Brault (2012) -- Pleistocene Megafaunal Extinction n' Climate

Assessing the impact of late Pleistocene megafaunal

extinctions on global vegetation and climate

Marc-Olivier BRAULT

Master of Science

Department of Atmospheric and Oceanic Sciences

McGill University

Montreal, Quebec, Canada

June 28, 2012

A thesis submitted to the Faculty of Graduate and Postdoctoral Studies in partial

fulfillment of the requirements for the degree of Master of Science

© Marc-Olivier BRAULT, June 2012. All rights reserved

iii

ABSTRACT

The end of the Pleistocene marked a turning point for the Earth system, as climate

gradually emerged from millennia of severe glaciation in the Northern Hemisphere. It is widely

known that the deglacial climate change then was accompanied by an unprecedented decline in

many species of large terrestrial mammals, featuring among others the near-total eradication of

the woolly mammoth. Due to a herbivorous diet that involved the grazing of a large number of

trees, their extinction is thought to have contributed to the rapid and well-documented expansion

of dwarf deciduous trees in Siberia and Beringia, which in turn would have resulted in a

significant reduction in surface albedo, leading to an increase in global temperature.

In this study, we use the UVic ESCM to simulate various scenarios of the megafaunal

extinctions, ranging from the catastrophic to more realistic cases, in order to quantify their

potential impact on the climate system, and investigate the associated biogeophysical feedbacks

between the growing vegetation and rising temperatures. The more realistic experiments include

sensitivity tests based on the timing of extinction, tree clearance ration, and size of habitat, as

well as a gradual extinction and a simulation involving free (non-prescribed) atmospheric CO2.

Overall, most of the paleoclimate simulations and the sensitivity tests yield results that

correspond well with our intuition. For the maximum impact scenario, we obtain a surface

albedo increase of 0.006, which translates into a global warming of 0.175°C; these numbers are

comparable in magnitude to those in similar studies.

iv

ABRÉGÉ

La fin de l’époque du Pléistocène est une étape importante de l’histoire climatique de la

Terre. En effet, c’est lors de cette période mouvementée que notre planète s’est pour une ultime

fois libérée des conditions glaciales qui perduraient depuis des dizaines de millénaires, et souvent

marquées par la présence d’imposante calottes glaciaires dans l’hémisphère nord. Il est bien

connu que ce changement climatique fut également accompagné d’un déclin sans précédent de

plusieurs espèces de grands mammifères terrestres, y compris une extermination rapide et brutale

du mammouth laineux. En raison d’une diète composée en partie de végétaux provenant

d’arbres prolifiques durant cette période, il y a de fortes raisons de croire que les ceux-ci auraient

pu contribuer au maintien d’une faible densité forestière au sein de leur habitat. Par conséquent,

leur extinction aurait contribué à une rapide émergence d’une variété de petits arbres feuillus tant

en Sibérie qu’en Béringie, provoquant par la même occasion une réduction considérable de

l’albédo de surface, qui à son tour aurait entrainé une augmentation globale de la température.

L’objectif visé par cette étude est de quantifier l’impact potentiel qu’aurait pu avoir une

extinction majeure de la mégafaune sur le climat de la Terre, par le biais d’une modification de la

carte végétale menant à une hausse de la température. Afin d’examiner en détail la rétroaction

de processus biogéophysiques à ce changement de température, nous employons le modèle de

complexité intermédiaire de l’Université de Victoria (UVic) avec des scénarios plus ou moins

réalistes, dont une catastrophe aux proportions exagérées servant à déterminer les limites de que

peut offrir le modèle UVic. Parmi les cas plus terre-à-terre figurent quelques tests de sensibilité

menés sur des paramètres tels que le taux de déboisement des mammouths, la grandeur de leur

habitat, ainsi que l’année de leur extinction. D’autres expériences ayant été menées portent sur

un étalement graduel d’un déclin des populations de mégaherbivores, ainsi qu’une simulation

laissant libre cours aux échanges de carbone entre l’atmosphère et les autres constituants du

système climatique, en autres mots une libre variation du niveau de CO2 dans l’atmosphère.

En général, nous obtenons des résultats qui se conforment assez bien avec ceux d’études

similaires. Dans le cas d’un scénario catastrophique, nous enregistrons une baisse de l’albédo

terrestre équivalent à un peu moins de 0.006, donnant lieu à une hausse de la température se

chiffrant à 0.175°C globalement. Quant aux expériences plus réalistes, les résultats en très

grande majorité confirment notre intuition.

ACKNOWLEDGEMENTS v

First and foremost, this project would not have been possible without the support and

guidance of Dr. Lawrence Mysak. Since the day he introduced me to the topic, he has

contributed to the project in a variety of ways, from our numerous meetings, the friendly advice,

and through his careful editing of this thesis and other paperwork. I am also appreciative of all

the opportunities he offered me, and for getting me to meet with all sorts of new people. But

above all else, I must commend his positive energy, unshakeable enthusiasm, and the patience

which he has shown me over the course of the past year.

I owe many thanks to Dr. Damon Matthews for providing assistance with the UVic

model and for giving crucial suggestions that helped bring the project forward. Our meetings

were few and with often with very short notice, but somehow he always managed to make me

put things into perspective, and find answers to many of my questions. I am also indebted

towards Dr. Jaime Palter, who agreed to act as supervisor to this project within the Department

of Atmospheric and Oceanic Sciences. Her involvement with the project at different levels,

especially in the writing of this thesis, is greatly appreciated.

I am much obliged towards my good friend and fellow graduate student Christopher

Simmons, who so generously offered his own time when I needed it the most. In providing me

with an IDL script to simulate the megafaunal extinctions within the UVic model, he effectively

put me on the right track to get started with the experimentation. Besides that, our discussions

were always interesting and constructive, and they often helped me clarify things about the

model and its underlying physics. A special mention should also be given to the AOS network

administrator Michael Havas, who frequently aided me in the constant fight against my greatest

foe – computers! I was especially impressed when I sent a complaint on a Saturday evening,

only to find that on the following Sunday the problem had already been resolved!

This work has been funded by scholarships awarded to Marc-Olivier Brault by the

Natural Sciences and Engineering Research Council (NSERC), the Global Environmental and

Climate Change Centre (GEC3), and an NSERC Discovery grant awarded to Lawrence Mysak. I

am thankful for this financial support.

Finally, much love towards my immediate family, who as usual went beyond the call of

duty in their unconditional support and faith in me throughout all these years. The endless hours

spent on the phone and the countless Ottawa-Montreal trips testify to their patience and

generosity; they are the very reason I have come this far.

To all these people, those few words cannot even begin to express my gratitude.

vii

Contents

ABSTRACT iii

ABRÉGÉ iv

ACKNOWLEDGEMENTS v

LIST OF TABLES AND FIGURES xi

1 INTRODUCTION 1

2 CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 7

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Forests as interactive components of the climate system . . . . . 8

2.2.1 Early work: biosphere-atmosphere interaction in the tropics . . . 8

2.2.2 Biogeophysical mechanism in high latitude forests . . . . . . . . 9

2.2.3 Investigating the climatic impacts of boreal deforestation: the

major numerical experiments . . . . . . . . . . . . . . . . . . . . 10

2.2.4 Climatic impact of the global forest cover changes. . . . . . . . 13

2.3 Climate-biosphere interactions in paleoclimate simulations . 15

2.3.1 The Mid-Holocene . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3.2 The Last Glacial Maximum . . . . . . . . . . . . . . . . . . . . . 17

2.3.3 Earlier periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3.4 Stability of the climate-vegetation system . . . . . . . . . . . . . 21

2.4 Present-day interactions between climate and the biosphere:

analyzing vegetation response to climate change . . . . . . . . . 23

CONTENTS viii

2.4.1 Global vegetation feedback to increases in atmospheric CO2 . . 23

2.4.2 Climate response to high-latitude afforestation . . . . . . . . . . 24

2.4.3 Climate response to anthropogenic land cover change . . . . . . 25

2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3 MODEL DESCRIPTION 27

3.1 Earth system Models of Intermediate Complexity . . . . . . . . 27

3.2 General description of the UVic ESCM . . . . . . . . . . . . . . . 28

3.2.1 Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2.2 Sea ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.2.3 Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2.4 Coupling strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Recent additions and improvements to the model . . . . . . . . 33

3.3.1 Enhanced radiative transfer model . . . . . . . . . . . . . . . . . 33

3.3.2 Land surface scheme . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.4 Description of the vegetation module . . . . . . . . . . . . . . . . 35

3.4.1 Evolution of vegetation modeling . . . . . . . . . . . . . . . . . . 35

3.4.2 An overview of Dynamic Global Vegetation Models (DGVMs) 36

3.4.3 The Plant Functional Type (PFT) approach . . . . . . . . . . . . 38

3.4.4 General description of TRIFFID . . . . . . . . . . . . . . . . . . 38

3.4.5 Vegetation dynamics . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.4.6 Leaf phenology and soil carbon . . . . . . . . . . . . . . . . . . . 40

3.4.7 Biophysical parameters in MOSES-2 . . . . . . . . . . . . . . . 41

CONTENTS ix

3.4.8 Coupling with the UVic ESCM . . . . . . . . . . . . . . . . . . . 42

4 RESULTS OF THE TRANSIENT SIMULATIONS 43

4.1 An overview of the original study by Doughty et al. (2010) . . 43

4.2 Description of the present experiment . . . . . . . . . . . . . . . 45

4.2.1 Differences with the original study . . . . . . . . . . . . . . . . . 45

4.2.2 Experimental approach . . . . . . . . . . . . . . . . . . . . . . . . 46

4.3 The maximum impact scenario . . . . . . . . . . . . . . . . . . . . 47

4.3.1 Short description and parameter tuning . . . . . . . . . . . . . . 47

4.3.2 Vegetation and surface albedo changes . . . . . . . . . . . . . . 48

4.3.3 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.3.4 Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.3.5 Sea ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.4 A set of more realistic experiments . . . . . . . . . . . . . . . . . . 59

4.4.1 Description of the experiments . . . . . . . . . . . . . . . . . . . 59

4.4.2 Sensitivity tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.4.3 Gradual extinction experiment . . . . . . . . . . . . . . . . . . . 64

4.4.4 Free CO2 experiment . . . . . . . . . . . . . . . . . . . . . . . . . 66

5 CONCLUSIONS 69

5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

REFERENCES 73

x

List of tables and figures

Tables

4.1 List of experiments used in the sensitivity study and their

parameterizations. Results from entries in bold are shown in Figure 4.13

in the form of a world map of temperature anomalies 500 years after the

prescribed extinction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Figures

4.1 Change in vegetation fraction over the mammoth habitat (all land north of

30°N) simulated by the UVic ESCM in the context of a maximum impact

scenario. This figure and every subsequent one represent the difference

between a simulation where mammoths go extinct, and a simulation where

their extinction does not occur. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.2 Change in surface albedo over the mammoth habitat (all land north of

30°N) simulated by the UVic ESCM in the context of a maximum impact

scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.3 (a) World map showing the spatial distribution of albedo changes after

500 years of climate model simulations; (b) Map depicting the size and

location of ice sheets at the end of the simulation. . . . . . . . . . . . . . . . . . 51

4.4 Annual cycle of land surface albedo anomaly in the Northern Hemisphere

during the last year of climate model simulations. Solid line represent

positive contours, while dotted lines represent negative values. On the

abscissa, months are displayed from January to December according to

their numerical order. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.5 Globally-averaged temperature increase due to biogeophysical effects only,

in the context of a maximum impact scenario. . . . . . . . . . . . . . . . . . . . 53

4.6 (a) Zonally-averaged temperature difference between the “extinction” and

“no-extinction” runs; (b) spatial distribution of the temperature anomaly.

The dotted lines represent 0.05°C isotherms. . . . . . . . . . . . . . . . . . . . . 54

4.7 Zonally-averaged, annual cycle of temperature anomalies over the

northern Hemisphere. The contour interval of the isotherms is 0.1°C. On

the abscissa, months are displayed from January to December in their

numerical order. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

LIST OF TABLES AND FIGURES xi

4.8 Variations in δ14

C anomaly as a function of depth. This particular

snapshot is taken in the Weddell Sea, in the middle of the cold anomaly in

Fig. 4.6(b), and averaged for the entire last year of the simulation. . . . . . . . 56

4.9 Change in total precipitation rates, shown for land only and land + ocean. . . . 57

4.10 Annual cycle of precipitation anomalies in the Northern Hemisphere

during the last year of model simulations. Solid lines represent positive

contours, while dotted lines represent negative values. The contour

interval is in units of 10-7

kg m2 s

-1. On the abscissa, months are displayed

from January to December according to their numerical order.. . . . . . . . . . 58

4.11 Sea-ice thickness anomaly. Left panel : Global change in sea ice volume,

over the course of the simulation. Right panel : Thickness anomaly over

the Arctic Ocean. This particular snapshot represents the 5-day average of

days 255-260 (out of 365) of simulation year -11500, or 500 years after

the extinction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.12 Results of the sensitivity tests, presented here as a timeseries of

temperature anomalies. The maximum impact scenario is shown in red for

the sake of comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.13 Spatial distribution of temperature anomalies for various simulations in

the set of sensitivity experiments. The number besides each panel refers

to the that of the specific experiment in Table 4.1. All of these figures are

one-year averaged differences in temperature between the simulation and

a related “no extinction” simulation with similar parameterizations. The

year of averaging is 500 yrs after extinction. . . . . . . . . . . . . . . . . . . . . 62

4.14 Results of the gradual extinction experiments, presented in the form of

temperature-albedo graphs. The four panels represent each of the

individual simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.15 A selection of results from the free CO2 experiment. (a) a comparison of

the temperature anomaly between the free and prescribed CO2

experiments; (b) difference in atmospheric CO2 between the two

simulations; (c) change in total soil carbon resulting from the vegetation

change; (d) carbon flux from the atmosphere to the land (since it is mostly

negative, it indicates a land-to-atmosphere flux. . . . . . . . . . . . . . . . . . . 67

1

Chapter 1

Introduction

The spatial and temporal fluctuations in climate have attracted the attention of a number

of scientific minds over the course of the last century. On the one hand, studies of short-term

future variations of global climate and its implications on the human society are becoming

increasingly popular. On the other hand, many have turned to the study of paleoclimates, not

only in order to explore the rich history of Earth’s climatic evolution, but also as a means to

provide more information to the former group by relating past climatic phenomena to similar

occurrences in present-day or near-future climatic conditions. In particular, the past few decades

have seen a considerable improvement of research tools for climate studies, leading to the

emergence of several research efforts to document and describe some of the most intriguing

events or periods in the Earth’s climatic history. Different means could be employed for this end:

whereas some used an exhaustive analysis of various proxies to reconstruct past states of the

Earth system, others would draw conclusions based on simulations of the major interactions and

feedback mechanisms obtained through climate modeling. While somewhat different in their

methodological approach, both strategies aimed towards a better understanding of climate system

evolution at all time scales, and its sensitivity to external forcings.

Extensive analyses of various climate proxies, notably Greenland ice cores and deep sea

sediment records, revealed that the Earth’s climatic history through the greater part of the

Quaternary (beginning approx. 1.8 My ago) was characterized by colder temperatures, lower sea

levels and severe glaciations in the Northern Hemisphere (NH). Extended periods of glacial

climate were punctuated by brief interglacials marked by a return to temperate conditions in the

NH, forming a cycle of NH glaciations that repeated itself over time with striking periodicity.

The pioneering work of Hays et al. (1976) and Imbrie et al. (1980) identified the Milankovitch

cycles of solar insolation (periodic variations in the Earth’s orbital cycle that lead to latitudinally-

dependent seasonal changes in incoming solar radiation) as the main source of geologic-scale

CHAPTER 1. INTRODUCTION 2

climate variability during the Quaternary, establishing the framework for the astronomical theory

of climate. However, if orbital forcing is now generally recognized as the underlying cause

behind the glacial-interglacial cycles, it alone comes far from explaining many aspects of the

temperature and ice-volume profiles obtained from climate proxies, especially at sub-

Milankovitch timescales (<20 ky). Even today, the diagnosis of physical and biogeochemical

processes and feedback mechanisms behind deglacial climate change (and its opposite, glacial

inception) remains one of the most challenging problems in paleoclimate research.

The final millennia of the Pleistocene epoch (which lasted from the start of the

Quaternary until about 11.7 ky ago) marked an important transitional period in the climatic

timeline as the Earth emerged from the latest recorded bout of widespread NH glaciations and

entered the warm and stable conditions of the current Holocene epoch (11.7 kyr ago – present).

This transition involved major changes in the land surface configuration. Ice sheets which had

dominated the continental landscape at the Last Glacial Maximum (LGM, 21 ky BP) began to

recede, and eventually disappeared from the mainland. In their wake came tundra vegetation – a

combination of cold-adapted short grasses lichens, and mosses – which in turn would be replaced

by boreal forests of evergreen needleleaf trees and dwarf deciduous trees, in locations where

climate became favorable to the maintenance of such ecosystems. These changes in the land

surface had a profound impact on the climate of the late Pleistocene, often acting as a positive

feedback to global warming and reinforcing the positive energy imbalance.

Of course, the last Pleistocene deglaciation becomes especially important – relative to

other similar occurrences in the cycle of NH glaciations – in light of the events that followed it,

notably the rise of the human civilization and the onset of the anthropogenic era, both of which

occurred during the relatively short time span of the Holocene. In any assessment of present-day

or near-future climate it is impossible to avoid the effects of human activity, especially in regards

to an increase in atmospheric carbon dioxide of unparalleled abruptness. The Holocene

interglacial is also unusual in its length, and some would argue that a delay in the next glacial


inception (and a possible termination of the Quaternary glacial cycles) would be directly

attributable to the impacts of increased greenhouse gas levels on the Earth’s energy imbalance

(see, for example, Mysak, 2008).

Another uncharacteristic aspect of the late Pleistocene deglaciation is that it coincided

with the extinction of at least 34 genera of megafaunal mammals (also called Late Quaternary

Extinctions, or LQE), one of the most significant shocks on faunal biodiversity during the past

55 million years (Koch and Barnosky, 2006). The mass extinction was a discontinuous event

spread over 50,000 years (and thus not entirely constrained within the time frame of deglacial

climate change), and consisting of a series of short-term diachronous pulses; nonetheless, it is

generally recognized that most of the extinctions did not continue into the Holocene (Barnosky et

al., 2004).

Numerous theories and hypotheses have been put forward to provide a tentative

explanation for the rapid decline of the Pleistocene megafauna. Most of these hypotheses would

fall into one of two categories: those that favored environmental causes (for example, Thomas et

al., 2004), and those who insisted on the role of human intervention (for example, Alroy, 2001;

Wroe et al., 2004). Included among the former category were topics such as: a direct or indirect

impact of climate change (e.g. through a change in vegetation that would reduce access to

optimal food, or a loss of habitat due to the rise in sea level), a change in population dynamics

leading to overwhelming competition, and a regional catastrophe (bolide impact!). Proposals in

the latter category emphasized the role of man through various scenarios: an artificial

modification of the habitat, the introduction of new predators and alien diseases, or any form of

the overkill hypothesis (Webb and Barnosky, 1989).

For many years there was no perceived middle-ground between the two set of hypotheses,

and a fierce debate raged between proponents of either faction (Barnosky et al., 2004).

Detractors of the overkill hypothesis argued that some of the extinct species included mammal

and avian genera that were not vulnerable to hunting (i.e., not attractive to the presumed hunters),


and that in any case the evidence supporting the systematic hunting of megafauna by human

tribes was defined based on ambiguous parameters, and was at best inconclusive. On the other

hand, many criticized the environmental hypotheses because they could not explain what was so

different about the late Pleistocene that would have driven such a large number of species to the

brink of extinction, whereas previous deglaciations had witnessed nothing of the sort. Theories

giving most of the credit to climate change also failed to explain why extinction patterns were

localized; indeed, the presumed date of extinction for each species varied inconsistently with

geographical location, and in some cases a descendant species was shown to have survived many

thousand years into the Holocene after relocating to a remote location (for example, a smaller

version of the woolly mammoth, often called dwarf mammoth, is believed to have survived until,

2000 BC on small islands off the Siberian coast).

Recent investigations have reinforced the claim of human intervention in the catastrophe.

Using evidence from paleontology, climatology, archaeology and ecology, it was determined that

early human tribes likely had a role in the extinction of some species, with a strong level of

confidence for human activity in North America, Africa, and Australia (Barnosky et al., 2004;

Koch and Barnosky, 2006). The evidence also appeared to be stronger on islands where humans

were known to have settled. However, it was noted that humans could not be responsible for

extinctions everywhere on the planet, and that it would be “oversimplistic” to pretend that

hunting alone could have caused the eradication of so many species prior to the Holocene.

Instead, the authors wrote off the human factor as an additional stress on the endangered species,

which when combined with a rapidly evolving environmental context, would have driven them

to famine, exhaustion, and eventually extinction. As a result, while the debate is still ongoing,

many have adopted this point of view, accepting that in the end the LQE are likely a combination

of both natural and anthropogenic factors.

Some of the larger extinct genera, known to have a strong interaction with the

surrounding vegetation, have long been thought to play a central role in the mass extinction


because their departure would have triggered a positive feedback from the vegetation, further

aggravating the situation for other threatened species (Owen-Smith, 1987). Among these

terrestrial megaherbivores, the case for human implication in the extinction of the woolly

mammoth is especially strong due to their body size, exceptionally slow gestation period, and

abundance of archaeological evidence found at Paleolithic sites in Siberia (Guthrie, 2006). Due

to the perceived resemblance with their elephant successors, as well as strong evidence for the

inclusion of various Pleistocene tree species into their diet, there is compelling reason to believe

that mammoths played a dominant role in the maintenance of grasslands over the expansion of

trees in the Eurasian taiga – much like elephants are maintaining the African savanna – and

therefore their extinction would have triggered a significant recovery of forest biomes at the NH

northern latitudes. In such a mindset, should the suspicions of human involvement in the

mammoth extinction happen to be well-founded, the combination of all of the above would have

the surprising consequence of redefining the onset of anthropogenic influences on climate.

It is not the first time that scientists challenge the idea that the Anthropocene is entirely

constrained within the past two hundred years. In a novel paper, Ruddiman (2003) proposed an

alternative explanation to the observed positive trend in greenhouse gas levels during the mid-

Holocene (which should have been negative in casual circumstances), by linking them with the

start of agriculture in Eurasia, and thus associating the increases in CO2 and CH4 to related

activities such as forest clearance (starting 8000 years ago) and rice irrigation (starting 5000

years ago). In a similar manner, Doughty et al. (2010) proposed that the start of the

anthropogenic era should be pushed back an additional several thousand years, by associating the

megafaunal extinctions (and the likely involvement of human hunters) with significant

modifications in the distribution of terrestrial vegetation. In particular, they suggest that the

extinctions played a pivotal role in the rapid expansion of Betula trees in Siberia and parts of

Beringia, and that an increase in surface darkness led to a significant warming over these regions.

In their paper, this assertion is backed up by a combination of proxy data analysis and climate

model simulations.


Following on from the work of Doughty et al. (2010), the main focus of this thesis is to

simulate the deglacial climate of the late Pleistocene in the context of megafaunal extinctions for

the purpose of obtaining a quantitative measurement of the latter’s impact on the climate system.

However, our objective varies from that of Doughty et al. (2010): whereas the aim of the original

paper was to provide solid argumentation in support of the authors’ novel claim on the first

potential case of human-induced global warming, in this thesis we propose an in-depth

assessment of biophysical interactions between the fauna, flora, and climate. Concurrently, we

wish to extend the modeling effort presented in Doughty et al. (2010), by executing long-term

transient simulations of the Earth system with the University of Victoria Earth System Climate

Model (henceforth UVic ESCM), a fully coupled global climate model of intermediate

complexity which includes, among others, a dynamical treatment of vegetation feedbacks. The

impacts of the megafaunal extinctions are to be prescribed directly into the model’s vegetation

component, first as geographically-dependent perturbation that reduces tree cover (while the

mammoth are alive and roaming the land), and then as a release of that perturbation (when they

go extinct), with the subsequent recovery of forest biomes acting as the main driving force for

climate change. Most of our analysis will focus on a single scenario of the most extreme

catastrophe, which we dubbed “maximum impact scenario,” and whose purpose is to quantify

the largest response that can be obtained from the UVic as a result of the megafaunal extinctions.

However, due to the obvious lack of realism of the latter case, we have also included results from

simulations that represent a more likely outcome.

The thesis is structured as follows. Chapter 2 reviews the available literature on climate-

vegetation interactions, with a particular emphasis on boreal forest feedbacks and paleoclimate

studies. Chapter 3 describes the climate model used in this study, as well as the dynamical

vegetation model involved in the simulations. In Chapter 4, we present the experimental context,

the methodology, and our analysis of the model output. Finally, the thesis conclusions are given

in Chapter 5.

7

Chapter 2

Climate-vegetation interactions and feedbacks

2.1 Introduction

The deep and complex role of vegetation within the Earth system provides one of the

finest examples of climate-biosphere interactions, involving a set of biogeophysical and

biogeochemical processes that couple it with various components of the climate system. The

relationship is twofold: on the one hand, climate (as defined by the annual average in air

temperature and precipitation) has long been known as a prime factor in determining the spatial

coverage and distribution of vegetation as well as the structural and phenological properties of

plants. In fact, the first systems of climate classification used vegetation almost exclusively in

their definitions of climatic zones, because flora was thought of as an exact mirror of temperature

and precipitation patterns (Köppen, 1936). On the other hand, it has become increasingly clear

in recent decades that vegetation dynamics comprises a major climate forcing, exerting its

influence through biogeophysical processes which alter the radiative, hydrological and turbulent

properties of the land surface and through biogeochemical effects which modify the atmospheric

gas composition (carbon dioxide, methane and nitrogen dioxide, to name a few), ocean chemistry

and soil carbon content (Kabat et al., 2004).

The purpose of this literature review is to gain a better understanding of climate-

vegetation interactions. The chapter first discusses in section 2.2 the basic physical concepts

involved in climate-biosphere interactions, with a special focus on Arctic-boreal vegetation; it

also reviews numerical modeling papers which discuss boreal and global deforestation

experiments. Section 2.3 covers some of the literature on the role of high northern vegetation on

past climatic changes such as the mid-Holocene climatic optimum and the Last Glacial

Maximum. Finally, section 2.4 deals with contemporary issues surrounding forest vegetation as

a component of the climate system. It should also be noted that, although biogeophysical and

CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 8

biogeochemical processes are equally important in the broad spectrum of climate-vegetation

interactions, the former is central to this study and therefore the main topic of this chapter.

2.2 Forests as interactive components of the climate system

2.2.1 Early work: atmosphere-biosphere interaction in the tropics

The study of climate-vegetation interactions has attracted an increasing amount of

interest over the past few decades in the climate modeling community. Charney et al. (1975)

were the first to investigate feedback mechanisms between land surface processes and the

climate system. In a pioneering study, they used a then state-of-the-art General Circulation

Model (GCM) to simulate the climate response to a decrease in vegetation cover in the Sahara

region (parameterized as an increase in surface albedo). The model output revealed a significant

decrease in rainfall caused by the reduced surface heating, which led them to conclude that land

surface processes could be responsible for the self-induction of low-latitude deserts.

Another emerging issue at the time was the possible climatic impacts of tropical land

cover changes. Some of the earliest modeling studies on climate-vegetation interactions (e.g.,

Potter et al., 1975; Dickinson and Henderson-Sellers, 1988) were concerned with the short-term

impacts of large-scale deforestation in the Amazonian rainforest. In particular, Dickinson and

Henderson-Sellers (1988) observed that a replacement of tropical vegetation with impoverished

grassland resulted in warmer temperatures and a notoriously drier soil, which would not only be

detrimental to the survival of any remaining woodland, thus igniting a potentially irreversible

feedback between climate and vegetation loss, but would also compromise the very motivation

behind this massive deforestation – that is, to create more space for arable land. Further studies

(Shukla et al., 1990; Nobre et al., 1991, Henderson-Sellers et al., 1993) confirmed the

establishment of warmer, drier conditions, along with significant alterations in evaporation and

net surface radiation, and found that a reduction in vegetation cover would lead to the


development of a lengthy dry season in the affected regions, creating conditions similar to those

that are thought to have prevailed in the tropics during the last major glaciation.

The causes behind this climatic response to tropical deforestation are well known.

Vegetation in general, and especially broadleaved evergreen trees, contributes to moisture

recycling in many different ways. Aside from direct evapotranspiration, plants can also extract

additional water from deep soil layers as well as increase surface roughness (and therefore

atmospheric turbulence), both of which act to further increase water vapor input to the

atmosphere (Meir et al., 2006). Consequently, plant vegetation adds moisture to the surrounding

environment and promotes ambient air cooling through evaporative latent heat release, the sum

of which indirectly contributes to creating a cool, moist boundary layer that enhances

precipitation (Bonan, 2008). It comes as no surprise, then, that the loss of these processes upon

deforestation – along with a corresponding reduction in carbon sequestration – results in warmer,

drier conditions locally which can also act as a major perturbation on atmospheric dynamics in

the tropics.

2.2.2 Biogeophysical mechanism in high-latitude forests

Climate-vegetation interactions in high-latitude woodlands are dominated by radiative

feedbacks, rather than hydrologic processes. This can be partially attributed to the limited

amount of moisture in these regions, but it is mainly due to the very large difference in surface

reflectivity between the dark forest canopy and bare or grass-covered ground, most of which

becomes snow-covered during the winter and early spring. Data analyses from flux tower

measurements in the mid-latitudes (Betts and Ball, 1997) reveal that surface albedo over the

forests in winter can be as low as 0.3 (a little higher for deciduous trees), which is quite a

contrast to that of bare soil, which can exceed 0.8 in the wake of a decent snowfall. Such a

massive difference in surface reflectivity, due to a masking of snow-covered ground by tree

cover, leads to an important radiative feedback between vegetation and surface air temperature.

The expanding vegetation cover (often expressed as leaf area index) reduces surface albedo


during the cold season, which in turn favors an early spring snow melt, resulting in warmer

temperatures that further enhance the spread of vegetation. Therefore, it is widely understood

that high-latitude forest vegetation provides a significant warming contribution on both local and

global scales, and that any successful model simulation of high-latitude seasonal variability must

include these land surface processes in order to accurately reproduce the annual cycle of

temperature change (Wilson et al., 1987).

Since land cover changes are also an important issue in boreal ecosystems, many studies

have been made to better assess and quantify climate-vegetation feedbacks in high latitudes.

Among the different modeling options, large-scale deforestation experiments remain the most

popular as they allow climate models to isolate the individual contribution of the removed

vegetation in the context of realistic scenarios of medium-range future climate change. The

radiative feedback in boreal forests was first introduced using simple “energy balance” climate

models (Otterman et al., 1984, Harvey, 1988). The goal of both these studies was to assess the

sensitivity of boreal forest species to climate change and the potential impacts of forest removal

on climate. Both found a significant hemispheric cooling in the absence of snow masking by

forests, as well as increased climate sensitivity to solar forcing and external perturbations.

2.2.3 Investigating the climatic impacts of boreal deforestation: the major

numerical experiments

The climatic impact of high-latitude vegetation has been analyzed extensively with a

variety of numerical models, and the examples in peer-reviewed literature are plentiful. A better

assessment of climatic feedbacks to ecological processes has been made possible through the use

of increasingly complex representations of land-atmosphere interactions, including among others

a better representation of land surface processes and an improved parameterization of vegetation

feedbacks.

Among the earlier work, Thomas and Rowntree (1992) used the UKMO (United

Kingdom Meteorological Office) GCM to show that an increased wintertime and springtime


surface albedo associated with boreal deforestation resulted in a significant cool perturbation to

the surface energy balance, despite the fact that only half of the change in surface reflectivity is

transferred to planetary albedo due to cloud cover. The overall effect of boreal vegetation on the

global heat budget was estimated to be equivalent to that of doubling CO2 levels, with a

complete removal of the boreal forest yielding a net cooling effect of up to 2.8C.

Bonan et al. (1992) employed a more explicit approach by removing all forest vegetation

poleward of 45°N in the NCAR climate model CCM1, which also initiated a substantial spring

cooling in the high latitudes. In addition, the coupled model revealed that sea ice-albedo

feedbacks amplified and extended the effect well beyond the deforested area, while cool

anomalies tended to persist throughout the entire year in many locations due to the strong

thermal inertia of oceanic basins. Due to these results, they reasoned that climate feedbacks

associated with boreal deforestation could create unfavorable environmental conditions,

irreversibly turning the tides against eventual forest regeneration.

Chalita and le Treut (1994) examined the impact of increased albedo in the LMD

(Laboratoire de Météorologie Dynamique) Regional GCM, and argued that the cold perturbation

associated with higher surface albedo could modify soil moisture so as to enhance summer

precipitation in Europe. This result is interesting because it contradicts the notion that

precipitation increases monotonously with temperature (at high latitudes), at least locally.

Although regional model do account for far more processes than their global counterpart, it is

surprising to find that none of the global studies seem to acknowledge an increase in summer

precipitation over Europe due to the increased surface albedo.

Two subsequent model studies used the same experimental setup as Bonan et al. (1992)

in climate models GENESIS (Bonan et al., 1995) and ARPEGE (Douville and Royer, 1997).

Results from both experiments clearly indicated that deforestation at high latitudes cools the

surface air and decreases latent heat flux and atmospheric moisture at all times of the year.

Additionally, results from the latter suggest mid to high latitude deforestation could produce


significant climatic trends in other parts of the globe, notably a shift in the Asian monsoon and

the African ITCZ.

Snyder et al. (2004), in experiments with the coupled atmosphere-biosphere model

CCM3-IBIS, compared the climatic impact of the removal of six major vegetation biomes and

determined that boreal forests produce the largest temperature signal, even surpassing in

intensity some important shorter-term climate oscillations such as ENSO. Their results also

underlined several changes in the summertime hydrologic cycle – including a decrease in

evapotranspiration, atmospheric moisture and precipitation – and confirmed the potential of high

latitude vegetation to influence climate remotely, all largely in agreement with much of the

earlier GCM work focused on the radiative effects of high-latitude vegetation changes.

Climate-vegetation interactions have also been investigated with the far better resolved

regional climate models (RCM), allowing a more diverse representation of sub-continent-scale

(e.g., orographic) and therefore a better assessment of the impact of localized land surface

perturbations. For example, Heck et al. (2001) studied the climatic impact of regional-scale

deforestation in Europe, and found that the climate sensitivity to vegetation changes occurred in

two distinct phases: a cool, wet spring, followed by a warm, dry summer. These results would

imply that hydrologic processes override radiative feedbacks during summer. As another

example, Notaro and Liu (2008) examined vegetation feedbacks in Asiatic Russia with a

combined statistical-dynamical approach, and both methodologies supported a year-round

positive feedback of forest cover on both temperature and precipitation. Some of the interesting

consequences of the increased surface albedo include an extended snow season and increased

atmospheric stability, which in turn act to enhance the Siberian High and reduce convective

precipitation. The latter, combined with an expected decrease in plant transpiration and moisture

recycling because of the sparser vegetation, point toward a significant decrease in precipitation.


2.2.4 Climatic impact of the global forest cover changes

The previous three subsections have highlighted a strong competition between two

biogeophysical feedback mechanisms – namely, surface reflectivity and evapotranspiration –

both of which are driven by the absorption of energy by the land surface. Since these two

processes directly oppose each other, it is clear that the prevalence of one over the other will

determine the impact (e.g. a warming or a cooling trend) of vegetation cover on climate in a

particular region at a given time of the year. For example, reflectivity usually dominates in areas

of high seasonal variability, low precipitation and sparse vegetation cover, whereas

evapotranspiration has a dominant effect in the densely vegetated tropical areas, but can also be

important during the warm season in other parts of the world. This creates a diversity of

ecological responses to climate forcings, which becomes especially important not only for the

global evaluation of the climate impacts of forests, but also in the context of climate change

mitigation efforts – for example, it is useful to know that afforestation would provide the greatest

climate benefit when concentrated in tropical regions (see Bonan,, 2008).

There have been a number of global-scale climate-biosphere investigations in order to

evaluate the full impact of the world’s forests on climate and determine which feedback

mechanism dominates the temperature and precipitation signal on a global scale. The idea was

first initiated in a numerical study with the atmospheric GCM ECHAM4 (see Fraedrich et al.,

1999; Kleidon et al., 2000), which compared the two opposite extremes of the vegetation

spectrum: a fully vegetated “green” world, and a “desert” world devoid of vegetation. Among

the many substantial differences between the two simulations, the “green” planet featured twice

as much precipitation worldwide and tripled land evapotranspiration, resulting in a significant

surface cooling from latent heat release many times compensating for the increase in absorbed

solar irradiation at the surface.

A major criticism of the previous experiment, however, was its prescription of sea

surface temperatures and sea ice coverage, which were seen as a constraint on the study’s ability


land cover change effects. Subsequent model investigations with some level of ocean dynamics

included would prove these reservations to be well-founded. In particular, Fraedrich et al. (2005)

conducted a series of sensitivity tests with the Planet Simulator (which involves a mixed-layer

ocean and thermodynamic sea ice model) and found the green world to be warmer than the desert

world. While still displaying regional pockets that experienced cooling, the new simulations

clearly showed that the global temperature response to increased tree cover was being dominated

by radiative effects. Another study (Gibbard et al., 2005) used a coupled AGCM-slab ocean

model and reached similar results, evaluating the temperature difference between a “forest”

world and a “grass” world to be approximately 1.7°C, along with a change in surface albedo of -

0.07. Interestingly, an assumption of increased carbon sequestration has been agreed to not

affect the warming trend in the long term, because a change in surface albedo is perceived as

permanent whereas the anomalous carbon levels eventually vanish once the model equilibrates

with the ocean, and ultimately the sediment components.

Other similar experiments have since confirmed that the warming effect of forests at mid

to high latitudes dominates over the cooling effect of latent heat fluxes in the tropical forests

(Bala et al., 2007; Brovkin et al., 2009; Davin and de Noblet-Ducoudré, 2010). In particular, the

last study explicitly demonstrates that the climate response to changing surface albedo is the

most important biogeophysical effect of land cover change. In regards to the unequivocal role of

ocean dynamics in the success of recent investigations, it has been suggested that the ocean

might be unresponsive to nonradiative forcings (such as perturbations in the hydrologic cycle),

which would explain why the inclusion of an interactive ocean module only appears to

strengthen warm anomalies brought on by decreased surface albedo.


2.3 Climate-biosphere interactions in paleoclimate simulations

2.3.1 The Mid-Holocene

The past few decades of climate research have highlighted boreal forest-climate

interactions as an amplifier of externally-driven climate change, both in the present and in the

past. Nowhere is this truer than for the case of the mid-Holocene climatic optimum (about 6000

years ago). Reconstructions indicate warmer-than-present temperatures globally, most of which

can be attributed to the higher total insolation received during this period. However, orbital

forcing alone fails to explain why largest temperature departures (with regards to present-day

conditions) occur in the spring, at which time the Earth reaches the aphelion and it thus farthest

away from the Sun.

The first suggestion implicating climate-vegetation feedbacks in this discrepancy can be

traced back to the early work of Foley (1994), who first discovered, through the use of a process-

based ecosystem model, that the terrestrial biosphere responded to mid-Holocene warming with

a significant expansion of the boreal forest in high latitudes and an expansion of grassland in

subtropical Africa, both supported by palaeobotanical evidence. Building on this knowledge,

Foley et al. (1994) used a set of climate simulations and integrated palaeobotanical data to show

that these vegetation feedbacks could help account for the additional warming indicated by the

proxies.

These findings were soon reinforced by subsequent experiments including an exhaustive

study with two coupled AGCM-slab ocean models (TEMPO authors,, 1996), which added that

the simulated vegetation feedbacks contributed to as much as 50% of the temperature increase

that drove the northward boreal forest expansion (relative to present day distributions). The

latter idea, however, was not shared by Texier et al. (1997), who argued instead for a more

secondary role of vegetation feedbacks – as an amplifier of orbitally induced climate and


vegetation changes, perhaps, but not the main factor that would reconcile simulated with

observed conditions.

A study with a fully coupled atmosphere-ocean-vegetation model (Gallimore et al., 2005)

further corroborated the above results, finding a climate-vegetation response of comparable

intensity to that of Texier et al. (1997). The northern expansion of both the taiga at high-

latitudes and grassland over low-latitude deserts was reproduced, however orbital forcing

remained the dominant cause of temperature change. The positive vegetation feedback in their

simulation was not uniform however, as the southern extent of the taiga also retreated north due

to severe water limitations, leaving previously wooded areas in the form of grassland and

therefore more susceptible to late spring snowmelt.

In another study, Wang et al. (2005) examined the climate system response to changes in

both orbital parameters and ice sheet configuration in a number of sensitivity experiments with

the McGill Paleoclimate Model (MPM), “greened” with the dynamic global vegetation module

VECODE. They found that orbital forcing together with strong vegetation-albedo feedbacks

induced by a retreating Laurentide Ice Sheet were mostly responsible for the warming trend in

the mid-Holocene, and as a response the northern limit of the boreal forest moved northward

during this period. However, declining summer insolation reversed that trend in the following

centuries and a gradually cooling climate forced the boreal forest to retreat further south.

As it became increasingly clear that mid-Holocene warming was influenced in some

manner by vegetation feedbacks, a new experimental setup emerged which identifies and isolates

three individual contributions to the climate signal: vegetation-atmosphere interaction,

atmosphere-ocean interaction, and a synergy that arises from the coupling of these two processes.

This approach was initiated by Ganopolski et al. (1998), who found a significant contribution

from vegetation-atmosphere interactions, but concluded that overall agreement with paleodata is

very weak without the synergy between vegetation-atmosphere and atmosphere-ocean

interactions.


Following on these footsteps, Crucifix et al. (2002) and Wolfhart et al. (2004) initiated

several climate simulations with different Earth system Models of Intermediate Complexity

(EMICs), and found that most of the warming of the Northern Hemisphere during the mid-

Holocene could be attributed to vegetation-atmosphere interactions. Since, in both cases, ocean

and vegetation feedbacks often displayed opposite impacts on continental temperature trends, a

strong synergy was deemed unlikely.

In a more recent set of experimentations with an updated version of the ECHAM AGCM,

Otto et al. (2009) could reproduce neither the strong vegetation-atmosphere interaction featured

in the previous two studies, nor the strong synergy found by Ganopolski et al. (1998), prompting

the authors to suggest that the observed mid-Holocene warming signal was dominated by the

contribution from atmosphere-ocean interactions. These findings were further strengthened by a

full investigation of forest-albedo feedbacks in the mid-Holocene (Otto et al., 2011), which

found that factors to which the intensity of spring warming was most sensitive to (such as the

parameterization of snow albedo) had little impact on boreal forest cover. Because of these latest

developments, it is now believed that vegetation-atmosphere interactions have been over-

estimated in early climate simulations of the mid-Holocene.

2.3.2 The Last Glacial Maximum

Among the many different climatic periods of interest, the Last Glacial Maximum

(~21000 years BP) has also gathered considerable interest because there are signs of major

vegetation changes (as indicated by various palaeobotanical records). This period offers a

unique opportunity to test model performance in the context of severe glaciation in the Northern

Hemisphere and provide additional insight on atmosphere-biosphere interactions, especially with

regards to feedback mechanisms between the massive continental ice sheets and a rapidly

evolving land surface cover. Its relatively recent time frame (when considering the availability

of proxy data records) also contributes to make it an attractive candidate for paleoclimate

modeling.


The Last Glacial Maximum (LGM) was first investigated within the framework of the

Paleoclimate Model Intercomparison Project (PMIP): for example, Lorentz et al. (1997) found

major discrepancies with geologic reconstructions, especially in sub-glacial high latitudes. In an

effort to better represent land surface patterns, Kubatzki et al. (1998) added a vegetation module

to their climate model, and were much more successful in obtaining a better representation of the

LGM climate. The model performance was especially improved in the Siberian region, where

atmosphere-only simulations tended to overestimate the ability of cold-adapted vegetation to

resist the harsh winter cold.

These results were soon followed by another study (Levis et al., 1999), which also found

that LGM conditions conducive to the southward migration of the tree line, replacing much of

the high-latitude forestry with tundra. Their model output also hinted at a reduction of tropical

forest cover in favor of C4 grasses, due to physiological effects associated with lower

concentrations of atmospheric carbon dioxide. The latter result is especially interesting in light

of current large-scale forest decimation in the tropics, because it would make LGM climate in

these regions a possible analogue to near future climate – save for atmospheric CO2 – should the

deforestation continue uninterrupted.

More recently, the findings of Crucifix et al. (2005) corroborated most of the above

results, notably a disappearing Siberian taiga, increased shrub cover in Europe and expanded

subtropical deserts. An analysis of bioclimatic relationships also revealed that the position of the

boreal treeline was primarily constrained by water stress and soil properties (rather than summer

temperature), and confirmed that a depletion of atmospheric CO2 (relative to pre-industrial

values) would result in environmental conditions more favorable to grasses and shrubs by

narrowing the climatic range where trees dominate the vegetation spectrum.

All of the above papers made note of the profound impact of LGM climate on vegetation,

especially at high latitudes, and their general success in using a coupled climate-vegetation

model to reconcile simulated climate with observations would suggest some influence of


vegetation processes on the climate system during the same period. Crowley and Baum (1997)

first tried to evaluate the potential role of vegetation changes by prescribing reconstructed LGM

vegetation in a GCM, and found a significant impact on the terrestrial response to ice and SST

changes. A more recent study (Crucifix and Hewitt, 2005) examined the regional and global

impact of land cover changes in a series of LGM simulations involving the vegetation module

“TRIFFID”. They found that the temperature and precipitation response on the continents was

driven by regional interactions with vegetation, and the overall impact of vegetation dynamics

(relative to present-day) resulted in additional surface cooling despite a warming trend in the

tropics caused by the reduced tree cover. Furthermore, a strong correlation was found between

enhanced glacial cooling in Siberia (caused by high surface albedo) and atmospheric dynamics in

the tropics, suggesting a possible remote impact of high latitude vegetation changes on tropical

climate.

In an attempt to better quantify the impact of vegetation dynamics during the LGM, Jahn

et al. (2005) used a factor separation technique (see previous section for other examples) in order

to isolate the individual contributions of continental ice sheets, changes in CO2 concentrations,

and vegetation feedbacks on the global climate signal. Their results highlighted previous

findings that the impact of vegetation changes would be mostly limited to regional-scale effects.

Although a global cooling similar to that of Crucifix and Hewitt (2005) was found, further

investigation revealed that the contribution from vegetation feedbacks to the temperature signal

could have been indirect, for example by triggering a change in ocean circulation regime that

would have caused further cooling.

2.3.3 Earlier periods

Among the earlier periods, ice age inception offers another potential interesting topic of

study. Given the strong forest-albedo feedback mechanisms discussed above, the retreating

boreal forest in favor of cold-adapted grasses (as observed in the palaeobotanical record during

glacial inception) is sometimes credited with a major role in the expansion of continental ice


sheets by enhancing cold continental air masses and severely limiting warm-season snowmelt.

This idea can be found, for example, in the work of Gallimore and Kutzbach (1996), as well as

De Noblet et al. (1996). Both studies indicate that prescribing an expansion of tundra (or letting

vegetation adapt naturally to decreasing summer insolation) results in a significant decrease in

summer temperature in high-latitude North America and Eurasia, which increases the likelihood

of summer snowfall and allows the snowpack to survive the warm season in a considerably

larger area, a crucial part of initiating an ice age. Glacial inception has also been studied with the

UVic ESCM (see Meissner et al., 2003). The model confirms a global decline in tree vegetation,

occurring in both tropics and high latitude as an expansion of grasses or shrubs at the expense of

forests, the consequence of which appear to double the effective atmospheric cooling during ice

age inception, as well as reduce meridional overturning in the North Atlantic and significantly

perturb precipitation patterns over the continents.

Finally, it is always interesting to contemplate how vegetation dynamics could have

impacted climate at various epochs of Earth’s history. For example, Kubatzki et al. (2000)

underline several differences between the climate of the Holocene (present day) and that of the

Eemian (last interglacial) which might be caused or amplified by climate-biosphere interactions.

In particular, they note that ecological feedbacks amplify the orbitally induced warming in the

Arctic (where temperature departures from present day conditions are greatest) and result in

overall warmer conditions across the globe. An apparent expansion of subtropical vegetation

also intensifies the monsoonal response to orbital forcing, while indirect interaction between

vegetation and the ocean indirectly results in a reduced Atlantic meridional overturning

circulation.

Another example can be found in Schneck et al. (2012), who use an EMIC to evaluate the

sensitivity of climate to vegetation changes during the Late Miocene, situated prior to the cycle

of Quaternary glaciations in the geologic timeline and believed to have been warmer – especially

at the poles – and more densely vegetated compared to the present day. In particular, one of their


simulations prescribes modern day high-latitude vegetation on Late Miocene climate. This

causes a strong cooling effect extending up to the mid-latitudes, an expected result of increased

surface albedo on boreal climate. Due to the geography of the time – or perhaps a model

shortcoming – this apparent cooling does not lead to an intensification of Northern Hemisphere

heat transport. However, the authors note that the inclusion of vegetation feedbacks eliminate a

part of the discrepancy between simulations and paleorecords, bringing the research one step

closer to explaining the weak equator-to-pole temperature gradient as suggested by data records.

2.3.4 Stability of the climate-vegetation system

One of the most intriguing aspects of the nonlinear climate system and its complex

entourage of interacting components is its ability to produce several equilibrium states depending

on the “initial” condition. The concept of dual equilibriums in the climate-biosphere system was

first hinted at in early studies of the mid-Holocene climatic optimum as a possible explanation

for the presence of a “green” Sahara in GCM simulations – an alternate solution of climate

system dynamics in place of the modern-day arid desert. For example, Claussen and Gayler

(1997) found a northward expansion of savanna vegetation into the Sahara as well as generally

wetter conditions in the northern half of Africa, especially in the west. These results, which were

obtained with a coupled atmosphere-biome model, were much closer to paleogeological and

palaeobotanical records than an atmosphere-only simulation, the latter tending to reproduce

modern-day conditions even with mid-Holocene orbital forcing and sea surface temperatures. In

other words, the single addition of vegetation dynamics would have provoked a change in

tropical circulation in response to mid-Holocene orbital forcing and SST that would have

drastically enhanced precipitation in the Sahel, hence the argument for a possibly crucial role of

atmosphere-biosphere interactions in the emergence of the “green” state of the Sahara.

The stability of the climate system in the Sahel has also been investigated for LGM

(Kubatzki and Claussen, 1998) and present-day (Claussen, 1998) climates. Sensitivity tests in

both of these periods revealed that initiating the ice-free land surface as a uniform forest, steppe


or dark (low albedo) desert would ultimately yield the “greened” state of the Sahara as described

in mid-Holocene reconstructions and simulations; in order to obtain the actual (and LGM

reconstructed) distribution of subtropical deserts, bright (high albedo) sand deserts had to be

prescribed from the beginning of a simulation. These results were explained in the context of

Charney’s theory of self-perpetuating deserts through albedo enhancement, associated with the

presence (or absence) of vegetation with a substantial, long-lasting impact on sub-tropical

convection and monsoonal patterns.

Another study (Claussen et al., 1998) confirmed the existence of a dual equilibrium for

LGM and present-day climate, and revealed that the “green” Sahara was the only possible

solution in the case of mid-Holocene boundary conditions. Using a conceptual bifurcation model,

the authors argued that orbital forcing (through its effects on atmospheric circulation) could be

responsible for locking the atmosphere-biome system into the “green” mode during the mid-

Holocene, which would also explain the observed decrease in sub-tropical aridity during that

period. However, some questions remain unanswered – such as the subsequent shift back to

“desert” mode – and the authors insist that further investigations with fully a coupled

(atmosphere-vegetation-ocean) model will likely be necessary in order to better understand the

role of vegetation in climate system stability.

Finally, the question of atmosphere-biome stability has also been raised regarding high-

latitude vegetation. Since boreal forests tend to create warmer, moister environments, it was

hypothesized that the sole presence of evergreen needleleaf trees over present-day tundra and

polar deserts could modify the high-latitude climate sufficiently enough to make it adequate for

their survival. This idea was quickly dismissed however, as experiments with coupled

atmosphere-biome models (Claussen, 1998; Levis et al., 1999) did not find multiple solutions of

the Arctic climate-vegetation system; in a “forested Arctic” start, for example, the initial

warming signal was insufficient to allow boreal evergreen trees to persist in higher latitudes, and

the northern extent of the boreal forest gradually drifted back towards present-day values.


Brovkin et al. (2003) extended the stability analysis to several other climate models, none of

which produced more than one steady state for high-latitude vegetation, even with doubled CO2

levels. However, they noted an increase in climate sensitivity for low vegetation cover, as well

as an increased sensitivity with interactive ocean and sea ice, reiterating the key importance of

ocean dynamics in assessing the influence of vegetation feedbacks on the climate system.

2.4 Present-day interactions between climate and the biosphere:

analyzing vegetation response to climate change

2.4.1 Global vegetation feedback to increases in atmospheric CO2

The potential climatic impacts of anthropogenic increases in atmospheric CO2 have been

a topical issue of the past decades, and the scientific community is only starting to grasp the full

extent of its influence on various aspects of our environment. Because of its central role in the

chemical equations that define plant life, changes in carbon dioxide have long been suspected to

have a major impact on vegetation, in addition to a spatial redistribution of plant biomes because

of the elevated surface temperatures. For example, Prentice et al. (1991) used a forest succession

model to analyze its implication on forest composition and biomass dynamics. While some

species reacted better than others, creating a rather complex spatial shift of vegetation boundaries,

the model displayed an unequivocal northward shift of the boreal treeline as a direct consequence

of anthropogenic changes in CO2.

Another known impact of increased atmospheric CO2 is to cause physiological changes

such as a reduction of plant stomatal conductance, which limits the loss of water through

transpiration and thereby mitigates the cooling effect of tropical forests through latent heat

release. However, climate model experiments with doubled CO2 levels (see for example, Betts

et al., 1997; Foley et al., 1999; Levis et al., 2000) have shown that that, despite appreciable

decreases in evapotranspiration on local scales, global physiological climate-vegetation

feedbacks are mostly offset by a widespread increase in leaf area index (e.g. by expanding tree


cover). The main exception is in the continental mid-latitudes, where a reduction in plant

transpiration leads to a depletion of soil moisture This further limits the availability atmospheric

moisture through recycling, the region’s main source of precipitation, therefore resulting in a

noticeable aridification of the mid-latitudes. This effect is not as important in the high latitudes,

where evapotranspiration is not a significant component of the hydrologic cycle; however, the

region remains sensitive to climate change, mainly because of radiative feedbacks from the

northward expansion of the boreal forest.

2.4.2 Climate response to high-latitude afforestation

As mentioned in the previous paragraphs, one of the major consequences of climate

warming due to anthropogenic increases in CO2 – hence a possible outcome for the medium-

range future – is a northward expansion of the boreal tree line. In order to better understand the

many climatic impacts of afforestation in the northern hemisphere, there have been a number of

numerical experiments simulating “increased greenness” in both the mid (Swann et al., 2011)

and high (Zhang et al., 2006; Swann et al., 2009) latitudes. In general, mid-latitude afforestation

seems to have little impact on global temperature and CO2, but regional warming can occur due

to the increased solar energy absorption, especially in regions where water limitation prevents

compensation through latent heat release. However, these local effects can influence remote

circulation patterns: for example, the model results from Swann et al. (2011) suggest that an

anomalous heating redistribution through atmospheric circulation changes could alter the Hadley

circulation, impacting precipitation patterns across tropical and sub-tropical latitudes. Of course,

a better understanding of these patterns and the role of vegetation dynamics would be crucial in,

for example, designing strategies for climate change mitigation.

At higher latitudes, added forestry results in a warming and moistening of the atmosphere

mostly driven by springtime increases in net surface radiation from the snow-albedo feedback.

This combines with the projected climate warming due to increase in atmospheric CO2 to further

exacerbate the warming trend (see above section). Furthermore, while not an important part of


the water cycle, plant transpiration nevertheless contributes to a net soil-to-atmosphere moisture

transfer, creating a more unstable atmosphere that enhances convective cloud formation during

summer. Interestingly, in the long term there are indications among model results from (Swann

et al., 2009) that the increased cloud cover would provoke a top-of-atmosphere radiative

imbalance of comparable effect to that caused by changes in albedo, overriding the warming

trend established by the latter. Further investigation will likely be necessary in order to gain a

better understanding of this matter.

2.4.3 Climate response to anthropogenic land cover change

Aside from injecting a large amount of carbon into the atmospheric reservoir, another

known climatic impact of human activities comes from the large-scale alteration of land surface,

chiefly for agricultural and, to a lesser extent, urbanization purposes. Recorded history of

humankind’s agricultural traditions suggest that significant influence on climate from land use

changes may predate the industrial era by at least a few centuries, but a recent hypothesis

(Ruddiman,, 2003) suggests that it could push back the beginning of the anthropogenic era by

several thousand years.

The impact of historical land cover change has been shown to be of comparable

importance to the effect of CO2 emissions (mainly from land conversion) for at least a thousand

years prior to the industrial era (Brovkin et al., 1999; Brovkin et al., 2006). In particular,

Brovkin et al. (2001) prescribed a scenario of land use change over the past millennium with six

different Earth models of intermediate complexity, all of which agreed on a crucial role of land

surface changes as a climate forcing for several centuries. It is interesting to note that the

cooling effect was strongest during the, 19th

century, and lasted until the mid-20th

century, when

the trend was apparently reversed, likely in result to the rising atmospheric CO2 levels.

The potential impact of modern-day land cover changes has also been studied extensively,

both through data collection (e.g., see Lee et al., 2011) and numerical model studies (Claussen et


al., 2001; Bathiany et al., 2010). In general, results are akin to those presented in large-scale

deforestation experiments (see section 2.2.5), which is not surprising since most human-induced

land use change consists of replacing forested areas with agricultural cropland. In particular, the

sign and magnitude of the temperature response depends on vegetation type and latitude:

removing tropical forests results in a temperature signal dominated by biogeochemical effects,

with a CO2 increase of approximately 60 ppmv driving the global warming, while land cover

changes in nontropical latitudes produce a temperature anomaly mostly driven by springtime

biogeophysical feedbacks. These observations are critically important in light of future land

cover changes, such as the use of reforestation as an option for the enhancement of carbon

sequestration.

2.5 Summary

A number of climate modeling studies of atmosphere-biosphere feedbacks have

illustrated the important role of vegetation within the Earth system. The dominant

biogeophysical effects vary depending on the location and time of the year; for example,

hydrologic processes are predominant in the densely wooded tropical evergreen forests, while

radiative effects are the main factor at mid to high latitudes during winter and spring. Globally,

the latter have been shown to be the most important, such that the total effect of the world’s

forests on climate is to increase global temperatures. Radiative effects are also crucially

important in assessing the influence of the boreal forest on high latitude climates, both in

paleoclimate simulation and in scenarios of future climate change.

Ultimately, one of the most striking aspects of climate-biosphere interactions is the

natural tendency of vegetation to modify its environment in such a way as to increase its

survivability. On either side of the climatic spectrum, plants act to mitigate temperature

extremes and enhance precipitation, all of which contribute in making the land more hospitable.

This reflection suggests an interesting analogy between the above and the theory of self-

perpetuating deserts (Charney, 1975).

27

Chapter 3

Model description

3.1 Earth system Models of Intermediate Complexity

Earth system models of intermediate complexity (EMICs) – such as the UVic ESCM –

exist in order to bridge the gap between inductive models, which focus on a limited set of

processes and mechanisms (for example, most box models), and the computationally expensive

quasi-deductive models (for example, GCMs). Considered as middle-of-the-road regarding

model complexity and computational efficiency, EMICs maintain a large spectrum of interacting

components typical of their comprehensive counterparts (atmosphere, ocean, sea ice, land

surface / vegetation modules as well as biogeochemical cycles are common), albeit in a more

simple form allowing for longer-term simulations of the climate system (Claussen et al., 2002).

Unlike a general circulation model, each EMIC is characterized by its own field of

specialization, making it more suited to a particular set of experiments than other EMICs. For

example, the McGill Paleoclimate Model of Wang and Mysak (2000) was specifically designed

for the study of ice age inception and millennial- to Milankovitch-scale climate variability during

the Quaternary, and in order to simulate the long timescales several degrees of complexity were

abandoned in favor of improved overall performance. According to the most recent table of

EMICs (Claussen, 2005), the range of model expertise covers a large spectrum of possible

research interests, such as atmospheric dynamics (CLIMBER-2), ocean circulation (Bern 2.5D),

biogeochemical cycles (ISAM-2: terrestrial; UVic: oceanic), global environmental change

(MoBiDiC: orbital-scale; MIT: anthropogenic), and extraterrestrial climate dynamics (Planet

Simulator). Although computational performance varies greatly among EMICs, it is usually

possible to simulate climate system evolution for periods spanning up to tens of thousands of

model years within a reasonable lapse of computing time, making this brand of models

particularly attractive for paleoclimate research.

CHAPTER 3. MODEL DESCRIPTION 28

3.2 General description of the UVic ESCM

The model used in this study is the University of Victoria Earth System Climate Model,

an intermediate complexity coupled atmosphere/ocean/sea-ice model introduced and described in

great detail by Weaver et al. (2001). It consists of a three-dimensional ocean general circulation

model (OGCM) with ocean chemistry, coupled to a thermodynamic/dynamic sea-ice model and

an energy-moisture balance atmospheric model with parameterized dynamical feedbacks. The

model was originally equipped with a thermomechanical land-ice model, but this approach has

been abandoned in recent versions in favor of prescribed continental ice sheets. Because of the

simplified atmospheric component in the UVic ESCM, the model is computationally efficient

compared to a fully coupled atmosphere-ocean GCM.

The land-sea configuration used in the UVic ESCM is coarse. The spatial domain is

global, and features a spherical grid resolution of 3.6° (zonal) by 1.8° (meridional), which is

comparable to most coupled coarse-resolution AOGCM’s. The model once employed the Euler

frame of reference with the North Pole shifted to Greenland in order to avoid grid convergence

problems. In more recent versions of the model, this approach has been abandoned in favor of

an artificial island at the North Pole.

Below are descriptions of the major components of the original, 2001 model: atmosphere,

ocean, and sea-ice. The vegetation module and its supporting land-surface scheme were added

later to the UVic model, and will be discussed in the following sections of this chapter.

3.2.1 Atmosphere

The atmospheric module is a vertically-integrated energy-moisture balance model loosely

based on Fanning and Weaver (1996), with two major simplifications. First, the conservation of

momentum is achieved through a combination of specified wind data and dynamical wind

feedbacks, removing the need for computationally demanding prognostic equations. Second, the

fluxes of energy and moisture are parameterized by diffusive processes only, although heat and


moisture advection by winds is left as an option. In other words, the transport of heat and water

vapor in the atmosphere is dictated mainly by meridional gradients (i.e., the inherent pole-to-

equator temperature and moisture gradients), while wind velocities are not explicitly prescribed.

The main feature of this simplified atmosphere is the energy-balance equation, an

evolution equation for the prediction of surface air temperature Ta:

where ρa is the surface air density, ht a representative scale height for temperature, and cpa the

specific heat of air at constant pressure. Terms on the right-hand side represent the sources and

sinks of heat which parameterize energy exchanges between the atmosphere and the underlying

surface.

The first term QT is the horizontal heat flux, which involves a combination of advective

and Fickian diffusive processes. The term QLH denotes the transfer of energy through latent heat

release, which is assumed to occur solely through precipitation, either as rain or snow.

Energy exchanges with the outer space in the form of incoming shortwave and outgoing

longwave radiation are represented by the terms QSSW and QPLW, respectively. The incoming

solar radiation is written as:

where S⨀ is the solar constant, α is the latitudinally- and time-dependant planetary albedo, and

CA is a reduction parameter accounting for the absorption/scattering of about 30% of shortwave

radiation in the atmosphere (from water vapor, dust, ozone and clouds, to name a few). Also, in

its definition of top-of-atmosphere incoming radiation I, the model accounts for orbital

configuration when establishing the annual cycle of solar insolation, as per the calculations of


Berger (1978). The parameterization of outgoing longwave radiation is based on Thompson and

Warren (1982), and modified in order to depend on surface air temperature, relative humidity,

and the atmospheric concentration of carbon dioxide. In particular, CO2 radiative forcing is

applied in the model through a decrease in outgoing longwave radiation.

Since the model does not permit the storage of heat or moisture on the land surface, the

final two terms can only assume nonzero values over the ocean. One term, QLW, accounts for the

strong longwave flux at the atmosphere-ocean interface (due to the oceanic heat reservoir),

which is modeled according to a “gray body” version of the Stefan-Boltzmann law. The other

term, QSH, denotes sensible heat exchanges between the surface and the atmosphere, which are

evaluated using a bulk parameterization of surface variables.

The model uses prescribed present-day winds in its climatology, and includes a set of

dynamical wind feedbacks based on a latitudinally-dependent empirical relationship between air

temperature and density. In order to account for the dynamic response of the atmosphere to

variations in sea surface temperatures, wind stress anomalies are parameterized in terms of

surface air temperature anomalies.

The hydrologic cycle in Fanning and Weaver (1996) is parameterized by a simplified –

and vertically-integrated – version of the balance equation for water vapor, in which the

horizontal advection term is replaced by an eddy diffusive term. In the UVic model this setup is

essentially untouched, although there is an option for moisture advection by vertically-integrated

atmospheric winds specified from NCEP reanalysis data. The model’s equation for moisture

balance also involves, among others, the use of a bulk parameterization to calculate evaporation

and precipitation, the latter being assumed to occur whenever the relative humidity exceeds a

certain threshold (usually 85%). A specified lapse rate is used to calculate temperature and

precipitation anomalies due to orographic influences, allowing among other things a more

realistic configuration of each of the 33 specified river basins.


3.2.2 Sea ice

The treatment of sea ice in the UVic model is done with a standard model involving

simple two-category (sea ice, open water) thermodynamics and elastic-viscous plastic dynamics;

however, several options are offered for a more sophisticated representation of sea-ice

thermodynamics and ice-thickness distribution. The standard model evaluates ice thickness,

areal fraction and ice surface temperature based on the zero-layer formulation of Semtner (1976)

and the lateral growth and melt parameterization of Hibler (1979), while the momentum balance

equation for ice dynamics is solved using the rheology developed by Hunke and Dukowicz

(1997). Snow follows the same parameterization as other types of precipitation, and is assumed

to occur when air temperature at the surface falls below a critical value (usually -5°C). Snow

accumulation on the ground is treated as another form of moisture storage, and it can be used as

an elementary ice sheet model (in the sense increasing surface and planetary albedo). Over sea

ice or ocean the accumulation of snow is treated as part of the surface energy balance.

Several ice-thickness distribution options are incorporated in the UVic model as

alternatives to the standard representation of sea ice. The most commonly used improvement,

described in great detail in (Bitz et al., 2001), involves a multi-layer thermodynamic model with

heat capacity (Bitz and Lipscomb, 1999) and a Lagrangian formulation of the sub-gridscale ice

thickness distribution developed by Thorndike et al. (1975), allowing among others a better

resolution of the vertical temperature profile.

Continental ice in the UVic model consists of a simple prescription of the spatial

coverage and height of ice sheets based on paleoclimate data records. In the version of the

model used for this study, the land-ice configuration is updated every few thousand years based

on data from the model ICE-5G (Peltier, 2004).


3.2.3 Ocean

The central piece of the UVic model is the Geophysical Fluid Dynamics Laboratory

(GFDL) Modular Ocean Model (MOM), v2.2 (Pacanowski, 1995), a full-fledged 3-D OGCM

based on the Navier-Stokes equations subject to Boussinesq and hydrostatic approximations.

The horizontal grid resolution is the same as in the atmospheric and sea-ice components of the

model, while the vertical grid consists of, 19 unequally spaced levels that vary gradually in size,

from very small near the surface to very large near the bottom. Ocean bathymetry is included

and taken from the Suarez and Takacs (1986) dataset. The density of seawater is given by a

nonlinear function of potential temperature, salinity and pressure. The ocean top layer is driven

by wind stresses and surface buoyancy forcing. In order to avoid subfreezing ocean

temperatures, the model calculates the maximum available heat in the top layer, which can then

be redistributed to the atmosphere or sea ice. This allows for a (relatively) simple definition of

the net heat flux into the ocean QH:

where Qto, Qb are adjusted downward heat fluxes from the atmosphere and sea ice, respectively,

and Ai is the areal fraction of ice. Similarly, the implied surface salinity flux Qs is given by:

(

)

where ρ0, S*, Lf are representative constants for water density, salinity and latent heat, and R

represents freshwater supplied from land runoff. The total heat flux from the atmosphere

is distributed between the ocean (Qto) and ice (Qti) according to the

areal fraction of ice. These transfer equations combine with ocean mixing by parameterized

wind stresses and the primitive-equation ocean dynamics to form the backbone of this ocean

component.


3.2.4 Coupling strategy

The atmosphere and sea-ice components are coupled using a time step of 30 hours. The

ocean model time step is double that of the previous two, and coupling between the

atmosphere/sea-ice models and ocean model is done every two ocean time steps. Each

component uses an intermittent Forward Euler time step with leapfrog time stepping scheme,

which forces the use of a special calculation technique of flux exchanges between components in

order to ensure the conservation of heat and salinity. The ocean model is first spun up for 5000

years under specified orbital forcing year and atmospheric carbon dioxide concentrations, and

coupled to the other components when equilibrium is reached. Overall model efficiency varies

between 50-150 years per CPU day depending on computer performance.

3.3 Recent additions and improvements to the model

The current version of the model is 2.9, which carries a number of differences from that

described by Weaver et al. (2001). These include an improved radiative transfer scheme, the

inclusion of a land surface model, the addition of sulfates and aerosols as potential climate

forcings, the introduction of a dynamic global vegetation model, and a coupling of the latter’s

terrestrial carbon cycle and the ocean’s inorganic carbon cycle (Matthews et al., 2004). More

recently, ocean biogeochemistry (Schmittner et al., 2008) and a sediment model (Eby et al., 2009)

have been incorporated into the UVic model. The improved radiative scheme and new land

surface model will be discussed below, while the new vegetation module will be the focus of

section 3.4.

3.3.1 Enhanced radiative transfer model

In earlier versions of the UVic ESCM the top-of-atmosphere reflectivity was specified by

a zonally averaged planetary albedo calculated in the single-layer atmosphere. The recent

inclusion of land surface and vegetation schemes has prompted a modification to the radiative

transfer scheme to provide an explicit representation of surface albedo as part of a two-


dimensional albedo field, which distinguishes it from atmospheric albedo. Based on the theory

outlined in Haney (1971), planetary albedo αp would then be defined as a function of surface

albedo αs, atmospheric albedo αa and atmospheric absorption Aa:

In this new scheme surface albedo is taken from the land surface module, which evaluates it

according to snow/ice cover and vegetation distribution, while atmospheric albedo is calculated

as a sum of a background (clear-sky) albedo of 0.08 and cloud reflectivity. Because cloud cover

is not explicitly represented in the UVic model, the cloud reflectivity is computed through a

specified zonally-averaged combination of albedos from other inputs, including the original

zonally-averaged planetary albedo. The net shortwave radiation at the surface is then given by:

( )

where IS is the incident shortwave radiation at the top of the atmosphere. While a definite

improvement over its predecessor, the model is still lacking a dynamical treatment of clouds

because of its zonally constant atmospheric albedo, and therefore a feedback between clouds and

climate is still excluded.

3.3.2 Land surface scheme

The current version of the UVic ESCM has integrated a single soil layer version of the

Meteorological Office Surface Exchange Scheme version 2 (MOSES-2), which defines the state

of the land surface in terms of surface temperature, soil temperature and moisture content, and

snow cover. It features among others an interactive representation of plant photosynthesis and

conductance, and a parameterization of evapotranspiration as a function of canopy resistance.

MOSES-2 in its standard configuration recognizes the five TRIFFID vegetation types, in

addition to four types of non-vegetation landcover (bare soil, land ice, inland water and urban

areas). A new soil thermodynamic scheme is introduced to account for the melting and freezing


of soil water and the impact of frozen and unfrozen water on the soil’s thermal characteristics.

Soil moisture is increased by precipitation and snow melt, and is decreased by evaporation and

continental runoff. The size of the snowpack is updated according to snow accumulation, snow

melt and the rate of sublimation. Solar radiation unto the surface is balanced by latent heat

release due to phase changes, sensible heat fluxes, and direct accumulation of heat into the soil.

For a complete description of the land surface scheme as well as the original formulation, see

Cox et al. (1999).

3.4 Description of the vegetation module

3.4.1 Evolution of vegetation modeling

Before the appearance of vegetation models, many AGCMs employed simple transfer

schemes involving a representation of short-term biophysical processes of energy, moisture,

carbon and momentum exchanges between the land surface and the atmosphere. The Biosphere-

Atmosphere Transfer Scheme (BATS), developed for use within NCAR’s climate models

(Dickinson et al., 1986), and the Simple Biosphere (SiB) model of Sellers et al. (1986) are

examples commonly found in the literature. In such models, the land surface was parameterized

by fixing the geographical distribution of vegetation – in most cases based on the modern-day

configuration – and assigning each grid cell to one of several pre-defined biomes with specified

leaf area index, albedo, rooting depth and roughness length (Foley et al., 1998).

Due to their static vegetation distribution, it was impossible for simple land surface

schemes to capture long-term feedback processes that would arise from changes in vegetation

cover. Many preliminary attempts to introduce vegetation as an interactive component of the

climate system involved the use of equilibrium biogeographical models to update the

geographical distribution of vegetation. For example, Claussen (1994) linked the BIOME model

of Prentice et al. (1992) to the atmospheric GCM ECHAM through an asynchronous coupling

procedure using multi-year averages of the climate model simulation to drive changes in


vegetation cover, and bringing the coupled system to equilibrium through multiple iterations.

The coupled behavior was found to be stable but heavily dependent on initial conditions (as

discussed previously in section 2.3.4). Another class of early vegetation models, referred to as

transient ecosystem models, are also found in the literature (Kittel et al., 2000). These models

place a heavier focus on the transient dynamics of vegetation changes, by modeling a wide array

of ecosystems and each possible vegetation transition in independent sub-modules. They are

most useful when examining differences among ecosystems in terms of rates of succession,

transition probabilities, and sensitivity to climate and environmental disturbances.

While undoubtedly a step forward in vegetation modeling, the set of iteratively linked

climate-vegetation models was found to have two major limitations due to the asynchronous

coupling strategy, leaving room for further model development. First, the existing models could

only simulate the equilibrium response of vegetation cover to changes in climate, without

addressing the transient nature of atmosphere-biosphere response to climate variability. Second,

the model sometimes required two independent treatments of physical processes (from both the

vegetation model and the AGCM), leading to inconsistencies in land surface parameterization.

This is notably the case for the coupled ECHAM-BIOME model, where energy and moisture

exchanges at the surface must be defined in both the AGCM for the evaluation of land-

atmosphere processes, and the vegetation model in order to estimate the soil moisture

requirements of plants.

3.4.2 An overview of Dynamic Global Vegetation Models (DGVMs)

The last decade saw the emergence of a new class of vegetation models, which were

created specifically to address the issues outlined in the above paragraph by incorporating the

latest advancements in plant geography, plant physiology and biogeochemistry, vegetation

dynamics, and biophysics (Prentice et al., 2007). In particular, DGVMs feature a transient, more

integrated and physically consistent simulation of vegetation structure, land surface and

ecological processes when compared to earlier models, and they are designed to be directly


incorporated into AGCMs. Some of the most commonly used vegetation models in current

research are DGVMs: IBIS (Foley et al., 1996), VECODE (Brovkin et al., 1997), LPJ (Smith et

al., 2001; Sitch et al., 2001) and TRIFFID (Cox et al., 2001) are but a few examples in this new

category which has come to play a dominant role in vegetation modeling.

Many DGVMs have been developed from existing models using one of two approaches.

The first consists in expanding an equilibrium biogeographical model to include vegetation

dynamics, by coupling it to models that simulate rates of vegetation growth and disturbance rates;

this method is usually referred to as the top-down approach. Conversely, it is also possible to

build a DGVM from a regional model by bringing it up to the global scale and coupling it with a

biogeochemistry model, a method also known as the bottom-up approach.

One of the challenges in efficient dynamic vegetation modeling comes from the large

range of time scales involved. For example, DGVMs need to account for short-term dynamics of

photosynthesis and moisture/energy exchanges (seconds to minutes), seasonal patterns of carbon

assimilation (weeks to months), and changes in vegetation structure due to competition, mortality

and disturbance rates (years to decades). In general, the timescales associated with changes in

ecosystem structure tend to be up to several orders of magnitude higher than for physiological

processes.

Kittel et al. (2000) identify some limitations of DGVMs, especially with regards to high-

latitude climate modeling. They note that most DGVMs still lack an adequate representation of

sub-gridscale processes associated with unusual biomes such as inundated landscapes (marshes

and bogs), anaerobic soils, and permafrost. Another possible improvement to high-latitude

vegetation modeling would come from better defining the role of small plant organisms such as

moss and lichens in the biogeochemical dynamics of tundra and boreal ecosystems.


3.4.3 The Plant Functional Type (PFT) approach

The concept of plant functional types, which consists of classifying plants functionally

rather than by evolutionary development, was introduced in order to reduce the complexity of

global vegetation structure and diversity to a manageable level in the more expensive vegetation

models (Woodward, 1987). Using this strategy implies several assumptions regarding the

terrestrial biosphere: (i) that plant species can indeed be grouped according to broad structural or

functional characteristics; (ii) that parameterizations for each PFT can adequately represent the

physiological properties of each individual species; (iii) that the definition of a PFT is

independent of geographical location; and (iv) that most biomes can be recovered from the

dominant PFT and climatic regime. In general, most PFT schemes differentiate between woody

and herbaceous types, with further subdivisions based on attributes such as leaf longevity,

temperature tolerance, and photosynthetic processes; ultimately, the PFT configuration is chosen

according to the modeling framework and the desired level of complexity.

The PFT approach is often used within DGVMs as an efficient way to simulate

vegetation dynamics and evaluate land surface properties. Most DGVMs allow for multiple

PFTs to coexist within a single grid cell (by defining the areal fraction of each PFT) in order to

provide a transient representation of vegetation changes due to climate forcings, as well as a

more realistic simulation of structural changes within a biome. The fractional distribution is

determined by PFT competition for nutrients (in the form of net primary productivity) and space,

which in turn is highly influenced by climate variability and natural disturbances.

3.4.4 General description of TRIFFID

The vegetation module “TRIFFID” (Top-down Representation of Interactive Foliage and

Flora Including Dynamics) is a DGVM developed at the Hadley Centre for use in coupled

climate-carbon cycle simulations, fully described in Cox et al. (2001). It describes the state of

the terrestrial biosphere in terms of soil carbon content and vegetation distribution, which is

expressed through the structure and coverage of five plant functional types (PFT): broadleaf tree,


needleleaf tree, C3 grass, C4 grass, and shrub. Plant distribution and soil carbon levels are

updated based on a “carbon balance” approach, using land-atmosphere carbon fluxes (for

example, plant photosynthesis and respiration) supplied by the land surface scheme MOSES-2 to

drive vegetation changes. These fluxes are derived for each PFT using the photosynthesis-

stomatal approach of Cox et al. (1999). Areal coverage is determined by the net available carbon

and interspecies competition, which is modeled using a Lotka-Volterra approach. The model

also accounts for bud-burst, leaf-drop and large-scale vegetation disturbances that increase the

soil carbon content.

3.4.5 Vegetation dynamics

The state of the terrestrial vegetation in TRIFFID depends on net primary productivity

(NPP) Πi, which is provided for each plant functional type i by the MOSES-2 land surface

scheme. A fraction λi of this NPP is employed to increase the area of the particular PFT, while

the remainder serves towards the growth of the existing vegetated area (in terms of leaf area

index and canopy height). The evolution of its fractional coverage νi is therefore governed by

the following differential equation:

( ∑

)

where is the PFT’s vegetation carbon and . Here the first term on the

right-hand side denotes the expansion of the PFT’s fractional cover in the grid cell, which is met

however with a certain amount of resistance from other PFTs (as given by the term in brackets).

The competition terms cij, which can range from zero to unity, represent the ability of vegetation

type “j” to dominate over vegetation type “i" and reduce the growth of νi. They are determined

through the Lotka-Volterra approach, which emphasizes the role of height in the vegetation

dominance hierarchy. In addition to pressure from other vegetation types, each PFT experiences

“intraspecies” competition (cii = 1) to prevent it from expanding into territory which it already


occupies. In order to allow a vegetation type to appear in a previously unoccupied grid cell (for

example, when climate and competition levels become favorable), each PFT is “seeded” by

never letting the effective fractional cover drop below a specified seed fraction. For the sake

of total carbon conservation, the fraction of NPP which cannot contribute to the expansion of this

PFT due to competition is considered “wasted” and is returned to the soil. Finally, the second

term on the right-hand side accounts for large-scale disturbance events, such as forest fires or

insect swarms, which result in the loss of vegetated area at a prescribed rate .

The total amount of vegetation carbon for a PFT, denoted by the variable , combines

all of the carbon accumulated in the leaves, stems, and roots. Its evolution, which is coupled to

that of areal fraction, is given by the relatively simple equation

In the first term on the right-hand side, all of the primary production not used to expand the

fractional coverage of the PFT (or lost to PFT competition) goes towards increasing vegetation

carbon. The second term accounts for the loss of vegetation carbon through litterfall, which is

parameterized according to the turnover rates for leaves, roots and stems. There is an additional

litter contribution from large-scale disturbances that destroy vegetation, but it is not explicitly

included in the definition of because the phenomenon is already accounted for in equation 7.

3.4.6 Leaf phenology and soil carbon

The phenological state of the vegetation is calculated based on the maximum potential

leaf area index Lb of trees and shrubs: , where L is the actual LAI of the canopy and p is

a fraction between zero and unity. Bud burst and leaf turnover rates are set to be equal under

normal circumstances, but leaf mortality increases if the surface temperature drops below a

critical threshold. In order to ensure conservation of carbon during phenological changes, the

actual rate of leaf drop (used to calculate litterfall in equation 8) is computed separately. Trees


and shrubs are allowed to grow towards “full leaf” status ( ) whenever the rate of leaf

turnover does not exceed twice that of bud burst; otherwise, p steadily decreases, leading to a

decline in LAI. Overall, this parameterization of leaf phenology results in a seasonal variation of

the canopy LAI of vegetation; this is limited only to trees and shrubs, however, as a similar

approach has not yet been included for grasses.

Soil carbon comprises all of the carbon which is stored on the land surface but not

currently used by any plant functional type. It is increased by total plant litterfall, and a fraction

of it is released on each timestep as CO2 to the atmosphere due to microbial respiration. The

total litterfall, Λc, tallies all of the dead vegetation carbon accumulated from fallen leaves, large-

scale perturbation events, and wasted NPP due to PFT competition. The latter term implies that

all of the NPP devoted to areal expansion will be converted to soil carbon once a PFT occupies

all of the space available to it. The rate of respiration, RS, is given by a complex

parameterization based on soil temperature, volumetric soil moisture and soil carbon content.

The temperature dependence is assumed to be weakly exponential (in a “Q10” form), while

moisture dependence takes a quasi-parabolic shape reaching a maximum upon a specified

“optimum moisture level.”

3.4.7 Biophysical parameters in MOSES-2

In the biophysical feedback loop, TRIFFID employs several parameters supplied by the

land surface scheme in its evaluation of vegetation changes, and then returns information on leaf

area index and canopy height for each PFT that are used by MOSES-2 to recalculate its own

biophysical parameters (while not explicitly computed in TRIFFID, canopy height is diagnosed

directly from total stem biomass). Three such parameters are obtained in this manner:

aerodynamic roughness length, canopy catchment capacity, and surface albedo. Roughness

length, which modifies the transport of heat, moisture, CO2 and momentum near the surface, is

taken to be directly proportional to height. Canopy catchment, which affects the amount of

moisture available for evaporation, has an assumed linear dependence on leaf area index.


More relevant to this study is surface albedo, which is calculated for each vegetation tile

as the sum of soil albedo α00 and canopy albedo α0∞, weighted by leaf area index L:

where represents the fraction of incoming light that passes through the vegetation

canopy and reaches soil level. In the case of snow-free land surface, canopy albedo is specified

as for tree types, and for grasses and shrubs, and soil albedo takes the form

of a geographically-varying field as presented in (Wilson and Henderson-Sellers, 1985). When

blanketed by snow both albedos become prescribed, PFT-dependent parameters. Snow albedo at

the surface takes the value of for trees and for grasses and shrubs, while

canopy albedo is prescribed as for tree types, for grass types and

for shrubs.

3.4.8 Coupling with the UVic ESCM

All information required by the land surface scheme (radiation, heat fluxes and

precipitation rates) are computed within the atmospheric model and passed to MOSES-2, which

uses it to evaluate land-atmosphere heat and carbon fluxes and continental runoff. Net primary

productivity is calculated in the land surface scheme, and passed to TRIFFID which distributes it

into the growth and expansion of each PFT. The distribution and physical characteristics of the

terrestrial vegetation (canopy height, leaf area index, etc…), as well as their associated land

surface parameters (albedo, roughness length, canopy catchment) are updated and returned to

MOSES-2 every 30 days. The only exception concerns the phenological status of leaves, which

is updated daily based on accumulated temperature-dependent leaf mortality rates. The typical

coupling period between the atmosphere-ocean-sea ice system and the land surface scheme is 60

hours, while information concerning the net primary productivity of plants is sent to TRIFFID on

a monthly basis (Meissner et al., 2003).

43

Chapter 4

Results of the transient simulations

4.1 An overview of the original study by Doughty et al. (2010)

The idea of associating the megafaunal extinction with climate change due to alterations

in the vegetation cover was formulated by Doughty, Wolff and Field (2010, henceforth

DWF2010), who used both an observational and a modeling approach to justify their assertion.

In particular, they noted that the mass extinction coincided with a drastic change in vegetation,

especially in Alaska and northern Asia, and sought to link the two events causally. Due to the

general lack of paleoevidence, mostly in regards to megafauna remains (which makes it difficult

to determine the exact date of extinction in a particular region), several assumptions were

required, notably (1) that vegetation change followed the megafauna extinction rather that

preceded it (Gill et al. 2009), and (2) that some of the larger species had a diet which would have

involved the uprooting of a large number of trees (Owens-Smith 1988). In this regard, the case

of the woolly mammoth is especially strong because their behavior can be directly related to that

of their modern-day elephant cousins, which are known to play a determinant role in the

maintenance of grassland and the expansion of trees in the African savanna (Caughley 1976).

In their pioneering work, DWF2010 showed that pollen data records indicate a rapid

increase in Betula over Siberia and Beringia (which encompasses territory within current-day

Alaska and the Yukon) close to the time of the megafaunal extinction. Based on this information,

they hypothesized that this increase in vegetation cover could not be entirely attributable to

climate change and accordingly they estimated the part of the increase in Betula that would be

caused by the extinction of the terrestrial megaherbivores. The pollen data were obtained from a

compilation of the Global Pollen Database for the above regions between 10 and 20 ky BP, and

these data were used to reconstruct vegetation cover during that time span and hence estimate the

percentage cover of Betula. In addition, archaeological evidence for human and mammoth

CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 44

presence was used to estimate the time of the mass extinction. In the modeling effort, several

scenarios of elephant-tree interactions were examined using an extended Lotka-Volterra

predator-prey model (Duffy et al. 1999) with a range of mature Siberian vegetation densities and

a wide range of mammoth behavior scenarios (40-1200 trees uprooted/mammoth/year, following

a Monte Carlo approach) in order to predict the impact of megafauna on vegetation cover and

estimate the reduced Siberian and Beringian dwarf deciduous tree cover prior to the Holocene.

A reduced percentage cover of dwarf deciduous trees was then prescribed in the NCAR CAM

3.0, a dynamic atmospheric model coupled to a slab ocean model, in order to evaluate the impact

of this vegetation change on global temperatures. Finally, results from both the predator-prey

and climate system models were combined to obtain a quantitative measure of temperature

changes that would be directly attributable to the megafaunal extinction.

In DWF2010, analysis of the pollen database revealed an average increase in Betula

pollen of 26% over a span of roughly 850 years, corresponding in time with the archaeological

evidence for the occupation of the land by humans and the extinction of mammoths in the area.

Atmospheric temperature and carbon dioxide concentrations obtained from Greenland ice core

temperatures and CO2 proxies confirmed that this time period also coincided with rapidly

changing climate conditions, resulting in increasingly hospitable conditions for dwarf deciduous

trees in northern high latitudes. Results from the predator-prey model suggested that on average

23% of the increase in Betula could be attributed to the mammoth extinction (up to 50% in

regions of dense vegetation and large mammoth population), with the rest caused by natural

climate change. Climate simulations indicated that each percent increase in high latitude

deciduous dwarf tree cover would results in a globally averaged 2-meter air temperature increase

of 0.0043°C (up to 0.021°C locally); these numbers take into consideration both the decrease in

surface albedo (positive feedback) and increased carbon sequestration (negative feedback) that

result from an increase in tree cover. In their paper, DWF2010 combined these results to obtain

an additional 6% increase in Betula (23% of 26%) due to the mammoth extinction, yielding an


additional regional warming of 0.13°C (globally : 0.026°C). These numbers were used to

suggest that the Pleistocene megafaunal extinction had some impact on land cover.

4.2 Description of the present experiment

4.2.1 Differences with the original study

The objective of the study in this thesis is to extend the modeling effort of DWF2010

with a more detailed experimental approach. In DWF2010 the temperature response to changes

in vegetation cover is obtained by comparing 100-year equilibrium (snapshot) simulations of the

climate system with different areal coverage of deciduous dwarf trees in the high latitudes (for

example, 20% cover vs. 40% cover). In a similar manner, feedbacks from carbon-cycle effects

are determined by comparing the equilibrium global temperature for different values of

prescribed CO2 levels. Other than these two factors, it is assumed that the climate is simulated

within the context of pre-industrial boundary conditions (i.e. orbital parameters, extent of the ice

sheets). In contrast, (1) this study examines the transient response (over 1000 years) of the

climate system to changes in vegetation cover, and (2) uses an Earth system model of

intermediate complexity with late Pleistocene boundary conditions (around 12-17 y BP) to

simulate this climate change. (3) Another important difference lies in the treatment of vegetation:

in DWF2010 different values of deciduous tree cover are prescribed within the same climatic

context, and the effect on temperature can be directly calculated by comparing two simulations;

in our study vegetation is constrained by the presence of mammoths, but allowed to evolve over

time (in reaction to changes in climate) with the use of the dynamic vegetation model TRIFFID.

As a modeling tool, the UVic ESCM is well suited to the project for a number of reasons.

First, it is relatively inexpensive, allowing as much as a thousand model years to be computed in

less than two weeks. This comes at the substantial cost of reducing the atmosphere to a

somewhat simplistic energy-moisture balance model with fixed winds, which severely limits the

number of processes in the atmosphere's response to forcing in the land surface scheme (and thus


the energy exchanges with all other components of the climate system). The model therefore

sacrifices short-term variability (i.e., “weather”) and the ability to produce a dynamical

atmospheric response for the sake of computational efficiency. However, these limitations are

not so detrimental to long-term simulations of the climate system (spanning an interval of time

many orders of magnitude longer than the characteristic timescale for atmospheric response),

which focus on climatic feedbacks that arise from energy imbalances in the atmosphere. Second,

the UVic model incorporates a full ocean general circulation model, which is an important asset

to this study because millennial-scale climate variability is mostly driven by ocean dynamics and

by the very long timescale of oceanic response to external forcings (such as orbital cycles and

CO2 fluctuations in the atmosphere). Finally, the land surface scheme accounts for a dynamical

treatment of vegetation feedbacks, and the plant functional type (PFT) approach in TRIFFID

allows a very simple parameterization of the megafaunal extinction (see below) to be used within

climate model simulations.

4.2.2 Experimental approach

The first step in this experiment consists of adding a slight modification to the UVic

model in order to simulate the mammoth extinction and its impact on global climate through an

increase in high latitude tree cover (as per the hypothesis formulated in DWF2010). Since it

would be unphysical to force the growth of trees beyond the model’s conceivable limits, a

measurable change in climate can only be achieved by first removing a fraction of the tree

vegetation and then letting it grow back. Therefore, our strategy for implementing the

megafaunal extinctions within the UVic model must first start by introducing a perturbation into

the model in the form of reduced tree cover, and specifying an area of the world’s land surface

(the “mammoth habitat”) in which to apply it. In the context of the PFT approach in TRIFFID,

this perturbation amounts to limiting the growth of trees and shrubs in favor of C3 and C4

grasses, much in the same way as one would account for agricultural lands in the present-day

configuration of vegetation – in fact, the “mammoth habitat” is defined as croplands in the model


and overwrites the map of agricultural lands provided in the model package; this is not an issue,

however, as there is little evidence for human agriculture during the late Pleistocene.

There are two ways for which such a perturbation can be introduced in the climate model.

One way is to spin up the model to equilibrium from rest with the reduced tree cover in the

northern high latitudes (for boundary conditions corresponding to the approximate time of

mammoth extinction), a lengthy process due to the long response time of the ocean (at least 5000

model years). In the context of this study we favored a less time-consuming alternative which

consists of inserting the tree cover perturbation at some point of a transient model simulation (i.e.,

start the simulation with an already spun-up model), and letting the climate system evolve until a

new, approximate equilibrium is reached (a few preliminary tests revealed that 500 years of

climate model simulation were sufficient for the temperature signal to stabilize). The starting

point for all of our simulations is an extensive model run spanning over 25 thousand model years

(initiated in the context of another study), which was selected due to its time-dependent

prescription of carbon dioxide levels in the atmosphere (necessary in order to isolate the effect of

biogeophysical feedbacks.

In the final step of this experimental strategy, we eliminate the perturbation to high

latitude tree cover – an event which symbolizes the extinction of the Pleistocene megafauna –

and we let the subsequent recovery of forest biomes act as the main driver of climate change for

the rest of the simulation. Due to the strong dominance of tree and shrub PFT in the competition

scheme, it takes only a few hundred years for boreal forests to fully recover from the

perturbation. In order to isolate the warming signal due to biophysical feedbacks only, it is

necessary to compare the output with that of a “no extinction” simulation, in which trees and

shrubs are not allowed to grow back even after the supposed time of extinction.


4.3 The maximum impact scenario

4.3.1 Short description and parameter tuning

The purpose of this experiment is to quantify the highest amount of warming that can be

obtained in climate simulations with the UVic model as a result of high latitude vegetation

changes. Most of the parameters related to the mammoth extinction are optimized to have the

greatest possible impact on the climate system; this particular simulation is therefore not

intended to offer a realistic portrait of climatic feedbacks to the megafaunal extinction. However,

since these parameterizations essentially lead to a quick replacement of grassland by small trees

(akin to several afforestation experiments), results from this experiment will also serve the

second purpose of identifying general climatic feedbacks to high latitude vegetation change in

the context of late Pleistocene boundary conditions, a novelty for this particular era.

In this experiment we define the mammoth habitat as any land grid cell located north of

the 30°N latitude. This particular number was chosen because it represents the approximate

southernmost limit of boreal forests in the northern hemisphere (needleleaf trees in TRIFFID),

and also because most of the vegetation in North America is constrained between 30°N and 45°N

due to the overwhelming presence of the Laurentide ice sheet at higher latitudes during the late

Pleistocene. Additionally, mammoth are assumed to uproot every single tree within their habitat,

which we represent my setting the perturbed tree and shrub fraction to zero everywhere on this

large territory. Finally, the extinction of the Pleistocene mammals is assumed to be

instantaneous, in order to minimize the time required for forest biomes to regain their original

status. The catastrophic extinction is taken to occur during the year 12000 BC (14 ky BP), as

suggested by the evidence from various burial sites mentioned in DWF2010; this last parameter

is not optimized, but its impact on the temperature signal will be examined in section 4.4. In the

following sub-sections we will examine the evolution of various climate parameters over the

following 500 years (from 14 ky BP to 13.5 ky BP).


4.3.1 Vegetation and surface albedo changes

The change in the fractions of trees, grasses and shrubs (in the mammoth habitat) is

displayed in Figure 4.1. There are two striking features in the results shown here. First, we note

that the major part of vegetation change occurs within the first 100 years of the simulation.

While it is certainly unreasonable to expect the boreal forest to recover so quickly, this

issue might be more associated with the model itself rather that the unrealistic scenario. In

particular, the parameterization of competitiveness and the height-based dominance hierarchy in

TRIFFID are likely to be responsible for this aggressive invasion of the boreal forest. Second,

the forest recovery (which extends to the southern limit of 30°N) is made up almost exclusively

of shrubs, until needleleaf trees start appearing in Europe and southwestern North America

during the last century of the simulation, in large part due to the natural increase in global

Figure 4.1: Change in vegetation fraction over the mammoth habitat (all land north of 30°N) simulated by the UVic ESCM in the context of a maximum impact scenario. This figure and every subsequent one represent the difference between a simulation where mammoths go extinct, and a simulation where their extinction does not occur.


temperatures. While this result does not contradict the conclusions of DWF2010 (based on its

physical characteristics, and height in particular, the Betula species would be classified as shrub

in the PFT scheme), it is surprising to find that needleleaf trees are almost non-existent north of

30°N despite their high tolerance to cold (in TRIFFID they are prescribed to survive

temperatures as low as -30°C).

Figure 4.2 displays the timeseries of surface albedo, which closely follows the change in

vegetation. After 500 years of climate model simulations, the change in surface albedo that

arises from biogeophysical feedbacks only amounts to -0.026 locally (averaged over mammoth

habitat, see Fig. 4.2), and approximately -0.006 globally (not shown). The spatial distribution of

this increase in albedo can be found in Figure 4.3a. We note that a large portion of the northern

landmasses, especially in North America, is unavailable for tree growth due to the presence of

massive ice sheets (see Figure 4.3b). Furthermore, several places in Asia are either too cold (in

Northern Siberia) or too dry (all of the southern half, with the notable exception of the

Himalayan mountain range) to support the growth of trees, limiting the appearance of shrubs to a

(relatively) narrow strip of land stretching from Europe to the Pacific coast and western Alaska,

as well as isolated blobs (the Himalayas, southwestern North America).

Since shrubs are very similar to grasses in terms of snow-free surface reflectivity, a large

part of this albedo decrease is due to the difference in snow-covered canopy albedo (0.6 for

Figure 4.2 : Change in surface albedo over the mammoth habitat (all land north of 30°N) simulated by the UVic ESCM in the context of a maximum impact scenario.


grasses versus 0.4 for shrubs). This fact is strongly evidenced in Figure 4.4, which displays the

full annual cycle of surface albedo anomalies in the Northern Hemisphere. The largest departure

in surface albedo are clearly located at the reforested latitudes, and the anomaly all but vanishes

during summer and early fall when the ground is assumed to be snow free. In the context of late

Pleistocene boundary conditions, the glacial climate regime in the Northern Hemisphere results

in a much later melting of the snowpack, as characterized by albedo anomalies that persist until

mid-June in these latitudes. This contrasts with earlier studies (notably, Thomas and Rowntree

(1992), Chalita and Le Treut (1994)), in which present-day boundary conditions result in a

March or April meltdown. It is also interesting to note that a minimum in surface albedo

anomaly seems to occur during June, right before the anomalies vanish altogether at the NH mid-

latitudes. An analysis of surface air temperature anomalies (see Figure 4.7 below) and land

surface temperature anomalies (not shown) also reveals that during the same time period this

region is much warmer in the reforestation run than in the control run. These observations lead

B


us to conclude that the darker canopy of dwarf trees leads to an earlier spring, and snow melt is

hastened by up to a few weeks due to the snow-albedo feedback.

4.3.3 Temperature

The impacts of this decrease in albedo on temperature are displayed in the form of a

globally-averaged (Figure 4.5) and zonally-averaged (Figure 4.6 (a)) timeseries of temperature

changes due to biogeophysical feedbacks only. The temperature trend is well-correlated with the

decrease in albedo, and 100 years into the simulation the temperature anomaly is approximately

0.110°C globally (in Figure 4.5) and an average 0.275°C over the mammoth habitat (not shown).

In a similar fashion to surface albedo, temperature keeps increasing over the following 400 years,

albeit at a reduced rate, reaching 0.175°C globally and 0.420°C over the mammoth habitat. The

additional warming is to be expected since natural deglacial climate change causes the Earth to

become warmer and wetter and ice sheets to recede, leaving an ever increasing amount of space

to be conquered by trees and shrubs, and thus further lowering the surface albedo compared to

the “no extinction” simulation in which the tree cover is not allowed to expand north. Due to

A

Figure 4.4 : Annual cycle of land surface albedo anomaly in the Northern Hemisphere during the last year of climate model simulations. Solid line represent positive contours, while dotted lines represent negative values. On the abscissa, months are displayed from January to December according to their numerical order.


this factor it is expected that the temperature departure between the two simulations will keep

increasing beyond 500 years, perhaps even indefinitely.

Not surprisingly, our numbers are several times higher than those obtained by DWF2010,

even after only 100 years of model simulations. However, this difference can be explained by

both the use of a much larger territory (although a large part of that area is covered by ice sheets

during the late Pleistocene); and the fact that our maximum impact scenario effectively compares

0% tree cover against 100% tree cover (20% vs. 26% for DWF2010).

The spatial distribution of temperature changes is displayed in Figure 4.6 (b). At first

glance, it would seem that the warming pattern is directly related to changes in surface albedo,

with areas that experience rapid reforestation following the megafaunal extinctions (see Fig. 4.3

(a)) also observing the largest increase in temperature. This is especially true for southwestern

North America and the Himalayas. However, a few details cannot be fully explained by

comparing with Fig. 4.3 (a) only, with two cases standing out in particular. First, we notice that

the largest departure (0.6°C) occurs over extreme northeastern Asia, which sees a change in

albedo comparable to that experienced in central Europe, although the latter only experience half

as much warming. Here, we suggest that the additional warming is caused by a strong snow-

Figure 4.5 : Globally-averaged temperature increase due to biogeophysical effects only, in the context of a maximum impact scenario.


Figure 4.6 : (a) Zonally-averaged temperature difference between the “extinction” and “no-extinction” runs; (b) spatial distribution of the temperature anomaly. The dotted lines represent 0.05°C isotherms.

A

B


albedo feedback, promoting warmer temperatures during the winter and spring (for a complete

discussion, see Chapter 2). As mentioned above, there is compelling reason to believe that the

warmer spring temperatures due to biogeophysical effects hasten the snowpack melt by a few

weeks, creating a strong temperature anomaly during the late spring (see Fig. 4.7).

A second case of interest comes from an unexpected area of (slightly) cooler

temperatures off the coast of Antarctica, in the Weddell Sea. Since all of the forcing is

happening in the Northern Hemisphere, it makes sense that the distribution of temperature

anomalies should also be heavily biased towards the latter. However, it is also known that

temperature anomalies over the North Atlantic can lead to significant changes in the oceanic

thermohaline circulation, impacting the rate of deep water formation near Antarctica. In our case

an analysis of the 14

C content of the deep waters in the Weddell Sea reveals a strengthening

vertical gradient of δ14C along with a reduced

14C ratio in the bottom waters (see Fig. 4.8), often

indicative of stunted deep water formation and lower ocean temperatures (because sinking ocean

Figure 4.7 : Zonally-averaged, annual cycle of temperature anomalies over the northern Hemisphere. The contour interval of the isotherms is 0.1°C. On the abscissa, months are displayed from January to December in their numerical order.


water loses heat which it releases into the atmosphere). These cooler waters can also lead to

more sea ice formation, triggering a local sea ice-albedo feedback with negative impacts on

temperature. The combination of all of the above factors would then explain why this region

could experience cooling despite a major warming in the Northern Hemisphere.

As mentioned earlier, the annual cycle of temperature anomalies is displayed in Figure

4.7. Although the peak anomalies tend to occur later due to the cooler glacial climate, the

seasonal cycle is very consistent with results from other similar studies (Thomas and Rowntree

(1992); Bonan et al. (1992); Douville and Royer (1996)), successfully reproducing the strong

temperature anomalies during the winter and spring.

4.3.4 Precipitation

Another important climatic factor, especially when considering the growth of vegetation,

is the global distribution of precipitation. Figure 4.9 displays the change in total precipitation

that would arise from biophysical feedbacks only. Contrary to our intuition and the results of

several studies, notably Thomas and Rowntree (1992), and Bonan et al. (1995), the model output

suggests that the increase in temperature brought by the change in vegetation cover is

accompanied by a considerable decrease of precipitation rates over land. As shown in Figure 4.9,

the total worldwide precipitation eventually recovers, but this is in large part thanks to a

compensating increase of precipitation over the oceans.

Figure 4.8 : Variations in δ14

C anomaly as a function of depth. This particular snapshot is taken in the Weddell Sea, in the middle of the cold anomaly in Fig. 4.6(b), and averaged for the entire last year of the simulation.


There are several arguments that could be used to explain the initial drop in precipitation

rates that seem to coincide with the drop in surface albedo. Since the maxima of precipitation

decrease are centered on the reforested areas (not shown) and occur mostly during the summer

months (see Fig. 4.10), one possible explanation could come from parameterization of soil

moisture in MOSES and TRIFFID.

Looking at the annual cycle of precipitation anomalies can also offer some clues. In our

case, it is interesting to note that during most of spring the precipitation anomaly is slightly

positive, which would be in line with most studies associating an increase in boreal forest with

warmer, wetter conditions. However, this anomaly takes a sudden reversal in June, and stays

strongly negative throughout the summer. In this manner, our results are quite similar to those of

Chalita and Le Treut (1994), who found that climate-vegetation interaction in Europe resulted in

a wet spring followed by a warm, dry summer. However, their paper noted that soil moisture

Figure 4.9 : Change in total precipitation rates, shown for land only and land + ocean.


levels were dramatically low in the early summer, likely because of the increased evaporation

during spring, and dropped to a critical point below which precipitation was not possible. This is

not the case in our model run, especially over the concerned regions, and so it is deemed unlikely

that critically low soil moisture level would explain the anomalous precipitation rates. Similarly,

changes in evaporation over land do not provide a convincing lead. Further experimentation

with the UVic model will be necessary in order to gain a better understanding of this issue.

4.3.5 Sea ice

The impacts on sea ice in the Northern Hemisphere are clearly visible in the model output.

The timeseries of global sea ice volume anomalies, as well as the anomaly in sea ice thickness

over the Arctic Ocean in late summer, are displayed in Figure 4.11. The change in ice volume

follows the same pattern as surface albedo and temperature, and can therefore be directly

associated with the decrease in surface albedo. Not surprisingly, the largest thickness change

occurs on the Asian side of the Arctic, where most of the vegetation change occurs. Further

Figure 4.10 : Annual cycle of precipitation anomalies in the Northern Hemisphere during the last year of model simulations. Solid lines represent positive contours, while dotted lines represent negative values. The contour interval is in units of 10

-7

kg m2 s

-1. On the abscissa, months are displayed from January to December according to their numerical order.


investigation will be required to determine whether this change in sea ice has a significant impact

on temperature (through the sea ice-albedo feedback mechanism).

4.4 A set of more realistic experiments

4.4.1 Description of the experiments

The above is a hypothetical maximum impact scenario, with parameters set to

unrealistically create a large perturbation. A criticism often heard when presenting the above

results in seminars was that the mammoths could not survive if the very source of their diet (trees

and shrubs) was completely removed. Or that their habitat could not possibly span such a large

area as suggested in the maximum impact scenario. With these criticisms in mind, and given the

large uncertainty regarding the mammoth diet and their time of extinction, we next designed a set

of simulations to explore the parameter span more likely to have been encountered.

The following subsection 4.4.2 deals with a set of sensitivity experiments concerning

three circumstantial parameters of the mammoths’ presence and extinction: rate of tree clearing

(referred to as “herbivory” in some papers), surface area of habitat, and timing of extinction.

The first of these terms is parameterized as a reduction of the mammoths’ influence on the

biosphere: instead of systematically causing the disappearance of forestry in their habitat, trees


and shrubs are allowed to grow at a rate equivalent to a certain fraction of their unperturbed state.

The second factor is treated simply as a northward shift of the southernmost limit of the

mammoths’ habitat. This is the only logical way to proceed since there are insufficient paleodata

to reconstruct the exact surface area of their habitat; regardless, it would make but a small

difference with the approach employed here, and would therefore be irrelevant to the purpose of

this study. The last of these terms consists of starting the simulation at a different time (with

different boundary conditions) in order for the extinction to occur in a different context of natural

climate variability; in particular, we want to test whether any significant change can arise from

the experiment being conducted at a different stage of deglacial climate change. Results from all

three of these experiments are put together for the sake of comparison.

In the final two subsections of this chapter, we relax two other assumption made in the

maximum impact scenario. In subsection 4.4.3, we abandon the instantaneous extinction and

look at the climate system evolution in the case of several different curves of gradual extinction :

linear, sinusoidal, and exponential. In subsection 4.4.4, we question the use of prescribed CO2

levels in the atmosphere, and try to determine the impact of biogeochemical effects on the total

temperature signal in response to changes in vegetation cover.

4.4.2 Sensitivity tests

Out of many different model runs for this part of the project, we have selected six

individual simulations to outline the results from the sensitivity study, two from each of the three

parameters described in the above subsection. The details of each experiment are presented in

Table 4.1, and these experimental parameterizations are compared with those used in the

maximum impact scenario.


#

Date started

(yrs after 0

AD)

Length of

simulation

(yrs)

Parameters of sensitivity study

Notes Year of

extinction

Fraction

of trees

cleared

Southmost

extent of

habitat

-- -12500 1000 -12000 1.00 30°N Maximum impact scenario

1 -12500 1000 -12000 0.67 30°N Tree clearance decreased by

1/3

2 -12500 1000 -12000 0.33 30°N Tree clearance decreased by

2/3

3 -12500 1000 -12000 1.00 45°N Extent of habitat further north

4 -12500 1000 -12000 1.00 60°N Extent of habitat much further

north

5 -15500 1000 -15000 1.00 30°N Earlier time of extinction

6 -10500 1000 -10000 1.00 30°N Later time of extinction

Table 4.1 : List of experiments used in the sensitivity study and their parameterizations. Results from entries in bold are shown in Figure 4.13 in the form of a world map of temperature anomalies 500 years after the prescribed extinction.

Figure 4.12 : Results of the sensitivity tests, presented here as a timeseries of temperature anomalies. The maximum impact scenario is shown in red for the sake of comparison.


1 3

5 6

Figure 4.13 : Spatial distribution of temperature anomalies for various simulations in the set of sensitivity experiments. The number besides each panel refers to the that of the specific experiment in Table 4.1. All of these figures are one-year averaged differences in temperature between the simulation and a related “no extinction” simulation with similar parameterizations. The year of averaging is 500 yrs after extinction.


The results from the sensitivity study are shown in Figs. 4.12 and 4.13. Overall these

results correspond well with our intuition. Decreasing the rate of tree clearing effectively

reduces the forest recovery in the years following the instantaneous extinction, and therefore

reduces the loss of albedo (when compared to the “no extinction” run) and the increase in

temperature. Decreasing herbivory by 33% approximately halves the impact on global

temperatures, while a decrease of herbivory by 67% yields anomalies so insignificant they can

hardly be distinguished from background noise. As can be seen in the top left panel of Figure

4.13, all of the main features of the maximum impact scenario (geographical distribution of

anomalies, location of the largest departure in eastern Siberia, slight cooling in the Southern

Ocean) can be retrieved for these experiments, albeit with diluted numbers.

Results from the sensitivity to the area of habitat are also fairly straightforward in their

interpretation. A reduction in the area of habitat effectively removes all potential input (in terms

of albedo effects) from the areas which are excluded from the smaller habitat. Moving the

southern border of the habitat to 45°N reduces the impact by about half, while a displacement of

this border to 60°N essentially negates all possible effects, since there are very few ice-free

locations north of this boundary that can support any kind of vegetation at all. The top right

panel of Fig. 4.13 still shows several of the aspects of the maximum impact scenario, notably the

maximum in Siberia and the negative anomaly near Antarctica.

Experimenting on the time of extinction creates a little more interesting impact, since a

different time period corresponds to a different stage of deglacial climate change, and a different

potential extent of forest recovery. An earlier extinction by 3000 years (15 ky BP) yields a

temperature anomaly slightly lower than the maximum impact scenario, but the difference

becomes negligible with time. In the end-of-simulation output (bottom left panel of figure 4.13),

we note large similarities between the two experiments, with the notable exception that the early

extinction scenario does not reproduce the cold temperature anomaly in the Weddell Sea. We

hypothesize that slightly lower global temperatures during that period (and therefore a lower


freshwater flux into the North Atlantic Ocean) prevented a reversal of the thermohaline

circulation due to additional warming from the biogeophysical feedbacks. As expected, a later

extinction by 2000 years (10 ky BP) results in an even larger increase in SAT anomalies than the

maximum impact scenario. The spatial distribution of temperature anomalies (bottom right

panel of Figure 4.13) is slightly different at the northern high latitudes due to a moderately large

fraction of the continental ice sheets disappearing between 12 ky BP and 10 ky BP (a prescribed

feature in the UVic ESCM), which opens up more room for the expansion of various vegetation

types.

4.4.3 Gradual extinction experiment

It is clear that any kind of megafauna species did not disappear all at once; instead, their

extinction is likely the end result of a slow decline due to a combination of climatic and

anthropogenic stresses acting over thousands of years. In order to verify whether a gradual

decline in population has a significant impact on the overall result, we designed a series of four

simulations with slightly different patterns of population decline (in order to represent the

decline in population, we set the fraction of trees cleared in a grid cell as equal to the fraction of

the original population left). In the first two, we examine the basic linear pattern, with one being

spread over a longer time than the other. The final two simulations both have an intermediate

duration of 1000 years, and one simulates an exponential decay (fast early, slow late) of the

population while the other follows a “sine” pattern (slow early and late, fast in between). In each

of these simulations, the model is run for an additional 500 years after the population reaches

zero.

Results from these simulations are shown as a combined timeseries of temperature and

albedo in Figure 4.14. Again, changes temperature seems to be very closely correlated to

changes in surface albedo. Because of the length of these simulations, we can see some events

that could not be witnessed in the 500 year-long maximum impact scenario. For example, in

panels (a), (c) and (d), a sharp drop in albedo can be observed which is linked to a considerable


Figure 4.14 : Results of the gradual extinction experiments, presented in the form of temperature-albedo graphs. The four panels represent each of the individual simulations.


decrease in the prescribed land ice. The temperature anomaly is also significantly larger in each

of these simulations (0.27 °C for each of them, compared to 0.175 °C), owing to the simulation

lasting a longer time (more time for trees to grow and for slower components to respond) and

extending to a later period than the maximum impact scenario (all of these end in 9500 YBP). A

quick glance at the spatial distribution reveals little difference in the spatial pattern compared to

the maximum impact scenario.

This experiment was only intended to find out whether a non-instantaneous extinction

pattern would result in a different temperature output, possibly due to long-term nonlinear

interactions between various components of the climate system. There are numerous possible

ways upon which this could be improved or extended: for example, one could subdivide the

mammoth habitat into smaller regions, each with its own timing and pattern of mammoth

extinction. Whether it would have a measurable impact on the overall result is still questionable,

based on what was obtained here.

4.4.4 Free CO2 experiment

Since the main objective of this study was to quantify the climate response to

biogeophysical feedbacks alone, it was only fitting to prescribe levels of CO2 in the atmosphere

in order to avoid any interference from carbon cycle effects. In this final experiment, however,

we turn off this prescription of CO2 and attempt to quantify the combined biogeophysical and

biogeochemical effects on the climate response. Apart from this new element, all other

characteristics of the experimentation are left unchanged from section 4.3.

Results from this simulation, which are shown in Figure 4.15, are rather counterintuitive.

We would have expected the temperature effects from biogeophysical effects to be at least partly

offset by interaction with the carbon cycle due to the increased carbon sequestration by trees and

shrubs. Instead, they appear to enhance each other, resulting in a combined warming that

exceeds double that from biogeophysical effects alone (Fig. 4.15 (a)). Naturally, this results


from an opposite trend of atmospheric carbon dioxide: as displayed in Fig. 4.15 (b), CO2 levels

are increased by about 15 ppm in the first 150 years following the extinction, after which the

trend is reversed, but the overall anomaly after 500 climate model years is still overwhelmingly

positive. While the increase of the last 350 years can undoubtedly by attributed to the increase in

carbon sequestration, the sudden increase early on cannot.

An analysis of carbon fluxes between the land and atmosphere reveals that the initial

increase in CO2 is closely related to the vegetation change. While this could be clear from the

coincidence of both the increase in CO2 and the rapid afforestation (as represented by the

decrease in surface albedo), it appears the UVic model, and TRIFFID in particular, defines

Figure 4.15 : A selection of results from the free CO2 experiment. (a) a comparison of the temperature anomaly between the free and prescribed CO2 experiments; (b) difference in atmospheric CO2 between the two simulations; (c) change in total soil carbon resulting from the vegetation change; (d) carbon flux from the atmosphere to the land (since it is mostly negative, it indicates a land-to-atmosphere flux.


different limits of soil carbon content depending on the dominant PFT. Since this limit is lower

for shrubs than C3 grasses, the replacement of the latter by the former in several grid cells north

of 30°N causes a massive release of soil carbon to the atmosphere. The change in soil carbon

content is portrayed in Fig. 4.15 (c), and Fig. 4.15 (d) confirms that most of it is transferred into

the atmosphere. As a side note, the “blips” that appear on Fig. 4.15 (d) are in fact caused by a

decrease in the (prescribed) continental ice sheets. A bug in the model causes all of the land

carbon in a grid cell to be trapped under the ice when the continental ice sheets are prescribed to

appear; this carbon is then suddenly released into the atmosphere when the ice is removed. The

changes visible in Fig. 4.15 (d) are very small (compared to those that can be seen in Fig. 4.14),

and thus would not be easily observed in a temperature anomaly plot.

It is difficult at this point to determine whether the carbon cycle response to the

vegetation change is genuine or a product of some model artifact. Since logic would dictate that

the increase in plant carbon sequestration should dominate over any other effects, a much more

detailed experimental setup would be required in order to draw conclusions from these results.

69

Chapter 5

Conclusions

5.1 Summary

The objective of this thesis was to investigate biophysical feedbacks between the fauna,

flora, and climate in the context of the Late Quaternary Extinctions, and provide a quantitative

assessment of those feedbacks in terms of temperature and other climate parameters. To this end,

we developed an experimental strategy that consisted of first prescribing a decrease in tree cover

over a pre-defined mammoth habitat in the coupled UVic-TRIFFID ESCM, then allowing the

model to reach an approximate equilibrium with the new environmental conditions, and finally

terminating the perturbation in order to examine the transient climatic response to a subsequent

recovery of tree fraction. This setup was used to explore several hypothetical cases of the

mammoth extinction, including a catastrophic “maximum impact scenario” and a collection of

more realistic variations of the former.

Results from the maximum impact scenario were mixed, with some being rather

unsurprising while others were more difficult to explain within a physical context. Due to a

strong height-based plant dominance hierarchy in TRIFFID, shrub types were found to quickly

recover their unperturbed territory following the mammoth extinction, perhaps too quickly for a

natural reforestation. Tree types, on the other hand, did not conquer much terrain during the 500

simulation years, and thus had on their own a limited impact on the climate system.

Since the experiment was set up specifically for changes in vegetation to drive the

climate response, it follows that many other climate parameters observed a similar trend to that

experienced by vegetation distribution. We noted a sharp decrease of surface albedo for the

initial 100 years (after extinction), which then became very subtle for the rest of the simulation.

Overall, after 500 years of climate model simulations, the albedo over land decreased by a little

CHAPTER 5. CONCLUSIONS 70

under 0.006, while strictly over the mammoth habitat the decrease was approximately 0.026.

There was little change outside of the Eurasian subcontinent, mostly because of the Laurentide

ice sheet, which was still quite large at the given time of simulation. Accordingly, it goes

without saying that under warmer, ice-free conditions in North America the areal cover of albedo

reduction would have been much larger. Another interesting consequence of late Pleistocene

boundary conditions was found in the seasonal cycle of albedo anomalies, which revealed that

the snow-masking effect of shrubs lasted several months longer into the spring season,

suggesting that the change in vegetation might have had a greater effect than it would for

present-day conditions.

We found the change in temperature to be dominated by the albedo feedback, and at the

surface the estimated warming was 0.175°C globally and 0.420°C over the mammoth habitat.

We hypothesize that any warming happening after the initial 100-year pulse would be an indirect

consequence of deglacial climate change, which over time makes more room for trees and shrubs

to move in (as a result of melting ice sheets, as well as warmer and wetter conditions). As

outlined in previous studies (Thomas and Rowntree (1992); Bonan et al. (1992); Douville and

Royer (1996)) the warming is found to be greatest during winter and spring. Although neutral or

positive temperature anomalies are observed almost everywhere on the globe, there is an area of

slightly cooler temperatures in the Southern Ocean. After examining a depth profile of the δ14C

tracer, we suggest that a cooling at this location would likely be caused by a weakening of the

overturning circulation, a result of which is to mitigate deep water formation in the Southern

Ocean. This sporadically cooler anomalies could also enhance the formation of sea ice over

these regions, a possible explanation to the albedo anomalies observed in that region.

Still within the context of a maximum impact scenario, we examined the transient

response of two other climate parameters, namely precipitation and sea ice. In regards to

precipitation, we observed an unequivocal and unexpected drop in precipitation rates, especially

during summer over the reforested latitudes. While these results can be related to at least one

major study (Chalita and le Treut (1994)), they seem to contradict the notion that with warmer


temperatures come more moisture availability for precipitation. More investigations with the

UVic are likely necessary to get a satisfying answer. On the other hand, sea ice anomalies were

found to go in the expected direction, and correspondingly the UVic model calculated a drop in

sea ice volume during the melting season in the Arctic Ocean.

In general, the range of sensitivity and gradual extinction studies were found to

correspond well with our intuition. Decreasing the mammoth habitat or the tree clearance ratio

resulted in a drop of the temperature response, while the timing of extinction did not seem to

have a major impact (among the range tested, from 15 ky BP to 10 ky BP). The main conclusion

from the gradual extinction tests was that the shape of the extinction pattern does not have a

significant impact on the long-term temperature response. However, these simulations

confirmed that temperature anomalies continue increasing at least several thousands of years

after the extinction. A simulation of free carbon exchanges with the atmosphere yielded

counterintuitive results, as the reforestation was found to coincide with a large CO2 anomaly in

the atmosphere, most of it coming from the land surface reservoir. These results are likely not

representative of reality, and further investigation with the UVic treatment of the land carbon

cycle will be necessary before these results are taken into consideration.

We therefore conclude that, with our experimental strategy, it was possible to reproduce

and quantify climatic effects of the megafaunal extinctions within the UVic ESCM. Our results

were of comparable significance to that obtained by several other studies which examined the

impact of high-latitude vegetation change on either present-day or past (i.e., LGM or mid-

Holocene) climate conditions. However, some of our simulations produced surprising, or at

times unprecedented, results which could not be related to previous studies; for example, the

decrease in precipitation observed in all simulations, or an increase in atmospheric carbon

dioxide caused by the recovery of tree fraction, both of which could be caused by physical

inconsistencies within the UVic model. Further experimentation with the UVic model will tell

whether they are genuine physical processes or unwanted model artifacts.


5.2 Future work

In general, many paleoclimate model studies involving vegetation feedbacks are focused

on either the mid-Holocene or the Last Glacial Maximum, both of which are peripheral to the

period studied in this thesis. In a way, our work is a bit of an oddity for its temporal frame,

which makes it a bit difficult to compare our numbers with other works. In that sense, our

maximum impact scenario can also be looked at as a high-latitude (>30°N) reforestation

experiment within late Pleistocene conditions, which is a bit of a novelty for paleoclimate studies.

It could be useful for future work to look at the climate response in other models (of intermediate

complexity), since numerical results from one single model can hardly be taken without a grain

of salt.

An element of the Doughty et al. (2010) study that we overlooked throughout the entire

project is the predator-prey relationship between the mammoth and the tree vegetation. Whereas

the original paper used a Lotka-Volterra approach to simulate a range of realistic tree-mammoth

scenarios, in this project we simply wrote of this relationship in terms of a pre-defined tree

grazing rate, whose influence we examine in Section 4.4.2. Hence, a further extension could be

made to our work by including the results of a predator-prey model as a dynamical component

impacting vegetation cover. Of course, this could be rather difficult with the current version of

TRIFFID, as it does not clearly define any measure of tree density which would be used in such

a case; an altogether different model could be the best choice in order to apply this suggestion.

Finally, as it was mentioned several times in the thesis, a thorough investigation of land

surface processes would be required in order to shed some light on peculiar results obtained in

the context of our study. In our case, both the counterintuitive precipitation and carbon dioxide

trends seem to happen during a sudden change in the dominant PFT (and then, it could be argued

that the abruptness itself is also caused by an irregularity in the competition scheme). A similar

experiment with a different vegetation model might also enlighten us as to whether or not the

processes suggested by TRIFFID are physically meaningful.

73

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