Although plant photosynthetic capacity as determined by the maximum
carboxylation rate (i.e.,
Photosynthesis is one of the major components of the ecosystem carbon cycle
(Canadell et al., 2007; Sellers et al., 1997) and is thus a key
ingredient of Earth system models (ESMs) (Block and Mauritsen, 2013;
Hurrell et al., 2013). Most ESMs are based on the photosynthesis model
developed by Farquhar et al. (1980). The maximum carboxylation rate
scaled to 25
Many studies have demonstrated that it is particularly difficult to predict
accurately the global scale variations in
To better describe the relationship between photosynthetic capacity and
its driving environmental conditions, we have developed a global scale
mechanistic model of leaf utilization of nitrogen for assimilation (LUNA). This
model takes into explicit consideration the key environmental
variables including temperature, radiation, humidity, CO
The LUNA model (version 1.0) is based on the nitrogen allocation model
developed by Xu et al. (2012), which optimizes nitrogen
allocated to light capture, electron transport, carboxylation and
respiration. Xu et al. (2012) considered a few model
assumptions to derive the optimized nitrogen distributions, including (i)
storage nitrogen is allocated to meet requirements to support new tissue
production; (ii) respiratory nitrogen is equal to the demand implied by the
sum of maintenance respiration and growth respiration; and (iii) light capture,
electron transport and carboxylation are co-limiting to maximize
photosynthesis. The model of Xu et al. (2012) has been tested for three
different sites only without global-scale calibration of its parameters.
Here, we expand the work of Xu et al. (2012) by using a global data set of
observations of photosynthetic capacity to derive accurate values of the
model parameters and by incorporation of several refinements to support
global-scale prediction of
The structure of the LUNA model is based on Xu et al. (2012),
where the leaf nitrogen is divided into four different pools including
structural nitrogen, photosynthetic nitrogen, storage nitrogen and
respiratory nitrogen. We assume that plants optimize their nitrogen
allocation to maximize the net photosynthetic carbon gain, defined as the
gross photosynthesis (
It is important to stress here that the outcome of the optimality concept
used in LUNA is conditional on the plant's nitrogen use strategies built
into the model. Thus, it is possible that the optimal values of
Details of data collection are reported in Ali et al. (2015).
Specifically, we conducted an exhaustive literature search with Google Scholar
to obtain publications that contained the words
To allow comparisons of
Because the LUNA model is based on the C
The four parameters of LUNA are difficult to measure directly in the field.
In this study, we estimate their values by fitting our model against
observations of
In this study, two different goodness-of-fit metrics are used to quantify
the performance of the LUNA model against the
To better understand how the simulated LUNA output of
The global surface temperature could increase as much of 3.9
We use model outputs from the climate carbon cycle Coupled Model Intercomparison Project Phase 5 (CMIP5) (Meehl et al.,
2000) to obtain projections of future climate. Climate modelers have
developed four representative concentration pathways (RCPs) for the
21st century (Taylor et al., 2013). Each of them corresponds
to a different level of greenhouse gas emission. In this study, we use
historic and future climate conditions simulated by the CCSM 4.0 model under
scenario RCP8.5, which considers the largest greenhouse gas emissions. We do
not consider herein other models and emission scenarios as the main purpose
of our study is not to do a complete analysis under all CMIP5 outputs but
rather to estimate the potential impact of nitrogen allocation on
photosynthesis. Specifically, the 10-year climate record between 1995 and
2004 is used as a benchmark for historic conditions, whereas the climate
data between 2090 and 2099 is used for future conditions. We present the
LUNA's predicted
We conduct a third sensitivity analysis to quantify the impacts of future
changes in climate variables such as temperature, CO
Mean values and standard deviations (parentheses) of LUNA parameters
estimated by using the Differential Evolution Adaptive Metropolis Snooker
updater (DREAM
The DREAM
Percentage of variance (
Our model also performs well for different PFTs. With TRF1, the LUNA model
explains about 57, 58 and 47 % of the variance in the observed values of
Sensitivities of
Sensitivity analysis shows that all four LUNA model parameters (Table 1)
have a positive effect on
Sensitivity analysis of the LUNA model output to its main climate variables
shows that radiation most strongly affects the simulated
Sensitivities of
Across the globe, a similar pattern is observed for TRF1 and TRF2 in the
simulated values of
Summer-season photosynthetic capacity for the top leaf layer in the
canopy under historical climatic conditions (
Our results show that the LUNA-simulated
Sensitivity of
The simulations of LUNA demonstrate that the future summer-season mean
photosynthetic rate at the top leaf layer might be substantially
overestimated if acclimation of
Sensitivity of
Percentage differences in estimated net photosynthetic rate for the leaf layer at the top of the canopy (
The LUNA model built on the assumption that nitrogen is allocated according
to optimality principles explains a large part of the global-scale
variability observed in
Other deficiencies of LUNA might be related to unexplored nutrient limitations and other plant physiological properties. For example, low phosphorus concentrations can reduce considerably the nitrogen use efficiency of tropical plants with typically modest to low nitrogen (Cernusak et al., 2010; Reich and Oleksyn, 2004), suggesting that our LUNA model can be enhanced by considering multiple different nutrient limitations simultaneously (Goll et al., 2012; Walker et al., 2014; Wang et al., 2010). Our treatment of the photosynthetic capacity can also be improved by incorporating a species-specific mesophyll and stomatal conductance (Medlyn et al., 2011), by analyzing properties such as leaf life span (Wright et al., 2004), and by considering soil pH, nutrient availability and water availability (Maire et al., 2015). Unexplored nutrient limitations and other plant physiological properties could also play a factor in the limitation of our model. For example, the nitrogen use efficiency of tropical plants (typically modest to low nitrogen) can be diminished by low phosphorus (Cernusak et al., 2010; Reich and Oleksyn, 2004), suggesting that our model could be improved by considering multiple nutrient limitations (Goll et al., 2012; Walker et al., 2014; Wang et al., 2010). Our treatment of photosynthetic capacity could also be improved by incorporating species-specific mesophyll and stomatal conductance (Medlyn et al., 2011), by analyzing leaf properties such as leaf life span (Wright et al., 2004), or by considering soil nutrient, soil water availability and soil pH (Maire et al., 2015).
Measurement errors of
Our model predicts that higher temperatures generally lead to lower values
of
Our model predicts that the future changes in atmospheric CO
There are many different ways to incorporate environmental controls on
Our model suggests that most regions of the world will likely experience
reductions in
The LUNA model has been implemented in CLM5.0 and will be made publicly available with its release in 2016. Stand-alone codes of LUNA are available in MATLAB, FORTRAN and C. These source codes can be obtained from the corresponding author upon request.
The LUNA model considers nitrogen allocation within a given leaf layer in
the canopy that has a predefined leaf-area-based plant leaf nitrogen content
availability (LNC
The photosynthetic nitrogen,
The structural nitrogen,
We assume that plants optimize their nitrogen allocations (i.e.,
The gross photosynthesis,
The baseline electron transport rate,
Based on Farquhar et al. (1980) and Wullschleger (1993), we
calculate the electron-limited photosynthetic rate under daily maximum
radiation (
Replacing Eq. (A16) with Eqs. (A14), (A15) and (A17), we are able to
estimate the maximum carboxylation rate (
Accounting for the daytime and nighttime temperature, we are able to
estimate the daily respiration as follows,
In summary, given an initial estimation of
Then, based on Eq. (A18), we are able to estimate the corresponding the
maximum carboxylation rate
Note that this storage nitrogen is mainly a remaining component of
FNC increase the nitrogen allocated ( calculate calculate calculate the total respiration calculate the total nitrogen invest in photosynthetic enzymes including
nitrogen for electron transport, carboxylation and light capture using Eq. (A2); calculate the gross photosynthetic rate, repeat steps (1) to (6) until the increase from previous time step in
Since the response of
After we get the optimal nitrogen allocations (
The temperature dependence of Rubisco kinetic parameters (
Community land model version 4.5 (CLM4.5)
(Oleson et al., 2013) uses the
partial pressures of oxygen,
Temperature sensitivities of
Fundamentally, TRF1 is a temperature dependence function for
TRF1 is implemented in CLM4.5 by Oleson et al. (2013), who use the form of
temperature dependence function for
An equation similar to Eq. (B6),
TRF2 does not consider the thermal acclimation. The formulation of TRF2 is
same as TRF1 except that in TRF2, the entropy term;
Photosynthesis is described using a system of three equations and three
unknown variables. The three unknown variables include (1) the net rate of
leaf photosynthesis (
The photosynthetic rate (
The stomatal conductance (
The estimation of
We solved Farquhar's non-linear equation ( Given the initial values of CO Given Using the leaf energy balance based on absorbed shortwave radiation, molar
latent heat content of water vapor, air temperature, and a parameter that
governs the rate of convective cooling (38.4 J m
The above five steps are repeated in a systematic way until
The nitrogen use efficiency for
The nitrogen use efficiency for
The nitrogen use efficiency of enzymes for respiration (
The maintenance respiration cost for all photosynthetic enzymes (NUE
The nitrogen use efficiency for electron transport (NUE
This work is funded by UC Lab Research Program (ID: 237285) and by the DOE Office of Science, Next Generation Ecosystem Experiment (NGEE) programs in the arctic and in the tropics. This submission is under public release with the approved LA-UR-14-23309. Edited by: G. A. Folberth