Application of paleosol-based climate models to modern soil samples across the contiguous United States

Alexander W.T. Bynum Senior Integrative Exercise

March 9, 2018

Submitted in partial fulfillment of the requirements for a

Bachelor of Arts degree from Carleton College, Northfield, Minnesota.

Table of Contents

ABSTRACT INTRODUCTION .............................................................................................................1 GEOLOGIC BACKGROUND .........................................................................................2

Paleosol-based Paleoclimate Proxies ........................................................................................ 3 Soil Weathering Mineralogy ..................................................................................................... 4

METHODS .........................................................................................................................7 RESULTS .........................................................................................................................10

Mean Annual Precipitation Predictors .................................................................................. 10 Mean Annual Temperature Predictors ................................................................................. 12 Multiple Regression Analysis .................................................................................................. 17

DISCUSSION ...................................................................................................................17

Mineralogic Findings ............................................................................................................... 19 Limitations ............................................................................................................................... 21

CONCLUSION ................................................................................................................24 ACKNOWLEDGEMENTS ............................................................................................25 REFERENCES .................................................................................................................26

Application of paleosol-based climate models to modern soil samples across the

contiguous United States

Alexander W.T. Bynum Carleton College

Senior Integrative Exercise March 9, 2018

Advisor: Dr. Daniel Maxbauer

ABSTRACT

Analysis of soil geochemistry through paleosol-based climate proxy models allows the constraint of precipitation and temperature in ancient Earth environments. Three widely accepted paleosol-based precipitation models — CALMAG, CIA, CIA-K — and two paleosol-based temperature models were applied to geographically and environmentally diverse soils across the United States to determine their broad scale efficacy. Proxy models were unable to capture variation in mean annual temperature (R2 < 0.01), but did poorly capture variation in mean annual precipitation (R2 < 0.25). Climate proxy models were tested for possible improvement by the inclusion of 26 soil minerals in conjunction with each climate proxy model. A multiple regression linear model containing both the original climate proxy model and mineralogic measure for each individual mineral was performed. Testing these multiple regression models and their geochemistry-exclusive equivalents for statistically significant differences revealed six mineral measures which improved models: K feldspar, plagioclase feldspar, total feldspar minerals, kaolinite, total clay minerals, and hematite. Other minerals that were found to improve some models and not others include gibbsite and quartz.

Keywords: climate, paleosols, geochemistry, mineralogy, United States

1

INTRODUCTION

Mounting evidence suggests that anthropogenic climate change will be a major

threat to coming generations (IPCC, 2013; USGCRP, 2017). In order to better

understand how the biosphere will be altered, scientists examine Earth’s climate

history. Accurately constraining paleoclimatic variables, such as precipitation and

temperature, allows us not only to examine our planet broadly, but also to

improve our understanding of how life adapted to paleoclimatic changes. One of

the principal ways in which scientists understand Earth’s climate history from

terrestrial systems is through the study of fossilized soils (paleosols; Sheldon and

Tabor, 2009). Paleosols represent buried landscapes of the past that are preserved

in the rock record (Sheldon and Tabor, 2009). Paleosol-based climate proxies are

useful because of a soil’s high sensitivity to climate-dependent weathering

(Sheldon et al., 2002; Gallagher and Sheldon, 2013).

Efforts to produce robust proxy methods from modern soil data have improved

our ability to constrain paleoclimate conditions when applied to paleosol systems

in the geologic record. But producing accurate predictors is fraught with

challenges, as modern Earth is quite changed when compared to much of Earth’s

history, when life existed as entirely different flora and fauna living under

variable atmospheric conditions. This paper seeks to examine performance of

previously determined climate proxies on a broad scale, and determine whether or

not the addition of specific mineralogical measures could improve their predictive

accuracy. Specifically, three well-established proxy models of climate were tested

2

in their ability to predict local precipitation and temperature using geochemical

data from two national datasets. The analysis was then repeated with the addition

of mineralogic data, to determine whether a mineral’s inclusion in the linear

model had a significant impact on the proxy model’s performance.

GEOLOGIC BACKGROUND

Soil is more than just dirt. Soil is “a natural body, differentiated into horizons of

minerals and organic constituents, usually unconsolidated, of variable depth,

which differs from the parent material below” (as cited in Jenny, 1941). Soils are

composed of a complex mixture of tiny, broken-up pieces of parent rock that

mature and develop into distinct layers, called horizons, over hundreds to

thousands of years. These bits of rock form the mineral phase of soil, which

surrounds and interacts with pore spaces that contain mixtures of gases and

liquids (Tan, 1993). Soils vary widely in composition, structure, texture, and

mineralogy depending on the conditions present during their formation. Many

factors influence soil characteristics, but the principal soil forming factors as

outlined by Jenny (1941) include climate, organisms, topography, parent material,

and time. The major soil forming factor important to this paper is that of climate,

which encompasses both precipitation and temperature.

Precipitation, and more generally soil moisture, is crucial to developing soil

character as it drives physical and chemical breakdown, and leaches elements

downwards to remove them from the soil system (McLaren and Cameron, 1996).

3

Chemical weathering from precipitation is due to the acidity of rainwater

introducing protons to a soil system (McLaren and Cameron, 1996, pg 161).

Increasing temperature essentially has a similar effect on chemical weathering, as

it works by increasing the amount of dissociation of water molecules in the pore

space (Jenny, 1941). Warmer climates also allow for more weathering simply

because once a soil freezes, little weathering occurs (Jenny, 1941). Because both

precipitation and temperature are primary controls on a soil’s characteristics

according to our framework, geologists apply analyses to paleosols that examine

their geochemistry in an effort to reconstruct climate during the paleosol’s

maturation.

Paleosol-based Paleoclimate Proxies

The major paleoclimate proxy models used in the literature are precipitation

estimators called CALMAG, CIA, and CIA-K (Sheldon et al., 2002). Proxy

models have also been developed to estimate temperature, but all geochemical

paleoclimate proxies work on the same fundamental principle. Different ions

behave predictably in varied soil maturation environments. For example, Al2O3

generally remains within the soil when it rains, while the bases (MgO, CaO, K2O,

Na2O) tend to leach out with precipitation/moisture (Sheldon and Tabor, 2009).

This means that we expect soils that developed in environments with high

precipitation to have a relatively high ratio of Al2O3 to bases (Sheldon et al. 2002;

Sheldon and Tabor, 2009). Various estimators have been found and reported that

4

utilize different soil processes (not just leaching) including hydrolysis, oxidation,

acidification, and salinization (Sheldon and Tabor, 2009).

The three major proxy models have proved useful in studying various

paleoclimates throughout geologic history, including the Paleocene-Eocene

Thermal Maximum (PETM; Andrews et al., 2017). Here each proxy model shows

that rainfall increased by approximately 500 !!"# across the PETM (Andrews et

al., 2017). These data support paleoprecipitation constraints produced via

examination of preserved alluvial fan complexes in a separate region (Andrews et

al., 2017). Although these proxy models that rely solely on geochemistry are

demonstrably useful corroborating evidence, they still fail to harness the full

scope of weathering characteristics in a developed soil. Soil mineralogy is another

example of an important soil measure that helps identify a weathering

environment. The presence or absence of minerals with specific weathering

characteristics is ignored by these paleosol-based paleoclimate proxy models.

Soil Weathering Mineralogy

Soils are composed of a mixture of different minerals that vary widely in their

susceptibility to chemical weathering, which is specific to the chemical

composition and crystal structure for a given mineral. Weathering of

phyllosilicate minerals provides an illustrative example of how weathering

processes involved in soil formation are varied, with the added benefit that clay

minerals (and therefore phyllosilicates) are pertinent to this paper’s results.

5

Muscovite (KAl3Si3O10(OH)2) is a common phyllosilicate that is prone to weathering

in soils. In the presence of acid (like rainfall), the hydroxyl groups on the outer

edge of a mica become protonated and reduce to overall negative charge of the

sheet (McLaren and Cameron, 1996). In other phyllosilicates, the ferrous iron

may convert to ferric iron over long periods of time in oxygenated soil which also

reduces the negative charge (Ismail, 1969). The moderate loss of negative charge

of the silica and alumina layers in turn causes the potassium ions that bind the

layers together to fall out of the structure (McLaren and Cameron, 1996) (Fig. 1).

When common soil cations in the soil solution (Ca2+ and Mg2+) replace the

potassium that fell out of structure, they wedge apart the layers further and drive

physical weathering (McLaren and Cameron, 1996).

Despite a relative resistance to chemical weathering, clay minerals still play a

crucial role in the chemical weathering process. This is due to their ability to

adsorb ions, called exchangeable ions, onto their slightly negative surface (Jenny,

1941). Exchangeable ions are predominantly cations, although some local anion

pockets may exist (McLaren and Cameron, 1996). These are relevant to soil

weathering because they can replace some ions that are lost due to leaching

(Jenny, 1941; McLaren and Cameron, 1996).

Both physical and chemical weathering processes described above are what get

reflected in soil measures that are interpreted through paleosol-based proxy

models. In this paper, the existing proxy models, CALMAG, CIA, and CIA-K, are

Exchangable IonsSoil Solution Ions

Ca2+ Na+

K+ NH4+

Fixed Ions

Figure 1. Soil clay weathering process releasing potassium ions into soil solution. Exchangeable (cat)ions stick to clay mineral surface because of its negative charge. Figure adapted from McLaren and Cameron (1996).

6

7

evaluated through their application to large, national datasets of soil geochemistry

and mineralogy. First, the effectiveness of each proxy model at predicting the

measured annual precipitation and temperature values based on a site’s

geochemistry is evaluated using a large volume of geographically and

developmentally diverse soils. Finally, a statistical framework which adds

mineralogy to geochemical proxy models is evaluated and compared to the

geochemistry-exclusive models.

METHODS

All data from this study are freely available through online databases from the

United States Geological Survey (USGS) and the United States Department of

Agriculture (USDA). The Mineral Resources On-Line Spatial Data were

downloaded from the USGS for both geochemical and mineralogical data of

4,857 sites across the contiguous United States (Fig. 2) (Smith et al., 2014;

Wickham et al., 2017b). The data included are 47 chemical measures (eg. Ca, Fe,

Sr) and 26 mineral measures, as well as land cover, sample depth, and coordinates

(Smith et al., 2014). Climate data were then obtained from the USDA’s National

Resources Conservation Service through their Geospatial Data Gateway. The

Parameter elevation Regression on Independent Slopes Model (PRISM) dataset

was pulled from this site to provide climate data collected between 1981 and 2010

at an 800m resolution (PRISM Climate Group, 2017; Wickham and Chang,

2017). The pertinent data included are mean annual precipitation (MAP) and

mean annual temperature (MAT) (PRISM Climate Group, 2017).

0

600 km

500 mi

0

Figure 2. Map of contiguous United States with each dot representing one of 4,857 sample sites. Adapted from Smith et al. (2014).

8

9

Prior to analysis, all data were compiled and formatted into a single dataset using

the R statistical programing language (R Core Team, 2018; Wickham et al.,

2017a). PRISM data were converted from raster data to a point data frame

(Hijmans, 2017). MAT and MAP data from the PRISM dataset were then

extracted onto the coordinates of the geochemical data (Kahle, 2013).

Geochemical data provided by the USGS are in units of !$%%$&#'!($%)&#'! . Most paleosol-

based proxies use weight percent oxide (e.g., Sheldon et al., 2001; Stinchcomb et

al., 2016). Accordingly, all geochemical data reported here were converted to

weight percent oxide prior to any statistical analysis.

To determine the accuracy of available paleoclimate proxies, regression analyses

were performed on all data with the exception of soils identified in the data as

being developed or directly associated with agricultural practices. These disturbed

soils have measures which are altered by human interference, and therefore may

not accurately represent their climate environment. The value for each paleosol-

based climate proxy (CALMAG, CIA, CIA-K) was logged for each site and

plotted against the climate variable it is supposed to estimate (Wickham and

Chang, 2016). This analysis was run for soil sample datasets from both the A

horizon, and the C horizon as they are defined by Smith et al. (2014). To

determine the efficacy of adding mineralogy to each climate proxy, a new

multiple regression linear model was run with both the climate proxy and

mineralogy for each individual mineral. Analysis of variance (ANOVA) tests

were then run for each multiple regression linear model against the original linear

10

model that excludes mineralogy to test for statistically significant differences

between the two. All data were analyzed using Rstudio software. Figures were

adapted and created using a combination of Rstudio and Adobe Illustrator

software.

RESULTS

Mean Annual Precipitation Predictors

Three different paleosol-based climate proxies — CALMAG, CIA, and CIA-K —

were employed to test whether or not each accurately captured variation in mean

annual precipitation (MAP) across the dataset. In general, all statistical

correlations are low (R2 < .25). However, considering the wide range in soil

forming factors across the dataset in addition to climate, the fact that significant

correlations exist at all highlights the strong relationship between MAP and soil

geochemistry.

CALMAG estimation compares the ratio of Al2O3 to the sum of CaO, Al2O3, and

MgO (CALMAG) to estimate MAP for a given soil. Comparing a plot of the

CALMAG ratio to MAP for A horizons across soil samples yielded a linear

model with a .08697 R2 value (Fig. 3a). The C horizon fared somewhat better,

with a linear model yielding an R2 value of .1496 (Fig. 3b).

The chemical index of alteration (CIA) compares the ratio of alumina to the sum

of bases (Al2O3 to the sum of CaO, MgO, K2O, Na2O). Comparing a plot of the

20 40 60 80 100

050

010

0015

0020

00

CALMAG (%)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m)

0 20 40 60 80 100

050

010

0015

0020

00

CALMAG (%)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m)

A

B

Figure 3. CALMAG (%) plotted against MAP (mm) for undisturbed soil samples from A horizons (A) and C horizons (B). Red line represents simple linear regres-sion. Created using Rstudio (R Core Team, 2018).

11

12

ratio of the basesto MAP in A horizons for our samples yielded a linear model

with an R2 of 0.2262 (Fig. 4a). The same plot for the C horizon gave an R2 of

0.1394 (Fig. 4b).

The CIA-K is the CIA ratio with the potassium (K2O) removed so as to better

control for potassium metasomatism (Sheldon et al., 2002). Removing K2O

weakened the R2 of the linear model for A horizons to .1571, while it strengthened

the R2 of sample C horizons to .2022. Log transforming the MAP, however,

improves the R2 values for both A horizon and C horizon CIA-K estimation

(.1872 and .2182 respectively) (Fig. 5).

Mean Annual Temperature Predictors

Mean annual temperature (MAT) has no significant correlations for the two

separate predictor models evaluated in this study. The first model predicts

temperature by comparing the ratio of barium to strontium in the A and C soil

horizons (Sheldon and Tabor, 2009). Neither the A horizon nor C horizon were

associated with *'+# ratios that were able to explain more than 1% of the

temperature data (R2 = .01 and R2 = .007 respectively) (Fig. 6). The second model

compares the sum of measured K2O and Na2O molecules over Al2O3 molecules

(Sheldon and Tabor, 2009). Again, neither the A horizon nor C horizon were

associated with a K2O and Na2O to Al2O3 ratio that could explain more than 1% of

the data (R2 = .002 and R2 = .009 respectively) (Fig. 7).

0 2 4 6 8 10

050

010

0015

0020

00

Al2O3 / (CaO+MgO+Na2O+K2O)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m)

0 2 4 6 8 10

050

010

0015

0020

00

Al2O3 / (CaO+MgO+Na2O+K2O)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m)

A

B

Figure 4. (Al2O3 / (CaO+MgO+Na2O+K2O)) plotted against MAP (mm) for undisturbed soil samples from A horizons (A) and C horizons (B). Red line represents simple linear regression. Created using Rstudio (R Core Team, 2018).

13

0 20 40 60 80 100

45

67

8

CIA−K (%)20 40 60 80 100

45

67

8

CIA−K (%)

log(

Mea

n A

nnua

l Pre

cipi

tatio

n lo

g(m

m))

log(

Mea

n A

nnua

l Pre

cipi

tatio

n lo

g(m

m))

20 40 60 80 100

050

010

0015

0020

00

CIA−K (%)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m)

0 20 40 60 80 100

050

010

0015

0020

00CIA−K (%)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m)

A B

C D

Figure 5. CIA-K (%) plotted against MAP (mm) for undisturbed soil samples from A horizons (A) and C horizons (B). CIA-K (%) plotted against log(MAP) (log(mm)) for undisturbed soil samples from A horizons (C) and C horizons (D). Red line represents simple linear regression. Created using Rstudio (R Core Team, 2018).

14

0.0 0.2 0.4 0.6 0.8 1.0

05

1015

2025

Ba/Sr

Mea

n A

nnua

l Tem

pera

ture

(˚C

)

0 5 10 15 20 25 30

05

1015

2025

Ba/Sr

Mea

n A

nnua

l Tem

pera

ture

(˚C

)

A

B

Figure 6. Ba/Sr plotted against MAT (˚C) for undis-turbed soil samples from A horizons (A) and C horizons (B). Created using Rstudio (R Core Team, 2018).

15

0.0 0.2 0.4 0.6 0.8 1.0

05

1015

2025

(K2O + Na2O) / Al2O3

Mea

n A

nnua

l Tem

pera

ture

(˚C

)

0.0 0.2 0.4 0.6 0.8 1.0

05

1015

2025

(K2O + Na2O) / Al2O3

Mea

n A

nnua

l Tem

pera

ture

(˚C

)

A

B

Figure 7. ((K2O + Na2O) / Al2O3) plotted against MAT (˚C) for undisturbed soil samples from A horizons (A) and C horizons (B). Created using Rstudio (R Core Team, 2018).

16

17

Multiple Regression Analysis

Each mineral was run in a separate multiple linear regression analysis alongside

the paleosol-based precipitation proxy to determine whether or not their inclusion

in the linear model significantly improved the R2 value. Significance was

determined using an analysis of variance (ANOVA) test. Of the 26 minerals

tested, four significantly improved R2 values for both log transformed CIA-K and

CALMAG in the A and C horizon: K feldspar (KAlSi3O8), plagioclase feldspar

((Na,Ca)Al(1,2)Si(2,3)O8), total feldspar minerals, kaolinite (Al2Si2O5(OH)4), and total

clay minerals, and hematite (Fe2O3) (see appendix for p-values). Gibbsite

(Al(OH)3) significantly improved the R2 values for both models, but only in the A

horizons. Lastly, quartz (SiO2) improved R2 values for both models, but failed to

improve both horizons in each model.

DISCUSSION

CIA and CIA-K models performed better than CALMAG with respect to their

ability to capture the variability of precipitation in our dataset. Previous studies

have found the CALMAG proxy model to perform slightly better than the CIA-K

model, meaning this paper’s results are somewhat unexpected (Nordt and Driese,

2010). Additionally, previous studies have found that the removal of potassium

improves the efficacy of CIA as a paleosol-based precipitation proxy model

(Maynard 1992). Although the CIA-K model performed better than CIA for this

study’s C horizon data, the inverse result was found in A horizon analysis. This is

not surprising, as the removal of potassium is done to control diagenetic

18

alterations which would not be present in modern soils (Maynard 1992; Sheldon

and Tabor, 2009).

These paleosol-based precipitation proxy models also exhibited improved

correlations when plotted against log-transformed values for MAP. This is

consistent in the case of the CIA-K model, because it theoretically contains an

asymptotic relationship at CIA-K = 100 (Sheldon et al., 2002). Therefore, an

exponential fit should have a slightly higher correlation than a linear fit, although

both are used in the scientific literature (Sheldon et al., 2002). The same

relationship should exist for CALMAG due to the nature of its calculation,

although the literature fails to explicitly affirm its existence (Nordt and Driese,

2010).

Neither of the paleosol-based temperature proxy models examined demonstrated

any predictive power with respect to MAT. This may imply that temperature is

simply less strong of a control on soil geochemistry when compared to

precipitation, or even that the temperature proxy models themselves are less

powerful. Regardless, mineralogic data couldn’t be incorporated in these models

because almost any variable would improve such weak correlations, yielding

trivial results.

19

Mineralogic Findings

My findings suggest that the inclusion of clay minerals and their unweathered

precursors (feldspars) into regression analyses for paleosol-based MAP proxies

could improve the accuracy of these estimations. Feldspar minerals weather into

kaolinite via a hydrolysis reaction, and kaolinite can then weather into gibbsite

(Tan, 1993). The literature emphasizes the role of clay minerals in chemical

weathering and leaching processes (Jenny, 1941; Tan, 1993). Both kaolinite and

gibbsite are clay minerals that may represent substantial portions of inorganic soil

colloids that hold exchangeable ions on their surfaces. Soils with larger clay

mineral compositions may have the ability to leach more, which might explain

why they help provide greater predictive power. As the amount of leaching is

directly correlated with the amount of precipitation, gaining leaching sensitivity

will improve the overall accuracy of the estimation. My findings support previous

work’s emphasis on clay minerals’ importance in soil processes, and suggest they

may improve paleosol-based precipitation proxy models.

Although gibbsite seems only seems to be relevant in the A horizon, this may be

due to its relationship with kaolinite (Tan, 1993). Because gibbsite is the product

of the weathering of kaolinite, it is possible that it must be near the surface to

surpass the threshold of weathering required to produce it. Another explanation

could be that gibbsite is unstable over the longer periods of time required to

develop a C horizon. Both ideas may be supported by the lack of samples in the C

horizon that contain gibbsite. The general lack of samples may be responsible for

20

the failure to produce a statistically significant improvement of the R2 value.

Despite the lack of significance (high p-value), the R2 value did improve in C

horizon tests. Perhaps additional samples containing gibbsite would reveal this as

a statistically significant relationship. To examine the role of gibbsite in climate

estimation models further, we should expand our databases with more gibbsite

containing samples, and rerun these analyses.

As hematite is a mineral whose creation is sensitive to soil moisture, it seems

consistent that it would be associated with an improvement in paleosol-based

precipitation proxies. Some iron oxides weather into either hematite or goethite

depending on frequency of dry periods in a soil environment (Schwertmann,

1988). Goethite, however, was not as effective at improving our precipitation

models. Because hematite and goethite are both products of these iron oxide

transformation reactions, we might expect our dataset to contain a higher number

of soils which experience a frequency of dry periods associated with hematite

production. This may be inconsistent with the literature, as the dry periods

necessary for hematite production are generally associated with subtropical and

tropical environments that are unlikely to be abundant in the contiguous United

States (Cornell and Schwertmann, 2003).

Curiosities also arose with this analysis, such as why quartz improves MAP

estimators. This defies expectation as quartz is intensely resistant to chemical

weathering and is often considered to be the product of physical breakdown of

21

parent material when found in soils. One possible explanation is that quartz could

be a secondary product of the chemical weathering processes of feldspars into

clay minerals. This possibility, however, seems unlikely given our knowledge of

how feldspars weather (Tan, 1993).

Limitations

Considering these models do not take into account parent material, organisms,

topography, or time as conditioning factors of soil character, they appear to be

impressively good at constraining climate. Yet because none of these factors are

taken into account, they still represent a limitation of this study. We can only

expect so much of our data to be explained through our conditioning variables

(climate and temperature), if so many of the other variables remain uncontrolled.

For example, vegetation clearly affects both CALMAG and CIA-K. Different

vegetation types have distinct relationships within these paleosol-based climate

proxies as shown by least-squares lines with varying slopes (Fig. 8, Fig. 9).

Another major limitation of this study is that the contiguous United States does

not necessarily represent climate across the planet. This analysis should be done

across not just the United States, but on a random sampling of soils more broadly

across the world. Special focus ought to be placed on tundra and tropical climates

underrepresented in the contiguous United States. However, such robust datasets

don’t yet exist, so our scope must remain national.

0

500

1000

1500

2000

60 70 80 90 100CALMAG (%)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m) Land Cover

Bare Rock/Sand/Clay

Deciduous Forest

Evergreen Forest

Grasslands/Herbaceous

Mixed Forest

Shrubland

Transitional

Figure 8. CALMAG (%) plotted against MAP (mm) for undisturbed C horizon samples with samples color coded by land cover. Colored lines correspond to simple linear models with shaded regions representing standard error. Created using Rstudio (R Core Team, 2018; Wickham 2016).

22

0

500

1000

1500

2000

60 70 80 90 100

CIA-K (%)

Mea

n A

nnua

l Pre

cipi

tatio

n (m

m) Land Cover

Bare Rock/Sand/Clay

Deciduous Forest

Evergreen Forest

Grasslands/Herbaceous

Mixed Forest

Shrubland

Transitional

Figure 9. CIA-K (%) plotted against MAP (mm) for undis-turbed C horizon samples with samples color coded by land cover. Colored lines correspond to simple linear models with shaded regions representing standard error. Created using Rstudio (R Core Team, 2018; Wickham 2016).

23

24

CONCLUSION

Paleosol-based climate proxies were able to poorly capture variation in MAP

across a modern dataset, while they failed to capture variation in MAT. Although

paleosol-based climate proxies poorly capture the variation in MAP, the fact that

any correlations exist points towards the efficacy of paleosol-based climate

proxies as tools. CIA-K was stronger than CALMAG at capturing variation in

MAP, with R2 values for the A and C horizons about 0.07 and .05 higher

respectively. Running multiple linear regression analyses for each mineral

measure on top of our two major paleo-based climate proxies revealed four

specific minerals as significantly improving both CIA-K and CALMAG analyses

for the A and C horizons: K feldspar, plagioclase feldspar, total feldspars,

kaolinite, total clay minerals, and hematite. Minerals which improved some

portion of analyses, although less broadly, included gibbsite and quartz. These

results point towards the likelihood that incorporation of mineralogy into

paleosol-based climate proxy models could significantly improve these models,

although questions remain about the quantity of this improvement.

Future studies should examine why the addition of specific minerals improves

paleosol-based climate proxies, and whether or not they serve to improve models

working on a narrow set of soils. Studies could also examine soil moisture

balance as a variable, in lieu of precipitation. Moisture balance may have a closer

correlation with CALMAG, CIA, and CIA-K than precipitation, as it more

accurately represents the presence of water within the soil. Analyzing ratios of

25

potential evapotranspiration and mean annual precipitation have precedent as

representing moisture balance, yet moisture analyses were outside the scope of

this project (Orgeira et al., 2011). Studies may also examine how vegetation cover

and soil types can be incorporated into proxy models to examine their

improvement of correlations.

26

ACKNOWLEDGEMENTS

Thanks to Dr. Daniel Maxbauer for his expertise and guidance. Thank you to the

Carleton Geology Department and my peers for advice and company. Thanks to

my friends and teammates for keeping me sane throughout the process.

Thank you to my family, for their continued love and support, and for making

opportunities like this one possible.

27

REFERENCES Andrews, E., White, T., and del Papa, C., 2017, Paleosol-based paleoclimate

reconstruction of the Paleocene-Eocene Thermal Maximum, northern Argentina: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 471, p. 181-195.

Cornell, R. M., and Schwertmann, U., 2003, The iron oxides: structure, properties, reactions, occurrences and uses, John Wiley & Sons.

Gallagher, T. M., and Sheldon, N. D., 2013, A new paleothermometer for forest paleosols and its implications for Cenozoic climate: Geology, v. 41, no. 6, p. 647-650.

Hijmans, R. J., 2017, raster: Geographic Data Analysis and Modeling. Ismail, F. T., 1969, ROLE OF FERROUS IRON OXIDATION IN

ALTERATION OF BIOTITE AND ITS EFFECT ON TYPE OF CLAY MINERALS FORMED IN SOILS OF ARID AND HUMID REGIONS: American Mineralogist, v. 54, no. 9-10, p. 1460-&.

Jenny, H., 1941, Factors of Soil Formation, New York, New York, McGraw-Hill Book Company Inc.

Kahle, D., and Wickham, H., 2013, ggmap: Spatial Visualization with ggplot2: The R Journal, v. 5, no. 1, p. 141-161.

Maynard, J., 1992, Chemistry of modern soils as a guide to interpreting Precambrian paleosols: The Journal of Geology, v. 100, no. 3, p. 279-289.

McLaren, R. G., Cameron, K. C., 1996, Soil Science, New York, New York, Oxford University Press.

Nordt, L., and Driese, S., 2010, New weathering index improves paleorainfall estimates from Vertisols: Geology, v. 38, no. 5, p. 407-410.

Orgeira, M. J., Egli, R., and Compagnucci, R. H., 2011, A quantitative model of magnetic enhancement in loessic soils, The Earth's Magnetic Interior, Springer, p. 361-397.

PRISM Core Group, 2017. R Core Team, 2018, R: A language and environment for statistical computing:

Vienna, Austria, R Foundation for Statistical Computing. Schwertmann, U., 1988, Occurrence and formation of iron oxides in various

pedoenvironments, Iron in soils and clay minerals, Springer, p. 267-308. Sheldon, N. D., Retallack, G. J., and Tanaka, S., 2002, Geochemical

climofunctions from North American soils and application to paleosols across the Eocene-Oligocene boundary in Oregon: The Journal of geology, v. 110, no. 6, p. 687-696.

Sheldon, N. D., and Tabor, N. J., 2009, Quantitative paleoenvironmental and paleoclimatic reconstruction using paleosols: Earth-Science Reviews, v. 95, no. 1-2, p. 1-52.

Smith, D. B., Cannon, W. F., Woodruff, L. G., Solano, F., and Ellefsen, K. J., 2014, Geochemical and mineralogical maps for soils of the conterminous United States: US Geological Survey, 2331-1258.

Stinchcomb, G. E., Nordt, L. C., Driese, S. G., Lukens, W. E., Williamson, F. C., and Tubbs, J. D., 2016, A data-driven spline model designed to predict

28

paleoclimate using paleosol geochemistry: American Journal of Science, v. 316, no. 8, p. 746-777.

Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., 2013, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, in IPCC, ed.: Cambridge, UK, and New York, Cambridge Univ. Press, p. 1535 pp.

Tan, K. H., 1993, Principles of Soil Chemistry, New York, New York, Marcel Decker Inc.

Wickham, H., 2007, Reshaping Data with the {reshape} Package: Journal of Statistical Software, v. 21, no. 12, p. 1-20.

-, 2016, ggplot2: Elegant Graphics for Data Analysis: New York, Springer-Verlag.

Wickham, H., and Chang, W., 2017, devtools: Tools to Make Developing R Packages Easier.

Wickham, H., Romain, F., Lionel, H., and Müller, K., 2017a, dplyr: A Grammar of Data Manipulation.

Wickham, H., Hester, J., and Francois, R., 2017b, readr: Read Rectangular Text Data.

Wuebbles, D. J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and Maycock, T. K., 2017, Climate Science Special Report: Fourth National Climate Assessment, in USGCRP, ed., Volume Volume 1: Washington, DC, USA, U.S. Global Change Research Program, p. 470 pp.