Volume 3, Issue 1 (4-2016)                   Jsaeh 2016, 3(1): 1-14 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

molodi G, khorani A, moradi A. Impacts of climate change on heat waves in northern coast of Persian Gulf. Jsaeh. 2016; 3 (1) :1-14
URL: http://jsaeh.khu.ac.ir/article-1-2541-en.html
1- MA in spatial planning
2- Associate Professor of climatology , khoorani@hormozgan.ac.ir
3- Assistant Professor of geomorphology
Abstract:   (1761 Views)

Climate change is one of the most significant threats facing the world today. One of the most important consequences of climate change is increasing frequency of climate hazards, mainly heat waves. This phenomena has a robust impacts on human and other ecosystems. The aim of this study is investigating changes of heat waves in historical (1980-2014) and projected (2040-2074) data in northern cost of Persian Gulf.

The focus here is on Mean daily maximum temperature and Fujibe index to extract heat waves. For this purpose 6 weather stations locating in north coast of Persian Gulf, Iran, are used (table 1).

Table1: weather stations

Station

Latitude

Longitude

Elevation(m)

Abadan

30° 22' N

48° 20' E

6.6

Boushehr

28° 55' N

50° 55' E

9

Bandarabbas

27° 15' N

56° 15' E

9.8

Bandarlengeh

26° 35' N

54° 58' E

22.7

Kish

26° 54' N

53° 54' E

30

  In addition, 4 model ensemble outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves (2040 to 2070), under Representative Concentration Pathways 8.5 (RCP8.5), adopted by the Intergovernmental Panel on Climate Change for its Fifth Assessment Report (AR5) (table 2).

Table2: List of the AR5 CMIP5 Used Models

Model

Modeling Cener

Country

CanESM2

Canadian Earth System Model

Canada

MPI-ESM-MR

Max-Planck-Institut für Meteorologie

Germany

CSIRO-Mk3-6-0

Commonwealth Scientific and Industrial Research Organization

Australia

CMCC-CESM        

CMCC Carbon Earth System Model

Italy

The output of models is downscaled using artificial neural network method (ANN). A feed-forward network of multi-layer perceptron with an input layer, a hidden layer and an output layer is used for this purpose. 73 percent (1980 – 2000) of the data is used for training and 27 percent (2000-2005) for testing ANN models. Root Mean Square Error (RMSE) is used as an indicator of the accuracy of Models.

RMSE=AWT IMAGE

Here  AWT IMAGE is the outputs of ANN models (downscaled data) and AWT IMAGEis the observation data.

Fujibe et all (2007) used an index based on Normalized Thermal Deviation (NTD) for extracting long-term changes of temperature extremes and day to day variability using following equations:

AWT IMAGE

Where N is the number of days in the summation except missing values. Then nine-day running average was applied three times in order to filter out day-to-day irregularities.

AWT IMAGE=(i,j,n)=T(i,j,n)-T(I,j)

The departure from the climatic mean is given by

AWT IMAGE=AWT IMAGE

AWT IMAGE

If NTD >2 and at least lasts for 2 days it determine as a heat wave.

Results

Table 3 shows the results of downscaling selected GCM models.

nodes

RMSE

Average RMSE

Sigmoid function

Linear function

Abadan

Bushehr

Bandarabbas

Bandar-e-Lengeh

Kish

CanESM2

5

1

9.6

6.1

4.85

4.7

4.5

5.97

MPI-ESM-MR

5

1

9.3

7.1

3.9

5

4.3

5.9

CSIRO-MK3-6-0

15

1

8.8

5.6

3.6

3.4

3.6

5

CMCC-CESM

10

1

9.2

5.8

3.9

4.7

3.9

5.5

Table 4 compares the frequency of heat waves for GCMs and historical data.

CanESM2

MPI-ESM-MR

CSIRO-Mk3-6-0

CMCC-CESM

Historical data

Abadan

434

401

448

387

430

Bushehr

376

423

420

406

407

Bandarabbas

441

405

457

382

410

Bandar-e-Lengeh

380

414

388

401

400

Kish

421

442

415

442

399

For historical data, heat waves are more frequent in Abadan station than other stations. There is an increasing trend in the occurrence of heat waves in historical data and monthly frequency of heat waves show the highest amounts for summer.

For both historical and future data 2 days listening heat waves are more frequent.

Table 5 shows seasonal changes of heat waves for historical data and GCMs.

season

The ratio of heat waves from total historical data (percent)

The ratio of heat waves from total projected data (percent)

Abadan

Spring

30.43

24.02

Summer

29.19

27.87

Autumn

17.39

22.61

Winter

22.98

25.48

Bushehr

Spring

21.42

24.23

Summer

25

26.21

Autumn

28.57

24.82

Winter

24

25.32

Bandarabbas

Spring

21.73

24.7

Summer

26.81

27.01

Autumn

25.81

25.17

Winter

24.1

24.63

Bandar-e-Lengeh

Spring

23.55

23.74

Summer

23.33

29.82

Autumn

23.74

25.81

Winter

25.17

20.8

Kish

Spring

24.27

24.8

Summer

25.53      

28.32

Autumn

23.35

25.21

Winter

23.1

23.8

In recent years the frequency of heat waves is increasing in all studied stations. Coincide with Russia and Europe, the highest amounts of heat waves is occurred in 2010 in northern coast of Persian Gulf and this is adopted Esmaeilnezhad et all (2013), Gavidel (2015) and Azizi (2011).

Full-Text [PDF 888 kb]   (585 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/10/18 | Accepted: 2016/10/18 | Published: 2016/10/18

Add your comments about this article : Your username or Email:
CAPTCHA code

Send email to the article author


© 2018 All Rights Reserved | Journal of Spatial Analysis Environmental hazarts

Designed & Developed by : Yektaweb