Summer temperature variability since 1730 CE across the low-to-mid latitudes of western North America from a tree ring blue intensity network
Introduction
The instrumental temperature record of the past ca. 120 years is too short for contextualizing recent temperature trends over longer timescales. Paleoclimate reconstructions, particularly derived from tree ring (TR) records, provide valuable estimates of past temperature variability that extend beyond the observational period (Jones et al., 1998; Mann et al., 1999, 2009; Wahl and Ammann, 2007; Christiansen and Charpentier-Ljungqvist, 2012; Cook et al., 2013; Linderholm et al., 2015; Esper et al., 2018). Currently, the Northern Hemisphere (NH) contains numerous regions that are underrepresented by the coverage of long (e.g. multi-century) TR-based paleoclimate proxies (Wilson et al., 2016; Anchukaitis et al., 2017; Köse et al., 2017). This study addresses the develop-ment of a collection of TR-derived proxy records in a region underrepresented by updated paleo-temperature records—the temperate zone of western North America—and presents a substantial improvement to the spatial coverage of paleo-temperature TR proxy record coverage across the NH paleo-network.
Within the family of paleoclimate proxy records, tree rings are valuable because they provide exactly-dated, well-replicated and sub-annually-resolved data that can extend back in time for multiple millennia, allowing for the analysis of low-frequency variability and trends (Cook and Briffa, 1990; Briffa et al., 2004). As novel dendrochronological techniques are developed and refined, tree rings have become one of the most important sources of late Holocene paleoclimate information, especially in the context of providing information about past hydroclimate and temperatures over increasingly longer time periods and across broader areas of the NH (D'Arrigo et al., 2006; Cook et al., 2007; Schneider et al., 2015; Stoffel et al., 2015; Cook et al., 2015; Wilson et al., 2016; Anchukaitis et al., 2017; Esper et al., 2018; Cook et al., 2020). Most large-scale temperature reconstructions are based on either a single index, such as one hemispheric or global mean derived from many points (e.g. Frank et al., 2010; Masson-Delmotte et al., 2013) or a spatially-resolved climate field reconstruction, emphasizing regionality before the calculation of large-scale means (e.g. Tingley et al., 2012; Anchukaitis et al., 2017).
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, 2019; Wilson et al., 2016; Christiansen and 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. function of distance decay), especially in places where the data network is spatially heterogeneous or sparse. The concurrent assumptions that [1] proxies must be robust estimators of local temperature and [2] the large-scale mean is well-represented by a network of local temperature datasets (Christiansen and Ljungqvist, 2017) may not be well-maintained as the number of records decreases back in time. Relationships between local and NH mean temperatures are largely dependent upon geography, thus, the correspondence of interannual local temperature variability to NH mean temperatures varies across space (Christiansen and Ljungqvist, 2017). To account for regional variability that is often muted in large-scale reconstructions, finer-scale models offer the benefits of more accurately characterizing local-to-regional scale climate variability and geographic expressions of atmospheric circulation patterns, radiative forcing, and ocean-atmosphere variability (Anchukaitis et al., 2017).
Across the NH, TR-derived temperature reconstructions are most highly concentrated at high latitudes (>50°N), where temperature is expected to be the most limiting factor on tree growth (e.g. Fritts, 1976; Jacoby and D’Arrigo, 1989; Briffa et al., 1992, 2001; Anchukaitis et al., 2013; Wilson et al., 2014; Björklund et al., 2014; Rydval et al., 2014; Linderholm et al., 2015; Björklund et al., 2015; Wilson et al., 2017; Rydval et al., 2017; Fuentes et al., 2018; Wilson et al., 2019; Björklund et al., 2019). At high latitudes, spatially-resolved TR proxies have been applied successfully for the evaluation of past temperature forcing by vol-canism (Anchukaitis et al., 2017; Edwards et al., 2021) and the timing and amplitude of past cool and warm events such as the Medieval Climate Anomaly (MCA), the Little Ice Agre (LIA), and the 20th-21st century warming trend (D'Arrigo et al., 2006; Schneider et al., 2015; Wilson et al., 2016). The same principle of temperature as a limiting factor for trees at high-latitudes (e.g. D’Arrigo et al., 2001; Gervais and MacDonald, 2001; Porter et al., 2013), also applies in high-elevation zones of low-to-mid-latitude, montane environments.
Recent progress to broaden the latitudinal extent of the NH TR temperature proxy network to the lower latitudes (< 45°N, e.g. Briffa et al., 2001; Büntgen et al., 2008; Dorado Liñan et al., 2012; Büntgen et al., 2017; Heeter et al., 2019; Esper et al., 2020; Reid and Wilson, 2020; Heeter et al., 2020; Harley et al., 2021; Büntgen et al., 2005; Fan et al., 2009; Buckley et al., 2018) can be attributed to [1] an increased number of investigations of high-elevation, temperature-sensitive trees, and [2] the development and application of additional TR metrics other than tree ring width (TRW) such as maximum latewood density (MXD; Schweingruber et al., 1978) and blue intensity (BI; McCarroll et al., 2002). These studies demonstrate that when TRW serves as a weak temperature predictor due to complex climate-growth relationships at lower latitudes (<40°N) (George and Ault, 2014; Fritts, 1976; Wilson et al., 2016; Büntgen et al., 2008; Reid and Wilson, 2020), ring-density parameters (e.g. MXD and BI) can still be strongly representative of local to regional temperatures.
Over the last decade, BI-derived temperature proxies have become important additions to the MXD and TRW temperature proxy network across the NH. BI uses the light absorbance properties of wood compounds that comprise the cell walls (e.g. lignin) to obtain a measure of raw light reflectance across the earlywood and latewood zones of an annual growth ring. Examination of minimum BI by McCarroll et al. (2002) showed the latewood reflectance exhibited a strong, negative relationship with MXD (r = −0.95, p <0.01), and thereby were the first to suggest that BI could be an important and effective surrogate for MXD to examine annual to decadal-scale changes in temperature. As raw BI measurement data are inversely correlated with MXD (i.e. a dense, dark late-wood will express low reflectance), current protocol inverts the raw latewood BI (latewood blue intensity; LWB) to allow the same detrending procedures to be used for both LWB and MXD data (Wilson et al., 2014; Rydval et al., 2014). As such, the LWB metric response to summer maximum temperature (Tmax) is typically very similar to that of MXD. Aside from exhibiting stronger, positive relationships with instrumental summer temperature data than TRW (e.g. Wilson et al., 2014), both MXD and LWB are shown to exhibit less signal contamination from biological memory of non-climatic factors and express similar auto-correlative properties to the instrumental data (Esper et al., 2014; Rydval et al., 2018; Lücke et al., 2019).
Continued efforts by the paleoclimate community to develop and archive robust temperature-proxy data are apparent by chronology network syntheses (e.g. Briffa et al., 1988, 1992; Schweingruber et al., 1993; Schweingruber and Briffa, 1996), and most recently, the creation of NTREND-2016, a publicly available multi-TR-proxy dataset (Fig. 1), as well as the PAGES 2K multi-proxy dataset (Consortium et al., 2017). While the spatial representation of paleo-temperature proxies in many parts of the low- and mid-latitudes of the NH continues to improve, many of these pre-existing proxies do not include the most recent period (e.g. Briffa et al., 2001); currently only a few TR based temperature reconstructions from the low-to-mid-latitudes of the NH extend past ca. 1990. As such, many of these records cannot be calibrated with instrumental climate data over the last ca. 20−30 years—a period characterized by unprecedented and extreme climatic trend. Therefore, temperature-sensitive records that extend to the most recent period are valuable, because they provide a more complete understanding of climate-tree growth relationships and past climate variability (Larson et al., 2013).
To date, few TR-based temperature reconstructions, which characterize historical regional temperature variability, exist for the western continental United States (US) (Douglas and Stockton, 1975; Graumlich and Brubaker, 1986; Briffa et al., 1992; Graumlich, 1993; Biondi et al., 1999; Salzer et al., 2014a; Heeter et al., 2020, 2021, 2021; Martin et al., 2020). However, most of these records end prior to ca. 2000, and thus do not contextualize recent warming trends. The western US is a region within the NH where improvements to the temperature proxy network are needed both spatially and temporally. In this paper, we present a temperature-sensitive TR proxy network comprised of LWB records from 26 sites across the western US. We use this new network to produce 4 regional reconstructions of growing-season Tmax that represent the spatial temperature variability not only across the low-to-mid latitudes of the western US, but also throughout southern Canada and northern Mexico. Temporally, these regional reconstructions include the most recent decade (post 2010 CE) and span several hundred years back in time. The temporal span of these reconstructions is valuable, because they track recent trends in regional temperature and their multi-century contextualization. Further, we emphasize the potential for BI parameters to fill data gaps in other low-to-mid-latitude regions globally.
Section snippets
Study location
Data for this study are derived from a network of 26 sample sites across various regions of the western continental US (Fig. 1; Table 1). All site-level TR chronologies are derived from living Engelmann spruce (Picea engelmanni Parry ex Engelm.). Sites were visited between 2001 and 2020, with the majority of samples collected during the summers of 2017 and 2018. At each site, we used a hand-held increment borer to extract cores from 10 to 25 living trees, with 2 cores taken per tree at 1.3 m
LWB as a temperature proxy in western North America
The preliminary response of the individual LWB chronologies and their TRW counterparts to local temperature data consistently shows that LWB chronologies exhibit a stronger relationship with current-year growing (warm) season temperatures than TRW (Figure S1). With the exception of a few sites, the LWB chronologies in this network are not highly correlated with their respective TRW site chronologies (Table S1). While Salzer et al. (2014a) demonstrate that TRW can be a successful parameter for
Conclusions and future work
Using the LWB parameter, we provide 4 regional reconstructions of growing season Tmax over much of the western North America temperate zone spanning the past ca. 300−400 years to present. Strong calibration/verification statistics for each reconstruction model indicates that LWB is a robust predictor of growing-season Tmax, especially during late-summer. This LWB network provides paleo-temperature estimates which could further contribute increased understanding of the role of temperature on
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This project was supported by the National Science Foundation under BCS- 2012482, BCS-1759694, and AGS-2002524, the United States Forest Service, the University of Idaho, and Indiana University Institute for Advanced Studies. We would like to thank the following: Yellowstone National Park for facilitating access to the MWS and SLS sites and the Shoshone National Forest to the FLS and RPS sites, Dr. Lauren Stachowiak, Zach Merrill, Kyle Landolt, Dr. Jessie Pearl, April Kaiser, and Dr. Bryan
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