Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 3085-3104, 2017
https://doi.org/10.5194/gmd-10-3085-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model evaluation paper
21 Aug 2017
Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC4RS and DISCOVER-AQ airborne campaigns
Min Huang1,2, Gregory R. Carmichael3, James H. Crawford4, Armin Wisthaler5,6, Xiwu Zhan7, Christopher R. Hain2,a, Pius Lee8, and Alex B. Guenther9 1George Mason University, Fairfax, VA, USA
2University of Maryland, College Park, MD, USA
3University of Iowa, Iowa City, IA, USA
4NASA Langley Research Center, Hampton, VA, USA
5University of Oslo, Oslo, Norway
6University of Innsbruck, Innsbruck, Austria
7NOAA National Environmental Satellite, Data, and Information Service, College Park, MD, USA
8NOAA Air Resources Laboratory, College Park, MD, USA
9University of California, Irvine, CA, USA
anow at: NASA Marshall Space Flight Center, Huntsville, AL, USA
Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

Citation: Huang, M., Carmichael, G. R., Crawford, J. H., Wisthaler, A., Zhan, X., Hain, C. R., Lee, P., and Guenther, A. B.: Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC4RS and DISCOVER-AQ airborne campaigns, Geosci. Model Dev., 10, 3085-3104, https://doi.org/10.5194/gmd-10-3085-2017, 2017.
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Short summary
Various sensitivity simulations during two airborne campaigns were performed to assess the impact of different initialization methods and model resolutions on NUWRF-modeled weather states, heat fluxes, and the follow-on MEGAN isoprene emission calculations. Proper land initialization is shown to be important to the coupled weather modeling and the follow-on emission modeling, which is also critical to accurately representing other processes in air quality modeling and data assimilation.
Various sensitivity simulations during two airborne campaigns were performed to assess the...
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