Improving Land Surface Temperature Simulation in CoLM Over the Tibetan Plateau Through Fractional Vegetation Cover Derived From a Remotely Sensed Clumping Index and Model‐Simulated Leaf Area Index

Abstract

Parameterizations of fractional vegetation cover (FVC) in land surface models have important effects on simulations of surface energy budget, especially in arid and semiarid regions. This study uses a FVC scheme in which FVC is derived from leaf area index and a remotely sensed clumping index. The performance of the new scheme (SMFVC) is evaluated against Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and in situ soil temperature observations, together with two other FVC schemes, a general FVC scheme (CTL) based on land cover map and a climatology‐based FVC scheme (RSFVC) that uses long‐term remotely sensed Normalized Difference Vegetation Index of MODIS. The three FVC schemes were implemented in the Common Land Model (CoLM) and applied in the Tibetan Plateau using the same forcing data and default parameters. Our results demonstrate that FVC schemes have significant influence on the CoLM performance: (1) the RSFVC and SMFVC schemes significantly reduce the LST biases found in CTL, particularly in grassland and during summer; (2) soil temperature evaluation by in situ observations from three networks on the Tibetan Plateau corroborates the LST results; and (3) the improvements are mainly related to representing temporal (seasonal) variability and subgrid heterogeneity of FVC, which improves surface albedo and surface energy balance. In other words, by including more vegetation characteristics, such as using a clumping index, land surface models may better simulate surface vegetation condition and further better represent the land surface energy budget over the Tibetan Plateau.

Plant Hydraulic Stress Strategy Improves Model Predictions of the Response of Gross Primary Productivity to Drought Across China

“…The overestimation could be attributed to the uncertainty in meteorological forcing data, which was caused by insufficient meteorological stations (Wang et al, 2018) and the overestimated precipitation in the satellite‐based reanalysis data (e.g., TRMM) in the TI region (Tong et al, 2014). The uncertainty in the meteorological forcing data clearly affected the simulation of vegetation growth process in LSMs (Li et al, 2019), and eventually induced bias in GPP estimation. For example, the LAI derived from MODIS product was reported to be averagely 0.70 during 2000–2015 in the TI region (Tian et al, 2018), while the simulated LAI in the present study was larger than MODIS in the majority area of TI region (Figure S8), with the mean annual value being 0.94–0.98 and 1.38–1.47 from simulations with CRUF and CHNF data, respectively.…”

“…The overestimation could be attributed to the uncertainty in meteorological forcing data, which was caused by insufficient meteorological stations (Wang et al, 2018) and the overestimated precipitation in the satellite-based reanalysis data (e.g., TRMM) in the TI region (Tong et al, 2014). The uncertainty in the meteorological forcing data clearly affected the simulation of vegetation growth process in LSMs (Li et al, 2019), and eventually induced bias in GPP estimation. For example, the LAI derived from MODIS product was reported to be averagely 0.70 during…”

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

“…For example, based on the SRTM DEM, Zhang et al (2018) quantified the topographic effects of solar radiation on permafrost distribution in the northeast Tibetan Plateau and the simulated permafrost coverage increased by 8% when accounting for the impact of topographic shadows on surface energy budget. Li et al (2019) found that introducing MODISbased vegetation coverage data into the CoLM can reduce the warming biases in land surface temperature and surface soil temperature by ∼10°C in sparse vegetated areas in the Tibetan Plateau. assimilated the satellite microwavebased brightness temperature into SHAW model at the Amdo station in the central Tibetan Plateau, and found that the errors of simulated upper (0-2.58 m) soil temperature during wintertime reduced by 0.76°C.…”

Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau

“…LST derived from satellite remote sensing usually covers the entire TP, providing indispensable observed evidence over this data-sparse region. More specifically, LST products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) are some of the best quality data ( Phan & Kappas, 2018 ; Wan et al, 2017 ; Wan, 2008 ; Wang et al, 2007 ), with high temporal frequency (four daily satellite overpasses) and spatial resolution (500 m), and have been trustworthily employed as a surrogate for or a supplementary source to LST changes since 2000 ( Jin & Mullens, 2012 ; Li et al, 2019a ; Zhang et al, 2014 ; Zhong et al, 2010 ). However, it is difficult to fully understand the physical processes and mechanisms of LST changes and to quantitatively analyze the contributions of various elements to the TP land surface energy and water changes only by relying on remote sensing data ( Chang et al, 2020 ; Ji, Yuan & Li, 2020 ).…”

“…LSMs are based on physical mechanism and parameterization schemes, providing the possibility to further understand the mechanisms related to LST changes ( Hu et al, 2020 ; Orth et al, 2017 ; Trigo et al, 2015 ). LSMs usually generate LSTs that significant deviate from observations due to simplistic model representations of land surface heterogeneities, such as land surface cover type, soil properties, and soil moisture ( Johannsen et al, 2019 ; Li et al, 2019a ; Nogueira et al, 2020 ; Trigo et al, 2015 ; Wang et al, 2014 ). Many simulation results showed that the underestimation of LST and overestimation of sensible heat flux on bare-ground or sparsely vegetated surface during daytime are notable deficiencies in Noah LSM and Community Land Model version 3.5 (CLM3.5) ( Zeng, Wang & Wang, 2012 ; Zheng et al, 2014 ).…”