CO2 and carbon uptake by land – towards a synthesis of theory, observations and models

Iain Colin Prentice AXA Professor of and Climate Impacts, Honorary Professor in and , Macquarie University Foreign Expert, Department of Earth System Science, Tsinghua University

including unpublished results by: Manuela Balzarolo (CREAF, Barcelona) César Terrer Moreno (CREAF, Barcelona) Beni Stocker (CFEAF, Barcelona) Han Wang (Tsinghua University) We need OBSERVATIONS We need MODELS

• To scale up from leaves to ecosystems • And from ecosystems to the world • To establish consistency among different space and time scales of observation

• ....and to make future projections! We need ΤΗΕΟRY (a set of mathematical statements that can be tested)

• To interpret observations, including experiments • To provide a clear and consistent basis for models science HAS NO ΤΗΕΟRY

It has MYTHS instead... For example, these incorrect statements: • Photosynthetic capacity is controlled by nitrogen • NPP/GPP decreases with temperature • Liebig’s “law” – GPP is controlled by the most limiting resource (or even, the most limiting “factor”) • Nitrogen isotope discrimination depends on the “openness” of the nitrogen cycle • The world is getting dryer

• “Greening” of dry environments will saturate as CO2 increases • Wildfire frequency increases with population density • etc., etc. CO2 uptake and ✖ tropical temperatures (in CMIP5 models)

OBSERVATIONS

Wenzel et al. (2014) JGR CO2 seasonal cycle high in the northern atmosphere (in CMIP5 and MsTMIP ensembles)

Graven et al. (2013) Science Thomas et al. (2016) GRL Time for a new approach!

• We now live in a data-rich world.... • How about using data in the development of theory and models? • But what principles should guide theory development? Natural selection => optimality

“Nothing in biology makes any sense except in the light of evolution” “The unity of life is no less remarkable than its diversity” (Dobzhansky, 1973) Successful theoretical predictions of traits and rates for plants and ecosystems (1)

• Isoprene emission: Morfopoulos et al. (2014) New Phytologist

• ci:ca ratio: Prentice et al. (2014) Ecology Letters, and Wang et al. (2017) Nature Plants • Elevation effects : Wang et al. (2017) New Phytologist • Leaf nitrogen: Dong et al. (2017) Biogeosciences

• Vcmax: Togashi et al. (2018) Biogeosciences, and Smith et al. (2018) Ecology Letters

• Jmax:Vcmax ratio: Wang et al. (2017) Nature Plants • Multiple leaf traits: Bloomfield et al. (2019) New Phytologist • GPP: Wang et al. (2017) Nature Plants Successful theoretical predictions of traits and rates for plants and ecosystems (2)

• Rdark: H Wang et al. (submitted) • Recent GPP trends: Cai et al. (in prep.) • Elevation trends in leaf traits, GPP and NPP: Y Peng et al. (in prep.) • Leaf-to-air ΔT: A Kamolphat et al. (in prep.) • Leaf mass-per-area and lifespan: H Wang & IC Prentice (in prep.) Photosynthetic physiology

The FvCB model:

A = min {AC, AJ} – Rdark where: AC = Vcmax (ci – Γ*)/(ci + K)

AJ ≈ φ0 Iabs (ci – Γ*)/(ci + 2Γ*) at low light...

...asymptotic approach to Jmax / 4 at high light Rdark = 0.015 Vcmax at 25˚C

Diffusion equations for photosynthesis (A) and transpiration (E):

A = gs (ca – ci) gs is stomatal conductance to CO2

E = 1.6 gs D D is vapour pressure deficit

Green symbols are unknown quantities, controlled by plants! The A-ci curve

co-limitation point AJ

A = gs(ca – ci)

AC

Γ* What controls the ci:ca ratio (χ)?

• Nearly constant with ca and Iabs • Declines with D • “Empirical” equations used in models:

Ball-Berry χ ≈ 1 – 1/mh (h is relative humidity)

Leuning χ ≈ f0 (1 – D/D00) An alternative, theoretical approach

• Plants must transport water, in order to take up CO2

• Minimize the cost function a (E/A) + b (Vcmax/A) • Take derivative wrt χ and equate to zero => χ = γ + (1 – γ) ξ/(ξ + √D) ≈ ξ/(ξ + √D) where:

γ = Γ*/ca (≈ 0.1) ξ = √(bK/1.6a)

K = KC (1 + O/KO) b/a is roughly constant (at any given temperature)....

Prentice et al. (2014) Ecology Letters Consequences of the theory

• Environmental responses:

 χ increases with temperature (Kc, Ko, η)  χ decreases with vapour pressure deficit (D)  χ decreases with elevation (O, D) • All correct • Some were known, but not understood properly Quantitative effects: Predictions versus data (leaf δ13C)

predicted fitted (by theory) (by regression) temperature (K) 0.054 0.052 ± 0.006 ln vpd –0.5 –0.55 ± 0.06 elevation (km) –0.08 –0.11 ± 0.03

Wang et al. (2017) Nature Plants χ around Australia

Bloomfield et al. (2019) New Phytologist χ worldwide

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“Co-ordination hypothesis”: Vcmax is set to just use available light • not more, not less...

• co-limitation by Vcmax and light under average daytime conditions • not a new idea, but its profound implications for modelling have been largely missed Regulation of Vcmax according to the co-ordination hypothesis

Predictions:

1) Vcmax increases in proportion to light (not a saturating response, as in most models)

2) Vcmax increases weakly with temperature (less steeply than enzyme kinetics, as used in most models) Some additional correct predictions

• high Vcmax in dry environments • high Vcmax at high elevations

• reduced Vcmax at elevated CO2

=> these statements also apply to leaf N...

Wang et al. (2017) New Phytologist Seasonal acclimation of Vcmax (repeat measurements on the same plants: Great Western Woodlands, Australia)

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A.aneura C.columellaris E.salmonophloia E.scoparia species A.hemiteles E.clelandii E.salubris E.transcontinentalis Vcmax around Australia

Bloomfield et al. (2019) New Phytologist Vcmax worldwide

Smith et al. (2018) Ecology Letters What controls Jmax?

An extension: maximize the benefit (added photosynthesis) minus cost of an increment in Jmax

• predicts a conservative ratio of Jmax to Vcmax • ratio declines steeply with growth temperature Jmax:Vcmax ratios around Australia

Bloomfield et al. 2018 New Phytologist Jmax:Vcmax ratios in experiments

H Wang et al. (unpublished results) What controls Rdark?

FvCB model: at constant temperature (e.g. 25˚C), Rdark is a fixed fraction of Vcmax

• predicts a conservative ratio of Rdark to Vcmax • the optimal ratio declines slightly with growth temperature R dark R d , V cmax and theirand ratios, worldwide V cmax H WangH etal. R d (submitted) / V cmax

at 25˚C at growth temp. Leaf N: cause or effect?

• Most models assume Vcmax, Jmax and Rd are controlled by leaf N. • They are correlated with leaf N.

• An alternative interpretation: Vcmax is tightly regulated, and

controls Jmax, Rdark and leaf N. Leaf N (ln Narea ) around Australia

predicted fitted

χ (from δ13C) –0.62 –0.61 ± 0.25 ln PPFD 1 0.87 ± 0.10 mean annual T –0.048 –0.047 ± 0.007

Dong et al. (2017) Biogeosciences Predicting monthly GPP

2 æ c* ö 3 A = j I m 1- J 0 abs èç m ø÷ c - G* m = a 1.6Dh* c + 2G* + 3G* a b (K + G* )

φ0 = 0.085 from literature

c* = 0.41 from experiments

β = b/a at 25˚C = 146 13 Green vegetation cover = MODIS EVI from δ C data Temperature, vpd, sunshine hours = CRU CL2.0 CO = NOAA GlobalView Wang et al. (2017) 2 Nature Plants TerrA-P validation sites: flux data (black), model (red, blue)

Balzarolo et al. (2018) Comparison with experimental CO2 effects

Comparison with Ainsworth & Long’s (2005) meta-analysis of FACE experiments (≈ 200 ppm CO2 enhancement):

meta-analysis model Light use efficiency 12.2 ± 9 % 15.2 % Water use efficiency 54.3 ± 17 % 55 % Stomatal conductance –20.0 ± 3 % –15.0 %

Other satellite-based models, e.g. the widely used MODIS GPP, cannot show any of these responses.

Wang et al. (2017) Nature Plants Swiss FACE: LAI 7 low CO2, low N high CO2, low N 6 low CO2, high N high CO2, high N

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B Stocker et al. (in prep.) Have we solved all the problems? Have we solved all the problems?

NO Some of the outstanding questions

• Why do tree-ring studies not show increased growth? • How is carbon allocation controlled (to fine roots, leaves and wood)? • Where is the carbon sink?

• Are there negative consequences of rising CO2 (apart from clmate change) for plants, e.g. reduced nitrate assimilation, increased leaf temperatures?

• Does (or will) N supply limit the CO2 fertilization effect?

• Inconsistent response of plant growth to CO2 and N addition in ecosystem experiments... Mycorrhizae explain varying effects of CO2 on biomass (meta-analysis) Chapter II:Mycorrhizal association as a primary control of the CO2 fertilization effect total biomass increase:

Figure II.2 Overall effects of CO2 on plant biomass. Effects on (A) total, (BTerrer) abovegro uetnd, al.and (2016)(C) Science belowground biomass for two types of mycorrhizal plants species (AM: arbuscular mycorrhizae and ECM: ectomycorrhizae) in N limited experiments (low N) or experiments that are unlikely N limited (high N). Overall means and 95% confidence intervals are given; we interpret CO2 effects when the zero line is not crossed.

The patterns observed for total biomass were reflected in both aboveground and

belowground biomass. Under low N availability, eCO2 stimulated aboveground biomass significantly in ECM plants (P<0.001), with no effect in AM plants (P=0.584) (Fig. II.2B).

Similarly, eCO2 enhanced belowground biomass in ECM plants at low N (P=0.003), but not in AM plants (P=0.907) (Fig. II.2C).

We conducted a sensitivity analysis to ensure the findings were robust. First, we added an intermediate level of N availability (Table S-II.2) by assigning some ecosystems that were initially classified as “low” to a “medium” class (e.g. Duke, Aspen, ORNL) (Fig. S-

II.5). This enabled testing whether the large CO2 stimulation in ECM plants was driven by experiments with intermediate N availability. Second, we weighted individual experiments by the inverse of the mixed-model variance (Fig. S-II.6), to ensure that the weights of the meta- analysis did not affect the outcome. Third, we ran a separate meta-analysis with the subset of

32 Soil C:N ratioChap teindexesr V: Quantificatio nthe and dis trcostibution of tofhe C O2N fe racquisitiontilization effect on plant b ioviamass AM

C Terrer et al. (in prep.) Figure S-V.3 Relationship between soil C:N ratio and the aboveground biomass response to elevated CO2 in AM (A) and ECM (B) plants. Line in (A) is based on a non-linear mixed-effects meta-regression model. Dots represent the individual studies in the dataset, with the size of the dots drawn proportional to the weights in the model. No line was drawn in (B) because the relationship was not significant.

131 Conclusions (1)

• One key to theory development is eco-evolutionary optimality: the “missing law” of biology for Earth System modelling! • The other key to theory development is to proceed hand-in- hand with empirical science. • Without well-tested theory, models should not be trusted. Conclusions (2)

• We have an initial theory for plant carbon and water exchanges, with each element tested against observations. • Simpler and better (than “state-of-the-art”) ecosystem models can be made, based on this theory. • A great deal of work remains to be done to develop theory and soundly based models for: – carbon allocation – responses to drought – nutrient acquisition (incl. exudation) – implications of reduced stomatal conductance – and much else...