場所：筑波大学 計算科学研究センター 会議室A
Understand and improve uncertainties in land-surface model
Research Applications Laboratory (RAL)
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Uncertainties in land-surface models (LSMs) parameterization and parameters, in atmosphere forcing conditions used to drive LSMs severely limit their applications in understanding the role of land-atmosphere interactions on weather and climate. To assess these uncertainties, we conducted physical ensemble simulations for selected observation sites. The Noah with multiparameterization (Noah-MP) community land model was used to perform 1152 physics ensemble experiments. We will present results of using two statistical methods (natural selection and Tukey’s test) to identify the range of uncertainties associated with atmospheric forcing conditions, vegetation parameter, and sub-processes, and to mitigate those uncertainties to obtain similar performance to the ensemble mean of the “best” ensemble experiment. We will also discuss impacts of these uncertainties on regional climate simulations.
Advancing the understanding of the nexus among food, energy, and water systems has recently emerged as a new science frontier, and the research community started modeling agricultural management in earth-system models to develop an integrated modeling tool for investigating relevant land-atmosphere interactions and agriculture sustainability issues. We discuss progress in developing of agricultural management (crop-growth, irrigation, tile drainage) models in Noah-MP and WRF-Crop. Especially, we will focus on the uncertainties in transitioning these agriculture models from field scales to continental scales.