Introduction
Precipitation (P) is a key climatic determinant of ecosystem productivity,
especially in arid and semi-arid grasslands (Lambers et al., 2008; Sala et
al., 1988; Hsu et al., 2012; Beer et al., 2010). Climate models project
substantial changes in amounts and frequencies of precipitation regimes
worldwide, and this is supported by observational data (Karl and Trenberth,
2003; Donat et al., 2016; Fischer and Knutti, 2016). Potential for
increasing occurrence and severity of droughts and increased heavy rainfall
events related to global warming will likely affect grassland growth (Knapp
et al., 2008, 2017a; Gherardi and Sala, 2015; Lau et al., 2013; Reichstein et al.,
2013). As a consequence, better understanding of the responses of grassland
productivity to altered precipitation is needed to project future
climate–carbon interactions, changes in ecosystem states, and to gain better
insights on the role of grasslands in supporting crucial ecosystem services
(e.g., livestock production).
Gross primary productivity (GPP) of ecosystems is controlled by environmental
conditions, in particular water availability (Jung et al., 2017), and by
biotic factors affecting leaf photosynthetic rates and stomatal conductance,
which scale up to canopy-level functioning (Chapin III et al., 2011). About
half of GPP is respired while the remainder, net primary productivity (NPP),
is primarily invested in plant biomass production, including photosynthetic
and structural pools aboveground (foliage and stem) and belowground (roots)
(Waring et al., 1998; Chapin III et al., 2011). NPP responses to
precipitation have been observed using multi-year, multi-site observations
(Hsu et al., 2012; Estiarte et al., 2016; Knapp and Smith, 2001; Wilcox et
al., 2015). Positive empirical relationships between grassland aboveground
NPP (ANPP) and precipitation (P) have been found in spatial gradients across
sites (Sala et al., 1988) and from temporal variability at individual sites
(Huxman et al., 2004; Knapp and Smith, 2001; Roy et al., 2001; Hsu et al.,
2012). The ANPP–P sensitivities obtained from spatial relationships are
usually higher than those obtained by temporal relationships (Estiarte et
al., 2016; Fatichi and Ivanov, 2014; Sala et al., 2012). Possible mechanisms
behind the steeper spatial relationship may be (1) a “vegetation
constraint” reflecting the adaptation of plant communities over long
timescales in such a way that grasslands make the best use of the typical water
received from rainfall for growth (Knapp et al., 2017b) and (2) the spatial
variation in structural and functional traits of ecosystems (soil properties,
nutrient pools, plant and microbial community composition) that constrain
local ANPP–P sensitivities (Lauenroth and Sala, 1992; Smith et al., 2009;
Wilcox et al., 2016). For projecting the effect of climate change on
grassland productivity in the near to mid-term (coming decades), inter-annual
relationships are arguably more informative than spatial relationships
because spatial relationships reflect long-term adaptation of ecosystems and
because ANPP–P relationships from spatial gradients are confounded by the
covariation of gradients in other environmental variables (e.g., temperature
and radiation) and soil properties (Estiarte et al., 2016; Knapp et al.,
2017b).
In temporal ANPP–P relationships, an important observation is the asymmetric
responses of productivity in grasslands to altered precipitation (Knapp et
al., 2017b; Wilcox et al., 2017). Compared to negative anomalies of ANPP
from years with decreased precipitation, positive anomalies of ANPP during
years with increased precipitation were usually found to have a larger
absolute magnitude, suggesting a convex positive response (positive
asymmetry) (Bai et al., 2008; Knapp and Smith, 2001; Yang et al., 2008).
Yet, when grasslands are subject to extreme precipitation anomalies that
fall beyond the range of normal inter-annual variability, an extreme dry
year is associated with a larger absolute ANPP loss than the gain found
during an extreme wet year. This suggests a convex negative response
(negative asymmetry) when considering a larger range of rainfall anomalies
than the current inter-annual regime (Knapp et al., 2017b). This is also
supported by current dynamical global vegetation models, which suggest a
stronger response to extreme dry conditions compared to extreme wet
conditions (Zscheischler et al., 2014). The sign of the asymmetric response
of grassland productivity to altered rainfall thus depends on the magnitude
of rainfall anomalies, the size distribution of rainfall events and
ecosystem mean state (Gherardi and Sala, 2015; Hoover and Rogers, 2016;
Parolari et al., 2015; Peng et al., 2013).
Key plant, soil and climate characteristics of the three grassland
sites. MAT, mean annual temperature; and MAP, mean annual precipitation. MAT
and MAP are based on the periods for the three sites with ANPP measurements.
SGS
KNZ
STU
Latitude
40∘49′ N
39∘05′ N
47∘07′ N
Longitude
104∘46′ W
96∘35′ W
11∘19′ E
MAT (∘C)
8.6 ± 0.7
13.0 ± 0.9
6.2 ± 0.8
MAP (mm yr-1)
304 ± 118
827 ± 175
1429 ± 198
ANPP (g DM m-2 yr-1)
91 ± 36
387 ± 82
525 ± 210
Measurement period
1986–2009
1982–2012
2009–2013
Grassland type
Shortgrass steppe
Mesic tallgrass prairie
Subalpine meadow
C3 species (%)
30
15
100
C4 species (%)
70
85
0
Soil type
Aridic Argiustoll
Typic Argiustoll
Dystric Cambisol
Sand (%)
14
8
42
Silt (%)
58
60
31
Clay (%)
27
32
27
Relationships between precipitation and grassland productivity have
previously been studied with site observations (Hsu et al., 2012; Knapp et
al., 2017b; Luo et al., 2017; Wilcox et al., 2017; Estiarte et al., 2016),
but they remain to be quantified and characterized in ecosystem models used
for diagnostic and future projections of the coupled carbon–water system in
grasslands, in particular grid-based models used as the land surface
component of Earth system models. In this study, we aim to evaluate the
responses of simulated productivity to altered precipitation from 14
ecosystem models at three sites representing dry
(304 ± 118 mm yr-1), mesic (827 ± 175 mm yr-1) and
moist (1429 ± 198 mm yr-1) rainfall regimes. The specific
objectives of this study are to (1) test if the productivity–P sensitivities
of spatial relationships are greater than the temporal ones in the models
such as those found in the observations; (2) test if models reproduce the observed
asymmetric responses under inter-annual precipitation conditions; (3) assess
the simulated productivity–P sensitivities related to different precipitation
regimes including normal and extreme conditions, and to test in particular if
sensitivities for extreme drought conditions are stronger than those for
high-rainfall conditions; (4) analyze the simulated curvilinear
productivity–P relationships for a large range of altered precipitation
amounts across the three sites.
Materials and methods
Experimental sites
We conducted model simulations using three sites: the Shortgrass steppe (SGS)
site at the Central Plains Experimental Range, the Konza Prairie Biological
Station (KNZ) site and the Stubai Valley meadow (STU) site. These sites
represent three grassland types spanning a productivity gradient from dry to
moist climatic conditions. The dry SGS site is located in northern Colorado,
USA (Knapp et al., 2015; Wilcox et al., 2015). The KNZ site is a native
C4-dominated mesic tallgrass prairie in the Flint Hills of northeastern
Kansas, USA (Heisler-White et al., 2009; Hoover et al., 2014). The moist site
of STU is a subalpine meadow located in the Austrian Central Alps near the village
of Neustift (Bahn et al., 2006, 2008; Schmitt et al., 2010). Experimental
measurements of annual ANPP were carried out spanning different time ranges.
Estimated mean ANPP for SGS, KNZ and STU sites are 91 ± 36,
387 ± 82 and 525 ± 210 g DM (dry mass) m-2 yr-1.
Details of the ecological and environmental factors are summarized in
Table 1.
These three grasslands were selected because they lie along a mean annual
precipitation (MAP) gradient and have detailed meteorological data to force
the models. While two are “natural” grasslands (KNZ and SGS) and one (STU)
is not, global land surface models do not typically differentiate regarding
the origin of ecosystem types and heavily managed grasslands and pastures
represent a significant fraction of mesic grasslands globally. Semi-natural
subalpine grasslands in the Alps were created several centuries ago, are very
lightly managed and should be in equilibrium concerning soil physical
conditions. It should be noted though that the grassland at STU is cut once a
year and lightly fertilized every 2–4 years and in consequence differs in
plant composition and soil fungi : bacteria ratio, which leads to different
drought responses compared to abandoned grassland (Ingrisch et al., 2017;
Karlowsky et al., 2018). Further, it is worth noting that the mesic grassland
in the USA would also be forested if human-initiated prescribed fires were to
be removed from the system (Briggs et al., 2005). Thus, these grassland sites
lie along a continuum of dry natural grassland, mesic natural grassland
maintained by human management and anthropogenic moist grassland maintained
by human management.
Ecosystem model simulations
In order to test the hypothesis of an asymmetric response of productivity to
variable rainfall (Knapp et al., 2017b), simulations were conducted with
14 ecosystem models – CABLE, CLM45-ORNL, DLEM, DOS-TEM, JSBACH, JULES,
LPJ-GUESS, LPJmL-V3.5, ORCHIDEE-2, ORCHIDEE-11, T&C, TECO, TRIPLEX-GHG and
VISIT – all using the same protocol defined by the precipitation subgroup of
the model–experiment interaction study (Table 2). At all three grassland
sites, observed and altered multi-annual hourly rainfall forcing time series
were combined with observations of other climate variables. These variables
were air temperature, incoming solar radiation, air humidity, wind speed and
surface pressure. Model simulations were carried out using soil texture
properties measured at each site as reported in Table 1. Simulated
productivity during the observational period is influenced at least in some
models (for instance those having C–N interactions) by historical climate
change and CO2 changes since the preindustrial period. Thus, instead of
assuming that productivity was in equilibrium with current climate,
historical reconstructions of meteorological variables from gridded CRUNCEP
data at half-hourly time step (Wei et al., 2014) were combined and
bias corrected with site observations to provide bias corrected historical
forcing time series from 1901 to 2013 (CRUNCEP-BC). In addition to the
observed current climate defining the ambient simulation, nine altered
rainfall forcing datasets were constructed by decreasing or increasing the
amount of precipitation in each precipitation event by -80, -70, -60,
-50, -20, +20, +50, +100 and +200 % during the time span of
productivity observations at each site, leaving all other meteorological
variables unchanged and equal to the observed values. Modelers performed all
simulations described below based on the same protocol (see below) and the
model output was compared with measured ecosystem productivities (GPP; NPP;
ANPP; and BNPP, belowground NPP), whenever available.
Summary of ecosystem models used in this study, including
model name, nitrogen (N) cycle and relevant references. Also see
Tables S1–S14 in the Supplement
for details of the simulated processes for grasslands in the
ecosystem models, including the N cycle, phosphorus (P) cycle, carbon (C)
allocation scheme, carbohydrate reserves, leaf photosynthesis and stomatal
conductance including treatment of water stress, scaling of photosynthesis
from leaf to canopy, phenology, mortality, soil hydrology, surface energy
budget, root profile and dynamics, and grassland species.
Model
Expanded name
N cycle
References
CABLE
CSIRO Atmosphere Biosphere Land Exchange model
No
Kowalczyk et al. (2006), Wang et al. (2011)
CLM45-ORNL
Version 4.5 of the Community Land Model
Yes
Oleson et al. (2013)
DLEM
Dynamic Land Ecosystem Model
Yes
Tian et al. (2011, 2015)
DOS-TEM
Dynamic organic soil structure in the Terrestrial Ecosystem Model
Yes
Yi et al. (2010), McGuire et al. (1992)
JSBACH
Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg
No
Kaminski et al. (2013), Reick et al. (2013)
JULES
Joint UK Land Environment Simulator
No
Best et al. (2011), Clark et al. (2011)
LPJ-GUESS
Lund–Potsdam–Jena General Ecosystem Simulator
Yes
Smith et al. (2001), B. Smith et al. (2014)
LPJmL-V3.5
Lund–Potsdam–Jena managed Land
No
Bondeau et al. (2007)
ORCHIDEE-2
Organizing Carbon and Hydrology in Dynamic Ecosystems (2 soil layers)
No
Krinner et al. (2005)
ORCHIDEE-11
Organizing Carbon and Hydrology in Dynamic Ecosystems (11 soil layers)
No
Krinner et al. (2005)
T&C
Tethys–Chloris
No
Fatichi et al. (2012, 2016)
TECO
Process-based Terrestrial Ecosystem model
No
Weng and Luo (2008)
TRIPLEX-GHG
An integrated process model of forest growth, carbon and greenhouse gases
Yes
Peng et al. (2002), Zhu et al. (2014)
VISIT
Vegetation Integrative Simulator for Trace gases model
No
Inatomi et al. (2010), Ito (2010)
Simulation S0 spin-up: models simulated an initial steady state spin-up run
for water and biomass pools under preindustrial conditions using the
1901–1910 CRUNCEP-BC climate forcing in a loop and applying fixed
atmospheric CO2 concentration at the 1850 level.
Simulation S1 historical simulation from 1850 until the first year of
measurement (1986 for SGS, 1982 for KNZ and 2009 for STU): starting from the
spin-up state, models were prescribed with increasing atmospheric CO2
concentrations and dynamic historical climate from CRUNCEP-BC. Because there
is no CRUNCEP-BC data for 1850–1900, the CRUNCEP-BC climate data from 1901
to 1910 was repeated in a loop instead.
Simulation SC1 ambient simulation for the measurement periods (1986–2009 for
SGS, 1982–2012 for KNZ and 2009–2013 for STU): starting from the
initial state in the start year of the period and run with observed CO2
concentrations and meteorological data corresponding to site observations at the hourly or half-hourly scale.
Simulations SP1–SP9 altered precipitation simulations for the measurement
periods (1986–2009 for SGS, 1982–2012 for KNZ and 2009–2013 for STU):
starting from the initial state in the start year of the period and run using
the nine altered rainfall forcing datasets with observed CO2
concentration.
Metrics of the response of productivity to precipitation
changes
In the analysis, we begin with testing our first specific objective, i.e.,
if the productivity–P sensitivities of spatial relationships are greater
than the temporal ones in the models as found in the observations. We
calculated the temporal slopes and spatial slopes between productivities and
precipitation from multi-year ambient simulations (SC1). Temporal slopes are
site based and relate inter-annual variability in precipitation to
inter-annual variability in the productivities using linear regression
analysis. Spatial slopes relate mean annual precipitation to mean annual
productivity across the three sites.
We then calculated two indices to analyze the asymmetric responses of primary
productivity to precipitation simulated by ecosystem models and derived by
observations whenever data were available. The two indices are (1) the
asymmetry of productivity–P for current inter-annual variability, based on
SC1 where observations for ANPP are also available; and (2) the sensitivity
of productivity to P for simulations where mean precipitation was altered,
based on SP results. With these metrics, we test our second and third
specific objectives, i.e., whether models could reproduce the observed
asymmetric responses of productivity in grasslands to altered precipitation
under normal and extreme conditions.
Finally, we analyze the nonlinearity of modeled response of productivity to
precipitation, which is described by the parameters of the curvilinear
productivity–P relationships across the full range of altered precipitation
scenarios, based on fits to model output for the ambient (SC1) and altered
(SP) simulations. Detailed methods for the two indices used to analyze the
asymmetric responses of primary productivity to altered precipitation and
the curvilinear productivity–P relationships are introduced in the
following.
Asymmetry index from inter-annual productivity and
precipitation
In order to characterize the asymmetry of productivity to precipitation, we
define the asymmetry index (AI) from inter-annual productivity and
precipitation data as follows:
AI=Rp-Rd,
where Rp is the relative productivity pulse in wet years and
Rd is the relative productivity decline in dry years defined
by
Rp=(med(fp90)-f‾)/f‾,Rd=(f‾-med(fp10))/f‾,
where f is the inter-annual productivity, being a function of environmental
factors from models or observation; f‾ is mean annual
productivity in the period of measurements (Table 1);
med(fp90) is the median value of productivities in wet
years with annual precipitation higher than the 90th percentile level; and
med(fp10) is median value of productivities in all the
dry years when annual precipitation is lower than the 10th percentile level.
In general, Rp > 0 indicates that the median value of
productivities in wet years is higher than the mean annual productivity in
the period of measurements; and Rd > 0 indicates that the
median value of productivities in dry years is smaller than the mean annual
productivity in the period of measurements. Therefore, AI > 0, i.e., a
positive asymmetry, means that there is a greater increase of productivity in
wet years than decline in dry years; and AI < 0, i.e., a negative
asymmetry, means that there is a greater decline of productivity in dry years
than increase in wet years.
Furthermore, uncertainty ranges of Rp, Rd and AI
were estimated as follows:
Rp∈Rplow,Rpup=[(medfp90-mad(fp90))-f‾f‾,(medfp90+mad(fp90))-f‾f‾],Rd∈Rdlow,Rdup=[f‾-(medfp10+mad(fp10))f‾,f‾-(medfp10-mad(fp10))f‾],AI∈AIlow,AIup=[Rplow-Rdup,Rpup-Rdlow],
where Rplow and Rpup are the
lower and upper bounds of Rp using one median absolute deviation,
i.e., madfp90;
Rdlow and Rdup are the lower
and upper bounds of Rd using one median absolute deviation,
i.e., madfp10; and
AIlow and AIup are the lower and
upper bounds of AI corresponding to estimated Rp and
Rd ranges.
Sensitivity of productivity to altered versus inter-annual
precipitation variability
For altered precipitation, in particular for the extreme SP simulations where
mean precipitation was altered and annual precipitation of a few years was
outside the range of observed precipitation variation, we tested the
hypothesis of whether the asymmetry response becomes negative – that is the
impacts of extreme dry conditions on productivity are much greater than the
positive effects of extreme wet scenarios (Knapp et al., 2017b). Thus, we
tested the mean change in productivity imposed by the change in
precipitation, and we defined the sensitivity of productivity to altered
rainfall conditions (S) as
S=(fPa‾-fPc‾)/(Pa‾-Pc‾),
where fPa‾ and fPc‾ are the
mean productivities of altered and ambient simulations;
Pa‾ and Pc‾ are the mean annual
precipitation amounts in altered and ambient simulations. It should be noted
that the sensitivity of productivity to altered rainfall conditions could
present the asymmetry response from normal to extreme conditions.
Relationships between GPP (a), NPP (b),
ANPP (c), and BNPP (d) and precipitation (P) derived from
multi-year ambient simulations (SC1) in two ways. Temporal slopes are site
based and relate inter-annual variability in P to inter-annual variability
in the productivities using linear regression analysis. Spatial slopes relate
mean annual P to mean annual productivity across three sites. In each
panel, SGS, KNZ and STU are from dry to moist, given from left to right. The
red lines are the ensemble mean of modeled temporal slopes, and the red
shading represents the model uncertainty range using the interquartile spread of
the temporal slopes between individual simulations (10th and 90th
percentiles). The blue line is the ensemble mean of modeled productivities,
and the blue error bar represents the model uncertainty range using
the interquartile spread of the productivities between individual simulations
(10th and 90th percentiles). In (c), the grey lines are the observed
temporal slopes, and the black line shows the observed spatial slope. The
grey shading represents the observed uncertainty range using the bootstrap
sampling method (10th and 90th percentiles), and the black error bar
represents the observed uncertainty range using the interquartile spread of the
inter-annual productivities (10th and 90th percentiles). Note that we simply
converted observed ANPP from dry mass (g DM m-2 yr-1) to carbon
mass (g C m-2 yr-1) with a factor of 0.5.
![]()
Curvilinear productivity–<inline-formula><mml:math id="M111" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> relationships across the entire
range of altered <inline-formula><mml:math id="M112" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula>
In general, plant productivity increases with increasing precipitation and
saturates when photosynthesis becomes less limited by water scarcity. We
fitted the response of simulated productivity to altered precipitation using
the Eq. (8):
y=a1-e-bx,
where the independent variable x is the mean annual precipitation (mm) and
the dependent variable y one of the productivities (GPP, NPP, ANPP and
BNPP). Parameter a (g C m-2 yr-1) is the maximum value of
productivity at high precipitation and parameter b (mm-1) is the
curvature of modeled productivity to altered precipitation.
Asymmetry responses of inter-annual GPP (a),
NPP (b), ANPP (c) and BNPP (d) to precipitation in
ambient simulations at the three sites SGS, KNZ and STU. The asymmetry index
was calculated as the difference between the relative productivity pulses
(Rp) and declines (Rd) in wet years and dry years
(see Eqs. 1–3). Black pentagrams in (c) represent asymmetry indices
from observations. The corresponding black error bars represent the observed
uncertainty ranges using Eqs. (4)–(6). A black asterisk at the bottom of a
panel indicates a significant asymmetry response of the model ensemble at a 0.1
significance level by a non-parametric statistical hypothesis test (Wilcoxon
signed-rank test).
![]()
Results
Temporal versus spatial slopes of productivity–<inline-formula><mml:math id="M123" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula>
From the ambient simulations, ensemble model results indicate that the slopes
of the spatial relationships were steeper than the temporal slopes for GPP,
NPP and ANPP for the subset of models that simulated this flux, while these
differences in slopes were less obvious for BNPP (Fig. 1). We compared model
results with site observations for ANPP–P temporal slopes of the ambient
simulation across the three sites (Fig. 1c). Observed and modeled temporal
slopes decreased from the dry (SGS) to moist (STU) site, from
0.10 g C m-2 mm-1 (0.05 to 0.14 for the 10th and 90th
percentiles) to 0.05 g C m-2 mm-1 (-0.14 to 0.55 for the 10th
and 90th percentiles) in the observations, and from
0.14 g C m-2 mm-1 (0.02 to 0.36 for the 10th and 90th
percentiles) to 0.03 g C m-2 mm-1 (-0.04 to 0.29 for the 10th
and 90th percentiles) for the model ensemble mean. Although there were some
discrepancies in the range of spatial and temporal slopes across models
(Fig. S1 in the Supplement), the multi-model ensemble mean captured the key observation of
spatial slopes steeper than temporal slopes for ANPP (Fig. 1).
Sensitivity of GPP (a), NPP (b), ANPP (c)
and BNPP (d) for altered precipitation simulations at the three
sites SGS, KNZ and STU. Curves show the ensemble mean of models, and the
shading represents the model uncertainty range using the interquartile spread of
the sensitivities between individual simulations (10th and 90th percentiles).
Curves above the zero line represent responses under increasing precipitation
conditions relative to the control, and curves below the zero line show
responses under decreasing precipitation conditions relative to the control.
Vertical dashed lines represent precipitation variations of 1 standard
deviation (1σ), 2 standard deviations (2σ) and 3 standard deviations (3σ),
which were derived from long-term annual
precipitation at the three sites respectively.
![]()
Asymmetry of the inter-annual primary productivity response to
precipitation
The asymmetry of each model was diagnosed using the asymmetry index (Eq. 1),
which showed large variation across models (Figs. 2, S2). Considering
all the models as independent ensemble members, the mean AI of GPP and NPP
showed significantly negative values at p < 0.1 level for SGS (ensemble
value of -0.11-0.310.12 and -0.20-0.480.11 respectively
with 10th and 90th percentiles). Hence, for SGS simulated declines of GPP and
NPP in dry years were larger than the increases in wet years. For STU, the
mean AI values were only slightly negative (ensemble value for GPP
-0.03-0.070.02 and for NPP -0.04-0.090.01 with 10th and
90th percentiles), while AI was very close to zero at KNZ. By contrast,
observation-based AI values, estimated from long-term inter-annual ANPP
measurements, suggest a decrease from positive (0.320.140.49 for
SGS and 0.200.040.37 for KNZ) to negative (-0.21 for STU). At the
dry (SGS) and mesic (KNZ) sites (Fig. S2), most of the model simulations
overestimated the extent of negative drought effects in dry years
(Rd) and/or underestimated the positive impacts on ANPP in wet
years (Rp). For example, CABLE and ORCHIDEE-2 overestimated the
drought effects in dry years at both of the two sites, and CLM45-ORNL and VISIT
underestimated the positive impacts in wet years at both of the two sites
(Fig. S2). At the moist site (STU), models agreed with observations regarding
the negative sign of AI (negative asymmetry) but AI magnitude is not well
captured.
Sensitivities of primary productivity to altered precipitation
The model-derived sensitivities given by Eq. (7) generally presented greater
negative impacts of reduced precipitation than positive effects of increased
precipitation under both normal (inter-annual) and extreme conditions
(Fig. 3). The results also indicated that models represented a constant
asymmetry pattern (negative asymmetry under normal and extreme conditions)
across the full range of altered precipitation rather than a double asymmetry
pattern (positive asymmetry under normal condition and negative asymmetry
under extreme condition) established by Knapp et al. (2017b), which confirmed
that models did not capture the positive asymmetric responses of
productivities to altered precipitation under normal conditions for the dry
(SGS) and mesic (KNZ) sites.
Primary productivity at the dry site (SGS) was more sensitive to
precipitation changes compared to the moist site (STU). Along with increases
in precipitation, the largest sensitivity values were found for SGS (ensemble
mean of 1.350.422.49 g C m-2 mm-1 for GPP with 10th
and 90th percentiles, 0.680.241.47 g C m-2 mm-1 for
NPP, 0.240.080.61 g C m-2 mm-1 for ANPP and
0.160.140.18 g C m-2 mm-1 for BNPP) and then KNZ
(0.32-0.091.23 g C m-2 mm-1 for GPP,
0.20-0.050.72 g C m-2 mm-1 for NPP,
0.130.010.21 g C m-2 mm-1 ANPP and
0.060.010.28 g C m-2 mm-1 for BNPP with 10th and 90th
percentiles) when precipitation was altered by +20 %. The values of S
decreased with further increased precipitation, indicating that additional
water does not increase productivity in the same proportion exceeding a
certain threshold. In contrast to SGS, the values of sensitivity for both GPP
and NPP at STU are close to zero in response to added precipitation
conditions, implying that the precipitation above ambient was not a limiting
factor for grassland production in the models at this site.
Responses of simulated annual GPP (a, d, g), NPP (b, e, h)
and CUE (NPP/GPP; c, f, i) to altered and ambient precipitation
(P) levels at the three sites STU, KNZ and SGS. The fitted equation is
Eq. (8) for GPP and NPP (see Fig. S3 for fitted a and b). The grey dashed
line represents ambient precipitation. It should be noted that the x-axis
scales are different between the sites.
![]()
The values of sensitivity decreased with reduced precipitation at KNZ and
SGS, indicating larger negative impacts on primary productivity when
conditions become drier. For the moist site of STU, primary productivities
showed less sensitivity to moderately dry conditions, and sensitivity only
increased with more extreme rainfall alterations out of 3σ
(∼ 40 % precipitation change). Additionally, the values of S for
ANPP were smaller than those of BNPP at KNZ and SGS, while there were no
differences between ANPP and BNPP at STU (Fig. 3). Thus, model results
suggest that the dry site (SGS) can be particularly vulnerable to altered
rainfall compared to the moist site (STU), which was more robust in response to
altered rainfall.
Responses of simulated annual ANPP (a, d, g), BNPP (b, e, h),
and the ratio of ANPP and NPP (c, f, i) to altered and ambient
precipitation (P) levels at the three sites STU, KNZ and SGS. The fitted
equation is Eq. (8) for ANPP and BNPP (see Fig. S4 for fitted a and b).
The grey dashed line represents ambient precipitation. It should be noted
that the x-axis scales are different between the sites.
![]()
Curvilinear responses of productivity to altered precipitation
At SGS and KNZ, simulated GPP and NPP increased with increasing
precipitation. In contrast, at the moist STU, most models showed saturation
in productivity for precipitation above ambient values (Fig. 4). Along with
increasing precipitation, GPP and NPP showed nonlinear concave-down response
curves in all models, with different curvatures b and maximum productivity
a (Fig. S3). The ensemble mean values of the curvature parameter b fitted
from Eq. (8) to each modeled GPP across the full range of altered
precipitation are 5.12.79.2 × 10-3 mm-1 at STU,
3.30.98.0 × 10-3 mm-1 at KNZ and
1.40.02.3 × 10-3 mm-1 at SGS with 10th and 90th
percentiles (Fig. S3).
The responses of GPP and NPP to altered precipitation were proportional to
each other for each model, and as a result changes in carbon use efficiency
(CUE) were very small compared to the background CUE differences diagnosed in
the ambient simulation (Fig. 4c, f, i). However, JSBACH and LPJmL-V3.5
produced a sharp decline of CUE below ambient precipitation at SGS and KNZ.
Only seven models simulated ANPP and BNPP separately (Fig. 5). The responses
of ANPP and BNPP to altered precipitation were similar to those of GPP and
NPP. When fitting Eq. (8) to ANPP–P (Fig. S4), the curvatures b ranged from
3.0 × 10-3 mm-1 (ORCHIDEE-11) to
9.2 × 10-3 mm-1 (TECO) at STU, from
0.7 × 10-3 mm-1 (TRIPLEX-GHG) to
6.1 × 10-3 mm-1 (VISIT) at KNZ, and from
0.9 × 10-3 mm-1 (T&C) to
2.3 × 10-3 mm-1 (CLM45-ORNL) at SGS; the modeled maximum
values a for ANPP ranged between 173 g C m-2 yr-1 (VISIT) and
827 g C m-2 yr-1 (TECO) at STU, 49 g C m-2 yr-1
(CLM45-ORNL) and 557 g C m-2 yr-1 (ORCHIDEE-2) at KNZ, and
94 g C m-2 yr-1 (CLM45-ORNL) and 523 g C m-2 yr-1
(ORCHIDEE-2) at SGS.
The ANPP : NPP ratio, i.e., aboveground carbon allocation, showed a nonlinear
increase (concave-down) with increasing precipitation in ORCHIDEE-2 and
ORCHIDEE-11, a nonlinear decrease (concave-up) in T&C due to translocation
of C reserves from roots and only minor changes in other models (Fig. 5c, f,
i).
Discussion
Comparison of modeled and observed responses of productivity to
altered precipitation
Spatial slopes steeper than temporal slopes of ANPP to precipitation are usually
explained by two hypotheses: (1) vegetation constraint effects on ANPP
responses to precipitation play a more important role in the temporal as
compared to the spatial domain (Knapp et al., 2017b; Estiarte et al., 2016);
(2) biogeochemistry (mainly resource limitations) and confounding factors
(e.g., temperature and radiation), rather than species attributes, constrain
community-level ANPP in response to precipitation (Huxman et al., 2004).
Thus, the former theory stresses more long-term intrinsic ecosystem
properties, while the latter underlines the effects of external
environmental factors. The current models tested here captured the relative
magnitude of the difference between temporal and spatial slopes (Fig. 1c),
which suggested that the models adequately considered the key processes
underlying carbon–water interactions across different grassland sites. Only
few grassland experiments have assessed BNPP (Luo et al., 2017), leaving the
question open of whether the minor differences between temporal and spatial
slopes for BNPP responses to precipitation as simulated by the models
correspond to experimental observations (Fig. 1d).
The asymmetry index obtained from available long-term ANPP and precipitation
observations reported positive values at SGS and KNZ (Fig. 2c), which
suggested greater declines of ANPP in dry years than increases in wet years
(Knapp and Smith, 2001). Knapp et al. (2017b) proposed the following
underlying mechanisms. (1) In dry years, the carryover effects of soil
moisture from previous years alleviate strong declines of ANPP (Sala et al.,
2012), which is usually treated as a time-lag effect (Petrie et al., 2018; Wu
et al., 2015). Additionally, rain use efficiency also increases with water
scarcity, meaning that less water is lost through runoff (Gutschick and
BassiriRad, 2003; Huxman et al., 2004). (2) In wet years, other resources
such as nutrient availability may increase with increasing precipitation,
contributing to a supplementary increase of ANPP (Knapp et al., 2017b;
Seastedt and Knapp, 1993). In contrast, the negative asymmetry index derived
from observations at the moist STU suggests that this process is not dominant
for this site, while temperature and/or light limitations that are associated
with rainy periods may become important during wet years and neutralize the
effect of increased precipitation on ANPP (Fig. S4) (Nemani et al., 2003; Wu
et al., 2015; Wohlfahrt et al., 2008).
In our results, most models did not capture the sign of observed asymmetry
indices across the three sites (Fig. 2c), which suggests that some of the
underlying processes (combined carbon–nutrient interactions, time-lag
effects, dynamic root growth allowing variation in accessible soil water) are
not accurately represented in the models. For example, grassland root depth
affects ecosystem resilience to environmental stress such as drought, and
arid and semi-arid grasses that have extensive lateral roots or possibly deep
roots show relatively strong resistance (Fan et al., 2017). However, most
models currently consider only two types of grasslands – C3 and C4
(Table S14), with fixed root fractions in each prescribed soil layers (Table S13). This is
potentially unrealistic for semi-arid grass roots and can lead to
underestimating the amount of soil water available to plants and their
resistance to drought. The latter is a key candidate especially for
explaining the negative asymmetry index at the dry SGS.
The sensitivity of productivity to increased and decreased precipitation for
simulations where mean precipitation was normally altered generally suggested
negative asymmetric responses at dry (SGS) and mesic (KNZ) sites (Fig. 3c).
This contrasts with a meta-analysis of grassland precipitation manipulation
experiments (Wilcox et al., 2017) and with the ANPP–P conceptual model (Knapp
et al., 2017b), which suggest a positive asymmetry response in the range of
normal rainfall variation. This emphasizes the finding that most models
overestimate drought effects and/or underestimate wet year impacts on primary
productivity of dry and mesic sites for current precipitation variability.
Under extreme conditions with modified precipitation, models were in line
with the hypothesis and the data showing that ANPP saturates in very wet
conditions but declines strongly in very dry conditions (Knapp et al.,
2017b). For BNPP sensitivities to altered precipitation, meta-analysis of
previous experiments indicated symmetric responses to increasing and
decreasing rainfall (Luo et al., 2017; Wilcox et al., 2017), which may be
regulated by allocation controls on the ratio of ANPP and BNPP to total NPP
in response to altered precipitation. However, in the participating models,
BNPP shows a negative asymmetric response to altered rainfall (Fig. 3d),
which may reflect a shortcoming of carbon–water interactions in the
belowground ecosystems.
Curvilinear responses of productivities to altered precipitation
by models
In general, precipitation in ecosystem models is distributed through three
pathways (N. G. Smith et al., 2014): (1) intercepted by vegetation and
subsequently evaporated or falling on the ground; (2) infiltrated into the
upper soil layers with subsequent evaporation, root water uptake and plant
transpiration, or percolated down to deeper layers to form ground water;
(3) runoff from the soil surface if the intensity of precipitation exceeds
infiltration rates. In reality as well as in models, soil moisture rather
than precipitation is the variable regulating vegetation growth, and
biological responses to changes in precipitation are manifested as functions
of soil moisture in different soil layers (Sitch et al., 2003; N. G. Smith et
al., 2014; Vicca et al., 2012). We calculated the surface soil water content
(SSWC, 0–20 cm depth converted from reported soil layers) and total soil
water content (TSWC) under ambient and altered precipitation as simulated by
the 14 models, and we found different patterns with parabolic,
asymptotic and threshold-like nonlinear curves, which is similar to the
response curves of primary productivity at the three sites (Figs. S5, S6).
For the moist STU, SSWC and TWSC did not show obvious changes in response to
increased precipitation since soil moisture at this site is often relatively
near field capacity, while the SSWC and TSWC quickly decreased with
decreasing in precipitation (Figs. S5, S6). In contrast, SSWC and TSWC at SGS
showed significant increases in response to altered increased precipitation
and slow decreases for decreased precipitation, because the soil was already
very dry under average ambient conditions. Thus, changes of SWC in response
to precipitation contribute to driving the different response patterns of
simulated primary productivity across the grassland sites.
The responses of primary productivity to precipitation in models might also
be driven by the intrinsic structure and parameterizations of vegetation
functioning besides changes of soil moisture (Gerten et al., 2008), which
account for the large spread in the values of b and a among models at the
three sites (Figs. 4, 5, S3, S4). For example, carbon–nitrogen cycle coupling
in ecosystem models reduced the simulated vegetation productivity relative to
a carbon-only counterpart model (Thornton et al., 2007; Zaehle et al., 2010).
Of those models used in this study, only five of the 14 models include
carbon–nitrogen–water interactions (Tables 2, S1, S2). We calculated the
ensemble mean of productivity for this group of carbon–nitrogen models
(CLM45-ORNL, DLEM, DOS-TEM, LPJ-GUESS and TRIPLEX-GHG) and carbon-only models
(CABLE, JSBACH, JULES, LPJmL-V3.5, ORCHIDEE-2, ORCHIDEE-11, T&C, TECO and
VISIT) across altered and ambient precipitation simulations at the three
sites, and then fitted the productivity–P responses with Eq. (8) (Figs. S7,
S8, S9). We found that ensemble mean of carbon–nitrogen models generally
produce a weaker GPP, NPP and ANPP response to precipitation than ensemble
mean of carbon-only models and similar responses for BNPP. The latter may be
explained by fixed root profiles in most models (Table S13). Our findings
suggest that N interactions in ecosystem models reduced the productivity–P
sensitivities, but should be confirmed using the same model prescribed with
different N availability. In addition to the influence of nutrient cycling,
different definitions of vegetation compositions (C3/C4) (Table S14), root
profiles (Table S13), phenology (Table S9) and carbon allocation (Table S4)
at the three sites may also contribute to the large variations of modeled
productivity–P responses and demands for more accurate calibration of models
to the specificity of the local sites in future model intercomparison
studies.
Uncertainties, knowledge gaps and suggestions of further work
In this work, we applied two indices to characterize the asymmetry responses
in the normal precipitation range using inter-annual variability of present
conditions and forcing models with continuously modified precipitation
amounts. Asymmetry indices from the inter-annual gross and net primary
productivities suggest large uncertainties (Fig. 2), while the sensitivity
analysis to changes in mean precipitation reported clear responses (Fig. 3).
This can be explained by the differences in other climatic factors (for
example, temperature, radiation and vapor pressure), or timing and frequency
of precipitation between dry and wet years. All these uncontrolled factors
may contribute to the large uncertainties of asymmetric responses from
inter-annual variations (Chou et al., 2008; Peng et al., 2013; Robertson et
al., 2009).
Although the carbon–water interactions in current models have been improved
during the last decades, there still exist large gaps for accurately
diagnosing the errors in the representation of key processes and
parameterizations. Suggestions that should be considered in future studies
aimed at model–data interaction include the following. (1) Models should report SWC at the
same depth of experiments and experimental data should be made available for
better comparisons in following studies. This can provide insights into the
bias of modeled sensitivities to precipitation and check explicitly the
sensitivity of vegetation productivity to change in SWC. (2) More experiments
are needed that assess also BNPP in order to evaluate the corresponding
processes in models (Luo et al., 2017; Wilcox et al., 2017). (3) There still
exist large gaps between changes of precipitation occurrence and intensity in
reality and how we simulated them in the current work, i.e., the altered
rainfall forcing datasets were constructed by decreasing or increasing the
amount of precipitation in each precipitation event by a fixed percentage
during the time span of productivity observations at each site and not by
modifying precipitation structure or reproducing the real treatment. Further
studies need to consider better different scenarios of precipitation
occurrence and intensity under climate change (Lauenroth and Bradford, 2012),
which will likely help to better understand the responses of productivities
to altered precipitation in the next decades. In addition, modelers will need
to simulate the control experiments corresponding to the real local
precipitation manipulations applied by field scientists, e.g., considering
the observed time series of modified precipitation and vegetation
composition, root profiles, nutrient cycling, phenology and carbon allocation
as close as possible to local conditions. This should be a priority for
future model–experiment interaction studies.