brault (2012) -- pleistocene megafaunal extinction n' climate
TRANSCRIPT
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
CHAPTER 1. INTRODUCTION 3
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),
CHAPTER 1. INTRODUCTION 4
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
CHAPTER 1. INTRODUCTION 5
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.
CHAPTER 1. INTRODUCTION 6
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 9
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 10
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 11
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 12
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.
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 13
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 14
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.
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 15
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 16
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.
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 17
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.
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 18
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 19
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 20
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 21
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 22
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.
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 23
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 24
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 25
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
CHAPTER 2. CLIMATE-VEGETATION INTERACTIONS AND FEEDBACKS 26
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
CHAPTER 3. MODEL DESCRIPTION 29
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
CHAPTER 3. MODEL DESCRIPTION 30
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.
CHAPTER 3. MODEL DESCRIPTION 31
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).
CHAPTER 3. MODEL DESCRIPTION 32
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.
CHAPTER 3. MODEL DESCRIPTION 33
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-
CHAPTER 3. MODEL DESCRIPTION 34
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
CHAPTER 3. MODEL DESCRIPTION 35
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
CHAPTER 3. MODEL DESCRIPTION 36
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
CHAPTER 3. MODEL DESCRIPTION 37
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.
CHAPTER 3. MODEL DESCRIPTION 38
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,
CHAPTER 3. MODEL DESCRIPTION 39
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
CHAPTER 3. MODEL DESCRIPTION 40
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
CHAPTER 3. MODEL DESCRIPTION 41
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.
CHAPTER 3. MODEL DESCRIPTION 42
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 45
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 46
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 47
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 48
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).
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 49
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 50
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 51
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 52
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 53
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 54
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 55
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 56
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 57
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 58
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 59
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 60
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 61
#
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 62
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 63
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 64
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 65
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 66
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
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 67
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.
CHAPTER 4. RESULTS OF THE TRANSIENT SIMULATIONS 68
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
CHAPTER 5. CONCLUSIONS 71
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.
CHAPTER 5. CONCLUSIONS 72
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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