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Volume 11, issue 2 | Copyright
Geosci. Model Dev., 11, 631-663, 2018
https://doi.org/10.5194/gmd-11-631-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 20 Feb 2018

Development and technical paper | 20 Feb 2018

Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)

Bakr Badawy1,a, Saroja Polavarapu1, Dylan B. A. Jones2, Feng Deng2, Michael Neish1, Joe R. Melton3, Ray Nassar1, and Vivek K. Arora3 Bakr Badawy et al.
  • 1Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
  • 2Department of Physics, University of Toronto, Toronto, Canada
  • 3Climate Research Division, Environment and Climate Change Canada, Victoria, Canada
  • anow at: Faculty of Environment, University of Waterloo, Waterloo, Canada

Abstract. The Canadian Land Surface Scheme and the Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component in the family of Canadian Earth system models (CanESMs). Here, CLASS-CTEM is coupled to Environment and Climate Change Canada (ECCC)'s weather and greenhouse gas forecast model (GEM-MACH-GHG) to consistently model atmosphere–land exchange of CO2. The coupling between the land and the atmospheric transport model ensures consistency between meteorological forcing of CO2 fluxes and CO2 transport. The procedure used to spin up carbon pools for CLASS-CTEM for multi-decadal simulations needed to be significantly altered to deal with the limited availability of consistent meteorological information from a constantly changing operational environment in the GEM-MACH-GHG model. Despite the limitations in the spin-up procedure, the simulated fluxes obtained by driving the CLASS-CTEM model with meteorological forcing from GEM-MACH-GHG were comparable to those obtained from CLASS-CTEM when it is driven with standard meteorological forcing from the Climate Research Unit (CRU) combined with reanalysis fields from the National Centers for Environmental Prediction (NCEP) to form CRU-NCEP dataset. This is due to the similarity of the two meteorological datasets in terms of temperature and radiation. However, notable discrepancies in the seasonal variation and spatial patterns of precipitation estimates, especially in the tropics, were reflected in the estimated carbon fluxes, as they significantly affected the magnitude of the vegetation productivity and, to a lesser extent, the seasonal variations in carbon fluxes. Nevertheless, the simulated fluxes based on the meteorological forcing from the GEM-MACH-GHG model are consistent to some extent with other estimates from bottom-up or top-down approaches. Indeed, when simulated fluxes obtained by driving the CLASS-CTEM model with meteorological data from the GEM-MACH-GHG model are used as prior estimates for an atmospheric CO2 inversion analysis using the adjoint of the GEOS-Chem model, the retrieved CO2 flux estimates are comparable to those obtained from other systems in terms of the global budget and the total flux estimates for the northern extratropical regions, which have good observational coverage. In data-poor regions, as expected, differences in the retrieved fluxes due to the prior fluxes become apparent. Coupling CLASS-CTEM into the Environment Canada Carbon Assimilation System (EC-CAS) is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.

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We assess the impact of using the meteorological fields from GEM-MACH-GHG to drive CLASS-CTEM. This coupling is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.
We assess the impact of using the meteorological fields from GEM-MACH-GHG to drive CLASS-CTEM....
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