Correlations between precipitation patterns in southern Mexico and the El Niño sea surface temperature index

This source preferred by Duncan Golicher

Authors: Golicher, D., Ramírez-Marcial, N. and Levy-Tacher, S.I.

Journal: Interciencia

Volume: 31

Pages: 80-87

ISSN: 0378-1844

Historical data for regional climate research in the tropics is typified by extreme unevenness in both temporal and spatial coverage.

Spatial interpolation techniques were applied in order to fill the gaps in time series of rainfall records for the state of Chiapas. The method involved iterated universal Kriging that combines a spatial covariance function with a polynomial trend surface. Automated outlier removal was used to prevent spurious records distorting the results. The procedure was applied 612 times in order to produce complete monthly time series from 1950 to 2000. In order to trace temporal trends the time series were decomposed into seasonal, trend and irregular components and analyzed using loess smoothing (STL). The seasonal values were removed, and the remainder smoothed to find the trend. An identical procedure was applied to the El Niño3.4 index. The trend component of each data set was analyzed for autocorrelation and cross correlation. The autocorrelation function for the standardized number of days with rainfall shows significant positive correlations between data points around three to four months apart. There is significant negative cross correlation between the standardized El Niño sea surface temperature index and rainfall.

The technique thus led to a clear description of a pattern that might be used in order to partly predict precipitation driven events such as floods and wildfire.

This data was imported from Scopus:

Authors: Golicher, J.D., Ramírez-Marcíal, N. and Tacher, S.I.L.

Journal: Handbook of Environmental Chemistry, Volume 5: Water Pollution

Volume: 31

Issue: 2

Pages: 80-86

eISSN: 0378-1844

ISSN: 1433-6863

Historical data for regional climate research in the tropics is typified by extreme unevenness in both temporal and spatial coverage. Spatial interpolation techniques were applied in order to fill the gaps in time series of rainfall records for the state of Chiapas. The method involved iterated universal Kriging that combines a spatial covariance function with a polynomial trend surface. Automated outlier removal was used to prevent spurious records distorting the results. The procedure was applied 612 times in order to produce complete monthly time series from 1950 to 2000. In order to trace temporal trends the time series were decomposed into seasonal, trend and irregular components and analyzed using loess smoothing (STL). The seasonal valueswere removed, and the remainder smoothed to find the trend. An identical procedure was applied to the El Ninõ3.4 index. The trend component of each data set was analyzed for autocorrelation and cross correlation. The autocorrelation function for the standardized number of days with rainfall shows significant positive correlations between data points around three to four months apart. There is significant negative cross correlation between the standardized El Niño sea surface temperature index and rainfall. The technique thus led to a clear description of a pattern that might be used in order to partly predict precipitation driven events such as floods and wildfire.

The data on this page was last updated at 05:18 on July 23, 2019.