scholarly journals On the formation of high-latitude soil carbon stocks: Effects of cryoturbation and insulation by organic matter in a land surface model

2009 ◽  
Vol 36 (21) ◽  
Author(s):  
C. Koven ◽  
P. Friedlingstein ◽  
P. Ciais ◽  
D. Khvorostyanov ◽  
G. Krinner ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Carbon ◽  
High Latitude ◽  
Land Surface ◽  
Land Surface Model ◽  
Carbon Stocks ◽  
Surface Model ◽  
Soil Carbon Stocks

scholarly journals Evaluating two soil carbon models within a global land surface model using surface and spaceborne observations of atmospheric CO<sub>2</sub> mole fractions

2020 ◽  
Author(s):  
Tea Thum ◽  
Julia E. S. M. Nabel ◽  
Aki Tsuruta ◽  
Tuula Aalto ◽  
Edward J. Dlugokencky ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Land Surface ◽  
Seasonal Cycle ◽  
Land Surface Model ◽  
Carbon Stocks ◽  
Atmospheric Co2 ◽  
Surface Model ◽  
Global Land ◽  
Soil Carbon Stocks ◽  
Surface Observations

Abstract. The trajectories of soil carbon (C) in the changing climate are of utmost importance, as soil carbon is a substantial carbon storage with a large potential to impact the atmospheric carbon dioxide (CO2) burden. Atmospheric CO2 observations integrate all processes affecting C exchange between the surface and the atmosphere. Therefore they provide a benchmark for carbon cycle models. We evaluated two distinct soil carbon models (CBALANCE and YASSO) that were implemented to a global land surface model (JSBACH) against atmospheric CO2 observations. We transported the biospheric carbon fluxes obtained by JSBACH using the atmospheric transport model TM5 to obtain atmospheric CO2. We then compared these results with surface observations from Global Atmosphere Watch (GAW) stations as well as with column XCO2 retrievals from the GOSAT satellite. The seasonal cycles of atmospheric CO2 estimated by the two different soil models differed. The estimates from the CBALANCE soil model were more in line with the surface observations at low latitudes (0 N–45 N) with only 1 % bias in the seasonal cycle amplitude (SCA), whereas YASSO was underestimating the SCA in this region by 32 %. YASSO gave more realistic seasonal cycle amplitudes of CO2 at northern boreal sites (north of 45 N) with underestimation of 15 % compared to 30 % overestimation by CBALANCE. Generally, the estimates from CBALANCE were more successful in capturing the seasonal patterns and seasonal cycle amplitudes of atmospheric CO2 even though it overestimated soil carbon stocks by 225 % (compared to underestimation of 36 % by YASSO) and its predictions of the global distribution of soil carbon stocks was unrealistic. The reasons for these differences in the results are related to the different environmental drivers and their functional dependencies of these two soil carbon models. In the tropical region the YASSO model showed earlier increase in season of the heterotophic respiration since it is driven by precipitation instead of soil moisture as CBALANCE. In the temperate and boreal region the role of temperature is more dominant. There the heterotophic respiration from the YASSO model had larger annual variability, driven by air temperature, compared to the CBALANCE which is driven by soil temperature. The results underline the importance of using sub-yearly data in the development of soil carbon models when they are used in shorter than annual time scales.

scholarly journals Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO<sub>2</sub>

2020 ◽  
Vol 17 (22) ◽  
pp. 5721-5743
Author(s):  
Tea Thum ◽  
Julia E. M. S. Nabel ◽  
Aki Tsuruta ◽  
Tuula Aalto ◽  
Edward J. Dlugokencky ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Land Surface ◽  
Seasonal Cycle ◽  
Land Surface Model ◽  
Carbon Stocks ◽  
Atmospheric Co2 ◽  
Surface Model ◽  
Cycle Amplitude ◽  
Global Land ◽  
Soil Carbon Stocks

Abstract. The trajectories of soil carbon in our changing climate are of the utmost importance as soil is a substantial carbon reservoir with a large potential to impact the atmospheric carbon dioxide (CO2) burden. Atmospheric CO2 observations integrate all processes affecting carbon exchange between the surface and the atmosphere and therefore are suitable for carbon cycle model evaluation. In this study, we present a framework for how to use atmospheric CO2 observations to evaluate two distinct soil carbon models (CBALANCE, CBA, and Yasso, YAS) that are implemented in a global land surface model (JSBACH). We transported the biospheric carbon fluxes obtained by JSBACH using the atmospheric transport model TM5 to obtain atmospheric CO2. We then compared these results with surface observations from Global Atmosphere Watch stations, as well as with column XCO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The seasonal cycles of atmospheric CO2 estimated by the two different soil models differed. The estimates from the CBALANCE soil model were more in line with the surface observations at low latitudes (0–45∘ N) with only a 1 % bias in the seasonal cycle amplitude, whereas Yasso underestimated the seasonal cycle amplitude in this region by 32 %. Yasso, on the other hand, gave more realistic seasonal cycle amplitudes of CO2 at northern boreal sites (north of 45∘ N) with an underestimation of 15 % compared to a 30 % overestimation by CBALANCE. Generally, the estimates from CBALANCE were more successful in capturing the seasonal patterns and seasonal cycle amplitudes of atmospheric CO2 even though it overestimated soil carbon stocks by 225 % (compared to an underestimation of 36 % by Yasso), and its estimations of the global distribution of soil carbon stocks were unrealistic. The reasons for these differences in the results are related to the different environmental drivers and their functional dependencies on the two soil carbon models. In the tropics, heterotrophic respiration in the Yasso model increased earlier in the season since it is driven by precipitation instead of soil moisture, as in CBALANCE. In temperate and boreal regions, the role of temperature is more dominant. There, heterotrophic respiration from the Yasso model had a larger seasonal amplitude, which is driven by air temperature, compared to CBALANCE, which is driven by soil temperature. The results underline the importance of using sub-annual data in the development of soil carbon models when they are used at shorter than annual timescales.

scholarly journals Greater soil carbon stocks and faster turnover rates with increasing agricultural productivity

SOIL ◽  
2017 ◽  
Vol 3 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Jonathan Sanderman ◽  
Courtney Creamer ◽  
W. Troy Baisden ◽  
Mark Farrell ◽  
Stewart Fallon
Keyword(s):  
Organic Matter ◽  
Soil Carbon ◽  
Sugar Content ◽  
Bulk Composition ◽  
Agricultural Productivity ◽  
Carbon Stocks ◽  
Decay Rates ◽  
Turnover Rates ◽  
Soil Carbon Stocks

Abstract. Devising agricultural management schemes that enhance food security and soil carbon levels is a high priority for many nations. However, the coupling between agricultural productivity, soil carbon stocks and organic matter turnover rates is still unclear. Archived soil samples from four decades of a long-term crop rotation trial were analyzed for soil organic matter (SOM) cycling-relevant properties: C and N content, bulk composition by nuclear magnetic resonance (NMR) spectroscopy, amino sugar content, short-term C bioavailability assays, and long-term C turnover rates by modeling the incorporation of the bomb spike in atmospheric 14C into the soil. After > 40 years under consistent management, topsoil carbon stocks ranged from 14 to 33 Mg C ha−1 and were linearly related to the mean productivity of each treatment. Measurements of SOM composition demonstrated increasing amounts of plant- and microbially derived SOM along the productivity gradient. Under two modeling scenarios, radiocarbon data indicated overall SOM turnover time decreased from 40 to 13 years with increasing productivity – twice the rate of decline predicted from simple steady-state models or static three-pool decay rates of measured C pool distributions. Similarly, the half-life of synthetic root exudates decreased from 30.4 to 21.5 h with increasing productivity, indicating accelerated microbial activity. These findings suggest that there is a direct feedback between accelerated biological activity, carbon cycling rates and rates of carbon stabilization with important implications for how SOM dynamics are represented in models.

scholarly journals The use of radiocarbon <sup>14</sup>C to constrain carbon dynamics in the soil module of the land surface model ORCHIDEE (SVN r5165)

2018 ◽  
Author(s):  
Marwa Tifafi ◽  
Marta Camino-Serrano ◽  
Christine Hatté ◽  
Hector Morras ◽  
Lucas Moretti ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Carbon ◽  
Land Surface ◽  
Carbon Stock ◽  
Land Surface Model ◽  
Carbon Dynamics ◽  
Total Carbon ◽  
Soil Parameters ◽  
Surface Model

Abstract. Despite the importance of soil as a large component of the terrestrial ecosystems, the soil compartments are not well represented in the Land Surface Models (LSMs). Indeed, soils in current LSMs are generally represented based on a very simplified schema that can induce a misrepresentation of the deep dynamics of soil carbon. Here, we present a new version of the IPSL-Land Surface Model called ORCHIDEE-SOM, incorporating the 14C dynamic in the soil. ORCHIDEE-SOM, first, simulates soil carbon dynamics for different layers, down to 2 m depth. Second, concentration of dissolved organic carbon (DOC) and its transport are modeled. Finally, soil organic carbon (SOC) decomposition is considered taking into account the priming effect. After implementing the 14C in the soil module of the model, we evaluated model outputs against observations of soil organic carbon and 14C activity (F14C) for different sites with different characteristics. The model managed to reproduce the soil organic carbon stocks and the F14C along the vertical profiles. However, an overestimation of the total carbon stock was noted, but was mostly marked on the surface. Then, thanks to the introduction of 14C, it has been possible to highlight an underestimation of the age of carbon in the soil. Thereafter, two different tests on this new version have been established. The first was to increase carbon residence time of the passive pool and decrease the flux from the slow pool to the passive pool. The second was to establish an equation of diffusion, initially constant throughout the profile, making it vary exponentially as a function of depth. The first modifications did not improve the capacity of the model to reproduce observations whereas the second test showed a decrease of the soil carbon stock overestimation, especially at the surface and an improvement of the estimates of the carbon age. This assumes that we should focus more on vertical variation of soil parameters as a function of depth, mainly for diffusion, in order to upgrade the representation of global carbon cycle in LSMs, thereby helping to improve predictions of the future response of soil organic carbon to global warming.

scholarly journals Including Stable Carbon Isotopes to Evaluate the Dynamics of Soil Carbon in the Land‐Surface Model ORCHIDEE

2019 ◽  
Vol 11 (11) ◽  
pp. 3650-3669 ◽  
Author(s):  
Marta Camino‐Serrano ◽  
Marwa Tifafi ◽  
Jérôme Balesdent ◽  
Christine Hatté ◽  
Josep Peñuelas ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Isotopes ◽  
Land Surface ◽  
Stable Carbon Isotopes ◽  
Land Surface Model ◽  
Surface Model ◽  
Stable Carbon

scholarly journals Identification of key parameters controlling demographicallystructured vegetation dynamics in a Land Surface Model [CLM4.5(ED)]

2019 ◽  
Author(s):  
Elias C. Massoud ◽  
Chonggang Xu ◽  
Rosie Fisher ◽  
Ryan Knox ◽  
Anthony Walker ◽  
...  
Keyword(s):  
Land Surface ◽  
Canopy Structure ◽  
Land Surface Model ◽  
Carbon Stocks ◽  
Surface Model ◽  
Model Parameters ◽  
Growth Strategies ◽  
Community Land Model ◽  
Vegetation Models ◽  
The Impact

Abstract. Vegetation plays a key role in regulating global carbon cycles and is a key component of the Earth System Models (ESMs) aimed to project Earth's future climates. In the last decade, the vegetation component within ESMs has witnessed great progresses from simple 'big-leaf' approaches to demographically-structured approaches, which has a better representation of plant size, canopy structure, and disturbances. The demographically-structured vegetation models are typically controlled by a large number of parameters, and sensitivity analysis is generally needed to quantify the impact of each parameter on the model outputs for a better understanding of model behaviors. In this study, we use the Fourier Amplitude Sensitivity Test (FAST) to diagnose the Community Land Model coupled to the Ecosystem Demography Model, or CLM4.5(ED). We investigate the first and second order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks. While the photosynthetic capacity parameter Vc,max25 is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which are shown here to determine vegetation demography and carbon stocks through their impacts on survival and growth strategies. The results of this study highlights the importance of understanding the dynamics of the next generation of demographically-enabled vegetation models within ESMs toward improved model parameterization and model structure for better model fidelity.

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