Why has the amplitude of the seasonal cycle in the NH increased? By Rebecca Thomas

Observations show that the amplitude of the seasonal cycle (SCA) of atmospheric CO2 in the higher latitudes of the northern hemisphere (45N-90N) has increased by 57±7% over the last 50 years. The seasonal cycle at these latitudes can be almost entirely attributed to land regions between 30-90N (Graven et al. 2013). However, terrestrial biosphere models are unable to reproduce the observed magnitude or change in seasonal cycle amplitude. Changes in the seasonal cycle have occurred as the terrestrial biosphere has responded to the rise in atmospheric CO2 (by ~70 ppm), changes in mean surface temperature (increase of ~1°C) and changes in land use. But how much have each of these changes in environmental drivers resulted in the observed increase in SCA?

Despite their shortcomings, models are doing something right. They do produce a seasonal cycle that is broadly in line with observations (in terms of timing and shape at least), the seasonal cycle does increase in amplitude in all models and they do show a greening trend. Their spread in values and variety of behaviours can be utilised because finding relationships between these models, which are all set up differently, is extremely powerful and suggestive of an underlying mechanism. This concept was demonstrated recently in Cox et al. 2013 to constrain the sensitivity of tropical land carbon storage to temperature. So, does a similar relationship exist between terrestrial biosphere models that can quantify the contribution from CO2 and temperature on the SCA? The figure below sets out a framework to analyse this question.

Rebecca Thomas. Greeness

Initial results suggest that these inter-model relationships do exist. NPP is well correlated to SCA between models. If we then look at the CO2 only part of this, both the direct (CO2 fertilisation) and indirect (through vegetation greening) effects on NPP are also well correlated to SCA.

This is still a work in progress, and so a number of questions remain regarding (but not limited to) the regions of influence from greening and the effects of temperature.

References:

  1. Graven et al. 2013 “Enhanced seasonal exchange of CO2 by Northern Ecosystems since 1960”. Science
  2. Cox et al. 2013 “Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability”. Nature
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The co-ordination hypothesis, the predictability of plant traits, and the end of despairing empiricism. By Prof Colin Prentice

“Despairing empiricism” is a phrase I invented some years ago to describe a disease that has infected modellers trying to describe ecosystems numerically since they first started to do so, circa 1980. The underlying idea is that since biological phenomena are (supposedly) not subject to universal laws, the only way they can be modelled is as physical systems with large numbers of parameters that have to be measured, and prescribed – as they can’t be predicted.

This view is fundamentally wrong because it neglects the most universal law of biology. Natural selection ensures that inefficient combinations of traits are eliminated – and thus, it enforces strong optimality criteria that vastly reduce the dimensionality of variation in the morphology and physiology of organisms. Unfortunately, the principle of optimization by natural selection has been largely neglected in the development of ecosystem models. As a result, models are far more complex than they need to be and contain far more uncertain parameters than they should.

A key theme of my current programme is the intrinsic predictability of leaf photosynthetic traits. Graham Farquhar’s model of photosynthesis lies at the core of plant ecophysiology, and most terrestrial models use it in some form or other. But it describes a leaf-level, instantaneous, physiological process. To make predictions with the Farquhar model for larger spatial and temporal scales requires certain highly variable quantities to be known: the maximum rate of carboxylation (Vcmax), the maximum rate of electron transport (Jmax), and the ratio of leaf-internal to ambient CO2 concentration (χ). Certain regularities in the relationships between these quantities have been noted repeatedly. In particular, the ratio Jmax/V­­cmax seems to be quite conservative, although lower at higher temperatures; χ is very conservative, although lower in dry environments; Jmax and Vcmax tend to be higher under high illumination and lower in the shade, as seen for example in the vertical gradient of maximum assimilation rates in dense canopies. From time to time, optimality considerations have been invoked to explain such observations, and as empirical generalizations they have been used to simplify models in various ways. But there has not previously been any systematic attempt to develop and test theory about just what is optimized, or to test previously unappreciated predictions of the theory.

A previous post by Wang Han verified some of the consequences of the ‘least cost hypothesis’ as first propounded by Ian Wright (Macquarie). Wang Han showed how this hypothesis – that plants tend to minimize the sum of the unit costs of carboxylation and transpiration, which are intimately coupled through stomatal behaviour – leads to quantitatively correct predictions not only of the accepted response of χ to vapour pressure deficit, but also of the relationship of χ to temperature (discovered in field data by Dong Ning), and a relationship to elevation that has actually been known, but previously eluded explanation, for over thirty years. Moreover, this trait appears to be highly plastic, showing a ‘universal’ response to environment that is identical within and between species.

Another plank of our theory in development is the ‘co-ordination hypothesis’ – that under typical daytime field conditions the rates of Rubisco- and electron transport-limited photosynthesis tend to be equal. This is a long-standing idea, obviously corresponding to an optimality hypothesis (plants gain nothing from over-investment in either carboxylation or electron transport) although alternative interpretations have been put forward. The idea had its origin in Aci curves, as it is found that the typical operating point (typical values of A and ci) is usually near the co-limitation point. (The idea can easily be forgotten, however, when photosynthesis measurements are routinely made at saturating, as opposed to ambient, illumination – thus ensuring that Rubisco limitation holds, and in doing so, setting up temperature and CO2 responses that are quite different from those that apply in the real world.)

We have refined the co-ordination hypothesis by taking into account the costs of maintaining electron transport capacity, which lead to a specified degree of curvature in the light response curve of assimilation below the co-limitation point. The presumed mechanism behind the co-ordination hypothesis is the acclimation of the photosynthetic traits Vcmax and Jmax to environmental variations, on time scales that are not quite clear, but are evidently longer than the diurnal cycle and shorter than the seasonal cycle.

The co-ordination hypothesis is extraordinarily powerful. The following are some of its predictions.

  • Vcmax when measured at ambient growth temperature should increase with temperature, but less steeply than the kinetic response of Rubisco. The increase is due to the declining affinity of Rubisco for CO2 with increasing temperature. On the other hand, Vcmax when corrected to a standard temperature, e.g. 25˚C, should decline with temperature. That is, the quantity of active Rubisco should be reduced, compensating for the enzyme’s greater efficiency at higher temperatures. Henrique Togashi’s fieldwork has quantitatively confirmed both of these predictions for multiple woody species in the Great Western Woodlands, Australia.
  • Jmax/Vcmax has a predictable value that varies with growth temperature in the same way as has been shown in experiments (demonstrated by Wang Han). This variation, again, is not due to the kinetics of the enzymes involved in the processes, but arises because of the different intrinsic temperature responses of the two limiting reaction rates in the Farquhar model. On the other hand, the ratio after both quantities have been corrected to 25˚C shows little variation with growth temperature – as has been observed.
  • The metabolic component of leaf N, generally assumed to be proportional to Vcmax at a standard temperature, should be proportional to illumination; decline with increasing growth temperature; decrease with increasing χ; and be independent of N supply (e.g., whether the species is N-fixing or not). Analyses by Dong Ning have recently confirmed all four predictions, for community mean values derived from Ausplots field collections on a geographic gradient across the centre of Australia.

There are many more predictions, and many ways to test them. Yan-Shih Lin is assembling a large set of Aci curves to test the global predictability of Vcmax and Jmax. We have access to a large recent global compilation of leaf respiration (Rdark) data. According to the Farquhar model, leaf respiration should be proportional to Vcmax. A recent multi-author paper by Owen Atkin (ANU) and others indicates that Rdark acclimates to temperature, so that the ratio of Rdark in cold and warm climates is much less than would be predicted by short-term enzyme kinetics.

The hypothesis also predicts how photosynthetic traits should respond to increased CO2 concentration. Vcmax and leaf N should decline – just as they are observed to do. This is usually explained in the literature as an effect of dilution or limiting N uptake. These explanations are redundant, and anyway they fail to explain why the actual assimilation rate always increases.

The co-ordination hypothesis has an important practical consequence for modelling gross primary production, GPP. On monthly timescales GPP should be co-limited by Rubisco activity and electron transport, so either equation could be used to predict GPP. But where we have flux measurements, we don’t know the effective canopy value of Vcmax. We do know the illumination (from meteorological data) and we know the green vegetation cover (from satellite measurements), so we know how much light is absorbed (the product of these two quantities) and we can apply the equation for electron transport-limited photosynthesis at the predicted co-limitation point – which turns out to be proportional to absorbed light.

Hardly a month seems to go by without the publication of yet another ‘Light Use Efficiency’ (LUE) model – models in which GPP is, indeed, assumed to be proportional to absorbed light. It is a good empirical generalization, as shown in work with flux data over the last couple of years by Brad Evans (using the Ozflux data) and Tyler Davis (using the ‘free and fair use’ publicly available subset of the global FLUXNET data set). What the co-ordination hypothesis does, however, and the authors of LUE models do not, is to explain why it works so well. Moreover, the co-ordination hypothesis allows us to predict how GPP as predicted by LUE models should be expected to respond to environmental variables, including ambient CO2.

Finally, all of this has a bearing on the response of GPP to temperature. We profess to be interested in the effects of global warming on primary production. An outsider to the field might expect that this response would be well established. It is not. Current models don’t even agree on whether the net effect of warming, at a global scale, is an increase or a decrease in GPP. The co-ordination hypothesis appears to make a bold (and counter-intuitive) prediction that so long as temperatures stay above the threshold for cold inhibition (10˚ or 15˚C?) the effect of increasing temperature on GPP should be a gradual decline. It remains to be seen whether this is true. Watch this space.

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The three R’s: How can land models be made reliable, robust and realistic? By Dr Han-Wang

Prentice et al. (2014a) raised this question about the development of next-generation land surface models. My interpretation of their ‘three R’s’ criteria for model development is as follows:

  • A reliable model should give the right answer. Therefore, reliability is the ultimate target of the models developed for the purpose of projecting the impacts of environmental change.
  • A robust model should not be sensitive to the specification of one or more uncertain parameter values. And the more uncertain parameters are included in a model, the greater the risk of losing robustness.
  • A realistic model should represent the Earth system accurately. Since the Earth system is complex, it is not surprising that anything approaching the full behaviour of the complex system cannot be described with a very simple model.

Therefore, there is a connection between realism and complexity; however, it should be borne in mind that complexity does not guarantee realism, and that there may be a trade-off between realism and robustness in the search for reliability.

So how are the three R’s to be achieved for state-of-the-art land models? Despite the apparent successes of land models that have been developed during the past several decades (Ciais et al. 2014), the ‘three R’s status’ of current models is becoming more and more worrying. Inconsistencies among models have persisted stubbornly through successive model generations (Friedlingstein et al. 2014) and the terrestrial carbon cycle has become one of the largest sources of uncertainty in climate projections. One original cause of this awkward situation was the limited data availability in the early days when land models were first developed. Many processes related to plant ecophysiology and ecosystem function were not very well understood, and when incorporated into models they had to be represented by equations with large numbers of parameters.

Many model parameters in current models, such as photosynthetic capacity, are still assigned fixed values (usually per plant functional type, PFT) even though they should really be variables that adapt to the environment. The typical modeller’s response to such criticisms is to represent processes in an even more complex way and in doing so to still further increase the number of parameters. However, realism does not inevitably increase with burgeoning complexity, whereas the robustness of models is inevitably lost. No wonder we end up with less reliable models!

On the other hand, a vast expansion of observations has happened in past couple of decades, and this situation has provided an opportunity for modellers to pursue a quantitative explanation of what is observed, and predict the variations of those fixed parameters. This opportunity unfortunately has been largely overlooked during business-as-usual model development.

Learning from previous model development work, we can propose a new strategy to improve the reliability of the next generation of land models. The key to this new strategy is optimal allocation theory, which has its roots in the principle of optimization by natural selection. This principle is central to understanding how and why plants allocate resources to different compartments and functions, and how allocation ‘decisions’ by plants vary in time and space. If the right optimality hypothesis is posed, remarkable predictive power can be obtained from a simple model with very few parameters (Wang et al. 2014) – paving the way for simpler, but also more powerful and robust models. Notably, optimality hypotheses can be formulated to maximize different criteria, and not all of them will make correct predictions. Therefore, the basis of our modelling strategy is to make use of the extensive observed data to test those hypotheses generated from optimality, select the one that gives the right answer, and finally meet the requirement of reliability.

To illustrate explicitly how to apply optimal allocation theory in pursuit of the ‘three R’s’, here I provide an example from my current work on predicting the variation of the ratio of leaf-internal to ambient CO2 concentration (the so-called ci:ca ratio, denoted here by χ). This ratio is regulated by stomatal behaviour in response to environmental conditions, and plays a crucial role in determining the photosynthetic assimilation rate under CO2 limitation. In current land models the ratio is either considered as a constant, or allowed to respond to vapour pressure deficit (vpd) following one or another empirical equation. My work has shown that the behaviour of this ratio, inferred from stable isotope (δ13C) data, is predictable from optimal allocation theory.

According to the ‘least-cost’ hypothesis (Wright et al. 2003; Prentice et al. 2014b), evolutionary optimality requires plants to adjust χ so as to minimize the total respiratory costs required to maintain the capacities for both carboxylation and transpiration that are needed to support a given assimilation rate. The value of χ that minimizes these costs can be mathematically expressed as a function of vpd, the effective Michaelis-Menten coefficient of Rubisco (K), and the ratio of two carbon cost parameters (a for transpiration, b for carboxylation). The parameter b is the ratio of leaf dark respiration to carboxylation capacity, assumed to be constant. The parameter a is related to properties of water transport through sapwood, including the viscosity of water, the permeability of sapwood, and difference in water potential maintained between soil and leaves (Prentice et al. 2014b). Based on the well-established equations describing the relationships of K to temperature and atmospheric pressure, the viscosity of water to temperature, and vpd to atmospheric pressure, it can be predicted that the derivatives of ln [χ/(1 – χ)] with respect to temperature, ln (vpd) and elevation are  respectively 0.0545 K–1, –0.5 and –0.0815 km–1.

Therefore we have a hypothesized model for χ; the next step is to test it. An extensive global dataset of 3549 δ13C measurements on leaves (compiled by Will Cornwell, University of New South Wales) was used. The data are from all biomes, including low and high elevations. δ13C provides a measure of the long-term response of χ to environment, which is what optimality theory predicts. The environmental predictors should also be long-term mean values, therefore I used the growing-season mean values of temperature and vpd. A standard equation was used to transform δ13C to χ. Multiple linear regression was performed on logit-transformed χ values using temperature, ln (vpd) and elevation as predictors.

The fitted regression coefficients of temperature, VPD and air pressure were all highly significant, and quantitatively consistent with predictions. Predicted values of χ based on the regression model are consistent with this data across all biomes and plant functional types (see Figure, below).

Han Wang

These results show that optimal allocation theory can successfully predict stomatal regulation of the leaf-internal to ambient CO2 concentration ratio as function of environmental factors. The prediction is more realistic than that of current models that treat this ratio as a function of vpd alone. No new empirical parameters were introduced during this work, so the model is robust. Finally, the comparison with observed data tells us that it is a reliable model.

A quick test was performed in which the theoretically predicted χ values were inserted into a simple light-use efficiency model of gross primary production (GPP). The results were compared with annual GPP data derived from publicly available FLUXNET measurements by Tyler Davis, and contrasted with the same model using a fixed value of χ. The use of predicted values of χ accounted for 8% more variation in the observations than the use of a fixed value, showing the potential for this model for χ to improve land models.

References:

Ciais, P. et al. (2014) Carbon and Other Biogeochemical Cycles. In Climate Change 2013: The Physical Science Basis. Cambridge University Press, Cambridge.

Friedlingstein, P., Meinshausen, M., Arora, V.K., Jones, C.D., Anav, A., Liddicoat, S.K., and Knutti, R. (2014): Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks, J. Clim. 27: 511-526.

Prentice, I.C., X. Liang, B. Medlyn and Y. Wang (2014a) Reliable, robust and realistic: the three R’s of next-generation land-surface modelling. Atmospheric Chemistry and Physics Discussions 14: 24811-24861.

Prentice, I.C., N. Dong, S.M. Gleason, V. Maire and I.J. Wright (2014b) Balancing the costs of carbon gain and water loss: testing a new quantitative framework for plant functional ecology. Ecology Letters 17: 82-91.

Wang, H., Prentice I.C. and Davis T.W. (2014) Biophysical constraints on gross primary production by the terrestrial biosphere. Biogeosciences 11: 5987-6001.

Wright, I.J., Reich, P.B. & Westoby, M. (2003). Least-cost input mixtures of water and nitrogen for photosynthesis. Am. Nat., 161: 98–111.

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Hungate challenge: How much CO2 can be taken up by the terrestrial biosphere? By Shun Hasegawa

Hungate et al. (2003) raised the importance of nutrient feedbacks on carbon (C) uptake by terrestrial ecosystems to determine associated additional C storage under future predicted carbon dioxide (CO2) concentrations in the atmosphere. Nevertheless, there is only little research investigating how rising CO2 levels affect soil nutrient status.  To address this, here, I present my PhD research looking at effects of elevated CO2 (eCO2) on soil nitrogen and phosphorus cycling in a mature Eucalyptus woodland in Australia.

Effect of elevated carbon dioxide on soil N and P cycling in a P-limited Eucalyptus woodland

Free Air CO2 Enrichment (FACE) experiments have consistently demonstrated increased plant productivity in response to eCO2, with the magnitude of responses related to soil nutrient status. Most experiments to date have been carried out in the northern hemisphere where nitrogen (N) is the primary growth-limiting nutrient. Whilst understanding nutrient constraints on productivity responses to CO2 is crucial to predicting C uptake and storage, and thus terrestrial feedbacks on climate change, very little is known about how eCO2 affects nutrient cycling in the nutrient poor, P-limited ecosystems that dominate in many parts of the Southern Hemisphere. Our study investigates the effects of eCO2 on soil N and P dynamics at the recently established EucFACE experiment in a P-limited woodland, in western Sydney, Australia (Figure 1). Three ambient and three eCO2 (+ 150 ppm) FACE rings (25 m diameter) were installed in a native Cumberland Plain Eucalyptus woodland, and CO2 treatments were initiated in September 2012. Instantaneous measurements of soil extracts and soil solution chemistry showed seasonally-dependant effects of eCO2 on ammonium, phosphate and DOC concentrations, representing 23 %, 14 % and 21 % increases over ambient rings, respectively, over the 18 month study period, with significant CO2 x Time interactions (P < 0.05). Extractable and soil solution nitrate concentrations and soil enzyme activities were not affected by CO2 treatment. Integrating measures of nutrient concentrations (in situ incubation of ion exchange resin strips) showed significantly higher levels of plant accessible nitrate (+95 %) with a CO2 x Time interaction (P = 0.05), ammonium (+12 %, P = 0.06), and phosphate (+54 %) with a CO2 x Time interaction (P < 0.001) under eCO2. Elevated CO2 was also associated with faster rates of nutrient turnover in the early part of the experiment, with P mineralisation rates 211 % higher in eCO2 rings compared to ambient in the first six months of CO2 fumigation, although this difference did not persist. Taken together, these results demonstrate that CO2 fertilisation increases nutrient turnover and availability – particularly for phosphate – in strongly P-limited soils, likely via increased plant belowground investment in labile C and associated enhancement of microbial turnover of soil organic matter. Early evidence therefore suggests that nutrient mining may reduce constraints on productivity responses to eCO2 in P-limited woodland ecosystems, at least in the short term.

Eucalyptus Free-CO2 enrichment (EucFACE)

Eucalyptus Free-CO2 enrichment (EucFACE)

References

Hungate BA, Dukes JS, Shaw MR, Luo YQ, Field CB (2003) Nitrogen and climate change. Science, 302, 1512-1513.

 

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The Big N Question: What controls rates of nitrogen fixation and losses? By Beni Stocker

To address this question, we have to start with the notion that, on the long term, N losses from ecosystems via gaseous and leaching pathways have to be compensated by N inputs. Natural, that is non-human affected, N inputs are small and have been shown not to be sufficient to explain the measured N balance. Biological N fixation (BNF) must account for the remainder. However, drawing a complete picture of the N budget in terrestrial ecosystems has been notoriously difficult, because BNF is practically impossible to measure and estimated rates of global BNF range from around 200 TgN (Cleveland et al., 1999) to 58 TgN (Vitousek et al., 2013). Today, research is stuck.

This lack of understanding of the amount, but also of the controls on BNF introduces a “wildcard” for carbon (C) cycle projections under future climate change. Early C-only vegetation models suggested massive C sequestration under rising atmospheric CO2. However, for every C fixed into organic compounds, a relatively tightly constrained amount of N is required. But where does this N come from? Hungate et al. (2003) argued that these C-only model’s predictions were unrealistic because the implied additional N required for C sequestration would have to be provided by additional BNF, and that the required amounts of additional BNF were not “realistic”. But what is realistic?

More recently, vegetation models have been developed with the aim of introducing more “realism”. One such aspect is the coupling of C and N cycles. Indeed, this new generation of models generally does suggest less C sequestration, mostly for reasons of progressive N limitation (PNL) (Luo et al., 2004), meaning that additional N fixed in organic compounds leads to a depletion of N availability in soils because ecosystems can’t bring in enough N and thus run into a PNL. Is this realistic?

Ecosystem manipulation experiments and observations along gradients of varying N availability draw a different picture. The C allocation of plants appears to be flexible and responding to the availability of nutrients in the soil. Symbiotic associations between plants and mycorrhiza (root fungi) and N-fixing bacteria are away for plants to overcome nutrient shortage. However, these come at a cost of feeding C to symbionts. Also the “blind” exudation of labile C by plants into the soil stimulates microbial soil decomposition and N mineralisation and thus improves N availability. Apparently, getting enough N is not impossible but is also not free.

N cycling in land ecosystems. Left: reference state. Right: N-limited state.

N cycling in land ecosystems. Reference state.

N cycling in land ecosystems. N-limited state.

N cycling in land ecosystems. N-limited state.

Hence, a way of advancing the mechanistic understanding of the controls on N fixation could be by looking at the ecosystem energy economy – that is the C economy. N fixation is a particularly costly option to acquire N. Feeding the nitrogenase enzyme to break up the sturdy N2 bond is expensive. Modeling these trade-offs is one of the big goals of my work here at Colin Prentice’s lab.

What observations can be used to test such modeling approaches? Free Air CO2 Enrichment (FACE) experiments have generated plenty of data to gain a better insight into C allocation changes under rising CO2. And indeed, more N appears to get bound into plant biomass when photosynthesis increases. However, knowing where this additional N is coming from remains unresolved. Either, it’s provided by accelerated soil organic matter decomposition, stimulated by more below-ground plant C allocation, or it’s brought in from the atmosphere via BNF.

If the first option is the case, then what happens to soil organic matter (SOM) stocks under continuing CO2 increase in the decades and centuries to come? A way to gain a better understanding of ecosystem responses to large-scale environmental change is to look into the past. The shift from the last ice age to the present warm period provides an analogue for future climate change (although from a different background and at a much slower pace). Again, if no additional N was brought into ecosystems back then, SOM stocks would have had to be tapped. Did they decrease indeed? Also, if no additional N was brought in, then ecosystems would indeed have run into a progressive N limitation. This would also imply that inorganic N stocks were depleted and lead to much less gaseous N losses and hence N2O emissions. Is this the case?

Spahni et al. (in prep.) now show that terrestrial N2O emissions did indeed increase as CO2 went up after the last ice age. This may be interpreted as a strong indication that no such PNL occurred. Hence, BNF must have increased along with the demand for N as ecosystems sequestered more C.

The bottom line is that measurements of BNF are notoriously difficult but there are other, indirect constraints on how ecosystems respond to changes in N availability. Apart from manipulation experiments, analyses along environmental gradients, and evidence from palaeo-information, also the isotopic composition of 15N/14N may be used to estimate rates BNF. All this data has to be exploited to constrain models and get a better understanding of whether BNF increases in the future and permits more C to be sequestered in land ecosystems.

References

Cleveland, C., Townsend, A., Schimel, D., Fisher, H., Howarth, R., Hedin, L., Perakis, S., Latty, E., Von Fischer, J., Elseroad, A., and Wasson, M.: Global patterns of terrestrial biological nitrogen (N-2) fixation in natural ecosystems, Global Biogeochem. Cycles, 13, 623–645, doi:{10.1029/1999GB900014}, 1999.

Vitousek, P. M., Menge, D. N. L., Reed, S. C., and Cleveland, C. C.: Biological nitrogen fixation: rates, patterns and ecological controls in terrestrial ecosystems, Philos. T. Roy. Soc. B, 368, doi:{10.1098/rstb.2013.0119}, 2013.

Hungate, B., Dukes, J., Shaw, M., Luo, Y., and Field, C.: Nitrogen and climate change, Science, 302, 1512–1513, doi:{10.1126/science.1091390}, 2003

Luo, Y., Su, B., Currie, W., Dukes, J., Finzi, A., Hartwig, U., Hungate, B., McMurtrie, R., Oren, R., Parton, W., Pataki, D., Shaw, M., Zak, D., and Field, C.: Progressive nitrogen limitation of ecosystem responses to rising atmospheric carbon dioxide, Bioscience, 54, 731–739, 2004.

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The Global ecosystem Production in Space and Time (GePiSaT) Model of the Terrestrial Biosphere. By Tyler Davis

The motivation for this work is to create a simple diagnostic model of gross primary production (GPP). The purpose of this is meant to contrast the complex process-based models that are currently being developed and used in earth system modelling that are generally not well tested and tend to produce widely divergent results.

Our data-based model is founded on in situ measurements from the free and fair use FLUXNET archives (http://fluxnet.ornl.gov/obtain-data). We drive our model using basic geophysical and meteorological observations provided by a variety of remote sensing products. Lastly, our model is founded on the principle of optimal acclimation of photosynthesis to the environment.

The key outputs of our modeling work include the daily, monthly and annual GPP; the monthly light-use efficiency (LUE); a generalized LUE model; and, based on the LUE model, the ability to extrapolate GPP globally with the possibility of doing some predictions of future climate scenarios.

The model is based on an open-source framework that consists of a database that stores all the observations and measurements to provide unified data access and a Python-based application code, which is written to be readable and reproducible while still being computationally powerful.

The challenge is to create a general LUE model. Starting with the basic definition of light-use efficiency, the ratio of monthly GPP to monthly absorbed light, this can be expressed as a linear equation where monthly GPP is equal to the light-use efficiency multiplied by the monthly absorbed light. This ‘basic’ model, for some flux towers, is capable of explaining 90% or more of variance in GPP; however, it is apparent that for other flux towers something is still missing.

 This ‘missing information’ helped motivate the creation of an advanced LUE model, based on the optimality principle with consideration to temperature, elevation, and water-stress effects on GPP. This new theoretically-based observationally-constrained model for terrestrial GPP has promising implications for the future of vegetation and climate modeling.

Comparison between five years of observed monthly gap-filled gross primary production (GPP) and monthly GPP modeled using the basic light-use efficiency equation (left) and the advanced light-use efficiency equation (right) for an evergreen needleleaf forest in Spain.

Comparison between five years of observed monthly gap-filled gross primary production (GPP) and monthly GPP modeled using the basic light-use efficiency equation (left) and the advanced light-use efficiency equation (right) for an evergreen needleleaf forest in Spain.

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Why has the seasonal cycle of CO2 increased? By Rebecca Thomas

An increase in the amplitude of the seasonal cycle of CO2 has been observed in the higher latitudes of the northern hemisphere. In situ measurements between 1960-2011 show increases at Point Barrow, Alaska and Mauna Loa Observatory, Hawaii of 35% (range 30 to 49%) and 15±5% respectively, and recent aircraft campaign data have confirmed that this is a large-scale trend, with increases of 57±7% in seasonal cycle amplitude at latitudes north of 45°N (from aircraft campaigns in 1958-63 and 2009-2010). A number of processes can influence the concentration of CO2 in the atmosphere: fire, fossil fuel emissions, the ocean and the land biosphere. Using an atmospheric transport model, it has been shown that the seasonal cycle of atmospheric CO2 in the higher latitudes of the northern hemisphere is predominantly attributed to the terrestrial biosphere (Graven et al. 2013).  The terrestrial biosphere imparts its signal on atmospheric CO2 concentration through net ecosystem exchange (NEE), the balance of net primary production (NPP) and heterotrophic respiration (Rh). The phase imbalance between these fluxes creates the seasonal cycle that is observed.

Using 13 models from the Multi-synthesis Terrestrial Model Intercomparison Project (MsTMIP), the behaviour of current terrestrial biosphere models (TBMs) was analysed. The TBMs use several equations to represent NEE, with many following the set up by Collatz et al. (1991) for photosynthetic assimilation of carbon, and a variety of different equations used for respiration. To investigate influences on NEE, the models held all driving data constant at preindustrial levels and then successively added dynamic forcing from climate, LUC, CO2 and nitrogen deposition (Huntzinger et al. 2013). Figure 1 shows that none of the TBMs were able to capture the current magnitude or the change in seasonal cycle amplitude that has been observed over the last 60 years. I therefore ask, can the distribution of the MsTMIP models in Figure 1 be explained, and why do these TBMs underestimate the CO2 amplitude increase?

Figure 1. Absolute and relative change in CO2 amplitude in MsTMIP models compared to aircraft observations. Models are detrended following the methods in Graven et al. 2013, and CMIP5 output from this paper is also shown.

Changes in NEE result from changes in the timing or magnitude of NPP and/or Rh and this can occur because of warmer temperatures or the increase in atmospheric CO2. Preliminary results suggest that modelled changes in CO2 amplitude are not a response to climatic changes as neither the climate only simulations or Rh seem to explain any of the variance in the modelled CO2 amplitude change. Therefore modelled increases are likely due to CO2 fertilisation. Changes in NPP amplitude and changes in NEE amplitude are well correlated to changes in CO2 amplitude, reinforcing this idea. However, the extent of CO2 fertilisation is difficult to isolate and quantify since none of the MsTMIP experiments had only CO2 forcing.

Continued investigation into these models will attempt to further constrain the CO2 fertilisation effect on the models.

References:

Graven HD, et al. 2013. Enhanced Seasonal Exchange of CO2 by Northern Ecosystems Since 1960. Science 341: 1085-1089.

Huntzinger DN, et al. 2013. The North American Carbon Program Multi-scale synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design. Geosci. Model Dev. Discuss. 6: 3977-4008.

Collatz et al. 1991 Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar bound- ary layer. Agric. For. Meteorol,, 54: 107-136.

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