community land model
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Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 126
Author(s):  
Minzhuo Ou ◽  
Shupeng Zhang

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.


2022 ◽  
Author(s):  
Cynthia Nevison ◽  
Christine Goodale ◽  
Peter Hess ◽  
William R. Wieder ◽  
Julius Vira ◽  
...  

2021 ◽  
Author(s):  
Evgenii Churiulin ◽  
Vladimir Kopeikin ◽  
Markus Übel ◽  
Jürgen Helmert ◽  
Jean-Maria Bettems ◽  
...  

Abstract. Climatic changes towards warmer temperatures require the need to improve the simplified vegetation scheme of the regional climate model COSMO-CLM, which is not capable of modelling complex processes which depend on temperature, water availability, and day length. Thus, we have implemented the physically based Ball-Berry approach coupled with photosynthesis processes based on Farquhar and Collatz models for C3 and C4 plants in the regional climate model COSMO-CLM (CCLM v 5.16). The implementation of the new algorithms includes the replacement of the “one-big leaf” approach by a “two-big leaf” one. We performed single column simulations with COSMO-CLM over three observational sites with C3 grass plants in Germany for the period from 2010 to 2015 (Parc, Linden and Lindenberg domain). Hereby, we tested three alternative formulations of the new algorithms against a reference simulation (CCLMref) with no changes. The first formulation (CCLM3.5) adapts the algorithms for stomatal resistance from the Community Land Model (CLM v3.5), which depend on leaf photosynthesis, CO2 partial and vapor pressure and maximum stomatal resistance. The second one (CCLM4.5) includes a soil water stress function as in CLM v4.5. The third one (CCLM4.5e) is similar to CCLM4.5, but with adapted equations for dry leaf calculations. The results revealed major differences in the annual cycle of stomatal resistance compared to the original algorithm (CCLMref) of the reference simulation. The largest changes in stomatal resistance are observed from October to April when stomata are closed while summer values are generally less than control values that come closer to measured values. The results indicate that changes in stomatal resistance and photosynthesis algorithms can improve the accuracy of other parameters of the COSMO-CLM model (e.g.: transpiration rate or total evapotranspiration). These results were received by comparing COSMO-CLM parameters with FLUXNET data, meteorological observations at the sites, and GLEAM and HYRAS datasets.


Author(s):  
Chong Zhang ◽  
Peyman Abbaszadeh ◽  
Lei Xu ◽  
Hamid Moradkhani ◽  
Qingyun Duan ◽  
...  

2021 ◽  
Vol 13 (21) ◽  
pp. 4460
Author(s):  
Dayang Wang ◽  
Dagang Wang ◽  
Chongxun Mo

Terrestrial evapotranspiration (ET) is a critical component of water and energy cycles, and improving global land evapotranspiration is one of the challenging works in the development of land surface models (LSMs). In this study, we apply a bias correction approach into the Community Land Model version 5.0 (CLM5) globally by utilizing the remote sensing-based ET dataset. Results reveal that the correction approach can alleviate both overestimation and underestimation of ET by CLM5 over the globe. The adjustment to overestimation is generally effective, whereas the effectiveness for underestimation is determined by the ET regime, namely water-limited or energy-limited. In the areas with abundant precipitation, the underestimation is effectively corrected by increasing ET without the water supply limit. In areas with rare precipitation, however, increasing ET is limited by water supply, which leads to an undesirable correction effect. Compared with the ET simulated by CLM5, the bias correction approach can reduce the global-averaged relative bias (RB) and the root mean square error (RMSE) by 51.8% and 65.9% against Global Land Evaporation Amsterdam Model (GLEAM) ET data, respectively. Meanwhile, the correlation coefficient (CC) can also be improved from 0.93 to 0.98. Continentally, the most substantial ET improvement occurs in Asia, with the RB and RMSE decreased by 69.7% (from 7.04% to 2.14%) and 70.2% (from 0.312 mm day−1 to 0.093 mm day−1, equivalent to from 114 mm year−1 to 34 mm year−1), and the CC increased from 0.92 to 0.99, respectively. Consequently, benefiting from the improvement of ET, the simulations of runoff and soil moisture are also improved over the globe and each of the six continents, and the improvement varies with region. This study demonstrates that the use of satellite-based ET products is beneficial to hydrological simulations in land surface models over the globe.


2021 ◽  
Author(s):  
Victoria R. Dutch ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Melody Sandells ◽  
Chris Derksen ◽  
...  

Abstract. Snowpack microstructure controls the transfer of heat to, and the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two different winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow MicroPenetrometer profiles allowed snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n = 1050) compared to traditional snowpit observations (3 cm vertical resolution; n = 115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE = 5.8 °C). Bias-correction of the simulated thermal conductivity (relative to field measurements) improved simulated soil temperatures (RMSE = 2.1 °C). Multiple linear regression shows the required correction factor is strongly related to snow depth (R2 = 0.77, RMSE = 0.066) particularly early in the winter. Furthermore, CLM simulations did not adequately represent the observed high proportions of depth hoar. Addressing uncertainty in simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures act as a control on subnivean soil respiration, and hence impact Arctic winter carbon fluxes and budgets.


Author(s):  
Samy A. Anwar ◽  
Ossénatou Mamadou ◽  
Ismaila Diallo ◽  
Mouhamadou Bamba Sylla

AbstractThe community land model version 4.5 provides two ways for treating the vegetation cover changes (a static versus an interactive) and two runoff schemes for tracking the soil moisture changes. In this study, we examined the sensitivity of the simulated boreal summer potential evapotranspiration (PET) to the aforementioned options using a regional climate model. Three different experiments with each one covering 16 years have been performed. The two runoff schemes were designated as SIMTOP (TOP) and variable infiltration capacity (VIC). Both runoff schemes were coupled to the carbon–nitrogen (CN) module, thus the vegetation status can be influenced by soil moisture changes. Results show that vegetation cover changes alone affect considerably the simulated 2-m mean air temperature (T2M). However, they do not affect the global incident solar radiation (RSDS) and PET. Conversely to the vegetation cover changes alone, the vegetation-runoff systems affect both the T2M and RSDS. Therefore, they considerably affect the simulated PET. Also, the CN-VIC overestimates the PET more than the CN-TOP compared to the Climatic Research Unit observational dataset. In comparison with the static vegetation case and CN-VIC, the CN-TOP shows the least bias of the simulated PET. Overall, our results show that the vegetation-runoff system is relevant in constraining the PET, though the CN-TOP can be recommended for future studies concerning the PET of tropical Africa.


2021 ◽  
Vol 13 (9) ◽  
pp. 4437-4464
Author(s):  
Bernd Schalge ◽  
Gabriele Baroni ◽  
Barbara Haese ◽  
Daniel Erdal ◽  
Gernot Geppert ◽  
...  

Abstract. Coupled numerical models, which simulate water and energy fluxes in the subsurface–land-surface–atmosphere system in a physically consistent way, are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a simulated reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the meteorological Consortium for Small-scale Modeling (COSMO) model, the land-surface Community Land Model (CLM), and the subsurface ParFlow model, to generate a simulated catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany, the virtual Neckar catchment. Simulations for this catchment are made for the period 2007–2015 and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modeling challenges, the model performance is evaluated based on observations covering several variables of the water cycle. We find that the simulated catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g., concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement, such as overland flow, and in the realism of some observation operators like the satellite-based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar_VCS_v1.


2021 ◽  
Author(s):  
Lukas Strebel ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen

Abstract. Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data.


2021 ◽  
Author(s):  
Ronny Meier ◽  
Edouard Léopold Davin ◽  
Gordon B. Bonan ◽  
David M. Lawrence ◽  
Xiaolong Hu ◽  
...  

Abstract. The roughness of the land surface (z0) is a key property for the amount of turbulent activity above the land surface and through that for the turbulent exchange of energy, water, momentum, and chemical species between the land and the atmosphere. Variations in z0 are substantial across different types of land cover from typically less than 1 mm over fresh snow or sand deserts up to more than 1 m over urban areas or forests. In this study, we revise the parameterizations and parameter choices related to z0 in the Community Land Model 5.1 (CLM), the land component of the Community Earth System Model 2.1.2 (CESM). We propose a number modifications for z0 in CLM, which are guided by observational data. Most importantly, we increase the z0 for all types of forests, while we decrease the momentum z0 for bare soil, snow, glaciers, and crops. We then assess the effect of those modifications in land-only (CLM) and land-atmosphere coupled (CESM) simulations. Diurnal variations of the land surface temperature (LST) are dampened in regions with forests, while they are amplified over warm deserts. These changes mitigate model biases compared to MODIS remote sensing observations, which have been identified in several earlier studies. The alterations in LST are mostly stronger during the day than at night. For example, the LST at 13:30 increases by more than 4.80 K during boreal summer across the entire Sahara. The induced changes in the diurnal variability of air temperatures at the bottom of the atmosphere generally oppose changes in LST in sign and are of smaller magnitude. Further, winds close to the land surface accelerate in areas where the momentum z0 was lowered, such as the Sahara desert, the Middle East, or the Antarctica, and decelerate in regions with forests. Overall, this study highlights that the current representation of z0 in CLM is not in agreement with observational constraints for several types of land cover. The resultant model modifications are shown to considerably alter the simulated climate in terms of temperatures and wind speed at the land surface.


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