Bias and Trend Correction of Precipitation Datasets to Force Ocean Models

A novel method to adjust the precipitation produced by atmospheric reanalyses using observational con-straints to force ocean models is described. The method allows the preservation of the qualities of the high-resolution and high-frequency output from the reanalyses while eliminating their bias and spurious trends. The method is shown to be ro-bust to degradation in both space and time of the observation dataset. This method is applied to the ERA-Interim precipitation dataset using the Global Precipitation Climatology Project (GPCP) v2.3 as the observational reference in order to create a debiased dataset that can be used to force ocean models. The produced debiased dataset is then compared to ERA-Interim and GPCP in a suite of forced ice–ocean numerical experiments using the GFDL OM4 model. Ocean states obtained with the new precipitation dataset are consistent with results from GPCP-forced experiments with respect to global metrics but produces the extra sea surface salinity variability at the time scales unresolved by the observation-based dataset. Discrepancies between modeled and observed freshwater fluxes are discussed as well as the strategies to mitigate them and their impacts.