PRECIPITATION AND TEMPERATURE CLIMATE INDICES IN SOUTH AMERICA AND SÃO PAULO STATE, BRAZIL

PRECIPITATION AND TEMPERATURE PRECIPITATION AND TEMPERATURE CLIMATE INDICES IN SOUTH AMERICA CLIMATE INDICES IN SOUTH AMERICA

AND SÃO PAULO STATE, BRAZILAND SÃO PAULO STATE, BRAZIL

Amanda Sabatini DufekAmanda Sabatini DufekTércio AmbrizziTércio Ambrizzi

Rosmeri Porfirio da RochaRosmeri Porfirio da Rocha

Department of Atmospheric Sciences University of São Paulo, BrazilDepartment of Atmospheric Sciences University of São Paulo, Brazil

OBJECTIVESOBJECTIVES

• Some indices based on daily data of precipitation and maximum and minimum

temperature will be calculated for present climate (1960-1990) over South America

and São Paulo State, Brazil, based on climate simulations data from a global

climate model (HadAM3) and a regional climate model (RegCM3) and also using

observational data from many available stations in the region.

• The main goal of this work is to compare the simulated and observed indices

over South America and São Paulo State, Brazil, during the period 1960-1990.

DATADATA

SIMULATION DATA

• The HadAM3 global climate model has a horizontal resolution of 1.25 of latitude by

1.875 of longitude and 19 vertical hybrid coordinates levels. The simulation data is

from 1960 to 1990.

• The RegCM3 regional climate model was integrated over South America with a

spatial resolution of 60km and 23 vertical sigma levels (top of the model at 50hPa).

The simulation was done for the same period of the HadAM3. The atmospheric initial

boundary conditions and the Sea Surface Temperature (SST) for the RegCM3

simulation were provided by the HadAM3 model.

OBSERVATIONAL DATA

• Precipitation and Temperature maximum and minimum daily data obtained from the

Agronomic Institute of Campinas (IAC), Department of Water and Electric Energy

(DAEE), National Institute of Meteorology (INMET) and Água Funda station.

INDICESINDICES

• Maximum Temperature

Warm days (wd) – Percentage of days with Tmax>90th percentis (%)

Cold days (cd) – Percentage of days with Tmax<10th percentis (%)

• Minimum Temperature

Warm nights (wn) – Percentage of days with Tmin>90th percentis (%)

Cold nights (cn) – Percentage of days with Tmin<10th percentis (%)

• Precipitation

prcptot – represents the total annual precipitation amount (mm)

r95p – represents the amount of rainfall falling above the 95th percentiles (mm)

rx5day – is the maximum precipitation in a year falling over 5 day (mm)

cdd – is the length of the longest dry spell in a year (day)

cwd – defines the longest wet spell in a year (day)

• MAXIMUM TEMPERATURE INDICES

• CORRELATION BETWEEN MODELXOBS

• ANNUAL TRENDS MODELXOBS

• MINIMUM TEMPERATURE INDICES



• PRECIPITATION INDICES



RESULTSRESULTS

RESULTS RESULTS – MAXIMUM TEMPERATURE INDICE: COLD DAYS– MAXIMUM TEMPERATURE INDICE: COLD DAYS CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS

HadAM3 Simulation x ObservationHadAM3 Simulation x Observation RegCM3 Simulation x ObservationRegCM3 Simulation x Observation

+ = 10o = 3 (se,co)

+ = 11o = 2 (co)

RESULTS RESULTS – MAXIMUM TEMPERATURE INDICE: COLD DAYS– MAXIMUM TEMPERATURE INDICE: COLD DAYS CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 6o = 0

+ = 3o = 3

RESULTSRESULTS










RESULTS RESULTS – MAXIMUM TEMPERATURE INDICE: COLD DAYS– MAXIMUM TEMPERATURE INDICE: COLD DAYS ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS


OBS. SIM. OBS. SIM.

++(oo) = 10o+(+o) = 3 (su,se)

++(oo) = 7o+(+o) = 6

RESULTS RESULTS – MAXIMUM TEMPERATURE INDICE: COLD DAYS– MAXIMUM TEMPERATURE INDICE: COLD DAYS ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS


OBS. SIM. OBS. SIM.

++(oo) = 1o+(+o) = 5

++(oo) = 2o+(+o) = 4

RESULTSRESULTS










RESULTS RESULTS – MINIMUM TEMPERATURE INDICE: WARM NIGHTS– MINIMUM TEMPERATURE INDICE: WARM NIGHTS CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 13o = 1 (su)

+ = 14o = 0

RESULTS RESULTS – MINIMUM TEMPERATURE INDICE: WARM NIGHTS– MINIMUM TEMPERATURE INDICE: WARM NIGHTS CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 6o = 0

+ = 6o = 0

RESULTSRESULTS










RESULTS RESULTS – MINIMUM TEMPERATURE INDICE: WARM NIGHTS– MINIMUM TEMPERATURE INDICE: WARM NIGHTS ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS


OBS. SIM. OBS. SIM.

++(oo) = 12o+(+o) = 2 (no,ne)

++(oo) = 12o+(+o) = 2 (su,no)

RESULTS RESULTS – MINIMUM TEMPERATURE INDICE: WARM NIGHTS– MINIMUM TEMPERATURE INDICE: WARM NIGHTS ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS


OBS. SIM. OBS. SIM.

++(oo) = 4o+(+o) = 2

++(oo) = 5o+(+o) = 1

RESULTSRESULTS










RESULTS RESULTS – PRECIPITATION INDICE: PRCPTOT– PRECIPITATION INDICE: PRCPTOT CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 13o = 3 (su,co)

+ = 11o = 5 (su,se,co,no)

RESULTS RESULTS – PRECIPITATION INDICE: PRCPTOT– PRECIPITATION INDICE: PRCPTOT CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 129o = 34

+ = 110o = 53

RESULTS RESULTS – PRECIPITATION INDICE: R95P– PRECIPITATION INDICE: R95P CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 12o = 4 (su,se,co,no)

+ = 10o = 6 (su,se,co,no)

RESULTS RESULTS – PRECIPITATION INDICE: R95P– PRECIPITATION INDICE: R95P CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 91o = 72

+ = 84o = 79

RESULTS RESULTS – PRECIPITATION INDICE: CWD– PRECIPITATION INDICE: CWD CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 5 (su,co,ne)o = 9

+ = 1 (co)o = 14

RESULTS RESULTS – PRECIPITATION INDICE: CWD– PRECIPITATION INDICE: CWD CORRELATION BETWEEN MODELXOBSCORRELATION BETWEEN MODELXOBS


+ = 109o = 50

+ = 64o = 96

RESULTSRESULTS










RESULTS RESULTS – PRECIPITATION INDICE: PRCPTOT– PRECIPITATION INDICE: PRCPTOT ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS


OBS. SIM. OBS. SIM.

++(oo) = 9o+(+o) = 7

++(oo) = 8o+(+o) = 8

RESULTS RESULTS – PRECIPITATION INDICE: PRCPTOT– PRECIPITATION INDICE: PRCPTOT ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS

HadAM3HadAM3 RegCM3RegCM3

ObservationObservation

++(oo) = 140o+(+o) = 23

++(oo) = 65o+(+o) = 98

RESULTS RESULTS – PRECIPITATION INDICE: R95P– PRECIPITATION INDICE: R95P ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS


OBS. SIM. OBS. SIM.

++(oo) = 9o+(+o) = 7

++(oo) = 9o+(+o) = 7

RESULTS RESULTS – PRECIPITATION INDICE: R95P– PRECIPITATION INDICE: R95P ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS



++(oo) = 81o+(+o) = 82

++(oo) = 64o+(+o) = 99

RESULTS RESULTS – PRECIPITATION INDICE: CWD– PRECIPITATION INDICE: CWD ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS


OBS. SIM. OBS. SIM.

++(oo) = 11o+(+o) = 5 (su,se,no,ne)

++(oo) = 6o+(+o) = 10

RESULTS RESULTS – PRECIPITATION INDICE: CWD– PRECIPITATION INDICE: CWD ANNUAL TREND MODELXOBSANNUAL TREND MODELXOBS



++(oo) = 96o+(+o) = 67

++(oo) = 69o+(+o) = 94

CONCLUSIONSCONCLUSIONS

• In general, the RegCM3 and HadAM3 models present very similar results, however,

the behavior of some indices was better simulated by the HadAM3 model, specially

in the region of São Paulo State, Brazil, and for the precipitation indices.

• Most of the correlations between simulation and observation for the indices have a

weak positive relationship.

• For the most of the indices, the results of the annual trends were not as good as the

correlations.

• The models do not simulate well the precipitation and the temperature indices.

• The warm night, based on minimum temperature, was the index better simulated by

the RegCM3 and HadAM3 when compared to the other annual temperature indices.

• The RegCM3 worst simulation was for the CWD index.

PRECIPITATION AND TEMPERATURE CLIMATE INDICES IN SOUTH AMERICA AND SÃO PAULO STATE, BRAZIL

Documents

Transcript of PRECIPITATION AND TEMPERATURE CLIMATE INDICES IN SOUTH AMERICA AND SÃO PAULO STATE, BRAZIL