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.
Understanding the mechanisms that drive climate variability, particularly
before the modern instrumental record (ca. 1900 CE in the United States),
depends on proxy-based reconstructions of climate. Precisely dated tree-ring
chronologies are one of the primary proxies that can reconstruct
interannual climate variability over recent centuries to millennia
(Fritts, 1976). Tree rings provide robust historical and prehistorical
context for droughts and pluvials (wet periods) captured in the instrumental
record throughout the midlatitudes (e.g., Stahle and Cleaveland, 1994; Woodhouse and Overpeck, 1998; Cook et al., 2010; Fang et al., 2010; Chen et al., 2013; Pederson et al., 2013;
Güner et al., 2017; Oliver et al., 2019; Morales et al., 2020). Most of our understanding of past drought severity and variability in North America is the result of the North American Drought Atlas (NADA; Cook et al., 1999). The NADA comprises a network of tree-ring chronologies across North America from the International Tree-Ring Data Bank (ITRDB;
While the NADA and LBDA have provided invaluable information of past droughts and pluvials in North America, they were generated to compare large, sub-continental events. The reconstruction at each grid cell uses tree-ring data that are within a 450 km radius of that grid point. By pulling from such a wide range of predictors, the NADA and LBDA models excel at representing large-scale hydroclimate variability as they tend to average out smaller-scale features. However, these drought atlases may not represent local conditions in areas with sparse coverage of tree-ring chronologies, such as certain regions of the midwestern US (Maxwell and Harley, 2017; Strange et al., 2019). The tree-ring chronologies from the ITRDB can have biases related to tree species used and the spatial density of the tree-ring network (Zhao et al., 2019; Coulthard et al., 2020). When collecting tree-ring data for the purpose of reconstructing climate, the general goal is to target long-lived species that are sensitive to the climate variable to be reconstructed while also maximizing the length of the reconstruction. However, inclusion of multiple species in a reconstruction can improve model performance and skill (Pederson et al., 2001, 2013; Frank and Esper, 2005; Cook and Pederson, 2011; Maxwell et al., 2011, 2015). In the US, the ITRDB has excellent spatial replication in certain regions, such as the American Southwest, but other regions are poorly represented, such as the Ohio River valley (ORV; Zhao et al., 2019). Due to changes in the density of the tree-ring network of the ITRDB and the use of a large radius (450 km) to reconstruct drought for the LBDA, soil moisture variability at local scales is potentially absent in areas that are underrepresented in the tree-ring network. Further, many of the chronologies that are available on the ITRDB were collected in the 1980s and have not been updated, limiting the range of climatic conditions to calibrate reconstruction models (Larson et al., 2013; Zhao et al., 2019).
The wealth of climate information derived from tree rings is based on the
key assertion that their physiological development is related to specific
climatic conditions. An explicit relationship between climate and tree
growth can be estimated during the instrumental period. Yet, developing a
reconstruction assumes that this climate–tree growth relationship is
stationary over time. This assumption was generally true in the early
development of the field of dendrochronology (Fritts, 1976). However, as
human activities drive the Earth's climate system into historically
unprecedented and potentially non-stationary and non-analogous conditions
(Milly et al., 2008), exceptions to this assumption have emerged. Changes in the
drought signal recorded by tree rings have been established only recently in
the eastern US (Larson et al., 2013; Maxwell et al., 2015, 2016, 2019; Helcoski et al., 2019),
making an investigation of its causes essential to ensuring the
interpretability of tree-ring-based hydroclimate reconstructions. Of these
recent studies, Maxwell et al. (2016) provided the first documentation of an
apparent deteriorating relationship between radial tree growth and summer
soil moisture that is not accompanied by an increase in signal strength
during another season. The declining relationship – referred to as the
“Fading Drought Signal” – was consistent across multiple species and sites
within the Central Hardwoods Forest region of the midwestern US. However,
Maxwell et al. (2019) found that
For the LBDA, Cook et al. (2010) created a gridded instrumental dataset of PMDI to
calibrate tree-ring reconstruction models. The instrumental data were
created using observations for temperature and precipitation from over 5000
and 7000 weather stations, respectively, which were spatially interpolated
with a trivariate thin-plate spline in the ANUSPLIN program (Hutchinson,
1995). Cook et al. (2010) derived the reconstructions by gathering standardized
tree-ring chronologies within 450 km of each instrumental grid point center.
However, because the LBDA was developed across North America, Cook et al. (2010)
used a dynamic search radius, with the requirement of having a minimum of
five chronologies as possible predictors; so in certain regions, the radius
was larger than 450 km. Therefore, in sparsely covered areas such as the
ORV, the actual search radius for the LBDA could be larger than 450 km.
Chronologies that were significantly (
Map of
To examine how the density of the tree-ring network could impact the
reconstruction, we gathered recently published chronologies and collected
new chronologies across the ORV to fill the spatial gaps of the ITRDB
(Fig. 1; Table S1 in the Supplement). For the new chronologies, we either (1)
updated existing chronologies from the ITRDB, (2) sampled new co-occurring
species at an ITRDB site, or (3) created new chronologies from previously
unsampled sites. For this study, we used a total of 72 chronologies across
15 species. Of these chronologies, 37 were published, 3 were newly
updated ITRDB records, and 32 were new collections (Fig. 1;
Table S1 in the Supplement). For the new (
For all chronologies, we removed both age-related growth trends and non-climatic influences of tree growth (e.g., forest dynamics or insect outbreaks) by using signal-free standardization (Melvin and Briffa, 2008) with a two-thirds smoothing spline applied to each measured series (Cook and Peters, 1981). To ensure we achieved the desired spline flexibility of the two-thirds spline in the standardization, we used the approximation suggested by Bussberg et al. (2020) and used an 83 % spline to account for endpoint adjustments. We stabilized the variance of the standardized chronologies using the data-adaptive power transformation (Cook and Peters, 1997). Signal-free standardization can reduce “trend distortion” problems near the ends of the record (Melvin and Briffa, 2008). We trimmed each chronology to remove the portion of the record where low sample depth inflated the variance in standardized growth using an expressed population signal (EPS) value of 0.80 (Wigley et al., 1984).
We replicated the point-by-point regression procedure for the LBDA in Cook et al. (2010) and described in Cook et al. (1999) for the ORV tree-ring network. We developed a network of
To examine how increasing the density of the tree-ring network influences
the reconstruction, we gathered tree-ring chronologies within a 250 km
radius from the center of each grid point instead of the 450 km minimum
radius used for LBDA. For the ORV gridded reconstructions, the use of a
250 km radius ensured that each gridded reconstruction could have at least
five chronologies as possible predictors (Fig. S1 in the Supplement). For each
grid point, we built a reconstruction model by taking the screened
standardized chronologies and using both the current year (
We used Pearson's correlation to compare the reconstructed PMDI values from the LBDA to the ORV reconstruction at each grid point. We further chose well-known drought and pluvial years in the instrumental period to examine how the ORV and LBDA compared spatially. Specifically, we examined the droughts of 1988, 1954, 1936, 1816, and 1774 and the pluvials of 1945–1951, 1882–1883, and 1811 (Trenberth et al., 1988; Stambaugh et al., 2011; Heim, 2017). To compare the reconstructions with the instrumental data, we calculated the mean absolute error for each extreme event. We also correlated the instrumental PMDI at each grid point to every other grid point and then examined those correlations as a function of distance. Similarly, the reconstructed PMDI values were correlated for each grid point for the ORV and LBDA and compared across distance. To examine the species contribution to the overall ORV reconstruction, we gathered the correlation of each species chronology to the PMDI for each grid reconstruction that the given species were included.
To determine if the ORV and LBDA reconstructions had differences in the
amount of extreme hydroclimatic conditions, we calculated the number of
years in each gridded reconstruction that had a JJA PMDI value of
To examine the temporal stability of the relationship between tree growth
and PMDI, we followed the same validation procedures used for the LBDA (Cook et al., 2010). We used the early half of the common period (1901–1955) to calibrate a model between tree growth and PMDI to validate the late half
(1956–2010). We used two tests of fit, the reduction of error statistic
(RE) and the coefficient of efficiency (CE; Fritts, 1976; Cook et al., 1999), to validate our calibration models. RE and CE both range from
To examine how validation statistics may change based on when the trees were
sampled, we created a second ORV reconstruction where the most recent year
was 1980. This year was chosen because several chronologies available on the
ITRDB were sampled in the 1980s, and this marked the beginning of a
weakening relationship between radial growth and soil moisture in this
region (Maxwell et al., 2016). We used the same validation process described above
except the early period was from 1901 to 1940 and the late period was from
1941 to 1980. We then calculated the difference between the 1980 and the
2010 reconstruction for
Our first comparisons of chronologies distributed for the LBDA and ORV
networks revealed broad spatial discrepancies. PMDI point-by-point
regressions for the LBDA included 20 chronologies from 6 species over the
study region, whereas the ORV network included 72 chronologies from 15 tree
species. Not only is the spatial density of sites sparser for the LBDA
network, but it mostly only included single-chronology sites, whereas 18 of
the sites included in the ORV are multiple-species sites (two to six co-occurring
species) (Figs. 1a and b). Although site coverage is sparse for both networks
along the west-central, northwest, and southeast sectors, the ORV network
included major spatial coverage improvements in other sectors (Fig. 1).
The ORV particularly increased spatial coverage in south-central Indiana,
where many of the sites included four to six co-occurring species
chronologies (
Map of correlation values between the LBDA and ORV reconstruction during the period of 1830–2005 CE. The correlations of each grid shown in the map are all significant at the 0.05 level. The black cells represent locations over the Great Lakes, and therefore no data are available for correlation analysis.
The ORV reconstructions were shorter in length (maximum of 343 years) compared to the LBDA reconstructions (maximum of 1645 years) due to needing numerous old chronologies to load into each grid reconstruction. While this is true for the LBDA, having a larger search radius allows a longer chronology to be included in many gridded reconstructions. A smaller search radius for chronology inclusion requires a denser network of longer chronologies to reach a similar length as the LBDA. Secondly, we focused on increasing the spatial density of the network, which resulted in sampling younger sites (e.g., the earliest years are in the early to late 19th century). While the ORV reconstructions were shorter, comparing certain well-known extreme climatic years during the period of the overlap between the LBDA shows some important differences.
We chose a series of well-known drought and pluvial years (events) to compare the reconstructions between ORV and LBDA. In general, the increased spatial density of tree-ring chronologies used in the ORV reconstruction displayed more local variation in the reconstructions of extreme climatic events (Fig. 3). However, in a few examples, such as those of 1774 and 1816, the spatial pattern of where extreme drought was located changed between the two reconstructions (Fig. 3). Using extreme events in the observed record (three droughts and one pluvial), both the ORV and LBDA underestimated wet and dry extremes. However, the ORV reconstruction better matched the distribution of soil moisture values and the spatial patterns of the instrumental data, particularly for the extreme values, compared to the LBDA reconstruction (Fig. 4; Figs. S2–4 in the Supplement). For droughts, the ORV consistently had lower mean absolute errors (differences ranging from 0.21 to 0.41) compared to the LBDA (Fig. 4; Figs. S2–S4 in the Supplement). However, for the pluvial event, the two reconstructions had similar mean absolute errors (difference of 0.03) with the LBDA being slightly smaller (Fig. S4 in the Supplement). When examining the correlation in PMDI (instrumental or reconstructed) between all grid points as a function of distance, the ORV better matched the instrumental PMDI with a steeper decline in correlation across distance compared to the LBDA (Fig. 5). The LBDA showed the most spatial autocorrelation with a gradual decrease in correlation across distance, while the instrumental had the least spatial autocorrelation with a lower correlation between close grid points and more variability (Fig. 5). The ORV better matched the overall pattern and variability of the instrumental PMDI across distance but had more spatial autocorrelation (Fig. 5).
Spatial comparison of the ORV (left column) and the LBDA (right column) of reconstructed PMDI during years that experienced hydroclimatic extremes. Red cells represent below-average PMDI and blue cells represent above-average PMDI. Black cells represent no data, either due to being over water or from not having at least five chronologies to create a reconstruction.
In general, the probability distribution function (PDF) of the ORV
reconstruction had a lower occurrence (densities of 0.17 compared to 0.23)
of near-average years but higher densities (differences ranging from 0.01 to
0.05) for extremes, particularly drought, compared to the LBDA (Fig. 6).
The ORV distribution was nearly identical to the instrumental, while the LBDA
had lower densities of extremes (Fig. 6). Similarly, the ORV had a larger
number of reconstructed drought (median difference of 9 years) conditions
that better matched the instrumental record. The pluvial conditions were
closer between the three datasets, with the LBDA having the highest median
and the instrumental the lowest median (Fig. 6). Due to the larger number
of extreme drought years, the ORV reconstructions had more frequent flips
according to the flip index values compared to the LBDA (Fig. 7). The
central and southeastern portions of the region, in particular, showed a
greater number of wet, dry, and total flips, resulting in
Maps showing PMDI values for the instrumental data, ORV, and LBDA reconstructions for the year 1954. The histogram represents the frequency of PMDI values for the instrumental, ORV, and LBDA PMDI values. The mean absolute error values show that the ORV reconstruction more accurately matches the instrumental data compared to the LBDA reconstruction. Black grids represent areas over water and therefore, no data.
Average correlation coefficients between PMDI values across all grid points as a function of distance. LBDA and ORV are reconstructed PMDI values.
Maps of the number of wet flips
With the highest average correlation values,
Correlation values between species chronologies and PMDI for the
gridded reconstruction models. The “
Comparing how well each reconstruction model represented the instrumental
data, we find that the variance explained (
Comparison of the calibration (1901–1955) and validation (1956–2010) statistics between the ORV (left column) and LBDA (right column) reconstructions. Difference represents LBDA values subtracted from ORV. Black cells represent values over water and therefore no data.
Previous work has shown that radial growth from trees in the south-central
portion of the region are becoming less sensitive to soil moisture compared
to earlier time periods (Maxwell et al., 2016). The comparison between a
point-by-point reconstruction that ended in 1980 to a reconstruction that
ended in 2010 demonstrates that while the calibration
Maps of the difference between the ORV reconstruction when ending
the calibration period in 2010 compared to 1980 (i.e., ORV
Tree rings have long been used to provide an historical context to
hydroclimatic extremes (Stahle and Cleaveland, 1994; Woodhouse and Overpeck,
1998; Cook et al., 1999, 2010; Pederson et al., 2013). However, in some regions
of the US, the tree-ring sites are sparsely distributed, and it is unknown
what kind of impact that has on the representation of past climate. Due to
the higher density of tree-ring chronologies and the smaller search radius
(250 km for the ORV compared to 450
In addition to the increase in spatial variability of extremes that we find, previous work suggests that increasing the density of the tree-ring network can uncover previously unknown droughts and pluvials at more local scales (Maxwell and Harley, 2017; Strange et al., 2019; Pearl et al., 2020). Here, we find the support of better-localized representations of extremes by increasing the density of the tree-ring network, with the ORV having a larger number of droughts and pluvials compared to the LBDA (Fig. 6). The increase in extremes has important implications on the long-term variability of past hydroclimate and to the interannual volatility of PMDI. Recent work has shown increases in interannual volatility has important impacts on agriculture (Locke et al., 2017) and social and ecological systems (Casson et al., 2019). Our finding suggests that in areas with a sparse tree-ring network, such as in the ORV, tree-ring reconstructions underestimate extremes, and therefore volatility in extremes is also underestimated. By increasing the density of the network and better representing localized extremes, we find a higher number of flips (Fig. 7). The better representation of localized extremes results in a more accurate representation of past climatic volatility and can be used to better place current and future projected changes into context. With gridded reconstructions of both soil moisture and temperature becoming more common with the increase in available tree-chronologies (e.g., Anchukaitis et al., 2017; Morales et al., 2020; Pearl et al., 2020), we show the importance of valuing higher density from a larger range of species within the network in addition to the length of the chronologies.
Historically, soil moisture reconstructions from tree rings in the eastern
US have been dominated by a few species, such as
While increasing the spatial density of the tree-ring network allowed the reconstructions to more accurately capture the spatial variability of extreme conditions, the reconstruction models of the ORV have less predictive skill compared to those of the LBDA, especially during the verification period (Fig. 9). The two networks have some overlap in chronologies, but while the ORV has a higher density of chronologies within the Ohio River valley region, the LBDA can draw from more chronologies across a larger region. While the larger radius increases the number of samples in the model and could lead to more explained variance for the LBDA, the ORV reconstruction better spatially replicates extremes in the instrumental period (Fig. 4; Figs. S2–S4 in the Supplement).
Interestingly, the decrease in variance explained in the southern portion of
the region may not be attributable to differences in sample depth in the
tree-ring network. When using the same chronologies while ending the
calibration period at 1980 instead of 2010 for the ORV reconstruction, the
validation statistics compare very well with the LBDA. However, by updating
the chronologies to 2010, the
By increasing the density of the tree-ring network in a region that is poorly represented in the LBDA, we created a gridded PMDI reconstruction for the ORV region. We compared our gridded reconstruction with the LBDA and found that increasing the density of the tree-ring network resulted in an increase in localized hydroclimatic extremes that better match the spatial and temporal patterns of the instrumental data. However, calibrating our models with more recent data (up to the year 2010) resulted in a decrease in variance explained and validation statistics for the southern portion of the region. This region has not experienced extreme droughts recently, which is likely driving the decrease in model performance. Increasing spatial density of the tree-ring network is important to better represent localized extremes in the past, indicating that researchers should continue to target previously unsampled old-growth forests. Similarly, the time in which the trees are sampled is also important to model performance. Long periods without extreme hydroclimate variability can result in reconstruction models that are less representative of climatic conditions. We stress the need to update previously sampled chronologies to the current period so that longer calibration models can have the chance to better represent the range of sensitivity of trees rings to climate. Further, more work is needed to extend more of the ORV chronologies to better represent climate further in the past. Targeting wood from historical structures and combining with surrounding living chronologies of the same species could be one way of achieving longer chronologies in this region (Harley et al., 2011; Matheus et al., 2017). Overall, we find that a higher spatial density of the tree-ring network will improve the local representation of reconstructed climate. However, more work is needed to better quantify how the strength of the relationship between tree growth and climate varies through time.
All reconstructions will be uploaded onto the NOAA paleoclimate page. All tree-ring chronologies used in this paper will be uploaded to the International Tree-Ring Databank.
The supplement related to this article is available online at:
JTM and GLH designed the methods of the paper. JTM performed analyses with feedback from GLH. TJM, BMS, KVK, and TFA helped develop tree-ring chronologies with assistance from JTM and GLH. All authors contributed to data collection and the preparation of the manuscript.
The authors declare that they have no conflict of interest.
We would like to thank James Dickens, James McGee, Josh Oliver, Karly Schmidt-Simard, Brynn Taylor, Michael Thornton, Senna Robeson, Matt Wenzel, and Luke Wylie for their assistance in the field and the laboratory.
This research has been supported by the USDA Agriculture and Food Research Initiative grant (grant no. 2017-67013-26191) and the Indiana University Vice-Provost for Research Faculty Research Program (FRSP grant).
This paper was edited by Hans Linderholm and reviewed by two anonymous referees.