Better constraining the CO2 plant uptake at global scale: joint assimilation of COS and CO2 atmospheric measurements into a transport model.

2020 ◽  
Author(s):  
Marine Remaud ◽  
Frédéric Chevallier ◽  
Philippe Peylin ◽  
Antoine Berchet ◽  
Fabienne Maignan
Keyword(s):  
Plant Uptake ◽  
Land Surface ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Gross Primary Production ◽  
Surface Fluxes ◽  
Surface Model ◽  
Atmospheric Measurements ◽  
Inverse Systems ◽  
Atmospheric Transport Model

<p>Inverse systems that assimilate atmospheric carbon dioxide measurements (CO2) into a global atmospheric transport model, are commonly used together with anthropogenic emission inventories to infer net biospheric surface fluxes. However, when assimilating CO2 measurements only, the respiration fluxes cannot be disentangled from the gross primary production (GPP) fluxes, leaving few possibilities to interpret the inferred fluxes from a mechanistic point of view. Measurements of carbonyl sulfide (COS) may help to fill this gap: COS has similar diffusion pathway inside leaves as CO2 but is not re-emitted into the atmosphere by the plant respiration. We explore here the benefit of assimilating both COS and CO2 measurements into the LMDz atmospheric transport model to constrain GPP and respiration fluxes separately. To this end, we develop an analytic inverse system based on the 14 Plant functional Type (PFTs) as defined in the ORCHIDEE land surface model. The vegetation uptake of COS is parameterized as a linear function of GPP and of the leaf relative uptake (LRU), which is the ratio of COS to CO2 deposition velocities in plants. A new parameterization of the atmosphere soil exchanges is also included. We use the system to optimize GPP and respiration fluxes separately at the seasonal scale over the globe. The results lead to a balanced COS global budget and a seasonality of the COS fluxes in better agreement with observations. We find a large sensitivity of the partition between the ocean emissions and the COS plant uptake to the LRU parameterizations.</p>

scholarly journals Inverse modelling of CF<sub>4</sub> and NF<sub>3</sub> emissions in East Asia

2018 ◽  
Vol 18 (18) ◽  
pp. 13305-13320 ◽  
Author(s):  
Tim Arnold ◽  
Alistair J. Manning ◽  
Jooil Kim ◽  
Shanlan Li ◽  
Helen Webster ◽  
...  
Keyword(s):  
South Korea ◽  
East Asia ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Atmospheric Gases ◽  
Atmospheric Measurements ◽  
Small Uncertainty ◽  
Measurement Location ◽  
Atmospheric Transport Model ◽  
Good Agreement

Abstract. Decadal trends in the atmospheric abundances of carbon tetrafluoride (CF4) and nitrogen trifluoride (NF3) have been well characterised and have provided a time series of global total emissions. Information on locations of emissions contributing to the global total, however, is currently poor. We use a unique set of measurements between 2008 and 2015 from the Gosan station, Jeju Island, South Korea (part of the Advanced Global Atmospheric Gases Experiment network), together with an atmospheric transport model, to make spatially disaggregated emission estimates of these gases in East Asia. Due to the poor availability of good prior information for this study, our emission estimates are largely influenced by the atmospheric measurements. Notably, we are able to highlight emission hotspots of NF3 and CF4 in South Korea due to the measurement location. We calculate emissions of CF4 to be quite constant between the years 2008 and 2015 for both China and South Korea, with 2015 emissions calculated at 4.3±2.7 and 0.36±0.11 Gg yr−1, respectively. Emission estimates of NF3 from South Korea could be made with relatively small uncertainty at 0.6±0.07 Gg yr−1 in 2015, which equates to ∼1.6 % of the country's CO2 emissions. We also apply our method to calculate emissions of CHF3 (HFC-23) between 2008 and 2012, for which our results find good agreement with other studies and which helps support our choice in methodology for CF4 and NF3.

scholarly journals Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements

2017 ◽  
Author(s):  
Thibaud Thonat ◽  
Marielle Saunois ◽  
Philippe Bousquet ◽  
Isabelle Pison ◽  
Zeli Tan ◽  
...  
Keyword(s):  
Land Surface ◽  
Transport Model ◽  
Land Surface Model ◽  
Arctic Region ◽  
The Arctic ◽  
Surface Model ◽  
Multiple Sources ◽  
Atmospheric Measurements ◽  
Methane Cycle ◽  
Methane Budget

Abstract. Understanding the recent evolution of methane emissions in the Arctic is necessary to interpret the global methane cycle. Emissions are affected by significant uncertainties and are sensitive to climate change, leading to potential feedbacks. A polar version of the CHIMERE chemistry-transport model is used to simulate the evolution of tropospheric methane in the Arctic during 2012, including all known regional anthropogenic and natural sources. CHIMERE simulations are compared to atmospheric continuous observations at six measurement sites in the Arctic region. In winter, the Arctic is dominated by anthropogenic emissions; emissions from continental seepages and oceans, including from the East Siberian Arctic Shelf, can contribute significantly in more limited areas. In summer, emissions from wetland and freshwater sources dominate across the whole region. The model is able to reproduce the seasonality and synoptic variations of methane measured at the different sites. We find that all methane sources significantly affect the measurements at all stations at least at the synoptic scale, except for biomass burning; this indicates the relevance of continuous observations to gain a mechanistic understanding of Arctic methane sources. Sensitivity tests reveal that the choice of the land surface model used to prescribe wetland emissions can be critical in correctly representing methane concentrations. Also testing different freshwater emission inventories leads to large differences in modelled methane. Attempts to include methane sinks (OH oxidation and soil uptake) reduced the model bias relative to observed atmospheric CH4. The study illustrates how multiple sources, having different spatiotemporal dynamics and magnitudes, jointly influence the overall Arctic methane budget, and highlights ways towards further improved assessments.

scholarly journals Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements

2017 ◽  
Vol 17 (13) ◽  
pp. 8371-8394 ◽  
Author(s):  
Thibaud Thonat ◽  
Marielle Saunois ◽  
Philippe Bousquet ◽  
Isabelle Pison ◽  
Zeli Tan ◽  
...  
Keyword(s):  
Land Surface ◽  
Transport Model ◽  
Land Surface Model ◽  
Arctic Region ◽  
The Arctic ◽  
Surface Model ◽  
Atmospheric Methane ◽  
Multiple Sources ◽  
Atmospheric Measurements ◽  
Methane Cycle

Abstract. Understanding the recent evolution of methane emissions in the Arctic is necessary to interpret the global methane cycle. Emissions are affected by significant uncertainties and are sensitive to climate change, leading to potential feedbacks. A polar version of the CHIMERE chemistry-transport model is used to simulate the evolution of tropospheric methane in the Arctic during 2012, including all known regional anthropogenic and natural sources, in particular freshwater emissions which are often overlooked in methane modelling. CHIMERE simulations are compared to atmospheric continuous observations at six measurement sites in the Arctic region. In winter, the Arctic is dominated by anthropogenic emissions; emissions from continental seepages and oceans, including from the East Siberian Arctic Shelf, can contribute significantly in more limited areas. In summer, emissions from wetland and freshwater sources dominate across the whole region. The model is able to reproduce the seasonality and synoptic variations of methane measured at the different sites. We find that all methane sources significantly affect the measurements at all stations at least at the synoptic scale, except for biomass burning. In particular, freshwater systems play a decisive part in summer, representing on average between 11 and 26 % of the simulated Arctic methane signal at the sites. This indicates the relevance of continuous observations to gain a mechanistic understanding of Arctic methane sources. Sensitivity tests reveal that the choice of the land-surface model used to prescribe wetland emissions can be critical in correctly representing methane mixing ratios. The closest agreement with the observations is reached when using the two wetland models which have emissions peaking in August–September, while all others reach their maximum in June–July. Such phasing provides an interesting constraint on wetland models which still have large uncertainties at present. Also testing different freshwater emission inventories leads to large differences in modelled methane. Attempts to include methane sinks (OH oxidation and soil uptake) reduced the model bias relative to observed atmospheric methane. The study illustrates how multiple sources, having different spatiotemporal dynamics and magnitudes, jointly influence the overall Arctic methane budget, and highlights ways towards further improved assessments.

A description of the COS budget inferred by inverse modelling

2021 ◽  
Author(s):  
Marine Remaud ◽  
Camille Abadie ◽  
Sauveur Belviso ◽  
Antoine Berchet ◽  
Frédéric Chevallier ◽  
...  
Keyword(s):  
Land Surface ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Land Surface Model ◽  
Diffusion Path ◽  
Global Scale ◽  
Striking Similarity ◽  
Surface Model ◽  
Terrestrial Vegetation ◽  
The Tropics

&lt;p&gt;Carbonyle Sulphide, a trace gas exhibiting a striking similarity with CO2 in the biochemical diffusion path of leaves, has been recognized to be a promising surrogate of CO2 for estimating carbon storage in the terrestrial vegetation. Based on the similarity between COS and CO2, an empirical linear model relating both gas concentrations provides constraints on the estimation of the Gross Primary Productivity (GPP), the amount of carbon dioxide that is absorbed by ecosystems. However, large uncertainties on the other components of its atmospheric budget prevent us from directly relating the atmospheric COS measurements to the the GPP at global scale. The largest uncertainty arises from the closure of its atmospheric budget, with a source component missing. We explore here the benefit of assimilating both COS and CO2 measurements into the LMDz atmospheric transport model to gain insight on the COS budget. We develop an analytic inverse system which optimized the biospheric fluxes within the 14 Plant functional Type (PFTs) as defined in the ORCHIDEE land surface model. The vegetation uptake of COS is parameterized as a linear function of GPP and of the leaf relative uptake (LRU), which is the ratio of COS to CO2 deposition velocities in plants. A possible scenario leads to a global biospheric sink between 800-900 GgS/y, with a higher GPP in the high latitudes and higher total oceanic emissions between 400 and 600 GgS/y over the tropics. The COS inter-hemispheric gradient is in better agreement with HIPPO independent aircraft measurements. The comparison against NOAA COS airborne profiles and Solar Induced Fluorescence shed light on a too strong GPP in spring in ORCHIDEE in northern America,&amp;#160; leaving room for improvements. &lt;span&gt;We also show that uncertainty in the location of hot spots in the prior anthropogenic inventory limits the use of atmospheric COS measurements in inverse modeling.&lt;/span&gt;&lt;/p&gt;

scholarly journals Inverse modelling of CF<sub>4</sub> and NF<sub>3</sub> emissions in East Asia

2018 ◽  
Author(s):  
Tim Arnold ◽  
Alistair Manning ◽  
Jooil Kim ◽  
Shanlan Li ◽  
Helen Webster ◽  
...  
Keyword(s):  
South Korea ◽  
East Asia ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Atmospheric Gases ◽  
Atmospheric Measurements ◽  
Small Uncertainty ◽  
Measurement Location ◽  
Atmospheric Transport Model ◽  
Good Agreement

Abstract. Well mixed abundances and decadal trends of carbon tetrafluoride (CF4) and nitrogen trifluoride (NF3) have been well characterised and have provided a time series of global total emissions. Information on locations of emissions contributing to the global total, however, is currently poor. We use a unique set of measurements between 2008 and 2015 from the Gosan station, Jeju Island, South Korea (part of the Advanced Global Atmospheric Gases Experiment network), together with an atmospheric transport model to make spatially disaggregated emission estimates of these gases in East Asia. Owing to the poor availability of good prior information for this study our results are strongly constrained by the atmospheric measurements. Notably, we are able to highlight emissions hotspots of NF3 and CF4 in South Korea, owing to the measurement location. We calculate emissions of CF4 to be quite constant between years 2008 and 2015 for both China and South Korea with 2015 emissions calculated at 4.33 ± 2.65 Gg yr−1 and 0.36 ± 0.11 Gg yr−1, respectively. Emission estimates of NF3 from South Korea could be made with relatively small uncertainty at 0.6 ± 0.07 Gg yr−1 in 2015, which equates to ~ 1.6 % of the country's CO2 emissions. We also apply our method to calculate emissions of CHF3 (HFC-23) between 2008 and 2012, for which our results find good agreement with other studies and which helps support our choice in methodology for CF4 and NF3.

scholarly journals Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model

2017 ◽  
Author(s):  
Bakr Badawy ◽  
Saroja Polavarapu ◽  
Dylan B. A. Jones ◽  
Feng Deng ◽  
Michael Neish ◽  
...  
Keyword(s):  
Land Surface ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Terrestrial Ecosystem ◽  
Forecast Model ◽  
Ecosystem Model ◽  
Co2 Fluxes ◽  
Meteorological Forcing ◽  
Terrestrial Ecosystem Model ◽  
Atmospheric Transport Model

Abstract. The Canadian Land Surface Scheme and the Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component in the family of Canadian Earth System Models (CanESM). Here, CLASS-CTEM is coupled to Environment and Climate Change Canada (ECCC)'s weather and greenhouse gas forecast model (GEM-MACH-GHG) to consistently model atmosphere-land exchange of CO2. The coupling between the land and the atmospheric transport model ensures consistency between meteorological forcing of CO2 fluxes and CO2 transport. The procedure used to spin up carbon pools for CLASS-CTEM for multi-decadal simulations needed to be significantly altered to deal with the limited availability of consistent meteorological information from a constantly changing operational environment in the GEM-MACH-GHG model. Despite the limitations in the spin up procedure, the simulated fluxes obtained by driving the CLASS-CTEM model with meteorological forcing from GEM-MACH-GHG were comparable to those obtained from CLASS-CTEM when it is driven with standard meteorological forcing (CRU-NCEP). This is due to the similarity of the two meteorological datasets in terms of temperature and radiation. However notable discrepancies in the seasonal variation and spatial patterns of precipitation estimates, especially in the tropics, were reflected in the estimated carbon fluxes, as they significantly affected the magnitude of the vegetation productivity and, to a lesser extent, the seasonal variations in carbon fluxes. Nevertheless, the simulated fluxes based on the meteorological forcing from the GEM-MACH-GHG model are within the range of other estimates from bottom-up or top-down approaches. Indeed, when simulated fluxes obtained by driving the CLASS-CTEM model with meteorological data from the GEM-MACH-GHG model are used as prior estimates for an atmospheric CO2 inversion analysis using the adjoint of the GEOS-Chem model, the retrieved CO2 flux estimates are comparable to those obtained from other systems in terms of the global budget and the total flux estimates for the northern extratropical regions, which have good observational coverage. In data poor regions, as expected, differences in the retrieved fluxes due to the prior fluxes become apparent, but fall within the uncertainty bounds based on multi-inversion analyses. The coupling of CLASS-CTEM to an atmospheric transport model with carbon assimilation capabilities also provides insights into the limitations of CLASS-CTEM simulated CO2 fluxes through comparisons of simulated atmospheric CO2 with observations at selected flask stations. This capability can be used to continually assess and improve the terrestrial ecosystem modules of the CLASS-CTEM model.

scholarly journals Biases in regional carbon budgets from covariation of surface fluxes and weather in transport model inversions

2014 ◽  
Vol 14 (3) ◽  
pp. 1571-1585 ◽  
Author(s):  
I. N. Williams ◽  
W. J. Riley ◽  
M. S. Torn ◽  
S. C. Biraud ◽  
M. L. Fischer
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Interannual Variability ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Carbon Uptake ◽  
Atmospheric Transport Model

Abstract. Recent advances in atmospheric transport model inversions could significantly reduce uncertainties in land carbon uptake through the assimilation of CO2 concentration measurements at weekly and shorter timescales. The potential of these measurements for reducing biases in estimated land carbon sinks depends on the strength of covariation between surface fluxes and atmospheric transport at these timescales and how well transport models represent this covariation. Daily to seasonal covariation of surface fluxes and atmospheric transport was estimated in observations at the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, and compared to an atmospheric transport model inversion (CarbonTracker). Covariation of transport and surface fluxes was stronger in CarbonTracker than in observations on synoptic (daily to weekly) timescales, with a wet year (2007) having significant covariation compared to a dry year (2006). Differences between observed and CarbonTracker synoptic covariation resulted in a 0.3 ppm CO2 enhancement in boundary layer concentrations during the growing season, and a corresponding enhancement in carbon uptake by 13% of the seasonal cycle amplitude in 2007, as estimated by an offline simplified transport model. This synoptic rectification of surface flux variability was of similar magnitude to the interannual variability in carbon sinks alone, and indicates that interannual variability in the inversions can be affected by biases in simulated synoptic rectifier effects. The most significant covariation of surface fluxes and transport had periodicities of 10 days and greater, suggesting that surface flux inversions would benefit from improved simulations of the effects of soil moisture on boundary layer heights and surface CO2 fluxes. Soil moisture remote sensing could be used along with CO2 concentration measurements to further constrain atmospheric transport model inversions.

scholarly journals Global modelling of soil carbonyl sulfide exchange

2021 ◽  
Author(s):  
Camille Abadie ◽  
Fabienne Maignan ◽  
Marine Remaud ◽  
Jérôme Ogée ◽  
J. Elliott Campbell ◽  
...  
Keyword(s):  
Land Surface ◽  
Latitudinal Gradient ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Carbonyl Sulfide ◽  
Sensitivity Analyses ◽  
Surface Model ◽  
Small Magnitude ◽  
C Cycle ◽  
The Impact

Abstract. Carbonyl sulfide (COS) is an atmospheric trace gas of interest for C cycle research because COS uptake by continental vegetation is strongly related to terrestrial gross primary productivity (GPP), the largest and most uncertain flux in atmospheric CO2 budgets. However, to use atmospheric COS budgets as an additional tracer of GPP, an accurate quantification of COS exchange by soils is also needed. At present, the atmospheric COS budget is unbalanced globally, with total COS flux estimates from oxic and anoxic soils that vary between −409 and −104 GgS yr−1. This uncertainty hampers the use of atmospheric COS concentrations to constrain GPP estimates through atmospheric transport inversions. In this study we implemented a mechanistic soil COS model in the ORCHIDEE land surface model to simulate COS fluxes in oxic and anoxic soils. Evaluation of the model against flux measurements at 7 sites yields a mean root mean square deviation of 1.6 pmol m−2 s−1, instead of 2 pmol m−2 s−1 when using a previous empirical approach that links soil COS uptake to soil heterotrophic respiration. The new model predicts that, globally and over the 2009–2016 period, oxic soils act as a net uptake of −126 GgS yr−1, and anoxic soils are a source of +96 GgS yr−1, leading to a global net soil sink of only −30 GgS yr−1, i.e., much smaller than previous estimates. The small magnitude of the soil fluxes suggests that the error in the COS budget is dominated by the much larger fluxes from plants, oceans, and industrial activities. The predicted spatial distribution of soil COS fluxes, with large emissions in the tropics from oxic (up to 68.2 pmol COS m−2 s−1) and anoxic (up to 36.8 pmol COS m−2 s−1) soils, marginally improves the latitudinal gradient of atmospheric COS concentrations, after transport by the LMDZ atmospheric transport model. The impact of different soil COS flux representations on the latitudinal gradient of the atmospheric COS concentrations is strongest in the northern hemisphere. We also implemented spatio-temporal variations of near-ground atmospheric COS concentrations in the modelling of biospheric COS fluxes, which helped reduce the imbalance of the atmospheric COS budget by lowering COS uptake by soils and vegetation globally (−10 % for soil, and −8 % for vegetation with a revised mean estimate of −576 GgS y−r1 over 2009–2016). Sensitivity analyses highlighted the different parameters to which each soil COS flux model is the most responsive, selected in a parameter optimization framework. Having both vegetation and soil COS fluxes modelled within ORCHIDEE opens the way for using observed ecosystem COS fluxes and larger scale atmospheric COS mixing ratios to improve the simulated GPP, through data assimilation techniques.

scholarly journals Simulating CH<sub>4</sub> and CO<sub>2</sub> over South and East Asia using the zoomed chemistry transport model LMDz-INCA

2018 ◽  
Vol 18 (13) ◽  
pp. 9475-9497 ◽  
Author(s):  
Xin Lin ◽  
Philippe Ciais ◽  
Philippe Bousquet ◽  
Michel Ramonet ◽  
Yi Yin ◽  
...  
Keyword(s):  
East Asia ◽  
Land Surface ◽  
Transport Model ◽  
A Priori ◽  
Model Performance ◽  
Surface Fluxes ◽  
Monitoring Networks ◽  
Atmospheric Measurements ◽  
Short Term ◽  
Chemistry Transport Model

Abstract. The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO2) and methane (CH4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH4 and CO2 using zoomed versions (abbreviated as “ZAs”) of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of ∼0.66∘ in longitude and ∼0.51∘ in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH4 and CO2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50∘ in longitude and 1.27∘ in latitude (abbreviated as “STs”), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH4 annual gradients between stations in SEA, with reduction of RMSE by 16–20 % compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO2 suggests that the CO2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH4 and CO2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions.

scholarly journals Land surface model photosynthesis and parameter calibration for boreal sites with adaptive population importance sampler

2019 ◽  
Author(s):  
Jarmo Mäkelä ◽  
Jürgen Knauer ◽  
Mika Aurela ◽  
Andrew Black ◽  
Martin Heimann ◽  
...  
Keyword(s):  
Stomatal Conductance ◽  
Land Surface ◽  
Carbon Fixation ◽  
Gross Primary Production ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Drought Event ◽  
Forest Site ◽  
Delayed Effect

Abstract. We calibrated the JSBACH model with six different stomatal conductance formulations using measurements from 10 FLUXNET coniferous evergreen sites in the Boreal zone. The parameter posterior distributions were generated by adaptive population importance sampler and the optimal values by a simple stochastic optimisation algorithm. The observations used to constrain the model are evapotranspiration (ET) and gross primary production (GPP). We identified the key parameters in the calibration process. These parameters control the soil moisture stress function and the overall rate of carbon fixation. We were able to improve the coefficient of determination and the model bias with all stomatal conductance formulations. There was no clear candidate for the best stomatal conductance model, although certain versions produced better estimates depending on the examined variable (ET, GPP) and the used metric. We were also able to significantly enhance the model behaviour during a drought event in a Finnish Scots pine forest site. The JSBACH model was also modified to use a delayed effect of temperature for photosynthetic activity. This modification enabled the model to correctly time and replicate the springtime increase in GPP (and ET) for conifers throughout the measurements sites used in this study.

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