Multi-site evaluation of the JULES land surface model using global and local data

Slevin, Darren; Tett, Simon F. B.; Williams, Mathew

doi:10.5194/gmd-8-295-2015

Cited by 20 publications

(12 citation statements)

References 78 publications

(69 reference statements)

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“…Different methods are used to create time series of global gridded climate data in order to drive LSMs, and this can introduce uncertainty that can propagate through model simulations (Zhao et al, 2006). Even on the point scale, differences in simulated GPP were observed when driving JULES with the WFDEI-GPCC and PRINCETON datasets (Slevin et al, 2015). As in this study, it also occurred in the tropics.…”

Section: Is the Meteorological Dataset Used To Drive The Model Importmentioning

confidence: 77%

“…The Joint UK Land Environment Simulator (JULES) has been evaluated on various scales: point Slevin et al, 2015;Ménard et al, 2015), regional (Galbraith et al, 2010;Burke et al, 2013;Chadburn et al, 2015) and globally as part of model-intercomparison studies (Anav et al, 2015;Sitch et al, 2015). Evaluating simulated GPP on a range of scales and its sensitivity to spatial resolution and meteorological data is essential for informing future model developments.…”

Section: Introductionmentioning

confidence: 99%

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Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

et al. 2017

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Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010]. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0.5 • × 0.5 • spatial resolution), the annual average global GPP simulated by JULES for 2001-2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CAR-DAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0.5 • × 0.5 • , 1 • × 1 • and 2 • × 2 • ) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year −1 . Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year −1 . On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5 • N-5 • S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30-60 • N).

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Section: Is the Meteorological Dataset Used To Drive The Model Importmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

et al. 2017

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“…The input range for LAI was based on, e.g., Slevin et al (2015) and range of anoxic respiration on, e.g., Scanlon and Moore (2000) and Szafranek-Nakonieczna and Stepniewska (2014).…”

Section: Testing Model Sensitivity To Input Datamentioning

confidence: 99%

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

et al. 2017

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Abstract. Wetlands are one of the most significant natural sources of methane (CH 4 ) to the atmosphere. They emit CH 4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH 4 , in addition to carbon dioxide (CO 2 ). Production of CH 4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH 4 emissions are thus needed for upscaling observations to estimate present CH 4 emissions and for producing scenarios of future atmospheric CH 4 concentrations. Aiming at a CH 4 model that can be added to models describing peatland carbon cycling, we composed a model called HIMMELI that describes CH 4 build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peatland vegetation, and water table depth (WTD), it simulates the concentrations and transport of CH 4 , CO 2 , and oxygen (O 2 ) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure and results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and demonstrate that HIMMELI outputs realistic fluxes by comparing modeled and measured fluxes at two peatland sites. As HIMMELI describes only the CH 4 -related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH 4 models connected to complete peatland carbon models, which is usually the case. Our results indicated that (1) the model is flexible and robust and thus suitable for different environments; (2) the simulated CH 4 emissions largely depend on the prescribed rate of anoxic respiration; (3) the sensitivity of Published by Copernicus Publications on behalf of the European Geosciences Union. 4666M. Raivonen et al.: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands the total CH 4 emission to other input variables is mainly mediated via the concentrations of dissolved gases, in particular, the O 2 concentrations that affect the CH 4 production and oxidation rates; (4) with given input respiration, the peat column description does not significantly affect the simulated CH 4 emissions in this model version.

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“…The procedures to perform the task of validation appear in several forms, depending on data availability, system characteristics and researchers' opinions (Hsu et al, 1999). A common strategy is to examine the model's simulation versus actual observations acquired from the real world using common statistical metrics, and several validation studies of this type have been undertaken globally (Henderson-Sellers et al, 1995;Viterbo and Beljaars, 1995;Liang et al, 1998;Wang et al, 2007;Abramowitz et al, 2008;Slevin et al, 2015). In addition, Kramer et al (2002), in an attempt to holistically assess the capability of a model for portraying a real world system, have proposed a set of model assessment criteria, namely accuracy, generality and realism.…”

Section: G P Petropoulos Et Al: Quantifying the Prediction Accuracmentioning

confidence: 99%

Quantifying the prediction accuracy of a 1-D SVAT model at a range of ecosystems in the USA and Australia: evidence towards its use as a tool to study Earth's system interactions

et al. 2015

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Abstract. This paper describes the validation of the SimSphere SVAT (Soil-Vegetation-Atmosphere Transfer) model conducted at a range of US and Australian ecosystem types. Specific focus was given to examining the models' ability in predicting shortwave incoming solar radiation (R g ), net radiation (R net ), latent heat (LE), sensible heat (H ), air temperature at 1.3 m (T air 1.3 m ) and air temperature at 50 m (T air 50 m ). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from eight sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation.Overall, results showed a good agreement between the model predictions and the in situ measurements, particularly so for the R g , R net , T air 1.3 m and T air 50 m parameters. The simulated R g parameter exhibited a root mean square deviation (RMSD) within 25 % of the observed fluxes for 58 of the 72 selected days, whereas an RMSD within ∼ 24 % of the observed fluxes was reported for the R net parameter for all days of study (RMSD = 58.69 W m −2 ). A systematic underestimation of R g and R net (mean bias error (MBE) = −19.48 and −16.46 W m −2 ) was also found. Simulations for the T air 1.3 m and T air 50 m showed good agreement with the in situ observations, exhibiting RMSDs of 3.23 and 3.77 • C (within ∼ 15 and ∼ 18 % of the observed) for all days of analysis, respectively. Comparable, yet slightly less satisfactory simulation accuracies were exhibited for the H and LE parameters (RMSDs = 38.47 and 55.06 W m −2 , ∼ 34 and ∼ 28 % of the observed). Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. The Nash-Sutcliffe efficiency index for all parameters ranges from 0.720 to 0.998, suggesting a very good model representation of the observations.To our knowledge, this study presents the most detailed evaluation of SimSphere done so far, and the first validation of it conducted in Australian ecosystem types. Findings are important and timely, given the expanding use of the model both as an educational and research tool today. This includes ongoing research by different space agencies examining its synergistic use with Earth observation data towards the development of global operational products.

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Multi-site evaluation of the JULES land surface model using global and local data

Cited by 20 publications

References 78 publications

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

Quantifying the prediction accuracy of a 1-D SVAT model at a range of ecosystems in the USA and Australia: evidence towards its use as a tool to study Earth's system interactions

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