Assessing seasonality of biochemical CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; exchange model parameters from micrometeorological flux observations at boreal coniferous forest

Thum, Tea; Aalto, Tuula; Laurila, Tuomas; Aurela, Mika; Lindroth, Anders; Vesala, Timo

doi:10.5194/bg-5-1625-2008

Cited by 33 publications

(28 citation statements)

References 81 publications

(93 reference statements)

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“…The photosynthesis module in JSBACH has the same temperature response for parameters describing the potential electron transport rate and maximum carboxylation rate for the whole year, thus indicating that the simulated vegetation is ready to immediately photosynthesize in spring with increasing air temperatures without any recovery period required, providing there is foliage present as in the case of ENF. However, the boreal coniferous forests have a recovery period in spring after the winter dormancy before they reach their full summertime photosynthetic capacity [89,90]. This may explain the differences between observed GPP at Sodankylä and simulated GPP using local weather observations (Figures 4 and 5).…”

Section: Modelling Of Springtime Development and The Start Of Season mentioning

confidence: 78%

Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations

et al. 2016

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Abstract:The objective of this study was to assess the performance of the simulated start of the photosynthetically active season by a large-scale biosphere model in boreal forests in Finland with remote sensing observations. The start of season for two forest types, evergreen needle-and deciduous broad-leaf, was obtained for the period 2003-2011 from regional JSBACH (Jena Scheme for Biosphere-Atmosphere Hamburg) runs, driven with climate variables from a regional climate model. The satellite-derived start of season was determined from daily Moderate Resolution Imaging Spectrometer (MODIS) time series of Fractional Snow Cover and the Normalized Difference Water Index by applying methods that were targeted to the two forest types. The accuracy of the satellite-derived start of season in deciduous forest was assessed with bud break observations of birch and a root mean square error of seven days was obtained. The evaluation of JSBACH modelled start of season dates with satellite observations revealed high spatial correspondence. The bias was less than five days for both forest types but showed regional differences that need further consideration. The agreement with satellite observations was slightly better for the evergreen than for the deciduous forest. Nonetheless, comparison with gross primary production (GPP) determined from CO 2 flux measurements at two eddy covariance sites in evergreen forest revealed that the JSBACH-simulated GPP was higher in early spring and led to too-early simulated start of season dates. Photosynthetic activity recovers differently in evergreen and deciduous forests. While for the deciduous forest calibration of phenology alone could improve the performance of JSBACH, for the evergreen forest, changes such as seasonality of temperature response, would need to be introduced to the photosynthetic capacity to improve the temporal development of gross primary production.

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Section: Modelling Of Springtime Development and The Start Of Season mentioning

confidence: 78%

Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations

et al. 2016

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“…Kuppel et al, 2014). Medvigy et al (2009) and Verbeeck et al (2011) both showed that parameters derived at one site can perform well on a similar site and over the surrounding region (Medvigy and Moorcroft, 2011). However, a contradictory study by Groenendijk et al (2010) found that there was cross-site parameter variability after optimisation within the PFT groupings.…”

Section: Introductionmentioning

confidence: 98%

“…of defining a model adjoint and minimising the error in the fit to data), are very similar in these two applications of data assimilation. This paper is certainly not the first to define parameter estimation of this form as data assimilation (Braswell et al, 2005;Stöckli et al, 2008;Verbeeck et al, 2011;Kuppel et al, 2012;Hararuk et al, 2014), but the reader should note the subtle difference between our definition of data assimilation and that commonly used in weather forecasting. Even a relatively simplistic land-surface representation such as JULES has over a hundred internal parameters representing the environmental sensitivities of the various land-surface types and PFTs within the model.…”

Section: Data Assimilation Systemmentioning

confidence: 99%

“…These approaches have been used in a number of studies, covering various LSMs, to derive vectors of parameters that improve model-data fit significantly (e.g. Wang et al, 2001Wang et al, , 2007Reichstein et al, 2003;Knorr and Kattge, 2005;Raupach et al, 2005;Santaren et al, 2007;Thum et al, 2008;Williams et al, 2009;Peng et al, 2011). Many of these studies calibrate the model at individual measurement sites.…”

Section: Introductionmentioning

confidence: 99%

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Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

2016

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Abstract. Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model-data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

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“…As observation sites have their own characteristics, it is necessary to make local site simulations for model evaluation and calibration as they may reveal new insight into model behaviour and guide further development. Model-data fusion has been applied for boreal forest sites by, e.g., Aalto et al (2004) Peltoniemi et al (2015b), Thum et al (2007Thum et al ( , 2008 and Wu et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

Mäkelä

Susiluoto

Markkanen

et al. 2016

Nonlin. Processes Geophys.

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Abstract. We examined parameter optimisation in the JS-BACH (Kaminski et al., 2013;Knorr and Kattge, 2005;Reick et al., 2013) ecosystem model, applied to two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon-exchange-related formulations, and optimised them using the adaptive Metropolis (AM) algorithm for Hyytiälä with a 5-year calibration period (2000)(2001)(2002)(2003)(2004) followed by a 4-year validation period (2005)(2006)(2007)(2008). Sodankylä acted as an independent validation site, where optimisations were not made.The tuning provided estimates for full distribution of possible parameters, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to a phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large contribution from vegetation transpiration.In addition to leaf area index and the maximum carboxylation rate, the most effective parameters adjusting the gross primary production (GPP) and evapotranspiration (ET) fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO 2 concentration to external CO 2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and daily and half-hourly tunings reversed this process.The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use efficiency. When compared to the seasonal tuning, the daily tuning is worse on the seasonal scale. However, daily parametrisation reproduced the observations for average diurnal cycle best, except for the GPP for Sodankylä validation period, where half-hourly tuned parameters were better. In general, the daily tuning provided the largest reduction in model-data mismatch.The models response to drought was unaffected by our parametrisations and further studies are needed into enhancing the dry response in JSBACH.

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Assessing seasonality of biochemical CO<sub>2</sub> exchange model parameters from micrometeorological flux observations at boreal coniferous forest

Cited by 33 publications

References 81 publications

Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations

Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations

Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

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