EGU2020-6374
https://doi.org/10.5194/egusphere-egu2020-6374
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Impacts of soil hydrologial modeling on long-term terrestrial carbon cycle inferred from CCDAS (Carbon Cycle Data Assimilation System)

Mousong Wu1,2, Marko Scholze2, Fei Jiang1, Hengmao Wang1, Wenxin Zhang2, Zhengyao Lu2, Wei He1, Songhan Wang1, Thomas Kaminski3, Michael Vossbeck3, Jun Wang1, and Weimin Ju1
Mousong Wu et al.
  • 1International Institute for Earth System Science, Nanjing University, Nanjing, China (mousongwu@nju.edu.cn)
  • 2Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 3The Inversion Lab, Hamburg, Germany

The terrestrial carbon cycle is an important part of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in estimating carbon fluxes, which impacts global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. In this context, accurate soil moisture data will improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36 years (1980-2015) of surface soil moisture data as provided by the ESA CCI in combination with atmospheric CO2 concentration observations at global scale. We will present the methods used for assimilating long-term remotely sensed soil moisture into the terrestrial biosphere model, and demonstrate the importance of soil moisture in modeling ecosystem carbon cycle processes. We will also investigate the impacts of soil moisture on the terrestrial carbon cycle during climate extremes at various scales.

How to cite: Wu, M., Scholze, M., Jiang, F., Wang, H., Zhang, W., Lu, Z., He, W., Wang, S., Kaminski, T., Vossbeck, M., Wang, J., and Ju, W.: Impacts of soil hydrologial modeling on long-term terrestrial carbon cycle inferred from CCDAS (Carbon Cycle Data Assimilation System), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6374, https://doi.org/10.5194/egusphere-egu2020-6374, 2020.