Description
This thesis provides a multivariate regression method to estimate the sampling errors of the annual global precipitation reconstructed by an empirical orthogonal function (EOF) expansion. The EOFs are computed using the Global Precipitation Climatology Project (GPCP) precipitation data from 1979-2008. The Global Historical Climatology Network (GHCN) gridded data (1900-2011) are used to calculate the regression coefficients for the reconstructions. A detailed analysis of our reconstructions sampling error is conducted for different EOF modes. The time series of the global average annual precipitation from 1900 to 2011 shows a trend of 0.024 (mm/day)/100a, which is in agreement to the trend derived from the mean of 25 models of the Coupled Model Intercomparison Project Phase 5. The reconstruction demonstrates that El Niño and La Niña precipitation are well reflected in the first two EOFs. The reconstruction validation in the GPCP period demonstrates skill at predicting oceanic precipitation from land stations. The error pattern analysis through comparison between reconstruction and GHCN suggests the importance of improving oceanic measurement of precipitation
