Introduction
Overview
Extreme climatic events such as heat or drought are key features of Earth's
climatic variability and occur on a wide range of timescales . Extreme climatic events directly propagate into
the terrestrial biosphere, thus affecting ecosystem functioning
and land surface properties (e.g., soil
moisture), which in turn triggers ecosystem–atmosphere feedback loops
e.g.,. For example, drought in
conjunction with severe heat reversed several years of ecosystem carbon
sequestration in Europe in 2003 , and strong land–atmosphere
feedbacks exacerbated the event while it was occurring .
However, ecosystem impacts of extreme climatic events are often nonlinear
and interact with concurrent climatic conditions. Additionally, potential
impacts can cancel each other out depending on the type and state of the
ecosystem and the magnitude of the climatic event. For instance, extremely
warm conditions at the beginning of the growing season during spring 2012 in
the contiguous US increased ecosystem carbon uptake, which subsequently
compensated for ecosystem carbon losses later during the same year's summer
heat and drought. Nonetheless, warm spring conditions and corresponding
earlier vegetation activity likely also contributed to exacerbating drought
impacts through reduced initial soil moisture at the onset of summer drought
.
Because extreme climate events have been changing in recent
decades, with, for example, a general increase in the amount of warm days and
the duration of warm spells and the opposite trend for cold days and spells
, and are projected to continue to change
, an understanding of their impacts on ecosystems is
crucial. Ideally, this understanding would cover ecological processes that
operate on both local and global scales. However, due to nonlinear and
interacting ecosystem effects of climate extremes, differences in ecosystem
responses across various growing season stages and various
ways in which different ecosystem types mediate climatic extremes
e.g.,, it currently remains unclear whether a global
perspective on ecosystem responses to climate extremes can emerge from
local-scale observations alone. Moreover, understanding ecosystem responses
to climate extremes is crucial in the context of potentially increasing
intensities or frequencies of climatic extremes that could lead to a positive
carbon-cycle–climate feedback via a reduction in the land carbon sink.
Stress ecophysiology of photosynthesis and respiration
Gross primary production (GPP), which is carboxylation rate (i.e., true
photosynthesis) minus photorespiration , is strongly
impacted by temperature and water stress . Besides its
other main environmental drivers (radiation, humidity (i.e., vapor pressure
deficit, VPD) and CO2 concentration, cf. ),
temperature directly influences photosynthesis by
affecting the kinetics of its two main chemical processes, namely the maximum
rates of carboxylation (i.e., Vc, max; ) and
electron transport (i.e., Jmax; e.g., ).
Both rates initially increase with rising temperature but decrease above a
certain optimum temperature . Leaf (i.e., light) respiration
similarly increases with temperature , which additionally
reduces GPP. As a result, extremely high temperatures can severely reduce
photosynthesis (and, hence, GPP) .
Soil water stress impacts photosynthesis see, e.g.,for a
review by causing either ecophysiological or structural changes
to the plant . For instance, a physiological
reduction in photosynthesis can be caused by reductions in enzymatic activity
or a reduction in mesophyll and
stomatal conductance e.g.,. Structural
changes reducing photosynthesis include reductions in leaf area and specific
leaf area or changes in leaf geometry or orientation
. Via increased tree mortality, droughts can also
severely impact ecosystem-level photosynthesis long after the drought event
itself e.g.,.
All these responses are highly species dependent, highlighting the need for
global cross-site analyses. For example, forest species generally close their
stomata much earlier compared to species from grassland or savannah
ecosystems, which often keep transpiring until their water storage is
depleted . In addition, anisohydric plants in general have no
control over their stomata . A soil-dependent factor
increasing the ecosystem's drought resilience is the rooting depth and the
general availability of fine roots .
In addition, interactions between heat and drought may affect GPP. For
example, drought-induced closing of the stomata and the subsequent reduction
in evaporative cooling can further increase heat stress when water stress
co-occurs with a high-temperature anomaly .
Conversely, high-temperature impacts can be alleviated by evaporative cooling
as long as enough water for transpiration is available .
Ecosystem respiration (Reco) is the sum of autotrophic respiration
and the CO2 emissions arising from the heterotrophic decomposition of
organic matter in soil e.g.,. Like
GPP, it is affected by changing soil (and, hence, ambient air) temperatures
. Rising
temperatures directly increase the kinetics of microbial decomposition, root
respiration and the diffusion of enzymes. Hence, soil respiration is commonly
modeled as an exponential function of temperature using the van't Hoff type
Q10 model or other
functions of a similar shape e.g.,. Even though enzyme activity generally decreases above a
certain temperature optimum , such high temperatures
rarely occur in extratropical soils , so high
temperatures alone are rarely an inhibiting stressor for soil respiration.
In addition, the activity of soil microorganisms depends on soil moisture
. Drought conditions
strongly reduce soil respiration because the microbial activity causing soil
respiration is dependent on the presence of water films for substrate
diffusion and exoenzyme activity . In addition, low soil water status may even cause microbial
dormancy and/or death . Indirectly, drought reduces
microbial activity through different processes like the alteration of soil
nutrient retention and availability or changes in
microbial community structure . Finally,
interactions between the response to temperature and water status, such as
changing temperature dependency due to changing soil water status
, further complicate the picture.
As described above, both heat and drought affect GPP and Reco in a
similar fashion, although the amplitude and onset of this impact may differ.
Hence, one important, partly unanswered question is the impact of climate
extremes on the balance of these two fluxes: the net ecosystem production
(NEP). Models tend to agree that drought affects GPP more strongly than
Reco, but their spread is large and predictions for the C balance
are uncertain . In addition, observational studies
on large drought and heat events like the 2003 European heat wave
or the 2000–2004
drought in North America have shown, for example, that
drought may cause a much stronger reduction in GPP compared to
Reco, leading to a reduction in the ecosystem's CO2 uptake.
However, it is important to understand that the tight coupling between GPP
and Reco in most ecosystems complicates systematic
assessments across sites. For example, heterotrophic respiration is not only
a function of the environment but is also strongly driven by the availability
of recently assimilated carbon .
Hence, a reduction in photosynthesis may cause a lagged reduction in soil
respiration in the absence of a large
labile carbon stock.
Today's opportunities
Comparison of the gross primary productivity (GPP) reductions during
the 2003 European heat wave for several FLUXNET sites.
quantified this reduction by comparing the 2003 fluxes to the previous year,
whereas we are able to use all available site years as a baseline.
The majority of studies so far focus on individual sites and predefined
extreme events seefor a review and only a few have
focused on comparisons of extreme-event impacts globally across sites and/or
across broader regions and different ecosystems
. The La Thuile dataset collected by FLUXNET
consists of 252 sites of eddy covariance flux observations in a standardized
way . These data provide a basis for a robust assessment
of the impacts of climatic extremes on ecosystem CO2 fluxes. The
opportunities arising from this trove of observations are exemplified in Fig. . The figure
recalculates the impacts of the 2003 heat wave on land fluxes as estimated by
using more reference years based on the data available. The
general findings of , who showed a strong reduction in C
uptake, are confirmed, but we now estimate a lower reduction in CO2 uptake
when considering more reference years, which is consistent with
, who found a similar pattern using models. Consequently,
the length of today's data records and in particular the tremendous work of
the numerous networks and initiatives (see Acknowledgements) who collect
these data and provide them to the scientific community allow us to update
previous quantifications of the CO2 flux impacts of climate extremes.
Objectives of this study
The objectives of this study are threefold: first, we want to exploit the
available FLUXNET data to systematically assess if extreme events corroborate
our assumptions about ecosystem behavior and to empirically describe the
spectrum of extreme responses across the globe. To do so, we extract
information about the occurrence of an extreme climatic event directly from
the observed data, not by first assuming the occurrence of an extreme event
(i.e., by identifying an extreme response of the observed ecosystem). Second,
our goal is to develop an extreme-event detection framework with a focus not
only on the extremeness of the climate forcing but which simultaneously takes
into account the resulting extremeness of the ecosystem's response or lack
thereof . Finally, we aim to bridge the gap
between local site-level studies and global assessments,which most often are
based on models e.g., or upscaling
studies by providing some helpful benchmarks for
the models and their underlying assumptions .
Methods
Study concept and overview
Conceptual overview of the different data streams and successive
steps of our analysis.
Our study can be outlined as a three-step process (Fig. ):
first, we use consistently downscaled climate data
(Sect. ) to detect climatic extreme events
(Sect. ) during the growing season in a set of
ecosystems. Second, we compare CO2 fluxes
(Sect. ) during these extreme events with reference
fluxes during comparable, non-extreme periods to quantify the impact of each
extreme event (Sect. ). Third, we use site-specific
information like plant functional type (PFT) or ecoclimatic zone (Geiger–Köppen climate classes) as well as climate extreme characteristics
(including type and duration) to systematically assess potential causes of
differences between extreme-event responses in the different ecosystems.
CO2 flux data
Measurements of CO2 flux and climate parameters collected through a
network of measurement sites were used in this study. CO2 fluxes were
measured using the eddy covariance technique e.g.,. The measured net carbon flux (i.e., net ecosystem exchange,
NEE) was partitioned into GPP and Reco at
each site. The empirical relationships used by this partitioning scheme
assume similar ecophysiological conditions for any given time step (e.g., for
one of the extreme events detected here) and a short reference period is used
to fit these empirical functions. Environmental stress, however, could also
directly impact the processes governing these empirical relationships and
hence the validity of this assumption. To assess whether this could bias our
analysis, we also performed all of our calculations using midday NEE as a
rough estimate for GPP and averaged nighttime NEE as a proxy for
Reco .
Throughout the rest of the paper, we refer to NEP instead of NEE (i.e.,
NEP = GPP - Reco=(-1)⋅NEE) because NEP is
centered on the ecosystem (i.e., positive NEP equals CO2 uptake) and
facilitates a more intuitive interpretation together with the component
fluxes GPP and Reco.
Eddy covariance measurements are continuously taken at various sites across
the globe by individual research teams and are collected and consistently
processed by the FLUXNET network . For this analysis, we used the FLUXNET La Thuile dataset,
which consists of a total of 252 sites. We used additional data from the
European eddy fluxes database cluster (http://www.europe-fluxdata.eu/)
for site years collected since the creation of the La Thuile dataset in 2007.
Both networks consistently filter the submitted data for potential outliers.
The half-hourly measurements supplied by the data providers are consistently
gap-filled via marginal distribution sampling (MDS) ,
i.e., by filling missing values with measurements taken under similar
meteorological conditions, and aggregated to daily mean values. For this
analysis we used only daily aggregates and excluded data for days with less
than 85 % original measurements or high confidence gap-filled data.
To be able to compare flux measurements during a potential extreme event with
fluxes during non-extreme conditions during comparable stages of the
phenological cycle in other years, we selected 102 sites with time series
longer than 3 years. In addition, we removed four sites where the correlation
between downscaled climate data and measured site meteorology was too low
(R2<0.6) (Sect. ) and four sites where water
availability (Sect. ) could not be calculated due to
missing data. Finally, we excluded 25 managed and disturbed sites where
disturbances such as fire and thinning would have resulted in biases in the
calculations of the non-extreme reference data in years before or after the
disturbance. This resulted in a subset of 69 sites
(Table ) out of the original 252 La Thuile sites, with a
total of 433 site years of data (i.e., years with data available for more
than 75 % of all days). These sites span 11 PFTs including grasslands,
wetlands and forest type ecosystems (Table ) and all
major Geiger–Köppen climate zones
(Table ) (i.e., first category zones A–E),
as well as half of the 24 Geiger–Köppen subzones (i.e., the secondary
categories).
Climate data
To be able to identify extreme events over sufficiently long and consistent
time periods for all sites, compared to the much shorter time periods where
actual measurements were available, we used downscaled climate data for the
extreme-event detection. We used daily air temperature and, for the
calculation of the water availability (see below), global radiation and
precipitation from ERA-Interim data at a 0.5∘ spatial
resolution (i.e., the area of 1 pixel ≤ (55 km)2).
Multiple linear regression models of the nearest nine grid boxes (i.e., the
grid box with the tower and its direct neighbor pixels) were fitted to FLUXNET
site-level meteorology measurements. The resulting models were used to
predict site-level values for a time period of 30 years between 1983 and 2012.
The resulting time series were then used to detect climate extremes. The
correlation between downscaled and site-level data for air temperature was
R2>0.9 for nearly 90 % of the sites. Sites with R2 < 0.6
(≈ 5 % of the sites, mainly tropical evergreen broad-leaved
forests) were removed from the analysis due to the low quality of the
downscaling.
To consistently quantify the amount of soil water available to the plant, a
water availability index (WAI) was calculated. This index was based on the
water balance between precipitation and evapotranspiration and was calculated
as a simple two-layer bucket model see Supplement 3 infor detailed
equations, etc.. At each time step, the soil is recharged
with water by precipitation up to a maximum value defined by the storage
capacity (125 mm). Losses of water by evapotranspiration are taken as the
minimum of either potential evapotranspiration or supply-limited
evapotranspiration. Potential evapotranspiration is estimated based on
from net radiation (also taken from the reanalysis
data) using a Priestley–Taylor coefficient of 1.26. Potential
evapotranspiration is then finally scaled with smoothed fAPAR (fraction of
absorbed photosynthetically active radiation) (from MODIS,
Moderate-resolution Imaging Spectroradiometer). Supply-limited
evapotranspiration is calculated following and is simply
defined as a fraction (i.e., 0.05, the median of the values determined by
) of current WAI. Assuming that both water recharge
(i.e., precipitation) and water loss (i.e., evapotranspiration) operate from
top to bottom, WAI was computed for a simple two-layer model, where the
storage capacity of the upper layer was set to 25 mm and that of the lower
layer to 100 mm. Only WAI of the lower layer was used in the subsequent
analysis and scaled to 0–1 (by dividing by the maximum capacity of 100).
The WAI does not account for local soil or vegetation specific properties
such as soil texture or rooting depth. such that the WAI may be interpreted
as a “climatological water availability metric”. The results are sensitive
to the fixed value of storage capacity, which influences the timing and
magnitude of extreme drought events. For example, a larger (smaller) storage
capacity value would tend to result in a later (earlier) extreme-drought
detection. We are confident, however, that these changes would not strongly
bias the qualitative and global patterns of the flux impacts investigated in
this analysis.
Extreme-event detection
Extreme events were defined and detected in the following stepwise procedure:
identification of single extreme data points (i.e., days) crossing the upper
and lower 5th percentile threshold
combination of temporally connected single extreme data points into extreme events
identification of co-occurring extreme events of different variables
to classify concurrent extremes.
A percentile-based approach was
used to define the upper and lower 5th percentile of the original
distribution as extreme (subscript max and min; cf. Table 1). Due to the
strong seasonal cycles of air temperature and WAI at most outer tropical
sites, this definition resulted in extreme events mainly being detected in
summer and winter and represents a means for capturing extreme conditions
beyond an actual value with direct physiological meaning.
However, from an ecosystem physiological perspective, an extreme climatic
event can also occur outside the maximum or minimum period of the year (e.g.,
during spring or fall for temperature). To detect such extreme events, air
temperature time series were deseasonalized by subtracting a mean annual cycle (MAC) to yield anomalies. The MAC was computed as the
daily average of all 30 years and smoothed with a 2-week moving average. The
upper (and lower) 5th percentiles of these anomalies were defined as extreme
(subscript anom, max and anom, min) (Table for all
extreme-event notations used). Such anomaly extremes were only detected for
air temperature because seasonally varying sensitivity to water availability
is not expected.
After the identification of single extreme time steps (i.e., days),
contiguous extreme time steps were concatenated into extreme events.
Additionally, two successive but not contiguous extreme events were
subsequently treated as one single long extreme event if the non-extreme
period between them was shorter than 20 % of the combined length of the
two extreme events together. This prevented short-term fluctuations in
temperature (WAI did not usually fluctuate so quickly) below the extreme
threshold during one long period of high temperature from separating this
period into smaller extreme events and allowed for a more realistic
assessment of the extreme-event duration (see below).
To differentiate between the effects of univariate extremes and the
possibility of different impacts of simultaneous extremes of heat and
drought, the following types of extreme events were differentiated:
(1) single variable extreme events irrespective of the possible extremeness
of other variables (denoted T/WAImin), (2) single
variable extreme events without other variables being extreme (denoted, for
example, as Tmax,s or WAImin,s) and
(3) concurrent extremes , i.e., coupled
extreme events with multiple variables being extreme (Tmax+WAImin) (Table for an overview).
Finally, all extreme events were described by characteristics such as
duration and type (see above) to identify which of these factors influence
the type and magnitude of possible impacts. At this first stage we did not
consider several other ecosystem specific important factors which influence
the ecosystem's response to climatic extremes such as site history and
detailed species composition e.g.,. Such an analysis
should be generally possible at future stages
(Sect. ); however, the relevant information first has
to be gathered across all sites in a standardized and comparable way.
Overview of different extreme-event types and the suffixes denoting them.
Label
Extreme type
Tmax/Tmin
temperature maximum/minimum
WAImin
water availability minimum (i.e., drought)
Tanom, max
temperature anomaly maximum
Tmax,s/WAImin, s
temperature/WAI extreme without the other variable being extreme
Tmax+WAImin
concurrent extreme with both temperature and WAI being extreme
Flux impact calculations
To identify those events that actually have a physiological impact among all
the detected climatic extreme events, a consistent quantification of the
actual impact on the ecosystem was required.
To do so, differences between the mean of the fluxes during the
extreme event and comparable reference periods were
computed see, e.g.,for a similar
approach. These reference
periods were defined to be non-extreme, identical days of the year (DOY) from
all other available years. For the reference period, the mean was computed
from a moving-average smoothed time series (i.e., 14-day moving-average
filtering computing the median) to minimize the influence of stochastic
fluctuations. During the actual extreme event, however, non-smoothed data
were used to compute these means.
Δf=f‾extr-f‾ref=1n∑k=ii+n-1fk-1ny∑k=jfk
Here, f denotes the respective CO2 flux (NEP, GPP or Reco),
i denotes the first day of one particular extreme event of length n, j
denotes the identical (and not extreme) days of the year (DOY) in all other
years, and y is the number of reference years.
As the amplitudes of Reco and GPP differ significantly between
highly productive and less productive ecosystems, all analyses were done for
original (Eq. ) and for z-transformed time series:
Δz=z‾extr-z‾ref=1n∑k=ii+n-1zk-1ny∑k=jzk,
with
zk=fk-f‾σ^(f)
for all k.
Even though extreme events outside the growing season, such as extreme-frost
periods in winter, can have impacts on the ecosystem's carbon fluxes, such
impacts would be lagged in many cases (i.e., visible during the following
growing season). Because only instantaneous responses were investigated with
our framework, it was necessary to exclude such extreme events from the
analysis. To identify the growing season, a spline function was used to
smooth the time series of GPP. In the first step, all smoothed values above
the 25th percentile were considered to be the growing season. Subsequently,
in each year these periods were extended at the beginning and end of the
detected period by identifying the first day when the smoothed series dropped
below the 5th percentile.
Results and discussion
We begin by discussing the different effects of heat and drought on primary
production and respiration observed on a global scale (i.e., averaged over all
ecosystems). The different responses to concurrent heat and drought extreme
events in contrast to heat- or drought-only events are highlighted and
discussed in Sect. . The crucial
role that the duration of the extreme event plays with regard to its impact is discussed
in Sect. , and the response of different ecosystem
types or PFTs that may explain the large spread of the impacts is considered in
Sect. . We conclude by discussing strengths and
limitations of the approach presented here
(Sect. ) and examining future directions
(Sect. ).
Impacts of different extreme-event types for a selection of
different extreme-event types (heat (Tmax), heat only
(Tmax, s), temperature anomalies extreme (Tanom, s), cold
(Tmin), drought (WAImin), drought only (WAImin, s) and
combined drought and heat (Tmax+WAImin); see
Table for details on all extreme types) on gross
primary production (GPP), ecosystem respiration (Reco) and net
ecosystem production (NEP). Shown are differences between the normalized
fluxes (i.e., their z scores) during a non-extreme reference period and the
fluxes during the extreme event (Δz=zextr-zref; see Eqs. and for details). Box
plots are color-coded according to the median of the distribution with shades
of blue (for positive values, i.e., a flux increase during the extreme event)
and red (for negative/decreased values). Panel (a) shows the amount
of growing season extreme events detected for each event type.
Panel (b) shows the impacts on GPP, (c) on Reco
and (d) on NEP.
Contrasting impacts of heat vs. drought on GPP and Reco
High-temperature extremes without particularly low water availability (i.e.,
Tmax, Tanom, max, Tmax, s and
Tanom, max, s) had only small or virtually zero impacts on GPP
(Fig. ), which is consistent with earlier findings
(e.g., for the European heat wave 2003; ). This
averaged effect can be partly explained by the specific response of different
ecosystem types (see Sect. ). Heat extremes in
general tended to have no or only a small negative impact on observed rates
of GPP in most cases. Even though GPP has been shown to have clear
temperature optima and decreases at high temperatures due to enzyme
inhibition , such conditions
(i.e., temperatures well above 30 ∘C) are experienced only rarely in
the (mostly temperate and Mediterranean) sites investigated. Other studies
also confirm the small impact of heat alone on GPP . Only
for very long and pronounced extreme events was a clear negative impact on
GPP observed (discussed in detail in Sect. ).
In our analysis, water scarcity events (i.e., WAImin and
WAImin, s) in general showed a reduction in GPP and Reco,
which, due to compensation of these component fluxes, led to no discernible
changes in NEP on average over the considered FLUXNET sites
(Fig. ). In contrast, events in which low water
availability coincided with heat led to a very strong reduction in GPP but a
lesser reduction in Reco and as a consequence to the strongest
reduction in carbon uptake (see
Sect. for a more detailed
discussion). Such a strong effect of droughts (compared to high temperatures
alone) on GPP and the generally decreasing effect of drought on GPP is
consistent with other studies
e.g.,
where water stress directly forces plants to close their stomata to limit
transpiration, reducing photosynthesis. Similarly, found
that water availability is a much bigger control on the interannual
variability in GPP (IAV, which is controlled to a large degree by extreme
events) compared to a smaller temperature control on a global level.
In contrast to the small response of GPP to heat, however, Reco
generally increased during most high-temperature extreme events
(Fig. ). As a consequence, NEP decreased, which
represents reduced carbon uptake of the ecosystem. Rising temperatures in
general lead to an increase in the microbial degradation of biomass
, which explains rising Reco rates during short
periods of high temperatures as observed in other studies
. An
additional factor could be higher radiation inputs, which result in increased
photodegradation in relatively open non-forest ecosystems.
Influence of extreme-event duration on extreme-event impact. Shown
are normalized flux differences between extreme events and a reference period
(Δz=zextr-zref; see Eqs. and
for details) for gross primary production (GPP), ecosystem
respiration (Reco) and net ecosystem production (NEP) (in rows
1–3) for a selection of different extreme-event types (in columns 1–5: heat
(Tmax), heat only (Tmax, s), cold (Tmin), drought only
(WAImin, s), and combined drought and heat
(Tmax+WAImin); see Table for details
on all extreme types). Blue numbers at the top margin of the figure denote
the amount of extreme events in each class. Blue numbers at the top margin of
the figure denote the amount of extreme events in each class.
Compared to temperature, soil respiration as the main component of
Reco is regulated much more strongly by soil water availability
. Droughts in general in our study led to a similar reduction
in Reco compared to GPP. The reason for this could be the
inhibition of soil microbial processes due to moisture limitation.
Additionally, a decrease in GPP also results in a coupling of the two fluxes
and, hence, also leads to a reduction in Reco
. The compensating effect of drought-induced
reductions in both GPP and Reco resulted in small or negligible
changes in NEP, which also has been demonstrated at local
e.g., and global levels.
The differentiated impacts of concurrent heat and drought events on GPP and Reco
In contrast to the single-factor extreme events discussed above, concurrent
heat and drought extremes (Tmax+WAImin) led to a
much stronger reduction in GPP in most cases. By contrast, Reco was
not so strongly (or not at all) reduced. This resulted in the strongest NEP
(i.e., C sink) reduction in any extreme event
(Fig. ).
Several studies have found a lower drought sensitivity of Reco
compared to GPP whereas we observed comparable or even slightly greater
reductions during drought-only (WAImin, s) extremes on the global
scale. The strong drought extremes investigated in these studies, however,
usually coincided with heat extremes and are hence more comparable to our
concurrent heat and drought extremes (Tmax+WAImin, s) where we also see a nearly negligible mean effect
on Reco (compared to GPP).
NEP is the sum of the opposing fluxes of GPP and Reco, and hence,
the direction and amplitude of its change is always determined by the sum of
the extreme-event impacts on the gross fluxes. For heat extremes, the general
increase in Reco adds to slight decreases (or no change) in GPP,
leading to a generally reduced rate of net carbon uptake. For only drought
(and no heat) extremes, the reductions in both GPP and Reco seem to
roughly cancel each other out, leading to no strong effects on NEP (again, as
a FLUXNET average). However, during the concurrent heat and drought extremes,
Reco is less strongly reduced than GPP (and also compared to only
drought extremes), leading to strong reductions in net carbon uptake compared
to non-extreme conditions. Part of this effect can be explained by the
compensating and opposite effects of heat and drought on Reco
.
While our analysis confirms a crucial impact of dryness on the individual
carbon fluxes GPP and Reco, it also shows that drought extreme
events in which dryness coincides with Tmax extremes have a
disproportionately large negative impact on the net carbon balance (i.e.,
compare also Fig. lowest panels on the right
side), which is consistent with model results . The
combined effect of dryness and heat might be interpreted in a
process-oriented way in that dryness acts primarily to reduce GPP, while heat
increases Reco, thus both leading to a severe reduction in net
ecosystem carbon sequestration. Hence, we conclude that an assessment of
combinations of extreme climate variables, in particular heat and drought
, is indeed crucial for understanding ecosystem
impacts .
Event duration crucially affects extreme-event impacts
Extreme-event duration is an important factor that influences ecosystem
impacts . In our study, with increasing duration of the
extreme climatic event, the impact on GPP generally emerged more clearly
(Fig. ). For Tmax extreme events there was
a threshold at a duration of > 27 days at which GPP strongly decreased by
approximately 1–2σ. This effect was also visible for Reco,
albeit less pronounced. With increasing duration, the response in
Reco was reversed: for short heat events (i.e., with a duration of
less than 18 days), Reco increased with respect to normal
conditions by up to 2σ, whereas for events that last longer than a
month, the response of Reco was predominantly negative.
The influence of the ecosystem's plant functional type (PFT) on the
extreme-event impact. Shown are the differences between z-transformed
CO2 fluxes during extreme events and reference periods for the different
extreme-event types and the different fluxes (Δz=zextr-zref; see Eqs. and for details)
according to the PFT (see Table for the
abbreviations used) of the respective ecosystem
(Fig. for a detailed description of the box
plots shown).
During concurrent Tmax and WAImin extremes, GPP and
Reco were reduced only for extreme events longer than 18 days. For
all other extreme types and for the other fluxes, no clear relationship
between extreme length and impact was observed
(Fig. ).
The impact of extreme climate events on GPP ranged from a neutral impact
(heat lasting less than 1 week, not coinciding with dryness) to severe
impacts (if temperature extremes persisted for more than 1 month). The
reversal from positive impacts for short durations to negative impacts for
long extreme events in the case of Reco might be interpreted as an
initial pulse of microbial activity in the soil, which is reduced after some
time when the supply limitation of respiration (i.e., GPP effects) kicks in.
Hence, these findings highlight that event duration is a critical parameter
that might qualitatively affect the directionality of the response and thus
lead to highly nonlinear ecosystem responses. These duration effects are
often not explicitly considered in the analysis of climate extreme effects on
ecosystems e.g.,. Future research should
address the question of whether such nontrivial patterns can be reproduced in
model simulations.
Most climate extreme indices for temperature consider only relatively short
temperature extremes, such as monthly maximum values of temperature or the
count or percentage of days that exceed an absolute or relative threshold.
Furthermore, currently used climate extreme indices are based on univariate
metrics . Our empirical analysis shows that ecosystem
impacts of climate extremes critically depend on the duration of an extreme
event and the coincidence of several climate variables. Hence, most
critical/negative ecosystem impacts are seen on timescales of 2–3 weeks to a
few months see also and when heat coincides
with dryness.
Different impacts in different ecosystems
Compared to the differences between the means of the impacts discussed above,
the spread of the impacts is rather large (Fig. ).
One reason for this is that differences between ecosystems are hidden by the
global (i.e., averaged) focus investigated and discussed above.
Figure shows the extreme-event impacts for the
different extreme-event types separated for the different PFTs,
Fig. shows this for different Geiger-Köppen climate
classes, and Fig. shows this for the combination of the two factors.
The clearest differences between impacts for ecosystems in particular climate
zones appeared in the open shrublands (OSH) of the polar climate zone (ET)
(Fig. ). Both GPP and Reco were
increased by more than ∼ 1σ during Tmax extremes
(Fig. ). A stronger increase in GPP led to a slight
overall increase in NEP (i.e., a C gain). No drought extremes occurred during
the investigated growing seasons in these ecosystems.
A similar but smaller (∼ 0.3σ) GPP increase during heat extremes
occurred in the cold arid (BSk), mostly GRA and OSH
(Fig. ), ecosystems
(Fig. ). (For an explanation of ecosystem and climate
class abbreviations, please see Tables A1 and A2 in Appendix A.) Here,
however, Reco was increased by a similar magnitude, resulting in
only a slight increase in NEP. Again, drought extremes did not occur during
the investigated growing seasons. In contrast, in the warm arid (i.e., BSh
climate zone) and exclusively ENF ecosystems, GPP experienced moderate
decreases during the Tmax extremes. In combination with an increase in
Reco comparable to the impact in the warm steppe climates (BSh),
this resulted in a general NEP decrease.
The influence of the ecosystem's ecoclimatic zone on the
extreme-event impact. Shown are the differences between z-transformed
CO2 fluxes during extreme events and reference periods (Δz=zextr-zref; see Eqs. and
for details) for the different extreme-event types and the different fluxes
according to the Geiger–Köppen climate class
(Table for the abbreviations used) of the
respective ecosystem. (See Fig. for a detailed
description of the box plots shown.)
The ecosystems in the mostly North American and continental European and
Asian “snow” climate zones (Dfa, Dfb, Dfc) experienced mean increases in
Reco during heat extremes of around 0.5σ
(Fig. ). GPP, however, showed almost no changes
averaged over the whole Dfc (i.e., cold summer) climate zone during heat
extremes but with this being the result of a reduction in its open shrublands
(OSH) and opposing increases in the wetlands (WET) of this climate zone
(Fig. ). In hot and warm summer ecosystems of this
climate zone (Dfa and Dfb), GPP was slightly increased. As a consequence,
this resulted in a relatively strong NEP decrease in Dfc ecosystems but only
in a moderate decrease in Dfa and Dfb climates. For drought extremes,
however, only the summer hot Dfa cropland (CRO) ecosystems showed reductions
in Reco and, to a lesser extent, in GPP during drought extremes.
Temperate and summer hot and dry (Csa, mainly Mediterranean) ecosystems
experienced the strongest GPP reductions (0.3σ), with particularly
strong impacts in the forest and savannah ecosystem compared to grasslands
and open shrublands (Fig. ), during heat extremes,
whereas Reco in general was not impacted, resulting in an NEP
decrease during heat extremes (Fig. ). During drought
periods, these Csa sites were among the ecosystems with the strongest
reductions in Reco for all forest and savannah ecosystems but not
the open shrublands which experienced increases in respiration
(Fig. ) and to a lesser extent in GPP. In
contrast, temperate summer dry ecosystems with only warm summers (Csb) did
not experience such strong reductions in GPP and even increases in
Reco during heat extremes and a smaller decrease in GPP during
drought extremes (compared to Csa). Most other ecosystems in humid temperate
climate zones (Cfa and Cfb) showed impacts consistent with the general
patterns (i.e slight GPP and stronger Reco increases during heat
extremes, a reduction in both fluxes during drought and a smaller reduction
in Reco during concurrent heat and drought) which is in line with
other research i.e.,.
The few equatorial winter dry (Aw) woody savanna ecosystems under
investigation experienced slight reductions in GPP during heat extremes. They
were one of the few climate zones where Reco was slightly reduced
during Tmax extremes. Due to the few sites and short time series,
drought extremes did not occur here often enough to reliably investigate
their impacts.
Z-transformed flux differences Differences between z-transformed
CO2 fluxes during extreme events and reference periods for the different
extreme-event types and the different fluxes (Δz=zextr-zref; see Eqs. and for details) of
the different extreme-event types on GPP, Reco and NEP (rows 1–3)
separated according to plant functional types (PFT) (x axis in each plot;
see Table for the abbreviations used) and
Geiger–Köppen climate class (y axis in each plot; see
Table for the abbreviations used) for
different types of extreme events (columns 1–5: heat (Tmax), heat
alone (Tmax, s), cold (Tmin, s), drought alone
(WAImin, s), and combined heat and drought
(Tmax+WAImin)). Shades of red indicate reductions of
different size in the respective fluxes; shades of blue indicate increases.
Refer to Figs. and for a
visualization or quantification of the actual magnitude of these impacts.
Whether temperature or water availability governs an ecosystem's response to
extreme events is mainly dependent on whether the ecosystem is located in a
temperature- or water-limited environment . This explains
the strong increases in both GPP and Reco during high-temperature
extremes in the open shrublands of the temperature-limited polar ET climate
zone compared to all other climatic zones (Fig. ).
Similar results have been found by . In addition, the detected
extreme events are at relatively low temperatures below 20 ∘C, which
are probably well below a possible heat stress for the affected plants and
still in the range where increasing temperatures increase both GPP rates and
the decomposition processes which govern Reco. An additional factor
could have been the increased sunlight during the extreme events (which may
have caused the heat extreme in the first place) in these energy-limited
regions.
Temperature extremes at sites in the arid steppe climates (BSh and BSk) have
comparatively small impacts, probably because most heat extremes occur during
dry periods with very low biological activity (Fig. ).
For one BSk site, however, the period of high temperatures and high fluxes
coincides with GPP increases during these extreme events, causing the general
mean GPP increase in this climate class compared to the BSh sites.
For the one available tropical Aw site, very small seasonal temperature
changes between ≈ 30 and 32 ∘C are observed
(Fig. ). As a result, our extreme-detection framework
detects all extreme events during the slightly hotter rainy season at the
beginning and end of the year (in the Southern Hemisphere). Still, such small
temperature differences are unlikely to cause visible physiological impacts,
which is demonstrated by the nearly nonexistent mean impact on GPP,
Reco and NEP in this climate zone. However, station density in
tropical ecosystems is very low compared to temperate Northern European or
North American sites so this may also be a consequence of the small amount of
extreme events detected.
Yearly cycles for climatic forcing variables (air temperature and
the water availability index (WAI)) and carbon fluxes (gross primary
production (GPP), ecosystem respiration (Reco) and net ecosystem
production (NEP)) for one example site for each different climatic region
(i.e., Geiger–Köppen climate class; see
Table ). One example year (black dots) is
shown, with various detected extreme events (red dots). Grey dots represent
all reference data from other years. Colored backgrounds indicate the
different extreme events detected in the example year.
Opportunities and limitations of our approach
The approach presented in this paper is based on a global, empirical
characterization of the impacts of climate extremes on ecosystem–atmosphere
carbon fluxes, which has several advantages but also limitations for
addressing global ecological questions. Classical extreme-event research has
often focused on events where the response was already known a priori to be
strong and has possibly neglected several comparable climatic periods with
similar conditions but with smaller or even opposite impacts. In contrast,
all periods are included in our analysis because we did not select our
extreme events a priori. Our results show that comparable extreme events can
lead to contrasting impacts, which depend on ecosystem type or extreme-event
timing.
In addition, this research is one of the few global and cross-site/ecosystem
investigations of extreme climate impacts on (measured) CO2 fluxes. We
try to extend the sometimes limiting (but still valuable) focus on particular
sites and compare such responses globally. This allows for a holistic picture
with which such local site observations can be compared.
Our global results highlight the importance of drought events for the
ecosystem carbon cycle. Hence, a reliable estimate of water availability is
crucial for the identification of climatic extreme events. As soil water
measurements at FLUXNET sites differ strongly between sites in quality, depth
and duration, we chose to use the modeled WAI for better between-site
comparability and consistency e.g.,. Even though we
see responses of the fluxes to decreasing WAI, the detailed investigation of
individual drought events (e.g., the 2003 heat wave:
Fig. ) highlighted the possible sudden decrease in
the fluxes to gradual changes in WAI, emphasizing the need for a reliable
estimate of WAI. At this stage, WAI was not optimized for the individual
sites and represents a purely hydrometeorological variable rather than a
direct measure of ecosystem-specific water stress.
We applied the 95th (or 5th) percentile threshold to define extreme events
throughout our study to allow for a comparable extreme definition for all
ecosystems. Importantly, this approach has as few a priori assumptions as
possible (compared to identifying extreme events via somewhat subjective
expert knowledge or by identifying extreme events using extreme responses)
and allowed us to thoroughly test such assumptions. However, this approach
also has some limitations. First, enforcing this extreme definition always
leads to a fixed number (i.e., 5 %) of extreme days per site. For long
enough and strongly varying time series, this approach yields actual extreme
events. However, for shorter time series or sites with weakly varying climate
(e.g., tropical sites), this method may lead to a false positive
extreme-event identification of non-extreme conditions. The WAI extreme
detection is probably more strongly affected by this problem. For the fairly
smooth time series with long periods of low and only slightly varying WAI at
several sites (see for example the IT Ro1 WAI time series of 2003 in
Fig. ), this approach probably led to rather
arbitrary breaks between extreme and non-extreme time spans caused by only
very small WAI differences. A more flexible data-driven approach to determine
site-specific extreme thresholds may be helpful for alleviating this problem
in future approaches. For WAI in particular, an ecosystem and
soil-type-specific threshold may lead to improved results. Finally, future
approaches should take additional extreme-strength indicators like amplitude
or occurrence into account when defining the extreme threshold. One also has
to note that FLUXNET sites are not necessarily well placed to capture extreme
events .
We used changes in the CO2 fluxes to quantify the impact of the extreme
events. Such changes, however, can only be defined relative to an undisturbed
reference period. Due to the strong seasonal cycles at many of the
investigated sites, we used fluxes from other years but identical periods (in
the year) as these reference values. However, shifts in the phenological
cycle between years could bias these reference values, especially during
stages of steep phenological changes at the beginning and end of the growing
season. We used smoothed data from multiple years to attenuate this effect. A
promising future improvement would be to synchronize each yearly cycle with a
reference by shifting it in time until a maximum agreement is reached. For
short extreme events, the impact could alternatively be calculated with
regard to the fluxes before and or after the extreme.
Future directions
In addition to the methodological modifications and improvements outlined
above (Sect. ) there are several promising
methodological extensions and possibilities.
For strongly fluctuating time series such as air temperature, our method of
defining individual days as extreme and subsequently joining them into
concurrent extreme events often resulted in the identification of several
successive but interrupted events. These were then analyzed and treated
independently, which may neglect their cumulative impact
e.g., on the ecosystem. We alleviated this
effect by joining large extreme events with small gaps in between, but our
choice of when to join the extreme events and when to treat them separately
was rather ad hoc. Such problems could be solved by applying a
moving-window-based approach when detecting the extreme events, which takes
into account the “extremeness” of a defined period before each individual
day. In particular, this approach could improve the results for the
multivariate extreme events where the fluctuations in temperature led to many
small, fragmented extreme events.
In addition, our method for defining multivariate extreme events is
(intentionally) simple and suffers from some restrictions. By independently
identifying extreme events in each climate forcing (i.e., temperature and
WAI), we may miss out potentially differing impact thresholds in situations
when both forcings are extreme. A true multivariate extreme-detection
methodology, possibly also including other variables such as vapor pressure
deficit or radiation, could overcome this limitation
e.g.,.
One important aspect of extreme-event impacts on ecosystems not covered by
the approach presented here are lagged or carry-over (i.e.,
memory) effects
e.g.,.
These are impacts which persist even after the end of the actual extreme or
occur only after the event or during subsequent growing seasons. In addition,
extreme events outside of the growing season (i.e., frost events during
winter) are not investigated here. We chose to focus on instantaneous effects
and neglect such lagged aspects because only the direct and unambiguous
connection of possible impacts to one unique extreme event ensured a large
enough sample size to apply the assumption-free approach and test all
possible extreme-event sizes and types for impacts. However, focusing on a
subset of long and pronounced extreme events, an identical approach could be
used to assess non-instantaneous effects. An additional interesting aspect
would be to examine the effect of the size of the time span between the
extreme-event onset and the flux response for Reco and GPP (i.e.,
the size of the “lag”) and a possible difference between the two fluxes
e.g.,.
Conclusions
In this study we evaluated and corroborated the current understanding and
hypotheses about the response of ecosystem CO2 fluxes to extreme
climatic events. We aimed for a strictly data-driven and assumption-free
approach that takes into account both the extremeness of the climate
forcing and that of the response.
Our approach first defines extreme values in the climate data (i.e., the
highest and lowest 5 %) to detect extreme events of varying length and
then calculates the difference between CO2 fluxes during these events
compared to non-extreme reference periods.
We found that periods of dryness (without extraordinary heat) reduce both GPP
and Reco, which led to a relatively neutral across-site impact in
net ecosystem carbon sequestration. In contrast, heat without dryness
increased Reco but did not consistently affect GPP (partly because
of differentiated effects across ecosystem types and event duration), which
overall led to a reduction in NEP. If heat coincided with drought, these
events strongly reduced GPP but yielded smaller reductions in Reco,
which led to strong reductions in NEP. A crucial contributing factor to these
differentiated impacts was the duration of the respective climate extreme
events: for instance, under heat extremes, Reco initially increased
(for the first 18 days on average) relative to non-extreme conditions but
decreased for longer events, presumably due to a reduction in GPP and thus in
soil carbon pools for long heat events.
Similar extreme events at similar sites in several cases led to decreases but
also to increases in CO2 fluxes, i.e., a large spread remained in the
data. These different responses could be partly linked to ecosystem-specific
factors. For example, boreal ecosystems experienced strong increases in GPP
and Reco during heat extremes compared to smaller changes in most
other ecosystems, whereas Mediterranean summer dry ecosystems showed
particularly strong flux decreases during drought extremes. However,
uncertainties and somewhat diverging impacts still remain unexplained after
accounting for ecosystem type, climate zone and event duration.
The framework proposed here forms a suitable basis for several promising
modifications and more in-depth analyses in the future. We plan to address
these open questions by improving the extreme-detection methodology and
performing an in-depth investigation of several additional aspects. As
responses to heat and drought also influence the exchange of water and,
hence, the fluxes of water and energy e.g., and such
fluxes are also measured by the eddy covariance technique (i.e., their net
balance), we plan to conduct a similar analysis with these fluxes, as has
been done for individual events e.g.,. Other important
aspects to include in future studies are the timing of the extreme during the
growing season, which can significantly influence the response
. Eddy covariance measurements
continue to be collected, so for several FLUXNET sites increasingly long time
series are becoming available. Hence, we are looking forward to future data
releases and to the possibility of extreme-event detection using the measured
data directly, without the constraints and possible biases of the
downscaling, which highlights the crucial importance of continuous long-term
measurements for meaningful ecosystem and climate research.