Fire, Carbon, and Climate Change Fire Ecology and Management 12 April 2013

Fire, Carbon, and Climate Change

Fire Ecology and Management

12 April 2013

Overview

- Climate change (brief overview of concepts)

- Climate change – potential effects - Wildland fires

- Fire ecology

- Carbon cycle (brief overview of relevant terminology)

- Local and global fire-C interactions

Climate change

- Definition

Climate change refers to any significant change in the measures of climate lasting for an extended period of time.

In other words, climate change includes major changes in temperature, precipitation, or wind patterns, among others, that occur over several decades or longer.

-U.S. Environmental Protection Agency

Climate change

- Definition

- Causes

- Thermohaline circulation

Climate forcing – internal vs external- Internal forcing mechanisms

- Example: Plant life

- Example: Greenhouse effect

Climate forcing – internal vs external- External forcing mechanisms

- Example: Orbital variations

- Example: Solar output

Climate change

- Definition

- Causes

- Effects

Climate change

- Definition

- Causes

- Effects

- Evidence

Real-world results:

Problem:

- We have only limited information about the complex suite of factors driving wildfires…

(Result: highly complex models that aren’t yet perfect)

Implied question:

- Can we reconstruct complex system dynamics from a limited amount of information?

Phase-space diagram

Analogy from other complex systems to fire?

- Example: Lorenz Atmospheric Convection Model

δx / δt = σ ( y – x )

δy/δt = ρx – y - xz

δz/δt = -βz + xy

( )x t

( )y t

( )z t

Analogy from other complex systems to fire?

- Example: Lorenz Atmospheric Convection Model

δx / δt = σ ( y – x )

δy/δt = ρx – y - xz

δz/δt = -βz + xy

( )x t

( )y t

( )z t

Reconstructing system dynamics from limited information:

Application of Takens’s Theorem (1981):

Time-lag embedding can be used to reconstruct

system dynamics given only a limited amount of

information.

Lorenz data sequence (top) courtesy of Prof. Eric Weeks, Emory University Department of Physics

Destroys temporal autocorrelation

(structure)

SSA - SV Decomposition

Decomposes time series into sum of additive components

1. Trajectory matrix construction

2. Singular Value Decomposition (SVD)

3. Grouping of SVD components

4. Reconstruction by diagonal averaging

= eigenvalue, = eigenvector of

= eigenvector of ,

See: T. ALexandrov and N Golyandina “the_autossa_files_AutoSSA-slides-EN”

15012510075

5025

0 0.050.1

0.150.2

0.250.3

0.350.4

0.45Time

Frequency

00

5050

100100

150150

200200

250250

300300

350350

400400

450450

Int=

TIS

A

Int=

TIS

A

Alaska FireShort-Time Fourier Transform Frequency Spectrum

Diagnostic Strategy

Test for Spectral Stationarity[e.g., Short-time Fourier Transform]

Signal-Noise Separation

Surrogate Data Analysisiid, AAFT, PPS Surrogates

Attractor Reconstruction

Deterministic Signal

Surrogate Data Analysisiid, AAFT Surrogates

Extreme Value Statistics

Unstructured Noise

[Original data set]

-Correlation dimension: indicator of stable attractor-Low CD and difference in surrogated vs original data indicate “chaotic dynamics”-No difference appearance of chaos due to periodicity or noisy linear dynamics

-Lyapunov exponent: indicator of sensitivity to initial conditions

-Surrogate data analysis tests the hypothesis that apparent structure is due to stochastic processes, rather than deterministic ones

Output

Application of SSA - NLTSA to Fire Prediction

Potential advantages:-Could detect trends AND “hidden” deterministic structure-Detection of chaotic behavior

sensitivity to initial conditionsobvious problems for prediction

-Forecasting implications:Predictions based on dynamical behavior, prior dataAnalytical technique to “validate” structure of other model outputDifferent method: possible new insights

Analysis of Fire Data using SSA - NLTSA

Preferences for initial analysis:-Multiple sites with diverse climates/fire seasons-Few “confounding factors” to introduce additional noise:

Low level of intervention (e.g. tree harvests)No human-caused fires (i.e. arson, Rx)

Analysis of Fire Data using SSA - NLTSA

Preferences for initial analysis:-Multiple sites with diverse climates/fire seasons-Few “confounding factors” to introduce additional noise:

Low level of intervention (e.g. tree harvests)No human-caused fires (i.e. arson, Rx)

Occurrence (i.e. # of fires by month) vs. # Acres Burned-preliminary analysis indicated occurrence data preferable

Dataset: US NPS Fire Data, 1980-2010

Alaska*:23 Properties52.6M acresLimited fire season

Maps: University of Texas

Florida*11 Properties2.4M acresYear-round fires

*Both of interest due to potential GCC/fire interactions

Dataset: US NPS Fire Data, 1980-2010

Alaska*:23 Properties52.6M acresLimited fire season

Florida*11 Properties2.4M acresYear-round fires

*Both of interest due to potential GCC/fire interactions

http://linda.ullrich.angelfire.com/Alaska.html Orlando Sentinel

Data preparation

- Screened NPS fire data set (41K+ fires) AK and FL fires- AK: 491 natural ignitions in 30y; FL: 1245 natural wildfires

Preliminary SSA for spectral trend detection

-Quasi-oscillatory behavior; low signal:noise-De-trending not necessary

Data analysis I: NLTS-SDA

Data analysis I: NLTS-SDA

Nonlinear dynamics, presence of attractorNonlinear dynamics, presence of attractor

Data analysis I: NLTS-SDA

Sensitivity to initial conditions

Sensitivity to initial conditions

Data analysis I: PSR visualization

Alaska Fire Occurrence

Florida Fire Occurrence

Data analysis I: PSR visualization

Alaska Fire Occurrence

Florida Fire Occurrence

Data analysis II: SSA; hindcasting

Florida Fire Occurrence, hindcast/forecast

ForecastAK Fires Occ by mo.xls [Sheet1]; Var:Var2; DECOMP.-K=108,Cent.(No); RECONSTR.-ET:(1-5);

FORECAST - start:120, #pnt.:120, base:1, method:2;

198005May 198409September 198908August 199407July 199906June 200405May 200809September

-5.3

-2.8

-0.3

2.3

4.8

7.3

9.8

12.3

14.8

17.4

19.9

22.4

24.9

27.4

29.9

32.5

35.0

37.5

40.0

Data analysis II: SSA; hindcasting

Alaska Fire Occurrence, hindcast/forecast

Initial Findings: Summary

-Nonlinear, deterministic structure detected in fire occurrence data

-Chaotic dynamics also detected in fire occurrence data(i.e. sensitivity to initial conditions)

-SSA hindcasting reproduces some structure of original data(but misses “extreme” episodes)

Some Implications

-Use to ask: Do forecasts of future fire scenarios possess similar “hidden” dynamics?

-Initial-conditions sensitivity a concern for forecasting

-Shows potential for applicability in forecasting

Next Steps

-Re-analyze using occurrence-size index

-Additional subregions (including other global regions)

-Incorporation of climate or environmental data to reduce noise

-Larger and “noisier” datasets (e.g., USFS)

-General improvements to occur with increased learning

Acknowledgments

Fire Data: Andy Kirsch, Nate Benson: USNPS

Funding: JFSP/AFE (GRIN Award) University of Florida Alumni Foundation

Contact: acwatts@ufl.edu

Alaska Hindcasting/Forecasting Phenomenon:

Increased periodic amplitude based on more-recent data:

Forecast based on first 100 observations

ForecastAK Fires Occ by mo.xls [Sheet1]; Var:Var2; DECOMP.-K=108,Cent.(No); RECONSTR.-ET:(1-5);

FORECAST - start:100, #pnt.:120, base:1, method:2;

Var2Forecast baseVar2(forecast)Forecast start point

198005May 198407July 198809September 199306June 199708August 200205May 200607July 201009September

-5.3

-2.8

-0.3

2.3

4.8

7.3

9.8

12.3

14.8

17.4

19.9

22.4

24.9

27.4

29.9

32.5

35.0

37.5

40.0

Alaska Fire Occurrence, hindcast/forecast (using first 100 observations)

ForecastAK Fires Occ by mo.xls [Sheet1]; Var:Var2; DECOMP.-K=108,Cent.(No); RECONSTR.-ET:(1-5);

FORECAST - start:120, #pnt.:120, base:1, method:2;

198005May 198409September 198908August 199407July 199906June 200405May 200809September

-5.3

-2.8

-0.3

2.3

4.8

7.3

9.8

12.3

14.8

17.4

19.9

22.4

24.9

27.4

29.9

32.5

35.0

37.5

40.0

Alaska Fire Occurrence, hindcast/forecast (using first 120 observations)

Forecast based on first 120 observations

ForecastAK Fires Occ by mo.xls [Sheet1]; Var:Var2; DECOMP.-K=108,Cent.(No); RECONSTR.-ET:(1-5);

FORECAST - start:140, #pnt.:120, base:1, method:2;

Var2Forecast baseVar2(forecast)Forecast start point

198005May 198506June 199007July 199508August 200009September 200605May

-5.3

-2.8

-0.3

2.3

4.8

7.3

9.8

12.3

14.8

17.4

19.9

22.4

24.9

27.4

29.9

32.5

35.0

37.5

40.0

Alaska Fire Occurrence, hindcast/forecast (using first 140 observations)

Forecast based on first 140 observations