Benchmark, predictability, and forecast skill of terrestrial water storage based on CMIP6 decadal hindcasts and land surface ensemble simulations

Terrestrial water storage (TWS), including surface water storage, soil water storage, and groundwater storage, is critical for the global hydrological cycle and freshwater resources. A reliable decadal prediction of TWS can provide valuable information for sustainable managements of water resources and infrastructures in the face of climate change. Generally, the hydrological predictability mainly comes from two sources, i.e., initial conditions and boundary conditions. To date, the dependence of TWS forecast skill on the accuracy of initial hydrological conditions and decadal climate forecasts is not clear, and the benchmark skill remains unknown. In this work, we use decadal climate hindcasts from CMIP and perform hydrological ensemble simulations to estimate a baseline decadal forecast skill containing the two predictability sources information for TWS over global major river basins with an elasticity framework that considers varying skill of initial conditions and climate forecasts. With the incorporation of decadal climate forecast, our benchmark skill for TWS incorporated is significantly higher than initial conditions-based forecast skill over 25% and 31% basins for the leads of 1–4 and 3–6 years, especially over mid- and high-latitudes. Although the decadal precipitation forecast skill based on individual model is limited, the ensemble forecasts from multiple climate models are better than individuals. In addition, the standardized precipitation index (SPI) predictability and forecast skill from the latest CMIP6 decadal hindcast data are being investigated. Preliminary results suggest that predictability and forecast skill of SPI are positively correlated in general, and the predictability is higher than forecast skill, indicating the room for improving hydro-climate forecast. Our findings provide a new benchmark for verifying the success of decadal TWS forecasts and imply the possibility of improving decadal hydrological forecasts by using dynamical climate prediction information which still has room for improvement.