Title of article :
Understanding the predictability of seasonal precipitation over northeast Brazil
Author/Authors :
VASUB، نويسنده , , HU MISRA، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2006
Abstract :
Using multiple long-term simulations of the Center for Ocean–Land–Atmosphere Studies (COLA) atmospheric general
circulation model (AGCM) forced with observed sea surface temperature (SST), it is shown that the model has high
skill in simulating the February–March-April (FMA) rainy season over northeast Brazil (Nordeste). Separate sensitivity
experiments conducted with the same model that entails suppression of all variability except for the climatological annual
cycle in SST over the Pacific and Atlantic Oceans reveal that this skill over Nordeste is sensitive to SST anomalies in
the tropical Atlantic Ocean. However, the spatial pattern of SST anomalies in the tropical Atlantic Ocean that correlate
with FMA Nordeste rainfall are in fact a manifestation of El Ni˜no Southern Oscillation (ENSO) phenomenon in the
Pacific Ocean.
This study also analyzes the failure of the COLA AGCM in capturing the correct FMA precipitation anomalies over
Nordeste in several years of the simulation. It is found that this failure occurs when the SST anomalies over the northern
tropical Atlantic Ocean are large and not significantly correlated with contemporaneous SST anomalies over the eastern
Pacific Ocean. In two of the relatively large ENSO years when the model failed to capture the correct signal of the
interannual variability of precipitation over Nordeste, it was found that the meridional gradient of SST anomalies over
the tropical Atlantic Ocean was inconsistent with the canonical development of ENSO. The analysis of the probabilistic
skill of the model revealed that it has more skill in predicting flood years than drought. Furthermore, the model has no
skill in predicting normal seasons. These model features are consistent with the model systematic errors.
Journal title :
Tellus. Series A
Journal title :
Tellus. Series A