the ecology of numerical models and other baked goods andy ... · the ecology of numerical models...

The ecology of numerical models and other baked goods

University of Bristol, University of California – Riverside

Andy Ridgwell

OutlineComputer models and

other baked goods

(1) Muffins.

(2) Cupcakes.

(3) Other baked goods.

OutlineComputer models and

other baked goods

(1) Some GENIE results.

(2) Current/emerging GENIE developments and potential for a GUI-based ‘teaching model’.

(3) Future directions in ecological modelling.

introductionComputer models and

other baked goods

GENIEGrid ENabled Integrated Earth system model

www.genie.ac.uk


other baked goods

open oceansea ice coastal seas

terrestrialbiota

atmosphere

marine biota

sed

imen

ts

soils

land surface and hydrology

icesheet

2D energy-moisture balance(no clouds, dynamics)

fully 3D (‘reducedphysics’)

ocean

simplifiedthermo-dynamic

cGENIECarbon-GENIE

www.seao2.org


other baked goods


terrestrialbiota

atmosphere

marine biota

sed

imen

ts

soils


icesheet



ocean

0 30 60 90 120 150 180 210 240 270 300 330 360

-90

-60

-30

0

30

60

90

36x36x8

175

411

729

1158

1738

2520

3576

5000

Depth (m)

0 30 60 90 120 150 180 210 240 270 300 330 360

-90

-60

-30

0

30

60

90

64x32x8



other baked goods


terrestrialbiota

atmosphere

marine biota

sed

imen

ts

soils


icesheet



ocean

surf

ace layer

bioturbatedzone of 1 cm

sedimentstack layers

partially-filledupper-most

layerb

iotu

rbati

on

al

mix

ing

non-bioturbated

zone ofburied layers


major changes vs. ‘GENIE’

deletion of ‘legacy’ science modules, e.g. IGCM, GLIMMER, MOSES/TRIFFID, etc.(improving compiler compatibility)

attempt at parallelization(concurrent GOLDSTEIN/BIOGEM, domain de-compositoin of GOLDSTEIN)

reorganization of main GENIE.F loop and introduction

(completion) of ‘GEMlite’

simplification of runmuffin.sh script configuration, name-

list checking

complete reorganisation of redox (in progress)

continued tracer addition and functionality such as proxy ‘inversion’ methodology

added netCDF restarts for biogeochem modules

added netCDF sedcor data saving

MATLAB plotting and analysis function development

muffinComputer models and

other baked goods

0

Age (Ma)

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

10 20 30 40 50 60

18

d0 (

‰)

Zachos et al. [2001, 2008]

muffin application: Paleocene-Eocene Thermal MaximumComputer models and

other baked goods

-0.5

Age relative to the PETM (Ma)0.5 1.0 1.5-1.0 0.0-1.5-2.0-2.5-3.0-3.5

4.0

3.0

2.0

1.0

0.0

13

dC

(‰)

PE

TM

(ET

M1)

Zachos et al. [2010]Lunt et al. [2011]

0

Age (Ma)

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

10 20 30 40 50 60

18

d0 (

‰)

-0.5

Age relative to the PETM (Ma)0.5 1.0 1.5-1.0 0.0-1.5-2.0-2.5-3.0-3.5

4.0

3.0

2.0

1.0

0.0

13

dC

(‰)

PE

TM

(ET

M1)

Zachos et al. [2010]Lunt et al. [2011]

Computer models andother baked goods

Zachos et al. [2001, 2008]

muffin application: Paleocene-Eocene Thermal Maximum

13d C (‰)

To

tal carb

on

rele

ase (

Pg

C)

0

2,000

0 -10 -20 -40 -60-30 -50

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

mantle CO2 @ -6‰

marine organic matter

thermogenic methane

biogenic methane

13

+6‰

dC

ini

13

-2‰

dC

ini

terrestrial:C3 plants

terrestrial:C4 plants

Contours of carbon release vs. source isotopic signature for a global -4‰ carbon isotopic excursion. Contours differ according to the initial mean

13global d C.

Ridgwell and Arndt [2014]

Computer models andother baked goodsmuffin application: Paleocene-Eocene Thermal Maximum

+ - + 2-CO +H O « H +HCO « 2H +CO2(aq) 2 3 3

pCO2(g) me

tam

orp

his

m,

vo

lca

nis

m (

0.1

)

sil

ica

tew

ea

the

rin

g (

0.1

)

sh

all

ow

wa

ter

C b

uri

al

(0.1

)o

rg

carbonate (0.1), kerogen (0.1)

weathering

calcification (1.1)net CO fixation2

(10)

CaCO dissolution (0.4)3

C oxidation (9.9)org

C oxidation (0.1)org

deep sea CaCO burial (0.1)3

vo

lca

nis

m

low-temperaturebasaltic alteration

CO emissions2

ocean [38000]

terrestrial biosphere [2000]

CaCO dissolution (0.6)3

sh

all

ow

wa

ter

Ca

CO

bu

ria

l (0

.1)

3

atmosphere [560]


1. Calculate model-data errorat a weekly time-step:

too high Þ emit carbon‘OK’ Þ do nothing

(too low Þ remove carbon)

-1emissions (PgC yr )

cumulativeemissions (PgC)


13model d C trajectory

13observed d C

13d C error

time

Earth systemmodel (’GENIE’)


13observed d C

13d C error

time




2. If emissions required:Add CO to atmosphere2

in an Earth system model

CO 2

assume:13d C signature

of fossil fuelsfor emissions





assume:13d C signature

of fossil fuelsfor emissions

3. Calculate new atmospheric13

CO d C value in model2

<REPEAT>


13observed d C

13d C error

time

-8.6

-8.4

-8.2

-8.0

-7.8

-7.6

-7.4

-7.2

-7.0

13

atm

osp

he

ric

dC

O (

‰)

2

-6.8

-6.6

200019401920 198019601900

year

310

320

330

340

350

360

370

380

atm

osp

he

ric

CO

co

nc

en

tra

tio

n (

pp

m)

2

300

290

280

-27‰


other baked goods

-8.6

-8.4

-8.2

-8.0

-7.8

-7.6

-7.4

-7.2

-7.0

13

atm

osp

he

ric

dC

O (

‰)

2

-6.8

-6.6

200019401920 198019601900

year

310

320

330

340

350

360

370

380

atm

osp

he

ric

CO

co

nc

en

tra

tio

n (

pp

m)

2

300

290

280

-12‰

-60‰


other baked goods

0 100 200 3007.2

7.6

7.4

7.8

time (kyr)

year 2100 seawater pH

peak PETM

sur. pH (pH )(sws)


other baked goods

0 100 200 3007.2

7.6

7.4

7.8

time (kyr)

sur. pH (pH )(sws)


other baked goods





-1.0

0.0

1.0

2.0

3.0

0 100 200 300

time (kyr)

13sur. d C (‰)DIC




other baked goods

0-100

100 200 300

0

-50


-1.0

0.0

1.0

2.0

3.0

0 100 200 300

time (kyr)

13sur. d C (‰)DIC

0 100 200 3007.2

7.6

7.4

7.8

time (kyr)

sur. pH (pH )(sws)




other baked goods

-1.0

0.0

1.0

0 100 200 300 400 500

Model f

orc

ing &

resp

onse 2.0

3.0

13

su

r. d

C (

‰)

DIC

7.2

7.6

7.4

7.8

su

r. p

H (

pH

)(s

ws

)A

tm.

tem

p.

(°C

)

022

24

26

100 200 300 400 500

28

30

32


other baked goods

Time since start of model experiment (ka)


other baked goods

Time since start of model experiment (ka)

å (

Eg

C)

15

0-100

100

100 200 300 400 500

Dia

gnose

d c

arb

on e

mis

sions

Em

iss

ion

s-1

(P

gC

yr

)

-0.5

0.0

0.5

1.0

5

0

13

Em

issio

ns d

C (

‰)

DIC

10

50

0

-50

muffinComputer models and

other baked goods

issues

continued use of FORTRAN77 code, e.g. preventing compile-time array dimensioning

netCDF installation issues (partly an issue of the use of C++ code in the netCDF comparison model ‘test’)

linux-only (a problem?) (actually, can also now be natively on a Mac)

non-intuative construction of experiments

limited applicability in teaching due to linux and command-line basis of the model

cupcakeComputer models and

other baked goods

major changes

code management under git (not svn), so has lost its

explicit historical link with ‘GENIE’

conversion to F90 throughout

progress towards all run-time dimensioning of arrays (and no need for re-compilation)

simpler directory structure and job creation/submission

xml removed (and hence no need for python xml libraries (and hence a simpler install))

cross platform support ... can be run under linux/MacOS/Windows


other baked goods

to-do thorough performance profiling and optimization

on-line /off-line matrix transport option (eventually replacing GEMlite)

addition of the Darwin ecosystem model

addition of the PALEOGENiE project ‘PAM’ (paleo-assemblage model)

addition of ECBILT AGCM??

addition of JeDi terrestrial ecosystem model??


other baked goods


other baked goods

to-do GUI development ... ?

PALEOGENiE – motivationComputer models and

other baked goods

Terrestrial weathering inputs

Organic matter burial, scavenging

FeMo

pCO2

(climate)

O2

- +NO , NH3 4

3-PO4

The nature marine ecosystems and strength of biological productivity and remineralization affects:

Oceanic macros nutrient inventories,

esp. P and the form of fixed N.

Ocean oxygenation and hence micro

nutrient inventories, esp. Fe – scavanged in an oxic ocean, and Mo – scavenged in a sulphidic ocean.

Atmospheric pCO and climate.2


other baked goods

The nature marine ecosystems and strength of biological productivity and remineralization affects:

Oceanic macros nutrient inventories,

esp. P and the form of fixed N.

Ocean oxygenation and hence micro

nutrient inventories, esp. Fe – scavanged in an oxic ocean, and Mo – scavenged in a sulphidic ocean.

Atmospheric pCO and climate.2

/end speculation

In turn, changes in the physical and biogeochemial (nutrient) environment will affect ecosystem composition and drive selection.

The approximate coincidence between plankton evolutionary time-scales and the residence time of many of the key ocean and atmospheric tracers raises the possibility of interesting dynamical behaviours of the full system.

Terrestrial weathering inputs

Organic matter burial, scavenging

FeMo

pCO2

(climate)

O2

- +NO , NH3 4

3-PO4

PALEOGENiE – motivation

Gib

bs e

t al. [

2006] (S

cie

nce

)

Fragile Robust Robust Robust Fragile

Originations Extinctions

PETM onset

tim

e‘PETM’

time


other baked goods

time

recovery

‘gap’

‘K-Pg’

65.2 65.3 65.4 65.5 65.6

Time (Ma)

13

dC

(‰

)

1.0

1.5

2.0

2.5

3.0

1210

465

sea ice

heat loss

winds

mixing

eva

po

rati

on

pre

cip

ita

tio

n

Eq

ua

tor

gyr

e

Po

le

coastal ocean

open ocean

deepocean

atmosphere

surfaceocean

strategies for modelling complex (marine) systemsComputer models and

other baked goods


other baked goods

Creating models is effectively, the art of encapsulation of one’s understanding (or preconceptions) of a system, numerically.

BUT ...


other baked goods

What happens under climate change?

What did the system look like in the past (e.g. Cretaceous)??

What if the structure of the system is not correctly understood???


other baked goods

heat loss

winds

mixing

eva

po

rati

on

pre

cip

ita

tio

n

Eq

ua

tor

Po

le


other baked goods

heat loss

winds

mixing

eva

po

rati

on

pre

cip

ita

tio

n

Eq

ua

tor

Po

le

sea icegyr

e

Ocean circulation becomes an emergent rather than prescribed property of the system.


other baked goods

phytoplankton

zooplankton


other baked goods

zooplankton

#2#1

Creating models is effectively, the art of encapsulation of one’s understanding (or preconceptions) of a system, numerically.

BUT ...


other baked goods

zooplankton

#2phytoplankton #1

str

ain

#1 in v

itro

str

ain

#2 in v

itro

predominantly short-term laboratoryperturbation experiments

model with measured properties of ~2-5 cultured strains encoded

Again:

What happens under climate change?What did the system look like in the past (e.g. Cretaceous)?What if the structure of the system is not correctly understood?

But also:

What about adaptation (or even evolutionary responses) to global change?


other baked goods

(Ocean) General Ecological Models? (O-GEMs?)

?


other baked goods

‘in s

ilico’

#1 #2 #3 #4 #n#5 .......

Follo

ws

et al.

[2007] (S

cience

)

Marine ecosystems in silico:

The MIT ‘Darwin’ model typically considered ca. n = 76 randomly-generated trait vectors (’plankton’).

Plankton trait vectors set according to physiological ‘rules’, e.g. larger cells have a higher nutrient limitation threshold, the ability to fixed N comes at the expense of reduced 2

growth rate, etc.

Plankton compete and the ecosystem is an emergent rather than prescribed property.But ...

‘PALEOGENiE’Computer models and

other baked goods

‘in s

ilico’

#1 #2 #3 #4 #n#5 .......

terrestrialweathering

terrestrialbiosphere

se

dim

en

tb

uri

al3D

circulationocean

sea-ice

‘paleoassembledge

model’


The MIT ‘Darwin’ model typically considered ca. n = 76 randomly-generated trait vectors (’plankton’).


growth rate, etc.

Plankton compete and the ecosystem is an emergent rather than prescribed property.But ...... the geochemical environment and climate co-evolves as global nutrient cycles are modified.


other baked goods

‘in s

ilico’

#1 #2 #3 #4 #n#5 .......



se

dim

en

tb

uri

al3D

circulationocean

sea-ice

‘paleoassembledge

model’


n = 1,000-10,000 randomly-generated trait vectors (’plankton’).


growth rate, etc.

Plankton compete and the ecosystem is an emergent rather than prescribed property.But ...... the geochemical environment and climate co-evolves as global nutrient cycles are modified.

At very high resolved diversity, we can explore questions of adaptation and rates of evolutionary change by spawning new plankton with perturbed characteristics.


other baked goods

reproductive regime

most happy

would rather be elsewhere

environmental variable (e.g. temperature)

gro

wth

ra

te

fatallyunhappy

‘re

-dra

wn

’, w

ith

sin

ce

re a

po

log

ies,

fro

m S

ch

mid

t e

t a

l. [2

00

6]


other baked goods


gro

wth

ra

te

am

bie

nt S

ST

‘Epp

ly cur

ve’

climate warmingclimate coolingIn traditional ‘functional type’ ecosystem models, diversity is not resolved, but instead its effects highly parameterized (e.g. the ‘Epply curve’).

The response to a change in climate is then instantaneous and fully reversible.


other baked goods


gro

wth

ra

te

.......sp

ecie

s #

1

sp

ecie

s #

2

sp

ecie

s #

3

sp

ecie

s #

4

sp

ecie

s #

5

Instead, in a highly diverse model, the environmental response of individual ‘species’ can be resolved ...


other baked goods


gro

wth

ra

te

.......sp

ecie

s #

1

sp

ecie

s #

2

sp

ecie

s #

2

sp

ecie

s #

2

sp

ecie

s #

2

sp

ecie

s #

2

sp

ecie

s #

1

sp

ecie

s #

1

sp

ecie

s #

1

sp

ecie

s #

1

....... sp

ecie

s #

3

Instead, in a highly diverse model, the environmental response of individual ‘species’ can be resolved ...

... or instead, the capability for adaptation (environmental selection within existing genetic diversity) can be represented(?)


other baked goods


gro

wth

ra

te

climate warming


other baked goods


gro

wth

ra

te

climate cooling

If climate cools, the low SST optimized species/varients no longer exist. Ecosystem dynamics are presumably different.

Niches are unfilled, so ...


other baked goods


gro

wth

ra

te

climate cooling

mutation

Allow non-viable plankton to be replaced with ‘mutations’ of surviving species, using the trait based trade-offs.

Q. How ‘frequently’ to mutate, and as a function of what?

Q. What ‘step size’ to take for mutation?


other baked goods

#1 #2 #3 #4 #n#5 .......



se

dim

en

tb

uri

al3D

circulationocean

sea-ice

‘paleoassembledge

model’

‘PALEOGENiE’: A radical paleo model-data

concept for theoretically exploring questions of marine plankton adaptation and evolution.

Specific questions:Cause(s) of the delayed recovery (100s of kyr) from end Cretaceous extinctionDetermining which factor(s) best explain ecological responses to PETM carbon release.

A tool for gaining understanding about future ecosystem stability (+ proof concepts for future models).


other baked goods

‘in s

ilico’

#1 #2 #3 #4 #n#5 .......



se

dim

en

tb

uri

al3D

circulationocean

sea-ice

‘paleoassembledge

model’


n = randomly-generated trait vectors (’plankton’).......

At very high resolved diversity, we can explore questions of adaptation and rates of evolutionary change by spawning new plankton with

1,000-10,000

There is clearly a very significant computational expense involved, even if using low resolution/efficient Earth system models such as ‘GENIE’.

‘PALEOGENiE’ – computational strategiesComputer models and

other baked goods

60-90

0

90

0120 180

-120 -600

‘Color’ tracer pattern to unambiguously diagnose surface ocean transport


other baked goods

=> Diagnose full 3D circulation, and employ (sparse) parallelized matrix multiplication.

=> Calculate plankton transport separately from nutrients (and other dissolved tracers)?

60-90

0

90

0 120 180

0-120 -60

Dispersal of a single ‘color’ after 1 year


other baked goods

=> Diagnose full 3D circulation, and employ (sparse) parallelized matrix multiplication.

=> Calculate plankton transport separately from nutrients (and other dissolved tracers)?

Terrestrial ecosystem modellingComputer models and

other baked goods

A B


other baked goods

Use a crop (plant) model based on carbon resource allocation.

Incorporate basic plant biomechanics (60ft long leaves == not a good idea ...).


other baked goods

Explore the coupled evolution of terrestrial plants and environment?

Mutate the plants across millions of generations and across millions of ‘fields’ on a massively parallel

computing basis.

Select for yield (but at the field scale, hence dealing with ‘competition’).

Can also select for e.g. water use efficiency, tolerance to gusty wind conditions, etc. etc.

Q. Would an ‘optimal’ crop plant have 6 triangular leaves that enables a hexagonal space-filling

tessellation across the soil surface??

Terrestrial ecosystem modelling ... and cropsComputer models and

other baked goods

the ecology of numerical models and other baked goods andy ... · the ecology of numerical models...

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