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 1.Extended the drought record father back in time with dendroclimatic reconstructions of summer drought (PDSI) patterns over the conterminous U.S back to Extended the atmospheric circulation record back in time through proxy-based reconstructions of boreal cold- and warm-season global SLP patterns back through the 17 th 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 2 lat. 3 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 Proxy- Based Reconstructions of Past Climate, Journal of Climate, 18, , 2005. PDSI dataset missing data need to be recon. PDSI gridpoints Tree-ring chronologies 1700yr To calculate the reconstruction scores, we only used part of the available instrumental data for calibration ( ) and keep some instrumental data ( ) free for verification. For final reconstruction, we employed all available instrumental data. Code was fromu/~tapio/imputation/. 1895yr 1927yr 1978yr U.S. drought reconstructions Present years past years ional PDSI) Mean=.3614 U.S. drought reconstructions Time series of regional and domain mean drought back to s Dust Bowl RegEMCook et al. The spatial patterns of reconstructed U.S. drought based on RegEM The spatial patterns of reconstructed U.S. drought based on RegEM 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 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(dens e tree- ring) Reconstructing high-frequency climate Full proxies Global SLP reconstructions Boreal warm-seasonBoreal cold-season Spatial verification scores Boreal warm-seasonBoreal cold-season Verification using long-term European SLP data(Luterbacher et al.,2002) 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 Luterbacher: 0.43 Cook: 0.37 Vinther: 0.31 Jones: 0.83 Analysis of Modern Relationship between Patterns of SLP and U.S. Drought ( ) 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. ENSO signal ENSO signal Bi-decadal signal Spatial reconstructions of peak ENSO signal ( 5-yr) coincident with peak positive ENSO (TNH) extratropical teleconnection pattern (Livezey and Mo 1987) Cold-seasonWarm-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 data Domain mean Great plains South west Schubert et al 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 ( ) MTM-SVD spectra (recon. data) Weak ENSO Weak ENSO 99% sign. ENSO signal Bi-decadal signal Mann % sign. ENSO signal Quasi-decadal signal coincident with peak positive ENSO (TNH) extratropical teleconnection pattern (Livezey and Mo 1987) Spatial reconstructions of peak ENSO signal ( 3.5 yr) Cold-season Warm-season 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 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 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 19 th 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 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):