Sampling density and date along with species selection influence spatial representation of tree-ring reconstructions

Abstract

Abstract. Our understanding of the natural variability of hydroclimate before the instrumental period (ca. 1900 CE in the United States) is largely dependent on tree-ring-based reconstructions. Large-scale soil moisture reconstructions from a network of tree-ring chronologies have greatly improved our understanding of the spatial and temporal variability in hydroclimate conditions, particularly extremes of both drought and pluvial (wet) events. However, certain regions within these large-scale network reconstructions in the US are modeled by few tree-ring chronologies. Further, many of the chronologies currently publicly available on the International Tree-Ring Data Bank (ITRDB) were collected in the 1980s and 1990s, and thus our understanding of the sensitivity of radial growth to soil moisture in the US is based on a period that experienced multiple extremely severe droughts and neglects the impacts of recent, rapid global change. In this study, we expanded the tree-ring network of the Ohio River valley in the US, a region with sparse coverage. We used a total of 72 chronologies across 15 species to examine how increasing the density of the tree-ring network influences the representation of reconstructing the Palmer Meteorological Drought Index (PMDI). Further, we tested how the sampling date and therefore the calibration period influenced the reconstruction models by creating reconstructions that ended in the year 1980 and compared them to reconstructions ending in 2010 from the same chronologies. We found that increasing the density of the tree-ring network resulted in reconstructed values that better matched the spatial variability of instrumentally recorded droughts and, to a lesser extent, pluvials. By extending the calibration period to 2010 compared to 1980, the sensitivity of tree rings to PMDI decreased in the southern portion of our region where severe drought conditions have been absent over recent decades. We emphasize the need of building a high-density tree-ring network to better represent the spatial variability of past droughts and pluvials. Further, chronologies on the ITRDB need updating regularly to better understand how the sensitivity of tree rings to climate may vary through time.

The 1820s Marks a Shift to Hotter‐Drier Summers in Western Europe Since 1360

“…Additionally, we also found a generally consistent centurial fluctuation between summer SM in WE and summer PDSI in central Europe (Figure S9a in Supporting Information S1, Büntgen et al, 2021) as well as summer PDSI reconstruction in WE from Old World Drought Atlas (OWDA) (Figure S9b in Supporting Information S1, E. R. Cook et al, 2015), despite they exerted some disparities particularly on the annual timescale, possibly because of the different investigating regions or reconstructed variables. Though our δ 18 O TR -based SM reconstruction shows satisfactory regional representation (Figure S3 in Supporting Information S1), a denser tree-ring network is encouraged to detect more local variability of SM in WE and other regions (Alexander et al, 2019;Maxwell & Harley, 2017;Maxwell et al, 2020).…”

Warm springs alter timing but not total growth of temperate deciduous trees

“…The tree-ring records from our focal sites were complemented with a much larger collection spanning 106 deciduous and mixed forest sites in Eastern North America 26,65 . Again, records were limited to broadleaf deciduous species with clearly defined xylem porosity (i.e., excluding semi-ring porous).…”

Summer temperature variability since 1730 CE across the low-to-mid latitudes of western North America from a tree ring blue intensity network

“…A synthesis of continental-to hemispheric-scale temperature reconstructions indicates a coherent, unprecedented increase of surface air temperatures within the last century (Mann et al, 1999;Ahmed et al, 2013;Masson-Delmotte et al, 2013). While such large-scale, single-point mean climate indices provide robust, large-scale estimates for attribution studies (Zhai et al, 2018;Stott et al, 2010), they do not perform well for examining regional-scale (100-500 km) temperature variability and relationships with internal modes of climate variability (Neukom et al, 2014;Wilson et al, 2016;Neukom et al, 2019;Christiansen & Ljungqvist, 2017;Maxwell et al, 2020). The challenges associated with the accuracy and reliability of large-scale temperature reconstructions could be due to changes in strength of the predictor-predictand relationship across geographic space (e.g.…”