Ling et al., Assimilation of remotely sensed LAI into CLM4CN Using DART, Journal of Advances in Modeling Earth Systems, 2019
Plant leaves play an important role in water, carbon, and energy exchanges between terrestrial ecosystems and atmosphere. Assimilating remotely sensed leaf area index (LAI) into land surface models is a promising approach to improve our understanding of those processes. Toward this goal, this study uses the Community Land Model with carbon and nitrogen components (CLM4CN) coupled with the Data Assimilation Research Testbed (DART). Global Land Surface Satellite (GLASS) LAI data are assimilated via the Ensemble Adjustment Kalman Filter. A random 40-member atmospheric forcing ensemble is used to drive the CLM4CN to provide background error covariance. The results show that assimilating GLASS LAI and updating both LAI and leaf C/N is an effective method to provide a high-accuracy estimate of LAI. The simulations always systematically overestimate LAI, especially in low-latitude regions, with the largest bias up to 5 m2/m2, which are effectively corrected in the analyzed LAI, with the bias reduced to +/- 1 m2/m2. Significantly improved regions are located in central Africa, Amazonia, southern Eurasia, northeastern China, and western Europe, where evergreen/deciduous forests and mixed forests are dominant. Except for the temperate zone in the Southern Hemisphere, the analyzed LAI can well represent seasonal variations. The most pronounced assimilation impact in low-latitude regions is attributed to large initial forecast error covariance and sufficient background errors. The MOD 16 evapotranspiration estimates and upscaled gross primary production have been used to evaluate the assimilation impact, which highlight neutral to highly positive improvement.
Ling et al., Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai), Geoscientific Modeling Development, 2019
The leaf area index (LAI) is a crucial parameter for understanding the exchanges of mass and energy between terrestrial ecosystems and the atmosphere. In this study, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model with explicit carbon and nitrogen components (CLM4CN) by assimilating Global Land Surface Satellite (GLASS) LAI data. Within this framework, four sequential assimilation algorithms, including the kernel filter (KF), the ensemble Kalman filter (EnKF), the ensemble adjust Kalman filter (EAKF), and the particle filter (PF), are thoroughly analyzed and compared. The results show that assimilating GLASS LAI into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the EnKF and EAKF algorithms are better than those of the KF and PF algorithm. From the perspective of the average and RMSD, the PF algorithm performs worse than the EAKF and EnKF algorithms because of the gradually reduced acceptance of observations with assimilation steps. In other words, the contribution of the observations to the posterior probability during the assimilation process is reduced. The EAKF algorithm is the best method because the matrix is adjusted at each time step during the assimilation procedure. If all the observations are accepted, the analyzed LAI seem to be better than that when some observations are rejected, especially in low-latitude regions.
Qiu et al., Satellite chlorophyll fluorescence and soil moisture observations lead to advances in the predictive understanding of global terrestrial coupled carbon-cater cycles, Global Biogeochemical Cycles, 2018
The terrestrial carbon and water cycles are coupled through a multitude of connected processes among soil, roots, leaves, and the atmosphere. The strength and sensitivity of these couplings are not yet well known at the global scale, which contributes to uncertainty in predicting the terrestrial water and carbon budgets. We now have synchronous, global-scale satellite observations of critical terrestrial carbon and water cycle components: solar-induced chlorophyll fluorescence (SIF) and soil moisture. We used these observations within the framework of a global terrestrial biosphere model (Simplified Simple Biosphere Model version 2.0, SSiB2) to investigate carbon-water coupling processes. We updated SSiB2 to include a mechanistic representation of SIF and tested the sensitivity of model parameters to improve the simulation of both SIF and soil moisture with the ultimate objective of improving the first-order terrestrial carbon component, gross primary production. Although several vegetation parameters, such as leaf area index and the green leaf fraction, improved the simulated SIF, and several soil parameters, such as hydraulic conductivity, improved simulated soil moisture, their effects were mainly limited to their respective cycles. One root-mean-square error parameter emerged as the key coupler between the carbon and water cycles: the wilting point. Updates to the wilting point significantly improved the simulations for SIF and gross primary production although substantial mismatches with the satellite data still existed. This study demonstrates the value of synchronous global measurements of the terrestrial carbon and water cycles in improving the understanding of coupled carbon-water cycles.
Qiu et al., Implementation and evaluation of a generalized radiative transfer scheme within canopy in the soil-vegetation-atmosphere transfer (SVAT) model, Journal of Geophysical Research-Atmospheres, 2016
The process of radiative transfer over vegetated areas has a profound impact on energy, water, and carbon balances over the terrestrial surface. In this paper, a generalized radiative transfer scheme (GRTS) within canopy is implemented in the Simplified Simple Biosphere land surface model (SSiB). The main concept and structure of GRTS and its coupling methodology to a land model are presented. Different from the two-stream method, the GRTS takes into account the effects of complex canopy morphology and inhomogeneous optical properties of leaves on radiative transfer process within the canopy. In the offline SSiB/GRTS simulation for the period of 2001-2012, the nonuniform leaf angle distribution within canopy layers is considered in SSiB/GRTS in the areas of evergreen broadleaf trees. Compared with the SSiB/two stream method, SSiB/GRTS produces lower canopy reflectance and higher transmittance, which leads to more realistic albedo simulation. The canopy-absorbed radiation flux in SSiB/GRTS simulation is lower than that in SSiB/two stream method simulation throughout the year in the areas of evergreen broadleaf trees. The largest difference of -18.4W/m2 occurs in the Amazon region in the autumn. The ground-absorbed radiation flux increases in the SSiB/GRTS simulation, especially in the spring and autumn. The largest difference in the ground-absorbed radiation flux between SSiB/GRTS simulation and SSiB/two stream method simulation is 25.45W/m2. In the boreal winter season, compared with the two-stream method in the SSiB, the GRTS gives higher surface albedo in the areas with high snow cover fraction over leaf.
Liu et al., Estimation of key surface parameters in semi-arid region and their impacts on improvement of surface fluxes simulation, Science China-Earth Sciences, 2016
Uncertainties in some key parameters in land surface models severely restrict the improvement of model capacity for successful simulation of surface-atmosphere interaction. These key parameters are related to soil moisture and heat transfer and physical processes in the vegetation canopy as well as other important aerodynamic processes. In the present study, measurements of surface-atmosphere interaction at two observation stations that are located in the typical semi-arid region of China, Tongyu Station in Jilin Province and Yuzhong Station in Gansu Province, are combined with the planetary boundary layer theory to estimate the value of two key aerodynamic parameters, i.e., surface roughness length z0m and excess resistance kB-1. Multiple parameterization schemes have been used in the study to obtain values for surface roughness length and excess resistance kB-1 at the two stations. Results indicate that z0m has distinct seasonal and inter-annual variability. For the type of surface with low-height vegetation, there is a large difference between the default value of z0m in the land surface model and that obtained from this study. kB-1 demonstrates a significant diurnal variation and seasonal variability. Using the modified scheme for the estimation of z0m and kB-1 in the land surface model, it is found that simulations of sensible heat flux over the semi-arid region have been greatly improved. These results suggest that it is necessary to further evaluate the default values of various parameters used in land surface models based on field measurements. The approach to combine field measurements with atmospheric boundary layer theory to retrieve realistic values for key parameters in land surface models presents a great potential in the improvement of modeling studies of surface-atmosphere interaction.