Land surface initialization using an offline CLM3 simulation with the GSWP-2 forcing dataset and its impacton CAM3 simulations of the boreal summer climate

The impacts of initialized land surface conditions on the monthly prediction were investigated using ensemble simulations from the Community Atmosphere Model version 3 (CAM3). The land surface initialization was based on an offline calculation of Community Land Model version 3 driven by observation-based meteorological forcings from the Global Soil Wetness Project 2 (GSWP2). A simple but effective correction method was applied to the GSWP2 forcings prior to the offline calculation to reduce the discrepancies between the observation-forced land surface conditions and the modeling system, which can cause climate drift and initial shock problems. The climatological mean of GSWP2 forcings was adjusted to that of the target model (CAM3), while the monthly anomalies were scaled to the model statistics and high-frequency synoptic variabilities were included. Ensemble hindcast experiments with and without land surface initialization were conducted for the boreal summer (May-September), for 1983-95. The initialization process is shown to prevent climate drift and to transfer the atmospheric anomalies to the land surface memory. Statistical analyses of the simulation results reveal that the land surface initialization increased the externally forced variance over most continental regions, which is translated to enhanced potential predictability, particularly for regions with strong land-atmosphere coupling.