Relationships Between Patterns
of Atmospheric Circulation and U.S. Drought
over the Past Several Centuries
Zhang, Zhihua
Department of Environmental Sciences
University of Virginia
Committee:
Professor Michael Mann (adviser), Department of Environmental Sciences
Professor Jose Fuentes, Department of Environmental Sciences
Professor Bruce Hayden, Department of Environmental Sciences
Professor Henry Shugart, Department of Environmental Sciences
Professor Ted Chang, Department of Statistics
“And it never failed
that during the dry years
the people forgot about the rich years,
and during the wet years
they lost all memory of the dry years.
It was always that way.”
—John Steinbeck
East of Eden
Is it going to be dry or wet this
year?
We need to
understand the
past history of
drought to better
assess future
prospects for
drought.
The goal of my research is to address
such questions as:
1. In what ways do the temporal and spatial patterns
of US drought change over time?
2. To what degree are those drought patterns linked
with larger-scale atmospheric circulation changes?
3. What is the relative importance of climate
variability in various regions of the tropics and
extratropics in determining patterns of
conterminous U.S. drought?
OUTLINE
To place modern climate changes in a longerterm context and explore the fuller range of
potential variability, I have:
1. Extended the drought record father back in time with
dendroclimatic reconstructions of summer drought
(PDSI) patterns over the conterminous U.S back to
1700
2. Extended the atmospheric circulation record back in
time through proxy-based reconstructions of boreal
cold- and warm-season global SLP patterns back
through the 17th century
OUTLINE
To more fully assess the potential relationships
between U.S. drought and larger-scale influences by
atmospheric circulation patterns and dynamical modes
of climate variability, I have
3. Analyzed the evidence for coherent modes of variability
in the joint U.S. drought/seasonal SLP field over the
modern instrumental period
4. Investigated the longer-term relationship between U.S.
summer drought and atmospheric circulation anomaly,
making use of proxy-based pre-reconstructions of past
centuries
Reconstructions of U.S.
summer (JJA) drought (PDSI)
patterns back to 1700
U.S. drought reconstructions
Proxy network:
483 tree ring
chronologies
U.S. drought reconstructions
This grid spacing is 2º lat. × 3º long.
U.S. drought reconstructions
Method (RegEM):
•
The method is based on a regularized expectation
maximization algorithm (RegEM), which offers some
theoretical advantages over previous methods of CFR.
•
This approach calibrates the proxy data set against the
instrumental record by treating the reconstruction as
initially missing data in the combined proxy/instrumental
data matrix.
•
With optimally estimating the mean and covariance of the
combined data matrix through an iterative procedure,
RegEM can produce a reconstruction of climate field with
minimal error variance (Schneider, T., 2001; Rutherford et
al, 2003; Mann et al, 2002).
RegEM CFR approach
Mann, M.E., Rutherford, S., Wahl, E., Ammann, C., Testing the Fidelity of Methods Used in ProxyBased Reconstructions of Past Climate, Journal of Climate, 18, 4097-4107, 2005.
U.S. drought
reconstructions
•To calculate the
reconstruction scores,
we only used part of
the available
instrumental data for
calibration (19281978) and keep some
instrumental data
(1895-1927) free for
verification.
• For final
reconstruction, we
employed all available
instrumental data.
•Code was from
http://www.math.nyn.ed
u/~tapio/imputation/.
1978yr
1927yr
PDSI
dataset
1895yr
missing
data
need
to be
recon.
1700yr
PDSI
gridpoints
Tree-ring chronologies
U.S. drought reconstructions
RE distributionforverificationinterval (global proxydatarecon.regional PDSI)
0.45
Mean=.3614
Time series of regional and domain mean drought back to 1700
1930’s Dust Bowl
RegEM
Cook et al.
The spatial patterns of reconstructed
U.S. drought based on RegEM
1736
1708
1736 PDSI pattern with regEM
1
0
0
0
2
1
-3
-2
-1
0 1
-2
-1
-4
-3
-1
0
0 -1
-1
-4
-2
-1
-1
-2
-5
-2
0
-3
-2
-1
-2
-1
-1
-2
-1
0
-3
0
-3
-6
-2
-3
-2
-6
-7
-1 0
0 1 2
-1
1708 PDSI pattern with regEM
-1
2
2
1
-1
1
1864
1800
0 1
-2
-1
0
1
-1
-1
-1
-2
-1
-1
-2
-2
-1
0
-1
-2
-3
0
-1
-1
-1
0
-1
-4
-3
-2
1
-1
-4
-1
0
-3
-4
-2
-4
0
-1
-3
-3 -4
-2
-1
0
-1
-4
-1
-3
1864 PDSI pattern with regEM
1800 PDSI pattern with regEM
-3
-1
0
0
The spatial patterns of reconstructed
U.S. drought based on RegEM
1745
1726
1726 PDSI pattern with regEM
0
2
2
3
2
1
1
1793 PDSI pattern with regEM
5
1
0
2
2
1
0
3
1
4
0
1
1
3
1
0
1
2
3
0
2
-1
1
1
1
1
2
2
1
0
-1
0
1
0
4
0 0
1
3
1
-1
0
1
1
00
3
4
2
0
0
2 3
4
0
1833 PDSI pattern with regEM
0
-1
2
1833
1793
1
-1
3
-1
1
0
1
4
2
4
3
0
-1
2
2
1
0
0
3
1
1
0
0
4
0
1
1
0
1
4
3
2
0
5
0
1
1
0
1
-1
1745 PDSI pattern with regEM
1
Reconstructions of cold-season
(Oct-Mar) and warm-season
(Apr-Sep) global SLP patterns
back to 1601
Global SLP reconstructions
• Hybrid frequency-domain RegEM
•
•
Different types of proxy data exhibit fundamentally different
frequency-domain fidelity characteristics.
Some variables such as sediments, ice core and historical records
are only decadal/low-frequency resolved proxy indicators.
• Stepwise RegEM
•
Proxy data do not share a common length, stepwise procedure
can better use climate information in the calibration process.
(Rutherford et al, 2005; Mann et al, 2005)
Global SLP reconstructions
Spatial distribution of full proxy database (high-frequency)
Year (before 2000 AD)
Global SLP reconstructions
Spatial distribution of full proxy database (low-frequency)
Year (before 2000 AD)
Global SLP
reconstructions
Procedures of reconstructing global SLP
Climat
e
Highfrequency
band
Full proxies
(including
lag+1,0,-1)
Reconstructing
high-frequency
climate
Proxy
PCs(dens
e treering)
Lowfrequency
band
Full
proxies
Screened proxies
(95%) with local
climate
Reconstructing
low-frequency
climate
Summing
reconstructed
low/high-frequency
climate
Global SLP
reconstructions
Global SLP
reconstructions
Spatial verification scores
Boreal cold-season
Boreal warm-season
Verification using long-term European
SLP data(Luterbacher et al.,2002)
Boreal cold-season
Boreal warm-season
Nodal area
No real
data
1982/83
ENSO
ENSO-like patterns
NAO-like patterns
Correlations between SLP-related
climate indices
Comparison with other reconstructions
Mann: 0.41 Stahle: 0.42
Jones: 0.83
Luterbacher: 0.43 Cook: 0.37 Vinther: 0.31
Analysis of Modern Relationship
between Patterns of SLP and
U.S. Drought (1895-1995)
The MTM-SVD method
•
The MTM-SVD method [Mann and Park, 1994; 1999] has
been widely used in the detection of spatiotemporal
oscillatory signals in one or several simultaneous climate
data fields.
•
The MTM-SVD method identifies distinct frequency bands
within which there is a pattern of spatially-coherent variance
in the data that is greater in amplitude than would be expected
under the null hypothesis of spatiotemporal colored noise.
•
This method differs from conventional EOF-based
approaches in that both phase and amplitude information are
retained in the data decomposition.
MTM-SVD spectra
Cold-season SLP/U.S.
summer drought
ENSO
signal
99% sign.
Warm-season SLP/U.S.
summer drought
Bi-decadal
signal
ENSO
signal
99% sign.
coincident with peak positive ENSO
(TNH) extratropical teleconnection pattern (Livezey and Mo 1987)
Cold-season
Warm-season
Comparison with standard composites (cold-season)
recon.
obs.
sign.
Comparison with standard composites (warm-season)
recon.
obs.
sign.
Spatial reconstructions of warm-season bidecadal
(22 yr) signal
coincident with peak domain wet
Spatial reconstructions of warm-season bidecadal
(22 yr) signal
Time-domain recon. vs. raw data
Domain mean
Great plains
Schubert et al. 2004
South west
Analysis of Past Relationship
between Patterns of SLP and
U.S. Drought with proxy-based
data (1700-1870)
MTM-SVD spectra (recon. data)
Quasi-decadal
signal
ENSO
signal
99% sign.
Weak
ENSO
Bi-decadal
signal
ENSO
signal
99% sign.
Weak
ENSO
Mann
2000
coincident with peak positive ENSO
(TNH) extratropical teleconnection pattern (Livezey and Mo 1987)
Warm-season
Time-domain reconstructions associated
with 3.5 yr period ENSO signal
Cold-season
Warm-season
Spatial reconstructions of cold-season quasidecadal
(11 year) signal
coincident with peak domain wet
Spatial reconstructions of cold-season quasidecadal
(11 year) signal
Time-domain reconstructions
Tourre et al. 2001
Spatial reconstructions of warm-season bidecadal
(24 year) signal
coincident with peak domain wet
Spatial reconstructions of warm-season bidecadal
(24 year) signal
Time-domain reconstructions
Schubert et al. 2004
Conclusions:
•
The 1930s Dust Bowl and the 1982/1983 El Nino
event appear to be relatively unusual events in
the context of the past few centuries, though
sizable uncertainties preclude definitive
conclusions.
•
The El Nino/Southern Oscillation (ENSO) has
been a robust interannual climate signal
influencing conterminous U.S. summer drought
over the past three centuries, with apparent
weak signals during the early and mid 19th
century .
Conclusions:
• A quasidecadal (10-11 year period) oscillatory
signal in cold-season SLP is found to represent
a low-frequency component of ENSO, with
similar influences on conterminous U.S.
drought.
•
A roughly bidecadal climate signal in warmseason SLP is found to influence drought of the
U.S. primarily through a long-term modulation
in the strength of Bermuda high pressure
system.
U.S. drought reconstructions
Climate
studies
1. precipitation is often the
most limiting factor to
plant growth in arid and
semiarid areas.
2. in the higher latitudes or
altitudes, temperature is
often the most limiting
factor that affects tree
growth rates.
Log
industry
The Reduction of Error (Lorenz, 1956; Fritts, 1976) statistic (RE) and Coefficient of
Efficiency (CE) (Cook et al., 1994) statistical skill metrics in this study are used for
gauging the fidelity of the reconstructions. The RE and CE have been widely used
as diagnostics of reconstructive skill in most previous climate/paleoclimate
reconstruction work
RE  1.0   ( xi  xˆi ) /  ( xi  xc )
2
CE  1.0   ( xi  xˆi ) /  ( xi  xv )
2
2
2
Defination of SLP-related indices
• The Southern Oscillation Index (SOI) is defined as the
normalized pressure difference between Tahiti (17S,
149W) and Darwin (12S, 131E) (Allan et al., 1991)
• The North Atlantic Oscillation (NAO) index is defined
as the difference between the normalized pressure at
Gibralter and Reykjavik (Jones et al. 1997).
• The Arctic Oscillation (AO) and Antarctic Oscillation
(AAO) indices are defined as the projections of the
leading Empirical Orthogonal Function (EOF) of the
instrumental SLP field (Thompson and Wallace, 2000)
over the extratropical Northern Hemisphere (poleward
of 20N) and Southern Hemisphere (poleward of 20S)
respectively.
Assumptions
The anomalous atmospheric circulation
patterns, which reflect the underlying
surface properties of oceans (SST) and
subject to associated dispersion and
propagation of atmospheric waves, are
the most important features that influence
regional and global scale U.S drought at
interannual and decadal time scales.
U.S. drought reconstructions
Method (RegEM):
The regularized expectation maximization (RegEM)
algorithm is an iterative method for the estimate of mean
and covariance matrices from incomplete data under the
assumption that the missing values in the dataset are
missing at random(Schneider, 2001).
•
With iterative approach, the reconstruction can be nonlinear, and
all available values (including incomplete dataset) were involved
in simulating.
•
With ridge regression, the principal components were truncated by
gradually damping the amplitude of higher order PCs