Transcript FIRE & CLIMATE IN THE AMERICAN NORTHWEST

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
2.0
PDOI
1.0
0.0
-1.0
-2.0
1825
1850
1875
1900
1925
Year
1950
1975
2000
R = 0.64
•
Based on leading principal component
of five published paleoproxy
reconstructions.
•
Collective skill better than individual
skill
Mean Interorrelation
Mean Intercorrelation...
1.00
Interval of Reconstruction
0.75
0.50
0.25
0.00
Linsley
Urban
Evans
Biondi
Gedalof
1600
1650
1700
1750
1800
1850
1900
1950
Year
Note Interval of Poor Intercorrelation
2000
Period of Poor Intercorrelation...
0.50
period ~ 23 yrs. 10.8 % var. exp.
0.25
0.00
-0.25
-0.50
0.50
period ~ 20 yrs. 20.4 % var. exp.
0.25
0.00
-0.25
-0.50
0.50
period ~ 80 yrs. 21.4 % var. exp.
0.25
0.00
-0.25
-0.50
1825
1850
1875
1900
1925
Year
1950
1975
2000
Linsley
Evans
Gedalof
APPEARING SOON IN
GEOPHYSICAL RESEARCH
LETTERS
COLUMBIA RIVER FLOW SINCE
A.D. 1750 RECONSTRUCTED
FROM TREE RINGS
A Gedalof / Peterson / Mantua Joint
Based on 32 tree-ring sites
Log10 mean flow, The Dalles, OR (cfs)
5.5
5.4
5.3
5.2
5.1
Observed Flow
Reconstructed Flow
R = 0.59
5.0
1750
1775
1800
1825
1850
1875
Year
1900
1925
1950
1975
2000
Regression Residual
0.2
0.1
0
-0.1
-0.2
1930
1940
1950
1960
Year
1970
1980
• Residuals exhibit positive trend over
time (ca. +1.2 percent per century)
• Validates model results of Matheussen
et al. (2000).
1990
1
1
6
6
11
11
Flow Rank
Flow Rank
Persistent Droughts:
16
21
26
21
26
31
31
36
36
1750
1
1800
1850
1900
5-year moving average
1950
2000
6
6
11
11
16
21
26
2000
1750
1800
1850
1900
1950
11-year moving average
2000
21
26
31
36
36
1800
1850
1900
1950
Single Year low-flow events
1800
1850
1900
1950
25-year moving average
16
31
1750
1750
1
Flow Rank
Flow Rank
16
2000
• The 1930s were not an anomaly...
MANUSCRIPT IN INTERNAL
REVIEW...
FEATURE PRESENTATION
Douglas-Fir
FIRE & CLIMATE IN THE
AMERICAN NORTHWEST
CO-CONSPIRATORS
Lolita (and Dave)
Ze’ev
Nate
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)
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)
Q. What causes wildfire?
C. You
Evidence for fuels...
Area burned by wildfire in 11 Western States
Source: National Interagency Fire Center
…for climate...
250
Cool PDO
Warm PDO
Burned Area Index
200
150
100
50
0
1900
1920
1940
1960
1980
2000
Year
On national forest lands in the Pacific Northwest
wildfires are more frequent and more extensive
during the warm phase of the PDO.
…and you.
250
Burned Area Index
200
150
100
50
0
1900
1920
1940
1960
Year
1980
2000
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
Literature
Review
• Lots of work
in the
Canadian
boreal forest.
• Very little
work in the
Pacific
Northwest
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)
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
?
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
Spatial Regressions
• Can “regress” fields of climate data onto
time series
• Produces characteristic response of
climate field to 1s perturbation in time
series
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
EOF 1 - 17%
PC1 / 500 hPa
Regression
May
June
August
September
Shaded areas
indicate
significant
correlation
Pattern
exhibits strong
blocking
May
June
PC1 / 500 hPa
Composite
• Five largest fire
years minus five
smallest fire years
July
August
September
Small Years:1971 1974 1975 1957 1978
Large Years:1987 1988 1994 1992 1959
• Patterns
consistent, but
magnitude greater
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.
EOF 2 - 13%
PC2 / 500 hPa
Regression
June
July
August
Resembles
“Summer
PNA”
Matches
results across
border
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.
EOF 3 - 12%
May
June
July
August
September
PC3 / 500 hPa
Composite
• 3 only large fire
years represented
by PC-3
• Characterized by
very strong, highly
persistent blocking
Correlations
Composite
EOF 4 - 10%
May
June
July
September
Small Years:1951 1990 1994 1970 1987
Large Years:1992 1988 1977 1981 1955
PC4 / 500 hPa
Composite
• Large fire years
correspond to fire
season cyclone
activity
Fire season is
wet on the
west side, dry
on the east
side
Preceding
season is drier
than normal
Summary
1. Climate Matters
• Region wide increases in area burned
are characterized by antecedent
drought accompanied by persistent
blocking events
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
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
Discussion ?