Predictability of the Terrestrial Carbon Cycle Yiqi Luo University of Oklahoma, USA Tsinghua University, China Trevor Keenan Harvard University, USA Matthew Smith Microsoft Research, Cambridge, UK YingPing WangCSIRO Jianyang Xia University of Oklahoma, USA Sasha Hararuk University of Oklahoma, USA Ensheng Weng Princeton University, USA Yaner Yan Fudan University, China

Soil carbon modeled in CMIP5 vs. HWSD Yan et al. submitted IPCC assessment report

Friedlingsterin et al Great uncertainty among models 2.Does the uncertainty reflect variability in the nature or result from artifacts?

Current efforts to improve the predictive understanding

Satellite measurement of CO 2

中科院碳循环研究专项

Observation and experiment Can they make IPCC assessment report better?

Modeling MethodMeritIssue Adding components Simulating more processes realistically Complexity Intractability Model intercomparison Illustrating uncertainty Attribution to its sources Benchmark analysis Performance skillsObjective benchmarks Data assimilationImproving modelsVarious challenges

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

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

A: Basic processes B: Shared model structure C: Similar algorithm D: General model Model development Encoding Theoretical analysis Generalization Leaf (X 1 ) Wood (X 3 ) Metabolic litter (X 4 ) Microbes (X 6 ) Structure litter (X 5 ) Slow SOM (X 7 ) Passive SOM (X 8 ) CO 2 Photosynthesis Root (X 2 ) Luo et al GBC Luo and Weng 2011 TREE Luo et al Luo et al. submitted Theoretical analysis

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)

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.

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

Periodic climate (e.g., seasonal) Periodicity Disturbance event (e.g. fire and land use) Pulse-recovery Climate change (e.g., rising CO 2 ) 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

working group Nonautonamous system The 3-D parameter space is expected to bound results of all global land models and to analyze their uncertainty and traceability.

Computational efficiency of spin-up Xia et al GMD

Computational efficiency of spin-up Xia et al GMD

Semi-analytical spin-up with CABLE Initial step: 200 years Final step: 201 years Initial step: 200 years Final step: 483 years Traditional: 2780 years Traditional: 5099 years -92.4% -86.6% Xia et al GMD

Xia et al GCB

Traceability of carbon cycle in land models

NPP ( ) Preset Residence times Soil textureLitter lignin fraction Climate forcing PrecipitationTemperature Xia et al GCB Traceability of carbon cycle in land models

Traceability for differences among biomes Based on spin-up results from CABLE with 1990 forcings. (τE)(τE) (τE)(τE) Long τ E but low NPP. High NPP but short τ E. Xia et al GCB

Traceability for model intercomparison Model-model Intercomparison CABLE CLM3.5 ENFEBF DNFDBF ShrubC3G C4GTundra

Traceability for impact of additional model component Xia et al GCB

Carbon influx Initial values of carbon pools Environmental scalar Transfer coefficient Partitioning coefficient Residence time Sources of model uncertainty

Carbon influx Initial values of carbon pools Residence time

Soil carbon modeled in CMIP5 vs. HWSD Todd-Brown et al BG Initial values of carbon pools Carbon influx Residence time

Soil carbon modeled in CMIP5 vs. HWSD Yan et al. submitted

Data assimilation to reduce uncertainty

A: Basic processes B: Shared model structure C: Similar algorithm D: General model Model development Encoding Theoretical analysis Generalization Leaf (X 1 ) Wood (X 3 ) Metabolic litter (X 4 ) Microbes (X 6 ) Structure litter (X 5 ) Slow SOM (X 7 ) Passive SOM (X 8 ) CO 2 Photosynthesis Root (X 2 ) Luo et al GBC Luo and Weng 2011 TREE Luo et al Luo et al. submitted Summary

A: Basic processes D: General model Theoretical analysis Luo et al GBC Luo and Weng 2011 TREE Luo et al Luo 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)

Periodic climate (e.g., seasonal) Periodicity Disturbance event (e.g. fire and land use) Pulse-recovery Climate change (e.g., rising CO 2 ) Gradual change Disturbance regime disequilibrium Ecosystem state change (e.g., tipping point) Abrupt change External forcing Response Relevance to empirical research Terrestrial carbon cycle 1.Find examples to refute this equation 2.Disequilibrium vs. equilibrium states 3.Disturbance regimes have not been quantified 4.Mechanisms underlying state change have not been understood 5.Response functions to link carbon cycle processes to forcing are not well characterized. 6.Disturbance-recovery trajectory, especially to different states, is not quantified

Relevance to modeling studies MethodMeritIssue Adding componentsSimulating more processes realisticallyComplexity Intractability Model intercomparisonIllustrating uncertaintyAttribution to its sources Benchmark analysisPerformance skillsObjective benchmarks Data assimilationImproving modelsVarious challenges 1.Benchmarks to be developed from data and used to evaluate models 2.Traceability of modeled processes 3.Pinpointing model uncertainty to its sources 4.Data assimilation to improve models 5.Standardize model structure for those processes we well understood but allow variations among models for processes we have alternative hypotheses 6.Parameter ensemble analysis