Climate Impacts of Making Evapotranspiration in the Community Land Model (CLM3) Consistent with the Simple Biosphere Model (SiB)

Abstract In recent climate sensitivity experiments with the Community Climate System Model, version 3 (CCSM3), a wide range of studies have found that the Community Land Model, version 3 (CLM3), simulates mean global evapotranspiration with low contributions from transpiration (15%), and high contributions from soil and canopy evaporation (47% and 38%, respectively). This evapotranspiration partitioning is inconsistent with the consensus of other land surface models used in GCMs. To understand the high soil and canopy evaporation and the low transpiration observed in the CLM3, select individual components of the land surface parameterizations that control transpiration, canopy and soil evaporation, and soil hydrology are compared against the equivalent parameterizations used in the Simple Biosphere Model, versions 2 and 3 (SiB2 and SiB3), and against more recent developments with CLM. The findings of these investigations are used to develop new parameterizations for CLM3 that would reproduce the functional dynamics of land surface processes found in SiB and other alternative land surface parameterizations. Global climate sensitivity experiments are performed with the new land surface parameterizations to assess how the new SiB, consistent CLM land surface parameterizations, influence the surface energy balance, hydrology, and atmospheric fluxes in CLM3, and through that the larger-scale climate modeled in CCSM3. It is found that the new parameterizations enable CLM to simulate evapotranspiration partitioning consistently with the multimodel average of other land surface models used in GCMs, as evaluated by Dirmeyer et al. (2005). The changes in surface fluxes also resulted in a number of improvements in the simulation of precipitation and near-surface air temperature in CCSM3. The new model is fully coupled in the CCSM3 framework, allowing a wide range of climate modeling investigations without the surface hydrology issues found in the current CLM3 model. This provides a substantially more robust framework for performing climate modeling experiments investigating the influence of land cover change and surface hydrology in CLM and CCSM than the existing CLM3 parameterizations. The study also shows that changes in land surface hydrology have global scale impacts on model climatology.

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Evaluation of key parameters controlling phenology-induced variability of surface fluxes in land surface models

10.5194/egusphere-egu21-11445 ◽

2021 ◽

Author(s):

Jan De Pue ◽

José Miguel Barrios ◽

Liyang Liu ◽

Philippe Ciais ◽

Alirio Arboleda ◽

...

Keyword(s):

Land Surface ◽

Gross Primary Production ◽

Global Scale ◽

Plant Phenology ◽

Surface Fluxes ◽

Terrestrial Vegetation ◽

Land Surface Models ◽

Area Index ◽

Wide Range ◽

Surface Models

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. 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. 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.

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Evaluation of leaf-level optical properties employed in land surface models – example with CLM 5.0

10.5194/gmd-2019-59 ◽

2019 ◽

Author(s):

Titta Majasalmi ◽

Ryan M. Bright

Keyword(s):

Optical Properties ◽

Optical Property ◽

Land Surface ◽

Surface Fluxes ◽

Estimation Methods ◽

Leaf Angle ◽

Land Surface Models ◽

Community Land Model ◽

Canopy Volume ◽

Surface Models

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.

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Phenology-induced energy and carbon fluxes in land surface models

10.5194/egusphere-egu2020-17702 ◽

2020 ◽

Author(s):

Jan De Pue ◽

José Miguel Barrios ◽

Fabienne Maignan ◽

Liyang Liu ◽

Philippe Ciais ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Terrestrial Ecosystems ◽

Carbon Fluxes ◽

Land Surface Models ◽

Energy Fluxes ◽

Wide Range ◽

Surface Models ◽

Modelling Strategies ◽

Phenological Cycle

The annual phenological cycle is of key importance for the carbon and energy fluxes in terrestrial ecosystems. Although the processes controlling budburst and leaf senescence are fairly well known, the connection between plant phenology and the carbon fluxes remains a challenging aspect in land surface modelling (LSM). In this study, the modelling strategies of three well stablished LSM are compared. The LSM considered in this study were: ORCHIDEE, ISBA-A-gs and the model driving the LSA-SAF evapotranspiration product (https://landsaf.ipma.pt). The latter model does not simulate the carbon fluxes but focuses on the computation of evapotranspiration and energy fluxes. The phenological cycle is simulated explicitly in the ORCHIDEE model, using empirical relations based on temperature sum, water availability, and other variables. In the ISBA-A-gs model, phenology and LAI development is fully photosynthesis-driven. The phenology in the LSA-SAF model is driven by remote sensing forcing variables, such as LAI observations. Alternatively, the assimilation of remote sensing LAI products is a convenient method to improve the simulated phenological cycle in land surface models. A dedicated module for this operation is available in ISBA-A-gs. Simulations were performed over a wide range of climatological conditions and plant functional types. The results were then validated with in-situ measurements conducted at Fluxnet stations. In addition to the comparison between measured and modelled carbon fluxes, the validation in this study included the intra-annual variation in the simulated phenological cycle.

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Comparison and Assessment of Three Advanced Land Surface Models in Simulating Terrestrial Water Storage Components over the United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0112.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 625-649 ◽

Cited By ~ 31

Author(s):

Youlong Xia ◽

David Mocko ◽

Maoyi Huang ◽

Bailing Li ◽

Matthew Rodell ◽

...

Keyword(s):

Interannual Variability ◽

Land Surface ◽

Water Storage ◽

The United States ◽

Major Contributor ◽

Terrestrial Water Storage ◽

Land Surface Models ◽

Community Land Model ◽

Surface Models ◽

Terrestrial Water

Abstract To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979–2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams.

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Evaluating the value of a network of cosmic-ray probes for improving land surface modelling

10.5194/hess-2016-432 ◽

2016 ◽

Cited By ~ 1

Author(s):

R. Baatz ◽

Harrie-Jan Hendricks Franssen ◽

Xujun Han ◽

Tim Hoar ◽

Heye R. Bogena ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Cosmic Ray ◽

Land Surface Models ◽

Lower Boundary Condition ◽

Soil Map ◽

Community Land Model ◽

Verification Period ◽

Surface Models ◽

Soil Hydraulic

Abstract. Land surface models can model matter and energy fluxes between the land surface and atmosphere, and provide a lower boundary condition to atmospheric circulation models. For these applications, accurate soil moisture quantification is highly desirable but not always possible given limited observations and limited subsurface data accuracy. Cosmic-ray probes (CRPs) offer an interesting alternative to indirectly measure soil moisture and provide an observation that can be assimilated into land surface models for improved soil moisture prediction. Synthetic studies have shown the potential to estimate subsurface parameters of land surface models with the assimilation of CRP observations. In this study, the potential of a network of CRPs for estimating subsurface parameters and improved soil moisture states is tested in a real-world case scenario using the local ensemble transform Kalman filter with the Community Land Model. The potential of the CRP network was tested by assimilating CRP-data for the years 2011 and 2012 (with or without soil hydraulic parameter estimation), followed by the verification year 2013. This was done using (i) the regional soil map as input information for the simulations, and (ii) an erroneous, biased soil map. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the biased soil map, soil moisture characterization improved in both periods strongly from a ERMS of 0.11 cm3/cm3 to 0.03 cm3/cm3 (assimilation period) and from 0.12 cm3/cm3 to 0.05 cm3/cm3 (verification period) and the estimated soil hydraulic parameters were after assimilation closer to the ones of the regional soil map. Finally, the value of the CRP network was also evaluated with jackknifing data assimilation experiments. It was found that the CRP network is able to improve soil moisture estimates at locations between the assimilation sites from a ERMS of 0.12 cm3/cm3 to 0.06 cm3/cm3 (verification period), but again only if the initial soil map was biased.

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Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

10.5194/gmd-2021-38 ◽

2021 ◽

Author(s):

Lukas Strebel ◽

Heye Bogena ◽

Harry Vereecken ◽

Harrie-Jan Hendricks Franssen

Keyword(s):

Data Assimilation ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Land Surface ◽

Land Surface Models ◽

Community Land Model ◽

Model Version ◽

Parallel Data ◽

Surface Models

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.

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Modeling Evapotranspiration over China’s Landmass from 1979 to 2012 Using Multiple Land Surface Models: Evaluations and Analyses

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0212.1 ◽

2017 ◽

Vol 18 (4) ◽

pp. 1185-1203 ◽

Cited By ~ 14

Author(s):

Shaobo Sun ◽

Baozhang Chen ◽

Quanqin Shao ◽

Jing Chen ◽

Jiyuan Liu ◽

...

Keyword(s):

Land Surface ◽

Regional Scale ◽

Temporal Variations ◽

The Tibetan Plateau ◽

Land Surface Models ◽

Infiltration Capacity ◽

Community Land Model ◽

Variable Infiltration Capacity Model ◽

Capacity Model ◽

Surface Models

Abstract Land surface models (LSMs) are useful tools to estimate land evapotranspiration at a grid scale and for long-term applications. Here, the Community Land Model, version 4.0 (CLM4.0); Dynamic Land Model (DLM); and Variable Infiltration Capacity model (VIC) were driven with observation-based forcing datasets, and a multiple-LSM ensemble-averaged evapotranspiration (ET) product (LSMs-ET) was developed and its spatial–temporal variations were analyzed for the China landmass over the period 1979–2012. Evaluations against measurements from nine flux towers at site scale and surface water budget–based ET at regional scale showed that the LSMs-ET had good performance in most areas of China’s landmass. The intercomparisons between the ET estimates and the independent ET products from remote sensing and upscaling methods suggested that there were fairly consistent patterns between each dataset. The LSMs-ET produced a mean annual ET of 351.24 ± 10.7 mm yr−1 over 1979–2012, and its spatial–temporal variation analyses showed that (i) there was an overall significant ET increasing trend, with a value of 0.72 mm yr−1 (p < 0.01), and (ii) 36.01% of Chinese land had significant increasing trends, ranging from 1 to 9 mm yr−1, while only 6.41% of the area showed significant decreasing trends, ranging from −6.28 to −0.08 mm yr−1. Analyses of ET variations in each climate region clearly showed that the Tibetan Plateau areas were the main contributors to the overall increasing ET trends of China.

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Enhancing the representation of subgrid land surface characteristics in land surface models

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-2177-2013 ◽

2013 ◽

Vol 6 (1) ◽

pp. 2177-2212

Author(s):

Y. Ke ◽

L. R. Leung ◽

M. Huang ◽

H. Li

Keyword(s):

Spatial Variability ◽

Land Surface ◽

Grid Cell ◽

New Method ◽

Vegetation Types ◽

Land Surface Models ◽

Surface Cover ◽

Community Land Model ◽

Surface Models ◽

Number Of Classes

Abstract. Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types. In this study, we developed a new subgrid classification method (SGC) that accounts for the topographic variability of the vegetation cover. Each model grid cell was represented with a number of elevation classes and each elevation class was further described by a number of vegetation types. The numbers of elevation classes and vegetation types were variable and optimized for each model grid so that the spatial variability of both elevation and vegetation can be reasonably explained given a pre-determined total number of classes. The subgrid structure of the Community Land Model (CLM) was used as an example to illustrate the newly developed method in this study. With similar computational burden as the current subgrid vegetation representation in CLM, the new method is able to explain at least 80% of the total subgrid Plant Functional Types (PFTs) and greatly reduced the variations of elevation within each subgrid class compared to the baseline method where a single elevation class is assigned to each subgrid PFT. The new method was also evaluated against two other subgrid methods (SGC1 and SGC2) that assigned fixed numbers of elevation and vegetation classes for each model grid with different perspectives of surface cover classification. Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0° and 2.0°) with three maximum-allowed total number of classes Nclass of 24, 18 and 12 representing different computational burdens over the North America (NA) continent, the new method showed variable performances compared to the SGC1 and SGC2 methods. However, the advantage of the SGC method over the other two methods clearly emerged at coarser model resolutions and with moderate computational intensity (Nclass = 18) as it explained the most PFTs and elevation variability among the three subgrid methods. Spatially, the SGC method explained more elevation variability in topography-complex areas and more vegetation variability in flat areas. Furthermore, the variability of both elevation and vegetation explained by the new method was more spatially homogeneous regardless of the model resolutions and computational burdens. The SGC method will be implemented in CLM over the NA continent to assess its impacts on simulating land surface processes.

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The Use of Remote Sensing-Based ET Estimates to Improve Global Hydrological Simulations in the Community Land Model Version 5.0

Remote Sensing ◽

10.3390/rs13214460 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4460

Author(s):

Dayang Wang ◽

Dagang Wang ◽

Chongxun Mo

Keyword(s):

Remote Sensing ◽

Water Supply ◽

Bias Correction ◽

Land Surface ◽

Land Surface Models ◽

Community Land Model ◽

Model Version ◽

Correction Effect ◽

Surface Models ◽

Global Land

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.

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HESS Opinions "A perspective on isotope versus non-isotope approaches to determine the contribution of transpiration to total evaporation"

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2815-2014 ◽

2014 ◽

Vol 18 (8) ◽

pp. 2815-2827 ◽

Cited By ~ 53

Author(s):

S. J. Sutanto ◽

B. van den Hurk ◽

P. A. Dirmeyer ◽

S. I. Seneviratne ◽

T. Röckmann ◽

...

Keyword(s):

Land Surface ◽

Hydrological Cycle ◽

Climate Modeling ◽

Global Climate ◽

Climatic Conditions ◽

Data Sets ◽

Land Surface Models ◽

Total Evaporation ◽

Surface Models ◽

Methodological Aspects

Abstract. Current techniques to disentangle the evaporative fluxes from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants, are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total evaporation, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based versus non-isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the partitioning analysis are considered, including their limitations, and explanations of possible discrepancies between the methods are discussed. We suggest sources of systematic error that may lead to biases in the results, e.g., instruments inaccuracy, assumptions used in analyses, and calibration parameters. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors; however, models tend to produce lower transpiration fractions. The relatively low transpiration fraction in current state-of-the-art land-surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope-enabled land-surface and global climate modeling studies allow for the evaluation of the parameterization of land-surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow for this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

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