the far future.

Due to the above mentioned problems, the uncertainty analysis for AOGCMs should be done in climate change studies.

The performance of civil, economic and social plans requires the recognising the climatic changes to gain reasonable schedules. Therefore, those necessities in any scales relating to the schematisation from small scale such as a town to massive ones such as a nation are completely vital.


Acknowledgements

The authors sincerely thank anonymous reviewers for reviewing the manuscript and providing critical comments to improve the paper. Special thanks are given to Iranian Meteorological Organization and Water Resources Organization of Iran that helped us in providing data for this study.

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An appropriate general circulation model (GCM) to investigate climate change impact

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