Parameter Sensitivities of the Community Land Model at Two Alpine Sites in the Three-River Source Region

The three-river source region plays an important role on China’s ecological security and Asia’s water supply. Historically, the region has experienced severe ecological degradation due to climate change and human activities. Reasonable simulations of the energy and water cycles are essential to predict the responses of land surface processes to future climate change. Current land surface models involve empirical functions that are associated with many parameters. These parameter uncertainties will largely affect the simulation when applied to a new domain. The Community Land Model(CLM) is a widely used land surface model, and version 5.0 is the newest version. Compared to the prior version CLM4.5, CLM5.0 has largely updated plant hydraulic and stomatal conductance schemes. How these changes affect parameter sensitivities is unknown. In our work, we tested 17 key parameters in CLM4.5 and 19 parameters in CLM5.0 at two eddy flux sites in the three-river source region: the Maqu and Maduo sites. We adopted the simplest one-at-a-time changes on each parameter and quantified their sensitivities by the parameter effect(PE).We found that the Maqu site was more sensitive to vegetation parameters, while the Maduo site was more sensitive to the initial soil water content in both CLM4.5 and CLM5.0. This is because Maduo grid cell has wetland that does not respond to vegetation parameters in CLM, which may not reflect the reality. Further model development on wetland vegetation parameterization is important. Our validation on the default simulation showed CLM5.0 did not always improve the simulations. The largest difference between CLM5.0 and CLM4.5 was that soil moisture(SM) showed a much stronger decrease in response to a higher leaf area index(LAI) in CLM5.0 than in CLM4.5, suggesting that SM is more sensitive to vegetation changes in CLM5.0.

Estimates of net primary productivity and actual evapotranspiration over the Tibetan Plateau from the Community Land Model version 4.5 with four atmospheric forcing datasets

The accuracy of the simulation of carbon and water processes largely relies on the selection of atmospheric forcing datasets when driving land surface models(LSM).Particularly in high-altitude regions,choosing appropriate atmospheric forcing datasets can effectively reduce uncertainties in the LSM simulations.Therefore,this study conducted four offline LSM simulations over the Tibetan Plateau(TP)using the Community Land Model version 4.5(CLM4.5)driven by four state-of-the-art atmospheric forcing datasets.The performances of CRUNCEP(CLM4.5 model default)and three other reanalysis-based atmospheric forcing datasets(i.e.ITPCAS,GSWP3 and WFDEI)in simulating the net primary productivity(NPP)and actual evapotranspiration(ET)were evaluated based on in situ and gridded reference datasets.Compared with in situ observations,simulated results exhibited determination coefficients(R2)ranging from 0.58 to 0.84 and 0.59 to 0.87 for observed NPP and ET,respectively,among which GSWP3 and ITPCAS showed superior performance.At the plateau level,CRUNCEP-based simulations displayed the largest bias compared with the reference NPP and ET.GSWP3-based simulations demonstrated the best performance when comprehensively considering both the magnitudes and change trends of TP-averaged NPP and ET.The simulated ET increase over the TP during 1982-2010 based on ITPCAS was significantly greater than in the other three simulations and reference ET,suggesting that ITPCAS may not be appropriate for studying long-term ET changes over the TP.These results suggest that GSWP3 is recommended for driving CLM4.5 in conducting long-term carbon and water processes simulations over the TP.This study contributes to enhancing the accuracy of LSM in water-carbon simulations over alpine regions.

Evaluating the Dependence of Vegetation on Climate in an Improved Dynamic Global Vegetation Model

The capability of an improved Dynamic Global Vegetation Model （DGVM） in reproducing the impact of climate on the terrestrial ecosystem is evaluated. The new model incorporates the Community Land Model- DGVM （CLM3.0-DGVM） with a submodel for temperate and boreal shrubs, as well as other revisions such as the ＂two-leaf＂ scheme for photosynthesis and the definition of fractional coverage of plant functional types （PFTs）. Results show that the revised model may correctly reproduce the global distribution of temperate and boreal shrubs, and improves the model performance with more realistic distribution of di？erent vege- tation types. The revised model also correctly reproduces the zonal distributions of vegetation types. In reproducing the dependence of the vegetation distribution on climate conditions, the model shows that the dominant regions for trees, grasses, shrubs, and bare soil are clearly separated by a climate index derived from mean annual precipitation and temperature, in good agreement with the CLM4 surface data. The dominant plant functional type mapping to a two dimensional parameter space of mean annual temperature and precipitation also qualitatively agrees with the results from observations and theoretical ecology studies.

Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach

The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.

Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis

Runoff is a major component of the water cycle, and its multi-scale fluctuations are important to water resources management across arid and semi-arid regions. This paper coupled the Distributed Time Variant Gain Model （DTVGM） into the Community Land Model （CLM 3.5）, replacing the TOPMODEL-based method to simulate runoff in the arid and semi-arid regions of China. The coupled model was calibrated at five gauging stations for the period 1980-2005 and validated for the period 2006-2010. Then, future runoff （2010-2100） was simulated for different Representative Concentration Pathways （RCP） emission scenarios. After that, the spatial distributions of the future runoff for these scenarios were discussed, and the multi-scale fluctuation characteristics of the future annual runoff for the RCP scenarios were explored using the Ensemble Empirical Mode Decomposition （EEMD） analysis method. Finally, the decadal variabilities of the future annual runoff for the entire study area and the five catchments in it were investigated. The results showed that the future annual runoff had slowly decreasing trends for scenarios RCP 2.6 and RCP 8.5 during the period 2010-2100, whereas it had a non-monotonic trend for the RCP 4.5 scenario, with a slow increase after the 2050s. Additionally, the future annual runoff clearly varied over a decadal time scale, indicating that it had clear divisions between dry and wet periods. The longest dry period was approximately 15 years （2040-2055） for the RCP 2.6 scenario and 25 years （2045-2070） for the RCP 4.5 scenario. However, the RCP 8.5 scenario was predicted to have a long dry period starting from 2045. Under these scenarios, the water resources situation of the study area will be extremely severe. Therefore, adaptive water management measures addressing climate change should be adopted to proactively confront the risks of water resources.