Introduction
Continuing increases in atmospheric CO2 are likely to cause increases
in temperature and changes in precipitation across Amazonia (Good et al.,
2013; Jupp et al., 2010; Malhi et al., 2009; Marengo et al., 2012). However,
significant uncertainty remains regarding the response of tropical forests
to warming (Corlett, 2011; Reed et al., 2012; Wood et al., 2012), altered
precipitation (Meir et al., 2008; Meir and Woodward, 2010) and short-term
abrupt changes in both precipitation and temperature (Cox et al., 2008;
Marengo et al., 2011; Reichstein et al., 2013). Such uncertainties are
propagated into models, resulting in substantial variability in modelled
responses to changes in temperature and drought (Friedlingstein et al.,
2006; Galbraith et al., 2010; Powell et al., 2013; Sitch et al., 2008).
These responses need to be rigorously assessed to enable further improvement
in our ability to predict the impacts of climate change on rain forest
functioning.
The ecosystem responses of models to multi-factor changes in climate can be
difficult to interpret because of complex non-linear responses (Zhou et al.,
2008), which can vary substantially between vegetation models with different
model structures. Previous modelling analyses have shown a greater
sensitivity of carbon storage in Amazonian forests to increased temperature
than reduced precipitation (Galbraith et al., 2010). However, model responses
to simultaneous changes in precipitation and temperature complex are
difficult to evaluate due to the compound effect of drought and temperature
responses (Luo et al., 2008). There are particular challenges when
considering short-to-medium-term responses (Luo et al., 2008) linked to
climatic extremes, such as severe drought (Cox et al., 2008; Marengo et al.,
2011).
Schematic diagram showing how droughts, via the combined effects
of increased air temperature (T) and reduced precipitation (PPT), affect the
carbon cycle of a tropical forest, including the effects on vapour pressure
deficit (VPD), evapo-transpiration (Et), stomatal conductance
(gs), soil water content (SWC), net photosynthesis (An), leaf area
index (LAI), the maximum rates of RuBP carboxylation and electron transport
(Vcmax and Jmax, respectively), autotrophic respiration (Ra)
heterotrophic respiration (Rh), gross primary productivity (GPP), and
net ecosystem exchange (NEE); + signs indicate a positive feedback effect
between variables (i.e. an increase in one variable can only cause an
increase in another if all else is equal), - signs indicate a negative
feedback effect, and +/- indicate the possibility of both a positive and
negative effect. Solid arrows represent responses which occur over short
timescales of minutes to hours, whereas dashes arrows represent responses
which can occur over longer timescales from days to months.
Concurrent changes in temperature and precipitation can cause a complex
chain of positive and negative feedbacks on different timescales (Fig. 1).
Increased temperature and reduced precipitation can directly affect stomatal
conductance (gs) through increasing vapour pressure deficit (VPD), or
indirectly affect gs on longer timescales through reducing soil water
content (SWC; Fig. 1). Stomatal conductance, gs, limits photosynthesis
(An), and therefore gross primary productivity (GPP). However, An can also be limited by changes in leaf biochemistry (Vcmax and
Jmax; Fig. 1). How An is limited by temperature increase is
important as changes in leaf biochemistry at very high temperatures can
result from permanent alteration and possible damage to proteins, whereas
changes in gs are less permanent, but alter water use, and potentially
water use efficiency. Currently, there is no consensus on how An will
respond to temperature: some studies find a direct impact through leaf
biochemistry (Doughty, 2011; Doughty and Goulden, 2008), and others an
indirect effect initiated by changes in gs, because the limitation of
increasing VPD on gs occurs at lower temperatures than those that cause
protein damage (Lloyd and Farquhar, 2008). The lack of data for tropical
trees means these responses remain poorly constrained, though drought and
warming can be examined using limited field data from drought and warming
experiments (da Costa et al., 2014, 2010; Nepstad et al.,
2002) and from extreme events within the natural range of the climate
(Marengo et al., 2011).
The response of vegetation models to temperature change or drought occurs
through the aggregated changes in finer-scale processes, for example at the
leaf level. Correctly simulating the mechanisms at the leaf scale is
therefore important to maintain confidence in canopy-scale predictions.
Leaf-scale responses in models are scaled using leaf area index (LAI) to simulate the
processes at the canopy-scale; therefore, inaccuracies in both leaf-scale
fluxes and how they are scaled can produce substantial errors in ecosystem-scale fluxes (Bonan et al., 2012). Currently, no model–data comparisons exist
that allow for the evaluation of combined temperature and
precipitation/drought sensitivity of ecosystem fluxes in relation to LAI and
leaf-scale processes in tropical forests. However, if we are to identify
accurately how to improve simulated responses of Amazonian forests to future
climate change, it is vital that model output is evaluated against data from
the leaf to the canopy scale.
At the Tapajós National Forest (TNF) in north-east Brazil, Doughty and Goulden (2008) collected data on the response of net ecosystem exchange (NEE) to
change in atmospheric temperature and the response of An and gs to
short-term artificial leaf warming. Doughty and Goulden (2008) found
reductions in forest productivity at air temperatures above 28 ∘C,
which corresponds to significant reductions in An and gs at leaf
temperatures above 30–33 ∘C. They suggested that tropical forests
may therefore already be close to a temperature threshold, beyond which
productivity will decline.
Here we use the data published by Doughty and Goulden (2008) to evaluate the
short-term temperature responses within models at both the leaf and
canopy-scale and investigate how the model formulations might impact
predicted responses to multiple climatic factors. Our model simulations
represent short-term non-equilibrium responses to changes in temperature to
make them comparable to the perturbation data collected by Doughty and
Goulden (2008). Evaluation of non-equilibrium changes in models is valuable
for assessing how models will perform when simulating responses to extreme
shifts in temperature and precipitation which are predicted to increase
across Amazonia (Cox et al., 2008; Marengo et al., 2011). If the models
run their equilibrium response to a simulated climate shift, the changes in
some of the key variables in the study (An, gs) are more likely to
be dominated by the effect of long-term soil drying rather than direct
temperature responses (e.g. the dashed lines in Fig. 1). This study is part
of a wider model inter-comparison project which aims to explore how well
vegetation models simulate drought in the eastern Amazon (Powell et al.,
2013). In this study we evaluate (1) how the forest productivity of five
vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model – CLM3.5–DGVM (hereafter CLM3.5); Ecosystem Demography model version 2 – ED2; the Joint UK Land
Environment Simulator version 2.1 – JULES; Simple Biosphere model
version 3 – SiB3; and the soil–plant–atmosphere model – SPA) responds to changes in
temperature, (2) what leaf-scale processes drive canopy-scale changes in
productivity and (3) how both leaf- and canopy-scale temperature
sensitivities are influenced by concurrent changes in precipitation at the
Tapajós forest site in eastern Brazil. In all models we simulate first
an ambient and then a 50 % reduction in the incoming precipitation during
the wet season from 2000 to 2006 analogous to the drought treatment imposed at
the Tapajós forest site, linked to a -5, 0,
+2, +4, and +6 ∘C change to the
ambient air temperature (Tair). These simulations cover a range of
likely and possible increases in temperature for the Amazon region in the
coming century (Christensen et al., 2007; Collins et al., 2013; Malhi et al.,
2009) and can be evaluated against existing data from Doughty and Goulden (2008). This study is the first to evaluate, using data, the inter-model
variability in the leaf and canopy responses to changes in temperature and
precipitation at a tropical forest site.
Materials and methods
Model description
Summary of the characteristics of each of the five vegetation models
(CLM3.5, ED2, JULES, SiB3, and SPA).
CLM3.5
ED2
JULES
SiB3
SPA
No. of plant function types
5
4
10
1
1
Canopy structure
Big leaf
Gap model
Layered canopy
Big leaf
Layered canopy
Leaf area index
Dynamic
Dynamic
Dynamic
Fixed
Dynamic
Division of sunlit and shaded leaf
Y (discrete division)
N
N
N
Y (discrete division)
Simulation of water stress on An and gs.
Water stress factor
Water stress factor
Water stress factor
Water stress factor
Linked soil–leaf water potential/resistance model to gs model.
Origin of photosynthesis model
Farquhar et al. (1980), Farquhar and Sharkey(1982), Collatz et al. (1991)
Farquhar et al. (1980), Farquhar and Sharkey(1982),Collatz et al. (1991)
Farquhar et al. (1980), Farquhar and Sharkey (1982),Collatz et al. (1991)
Farquhar et al. (1980), Farquhar and Sharkey (1982), Collatz et al. (1991)
Farquhar et al. (1980), Kirschbaum and Farquhar(1984),McMurtrie et al. (1992)
Key model references
Bonan et al. (2003), Levis et al. (2004),Oleson et al. (2008)
Medvigy et al. (2009),Kim et al. (2012)
Best et al. (2011),Clark et al. (2011)
Sellers et al. (1992), Sellers et al. (1996),Baker et al. (2008)
Williams (1996), Williams et al. (2005),Fisher et al. (2006)
The five models used in this study are the CLM3.5, the ED2, JULES, SiB3, and the SPA. A brief
description of each of the models is given here and in Table 1 (also see
Powell et al., 2013). The simplest canopy structure is in SiB3. SiB3 has a
fixed LAI and uses a big-leaf model which simulates the response of the top
canopy and integrates this response throughout the canopy according to a
light and leaf nitrogen (N) extinction coefficient (Baker et al., 2008;
Sellers et al., 1992, 1996). CLM3.5 is also a big-leaf
model; however, it separates the canopy into a sunlit leaf fraction (leaves
which receive both direct and diffuse light) and a shaded leaf fraction
(leaves which receive only diffuse light), which change dynamically with sun
angle and canopy light penetration (Oleson et al., 2004,
2008). The version of JULES used in this study simulates 10 canopy layers
with equal leaf area increments. Leaf nitrogen decays exponentially through
the canopy and radiation interception is simulated following the two-stream
approximation of Sellers (1985). SPA also has a layered canopy model, and
here used three canopy layers, with separate sunlit and shaded fractions
(Williams, 1996; Williams et al., 2005). ED2 mathematically approximates the
properties of an individual-based forest gap model, separately modelling the
stems of three successional stages (pioneer, mid-successional and
late successional) of, in this study, tropical trees and grasses on a
continuum of leaf light levels from fully shaded to fully sunlit (Kim et
al., 2012; Medvigy et al., 2009b; Moorcroft et al., 2001). SiB3 and SPA
simulate only one plant functional type (PFT), set to tropical evergreen
broadleaf; JULES and CLM3.5 simulate five PFT's, but this site simulated a
fractional cover > 95 % evergreen broadleaf trees. As the focus
of this study is the responses within tropical forests, results were not
considered if a model simulated a shift in the PFT away from the dominance
of tropical forest.
All of the models use enzyme-kinetic An equations, derived from Farquhar
et al. (1980), Farquhar and Sharkey (1982), Kirschbaum and Farquhar (1984)
and Collatz et al. (1991). In all models, temperature can affect An
directly through temperature response functions on the maximum rate of
carboxylation of RuBP (Vcmax), the CO2 compensation point, and the
Michaelis–Menten constants (Kc and Ko) and in SPA the maximum
rate of electron transport (Jmax). Temperature can also indirectly
change An through changing the VPD at the leaf surface, which alters
gs. CLM3.5, ED2 and SiB3 use the Ball–Berry stomatal conductance model
(Collatz et al., 1991). JULES calculates gs by relating the ratio of
internal to external CO2 to the humidity deficit (Cox et al., 1998).
SPA is unique in that it models stomatal conductance by simulating an
aqueous continuum between the soil and leaf water: gs and photosynthesis
are maximised using an isohydric assumption that at each time step leaf
water potential does not drop below a critical level (-2.5 MPa; see Williams
et al., 1996; Fisher et al., 2007). CLM3.5, ED2, SiB3 and JULES alter
gs using a water stress factor (β; a value ranging 0–1 where 1
indicates no soil water stress and 0 indicates complete soil water
limitation). A detailed description of the effect of soil water stress on
gs and An in these models is given by Powell et al. (2013).
Site
The throughfall exclusion in the TNF (2.897∘ S,
54.952∘ W) is located on an Oxisol soil, and has a mean annual precipitation
of approximately 2 m per year; the site is described in detail by Nepstad et
al. (2002). This plot was selected for this experiment because the
temperature response of canopy-level NEE was
collected at a nearby site (km83; Doughty and Goulden, 2008). The canopy NEE
measurements were from an eddy covariance tower from July 2000 to July 2001,
when light levels were above 1000 µmol m-2 s-1 (Doughty and
Goulden, 2008). Leaf-level responses of stomata conductance and
photosynthesis to increases in leaf temperature in fully sunlit canopy
leaves were from three species in 2004 (see Doughty and Goulden, 2008 and
Goulden et al., 2004).
Meteorological data and soil properties
The model simulations were driven using hourly meteorological data
(precipitation, Tair, specific humidity, short and long-wave radiation
and air pressure) measured above the canopy at the site from
1 January 2002 to 31 December 2004. The short-wave radiation was split into 68 % direct
and 32 % diffuse, and then this was split into 43 % visible and 57 %
near-infrared for direct, and 52 % visible and 48 % near-infrared for
diffuse (Goudriaan, 1977).
The soil properties were standardised across all models to create a similar
soil physical environment, thereby testing only for differences in
vegetation functioning (see Powell et al., 2013). Only biological properties
such as rooting depth, root biomass, as well as the total number of soil
layers were left as model specific soil properties.
Experimental design
All of the models went through a standard spin-up procedure prior to
simulations (see Powell et al., 2013). Following the spin-up period, a
series of five model simulations, with varying Tair, were performed for
an 8-year period (which was intended to simulate 1999–2006; see Powell
et al., 2013) for ambient precipitation (control simulations) and for
simulations with a 50 % reduction in wet season rainfall (drought
simulations). The 2002–2004 meteorological data were recycled over the
8-year simulation period. To explore the effects of changes in Tair on the
models, we performed five model simulations which consisted of simulations
with the hourly 2000–2006 ambient Tair adjusted by -5, 0 ∘C
(ambient Tair), +2, +4 and
+6 ∘C; 1999 was the baseline year for which no changes from
ambient temperature and precipitation were implemented. Our analysis was
focused on increases in temperature; however, we included a simulation with
temperatures 5 ∘C lower than ambient temperatures, on the basis
that some models may have processes optimised for temperate regions where
average Tair is lower. VPD was adjusted according to the changes in air
temperature.
Model output and evaluation
All the data in this study was processed to match the collection methods and
processing done by Doughty and Goulden (2008; hereafter referred to as DG),
as closely as possible. Therefore, to compare the models' predictions NEE
with the flux data, we extracted canopy-level fluxes when photosynthetic
photon flux density (PPFD) was > 1000 µmol m-2 s-1, the conditions used by DG. PPFD was not available for the whole
period; therefore, we used the measured short-wave radiation to estimate PPFD.
A conversion factor of 2 is used to convert from short-wave radiation (W m-2) to PPFD (µmol m-2 s-1) based on an empirical
relationship calculated from the flux tower at the study site (Doughty,
unpublished data). The results on hourly time steps from each model for the
period of 2000–2006 for the five temperature simulations (with offset of
-5, +0, +2, +4 and
+6 ∘C) were pooled. Model output was then placed into 1 ∘C bins of Tair for the canopy-scale analysis (GPP, NEE,
ecosystem respiration (Reco)) or of leaf temperature (Tleaf) for
leaf-scale analysis, as done in the DG study. Accounting for non-gaussian
distributions in model output, the median and the 15.9th and 84.1th
quantiles of the binned model output are plotted to represent the mean and 1
standard deviation of the temperature response curve of any model variable.
The data from the drought and control simulations are considered separately.
To explore the relative sensitivity of models to changes in temperature and
drought, a linear relationship between the temperature increase per control
simulation (-5, 0, 2, 4, 6 ∘C) and final year (2006) GPP was used to
calculate the change in GPP per 1 ∘C increase Tair for each
model (Table 2). This value was used to calculate the increase in
temperature necessary to produce the same loss of GPP as the ambient
Tair drought simulation, where there is a 50 % reduction in wet
season rainfall (Table 2).
Model values for GPP (Mg C ha-1 yr-1) for the last year
(2006) of the ambient air temperature control plot simulation (Tair+0 ∘C), the control plot simulation -5 ∘C
(Tair-5 ∘C), the control plot simulation +6 ∘C
(Tair+6 ∘C) and the ambient air temperature drought plot
simulation (Tair+0 ∘C). The equivalent temperature is the
elevation in the control plot simulation temperature needed to replicate the
same magnitude reduction in GPP as the drought simulation, for the year 2006
and at ambient temperatures. The equivalent temperature is derived from a
linear relationship between GPP values in 2006 and the air temperatures in
the 5 temperature simulations per model.
CLM3.5
ED2
JULES
SiB3
SPA
Control GPP Tair-5 ∘C
40.74
31.74
36.73
35.27
38.23
Control GPP Tair+0 ∘C
36.68
28.31
31.16
31.95
29.55
Control GPP Tair+6 ∘C
28.03
20.70
20.08
27.50
15.89
Drought GPP Tair+0 ∘C
26.47
10.79
18.13
20.86
19.55
Equivalent Tair
8.83
17.50
8.61
15.70
4.92
DG published data for the temperature response of An and gs of
sunlit leaves during the dry season when PPFD is > 1000 µmol m-2 s-1. CLM3.5 and SPA are the only models which have
separate output for sunlit and shaded leaves. Consequently, data from the
sunlit leaves of these models from periods of high PPFD (> 1000 µmol m-2 s-1) during the dry season (July–December) were
used for comparison. The effect of increasing Tair reducing modelled
soil water content (via increased VPD and consequent leaf transpiration) had
to be removed from the model outputs to make it comparable to the DG data,
where individual leaves were artificially warmed. Therefore, we only selected
model outputs from the temperature simulations if the soil water content in
the rooting zone was in the top quartile of the values from the ambient
control simulation, this corresponded to β values of > 0.9
in CLM3.5. For consistency with the sunlit leaf analysis, the analysis of
canopy average leaf data from all models was done using dry season data with
PPFD > 1000 µmol m-2 s-1.
The relative sensitivity of the five models to changes in temperature and
precipitation is assessed by comparing the interactive and non-interactive
effects of the 50 % reduction in wet season precipitation (drought
simulation) with the -5, 0, and +6 ∘C change in
Tair on ecosystem fluxes at the end of the 8-year simulation (2006).
Results
Canopy-scale responses
The models have similar responses of NEE and GPP to increasing Tair. DG
observed a reduction in carbon uptake as NEE went from -17.4 ± 0.3 to
-7.9 ± 1.1 µmol m-2 s-1, corresponding to an increase
in Tair from 28 to 32 ∘C (Fig. 2a). The modelled
NEE begins to increase at a lower Tair (22–25 ∘C) in the
models and the 28–32 ∘C increase in NEE is
generally substantially less than observed by DG (2.5–3.9 µmol m-2 s-1), except in SPA which experiences a similar increase in
NEE as DG from 28 to 32 ∘C (8.8 µmol m-2 s-1),
across the same range of values (-15.8 to -7.0 µmol m-2 s-1; Fig. 2a). The increase in modelled NEE at high
temperatures is caused by a decline in GPP across all models (Fig. 2b). As
Tair increases from 16 to 38 ∘C, the average
decline in GPP from all models is 20.9 ± 3.2 µmol m-2 s-1. In contrast the mean model decline in Reco over the same
modelled Tair range was 4.2 ± 1.8 µmol m-2 s-1
(Fig. 2c). The decline in modelled ecosystem respiration is low because in
all models a decline in autotrophic respiration with increasing temperature
(linked in the models with reduced GPP) is opposed by an increase in
heterotrophic respiration (data not shown).
Comparison of the air temperature (Tair; ∘C)
response of (a) daytime net ecosystem exchange (NEE; µmol m-2 s-1 ; note that negative values of NEE indicate carbon sequestration),
(b) gross primary productivity (GPP; µmol m-2 s-1), (c)
ecosystem respiration (Reco; µmol m-2 s-1), (d) leaf
area index (LAI; m2 m-2). The lines show the median model
responses from the five control temperature runs per model pooled and
divided into 1 ∘C temperature bins. The grey shaded area shows the
combined 15.9th and 84.1th quantiles for all models. The black
points and error bars in panel (a) show the daytime eddy-flux inferred NEE
(cf. Fig. 4 in Doughty and Goulden, 2008).
Declines in GPP corresponded to declines in LAI. Between 25 and
38 ∘C, the decline in GPP in CLM3.5 (89 ± 38 %), and SPA
(82 ± 26 %) was greater than in other models (Fig. 2b) and matched
by greater declines in LAI over the same temperature range (4.2 ± 1.0 m2 m-2, CLM3.5 and 4.4 ± 0.9 m2 m-2 in SPA,
relative to only 0.6 ± 0.3 m2 m-2 in ED2 and 0.4 ± 0.1 m2 m-2
in JULES; Fig. 2d). The inter-model variability in LAI is
large; at 25 ∘C the median LAI value in ED2 (3.6 ± 0.3 m2 m-2) is 3 times smaller than the median values in CLM3.5
(10.7 ± 1.0 m2 m-2). Observed mean LAI at the TNF under
non-drought conditions ranged from 5.5 to 6.3 m2 m-2 in 2000–2005 (Brando et al., 2008) and therefore the modelled values span a range
∼ 70 % above and below the measured LAI (Fig. 2d).
Modelled effect of short-term variations in temperature and
drought expressed as 1 minus the changes in (a) gross primary productivity
(GPP), (b) ecosystem respiration (Reco) and (c) leaf area index (LAI) in
the final year (2006) of the drought run, as a fraction of the value of the
final year (2006) of the control run, for the Tair -5 ∘C (grey
bars) and Tair+6 ∘C (white bars) simulations.
Combined drought and warming had compound effects on GPP, Reco and LAI.
In CLM3.5, GPP remained the same in the Tair -5 ∘C simulation
at the end of the drought and control simulation; however, in the Tair
+6 ∘C simulation, the forest which existed at the end of the
control simulation was replaced with grassland in the drought simulation
(GPP values for grassland are not shown; Fig. 3a). In JULES, SiB3 and SPA,
the GPP was the same in the control and the drought simulation at
Tair -5 ∘C; however, GPP is 61, 58 and 44 % lower
respectively at the end of the drought relative to the control simulation
(Fig. 3a). The combined effect of temperature and drought on GPP and
Reco is lowest in ED2, because it was the only model to have a strong
drought effect on GPP, Reco and LAI in the Tair -5 ∘C
simulation (Fig. 3). In CLM3.5 and SPA, GPP and LAI have the same
fractional reductions with drought, at higher temperatures (Fig. 3a and
c), indicating a tight coupling between the LAI and canopy productivity;
this contrasts the lack of or low GPP–LAI feedback in SiB3 and JULES.
Amongst the models there is a continuum of temperature versus drought
sensitivity. We express the temperature versus drought sensitivity as the
equivalent temperature increase necessary to produce the same GPP reduction
as between the last year of the control to the drought simulation at ambient
Tair (Table 2). A low equivalent temperature would represent a greater
GPP sensitivity to temperature increase and/or a lower GPP sensitivity to
drought; a higher equivalent temperature represents a lower GPP sensitivity
to temperature increase and/or a higher GPP sensitivity to drought. The
equivalent temperature increase necessary to reproduce the same GPP
reduction as from the last year of control and droughts simulation at
ambient temperature was lowest in SPA (4.92 ∘C), moderate in JULES
and CLM3.5 (8.61 and 8.83 ∘C, respectively), and
highest in SiB3 and ED2 (15.70 and 17.50 ∘C,
respectively; Table 2). However, across all the models a 5 ∘C
reduction in ambient Tair resulted in an increase in forest productivity
as GPP rose between 3.3 and 8.7 Mg C ha-1 yr-1 in all models (Table 2).
Values show the normalised intrinsic water use efficiency (IWUE)
calculated from the linear slope of normalised An plotted against
normalised gs (Fig. 6). The normalised IWUE is calculated separately
for each models' control and drought temperature simulations (ambient air
temperature (Tair) -5, +0, +2, +4, and +6 ∘C). (Note NA in CLM3.5 drought
simulations indicates the model changed from a forest to a grassland).
Control simulations
Drought simulations
CLM3.5
ED2
JULES
SiB3
SPA
CLM3.5
ED2
JULES
SiB3
SPA
Tair -5 ∘C
0.84
0.42
0.50
0.09
0.49
0.73
0.29
0.50
0.10
0.27
Tair+0 ∘C
0.93
0.56
0.83
0.49
0.68
0.93
0.40
0.60
0.93
0.24
Tair+2 ∘C
1.01
0.67
1.01
0.58
0.73
1.08
0.53
0.97
1.11
0.41
Tair+4 ∘C
1.05
0.79
1.18
0.65
1.00
NA
0.78
1.37
1.20
0.74
Tair+6 ∘C
1.11
0.95
1.32
0.69
1.50
NA
1.10
1.73
1.22
1.15
Leaf-scale responses
Leaf-scale An and gs oppose LAI responses; the model with
the largest change in LAI in response to temperature increase (CLM3.5) has
the lowest An values and the models with the smallest change in LAI
(ED2, JULES and SiB3) have the greatest An values and the strongest
responses of An to temperature change (Fig. 4). Model uncertainty
increases with temperature for An and Vcmax (Figs. 4a and 5). For
Vcmax this is caused by substantial variation in the optima
(10 ∘C; Fig. 5) and the rate of decline in Vcmax following
the optima; in CLM3.5 Vcmax declines 50 % at 9 ∘C over the
optimum, contrasting with the same decline 17 ∘C over the optimum
in SPA (Fig. 5).
Comparison of the dry season mean (sunlit + shaded leaves,
weighted by their respective LAIs) leaf-level response to temperature
(Tleaf; ∘C) of (a) net photosynthesis (An, µmol m-2 s-1), (b) stomatal conductance (gs, mmol m-2 s-1),
(c) leaf evapo-transpiration (Et, mm m-2 s-1) and (d) the
soil water stress factor (β) for average canopy leaves (Note SPA does
not simulate β) . The lines show the median model responses from the
control plot for the five temperature simulations pooled and divided into 1 ∘C
temperature bins for each model. The grey shaded area shows
the combined 15.9th and 84.1th quantiles for all models. (Note
JULES Et data is missing from these simulations).
The temperature response of Vcmax for each model shown
relative to the Vcmax at 25 ∘C per model.
The optimum An in SPA, SiB3, JULES, CLM3.5 and ED2 occurs at
Tleaf values of 25, 26, 27,
30 and 30 ∘C, respectively (Fig. 4a), and
significantly before the optimum point on Vcmax (Fig. 5). In all
models the An optimum and the initial decline in canopy average An is
linked to declines in gs (Fig. 4a–b). Consequently, for each model
there are apparent, but variable, relationships between gs and An
(Fig. 6), but no obvious relationships between An and Vcmax
(Fig. 7).
The relationship between 30 min values of modelled stomatal
conductance (gs) and photosynthesis (An) normalised by their
respective maximum values; An and gs values are taken only from the
dry season when PPFD > 1000 µmol m-2 s-1 .
Values are coloured separately from deep blue to red (see legend) for each
temperature simulations (ambient air temperature -5, +0, +2, +4, and +6 ∘C)
and panels separate the control (panels a–e) and drought simulations (panels
f–j), for each model. Values are from sunlit and shaded leaves, weighted by
their respective LAIs. A separate linear line is plotted through the
normalised An, gs data for each temperature simulations, the slope
of which represents the normalised intrinsic water use efficiency: the
normalised increase in Anper unit increase in normalised
gs. Linear lines are also coloured from deep blue to deep red to
differentiate the additions to ambient air temperature (see legend).
The relationship between Vcmax (µmol m-2 s-1)
and photosynthesis (An mmol m-2 s-1) for the half hourly
output from each model in the dry season of the control runs, with PPFD
> 1000 µmol m-2 s-1. Values are from sunlit
and shaded leaves, weighted by their respective LAIs. Results are shown
across all leaf temperatures explored in this study (colour change from blue
to red indicates increasing leaf temperature; see legend).
There was high variability in the magnitude and temperature response of
gs across the models. The maximum canopy average gs values in SiB3
(486 mmol m-2 s-1 at 25 ∘C) and ED2 (384 mmol m-2 s-1
at 23 ∘C) are substantially higher than CLM3.5 (49 mmol m-2 s-1 at 20 ∘C), JULES (70 mmol m-2 s-1
at 25 ∘C) and SPA (200 mmol m-2 s-1 at 24 ∘C; Fig. 4b). In CLM3.5 a strong constriction in Et is caused by the strong
influence of β on gs (Fig. 4c–d). β is reduced by
85 ± 31 % in CLM3.5 as Tleaf increase from 30 to 40 ∘C.
The decline in β over the same Tleaf range was only 14 ± 1 %
in ED2, 38 ± 5 % in JULES and 7.9 ± 1 % in SiB3 (Fig. 4d).
The slope of An against gs indicates intrinsic water use efficiency
(IWUE): the rate of increase of assimilation per unit increase in gs. If
An is plotted against gs separately for each model temperature
simulations (-5, 0, +2,
+4, +6 ∘C) and a linear fit is forced through the
gs and An data, it is apparent that all models simulate increasing
IWUE (an increase in slope) from the -5 ∘C up to the
+6 ∘C simulations (Fig. 6 and Table 3). The increase in slope
of An and gs from the -5 to +6 ∘C
temperature simulation is greater in the drought than control simulations in
all models (Fig. 6 and Table 3), suggesting that both increasing
temperature and reduced water availability increase IWUE.
The sunlit leaf-level response of dry season (a) net photosynthesis
(An, µmol m-2 s-1) and (b) stomatal conductance
(gs, µmol m-2 s-1) to leaf temperature
(Tleaf; ∘C) for CLM3.5 (orange) and SPA (red). The lines show the median
model responses from the control plot for the five temperature simulations
pooled and divided into 1 ∘C temperature bins for each model. The
shaded areas around each line show the 15.9th and 84.1th quantiles
for each model. Data from Doughty and Goulden is shown as black points;
error bars show the standard error. (Note only SPA and CLM3.5 output data on
sunlit leaf values of An and gs.)
When the effect of soil water stress is removed and sunlit leaf-level values
are compared to the DG data for the models which could output separate
sunlit leaf values of gs and An (only SPA and CLM3.5; Fig. 8), the
peak An of sunlit leaves in SPA at 25 ∘C (8.72 ± 0.24 µmol m-2 s-1) is similar to the peak in the DG leaf-scale
data at 30.5 ∘C (8.44 ± 0.17 µmol m-2 s-1;
Fig. 8a). In CLM3.5 the peak An at 29 ∘C is considerably
higher (13.48 ± 0.20 µmol m-2 s-1), although it occurs
at a similar temperature to the observed peak. Both CLM3.5 and SPA show a
decline of An with temperature similar to the data. Modelled
gs, however, shows a poor match to the observations (Fig. 8b).
Peak gs values occur at substantially lower Tleaf values in
CLM3.5 (27 ∘C) and SPA (25 ∘C) than observed
(33.5 ∘C; Fig. 5b). The peak sunlit gs in SPA are also
significantly higher (434 ± 88 mmol m-2 s-1) than the
observations (123 ± 4 mmol m-2 s-1) and show a very sharp
decline not observed in the data (Fig. 8b).
Discussion
Canopy- and leaf-scale feedbacks
The response of NEE and GPP to short-term changes in temperature
demonstrated substantially greater consistency across models than for LAI
(Fig. 2). Amongst the models which had dynamic LAI, the change in LAI from
the original value ranged from 4.5 m2 m-2 in SPA to 1.0 m2 m-2 in ED2. Interestingly, the change of LAI with Tair in ED2 and
JULES was so low that it was more comparable to SiB3, a model with fixed
LAI. This is in contrast with CLM3.5 and SPA, within which LAI declined substantially
as Tair rose above a threshold (Fig. 2d). The inter-model range in LAI
(maximum range 7.5 m2 m-2) was greater than the decline in LAI
with Tair in any model. If leaf-scale fluxes are scaled using an
inaccurate LAI, the simulation of both accurate leaf- and canopy-scale
fluxes is not possible (Bonan et al., 2012; Lloyd et al., 2010; Mercado et
al., 2006, 2009). Given the large variability in LAI
responses across the models, it would be expected that there should be a
greater variability in GPP and NEE than was observed. Therefore, the models
must compensate for variability in canopy structural parameters, such as
LAI, through adjustment in other leaf-scale parameters if the observed
consistency in ecosystem-scale responses is to be maintained (Bonan et al., 2012).
We found substantial variation in the magnitude and temperature responses of
leaf-scale parameters: peak Vcmax had a 10 ∘C
Tleaf range across the models (Fig. 5), gs values varied by over
an order of magnitude (Fig. 4b), the inter-model range of β and
Et increased with Tleaf (Fig. 4c–d), and there was a twofold increase
in the inter-model range of An as Tleaf rose from 25 to 40 ∘C
(Fig. 4a). Such variability across the models suggests that any similarity
in the response of NEE to Tair among models is caused by different
processes and feedbacks at the leaf scale. Had the models been run to their
equilibrium states, it is likely that there would have been greater
divergence of model responses at both canopy- and leaf-scales. Prolonged
higher temperatures reduce long-term moisture availability and cause more
severe changes in β; in dynamic PFT models this can result in a
substantial shift of PFT away from tropical forest. Without more data to
evaluate which models produced the correct responses to temperature, it is
hard to have confidence in predictions of climate change impacts in
Amazonian. Variability in the control of gs and leaf biochemistry on
An and changes in IWUE efficiency with increasing temperature or drought
will have significant consequences on the demand of water from a forest
(Harper et al., 2014). In this study we find gs had a greater control on
the change in An with increasing temperature because An started to
decline at Tleaf values which were lower than those at which peak
Vcmax occurred (Figs. 4b and 5), and An maintained a positive
relationship with gs across all models (Table 3; Fig. 6), but no
clear relationship with Vcmax (Fig. 7). All the models in this study
also predicted an increases in IWUE from the lowest (ambient Tair -5 ∘C) to the highest (ambient Tair+6 ∘C)
temperature simulation; this increase in IWUE was also always greater in the
drought temperature simulations relative to the control temperature
simulations (Table 3; Fig. 6). Increases in IWUE with increasing
temperature suggests that as the ecosystem warms An will become more
sensitive to reductions in gs and gs will maintain a greater control
on An than biochemical controls, even at very extreme increases in
temperature (ambient Tair+6 ∘C).
These results are consistent with the hypothesis that temperature increases
will mainly be manifest through the effect of increased VPD on stomatal
conductance (Lloyd and Farquhar, 2008). They are also consistent with leaf
warming data from the Tapajós forest which show that reductions in An
start to occur at 4–5 ∘C before the optimum point for Vcmax
and Jmax in sunlit leaves (Tribuzy, 2005). However, the responses from
longer-term leaf warming experiments at the same site showed that changes in
leaf biochemistry with increasing leaf temperatures were an important control
on An (Doughty, 2011), suggesting more data are required to test
effectively both the short- and long-term responses of An to changes in
temperature in models.
Comparing the short-term direct effect of temperature on the
An–gs relationships is complicated because of the differences in
the calculation and implementation of the effect of water stress amongst
models (Powell et al., 2013; Zhou et al., 2013). β is altered by
changes in SWC, which can be caused by changes in temperature (via increased
VPD altering SWC), as well as changes in precipitation; in turn β
alters both gs (Fig. S1) and An. The decrease in β with
temperature increase was highly variable among models (Fig. 4d).
Consequently, the direct influence of soil water stress on gs, An
and Et, versus the indirect effect of VPD, was inconsistent between models.
Resolving these inconsistencies is important, as water stress functions
impact the ratio of modelled latent to sensible heat fluxes and so when
coupled to global climate models they alter climate and vegetation feedbacks
(Harper et al., 2014). Improving how water stress is simulated in models is
therefore essential to improving temperature and drought responses in
tropical forests.
When focusing only on periods of high soil water content and therefore
removing the effects of water stress, An and gs values from fully
sunlit leaves still varied substantially from the response and magnitude of
the DG data (Fig. 8). Given the DG data were averaged from only three
top-canopy species, some degree of variation between the model and the data
is expected. The variability between the peak data and peak model gs was, however, > 4 times (Fig. 8b) and the modelled temperature optima
for gs (25–27 ∘C) was substantial lower than observed by DG
(33.5 ∘C). Given that CLM3.5 and SPA are in the lower range of the
total model variability for the gs and An of an average canopy leaf
(aggregated sunlit and shaded leaf; Fig. 4a–b), the variation from the
data is likely to be substantially larger if sunlit leaf data could be
extracted from all models. Considering the importance of gs in
controlling leaf productivity, the suitability of the empirical models of
gs used in these models requires further testing (Bonan et al., 2014). The use
of optimised rather than empirical models may provide an opportunity to
improve the capability to simulate gs responses to temperature and
water stress in greater detail (Heroult et al., 2013; Medlyn et al., 2013, 2011; Zhou et al., 2013).
Combined drought and temperature sensitivities
Previous modelling studies have shown that there is high variability in how
sensitive models are to temperature and drought (Friedlingstein et al.,
2006; Galbraith et al., 2010; Luo et al., 2008; Sitch et al., 2008), but
that vegetation models have embedded in them greater sensitivity to rises in
temperature than drought (Galbraith et al., 2010) despite the evidence for
strong drought sensitivity in natural rainforests (Gatti et al., 2014). The
responses of modelled forest production in this study to combined changes in
precipitation and temperature were, however, highly variable. CLM3.5 and SPA
had very strong compound effects of temperature on drought-induced
reductions in GPP, Reco and LAI (Fig. 3) relative to JULES and SiB3.
In ED2, the drought effect on GPP was always stronger than the temperature
effect (Fig. 3) because it has a strong drought–mortality effect at this
site (Powell et al., 2013). This study demonstrates that there is a
continuum in model responses from models that require a low increase in
ambient Tair to cause the same GPP loss as a 50 % reduction in wet
season rainfall (SPA, 4.9 ∘C), to models that have a very strong
drought response and therefore require a substantial increase in ambient
Tair to replicate the same GPP loss as a 50 % reduction in wet season
rainfall (ED2, 17.5 ∘C; Table 2). As a 6 ∘C rise in
temperature and a 50 % reduction in rainfall are changes which may occur
in Amazonia during the 21st century (Christensen et al., 2007; Collins
et al., 2013), we suggest that there is currently no consensus among
vegetation models as to whether there will be a stronger drought or
temperature response to future climate change within tropical forests.
Across all the models GPP increased when ambient Tair was reduced by
5 ∘C; this occurred because the ambient air temperature
-5 ∘C was closer to the modelled gs optima. This result
suggests models are currently predicting that Amazonian forests are
operating beyond a temperature and VPD optimum. Given that the models
underestimate the point at which NEE declines with Tair by
3–6 ∘C and the point at which gs declines with Tleaf by
7.5–9.5 ∘C (Figs. 2 and 4), it seems likely that the models in
this study may be biased towards temperature calibrations for temperate
ecosystems. Consequently, as well as moving towards implementing more
mechanistic responses to improve models, more research to test and adjust
their temperature responses in tropical ecosystem is necessary. The range of
model responses in this study is likely to stem from real uncertainty in our
understanding of the responses by tropical rain forest ecosystems to changes
in precipitation and temperature. Further analysis of the same questions
using models that vary in complexity (e.g. statistical or optimised models,
as well as purely mechanistic) might provide additional insight into
mechanistic and simulation bias (systematic or random), as well advancing
understanding about climate risk that we derive from them (Meir et al., 2015).