wavelet analysis of climate variability and savanna land-atmosphere interactions...

Declaration

i

Wavelet Analysis of Climate Variability and

Savanna Land-Atmosphere Interactions during the

20th

Century

Emily Goodale

Bachelor of Science (Honours)

Thesis submitted by Emily Goodale, BSc. already held in partial fulfilment of the

requirements for the Degree of Bachelor of Science with Honours in the School of

Environment, Faculty of Engineering, Health, Science and the Environment, Charles Darwin

University.

Submitted November, 2014

Declaration

ii

Declaration

I declare that this thesis is my own work and has not been submitted in any form for another

degree or diploma at any university or other institute of tertiary education. Information

derived from the published and unpublished work of others has been acknowledged in the

text and a list of references given.

Emily S Goodale, November 2014

Dedication

iii

Dedication

To my supervisors, Lindsay and Jason, whom without which my year would not have been a

success.

Acknowledgements

iv

Acknowledgements

First and foremost, I wish to thank my supervisors, Jason and Lindsay, you have gone above

and beyond to ensure this project was a success, for which I am endlessly grateful. This thesis

would not have been possible without your guidance, support and encouragement along the

way.

To my previous educators, especially Dr Cäcilia Ewenz of Flinders University, Adelaide,

thank you for inviting me into the wonderful world of research. Your wealth of knowledge

and passion for meteorology is inspired; I hope I can be half as knowledgeable as you

someday. Vielen dank für alles.

I am indebted to Jason, Rhys Whitley (Macquarie University) and Vanessa Harverd (CSIRO)

for their incredible work in gathering modelled historical data for me, yet another example of

the willingness to help others, which I am forever observing within this small network of

Australian and international scientists. Your efforts are not forgotten.

The wavelet analysis presented in this thesis could not have been achieved without the

endless help of Liang He. Liang, your wealth of knowledge in this area is exceptional,

especially as an early researcher and I’m eternally grateful for all your help and advice.谢谢.

Thank you to the Hutley Lab, your smiles and words of encouragement along the way are

undoubtedly the reason I have finished this thesis with my sanity (mostly) intact. I have never

met an academic group more willing to offer their help and advice, I hope that someday I will

be able to help out a lowly honours student beginning their research career in the same way

you’ve facilitated me this year.

Finally, I would like to thank Sinclair Knight Merz for their generous contribution and

scholarship granted to me this year; your award has demonstrated my capabilities as a

research scientist and I hope that you find my project worthy of your support.

Table of Contents

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Table of Contents

Declaration ............................................................................................................................................... i

Dedication .............................................................................................................................................. iii

Acknowledgements ................................................................................................................................ iv

Table of Contents .................................................................................................................................... v

List of Figures ...................................................................................................................................... viii

List of Tables ......................................................................................................................................... xi

List of Acronyms/ Abbreviations .......................................................................................................... xii

Abstract ................................................................................................................................................ xiv

Chapter 1: Introduction ........................................................................................................................... 1

1.1 Land-atmosphere exchanges ......................................................................................................... 2

1.2 Climate change in Australia .......................................................................................................... 4

Temperature and rainfall ................................................................................................................. 5

1.3 Climate change in savanna-dominated north Australia ................................................................ 5

1.4 Mathematical techniques for time-series analysis......................................................................... 7

Wavelet analysis ............................................................................................................................. 7

1.5 Aims and objectives ...................................................................................................................... 9

Chapter 2: Literature Review ................................................................................................................ 11

2.1 Drivers of tropical climatology ................................................................................................... 12

The North Australian Monsoon .................................................................................................... 12

El Nino Southern Oscillation ........................................................................................................ 13

Tasman Sea Index ......................................................................................................................... 15

Indonesia Index ............................................................................................................................. 15

2.2 Analysing climatic and biological cycles .................................................................................... 16

Fourier vs. wavelet analysis .......................................................................................................... 16

The wavelet approach ................................................................................................................... 17

Applications .................................................................................................................................. 18

Table of Contents

vi

2.3 Savanna – the dominant north Australian ecosystem ................................................................. 19

Savanna physiology ...................................................................................................................... 21

Savanna phenology ....................................................................................................................... 22

2.4 Land surface models ................................................................................................................... 23

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

Chapter 3: Approach and Methodology ................................................................................................ 27

Variables of interest ...................................................................................................................... 28

Tools of analysis ........................................................................................................................... 30

3.1 Site description ............................................................................................................................ 30

3.2 Data processing methods ............................................................................................................ 31

Time series trends ......................................................................................................................... 32

Power spectrum densities and Wavelet transform coherence ....................................................... 32

Software package .......................................................................................................................... 34

3.3 Data Sources and modelling approach ........................................................................................ 34

Tower observations ....................................................................................................................... 34

AWAP ........................................................................................................................................... 34

Climate Indices data ...................................................................................................................... 35

Modelling ...................................................................................................................................... 36

Chapter 4: Results ................................................................................................................................. 38

4.1 Time Series Analysis .................................................................................................................. 38

AWAP meteorological inputs ....................................................................................................... 39

Biological responses ..................................................................................................................... 40

4.2 Power Spectrum density graphs .................................................................................................. 44

4.3 Wavelet Coherence ..................................................................................................................... 51

Gross primary productivity ........................................................................................................... 52

Net ecosystem exchange ............................................................................................................... 52

Ecosystem respiration ................................................................................................................... 53

Latent heat flux ............................................................................................................................. 54

Southern Oscillation Index............................................................................................................ 55

Table of Contents

vii

Tasman Sea Index ......................................................................................................................... 57

Indonesian Index ........................................................................................................................... 58

Chapter 5: Discussion ........................................................................................................................... 64

5.1 Trends and temporal patterns ...................................................................................................... 64

Temperature .................................................................................................................................. 64

Rainfall and latent heat ................................................................................................................. 65

VPD............................................................................................................................................... 67

Carbon fluxes ................................................................................................................................ 67

Trends in ecosystem physiology ................................................................................................... 69

Evidence for ecosystem coupling to climate ................................................................................. 70

Climate indices .............................................................................................................................. 71

Limitations to the study................................................................................................................. 76

Chapter 6: Conclusions ......................................................................................................................... 78

References ............................................................................................................................................. 80

Appendices ............................................................................................................................................ 98

Appendix A: Climate Indices with high correlation to annual total rainfall over Australian continent

.......................................................................................................................................................... 98

Appendix B: Howard Springs Flux Tower Equipment ..................................................................... 99

Appendix C: Slope changes of annual mean carbon fluxes early vs. late century .......................... 100

Appendix D: Time series (anomaly plots) of climate indices ......................................................... 101

Appendix E: Rainfall coherence spectra and ecosystem fluxes ...................................................... 102

List of Figures

viii

List of Figures

Figure 1.1: Representation of the main processes and variables in the coupled land surface-ABL dynamic

system. The carbon and water cycles are represented as flux components within the ecosystem. Taken from

Vila-Guerau de Arellano et al. (2012)……………………………………………………………………………..3

Figure 1.2: Wavelet transform coherence of red and blue signal, observing the different properties of phase

shift, frequency and amplitude between the two signals. Taken from Xu Cui (2014)-

http://www.alivelearn.net/category/matlab/page/3/ ………………………………………………………………8

Figure 2.1 Conceptual diagram linking ecosystem and climate from ecosystem to global scale. (Source:

Beringer et al. 2011, adapted from Chapin et al. 2005 and Bonan 2008). ………………………………………11

Figure 2.2: The geographical distribution of the Australian savanna biome as defined by Fox et al. (2001).

Located north of the Tropic of Capricorn, it also includes the NATT and observation sites (Source: Hutley et al.

2011) ……………………………………………………………………………………………………………..20

Figure 2.3: Schematic of increasing levels of detail being added into surface modelling approaches. (Source:

Pitman 2003) …………………………………………………………………………………………………….25

Figure 3.1: General approach flow chart. The neural network is central to this modelling exercise, bringing

together two sources of information to generate a flux response from 1900-2013 on which the study is centred.

……………………………………………………………………………………………………………………28

Figure 3.2: Diurnal latent heat flux measured at 30 minute intervals and averaged over the 2001-2013 period for

observations (blue) and ANN (green). X axis is hours from midnight and y is Fe flux in W m-2

. ……………...37

Figure 4.1: Time series of three environmental drivers of ecosystem fluxes at the study site (a) temperature

(Ta), (b) rainfall and (c) vapour pressure deficit (VPD). Temperature and rainfall are AWAP inputs

(observations from BoM’s Darwin station) and VPD is a composite of temperature and humidity, demonstrating

the evaporative power of the atmosphere. ……………………………………………………………………….39

Figure 4.2: Linear trend of three carbon fluxes of interest. (a) Net ecosystem exchange (Fc), (b) gross primary

productivity (GPP) and (c) ecosystem respiration (Fre). Plots are ANN outputs over the 100 year period of

interest 1913-2013. For Fc, GPP and Fre -ve values indicate the atmospheric loss of carbon to the ecosystem, i.e.

a sink. …………………………………………………………………………………………………………….41

Figure 4.3: Linear trend of latent heat flux (Fe). Plots are ANN outputs over the 100 year period of interest

1913-2013. ……………………………………………………………………………………………………….42

Figure 4.4: Time series of (a) water use efficiency (WUE) and (b) light use efficiency (LUE) for the tower

observation period (2001-2013). GPP is divided Fe *0.49 to generate a time series for WUE and using the

conversion factor of 0.49 (Kanniah et al. 2010) in LUE gives GPP divided by short wave radiation *0.49, WUE

and LUE are shown at a monthly scale, and are reflective of ecological coupling. ……………………………..43

http://www.alivelearn.net/category/matlab/page/3/

List of Figures

ix

Figure 4.5: Wavelet power spectrum of (a) temperature, (c) rainfall and (e) vapour pressure deficits and

corresponding wavelet spectrum at Howard Springs (b, d and e respectively). The thick black contour in the

wavelet power spectrums and blue dash line in the wavelet spectrums indicate the 5% significance level against

red noise. Wavelet spectral analysis was fed daily data over the 113 year period. ……………………………...45

Figure 4.6: Wavelet power spectrum of (a) Fc, (c) GPP, (e) Fre and (g) Fe and corresponding wavelet spectrum

(b, d, f and h respectively) at Howard Springs. The thick black contour in the wavelet power spectrums and blue

dash line in the wavelet spectrums indicate the 5% significance level against red noise. Wavelet spectral analysis

was fed daily data over the 113 year period. …………………………………………………………………….47

Figure 4.7: Wavelet power spectrum of (a) Southern Oscillation Index (SOI), (c) Tasman Sea Index (TSI) and

(e) Indonesian Index (II) and corresponding wavelet spectrum (b, d and f respectively) at Howard Springs. The

think black contour in the wavelet power spectrums and blue dash line in the wavelet spectrums indicate the 5%

significance level against red noise. The very low resolution of these images is most likely due to the reduced

number of values (Indies are measured monthly over the 110 year period). ……………………………………50

Figure 4.8: Wavelet coherence of GPP and temperature GPP-temperature is over the whole study period (1900-

2013) using monthly data. The solid black line is the 5% significance level against red noise calculated from the

Monte Carlo simulation. Arrows are the relative phase angle (arrows pointing right are in-phase, arrows pointing

left are anti-phase). …………………………………………………………………………………………….52

Figure 4.9: Wavelet coherence of net ecosystem exchange and VPD. The solid black line is the 5% significance

level against red noise calculated from the Monte Carlo simulation. Arrows are the relative phase angle (arrows

pointing right are in-phase, arrows pointing left are anti-phase). ……………………………………………...53

Figure 4.10: Wavelet coherence of (a) respiration and temperature and (b) respiration and VPD. The solid black

line is the 5% significance level against red noise calculated from the Monte Carlo simulation. Arrows are the

relative phase angle (arrows pointing right are in-phase, arrows pointing left are anti-phase). ……………….53

Figure 4.11: Wavelet coherence of (a) latent heat and temperature and (b) latent heat and VPD. The solid black


relative phase angle (arrows pointing right are in-phase, arrows pointing left are anti-phase). ……………….54

Figure 4.12: Wavelet Coherence of SOI against the biological responses (a) ecosystem respiration, (b) gross

primary productivity, (c) latent heat flux and (d) net ecosystem exchange. The solid black line is the 5%

significance level against red noise calculated from the Monte Carlo simulation. Arrows are the relative phase

angle (arrows pointing right are in-phase, arrows pointing left are anti-phase). ………………………………55

Figure 4.13: Wavelet Coherence of TSI against the biological responses (a) net ecosystem exchange, (b) latent

heat flux, (c) ecosystem respiration and (d) gross primary productivity. The solid black line is the 5%



List of Figures

x

Figure 4.14.: Wavelet Coherence of II against the biological responses (a) net ecosystem exchange, (b) latent

heat flux, (c) ecosystem respiration and (d) gross primary productivity. The solid black line is the 5%



Figure 5.1: Variable temperature trends in the previous century at the Howard Springs site. (a) Temperature

trend 1913-1949 and (b) temperature trend 1950-2013. Annual average values are used to generate the time

series. …………………………………………………………………………………………………………….65

Figure 5.2: WTC analysis of rainfall and latent heat, demonstrating the driving environmental conditions at an

annual cycle, but little coherence at other periods, especially post 1970 where wetting of the ecosystem is

expected to increase latent heat and other biological drivers. …………………………………………………...66

Figure 5.3: Wavelet coherence of temperature and (left) Fe (middle) Fre and (right) GPP. Besides the annual

cycle, the strongest and most consistent cycle has a frequency greater than 10 years. ………………………….70

Figure 5.4: Wavelet coherence spectra of (a) temperature and (b) rainfall with SOI. Graphs show time series of

SOI (black) with a 10 year running mean (grey) of (top) temperature and (bottom) rainfall, displaying the

accuracy (or not) of the wavelet coherence analysis in picking up the decadal signals. ………………………..72

Figure 5.5: Variation in response to SOI conditions between carbon (Fc) and water (Fe) exchanges of savanna

ecosystem. 2.5 year periodicities of carbon exchanges respond to neutral conditions of SOI, water exchanges

respond to drier (El Nino) conditions. …………………………………………………………………………...74

Figure A.1: Australian map of climate indices with highest correlation with annual total rainfall from 1900-

2010. Resolution is 125km2. Rainfall at Howard Springs is shown to correlate with the Tasman Sea Index and

Indonesian Index. ……………………………………………………………………………………………….98

Figure A.2: Time series and linear trends of carbon flux variables showing the variation in rate of increase

between the first and second halves of the 20th

century. (a,b) gross primary productivity, (c,d) ecosystem

respiration and (e,f) net ecosystem exchange. ………………………………………………………………….100

Figure A.3: Time series of climate indices. SOI appears to have approximately equal distributions of positive

and negative (wet and dry) events while TSI and II show marked increasing in frequency of positive (warm)

SST anomalies during the last quarter of the time period. ……………………………………………………..101

Figure A.4: WTC plots demonstrating lack of coherence beyond the dominating annual cycle of rainfall and

ecosystem flux variables. (a) net ecosystem exchange, (b) latent heat flux, (c) ecosystem respiration and (d)

gross primary productivity. …………………………………………………………………………………….102

List of Tables

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List of Tables

Table 2.1: Summary of selected wavelet analysis studies on terrestrial ecosystems response to climate. Note the

recent emergence of the wavelet approach for land atmosphere exchanges and climate drivers. ……………....19

Table 3.1: Meteorological and flux response variables used for time series and wavelet coherence analysis. ...28

Table 3.2: Climate indices of interest in this study. Chosen for their influence on Australian rainfall and

climate……………………………………………………………………………………………………………29

Table 4.1: Summary table of climate and biological variables following spectral analysis. Observed periodicity

and associated statistically significant time periods are included, calculated as the number of years a periodicity

is observed over the total number of years in the analysis (113). Each variable is ranked by significance fraction,

to demonstrate the variability in periodicity across meteorological and biological variables. ………………….49

Table 4.2: Summary table of gross primary productivity responses to the climate indices, SOI, TSI and II,

following wavelet coherence spectral analysis. Observed periodicity and associated significant time periods and

phase angle are also listed. ………………………………………………………………………………………59

Table 4.3: Summary table of latent heat flux responses to the climate indices, SOI, TSI and II, following

wavelet coherence spectral analysis. Observed periodicity and associated significant time periods and phase

angle are also listed. ……………………………………………………………………………………………..60

Table 4.4: Summary table of ecosystem respiration responses to the climate indices, SOI, TSI and II, following


angle are also listed. ……………………………………………………………………………………………..61

Table 4.5: Summary table of net ecosystem exchange responses to the climate indices, SOI, TSI and II,


phase angle are also listed. ………………………………………………………………………………………62

Table A.1. List of instruments and location along the tower at the Howard Springs Flux site, collecting eddy-

covariance data capturing the land-atmosphere exchanges at a savanna site. ………………………………….99

List of Acronyms/ Abbreviations

xii


ABL …………………………………….………………………………..Atmospheric Boundary Layer

ACEAS………………………………...…… Australian Centre for Ecological Analysis and Synthesis

ANN………………………………………………………………………… Artificial Neural Network

AR4………………………………………………………………… Fourth Assessment Report (IPCC)

AR5………..…………………………………………………………. Fifth Assessment Report (IPCC)

AWAP …………………………………………………………...Australian Water Availability Project

BoM………………………………………………………...……… Australian Bureau of Meteorology

BVG…………………………………………………………………………... Broad Vegetation Group

CABLE/BIOS2 ………...…………..……. Community Atmosphere Biosphere Land Exchange Model

CAWCR ………………………………………...Centre for Australian Weather and Climate Research

CSIRO……………………………… Commonwealth Scientific and Industrial Research Organisation

ENSO………………………….…………….………………………….. El Niño Southern Oscillation

FE…………………………………………………………………………………….. Latent Heat Flux

fPAR…………………………………………….….. Fraction of Photosynthetically Active Radiation

FT…………………………………………………………………………………… Fourier Transform

GCM …………………………………………………………………………….Global Climate Model

GPP……………………………………………………………………….. Gross Primary Productivity

HWS………………………………………………………………………………….. Howard Springs

IOD……………………………………………………………………………… Indian Ocean Dipole

IPCC……………………….………………………….. Intergovernmental Panel on Climate Change

LAI…………………………………………………………………………………… Leaf Area Index

LSM………………………………………………………………………………. Land Surface Model

LUE……………………………………………………………………………… Light Use Efficiency


xiii

MODIS……………………………………………… Moderate-resolution Imaging Spectroradiometer

MSLP………………………………………………………………………… Mean Sea Level Pressure

NATT………………………………………………………….. Northern Australian Tropical Transect

NCAR…………………………………………….……….. National Centre for Atmospheric Research

FC…………………………………………………….……………………… Net Ecosystem Exchange

OZFLUX………………………………... Australian and New Zealand Flux Research and Monitoring

FRE………………………………………………………………………... Net Ecosystem Respiration

RN……………………………………...………………………………………………. Solar Radiation

SOTT………………………………………………………………… 2014 State of the Tropics Report

SST…………………………………………………………………………. Sea Surface Temperature

TERN………………………………………..….. Australian Terrestrial Ecosystem Research Network

VPD…………………………………………………………………………… Vapour Pressure Deficit

WFT …………………………………………………………………….Windowed Fourier Transform

WTC……………………………………..…………………………….. Wavelet Transform Coherence

WUE……………………………………...……………………………………… Water Use Efficiency

Abstract

xiv

Abstract

Land-atmosphere interactions and feedbacks mirror a dynamically coupled system that

functions on timescales from fractions of seconds to millions of years, driven by terrestrial

ecosystem processes and modes of climate variability. Land-atmosphere exchanges involve

the transfer of mass, energy and momentum and changes to these fluxes can modify regional-

scale climate. This thesis aimed to examine the variability and temporal cycles between

temperature (Ta), rainfall and vapour-pressure deficit (VPD) as drivers and gross primary

productivity (GPP), net ecosystem exchange (Fc), ecosystem respiration (Fre) and latent heat

flux (Fe) as ecosystem flux responses, over the 20th

century. It also sought to analyse how

influential modes of climate variability were to describing variability in ecosystem responses.

Climate indices such as the Southern Oscillation Index (SOI), Tasman Sea Index (TSI) and

Indonesian Index (II) were chosen to analyse this influence.

The study site is located at Howard Springs, NT (12.5⁰S, 131.2⁰E), chosen for its long-term

flux tower and proximity to a Class 1 Bureau of Meteorology station (Darwin). The site is a

typical coastal savanna with high annual rainfall (1690mm y-1

) experiencing frequent climatic

and fire disturbance. Tower flux data trained an Artificial Neural Network (ANN) which was

driven by meteorological observations to hind-cast ecosystem responses over a 113 year

period. Using a wavelet coherence analysis, interactions between eddy covariance fluxes and

meteorological drivers are quantified at multiple time scales over the previous 113 years,

using modelled and observational data, in an effort to establish the relationship between

climate and the terrestrial carbon and water cycle within tropical north Australia.

Spectral and temporal analysis revealed significant shifts in broad climate from the mid-

1960’s onwards with increased temperatures at the Howard Springs site in line with trends

observed for Australia, namely a 0.92 oC increase in temperature over the last 113 years.

VPD also increased by 24% relative to 1910 and rainfall by 17%. In effect, the Howard

Springs site has become hotter and wetter, with an increase in evaporative demand as

demonstrated by the increase in VPD. The wavelet analysis suggested increased coupling

between the climate and ecosystem exchanges with increased temperature, rainfall and CO2

concentrations. Water fluxes (Fe) showed sensitivity to El Nino phases (drier conditions)

while carbon fluxes (GPP, Fre and Fc) consistently correlated with neutral conditions,

indicating disconnect between the carbon and water exchanges in response to climate change.

Abstract

xv

These results can be further developed by analysing additional sites to unpack the

mechanisms involved between savanna and climate. The novel approach and unique study

period length has contributed to our understandings of the influence of past and present

climate variability to savanna land-atmosphere exchanges, providing insight to the behaviour

of coastal savanna ecosystems, which covers tens of thousands of square kilometres of

tropical north Australia.

Chapter 1: Introduction

1


Quantifying the association between ecosystem flux and climate at multiple time scales is

essential for a broad understanding of the role of climate and its impact on ecosystem

function (Stoy et al. 2009). The Earth’s surface is covered by just 29% land; however,

terrestrial ecosystems store approximately 1600 giga-tonnes of organic carbon, more than

twice that of the atmosphere (Pielke et al. 1998). Annually, ecosystems sequester more than a

quarter of all anthropogenically-released carbon emissions (ACREAS 2013), an important

ecosystem service. The carbon cycle is also of critical importance to atmospheric processes

through alteration to the planetary energy balance (Gornall et al. 2009) with increased

emissions of CO2 over the last century. The temporal and spatial distribution and magnitude

of this terrestrial sink is uncertain, particularly in tropical regions (Pan et al. 2011). The

weather and climate at the Earth’s surface influences many social and economic activities

calling for a realistic simulation of the land surface within climate models (Bounoua et al.

2002).

Within the framework of climate change, the carbon cycle is coupled with the water balance

through assimilation and water-use of vegetation (Seneviratne & Stockli, 2008). Mass

(carbon, water) and energy (radiation, heat, and momentum) is exchanged between the land

surface and the lower atmosphere. These land-atmosphere exchanges occur on temporal

scales from seconds to centuries and these processes that influence the meteorology from

regional to planetary scales. Interactions and feedbacks between vegetation and climate are

complex, operating over a range of temporal scales (Pitman 2003). The climate system and

terrestrial ecosystems are closely coupled by the cycling of carbon between vegetation, soils

and the atmosphere. The carbon cycle is hypothesised to have been modified by changes in

the climate and atmospheric CO2 concentrations, with an enhanced terrestrial sink and

storage proposed (Poulter et al. 2014) however, direct evidence for this is limited. Ecosystem

carbon stocks have changed, caused by shifts between stable climate states however, the

dynamic responses of ecosystem carbon fluxes to a changing climate are still poorly

understood (Cao & Woodward 1998).

This thesis aims to investigate this potentially changing relationship between climate

variability and the exchanges of mass and energy for a tropical savanna ecosystem. The


2

savannas of north Australia occupy a third of the land surface and understanding the coupling

between climate and ecosystem processes such as carbon sequestration, water use and energy

balance is critical for meaningful predictions of ecosystem response to climate change.

1.1 Land-atmosphere exchanges

The importance of understanding and quantifying land-atmosphere interactions and their

influence on the global climate system is increasingly being recognised (Seneviratne, &

Stockli, 2008). Climate modelling studies show that terrestrial ecosystem dynamics are as

important as changes in atmospheric composition in determining the rate of land-atmosphere

exchanges (Pielke et al. 1998). The exchange of mass and energy occurs within the

atmospheric boundary layer, the lower 500-2000m of the atmosphere. Early climate models

(1980’s- 1990’s) ignored this feedback with land surfaces, focusing solely on oceanic

processes which influenced climate variability (Pitman 2003). The sophistication of climate

modelling over the past forty years and current models integrate multi-component

representations of the full climate system, with functional plant types (FPT’s) defined that

capture spatial and temporal characteristics of land surfaces (Verburg et al. 2011). Terrestrial

ecosystem feedbacks can now be represented as a dynamically-coupled system with the ABL.


3

Figure 1.1 illustrates both energy (latent heat, sensible heat) and carbon fluxes

(photosynthesis, respiration) as the most important components of the land-atmosphere

interactions.

Climate models are most commonly used to explore the impact of trepidations in climate due

to human activity, however, a significant failure remains in many climate models and their

ability to quantify the land surface and land cover change for climate perturbation (Pitman

2003). This is worrisome since humans have significantly altered a huge fraction of the

Earth’s surface through land cover change, which is likely to accelerate over the next century

as a result of direct impacts due to deforestation, reafforestation and agricultural expansion,

the latter of which is a huge threat to the tropical land surface. With the suggestion that rising

temperatures may decrease the carbon sink by terrestrial ecosystems as a result of rising CO2

levels (Donohue et al. 2013; Pitman 2003), there is a need for improvements to

Figure 1.1: Representation of the main processes and variables in the coupled land surface-ABL dynamic

system. The carbon and water cycles are represented as flux components within the ecosystem. Taken from

Vila-Guerau de Arellano et al. (2012).


4

understandings of land surface and terrestrial ecosystems and their feedbacks to the climate

system.

1.2 Climate change in Australia

Two leading Australian scientific organisations recently released the State of the Climate

Report for 2014 (BoM & CSIRO 2014) summarising the change in climate since the

beginning of the 20th

century. This includes a continent-wide warming of 0.8⁰ C-0.9⁰ C since

1910 which has altered the frequency of extreme weather with more heating events and fewer

cooling extremes. Rainfall has increased on average since 1900, largest in the northwest since

1970. The south-east and south-west has experienced declined rainfall in the same period

(1970) as a result of reduced winter precipitation. The fire season has lengthened across large

parts of Australia since the 1970’s and ocean heat storage has increased as the global mean

sea level has risen by 225mm since 1880.

Modes of climate variability that affect Australian weather, such as the El Nino Southern

Oscillation (ENSO) and Indian Ocean Dipole (IOD) have also experience changes since the

1970’s. Since the mid-1970’s ENSO behaviour has changed and a stud y by Timmermann

(2001) revealed that global warming has shifted the ENSO from stable oscillatory behaviour

to an unstable oscillation, coinciding with an abrupt change in simulated ENSO activity and

changed Pacific Ocean dynamics. Cai et al. (2009) noted the recent high frequency of

positive IOD occurrences, indicating warmer SST across the Indian Ocean, which has

impacts for Australian rainfall. Positive IOD events have increased from four every 30 years

in the early 20th

century to over 10 in the last 30 years, at the same time, cooler SST events

have shifted from 10 over the same period to two. There are now more positive IOD events

than there are negative IOD’s which is also consistent with the reduction in rainfall over

south-eastern Australia since the 1950’s. The warming of the Indian Ocean has also led to the

synchronisation of the IOD with ENSO through the Walker circulation (Cleverly, pers.

Comm. May 2014).These climate phenomena are quantified using climate indices such as the

Southern Oscillation Index which describes Pacific Ocean conditions; Indonesian Index, an

anomaly of SST’s in the Indonesian region and the Tasman Sea Index, used to define extra-

tropical climate.

Building on a large body of work undertaken for the Australian region, climate change

projections for Australia are based on international climate change research. These changes


5

have the potential to create major impacts on human and natural systems and are likely to

continue if emissions of greenhouse gases increase (Climate Institute Report 2014).

Temperature and rainfall

The IPCC Fourth Assessment Report (AR4) predicts that temperatures are likely to warm at

amplitudes larger than that of surrounding oceans (AR4, 2007) within Australia. This

warming is expected to be smaller in the south and most prominent in the winter; frequencies

of extreme high daily temperatures are predicted to increase in Australia whilst vice versa, a

decrease in extreme cold conditions are expected (AR4, 2007). These conclusions are

reiterated in the IPCC’s Fifth Assessment Report (AR5), which noted that the rate of

warming above the land surface was twice that over oceans since 1979 (AR5, 2013).

Minimum temperatures have warmed faster than maximum temperatures and Australia has

warmed ~0.8⁰C over the last century, consistent with global trends (Hughes et al. 2003).

Rainfall trends in Australia are highly spatially-variable, with increases in the north-west of

about 300mm since the 1970’s with corresponding decreases in the south-east (CSIRO, BoM

2014; AR5 2013). These variable trends seem to be responding to changes in the surrounding

oceans, especially in the case of declined rainfall and the terrestrial water balance of south-

west Western Australia. England et al. (2006) observed a characteristic dipole pattern of

Indian Ocean sea surface temperature (SST) anomalies during extreme rainfall years, with

peak amplitudes in the eastern Indian Ocean adjacent to the west coast of Australia. Tropical

and extra-tropical processes in the Indian Ocean generate SST and wind anomalies off the

south-west coast leading to moisture transport and rainfall extremes in the region, a

significant agricultural area. Rainfall is a driver of ecosystem processes and changes in

rainfall seasonality will influence future ecosystem services such as carbon storage however,

these changes are not expected to turn vegetation into a carbon source which suggests that

CO2 fertilisation is the main driver of vegetation change (Scheiter et al. 2014).

1.3 Climate change in savanna-dominated north Australia

Understanding the effects of climate change in North Australia is important given its

contribution to the terrestrial carbon balance. It is of critical importance to the global carbon

and water budget as it accounts for 12% of global savanna land cover. Climate phenomenon

such as ENSO and IOD affect Australian rainfall which is turn influences the carbon sink of

terrestrial ecosystems through gross primary productivity (GPP). The influence of the IOD is


6

strongest in coastal north Australia and changes to these modes of climate variability will

have significant impact for the terrestrial carbon budget.

The Australian savannas experience one of the most seasonal of the world’s savanna climates

(Bowman & Prior 2005) as a result of the annual movement by the Australian summer

monsoon. The strong wet-dry seasonality resulting from the north-south movement of the

monsoon are compounded by the state of near-constant disturbance by other climate

extremes, such as cyclones and fire weather. These disturbances occur over large areas

creating a complex spatial and temporal mosaic of carbon sources and sinks, based on

previous disturbances, fire regime and long-term climate (Hutley et al. 2013).

In biomes dominated by C4 grasses, tree cover is suppressed by periodic fires causing

mortality of aboveground organs (Bond 2008). In the Northern Territory’s savanna, grass-tree

ratio increases with distance from coastline, following a decrease in rainfall and fire

occurrence (Hutley et al. 2011). Fire, tree cover and C4 grass feedbacks are vulnerable to

disruption via changes in climate, atmospheric CO2 concentrations and fire regimes (Bond &

Midgley 2012; Moncrieff et al. 2013). Tropical land cover change through agricultural

expansion will disrupt biogeochemical cycles and community structure (Parr et al. 2012),

which is particularly significant for a biome such as the extensive savanna, which stores

billions of tonnes of aboveground carbon (Lewis et al. 2009). Increased atmospheric CO2

concentrations may increase or decrease terrestrial carbon storage, given potential variations

to processes that may be affected by climate change, such as productivity, ecosystem

respiration rate or fire regimes. When these alterations are applied at the landscape level, they

may impact on climate through local changes in circulation patterns, regional heating,

precipitation and monsoon circulation (Beringer et al. 2003).

Savanna and tropical forest cover most of the land surface are of the tropics, dominating the

exchanges between land and atmosphere in this region. Thus it is important to quantify these

interactions accurately within global climate models (GCM’s). Quantifying these exchanges

within the tropics is difficult given the uncertainty of the source or sink status of tropical

terrestrial ecosystems to atmospheric carbon dioxide concentrations.

Savannas are important to global circulation given their size (approximately 20% of Earth’s

land is covered by savannas), current clearing rate and annual burning, which shifts surface

albedo and net exchange (Knapp et al. 2001). Annual burning emits large amounts of CO2,

methane and N2O to the atmosphere (van der Werf et al. 2010). Given this fire and


7

consumption of fuel, there is uncertainty surrounding the magnitude of the tropical savanna

sink. Beringer et al. (2014) reported a weak source for north Australian savanna, whereas

many studies have found tropical ecosystems to be a significant carbon sink (e.g. Pan et al.

2011; Phillips & Lewis 2013; Wright 2013), though the amplitude of that sink is argued

(Wright 2013). These uncertainties are further compounded by a lack of knowledge

surrounding climate variability and climate change in tropical north Australia; it is unknown

how variability will change in the future.

Some of this uncertainty can be resolved by understanding the climatic variability (cycles)

and the response of ecosystem to these climate cycles, manifest through shifts in productivity,

respiration and surface energy balance through time. Understanding past climate change and

ecosystem response is critical to understanding the future impacts to savanna ecosystems and

global carbon and water budgeting.

1.4 Mathematical techniques for time-series analysis

Ecosystems and climate are coupled through time and a time series analysis provides a

method to assess the strength and change of this link between climate and vegetation and the

associated driver-response mechanism. This can be done using a frequency-domain analysis

that unpacks the complex interactions between the savanna ecosystem and tropical climate

variability. The analysis needs to understand the spectral power and cycling of individual

variables before two can be coupled to assess the driver-response relationship. It also needs to

unpack the lagged response and phase shifts between two variables to understand savanna

function as a response to environmental conditions. Understanding these physical processes

and how they influence biological exchanges is a fundamental goal of functional ecology.

Wavelet analysis

Frequency-domain methods such as the Fourier series approximate a function by

decomposing it into sums of sinusoidal functions, making it capable of detecting dominant

frequencies in a signal (Tran 2006). However, the Fourier transform is only localised in

frequency, whilst wavelet analysis is localised in both time and frequency (Chang & Glover

2010). The Fourier transform can evaluate the size of the component of frequency with

precision, but in doing so loses all control of spatial duration so the time that the signal is

sounded cannot be determined. This is the limiting case of the Uncertainty Principle:

precision on frequency but zero control on temporal spread. The wavelet transform takes

advantage of the intermediate cases of the Uncertainty Principle as each wavelet


8

measurement (corresponding to a fixed parameter) describes both the temporal extent of the

signal as well as the frequency spectrum of the signal. Frequency precision is sacrificed in

order to gain temporal information in the wavelet transform and also ignores the assumption

of stationarity (a time series that does not change through time), making the wavelet a

superior method for analysis of ecological and geophysical time series. This trait is a strong

indicator of the suitability of the wavelet analysis in this study as it represents the coupling

through time between ecosystems and climate. The power of the wavelet transform to

understanding land-atmosphere exchanges between savanna ecosystems and atmospheric

processes is further exhibited through its ability to demonstrate the lagged response and phase

shifting at various cycles, by examining the driver-response mechanism as a mathematical

relationship.

Figure 1.2 presents two varying signals, red and blue analysed using the wavelet transform

coherence function demonstrating the wavelet’s ability to recognise what frequencies are

present and where in time, observing the signal’s change with time (ignoring the assumption

of stationarity).

On the coherence diagram, the thick black line surrounding the high correlating periods is the

cone of influence (COI), beyond which edge effects may distort the statistical confidence of

the coherence. The colour bar to the right shows the statistical strength in the degree of

Figure 1.2: Wavelet transform coherence of red and blue signal, observing the different properties of phase shift, frequency

and amplitude between the two signals. Taken from Xu Cui (2014)- http://www.alivelearn.net/category/matlab/page/3/

http://www.alivelearn.net/category/matlab/page/3/


9

coupling between two variables (significance level 0-1). The y axis is time frequency

(periodicity) and x is time. The black arrows demonstrate the phase shift, left and right

pointing arrows show anti-phase and in-phase (0⁰ and 180⁰ respectively) and down and up

pointing arrows show the blue signal to be lagging by 90⁰ and 360⁰ respectively. The time

series is shown at the bottom of the figure, correlating with the coherence plot.

This novel approach is an ideal method for this project given that the ecological and

geophysical variables to be used ignore the assumption of stationarity.

1.5 Aims and objectives

Given the known coupling between vegetation and climate and the uncertainty in the tropics,

this thesis will focus on one tropical savanna site in Howard Springs, NT. Rainfall variability

at this site correlates with climate indices such as the Southern Oscillation Index (SOI),

Tasman Sea Index (TSI) and Indonesian Index (II). It is also known to be influenced climate

extremes such as cyclones and annual fire regimes. The degree of coupling between cyclic

patterns of environmental conditions (temperature, rainfall) and climate variability (SOI, TSI,

II) with ecosystem responses will be investigated, focusing on the strength of cycling and

phase shifts throughout the 20th

century. This study is interested in the relative response and

coupling to determine coherence and relationships by savanna ecosystems to meteorological

drivers and climate indices, not the prediction of past fluxes.

In order to achieve this, a sophisticated time series analysis known as the wavelet transform

will elucidate the driver-response mechanisms between climate and ecosystem by quantifying

ecosystem sensitivity to climate variability and identify potential lag responses at various

time scales.

This project aims to significantly improve our understanding of temporal variability of key

climate indices and their impact on savanna ecosystem function, where function is captured

by variables describing carbon ( productivity, respiration) and energy fluxes (latent heat or

evapotranspiration). Specifically, the following questions are posed;

1) How much variability in savanna biotic responses (productivity, respiration and

evapotranspiration) is due to climatic changes at seasonal and longer (greater than

annual) time scales?

2) What is the nature of the temporal relationship between meteorological drivers

(temperature, precipitation, vapour pressure deficit) and these biological variables?


10

3) How influential is the role of modes of climate variability as integrated by climate

indices (e.g. SOI, TSI, and II) to describing variability in ecosystem responses?

4) What, if any, lags exist between drivers and biological responses and why?

This thesis is structured as follows. Chapter Two provides a literature review of recent work

on savanna land-atmosphere interactions in north Australia and probable climate drivers and

associated climate indices. Mathematical techniques to correlate biological and climate

indices are expanded in Chapter 3, which details approach and methods. Chapter 4 describes

results from the century long time series of modelling outputs describing climate and

biological variables. Both linear trends and power spectral density data are presented that

describe inherent temporal cycles. Wavelet coherence plots are also presented, where the

degree of coupling of climate and biological variables is analysed and changes over century

time scales are examined. In Chapter 5 these findings are discussed in light of previous

studies, with conclusions summarised and recommendations for future research in climate

and land surface modelling given in the context of tropical ecosystems detailed in Chapter 6.

Chapter 2: Literature Review

11


This chapter provides an overview of the modes of climate variability influencing tropical

meteorology, noting the recent emergence of two significant climate indices and their

implications for land-atmosphere exchanges in tropical north Australia. Current

understanding of carbon dynamics as driven by climate is also reviewed, highlighting the

cyclical nature and coupling of climate and ecosystem processes. The wavelet transform is a

frequency-domain time series analysis and has been established as a useful technique for

assessing coherence between ecological and geophysical time series (Hong & Kim 2011).

The ecological significance of the savanna biome is also examined, given its unknown

vulnerability to climate change. The literature review concludes with a short summary on the

history of land surface models.

A gap exists in the understanding of climate change impacts on a wet/ dry tropical savanna

ecosystem such as Howard Springs at various temporal scales. Given the importance of

climate for terrestrial ecosystems (e.g. Di Lorenzo et al. 2008; Eamus et al. 2013; Knapp et

al. 2008; Nemani et al. 2003; Overland et al. 2010; Peterson & Schwing 2003; Petheram et al.

2012), this uncertainty has major implications for the savanna biome and the terrestrial

carbon and water balance of tropical north Australia. Figure 2.1 demonstrates the linkages

between ecosystems and the atmosphere, as outlined by Beringer et al. (2011). Land-

atmosphere exchanges are modified by ecosystem properties such as structure, composition

and function and play a role in determining atmospheric conditions.

Figure 2.1 Conceptual diagram linking ecosystem and climate from ecosystem to global scale. (Source: Beringer et al. 2011, adapted from

Chapin et al. 2005 and Bonan 2008).


12

Variability in ecosystem characteristics modifies land-atmosphere exchanges at the regional

scale, influencing the overlaying atmosphere and boundary layer. However, at the meso-

scale, this spatial variability can create disparities that drive mesoscale climate, such as the

Australian summer monsoon.

2.1 Drivers of tropical climatology

A notable publication is the State of the Tropics Report of 2014 (SOTT

http://stateofthetropics.org ), which examines predicted change within the tropics and

implications for the sub-tropical, temperate and polar regions (SOTT 2014). Key interests of

this report are the climate drivers of variability within the tropics, most notably, the South-

East Asia and Northern Australian monsoon and El Nino Southern Oscillation (ENSO).

Absent are predictions for change to the Madden Julian Oscillation, a little-known climatic

phenomena with potentially significant influence on the climatic and thus tropical ecosystems

due to interactions with the monsoon and ENSO systems (Hendon 2004; Kumar et al. 1999;

McBride & Wheeler 2004; Smith et al. 2008; Tang & Yu 2008). Within the tropics of

northern Australia, climatological research has concentrated predominantly on the Australian

summer monsoon (Bowman et al. 2010; Cook & Herdegen 2001).

The North Australian Monsoon

The wet-dry tropical climatology of North Australia is dominated by the presence of the north

Australian summer monsoon system via land-ocean pressure differences driving convection

(stormy weather) and low pressure systems, resulting in heavy rainfall and cyclones. The

summer monsoon provides most of the annual rainfall in the north Australian region,

dynamically similar to the system that affects the South Asia region, but with much less

strength and geographical extent (Suppiah 1992).

A number of papers on the wet season in north Australia have focused on predicting and

characterising the onset and duration of monsoonal rain events such as Cook & Heredegen

(2001) and Smith et al. (2008). The spatial variation in duration of wet seasons is critical to

understanding how climate variation affects ecosystems (Cook & Heredegen 2001), which

has driven many studies to characterise the monsoonal influence on the tropical biosphere.

There is however a lack of understanding in the temporal variation of climate, as driven by

the monsoon wet season and its effects on terrestrial ecosystems, over long periods of time

(Beringer et al. 2011).

http://stateofthetropics.org/


13

Monsoon onsets are not a recent interest, Troup (1961), Holland (1986), Hendon & Liebmann

(1990a) and Drosdowsky (1996) examined onset dates and described various methods for

defining the Australian summer monsoon. The most recent undertook a re-examination of

monsoonal activity utilising 35 wet seasons, from 1957-1992 via what is described by the

author as an easily applied objective definition of active and break phases, based solely on

zonal winds at the Darwin International Airport (Drosdowsky 1996). By discarding the

previously used term of ‘wet westerlies’ since there is no clear relationship between westerly

winds and rainfall (Drowsdowsky, 1996), the new onset dates are significantly related to the

Southern Oscillation index (SOI). The inter-annual variability in monsoon onset and

monsoon intensity correlates well with the El Nino Southern Oscillation and using cross-

spectral analysis, onset dates have shown a strong relationship to SOI in the spring prior to

the wet season (Drosdowsky, 1996). This suggests that an investigation into the lags between

climate indices and ecosystem function, especially with SOI is needed to better understand

the dynamical interactions between drivers (climate) and responses (ecosystem exchanges).

El Nino Southern Oscillation

The El Nino Southern Oscillation (ENSO) primarily influences rainfall in the spring, summer

and autumn over the Australian continent, due to the direct impacts of ENSO on the active

South Pacific Convergence Zone during these months (Folland et al. 2002; Franks 2004).

Additionally, a number of authors (e.g. Kiem et al. 2003; Franks 2004; Power et al. 1999)

have demonstrated the influence of the Inter-decadal Pacific Oscillation (IPO) on Australian

rainfall variability at decadal and multi-decadal timescales. Fewer studies exist to quantify

the relationship of ENSO to local rainfall variability in tropical north Australia, though its

influence on monsoonal systems is acknowledged (Wang et al. 2003).

Numerous studies have acknowledged patterns of sea surface temperature (SST) variability

occurring over the Indian Ocean (e.g. Ashok et al., 2003; Nicholls, 1989; Saji et al.1999;

Smith, 1994) that are consequently linked to rainfall variability in Australia, Asia and East

Africa, raising the possibility of a climate phenomenon separate to ENSO influencing

Australian rainfall. This is particularly evident given the absence of ENSO influences over

winter, even though these months display a high degree of variability.

The occurrence of an El Nino phase results in a fluctuation in tropical Pacific Ocean sea

surface temperatures (SST) whereby the equatorial surface waters warm considerably from

approximately 180oE/W to the western shoreline of South America (Stenseth et al. 2003).


14

The atmospheric phenomenon coupled to the ENSO is called the Southern Oscillation,

quantified by the Southern Oscillation Index (SOI), in which exchanges of air occur between

the eastern and western hemispheres within tropical and sub-tropical latitudes.

Much of the climate literature in both the Southern and Northern Hemisphere has focused on

the ENSO phenomenon over the past several decades working on the pioneering papers by

Jacob Bjerknes in 1966. Before this, the term El Nino was used for centuries referencing the

annual incidence of warm, southward-flowing oceanic current (upwelling) off the coast of

Peru and Ecuador at Christmas-time (Trenberth & Hoar 1997). Qualitative listings of El Nino

events date back to 1726 by Quinn et al. (1987) based solely on this coastal phenomenon.

More recent literature defines the large-scale warming that occurs every few years, which

changes the local and regional ecological conditions, as ENSO (Strenseth et al. 2003). This

Pacific-basin-wide occurrence links with anomalous climate patterns around the world

(Chiew et al. 1998), including the Pacific North-American in the Northern Hemisphere and

the Pacific South American in the Southern Hemisphere (Strenseth 2003). ENSO forms two

phases, the cold, termed El Nino, and warm conditions are known as La Nina.

Within Australia, a relationship exists (though its strength varies with geographical proximity

to the Pacific) to rainfall, drought and streamflow (Chiew et al. 1998). The hydro climate of

Australia has captured the attention of many climate scientists given the spate of recent

extreme events, which can be coupled with the warming and cooling phase of ENSO.

Analyses demonstrate that dry conditions in Australia are typically associated with El Nino;

whilst rainfall and streamflow are statistically significant in most regions, ENSO is not

sufficiently strong enough to consistently predict such variables (Chiew et al. 1998). The

teleconnections vary in strength temporally, stronger in the latter half of the year, providing

an invaluable resource to land and water management in Australia where streamflow

variability is one of the highest in the world. Risbey et al. 2009 describe the ENSO as the

principle driver of climate in the tropics but concede that the Indian Ocean has some effect,

especially on rainfall variability. Recent work has highlighted the contribution of the Madden

Julian Oscillation (MJO) to tropical rainfall variability through its manifestation on the

Australian Summer Monsoon (Hendon & Liebmann 1990; Wheeler & Hendon 2004; Risbey

et al. 2009; Wheeler et al. 2009).


15

Tasman Sea Index

The Tasman Sea Index (TSI; Murphy & Timbal 2008) is a measure of sea surface

temperature anomalies in the Tasman Sea at 30-40⁰S, 150-160⁰E. Murphy & Timbal (2008)

have established an association between TSI and autumn rainfall in south-eastern Australia,

where warm SST anomalies in the Tasman Sea are connected with increased rainfall within

the region. One reason for the focus on this region is the high population and agricultural

production density

Schepen et al. (2012) also examined TSI in the same paper analysing the lagged relationship

between II and rainfall over the northern tips and eastern Australia and established a similar

relationship. There was strong to very strong evidence supporting the one month lagged TSI

to forecast rainfall in the western half of Australia in the months October – January. However

since the relationship between seasonal rainfall and TSI has not been studied in this region,

the mechanism for this connection is not understood.

Indonesia Index

The Indonesia Index (II) was first classified by Nicholls (1989) as an index of SST

differences between the Indonesian region (0-10⁰S, 120-130⁰E) and the central Indian Ocean

which was found to be strongly associated with a broad band of rainfall variability stretching

from the northwest to the southeast corners of Australia and negatively correlated with east

coast rainfall. This is indicative of yet another factor that influences Australian rainfall,

largely independent of the well-known Southern Oscillation. Based on limited data,

O’Mahony (1961), Priestley (1964) and Priestley & Troup (1966) appear to be the first to

suggest the link between SST’s and Australian rainfall but it was not until the 1980’s that

studies identified the positive correlation of SST’s around Indonesia with Australian rainfall

(e.g. Kep 1984; Streten 1981; 1983; Whetton 1986).

More recently, a paper by Verdon & Franks (2005) investigating the aforementioned winter

rainfall variability over Australia explored the II in depth and found its anomalous SST’s to

be the most promising for potential practical application due to its ability to identify a

balanced and realistic frequency of wet and dry years. The SST’s over the Indonesian region

were also found to provide a good indication of winter rainfall over eastern Australia,

contradicting earlier studies. The predictive use of II on Australian rainfall is further

evidenced in Schepen et al. 2012 who identified the strong correlations between SST around

Indonesia and rainfall over the central northern tips and parts of eastern Australia. The II was


16

lagged out by two months and used in a Bayesian joint probability modelling approach to

forecast spring rainfall with positive results.

Climate indices such as the SOI, TSI and II have seasonal and cyclic patterns, quantifying

complex and stochastic climate phenomena driving global exchanges of mass and energy

between the land surface and atmosphere. They are also known to have changed through

time, particularly during the last 100 years as a result of climate change and warming oceans.

A time series analysis is required to examine these indices and their coupling to ecosystem

exchanges in tropical north Australia.

2.2 Analysing climatic and biological cycles

Methods of time series analysis may be divided into two classes, time-domain methods and

frequency-domain methods. The latter method includes spectral analysis and the more recent

wavelet analysis. These techniques may be further categorised into parametric and non-

parametric methods, the former assumes that the underlying stationary stochastic process has

certain structures that can be described parametrically. Non-parametric approaches by

contrast explicitly estimate the spectrum of the process without assuming that the process has

particular structure (Lin et al. 2003). Spectral analyses examine cyclic behaviour, separating

components representing trend, seasonality, variation and cyclical irregularity in what is

known as decomposition of a time series.

Wavelet transforms in data analysis have been broadly formalised thanks to mathematicians,

physicists and engineering efforts (Morlet 1983), growing exponentially in popularity over

the past two decades since it represents a synthesis of old techniques coupled with robust

mathematic results and efficient computational algorithms (Daubechies et al. 1992). The

introduction of the time frequency decomposition to wavelet analysis allowed for its use in

ecological applications, where Fourier transforms were previously used (Cazelles et al. 2008).

These decomposition properties, particularly in its time-scale localisation, make it an

attractive tool within science and engineering for the analysis of non-stationary systems. It

can thus be used to examine the periodicity of climate and co-varying biological processes.

Fourier vs. wavelet analysis

Before the identification of the wavelet transform for use as a time series analysis, the FT was

a tool often used to study frequency distributions of geophysical time series. However, the FT

lacked in demonstrating the temporal locality of frequency distributions (Weng & Fau 1994)

which weakened its ability to identify changes through time. There was also a failure in the


17

Fourier series by way of non-stationary signals that occurred during a short time interval

which could not be detected, thus there was a need for an algorithm that identified an

alteration of a signal in a small neighbourhood at a time locality that did not affect the entire

spectrum series (Weng & Fau 1994).

Whereas the Fourier transform (FT) is only localised in frequency, wavelets are localised in

both frequency and time. It ignores the assumption of stationarity, making it the ideal choice

for analysing time series of ecological processes (Cazelles et al. 2008). The wavelet

transformation is a powerful tool for multi-scale analysis and interactions that display

localisation in frequency and time through continuous and orthogonal functions (Weng &

Lau 1994). Given its capability to identify variations of both time and frequency, the wavelet

makes for an ideal analysis tool in this study, using time series of climate and biotic

responses which are cyclic in nature.

Gabor (1946) first identified the windowed Fourier transform (WFT) during his study of non-

stationary time series by localising signals in both time and frequency domains. The WFT

had limitations pertaining to detecting high-frequency signals rooted in low-frequency

phenomena and a fixed window for time frequency width series (Weng & Fau 1994). The

wavelet analysis is based off the WFT.

Early studies using the wavelet analysis identified faults, within the analysis itself, for the

misconception that it only produced qualitative results by way of ‘colourful pictures’ as it

transforms a one-dimensional time series to a diffuse two-dimensional time-frequency image

(Torrence & Compo 1998a). The lack of quantitative results from this method was

compounded by the lack of significant statistical tests and use of arbitrary normalisations.

The wavelet approach

The aim of the wavelet approach is to expand a signal into different waveforms with local

time-frequency properties which are well-adapted to the signal’s structure (Cazelles et al.

2008). The wavelet analysis is now a widely-used tool in medical research (most often in

neurological studies) and is gaining popularity within studies of geophysical time series

since, dissimilarly to the Fourier analysis, it analyses the signals with changing spectra and

thus the spectral characteristics of a time series is a function of time (Velda et al. 2012).

The wavelet method allows the analyser to view correlations that may otherwise not appear

from other methods. Frequencies of high correlations are shown in red, which can occur at


18

different temporal scales and show common high power between two variables for a

particular periodicity. The wavelet analysis is appropriate for following gradual change in

forcing by exogenous variables, permitting an analysis of the relationships between two

signals; its versatility is exacerbated by its decomposition properties, especially its time-scale

localisation (Cazelles et al. 2008), making it a viable choice of methodology for this project.

Applications

The transform is notable for its significance in many neurological studies (Adeli et al. 2003;

Muthuswamy & Thakor 1998) and is becoming popular in geophysical studies, particularly in

its analysis of tropical convection by Weng & Lau (1994), El-Nino Southern Oscillation (Gu

& Philander 1995) and atmospheric cold fronts as analysed by Gamage & Blumen in 1993

(Torrence & Compo 1994). The wavelet analysis has been used on one other similar study

(Hong & Kim 2011) investigating climate and ecosystem response however, the period of

examination went over just three years using eddy covariance observations. This thesis is the

first of its kind using wavelets as a time series analysis over a 113 year period.

The relationships between environmental and ecological time series, as well as their specific

cyclic nature, gives rise to the attractiveness of wavelet analysis and Cazelles et al (2008)

provides abundant examples of such studies dating back to 1954, when spectral analysis in

ecology was first described. Table 2.1 lists recent studies that have used a wavelet approach

in the field of ecology, which gained popularity in the early 21st century. It has found a niche

for many other time series studies such as in Grenfell et al. (2001), who found synchrony

patterns of measles in the UK using wavelet analysis; Nezlin & Li (2003) used wavelets to

compare features of chlorophyll and environmental time series in a qualitative study; an

association between North American porcupine dynamics, local climate and the solar cycle

was demonstrated in 2004 by Klvana et al.; Cazelles et al. (2005) used the approach to

validate the highly significant and non-stationary association between El Nino, precipitation

and dengue epidemics in Thailand (Cazelles et al. 2008).

Table 2.1: Summary of selected wavelet analysis studies on terrestrial ecosystems response to climate. Note the

recent emergence of the wavelet approach for land atmosphere exchanges and climate drivers.

Reference Topic

Baldocchi et al. (2001) Spectral analysis of biosphere-atmosphere trace gas densities

Hong and Kim (2011) Impact of Asian Monsoon on ecosystem carbon and water fluxes

Katul et al. (2001) Multiscale analysis of vegetation surface fluxes

Stoy et al. (2006) Separating effects of climate and vegetation on evapotranspiration


19

Stoy et al. (2007) Coupling of ecosystem carbon inputs and outputs on short

timescales

Stoy et al. (2009) Biosphere-atmosphere exchange of CO2 in relation to climate

Vargas et al. (2010) Multi-scale analysis of temporal variability of soil CO2

productions

Vargas et al. (2011) Multi-temporal correlation between photosynthesis and soil CO2

efflux

Vargas et al. (2013) Drought influence on accuracy of simulated ecosystem fluxes

Post (2013) and Klvana et al. (2008) recognise the variety of ecosystems that are driven by

large-scale climatic oscillations (Cazelles et al. 2008) such as the ENSO and MJO, however

little literature exists to demonstrate the ecological impacts of climate variability globally, but

especially in Australia, due to a lack of data supporting such hypotheses, until now. Recent

advances in flux data management have been accelerated by the efforts of Beringer via the

development of data gap filling and analysis tools and this project is the first of a new

generation of analysis that can now been undertaken given the advanced state of data

availability, both climate and flux time series that describe biological processes (e.g. net

ecosystem exchange, gross primary productivity, evapotranspiration, light-use efficiency,

water-use efficiency).

2.3 Savanna – the dominant north Australian ecosystem

This project is focused on understanding climate drivers of productivity and

evapotranspiration of a high rainfall (1690 mm y-1

) tropical savanna. The tropical savannas of

north Australia occurs across the wet-dry tropics of north Australia, stretching from the

Kimberley region, across NT and into central Queensland above the 600mm isohyet as

defined by Fox et al. (2001), with an area of some two million km2 (Figure 2.2). Globally,

tropical savanna occupies ~ 27.6 million km2 of Australia, South America, Asia and Africa

(Hutley & Setterfield 2008), consisting of a mixture of competitive C3 (trees) and C4

(grasses) vegetation types, both distinct from grasslands and closed forests. The competitive

balance creates highly variable spatial and temporal landscape dynamics (House et al. 2003;

Hutley & Beringer 2011). Australian savanna is one of the most ecologically intact in the

world which is attributable to the indigenous lands and conservation reserves which occupy

about 40% (Hutley & Beringer 2011), and experiences one of the most seasonal climates in

the world (Prior et al. 2009).


20

The savanna biome provides a unique platform for research into ecology, ecosystem

functionality and climate change in both Australia and tropical regions around the world.

Given the size of the savanna biome globally and in Australia, well-calibrated land surface

models (LSM) and dynamic global vegetation models (DVM) are essential to predict

distribution and vegetation-atmosphere feedbacks at regional and global scale (van der Werf

2006). Despite this need, savanna is poorly characterised in current models, resulting in

difficulty in predicting future vegetation distributions (Scheiter & Higgins 2009). Current

research efforts are concentrated on improving model prediction of functional attributes

(gross primary productivity, net ecosystem exchange, water-use efficiency, light-use

efficiency). Ma et al. (2013) described the spatial patterns and temporal dynamics of savanna

phenology across north Australian savanna, a key parameter in climate and biogeochemical

cycle models, given the importance of leaf area.

There are no studies that have examined tropical land atmosphere exchanges and climatic

forcings at such an extensive temporal scale. Just as the literature on climate and ecology is

biased toward global (large-scale) climate patterns such as ENSO, this project is deliberately

focused on ecological processes such as gross primary productivity, evapotranspiration,

water-use efficiency and light-use efficiency. Stenseth et al. 2002; 2005 suggested that

biological effects to climate may be more strongly correlated to global indices than to any

single local climate variable.

Figure 2.2: The geographical distribution of the Australian savanna biome as defined by Fox et al. (2001).

Located north of the Tropic of Capricorn, it also includes the NATT and observation sites (Source: Hutley et al.

2011)


21

Savanna physiology

Physiological characteristics of savanna vegetation vary greatly within the context Australian

savannas due to the multiple plant functional types found in the tree-shrub-grass layers (Ma et

al. 2013). Scholes & Archer (1997) also identify the significantly variable leaf area index

(LAI) and tree-grass ratios among different savanna classes which contribute to physiological

variability. The multi-canopy strata have differing phenological and functional responses and

interactions to seasonal rainfall, temperature, vapour pressure deficits (VPD) and other

environmental variables (Scholes & Archer 1997; Whitley et al. 2013).

The biophysical and biogeochemical processes are altered significantly as a consequence of

fire events within the Australian savanna biome, though its current structure, composition and

distribution have coevolved with fire (Beringer et al. 2014). Savannas with a well-developed

grassy understory are at risk of fire during the dry season, given the high fuel load content

combined with hot, dry and often windy conditions from April to September. Fire events are

known to immediately affect the radiative balance through understory consumption and

blackening of the surface and leaf death following scorching (Beringer et al. 2003). The

intensity of the fire causes variable effects; low intensity fires cause little canopy damage and

surface energy balance alteration with a slight increase in Bowen ratio (ratio of latent to

sensible heat partitioning); moderate fires result in comprehensive damage in the following

weeks after the event such as canopy scorch and almost complete leaf drop. Thermal energy

partitioning is driven by land surface characteristics, such that following a fire, the shutdown

of most leaves within the canopy reduces transpiration and shifts net carbon exchange from a

sink to carbon source (to the atmosphere) through elevated respiration and low levels of

canopy photosynthesis (Beringer et al. 2003).

Savannas typically occur on a range of soil types given their biogeographic range, such as

oxisols, ultisols, entisols and asfisols (Hutley & Setterfield 2008). These soils are generally

highly weathered and low in organic matter, tending toward types of sands to sandy loams.

Deep and well-drained, they lack the capacity to hold moisture for long periods of time and

are considered nutrient-limited. Thus the interactions between C3 trees and C4 grasses in the

savanna context tend to be dominated by competition for water and nutrients, rather than light

or growing space (Hutley & Setterfield 2008).


22

Savanna phenology

Phenology is the analysis of the life cycles of flora and fauna as influenced by environmental

drivers such as climate and other seasonal influences (Ma et al. 2013). Landscape phenology

is a key parameter in climate and biogeochemical cycle models and despite their coverage of

approximately one eighth of the global land surface and sensitivity to climate change

(Beringer et al. 2012; Ma et al. 2013); little is known about the phenological triggers of

savanna vegetation (Prior & Bowman 2005). Much of the phenological research has been

carried out in northern mid-to-high latitude temperate regions (e.g. Chen et al. 2002; Zhang et

al. 2004), where the timing and duration of growing season is tightly coupled with

temperature. Within the savanna biome, phenological responses to seasonal climate are

complicated due to the heterogeneous assemblage of multiple tree, shrub and grass vegetation

strata, all of which vary independently to environmental factors (Ma et al. 2013). Within the

northern Australian savanna, key phenology transition dates such as the start, peak, end and

length of seasonal greening periods are subjugated by the Inter-Tropical Convergence Zone

(ITCZ) monsoon climate system, characterised by biogeographical patterns.

At a community scale, savanna phenology has been investigated comprehensively near

Darwin in the Northern Territory. Ground observations demonstrated that evergreen species

continue to flush and produce leaves throughout the dry season whilst brevi- and semi-

deciduous species commenced leafing just prior to the arrival of the rainy season (October

through March) (Williams et al. 1997). Distinct from these patterns, fully deciduous species

exhibit a variety of behaviours, flushing before and after rainfall (Williams et al. 1997).

Eucalyptus-dominated savannas observe an earlier greening period of enhanced vegetation

activity than their southern Acacia counterparts, resulting in a relatively stable length of

greening periods (Ma et al. 2013). This is due in part to the extreme rainfall variability

experienced in the north (strong wet-dry seasonality), but also due to the C3/ C4 grass-forb

understory which provides potential for extended greening periods. In contrast, their

responsiveness to hydro-climatic variability is significantly reduced. These phenological

characteristics are suggestive of a strong coupling to rainfall events. Predictions of increased

inter-annual variability and extreme climate events are likely to impact on savanna phenology

(Easterling et al. 2000; Seager et al. 2013).

Characterisation of biogeographical patterns in the phenological cycles of savanna is lacking,

despite their global importance and vulnerability to climate change, especially at landscape


23

and regional scales (Ma et al. 2013). These limitations means effective understandings of

future climate impacts on functionality, composition and fire behaviour are absent in the face

of mounting concern and evidence over the savanna biome’s sensitivity to climate change

(Scholes & Archer 1997; Scholes & Parsons 1997; Bowman et al. 2010; Bowman et al. 2010;

Hill & Hanan 2013).

Across the globe, the phenology of the savanna biome has been investigated at regional

scales using remote sensing (Archibald & Scholes 2007; Ferreira & Huete 2013; Higgens et

al. 2011; Ratana et al. 2005), investigating the range of characteristics from Africa to South

America. However, many papers such as Zhang et al. (2003) have identified challenges

within the scope of remote sensing for its inaccuracy to calculate phenology metrics. Process-

based models have sought to work in conjunction with remote sensing capabilities to enhance

understandings of the land surface and feedbacks with the atmosphere.

2.4 Land surface models

The land surface is a key component of climate models as it controls the partitioning of

available energy and water into sensible and latent heat and evaporation and runoff

respectively. Many papers published in the 1990’s addressed the issue of weather and climate

being influenced by the state of the land surface such as Avissar & Verstraete (1990), Betts et

al. (1996) and Pielke et al. (1998). Sellers et al. (1992) and Pielke et al. (1998) identified

strong evidence for this at timescales ranging from seconds to millions of years. The role of

the land surface varies between climate and climate models and micrometeorology and

mesoscale meteorology due to spatial differences i.e. the climate as modelled through climate

models is not defragmented by small-scale processes over short-temporal scales (Avissar &

Pielke 1991; Pitman 2003). Modelling the climate is a mature discipline driven by the need

for valid predictions in the wake of recognition that human activities are strongly modifying

atmospheric conditions.

The development of land surface models (LSM’s) came from a need to satisfy gaps in

numerical weather prediction and climate modelling by feeding information about land

exchange processes such as the transport of momentum, thermal energy, carbon and water

(Kowalczyk et al. 2013). The first LSM was applied by Manabe (1969) into a climate model

that intentionally included a simple and idealised distribution of ocean and continent and

ignored the diurnal and seasonal cycles (Pitman 2003). Despite these simplifications, which

Manabe defended on the basis that he was exploring climate within a simplified model, this


24

was a key first step to identifying the importance of land surface processes within the

framework of climate modelling.

Legg & Long (1975) and Thom (1972) idealised improvements to the basic LSM by

introducing the representation of heat and moisture transfer from the canopy which was built

heavily on the philosophy of Monteith and Szeicz (1962) for evaporation. Second generation

models included another fundamental step, first described in 1978 by Deardorff who

presented a method for simulating parameters of soil, such as temperature and moisture in

two layers, and vegetation as a single layer. Deardorff proposed that the single vegetative

layer protected a fraction of ground from solar radiation. LSM development provided a key

opportunity for a multi-disciplinary approach since processes were treated explicitly and

mathematically, allowing a new generation of micrometeorologists to contribute

significantly.

Third generation LSM’s identified the major advance and limitation in second-generation

LSM’s which modelled canopy conductance empirically. They accounted for plant and

environmental conditions but used this conductance to only model transpiration. A

supplementary explicit canopy conductance was identified in the 1980’s as a means to

improve the simulation of the evapotranspiration pathway and also address the issue of

carbon uptake by vegetation (Pitman 2003), a major revolution in modelling capabilities. The

support of the plant physiology community was integral to the addition of carbon to LSM’s.

It had been established that leaf assimilation of carbon was limited by the efficiency of

photosynthetic activity, the amount of photosynthetically-active radiation (PAR) and the

capacity of the leaf to utilise these products. Major fieldwork campaigns such as the HAPEX-

MOBILHY (Andre et al. 1988), FIFE (Sellers et al. 1992a), HARPEX-Sahel (Goutourbe et

al. 1997) and ABRACOS (Gash et al. 1996) brought together the plant physiologist,

micrometeorologist and hydrologist communities and led to the integration of photosynthesis

capabilities into LSM’s.

Over the previous decade, land surface models have evolved to improve the simulation of

canopy processes, plant physiology, and most importantly, the uptake of carbon at the land’s

surface. These improvements have led to some LSM’s having a complete and interactive

terrestrial carbon cycle with the incorporation of biogeochemical components and vegetation

dynamics (Pitman 2003; Kowalczyk et al. 2013). A schematic of the stages of increasing

development into LSM’s to its current model is shown in Figure 2.3.


25

Calculating surface fluxes from given meteorological forcings, LSM’s can operate as ‘stand-

alone’ models with applications at single sites (as demonstrated in this thesis) as well as over

continental-scale and global regions. Comparisons of LSM’s have occurred in stand-alone

mode (e.g. Abramowitz et al. 2008) and establishment of benchmarking schemes are

continuing (Abramowitz 2012; Kumar et al. 2012; Luo et al. 2012). Despite these

developments, comparisons of LSM’s within single atmospheric models are less common due

to the coupling work involved, complicating assessments (Kowalczyk et al. 2013). Projects

such as the Land Information System which provides software frameworks for surface

modelling and data assimilation are addressing these issues.

The sophistication of land surface models has seen marked improvements to our

understandings of surface behaviour and exchanges with the atmosphere, satisfying previous

gaps in climate modelling.

Figure 2.3: Schematic of increasing levels of detail being added into surface modelling approaches. (Source: Pitman

2003)


26

2.5 Summary

Given the highly seasonal dynamics of the tropical climate found in northern Australia,

changing seasonal duration has the potential to alter the current surface energy, water and

carbon balance through changes in the local meteorology driven by variations to meso-

climate circulations. This study aims to improve understandings of changing carbon and

water fluxes in response to climate by focusing on the temporal variability of response to

drivers at a century scale. This approach enables correlation between climate drivers and

biological responses at diurnal, seasonal, inter-annual and decadal scales. Savanna is the

dominant biome of North Australia with significant economic, biodiversity and cultural

heritage assets (Woinarski et al. 2007), but whose sensitivity to climate change is not yet

fully understood.

Improvements to the wavelet analysis over the past two decades have seen its validation in

statistical and quantitative studies, but only one study has been published that used this

approach. Hong & Kim (2011) analysed the influence of the monsoon and associated

environmental drivers (temperature, rainfall, vapour pressure deficit) on ecosystem processes

gross primary productivity (GPP), ecosystem respiration (Fre), net ecosystem exchange (Fc)

and latent heat flux (Fe). The wavelet analysis demonstrated the seasonal influence of the

monsoon on ecosystem function and showed the coupling present between environmental

drivers and ecosystem exchanges of carbon and water.

ENSO and SOI have been the focus of literature relating to their influence on temperature

and rainfall regimes in Australia. However, few studies have used the TSI and II to examine

temperature and rainfall patterns in Australia (Verdon & Franks, 2005; Murphy & Timbal,

2008), with no studies using these indices in wavelet analyses.

Chapter 3: Approach and Methodology

27


This thesis brings together two sources of data to train and drive an artificial neural network

(ANN) to hind-cast 100 years (1900-2000) of flux responses at the Howard Springs (HWS)

site (Figure 3.1). These hind-cast responses were analysed as a time series, along with the

driving data to examine the variable change through the 20th

century.

Twelve years of eddy covariance measurements collected at the HWS flux tower site were

used to train the ANN. This site is the longest-running data set representing coastal savanna

in tropical north Australia. Its extensive data set makes it a superior site for training the

neural network to detect seasonality, responses to climate extremes and fire disturbances, a

critical feature of savanna environments.

Meteorological data spanning the 20th

century from the Australian Water Availability Project

(AWAP) drove the ANN outputs of flux responses. These measurements are a combination

of observations and modelled predictions. Given the site’s proximity to the Bureau of

Meteorology’s class 1 Darwin site, the driving meteorology is highly accurate.

Tower derived

flux data

Meteorological

data

Artificial neural

network model

Flux response

outputs

Figure 3.1: General approach flow chart. The neural network is central to this modelling exercise, bringing

together two sources of information (tower derived flux data, GPP, Fc, Fre, Fe) and gridded meteorology

(AWAP data) to generate flux response variables hindcast from 1900-2013 on which the study is centred.


28

Variables of interest

In order to examine how climate and vegetation are coupled, ecosystem exchanges of carbon

and water are fluxes of interest, along with ecosystem physiological variables of water-use

efficiency (WUE) and light-use efficiency (LUE) (Table 3.1). Carbon exchanges are summed

as net ecosystem exchange (Fc), a net representation of carbon uptake through gross primary

productivity (GPP) and carbon loss as ecosystem respiration (Fre). Water exchange by the

ecosystem is defined as latent heat flux (Fe). These exchanges are driven by environmental

conditions such as temperature (Ta), rainfall and vapour pressure deficit (VPD) which in turn

are controlled by modes of climate variability (Table 3.2). In the tropics these modes may be

quantified using climate indices such as the Southern Oscillation Index (SOI), Tasman Sea

Index (TSI) and Indonesian Index (II).

Table 3.1: Meteorological and flux response variables used for time series and wavelet coherence analysis.

Variable Units Comments

FLUX RESPONSE VARIABLES

Gross Primary Productivity

(GPP)

t C ha-1

y-1

(unless otherwise

specified)

Total amount of carbon

materials created from

carbon sources such as

carbon dioxide

Ecosystem Respiration (Fre) t C ha-1

y-1

(unless otherwise

specified)

Release of carbon dioxide by

ecosystem to atmosphere

after oxidation process

Net Ecosystem Exchange

(Fc)

t C ha-1

y-1

(unless otherwise

specified)

Measures how much carbon

is entering and leaving the

ecosystem

Latent Heat Flux (Fe) W m-2

Energy release by ecosystem

during constant-temperature

process (change state of

matter known as

evapotranspiration)

Water Use Efficiency (WUE) GPP/(Fe*0.49) = umol C/

umol H2O

Ratio of carbon uptake per

water unit loss

Light Use Efficiency (LUE) GPP/ (shortwave

radiation*0.49) = umol/umol

Ratio of carbon uptake per

unit of light (radiation)


29

METEOROLOGICAL DRIVER VARIABLES

Temperature (Ta) oC Ambient temperature

Rainfall mm Rainfall at HWS grid cell

Vapour Pressure Deficit

(VPD)

kPa Difference between actual

water vapour pressure and

saturation water vapour

pressure at x temperature

Radiation (Rn) W m-2

Net incoming solar radiation

These variables quantify ecosystem function through carbon and water exchanges and

ecological coupling through water and light use efficiencies.

Table 3.2: Climate indices of interest in this study. , chosen for their influence on Australian rainfall and

climate.

Name Climate

Phenomenon

Description Time Scale

Southern Oscillation

Index (SOI)

ENSO Mean sea level

pressure difference

between Darwin and

Tahiti

Inter-annual to

decadal

Tasman Sea Index

(TSI)

Extra-tropical

Climate

Sea surface

temperature anomaly

(30⁰S-40⁰S, 150⁰E-

160⁰E)

Inter-annual to

decadal

Indonesian Index (II) Indonesia SST Sea surface

temperature anomaly

(0⁰-10⁰S, 120⁰E-

130⁰E)

Inter-annual to

decadal

The climate indices reflect climate phenomena that adjust sea surface temperatures (SST’s)

and drive Australian meteorology, especially rainfall. Their correlation with ecosystem flux

variables will provide further understandings of the degree of coupling present between

climate and vegetation processes in tropical north Australia. Indices used in this study


30

evolved from their influence on Australian rainfall. Appendix A demonstrates that rainfall at

Howard Springs correlates with the Tasman Sea Index, Indonesian Index and Southern

Oscillation Index

Tools of analysis

Linear trends demonstrate the change with time that a variable experiences. By analysing the

time series of each flux response and meteorological driver, trends and variability due to

climate change can be observed, beyond the seasonal cycle and over century time scales.

Correlating trends suggest tight coupling between environmental conditions and ecosystem

function, describing the temporal relationship and driver-response mechanism.

Power spectra density graphs detect the inherent cycling within a singular variable and its

changes in periodicity with time. This analysis is integral to the study and understandings of

common periodicities between two variables, which will come from the wavelet transform

coherence graphs (WTC). The WTC establishes the degree of coupling between the flux

variable and meteorological driver or climate index, analysing the strength of influence of

modes of climate variability on ecosystem function. A defining feature of the WTC is its

ability to determine lags through phase angles, demonstrating the temporal response of

ecosystem to environment.

Howard Springs is an ideal site, representative of coastal tropical north Australian savanna

that experiences influences by modes of climate variability. It is one of the longest flux sites

in the world with twelve years of observations, valid as a training tool for modelling past flux

responses.

3.1 Site description

The Howard Springs Flux Station (12⁰29.712’ S, 131⁰09.003’ E), located adjacent to the

Black Jungle Conservation Reserve in the Northern Territory (Beringer et al. 2007; Hutley &

Beringer 2011) has been operating since 2001 and is one of the longest flux records in

Australia. The instrument mast is 23m in height and using methods of open-path eddy

covariance, it measures heat, water vapour and CO2 fluxes above the 18m canopy (Hutley et

al. 2011). Meteorological parameters include temperature, humidity, wind speed/ direction,

precipitation, incoming and outgoing shortwave radiation and net radiation are also measured

in this region. Howard Springs receives approximately 1690mm annually, 95% of which falls

during the wet season (October to April). Maximum temperatures range seasonally by three

degrees Celsius, from 33.2⁰C in summer to 30.4⁰C in winter. Minimum temperatures range


31

slightly larger at 25.4⁰C in summer and 19.3⁰C in winter (TERN, 2013). Given its location

near the coast, temperatures are modulated by oceanic heat capacity, which also brings about

the presence of a sea breeze.

Average tree height is fourteen to sixteen metres, classified as open woodland savanna

dominated by Eucalyptus miniata and Eucalyptus tetrodonta overstory. The extreme tropical

location and high rainfall of Howard Springs results in a savanna characterised by a woody

overstory of evergreen Eucalyptus and Corymbia species and perennial C4 grasses and other

forb and shrub species making up the understory (Egan & Williams 1996; Hutley et al. 2011).

The location, geology and soil setting and domination of two species has allowed the site to

be classified into a Broad Vegetation Group (BVG) of Monsoon woodlands dominated by

Eucalyptus tectifica and Corymbia species, or BVG 4 (Fox et al. 2001). The highly-

weathered soil is low is nutrient and water-holding capacity, resulting in competition between

the C3 trees and C4 grasses for water and nutrients, rather than light and space. The

functional response of the C4 grass layer present in the northern Australian savanna is

primarily associated with the strongly seasonal presence of the monsoon system.

Unlike South America and Africa, Australia’s savannas are exhibit low levels of

fragmentation and are ecologically intact (Beringer et al. 2011; Woinarski et al. 2007)

providing us with greater capacity to understand the environmental controls of their growth

and functionality. This ‘living laboratory’ creates an ideal investigation of savanna phenology

and biogeographical variations (Hutley et al. 2011; Williams et al. 1996). Many studies have

utilised this region for investigations in biogeography, soils, ecophysiology, meteorology and

land-atmosphere processes (see review by Hutley et al. 2011).

3.2 Data processing methods

Now and hereafter, annual records of climate, meteorology or flux variables are organised as

July-June annual values, in order to capture the whole seasonal distribution of rainfall (a

significant ecosystem driver) within one time-step, as experienced in tropical Australia.

Initial data processing and sorting proved impossible with almost two and a half billion

values for 374 variables at 30 minute time steps from 1st January 1900 to 2014 (Beringer et

al., unpublished data). This was overcome by reducing temporal resolution to daily averages

before capturing the variables of interest (meteorological drivers, tower observations and

ANN outputs). Arbitrary classifications for wet and dry months were made in order to

compare seasonal patterns of influence, drives and lags. They are: May, June, July, August,


32

September and October (Dry); and November, December, January, February, March and

April (Wet).

We have assumed that the climate phenomena (i.e. ENSO, extra-tropical climate, SST’s in

Indonesian region) are the drivers of change in environmental conditions which in turn

influence these variables. GPP, Fre and Fc are carbon exchanges which have links to the

thermal regime while Fe is a constituent of the water cycle and as such is influenced by

moisture conditions in the environment, among other drivers of radiation (Rn) and vapour

pressure deficits (VPD). When analysing water and light use efficiencies (WUE and LUE

respectively), this could only be done for the tower data period (2001-2013). This is because

WUE and LUE are a measure of ecological coupling whereas GPP, Fre, Fc and Fe are

coupled physiologically. Thus, the ANN predicts GPP and Fe statistically and doesn’t know

about the coupling.

Time series trends

Time series were created to demonstrate the various trends and cycles of the meteorological

drivers and their biological counterparts, which respond to different drivers, such as

atmospheric inputs of solar radiation (evapotranspiration), precipitation (gross primary

productivity, ecosystem respiration) and air temperature, soil temperature and moisture also

have a significant influence on many flux responses (Pielke et al. 1998). Many responses are

driven by a combination of meteorological forcings, which will be demonstrated through

analysis of climate indices such as SOI, TSI and II, which influence precipitation and water

availability and radiation through various processes. Given the tropical scope of this project,

understanding the climate variability is of particular importance, due to the sensitivity of

tropical regions to climate change and the alterations that are expected to occur over the next

century with regards to temperature and precipitation extremes.

Anomalies of the time series were also plotted over the 100 year period (1913-2013) looking

at the annual variation. This was done to confirm modelled data suitability in further

correlative analyses with climate indices.

Power spectrum densities and Wavelet transform coherence

Wavelets provide time-frequency localisation, done by integrating the whole domain of time t

from -∞ to ∞, to find the frequency behaviour over a particular time period (Nason &

Silverman 1995), as is observed through the following steps of wavelet analysis.


33

The power spectrum density graphs detect inherent cycling within individual variables and

how these change with time (eq. 1). Variations throughout the 20th

century see changes in

periodicity length and strength of cycling. The wavelet transform coherence similarly detects

cyclic behaviour in the time series, however the algorithms (eq. 2-4) are fed two variables

and the common periodicities are found, along with their strength in coupling and the phase

angles, demonstrating the lagged effect by the response to the driver.

Initial analysis began with power spectrum density graphs displaying the global power

spectrum of meteorological drivers and ecosystem responses, using the Morlet wavelet as a

basis function in this study, following successful use in the same research context by Hong &

Kim (2011), defining the wavelet power spectrum (S) of Xn as

Sn (s) = |Wxn(s)|

2 eq. 1

Where s is the wavelet scale and Wn is a complex number with real and imaginary parts after

transform of Xn. The continuous wavelet transform (CWT) of Xn is calculated as (Torrence &

Compo 1998)

𝑊𝑛(𝑠) = ∑ �̂�𝑘ψ̂∗(𝑠𝜔𝑘)𝑒𝑖𝜔𝑘𝑛𝛿𝑡𝑁−1

𝑘=0 eq. 2

k is the scale index and N is the number of time series. x̂k is the Fourier transform of Xn, ψ̂∗

is the Fourier transform of the conjugate of the wavelet function; s is the scale; ωk is angular

frequency, δt is the time step, as documented in Collineau & Brunet (1994); Grinsted et al.

(2004) and Hong & Kim (2011). The cross wavelet transform the two time series Xn and Yn

are further analysed in preparation for the final wavelet coherence (WTC) spectrum, at scale

s, defined as

WnXY

(s) = WnX (s)Wn

Y* (s) eq. 3

Where (*) is the complex conjugate (a binomial formed by negating the second term of the

binomial) of wavelet coefficient WnY and the wavelet coherence becomes

𝑅𝑛2(s) =

|⟨𝑠−1𝑊𝑛𝑋𝑌(𝑠)⟩|

2

⟨𝑠−1|𝑊𝑛𝑋(𝑠)|

2⟩⟨𝑠−1|𝑊𝑛

𝑌(𝑠)|2

⟩ eq. 4

Where smoothing occurs in both scale and time and the factor s-1

is used to convert energy to

power spectral density using definitions0 ≤ 𝑅𝑛2(s) ≤ 1 (Hong & Kim 2011).


34

In the wavelet power spectra graphs and coherence plots, the x axis is time in years, from

1900-2013 and y axis is time scale and frequency ranging from six months to ten years.

The colour intensity exhibits the spectral power with dark red indicative of the highest

spectrum. The global power spectrum graphs specify the significant time-frequency variation

for each variable; where the x axis is time and y axis is spectrum.

Software package

Statistica (StatSoft Inc. Ver. 12) was used in conjunction with MATLAB (MathWorks Inc.

R2014a) to organise, summarise and conduct wavelet analyses. The wavelet analysis module

is a component of the WTC MATLAB program (Grinsted 2004) (MATLAB codes can be

downloaded from the website http://noc.ac.uk/using-science/crosswavelet-wavelet-

coherence) while the wavelet program was written by Christopher Torrence and Gilbert

Compo (Torrence & Compo 1998).

3.3 Data Sources and modelling approach

Tower observations

Twelve years of tower observations are used to train the ANN at the study site. Tower

observations at Howard were collected and calculated through eddy covariance, following an

open-path method, using the LI-7500A and LI-7700 (CH4) gas analysers (Burba & Anderson

2005). A full list of measurements and their descriptions can be found in Appendix B. Final

data output consists of an estimated ten per-cent error of which a number of issues can cause.

In this tropical region, the errors most likely result from spatial separation between sonic and

open-path analyser and precipitation events interfering with sensors (Burba & Anderson

2005).

AWAP

Feeding the ANN is AWAP meteorological data, prescribing the climatic conditions for the

site from 1900- present. The local meteorology used by the tower would usually be best here;

however it only goes back ten years.

The Australian Water Availability Project (AWAP) was created by CSIRO as a 0.05⁰ x 0.05⁰

gridded dataset to monitor the state and trend of the terrestrial water balance over the

Australian continent, using model-data fusion methods combining both observational data

and modelling (CSIRO 2014). However, observations haven’t existed in many parts of the

country since its inception (1900) and have had to use modelled data, creating a climatology,

http://noc.ac.uk/using-science/crosswavelet-wavelet-coherence

http://noc.ac.uk/using-science/crosswavelet-wavelet-coherence


35

or repeated trend for the period in which no data was taken. We acknowledge radiation as a

significant driver of ecosystem function however, given that records didn’t exist before 1990,

it would be futile to discuss the results of radiation as a driver in a century-long analysis.

Climate Indices data

Climate data was downloaded via online sources, at a monthly resolution provided by the

Centre for Australian Weather and Climate Research (CAWCR) and National Oceanic and

Atmospheric Administration (NOAA).

The Southern Oscillation Index (SOI; Troup 1965) is calculated as a standardised anomaly of

values of mean sea level pressure (MSLP) differences between Darwin and Tahiti from 1933-

1992. Sources use the Troup method as shown here:

SOI = 10(𝑃𝑑𝑖𝑓𝑓−𝑃𝑑𝑖𝑓𝑓𝑎𝑣

𝑆𝐷(𝑃𝑑𝑖𝑓𝑓)) eq. 5

Where Pdiff is the average Tahiti MSLP for the month minus average Darwin MSLP for the

month, Pdiffav is the long term average of Pdiff for the month in question and SD(Pdiff) is

the long-term standard deviation of Pdiff for the month in question (BoM 2014). The SOI is

related to the atmospheric response associated with ENSO; the Centre for Australian Weather

and Climate Research (CAWCR) believes that this may prove to be a better predictor for

Australian rainfall than indices of sea surface temperature, though the spatial variability of

this has not been explicitly examined.

The TSI is a measure of SST anomalies off the east coast of southern Australia (Table 3.2).

Its use as a quantifier of extra-tropical climate has only emerged recently (e.g. Murphy &

Timbal 2008; Schepen et al. 2012). The data used in the TSI analysis was extracted from the

Extended Reconstruction Sea Surface Temperature version 3b (ERSST.v3b) via the National

Oceanic and Atmospheric Administration (NOAA) – National Operational Model Archive

and Distribution System (NOMADS) Live Access Server (LAS)

(http://nomads.ncdc.noaa.gov/las/getUI.do).

The Indonesia Index characterises anomalies of sea surface temperatures in the Indonesian

region (Table 3.2). Australian winter rainfall was shown to display a high correlation to a

function of II when it was subtracted from SST’s over the central Indian Ocean (Nicholls

1989). This relationship was furthered by Verdon & Franks (2005) and Schepen et al. (2012)

http://nomads.ncdc.noaa.gov/las/getUI.do


36

used lagged II to demonstrate the correlation between II and rainfall over the Northern

Territory. II monthly anomalies were also extracted from NOAA’s distribution system.

By initiating a multi-disciplinary approach (eddy covariance flux measurements and

modelled data) with a multi-scaled methodology, a method advocated by Pittman (2003), it is

hoped that this study and series of experiments will minimise some of the knowledge gaps

that remain in understanding the processes that control the temporal variability of fluxes.

Modelling

A statistical-based model, known as an artificial neural network (ANN) was trained using the

flux observations from the Howard Springs Tower to hind-cast ecosystem flux responses

from 1900 onwards. Driving the ANN were meteorological variables, a combination of

observations and modelled predictions from AWAP. The ANN output was a 113 year trend

of ecosystem fluxes such as GPP, Fre, Fc and Fe.

This statistical approach has been used in space of a process-based model, given its ability to

accurately capture ecosystem responses, such as Fe (Figure 3.2). The diurnal dynamics of the

savanna water flux is represented in the ANN output, validating its use in this study.

ANN’s are highly empirical statistical models, but mechanistic constraints have been

imposed in this study, via selected meteorological drivers. Fractional photosynthetically

Figure 3.2: Diurnal latent heat flux measured at 30 minute intervals and averaged over the 2001-2013

period for observations (blue) and ANN (green). X axis is hours from midnight and y is Fe flux in W

m-2

.


37

active radiation (fPAR) has been used as a driver, which is significant as it allows the ANN to

capture structural components of the ecosystem such as leaf are index (LAI) permitting the

ANN to capture seasonal variability. The training data (tower observations) have enough

incidences of meteorological extremes, allowing the ANN to provide robust predictions.

Each time series analysis (linear trends, power spectral densities and wavelet transform

coherence) has been used to answer the research questions regarding savanna response

variability, cyclic patterns, and degree of coupling and lagged phase angles. The variables

used to characterise ecosystem function capture the exchange of carbon and water with the

atmosphere over the 20th

century, as hind-cast by the neural network.

Chapter 4: Results

38

Chapter 4: Results

Understanding and predicting the nature of ecosystem flux responses to climate change

begins with a basic trend line analysis of the meteorological and ecosystem variables, as

detailed in Table 3.1. The trend lines demonstrate the change experienced at Howard Springs

during the 20th

century, identifying similarities and differences between the drivers and

responses, as expected per the literature. Variability is also examined determining the

dynamics of change over time. A spectral analysis is then conducted on all variables,

including the climate indices (Table 3.2) demonstrating the inherent cycling present in

individual variables. These results contribute to our interpretation of the wavelet coherence

plots. These plots establish the temporal relationship and degree of coupling between

meteorological drivers and climate indices to flux responses, by demonstrating the coherence

and lagged effects between driver and response at common periodicities.

4.1 Time Series Analysis

In order to understand the temporal dynamics at Howard Springs, time series of each

meteorological and flux variable are shown (Figure 4.1, 4.2 and 4.3). Meteorological

variables were chosen for their known influence on ecosystem exchanges while flux variables

were chosen as representatives of carbon and water exchanges. It is useful to illustrate these

variable trends over time as it provides insight to how the environment at Howard Springs, a

representative of tropical north Australian mesic savanna ecosystems, has changed over time.

The linear trend analysis was conducted over the 1913-2013 time period, capturing 100 years

of mean annual change and variability. The first thirteen years was excluded from this

analysis due to the uncertain validity of AWAP inputs. All variables show an increasing trend

over the last 100 years but vary in rate of increase. Generally, the first half of the century

observed little to no change in slope while the second half of the century, particularly post

1970’s saw intensified warming and wetting, and increased rates of carbon and water

exchange.

Since tropical north Australia is dominated by the Australian monsoon system, annual

variability is expected as the wet season from year to year displays variable rainfall volumes

and distributions, thus confirming the variability seen in the meteorological time series.

Chapter 4: Results

39

AWAP meteorological inputs

The linear trends indicate that all meteorological model inputs have increased over the

previous century at various rates. Temperature at the Howard Springs site has increased on

average by almost one degree Celsius (0.92⁰C) over the 100 year period whilst rainfall has

increased by 300mm over the previous century. Inter-annual rainfall variability appears to

have increased during the second half of the 20th

century; however, no significant trend was

found in the coefficient of variability plot (result not shown). There appears to be an artefact

in VPD in the early 1940’s, at around the same period as the Darwin meteorological station

y = 0.0056x + 1.8391

1.5

1.7

1.9

2.1

2.3

2.5

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VP

D (

kP

a)

c)

Figure 4.1: Time series of three environmental drivers of ecosystem fluxes at the study site (a) temperature (Ta),

(b) rainfall and (c) vapour pressure deficit (VPD). Temperature and rainfall are AWAP inputs (observations

from BoM’s Darwin station) and VPD is a composite of temperature and humidity, demonstrating the

evaporative power of the atmosphere.

Chapter 4: Results

40

was moved from the post-office (CBD) to the airport, less than 10 km away. The increase in

VPD is probably a response to temperature, but is also varies with changes in absolute

moisture content, a variable that is not analysed in this study.

Atmospheric CO2 concentrations and radiation are significant drivers of ecosystem flux

however, their trends are not shown here for separate reasons. Firstly, CO2 concentrations are

not displayed as the trend is clearly established in the literature and secondly, radiation was

not measured until 1990 resulting in most of the 20th

century representing a climatology

(averaged 1990-2010 conditions). The trend has low variability so its use as a driver in the

wavelet plots is not suitable or valid, as the algorithms cannot handle the extreme change in

variability between averaged and observed periods.

Biological responses

Following their meteorological drivers, time series of the flux responses of carbon and water

have uniformly increased over the previous century, mostly due to increased ambient

temperature and rainfall.

ANN outputs of ecosystem flux responses (Figure 4.2 and 4.3) demonstrate change in both

carbon and water exchanges. Negative carbon fluxes indicate a loss of carbon by the

atmosphere to the ecosystem, i.e. a carbon sink. Gross primary productivity (GPP) and

ecosystem respiration (Fre) have both increased at Howard Springs, by over one tonne per

hectare and half a tonne per hectare over the previous 100 years respectively. The rate of

increase in net ecosystem exchange (Fc) is 0.01 t C ha-1

y-1

over the last century, with rates

intensifying after the mid-1960’s. This is a reflection of high rates of increase in GPP and less

increase in corresponding Fre. The net result is enhanced carbon sink shown as an increasing

trend of Fc. Latent heat (Fe) has also increased during the 20th

century, by an average 1 W m-

2 each year.

Chapter 4: Results

41

Figure 4.2: Linear trend of three carbon fluxes of interest. (a) Net ecosystem exchange (Fc), (b) gross primary

productivity (GPP) and (c) ecosystem respiration (Fre). Plots are ANN outputs over the 100 year period of

interest 1913-2013. For Fc, GPP and Fre -ve values indicate the atmospheric loss of carbon to the ecosystem, i.e.

a sink.

GPP has increased noticeably over the previous 100 years, possibly driven by increased

rainfall. The ANN has suggested an increased carbon uptake of almost one and a half tonnes

per year over the previous century in a consistently sinking trend. Respiration has a less

distinctive increase (less than 0.4 t C ha-1

y-1

), reflective of significant rainfall and

temperature patterns, which are driving variables of ecosystem respiration.

The net sink has increased by about 1 tonne per hectare per year, from -5.5 t ha-1 y-1 to -6.5 t

ha-1 y-1over the previous century, which is at odds with observations. Beringer et al. (2004)

found Fc to average 4 tonnes per hectare per year for the 2001-2006 period, significantly

Chapter 4: Results

42

lower than the ANN-predicted Fc. The increase in carbon sink (GPP) means that

photosynthesis has also increased over the previous century and as a result of the warming

and wetting observed in the temperature and rainfall trends, transpiration has also risen as a

by-product of increased growth and biomass. A slope change analysis has been done for the

carbon flux variables, to examine the dependence of these to environmental conditions. These

results are shown in Appendix C.

Between the two arbitrary periods (1913-1949 and 1950-2013), increases were observed in

all variables except 1913-1950 respiration, which had a decrease of about 0.007 t C ha-1

y-1

,

which is negligible, as was early century GPP increase with a slope of just 0.002 t C ha-1

y-1

.

The largest increase came in late century net ecosystem exchange (0.015 t C ha-1

y-1

), as a

result of a GPP increase of 0.01 t C ha-1

y-1

and minimal increase in respiration (just 0.0038 t

C ha-1

y-1

).

Figure 4.3: Linear trend of latent heat flux (Fe). Plots are ANN outputs over the 100 year period of interest

1913-2013.

Fe has increased consistently through the 20th

century, but it is beyond the scope of this thesis

to attribute this trend to a specific driver, especially since representation of all potential

drivers is limited in this study (only three meteorological drivers are shown). This is

particularly evident in Fe analysis since increased Fe would suggest a rise in radiation which

would imply reduced cloud cover and rainfall, which is not observed at this site.

y = 0.1042x + 77.3

70

75

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93

19

97

20

01

20

05

20

09

20

13

Fe

(W

m-2

)

a)

Chapter 4: Results

43

Figure 4.4: Time series of (a) water use efficiency (WUE) and (b) light use efficiency (LUE) for the tower observation period (2001-2013). GPP is divided Fe *0.49 to

generate a time series for WUE and using the conversion factor of 0.49 (Kanniah et al. 2010) in LUE gives GPP divided by short wave radiation *0.49, WUE and LUE are

shown at a monthly scale, and are reflective of ecological coupling.

-0.18

-0.13

-0.08

12001

72001

12002

72002

12003

72003

12004

72004

12005

72005

12006

72006

12007

72007

12008

72008

12009

72009

12010

72010

12011

72011

12012

72012

12013

WU

E (

um

ol /u

mol )

a)

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

12001

72001

12002

72002

12003

72003

12004

72004

12005

72005

12006

72006

12007

72007

12008

72008

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72009

12010

72010

12011

72011

12012

72012

12013

LU

E (

um

ol / um

ol )

b)

Chapter 4: Results

44

Water use efficiency (WUE) and light use efficiency (LUE) are ratios of carbon uptake (GPP)

and water loss and radiation use respectively. WUE and LUE are examples of ecologically-

coupled variables (Figure 4.4) which show no significant trend over the 13 year observational

period. Unlike WUE, LUE displayed a distinct seasonal pattern, with maximal LUE

occurring during the peak of the wet season (January-February) and minimal LUE occurring

during the late dry season (August-September).

The linear trend analysis displayed increases in all meteorological and ecosystem variables,

suggesting a dynamically-changing environment at Howard Springs. Whether this correlation

is a driver-response mechanism is still unknown.

4.2 Power Spectrum density graphs

The spectral analysis demonstrates the inherent cycling of singular variables which aids our

interpretation of the wavelet transform coherence graphs that follow. The following results

demonstrate the wavelet power spectra and global wavelet spectrum for the meteorological

and biological variables. The spectral analyses of meteorological drivers (Figure 4.5) feature

a distinctive annual periodicity due to the wet/ dry cycle. Ta also shows a clear six-month

periodicity as well as a 5 and 10 year cycle, though activity appears inconsistent across the

century. VPD exhibits similar patterns as temperature in terms of the larger-scale frequencies

however, the second quarter of the century ( ̴1930-1950) appears to show cycles from 2-10

year frequencies.

The power spectrum and wavelet analyses are conducted over the 1900-2013 period; early

century inconsistencies are partially removed from the plots as a result of a wavelet function

which detects where the data may be distorted due to edge effects.

Chapter 4: Results

45

The distinct annual cycle of rainfall is driven by monsoonal processes common of tropical

north Australia dominating annual dynamics, whereas temperature is more continuous. These

patterns of periods of high spectral power that are variable through time is repeated for all

Figure 4.5: Wavelet power spectrum of (a) temperature, (c) rainfall and (e) vapour pressure deficits and corresponding

wavelet spectrum at Howard Springs (b, d and e respectively). The thick black contour in the wavelet power spectrums

and blue dash line in the wavelet spectrums indicate the 5% significance level against red noise. Wavelet spectral

analysis was fed daily data over the 113 year period.

(c) Tair Wavelet Power Spectrum (d) Global Wavelet Spectrum

(a) Rainfall Wavelet Power Spectrum (b) Global Wavelet Spectrum

(e) VPD Wavelet Power Spectrum (f) Global Wavelet Spectrum

Chapter 4: Results

46

variables with periods of high and low power and significance evident. These temporal

dynamics have been summarised in Table 4.1 below for each meteorological and biological

variable, with an interpretation of results.

The biological variables (Figure 4.6) displayed similar annual patterns observed in the

meteorological spectral analysis. Similar patterns are emerging between carbon (Fc) and

water (Fe) variables. By contrast, GPP and Fre displayed less spectral strength and

consistency of larger cycles (2-10 years) across the 113 year period. Spectral analysis of Fc

stood out for its strong decadal signal present across the century as well as inconsistent 2-5

year cycles, which appeared again in the Fe analysis.

Chapter 4: Results

47

Figure 4.6: Wavelet power spectrum of (a) Fc, (c) GPP, (e) Fre and (g) Fe and corresponding wavelet spectrum (b, d, f and

h respectively) at Howard Springs. The thick black contour in the wavelet power spectrums and blue dash line in the

wavelet spectrums indicate the 5% significance level against red noise. Wavelet spectral analysis was fed daily data over

the 113 year period.

(c) GPP Wavelet Power Spectrum (d) Global Wavelet

(a) Fc Wavelet Power Spectrum (b) Global Wavelet

(e) Fre Wavelet Power Spectrum (f) Global Wavelet Spectrum

(g) Fe Wavelet Power Spectrum (h) Global Wavelet Spectrum

Chapter 4: Results

48

A summary of the periodicity length and occurrence of four environmental conditions and

four biological responses (Table 4.1) demonstrates the highly variable temporal dynamics and

cycling of individual variables, which will be assessed further as common periodicities

between the meteorological drivers and ecosystem flux responses.

Table 4.1: Summary table of climate and biological variables following spectral analysis. Observed periodicity

and associated statistically significant time periods are included, calculated as the number of years a periodicity

is observed over the total number of years in the analysis (113). Each variable is ranked by significance fraction,

to demonstrate the variability in periodicity across meteorological and biological variables.

Variable Periodicity (y) Time Periods Significance

fraction

Ta 0.5 1900-2013 0.95

5 1917-1952 0.35

10 1973-1983 0.09

VPD 0.5 1900-2013 (intermittent) 0.08

2-10 1940-1955 0.13

10 1925-1994 0.63

Rainfall 0.5 1900-2013 (intermittent) 0.1

2 1905-1909 0.03

Fc 0.5 1900-2013 (intermittent) 0.44

2-5 1910-1915, 1921-1937,

1938-1951, 1997-2003

0.36

10 1915-2001 0.78

Fe 0.5 1900-2013 (intermittent) 0.12

2-5 1922-1946, 1974-1983 0.3

10 1915-1955, 1965-2000 0.68

GPP 5 1927-1936 0.08

10 1915-1947, 1966-1999 0.59

Fre 10 1940-1975 0.31

Temperature and Fc showed the highest fraction of periodicity across the century in their

respective categories (meteorological and ecosystem fluxes) with over half of the century

observing multiple cycles from six months to ten years. This is expected as Fre is only driven

Chapter 4: Results

49

by Ta in the ANN. The longest periodicity observed in the power spectral analysis is that of

the six month cycle present in temperature, which was consistent throughout 95% of the last

113 years, with some notable exceptions during the early 1930’s and again in the mid to late

1940’s. Such a strong and consistent cycle was not observed in any other variable nor

periodicity. The strongest decadal cycle was observed in Fc with 78% of the last century

having some decadal frequency present in the net ecosystem exchange of carbon, suggesting

a significant cycle present at 10 years. This cycle is reiterated in other biological variables

such as Fe (68%), GPP (59%) and Fre (31%). Of the meteorological variables, only VPD

showed a significant 10 year cycle (occurring in 63% of the last century).

The climate indices were also put through the same analysis however, due to the reduced

number of values (indices are monthly values, Figures 4.5 and 4.6 used daily data), the

analyses are produced in a lower resolution. This could be improved in future studies by

adjusting (reducing) the colour spectrum that the spectral analysis uses. Whilst the above

tabular display of results cannot be recreated for the following results, inferences can be

made about the inter-annual and decadal cycles present in the Southern Ocean Index (SOI),

Tasman Sea Index (TSI) and Indonesian Index (II) gathered from Figure 4.7 below.

Chapter 4: Results

50

Whilst spectral analysis of the climate indices are not highly-resolved, they help to form an

understanding of the inherent cycling within the index and associated climate phenomena,

and how these might drive ecosystem responses at similar periodicities in coherence plots.

Figure 4.7: Wavelet power spectrum of (a) Southern Oscillation Index (SOI), (c) Tasman Sea Index (TSI) and (e)

Indonesian Index (II) and corresponding wavelet spectrum (b, d and f respectively) at Howard Springs. The think black

contour in the wavelet power spectrums and blue dash line in the wavelet spectrums indicate the 5% significance level

against red noise. The very low resolution of these images is most likely due to the reduced number of values (Indies are

measured monthly over the 110 year period).

(a) SOI Wavelet Power Spectrum (b) Global Wavelet Spectrum

(c) TSII Wavelet Power Spectrum (d) Global Wavelet Spectrum

(e) II Wavelet Power Spectrum (f) Global Wavelet Spectrum

Chapter 4: Results

51

The strongest pattern emerges from the SOI spectral analysis at around 5-7 year cycles across

the century, which is reflective of the literature (e.g. AchutaRao & Sperber 2002; Lau & Sheu

1988). In TSI and II, decadal cycles appear to emerge, however this is difficult to confirm

given the low resolution.

Significant 0.5 to 10 year temporal patterns have appeared within the variables of interest

using the power spectral analysis above. Various time frequencies have emerged from both

the meteorological and biological variables but these alone do not confirm the driver-

response mechanism through correlating cycles. A wavelet coherence analysis will allow

correlating patterns to emerge between the drivers (meteorological variables and climate

indices) and biological responses.

4.3 Wavelet Coherence

The following WTC analyses explores the different environmental driving factors of

biological responses such as GPP, Fe, Fre and Fc, through meteorological drivers and the

climate indices. It varies from the power spectrum analysis in that is shows the common high

powers and cycling of two variables, building on the individual temporal analysis in sections

4.1 and 4.2.

As shown and discussed in Figure 1.2 (conceptual diagram of the wavelet analysis and its

functionality) the arrows indicate phase angles, which demonstrate the lagged characteristics

of ecosystem responses to their meteorological and climate drivers. Following the previous

spectral analysis, only the significant meteorological and biological coherences have been

included here, categorised by biological responses.

Chapter 4: Results

52

Gross primary productivity

The only significant coupling to occur between the meteorological drivers and GPP (besides

annual cycles) was that of Ta, which showed anti-phased coupling in periodicity at a scale

larger than ten years. There are also two periods of 5 year cycling throughout the century,

between 1920-1930 and 1970-1990. Interestingly, rainfall showed little correlation with GPP,

suggesting no coherence in cycles between the two variables.

Net ecosystem exchange

An intermittent six month cycle is observed over about 25% of the century, from 1900-2000,

between Fc and VPD (Figure 4.9). This coherence appears to be anti-phased. At scales larger

than annual, very little coherence is observed, however patchy coupling appears at cycles of

2.5 to 10 years. Between 1915 and 1930 a 3-5 year cycle appears with the response (Fc) out

of phase by 180⁰. This is not reflected in later decades of the same time frequency with mid

1970’s coherence out of phase by 360⁰ and again post 1988 which appears in phase. A weak

ten year cycle appears between 1930 and 1955 which again appears out of phase by 180⁰.

Figure 4.8: Wavelet coherence of GPP and temperature GPP-temperature is over the whole study period (1900-2013) using

daily data. The solid black line is the 5% significance level against red noise calculated from the Monte Carlo simulation.

Arrows are the relative phase angle (arrows pointing right are in-phase, arrows pointing left are anti-phase).

Chapter 4: Results

53

Ecosystem respiration

Ecosystem respiration appears to correlate inconsistently at six month cycles with both Ta

and VPD. However this coupling occurs at all phase angles, exacerbating the inconsistency.

Fre also correlates with Ta and VPD at cycles larger than 2.5 years. A 5 year cycle exists

between Fre and Ta between 1915-1935 that appears in-phase, as does the 5-10 year cycles

between 1965 and 2000. A lengthy coherence exists between 1925 and 1980 at a scale larger

than 10 years, which is between 360⁰ and 0⁰ in phase, and appears weakened and at 180⁰ out

of phase with VPD. Coherences are also observed between Fre and VPD at smaller cycles (3-

5 years) in the latter half of the century, between 1960 and 1990 that is 90⁰ out of phase.

Figure 4.9: Wavelet coherence of net ecosystem exchange and VPD. The solid black line is the 5% significance

level against red noise calculated from the Monte Carlo simulation. Arrows are the relative phase angle (arrows

pointing right are in-phase, arrows pointing left are anti-phase).

Figure 4.10: Wavelet coherence of (a) respiration and temperature and (b) respiration and VPD. The solid black


relative phase angle (arrows pointing right are in-phase, arrows pointing left are anti-phase).

Chapter 4: Results

54

Latent heat flux

The six month cycle again appears inconsistently in coherences of Fe with temperature and

VPD. From 1915-1935, a 3-5 year cycle appears in both driving variables of latent heat flux

which is in phase. A similar cycle occurs again from 1965-1980 which is out of phase by 90⁰

followed by a weakened coherence from 1985-2000 that has a phase angle of 180⁰, for VPD.

Fe and temperature appears to couple at much larger periodicities, from 1930-1990 which is

mostly in phase with a larger than 10 year cycle and again from 1970-2000 with a 5-10 year

cycle, also in phase.

Climate indices quantify climate phenomenon at the meso-scale, integrating influences of

temperature, rainfall and VPD through various processes, including ocean-atmosphere

interactions. The indices that have been chosen for this analysis represent atmospheric

processes in the South Pacific Ocean region (SOI); extra-tropical sea surface temperatures

(SST) off the south-east coast of Australia (TSI) and SST’s in the Indonesian region (II).

Time series of the climate indices are shown in Appendix D observing the change and trend

over the 20th

century. Frequency of SOI positive and negative phases appears even through

time whilst positive SST anomalies increase in frequency toward the end of the century in

both the Tasman Sea and Indonesian region.

Figure 4.11: Wavelet coherence of (a) latent heat and temperature and (b) latent heat and VPD. The solid black line

is the 5% significance level against red noise calculated from the Monte Carlo simulation. Arrows are the relative

phase angle (arrows pointing right are in-phase, arrows pointing left are anti-phase).

Chapter 4: Results

55

Southern Oscillation Index

The SOI is calculated via the Troup method (eq. 5) which establishes a monthly averaged

value describing conditions of sea surface temperature and mean sea level pressure

differences between Darwin, Australia and Tahiti.

Figure 4.12: Wavelet Coherence of SOI against the biological responses (a) ecosystem respiration, (b) gross

primary productivity, (c) latent heat flux and (d) net ecosystem exchange. The solid black line is the 5%


angle (arrows pointing right are in-phase, arrows pointing left are anti-phase).

Chapter 4: Results

56

The standout coherence that exists in all four biological fluxes is the 5-7 year cycle that

appears toward the end of the century (1980’s and onwards), though varies in length and

strength of the relationship between the four measures of carbon and water exchange. Also of

significance are the intermittent patches of coherence that occur at periods of 2.5-5 years

throughout the century across all four signals, correlating with much of the current literature

that suggests a similar cycle that is emulated through the El Nino Southern Oscillation

phenomenon. This will be discussed further in the following chapter.

Tables 4.2, 4.3, 4.4 and 4.5 at the end of section 3.3 offer a summary of the results from the

WTC analysis of SOI, TSI and II on biological responses.

Chapter 4: Results

57

Tasman Sea Index

The TSI is a measure of SST’s off the east coast of southern Australia, with inter-annual to

decadal time scales, similar to that of SOI and II. TSI is bound by coordinates 30⁰-40⁰S and

150⁰-160⁰E. Similar to SOI is the long-term cycle is found at the end of the century (post

1980) in biological responses of GPP, Le and Fc. A one year intermittent cycle has also been

observed throughout the century across all four variables.

Again the emerging pattern is the ten year cycle present throughout the century, especially

with regards to ecosystem respiration, which is known to respond to moisture availability.

Figure 4.13: Wavelet Coherence of TSI against the biological responses (a) net ecosystem exchange, (b) latent heat flux, (c) ecosystem

respiration and (d) gross primary productivity. The solid black line is the 5% significance level against red noise calculated from the

Monte Carlo simulation. Arrows are the relative phase angle (arrows pointing right are in-phase, arrows pointing left are anti-phase).

Chapter 4: Results

58

Indonesian Index

The II measures the average SST anomaly in a region north-west of Australia in the south-

east tropical Indian Ocean (0⁰-10⁰S, 120⁰-130⁰E). Coherences between II and the biological

variables appear inconsistent across time frequencies of one to ten years; however the ten

year cycle at the end of the century seen in SOI and TSI has again emerged, as has the

century-long ten year cycle in ecosystem respiration.

Tables 4.2-4.5 (gross primary productivity, latent heat flux, ecosystem respiration and net

ecosystem exchange respectively) below will summarise and compare indices from the WTC

spectral analysis by biological response.

Figure 4.14.: Wavelet Coherence of II against the biological responses (a) net ecosystem exchange, (b) latent heat flux, (c)

ecosystem respiration and (d) gross primary productivity. The solid black line is the 5% significance level against red noise

calculated from the Monte Carlo simulation. Arrows are the relative phase angle (arrows pointing right are in-phase,

arrows pointing left are anti-phase).

Chapter 4: Results

59

Table 4.2: Summary table of gross primary productivity responses to the climate indices, SOI, TSI and II,


phase angle are also listed.

Climate

Index

Periodicity

(y)

Time Periods Phase Angle Significance

Fraction

SOI

1

1908-1912, 1924-

1926, 1940,1970,

1999-2002

180⁰ 0.1

2.5

1929-1935, 1940-

1950, 1977-1988,

2004-2006

0⁰ 0.26

5 1983-2000 180⁰ 0.15

TSI

0.5

1907, 1925, 1936,

1960, 1970, 1990,

2000

180⁰ 0.06

1 1900-2013

(intermittent) 0⁰-180⁰ 0.19

2.5 1918-1921, 1965-

1970 0⁰ 0.07

10 1974-1999 180⁰ 0.23

II

0.5 1910-1980

(intermittent) 0⁰ 0.07

1

1909-1911, 1930-

1933, 1959-1961,

1975, 1992, 2001-

2005

180⁰ 0.12

2.5-5

1917-1931, 1945-

1952, 1969-1988,

1990-2001

180⁰ 0.46

10 1967-1998 180⁰ 0.28

Table 4.2 above suggests that at smaller time scales (1-2.5 year periodicity), SOI has the most

influence on driving GPP at Howard Springs whilst the II drives GPP cycles from 2.5-5 to 10

years, lagged by 180⁰. At scales smaller than 1 year, little significant coherence is observed

Chapter 4: Results

60

between climate indices and ecosystem responses, which are expected given that these

indices are most active at inter-annual to decadal time cycles.

Table 4.3: Summary table of latent heat flux responses to the climate indices, SOI, TSI and II, following


angle are also listed.

Climate

Index

Periodicity (y) Time Period Phase Angle Significance

Fraction

SOI

0.5 1900-1980

(Intermittent) 0⁰-180⁰ 0.11

1

1908-1911, 1925-

1927, 1941, 1972-

1974, 2000-2002

0⁰ 0.09

2.5 1932-1936, 1943-

1951, 1980-1987 180⁰ 0.17

3 1940-1949 90⁰ 0.08

5 1988-2000 0⁰ 0.11

TSI

0.5 1900-1975


1 1900-2013


10 1983-1997 0⁰ 0.13

II

0.5 1900-2013


1 1900-2013


2.5-5 1917-1938, 1943-

1951, 1968-1998 360⁰-0⁰ 0.63

10 1974-1997 0⁰ 0.21

The climate index responsible for driving latent heat flux at larger time scales is again the II,

with a significance fraction of 0.63 at cycles of 2.5-5 years. Whilst the SOI does not stand out

at any particular periodicity, it does cohere with over half of the century at various cycles and

time periods, suggesting that SOI may be the overall driver of latent heat responses and the

water cycle with the 2.5, 3 and 5 year cycles moving from anti-phase to in phase with

increased periodicity.

Chapter 4: Results

61

Table 4.4: Summary table of ecosystem respiration responses to the climate indices, SOI, TSI and II, following


angle are also listed.

Climate

Index


Fraction

SOI

0.5 1900-1975


1 1900-2013


2.5-3

1908-1912, 1936-

1939, 1941-1952,

1956-1969, 1979-

2000

180⁰ 0.47

10 1995-2000 0⁰ 0.05

TSI

0.5 1900-2013


1 1900-2013


2.5 1963-1967 360⁰ 0.04

10 1924-1996 0⁰-90⁰ 0.65

II

0.5 1900-2013


1 1900-2013


2.5-5

1917-1921, 1925-

1935, 1942-1951,

1964-1984, 1990-

2001

360⁰-0⁰ 0.57

10 1924-1998 0⁰-90⁰ 0.67

Again at cycles 10 years and larger, the II appears to be the most significant driver of

ecosystem respiration, closely followed by TSI. At cycles between 2.5 years and 5 years, the

II and SOI drive respiration at different periods and different phase angles during the century.

This suggests that the exhalation of carbon by the ecosystem to the atmosphere is driven by

various indices at various periodicities over the century.

Chapter 4: Results

62

Table 4.5: Summary table of net ecosystem exchange responses to the climate indices, SOI, TSI and II,


phase angle are also listed.

Climate

Index


Fraction

SOI

0.5

1910,1919, 1932,

1937, 1940, 1965,

1970, 1993

0⁰

180⁰

0.07

1

1908-1910, 1926-

1927, 1930, 1938-

1942, 1972, 1999-

2001, 2005

90⁰-360⁰ 0.01

2.5

1923-1928, 1932-

1936, 1945-1952,

1980-1987

360⁰-0⁰ 0.21

3 1940-1955 360⁰ 0.14

5-10 1976-2000 180⁰ 0.22

TSI

0.5 1910-1975


1 1920-2000


2.5-5 1920-1927, 1965-

1980 0⁰-90⁰ 0.2

10 1980-1995 90⁰-180⁰ 0.14

II

0.5 1915-1990


1 1910-2000


2.5-5

1921-1930, 1937-

1945, 1947-1950,

1978-1990, 1991-

1996

90⁰-180⁰ 0.34

10 1972-1999 180⁰ 0.25

Chapter 4: Results

63

Of the four ecosystem responses, net exchange by the ecosystem appears to correlate with the

climate indices the least. Of the three indices, SOI and II correlate at ten year cycles with

some significance (greater than 20%), as do the 2.5-5 year periodicities. At these

periodicities, TSI also has a significance fraction of 0.2 and is the only index at this cycle to

lead Fc mostly in-phase (0⁰-90⁰).

Overall, TSI appears to have little coherence with ecosystem responses at small scales and

the only significant decadal cycle occurs with ecosystem respiration. The SOI seems to have

a greater influence on the carbon cycle, dictated through Fc, GPP and Fre, than the water

cycle (Fe) which correlates well with the II, especially at mid-size scales (2.5-5 years).

Carbon and water appear to respond to different phases of SOI, suggesting dissimilar drivers

of ecosystem carbon and water fluxes. Given the known cycles of the SOI (2.5-7 years), it

was expected that these periodicities would have displayed more coupling with ecosystem

fluxes, but instead it has appeared as a stronger driver of small scales, less than 2.5 years. At

the same time as increases in temperature, rainfall and VPD are observed in the linear trends,

coherence between climate indices and ecosystem responses are also observed, post 1970.

Chapter 5: Discussion

64


This thesis examined the temporal dynamics of climate and ecosystem fluxes at a typical

mesic savanna site in Howard Springs. Key climate variables (precipitation, air temperature

and VPD) increased from the mid-20th

century and exhibited spectral shifts and coherence

with modelled biological variables over the last four decades, suggesting an enhanced

coupling between climate and ecosystem processes. This shift in enhanced coherence is

reiterated in the climate indices plots, particularly at 10-12 year cycles post 1970. These

results suggest that SOI, TSI and II influence ecosystem function at inter-annual cycles

however, the source of this coupling (whether it is mechanistic or as a result of the statistical

methodology) is beyond the scope of this thesis.

5.1 Trends and temporal patterns

Meteorological time series at Howard Springs, NT correlate well with global and regional

trends of increasing temperature and vapour pressure deficit. By contrast, tropical rainfall has

decreased since the 1970’s (AR5, 2013) and increased over land surfaces above 30⁰ N/S.

Rainfall over Australia contradicts these patterns with increased rainfall in the tropics since

the 1970’s (BoM 2014), possibly due to increased SST’s in the Indian Ocean which has also

decreased rainfall significantly in the south and east (Pitman 2003).

Temperature

Temperature has increased globally by almost 1⁰C over the previous century (AR4 2007;

AR5, 2013; Karl & Knight 1998; New et al. 2001), in Australia by 0.9⁰C (BoM 2014) and

0.92⁰C at Howard Springs. Temperature across Australia has warmed significantly since

1950, a trend also reflected at Howard Springs, which has increased by 0.44⁰C from 1950-

2013 versus a decrease of 0.02⁰C from 1913-1950. These contrasting trends within the

century are shown in Figure 5.1.


65

Temperature impacts biological responses such as photosynthesis, respiration and

decomposition (e.g. Atkin & Tjoelker 2003; Kirschbaum 1995; Singh & Gupta 1977; Yi et al.

2010) however, many studies suggest that precipitation regimes hold more consequence for

ecosystem function (e.g. Huxman et al. 2004; Knapp et al. 2008), especially in arid and semi-

arid regions.

Rainfall and latent heat

Australian climate appears to have changed significantly since the early 1970’s (Nicholls et

al. 1998; CSIRO & BoM 2014) as was the finding at Howard Springs. The State of the

Climate Report (CSIRO & BoM 2014) observed higher maximum temperatures relative to

rainfall since this period, contributing to a climate where temperatures are warmer but rainfall

does not alter significantly, or even decline in some regions, such as south-west WA (Pitman

et al. 2004). These summations came from a continent-wide study and contrast our north

Australian site where both rainfall and temperature are increased.

Compared to consistent patterns of temperature, the complexity, interactions and scope of

global-scale climate processes (Knapp et al. 2008) has resulted in difficulty in predicting

potential changes in precipitation regimes (Lim & Roderick 2009). Many GCM’s project a

modest increase in rainfall at a global scale with large discrepancies at regional and local

scales.

Rainfall has had a net increase across Australia (1910-2013) (BoM 2014); however it has

declined since 1970 in the southwest via reduced winter rainfall (Cai et al. 2009). Long-term

predictions for Australia suggest this trend will continue in Southern Australia but increased

rainfall is forecast to increase over most other parts of Australia, (BoM, 2014). This reaffirms

Figure 5.1: Variable temperature trends in the previous century at the Howard Springs site. (a) Temperature trend

1913-1949 and (b) temperature trend 1950-2013. Annual average values are used to generate the time series.

y = -0.0009x + 26.527 25.5

26.5

27.5

1913 1918 1923 1928 1933 1938 1943 1948

Ta (

oC

)

a)

y = 0.0071x + 26.823

25.5

26.5

27.5

19

50

19

55

19

60

19

65

19

70

19

75

19

80

19

85

19

90

19

95

20

00

20

05

20

10

Ta (

oC

)

b)


66

the results of this study, which showed increases in the latter half of the century in the

Howard Springs grid (Figure 4.1).

The impact of increased rainfall on tropical ecosystems is not well understood, with most

studies focusing on subtropical or temperate regions (e.g. Ciais et al. 2005; Reichstein et al.

2007; Knapp et al. 2008), with an emphasis on the effects of changes in rainfall amount and

seasonality (e.g. Beier et al. 2004). The intensification of the hydrological cycle as a

consequence of global warming is known and expressed in the form of increased cloudiness,

latent heat fluxes and frequent climate extremes (AR4 2007). Further, less frequent but more

intense precipitation events may alter latent heat fluxes and generate greater runoff (Fay et al.

2003; MacCracken et al. 2003). The relationship is indirect, increased rainfall suggests

increased cloud cover and reduced radiation, decreasing latent heat (evapotranspiration) so

greater runoff results.

This relationship is not evident at the Howard Springs site through increased rainfall and Fe

over the 20th

century. There is little coherence observed between rainfall and latent heat

beyond the annual cycle (Figure 5.2); despite finding coherences between Fe and climate

indices intensifying post 1970. This is possibly due to the strongly seasonal wet-dry

characteristics of the region and the vegetative properties of savanna which do not appear

water-stressed, even during the dry season (O’Grady et al. 1999).

Figure 5.2: WTC analysis of rainfall and latent heat, demonstrating the driving environmental conditions at an

annual cycle, but little coherence at other periods, especially post 1970 where wetting of the ecosystem is

expected to increase latent heat and other biological drivers.


67

The coherence at the annual scale is expected as annual rainfall provides a store for dry

season Fe. These ecosystems are likely not water-limited, resulting in more runoff as rainfall

increases with no response from Fe. Also unknown are the ecological implications of more

extreme rainfall regimes (Jentsch et al. 2007), a significant knowledge gap given the impacts

of enhanced rainfall variability on terrestrial ecosystems are predicted to rival the ecological

impacts of other global-scale changes such as increasing CO2 concentrations (Easterling et al.

2000; AR4 2007). Analysis of model results from the Howard Springs site suggests that

while there is a strong increasing trend in Fe over the century (Figure 4.3), coherence with

rainfall was weak.

VPD

VPD has increased since the mid 1940’s (Figure 4.1), probably as a result of increased

temperature which will have substantial eco-physiological impacts. VPD is a significant

driver of leaf (Prior et al 1997) and canopy scale (Eamus et al. 2001) conductance and is

particularly significant in seasonal environments. In general, as VPD increases beyond a

threshold we see a feedback to stomatal conductance which declines (Atwell et al. 1999)

limiting Fe and ultimately Fc. VPD responses in north Australian savanna have been

extensively examined at leaf, tree and canopy scales (Drake et al. 2014; Eamus et al. 2001).

During the wet season there is limited impact on gas exchange with increasing sensitivity as

the dry season progresses. Beyond temperature and VPD thresholds, gas exchange will be

limited reducing carbon sequestration. Future carbon sink by the savanna ecosystem will be

restricted by the warming environment with declines in production as temperatures warm

throughout the century. A thermal regime may be a dominant driver of ecosystem exchanges

in the future.

Carbon fluxes

The trend line analysis from the ANN suggests an increasing sink as indicated by GPP and Fc

over the previous century (Figure 4.2). Over the last 50 years, GPP has increased at a greater

rate than Fre because GPP (Figure 4.2) responds to increased temperatures and rainfall as

well as increased atmospheric CO2 (Scheiter et al. 2014). As a consequence Fc has an

increasing trend (more –ve and a sink), despite increased respiratory losses from the

ecosystem.

This sink is consistent with global tropical forest trends, which has been large (1.2 Pg C y-1

in

1990-2007) in recent decades (Pan et al. 2011); however, the terrestrial carbon sink in the


68

tropics has had the most uncertainty in literature, with many papers citing source, sink or

neutrality of tropical ecosystems. For example, Sabine et al. (2004) claims that tropical

forests are the greatest carbon sink among terrestrial systems, locking up large quantities in

both biomass and soil, conflicting with the argument that deforestation and burning reduce

carbon storage and increase the CO2 release to the atmosphere in the tropics (e.g. DeFries et

al. 2002; Houghton 1991; 2004).

Whether or not tropical ecosystems are a source or sink of atmospheric carbon dioxide is a

contentious issue within the literature with the scientific community debating the results of

studies and the use of methods to fuel arguments. Recently, Wright (2013) challenged the

results of a global study by Pan et al. (2011) who quantified the carbon sink of intact tropical

forests using region-specific estimates of carbon density or change in carbon density times,

stating that their estimated sink of 1.2 petagrams of carbon per year between 1990-2007 was

too large. Wright argued that sources used for forest area and carbon stocks were also

inaccurate (there is no global agreement on defining forest area) and that the study

overlooked or ignored a source for carbon stocks in their global study. Defending the Pan et

al. study was Phillips and Lewis (2013) who in a return letter to Wright contested that he only

focused on one aspect of the paper and ignored the global results, making unsubstantiated

(Phillips & Lewis 2013) claims regarding the mechanisms of carbon dioxide fertilisation

which were largely beyond the scope of the study.

Much of the uncertainty involving terrestrial sources or sinks of carbon in Australia is due to

modelling inconsistencies and predictions (Roxburgh et al. 2004). Models vary in approach

and driving inputs resulting in estimates that range 5-fold with most contradictions occurring

in the arid zone of Australia. Haverd et al. (2013) attempted to solve some of these

discrepancies using the Community Atmosphere Biosphere Land Exchange (CABLE/BIOS2)

model, which suggests savanna is a weak sink in Australia. However, climate and fire

disturbances experienced by the coastal and sub-coastal north Australian savannas create a

spatial and temporal mosaic of carbon sources and sinks and this study did not include

additional disturbance. Beringer et al. (2014) found that when disturbances are added, the

ecosystem becomes a weak carbon source.

The results from this study contribute to the uncertainty in mechanisms present driving the

terrestrial carbon cycle. We have observed increased GPP and rainfall at the Howard Springs

site, which would suggest the greening occurred as a result of increased precipitation


69

regimes. However, Donohue et al. (2013) observed the same increased trend in greening

(enhanced photosynthesis) even in areas of reduced rainfall using satellite analysis. These

regions (arid environments) are predominantly water-limited, contrasting the characteristics

of coastal savanna. This would suggest another driver at play, such as increasing atmospheric

CO2 concentration. The effect on photosynthesis resulting from rising CO2 is yet to be

established however, the CO2 fertilisation effect appears as a significant driver in these warm

and arid environments, reflecting GPP and photosynthesis at Howard Springs which

measured an identical trend based on observations and expectations given the meteorology of

the last 100 years, driven by warming, wetting and CO2 perturbations.

Trends in ecosystem physiology

Analysis of ecosystem physiology provided this study with an overview of ecosystem

function. Despite increased VPD and rainfall, simulated ecosystem WUE remained

unchanged throughout the observation period (2001-2013). WUE is a ratio of GPP and Fe,

both of which increased at similar rates over the 20th

century, resulting in the unchanged

trend. WUE was reduced during the wet season in response to ample rainfall and low VPD,

however, the dry season also displayed periods of reduced WUE, often around July or August

of any year. Water use was most efficient following the end of the dry season, peaking

around May or June each year, followed by a second, stronger peak during the late dry. This

is likely due to disturbances such as fire, resulting in very dry atmospheric conditions,

maximising VPD. Studies are seeing a global increase in WUE due to increased atmospheric

CO2 concentrations (Fuhrer 2003), a trend that is not established at the Howard Springs site,

possibly due to the small time period (2001-2013). Both light use and water use efficiency

show high intra-annual variability, as would be expected in a region with strong seasonal

dynamics. This is especially apparent for LUE, as it responds to less atmospheric interference

with solar radiation (i.e. cloudiness) which decreases LUE as the dry season persists.

Analysis of savanna biotic responses and physiological trends demonstrated the variability

and change over the 20th

century resulting from increased temperature, rainfall, VPD and CO2

concentrations. The temporal relationships between these variables (meteorological drivers

and flux responses) appear strong with persistent cycling of ecosystem variables driven by

the meteorology at Howard Springs.


70

Evidence for ecosystem coupling to climate

WTC identified temporal correlations and time lags between climate drivers and ecosystem

processes, with coherence absent in the early 20th

century for many coupled variables, but

strengthened significantly by the mid 1970’s. Climate-ecosystem coupling strengthened at the

same time as the rate of climate change increased during the second half of the 20th

century.

Such relationships are evident in recent studies using similar analyses (e.g. Hong & Kim

2011) but no investigation has examined either the savanna biome or ecosystems of north

Australia.

Ecosystem variables GPP, Fre, Fc and Fe showed little coherence beyond the expected

annual cycles. This is surprising given that the global spectrum analyses suggested longer

cycling present in climate indices. It was hypothesised that environmental variables would

show little cycling beyond annual frequencies, whereas spectral analysis suggested weak 5-10

year cyclic patterns (Figure 4.3). Following these observations, one would expect to see

coherences at similar or longer temporal frequencies when correlated with meteorological

drivers. This was the case for temperature and Fe, Fre and GPP with strong decadal cycles

approximately mid to late century.

Fe was shown to have a significant and consistent coupling with temperature at frequencies

greater than 10 years. It also appears that throughout this coupling (beginning mid 1920’s) a

0⁰ lag is observed, meaning Fe responds immediately to temperature as a driver; although this

shifts to approximately 360⁰ (or 12 months) out of phase later on in the century and as the

periodicity got larger. Absent from coherence with temperature is Fc, interesting since the

other two carbon variables displayed strong coherence at a decadal cycle.

Figure 5.3: Wavelet coherence of temperature and (left) Fe (middle) Fre and (right) GPP. Besides the annual

cycle, the strongest and most consistent cycle has a frequency greater than 10 years.


71

There are few studies linking between increasing temperature and Fc of tropical ecosystems.

Zhou et al. (2012) observed increased Fc as a result of increased soil temperature, as tested in

an urban, temperate environment although the study had no temporal component. Similarly,

Hong & Kim (2011) observed a strong six-month cycle between Fc and rainfall in a

monsoon-driven temperate deciduous forest in Korea. In contrast to this study where there

was no observed coherence between these two variables (Appendix E) at annual scales. This

in dissimilar to Beringer et al. (2007) who observed that over the short-term (six annual

cycles), there was a strong relationship between rainfall and Fc and GPP. It is unknown why

this expected relationship showed no significant coherence using longer time steps.

Climate indices

Time series of the climate indices are illustrated in Appendix D to compare changing

conditions to ecosystem flux variables. The temporal cycles of the three indices were

evaluated throughout the 113 year period to analyse their cyclic patterns (Figure 4.7) which

in turn may influence environmental drivers of temperature, rainfall and VPD. SOI showed

the highest power spectrum at a seven year cycle across the century (Figure 4.7). As an index

of ENSO, it is thought of as a principal tropical influence (Allan 1988; Nicholls et al. 1997;

Wang & Hendon 2007). The 2.5-5 year cycles that appear in similar studies are not observed

in the power spectrum analysis but it does appear in the wavelet coherence investigation as a

coupling between SOI and ecosystem responses of carbon and water.

There have been numerous studies examining the influences of SOI on Australian rainfall and

tropical weather (e.g. Allan et al. 1991; Nicholls et al. 1996; Power et al. 1998; Risbey et al.

2009; Suppiah 2004). These studies in general contradict the inconsistent patterns in the

coherence spectra seen in these results. Studies such as Nicholls et al. (1996) suggest good

correlation between Australian rainfall, temperature and SOI between seasons using principal

component analyses. This is not the case at the Howard Springs site; rainfall appears mostly

independent from SOI prior to 1970, in contrast, temperature loses coherence with SOI by

1950. In the second half of the 20th

century, significant changes are observed in the cyclic

patterns between SOI and temperature and rainfall at decadal cycles. A similar decadal cycle

is observed in temperature and SOI earlier in the century.


72

(a) Temperature-SOI

-60

-40

-20

0

20

40

26.3

26.8

27.3

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SOI

Tem

pe

ratu

re (

oC

) SOI and Temperature as a 10 year running mean 1900-2013

(b) Rainfall-SOI

-60

-40

-20

0

20

40

120

140

160

180

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SOI

Rai

nfa

ll (m

m)

SOI and Rainfall as a 10 year running mean 1900-2013

Figure 5.4: Wavelet coherence spectra of (a) temperature and (b) rainfall with SOI. Graphs show time series

of SOI (black) with a 10 year running mean (grey) of (top) temperature and (bottom) rainfall, displaying the

accuracy (or not) of the wavelet coherence analysis in picking up the decadal signals.

Per

iod

1 Month

6 Months

1 Year

2.5 Years

5Year

10 Years

Per

iod

1 Month

6 Months

1 Year

2.5 Years

5Year

10 Years


73

The decadal cycle present in the wavelet coherence between SOI and temperature in the early

century is not easily identifiable in the time series (Figure 5.4.b), but there is a very

noticeable increase in the 10 year running mean of temperature between 1950 and 1960. This

suggests a period of warming which was maintained throughout the second half of the

century, which has been widely reported in the literature. The same decadal cycle observed in

the spectral analysis of rainfall and SOI post 1980’s appears very clearly in the time series

analysis, with less than a 90⁰ phase angle, thus confirming the acknowledged change in the

relationship between SOI and rainfall after the 1970’s.

Evidence of the changed relationship between these variables and SOI has been described in

many climatological studies (e.g. Fredericksen & Balgovind 1994; Nicholls et al. 1996); with

many suggesting these changes are results of spatially varied distribution of the global sea

surface temperature anomalies since the early 1970’s.

There is a recurring 2.5 year frequency in the coherence spectra of SOI with biological

variables which are distinctive, as is the late-century coherence at the 5-7 year (GPP, Fe, and

Fc) and 10 year (Fre) periodicities (Figure 4.12). The earliest 5-7 year coherence occurs in

the mid to late 1970’s coupling with Fc whilst the 10 year cycle doesn’t appear with Fre until

the mid-1990’s which is then not significant.

The 2.5 year cycle appears patchy in each analysis of GPP, Fc, Fre and Fe. GPP and Fc have

similar coherences to SOI, occurring mostly early to mid-century, coinciding with relatively

neutral SOI conditions (i.e. within 1 standard deviation, standardised to 10). The exception to

this carbon flux trend was Fre which correlated with a La Nina (wet) phase followed by the

persistent El Nino in the mid 1990’s. Fe cycling at the 2.5 year periodicity appears to

correlate to periods of El Nino (dry conditions over western Pacific) which are strictly anti-

phased. These occur around 1932, mid 1940’s and 1980. Figure 5.5 demonstrates the variable

response between carbon and water exchanges to SOI cycles.


74

These analyses suggest the carbon cycle correlates with neutral conditions of SOI whilst the

water use (Fe) responds to drier periods of significant El Nino phases, advocating the

correlation between ENSO and tropical Australia’s water balance. Phase angles also change

with SOI. In the five year periodicities, GPP and Fc lag SOI by 180⁰ while Fe is in phase (0⁰)

with SOI. This suggests that water exchanges respond at immediate time scales with drier

conditions whilst carbon exchanges lag by six months to neutral conditions of SOI. The

significance of these responses are currently unknown.

The use of TSI to quantify extra-tropical SST’s has only recently emerged in the literature

(e.g. Murphy and Timbal 2008 and Schepen et al. 2012) hence there are not many studies that

include this index.

Anthropogenic climate change has most likely caused the increase in ocean temperature over

the previous century, which has seen positive increases within the TSI (and II) since 1900,

reflecting the IPCC’s AR4 finding of increased oceanic temperatures between 1961 and

2003 (Bindoff et al. 2007). Another feature of the TSI is its inter-annual and decadal cycles,

which appear weak in the spectral analysis (probably due to the low-resolution), but emerge

clearly within the coherence analysis, confirming its influence on environmental conditions

Figure 5.5: Variation in response to SOI conditions between carbon (Fc) and water (Fe) exchanges of savanna ecosystem. 2.5 year

periodicities of carbon exchanges respond to neutral conditions of SOI, water exchanges respond to drier (El Nino) conditions.

-40

-20

0

20

40

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

MSL

P S

tan

dar

d D

evia

tio

n

SOI


75

such as the increased wetting. These changes to moisture availability (e.g. through enhanced

evaporation off a warmer ocean surface) drive increases in ecosystem fluxes such as

respiration, which may eventually see a shift from sinking Fc to a carbon source in the

tropics.

II correlates well with Australian winter rainfall, which is sensitive to SST variations in the

Indonesian ocean region. It has no observed seasonal cycle, but a previous honours thesis (see

Rogers 2012, Monash University) has suggested it is one of the three highest correlated

climate indices with rainfall distribution over the study site. The II also exhibits inter-annual

and decadal cycles which, like the TSI, emerge within the coherence plots. The spectral

analysis demonstrated the cyclic patterns within the individual variables, the knowledge of

which helps to understand the patterns of ecosystem coupling to the changing climate within

the wavelet coherence analysis.

The Tasman Sea Index (TSI) and Indonesian Index show little consistency in coupling to the

ecosystem fluxes prior to the mid-late 1970’s. The exception is Fre suggesting a correlation

to TSI and II at periodicities larger than 10 years (Figure 4.6, 4.7). It appears that as positive

anomalies of TSI and II (i.e. warmer SST in Tasman Sea and Indonesian region) become

more frequent (Appendix D), the coupling weakens between respiration and climate indices.

It is possible that this is a mathematical coincidence as the spectral density of Fre weakens at

the same time (mid-1970’s) and increased moisture availability is a known driver of

respiration (Chen et al. 2002). GPP, Fc and Fe appears strongly coupled with both indices

post 1970 with carbon exchanges anti-phased and water exchanges in-phase with ten year

cycles of TSI and II.

Autotrophic and heterotrophic respiration is largely driven by temperature and the simulated

increase in Fre occurred since the 1970’s (Figure 4.2) and is likely to be driven by the

temperature increase of the 1970’s, since the model uses this driver-response mechanism. If

predictive modelling were used in a future investigation, the relationship of ecosystem

respiration and SST’s in Indonesian waters and the Tasman Sea could prove to be correlated

as oceanic warming increases.

Previous studies have identified multi-decadal warming and cooling trends over the Pacific

and Indian oceans (Allan et al. 1995; Franks 2002; Power et al. 1999). Power et al. 1999

identified a switch to a warming phase in the mid-1970’s over the Pacific Ocean and it

appears that a similar switch in the Indian Ocean to a warming phase may also have occurred


76

around the same time, in particular the central and western Indian Ocean (Verdon & Franks

2005). This shift may help to explain the wetting observed at Howard Springs during this

time as well as changes to coupling between environmental drivers and ecosystem fluxes.

This is reflected across WTC analyses using climate indices and is likely to be driven by

observed global warming and increasing ocean temperatures. Responding to these changes is

the increased Fre.

Limitations to the study

There are limitations of the wavelet coherence analysis via its inability to pick up

coincidental (mathematical) patterns of coupling. It is a mathematical analysis running off a

statistical model, rather than a mechanistic model, which has allowed the wavelet to pick up

mathematical coincidences. Another limitation to the modelling is the exclusion of ecological

disturbances such as fire and cyclones which have a decadal to 50 year impact (Hutley et al.

2013). This is a significant factor for Howard Springs as it is likely to have been impacted by

Cyclone Tracy in December 1974.

Neural networks are highly empirical statistical models which creates a possible limitation to

this study’s methodology and results. In this case, the ANN was driven solely by meteorology

whereas many process-based models (such as CABLE) utilise variables defining ecosystem

structure in a demographic approach. The ANN is a mathematical tool but has had

mechanistic constraints imposed via selected meteorological drivers a priori such as

temperature of the air and soil, rainfall, wind speed and VPD. The use of fractional

photosynthetically active radiation (fPAR) would have allowed the ANN to capture structural

components of the ecosystem, such as LAI which would tolerate seasonal variability to be

picked up by the model. This means that the ANN assumes static relationships that are

extrapolated back in time, which these results have demonstrated is not the case. Given the

neural network is trained to predict fluxes within a certain variable space or range, if we

move out of that variable space e.g. via increased temperature over a century, there is no

certainty the model will provide meaningful projections for changed conditions. However,

the training (observations) data have enough incidences of high temperatures to provide

robust predictions thus, if climate change simply shifts the frequency of wet, dry, hot or cold

conditions, these trends are all within the domain of the training data. Therefore the ANN is

not necessarily a limitation of this study if used conservatively as an analysis tool, given its

performance can be superior to a mechanistic model.


77

Fire is also not specifically dealt with in this study, though the ANN was trained using fire-

affected variables, so it has not been excluded altogether. Beringer et al. 2003, 2007

examined fire impacts on albedo, Fe and Fc and demonstrated that at a canopy scale, fire had

significant impact on the surface energy balance and annual sequestration rate with sever fire

consuming almost 50% of the annual sink (Beringer et al. 2007). Fire periods were not

screened and will have introduced error as the ecosystem would appear in dis-equilibrium

relative to driving meteorology post-burn (Beringer et al. 2003, 2007) during the

observational period. This means that some error was introduced during ANN training as the

model is reliant on fPAR to determine post-fire fluxes. In addition, Fe recovers at a very

different rate to Fc. Beringer found recover in Fe to pre-fire rates after a month or less

following a severe fire event (80% canopy scorch), whereas Fc took three months and the

onset of the wet season to shift the ecosystem from the strong post-fire source to a net carbon

sink (Beringer et al. 2007). In effect, fire events are integrated into the vegetation structure

and function, so individual fire events are actually built into the ANN when looking at 100

year trends. Of course what hasn’t been tested is the extent to which the neural networks

perform during fire-affected periods. Increased CO2 impact on savanna fluxes are not

directly addressed in this study, though we know the influence of CO2 fertilisation will be a

major factor driving the greening, or photosynthesis of terrestrial ecosystems (Donohue et al.

2013).

All ecosystem carbon and water variables have demonstrated increased coupling to climate

phenomena such as ENSO and SST increases in tropical (II) and extra-tropical (TSI) regions,

with cycles of 5 years and larger, occurring since the mid 1970’s. The consistency of this

pattern is significant and suggests that with increasing temperature, rainfall and VPD (as

driven by these climate phenomena) in the Howard Springs region, ecosystem variables

across the spectrum of carbon and water (i.e. GPP, respiration, net exchange and latent heat)

have also increased, as a coupled response to warming and wetting of the tropical land

surface. This is further validated through the increased coupling observed between SOI and

temperature and rainfall at the Howard Springs site, and reflects global tropical patterns and

predictions in future climate scenarios.

Chapter 6: Conclusions

78


This investigation is the first of its kind to use wavelet spectral analysis over a century-long

period in such a context. There is evidence of increasing climatic influence on savanna

ecosystems in north Australia since the 1970’s. This has repercussions for our ability to

confirm both the results and compare to other studies across tropical terrestrial surfaces and

other savanna ecosystems. This complicates validation of the modelled data and limits the

conclusions that can be made from the results. These limitations are mostly derived from the

lack of observational data and the issue of feeding the wavelet algorithms modelled data.

The major conclusions are:

There is increased coupling of climate indices and ecosystem responses at decadal

cycles following a warming and wetting environment post 1970;

The role of SOI on rainfall, but not so much temperature, is influential in driving

ecosystem function, even though temperature appears as a stronger driver than

rainfall;

Both TSI and II influence ecosystem function at inter-annual cycles however, whether

this has a mechanistic basis is uncertain, given the statistical nature of the approach

used in this study;

Consistent with sensitivities to Fe to high and low rainfall years, latent heat constantly

correlates with El Nino phases of SOI, while the carbon fluxes correlate at neutral

phases, suggesting dissimilar climate drivers for carbon and water balances in the

tropics;

There is a disconnect between the carbon and water exchanges in response to

temperature and rainfall. Lags exist between biological responses and environmental

drivers which vary by 180⁰ (six months) between carbon and water fluxes.

These results can be further developed by analysing additional sites to unpack the

mechanisms involved between savanna and climate. A biome-scale assessment of

climate impacts on savanna ecosystems should integrate a spatial component and field

work validation.


79

The results from this study suggest that the ecosystem is adjusting to the changing climate,

which contradicts Moncreiff et al. (2014) who observed that future dynamic global vegetation

models predict narrow climate space for savannas and grasslands.

This study has provided insight to the behaviour of coastal savanna ecosystems. These

ecosystems cover tens of thousands square kilometres of the tropics and experience high

rainfall and frequent fire, storm and cycle disturbance (Hutley et al. 2013), modifying land-

atmosphere interactions for months after disturbance. The project made use of one of the

longest savanna flux towers in the world, situated 35 kilometres from a Class 1 (highly

accurate, long-term observations) Bureau of Meteorology station (Darwin) which contributes

to the validity of these results.

This unique study has improved our understanding of the influence of past and present

climate variability to savanna land-atmosphere exchanges using a novel time series analysis.

It has established the changing climate as a dominant driver of terrestrial ecosystem processes

in tropical north Australia, a region of uncertainty in the literature.

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Appendices

98

Appendices

Appendix A: Climate Indices with high correlation to annual total rainfall over Australian

continent

Figure A.1: Australian map of climate indices with highest correlation with annual total rainfall from 1900-2010. Resolution is

125km2. Rainfall at Howard Springs is shown to correlate with the Tasman Sea Index and Indonesian Index. (Source:

Cassandra Rogers 2012)

Appendices

99

Appendix B: Howard Springs Flux Tower Equipment

Table A.1. List of instruments and location along the tower at the Howard Springs Flux site, collecting eddy-

covariance data capturing the land-atmosphere exchanges at a savanna site.

INSTRUMENT TYPE Make Model Situation

Open path CO2, H2O LI-COR LI-7500 23m

Sonic anemometer

- wind velocities (u,v,w)

- sonic temperature

Campbell Scientific CSAT-3 23m

Solar Radiation (incoming) Kipp and Zonen CM-7B 23m

Solar Radiation (reflected) Kipp and Zonen CG-2 23m

Atmospheric Pressure Li-Cor LI-7500 23m

Soil Heat Flux (2 replicates) Campbell Scientific HFT3 Ground, -15 cm

Soil moisture Campbell Scientific CS616 10-100cm

Temperature and Relative Humidity Vaisala HMP45A 2m, 23m

Rain Gauge Campbell Scientific TB3 Ground

Data Loggers Campbell Scientific CR-3000

Power Supply

- 12V DC EC flux station Kyocera Solar panels (3) (BP) Ground

(Source:

http://www.ozflux.org.au/monitoringsites/howardsprings/howardsprings_measurements.html)

http://www.ozflux.org.au/monitoringsites/howardsprings/howardsprings_measurements.html

Appendices

100

Appendix C: Slope changes of annual mean carbon fluxes early vs. late century

y = 0.0021x + 19.775 18

19

20

21

22

19

13

19

16

19

19

19

22

19

25

19

28

19

31

19

34

19

37

19

40

19

43

19

46

19

49

GP

P (

t C

ha-1

y-1

)

a)

y = 0.014x + 20.193

18

19

20

21

22

19

51

19

56

19

61

19

66

19

71

19

76

19

81

19

86

19

91

19

96

20

01

20

06

20

11

GP

P (

t C

ha-1

y-1

)

b)

y = -0.0075x + 16.831

15.5

16.0

16.5

17.0

17.5

19

13

19

16

19

19

19

22

19

25

19

28

19

31

19

34

19

37

19

40

19

43

19

46

19

49

Fre

(t

C h

a-1 y

-1)

c)

y = 0.0038x + 16.807 16.0

16.5

17.0

17.5

18.0

19

51

19

56

19

61

19

66

19

71

19

76

19

81

19

86

19

91

19

96

20

01

20

06

20

11

Fre

(t

C h

a-1 y

-1)

d)

y = 0.0125x + 5.6466 4.0

5.0

6.0

7.0

8.0

19

13

19

16

19

19

19

22

19

25

19

28

19

31

19

34

19

37

19

40

19

43

19

46

19

49

Fc (

t C

ha

-1 y

-1)

e)

y = 0.0153x + 6.0846 5.0

6.0

7.0

8.0

9.0

19

51

19

56

19

61

19

66

19

71

19

76

19

81

19

86

19

91

19

96

20

01

20

06

20

11

Fc (

t C

ha-1

y-1

)

f)

Figure A.2: Time series and linear trends of carbon flux variables showing the variation in rate of increase between the first and

second halves of the 20th

century. (a,b) gross primary productivity, (c,d) ecosystem respiration and (e,f) net ecosystem exchange.

Appendices

101

Appendix D: Time series (anomaly plots) of climate indices

-40

-20

0

20

40

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

MSL

P S

tan

dar

d D

evia

tio

n

SOI

-0.0175

-0.0075

0.0025

0.0125

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SST

An

om

aly

TSI

-0.0175

-0.0075

0.0025

0.0125

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SST

An

om

aly

II

Figure A.3: Time series of climate indices. SOI appears to have approximately equal distributions of positive and negative (wet

and dry) events while TSI and II show marked increasing in frequency of positive (warm) SST anomalies during the last quarter of

the time period.

Appendices

102

Appendix E: Rainfall coherence spectra and ecosystem fluxes

Figure A.4: WTC plots demonstrating lack of coherence beyond the dominating annual cycle of rainfall and

ecosystem flux variables. (a) net ecosystem exchange, (b) latent heat flux, (c) ecosystem respiration and (d) gross

primary productivity.

wavelet analysis of climate variability and savanna land-atmosphere interactions...

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