scholarly journals Evaluation of the Effect of Stability Schemes on the Simulation of Land Surface Processes at a Western Tibetan Site

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 253
Author(s):  
Xingbing Zhao ◽  
Zhiwei Heng ◽  
Xingwen Jiang ◽  
Qidong Yang ◽  
Yubin Li ◽  
...  

The surface fluxes calculated in land surface models (LSMs) are sensitive to the determination of the stability parameter. Further, calculation of the surface fluxes over the Tibetan Plateau (TP) is crucial in the simulation of regional and global weather and climate. In this study, we use 2-year micrometeorological data measured from Shiquanhe, located in the western TP, to evaluate the performance of the widely used Noah LSM with five stability parameterization schemes. Results show that all five stability parameterization schemes can generally reproduce the observations, but the scheme proposed by Li has the smallest bias. The reason is that Li’s scheme is more accurate under the unstable condition, and the surface layer at Shiquanhe is mostly unstable. Further, the four non-iterative schemes show an advantage in terms of their computational efficiency compared to the iterative scheme adopted by the Noah LSM.

2013 ◽  
Vol 141 (2) ◽  
pp. 773-797 ◽  
Author(s):  
Gang Liu ◽  
Yangang Liu ◽  
Satoshi Endo

Abstract Surface momentum, sensible heat, and latent heat fluxes are critical for atmospheric processes such as clouds and precipitation, and are parameterized in a variety of models ranging from cloud-resolving models to large-scale weather and climate models. However, direct evaluation of the parameterization schemes for these surface fluxes is rare due to limited observations. This study takes advantage of the long-term observations of surface fluxes collected at the Southern Great Plains site by the Department of Energy Atmospheric Radiation Measurement program to evaluate the six surface flux parameterization schemes commonly used in the Weather Research and Forecasting (WRF) model and three U.S. general circulation models (GCMs). The unprecedented 7-yr-long measurements by the eddy correlation (EC) and energy balance Bowen ratio (EBBR) methods permit statistical evaluation of all six parameterizations under a variety of stability conditions, diurnal cycles, and seasonal variations. The statistical analyses show that the momentum flux parameterization agrees best with the EC observations, followed by latent heat flux, sensible heat flux, and evaporation ratio/Bowen ratio. The overall performance of the parameterizations depends on atmospheric stability, being best under neutral stratification and deteriorating toward both more stable and more unstable conditions. Further diagnostic analysis reveals that in addition to the parameterization schemes themselves, the discrepancies between observed and parameterized sensible and latent heat fluxes may stem from inadequate use of input variables such as surface temperature, moisture availability, and roughness length. The results demonstrate the need for improving the land surface models and measurements of surface properties, which would permit the evaluation of full land surface models.


2021 ◽  
Author(s):  
Jan De Pue ◽  
José Miguel Barrios ◽  
Liyang Liu ◽  
Philippe Ciais ◽  
Alirio Arboleda ◽  
...  

<p>Over the past decades, land surface models have evolved into advanced tools which comprise detailed process descriptions and interactions at a broad range of scales. One of the challenges in these models is the accurate simulation of plant phenology. It is a key element at the nexus of the simulated hydrological and carbon cycle, where the leaf area index (LAI) plays a major role in flux partitioning, water balance and gross primary production.<br>In this study, three well-established models are used to simulate the intrinsically coupled fluxes of water, energy and carbon from terrestrial vegetation. ORCHIDEE, ISBA-CC and the LSA-SAF algorithm each have a different approach to represent plant phenology. Whereas ISBA-CC has a fairly simple biomass allocation scheme to represent the phenological cycle, ORCHIDEE relies on a dedicated phenology module, and LSA-SAF is driven by remote-sensed forcing variables, such as LAI. Simulations were performed for a wide range of hydro-climatic biomes and plant functional types at field scale. The simulated fluxes were validated using eddy-covariance measurements, and the simulated phenology was compared to remote-sensed observations.<br>These models are tools to extrapolate leaf-level processes to global scale climate predictions. The origin of the parameters controlling phenology-induced variability in these models ranges from plant-scale lab experiments to global-scale calibration. The aim of this study is to investigate the key parameters controlling phenology-induced variability in these models.</p>


2009 ◽  
Vol 6 (1) ◽  
pp. 455-499 ◽  
Author(s):  
R. van der Velde ◽  
Z. Su ◽  
M. Ek ◽  
M. Rodell ◽  
Y. Ma

Abstract. In this paper, we investigate the ability of the Noah Land Surface model (LSm) to simulate temperature states in the soil profile and surface fluxes measured during a 7-day dry period at a micrometeorological station on the Tibetan Plateau. Adjustments in soil and vegetation parameterizations required to ameliorate the Noah simulation on these two aspects are presented, which include: (1) Differentiating the soil thermal properties of top- and subsoils, (2) Investigation of the different numerical soil discretizations and (3) Calibration of the parameters utilized to describe the transpiration dynamics of the Plateau vegetation. Through the adjustments in the parameterization of the soil thermal properties (STP) simulation of the soil heat transfer is improved, which results in a reduction of Root Mean Squared Differences (RMSD's) by 14%, 18% and 49% between measured and simulated skin, 5-cm and 25-cm soil temperatures, respectively. Further, decreasing the minimum stomatal resistance (Rc, min) and the optimum temperature for transpiration (Topt) of the vegetation parameterization reduces RMSD's between measured and simulated energy balance components by 30%, 20% and 5% for the sensible, latent and soil heat flux, respectively.


2019 ◽  
Author(s):  
Titta Majasalmi ◽  
Ryan M. Bright

Abstract. Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although Plant Functional Type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the time-invariant optical properties table of the Simple Biosphere (SiB) model (later referred as SiB-table) presented 30-years ago by Dorman and Sellers (1989) which has since become adopted by many LSMs. This revisit was needed as much of the data underlying the SiB-table was not formally reviewed or published or was based on older papers or personal communications (i.e. the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g. Community Land Model (CLM), Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB-table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e. tropical, boreal, temperate) and phenology (i.e. deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops and grasses and by photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to be appropriate. However, in the near-infrared and shortwave infrared bands (NIR+SWIR; e.g. 701–2500 nm, referred as NIR) notable differences between CLM default and measured estimates were observed, thus suggesting that NIR optical properties need updating in the model. For example, for conifer PFTs, the measured mean needle albedo estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat-leaves, the measured mean leaf albedo values in NIR were 20 %, 14 % and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle definitions were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and introduce the concept and application of photon recollision probability (p). The p may be used to upscale needle spectra into shoot spectra to meet the common assumption that foliage is located randomly within the canopy volume (behind canopy radiative transfer calculation) to account for multiple scattering effects caused by needles clustered into shoots.


2020 ◽  
Author(s):  
Dazhi Li ◽  
Xujun Han ◽  
Dhanya C.t. ◽  
Stefan Siebert ◽  
Harry Vereecken ◽  
...  

<p>Irrigation is very important for maintaining the agricultural production and sustaining the increasing population of India. The irrigation requirement can be estimated with land surface models by modeling water storage changes but the estimates are affected by various uncertainties such as regarding the spatiotemporal distribution of areas where and when irrigation is potentially applied. In the present work, this uncertainty is analyzed for the whole Indian domain. The irrigation requirements and hydrological fluxes over India were reconstructed by multiple simulation experiments with the Community Land Model (CLM) version 4.5 for the year of 2010.</p><p>These multiple simulation scenarios showed that the modeled irrigation requirement and the land surface fluxes differed between the scenarios, representing the spatiotemporal uncertainty of the irrigation maps. Using a season-specific irrigation map resulted in a higher transpiration-evapotranspiration ratio (T/ET) in the pre-monsoon season compared to the application of a static irrigation map, which implies a higher irrigation efficiency. The remote sensing based evapotranspiration products GLEAM and MODIS ET were used for comparison, showing a similar increasing ET-trend in the pre-monsoon season as the irrigation induced land surface modeling. The correspondence is better if the seasonal irrigation map is used as basis for simulations with CLM. We conclude that more accurate temporal information on irrigation results in modeled evapotranspiration closer to the spatiotemporal pattern of evapotranspiration deduced from remote sensing. Another conclusion is that irrigation modeling should consider the sub-grid heterogeneity to improve the estimation of soil water deficit and irrigation requirement.</p>


2016 ◽  
Vol 121 (20) ◽  
pp. 12,180-12,197 ◽  
Author(s):  
Peng Bai ◽  
Xiaomang Liu ◽  
Tiantian Yang ◽  
Kang Liang ◽  
Changming Liu

2021 ◽  
Author(s):  
Weiqiang Ma ◽  
Yaoming Ma ◽  
Yizhe Han ◽  
Wei Hu ◽  
Lei Zhong ◽  
...  

<p>Firstly, based on the difference of model and in-situ observations, a serious of sensitive experiments were done by using WRF. In order to use remote sensing products, a land-atmosphere model was initialized by ingesting land surface parameters, such as AMSR-E RS products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations.</p><p>Secondly, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations. The differences between the AMSR-E initialized model runs with the default model configuration and in situ data showed an apparent inconsistency in the model-simulated land surface heat fluxes. The results showed that the soil moisture was sensitive to the specific model configuration. To evaluate and verify the model stability, a long-term modeling study with AMSR-E soil moisture data ingestion was performed. Based on test simulations, AMSR-E data were assimilated into an atmospheric model for July and August 2007. The results showed that the land surface fluxes agreed well with both the in-situ data and the results of the default model configuration. Therefore, the simulation can be used to retrieve land surface heat fluxes from an atmospheric model over the Tibetan Plateau.</p><p>All of the different methods will clarify the land surface heating field in complex plateau, it also can affect atmospheric cycle over the Tibetan Plateau even all of the global atmospheric cycle pattern.</p>


2016 ◽  
Vol 10 (1) ◽  
pp. 287-306 ◽  
Author(s):  
W. Wang ◽  
A. Rinke ◽  
J. C. Moore ◽  
X. Cui ◽  
D. Ji ◽  
...  

Abstract. We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135  ×  104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101  × 104 km2). However the uncertainty (1 to 128  ×  104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.


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