气候变化领域集成服务门户(CCPortal): An Observation-Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

CCPortal

DOI	10.1029/2020MS002083
	An Observation-Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model
	Kolassa J.; Reichle R.H.; Koster R.D.; Liu Q.; Mahanama S.; Zeng F.-W.
发表日期	2020
ISSN	19422466
卷号	12 期号:9
英文摘要	An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment-CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment-CN model are divided into three subtypes, based on the bias between the model-simulated and MODIS-observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single-PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment-CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements. © 2020. The Authors.
英文关键词	model calibration; particle swarm optimization; photosynthesis; plant functional types; vegetation modeling
语种	英语
scopus关键词	Catchments; Forestry; Mean square error; Microgrids; NASA; Runoff; Surface measurement; Vegetation; Land surface modeling; Moderate resolution imaging spectroradiometer; Photosynthetically active radiation; Plant functional type; Root mean square errors; Temporal correlations; Vegetation parameters; Vegetation phenology; Radiometers; calibration; catchment; empirical analysis; global change; land surface; MODIS; numerical model; observational method; phenology; vegetation
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156650
作者单位	Universities Space Research Association, Columbia, MD, United States; Global Modeling and Assimilation Office, NASA Goddard Spaceflight Center, Greenbelt, MD, United States; Science Systems and Applications Inc., Lanham, MD, United States
推荐引用方式 GB/T 7714	Kolassa J.,Reichle R.H.,Koster R.D.,et al. An Observation-Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model[J],2020,12(9).
APA	Kolassa J.,Reichle R.H.,Koster R.D.,Liu Q.,Mahanama S.,&Zeng F.-W..(2020).An Observation-Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model.Journal of Advances in Modeling Earth Systems,12(9).
MLA	Kolassa J.,et al."An Observation-Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model".Journal of Advances in Modeling Earth Systems 12.9(2020).