Reconstructing the Climate of North America During the Past 2,000 Years Using Pollen Data

Abstract

July temperature (TJUL) and total annual precipitation (ANNP) are reconstructed to better understand the spatial and temporal patterns of change in North America over the last 2,000 years using pollen databases. Using a customized application in R, the reconstructions use a composite averaging of multiple site reconstructions that show a distinct warmer Medieval Warm Period (MWP) compared to the colder Little Ice Age (LIA). Results show that, both multi-centennial scale periods are re- constructed as cooler than the last 50 years. Regional time series from several forested ecoregions show positive anomalies up to 0.6ºC during the MWP and anomalies up to -0.3ºC during the LIA. In order to test whether the TJUL reconstructions are biased to the modern calibration climate data, we show a distinct difference between the reconstructions when using station versus reanalysis-based modern TJUL fields. Reconstructions using station-based modern calibration data sets better reflect the centennial to multi-centennial scale climate variability as compared to the reanalysis-based modern calibration data sets that reveal a warm-bias. We justify the choice of the Whitmore et al. (2005) modern data set for large-scale pollen-based paleoclimate reconstructions. Finally we use Local Indicators of Spatial Association (LISA) to spatially filter the ANNP reconstructions in order to distinguish regional hydroclimate patterns from local site-specific conditions. Results show that a La Nina, positive North Atlantic Oscillation (+NAO) and positive Atlantic Multidecadal Oscillation (+AMO) state-like dominated both the MWP and Roman Warm Period (RWP), although the MWP was generally drier. In contrast, the Dark Ages Cold (DAC) period was likely dominated by El Nino, negative NAO and negative AMO state-like circulation. Minimum solar and high volcanic activity is likely to have contributed to more complex hydroclimate regional patterns during the LIA. The results presented in this dissertation can be used as benchmark data sets for future climate data-model comparisons in order to improve our understanding of natural climate variability during the past 2,000 years in the context of modern human-induced climate change.