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

Atmospheric Aerosol Loadings Atmospheric Aerosol Loadings

U. ShankarU. Shankar11, A. Xiu, A. Xiu11, D. Fox, D. Fox22, S. McNulty, S. McNulty33, , J. Moore MeyersJ. Moore Meyers33, L. Ran, L. Ran11, and A. Holland, and A. Holland11

33rdrd International Fire Ecology & Management Congress International Fire Ecology & Management CongressNovember 14, 2006November 14, 2006

11 Carolina Environmental Program, University Carolina Environmental Program, University of North Carolina at Chapel Hillof North Carolina at Chapel Hill22 Cooperative Institute for Research in the Cooperative Institute for Research in the Atmosphere, Ft. Collins, COAtmosphere, Ft. Collins, CO33 USDA Forest Service, Southern Global Change USDA Forest Service, Southern Global Change ProgramProgram

Research Program GoalsResearch Program Goals

Project funding: EPA STAR Grant RD 83227701Project funding: EPA STAR Grant RD 83227701Aim is to support the EPA Global Change Aim is to support the EPA Global Change

Research Program goals byResearch Program goals byExamining consequences of climate change for wild Examining consequences of climate change for wild

fire occurrence and consequently for U.S. air qualityfire occurrence and consequently for U.S. air qualityCombining the effects of climate change with forest Combining the effects of climate change with forest

growth to examine impacts on fire frequency and growth to examine impacts on fire frequency and intensityintensity

Investigating methods to credibly project changes in Investigating methods to credibly project changes in biogenic emissions from 2002-2050 due to firesbiogenic emissions from 2002-2050 due to fires

AcknowledgmentsAcknowledgments

Participation and Outreach: USDA Forest ServiceParticipation and Outreach: USDA Forest ServiceD. McKenzie, Pacific Wildland Fire Sciences Lab, future D. McKenzie, Pacific Wildland Fire Sciences Lab, future

firesfires J. Prestemon and E. Mercer, Southern Research J. Prestemon and E. Mercer, Southern Research

Station, human-induced fire ignitionStation, human-induced fire ignitionS. McNulty and J. Moore Myers, Southern Global S. McNulty and J. Moore Myers, Southern Global

Change Program, forest growth modelingChange Program, forest growth modeling

Project PersonnelProject Personnel

Uma Shankar (PI): Aerosol modeling and Uma Shankar (PI): Aerosol modeling and analysisanalysis

Aijun Xiu (co-PI): Meteorology, chemistry-Aijun Xiu (co-PI): Meteorology, chemistry-climate couplingclimate coupling

Doug Fox (co-PI): Fire modeling Doug Fox (co-PI): Fire modeling Andy Holland: Fire model data linkages, Andy Holland: Fire model data linkages,

emissions processingemissions processing Limei Ran: Forest growth model and data Limei Ran: Forest growth model and data

linkageslinkages Frank Binkowski: Radiative transfer Frank Binkowski: Radiative transfer

modeling, analysis modeling, analysis Sarav Arunachalam: Air quality data Sarav Arunachalam: Air quality data

analysis, website mgmt analysis, website mgmt

Air Quality and Climate ImpactsAir Quality and Climate Impacts of Firesof Fires

Impacts of wild fires Impacts of wild fires felt at the regional felt at the regional and global scaleand global scale

> 8M acres burned last year> 8M acres burned last year Black carbon => positive Black carbon => positive

forcing on climate; SOforcing on climate; SO22 emissions => negative emissions => negative forcing on climate from forcing on climate from secondarily produced SOsecondarily produced SO44

Dioxins and GHGsDioxins and GHGs also also associated with fire plumes associated with fire plumes (Gullett and Tuotti, (Gullett and Tuotti, AE 37AE 37, , 2003; Simmonds et al., 2003; Simmonds et al., AE 39AE 39, , 2005)2005)

Effect of radiatively important Effect of radiatively important pollutants on short-term pollutants on short-term climate variability affects climate variability affects forest growth, and thus the forest growth, and thus the biogenic emissions as well as biogenic emissions as well as fuel available for potential fuel available for potential firesfires

CO O3

Carbonaceous

Aerosol

Model predictions of the effects of Canadian boreal fires on aerosols and ozone, July 1995

Modeling IssuesModeling Issues

Feedback of short-term climate variability Feedback of short-term climate variability to forest growth is not represented in most to forest growth is not represented in most modelsmodels

Most regional air quality models do not Most regional air quality models do not include feedback of scattering and include feedback of scattering and absorbing aerosols or ozone to absorbing aerosols or ozone to atmospheric dynamicsatmospheric dynamics

Understanding these feedbacks and effect Understanding these feedbacks and effect on short-term climate variability is on short-term climate variability is essential to fully assess impacts of essential to fully assess impacts of managed vs. uncontrolled fires on forest managed vs. uncontrolled fires on forest land and the net benefits of fire land and the net benefits of fire management plansmanagement plans

ObjectivesObjectives

To examine impacts of climate change and To examine impacts of climate change and variability on:variability on: forest growth -> fuel loads -> fire frequency, forest growth -> fuel loads -> fire frequency,

fire emissionsfire emissions feedbacks to forest biomass and biogenic feedbacks to forest biomass and biogenic

emissionsemissions To investigate the changes in air quality due to To investigate the changes in air quality due to

evolution of emissions in response to fires in evolution of emissions in response to fires in successive years under various fire scenariossuccessive years under various fire scenarios

To study the feedbacks of these air quality To study the feedbacks of these air quality changes to climate variabilitychanges to climate variability

In the process, to build a modeling system that In the process, to build a modeling system that can be further refined for similar assessmentscan be further refined for similar assessments

Modeling SystemModeling System

PnETCCSM

METCHEM(MM5-MCPL /

MAQSIP)

BlueSky-EM-SMOKE-

BEIS3

Monthly met.

Base & future yearfuel data

Fire Simulator

Hourly met

Fireactivity data

Modifiedbiogenic

land use data

Anthropogenicinventoriedemissions

Gridded &SpeciatedEmissions

Initial &boundary

met.

Forest Growth ModelForest Growth Model

Used by the US Forest Service’s Southern Global Used by the US Forest Service’s Southern Global Change Program to model 11 states in the Change Program to model 11 states in the Southeast Southeast Modeling period for this application: 1990-Modeling period for this application: 1990-

2050 2050 University of NH model coupled to forestry University of NH model coupled to forestry

economics model (SRTS) to create PEconeconomics model (SRTS) to create PEcon Ecological process model of forest Ecological process model of forest

productivity, species composition, and productivity, species composition, and hydrology (PnET II); predictions of forest hydrology (PnET II); predictions of forest biomass scaled up from the FIA plot to the biomass scaled up from the FIA plot to the county levelcounty level

Removal due to disturbances including Removal due to disturbances including climate change impacts, ozone levels, fire, climate change impacts, ozone levels, fire, pests, etc. pests, etc.

Being adapted to track dead wood biomass Being adapted to track dead wood biomass for future year fuel loadsfor future year fuel loads

Developing linkages to fire simulator and Developing linkages to fire simulator and biogenic land cover biogenic land cover

Climate Spatial

FIA

FIA Plot

PnET-CN

Volume1

Volume2

Volume3

Inventoryand

Harvest

SRTS

Update Acres

Calculate Acres

Harvested

Allocate HarvestCalculate

Growth

Update Inventory

Update Equilibrium

Flow Chart of PEcon

Fire/Smoke Emissions ModelingFire/Smoke Emissions Modeling BlueSky-EM, a smoke emissions model linked to BlueSky-EM, a smoke emissions model linked to

the Sparse Matrix Operator Kernel Emissions the Sparse Matrix Operator Kernel Emissions Model (SMOKE) for processing and merging with Model (SMOKE) for processing and merging with emissions from other sources (industry, emissions from other sources (industry, transport, biogenic, sea salt, etc.)transport, biogenic, sea salt, etc.)

Directly linked to the FCCS fuel databaseDirectly linked to the FCCS fuel database ConUS fire emissions data at 36-km resolution, ConUS fire emissions data at 36-km resolution,

nesting down to 12-km res domain over the nesting down to 12-km res domain over the SoutheastSoutheast

Future-year fire modeling expertise from USDA Future-year fire modeling expertise from USDA FS consultantsFS consultants Adapt Fire Scenario Builder developed by Pacific

Wildland Fire Lab Modify fire ignition mechanism to use a probabilistic Modify fire ignition mechanism to use a probabilistic

model developed by Southern Research Station, model developed by Southern Research Station, USFS for arsonUSFS for arson

Ignition Avail

Fire Scenario Builder – Fire Scenario Builder – modelmodel

Flammability

Fire frequency & fuel maps

Management RxFire/suppression

MM5 (mesoscale model)

AtmosphericInstability- CAPE

MapTypes-500mb-700mb

Fire Generator

Fire Starts

Fire Sizes

Equations predict fuel moisture in fuel size classes that carry fire.

NFDRSHuman ignitions

(East)

Days since July 1, 2003

Are

a b

urn

ed

(h

a *

10

00

)

0

5

10

15

20

25

30

0 10 20 30 40 50 60

0

5

10

15

20

25

30

Nu

mb

er

of

fire

s

Area burnedNumber of fires

McKenzie et al. (2006) Ecol. Modell.

FSB output for the Pacific Northwest 12-km MM5 domain

Air Quality and Climate Feedback ModelingAir Quality and Climate Feedback Modeling Coupled meteorology-chemistry model Coupled meteorology-chemistry model

developed by CEP under a previous EPA grantdeveloped by CEP under a previous EPA grant Prior application results (1995, eastern U.S.) at Prior application results (1995, eastern U.S.) at

www.cep.unc.edu/empd/projects/integratedwww.cep.unc.edu/empd/projects/integrated Ongoing applications, eval (U.S. and South Asia) Ongoing applications, eval (U.S. and South Asia)

Recently added sea salt emissions algorithm, Recently added sea salt emissions algorithm, chemical reactions with anthropogenic aerosolschemical reactions with anthropogenic aerosols

Fast optics code to improve performance and Fast optics code to improve performance and prediction of aerosol optical depthsprediction of aerosol optical depths

Nested simulations at 36-km and 12-km Nested simulations at 36-km and 12-km resolutions to evaluate the whole system resolutions to evaluate the whole system against forest, fire and AQ observations over against forest, fire and AQ observations over the Southeast for 2002the Southeast for 2002

Future forest and fire simulations to 2050; AQ Future forest and fire simulations to 2050; AQ modeling for selected periods in 2015, 2030 and modeling for selected periods in 2015, 2030 and 20502050

Coupled Meteorology-Chemistry Model Coupled Meteorology-Chemistry Model (METCHEM)(METCHEM)

H & V Transport, Cloud Physics & Chemistry,

Gas/Particulate Chemistry, PM

Microphysics (Modal), Dry & Wet Removal (MAQSIP CTM)

Met. Couple(MCPL)

Meteorology

(MM5)

Emissions Processing(SMOKE)

Aerosol Direct Radiative FeedbackAerosol Direct Radiative Feedback

MM5 Modeling DomainsMM5 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

Next StepsNext Steps

Complete ConUS BlueSky-EM runs (36-km)Complete ConUS BlueSky-EM runs (36-km) ConUS METCHEM simulations for 2002ConUS METCHEM simulations for 2002 Extract boundary condition inputs for SEExtract boundary condition inputs for SE 12-km simulations with PEcon linked in12-km simulations with PEcon linked in Examine model performance in base yearExamine model performance in base year Proceed to “snap shot” simulations with full Proceed to “snap shot” simulations with full

system in 2015, 2030 and 2050 to analyze system in 2015, 2030 and 2050 to analyze effects of key climate parameterseffects of key climate parameters

Archive results on project website: Archive results on project website: http://cf.unc.edu/cep/empd/projects/FIREhttp://cf.unc.edu/cep/empd/projects/FIRE