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Title:   Author  item
                                            KUMAR, ANIL - National Aeronautics And Space Administration (NASA)item
                                            CHEN, FEI - National Center For Atmospheric Research (NCAR)item
                                            NIYOGI, DEV - Purdue Universityitem
                                            Alfieri, Josephitem
                                            EK, MICHAEL - National Oceanic & Atmospheric Administration (NOAA)item
                                            MITCHELL, K - National Oceanic & Atmospheric Administration (NOAA)
  Submitted to: Boundary Layer Meteorology
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 11/15/2010
                                    Publication  Date: 2/10/2011
                                    Citation: Kumar, A., Chen, F., Niyogi, D., Alfieri, J.G., Ek, M.B., Mitchell, K. 2011. Evaluation of a photosyntheses-based canopy resistance formulation in the Noah Land-surface model. Boundary Layer Meteorology. 138:263-284.Interpretive Summary: Numerical models, such as weather prediction models, are a key component of both research and decision support activities; application of these models range from investigating the fundamental linkages between various Earth system processes, such as the water, carbon and nutrient cycles, to predicting severe weather phenomena, such as drought, to providing information to natural resource managers and policy makers. Nonetheless, accurately representing the role of the land surface, and especially vegetation, in controlling weather and climate processes remains a major challenge for the modeling community. In order to improve the ability of WRF-Noah weather model, which is used for both research and operationally by NOAA, this study investigated the impacts of replacing the model’s current plant transpiration scheme – the so-called Jarvis scheme – with a physically-based scheme, namely the Gas Exchange Model (GEM). A comparison of model simulations using both the current default model and the modified WRF-Noah model with observational data showed that WRF-Noah-GEM was better able to represent transpiration and thus a number of related land-surface properties that impact weather and climate. As a result, this study strongly suggests that weather predictions could be improved by incorporating the GEM model in WRF-Noah.
                                Technical Abstract: Accurately representing complex land-surface processes balancing complexity and realism remains one challenge that the weather modelling community is facing nowadays. In this study, a photosynthesis-based Gas-exchange Evapotranspiration Model (GEM) is integrated into the Noah land-surface model replacing the traditional Jarvis scheme for estimating the canopy resistance and transpiration. Using 18-month simulations from the High Resolution Land Data Assimilation System (HRLDAS), the impact of the photosynthesis-based approach on the simulated canopy resistance, surface heat fluxes, soil moisture, and soil temperature over different vegetation types is evaluated using data from the Atmospheric Radiation Measurement (ARM) site, Oklahoma Mesonet, 2002 International H2O Project (IHOP_2002), and three Ameriflux sites. Incorporation of GEM into Noah improves
the surface energy fluxes as well as the associated diurnal cycle of soil moisture and soil temperature during both wet and dry periods. An analysis of midday, average canopy resistance shows similar day-to-day trends in the model fields as seen in observed patterns. Bias and standard deviation analyses for soil temperature and surface fluxes show that GEM responds somewhat better than the Jarvis scheme, mainly because the Jarvis approach relies on a
parametrised minimum canopy resistance and meteorological variables such as air temperature and incident radiation. The analyses suggest that adding a photosynthesis-based transpiration scheme such as GEM improves the ability of the land-data assimilation system to simulate evaporation and transpiration under a range of soil and vegetation conditions.


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