1. |
- Bertacchi Uvo, Cintia
(författare)
-
Influence of Sea Surface Temperature on Rainfall and Runof in Northeastern South America: Analysis and Modeling
- 1998
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This work represents an amalgam of a group of studies with the purpose of understanding the influence of the Pacific and Atlantic sea surface temperature on precipitation and river discharge in Northeastern South America. Sea surface temperature is a good representative of phenomena such as ENSO that, in turn, cause worldwide climate variability. The patterns of the Atlantic Ocean sea surface temperature anomaly also plays a very important role in the precipitation over the neighboring regions and a special investigation was also carried out to better understanding this influence. The influence of these oceans' sea surface temperature on the intraseasonal variability of precipitation in Northeast Brazil was also a particular subject of study. Statistical methods were largely used both during these investigations and in the development of models for forecasting discharge long term in advance at some sites in the Amazon, Orinoco and Tocantins River Basins. Sea surface temperature anomalies in both oceans significantly influence precipitation over northeastern South America. The Atlantic Ocean, however, plays a more important role in the case of precipitation over Northeast Brazil while the Pacific Ocean seems to have stronger influence over eastern and northern Amazonia. As a result of changes in precipitation, the river discharge in the Amazon Region is also influenced by changes in sea surface temperature patterns. The discharge of rivers located to the north of the Amazon River is mainly influenced by the Pacific sea surface temperature while the Atlantic influences the rivers to the south of the Amazon River. This influence could be clearly observed using the forecast models. Two different methodologies were used to develop forecast models: Canonical Correlation Analysis and Artificial Neural Network. The first is a linear technique and the second a non-linear one. In both cases, the models developed using Pacific sea surface temperature were better at forecasting discharge at sites to the north of the Amazon River and those developed from Atlantic sea surface temperature at forecasting discharges at sites to the south of the Amazon River. Even though the use of a non-linear technique improved the accuracy of the models in general, it considerably improved the capacity of Atlantic sea surface temperature to forecast discharge. This general improvement was to some degree expected considering the very complex and non-linear mechanisms that transform precipitation into discharge.
|
|
2. |
- Uvo, Cíntia Bertacchi, et al.
(författare)
-
Seasonal runoff forecast for northern South America : A statistical model
- 1998
-
Ingår i: Water Resources Research. - 0043-1397. ; 34:12, s. 3515-3524
-
Tidskriftsartikel (refereegranskat)abstract
- The variability of water availability in the Amazon Basin is highly related to the sea surface temperature (SST) pattern over the Atlantic and the Pacific Oceans. The possibility of forecasting this variability one season in advance is a necessity for the correct management of water in the region for water supply, energy production, and flood control. Here we present the development of a statistical model to forecast seasonal discharge, one season in advance, at 12 sites in northeastern South America from Pacific and Atlantic Ocean SSTs. Results show that the model has a better capacity to forecast discharge at some sites than at others and that different oceanic regions have different influences over the basins. Discharge at subbasins in the northern part of the Amazon Basin are better forecasted from Pacific Ocean SSTs while discharges at subbasins in the southern part are better forecasted from Atlantic Ocean SSTs.
|
|
3. |
- Uvo, Cintia Bertacchi, et al.
(författare)
-
The relationships between tropical Pacific and Atlantic SST and northeast Brazil monthly precipitation
- 1998
-
Ingår i: Journal of Climate. - 0894-8755. ; 11:4, s. 551-562
-
Tidskriftsartikel (refereegranskat)abstract
- The monthly patterns of northest Brazil (NEB) precipitation are analyzed in relation to sea surface temperature (SST) in the tropical Pacific and Atlantic Oceans, using singular value decomposition. It is found that the relationships between precipitation and SST in both basins vary considerably throughout the rainy season (February-May). In January, equatorial Pacific SST is weakly correlated with precipitation in small areas of southern NEB, but Atlantic SST shows no significant correlation with regional precipitation. In February, Pacific SST is not well related to precipitation, but south equatorial Atlantic SST is positively correlated with preciptiation over the nrothern Nordeste, the latter most likely reflecting an anomalously early (or late) southward migration of the ITCZ precipitation zone. During March, equatorial Pacific SST is negatively correlated with Nordste precipitation, but no consistent relationship between precipitation and Atlantic SST is found. Atlantic SST-precipitation correlations for April and May are the strongest found among all months or either ocean. Precipiation in the Nordeste is positively correlated with SST in the south tropical Atlantic and negatively correlated with SST in the north tropical Atlantic. These relationships are storng enough to determine the structure of the seasonal mean SST-precipitation correlations, even though the corresponding patterns for the earlier months of the season are quite different. Pacific SST-precipitation correlations for April and May are similar to those for March. Extreme wet (dry) years for the Nordeste occur when both Pacific and Atlantic SST patterns for April and May occur simultaneously. A separate analysis reinforces previous findings in showig that SST in the tropical Pacific and the northern tropical Atlantic are positively correlated and that tropical Pacific-south Atlantic correlations are negligible. Time-lagged analyses show the potential for forecasting either seasonal mean or monthly precipitation patterns with some degree of skill. In some instances, individual monthly mean SST versus seasonal mean (February-May) precipitation relationships differ considerably from the corresponding monthly SST versus monthly precipitation relationships. It is argued that the seasonal mean relationships result from the relatively strong monthly relationships toward the end of the season, combined with the considerable persistence of SST in both oceans.
|
|