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Baozhang Chen, Jing M. Chen, Gang Mo, Chiu-Wai Yuen, Hank Margolis, Kaz Higuchi, and Douglas Chan


Land surface models (LSMs) need to be coupled with atmospheric general circulation models (GCMs) to adequately simulate the exchanges of energy, water, and carbon between the atmosphere and terrestrial surfaces. The heterogeneity of the land surface and its interaction with temporally and spatially varying meteorological conditions result in nonlinear effects on fluxes of energy, water, and carbon, making it challenging to scale these fluxes accurately. The issue of up-scaling remains one of the critical unsolved problems in the parameterization of subgrid-scale fluxes in coupled LSM and GCM models.

A new distributed LSM, the Ecosystem–Atmosphere Simulation Scheme (EASS) was developed and coupled with the atmospheric Global Environmental Multiscale model (GEM) to simulate energy, water, and carbon fluxes over Canada’s landmass through the use of remote sensing and ancillary data. Two approaches (lumped case and distributed case) for handling subgrid heterogeneity were used to evaluate the effect of land-cover heterogeneity on regional flux simulations based on remote sensing. Online runs for a week in August 2003 provided an opportunity to investigate model performance and spatial scaling issues.

Comparisons of simulated results with available tower observations (five sites) across an east–west transect over Canada’s southern forest regions indicate that the model is reasonably successful in capturing both the spatial and temporal variations in carbon and energy fluxes, although there were still some biases in estimates of latent and sensible heat fluxes between the simulations and the tower observations. Moreover, the latent and sensible heat fluxes were found to be better modeled in the coupled EASS–GEM system than in the uncoupled GEM. There are marked spatial variations in simulated fluxes over Canada’s landmass. These patterns of spatial variation closely follow vegetation-cover types as well as leaf area index, both of which are highly correlated with the underlying soil types, soil moisture conditions, and soil carbon pools. The surface fluxes modeled by the two up-scaling approaches (lumped and distributed cases) differ by 5%–15% on average and by up to 15%–25% in highly heterogeneous regions. This suggests that different ways of treating subgrid land surface heterogeneities could lead to noticeable biases in model output.

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Jing M. Chen, T. Andrew Black, David T. Price, and Reid E. Carter


A model has been developed to calculate the spatial distribution of the photosynthetic photon flux density (PPFD) in elliptical forest openings of given slopes and orientations. The PPFD is separated into direct and diffuse components. The direct component is calculated according to the opening and radiation geometries, and pathlength of the solar beam through the forest canopy. The diffuse component is obtained from the sky, tree, and landscape view factors. In this model, the distribution of foliage area with height and the effect of foliage clumping on both direct and diffuse radiation transmission are considered.

The model has been verified using measurements for six quantum sensors (LI-COR Inc.) located at different positions in a small clear-cut (0.37 ha) in a 90-year-old western hemlock-Douglas fir forest.

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Eric D. Maloney, Andrew Gettelman, Yi Ming, J. David Neelin, Daniel Barrie, Annarita Mariotti, C.-C. Chen, Danielle R. B. Coleman, Yi-Hung Kuo, Bohar Singh, H. Annamalai, Alexis Berg, James F. Booth, Suzana J. Camargo, Aiguo Dai, Alex Gonzalez, Jan Hafner, Xianan Jiang, Xianwen Jing, Daehyun Kim, Arun Kumar, Yumin Moon, Catherine M. Naud, Adam H. Sobel, Kentaroh Suzuki, Fuchang Wang, Junhong Wang, Allison A. Wing, Xiaobiao Xu, and Ming Zhao


Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers’ model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.

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Stephen Baxter, Gerald D Bell, Eric S Blake, Francis G Bringas, Suzana J Camargo, Lin Chen, Caio A. S Coelho, Ricardo Domingues, Stanley B Goldenberg, Gustavo Goni, Nicolas Fauchereau, Michael S Halpert, Qiong He, Philip J Klotzbach, John A Knaff, Michelle L'Heureux, Chris W Landsea, I.-I Lin, Andrew M Lorrey, Jing-Jia Luo, Andrew D Magee, Richard J Pasch, Petra R Pearce, Alexandre B Pezza, Matthew Rosencrans, Blair C Trewin, Ryan E Truchelut, Bin Wang, H Wang, Kimberly M Wood, and John-Mark Woolley
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Howard J. Diamond, Carl J. Schreck III, Emily J. Becker, Gerald D. Bell, Eric S. Blake, Stephanie Bond, Francis G. Bringas, Suzana J. Camargo, Lin Chen, Caio A. S. Coelho, Ricardo Domingues, Stanley B. Goldenberg, Gustavo Goni, Nicolas Fauchereau, Michael S. Halpert, Qiong He, Philip J. Klotzbach, John A. Knaff, Michelle L'Heureux, Chris W. Landsea, I.-I. Lin, Andrew M. Lorrey, Jing-Jia Luo, Kyle MacRitchie, Andrew D. Magee, Ben Noll, Richard J. Pasch, Alexandre B. Pezza, Matthew Rosencrans, Michael K. Tippet, Blair C. Trewin, Ryan E. Truchelut, Bin Wang, Hui Wang, Kimberly M. Wood, John-Mark Woolley, and Steven H. Young
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