Relationships Between Patterns of Atmospheric Circulation and U.S. Drought

over the Past Several Centuries

Zhang, ZhihuaDepartment of Environmental Sciences

University of Virginia

Committee: Professor Michael Mann (adviser), Department of Environmental SciencesProfessor Jose Fuentes, Department of Environmental SciencesProfessor Bruce Hayden, Department of Environmental SciencesProfessor Henry Shugart, Department of Environmental SciencesProfessor 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 We need to understand the understand the past history of past history of

drought to better drought to better assess future assess future prospects for prospects for

drought.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

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

To place modern climate changes in a longer-term context and explore the fuller range of potential

variability, I have:

OUTLINE

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

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

Reconstructions of U.S. summer (JJA) drought (PDSI)

patterns back to 1700

U.S. drought reconstructions

Proxy network:

483 tree ring chronologies

This grid spacing is 2º lat. × 3º long.

U.S. drought reconstructions

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 Proxy-Based Reconstructions of Past Climate, Journal of Climate, 18, 4097-4107, 2005.

PDSIdataset

missingdataneedto berecon.

PDSIgridpoints

Tree-ring chronologies

1700yr

•To calculate the reconstruction scores, we only used part of the available instrumental data for calibration (1928-1978) and keep some instrumental data (1895-1927) free for verification.• For final reconstruction, we employed all available instrumental data.•Code was fromhttp://www.math.nyn.edu/~tapio/imputation/.

1895yr

1927yr

1978yr

U.S. drought reconstructions

Present years past years

RE distribution for verification interval (global proxy data recon. regional PDSI)

0.45

0.30

0.60 0.15

0.00-0.15

0.30

0.45

0.45

0.45

0.30

0.45

0.150.150.

60

0.45

0.300.15

0.00

0.15

0.30

0.15

0.30

0.45

0.15

0.45

0.300.60

0.30

0.45

0.30

0.15

0.60

0.60 0.30

0.30

0.30

0.15

0.60

0.15

0.15

Mean=.3614

U.S. drought reconstructions

Time series of regional and domain mean drought back to 1700 1930’s Dust Bowl

RegEM Cook et al.

-5

1708 PDSI pattern with regEM

1800 PDSI pattern with regEM

-2

The spatial patterns of reconstructed U.S. drought based on RegEM

1708

1800

1736 PDSI pattern with regEM

1864 PDSI pattern with regEM

0

-1

-4

-2

1864

1736

1726 PDSI pattern with regEM

3

4

-110

1745 PDSI pattern with regEM

2

1793 PDSI pattern with regEM

2

1833 PDSI pattern with regEM

1

1

The spatial patterns of reconstructed U.S. drought based on RegEM

1726

1793 1833

1745

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 reconstructionsSpatial distribution of full proxy database (high-frequency)

Year (before 2000 AD)

Global SLP reconstructionsSpatial distribution of full proxy database (low-frequency)

Year (before 2000 AD)

Global SLP reconstructions

Procedures of reconstructing global SLP

Climate

Screened proxies (95%) with local

climate

Reconstructing low-frequency

climate

High-frequency

band

Low-frequency

band

Full proxies(including

lag+1,0,-1)

Summing reconstructed low/high-frequency

climate

Proxy PCs(dense tree-ring)

Reconstructing high-frequency

climate

Full proxies

Global SLP reconstructions

Global SLP reconstructions

Boreal warm-season Boreal cold-season

Spatial verification scores

Boreal warm-season Boreal cold-season

Verification using long-term European SLP data(Luterbacher et al.,2002)

Nodal area

No real data

1982/83ENSO

ENSO-like patterns

NAO-like patterns

Correlations between SLP-related climate indices

Comparison with other reconstructions

Mann: 0.41 Stahle: 0.42

Luterbacher: 0.43 Cook: 0.37 Vinther: 0.31

Jones: 0.83

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

Warm-season SLP/U.S. summer drought

99% sign.

99% sign.

ENSOsignal

ENSOsignal

Bi-decadalsignal

Spatial reconstructions of peak ENSO signal (5-yr)

coincident with peak positive ENSO

(TNH) extratropical teleconnection pattern (Livezey and Mo 1987)

Cold-season Warm-season

Spatial reconstructions of peak ENSO signal (5-yr)

Comparison with standard composites (cold-season)

recon. obs. sign.

recon. obs. sign.

Comparison with standard composites (warm-season)

coincident with peak domain wet

Spatial reconstructions of warm-season bidecadal (22 yr) signal

Time-domain recon. vs. raw dataDomain mean

Great plains

South westSchubert et al. 2004

Spatial reconstructions of warm-season bidecadal (22 yr) signal

Analysis of Past Relationship between Patterns of SLP and

U.S. Drought with proxy-based data (1700-1870)

MTM-SVD spectra (recon. data)

WeakENSO

WeakENSO

99% sign.

ENSOsignal

Bi-decadalsignal

Mann2000

99% sign.

ENSOsignal

Quasi-decadalsignal

coincident with peak positive ENSO

(TNH) extratropical teleconnection pattern (Livezey and Mo 1987)

Spatial reconstructions of peak ENSO signal (3.5 yr)

Cold

-seaso

n

Warm

-seaso

n

Spatial reconstructions of peak ENSO signal (3.5 yr)

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

Time-domain reconstructions

Tourre et al. 2001

Spatial reconstructions of cold-season quasidecadal (11 year) signal

Spatial reconstructions of warm-season bidecadal (24 year) signal

coincident with peak domain wet

Time-domain reconstructions

Schubert et al. 2004

Spatial reconstructions of warm-season bidecadal (24 year) signal

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 warm-season 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

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

Climate studies

22 )(/)ˆ(0.1 ciii xxxxRE 22 )(/)ˆ(0.1 viii xxxxCE

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

• 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.

Defination of SLP-related indices

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.

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

U.S. drought reconstructions

Method (RegEM):