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Diffuse radiation forcing constraints on gross primary productivity and global terrestrial evapotranspiration
  • TC Chakraborty,
  • Xuhui Lee,
  • David Lawrence
TC Chakraborty
Pacific Northwest National lab

Corresponding Author:tc.chakraborty@pnnl.gov

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Xuhui Lee
Yale University, School of Forestry and Environmental Studies
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David Lawrence
National Center for Atmospheric Research
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The diffuse radiation fertilization effect – the increase in plant productivity in the presence of higher diffuse radiation (K↓,d) – is an important yet understudied aspect of atmosphere-biosphere interactions and can modify the terrestrial carbon, energy, and water budgets. The K↓,d fertilization effect links the carbon cycle with clouds and aerosols, all of which are large sources of uncertainties for our current understanding of the Earth system and for future climate projections. Here we establish to what extent observational and modeling uncertainty in sunlight’s diffuse fraction (kd) affects simulated gross primary productivity (GPP) and terrestrial evapotranspiration (λE). We find only 48 eddy covariance sites with simultaneous sufficient measurements of K↓,d with none in the tropical climate zone, making it difficult to constrain this mechanism globally using observations. Using a land modeling framework based on the latest version of the Community Land Model, we find that global GPP ranges from 114 Pg C year-1 when using kd forcing from the MERRA-2 reanalysis to a ~7% higher value of 122 Pg C year-1 when using the CERES satellite product, with especially strong differences apparent over the tropical region (mean increase ~9%). The differences in λE, although smaller (-0.4%) due to competing changes in shaded and sunlit leaf transpiration, can be greater than regional impacts of individual forcing agents like aerosols. Our results demonstrate the importance of comprehensively and systematically validating the simulated kd by atmosphere modules as well as the response differences in diffuse fraction within land modules across Earth System Models.