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Xiang Gao, Alexander Avramov, Eri Saikawa, and C. Adam Schlosser


Land surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

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Hanqin Tian, Jia Yang, Chaoqun Lu, Rongting Xu, Josep G. Canadell, Robert B. Jackson, Almut Arneth, Jinfeng Chang, Guangsheng Chen, Philippe Ciais, Stefan Gerber, Akihiko Ito, Yuanyuan Huang, Fortunat Joos, Sebastian Lienert, Palmira Messina, Stefan Olin, Shufen Pan, Changhui Peng, Eri Saikawa, Rona L. Thompson, Nicolas Vuichard, Wilfried Winiwarter, Sönke Zaehle, Bowen Zhang, Kerou Zhang, and Qiuan Zhu


Nitrous oxide (N2O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N2O fluxes and the key drivers of N2O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N2O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N2O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N2O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N2O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N2O dynamics; and 3) provide a benchmarking estimate of N2O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N2O estimation derived from the NMIP is a key component of the global N2O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.

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