Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction

Abstract

Abstract Land–atmosphere (LA) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. In this study, the authors examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled Weather Research and Forecasting Model (WRF) forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystems in NASA's Land Information System (LIS-OPT/LIS-UE). The impact of the calibration on the 1) spinup of the land surface used as initial conditions and 2) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. In addition, the sensitivity of this approach to the period of calibration (dry, wet, or average) is investigated. Results show that the offline calibration is successful in providing improved initial conditions and land surface physics for the coupled simulations and in turn leads to systematic improvements in land–PBL fluxes and near-surface temperature and humidity forecasts. Impacts are larger during dry regimes, but calibration during either primarily wet or dry periods leads to improvements in coupled simulations due to the reduction in land surface model bias. Overall, these results provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.