Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected uncertainties associated with physical parameterizations. In this study, we propose a framework quantify reduce parameterization surface fluxes L-A coupling. An ensemble WRF simulations different schemes is simulate over Amazon region. parameterizations investigated include cloud microphysics (MP), land processes (LSM), planetary boundary layer (PBL), (SL), cumulus (CU). We perform 120 using combinations six MPs, three LSMs, PBLs SLs CUs. measurements from GoAMAZON field campaign satellite data are evaluate performance. A Multi-way analysis variance (ANOVA) approach applied relative importance physics on Tukey's test sort that have no significant differences into one group. suite result best corresponding variables selected based Taylor skill score. Results show their interaction vary interest. For example, CU was most important process modulating soil moisture, 2 m-humidity, latent heat, net radiation. LSM showed dominant effects m-temperature also has largest impact sensible heat lifting condensation level. ensembles much narrower ranges interest than priori ensemble. study roles interactions, processes, provide insights for improving