Abstract
Abstract. The quality of soil-moisture simulation using land surface models depends largely on the accuracy of the meteorological forcing data. We investigated how to reduce the uncertainty arising from meteorological forcings in a simulation by adopting a multiple meteorological forcing ensemble approach. Simulations by the Community Land Model version 3.5 (CLM3.5) over mainland China were conducted using four different meteorological forcings, and the four sets of soil-moisture data related to the simulations were then merged using simple arithmetical averaging and Bayesian model averaging (BMA) ensemble approaches. BMA is a statistical post-processing procedure for producing calibrated and sharp predictive probability density functions (PDFs), which is a weighted average of PDFs centered on the bias-corrected forecasts from a set of individual ensemble members based on their probabilistic likelihood measures. Compared to in situ observations, the four simulations captured the spatial and seasonal variations of soil moisture in most cases with some mean bias. They performed differently when simulating the seasonal phases in the annual cycle, the interannual variation and the magnitude of observed soil moisture over different subregions of mainland China, but no individual meteorological forcing performed best for all subregions. The simple arithmetical average ensemble product outperformed most, but not all, individual members over most of the subregions. The BMA ensemble product performed better than simple arithmetical averaging, and performed best for all fields over most of the subregions. The BMA ensemble approach applied to the ensemble simulation reproduced anomalies and seasonal variations in observed soil-moisture values, and simulated the mean soil moisture. It is presented here as a promising way for reproducing long-term, high-resolution spatial and temporal soil-moisture data.
Highlights
Soil moisture plays a very important role in the global hydrological cycle and energy balance within the land–atmosphere interaction in the climate system (Robock et al, 1998)
Many previous studies, which applied the Bayesian model averaging (BMA) approach to a range of different weather and seasonal climate ensemble forecasts, have demonstrated that it is superior to the simple arithmetical averaging method and provides a quantitative description of total predictive uncertainty through the probability density functions (PDFs) (Raftery et al, 2005; Duan et al, 2007; Vrugt et al, 2008; Liu et al, 2013)
We briefly describe the land surface models (LSMs) CLM3.5, four various meteorological forcings and in situ observation of soil moisture in China in Sect
Summary
Soil moisture plays a very important role in the global hydrological cycle and energy balance within the land–atmosphere interaction in the climate system (Robock et al, 1998) It is a crucial variable for monitoring land surface conditions that force extreme events such as drought and flood (Wang et al, 2009, 2011; Albergel et al, 2012). Many previous studies, which applied the BMA approach to a range of different weather and seasonal climate ensemble forecasts, have demonstrated that it is superior to the simple arithmetical averaging method and provides a quantitative description of total predictive uncertainty through the PDF (Raftery et al, 2005; Duan et al, 2007; Vrugt et al, 2008; Liu et al, 2013). We briefly describe the LSM CLM3.5, four various meteorological forcings and in situ observation of soil moisture in China in Sect.
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