Integrated Modeling of Forest Growth, Fire Emissions, Air Quality and Its Climate Feedbacks

1. Integrated Modeling of Forest Growth, Fire Emissions, Air Quality and Its Climate Feedbacks L. Ran1, U. Shankar1, D. McKenzie2, A. Holland1A. Xiu1, S. Arunachalam1, S. McNulty3, J. Prestemon4, and D. Fox5 2007 EastFire Conference June 6-8 1Institute for the Environment, UNC-Chapel Hill 2 USDA Forest Service, Pacific Wildland Fire Sciences Laboratory 3 USDA Forest Service, Southern Global Change Program 4 USDA Forest Service, Southern Research Station, RTP, NC 5 Cooperative Institute for Research in the Atmosphere, Ft. Collins, CO

2. Research Program Goals • Project funding: EPA STAR Grant RD 83227701 • Aim: support the EPA Global Change Research Program goals by • Examining consequences of climate change for wild fire occurrence and consequently for U.S. air quality • Combining the effects of climate change with forest growth to examine impacts on fire frequency and intensity • Investigating methods to credibly project changes in biogenic emissions from 2002-2050 due to fires

3. Air Quality and Climate Impactsof Fires • Impacts of wild fires felt at the regional and global scale • Black carbon => positive climate feedback (warming); SO2 emissions => negative climate forcing from secondarily produced SO4 • Effect of radiatively important pollutants on short-term climate variability affects forest growth, biogenic emissions and future fuel loads CO O3 CarbonaceousAerosol Effects of Canadian boreal fires on CO, PM and ozone from a July 1995 simulation

4. Predictive Modeling Issues • Current fire inventoriescannot captureclimate impacts on forest biomass and fuel loads • effects not reflected in biogenic land cover data • Feedback of scattering and absorbing aerosols and ozone to atmospheric dynamics not captured in most air quality simulation models • Understanding these effects is essential to fully assess impacts of managed vs. wild fires on air quality and climate and the net benefits of fire management plans

5. Integrated Modeling System Monthly met. PnET CCSM Initial & boundary met. Base & future year fuel data Fire Scenario Builder Hourly met METCHEM (MM5-MCPL / MAQSIP) Fire activity data Anthropogenic inventoried emissions Modified biogenic land use data BlueSky-EM- SMOKE- MEGAN Gridded & Speciated Emissions

6. PnET Forest Growth Model • Used by the USFS Southern Global Change Program to model 13 states in the Southeast • Ecological process model of forest productivity, species composition, and hydrology (PnET II); predictions of forest biomass scaled up from the FIA plot level to the county level • Models the removal of forest biomass due to disturbances including climate change impacts, ozone levels, fire, and pests

7. Flow Chart of PEcon Climate Spatial FIA Update Equilibrium UpdateInventory Volume1 PnET-CN SRTS Volume2 Allocate Harvest Calculate Growth Volume3 Calculate Acres Harvested Update Acres FIA Plot Inventory and Harvest

9. PnET Modifications • Added LAI, foliar NPP, wood NPP, root NPP, plot ID, year, month, species and other variables to output • Obtained DWM and FIA plot level data from Forest Services South Research Station (Knoxville, TN) • Computed county center location, elevation, and water holding capacity for modeling at county level

10. PnET Linkages to Fire Emissions Models • Modeling period: 2000-2050; climate data will be provided by CCSM • BlueSky-EM uses fuel table with live fuel and DWM in various size classes • FIA inventory for DWM includes coarse and fine woody debris, litter, herb/shrubs, slash, duff, and fuel bed depth • Estimate future DWM at county level from current DWM, biomass, and related data (latitude, elevation, temperature, RH, winds) • Use SAS data mining tools (e.g., regression, clustering) • Allocate DWM to species used in BlueSky-EM based on their biomass proportions at county level

11. PnET Linkages to Biogenic Emissions Model • PnET outputs monthly biomass and LAI for 203 forest species at plot and county level • Will use the MEGAN model to import PnET output for estimating biogenic emissions • MEGAN takes gridded monthly LAI and climate data with plant function type and emission factor files to estimate gridded biogenic emissions • Includes isoprene (O3 precursor), mono- and sesquiterpenes (PM precursors)

12. Fire/Smoke Emissions Modeling • BlueSky-EM, a smoke emissions model linked to the Sparse Matrix Operator Kernel Emissions Model (SMOKE) for processing and merging with emissions from other sources (industry, transport, biogenic, sea salt, etc.) • Directly linked to the FCCS fuel database • Will be run at 12-km resolution over the Southeast • Future-year fire modeling will link BlueSky-EM to: • The Fire Scenario Builder developed by Pacific Wildland Fire Lab, and adapted for the Southeast

13. Atmospheric Instability - CAPE • Map • Types • 500mb • 700mb Flammability Ignition Avail Fire Generator Fire Scenario Builder MM5 (mesoscale model) Fire frequency & fuel maps Management RxFire/suppression NFDRS Equations predict fuel moisture in fuel size classes that carry fire. Fire Starts Fire Sizes Human ignitions (East)

14. FSB output for the Pacific Northwest 12-km MM5 domain McKenzie et al. (2006) Ecol. Modell.

15. Coupled Meteorology-Chemistry Model (METCHEM) Aerosol Direct Radiative Feedback H & V Transport, Cloud Physics & Chemistry, Gas/Particulate Chemistry, PM Microphysics (Modal), Dry & Wet Removal (MAQSIP CTM) Meteorology (MM5) Met. Couple (MCPL) Emissions Processing (SMOKE)

16. Next Steps • ConUS METCHEM simulations for 2002 using best available (RPO) emissions inventory • Extract boundary condition inputs for SE • 12-km Southeastern US simulations with full system integration • Examine model performance in 2002 • Proceed to “snap shot” simulations in 2015, 2030 and 2050 to analyze effects on key climate parameters