Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes

Simulating land surface hydrological states and fluxes requires a comprehensive set of atmospheric forcing data at consistent temporal and spatial scales. At the continental-to-global scale, such data are not available except in weather reanalysis products. Unfortunately, reanalysis products are often biased due to errors in the host weather forecast model. This paper explores whether the error in model predictions of the initial soil moisture status and hydrological fluxes can be minimized through a bias reduction scheme to the European Centre for Medium Range Weather Forecast and National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis products. The bias reduction scheme uses both difference and ratio corrections based upon global observational data sets. Both the corrected and original forcing data were used to simulate land surface states and fluxes with a land surface model (LSM) over North America. Soil moisture, snow depth, and runoff output from the LSM are compared to observations to assess the impact of the bias correction on simulation accuracy. Results of this study demonstrate the sensitivity of LSMs to bias in the forcing data, and that implementation of a bias reduction scheme reduces errors to the simulation of soil moisture, runoff, and snow water equivalence. Accordingly, the initial soil moisture fields produced should be more representative of actual conditions, and therefore more useful to the climate modeling community. Results suggest that modelers using reanalysis products for forcing LSMs, in particular for the establishment of initial conditions, should consider a bias reduction strategy when preparing their input forcing fields.