Understanding Monte Carlo Experiment and succeding investigations Zoltán Barcza, Ferenc Horváth...

Understanding Monte Carlo Experiment and succeding

investigations

Zoltán Barcza, Ferenc Horváth Department of Meteorology, Eötvös Loránd Universtiy, BudapestMTA ÖK Institute of Ecology and Botany, Vácrátót

Budapest29-30 of May, 2014

BOX WROTE THAT „Essentially, all models are wrong,

but some are useful"

Monte Carlo Experiment

Biome-BGC Monte Carlo Experiment

SpinUp INI Normal INI OUTPUT

dailymonthly avgannual avgannual sum

METDATA for SpinUpMETDATA for SpinUp METDATA for NormalMETDATA for Normal

CO2 (optional)CO2 (optional)

NDEP (optional)NDEP (optional)

EPC - ecophysiologyEPC - ecophysiologyEPC - ecophysiologyEPC - ecophysiology

SITE parametersSITE parameters SITE parametersSITE parameters

MANAGEMENT (opt.)MANAGEMENT (opt.)

MORTALI TY (opt.)MORTALI TY (opt.)

GROUNDWATER (opt.)GROUNDWATER (opt.)

MCE INI – parameter randomization


QUESTIONS:

1. WHAT DO I WANT TO RANDOMIZE?2. HOW SHOULD I DEFINE PARAMETER INTERVALS?


1. WHAT DO I WANT TO RANDOMIZE?

We should only fix parameters which are measured locally (we ‘believe’ in these parameters).But: consider structural problems that can cause bias in the parameter values!

MAIN ISSUE WITH PARAMETER ESTIMATION [CALIBRATION, OPTIMIZATION]: THE MODEL IS HIGHLY NON-LINEAR, AND HAS A LARGE DEGREE OF FREEDOM.


1. OK, BUT WHAT DO I WANT TO RANDOMIZE?

Typically the steps are:a) sensitivity analysis – use as many parameters as possible, and

check the effect of parameter variability on the results.b) parameter estimation [optimization] – restrict the number of

parameters to decrease the degree of freedom [literature also suggests that the number of parameters than can be estimated is quite low]


2. HOW SHOULD I DEFINE PARAMETER INTERVALS?

Parameterization of Biome-BGC:must read White et al. 2000:


White et al: 85 pages, vast amount of data

Biome-BGC Sensitivity Analysis

OUTPUT VARIABLES

=f (params)

0

5

10

15

20

25

30

year

day t

o sta

rt ne

w gro

wth

year

day t

o en

d litt

erfa

ll

trans

fer g

rowth

per

iod a

s fra

ction

of g

.s.

litter

fall a

s fra

ction

of g

rowing

seas

on

annu

al who

le-pla

nt m

orta

lity fr

actio

n

(ALL

OCATION) n

ew fin

e ro

ot C

: ne

w leaf

C

(ALL

OCATION) c

urre

nt g

rowth

pro

porti

on

C:N o

f leav

es

C:N o

f leaf

litte

r, af

ter r

etra

nsloc

ation

C:N o

f fine

root

s

cano

py w

ater

inte

rcep

tion

coef

ficien

t

cano

py lig

ht e

xtinc

tion

coef

ficien

t

cano

py a

vera

ge sp

ecific

leaf

are

a

fracti

on o

f leaf

N in

Rub

isco

max

imum

stom

atal

cond

ucta

nce

cutic

ular c

ondu

ctanc

e

boun

dary

laye

r con

ducta

nce

leaf w

ater

pot

entia

l: sta

rt of

redu

ction

leaf w

ater

pot

entia

l: com

plete

redu

ction

vapo

r pre

ssur

e de

ficit:

start

of re

ducti

on

vapo

r pre

ssur

e de

ficit:

com

plete

redu

ction

sens

itivi

ty [

%] modelled GPP

modelled Reco

Sensitivity analysis

Parameter interval is critical. Investigated output is critical.

Possible configurations:

- wide parameter interval- small interval, e.g. 1% of total interval, around mean

Note: check the sensitivity of the model to output which is interesting in your work! Slowly and quickly changing fluxes/pools are driven by different parameters….

Biome-BGC Generalized Likelihood Uncertainty Estimation (GLUE)

LHOODMISFIT

‘BEST’

?

OBSER-VATION

DATA

0. maximum root depth1. symbiotic+asymbiotic fixation of N2. annual whole-plant mortality fraction3. new fine root C : new leaf4. current growth proportion5. C:N of leaves6. canopy light extinction coeff7. canopy average specific leaf area 8. fraction of leaf N in Rubisco9. maximum stomatal conductance

Oensingen

Equifinality

We have to learn to live together with equifinality…

GLUE

Parameter uncertainty [confidence interval] can be calculated.

Additionally, uncertainty of the calibrated model can be estimated if we run the model with the retained parameter sets, or with a subset of the best parameter settings.

Parameter estimation can be performed for multiple years, for multiple sites, but it can also be performed for individual years… All depends on the scientific question that we want to answer.

Understanding Monte Carlo Experiment and succeding investigations Zoltán Barcza, Ferenc Horváth...

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