Quantifying the impacts of ENSO and IOD on rain gauge and remotely sensed precipitation products over Australia

Abstract

2016. Quantifying the impacts of ENSO and IOD on rain gauge and remotely sensed precipitation products over Australia. Remote Sensing of Environment 172 , pp. (2016) Quantifying the impacts of ENSO and IOD on rain gauge and remotely sensed precipitation products over Australia. AbstractLarge-scale ocean-atmospheric phenomena like the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) have significant influence on Australia's precipitation variability. In this study, multi-linear regression (MLR) and complex empirical orthogonal function (CEOF) analyses were applied to isolate (i) the continental precipitation variations likely associated with ENSO and IOD, here referred to as 'ENSO/IOD mode', and (ii) the variability not associated with ENSO/IOD (the 'non-ENSO/IOD mode'). The first is of interest due to its dominant influence on inter-annual variability, while the second may reveal lower frequency variability or trends. Precipitation products used for this study included gridded rainfall estimates derived by interpolation of rain gauge data from the Australian Bureau of Meteorology (BoM), two satellite remote sensing products (CHIRP and TRMM TMPA version 7), and two weather forecast model re-analysis products (ERA-Interim and MERRA). The products covered the period 1981-2014 except TMPA (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). Statistical and frequency-based inter-comparisons were performed to evaluate the seasonal and long-term skills of various rainfall products against the BoM product. The results indicate that linear trends in rainfall during 1981-2014 were largely attributable to ENSO and IOD. Both intra-annual and seasonal rainfall changes associated with ENSO and IOD increased from 1991 to 2014. Among the continent's 13 major river basins, the greatest precipitation variations associated to ENSO/IOD were found over the Northern and North East Coast, while the smallest contributions were for Tasmania and the South West Coast basins. We also found that although the assessed products show comparable spatial variability of rainfall over Australia, systematic seasonal differences exist that were more pronounced during the ENSO and IOD events.

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

“…ENSO is a large-scale ocean-atmosphere interaction in the Tropical Pacific, which affects the climate of many regions of the Earth (Trenberth 1990;Forootan et al 2016). El Niño refers to the negative phase on ENSO, and its opposite phase is known as La Niña.…”

“…In other words, to generate a synthetic TWS data, we account for ENSO's immediate and out-of-phase influence on TWS, with a fundamental assumption that the temporal behavior of TWS changes due to ENSO is similar to SOI. This might be in reality not perfectly true (see also Forootan et al 2016), but the influence of this assumption is not strong enough to alter the results of our assessment, during which we try to measure the efficiency of decomposition techniques rather than focusing on a realistic estimation of climate impacts on geophysical data. We exclude S 2 ðtÞ and HðS 2 ðtÞÞ while fitting Eq.…”

“…Non-stationary is introduced in these techniques by incorporating time and/or space lag information while estimating statistical moments (for more details, see, e.g., Hannachi et al 2007). Various applications indicate a better performance of these extensions when extracting non-stationary behaviors in a few dominant modes (see, e.g., Rangelova et al 2012;Forootan et al 2016).…”

Large-Scale Total Water Storage and Water Flux Changes over the Arid and Semiarid Parts of the Middle East from GRACE and Reanalysis Products

“…• ENSO is the largest inter-annual climate variability phenomenon in the Tropical Pacific, which affects the climate of many regions of the Earth (Trenberth 1990;Forootan et al 2016). El Niño refers to the negative phase on ENSO that brings winter precipitation to the Middle East and the opposite phase La Niña causes less than normal precipitation variability (Nazemosadat and Cordery 2000).…”

“…Their results indicate that the TWF derived from reanalysis products might represent limited skills particularly due to biases, and the effects of anthropogenic modification that are particularly difficult to quantify (Simmons et al 2010). Compared to in situ observations, skill of the reanalyses might change from a region to another (see, e.g., Forootan et al 2016;Khandu et al 2016). On the other hand, Rodell et al (2004a) indicated that the temporal rate of changes in GRACE TWS is directly related to the changes in TWF through the water balance equation.…”