Uncertainties in estimating spatial and interannual variations in precipitation climatology in the India–Tibet region from multiple gridded precipitation datasets

Abstract

Uncertainty in calculating the spatial-and interannual variability of precipitation over India and Tibet from widely used gridded precipitation datasets is examined for the 29-year period from 1979 to 2007. Uncertainty is defined in terms of the spread among the variability calculated from multiple datasets, a useful method when multiple datasets of similar or unknown accuracy are available for analyses. The resulting uncertainty varies for regions and seasons. Geographical variations are clearly seen in the signal-to-noise ratio (SNR), with the largest uncertainty in the Jammu and Kashmir (J&K), western India-eastern Pakistan, Tibet, Hindu-Kush mountains, and Western Ghats which are characterized by either dry climate or complex terrain, or both. Seasonally, the uncertainty is larger for the December-February period (DJF) than for the June-September period (JJAS) in most of the region except J&K which is characterized by two wet seasons: winter and summer. The uncertainty in the interannual variability also varies according to regions especially in J&K where the calculated interannual variability varies by nearly a factor of two among the datasets. The uncertainty range in the calculated interannual variability is determined largely by two gauge-based data of the finest resolution, Asian Precipitation -Highly-Resolved Observational Data Integration Towards Evaluation of water resources (smallest) and India Meteorological Department (largest) in all regions.The regional and seasonal variations in the uncertainty do not appear to depend on either the spatial resolution or the length of records. This implies that analysis methodology such as the quality control of input data, spatial/temporal interpolation, and retrieval algorithms used in producing these gridded datasets plays a crucial role in determining the characteristics of precipitation climatology represented by individual datasets. Our results show that calculating precipitation characteristics must be accompanied by careful examinations of uncertainty among available datasets, especially for dry seasons and arid/mountainous regions.

Uncertainties in calculating precipitation climatology in East Asia

“…In addition, the distance between the point (0 • , 1.0) and a data point in this diagram corresponds to the centered root mean square error (RMSE). This diagram has become one of the most widely used methodologies in climate studies for presenting the evaluations of multiple models and/or variables or intercomparison of multiple data sets (IPCC, 2001;Taylor, 2001;Duffy et al, 2006;Gleckler et al, 2008;Kim et al, 2013Kim et al, , 2015. The signal-to-noise ratio (SNR) for the properties shown in Fig.…”

“…Because it is practically impossible to determine which data set is more accurate, assessing the reliability of climatological properties calculated from various data sets as well as the expected range of uncertainty due to the diversity of these data sets is crucial in calculating regional climatology (Kim et al, 2015). In this section, the range of uncertainty in the three precipitation characteristics is measured in terms of the SNR and the agreement between individual data sets and the multi-data ensemble mean in terms of the spatial pattern correlation and the magnitude of spatial variability following the methodology of Kim et al (2015), using the Taylor diagram.…”

“…These two studies have revealed independently that there exist substantial amounts of differences amongst today's gridded precipitation data sets resulting in uncertainties in the calculated precipitation climatology and that the uncertainty and the spread amongst multiple data sets vary according to regions as well as seasons. Kim et al (2015) further revealed that uncertainties in the calculated precipitation climatology defined relative to their climatological means are generally larger in the dry regions and/or local dry seasons. These two studies strongly suggest that uncertainty due to the differences between various data sets needs to be examined and quantified in all climate studies because the absolute accuracy of individual data sets cannot be quantified in practice.…”

“…These new data sets also introduce uncertainties in calculating regional climate characteristics because of the differences amongst them. Based on these concerns, two recent studies by Prakash et al (2014) and Kim et al (2015) examined uncertainty in calculating precipitation climatology over India and its surrounding regions using multiple precipitation analysis data sets. These two studies have revealed independently that there exist substantial amounts of differences amongst today's gridded precipitation data sets resulting in uncertainties in the calculated precipitation climatology and that the uncertainty and the spread amongst multiple data sets vary according to regions as well as seasons.…”

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

“…While high-resolution RCMs are computationally less expensive and have the ability to resolve finer scale orographic precipitation, they require the specification of lateral boundary conditions, which inhibit self-consistent interactions between global and regional scales of motion (Fox-Rabinovitz et al 2006). Recently many evaluation studies using RCMs have been conducted through Coordinated Regional Climate Downscaling Experiment (CORDEX) program (Kim et al 2014(Kim et al , 2015Huang et al 2015;Zhou et al 2016;Zou et al 2016;Pattnayak et al 2017). Especially, there have been many interests and efforts regarding added value of RCMs (Di Luca et al 2015;Torma et al 2015).…”