Fire, Carbon, and Climate Change

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

2. 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

3. 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

4. Climate change • Definition • Causes

5. Climate forcing – internal vs external • Internal forcing mechanisms • Example: Plant life • Example: Greenhouse effect - Thermohaline circulation

6. Climate forcing – internal vs external • External forcing mechanisms • Example: Orbital variations • Example: Solar output

7. Climate change • Definition • Causes • Effects

9. Climate change • Definition • Causes • Effects • Evidence

11. Real-world results:

12. 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?

14. Phase-space diagram

15. 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

16. 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

17. 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

18. Destroys temporal autocorrelation (structure)

19. SSA - SV Decomposition Decomposes time series into sum of additive components • Trajectory matrix construction • Singular Value Decomposition (SVD) • Grouping of SVD components • Reconstruction by diagonal averaging = eigenvalue, = eigenvector of = eigenvector of , See: T. ALexandrov and N Golyandina “the_autossa_files_AutoSSA-slides-EN”

20. Diagnostic Strategy [Original data set] Test for Spectral Stationarity [e.g., Short-time Fourier Transform] Signal-Noise Separation Deterministic Signal Unstructured Noise Surrogate Data Analysis iid, AAFT, PPS Surrogates Attractor Reconstruction Surrogate Data Analysis iid, AAFT Surrogates Extreme Value Statistics

21. Output -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

22. Application of SSA - NLTSA to Fire Prediction Potential advantages: -Could detect trends AND “hidden” deterministic structure -Detection of chaotic behavior sensitivity to initial conditions obvious problems for prediction -Forecasting implications: Predictions based on dynamical behavior, prior data Analytical technique to “validate” structure of other model output Different method: possible new insights

23. 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)

24. 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

25. Dataset: US NPS Fire Data, 1980-2010 Florida* 11 Properties 2.4M acres Year-round fires Alaska*: 23 Properties 52.6M acres Limited fire season *Both of interest due to potential GCC/fire interactions Maps: University of Texas

26. Dataset: US NPS Fire Data, 1980-2010 Florida* 11 Properties 2.4M acres Year-round fires Alaska*: 23 Properties 52.6M acres Limited fire season *Both of interest due to potential GCC/fire interactions http://linda.ullrich.angelfire.com/Alaska.html OrlandoSentinel

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

28. Preliminary SSA for spectral trend detection • -Quasi-oscillatory behavior; low signal:noise • -De-trending not necessary

29. Data analysis I: NLTS-SDA

30. Data analysis I: NLTS-SDA Nonlinear dynamics, presence of attractor

31. Data analysis I: NLTS-SDA Sensitivity to initial conditions

32. Data analysis I: PSR visualization Alaska Fire Occurrence Florida Fire Occurrence

33. Data analysis I: PSR visualization Alaska Fire Occurrence Florida Fire Occurrence

34. Data analysis II: SSA; hindcasting Florida Fire Occurrence, hindcast/forecast

35. Data analysis II: SSA; hindcasting Alaska Fire Occurrence, hindcast/forecast

36. 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)

37. 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

38. 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

39. Acknowledgments Fire Data: Andy Kirsch, Nate Benson: USNPS Funding: JFSP/AFE (GRIN Award) University of Florida Alumni Foundation Contact: acwatts@ufl.edu

41. Alaska Hindcasting/Forecasting Phenomenon: Increased periodic amplitude based on more-recent data:

42. Forecast based on first 100 observations Alaska Fire Occurrence, hindcast/forecast (using first 100 observations)

43. Forecast based on first 120 observations Alaska Fire Occurrence, hindcast/forecast (using first 120 observations)

44. Forecast based on first 140 observations Alaska Fire Occurrence, hindcast/forecast (using first 140 observations)