Applications of RegCM3 model in the South Applications of RegCM3 model in the South America: Grec-USP experiencesAmerica: Grec-USP experiences

•Rosmeri P. da Rocha •Tércio Ambrizzi

and collaborates (Michele, Amanda, Santiago, Maria Cristina, Fabio, Gabriel, Rubinei....)

•Departamento de Ciências Atmosféricas

GrEC (Grupo de Estudos Climáticos) of the USP has been using RegCM3 in various studies

• Since 2002 (RegCM2) we are using the Regional Climate Model (RegCM3) to:

– Simulate interannual and inter season anomalies (precipitation and circulation over south and southeast of Brazil);

– Understand physical climate process (LLJ of east of Andes, ciclogeneses in the South Atlantic Ocean)

– Climate change scenarios (PROBIO project: RegCM3 nested in the HadAM3 global model)

– Tests in the seasonal forecasting (RegCM3 nested in the CPTEC/COLA global model).

- and more ….

• RegCM3 has been running at different horizontal resolutions (80 to 40

km) and vertical (14 to 23 levels) over different domains.

Interannual variability – Cuadra and Rocha (2005)

SDN

SDS

SDN

SDS

Two summers: 1990 and 1998

RegCM3 initial and boundary conditions from NCEP reanalysis

Daily precipitation data from NCDC

Precipitation anomalies

1998– RegCM3

1990 – NCDC

1990 – NCDC

1990 – RegCM3

Daily precipitation (mm/day) observed (CPC) and simulated by RegCM3 in the subdomains (SDN and SDS)

SDS - 1990

SDN - 1990

(a)

(b)

SDS - 1990

SDN - 1990

(a)

(b)

SDN - 1998

SDS - 1998

(c)

(d)

SDN - 1998

SDS - 1998

(c)

(d)

summer 1990 1998

SDN SDS SDN SDS

Precipitation (mm/day) 7.2

(7.7)

6.2

(6.2)

5.5

(4.0)

6.8

(7.2)

Relative bias (%) 7.0 0.0 -27.0 6.6

Correlation coefficient 0.82 0.44 0.46 0.39

Impact of SST on the seasonal simulations over southeastern South America during the summer – Cuadra and Rocha (2007)

•Two sets of regional simulations to investigate responses under different SST forcing:

- first set control experiment (CON): used the monthly averaged observed SST (OSST), obtained from Reynolds and Smith (1995);

- second set ESST: the SST is formed by persistence of November SST anomaly superimposed over the climatological cycle (PSST), of the four subsequent months (D, J, F, M). SST climatology was obtained considering the average of the monthly-observed SST from 1982 to 2000;

Additional experiments: in the 1992 summer the model internal variability was discussed through of the:

ICs (Initial Conditions) experiments: an ensemble of five simulations using OSST. PSST experiments: the ensemble procedure was followed using PSST to compare the

deviation between the ensemble means

They were initialized on the four subsequent days in relation to the control run (2, 3, 4 and 5 of November).

Period: ten summers seasons (DJF, summer) from 1989 to 1998 and they were initialized a month before the summer (0000 UTC 1 of November).

Initial and boundary conditions from NCEP reanalysis 2 (Kanamitsu et al. 2002)

Time series, from 1989 to 1998 summers, of the seasonal precipitation anomaly (mm day-1) simulated by CON (continuous line) and ESST (dashed-dotted line)

experiments for the subdomains.

•continental domains: only the 89 (SO subdomain) summer presents change in the sign of the rainfall anomaly as function of SST specification; however, the magnitude of the difference is very low;• ZCO region is directly affected by the differences between SSTs. The change in sign and amplitude changes of rainfall anomalies is more prominent in the summers of 89, 91, 92 and 93.

Continental subdomains Sea subdomain

Time series of the daily precipitation RMSD (mm day-1) between CON and ESST experiments (continuous line), and between CON and ICs experiments (symbols), for

the subdomains. SE

For precipitation: the RMSDs of the ESST and ICs experiments present the same order of magnitude.

This result shows that the signal generated by persisted SST does not predominate during the model integration, i.e., the deviations imposed by SST specification have the same magnitude as the deviations associated to the model internal variability

ZCO

1992 summer differences of precipitation between: ESST-CON

• five members ensemble simulations for the summer of 1992 showed that the small seasonal differences in the precipitation and air temperature as function of the SST specification are basically due to two factors:

• (a) smoothing of the deviations between simulations as a function of the number of days considered in the average;

• (b) predominance of the model internal variability over continental areas.

• Comparing the deviations due to SST specification and the initial condition, it could be concluded that noise overcomes the signal associated with SST differences over the continent and the noise can be eliminated with the utilization of the ensemble technique.

• However, this result does not apply over the subtropical South Atlantic Ocean, where the precipitation and air temperature differences were not eliminated by the ensemble mean, showing the predominance of the signal generated by SST differences over the atmospheric model noise.

One member of ESST and CON experiments

ensemble-mean

Same RegCM3 code, domain and resolution (50 km) was used to

investigate the summer precipitation diurnal cycle.

Period: DJF from 1998 to 2002

Observations: TRMM (Tropical Rainfall Measuring Mission)

Total Precipitation

Convective Precipitation

Air temperature diurnal cycle (only RegCM3)

LATENT AND SENSIBLE HEAT FLUXES SIMULATEDLATENT AND SENSIBLE HEAT FLUXES SIMULATED BY ABY A REGIONAL CLIMATE MODELREGIONAL CLIMATE MODEL OVER THE SOUTH ATLANTIC OCEAN: OVER THE SOUTH ATLANTIC OCEAN: SEASONAL MEAN VARIATIONSSEASONAL MEAN VARIATIONS

(Reboita et al. 2005 and Reboita et al. 2006)

Figure 1. Simulation domain (outer) and analysis domain (inner). The boxes 1, 2, and 3 indicate the favorable regions to cyclone development. The RegCM results over the continent were not included in the analysis.

Latent Heat Fluxes Sensible Heat Fluxes

RegCM WHOI ECMWF RegCM WHOI ECMWF

S 69.8 67.6 77.1 8.5 0.9 3.6

W 84.1 93.9 100.3 20.9 10.7 16.2

-Over South Atlantic Ocean:-heat fluxes are more intense in the winter than in the summer; -the simulated averages of the latent heat fluxes are closer to those in the WHOI analysis; - sensible heat the values simulated are higher than those of the analyses in the two seasons, but they are closer to the ECMWF analysis. As discussed in Reboita et al. (2005 b), this is due to the more intense temperature vertical gradients of the simulation.

J F M A M J J A S O N D0

50

100

150Latent Heat Fluxes

Wm-2

J F M A M J J A S O N D-20

0

20

40Sensible Heat Fluxes

Wm-2

J F M A M J J A S O N D0

50

100

150

Wm-2

J F M A M J J A S O N D-20

0

20

40

Wm-2

J F M A M J J A S O N D0

50

100

150

Wm-2

J F M A M J J A S O N D-20

0

20

40

Wm-2

J F M A M J J A S O N D0

50

100

150

Months

Wm-2

J F M A M J J A S O N D-20

0

20

40

Months

Wm-2

RegCMWHOIECMWF

Region 1

Region 2

Region 3

South AtlanticSouth Atlantic

Region 1

Region 2

Region 3

a)

b)

c)

d)

e)

g)

h)

f)

Annual cycle, obtained from 1990 to 1999, of latent and sensible heat fluxes simulated by Annual cycle, obtained from 1990 to 1999, of latent and sensible heat fluxes simulated by RegCM3 and analyzed (WHOI and ECMWF)RegCM3 and analyzed (WHOI and ECMWF)

two important characteristics can be identified: the annual cycle and the latitudinal dependence of the sensible heat fluxes;as the latitude increases the simulations become closer to the analyses. The reason for this behavior is probably because of the vertical temperature vertical gradients in the three regions. for the entire domain, the intensity of the latent heat fluxes is near to the WHOI and the sensible heat fluxes are more intense than the analyses, but it is closer to the ECMWF.

Winter latent heat fluxes (Wm-2) seasonal average (1990-1999)

RegCM3 WHOI ECMWF

-70 -60 -50 -40 -30 -20 -10 0 10-55

-50

-45

-40

-35

-30

-25

-20

-15LH - Winter (RegCM 90-99)

20

40

60

80

100

120

140

160

d)

-70 -60 -50 -40 -30 -20 -10 0 10

-50

-45

-40

-35

-30

-25

-20

-15LH - Winter (WHOI 90-99)

20

40

60

80

100

120

140

160

e)

-70 -60 -50 -40 -30 -20 -10 0 10-55

-50

-45

-40

-35

-30

-25

-20

-15LH - Winter (ECMWF 90-99)

20

40

60

80

100

120

140

160

f)

The spatial structure in the seasonal maps of the Brazil-Malvinas confluence is the most relevant feature. In this region, the fresh and cold waters of the Malvinas current are characterized by negative fluxes and the warm and salty waters of the Brazil current are characterized by positive fluxes.

EXTRATROPICAL CYCLONES CLIMATOLOGY IN THE SOUTH ATLANTIC EXTRATROPICAL CYCLONES CLIMATOLOGY IN THE SOUTH ATLANTIC OCEAN SIMULATED BY REGIONAL CLIMATE MODEL (RegCM3)OCEAN SIMULATED BY REGIONAL CLIMATE MODEL (RegCM3)

Reboita et al. (2007, Reboita et al. (2007, in preparation))

01002505007501000

1500

2000

3500

6000

-80 -70 -60 -50 -40 -30 -20 -10 0 10 -60

-55

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

RG 1

RG 2

RG 3

Domínio da Simulação

Domínio de Análise

Interannual variability of the number of cyclogeneses over South Atlantic Ocean (1990-1999)

a) b)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999270

275

280

285

290

295

300

305

310

315

Fre

qü

ên

cia

Ab

solu

ta

Total Anual de Ciclogêneses -1.5 X 10-5 s-1

RegCMNCEP

1990 1991 1992 1993 1994 1995 1996 1997 1998 199980

85

90

95

100

105

110

115

120

125

130

135

140

Fre

qü

en

cia

Ab

solu

ta

Total Anual de Ciclogêneses -2.5 X 10-5 s-1

RegCMNCEP

The number of RegCM3 initially weak and more strong ciclogenesis is smaller than NCEP;

Annual and seasonal number of cyclogeneses - 1990 – 1999

a) b)

V O I P64

66

68

70

72

74

76

Mé

dia

Sa

zon

al

Média Sazonal -1.5 X 10-5 s-1

RegCMNCEP

V O I P22

24

26

28

30

32

34

Mé

dia

Sa

zon

al

Média Sazonal -2.5 X 10-5 s-1

RegCMNCEP

Initially weak ciclogenes shows a maximum occurrence in May

Initially more strong ciclogeneses peaks in winter months

a) b)

J F M A M J J A S O N D6

7

8

9

10

11

12

13

14

Mé

dia

Me

nsa

l

Média Mensal -2.5 X 10-5 s-1

RegCMNCEP

J F M A M J J A S O N D18

19

20

21

22

23

24

25

26

27

28

29

30

Mé

dia

Me

nsa

l

Média Mensal -1.5 X 10-5 s-1

RegCMNCEP

Seasonal

Monthly

Seasonal cyclogeneses density

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

-70 -60 -50 -40 -30 -20 -10 0 10-55

-50

-45

-40

-35

-30

-25

-20

-15Summer - NCEP ( -1.5x10-5 s-1)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

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2.4

2.6

-70 -60 -50 -40 -30 -20 -10 0 10-55

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-25

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-15Autumn - NCEP ( -1.5x10-5 s-1)

0

0.2

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0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

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-70 -60 -50 -40 -30 -20 -10 0 10-55

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-45

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-25

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-15Winter - NCEP ( -1.5x10-5 s-1)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

-70 -60 -50 -40 -30 -20 -10 0 10-55

-50

-45

-40

-35

-30

-25

-20

-15Spring - NCEP ( -1.5x10-5 s-1)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

-70 -60 -50 -40 -30 -20 -10 0 10-55

-50

-45

-40

-35

-30

-25

-20

-15Summer - RegCM3 ( -1.5x10-5 s-1)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

-70 -60 -50 -40 -30 -20 -10 0 10-55

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-45

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-30

-25

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-15Autumn - RegCM3 ( -1.5x10-5 s-1)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

-70 -60 -50 -40 -30 -20 -10 0 10-55

-50

-45

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-15Winter - RegCM3 ( -1.5x10-5 s-1)

0

0.2

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0.8

1

1.2

1.4

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1.8

2

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-70 -60 -50 -40 -30 -20 -10 0 10-55

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-15Spring - RegCM3 ( -1.5x10-5 s-1)

a)

b)

c)

d)

e)

f)

g)

h)

NCEP RegCM3

Summer

Autumn

Winter

Spring

REGCM3 SIMULATIONS NESTED IN THE HADLEY CENTER MODEL OVER SOUTH AMERICA: THE PRESENT DAY CLIMATE

Rocha et al. (2006)

As part of the PROBIO project that conduced a downscaling of Hadley Center model (present, A2 and B2 scenarios) over South America with three

regional models:

ETA – CPTEC/INPE

Regional Hadley Center Model – CPTEC/INPE

RegCM3 model – GrEC/IAG/USP

RegCM3 Setup

• For Probio the simulation used the Grell convective scheme with the Fritsch Chappell closure (GFC);

• New simulation with the Emanuel convective scheme;

• Ensemble: (GFC+Emanuel)/2• Initial and boundary condition from HadAM3

model;• Period: 1960 to 1990

Climatology of annual air temperature (oC)1961-1990

Emanuel

CRU

Grell

Ensemble

Annual Temperature BIAS (RegCM3 - CRU)

Emanuel : warmer than the CRU

Grell: colder than the CRU

Ensemble (mean from Emanuel and Grell) – reduce the cold bias in great part of South America

Emanuel Grell Ensemble

Climatology of annual mean precipitation (mm/day)1961-1990

Emanuel

ENSEMBLE

Grell

CRU

Annual precipitation BIAS (RegCM3-CRU)Emanuel Grell+FC Ensemble

Emanuel: intense dry bias in east of Amazon (Para) where the Grell scheme present a small bias; intense moist bias in the east of South and Southeast of Brazil;

Grell: the bias are smaller than Emanuel

Ensemble: reduce the precipitation biases in some parts of Brazil

Annual mean wind at 850 hPa

ENSEMBLE

Grell

NCEP

Emanuel

Annual mean wind at 200 hPa

Emanuel Grell

NCEP

ENSEMBLE

Precipitation seasonal cycle

DJF MAM JJA SON0123456789

101112

CRU WM GFC Ema had

Me

an

se

aso

na

l pre

cip

itatio

n (

mm

/da

y)

Season - AMZ

RegCM3 annual precipitation and temperatures anomalies for A2 and B2 scenarios

(scenarios – present climate)

(only using GFC convective scheme)

RegCM3 anomalies HadAM3 anomalies (scenarios – present climate)

East of Andes Low level Jet (LLJ) and precipitation anomalies as simulated by RegCM3 (Lemos da Silva 2006)

January 2003December - 2002 February 2003

NCEP shows the difference in the maximum velocity core from December to February. This characteristic was simulated by RegCM3.

start of simulation October 2002 end of simulation February 2003

Precipitation anomalies

GPCP January-December January-February

RegCM3 January-December January-February

Fog events over the city of São Paulo as simulated by Fog events over the city of São Paulo as simulated by regional climate modelregional climate model

Rocha et al. 2007Rocha et al. 2007

• ObjectiveObjective• investigated the performance of atmospheric numerical climate model to

simulate the climatology of fog events over Sao Paulo city.

• Numerical Simulations Setup Simulation: 2 years and 3 months;

From 00:00 UTC of October 1st 2002 to 00:00 UTC of January 1st 2005

initial and boundary conditions of the atmospheric variables from NCEP reanalysis 2 (Kanamitsu et al., 2002);

Monthly mean of sea surface temperature (SST) from Reynolds and Smith (1995).

Simulation DomainSimulation Domain

altitude of São Paulo:

model grid point: 628 mIAG/Station: 790 m

* there is a difference of 162 m;

São Paulo city localization in the simulation domain

The city of São Paulo is situated in one valley between Serra do Mar (in the east) and Serra da Cantareira (in the west).

50 km of horizontal resolution

118 east-west grid points

88 north-south

18 sigma levels in the vertical (top of the model at 80 hPa)

Fog identificationFog identification

• Observed fog events:• IAG/Station: Weather/Climate Station of Instituto de Astronomia, Geofísica

e Ciências Atmosféricas (IAG) of the Universidade de São Paulo, located in São Paulo City.

• this station observes the fog events at each hour from 6 to 24 LT.

• Simulated fog events: • a grid point in the RegCM3 domain (Figure), nearest IAG/Station, was

checked to identify fog events by considering:– relative humidity greater than 97.8% and without rain in the last 3

hours. • Period

• were investigated the number of observed and simulated fog events at 00:00 UTC (21 LT) and 09:00 UTC (6 LT) during June-July-August-September (JJAS - Wintertime) from 2003 and 2004, i.e., a total of 244 days.

• Note: the date of simulated and observed fog events are not necessarily the same

RegCM and IAG/Station daily values for JJAS 2003-2004

8 10 12 14 16 18 20 22 24 26 288

10121416182022242628

r= 0.90

Air

Tem

per

atu

re (

oC

) -

Reg

CM

3

Air Temperature (oC) - IAG Station

Pressure Air Temperature

922 924 926 928 930 932 934 936 938 940940942944946948950952954956958

Pre

ssu

re (

hP

a) -

Reg

CM

3

Pressure (hPa) - IAG Station

r=0.92

The scatter diagrams show that both daily pressure (r=0.93) and air temperature (r=0.90) simulated by RegCM3 are well correlated with the IAG/Station

observation;

Mean

IAG/Station: 16.8oC

RegCM3: 15.8 oC

Mean

IAG/Station: 931.0 hPa

RegCM3: 948.7 hPa

Number of fog events by month: RegCM3 x IAG/Station – JJAS of 2003 and 2004

RegCM3 presents a good skill to simulate the observed fog events, in both 2003 and 2004;

there are some differences in the total number of fogs by month between simulation and observation, but the main observed characteristics are well represented by the model;

Relative bias is around ± 10% for both 2003 and 2004 seasons.

jun jul aug sep4

6

8

10

12

14

16

18

20

22

Num

ber

of fo

g ev

ents

months - 2003

IAG RegCM3

Total number of fogs

JJAS

2003

JJAS

2004

IAG/

Station

45 41

RegCM3 48 40

jun jul aug sep4

6

8

10

12

14

16

18

20

22

Num

ber

of fo

g ev

ents

months - 2004

IAG RegCM3

Initial test of the RegCM3 using CPTEC/COLA forecast as initial and boundary conditions (Machado, 2007)

AMZ

NDE

SE2

SUL

Forecasts with RegCM3 model are initiated at 00:00 UTC from each month;

The results are verified in the 3 following months:

Example: initial condition 16.01.2005

Verification period: mean form February, March, April

RegCM3 horizontal resolution: 60 km

vertical resolution: 23 levels

AMZ

Validation of 3 months mean precipitation: RegCM3 and CPC daily precipitation analysis (ftp://ftp.cpc.ncep.noaa.gov/precip/wd52ws/AS)

SE2

Correlation: 0.89

CoE: 0.80 Correlation: 0.88

CoE: 0.73

2005 2006 2007 2005 2006 2007

Summary and conclusions • We are using the RegCM3 to:

– Understand physical climate process– Climate change scenarios– Tests in the seasonal forecasting

– When we used NCEP-reanalysis 2 and HadAM3 forcing with the GFC scheme the model appears to dry over the tropics. However it shows some important results when we search for patterns (LLJ, cyclogenesis, inter season variability, precipitation diurnal cycle, etc.).

– When we applied CPTEC/COLA forcing for the RegCM3 (with the same configuration) the mean precipitation simulated is well represented.

– Dependency of regional simulations of the boundary data and the necessity of validate the model for the different regions of the world.

• Work in progress.....– Cyclogenesis in the present and future climate,– Continue to test the RegCM3 in the forecasting

mode and verify the results in other regions...of South America..

– We need more apropriated regional analysis to validate present day model results.

• Interannual variability – Cuadra and Rocha (2006) • Impact of SST on the seasonal simulations over southeastern South

America during the summer – Cuadra and Rocha (2007) • Michele – fluxos de calor no oceano e ciclones no leste da América

do Sul e ciclones• Probio: Grell (Fritsch-Chappel) e Emanuel: 30 anos, campos médios e

ciclo annual • Fog events – Rose e Fabio • Ciclogeneses: clima presente e futuro.. • RegCM3 aninhado no CPTEC/COLA – alguns resultados para

previsão sazonal – Rubinei • Eventos extremos – Amanda• JBN – Cristina