Reconstructed 2.5-Degree Monthly Precipitation Data for the Tibetan Plateau Region Since 1901

Abstract:

This presentation describes a monthly 2.5-by-2.5 lat-lon Tibetan Plateau (TP) region’s precipitation data product since January 1901, reconstructed by a multivariate regression method using an empirical orthogonal function (EOF) expansion. The Global Precipitation Climatology Project (GPCP) precipitation data from 1981-2010 are used to calculate the EOFs. The Global Historical Climatology Network (GHCN) gridded data are used to calculate the regression coefficients for reconstructions. The sampling errors of the reconstruction are analyzed in detail for different number of EOF modes used in the reconstruction. The spatial average time series of the reconstructed TP precipitation from 1910-2001 has a small positive trend: 0.006 (mm/day)/100a. This positive trend is consistent with other studies based on the observed data from 71 TP stations from 1961-2001, in contrast to the negative trends in the reanalysis data from 1961-2001 from both the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the European Centre for Medium-Range Weather Forecasts (ERA-40).

The validation of our reconstruction data with GPCP from 1979-present makes it reliable to use this reconstruction dataset as the benchmark data for TP precipitation models. Our reconstructed TP precipitation for over 100 years will help the climate modeling community to improve TP precipitation mechanisms, reduce the systematic difference between the data from observations and climate models, and analyze the interdecadal variations of the TP climate.

Our TP precipitation product is publically available online, including digital data, precipitation animations, computer codes, readme files, and the user manual. This work is a joint effort between San Diego State University (Sam Shen, Nancy Tafolla, Gregori Clarke, Greg Behm, Barbara Sperberg, and Melanie Thorn), Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Tandong Yao, and Jing Gao), and University of Maryland (Phil Arkin, and Tom Smith) and is supported in part by the U.S. National Science Foundation (Awards No. AGS-1419526 and AGS-1015957).