1 / 50

COMING ATTRACTIONS

COMING ATTRACTIONS. CIG / JISAO PRESENTS. A GEDALOF, MANTUA, PETERSON PRODUCTION. A multi-century perspective of variability in the Pacific Decadal Oscillation: new insights from tree rings and coral. Reconstructed PDO Index. R = 0.64.

dixon
Télécharger la présentation

COMING ATTRACTIONS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. COMING ATTRACTIONS

  2. CIG / JISAO PRESENTS A GEDALOF, MANTUA, PETERSON PRODUCTION

  3. A multi-century perspective of variability in the Pacific Decadal Oscillation: new insights from tree rings and coral

  4. Reconstructed PDO Index R = 0.64 • Based on leading principal component of five published paleoproxy reconstructions. • Collective skill better than individual skill

  5. Mean Intercorrelation... Note Interval of Poor Intercorrelation

  6. Period of Poor Intercorrelation...

  7. Linsley Evans Gedalof

  8. APPEARING SOON IN GEOPHYSICAL RESEARCH LETTERS

  9. COLUMBIA RIVER FLOW SINCE A.D. 1750 RECONSTRUCTED FROM TREE RINGS A Gedalof / Peterson / Mantua Joint

  10. Based on 32 tree-ring sites R = 0.59

  11. Residuals exhibit positive trend over time (ca. +1.2 percent per century) • Validates model results of Matheussen et al. (2000).

  12. Persistent Droughts: • The 1930s were not an anomaly...

  13. MANUSCRIPT IN INTERNAL REVIEW...

  14. FEATURE PRESENTATION

  15. FIRE & CLIMATE IN THE AMERICAN NORTHWEST Douglas-Fir

  16. CO-CONSPIRATORS Lolita (and Dave) Nate Ze’ev

  17. Q. What causes wildfire? A. Fuels Accumulation "As with other areas of the country, we have experienced the unintended consequences of our very effective wildfire fighting program: The wildfires of today are getting bigger, more dangerous, harder to control, and are adversely affecting the safety of the public and our fire fighters.” National Fire Plan Strategy For the Pacific Northwest (2002)

  18. Q. What causes wildfire? B. Weather "…forest fire behavior is determined primarily by weather variation among years rather than fuel variation associated with stand age." Bessie and Johnson (1995)

  19. Q. What causes wildfire? C. You

  20. Evidence for fuels... Area burned by wildfire in 11 Western States Source: National Interagency Fire Center

  21. Cool PDO Warm PDO …for climate... On national forest lands in the Pacific Northwest wildfires are more frequent and more extensive during the warm phase of the PDO.

  22. …and you.

  23. Study Overview • To characterize patterns in annual area burned • To relate those patterns to climatic features and ecological context • To determine the extent to which climatic factors can be used to predict seasonal wildfire

  24. Literature Review • Lots of work in the Canadian boreal forest. • Very little work in the Pacific Northwest

  25. Previous Studies • Have generally treated area west of the Rocky Mountains as a single coherent unit • No allowance for spatial variability • No recognition of underlying ecology • Emphasis has been on weather (not climate)

  26. New Ideas: (1) I do not treat the area west of the Rocky Mountains as a single coherent unit (2) I address large fire seasons, rather than individual large fires (3) I identify several key atmospheric structures that can potentially be used to forecast fire-season severity

  27. ?

  28. EOF Analysis • Empirical Orthogonal Function (EOF) analysis identifies underlying patterns in large data sets • The EOFs describe the spatial variability in the data set • Associated principal components (PCs) describe the temporal variability

  29. Spatial Regressions • Can “regress” fields of climate data onto time series • Produces characteristic response of climate field to 1s perturbation in time series

  30. Superposed Epoch Analysis • Develop map composites for selected years (i.e. epochs) based on quantitative criteria • Derive descriptive statistics for subsets • Can focus on extreme events • More powerful than correlations / regressions • Does not assume linear relationship

  31. EOF 1 - 17%

  32. PC1 / 500 hPaRegression Shaded areas indicate significant correlation Pattern exhibits strong blocking

  33. PC1 / 500 hPaComposite • Five largest fire years minus five smallest fire years • Patterns consistent, but magnitude greater

  34. PC1 / PDSI Correlations Area burned is correlated to drought in winter and spring preceding the fire season Correlations: -0.59 -0.55 -0.61 June, July, Aug.

  35. EOF 2 - 13%

  36. PC2 / 500 hPaRegression Resembles “Summer PNA” Matches results across border

  37. PC2 / PDSI Correlations Area burned is weakly correlated to drought in winter preceding the fire season Correlations: -0.06 -0.09 -0.11 June, July, Aug.

  38. EOF 3 - 12%

  39. PC3 / 500 hPaComposite • 3 only large fire years represented by PC-3 • Characterized by very strong, highly persistent blocking

  40. Correlations Composite

  41. EOF 4 - 10%

  42. PC4 / 500 hPaComposite • Large fire years correspond to fire season cyclone activity

  43. Fire season is wet on the west side, dry on the east side Preceding season is drier than normal

  44. Summary 1. Climate Matters • Region wide increases in area burned are characterized by antecedent drought accompanied by persistent blocking events

  45. Summary 2. Ecology Matters • Underlying ecology appears to modulate the response to drought and circulation • more mesic forests require persistent drought, blocking events, and a source of ignition and spread • drier forests are more responsive to shorter-scale (i.e. synoptic) processes

  46. Summary 3. These relationship are non-linear • Implies that eigenvector techniques may not be the most appropriate method of investigation • Small changes in mean climate may lead to dramatic changes in wildfire activity

  47. Discussion ?

More Related