Sensitivity of global terrestrial gross primary production to hydrologic states simulated by the Community Land Model using two runoff parameterizations

Abstract

Soil moisture plays an important role in the coupled water, energy, and carbon cycles. In addition to surface processes such as evapotranspiration, the boundary fluxes that influence soil moisture are closely related to surface or subsurface runoff. To elucidate how uncertainties in representing surface and subsurface hydrology may influence simulations of the carbon cycle, numerical experiments were performed using version 4 of the Community Land Model with two widely adopted runoff generation parameterizations from the TOPMODEL and Variable Infiltration Capacity (VIC) model under the same protocol. The results showed that differences in the runoff generation schemes caused a relative difference of 36% and 34% in global mean total runoff and soil moisture, respectively, with substantial differences in their spatial distribution and seasonal variability. Changes in the simulated gross primary production (GPP) were found to correlate well with changes in soil moisture through its effects on leaf photosynthesis (An) and leaf area index (LAI), which are the two dominant components determining GPP. Soil temperature, which is influenced by soil moisture, also affects LAI and GPP for the seasonal-deciduous and stress-deciduous plant functional types that dominate in cold regions. Consequently, the simulated global mean GPP differs by 20.4% as a result of differences in soil moisture and soil temperature simulated between the two models. Our study highlights the significant interactions among the water, energy, and carbon cycles and the need for reducing uncertainty in the hydrologic parameterization of land surface models to better constrain carbon cycle modeling. Key Points Simulated terrestrial water cycle is sensitive to runoff generation schemes Hydrologic parameterizations have large impacts on the global C budgets Improving hydrologic representations to constrain C cycle modeling is important