This study assessed the uncertainty in estimating long-term (1971-2010) mean precipitation, its inter-annual variability, and linear trend of three network observation datasets over West Africa. A reference data, defined as a multi-dataset ensemble of precipitation observations of the Climate Research Unit (CRU) of the University of East Anglia, the Global Precipitation Climatology Centre (GPCC) and the University of Delaware (UDEL), all at horizontal resolutions of 0.5 ° by 0.5 ° were obtained and used in this study. Uncertainties in these climatological parameters of precipitation at both annual and seasonal time scales were examined in terms of inter-dataset variability using signal-to-noise ratio (SNR), correlation, root-mean-square errors and the normalised standard deviation. Results showed that the mean, inter-annual variability and trends climatology varied for different datasets. The three datasets had good agreement (SNR>5) in terms of the annual mean precipitation and its inter-annual variability in most parts of West Africa. However, the agreement between the datasets was poor in the very dry Sahel parts of northern Niger, Mali, and Mauritania (SNR ≤ 1) due to very little precipitation and possibility of relatively low station density in these regions of complex terrain. In terms of correlation (0.89 ≤ r ≤ 0.98), and normalised standard deviation, NSD (0.8 ≤ NSD ≤ 1.7), the uncertainties in the spatial variations in linear trend were larger than mean precipitation and their inter-annual variability for both annual and seasonal scales. The long-term annual precipitation trend in the region is highly uncertain except in a few small areas. observational errors are much more problematic, because their effects become relatively more pronounced as greater numbers of observations are aggregated. In this case, the author believed that averaging observations together from many different instruments/sources would tend to reduce the contribution of systematic observational errors to the uncertainty of the average. A number of researchers and institutions have developed observation-based gridded analysis datasets of global or regional coverage with fine spatial resolutions [8-14]. These network of observation datasets provide precipitation and/or surface air temperatures over extended periods of multiple decades at spatial resolutions of 0.5 ° or finer. This is, of course, a substantial improvement over previous generation data sets that are typically at much coarser (e.g. 2.5 °) horizontal resolutions [15]. These recent fine-scale datasets allow us to better examine the regional precipitation and temperature climatology and to perform more reliable evaluations of today's high-resolution climate simulations, especially over the regions of complex terrain, that are important for climate-change impact assessments and climate model evaluations [16].

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.

Climate extremes such as droughts and intense rainfall events are expected to strongly influence global/regional water resources in addition to the growing demands for freshwater. This study examines the impacts of precipitation extremes and human water usage on total water storage (TWS) over the Ganges-Brahmaputra-Meghna (GBM) River Basin in South Asia. Monthly TWS changes derived from the Gravity Recovery And Climate Experiment (GRACE) (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and soil moisture from three reanalyses are used to estimate new extreme indices. These indices are applied in conjunction with standardized precipitation indices (SPI) to explore the impacts of precipitation extremes on TWS in the region.The results indicate that although long-term precipitation do not indicate any significant trends over the two subbasins (Ganges and Brahmaputra-Meghna), there is significant decline in rainfall (9.0 6 4.0 mm/decade) over the Brahmaputra-Meghna River Basin from 1998 to 2014. Both river basins exhibit a rapid decline of TWS from 2002 to 2014 (Ganges: 12.2 6 3.4 km 3 /yr and Brahmaputra-Meghna: 9.1 6 2.7 km 3 /yr). While the Ganges River Basin has been regaining TWS (5.4 6 2.2 km 3 /yr) from 2010 onward, the BrahmaputraMeghna River Basin exhibits a further decline (13.0 6 3.2 km 3 /yr) in TWS from 2011 onward. The impact of human water consumption on TWS appears to be considerably higher in Ganges compared to Brahmaputra-Meghna, where it is mainly concentrated over Bangladesh. The interannual water storage dynamics are found to be strongly associated with meteorological forcing data such as precipitation. In particular, extreme drought conditions, such as those of 2006 and 2009, had profound negative impacts on the TWS, where groundwater resources are already being unsustainably exploited.