Why big data is a game changer for science and what have we learned over the last 30 years

1. Why
big
data
is
a
game
changer
for
terrestrial
ecosystem
science
and
what
have
we
learned
over
the
last
30
years
I.
Colin
Pren,ce
AXA
Chair
in
Biosphere
and
Climate
Impacts,
Imperial
College
London
Professor
in
Ecology
and
EvoluCon,
Macquarie
University
Chair,
ecosystem
Modelling
And
Scaling
infrasTructure
(eMAST)

2. The
significance
of
30
years
ago…
• Orwell’s
1984
• Murakami’s
1Q84
• Shugart
(1984)
A
Theory
of
Forest
Dynamics
– “gap
models”
for
tree
growth
and
compeCCon
– ecosystem-­‐specific,
required
data
on
every
tree
species
– lack
of
integraCon
of
vegetaCon
dynamics
with
ecophysiology,
biogeochemistry,
biogeography

3. Trends
in
ecosystem
science,
1984-­‐2004
• Recognizing
large-­‐scale
drivers
of
ecosystem
change
GCTE
launch
(1992):
promoCng
experimental
and
modelling
research
on
global
change
• From
ecosystem-­‐specific
models
to
DGVMs
Cramer
et
al.
(2001)
GCB:
C
cycle
projecCons,
six
models
• Revival
of
comparaCve
funcConal
ecology
(moCvaCon
to
improve
DGVMs)
Wright
et
al.
(2004)
Nature:
leaf
economics
spectrum

4. Big
data
for
ecosystem
science
• Steady
accumulaCon
of
precise
atmospheric
measurements
(ramp
up
in
1980s)
• Major
advances
in
remote
sensing
(MODIS
launch
2000;
Sciamachy,
GOME
etc.
for
atmospheric
consCtuents)
• ‘Bodom-­‐up’
syntheses
of
local
measurements
(flux,
traits)
=>
push
for
data
sharing
(N
America
first;
big
push
from
TERN;
WIRADA)
• ConCnuous
exponenCal
improvement
in
data
storage
and
computaConal
capacity
• Major
advances
in
computaConal
tools
(especially
open-­‐source
languages
and
codes)

5. What
can
we
do
with
big
data?
• Model
evaluaCon
and
benchmarking
(post
facto
comparison)
• Data
assimilaCon
(model
structure
pre-­‐defined:
variables
and/or
parameters
to
be
esCmated)
• New
model
development
(using
data
to
inform
model
structure)
1.
Process
understanding
flows
from
large-­‐scale
data
analysis.
2.
There
are
huge
unexploited
opportuniCes
–
hardly
conceivable
30
years
ago.

6. Role
of
eMAST
• PredicCve
models,
fully
informed
by
all
relevant
data
• Ecosystems
under
pressure
=>
requirement
for
predicCve
power

7. Role
of
eMAST
(cont.)
• Without
models,
there
is
no
predicCve
power.
• Without
data,
models
are
worthless.
• We
need
to
make
it
easy
for
models
and
data
to
talk
to
one
another.

8. Example
1:
CO2
seasonal
cycles
• Seasonal
cycles
at
different
locaCons
as
a
benchmark
for
modelled
NEE
• Increasing
high-­‐laCtude
seasonal
cycle
as
a
challenge
for
modelling
NPP
• Requires
intervenCon
of
an
atmospheric
transport
model
–
but
this
can
be
done
‘automaCcally’
through
inversion

9. NH
Tropics
SH
Kelley
et
al.
(2013)
Biogeosciences

10. Graven
et
al.
(2013)
Science

11. Graven
et
al.
(2013)
Science

12. Example
2:
Leaf
stable
carbon
isotopes
• Global
leaf
δ13C
data
(for
ci:ca
raCo
–
coupling
of
water
and
CO2
exchanges):
synthesis
of
>
3500
measurements
led
by
Will
Cornwell,
UNSW
• Leaf
economics
theory
(PrenCce
et
al.
2013
Ecology
LeIers)
=>
predicts
dependence
on
temperature,
aridity,
elevaCon
• Requires
climate
data
and
a
model
to
infer
bioclimate
variables,
e.g.
cumulaCve
water
deficit
(proxy
for
vpd)

13. ParCal
residual
plots
H.
Wang
et
al.
(unpublished
results)

14. Global
slopes:
ln
χ/(1
−
χ)
vs
predictors
Predicted
FiIed
(±
95%
CI)
temperature
0.055
0.050
±
0.004
ln
(dryness)
−
0.250
−
0.226
±
0.012
elevaCon
−
0.082
−
0.093
±
0.030
R2
=
0.450

15. Global
regression
slopes

16. Example
3:
IntegraCng
remotely
sensed
and
flux
measurements
(ePiSaT)
• OzFlux
synthesis
(all-­‐site
CO2
flux
measurements)
• fAPAR
synthesis
product
(Huete
et
al.)
! ParCConing
fluxes
into
respiraCon
and
GPP
! Analysis
of
monthly
integrated
GPP
versus
fAPAR
x
PPFD
! LUE
model
driven
by
fAPAR,
PPFD,
vpd…
• Also
requires
climate
data,
bioclimate
variables,
parCConing
and
gap-­‐filling
methods…
B.J.
Evans
et
al.
(2013)
unpublished
results

18. Where
do
we
go
from
here?
• Data-­‐model
comparison
and
evaluaCon
‘made
easy’.
• Data
assimilaCon
‘made
possible’.
• IntegraCon
of
data
sets
with
different
properCes
(e.g.
spaCally
versus
temporally
extensive)
‘made
rouCne’.