Analysis of South American climate and teleconnection indices
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abstract
Oceanic heat anomalies affect climate in remote regions through the atmospheric cycle. South America (SA) was the first region found associated with EI Niño, which affects the fishery, agriculture, forestry, and livestock industry of SA. As approximately 60% of the total water is used for agriculture, climate changes in SA caused by ocean anomalies have led to the variability of available water, especially for irrigation water. Where the precipitation is low and/or the temperature is high, the availability and quality of water resources are under pressure. For instance, droughts associated with La Niña severely limited water supply and irrigation requirements between 25°S - 40°S in west-central Argentina and central Chile. In order to study the relationship between ocean variability and the climate of SA, 19 teleconnection indices (TI) related to Ocean abnormity are considered. The 19 indices are: the sea surface temperature (SST) and their anomaly in 4 Niño regions (SST1 + 2, SST3, SST3.4, SST4, ANOM1 + 2, ANOM3, ANOM3.4, ANOM4), Southern Oscillation Index (SOI), Oceanic Niño Index (ONI), Outgoing Longwave Radiation (OLR), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Pacific-North America (PNA), Atlantic Multi-decadal Oscillation (AMO), West and East of Indian Ocean Dipole (IODW, IODE), and the difference between IODW and IODE (IODd). High-resolution gridded climate data (1982-2016) from the Global Precipitation Climatology Centre (GPCC), the Climate Prediction Center (CPC), and the National Centers for Environmental Prediction (NCEP) are applied for correlation analyses. The results show that the 89.4% area of South American climate has a significant correlation with the SST in Niño region 1 + 2, the mean correlation coefficient is 0.55 for NCEP precipitation and 0.54 for CPC temperature. The lag duration for the remote correlation is around 2-3 months. It is the first attempt to analyze the correlation relationship based on 19 TIs, which can provide comprehensive insight into the climate of SA at a high-resolution scale. These findings are helpful for identifying the sensitive factors that affect climate in SA, for projecting the climate variables of SA, and for managing the irrigation water resources of SA.
