Introduction
The Paleogene was the last major greenhouse period in Earth's history and is
characterized by extreme warming events and resultant biological shifts
(e.g., Greenwood and Wing, 1995; Wilf, 2000; Zachos et al., 2001, 2008;
McInerney and Wing, 2011), with prolonged warmth during the Early Eocene
Climatic Optimum (EECO) peaking from roughly 52 to 50 Ma (e.g., Zachos et
al., 2008; Hyland et al., 2017). The early Eocene in general is thought to
represent a warm and “equable” global climate state with high mean annual
temperatures (MATs; e.g., Wilf, 2000; Zachos et al., 2008), low mean annual
range of temperatures (MARTs; e.g., Wolfe, 1978, 1995; Greenwood and Wing,
1995), and low pole-to-Equator temperature gradients (LTGs; e.g., Spicer and
Parrish, 1990; Greenwood and Wing, 1995; Evans et al., 2018). While high MATs
during the Eocene now seems well established, the feasibility of “equable”
conditions defined by low MARTs and low LTGs is still in question as a result
of increasingly complex global climate models that are unable to reproduce
such conditions (e.g., Barron, 1987; Sloan and Barron, 1990; Sloan, 1994;
Huber and Caballero, 2011; Lunt et al., 2012).
Recent proxy work on Paleogene warm intervals and hyperthermals such as the
Paleocene–Eocene Thermal Maximum (PETM) has suggested that continental
interiors may maintain higher or near-modern MARTs during these periods,
implying that the “low seasonality” aspect of climate equability may not
be reasonable under all greenhouse conditions (e.g., Snell et al., 2013;
Eldrett et al., 2014). Despite this suggestion, it remains unclear whether
proxy estimates from other basins, regions, and greenhouse periods can be
reconciled with the range of feasible conditions provided by climate model
studies. Quantitative reconstructions of seasonality (MART) based on precise
proxy estimates of MAT, warm month mean
temperature (WMMT), and cold month mean temperature (CMMT) could help to
resolve some of these model–proxy discrepancies by providing a robust and
well constrained set of seasonal observations for comparison to available
climate model outputs. Robust proxy reconstructions of seasonality are
crucial for understanding this aspect of past greenhouse equability (Lunt et
al., 2012; Snell et al., 2013; Peppe, 2013).
Seasonality estimates have previously been made using a variety of proxy
paleothermometers in isolation, and can now be made with higher confidence
using recently developed methods that target each of these individual
temperature parameters: MAT can be estimated using a paleosol
geochemistry-based thermometer, WMMT can be estimated using the carbonate
clumped isotope (Δ47) thermometer, and CMMT can be estimated
using a nearest living relative (NLR) floral coexistence thermometer. The
bulk major-element geochemistry of modern soils has been used to quantify
the effects of weathering processes via a wide range of geochemical indices
(see Sheldon and Tabor, 2009). The relationship between modern climate
parameters like temperature and indices such as salinization (Sheldon et
al., 2002), the paleosol weathering index (Gallagher and Sheldon, 2013), and
the paleosol-paleoclimate model (Stinchcomb et al., 2016) has led to the
development of climofunctions for MAT that have been used to estimate
paleo-MAT during the Cenozoic (e.g., Retallack, 2007; Takeuchi et al., 2007;
Bader et al., 2015; Stinchcomb et al., 2016). The clumped isotope (Δ47) thermometer is based on the temperature-dependent relative
enrichment of multiply substituted isotopologues of CaCO3
(13C18O16O2) within the solid carbonate phase, which is
independent of the isotopic composition of the water in which the carbonate
precipitated (e.g., Ghosh et al., 2006; Eiler, 2007). For pedogenic
carbonates in temperate regions, this growth temperature is linked to mean
warm season soil temperatures (e.g., Quade et al., 2013; Hough et al.,
2014), and has been used to estimate paleo-WMMT during the Cenozoic (e.g.,
Snell et al., 2013; Garzione et al., 2014). The NLR coexistence method has been developed based on the sensitive and
highly conserved collective modern cold temperature tolerances of related
floras to calculate cold month temperatures (e.g., Wolfe, 1995; Mosbrugger
and Utescher, 1997). Those relationships have been refined and used to
estimate quantitative paleo-CMMT during the Cenozoic (e.g., Greenwood et
al., 2005, 2017; Thompson et al., 2012; Eldrett et al., 2014; Utescher et al.,
2014).
Map and stratigraphy of the Green River Basin. (a) Map of
the region, showing major sedimentary basins and topographic highs. Stars
show proxy record sampling sites (paleosols in yellow, paleoflora in red),
and the dashed box is the sampling region for modern climate stations and the
downscaling domain for both models. CF is the Cordilleran fold-thrust belt,
UU is the Uinta uplift, WR is the Wind River uplift, OC is the Owl Creek
uplift, GM is the Granite Mountains, and FR is the Front Range.
(b) Simplified stratigraphy of the central to eastern GRB, showing
facies for the Green River Formation (GRF) and the equivalent and
interfingering Wasatch Formation (WF) based on the work of Smith et
al. (2015) and Hyland and Sheldon (2013). LY is the Lysitean, BF is the
Blacksforkian, LU is the Luman Member, NT is the Niland Tongue, TM is the
Tipton Member, WPM is the Wilkins Peak Member, LA is the Laney Member, RR is
the Ramsey Ranch Member, and CB is the Cathedral Bluffs Member.
Here we employ a multi-proxy approach using paleosol geochemistry, clumped
isotope, and floral NLR coexistence thermometry methods from the same
localities in order to address seasonality in the past, specifically
applying it to the issue of early Eocene greenhouse equability in the North
American continental interior. We estimate MAT, WMMT, and CMMT throughout
the EECO including both defined peak (∼51 Ma) and non-peak
conditions (e.g., Hyland et al., 2017), and compare the resultant proxy
estimates of temperature seasonality (MART) to the modern climate state of
the region, as well as to downscaled climate model predictions of
temperature seasonality during the Eocene and for future emissions
scenarios.
Methods
The targeted early Eocene locality is the Green River Basin (GRB) in
southwestern Wyoming (USA; Fig. 1). The GRB sequence is composed of a
series of terrestrial clastic rocks deposited during the early Eocene and
EECO as a result of Laramide synorogenic fluvial and lacustrine
sedimentation along the margin of endorheic paleo-lake Gosiute (e.g., Clyde
et al., 2001; Smith et al., 2008, 2010, 2015). Contemporaneous multi-proxy
records of peak and non-peak conditions during the EECO are from the
interfingering Wasatch Formation, primarily fluvial sandstones and paleosols
of the Ramsey Ranch and Cathedral Bluffs members, and Green River Formation,
primarily lacustrine shales and carbonates of the Wilkins Peak Member
(Fig. 1). The paleosols and pedogenic carbonates were sampled from the
Honeycomb Buttes near South Pass, Wyoming (42.24∘ N,
108.53∘ W; Hyland and Sheldon, 2013), while the floral assemblages
were sampled from the Latham coal (41.68∘ N, 107.88∘ W),
Sourdough coal (41.91∘ N, 108.00∘ W), Niland Tongue
(41.06∘ N, 108.77∘ W), and Little Mountain quarry
(41.28∘ N, 109.30∘ W) outside Rock Springs, Wyoming
(Fig. 1; Wilf, 1998, 2000).
Temperature proxies
Paleosol geochemistry
The bulk major-element geochemistry of modern soils (specifically B horizons)
has been used extensively to develop a number of composition–climate
relationships, including those predicted by the paleosol-paleoclimate model
(PPM1.0), which relates a broad suite of major element compositions
to mean annual temperature (among other factors) at the site of soil
formation (Stinchcomb et al., 2016). Stinchcomb et al. (2016) developed this
nonlinear spline model using the largest available geochemical dataset from
685 modern soils across North America in order to derive proxy relationships
between 11 major and minor oxides and MAT. This new proxy is calibrated over
a wider range of climatic conditions, soil types, and parent materials than
other available proxies (cf. Sheldon et al., 2002; Gallagher and Sheldon,
2013), and has been validated via independent comparisons in both modern
climosequences (Stinchcomb et al., 2016) and Miocene paleosols (Driese et
al., 2016). Following associated procedures, our bulk paleosol samples from
selected upper Bt horizons of defined Alfisols (described in detail by Hyland
and Sheldon, 2013) were prepared for major-element geochemistry by cleaning
and grinding to a homogenous powder. Samples were analyzed using lithium
borate fusion preparation and X-ray fluorescence (XRF) measurements at the
ALS Chemex Laboratory (Vancouver, BC, Canada), where analytical uncertainty
for analyses was maintained at less than 0.1 % for all elements, and
replicate analyses had a mean standard deviation of 0.8 % (Table S1 in
the Supplement). Resultant major- and minor-element data were not corrected
for loss-on-ignition (e.g., Stinchcomb et al., 2016), and were input into the
open-access PPM1.0 model, which produces “low”, “best” and
“high” MAT estimates; we present the “high” estimates as MAT here (see
Sect. 4.1 for explanation; Table S1). Broadly, soil geochemical proxies are
consistent with other paleoclimate proxies (e.g., paleobotanical; Sheldon and
Tabor, 2009, and references therein), and are more robust to diagenetic
alteration under a wide variety of burial conditions (Hyland and Sheldon,
2016).
Paleosol carbonate descriptions. (a) Paired transmitted
light and cathodoluminescence (CL) images of carbonate nodules showing
primary micrite in sampled nodules (I–II) and diagenetically altered
material in unsampled nodules (III–IV). Images taken on a Premier ELM-3R
luminoscope at 8–10 kV, 0.5 mA, and 6.6–13.3 Pa with preset 1 s
exposure; scale bars ∼50 µm. (b) Clumped
isotope-based soil temperature profiles from discrete layers sampled within
analyzed paleosol exemplars. Profile HB-129 contained nodular carbonate
layers at 20–30, 50–65, and 80–100 cm; Profile HB-187 contained nodular
carbonate layers at 150–170, 190–205, and 240–260 cm.
Clumped isotope geochemistry
The clumped isotope (Δ47) thermometer is based on the theoretical
temperature dependence of the overabundance of multiply substituted carbonate
ion isotopologues (primarily 13C18O16O2-2) within the
solid carbonate phase, which is independent of the isotopic composition of
the waters from which the carbonate precipitated (e.g., Schauble et al.,
2006; Ghosh et al., 2006; Eiler, 2007). The enrichment of “clumped”
isotopologues relative to the abundance expected for a random distribution of
isotopes among isotopologues (Δ47) varies with the growth
temperature of the sampled carbonate (e.g., Ghosh et al., 2006; Dennis et
al., 2011; Zaarur et al., 2013; Kluge et al., 2015; Kelson et al., 2017).
Clumped isotope thermometry of soil carbonates is a useful paleoenvironmental
proxy in continental settings (e.g., Eiler, 2011; Quade et al., 2013), and
studies of recent pedogenic carbonates indicate that their clumped isotope
values record environmental temperature conditions during mineral growth. The
timing of pedogenic carbonate growth is controlled by a combination of soil
moisture, CO2, temperature, and other factors over
102–104 years (e.g., Cerling, 1984; Cerling and Quade, 1993;
Breecker et al., 2009; Zamanian et al., 2016), and clumped isotope analyses
show corresponding variability in recorded temperatures (e.g., Peters et al.,
2013; Hough et al., 2014; Burgener et al., 2016; Ringham et al., 2016;
Gallagher and Sheldon, 2016). However, for pedogenic carbonates forming in
forest soils from mid-latitude regions, this growth temperature has been
shown to be linked to mean warm season soil temperatures in most settings
(e.g., Breecker et al., 2009; Passey et al., 2010; Quade et al., 2013;
Garzione et al., 2014; Hough et al., 2014; Ringham et al., 2016), and has
been used to estimate paleo-WMMT during the Cenozoic (e.g., Suarez et al.,
2011; Snell et al., 2013; Quade et al., 2013; Garzione et al., 2014).
Pedogenic carbonate nodules from selected Bk horizons (paleosol depths ∼20–240 cm) were sliced into thin sections and analyzed under transmitted
light and cathodoluminescence to identify primary micritic carbonate
(Fig. 2), which was microdrilled and homogenized for clumped isotope (Δ47) analysis. Extremely
shallow (<50 cm) or deep (>200 cm) carbonates were analyzed to
specifically examine temperature depth profiles in paleosols (Fig. 2), while
pedogenic carbonates from commonly sampled depths (50–200 cm; e.g.,
Cerling, 1984; Koch, 1998; Zamanian et al., 2016) were used for calculating
and interpreting paleotemperature records. Powdered samples and carbonate
standards were analyzed in replicate at the University of Washington's
IsoLab, following methods of Burgener et al. (2016) and Kelson et al. (2017),
which are modified after Huntington et al. (2009) and Passey et al. (2010).
Briefly, CO2 is produced from 6 to 8 mg of pure carbonate reacted
in a common phosphoric acid bath (∼105 % H3PO4) at
90 ∘C. Evolved CO2 is then cleaned via passage through a
series of automated cryogenic traps and a cooled (-20 ∘C) Poropak
Q column using helium carrier gas through a nickel and stainless steel vacuum
line, and the purified CO2 is transferred to Pyrex break seals.
Each sample is then analyzed on a Thermo MAT253 mass spectrometer equipped
with an automated 10-port tube cracker inlet system and configured to measure
m/z 44–49, using data acquisition methods and scripts presented by
Schauer et al. (2016).
All analyses include an automatically measured pressure baseline
(He et al., 2012), are corrected
using heated gas (1000 ∘C; Huntington et al., 2009) and
CO2–water equilibration (4, 60 ∘C) lines during
the corresponding analysis period (Table S2), and are reported in the
absolute reference frame (Dennis et al., 2011). Following recent work
(Daëron et al., 2016; Schauer et al., 2016), mass spectrometer data are
corrected using the 17O correction values recommended by Brand et
al. (2010). Carbonate standards for these analyses include international
standards NBS-19 and ETH-2, as well as internal standards C64 and COR, which
are all reported relative to VPDB (δ13C, δ18O)
and absolute reference frame (Δ47) in Table S2. All samples were analyzed in
replicate (3–5) to minimize standard analytical error, and data were reduced
following Schauer et al. (2016). Carbonate growth temperatures
(T[Δ47]) were calculated using the most current and extensive
inorganic calcite calibration (Kelson et al., 2017), which was produced using
the updated 17O correction values of Brand et al. (2010) and is
consistent with our analytical methods. Based on preliminary comparisons, the
Kelson et al. (2017) calibration produces results that are not significantly
different from data calculated using previous calibrations at moderate
Earth-surface temperatures (∼20–40 ∘C; Daëron et al.,
2016; Cedric John and Matthieu Daëron, personal communication, 2016;
Table S2).
Floral coexistence analysis
Floral physiognomy and floral coexistence techniques are often applied in
concert to arrive at terrestrial paleoclimate estimates (e.g., Spicer et al.,
2014; Reichgelt et al., 2015; West et al., 2015). While floral leaf
physiognomy has been used to develop character–climate relationships for
parameters like CMMT and MART (e.g., Wolfe, 1995; Wolfe et al., 1998; Wing,
1998), other work has raised questions about the reliability of modern
calibrations and possible covariability of seasonal temperatures recorded by
floral methods (Jordan, 1997; Peppe et al., 2010). Similar questions have
been raised regarding the NLR coexistence method
(Grimm and Denk, 2012; Grimm and Potts, 2016). However, recent developments
have addressed these issues including (1) improvements or revisions to NLR
assignments for paleofloral assemblages (e.g., Manchester, 2014; SIMNHP,
2015), (2) new global datasets of modern floral distributions (e.g.,
TROPICOS, 2015; USDA, 2015; GBIF, 2016), (3) high-resolution linked climatic
datasets (e.g., Hijmans et al., 2005), and (4) the application of more
rigorous statistical analyses (e.g., Eldrett et al., 2014; Utescher et al.,
2014; Harbert and Nixon, 2015). As a result of this work, bioclimatic
analysis has emerged as a refined version of this approach, employing the
climatic range of modern living relatives of plants found together in a
fossil assemblage and statistically constraining the most likely climatic
co-occurrence envelope (e.g., Greenwood et al., 2005, 2017; Thompson et al.,
2012; Eldrett et al., 2014).
Floral methods description. (a) Probability density
functions of hypothetical Taxa A and Taxa B along climatic variable X to form a
probability density function representative of the maximum likelihood of co-occurrence.
(b) Hypothetical climatic envelope of Taxon Q with climatic
variables X and Y, where point R occurs outside the envelope of Taxon Q
but within its range of both variables (creating a false inclusion of
point R). (c) Probability density function distributions for
seasonal temperatures from sampled paleofloral sites, where arrows indicate
calculated mean temperatures for each parameter, and n is the number of
morphotypes included in each assemblage.
Fossil assemblages were selected from the literature (e.g., Wilf, 1998, 2000)
based on temporal fit, floristic diversity, and reliable taxonomy. Fossil
taxa were each attributed to a modern taxon based on NLR (e.g., MacGinitie,
1969; Hickey, 1977; Manchester and Dilcher, 1982; Wolfe and Wehr, 1987; Wing,
1998; Wilf, 1998, 2000; Manchester, 2014; SIMNHP, 2015), with unattributed or
disputed placements assigned conservatively at higher taxonomic levels
(Table S4). Climatic envelopes of modern groups in North America and Asia
were retained for the ancient taxa based on environmental niche conservation
(e.g., Wang et al., 2010; Fang et al., 2011). Modern taxa distributions
(GBIF, 2016) were linked to high-resolution gridded climatic maps (Hijmans et
al., 2005) to extract MAT, WMMT and CMMT using the Dismo Package in the R
statistical program (R Core Team, 2013). Prior to calculating climatic
ranges, plant distribution coordinate files were scrutinized for (1) plants
with dubious taxonomic assignments, as not all identifications were rigorous
and not all collected specimens were taxonomically assigned by experts (only
species-level identifications are included); (2) plants occurring outside of
their natural ranges, as many plants occur outside their adapted environment
due to agricultural or aesthetic translocation; and (3) redundant
occurrences, as many duplicate coordinates or researcher entries exist for
the same taxon and their inclusion may skew results toward given localities.
Quantitative paleotemperatures were estimated using a modified bioclimatic
analysis approach (e.g., Greenwood et al., 2005, 2017; Thompson et al., 2012;
Eldrett et al., 2014). Overlap ranges of climatic tolerances for coexisting
species from each assemblage were defined by calculating probability density
functions of those climatic envelopes (Fig. 3 and Table S5) consistent with
recent work (e.g., Thompson et al., 2012; Harbert and Nixon, 2015; Grimm and
Potts, 2016; Greenwood et al., 2017). In order to avoid inclusion of apparent
coexistence intervals in which no modern occurrence is recorded, we calculate
the collective probability density of taxa co-occurrence for each combination
of MAT (x), WMMT (y), and CMMT (z):
fx|t=12σ2πe-x-μ22σ2,fy|t=12σ2πe-y-μ22σ2,fz|t=12σ2πe-z-μ22σ2,fx,y,z,t=lnfx×fy×fzt1×…×fx×fy×fztn.
Calculations are repeated such that the likelihood (f) is calculated for
each climatic combination, for each taxon (t), dependent on the number of
taxa (n), using the mean and standard deviation of each taxon (Table S5).
Climate input parameters were individual occurrence data points (∼32 000) derived from GBIF (2016), excluding combinations unlikely to
represent the climatic envelope of the taxa in the assemblage by calculating
a maximum likelihood probability density function that defines a precise
estimate of temperature parameters with a low standard deviation for each
selected assemblage (Fig. 3).
Modern climate data and model downscaling
The modern temperature dataset was derived from 1981 to 2010 averaged climate
normals from the National Oceanic and Atmospheric Administration (NOAA)
weather observation stations within the Green River Basin (n=18; NCDC,
2010), defined as the area 40.5–43∘ N by 107–110.5∘ W
(Fig. 1). Future model temperature projection results used a 10-model
ensemble from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under
standard low (RCP4.5) and high (RCP8.5) emissions scenarios (IPCC, 2014); the
specifics of each model and configuration are available from the World
Climate Research Programme (2011). Results were averaged monthly for the
final 10 years of the model run (2090–2099) and calculated over the same
study area using standard bias-correction and spatial downscaling (BCSD)
methods developed by PCDMI (2014). Eocene model temperature results used data
from a modified three-dimensional regional climate model (RegCM3; Sewall and
Sloan, 2006; Pal et al., 2007) with established Eocene boundary conditions
including low (560 ppm; LoCO) and high (2240 ppm; HiCO) atmospheric
pCO2 scenarios (Sewall and Sloan, 2006; Thrasher and
Sloan, 2009, 2010); the specifics of the model configurations can be found in
Thrasher and Sloan (2009). Those results were averaged for the final 20 years
of the model run at equilibrium and calculated over the same study area
(40.5–43∘ N by 107–110.5∘ W) by integrating data across
grid cells monthly for each model year within the above-defined Green River
Basin (e.g., Snell et al., 2013). This particular set of Eocene model
configurations was chosen because it allows for the highest available
resolution over the basin domain using the best available set of boundary
conditions (cf., EoMIP; Lunt et al., 2012). All modern climate normals and
model downscaling results are reported in Table S7.
Results
PPM1.0 statistical model results for MAT from these paleosol
samples range from 13.5 to 17.6 ∘C (μ=15.2 ∘C;
σ=1.3 ∘C). Uncertainty for these estimates is reported as
the root mean squared error of the model fit regression (±2.5 ∘C). Petrographic observation of carbonate nodules from all
depths and selected soils identified dominantly micritic textures with minor
components of subangular quartz grains and occasional sparry (>20 µm) calcite veins and cements; however, we were able to
identify and micro-sample unaltered fine-grained (<5 µm) calcite
material in each of the examined samples (n=14; Fig. 2). Clumped isotope
Δ47 values for these samples range from 0.582 to 0.631 ‰
(μ=0.607 ‰; σ=0.014 ‰), which corresponds to
an estimated WMMT range of 18 to 34 ∘C (μ=25 ∘C;
σ=4 ∘C). Uncertainty for these estimates is reported as
propagated error from analytical and equilibrated CO2 reference
frame uncertainty (negligible); replicate standard error (μ=0.008 ‰) or standard error from long-term standards, whichever is
larger; and calibration standard error (e.g., Kelson et al., 2017); which
have a combined error averaging ±3 ∘C. Clumped isotope-based
temperature depth profiles in the sampled paleosols show no clear trend with
depth, and estimates are mostly within error for a given paleosol (Fig. 2).
Nearest living relative bioclimatic analysis minimum cold tolerances for
these samples range from -28 to 24 ∘C (μ=6 ∘C;
σ=7 ∘C), and maximum warm tolerances range from 10 to
43 ∘C (μ=28 ∘C; σ=5 ∘C).
Probability density functions define bioclimatic envelopes (Fig. 3)
corresponding to an estimated CMMT range of 4.2 to 7.6 ∘C (μ=5.9 ∘C; σ=1.2 ∘C), an MAT range of 15.2 to
18.2 ∘C (μ=16.5 ∘C; σ=1.1 ∘C), and
a WMMT range of 27.9 to 28.7 ∘C (μ=28.3 ∘C; σ=0.3 ∘C) for the collective floral assemblages. Uncertainty for
these estimates is reported as 2σ for individual assemblage
probability density function distributions, which
average ±2 ∘C. Proxy estimates from all three methods show a
trend of increasing temperatures from non-peak conditions into the peak EECO
(∼51 Ma), after which temperatures decreased back to lower values
(Fig. 4).
Temperature proxy estimates of CMMT (white plot), MAT (gray plot),
and WMMT (black plot) through the early Eocene. Triangles represent
paleobotanical coexistence estimates, squares represent paleosol geochemistry
estimates, stars represent revised paleobotanical physiognomy estimates, and
circles represent clumped isotope estimates. Error bars represent probability
density function 2σ (paleobotanical coexistence), root mean squared
error (PPM1.0 paleosol geochemistry), calibration standard error
(paleobotanical physiognomy), and propagated analytical and
calibration error (clumped isotopes). Shading highlights peak EECO
conditions based on previous work (e.g., Hyland et al., 2017), the long
dashed line highlights possible aliasing due to a long sampling interval, and
the short dashed line highlights exclusion of two clumped isotope data points
(see Discussion). Estimates of peak EECO (51±0.5 Ma) and non-peak EECO
MART are defined as described in Table 1 and the Discussion, with MAT shown
by vertical lines. Modern MART and MAT are from averaged climate normals for
NOAA weather stations in the GRB (NCDC, 2010).
Modern climate normals averaged monthly for the GRB range from -8.4 to
18.1 ∘C, with a MAT of 4.4 ∘C (Table S7). Downscaled Eocene
climate model results averaged monthly for the GRB range from 4 to
24 ∘C (LoCO) and 6 to 30 ∘C (HiCO), with MATs of 13 and
16 ∘C, respectively (Table S7). Downscaled future climate model
results averaged monthly for the GRB range from -5.0 to 20.4 ∘C
(RCP4.5) and -2.9 to 24.7 ∘C (RCP8.5), with MATs of 7.1 and
10.6 ∘C, respectively (Table S7). Monthly temperature trends
maintain roughly the same shape for modern observational data, future model
estimates, and Eocene model estimates. However, the Eocene-modeled cases show
substantially higher winter temperatures, and in both modern and
Eocene-modeled cases the higher emission or pCO2 scenario
shows an enhanced summer signal relative to the lower emission or
pCO2 scenario from the same time period
(Fig. 5).
Averaged monthly mean temperatures in the GRB, including: modern
instrumental data (filled black circles; NCDC, 2010); high (red squares;
RCP8.5) and low (red circles; RCP4.5) future emissions scenarios (PCDMI,
2014); high (blue squares; HiCO) and low (blue circles; LoCO) early Eocene
pCO2 scenarios (Thrasher and Sloan, 2009, 2010); and proxy
reconstructions of WMMT and CMMT for non-peak (filled triangles) and peak
EECO (open triangles) from this study. Method-averaged MART estimates shown
for each category (symbols and colors match the main panel).
Discussion
Temperature estimates
Temperature estimates from the PPM1.0 spline model are based on
specifically selected uppermost B horizons of paleosols with comparable
parent materials. These horizons were selected based on previous work
describing and sampling paleosols from the Cathedral Bluffs Member in the GRB
(Fig. 1; Hyland and Sheldon, 2013), and based on the characteristics of soils
sampled for the paleosol paleoclimate model dataset (Stinchcomb et al.,
2016), in order to generate the most robust input data for the
PPM1.0 spline model. While the PPM1.0 model produces
multiple possible estimates of paleo-MAT, the estimate shown to be most
reliable via concurrent comparisons with other paleotemperature methods
(paleobotanical and paleosol proxies) is the “high MAT” value we present
here (Michel et al., 2014; Stinchcomb et al., 2016; Driese et al., 2016). We
further justify our use of the “high” estimate because the PPM1.0
training dataset heavily samples soils from temperate regions (specifically
the conterminous USA), which tend to have lower MAT (≤10 ∘C) and
therefore could place excess weight on low values in the model predictive
space. This sampling bias likely produces the demonstrated pattern of
“best” MAT predictions generally exhibiting positive residuals (Stinchcomb
et al., 2016), which means that the PPM1.0 model would be more
likely to skew temperature estimates from paleosol and other modern samples
toward lower-than-observed MAT values. The presented MATs appear to coincide with a statistical mean between CMMT and WMMT estimates
(Fig. 4), and also agree within uncertainty with independent MAT estimates
from other types of paleosol geochemistry (salinization index, δ18O; Hyland and Sheldon, 2013) and broadly with updated physiognometric
(Table S6; Wilf, 2000) and coexistence analysis paleobotanical estimates from
the GRB (Fig. 4).
Based on the assessment of physical and isotopic data, our sampled pedogenic
carbonate nodules appear to be primary records of Earth surface temperatures
at the time of their formation. All sampled nodules preserve micritic
carbonate, and transmitted light and cathodoluminescence images show limited
recrystallization or void-filling spar and no evidence of pervasive
remineralization (Fig. 2). Clumped isotopic data also suggest primary and
uncontaminated carbonate material; Δ48 values remain low (≪1 ‰; Table S2), indicating a lack of hydrocarbon or sulfide
contamination (e.g., Guo and Eiler, 2007; Huntington et al., 2009).
Temperature and δ18O measurements remain well within the range
of reasonable terrestrial values, particularly for continental interior
basins with seasonal climates (Table S2; e.g., Quade et al., 2013; Hough et
al., 2014). Carbonates forming in temperate regions often exhibit summer or
warm-month temperatures due to warm, dry conditions
and low soil CO2 concentrations during those months (e.g., Breecker
et al., 2009; Quade et al., 2013). Such conditions are predicted for the GRB
during the early Eocene based on regional climate models (Thrasher and Sloan,
2009, 2010), and are evident in paleosol features (Clyde et al., 2001; Hyland
and Sheldon, 2013) as well as evaporative δ18O of source
waters from nearby paleo-lakes Gosiute and Uinta (Table S1; e.g., Sarg et
al., 2013; Frantz et al., 2014). Further warm biasing of soil temperature
with respect to air temperature can be imparted by radiant ground heating,
but such effects are likely negligible in shaded forest soils (e.g., Quade et
al., 2013; Ringham et al., 2016). Clumped isotope data from two soil depth
profiles collected in the GRB agree within uncertainty below ∼50 cm
(Fig. 2), suggesting that surface heating and depth attenuation of surface
temperature variability does not significantly affect the samples used for
our MART reconstructions (paleosol depths ∼50–200 cm; e.g., Ringham
et al., 2016).
These results imply that the temperatures measured from our pedogenic
carbonates broadly reflect WMMT of soil as observed in other records
(e.g., Peters et al., 2013; Hough et al., 2014; Burgener et al., 2016).
Possible exceptions are two samples at the base of the Honeycomb Buttes
section (HB-109 and HB-18; Table S2), which appear to correspond to MAT
estimates from the same paleosols (PPM1.0; Fig. 4). These lowest
temperature estimates from the base of the section may be artificially
“cool” as a function of seasonal precipitation regimes spreading carbonate
formation across other parts of the year, particularly in soils with deeper
Bk horizons like these (e.g., Gallagher and Sheldon, 2016). Because of the
likely bias toward MAT in these two samples, we exclude them from
calculations of WMMT or MART as indicated in Fig. 4; additionally, this
effect means that all of our clumped isotope-based estimates of WMMT may be
artificially low, suggesting that our calculated MART values could represent
a minimum value. However, our resultant clumped isotope-based temperature
estimates are mostly in agreement with both regional climate model
predictions of summer month air temperatures (e.g., Thrasher and Sloan, 2009;
Snell et al., 2013) and paleobotanical coexistence estimates of WMMTs
(Fig. 4).
Comparison of Eocene MART estimates using different constraining
temperatures and calculation methods.
Interval
CMMTa
MATa
WMMTa
MARTTb
MARTCb
MARTWb
Peak EECO (50.5–51.5 Ma)
–
15.4 ∘C
28.2 ∘C
–
–
26 ∘C (4)
Non-peak EECO (53.5– 51.5 Ma & 50.5–49.5 Ma)
5.9 ∘C
15.6 ∘C
26.8 ∘C
22 ∘C (1)
21 ∘C (1)
23 ∘C (1)
MARTT = WMMT - CMMT.
MARTC = (MAT - CMMT) × 2.
MARTW = (WMMT - MAT) × 2.
a Average of all available temperature proxy data across
indicated time interval. b Average MART estimate for each
calculation method, number in parentheses is the SD of calculation group.
Paleobotanical coexistence methods have been shown to reconstruct
paleo-temperatures robustly, particularly for warm and cold months in
well-sampled and taxonomically rich localities such as these (e.g., Thompson
et al., 2012; Grimm and Potts, 2016). However, uncertainties may be larger
than accounted for by the described statistical methods applied to these
assemblages because (1) many fossil classifications within the GRB
assemblages are not directly comparable to or identifiable as extant species,
and coexistence analyses at a generic or familial level may introduce bias by
broadening the temperature tolerance ranges of most groups (e.g., Wang et
al., 2010); and (2) evolutionary or climatic preferences of Paleogene fossil
taxa may not be fully conserved in extant groups, introducing potential
sources of error (e.g., Fang et al., 2011). If we double the estimated error
to account for these unquantifiable uncertainties, the collective coexistence
probability density functions from these assemblages still produce CMMT, MAT,
and WMMT estimates defined by narrow “maximum likelihood” bioclimatic
envelopes (<±3 ∘C; Fig. 3; Table S5), which suggest that the
environmental characteristics of these fossil assemblages are well
constrained despite some higher-level NLR assignments. Additionally, sampling
bias from well-sampled temperate regions (e.g., North America) in the modern
GBIF (2016) database may place undue weight on the cool end of plant
ranges (e.g., Greenwood et al., 2017), constraining paleotemperature
estimates to lower values or smaller ranges than is appropriate. This
suggests that, similar to clumped isotope-based estimates, our plant-based
MART values could also represent a minimum value. Despite this,
paleobotanical coexistence CMMT estimates agree with regional climate model
predictions of winter month temperatures in the GRB (e.g., Thrasher and
Sloan, 2009, 2010), MAT estimates agree broadly with multiple paleosol-based
proxy estimates (Fig. 4; Hyland and Sheldon, 2013) and with updated
paleobotanical physiognomy estimates (Fig. 4; Table S6; Wilf, 2000), and WMMT
estimates agree with regional climate model estimates (e.g., Thrasher and
Sloan, 2009; Snell et al., 2013) and broadly with clumped isotope-based
estimates (Fig. 4). Taken together these proxy results paint a consistent
picture of Earth-surface temperatures during the early Eocene, despite
uncertainties inherent in each individual method.
Temperature seasonality
Because each of these proxies appears to represent different seasonal
temperatures robustly, we combine these estimates to produce a new multiply
constrained investigation of paleo-MART. By calculating the differences
between CMMT from paleobotanical coexistence analysis, MAT from paleosol
geochemistry or paleobotanical analyses, and WMMT from Δ47
composition or paleobotanical coexistence analysis, we can directly estimate
MART in the past and compare differences in seasonal temperatures independent
of calculation method (cf., Snell et al., 2013). In other words, our approach
can define MART as (1) the difference between WMMT and CMMT or (2) twice
the difference between MAT and either WMMT or CMMT, assuming that MAT falls
half way between those estimates by definition (Table 1). Because our
approach can calculate MART using both methods and an average of multiple
proxies, this allows for a wide range of independent checks on our estimates,
providing the most robust available paleo-MART (Table 1). Each method
provides consistent answers that are statistically indistinguishable for a
given time period (Student's t test p values = 0.4–0.9), lending
confidence to calculations which show that MART ranged from
21–26 ∘C during the early Eocene (Table 1).
By averaging all data from each population (CMMT, MAT, WMMT) for the peak and
non-peak intervals separately, calculated MARTs suggest that seasonality was
generally slightly lower than modern across parts of the early Eocene (∼21–23 ∘C, non-peak), but appears to have increased to near-modern
ranges during the peak EECO (∼26 ∘C; Fig. 4; Table 1). The
calculated uncertainty in the difference between these populations (standard
error of a difference) is ∼4 ∘C, which makes the non-peak and
peak intervals statistically distinct though nearly overlapping. Overall,
this suggests that not only is seasonality not reduced during greenhouse
periods (e.g., Snell et al., 2013) but it may actually be expanded (Fig. 5).
Estimates from the lower end of our reconstructed MART range are still higher
than MART estimates from individual paleobotanical proxies
(15–18 ∘C; e.g., Greenwood and Wing, 1995; Wolfe et al., 1998), but
compare favorably to estimates from regional climate models with assumed
lacustrine or paludal land cover (20–22 ∘C; Thrasher and Sloan,
2010). However, estimates from the higher end of the reconstructed MART range
compare more favorably to modeled MART values with assumed woodland or
forested land cover (24–26 ∘C; Thrasher and Sloan, 2010; Snell et
al., 2013). The transient nature of paleo-lake Gosiute and the variable
evolution of environments within the GRB throughout the early Eocene is well
documented in stratigraphic archives, indicating that the basin may have been
alternately dominated by the paleo-lake or by forested floodplains during
this period (Smith et al., 2008, 2014). In this context, our results suggest
that both lower (though still in excess of any previous paleobotanical
estimates) and higher MART states may in fact be reasonable for this region
at different points during the early Eocene as the GRB evolved. Moreover,
proxy and modeling work does not appear to be contradictory, instead having
captured different portions of the range of possible MART values indicated
for the peak vs. non-peak EECO in this part of the continental interior
(Figs. 4 and 5). Regardless, these results suggest that MART values lower
than ∼20 ∘C (e.g., Greenwood and Wing, 1995; Wolfe et al.,
1998) may be unreasonable during any part of the EECO, even in the context of
variable climate and environmental conditions. This is particularly true
because MART estimates using these proxy methods are more likely to
underestimate than overestimate seasonality (see Sect. 4.1).
Seasonality implications
Our new proxy data and model comparisons have important implications for
continental climates, as they suggest two potential characteristics of
seasonality in interior regions during warming events: (1) proxies tend to
indicate continental temperatures on the high end of modeled ranges in all
seasons and (2) both proxies and regional models indicate that summer
temperatures may increase disproportionately, actually broadening MART, at
high atmospheric pCO2. While proxy and model estimates of
paleotemperature generally agree through the early Eocene in the GRB, proxy
estimates consistently fall in the top half of all modeled values (Fig. 5).
Although these model and proxy results are not statistically distinct, they
may suggest that realistic environmental responses could have a skewed
distribution within the range of model-predicted climate outcomes, an
observation which has been made previously for other regions and time periods
(e.g., Roe and Baker, 2007; Diffenbaugh and Field, 2013).
Winter temperatures were generally high during the Eocene (Figs. 4 and 5;
e.g., Greenwood and Wing, 1995), but during the peak EECO summer temperatures
appear to have increased disproportionally, broadening the range of MART
(Figs. 4 and 5). While this apparent trend may be related to the lack of
direct CMMT estimates during the peak EECO, the consistency of MART estimates
using both reconstruction methods (see Sect. 4.2) suggests the observation is
robust. Regional Eocene climate model output for the GRB predicts lower MART
(∼20 ∘C) under low pCO2 conditions (LoCO
scenario), and higher MART (∼24 ∘C) under high
pCO2 conditions (HiCO scenario; Fig. 5; Table S7).
Therefore, a theoretical transition from lower (≤500 ppm) to higher
(≥1000 ppm) atmospheric pCO2 during the peak EECO
(e.g., Hyland and Sheldon, 2013; Jagniecki et al., 2015) could effectively
broaden MART and result in extreme summer temperatures during that period,
which would be consistent with both regional model and proxy predictions in
the GRB (Fig. 5). Regional model–proxy agreement on the plausibility of
variable moderate-to-high MART (20–26 ∘C) in continental interiors
fits with global simulations employing a reasonable set of radiative forcings
and climate sensitivities, which project similar seasonality ranges during
this and other greenhouse events (Huber and Caballero, 2011; Lunt et al.,
2012). These temperature seasonality estimates also corroborate recent work
on other regions and warm periods (e.g., Snell et al., 2013; Eldrett et al.,
2014), and further support the interpretation that continental interiors were
less “equable” than previously thought under greenhouse conditions (Snell
et al., 2013; Peppe, 2013).
Increased seasonality and the disproportionate response of summer
temperatures during greenhouse climates also has significant implications for
predicting future change in continental interiors. Current projections for
the next century using downscaled global climate model ensembles (PCDMI,
2014; Table S7) indicate generally increased temperatures and changing
seasonality in North America, and GRB temperatures are projected to increase
particularly during winter months (Fig. 5). However, for high emissions
scenarios that may be closer in character to greenhouse conditions like the
peak EECO or the PETM (RCP8.5; e.g., IPCC, 2007; Lunt et al., 2012), summer
temperatures in the GRB increase more strongly, broadening MART (Fig. 5;
Table S7). This trend in MART from peak EECO proxy data and high-emission or
pCO2 model simulations in both the future and
Eocene suggests a potential atmospheric pCO2 threshold for
enhanced seasonality, and provides support for models and observations
indicating that continental interiors may experience more extreme seasonality
in the future under heightened greenhouse conditions (e.g., IPCC, 2007;
Diffenbaugh and Field, 2013; Diffenbaugh et al., 2017). The mechanism for
producing this increased seasonality remains unclear and requires further
study in terms of both proxy applications and model development, although
changes in land cover may play a crucial role at least in regional
variability (Thrasher and Sloan, 2010; Diffenbaugh and Field, 2013).