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Inclusion of a dry surface layer and modifications to the transpiration and canopy evaporation partitioning in the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC)
  • Gesa Meyer,
  • Joe R. Melton,
  • Elyn R. Humphreys
Gesa Meyer
Environment and Climate Change Canada

Corresponding Author:[email protected]

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Joe R. Melton
Environment Canada
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Elyn R. Humphreys
Carleton University
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Land surface/Earth System models depend upon accurate simulation of evapotranspiration (ET) to avoid excessive biases in simulated energy, water, and carbon cycles. The Canadian Land Surface Scheme including biogeochemical Cycles (CLASSIC), the land surface scheme of the Canadian Earth System Model (CanESM) shows reasonable ET fluxes globally, but CLASSIC’s partitioning into evaporation (E) and transpiration (T) can be improved. Specifically, CLASSIC exhibited a high soil evaporation (Es) bias in sparsely vegetated areas during wet periods, which can deplete soil water and decrease photosynthesis and T later in the year.
A dry surface layer (DSL) parameterization was implemented to address biases in Es through an increased surface resistance to water vapour and heat fluxes. In arid/semi-arid regions, the DSL decreased Es, leading to improved seasonality of ET and increased gross primary productivity (GPP) due to an increase in soil moisture. The DSL simulations significantly (t-test, p<0.01) increased T/ET from 0.25 in baseline CLASSIC to 0.30 in the DSL simulations. T/ET was further increased to 0.41 (p<0.01), comparable to the CMIP5 model mean, by allowing T to occur from the dry canopy fraction while water evaporates from the wet fraction. This mainly affected densely vegetated areas, where T and ET increased significantly (p<0.01) and canopy E was reduced (p<0.01). In seasonally dry tropical forests, higher T and ET reduced GPP. Despite increases in arid/semi-arid regions, the reduced GPP in tropical forests resulted in ∼1.6% lower global GPP (p=0.018) than baseline CLASSIC. Including these modifications in CanESM might reduce biases in climate.