Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

作     者：Kazuyoshi SUZUKI Milija ZUPANSKI 

作者机构：Institute of Arctic Climate and Environment Change Research (IACE)Japan Agency for Marine-Earth Science and Technology (JAMSTEC)Yokohama236-0001Japan Cooperative Institute for Research in the AtmosphereColorado State UniversityFort CollinsCO 80523-1375USA 

出 版 物：《Frontiers of Earth Science》 (地球科学前沿（英文版）)

年 卷 期：2018年第12卷第4期

页      面：672-682页

核心收录：

学科分类：07[理学] 0708[理学-地球物理学] 0704[理学-天文学] 

基　　金：supported by a Grant-in-Aid for Scientific Research (C) a Grant-in-Aid for Challenging Exploratory Research partial support from the Office of Naval Research under contract 

主　　题：ensemble simulation land-atmosphere interaction ensemble spread vertical temperature snow prediction 

摘      要：In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere–land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.