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David Meko
,
Edward R. Cook
,
David W. Stahle
,
Charles W. Stockton
, and
Malcolm K. Hughes

Abstract

A network of 248 tree-ring chronologies in the conterminous United States is assembled and analyzed by rotated principal components analysis (RPCA) to delineate “regions” of common tree-growth variation during the period 1705–1979. Spatial continuity of the tree-ring data is summarized by variogram analysis, and tree-ring data are gridded before RPCA to reduce effects of site clustering. Principal component drought information is evaluated by comparing PC scores and primary pattern coefficients with Palmer Drought Severity Index (PDSI) data from instrumental records.

High PC pattern coefficients group geographically into regions coinciding roughly with nine drought regions delineated by RPCA of PDSI by other researchers. The drought signal as measured by the correlation between tree-ring PC scores and July PDSI, 1929–79, is strongest in the South and the interior West (r>0.7), and weakest in the Northeast and Pacific Northwest (r<0.16). A count of years with large negative PC scores in multiple regions marks the 1950s as the extreme in widespread drought across the southern United States to 1705.

Tree-growth regions are sensitive to whether tree-ring data are gridded before RPCA. Principal components on ungridded tree-ring data tend to center on dense clusters of sites. The importance of site density is most noticeable in the RPCA results for the southeast, where the gridded data yield a PC centered on a group of climate-sensitive but widely spaced bald cypress chronologies. Cross-validation indicates that gridding of tree-ring anomalies over different species for drought reconstruction is more appropriate in the semiarid southwest than in cooler, moister regions—especially the northeast and the Pacific Northwest. Our results endorse the large-scale chronology network as a long-term proxy for the spatial and temporal patterns of past drought across the United States.

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Henry F. Diaz
,
Ricardo Trigo
,
Malcolm K. Hughes
,
Michael E. Mann
,
Elena Xoplaki
, and
David Barriopedro

Developing accurate reconstructions of past climate regimes and enhancing our understanding of the causal factors that may have contributed to their occurrence is important for a number of reasons; these include improvements in the attribution of climate change to natural and anthropogenic forcing, gaining a better appreciation for the range and magnitude of low-frequency variability and previous climatic regimes in comparison with the modern instrumental period, and developing greater insights into the relationship between human society and climatic changes. This paper examine upto- date evidence regarding the characteristics of the climate in medieval times (A.D. ~950–1400). Long and high-resolution climate proxy records reported in the scientific literature, which form the basis for the climate reconstructions, have greatly expanded in the last few decades, with greater numbers of sites that now cover more areas of the globe. Some comparisons with the modern climate record and discussion of potential mechanisms associated with the patterns of medieval climate are presented here, but our main goal is to provide the reader with some appreciation of the richness of past natural climate variability in terms of its spatial and temporal characteristics.

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Michael E. Mann
,
Ed Gille
,
Jonathan Overpeck
,
Wendy Gross
,
Raymond S. Bradley
,
Frank T. Keimig
, and
Malcolm K. Hughes

Abstract

The recent availability of global networks of annual or seasonal resolution proxy data, combined with the few long instrumental and historical climate records available during the past few centuries, make it possible now to reconstruct annual and seasonal spatial patterns of temperature variation, as well as hemispheric, global-mean, and regional temperature trends, several centuries back in time.

Reconstructions of large-scale global or hemispheric trends during centuries past can place the instrumental assessments of climate during the twentieth century in a longer-term perspective and provide more robust evidence regarding the roles of potential climate forcings over time. The reconstructed spatial patterns lead to important inferences regarding ENSO-scale variability, the spatial influences of climatic forcings, and the regional patterns that underlie large-scale climate variations. Here proxy-based annual global temperature pattern reconstructions described recently by Mann et al. are expanded upon. For the first time seasonally resolved versions of the proxy-reconstructed surface temperature patterns are presented, and the seasonal differences between key climate indices and patterns of variations are diagnosed. The reader is enabled to interactively examine spatial as well as temporal details (and their uncertainties) of yearly temperatures back in time for both annual-mean and seasonal windows. Annual and seasonal time histories of reconstructed Northern Hemisphere, Southern Hemisphere, and global-mean temperature are made available, as are time histories of the Niño-3 index describing El Niño–related variations, time histories for particular regions of interest such as North America and Europe, and time series for temperature variations in different (e.g., tropical and extratropical) latitude bands. Time histories for specific grid points are available along with their estimated uncertainties. Time histories for the different eigenvectors [i.e., the reconstructed principal components (RPCs)] are also available, along with the raw instrumental series, which underlie the temperature pattern reconstructions. For both the annual-mean and seasonally resolved temperature reconstructions, the reader can directly compare reconstructed patterns for different years, as well as the raw and reconstructed patterns during calibration and verification intervals, and view animated year-by-year sequences of reconstructed global temperature patterns. The statistical relationships between climate forcings and temperature variations are also analyzed in more detail, taking into account potential lagged responses to climate forcings in empirical attribution analyses.

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