EXP1 ==== EXP1 of GSWP3 is a long-term retrospective experiment. It aims to investigate how interactions among energy-water- carbon cycles have changed spanning 1850-2010 using a multi-model approach over a global 0.5 deg. land grid. Since most land surface models do not have full or uniform representations of water and carbon cycles, we will include various types of models (e.g., so-called first, second and third generation land surface models, hydrologic models, ecological models, and dynamic vegetation models) and will consider time evolution of coupled processes related parameters such as land use/cover, LAI, and CO2. Also, a standard product of the project will be an extensive set of land fluxes and state variables, which has the potential to serve as a long-term land-surface reanalysis. It also will serve as a reference set for the long-term variability of various processes in the terrestrial hydro-energy-eco system that respond to surrounding large-scale climate variability, such as changes in extremes (e.g., flood and drought), land carbon balances, and water and energy inputs to the atmosphere. Boundary Conditions ------------------- Surface meteorology ~~~~~~~~~~~~~~~~~~~ retrospective atmospheric boundary conditions (9 variables: Rainfall, Snowfall, 2m Air Temperature, 2m Specific Humidity, Surface Pressure, Downward Shortwave Radiation, Downward Longwave Radiation, 10m Wind Speed, and Cloud Cover Fraction) for 1901-2010 in 3-hourly resolution are generated. 20th Century Reanalysis (20CR) [compo2011]_ [Compo el al., 2011] on global 2° resolution is dynamically downscaled into T248 (~0.5°) grid using a spectral nudging technique [Yoshimura and Kanamitsu 2008] in a Global Spectral Model (GSM) :ref:`[Figure 2]`. This successfully keeps the low frequency signal of original reanalysis, providing additional high frequency signals, which are lacking in previous products [e.g., Weedon et al., 2011]. It is essential to investigate phenomena at higher spatiotemporal scales such as extreme events. In order to relieve known artifacts (e.g., ripple patterns and persistent overcast in high latitude region), additional techniques, such as single ensemble correction [Yoshimura and Kanamitsu, 2013] and vertically weighted damping [Hong and Chang, 2012], are applied. Model biases in the downscaled 20CR are corrected using observational data (e.g., GPCC for precipitation, SRB for short/long wave radiation, and CRU for air temperature and daily temperature range). In addition to previously introduced bias correction algorithms [e.g., Weedon et al., 2011], variability in higher temporal (