A 53‐year forcing data set for land surface models

Abstract

[1] As most variables describing the state of the surface are not directly observable, we have to use land surface models in order to reconstruct an estimate of their evolution. These large-scale land surface models often require high-quality forcing data with a subdiurnal sampling. Building these data sets is a major challenge but an essential step for estimating the land surface water budget, which is a crucial part of climate change prediction. To study the interannual variability of surface conditions over the last half century, we have built a 53-year forcing data set, named NCC. NCC has a 6-hourly time step from 1948 to 2000 and a spatial resolution of 1°Â 1°. It is based on the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project and a number of independent in situ observations. In this study we show the adjustments which need to be applied to the reanalysis and how they impact the simulated continental water balance. The model outputs are validated with the observed discharges of the world's 10 largest rivers to estimate the combined errors of the forcing data and the land surface model. The seasonal and interannual variations of these discharges are used for this validation. Five numerical experiments have been carried out. They used the forcing data sets obtained after each step of data adjustment and the forcing of the Global Soil Wetness Project 2 as inputs for the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model. The quality of forcing data is improved after each adjustment. The precipitation correction gives the most important improvement in the simulated river discharges, while the temperature correction has a significant effect only at high latitudes. The radiation correction also improves the forcing quality, especially in term of discharge amplitude. The NCC forcing data set can be used to study the water budget over many areas and catchment basins that have not been yet analyzed in this study. With its period of 53 years, NCC can also be used to evaluate the trends of terrestrial water storage in particular regions.Citation: Ngo-Duc, T., J. Polcher, and K. Laval (2005), A 53-year forcing data set for land surface models,

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

“…However, there are large differences in the wet-day frequency correction, rainfall/snowfall partition, and wind-induced undercatch correction. There is no wet-day frequency correction in some studies [Zhao and Dirmeyer, 2003;Ngo-Duc et al, 2005;Hanasaki et al, 2008]. However, many studies account for some wet-day frequency correction [Sheffield et al, 2006;Hirabayashi et al, 2008a;Weedon et al, 2011Weedon et al, , 2014Iizumi et al, 2014], although the methodological details vary by study.…”

“…The correction method used for the downward shortwave radiation flux was the same as that in previous work [Ngo-Duc et al, 2005;Iizumi et al, 2014], which was relatively simple compared to other forcing data sets [Zhao and Dirmeyer, 2003;Sheffield et al, 2006;Weedon et al, 2011Weedon et al, , 2014. Regarding the downward longwave radiation flux, we made the correction for the 12 monthly climatologies of the climatic variable as in Ngo-Duc et al [2005] and Iizumi et al [2014], which is different than the method based solely on elevation correction [Weedon et al, 2011[Weedon et al, , 2014 but simpler than the method of Sheffield et al [2006] that uses CRU-TS cloud cover data and surface radiation budget downward longwave radiation flux data as references.…”

“…All the forcing data sets used in this study, including S14FD, would commonly have this problem. There is a difference in the correction method for wind speed because this study corrected the 12 monthly climatologies of the climatic variable, as in Iizumi et al [2014], whereas many other forcing data sets use reanalysis wind speed data without any correction [Zhao and Dirmeyer, 2003;Ngo-Duc et al, 2005;Sheffield et al, 2006;Weedon et al, 2011Weedon et al, , 2014 or with elevation correction based on the logarithm profile [Hirabayashi et al, 2008b;Hanasaki et al, 2008].…”

“…A2.1.1. Surface Pressure The reanalysis surface pressure at the 0.5°CRU mean elevation (https://crudata.uea.ac.uk/~timm/grid/CRU_ TS_2_1.html) was calculated by incorporating the effect of elevation correction on the reanalysis temperature as in previous work [Ngo-Duc et al, 2005;Weedon et al, 2011Weedon et al, , 2014: …”

“…However, many studies account for some wet-day frequency correction [Sheffield et al, 2006;Hirabayashi et al, 2008a;Weedon et al, 2011Weedon et al, , 2014Iizumi et al, 2014], although the methodological details vary by study. The rainfall/snowfall partition was made using various methods including the use of original reanalysis rainfall and snowfall rates [Zhao and Dirmeyer, 2003;Weedon et al, 2011], a simple temperature threshold [Ngo-Duc et al, 2005] and an empirical function of wet bulb temperature [Hirabayashi et al, 2008b;Hanasaki et al, 2008]. Sheffield et al [2006] and Iizumi et al [2014] did not separate rainfall and snowfall.…”