interannual to decadal variabilities of the maritime continent monsoon from modeling and observation...
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Interannual to decadal variabilities Interannual to decadal variabilities of the maritime continent of the maritime continent
monsoon from modeling and monsoon from modeling and observation perspective observation perspective
Edvin AldrianEdvin AldrianAgency for the Assessment and Application of Technology (BPPT), Agency for the Assessment and Application of Technology (BPPT),
IndonesiaIndonesiaMarine Study Program, University of Indonesia, JakartaMarine Study Program, University of Indonesia, Jakarta
Email: Email: [email protected]@webmail.bppt.go.idhttp://geocities.com/e_aldrianhttp://geocities.com/e_aldrian
International Workshop on Asian Monsoon Year 2008, Beijing 23-25 April 2007International Workshop on Asian Monsoon Year 2008, Beijing 23-25 April 2007
Climatology of regional Climatology of regional rainfallrainfall
► Mainly monsoonalMainly monsoonal► Three distinct rainfall climate Three distinct rainfall climate
regionsregions
monsoonalmonsoonal
Semi-monsoonalSemi-monsoonal Anti-monsoonalAnti-monsoonal
Aldrian and Susanto, 2003, Intl J Climatol.
Interannual variability in Region AInterannual variability in Region A
Aldrian et al, 2007, Theo. Appl. Climatol.
Interannual variability in Region BInterannual variability in Region B
Aldrian et al, 2007, Theo. Appl. Climatol.
Interannual variability in Region CInterannual variability in Region C
Aldrian et al, 2007, Theo. Appl. Climatol.
Wide range SST responses to rainfall in Wide range SST responses to rainfall in region Aregion A
• Weak response in spring, no ENSO influenceWeak response in spring, no ENSO influence• Strong two dipoles in SON (Walker cell)Strong two dipoles in SON (Walker cell)• Role of SPCZ in SONRole of SPCZ in SON
Aldrian and Susanto, 2003, Intl J Climatol.
Wide range SST responses to rainfall in Wide range SST responses to rainfall in region Bregion B
• Weak response in all season, especially in springWeak response in all season, especially in spring• no ENSO influence and walker cellno ENSO influence and walker cell
Aldrian and Susanto, 2003, Intl J Climatol.
Wide range SST responses to rainfall in Wide range SST responses to rainfall in region Cregion C
• Weak response in springWeak response in spring• Strong two dipoles in SON (Walker cell) like region AStrong two dipoles in SON (Walker cell) like region A• Role of SPCZ in SON like in region ARole of SPCZ in SON like in region A
Aldrian and Susanto, 2003, Intl J Climatol.
Skills of monthly rainfall Skills of monthly rainfall variabilityvariability
Response to ENSO Response to ENSO
Variability in Variability in comparison to comparison to observationsobservations(correlation (correlation values)values)
•Negative Negative responses to responses to NINO3 SSTNINO3 SST•Significant in Significant in MJJASMJJAS(similar to Hendon, (similar to Hendon, 2003)2003)
•Strong Strong responses in responses in Region A and CRegion A and C•Spring is the Spring is the least responsive least responsive seasonseason•ECHAM4 ECHAM4 responds well to responds well to ENSOENSO
Aldrian et al, 2007, Theo. Appl. Climatol.
Several finished and on going Several finished and on going applications of climate modelingapplications of climate modeling►Rainfall simulation (dissertation project)Rainfall simulation (dissertation project)►Forest Fire smoke distribution (INSIDE Forest Fire smoke distribution (INSIDE
project). project). An EU Asia ProEco Project with a collaboration An EU Asia ProEco Project with a collaboration
between BPPT, MPI and Univ. Cambridgebetween BPPT, MPI and Univ. Cambridge
►Local ocean model simulation (LITHMOS Local ocean model simulation (LITHMOS project)project) A proposed DFG Project with a collaboration A proposed DFG Project with a collaboration
between BPPT, IfM Uni Hamburg and Uni Syiah between BPPT, IfM Uni Hamburg and Uni Syiah Kuala of Aceh IndonesiaKuala of Aceh Indonesia
The atmosphere and ocean The atmosphere and ocean modelmodel► REMO ECHAM PhysicsREMO ECHAM Physics
► allows only one surface type in each allows only one surface type in each grid cellgrid cell
► 0.50 : 101x55 grid cells for entire 0.50 : 101x55 grid cells for entire Indonesia, Indonesia,
► 1/60 : 73x61 grid cell over Sulawesi1/60 : 73x61 grid cell over Sulawesi► 20 layers20 layers► boundary forcing every 6 hr:boundary forcing every 6 hr:► ERA15 1979-1993, ERA15 1979-1993, ► NCEP/NCAR 1979-1993, NCEP/NCAR 1979-1993, ► ECHAM4 1979-1988ECHAM4 1979-1988► Higher resolution product Higher resolution product ► allow a detailed investigation allow a detailed investigation ► of five large islandsof five large islands► and three sea regionsand three sea regions
•MPI OM1 MPI OM1 •Using a conformal grid system with desired pole Using a conformal grid system with desired pole locationslocations• `North` pole: 112E 29N `North` pole: 112E 29N • `South` pole: 132E 22S`South` pole: 132E 22S • Two model resolutions: 182x105 and 362x210Two model resolutions: 182x105 and 362x210 • 20 layers for low resolution, 30 layers for high 20 layers for low resolution, 30 layers for high resolutionresolution• highest resolution over the Maritime Continenthighest resolution over the Maritime Continent• boundary forcings every 6 hr:boundary forcings every 6 hr: ERA15 1979-1993, ERA15 1979-1993, NCEP/NCAR 1948-1998,NCEP/NCAR 1948-1998, OMIP climatologyOMIP climatology
Improvement by higher resolution with Regional Improvement by higher resolution with Regional ModelModel
These two examples are taken from a normal or non-ENSO yearThese two examples are taken from a normal or non-ENSO year Aldrian et al, 2004, Clim Dyn.
Rainfall Predictability by Regional Rainfall Predictability by Regional ModelModel
Ensemble runs with REMO-ERA and REMO-ECHAM: 6 members with 12 hr diff. in initial conditionEnsemble runs with REMO-ERA and REMO-ECHAM: 6 members with 12 hr diff. in initial condition
RMSRMSErrorErroragainstagainstcontrolcontrolrunrun
Aldrian et al, 2004, Clim Dyn.
Coupling mechanismCoupling mechanism
The coupling is performed without flux correctionsThe coupling is performed without flux correctionsand performed only inside REMO domainand performed only inside REMO domain Aldrian et al, 2005, Clim Dyn.
Improvement by Improvement by couplingcoupling
uncoupleduncoupled coupledcoupledJava Java
Southern South China Sea Southern South China Sea
Molucca Sea Molucca Sea
Aldrian et al, 2005, Clim Dyn.
Local ocean aspect of Local ocean aspect of the three climate the three climate
regionsregions
122E-135E,6S-2N
0
2
4
6
8
10
12
14
16
18
20
25 26 27 28 29 30 31
Local SST (C)
Rai
nfa
ll (m
m/d
ay)
January
February
March
April
May
June
July
August
September
October
November
December
120E-135E,15S-5S
0
2
4
6
8
10
12
14
16
18
20
25 26 27 28 29 30 31Local SST (C)
Rai
nfa
ll (
mm
/day
)
January
February
March
April
May
June
July
August
September
October
November
December
102E-110E,1.5S-8N
0
2
4
6
8
10
12
14
16
18
20
25 26 27 28 29 30 31Local SST (C)
Ra
infa
ll (
mm
/da
y)
January
February
March
April
May
June
July
August
September
October
November
December
Similar results to Bony Similar results to Bony et alet al, 1997a,b, Lau , 1997a,b, Lau et alet al, 1997, 1997
Role of ocean circulation in driving the Role of ocean circulation in driving the rainfall characteristics of all three rainfall characteristics of all three
regionsregions
dxdzTvdydzTucdxdyQdt
dxdydzzyxcT
dt
dEsurf
tT ),,(15 yr Correlations North 15 yr Correlations North
MoluccaMolucca•Horz adv – heat:0.207Horz adv – heat:0.207•Heat – SST : 0.394Heat – SST : 0.394•Horz adv – SST: 0.194Horz adv – SST: 0.194
15 yr Correlations SSCS15 yr Correlations SSCS•Horz adv – heat:0.613Horz adv – heat:0.613•Heat – SST : 0.711Heat – SST : 0.711•Horz adv – SST: 0.842Horz adv – SST: 0.842
Using a heat budget calculationUsing a heat budget calculation
► An EU Asia ProEco programAn EU Asia ProEco program► The INSIDE project aims to The INSIDE project aims to
determine the amount and determine the amount and distribution of smoke haze distribution of smoke haze in Indonesia and the in Indonesia and the adjacent countries adjacent countries generated from vegetation generated from vegetation and peat fires, and the and peat fires, and the related implications for related implications for human health (e.g. human health (e.g. respiratory diseases) and respiratory diseases) and climate (droughts, floods, climate (droughts, floods, aerosol-cloud interactions, aerosol-cloud interactions, CO2 release). The main CO2 release). The main goal of the project is to goal of the project is to provide, optimize and apply provide, optimize and apply a regional model tool for a regional model tool for Indonesia. Due to the Indonesia. Due to the sparse air quality sparse air quality monitoring in Indonesia our monitoring in Indonesia our initiative with the country-initiative with the country-wide determination of wide determination of ambient air quality offers ambient air quality offers guide to local decision guide to local decision makersmakers
The INSIDE ProjectThe INSIDE Project
Examples of modeling Examples of modeling resultresult
K u ch in g
0
20 0
40 0
60 0
80 0
10 001.7
15.7
29.7
12.8
26.8
9.9
23.9
7.10
21.10
4.11
18.11
2.12
16.12
30.12
TPM
in μ
g/m
3
Peta ling Jaya
0
100
200
300
400
500
1.7
15.7
29.7
12.8
26.8
9.9
23.9
7.10
21.10
4.11
18.11
2.12
16.12
30.12
PM10
in μ
g/m
3
P o n t ia n a k
0
4 0 0
8 0 0
1 2 0 0
1 6 0 0
2 0 0 0
1.7
15.7
29.7
12.8
26.8
9.9
23.9
7.10
21.10
4.11
18.11
2.12
16.12
30.12
TPM
in μ
g/m
3
O b s e rv a t io n E X P _ R E F E X P _ N O P E A T E X P _ M E T 9 6
Pa
rtic
le C
on
ce
ntr
ati
on
in
μg
/m3
Distribution of PM10 concentration
Comparison of observed and simulated particle concentration over Kuching (northwest Borneo) and Petaling Jaya (Malaysian Peninsula)
Heil, Langmann, Aldrian, 2007 Mit. Adapt. Stra. Global Change
The LITHMOS ProjectThe LITHMOS Project► A DFG proposed projectA DFG proposed project► Simulate local ocean Simulate local ocean
with boundary forcing with boundary forcing from the coupled ocean from the coupled ocean atmosphere modelatmosphere model
► Use forcing from NCEP Use forcing from NCEP or ERA40or ERA40
► Use observed deep Use observed deep ocean data from the ocean data from the INSTANT project (2004-INSTANT project (2004-2006) of 11 mooring 2006) of 11 mooring locationslocations
Climate trend of rainfall in Climate trend of rainfall in IndonesiaIndonesia
► Investigation from 1960 to 1998 in 63 Investigation from 1960 to 1998 in 63 major stationsmajor stations
No Station name
Period
n
Mann-Kendall trend Sen's slope estimate location
from to Test Z Signific Q B longitude latitude
1 Banda Aceh 1952 1997 44 -1,884 + -4,88 1569,7 95,43 5,52
2 Meulaboh 1953 1997 43 -3,473 *** -26,17 3630,5 96,10 4,20
3 Medan 1950 1997 48 -0,462 -1,27 2197,7 98,48 3,57
4 Sibolga 1953 1997 45 -2,847 ** -24,25 4610,1 98,92 1,57
5 Padang 1950 1997 48 -1,449 -14,48 4297,0 100,35 -0,88
6 Pekanbaru 1953 1997 45 -1,722 + -9,47 2745,8 101,43 0,47
7 Tanjung Pinang 1951 1997 47 -1,843 + -13,41 3132,6 104,50 0,90
8 Jambi 1952 1997 46 -1,988 * -11,92 2481,6 103,70 -1,60
9 Bengkulu 1968 1997 30 -4,229 *** -71,79 5450,7 102,33 -3,88
10 Palembang 1950 1997 48 -1,120 -6,02 2582,7 104,70 -2,90
11 Rejosari 1951 1997 47 -1,229 -7,43 2139,3 105,11 -5,15
12 Pangkal Pinang 1951 1997 47 -4,347 *** -25,24 3070,7 106,13 -2,17
13 Tanjung Pandan 1950 1997 48 -2,498 * -17,73 3458,7 107,80 -2,80
14 Pontianak 1950 1997 48 -3,173 ** -14,38 3375,9 109,40 -0,10
15 Ketapang 1950 1997 48 -4,204 *** -29,71 3459,0 110,00 -1,90
16 Pangkalan Bun 1951 1997 47 -2,815 ** -18,16 3215,7 111,70 -2,70
17 Nangahpinoh 1951 1997 47 -2,889 ** -20,40 3551,2 111,70 -0,40
18 Muaratewe 1951 1997 47 -3,852 *** -30,30 3845,5 114,80 -0,40
19 Banjarmasin 1951 1997 47 -2,394 * -15,56 2911,0 114,80 -3,40
20 Balikpapan 1950 1997 48 -2,302 * -12,41 2827,2 116,90 -1,30
21 Tarakan 1950 1997 48 -2,409 * -18,24 3877,0 117,57 3,33
22 Palu 1953 1997 45 -1,468 -3,58 783,3 119,88 -0,90
23 Gorontalo 1961 1997 37 -0,392 -1,99 1192,4 123,10 0,50
24 Manado 1950 1997 48 -1,262 -9,85 3226,1 124,90 1,50
25 Luwuk 1960 1997 38 -1,358 -4,12 1184,9 122,70 -0,90
26 Poso 1974 1997 22 -0,769 -17,99 2607,8 120,80 -1,40
27 Majene 1960 1997 36 -0,767 -4,12 1404,4 118,97 -3,52
28 Kendari 1951 1997 47 -1,871 + -12,11 2291,9 122,40 -4,10
29 Bau Bau 1961 1997 37 -1,609 -13,39 2111,0 122,62 -5,47
30 Makassar 1950 1997 48 -1,200 -9,58 3328,7 119,60 -5,10
31 Jakarta 1950 1997 48 0,124 0,48 1874,9 106,82 -6,17
32 Bandung 1951 1997 47 -2,806 ** -14,50 2250,5 107,60 -6,90
33 Jatiwangi 1950 1997 48 -2,746 ** -16,06 3040,3 108,27 -6,75
34 Tegal 1950 1997 48 -2,293 * -10,60 1915,0 109,15 -6,85
35 Semarang 1950 1997 48 -0,844 -6,39 2346,0 110,40 -7,00
36 Cilacap 1951 1997 47 -2,384 * -31,80 4055,2 109,02 -7,73
37 Jogjakarta 1951 1997 47 -2,421 * -14,00 2129,0 110,26 -7,47
38 Madiun 1951 1997 47 -0,624 -3,31 1833,2 111,52 -7,62
39 Banyuwangi 1950 1997 48 0,924 3,38 1184,1 114,40 -8,20
40 Kalianget 1951 1997 47 -2,641 ** -9,92 1518,5 113,97 -7,05
41 Bawean 1961 1997 37 -2,564 * -26,01 3000,1 112,63 -5,85
42 Denpasar 1950 1997 48 -0,880 -5,31 1800,0 115,10 -8,45
43 Ampenan 1951 1997 47 0,862 5,11 1334,7 116,07 -8,53
44 Sumbawa 1961 1997 37 1,059 6,23 1047,7 117,42 -8,43
45 Waingapu 1950 1997 48 0,133 0,29 823,2 120,30 -9,70
46 Kupang 1950 1997 48 0,178 0,91 1483,7 123,70 -10,20
47 Dilli 1952 1997 46 -1,932 + -5,21 978,2 125,60 -8,60
48 Saumlaki 1961 1997 37 -2,681 ** -14,66 2096,2 131,30 -7,98
49 Tual 1951 1997 47 -1,311 -9,13 2445,1 132,80 -5,70
50 Geser 1951 1997 47 -1,862 + -12,39 2237,8 113,00 -7,00
51 Ambon 1950 1997 46 -1,244 -13,61 2922,3 128,10 -3,70
52 Sanana 1974 1997 22 -0,074 -1,56 1424,2 126,00 -2,30
53 Ternate 1971 1997 27 -1,501 -27,40 2931,0 127,40 0,80
54 Sorong 1950 1997 48 -4,826 *** -36,98 3443,5 131,12 -0,93
55 Manokwari 1955 1997 43 -1,361 -9,75 2745,3 134,05 -0,88
56 Biak 1955 1997 43 -1,225 -7,11 2881,9 136,12 -1,18
57 Sarmi 1974 1997 24 -2,779 ** -29,84 3363,6 138,75 -1,85
58 Sentani 1950 1997 48 -2,293 * -7,34 1936,2 140,72 -2,37
59 Wamena 1957 1997 41 -1,831 + -7,29 2021,6 138,92 -4,08
60 Nabire 1970 1997 28 -2,746 ** -57,36 5432,5 135,50 -3,33
61 Kaimana 1959 1997 39 -0,823 -8,00 2507,0 133,75 -3,67
62 Merauke 1952 1997 46 -1,212 -5,06 1474,3 140,38 -8,47
63 Agats 1972 1990 19 -1,225 -57,17 5927,2 138,10 -5,50
Example of climatic rainfall Example of climatic rainfall trendtrend
y = -71.79x + 5450.71
0
1000
2000
3000
4000
5000
6000
7000
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
mm
/ye
ar
Bengkulu
Linear (Bengkulu)
y = -29.71x + 4010.15
0500
100015002000250030003500400045005000
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
mm
/ye
ar
Ketapang
Linear (Ketapang)
Potency of water loss during 4 Potency of water loss during 4 decadesdecades
Island groupArea
(km2)*
Z Q (mm/year)annual water lost from Q x area (million m3)
ave max min ave max min ave min max
Sumatra 425000 -2,24 -0,46 -4,35 -18,00 -1,27 -71,79 7652 541 30509
Kalimantan **540615 -3,00 -2,30 -4,20 -19,89 -12,41 -30,30 10755 6707 16379
Sulawesi 174600 -1,19 -0,39 -1,87 -8,52 -1,99 -17,99 1488 347 3141
Java 126700 -1,66 0,92 -2,81 -11,70 3,38 -31,80 1483 -428 4029
Papua ***327160 -2,03 -0,82 -4,83 -22,59 -5,06 -57,36 7391 1654 18767
Monsoon Monsoon weakeningweakening
►Data from Data from Brantas Brantas catchment catchment east Java in east Java in the last 51 the last 51 years years (1955 – (1955 – 2005)2005)
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 20050
500
1000
Time (year)P
reci
pita
tion
(mm
)
a) Kertosono Station Monthly Precipitation (1955-2005)
0 1 2 3
x 105Power (mm2)
c) Global Wavelet Spectrum
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 20050
2
4
6
Time (year)
Pow
er (
mm
2 )
Reg.Gradient = -0.018652
d) One Year Period Wavelet Time Series
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
0.25
0.5
1
2
4
8
16
Time (year)
Per
iod
(yea
rs)
b) Monthly Precipitation Wavelet Power Spectrum (mm2)
-4
-2
0
2
4
Aldrian and Djamil 2007, Intl J Climatol
Extension of dryspellExtension of dryspellDry month period Mojokerto (lowland)
y = -4E-13x5 - 4E-11x4 + 5E-07x3 - 0,0003x2 + 0,0448x + 0,2508
0123456789
10
19
55
19
60
19
65
19
70
19
75
19
80
19
85
19
90
19
95
20
00
20
05
Year
mo
nth
s
Dry month period Pujon (highland)
y = -2E-12x5 + 2E-09x4 - 6E-07x3 - 3E-05x2 + 0,0278x + 0,0143
0123456789
10
19
55
19
60
19
65
19
70
19
75
19
80
19
85
19
90
19
95
20
00
20
05
Year
mo
nth
s
Aldrian and Djamil 2007, Intl J Climatol
Change of Change of ratio rain ratio rain
falls in wet falls in wet season to season to
totaltotal(east Java)(east Java)
►Higher risk of Higher risk of flood and flood and droughtdrought
Birowo (195m)
y = 0.1994x - 315.1
0102030405060708090
100
1955 1965 1975 1985 1995 2005Year
Ra
tio
(%
)
Wates Sawahan (620m)
y = 0.1614x - 241.69
0
10
20
30
40
50
60
70
80
90
100
1955 1965 1975 1985 1995 2005Year
Ra
tio
(%
)
Aldrian and Djamil 2007, Intl J Climatol
The leading annual eigen coefficients of PC1 and PC2 (black The leading annual eigen coefficients of PC1 and PC2 (black lines) along with the NINO3 aSST (grey lines; in unit oC) for lines) along with the NINO3 aSST (grey lines; in unit oC) for the PC1. For the PC2, the linear straight grey lines highlight the PC1. For the PC2, the linear straight grey lines highlight the bi-decadal variability at level 0.0, 0.2 and -0.18 during the the bi-decadal variability at level 0.0, 0.2 and -0.18 during the period 1955-1973, 1974-1988 and 1989-2005, respectively.period 1955-1973, 1974-1988 and 1989-2005, respectively.
Annual PC1-2.0-1.5-1.0-0.50.00.51.01.52.0
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
PC1-Coeff
NINO3-aSST
Annual PC2-0.6
-0.4
-0.2
0.0
0.2
0.4
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
PC2-Coeff
The leading The leading seasonal seasonal eigen eigen coefficients coefficients of PC1 (black of PC1 (black lines) after lines) after removing the removing the annual signal annual signal along with along with the NINO3 the NINO3 aSST (grey aSST (grey lines; in unit lines; in unit oC). oC).
MAM-2.0-1.5-1.0-0.50.00.51.01.52.0
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
PC1-Coeff
NINO3-aSST
JJA-2.0-1.5-1.0-0.50.00.51.01.52.0
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
PC1-Coeff
NINO3-aSST
SON-2.0-1.5-1.0-0.50.00.51.01.52.0
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
PC1-Coeff
NINO3-aSST
DJF-2.0-1.5-1.0-0.50.00.51.01.52.0
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
PC1-Coeff
NINO3-aSST
Aldrian and Djamil 2007, Intl J Climatol
ConclusionsConclusions► the rainfall climate of this region is predictable at monthly and the rainfall climate of this region is predictable at monthly and
seasonal scales, but only for aseasonal scales, but only for a limitedlimited and specificand specific periodperiod in in specificspecific regionsregions. The. The predictability barrierpredictability barrier is an intrinsic is an intrinsic character of Indonesian rainfall and a challenge to climate character of Indonesian rainfall and a challenge to climate modelling in the region because it limits model applications.modelling in the region because it limits model applications.
► besidebeside monsoonmonsoon, important rainfall variability from monthly to , important rainfall variability from monthly to interannually is the coherentinterannually is the coherent ENSOENSO..
► The global atmospheric models produce the large scale The global atmospheric models produce the large scale precipitationprecipitation characteristics characteristics well, but the regional model well, but the regional model shows a regional shows a regional phenomenon obscured within global models phenomenon obscured within global models throughthrough better orography better orography..
► A coupled regional atmosphere/ocean model showsA coupled regional atmosphere/ocean model shows improved improved dynamicsdynamics through the ocean and the atmosphere sea-air through the ocean and the atmosphere sea-air interaction and feedback. interaction and feedback.
► The ocean regulates the SST with persistence time lag at the The ocean regulates the SST with persistence time lag at the ocean surface. The horizontal advection fluxes changes the ocean surface. The horizontal advection fluxes changes the heat content of the water column, which eventually changes heat content of the water column, which eventually changes the surface condition (SST). The SST regulates the local the surface condition (SST). The SST regulates the local precipitation through a specific SST rainfall relationship.precipitation through a specific SST rainfall relationship.
► Decreasing climatic trend of rainfall due to global climate Decreasing climatic trend of rainfall due to global climate change with much decrease of rainfall in dry period than change with much decrease of rainfall in dry period than increase of rainfall in wet period.increase of rainfall in wet period.
Thank You very much for Thank You very much for your kind attentionyour kind attention