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Rise and fall of vegetation annual primary production resilience to climate variability projected by a large ensemble of Earth System Models’ simulations


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Climate change is affecting natural ecosystems and society. Anticipating its impacts on vegetation resilience is critical to estimate the ecosystems’ response to global changes and the reliability of the related ecosystem services, to support mitigation actions, and to define proper adaptation plans. Here, we compute the Annual Production Resilience Indicator from gross primary production (GPP) data simulated by a large ensemble of state-of-the-art Earth System Models (ESMs) involved in the last Coupled Model Intercomparison Project (CMIP6) of the Intergovernmental Panel on Climate Change (IPCC). In the Sustainability (Taking the Green Road) and Middle of the Road scenarios (ssp126 and ssp245), the areas where vegetation shows increasing GPP resilience are wider than the areas with decreasing resilience. The situation drastically reverses in the Fossil-fuel Development (Taking the Highway) scenario (ssp585). Among the larger countries, Brazil is exposed to the highest risk of experiencing ears with anomalously low GPP, especially in the Taking the Highway scenario.
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Rise and fall of vegetation annual primary production resilience to
climate variability projected by a large ensemble of Earth System
Models’ simulations
Matteo Zampieri1,, Bruna Grizzetti1, Andrea Toreti1, Pierluca de Palma2and Alessio Collalti3
1European Commission—Joint Research Centre (JRC), Ispra, Italy
2Fincons SPA, Vimercate, Italy
3Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), Perugia (PG),
Author to whom any correspondence should be addressed.
Keywords: climate change, resilience, agriculture, natural ecosystems, ecosystem services, annual production resilience indicator
Supplementary material for this article is available online
Climate change is affecting natural ecosystems and society. Anticipating its impacts on vegetation
resilience is critical to estimate the ecosystems’ response to global changes and the reliability of the
related ecosystem services, to support mitigation actions, and to define proper adaptation plans.
Here, we compute the Annual Production Resilience Indicator from gross primary production
(GPP) data simulated by a large ensemble of state-of-the-art Earth System Models involved in the
last Coupled Model Intercomparison Project (CMIP6) of the Intergovernmental Panel on Climate
Change. In the Sustainability (Taking the Green Road) and Middle of the Road scenarios (ssp126
and ssp245), the areas where vegetation shows increasing GPP resilience are wider than the
areas with decreasing resilience. The situation drastically reverses in the Fossil-fuel Development
(Taking the Highway) scenario (ssp585). Among the larger countries, Brazil is exposed to the
highest risk of experiencing years with anomalously low GPP, especially in the Taking the Highway
1. Introduction
The Sustainable Development Goals, formally
embraced by the 2010 Conference of Parties, recog-
nize the importance of ensuring conservation, restor-
ation and sustainable use of terrestrial ecosystems and
their services, and strengthening the resilience and
adaptive capacity to climate-related hazards (SDG-
15 and SDG-13, respectively; United Nations 2016).
Stable ecosystems, characterized by small variations
from their average state despite changes in environ-
mental conditions, are indeed considered healthy and
reliable in terms of the services they provide (MAE
2005, Costanza et al 2014). Ecosystems in good con-
dition are necessary to secure the sustainability of
human activities and human well-being (Maes et al
The concept of resilience is closely connected to
ecosystem stability. Resilience has been defined either
as the larger disturbance that a system can absorb
without losing its structure, relationships and func-
tionalities (Holling 1973,1996, Walker et al 2004,
Yi and Jackson 2021) or as the time required by an
ecosystem to recover and return back to the equilib-
rium state after a disturbance (Pimm 1984, Yi and
Jackson 2021). These definitions—termed ‘ecological
resilience’ and ‘engineering resilience’, respectively—
are conceptually clear but do not directly provide a
practical way to measure resilience (Morecroft et al
2012, Scheffer et al 2015). In fact, a quantitative
estimation of resilience requires objective methods to
identify and measure the external stresses and shocks
(Meyer 2016). Also as a result of such indeterminacy,
a large number of indicators was proposed to meas-
ure different aspects of resilience (de Keersmaecker
et al 2014, Scheffer et al 2015, Meyer 2016, Yi and
Jackson 2021). Up to date, none of these meth-
ods has been used to evaluate vegetation resilience
© 2021 The Author(s). Published by IOP Publishing Ltd
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
at the global level and in future climate scenarios
Gross primary production (GPP)—the total car-
bon fixation by plants—is a primarily important ter-
restrial ecosystem function, at the point that it was
also considered to be strongly related to resilience
itself (Moore et al 1993, Stone et al 1996, Bryant
et al 2019). Climate change is indeed expected to alter
vegetation GPP resilience by potentially comprom-
ising the availability of water for vegetation in dry
regions (Santini et al 2014, Zampieri et al 2019) and
in general by increasing the frequency, amplitude and
duration of extreme events that are detrimental for
vegetation productivity (Dosio et al 2018, Naumann
et al 2018). At the same time, the increase of atmo-
spheric CO2concentration coming along with global
warming is expected to bring positive effects in terms
of vegetation photosynthetic rate (although acclima-
tion should be also considered) i.e. the so called ‘CO2
fertilization effect’ (Sage and Kubian 2007) and water
use efficiency (Peters et al 2018).
Here, we used the Annual Production Resilience
Indicator (Rp), defined as the squared mean annual
GPP divided by its variance, which was recently pro-
posed for a statistical evaluation of the production
resilience of natural vegetation (Zampieri et al 2019)
and agricultural systems from annual production
time-series (Zampieri et al 2020b). Rpis a simple but
powerful indicator with several interesting properties.
Being inspired by the ecological definition of resili-
ence, Rpis proportional to the amplitude of the largest
disturbances that the system can absorb (measure by
their rareness) and it is potentially consistent with the
engineering definition (i.e. the return timing) as well
(Zampieri 2021). It increases with diversity (number
of species) and it accounts for memory effects, i.e. for
perturbation recovery timings longer than a season
(Zampieri et al 2020b).
We compute the Annual Production Resili-
ence Indicator from an ensemble of 480 Earth
System Models (ESMs) simulations included in
the Sixth Coupled Model Intercomparison Project
cmip6) and involved in the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change
(IPCC, ESMs are
global climate models with an explicit representa-
tion of carbon processes and cycling over land, atmo-
sphere and the oceans (Dahan 2010, Randall et al
2019), allowing to explore future climate variability
of vegetation GPP according to different greenhouse
gases emission scenarios.
We quantify the relative changes in resilience
of the GPP production with respect to period
1985–2015 for the near and the mid-long terms
(2021–2050 and 2051–2100) under three scenarios of
socio-economical global changes, corresponding to
different levels of greenhouse gases emissions and
land-use (i.e. the Sustainability, the Middle of the
Road, and the Fossil-fuel Development scenarios) and
we compute country level statistics of the larger pro-
jected changes.
2. Data and methods
The Annual Production Resilience Indicator (Rp),
is based on two assumptions (Zampieri 2021). For
annual producing systems such as agriculture or nat-
ural ecosystems that are sufficiently adapted to the
environmental conditions and to the local climate,
it is sensible to assume that the largest disturb-
ances are rarer compared to the ‘normal’ conditions
(assumption 1) and that the largest disturbances res-
ult in larger impacts of the annual production val-
ues (assumption 2). Under such conditions, the size
of the disturbance can be univocally measured by its
rareness e.g. the return period of production anom-
alies (T). Focusing on the production function only,
the ecological resilience can be then simply meas-
ured by T
MAX, which is the return period of the
largest adverse event that the system can cope with
before losing completely the production ability. This
approach is not sensitive to changes in composition
and structure of the ecosystems, so it may allow for
adaptation according to a more ‘modern’ interpreta-
tion of ecological resilience (Walker et al 2004).
For homogeneous production systems, it can
be demonstrated that T
MAX is proportional to the
annual production resilience indicator, defined as:
where µis the mean and σis the standard deviation
of the annual production (see appendix A available
online at In
case the annual production resilience indicator is
evaluated over a region including bare ground, the
indicator is sensitive to the vegetated portion only
(Zampieri et al 2019).
It is interesting and potentially useful to disen-
tangle the effects of changes in the mean and in the
variability of GPP on the annual production resilience
indicator. This can be accomplished by approximat-
ing the RPchange with a first order 2D Taylor expan-
sion of equation (1) as a function of the changes in the
mean and in the standard deviation of GPP as follows:
Rp_s =Rp_h + ∆Rp,(2)
where the s stands for ‘scenario, the h stands for ‘his-
torical’, and represents the difference between two
RpR/∂µ ×µ+R/∂σ ×σ(3)
where is the partial derivative. By computing the
derivatives and dividing both members of equation
(3) by Rpone obtains:
Rp/Rp=2µ/µ 2σ/σ. (4)
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
Table 1. ESM simulations producing the annual GPP data used in this study; with information on: modules delegated to the
representation of land surface processes and GPP simulations (and associated reference publications); number of simulations available
in the Earth System Grid Federation nodes (ESGF, until 31 December 2019 for the historical period and for ssp126, ssp245
and ssp585 future scenarios.
ESM Land model Historical ssp126 ssp245 ssp585
ACCESS-ESM-5 CABLE w/carbon cycle
(de Kauwe et al 2015)
1 1 1 1
CESM2-WACCM CLM5 (Lawrence et al 2019) 3 1 1 1
CESM2 CLM5 (Lawrence et al 2019) 10 2 3 2
CNRM-CM6-1 ISBA with fixed LAI
monthly climatology
(Garrigues et al 2015a,
Garrigues et al 2015b)
30 1 6 6
CNRM-ESM-1 ISBA with interactive LAI
(Garrigues et al 2015b,
Garrigues et al 2015a)
7 1 5 5
CanESM5-CanOE CLASS-CTEM (Arora and
Scinocca 2016)
3 3 3 3
CanESM5 CLASS-CTEM (Arora and
Scinocca 2016)
50 50 50 50
EC-Earth3-Veg LPJ-GUESS v4 (Forrest
et al 2018)
4 3 3 3
INM-CM4-8 No name (Volodin et al
1 1 1 1
INM-CM5-0 No name (Volodin et al
10 1 1 1
30 1 9 6
MIROC-ES2L VISIT-e (Ito and Inatomi
3 1 1 1
MPI-ESM1-2HR JSBACH (Reick et al 2013) 10 2 2 2
MPI-ESM2-2-LR JSBACH (Reick et al 2013) 10 10 10 10
NorESM2-LM GFDL-LM3.0 (Gerber et al
3 1 3 1
UKEMS1-0-LL JULES (Harper et al 2016) 19 5 4 5
Thus, an indication on the changes induced by the
mean and the variability on the production resilience
may be obtained by comparing the projected relat-
ive changes of the mean and of the variability, using
the same weight. Equation (4) provides a normalized
indicator of such comparison:
(|µ/µ| − |σ|)/(|µ/µ|+|σ|),(5)
which varies from 1 (variability dominates) to +1
(mean dominates), which is useful to assess and
compare the dominant relative changes in different
In this study, RPand its changes are computed on
a large ensemble composed of all the climate change
simulations for vegetation GPP available from all the
Earth System Grid Federation (ESGF) portals up to
31 December 2019. The full list of simulations is
provided in table 1, along with the detailed reference
to the land surface component of the ESMs.
The ESMs land surface components include a
prognostic representation of the biosphere with spa-
tially distributed vegetation processes such as evapo-
transpiration, photosynthesis, carbon allocation and
growth of leaves, stems and roots interacting with
near surface meteorological variables such as temper-
ature, radiation, wind and CO2concentration, and
soil variables such as moisture, carbon and nitrogen
(see references in table 1). Therefore, GPP variability
in ESMs is the result of both bio-geophysical and bio-
geochemical processes such as soil moisture dynamics
and energy budget, permafrost thawing, atmospheric
CO2fertilization and nitrogen limitation as well as
land use changes defined as a function of the different
future scenarios. Some of the models also include a
simplified representation of abiotic stresses on veget-
ation, such as fires (Lawrence et al 2019, Bastos et al
Memory effects linked to antecedent drought
conditions are well reproduced since soil mois-
ture dynamics and the related physical feedbacks
were quite well developed already in the GCMs
(Seneviratne et al 2010), which are the predecessors
of the ESMs and from which ESMs inherits the rep-
resentation of abiotic processes. In general, ESMs
provide a reasonable representation of the GPP
response to drought (see citations in table 1), which is,
however, largely variable among models (Knauer et al
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
Figure 1. Global changes of annual GPP resilience (Rp) computed from an ensemble of 16 ESMs simulations under ssp126
(panels (a) and (b)), ssp245 (panels (c) and (d)), and ssp585 (panels e and f) CMIP6 scenarios in the periods 2021–2050 (panels
(a), (c), and (e)) and 2051–2100 (panels (b), (d), and (f)) compared to the period 1985–2014.
2015, Huang et al 2016, Orth et al 2020). This motiv-
ates the use of a large ensemble for a robust assess-
ment of GPP changes such as the one used here.
The annual GPP is derived by summing up the
monthly GPP outputs for each simulation listed in
table 1consistently with the spatial variability of
vegetation seasonality (Peano et al 2019). The annual
GPP data of each simulation is interpolated on a
common 0.5 degrees regular grid with a second con-
servative remapping method (Jones 1999, Chen and
Knutson 2008). The simulations’ ensemble mean Rp
is computed for each ESMs and for each period and
scenario. Finally, the overall median of the RPchanges
with respect to the historical period is computed
for each future period and scenario. The robustness
of the results is assessed by highlighting the areas
where at least 75% of the models agree on the sign
of changes.
3. Results
Future climate projections display significant changes
of GPP variability resilience (figure 1) compared to
period 1985–2014. The annual vegetation primary
production resilience indicator is anticipated to gen-
erally increase in the lower emission scenarios (ssp126
and ssp245, figures 1(a)–(d), respectively). The lar-
ger positive changes are expected to occur especially
in the snow dominated bioclimatic regions (see table
S2 (available online at
mmedia)). The amplitude and the area covered by
these changes are comparatively larger in the ssp245
scenario than in the ssp126 scenario and increase with
time towards the end of the 21st century (table 2).
Positive changes are also estimated for Central Africa
and the Sahel regions, India and over the Himalayan
Plateau. However, regions with loss of GPP resilience
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
Table 2. Fraction of global land area where the relative resilience indicator (Rp/Rp) exceeds different thresholds (5%; 10%; 15%; 20%)
in the simulation ensemble median. The first estimation (third and fourth columns) considers all areas displaying changes larger than
the thresholds. A second estimate (fifth and sixth columns) is restricted to the areas where at least the 75% of the models agree on the
sign of changes.
Fraction of land area
with changing resilience
Fraction of land area considering
75% models’ agreement
Period Scenario Rp/Rp> 5% Rp/Rp< 5% Rp/Rp> 5% Rp/Rp< 5%
2021–2050 ssp126
2051–2100 ssp126
Rp/Rp> 10% Rp/Rp< 10% Rp/Rp> 10% Rp/Rp< 10%
2021–2050 ssp126
2051–2100 ssp126
Rp/Rp> 15% Rp/Rp< 15% Rp/Rp> 15% Rp/Rp< 15%
2021–2050 ssp126
2051–2100 ssp126
Rp/Rp> 20% Rp/Rp< 20% Rp/Rp> 20% Rp/Rp< 20%
2021–2050 ssp126
2051–2100 ssp126
appear as well, especially in Brazil, China and sur-
rounding countries of equatorial America. Under the
ssp245 socio-economic scenario, the CMIP6 ESMs
project resilience losses also in Mexico and the south-
ern part of the US, the Mediterranean region, South-
ern Africa and Australia. This occurs not only in the
mid-long term (2051–2100, figure 1(d)), but also in
the near term (2021–2050; figure 1(c)).
Under moderate emission scenarios (ssp126 and
ssp245), about one third of land area is going to exper-
ience an increase of vegetation annual GPP resilience
over the period 2021–2050 (see table 2). This propor-
tion is slightly lower, about one forth, when consider-
ing only the areas where 75% of the models agree on
the sign of changes. Differently, the area with positive
changes will cover almost half of the global land area
(less than one third when the constraint on models’
agreement is introduced) over the period 2051–2100.
Regions losing resilience cover a smaller percentage of
the global area, about 10% under ssp245 in the near
term. The differences between the plain estimate and
the one constrained on models’ agreement become
negligible for variations of resilience higher than 15%
(table 2).
The results for the ssp585 scenario stand out sig-
nificantly compared to the lower emission scenarios.
Broad areas with negative change (i.e. loss of veget-
ation GPP resilience) appear already in the period
2021–2050 (figure 1(e)) in the Amazon region, the
Unites States, South Canada, Western Europe, the
Mediterranean basin, as well as in the Middle East,
Central, Western and Southern Africa, Southeast Asia
and China, and Oceania. Areas with at least 5% loss
of vegetation GPP resilience are projected to cover
approximately one fifth of the global land (12% con-
sidering models’ agreement); while areas with more
than 15% losses are projected to be limited to 3%.
Positive gains of vegetation GPP resilience in boreal
regions are simulated to be more limited with respect
to the lower emission scenarios. Gains of at least 5%
are expected to cover about one fourth of the global
areas (15% considering models’ agreement); while
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
Figure 2. Ensemble mean share of the two factors triggering changes in the vegetation annual production resilience indicator (as
from equation (5), see section 2). Positive values (light and dark blue areas) point to changes in the resilience indicator mainly
due to changes in the mean GPP. Negative values (red and yellow areas) are associated with grid points where the changes in the
resilience indicator are mainly driven by changes in the GPP variability.
areas with changes larger than 15% are limited to the
6%, similarly to the ssp126 scenario.
The severity of the projected losses is expected to
further exacerbate in the period 2051–2100. In the
Taking the Highway scenario, less and less regions are
expected to experience gains in vegetation GPP resi-
lience. These regions are: La Plata basin in Argen-
tina, part of the Sahel region, Eastern Africa, Western
India, North-western China and some regions along
the coast of the Arctic Sea. In general, areas gaining at
least 5% resilience are simulated to be limited to 14%
(8% considering model agreement) of global areas,
while regions with more than 15% increase of veget-
ation primary production resilience are limited glob-
ally to 6% of the land area. The areas losing resilience
are expected to outbalance those ones increasing resi-
lience and cover 43% (25% considering models agree-
ment) of global land area with more than 5% resili-
ence losses. Globally, 13% of land areas are predicted
to lose more than 15% vegetation primary production
The GPP resilience changes can be determined
either by the change in the GPP mean and by the
changes in the GPP variability due to climate change
(see section 2). Positive resilience changes in the
near term under moderate emission scenarios are
often linked to positive changes in the mean annual
GPP (figures 2(a)–(e) and S4) connected to over-
all higher levels of atmospheric CO2concentration
and to higher mean growing temperature in Boreal
Regions. Negative resilience changes are generally
associated to an increase in the interannual variab-
ility of GPP (see figure S5). The areas affected by
an increase of variability largely change across the
scenarios and reach their maximum extent under the
scenario ssp585 (figures 2(e), (f), S5(e) and (f )).
Gain and losses of resilience are quantified at the
national level in order to provide country-specific
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
Figure 3. Percentages of area with more than 15% annual GPP resilience change for the ten wider countries, Russia (RUS),
Canada (CAN), the United States of America (USA), China (CHN), Brazil (BRA), Australia (AUS), the European Union (EUR),
India (IND), Kazakhstan (KAZ), and Argentina (ARG). Negative values refer to the percentage of areas with negative GPP
resilience changes.
information for adaptation options, and possibly to
support ambitious mitigation policies. This analysis
is displayed in figure 3for the ten largest coun-
tries (and in table S3 for all World countries). Rus-
sia is characterized by the widest gains of resilience,
which could cover almost 70% of the country area
in period 2051–2100 under the ssp245 scenario. The
spatial extent of areas expected to experience gains
is reduced up to about 15% in the near term under
the ssp585 scenario. This tendency continues towards
the end of the century, under the ssp585 scenario,
when also areas with GPP resilience losses start to
appear. Canada shows a similar picture, but with less
optimistic estimation of predicted losses largely out-
balancing the gains in the 2051–2100 period under
the ssp585 scenario. The US and China display similar
figures, with gains predicted to reach 20% in the low
emission scenarios (ssp126 and ssp245) and losses
ranging from 10% to 15% in the ssp585 over period
2051–2100. Among the largest countries, Brazil is the
one characterized by the most alarming projections,
with the risk of losing resilience in 50% of its total
territory under the ssp585 scenario at the end of the
21st Century. It is worth noting that these changes are
likely to represent an underestimation as the current
trend of land-use change (Freitas et al 2018) is only
partially considered in the ESMs (Hurtt et al 2020).
Australia is estimated to undergo negligible losses .
Nevertheless, Australia will experience comparatively
large losses of resilience towards the end of the cen-
tury under the ssp585 scenario. The European Union
is characterized by a more stable situation with sig-
nificant positive changes only under the ssp245 scen-
ario over the period 2051–2100. India shows projec-
tions similar to the EU, but with significant areas of
vegetation that both gain and lose resilience under the
high emission scenario at the end of the 21st Cen-
tury. Large positive and negative changes in resili-
ence are also estimated for Argentina under the high
emission scenario (2051–2100). Whether or not these
compensating changes in different area are beneficial
for the countries’ adaptive capacity could be subject
of specific follow-up investigations.
Results for the remaining World countries (see
table S3) allow identifying severe cases, such as
losses of resilience higher than 15% over more than
50% of the area under the ssp585 scenario over
the period 2051–2100 in Gabon, Bhutan, Venezuela,
Equatorial Guinea, Malaysia, Peru, Guyana, Lebanon,
Japan, Congo, Bolivia, Honduras, Zambia, South
Korea, Papua Nuova Guinea and other 16 coun-
tries. Under the same scenario, the list of ‘win-
ner’ countries is much shorter, with only Somalia
gaining at least 15% resilience over more than
50% of its territory. Countries having the largest
increase of vegetation production resilience under
the ssp245 scenario in the mid-long term are Rus-
sia and the Northern European countries. Under the
ssp126 scenario, the vegetation production resilience
increases are geographically spread into more con-
tinents. In both ssp126 and ssp245 scenarios, there
are almost no countries experiencing more than 15%
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
losses of resilience over more than 10% of their
4. Discussion and conclusions
This study implements a simple and robust statistical
indicator, the Annual Production Resilience Indicator
(Rp), able to provide a bulk estimation of ecological
resilience from annual production time-series. Being
focused on production, Rpis not sensitive to changes
in ecosystem structure and other state variables that
play a fundamental part in Holling’s definition of
ecological resilience (Holling 1973). Conversely, it is
formally consistent with more modern conception
of resilience allowing adaptation and other changes
in ecosystem structure in a resilience framework
(Walker et al 2004). Other indicators based on higher
temporal resolution data could be employed as well
in order to assess more detailed aspects of resilience
in a non-linear dynamics framework (Yi and Jackson
2021). However, Rpalready provides a quick and use-
ful indication of the changes in statistical stability of
the production time series in different periods, and it
is especially suitable for being computed on large data
volumes. Here, the vegetation GPP resilience changes
are estimated with reference to the climate variabil-
ity simulated by a large ensemble of state-of-the-art
While the main properties of Rpare already
demonstrated (Zampieri et al 2019,2020b, Zampieri
2021), as also summarised in the Appendix A
of the Supplementary Material (available online
at, here we
developed an interesting decomposition of the resili-
ence changes in terms of the mean GPP and its variab-
ility. Such decomposition provides a potentially use-
ful assessment for adaptation planning that should
presumably account differently for the changes in
the mean with respect to the changes in variability
of vegetation GPP in the future climate projections
(Mearns et al 1997, Smit et al 2000, van der Wiel
and Bintanja 2021). However, the implications of the
estimated GPP resilience changes need to be inter-
preted and confirmed through dedicated local-scale
assessments, also identifying the specific drivers of the
changes. For instance, while gains in resilience could
be generally considered a positive feature in dry envir-
onments, their consequences for wet environments
and over high latitudes should be further investig-
ated considering more complex approaches (Drews
and Greatbatch 2017, Laamrani et al 2020).
Our results aim at providing a useful first-
order indication that surely needs to be corrobor-
ated with further dedicated studies also account-
ing for different socio-economic perspectives. How-
ever, the analysis presented here is already poten-
tially very useful to identify the world regions
where there might be serious losses of vegetation
GPP resilience as well as the countries that are
subject to the most urgent necessity of improv-
ing adaptive capacity and resilience to climate-
related hazards under different future emission
Our results show large differences in the changes
of GPP resilience across the globe, depending on
greenhouse gases concentration of the projected scen-
arios. Under low emission scenarios, as found in pre-
vious studies (Hubau et al 2020), the CO2fertiliza-
tion effect often prevail over the negative effect due
global warming (e.g. an increase in metabolic costs
which in turn would lead to a reduction in forest pro-
ductivity and their efficiency, Collalti et al 2020) and
to the increase of climate variability. We find a gen-
eral increase of vegetation GPP resilience over boreals
regions that is in general agreement with the already
observed changes of vegetation distribution in those
areas (Myneni et al 1997). This tendency is expec-
ted to be reinforced in the future climate scenarios,
especially those with higher greenhouse gases emis-
sions (Zampieri and Lionello 2010). However, radi-
ation will always be a limiting factor for the vegeta-
tion development at very high latitudes (Seddon et al
The main findings point to areas in the mid-
latitudes where vegetation resilience is estimated to
decrease in the higher emission scenarios, such as
the Mediterranean, the mid-West in the US, Cent-
ral America, part of China, Southern Africa and Aus-
tralia. The stability of agricultural production and
the reliability of ecosystem services provided by the
natural vegetation might be compromised in these
regions, unless sensible adaptation actions are taken.
The importance of climate change mitigation is most
evident under the higher emission scenarios, where
vegetation resilience is affected in most land areas and
especially in tropical regions, where society is highly
dependent on ecosystem services and more vulner-
able to climatic changes.
The results of our analysis strongly support the
SDG-13 on taking action to combat climate change
and its impacts. Over areas with a high level of
anthropization, our results are relevant for agricul-
tural production, which is a main source of employ-
ment, livelihood and income for a large portion of
population especially in developing countries (SDG-
1, no poverty) as well as a main food source (SDG-2,
no hunger). Our results are also relevant for the SDG-
15, on the sustainable management of ecosystems and
halting land degradation and biodiversity loss.
Overall, in the scenario with lower climate change
mitigation (i.e. the Fossil-fuel Development scenario),
the areas losing vegetation resilience are larger than
the ones gaining resilience, jeopardizing the stability
of the ecosystems structure (and of the related ser-
vices). Adapting to changes in variability more than
to changes in the mean production of vegetation will
be critical for society and natural ecosystems in areas
experiencing large vegetation GPP resilience losses.
Environ. Res. Lett. 16 (2021) 105001 M Zampieri et al
Data availability statement
The data that support the findings of this study are
openly available at the following URL/DOI: https://
For transparency and reproducibility, this paper uses
publicly available data provided by the IPCC through
the Earth System Grid Federation LLNL, CEDA,
(ESGF, and open-
source software to compute the annual production
resilience indicator (Zampieri et al 2020a). This work
was supported by the FOCUS-AFRICA H2020 pro-
ject under Grant No 869575.
Author contributions
MZ conceived the study and drafted the paper, MZ
and BG performed the analysis, PDP downloaded
data and performed initial checking, all authors
reviewed the manuscript and contributed to the final
Conflict of interest
Authors declare no conflicts of interests.
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... Other recent work has analysed annual GPP from a large ensemble of climate models' future projections [85], using the ratio of the squared mean and variance of annual GPP as a 'resilience indicator', proportional to the return period of the largest tolerable perturbation. This assumes a multiyear timescale of recovery, when it can be sub-annual [14,15,24]. ...
... This assumes a multiyear timescale of recovery, when it can be sub-annual [14,15,24]. Notable is a predicted increase in inter-annual GPP variability across much of the tropics under high-end climate forcing, especially in the Amazon [85]. ...
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We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here, we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilizing the generic mathematical behaviour of complex systems—loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales could aid management and governance at these different scales, and identify priorities for further work. This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.
... Following the "wet areas getting wetter and dry areas getting drier" paradigm (Held and Soden, 2006;Toreti et al., 2013), the main climate change signal for precipitation over Africa broadly consists of the wetting of the Tropics and the drying of the Subtropics Spinoni et al., 2020). However, Africa experiences substantial inter-annual and intra-seasonal climate variability that is also increasing (Dosio et al., 2021;Nikulin et al., 2018) and that could affect vegetation seasonality (Peano et al., 2019) and stability (Zampieri et al., 2021). ...
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Precipitation seasonality is the main factor controlling vegetation phenology in many tropical and subtropical regions. Anticipating the rain onset is of paramount importance for field preparation and seeding. This is of particular importance in various African countries that rely on agriculture as a main source of food, subsistence and income. In such countries, skilful and accurate onset forecasts could also inform early warning and early actions, such as aids logistics planning, for food security. Here, we assess the skill of the seasonal forecast data provided by the Copernicus Climate Change Service in predicting the rain onset over Africa. The skill, i.e. the accuracy of the seasonal forecasts simulation ensemble compared to the climatology, is computed in a proba-bilistic fashion by accounting for the frequencies of normal, early and late onsets predicted by the forecast system. We compute the skill using the hindcasts (forecast simulations conducted for the past) starting at the beginning of each month in the period 1993-2016. We detect the onset timing of the rainy season using a non-parametric method that accounts for double seasonality and is suitable for the specific time-window of the seasonal forecast simulations. We find positive skills in some key African agricultural regions some months in advance. Overall, the multi-model ensemble outperforms any individual model ensemble. We provide targeted recommendations to develop a useful climate service for the agricultural sector in Africa.
... Biodiversity increases the stability and resilience of ecosystem functioning (Cardinale et al., 2012;IPBES, 2018;Isbell et al., 2015). Global changes are threatening the stability and resilience of ecosystems (IPBES, 2018;Zampieri et al., 2021). Safe planetary boundaries have been overstepped for biosphere integrity (biodiversity loss and extinctions), nitrogen and phosphorus biogeochemical flows, and climate and land-system changes (Rockström et al., 2009;Steffen et al., 2015). ...
Inland freshwaters, especially rivers and lakes, represent a very small fraction of the total amount of water on the planet, but at the same time, they are home to an immense biodiversity and support human well-being, providing many ecosystem services (such as food, recreational sites, climate mitigation, and pollution purification). Finite water resources are being exploited and degraded rapidly by growing human populations. Human activities cause multiple pressures on freshwater ecosystems via many avenues, including water withdrawal, pollution, changes in the natural landscape, overfishing, and the introduction of invasive species. These pressures affect many aspects of aquatic ecosystems, which can then result in changes in ecosystem services. Understanding the structure and function of freshwater ecosystems and adopting a holistic overview of human–environment relationships is needed to protect aquatic ecosystems and secure sustainable use of freshwater resources.
... In the GPP prediction of the short-term future (such as one week later), surface factor data (such as NDVI and EVI) can be obtained by using reasonable prediction methods, and climate factor data (such as temperature and precipitation) can be obtained by using meteorological forecasting methods [86,87]. In GPP projections for the long-term future (such as 2050), earth system models can predict future surface and climate factor data under different emission scenarios and provide strong support for future research in various fields [88][89][90]. GPP simulation, on the other hand, also has its applicable situations, for example, the 500 m resolution MOD17A2H. In 006 GPP products (, ...
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Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only −0.034). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction.
... The spatial patterns of vegetation resilience demonstrated in our study are similar to those reported in related studies [25,27,46]. In arid and semiarid regions (e.g., the west of the United States, Sub-Saharan Africa, and Australia), low resilience suggests strong self-memory of vegetation growth, which means that vegetation recovers slowly to its normal state during or after climatic disturbances. ...
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The quantitative assessment of vegetation resilience and resistance is worthwhile to deeply understand the responses of vegetation growth to climate anomalies. However, few studies comprehensively evaluate the spatiotemporal resilience and resistance of global vegetation responses to climate change (i.e., temperature, precipitation, and radiation). Furthermore, although ecosystem models are widely used to simulate global vegetation dynamics, it is still not clear whether ecosystem models can capture observation-based vegetation resilience and resistance. In this study, based on remotely sensed and model-simulated leaf area index (LAI) time series and climate datasets, we quantified spatial patterns and temporal changes in vegetation resilience and resistance from 1982–2015. The results reveal clear spatial patterns of observation-based vegetation resilience and resistance for the last three decades, which were closely related to the local environment. In general, most of the ecosystem models capture spatial patterns of vegetation resistance to climate to different extents at the grid scale (R = 0.43 ± 0.10 for temperature, R = 0.28 ± 0.12 for precipitation, and R = 0.22 ± 0.08 for radiation); however, they are unable to capture patterns of vegetation resilience (R = 0.05 ± 0.17). Furthermore, vegetation resilience and resistance to climate change have regionally changed over the last three decades. In particular, the results suggest that vegetation resilience has increased in tropical forests and that vegetation resistance to temperature has increased in northern Eurasia. In contrast, ecosystem models cannot capture changes in vegetation resilience and resistance over the past thirty years. Overall, this study establishes a benchmark of vegetation resilience and resistance to climate change at the global scale, which is useful for further understanding ecological mechanisms of vegetation dynamics and improving ecosystem models, especially for dynamic resilience and resistance.
CONTEXT The development of methods for improving the assessment of resilience of socio-ecological systems has become increasingly important as the severity and variability of global climate patterns continue to compound and intensify. OBJECTIVE The present study was conducted with the aim of using a dynamically-coupled bio-physical and socioeconomic model to test the efficacy of stakeholder-defined policy measures in conferring resilience to two agroecological variables, farm income and water-table depth, experiencing both socio-ecological shocks and climatic stress in the Rechna Doab basin of northeastern Pakistan. METHODS The objective was accomplished by using a dynamically-coupled physical and group-built system dynamics modelling framework for scenario testing and output generation, including three NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-DGGP), two relevant socio-ecological shock scenarios (market inflation and canal water supply variability), and three stakeholder-defined policy measures. Resilience was assessed using the following system functionality metrics: 1) The degree of return for each variable after a perturbation, i.e., the extent to which the observed variable returns to baseline functioning; 2) The return time of the variable to baseline functioning; 3) The rate of variable return to baseline; 4) Overall perturbation of the system post-disturbance; and 5) The corrective impact of the shock on system functionality. Differences in the resilience metrics were subsequently compared based on the behavioral change(s) of the study variables in response to the application of the three selected policy scenarios. RESULTS AND CONCLUSIONS The results presented here indicate that rainwater harvesting is the most effective stakeholder-defined policy measure for improving or maintaining resilience of the tested study variables in the Rechna Doab agricultural basin; this holds true for every climate and shock scenario with the exception of water-table depth in the upper and mid-watershed regions under canal supply shock conditions, for which canal lining is the most effective policy measure. The irrigation improvement and canal lining policies were more effective in improving the resilience metrics of water-table depth than those of the farm income variable, as the water-table depth variable is not bound so tightly by economic constraints; although there were regional differences in policy efficacy, these trends hold true, on average, for the entire watershed. The results presented herein align well with previous studies conducted in the region which note similar patterns in spatial resilience of agroecosystem variables (i.e., upper to lower watershed) in the Recha Doab basin as well as regional differences in the efficacy of the stakeholder-defined policy measures. SIGNIFICANCE This unique approach for assessing socio-environmental policies can help improve decision-making processes with respect to agro-infrastructure and climate change mitigation strategies in vulnerable agroecosystems.
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We present ResiPy, a Python object-oriented software to compute the annual production resilience indicator. This indicator can be applied to different anthropic and natural systems, e.g., agricultural production, natural vegetation and water resources, to quantify their stabilities and the risk of adverse events. We propose an illustrative application of ResiPy to agricultural production in Europe, expressed in economic terms. After estimating the single-country or single-crop resilience, we evaluate the overall resilience of diversified production systems, composed of different crops and different cultivation areas. ResiPy also includes a powerful graphical tool to visually estimate the impact of diversity on complex production systems. The robustness of the indicator and the simplicity of the code ensure its effective applicability in many fields and with different datasets.
Full-text available
We present ResiPy, a Python object-oriented software to compute the annual production resilience indicator. This indicator can be applied to different anthropic and natural systems, e.g., agricultural production, natural vegetation and water resources, to quantify their stabilities and the risk of adverse events. We propose an illustrative application of ResiPy to agricultural production in Europe, expressed in economic terms. After estimating the single-country or single-crop resilience, we evaluate the overall resilience of diversified production systems, composed of different crops and different cultivation areas. ResiPy also includes a powerful graphical tool to visually estimate the impact of diversity on complex production systems. The robustness of the indicator and the simplicity of the code ensure its effective applicability in many fields and with different datasets.
Full-text available
Assessing and managing the human influence on the natural and anthropic ecosystems strongly demands for robust measures of their resilience, especially in a world facing global changes as it is the Earth now. Many definitions of resilience have been proposed in order to cover different contexts. However, they are mostly derived either from the ecological or the engineering definitions of resilience, which substantially differ between each other. Here, following the strategy for measuring the system perturbations by the return period of their impacts on production (or equivalently by the inverse frequency), I demonstrate the mathematical equivalence of the ecological and engineering definitions of resilience for a special class of production systems. These finding provides additional robustness to resilience assessments based on the recently proposed annual production resilience indicator.
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Resilience is the central concept for understanding how an ecosystem responds to a strong perturbation, and is related to other concepts used to analyze system properties in the face of change such as resistance, recovery, sustainability, vulnerability, stability, adaptive capacity, regime shift, and tipping point. It is extremely challenging to formulate resilience thinking into practice. The current state-of-art approaches of assessing ecosystem resilience may be useful for policy makers and ecosystem resource managers to minimize climatological or natural disaster related impacts. Here, we review the methods of assessing resilience and classify and limit them to three cases: (a) forest resilience based mainly on remote sensing and tree-ring data; (b) soil microbial community resilience based on laboratory and field studies; and (c) hydrological resilience of terrestrial biomes based on the Budyko framework and climate data.
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The frequency of climate extremes will change in response to shifts in both mean climate and climate variability. These individual contributions, and thus the fundamental mechanisms behind changes in climate extremes, remain largely unknown. Here we apply the probability ratio concept in large-ensemble climate simulations to attribute changes in extreme events to either changes in mean climate or climate variability. We show that increased occurrence of monthly high-temperature events is governed by a warming mean climate. In contrast, future changes in monthly heavy-precipitation events depend to a considerable degree on trends in climate variability. Spatial variations are substantial however, highlighting the relevance of regional processes. The contributions of mean and variability to the probability ratio are largely independent of event threshold, magnitude of warming and climate model. Hence projections of temperature extremes are more robust than those of precipitation extremes, since the mean climate is better understood than climate variability.
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Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth’s surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth’s surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth systemHuman land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth’s surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations.
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Northern boreal forests are characterized by accumulation of accumulation of peat(e.g., known as paludification). The functioning of northern boreal forest species and their capacity to adapt to environmental changes appear to depend on soil conditions. Climate warming is expected to have particularly pronounced effects on paludified boreal ecosystems and can alter current forest species composition and adaptation by changing soil conditions such as moisture, temperature regimes, and soil respiration. In this paper, we review and synthesize results from various reported studies (i.e., 88 research articles cited hereafter) to assess the effects of climatic warming on soil conditions of paludified forests in North America. Predictions that global warming may increase the decomposition rate must be considered in combination with its impact on soil moisture, which appears to be a limiting factor. Local adaptation or acclimation to current climatic conditions is occurring in boreal forests, which is likely to be important for continued ecosystem stability in the context of climate change. The most commonly cited response of boreal forest species to global warming is a northward migration that tracks the climate and soil conditions (e.g., temperature and moisture) to which they are adapted. Yet, some constraints may influence this kind of adaptation, such as water availability, changes in fire regimes, decomposer adaptations, and the dynamic of peat accumulation. In this paper, as a study case, we examined an example of potential effects of climatic warming on future paludification changes in the eastern lowland region of Canada through three different combined hypothetical scenarios based on temperature and precipitation (e.g., unchanged, increase, or decrease). An increase scenario in precipitation will likely favor peat accumulation in boreal forest stands prone to paludification and facilitate forested peatland expansion into upland forest, while decreased or unchanged precipitation combined with an increase in temperature will probably favor succession of forested peatlands to upland boreal forests. Each of the three scenarios were discussed in this study, and consequent silvicultural treatment options were suggested for each scenario to cope with anticipated soil and species changes in the boreal forests. We concluded that, despite the fact boreal soils will not constrain adaptation of boreal forests, some consequences of climatic warming may reduce the ability of certain species to respond to natural disturbances such as pest and disease outbreaks, and extreme weather events
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Forest production efficiency (FPE) metric describes how efficiently the assimilated carbon is partitioned into plants organs (biomass production, BP) or-more generally-for the production of organic matter (net primary production, NPP). We present a global analysis of the relationship of FPE to stand-age and climate, based on a large compilation of data on gross primary production and either BP or NPP. FPE is important for both forest production and atmospheric carbon dioxide uptake. We find that FPE increases with absolute latitude, precipitation and (all else equal) with temperature. Earlier findings-FPE declining with age-are also supported by this analysis. However, the temperature effect is opposite to what would be expected based on the short-term physiological response of respiration rates to temperature, implying a top-down regulation of carbon loss, perhaps reflecting the higher carbon costs of nutrient acquisition in colder climates. Current ecosystem models do not reproduce this phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely to overestimate carbon losses in a warming climate.
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Soil moisture droughts have comprehensive implications for terrestrial ecosystems. Here we study time-accumulated impacts of the strongest observed droughts on vegetation. The results show that drought duration, the time during which surface soil moisture is below seasonal average, is a key diagnostic variable for predicting drought-integrated changes in (i) gross primary productivity, (ii) evapotranspiration, (iii) vegetation greenness, and (iv) crop yields. Drought-integrated anomalies in these vegetation-related variables scale linearly with drought duration with a slope depending on climate. In arid regions, the slope is steep such that vegetation drought response intensifies with drought duration, whereas in humid regions, it is small such that drought impacts on vegetation are weak even for long droughts. These emergent large-scale linearities are not well captured by state-of-the-art hydrological, land surface, and vegetation models. Overall, the linear relationship of drought duration versus vegetation response and crop yield reductions can serve as a model benchmark and support drought impact interpretation and prediction.
Structurally intact tropical forests sequestered about half of the global terrestrial carbon uptake over the 1990s and early 2000s, removing about 15 percent of 1–3 anthropogenic carbon dioxide emissions. Climate-driven vegetation models 4,5 typically predict that this tropical forest ‘carbon sink’ will continue for decades . Here we assess trends in the carbon sink using 244 structurally intact African tropical forests spanning 11 countries, compare them with 321 published plots from Amazonia and investigate the underlying drivers of the trends. The carbon sink in live aboveground biomass in intact African tropical forests has been stable for the three decades to 2015, at 0.66 tonnes of carbon per hectare per year (95 percent confidence 6 interval0.53–0.79), in contrast to the long-term decline in Amazonian forests. Therefore the carbon sink responses of Earth’s two largest expanses of tropical forest have diverged. The difference is largely driven by carbon losses from tree mortality, with no detectable multi-decadal trend in Africa and a long-term increase in Amazonia. Both continents show increasing tree growth, consistent with the expected 7–9 net effect of rising atmospheric carbon dioxide and air temperature. Despite the past stability of the African carbon sink, our most intensively monitored plots suggest a post-2010 increase in carbon losses, delayed compared to Amazonia, indicating asynchronous carbon sink saturation on the two continents. A statistical model including carbon dioxide, temperature, drought, and forest dynamics accounts for the observed trends and indicates a long-term future decline in the African sink, whereas the Amazonian sink continues to weaken rapidly. Overall, the uptake of carbon into Earth’s intact tropical forests peaked in the 1990s. Given that the global terrestrial carbon sink is increasing in size, independent observations indicating greater recent carbon uptake into the Northern Hemisphere landmass10 reinforce our conclusion that the intact tropical forest carbon sink has already peaked. This saturation and ongoing decline of the tropical forest carbon sink has consequences for policies intended to stabilize Earth’s climate.