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Page 1: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Predictability of the Terrestrial Carbon

Cycle

Yiqi Luo University of Oklahoma, USATsinghua University, China

Trevor Keenan Harvard University, USAMatthew Smith Microsoft Research, Cambridge, UKYingPing Wang CSIROJianyang Xia University of Oklahoma, USASasha Hararuk University of Oklahoma, USAEnsheng Weng Princeton University, USAYaner Yan Fudan University, China

http://[email protected]

Page 2: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Soil carbon modeled in CMIP5 vs. HWSD

Yan et al. submitted

IPCC assessment report

Page 3: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Friedlingsterin et al. 2006

1. Great uncertainty among models

2. Does the uncertainty reflect variability in the nature or result from artifacts?

Page 4: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Current efforts to improve the

predictive understanding

Page 5: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Satellite measurement of CO2

Page 6: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

中科院碳循环研究专项

http://159.226.234.56/
Page 7: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Observation and experiment

Can they make IPCC assessment report better?

Page 8: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Modeling

Method Merit Issue

Adding components

Simulating more processes realistically

Complexity Intractability

Model intercomparison

Illustrating uncertainty

Attribution to its sources

Benchmark analysis

Performance skills Objective benchmarks

Data assimilation Improving models Various challenges

Page 9: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Theoretical analysis

1. The terrestrial carbon cycle is a relatively simple system

2. It is intrinsically predictable.

3. The uncertainty shown in model intercomparison studies can be substantially reduced with relatively easy ways

4. We should sharpen our research focus on key issues

Page 10: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

1. Photosynthesis as the primary C influx pathway

2. Compartmentalization,

3. Partitioning among pools

4. Donor-pool dominated carbon transfers

5. 1st-order transfers from the donor pools

Properties of terrestrial carbon cycle

Luo and Weng 2011

Page 11: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

A: Basic processesB: Shared model structure

C: Similar algorithmD: General model

Model development

En

cod

ing

Th

eore

tica

l an

alys

is

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 GBCLuo and Weng 2011 TREELuo et al. 2012Luo et al. submitted

Theoretical analysis

Page 12: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

General equations

Empirical evidence First-order decay of litter decomposition (Zhang et al. 2008) Carbon release from soil incubation data (Schaedel et al. 2013) Ecosystem recovery after disturbance (Yang et al. 2011)

Model structure analysis 11 models in CMIP5 (Todd-Brown et al. 2013)

Page 13: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

1. Internal C processes to equilibrate efflux with influx as in an example of forest succession

2. C sink strength becomes smaller as efflux is equalized with influx

3. When initial values of C pools differ, the magnitude of disequilibrium varies without change in the equilibrium C storage capacity.

Page 14: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Focusing research on dynamic disequilibrium

Convergence

An ultimate goal of carbon research is to quantify

Carbon-climate feedback

Which occurs only when carbon cycle is at disequilibrium

Luo and Weng 2011

Page 15: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Periodic climate(e.g., seasonal)

Periodicity

Disturbance event(e.g. fire and land use)

Pulse-recovery

Climate change(e.g., rising CO2)

Gradual change

Disturbance regime disequilibrium

Ecosystem state change(e.g., tipping point)

Abrupt change

External forcing Response

Given one class of forcing, we likely see a highly predictable pattern of response

Predictability of the terrestrial carbon cycle

Luo, Smith, and Keenan, submitted

Terrestrial carbon cycle

Nonautomatous system

Page 16: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

working groupNonautonamous system

The 3-D parameter space is expected to

bound results of all global land models and

to analyze their uncertainty and traceability.

Page 17: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Computational efficiency of spin-up

Xia et al. 2012 GMD

Page 18: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Computational efficiency of spin-up

Xia et al. 2012 GMD

Page 19: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Semi-analytical spin-up with CABLE

Initial step: 200 yearsFinal step: 201 years

Initial step: 200 yearsFinal step: 483 years

Traditional: 2780 years

Traditional: 5099 years

-92.4%

-86.6%

Xia et al. 2012 GMD

Page 20: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

http://ecolab.ou.eduXia et al. 2013 GCB

Page 21: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

'1EE

WT

http://ecolab.ou.eduXia et al. 2013 GCB

Traceability of carbon cycle in land models

Page 22: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

'E

E

NPP ( )ssU

ssX

Preset Residence times

Soil textureLitter lignin fraction

Climate forcing

Precipitation Temperature

Tw

http://ecolab.ou.eduXia et al. 2013 GCB

Traceability of carbon cycle in land models

Page 23: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Traceability for differences among biomes

Based on spin-up results from CABLE with 1990 forcings.

http://ecolab.ou.edu

0

100

200

300

400

500

0 500 1000 1500 2000

Res

iden

ce ti

me

(Yea

r)

(a)

NPP (g C m-2 year-1)

0

40

80

120

160

0 500 1000 1500

ENF EBF

DNF DBF

Shrub C3G

C4G Tundra

Barren

4030

20101

(b)

NPP (g C m-2 year-1)

Res

iden

ce ti

me

(Yea

r)

(τE)

(τE)

Long τE but low NPP.

High NPP but short τE.

Xia et al. 2013 GCB

Page 24: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Act

ual

res

iden

ce t

ime

(yea

r)

Baseline residence time (year)

1 3 10 32 100 3161

3

10

32

100

316

Traceability for model intercomparison

http://ecolab.ou.eduModel-model Intercomparison

CABLECLM3.5

ENF EBF

DNF DBF

Shrub C3G

C4G Tundra

Page 25: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Traceability for impact of additional model component

http://ecolab.ou.edu

0

60

120

180

0 500 1000 1500

Res

iden

ce ti

me

(yea

r)

NPP (g C m-2 year-1)

ENF EBF

DNF DBF

Shrub C3G

C4G Tundra

Barrens

4030

20101

(a)

0

10

20

30

N in

du

ced

red

uct

ion

s (%

)

(b)

EN

F

EB

F

DN

F

DB

F

Shr

ub

C3G

C4G

Tun

dra

Bar

ren

NPPτE

'

Xia et al. 2013 GCB

Page 26: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Carbon influx

Initial values of carbon pools

Environmental scalar

Transfer coefficient

Partitioning coefficient

Residence time

Sources of model uncertainty

Page 27: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Sources of model uncertainty

Carbon influx

Initial values of carbon pools

Residence time

Page 28: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Soil carbon modeled in CMIP5 vs. HWSD

Todd-Brown et al. 2013 BG

Initial values of carbon pools

Carbon influx

Residence time

Page 29: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Soil carbon modeled in CMIP5 vs. HWSD

Yan et al. submitted

Page 30: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Data assimilation to reduce uncertainty

Page 31: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

A: Basic processesB: Shared model structure

C: Similar algorithmD: General model

Model development

En

cod

ing

Th

eore

tica

l an

alys

is

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 GBCLuo and Weng 2011 TREELuo et al. 2012Luo et al. submitted

Summary

Page 32: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

A: Basic processes

D: General model Th

eore

tica

l an

alys

is

Luo et al. 2003 GBCLuo and Weng 2011 TREELuo et al. 2012Luo et al. submitted

Applications

a. 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. submitted)

e. Sources of uncertainty

f. Data assimilation to improve models (Hararuk et al. submitted)

g. Parameter space (work in progress)

Page 33: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Periodic climate(e.g., seasonal)

Periodicity

Disturbance event(e.g. fire and land use)

Pulse-recovery

Climate change(e.g., rising CO2)

Gradual change

Disturbance regime disequilibriumEcosystem state change(e.g., tipping point) Abrupt change

External forcing Response

Relevance to empirical research

Terrestrial carbon cycle

1. Find examples to refute this equation2. Disequilibrium vs. equilibrium states3. Disturbance regimes have not been quantified4. Mechanisms underlying state change have not been understood5. Response functions to link carbon cycle processes to forcing are not well

characterized.6. Disturbance-recovery trajectory, especially to different states, is not

quantified

Page 34: Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew.

Relevance to modeling studiesMethod Merit IssueAdding components Simulating more processes realistically Complexity Intractability

Model intercomparison Illustrating uncertainty Attribution to its sources

Benchmark analysis Performance skills Objective benchmarks

Data assimilation Improving models Various challenges

1. Benchmarks to be developed from data and used to evaluate models2. Traceability of modeled processes3. Pinpointing model uncertainty to its sources 4. Data assimilation to improve models5. Standardize model structure for those processes we well understood but

allow variations among models for processes we have alternative hypotheses

6. Parameter ensemble analysis


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