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要旨:
Since 70% of global forests are managed and forests impact the global carbon cycle
and the energy exchange with the overlying atmosphere, forest management has
the potential to mitigate climate change. Yet, none of the land surface models used
5 in Earth system models, and therefore none of today’s predictions of future climate,
account for the interactions between climate and forest management. We addressed
this gap in modelling capability by developing and parametrizing a version of the land
surface model ORCHIDEE to simulate the biogeochemical and biophysical effects of
forest management. The most significant changes between the new branch called
10 ORCHIDEE-CAN (SVN r2290) and the trunk version of ORCHIDEE (SVN r2243) are
the allometric-based allocation of carbon to leaf, root, wood, fruit and reserve pools; the
transmittance, absorbance and reflectance of radiation within the canopy; and the vertical
discretisation of the energy budget calculations. In addition, conceptual changes
towards a better process representation occurred for the interaction of radiation with
15 snow, the hydraulic architecture of plants, the representation of forest management and
a numerical solution for the photosynthesis formalism of Farquhar, von Caemmerer and
Berry. For consistency reasons, these changes were extensively linked throughout the
code. Parametrization was revisited after introducing twelve new parameter sets that
represent specific tree species or genera rather than a group of unrelated species, as
20 is the case in widely used plant functional types. Performance of the new model was
compared against the trunk and validated against independent spatially explicit data
for basal area, tree height, canopy strucure, GPP, albedo and evapotranspiration over
Europe. For all tested variables ORCHIDEE-CAN outperformed the trunk regarding its
ability to reproduce large-scale spatial patterns as well as their inter-annual variabil25
ity over Europe. Depending on the data stream, ORCHIDEE-CAN had a 67 to 92%
chance to reproduce the spatial and temporal variability of the validation data.
