Detect signals of interdecadal climate variations from an enhanced suite of reconstructed precipitation products since 1850 using the historical station data from Global Historical Climatology Network and the dynamical patterns derived from Global Precipitation Climatology Project

Abstract:

This presentation describes the detection of interdecadal climate signals in a newly reconstructed precipitation data from 1850-present. Examples are on precipitation signatures of East Asian Monsoon (EAM), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillations (AMO). The new reconstruction dataset is an enhanced edition of a suite of global precipitation products reconstructed by Spectral Optimal Gridding of Precipitation Version 1.0 (SOGP 1.0). The maximum temporal coverage is 1850-present and the spatial coverage is quasi-global (75S, 75N). This enhanced version has three different temporal resolutions (5-day, monthly, and annual) and two different spatial resolutions (2.5 deg and 5.0 deg). It also has a friendly Graphical User Interface (GUI).

SOGP uses a multivariate regression method using an empirical orthogonal function (EOF) expansion. The Global Precipitation Climatology Project (GPCP) precipitation data from 1981-20010 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 according to the number of EOF modes used in the reconstruction. Our reconstructed 1900-2011 time series of the global average annual precipitation shows a 0.024 (mm/day)/100a trend, which is very close to the trend derived from the mean of 25 models of the CMIP5 (Coupled Model Intercomparison Project Phase 5). Our reconstruction has been validated by GPCP data after 1979. Our reconstruction successfully displays the 1877 El Nino (see the attached figure), which is considered a validation before 1900.

Our precipitation products are publically available online, including digital data, precipitation animations, computer codes, readme files, and the user manual. This work is a joint effort of San Diego State University (Sam Shen, Gregori Clarke, Christian Junjinger, Nancy Tafolla, Barbara Sperberg, and Melanie Thorn), UCLA (Yongkang Xue), and University of Maryland (Tom Smith and Phil Arkin) and supported in part by the U.S. National Science Foundation (Awards No. AGS-1419256 and AGS-1015957).