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Steve Edburg

Steve Edburg. Assistant Research Professor Laboratory for Atmospheric Research Washington State University sedburg@wsu.edu. My Background. Large-eddy simulation (LES) PhD work at WSU Earth system modeling ( EaSM ) Postdoctoral work at UI. SUN. OUTFLOW.

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Steve Edburg

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  1. Steve Edburg Assistant Research Professor Laboratory for Atmospheric Research Washington State University sedburg@wsu.edu

  2. My Background • Large-eddy simulation (LES) • PhD work at WSU • Earth system modeling (EaSM) • Postdoctoral work at UI

  3. SUN OUTFLOW Products and reactants from biosphere atmosphere interaction INFLOW air + trace gases Mixing & Chemical Reactions FOREST SOIL Gas emission from biological processes in forest and soil

  4. LES Overview • Gap in knowledge: The role of turbulence on chemical production or loss within a forest canopy is unknown • Objective: Our objective was to determine if reaction rates are modified by intermittent turbulent structures • Hypothesis: Our central hypothesis was that turbulent structures alter reactions rates by un-evenly mixing trace gases above the canopy with gases emitted from trees • Goal: Use large-eddy simulation to determine the influence of coherent structures on trace gas reaction rates

  5. Side View Animation

  6. Top View Animation

  7. Scalar Segregation

  8. Earth System Modeling

  9. EaSM Overview • Knowledge gap: Impact of bark beetle outbreak on carbon cycling is unknown • Objective: Quantify the impact of bark beetles on carbon cycling across the western US • Aims: • Create a regional insect disturbance product; • modify a Earth system model; • conduct simulations with and without outbreaks

  10. Why is this issue important? • Infestations are widespread throughout western US • In 2009, • 4.3 Mha/10.6 Macres affected by bark beetles • 3.6 Mha/8.8 Macres affected by mountain pine beetle USDA Forest Service, 2004

  11. Physical and biogeochemical characteristics compared with undamaged forest Year following attack After 3-5 years After several decades Photo by ArjanMeddens Photo by ArjanMeddens Photo by C. Schnepf, forestryimages.org Dead tree, needles on Needles off Snag fall/understory growth • Reduced LAI • Reduced Interception • Increased Rh • Initial recovery • Reduced GPP • Reduced ET

  12. Simulated Soil N Dynamics Play a Key Role in C Fluxes and Recovery 25 yr 10 yr 5 yr Point simulation in Idaho: 95% mortality over 3 years

  13. Future Research

  14. “Daily Forecasts of Wildland Fire Impacts on Air Quality in the Pacific Northwest: Enhancing the AIRPACT Decision Support System ” Team: S. Edburg, B. Lamb, J. Vaughan, A. Kochanski, M.A. Jenkins, J. Mandel, N. Larkin, T. Strand, and R. Mell Pending, submitted in December 2011 to NASA ROSES: Wildland Fires

  15. Project Overview • Our long-term goal is to continue the development of AIRPACT and evaluation tools to support decision making activities • The objective of this proposal is to improve the representation of wildland fires within AIRPACT • Our specific aim is to implement the WRF-Fire model within AIRPACT and evaluate simulations with satellite products • We expect this will improve the plume rise and emission estimates and our evaluation techniques • In our opinion, this will improve daily predictions of wildland fire impacts on air quality across the pacific northwest

  16. EOS inputs: MOPITT (CO) MODIS / GOES SMARTFIRE -Fire location -Fire area Proposed Additions AIRPACT WRF-Fire -Time rate of emissions -Plume Injection Heights -Influence of meteorology on fire spread and intensity BlueSky Modeling Framework -Speciated emissions -Time rate of emissions -Plume injection height of emissions S.M.O.K.E -Emissions preprocessor EOS Evaluation -OMI NO2 & O3 -MISR/CALPISO aerosol CMAQ -Influence of fire on the Air Quality forecast (e.g. PM2.5, O3, NO2, CO, NMHC) WRF -Meteorological Input -72 hour forecast

  17. Example of WRF-Fire

  18. Example of WRF-Fire

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