1. INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS
CENTRO DE PREVISÃO DE TEMPO E ESTUDOS CLIMÁTICOS
Summer rainfall forecast skill over South America derived from WCRP SMIP-2 models results.
Lincoln M. Alves, Paulo Nobre, Luciano Pezzi, José F. Pesquero
Climate Prediction Group
WCRP Workshop on Seasonal Prediction
Barcelona Spain June 4-7, 2007

2. OUTLINE
Part I – A brief discussion about the main climatological features of South America rainfall
Part II – Methodology and data
Part III – Skill of the models simulation
Part IV – Summary and Conclusions

3. OVERVIEW
Distribution of Climatological Precipitation for DJF and JJA (mm/day)
Diminuir purpose para uma frase…
PURPOSE
This work analyses the forecast skill of summer (DJF) rainfall for several SMIP-2 AGCMs over South America, relative to CPTEC’s both atmospheric and coupled ocean-atmosphere GCMs.

4. WCRP SMIP-2 prescribed_SST Datasets
DATA AND METHODOLOGY
WCRP SMIP-2 prescribed_SST Datasets
CPTEC AGCM: Centro de Previsão de Tempo e Estudos Climáticos
Resolution: 1.875x ; Members: 5;
HMC: Hydrometcentre of Russia
Resolution: 2.5x2.5; Members: 6;
IAP: Institute of Atmospheric Physics, China
Resolution: x ; Members: 6;
KMA: Korea Meteorological Agency
MGO: Main Geophysical Observatory, Russia
MRI: Meteorological Research Institute (Japan)
SCRIPPS: Scripps Institute of Oceanography
Resolution: 1.875x ; Members: 10;
CPTEC CGCM: Centro de Previsão de Tempo e Estudos Climáticos
Resolution: 1.875x1.875; Members: 10;

5. DATA AND METHODOLOGY Time: [DJF 1979-1999]; season
Data used for model evaluation: The Global Precipitation Climatology Project (GPCP)
Skill Measures:
Ensemble means are used to compare the models results with observational datasets
Bias
Correlation of seasonal rainfall anomalies
Interannual variability of observed and modeled normalized rainfall departures in several regions of South America
The multi-model simulations are combined by two methods: a simple average and the superensemble (Krishnamurti, 2000, 2001a, 2001b)
Conventional Superensemble methodology is given by:

6. MODEL PRECIP. CLIMATOLOGY: DJF (mm/day)
CPTEC AGCM
HMC
IAP
KMA
MGO
MRI
SCRIPPS
SIMPLE AVERAGE
CPTEC CGCM
OBS

7. DJF MODEL’S BIAS CPTEC AGCM HMC IAP KMA MGO MRI SCRIPPS SIMPLE AVERAGE
CPTEC CGCM
Bias na AM, forte bias nao é um bom modelo… erro sistematico sobre a AM…. Wet bias over south… sul da Argentina. Dry bias no sul do Brazil… diferencao de parametrizao no modelo acoplado RAS as you can see here…

8. Seasonal Anomaly Correlation
CPTEC AGCM
HMC
IAP
KMA
MGO
MRI
SCRIPPS
SIMPLE AVERAGE
SUPERENSEMBLE
CPTEC CGCM
Potential predictabily Couple and AGCM model actual prediction predictability… skill variavel nos varios modelos no sudeste… keeps both features
Deficience em nordeste because parametrization

9. Seasonal Anomaly Correlation
Models minus Superensemble
CPTEC AGCM
HMC
IAP
KMA
MGO
MRI
SCRIPPS
SIMPLE AVERAGE
CPTEC CGCM
worse

10. CPTEC’s AGCM & CGCM DJF PRECIP FORECAST ACC
DIFFERENCE
CGCM
Actual predict one month lead to predict djf… bom skill para prever enso variability and
AGCM

11. INTERANNUAL VARIABILITY

12. INTERANNUAL VARIABILITY

13. SUMMARY
The models simulate the patterns of DJF precipitation climatology reasonably well, despite a general dry bias over the Amazon.
A common feature of all AGCMs was the higher predictability over Northern Amazon, Northeast, Southern Brazil, and northern Argentina, and low predictability over Central and SE Brazil.
It is not possible to point out a model with characteristics outstandingly better than the others, on the contrary, each model has strong and weak points in different regions.
A multi-model fcst may contribute to more skillful predictions than any single AGCM;
Although a more sophisticated combination method does not necessarily result better fcst everywhere;
CGCM presented marginally better skill over the South-SW Tropical Atlantic and the SACZ region: seasonal rainfall predictability dependent on coupled interactions?