Search Results (5)

Soil moisture is a key state variable in land surface processes. Since field measurements of soil moisture are generally sparse and remote sensing is limited in terms of observation depth, land surface model simulations are usually used to continuously obtain soil moisture data in time and space. Therefore, it is crucial to evaluate the performance of models that simulate soil moisture under various land surface conditions. In this work, we evaluated and compared two land surface models, the Common Land Model version 2014 (CoLM2014) and the Community Land Model Version 5 (CLM5), using in situ soil moisture observations from the Soil Climate Analysis Network (SCAN). The meteorological and soil attribute data used to drive the models were obtained from SCAN station observations, as were the soil moisture data used to validate the simulation results. The validation results revealed that the correlation coefficients between the simulations by CLM5 (0.38) and observations are generally higher than those by CoLM2014 (0.11), especially in shallow soil (0–0.1016 m). The simulation results by CoLM2014 have smaller bias than those by CLM5 . Both models could simulate diurnal and seasonal variations of soil moisture at seven sites, but we found a large bias, which may be due to the two models’ representation of infiltration and lateral flow processes. The bias of the simulated infiltration rate can affect the soil moisture simulation, and the lack of a lateral flow scheme can affect the models’ division of saturated and unsaturated areas within the soil column. The parameterization schemes in land surface models still need to be improved, especially for soil simulations at small scales. Full article

The current evaluations of global land data assimilation system (GLDAS) runoff were generally limited to the observation-rich areas. At the global and hemispheric scales, we assessed different runoff components performance of GLDAS (1.0 and 2.1) using the University of New Hampshire and Global Runoff Data Centre (UNH-GRDC) dataset. The results suggest that GLDAS simulations show considerable uncertainties, particularly in partition of surface and subsurface runoffs, in snowmelt runoff modeling, and in capturing the northern peak time. GLDAS1.0-CLM (common land model) produced more surface runoff almost globally; GLDAS-Noah generated more surface runoff over the northern middle-high latitudes and more subsurface runoff in the remaining areas; while the partition in GLDAS1.0-VIC (variable infiltration capacity) is almost opposite to that in Noah. Comparing to GLDAS1.0-Noah, GLDAS2.1-Noah improved the premature snow-melting tendency, but its snowmelt-runoff peak magnitude was excessively high in June and July. The discrepancies in northern primary peak times among precipitation and runoff is partly caused by the combination of rainfall and melting-snow over high-latitude, as well as the very different temporal–spatial distributions for snowmelt runoff simulated by GLDAS models. This paper can provide valuable guidance for GLDAS users, and contribute to the further improvement of hydrological parameterized schemes. Full article

One of the most important anthropogenic influences on climate is land use change (LUC). In particular, the Amazon (AMZ) basin is a highly vulnerable area to climate change due to substantial modifications of the hydroclimatology of the region expected as a result of LUC. However, both the magnitude of these changes and the physical process underlying this scenario are still uncertain. This work aims to analyze the simulated Amazon deforestation and its impacts on local mean climate. We used the Common Land Model (CLM) version 4.5 coupled with the Regional Climate Model (RegCM4) over the Coordinated Regional Climate Downscaling Experiment (CORDEX) South America domain. We performed one simulation with the RegCM4 default land cover map (CTRL) and one simulation under a scenario of deforestation (LUC), i.e., replacing broadleaf evergreen trees with C₃ grass over the Amazon basin. Both simulations were driven by ERA Interim reanalysis from 1979 to 2009. The climate change signal due to AMZ deforestation was evaluated by comparing the climatology of CTRL with LUC. Concerning the temperature, the deforested areas are about 2 °C warmer compared to the CTRL experiment, which contributes to decrease the surface pressure. Higher air temperature is associated with a decrease of the latent heat flux and an increase of the sensible heat flux over the deforested areas. AMZ deforestation induces a dipole pattern response in the precipitation over the region: a reduction over the west (about 7.9%) and an increase over the east (about 8.3%). Analyzing the water balance in the atmospheric column over the AMZ basin, the results show that under the deforestation scenario the land surface processes play an important role and drive the precipitation in the western AMZ; on the other hand, on the east side, the large scale circulation drives the precipitation change signal. Dipole patterns over scenarios of deforestation in the Amazon was also found by other authors, but the precipitation decrease on the west side was never fully explained. Using budget equations, this work highlights the physical processes that control the climate in the Amazon basin under a deforestation scenario. Full article

Land surface models (LSMs) are important tools for simulating energy, water and momentum transfer across the land–atmosphere interface. Many LSMs have been developed over the past 50 years, including the Common Land Model (CoLM), a LSM that has primarily been developed and maintained by Chinese researchers. CoLM has been adopted by several Chinese Earth System Models (GCMs) that will participate in the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this study, we evaluate the performance of CoLM with respect to simulating the water and energy budgets. We compare simulations using the latest version of CoLM (CoLM2014), the previous version of CoLM (CoLM2005) that was used in the Beijing Normal University Earth System Model (BNU-GCM) for CMIP5, and the Community Land Model version 4.5 (CLM4.5) against global diagnostic data and observations. Our results demonstrate that CLM4.5 outperforms CoLM2005 and CoLM2014 in simulating runoff (R), although all three models overestimate runoff in northern Europe and underestimate runoff in North America and East Asia. Simulations of runoff and snow depth (SNDP) are substantially improved in CoLM2014 relative to CoLM2005, particularly in the Northern Hemisphere. The simulated global energy budget is also substantially improved in CoLM2014 relative to CoLM2005. Simulations of sensible heat (SH) based on CoLM2014 compare favorably to those based on CLM4.5, while root-mean-square errors (RMSEs) in net surface radiation indicate that CoLM2014 (RMSE = 16.02 W m⁻²) outperforms both CoLM2005 (17.41 W m⁻²) and CLM4.5 (23.73 W m⁻²). Comparisons at regional scales show that all three models perform poorly in the Amazon region but perform relatively well over the central United States, Siberia and the Tibetan Plateau. Overall, CoLM2014 is improved relative to CoLM2005, and is comparable to CLM4.5 with respect to many aspects of the energy and water budgets. Our evaluation confirms CoLM2014 is suitable for inclusion in Chinese GCMs, which will increase the diversity of LSMs considered during CMIP6. Full article

Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-E’s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R² values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques. Full article