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.

Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

2021 ◽  
Author(s):  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Valérie Daux ◽  
Camille Risi ◽  
Jina Jeong ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Forest Ecosystems ◽  
Ring Width ◽  
Land Surface Model ◽  
Surface Model ◽  
Growth Variability ◽  
Tree Ring Data ◽  
Surface Models ◽  
Global Land

&lt;p&gt;Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (&amp;#916;&lt;sup&gt;13&lt;/sup&gt;C) and oxygen (&amp;#948;&lt;sup&gt;18&lt;/sup&gt;O) stable isotopes in six sites in boreal and temperate Europe.&amp;#160; The model simulates &amp;#916;&lt;sup&gt;13&lt;/sup&gt;C (r = 0.31-0.80) and &amp;#948;&lt;sup&gt;18&lt;/sup&gt;O (r = 0.36-0.74) variability better than tree-ring width variability (r &lt; 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with &amp;#948;&lt;sup&gt;18&lt;/sup&gt;O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, &amp;#916;&lt;sup&gt;13&lt;/sup&gt;C and &amp;#948;&lt;sup&gt;18&lt;/sup&gt;O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.&lt;/p&gt;

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

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 The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

2014 ◽  
Vol 7 (1) ◽  
pp. 361-386 ◽  
Author(s):  
D. N. Walters ◽  
K. D. Williams ◽  
I. A. Boutle ◽  
A. C. Bushell ◽  
J. M. Edwards ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Regional Climate ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Development Cycle ◽  
Global Land ◽  
Overall Performance ◽  
Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.

scholarly journals Multiple stable states of tree cover in a global land surface model due to a fire-vegetation feedback

2016 ◽  
Vol 43 (12) ◽  
pp. 6324-6331 ◽  
Author(s):  
G. Lasslop ◽  
V. Brovkin ◽  
C. H. Reick ◽  
S. Bathiany ◽  
S. Kloster
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Tree Cover ◽  
Surface Model ◽  
Stable States ◽  
Vegetation Feedback ◽  
Global Land ◽  
Multiple Stable States

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 The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations

2017 ◽  
Vol 10 (4) ◽  
pp. 1487-1520 ◽  
Author(s):  
David Walters ◽  
Ian Boutle ◽  
Malcolm Brooks ◽  
Thomas Melvin ◽  
Rachel Stratton ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Model Performance ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Main Trunk ◽  
Global Land ◽  
Prediction Systems ◽  
The Tropics

Abstract. We describe Global Atmosphere 6.0 and Global Land 6.0 (GA6.0/GL6.0): the latest science configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment) dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model's physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe GA6.1/GL6.1, which includes a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global numerical weather prediction, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.

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