(RegCM) and Georgia

heehawultraMechanics

Feb 22, 2014 (3 years and 5 months ago)

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Reg
ional
C
limatic
M
odel
(
RegCM
) and
G
eorgia

Bagrat

Kikvadze

a_kikvadze@yahoo.com



PhD Student of Geography

Ivane

Javakhishvili

Tbilisi State University, Georgia


There

is

a

general

agreement

that

intensification

of

global

warming

will

increase

the

frequency

and

intensity

of

extreme

weather

and

climate

events

.

Over

the

last

decades

significant

changes

were

observed

in

Georgia
:

rise

of

average

temperature,

changes

in

desertification

and

redistribution

of

precipitation,

reduction

in

glaciers,

sea

level

rise,

variations

in

river

sedimentation

rates
.

Moreover,

against

the

background

of

climate

changes

extreme

climate

phenomena

have

become

more

frequent
:

droughts,

strong

winds,

torrential

rains,

floods

as

well

as

extreme

temperatures

and

other

phenomena

which

significantly

affect

agriculture,

economy,

health

of

the

population

and

even

the

security

of

the

country
.

Thus,

predicting

extreme

events

is

profoundly

important

for

the

regional

stability
.


Introduction

July

2012

i
n

Telavi

during

3

hours

-

72

mm

precipitation,

more

than

one

month

climatological

norm
.

In

May

2012

in

Tbilisi

90

mm

precipitation

in

less

than

one

day

.

The

work

in

using

RegCM
-
outputs

for

the

predictions

of

extreme

weather

and

climate

events

over

the

Georgia

has

already

been

started
.

Within

the

framework

of

guidelines

for

the

preparation

of

the

second

national

communication

to

the

United

Nations

Framework

Convention

on

Climate

Change

(on

the

web

at
:

http
:
//www
.
global
-
issues
-
rtd
.
info/programmes/
2177
.
html
)

various

future

climate

scenarios

for

socio
-
economic

development

of

Georgia

were

constructed
.

The

RegCM

-

PRECIS

(
P
roviding

Re
gional

C
limates

for

I
mpacts

S
tudies)

was

used

with

model

domain

and

boundary

conditions

defined

by

the

scientists

from

the

Hadley

Centre,

UK

using

two

different

global

models

(HadAM
3
P

and

ECHAM

4
)
.

Together

with

othefuture

forecasts

(
1961
-
1990
),

as

well

as

two

future

runs

(
2020
-
2050

and

2070
-
2100
)

for

IPCC

A
1
,

A
2

and

r

South

Caucasus

countries

several

runs

were

carried

out

for

the

baseline

period

used

to

correct

B
1
,

B
2

(
IPCC
,

2007
)

climate

scenarios
.

Based

on

these

simulations,

future

changes

in

average

values

of

major

climatic

parameters

were

estimated

over

the

territory

of

Georgia
.

In

addition

to

this,

under

the

scope

of

the

project

“Study

and

Modelling

of

Extreme

climate

Events

in

Georgia

by

Reg
ional

C
limate

M
odel

(
RegCM
)”

(funded

by

Shota

Rustaveli

National

Science

Foundation)

Dr
..
Elizbarashvili

has

visited

North

Carolina

State

University

(NCSU)
.

Within

3

months

(January

to

April,

2012
)

the

project

team

was

able

to

transfer

the

RegCM

model

from

the

Abdus

Salam

International

Centre

for

Theoretical

Physics

(ICTP),

Trieste,

Italy

to

Dr
.

Meskhidze’s

machine

at

High

Performance

Computing

(HPC)

system

at

NCSU
.

The

research

team

was

able

to

successfully

compile

and

run

the

code
.

The

team

created

spatial

distribution

maps

for

various

types

of

extreme

indices

including

five

temperature
-
based

indices

and

five

precipitation

based

indices,

using

model

outputs,

for

the

Georgia

and

Caucasus

region
.

T
he

aim

of

our

study

was

to

make

simulation

of

RegCM

and

analyze

model
-
predicted

distribution

of

extreme

precipitation

indices

over

Georgia

Territory
.


Climatic

peculiarities

in

Georgia

are

largely

conditioned

by

the

Greater

Caucasus

mountain

range

to

the

north

and

the

Black

Sea

to

the

west
.

The

Greater

Caucasus

range

serves

as

a

barrier

against

cold

air

from

the

north
.

Warm,

moist

air

from

the

Black

Sea

moves

easily

into

the

coastal

lowlands

from

the

west
.

Climatic

zones

are

determined

by

distance

from

the

Black

Sea

and

by

altitude
.

The

Lesser

Caucasus

range

runs

parallel

to

the

Turkish

and

Armenian

borders
.

The

Likhi

Range

stretching

from

the

north

to

the

south

connecting

the

Greater

Caucasus

and

the

Lesser

Caucasus

mountains

divides

the

country

into

two

distinct

climatic

zones

-

humid

subtropical

west

and

continental

east
.

Georgia

The Black Sea

The Black Sea

Number of days with precipitation above 30 mm

Climate and Agro
-
Climatic Atlas of Georgia, 2011

Number of days with precipitation above 10 mm

Climate and Agro
-
Climatic Atlas of Georgia, 2011

Number

of

days

with

precipitation



0
.
1

mm




Climate and Agro
-
Climatic Atlas of Georgia, 2011

Model and Method



We

have

applied

the

Abdus

Salam

International

Center

for

Theoretical

Physics

Regional

Climate

Model

Version
4
.
1

-

RegCM
4
.
1
.




For

the

model

simulation

the

Lambert

Conformal

projection

was

chosen
.

We

defined

central

latitude

and

central

longitude

of

model

domain

clat
=
42
.
00
,

clon

=
43
.
5

degrees

as

well

as

34

number

of

points

in

the

N/S

direction

and

48

number

of

points

in

the

E/W

direction

and

18

vertical

levels
.




The

domain

used

in

current

simulations

includes

Georgia,

Part

of

Armenia,

Azerbaijan,

Turkey

and

Russia
.



The

model

simulation

has

been

carried

out

for

1982

-

1996

time

period

with

the

minimal

horizontal

resolution

of

20

km
.




The

run

was

conducted

using

reanalysis

data

(ERA
40
)
.

ERA
-
40

is

the

European

Centre

for

Medium
-
Range

Weather

Forecasts

(
ECMWF)

re
-
analysis

of

the

global

atmosphere

and

surface

conditions

for

45
-
years,

over

the

period

from

September

1957

through

August

2002

available

at

2
.
5
°

x

2
.
5
°

(latitude
-
longitude)

and

23

pressure

levels
.

For

a

sea

surface

temperature

(SST)

the

optimum

interpolation

of

sea

surface

temperature

(OISST)

was

used,

which

is

available

weekly

on

a

1
.
0
°

x

1
.
0
°

grid
.

All

these

data

were

downloaded

from

web

page
:


http
:
//users
.
ictp
.
it/~pubregcm/RegCM
4
/globedat
.
htm





To

investigate

the

characteristics

of

precipitation

extremes

over

the

Georgian

territory,

we

consider

frequency,

intensity,

and

duration

properties
.

Table

presents

indices

used

in

this

study
.

All

these

indices

are

calculated

as

annual

values

for

the

years

examined

in

this

study
.














Extreme

Climate Indices

Variable

Abbreviation
(
unit
)

Definition of extreme indices

Temperature

FD (days)

Number of frost days (with T min below 0
°
)

HD (days)

Number of hot days (with T max above 30
°
)

HW (days)

Maximum duration of consecutive hot days

Precipitation

PN80 (days)

Number

of

days

with

precipitation

above

80

mm

intensity

PX1D (mm)

Greatest 1
-
day total precipitation

MDRY (days)

Maximum duration of consecutive dry days

MWET (days)

Maximum duration of consecutive wet days

Extreme

Climate Indices

Variable

Abbreviation
(
unit
)

Definition of extreme indices

Temperature

FD (days)

Number of frost days (with T min below 0
°
)

HD (days)

Number of hot days (with T max above 30
°
)

HW (days)

Maximum duration of consecutive hot days

Precipitation

PN80 (days)

Number

of

days

with

precipitation

above

80

mm

intensity

PX1D (mm)

Greatest 1
-
day total precipitation

MDRY (days)

Maximum duration of consecutive dry days

MWET (days)

Maximum duration of consecutive wet days

Extreme

Climate Indices

Variable

Abbreviation
(
unit
)

Definition of extreme indices

Temperature

FD (days)

Number of frost days (with T min below 0
°
)

HD (days)

Number of hot days (with T max above 30
°
)

HW (days)

Maximum duration of consecutive hot days

Precipitation

PN80 (days)

Number

of

days

with

precipitation

above

80

mm

intensity

PX1D (mm)

Greatest 1
-
day total precipitation

MDRY (days)

Maximum duration of consecutive dry days

MWET (days)

Maximum duration of consecutive wet days

Variable

Abbreviation
(unit)

Definition of extreme indices

Precipitation

PN80 (days)

Number

of

days

with

precipitation

above

80

mm

intensity

PX1D (mm)

Greatest 1
-
day total precipitation

MDRY (days)

Maximum duration of consecutive dry days,
precipitation ≤ 0.1mm

MWET (days)

Maximum duration of consecutive wet days,
precipitation ≥ 0.1mm

Extreme Precipitation Indices


Computer

codes

were

used

(written

in

MATLAB

language)

for

calculation

of

the

4

indices
.

Codes

were

developed

during

Elizbarashvili’s

visit

at

NCSU
.

Spatial

distribution

maps

were

created

for

various

types

of

extreme

indices

including

four

precipitation

based

indices,

using

model

outputs,

for

the

Georgia

and

Caucasus

region
.


Abbreviation

and

definition

of

the

indices




Model
-
predicted

distribution

of

number

of

PN
8
0

(days)
,

PX
1
D

(mm)

PX
1
D

(mm)

MDRY

(days)

MWET

(days)

for

1982
-
1996

time

period
.

Star

indicates

Georgian

capital,

thin

black

line

depicts

country

borders,

while

thick

black

line

shows

the

coastline
.

PN80 (days)




Results


Model
-
predicted distribution of
number of days with precipitation
above 80 mm intensity for 1982
-
1996
time period.

Model
-
predicted distribution of g
reatest
1
-
day total precipitation

for 1982
-
1996 time period

Th
e

highest

values

of

PN
80

concentrated

over

the

Caucasus

Mountains

and

near

the

Black

Sea

coast
.

Due

to

the

lack

of

measurement

stations

it

is

hard

to

verify

such

high

values

of

PN
80

over

the

mountains
.

However,

local

meteorological

observations

suggest

that

with

yearly

precipitation

of

2
,
500

mm,

Batumi

is

a

place

with

highest

precipitation

throughout

the

Georgia
.


Model
-
predicted distribution of
number of MWET days for
1982
-
1996 time period.

Model
-
predicted distribution of
number of MDRY days for
1982
-
1996 time period.

As

expected,

the

highest

values

for

the

maximum

duration

of

consecutive

dry

days

are

found

in

the

eastern

part

of

Georgia,

increasing

towards

Azerbaijan,

while

the

lowest

values

are

found

over

the

Caucasus

Mountains
.

The

spatial

distribution

map

of

MDRY

days

is

also

in

a

good

agreement

with

physical

geography

of

the

country
.

Conclusions and Future Work


Although

some

values

are

very

high

and

needs

corrections,

the

model

captures

well

the

influences

of

the

Caucasus

Mountains

and

the

Black

Sea

on

distribution

of

extreme

precipitation

events

over

the

Georgian

territory
.




In

the

future

by

using

RegCM
4

it

is

possible

to

investigate

the

effects

of

extreme

climate

and

weather

events

on

Georgian

Agricultural

sector,

with

particular

emphasis

on

viticulture
.


References



Filippo

Giorgi
,

Nellie

Elguindi
,

Stefano

Cozzini

and

Graziano

Giuliani
.

Regional

Climatic

Model

RegCM

User’s

Guide

Version

4
.
3
.

Trieste,

Italy

p
.
62
,

2011


.



Elizbarashvili

M
.
,

Meskhidze

N
.
,

Gantt

B
.
,

Mikava

D
.

Model

Simulation

Study

of

Temperature

and

Precipitation

Extremes

in

Georgia,

International

Multidisciplinary

Scientific

GeoConference

SGEM

2012
,

“Modern

Managment

of

Mine

Producing,

Geology

and

Environmental

Protection”

Conference

Proceedings,

Volume

IV,

355
-
362
,

2012
.




Climate

and

Agro
-
Climatic

Atlas

of

Georgia,

Institute

of

Hydrometeorology

at

the

Georgian

Technical

University,

p
.
120
,

2011
.



E
.

S
.

Im
,

I
.

W
.

Jung

and

D
.

H
.

Bae
.

The

temporal

and

spatial

structures

of

recent

and

future

trends

in

extreme

indices

over

Korea

from

a

regional

climate

Projection
.

INTERNATIONAL

JOURNAL

OF

CLIMATOLOGY
.

Int
.

J
.

Climatol
.

31
:

72

86
,

2011
.



Thank you for your attention!