Evaluation of land water storage prediction skill in CMIP5 decadal hindcasts by means of a GRACE-based data set

Reliable predictions of terrestrial water storage (TWS) changes for the next couple of years would be extremely valuable for, e.g., agriculture and water management. In contrast to long-term projections of future climate conditions, so-called decadal predictions do not depend on prescribed CO₂ scenarios but provide unconditional forecasts similar to numerical weather models. Therefore, opposed to climate projections, decadal predictions (or hindcasts, if run for the past) can directly be compared to observations. Here, we evaluate decadal hindcasts of TWS related variables from an ensemble of 5 coupled CMIP5 climate models against a TWS data set based on GRACE satellite observations.

Since data from the CMIP5 models and GRACE is jointly available in only 9 years, we access a GRACE-like reconstruction of TWS derived from precipitation and temperature data sets (Humphrey and Gudmundsson, 2019), which expands the analysis time-frame to 41 years. The skill of the decadal hindcasts is assessed by means of anomaly correlations and root-mean-square deviations (RMSD) for the yearly global average and aggregated over different climate zones. Furthermore, we compute global maps of correlation and RMSD.

We find that at least for the first two prediction years the decadal model experiments clearly outperform the classical climate projections, regionally even for the third year. We can thereby demonstrate that the observation type “terrestrial water storage” as available from the GRACE and GRACE-FO missions is suitable as additional data set in the validation and/or calibration of climate model experiments.