the big experiment-big model (bebm) challenges in ecology · outline •nature of experiment and...

The Big Experiment-Big Model

(BEBM) challenges in Ecology

Yiqi Luo

University of Oklahoma, USA

http://ecolab.ou.edu

[email protected]

AnaEE, Paris, French, March 2-3, 2016

"From Experimentation to Global Prediction"

Experiment ??global prediction

Outline

• Nature of experiment and big data challenges

• Current status of models toward ecological forecasting

• Challenges from experimentation to global prediction

• Work in my lab

• Future infrastructure needs to enable scaling from experimentations to global prediction

EcoCELLs

in Nevada

Warming

Oklahoma

FACE

North

Carolina

Coastal wetland

China

Grassland

Ukraine

Permafrost

Alaska

Drought

Great Plains

SPRUCE

3/1/2012

Duke FACE

3/1/2012Luo and Reynolds 1999 Ecology

Nature of experimentation

Manipulative experiment imposes a perturbation to

ecosystems.

Data records responses of ecosystems to the

perturbation.

We have to use inverse analysis to extract useful

information to advance our understanding

Luo and Reynolds 1999 Ecology

• Characterize response patterns

• Reveal underlying mechanisms

• Identify major factors in regulating the patterns

Norby et al. 2010 PNAS

Scientific values of experiment

1. Process representation: New algorithms to represent processes instead of a black-box approach

2.Parameterization: Data used to parameterize models

3.Data assimilation: Multiple streams of data ingested into model to improve its performance

4.Benchmarking: Data used to evaluate model performance

Experiment results model

Big data challenges

Current status of data

• Great advances in measurement technology to collect data at spatial scales from genomics to satellites

• One variable (e.g., biomass) measured by different methods, generating multiple, often contradictory data sets

• Data are often processed (merging, resampling, homogenizing) to generate data products, leading errors not well quantified.

• Users (e.g., modelers) do not know where the data come from, data collectors do not recognize their data in the data products.

Outline




• Work in my lab


Model

System

Forcing Variables

Output Variables

Forward

Simulation models

DATA

Traditional practice in ecology

Enough to explore ideasBut not for prediction or forecasting

Poor match with observation

Luo et al. 2015

High uncertainty

Low tractability

IPCC AR5, Ch. 6

Step 3: External forcings

The physical and biological environment the soil

experiences to perform its functioning

Step 1: Structure

A series of equations to represent the real-world

processes that control systems functions

Step 2: Parameterization

Model specification through parameter estimation to

constrain model projections

Realistic projection

Luo et al. 2016

Ecological Forecasting

• 2007 NSF workshop on data assimilation

• 2010-2016 NSF RCN Forecasts Of Resource and Environmental Changes: data Assimilation Science and Technology (FORECAST)

>10 workshops

• DOE workshop 2012: model-experiment integration – ModEX

• US CCIWG workshop 2016: Predictive carbon cycle science

NSF workshop on data assimilationOctober 2007

Data alone could not be used to inform decision making processes

Nor modeling alone

Data and model has to be integrated to do so.

Luo et al. 2011

Towards More Realistic Projections of Soil Carbon Dynamics by Earth System Models

Databases

Processes

Mic

robia

l pro

cesses

Dis

turb

ances (LU

C, etc

.)

SO

C s

tabili

zatio

nN

utrie

nt cyc

les

C in

put (N

PP

and li

tter, e

tc.)

Vertic

al d

ynam

ics

Prim

ing e

ffects

Resid

ence tim

eO

xygen

Aggre

gatio

n

Fre

quency

0

4

8

12

16

20

24

Databases

Soil

C p

ool

Soil

depth

/pro

files

NP

P/G

PP

Isoto

pe

Soil

N

Dis

turb

ances (LU

C, etc

.)Litt

er/lit

terfall

Soil

text

ure

Long-term

SO

C d

ynam

ics

Mic

robia

l C

Modeling techniques

Data

assim

ilatio

nB

enchm

ark

ing

Model s

implif

icatio

n

New

module

s/c

om

ponents

Com

bin

ing p

rocess a

nd e

mprical m

odels

Model i

nte

rcom

parison

Pro

cess m

odels

Spatia

l scalin

g

Uncertain

ty a

naly

sis

Bio

geochem

ical c

ouplin

g

Luo et al. 2016 GBC

Ecological forecasting system

Niu et al. 2014 ecosphere

Status of modeling

• Great progress on integrating components together for Earth system models

• Simulation modeling is still commonly practiced but not useful for forecasting

• Data assimilation is essential to improve model predictive ability

• We need to develop ecological forecasting system to enhance experimentation

Outline




• Work in my lab


DataSatellites

Meta-genomicsObs. Networks

GC experiments

To improve models with data

?Step 3: External forcings

The environment the soil experiences

Step 1: Structure Equations to represent the real-

world processes

Step 2: Parameterization Model specification through

parameter estimation

Realistic projection

Inte

rop

erab

ility

Computational cost

Co

mp

lexi

ty

Equ

ifin

alit

y

Earth system modeling

Big-Experiment-Big-Model challenges

Issue Challenge

Multiple, heterogeneous datasets Interoperability

Structural complexity Intractability

Numerous parameters Equifinality

Global optimization Computational cost

Outline




• Work in my lab


Roth-C

TEM

CASACNP

CASA

CASA’

DLEM LPJ

ORCHIDEE

CLM4.5

CENTURY

TECOCLM3.5

CLM4CN

JULES

NCAR LSM

CoLM

SDGVM

TRIFFID

TRIPLEX

O-CNForest BGC

Biome-BGC DayCent

… …

Model Outputs

Forcings

Traceable components shared among models

e.g., Ecosystem C stock

Fundamental properties of models

1. Photosynthesis as the

primary C influx pathway

2. Compartmentalization,

3. Partitioning among pools

4. Donor-pool dominated

carbon transfers

5. 1st-order kinetics of carbontransfers

Fundamental properties of the terrestrial carbon cycle

Luo and Weng 2011 TREE

dX(t)

dt= xAX(t)+ BU(t)

X(t = 0) = X0

ì

íï

îï

A: Basic processesB: Shared model structure

C: Similar algorithmD: General model

Model development

En

cod

ing

Th

eore

tica

l

anal

ysi

s

Generalization

Leaf (X1) Wood (X3)

Metabolic litter (X4)

Microbes (X6)

Structure litter (X5)

Slow SOM (X7)

Passive SOM (X8)

CO2

CO2CO2

CO2

CO2

CO2

CO2

CO2

Photosynthesis

Root (X2)

Luo et al. 2003 GBC


Luo et al. 2012

Luo et al. 2015

Luo et al. To be submitted

Our approach

dX(t)

dt= xACX(t)+ BU(t)

X(t = 0) = X0

ì

íï

îï

A: Basic processes

D: General model Th

eore

tica

l

anal

ysi

s

Luo et al. 2003 GBC


Luo et al. 2012

Luo et al. 2014 GCB

Applicationsa. Research focus on dynamic

disequilibrium (Luo and Weng 2011)

b. Computational efficiency of spin-up (Xia et al. 2012)

c. Traceability for structural analysis (Xia et al. 2013)

d. Predictability of the terrestrial carbon cycle (Luo et al. 2015)

e. Sources of uncertainty (Ahlström et al. 2015)

f. Data assimilation to improve models (Hararuk et al. 2014ab, Hararuk and Luo 2014)

g. Parameter space (Luo et al. in prep.)

BDBM challenges

Issue Challenge

Multiple, heterogeneous

datasets

Interoperability Ecoinformatics

Structural complexity Intractability Traceability

Numerous parameters Equifinality More data sets

Global optimization Computational

cost

High-fidelity

emulator

ESMsTractabilityUncertainty

Predictive abilityParameter space

DataSatellites

Meta-genomicsCMIP5

Obs. NetworksGC experiments Data mining

ThresholdsTipping pointsVulnerability

Disturbance regime

Meta-analysisParameter ranges Spatial variability

Temporal variabilityInteractive effects

BenchmarkingData products

Ecosystem production Vegetation dynamics

Carbon residence time

Our approach to data-model integration

Cyber-enabled workflow system

Data AssimilationParameterizationModel structure

Uncertainty analysisSampling design

Ecological forecasting system

Dat

a A

ssim

ilat

ion S

yst

em

Continuous datasets

Model Model output

Discrete datasets

Ecological forecasting at SPRUCE

Hand measurement

Online measurement

Model projections

Feedback to experimenters on which data sets need to be measured

Feedback to modelers on which parts of a model need to be improved.

Outline




• Work in my lab


Experiment Modeling

Scientific inquiry

Imperfect modelImperfect data

Theory delineates possibilities

Empirical studies discriminate the actualities

Robert May 1981

Experiment Modeling

Scientific inquiry

Process thinkingData

Gain best knowledge from imperfect data and imperfect models

?

DAAC, CDIAC NCAR CLM

Experiment Modeling

Scientific inquiry


Experiment Modeling

Scientific inquiry


International center for experiment-model integration (IceMi)

IceMi

International center for experiment-model integration (IceMi)

Eco-informaticsDecision supporting

Summary

1. Experimentglobal prediction requires substantial RI investment.

2. RI is mainly on information technology (IT) enabling data assimilation and ecological forecasting

3. Eco-informatics should be designed to enable model-data integration

4. Real-time experiment-model interaction is technically feasible now but needs commitment to realization.

the big experiment-big model (bebm) challenges in ecology · outline •nature of experiment and...

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