The Cyclical Relationships of Climate Change, Forest Biomass, Fire Emissions and

1. The Cyclical Relationships of Climate Change, Forest Biomass,Fire Emissions and Atmospheric Aerosol Loadings U. Shankar1, A. Xiu1, D. Fox2, S. McNulty3, J. Moore Meyers3, L. Ran1, and A. Holland1 3rd International Fire Ecology & Management Congress November 14, 2006 1Carolina Environmental Program, University of North Carolina at Chapel Hill 2 Cooperative Institute for Research in the Atmosphere, Ft. Collins, CO 3 USDA Forest Service, Southern Global Change Program

2. Research Program Goals • Project funding: EPA STAR Grant RD 83227701 • Aim is to 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. Acknowledgments • Participation and Outreach: USDA Forest Service • D. McKenzie, Pacific Wildland Fire Sciences Lab, future fires • J. Prestemon and E. Mercer, Southern Research Station, human-induced fire ignition • S. McNulty and J. Moore Myers, Southern Global Change Program, forest growth modeling

4. Project Personnel • Uma Shankar (PI): Aerosol modeling and analysis • Aijun Xiu (co-PI): Meteorology, chemistry-climate coupling • Doug Fox (co-PI): Fire modeling • Andy Holland: Fire model data linkages, emissions processing • Limei Ran: Forest growth model and data linkages • Frank Binkowski: Radiative transfer modeling, analysis • Sarav Arunachalam: Air quality data analysis, website mgmt

5. Air Quality and Climate Impactsof Fires • Impacts of wild fires felt at the regional and global scale • > 8M acres burned last year • Black carbon => positive forcing on climate; SO2 emissions => negative forcing on climate from secondarily produced SO4 • Dioxins and GHGsalso associated with fire plumes (Gullett and Tuotti, AE 37, 2003; Simmonds et al., AE 39, 2005) • Effect of radiatively important pollutants on short-term climate variability affects forest growth, and thus the biogenic emissions as well as fuel available for potential fires CO O3 CarbonaceousAerosol Model predictions of the effects of Canadian boreal fires on aerosols and ozone, July 1995

6. Modeling Issues • Feedback of short-term climate variability to forest growth is not represented in most models • Most regional air quality models do not include feedback of scattering and absorbing aerosols or ozone to atmospheric dynamics • Understanding these feedbacks and effect on short-term climate variability is essential to fully assess impacts of managed vs. uncontrolled fires on forest land and the net benefits of fire management plans

7. Objectives • To examine impacts of climate change and variability on: • forest growth -> fuel loads -> fire frequency, fire emissions • feedbacks to forest biomass and biogenic emissions • To investigate the changes in air quality due to evolution of emissions in response to fires in successive years under various fire scenarios • To study the feedbacks of these air quality changes to climate variability • In the process, to build a modeling system that can be further refined for similar assessments

8. Modeling System Monthly met. PnET CCSM Initial & boundary met. Base & future year fuel data Fire Simulator Hourly met METCHEM (MM5-MCPL / MAQSIP) Fire activity data Anthropogenic inventoried emissions Modified biogenic land use data BlueSky-EM- SMOKE- BEIS3 Gridded & Speciated Emissions

9. Forest Growth Model • Used by the US Forest Service’s Southern Global Change Program to model 11 states in the Southeast • Modeling period for this application: 1990-2050 • University of NH model coupled to forestry economics model (SRTS) to create PEcon • Ecological process model of forest productivity, species composition, and hydrology (PnET II); predictions of forest biomass scaled up from the FIA plot to the county level • Removal due to disturbances including climate change impacts, ozone levels, fire, pests, etc. • Being adapted to track dead wood biomass for future year fuel loads • Developing linkages to fire simulator and biogenic land cover

10. 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

11. 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 • ConUS fire emissions data at 36-km resolution, nesting down to 12-km res domain over the Southeast • Future-year fire modeling expertise from USDA FS consultants • Adapt Fire Scenario Builder developed by Pacific Wildland Fire Lab • Modify fire ignition mechanism to use a probabilistic model developed by Southern Research Station, USFS for arson

12. Atmospheric Instability - CAPE • Map • Types • 500mb • 700mb Flammability Ignition Avail Fire Generator Fire Scenario Builder – model 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)

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

14. Air Quality and Climate Feedback Modeling • Coupled meteorology-chemistry model developed by CEP under a previous EPA grant • Prior application results (1995, eastern U.S.) at www.cep.unc.edu/empd/projects/integrated • Ongoing applications, eval (U.S. and South Asia) • Recently added sea salt emissions algorithm, chemical reactions with anthropogenic aerosols • Fast optics code to improve performance and prediction of aerosol optical depths • Nested simulations at 36-km and 12-km resolutions to evaluate the whole system against forest, fire and AQ observations over the Southeast for 2002 • Future forest and fire simulations to 2050; AQ modeling for selected periods in 2015, 2030 and 2050

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. MM5 Modeling Domains • Purpose of ConUS simulation is mainly to provide adequate chemical • boundary conditions for the inner domain • MM5 grid is a few grid cells larger on all sides than respective AQ grid

17. Next Steps • Complete ConUS BlueSky-EM runs (36-km) • ConUS METCHEM simulations for 2002 • Extract boundary condition inputs for SE • 12-km simulations with PEcon linked in • Examine model performance in base year • Proceed to “snap shot” simulations with full system in 2015, 2030 and 2050 to analyze effects of key climate parameters • Archive results on project website: http://cf.unc.edu/cep/empd/projects/FIRE