Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data

Blyth, Eleanor; Gash, J. H. C.; Lloyd, Amanda; Pryor, Milton; Weedon, Graham P.; Shuttleworth, J.

doi:10.1175/2009jhm1183.1

Cited by 103 publications

(100 citation statements)

References 28 publications

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“…5b and d which show the mean seasonal cycle of ET(mm month −1 ) and give an approximation of the mean bias per month for each of the models. Similar trends have been observed by Blyth et al (2010), who used surface energy flux measurements from 10 FLUXNET sites around the world that represented a range of climate conditions and biome types, and found that the JULES-base evaporation is higher than that observed; the same results were also found by Van den Hoof et al (2011), who found that the JULES-base latent heat flux is overestimated over cropland in Europe. One possibility for this bias is an overestimation of canopy interception.…”

Section: Resultssupporting

confidence: 85%

Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

Tsarouchi

Buytaert

Mijic

2014

Hydrol. Earth Syst. Sci.

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Abstract. Land-Surface Models (LSMs) are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land-surface-atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the LSM JULES with the crop growth model InfoCrop. JULES in its current version (v3.4) does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parametrized it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges River basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm month −1 , depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 5.4-11.6 mm month −1 . For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm month −1 , depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 2.2-3.4 mm month −1 . The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops-atmosphere interactions.

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Section: Resultssupporting

confidence: 85%

Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

Tsarouchi

Buytaert

Mijic

2014

Hydrol. Earth Syst. Sci.

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“…The Bowen ratio closure method is often adopted to correct the non-closure. It takes on the assumption that the Bowen ratio (ratio of the sensible to the latent heat fluxes) is well estimated by the eddy covariance system, so that the turbulent fluxes can be adjusted while conserving the Bowen ratio to ensure closure (Blyth et al, 2010;Zheng et al, 2014;Er-Raki et al, 2008). The adjusted sensible and latent heat fluxes can be computed as…”

Section: Energy Balance Closurementioning

confidence: 99%

The interactions between soil–biosphere–atmosphere (ISBA) land surface model multi-energy balance (MEB) option in SURFEXv8 – Part 2: Introduction of a litter formulation and model evaluation for local-scale forest sites

et al. 2017

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Abstract. Land surface models (LSMs) need to balance a complicated trade-off between computational cost and complexity in order to adequately represent the exchanges of energy, water and matter with the atmosphere and the ocean. Some current generation LSMs use a simplified or composite canopy approach that generates recurrent errors in simulated soil temperature and turbulent fluxes. In response to these issues, a new version of the interactions between soilbiosphere-atmosphere (ISBA) land surface model has recently been developed that explicitly solves the transfer of energy and water from the upper canopy and the forest floor, which is characterized as a litter layer. The multi-energy balance (MEB) version of ISBA is first evaluated for three well-instrumented contrasting local-scale sites, and sensitivity tests are performed to explore the behavior of new model parameters. Second, ISBA-MEB is benchmarked against observations from 42 forested sites from the global micrometeorological network (FLUXNET) for multiple annual cycles.It is shown that ISBA-MEB outperforms the composite version of ISBA in improving the representation of soil temperature, ground, sensible and, to a lesser extent, latent heat fluxes. Both versions of ISBA give comparable results in terms of simulated latent heat flux because of the similar formulations of the water uptake and the stomatal resistance. However, MEB produces a better agreement with the observations of sensible heat flux than the previous version of ISBA for 87.5 % of the simulated years across the 42 forested FLUXNET sites. Most of this improvement arises owing to the improved simulation of the ground conduction flux, which is greatly improved using MEB, especially owing to the forest litter parameterization. It is also shown that certain processes are also modeled more realistically (such as the partitioning of evapotranspiration into transpiration and ground evaporation), even if certain statistical performances are neutral. The analyses demonstrate that the shading effect of the vegetation, the explicit treatment of turbulent transfer for the canopy and ground, and the insulating thermal and hydrological effects of the forest floor litter turn out to be essential for simulating the exchange of energy, water and matter across a large range of forest types and climates.

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“…Manipulative experiments can also be used to evaluate modeled responses of water and energy to global change ). Data sets from over 100 sites on soil and permafrost data and active layer depths from the Circumpolar Active Layer Mon- Data sets that are often used for benchmarking biogeochemical cycle models include atmospheric CO 2 records on seasonal to centennial time scales (Dargaville et al, 2002;Heimann et al, 1998) and satellite data at seasonal or longer time scales (Blyth et al, 2010;Maignan et al, 2011;Randerson et al, 2009). Other available data sets for biogeochemical cycle benchmarking include global gross primary production (GPP), NPP, soil respiration, ecosystem respiration, plant biomass, litter pool, litter decomposition rates, and soil carbon data products (Table 3).…”

Section: Candidate Benchmarks For Evaluation Of Various Aspects Of Lamentioning

confidence: 99%

A framework for benchmarking land models

et al. 2012

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Abstract. Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of Published by Copernicus Publications on behalf of the European Geosciences Union. Y. Q. Luo et al.: A framework for benchmarking land modelsbenchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data-model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

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Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data

Cited by 103 publications

References 28 publications

Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

The interactions between soil–biosphere–atmosphere (ISBA) land surface model multi-energy balance (MEB) option in SURFEXv8 – Part 2: Introduction of a litter formulation and model evaluation for local-scale forest sites

A framework for benchmarking land models

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