scholarly journals Soil related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model by integrating crop model components

2021 ◽  
Author(s):  
Dóra Hidy ◽  
Zoltán Barcza ◽  
Roland Hollós ◽  
Laura Dobor ◽  
Tamás Ács ◽  
...  

Abstract. Terrestrial biogeochemical models are essential tools to quantify climate-carbon cycle feedback and plant-soil relations from local to global scale. In this study, theoretical basis is provided for the latest version of Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. During the developments we took advantage of experiences from the crop modeller community where internal process representation has a long history. In this paper the improved soil hydrology and soil carbon/nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology and soil organic carbon content estimation. Soil hydrology related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC, and from 0.084 to 0.46 for soil evaporation; bias changed from −0.047 to 0.007 m3 m−3 for SWC, and from −0.68 mm day−1 to −0.2 mm day−1 for soil evaporation). Sensitivity analysis and optimization of the decomposition scheme is presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation and nitrification/denitrification process estimation which represents a major milestone.

2019 ◽  
Vol 16 (2) ◽  
pp. 207-222 ◽  
Author(s):  
Tong Yu ◽  
Qianlai Zhuang

Abstract. A group of soil microbes plays an important role in nitrogen cycling and N2O emissions from natural ecosystem soils. We developed a trait-based biogeochemical model based on an extant process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), by incorporating the detailed microbial physiological processes of nitrification. The effect of ammonia-oxidizing Archaea (AOA), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB) was considered in modeling nitrification. Microbial traits, including microbial biomass and density, were explicitly considered. In addition, nitrogen cycling was coupled with carbon dynamics based on stoichiometry theory between carbon and nitrogen. The model was parameterized using observational data and then applied to quantifying global N2O emissions from global terrestrial ecosystem soils from 1990 to 2000. Our estimates of 8.7±1.6 Tg N yr−1 generally agreed with previous estimates during the study period. Tropical forests are a major emitter, accounting for 42 % of the global emissions. The model was more sensitive to temperature and precipitation and less sensitive to soil organic carbon and nitrogen contents. Compared to the model without considering the detailed microbial activities, the new model shows more variations in response to seasonal changes in climate. Our study suggests that further information on microbial diversity and ecophysiology features is needed. The more specific guilds and their traits shall be considered in future soil N2O emission quantifications.


2018 ◽  
Author(s):  
Tong Yu ◽  
Qianlai Zhuang

Abstract. A group of soil microbes plays an important role in nitrogen cycling and N2O emissions from natural ecosystem soils. We developed a trait-based biogeochemical model based on an extant process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), by incorporating the detailed microbial physiological processes of nitrification. The effect of ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) was considered in modeling nitrification. The microbial traits including microbial biomass and density were explicitly considered. In addition, nitrogen cycling was coupled with carbon dynamics based on stoichiometry theory between carbon and nitrogen. The model was parameterized using observational data and then applied to quantifying global N2O emissions from global terrestrial ecosystem soils from 1990 to 2000. Our estimates of 8.7 ± 1.6 Tg N yr−1 generally agreed with previous estimates during the study period. Tropical forests are a major emitter, accounting for 42 % of the global emissions. The model was more sensitive to temperature and precipitation, and less sensitive to soil organic carbon and nitrogen contents. Compared to the model without considering the detailed microbial activities, the new model shows more variations in response to seasonal changes in climate. Our study suggests that further information on microbial diversity and eco-physiology features is needed. The more specific guilds and their traits shall be considered in future soil N2O emission quantifications.


2019 ◽  
Author(s):  
Thomas Luke Smallman ◽  
Mathew Williams

Abstract. Photosynthesis (gross primary production, GPP) and evapo-transpiration (ET) are ecosystem processes with global significance for the carbon cycle, climate, hydrology and a range of ecosystem services. The mechanisms governing these processes are complex but well understood. There is strong coupling between these processes, mediated directly by stomatal conductance and indirectly by root zone soil moisture. This coupling must be effectively modelled for robust predictions of earth system responses to global change. It is highly demanding to model cellular processes, like stomatal conductance or electron transport, with responses times of minutes, over decadal and global domains. computational demand means models resolving this level of complexity cannot be fully evaluated for their parameter sensitivity, nor calibrated using earth observation data through data assimilation approaches requiring large ensembles. To resolve this problem, here we describe a coupled photosynthesis evapo-transpiration model of intermediate complexity. The model reduces computational load and parameter numbers by operating at canopy scale and daily time steps. But by including simplified representation of key process interactions it retains sensitivity to variation in climate, leaf traits, soil states and atmospheric CO2. The new model is calibrated to match the biophysical responses of a complex terrestrial ecosystem model (TEM) of GPP and ET through a Bayesian model-data fusion process. The calibrated ACM-GPP-ET generates unbiased estimates of TEM GPP and ET, and captures 80–95 % percent of the sensitivity of carbon and water fluxes by the complex TEM. The ACM-GPP-ET model operates ∼ 2200 times faster than the complex TEM. Independent evaluation of ACM-GPP-ET at FLUXNET sites, using a single global parameterisation, shows good agreement with typical R2 ∼ 0.60 for both GPP and ET. This intermediate complexity modelling approach allows full Monte Carlo based quantification of model parameter and structural uncertainties, global scale sensitivity analyses for these processes, and is fast enough for use within terrestrial ecosystem model-data fusion frameworks requiring large ensembles.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kuang-Yu Chang ◽  
William J. Riley ◽  
Sara H. Knox ◽  
Robert B. Jackson ◽  
Gavin McNicol ◽  
...  

AbstractWetland methane (CH4) emissions ($${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.


Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 683-701 ◽  
Author(s):  
Z. Wan ◽  
J. She ◽  
M. Maar ◽  
L. Jonasson ◽  
J. Baasch-Larsen

Abstract. Thanks to the abundant observation data, we are able to deploy the traditional point-to-point comparison and statistical measures in combination with a comprehensive model validation scheme to assess the skills of the biogeochemical model ERGOM in providing an operational service for the Baltic Sea. The model assessment concludes that the operational products can resolve the main observed seasonal features for phytoplankton biomass, dissolved inorganic nitrogen, dissolved inorganic phosphorus and dissolved oxygen in euphotic layers as well as their vertical profiles. This assessment reflects that the model errors of the operational system at the current stage are mainly caused by insufficient light penetration, excessive organic particle export downward, insufficient regional adaptation and some from improper initialization. This study highlights the importance of applying multiple schemes in order to assess model skills rigidly and identify main causes for major model errors.


2017 ◽  
Author(s):  
Joe R. Melton ◽  
Reinel Sospedra-Alfonso ◽  
Kelly E. McCusker

Abstract. We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme – Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (ca. 2.8° × 2.8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms (OPTICS (Ankerst et al., 1999; Daszykowski et al., 2002) and Sander et al. (2003)) to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures, however differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global scale simulations using a simple grid-mean value.


2015 ◽  
Vol 6 (2) ◽  
pp. 1999-2042 ◽  
Author(s):  
S. Sippel ◽  
F. E. L. Otto ◽  
M. Forkel ◽  
M. R. Allen ◽  
B. P. Guillod ◽  
...  

Abstract. Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance to carefully consider statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying past, current and future extremes.


2021 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Corinne Le Quéré ◽  
Erik Buitenhuis ◽  
Lionel Guidi ◽  
Jean-Olivier Irisson

<p>A lot of effort has been put in the representation of surface ecosystem processes in global carbon cycle models, in particular through the grouping of organisms into Plankton Functional Types (PFTs) which have specific influences on the carbon cycle. In contrast, the transfer of ecosystem dynamics into carbon export to the deep ocean has received much less attention, so that changes in the representation of the PFTs do not necessarily translate into changes in sinking of particulate matter. Models constrain the air-sea CO<sub>2</sub> flux by drawing down carbon into the ocean interior. This export flux is five times as large as the CO<sub>2</sub> emitted to the atmosphere by human activities. When carbon is transported from the surface to intermediate and deep ocean, more CO<sub>2 </sub>can be absorbed at the surface. Therefore, even small variability in sinking organic carbon fluxes can have a large impact on air-sea CO<sub>2</sub> fluxes, and on the amount of CO<sub>2</sub> emissions that remain in the atmosphere.</p><p>In this work we focus on the representation of organic matter sinking in global biogeochemical models, using the PlankTOM model in its latest version representing 12 PFTs. We develop and test a methodology that will enable the systematic use of new observations to constrain sinking processes in the model. The approach is based on a Neural Network (NN) and is applied to the PlankTOM model output to test its ability to reconstruction small and large particulate organic carbon with a limited number of observations. We test the information content of geographical variables (location, depth, time of year), physical conditions (temperature, mixing depth, nutrients), and ecosystem information (CHL a, PFTs). These predictors are used in the NN to test their influence on the model-generation of organic particles and the robustness of the results. We show preliminary results using the NN approach with real plankton and particle size distribution observations from the Underwater Vision Profiler (UVP) and plankton diversity data from Tara Oceans expeditions and discuss limitations.</p>


2019 ◽  
Author(s):  
Tea Thum ◽  
Silvia Caldararu ◽  
Jan Engel ◽  
Melanie Kern ◽  
Marleen Pallandt ◽  
...  

Abstract. The dynamics of terrestrial ecosystems are shaped by the coupled cycles of carbon, nitrogen and phosphorus, and strongly depend on the availability of water and energy. These interactions shape future terrestrial biosphere responses to global change. Many process-based models of the terrestrial biosphere have been gradually extended from considering carbon-water interactions to also including nitrogen, and later, phosphorus dynamics. This evolutionary model development has hindered full integration of these biogeochemical cycles and the feedbacks amongst them. Here we present a new terrestrial ecosystem model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), which is formulated around a consistent representation of element cycling in terrestrial ecosystems. This new model includes i) a representation of plant growth which separates source (e.g. photosynthesis) and sink (growth rate of individual tissues, constrained by nutrients, temperature, and water availability) processes; ii) the acclimation of many ecophysiological processes to meteorological conditions and/or nutrient availabilities; iii) an explicit representation of vertical soil processes to separate litter and soil organic matter dynamics; iv) a range of new diagnostics (leaf chlorophyll content; 13C, 14C, and 15N isotope tracers) to allow for a more in-depth model evaluation. We present the model structure and provide an assessment of its performance against a range of observations from global-scale ecosystem monitoring networks. We demonstrate that the framework is capable of consistently simulating ecosystem dynamics across a large gradient in climate and soil conditions, as well as across different plant functional types. To aid this understanding we provide an assessment of the model's sensitivity to its parameterisation and the associated uncertainty.


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