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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: [email protected]
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