ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus PublicationsGöttingen, Germany10.5194/esd-7-327-2016Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 ∘C and 2 ∘CSchleussnerCarl-Friedrichcarl.schleussner@climateanalytics.orghttps://orcid.org/0000-0001-8471-848XLissnerTabea K.FischerErich M.https://orcid.org/0000-0003-1931-6737WohlandJanPerretteMahéGollyAntoniushttps://orcid.org/0000-0001-6213-0183RogeljJoerihttps://orcid.org/0000-0003-2056-9061ChildersKatelinScheweJacobhttps://orcid.org/0000-0001-9455-4159FrielerKatjaMengelMatthiashttps://orcid.org/0000-0001-6724-9685HareWilliamSchaefferMichielClimate Analytics, Friedrichstr. 231 – Haus B, 10969 Berlin, GermanyPotsdam Institute for Climate Impact Research, Potsdam, GermanyInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandGFZ German Research Centre for Geosciences, Potsdam, GermanyEnergy Program, International Institute for Applied Systems Analysis, Laxenburg, AustriaUniversity of Potsdam, Institute of Earth and Environmental Science, Potsdam, GermanyWageningen University and Research Centre, Environmental Systems Analysis Group, Wageningen, the NetherlandsCarl-Friedrich Schleussner (carl.schleussner@climateanalytics.org)21April2016723273518October201527November201531March20167April2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://esd.copernicus.org/articles/7/327/2016/esd-7-327-2016.htmlThe full text article is available as a PDF file from https://esd.copernicus.org/articles/7/327/2016/esd-7-327-2016.pdf
Robust appraisals of climate impacts at different levels of global-mean
temperature increase are vital to guide assessments of dangerous
anthropogenic interference with the climate system. The 2015 Paris Agreement
includes a two-headed temperature goal: “holding the increase in the global average temperature to well below
2 ∘C above pre-industrial levels and pursuing efforts to limit the
temperature increase to 1.5 ∘C”.
Despite the prominence of these two
temperature limits, a comprehensive overview of the differences in climate
impacts at these levels is still missing. Here we provide an assessment of
key impacts of climate change at warming levels of 1.5 ∘C and 2 ∘C,
including extreme weather events, water availability, agricultural yields,
sea-level rise and risk of coral reef loss. Our results reveal substantial
differences in impacts between a 1.5 ∘C and 2 ∘C warming that are highly
relevant for the assessment of dangerous anthropogenic interference with the
climate system. For heat-related extremes, the additional 0.5 ∘C
increase in global-mean temperature marks the difference between events at
the upper limit of present-day natural variability and a new climate regime,
particularly in tropical regions. Similarly, this warming difference is
likely to be decisive for the future of tropical coral reefs. In a scenario
with an end-of-century warming of 2 ∘C, virtually all tropical coral
reefs are projected to be at risk of severe degradation due to temperature-induced bleaching from 2050 onwards. This fraction is reduced to about 90 %
in 2050 and projected to decline to 70 % by 2100 for a 1.5 ∘C
scenario. Analyses of precipitation-related impacts reveal distinct regional
differences and hot-spots of change emerge. Regional reduction in median
water availability for the Mediterranean is found to nearly double from 9 %
to 17 % between 1.5 ∘C and 2 ∘C, and the projected lengthening of
regional dry spells increases from 7 to 11 %. Projections for agricultural
yields differ between crop types as well as world regions. While some (in
particular high-latitude) regions may benefit, tropical regions like West
Africa, South-East Asia, as well as Central and northern South America are
projected to face substantial local yield reductions, particularly for wheat
and maize. Best estimate sea-level rise projections based on two illustrative
scenarios indicate a 50 cm rise by 2100 relative to year 2000-levels for a
2 ∘C scenario, and about 10 cm lower levels for a 1.5 ∘C
scenario. In a 1.5 ∘C scenario, the rate of sea-level rise in 2100
would be reduced by about 30 % compared to a 2 ∘C scenario. Our
findings highlight the importance of regional differentiation to assess both
future climate risks and different vulnerabilities to incremental increases
in global-mean temperature. The article provides a consistent and
comprehensive assessment of existing projections and a good basis for future
work on refining our understanding of the difference between impacts at
1.5 ∘C and 2 ∘C warming.
Introduction
Recent decades have seen increasing climate impacts, many of which science is
now able to attribute to anthropogenic carbon dioxide emissions and
consequent global warming . On-going temperature
increase will escalate these impacts on ecological and human systems
, which has made climate change a political
issue of central importance. The response of the global community to that
challenge laid out in the Paris Agreement under the United Nations Framework
Convention on Climate Change (UNFCCC) provides a promising framework for
global climate protection . Specifically, the Agreement
includes two long-term global goals (LTGGs): “holding the increase in the global average temperature to well below
2 ∘C above pre-industrial levels and pursuing efforts to limit the
temperature increase to 1.5 ∘C, recognizing that this would
significantly reduce the risks and impacts of climate change”. LTGGs have
been proven useful to guide climate action and their
inclusion aims to operationalize the “ultimate objective” of the UNFCCC of
a “stabilization of greenhouse gas concentrations in the atmosphere at a
level that would prevent dangerous anthropogenic interference with the
climate system” . Although the assessment of levels of
dangerous interference is primarily a political process that requires value
judgements and depends on different world views , it needs
to be informed by the best available science outlining the impacts of climate
change and mitigation efforts implied by different LTGGs. Based on the Fifth
Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC),
a recent expert assessment focussing on the adequacy of the LTGGs in
light of the ultimate objective of the convention concluded that
“significant climate impacts are already occurring […] and additional
magnitudes of warming will only increase the risk of severe, pervasive and
irreversible impacts” . While the report emphasized that a
warming of global mean surface air temperature (GMT) of 2 ∘C above
pre-industrial levels should not be seen as a “safe” level, it also concluded
that substantial research gaps exist regarding the differences in climate
impacts between a 1.5 ∘C and 2 ∘C temperature increase
. In particular, comprehensive, multi-sectoral assessments
of differences in climate impacts between a 1.5 ∘C and 2 ∘C
warming are lacking. The assessment of such differences would greatly profit
from a regional and impact centred approach that allows for a more
differentiated picture than globally aggregated metrics
. For example, changes in the hydrological cycle as a
result of temperature increase will be regionally dependent .
The “Turn down the heat” report series issued by the World Bank
assessed
climate risks for a 2 ∘C and a 4 ∘C warming above
pre-industrial levels for different world regions. The report of the Working
Group 2 (WG2) of the IPCC AR5 includes both, impact and region specific
chapters, and provides warming level dependent information on impacts where
available. The range of emission scenarios which provide the basis for the
climate impact projections in the IPCC AR5, the Representative Concentration
Pathways (RCPs), however, do not allow for a straightforward differentiation
between impacts for warming levels of 1.5 ∘C and 2 ∘C. Only
the lowest emission pathway RCP2.6 is in line with keeping GMT increase
above pre-industrial levels to below 2 ∘C with a likely chance
66 % probability, and no pathway in line with a
1.5 ∘C limit is assessed in the AR5. Still, the IPCC AR5 WG2 report
provides an expert assessment of key impacts at different levels of warming,
summarized in five “Reasons-for-Concern” RFCs,.
The risks for three out of five of these RFCs are assessed as at least
moderate at 1.5 ∘C GMT increase above pre-industrial levels, and as
at least moderate-high at 2 ∘C. In the RFC framework, moderate risks
imply that associated impacts are both detectable and attributable to climate
change with at least medium confidence, whereas high risks are associated
with severe and widespread impacts . Among the three
RFCs that show high risks at 2 ∘C are Risks to unique and
threatened systems (RFC1) that include coral reefs and other highly
vulnerable human systems as well as ecosystems, Risks associated with
extreme weather events (RFC2) and Risks associated with the distribution of impacts (RFC3).
Based on the Coupled Model Intercomparison Project 5
CMIP5, and the Inter-Sectoral Impact Model
Intercomparison Project ISI-MIP,, this article
provides an extensive assessment of regionally differentiated climate impacts
at levels of 1.5 ∘C and 2 ∘C GMT increase above
pre-industrial levels (henceforth 1.5 ∘C and 2 ∘C) for
different climate impacts, including increases in extreme weather events
(Sect. ), changes in water availability (Sect. ),
crop yield projections (Sect. ), sea-level rise (SLR, Sect. )
and coral reef degradation (Sect. ).
The following Sect. outlines our methods for the
assessment of changes in extreme weather indices, water availability and
agricultural impacts. Analyses of sea-level rise and impacts on coral reefs
contain additional details on sector-specific methods. Where impact-specific
additional methodological specifications are needed, these are given in the
respective section, followed by a presentation of the main results and a
short discussion. A summarizing discussion and conclusions finalize this
contribution in Sect. . The Supplement (SM)
provides additional methodological information as well as further impact
maps, regional overviews and summary tables.
Methods
This section provides an overview of the methods applied for the assessment
of extreme weather indices, water availability and agricultural impacts. The
individual subsections provide additional information on sector- and
impact-specific methods as well as on the data analysed. The meteorological
extreme indices are derived from an ensemble of general circulation models (GCMs)
from CMIP5 while our assessment of water
availability and agricultural impacts at 1.5 ∘C and 2 ∘C is
based on the ISI-MIP Fast Track data .
For both data archives, the impacts for a GMT increase of 1.5 and
2 ∘C above pre-industrial levels are derived for 20-year time slices
with the respective mean warming for each model separately. To account for
model deviations from observations over the historical period, the warming
levels are derived relative to the reference period 1986-2005, (this
reference period is 0.6 ∘C warmer than pre-industrial levels
(1850–1900), ), which translates to a warming of
0.9 ∘Cand 1.4 ∘C above reference period levels for the
1.5 ∘C and 2 ∘C limit, respectively. All time slices are
derived from the RCP8.5 scenario (the time slices for the individual GCMs are
given in the SM Table S1). 1986–2005 is also the common reference period to
assess projected changes in extreme indices and climate impacts.
All our results are calculated with respect to this common reference period.
For consistency with the respective policy targets, however, we express the
GMT differences of 0.9 ∘C and 1.4 ∘C by their implied
pre-industrial warming of 1.5 ∘C and 2 ∘C.
Analysing time-slices centred around a specific level of warming relies on
the assumption that the changes in the climate and climate impact signals
studied here are dominantly driven by changes in GMT and that the effect of
changes in time-lagged systems such as large-scale ocean circulations
on the quantities studied are of
minor importance. In addition, this approach does not account for the effect
of other anthropogenic climate forcers that may differ for the same level of
total radiative forcing such as aerosols . It comes,
however, also with several advantages. In particular, it eliminates the
spread due to different transient climate responses across the model
ensemble, which can deviate by up to a factor of two .
Traditional approaches that analyse impacts over a given time period for all
models in a model ensemble and relate this to a median GMT increase across
the model ensemble do not account for this ensemble-intrinsic spread of
global warming levels and will consequently overestimate the ensemble
uncertainty of the GMT-dependent indices studied. The time-slice approach has
furthermore been shown to provide better accuracy than traditional pattern
scaled approaches . Although also relying on the debatable
assumption of scenario-independence of the projected signals, which does not
fully hold in climate stabilization scenarios ,
time-slicing avoids known short-comings of classical pattern scaling
analysis. In particular, it allows one to capture non-linearities in extreme
indices and precipitation-related signals that relate to non-linear local
feedbacks or large-scale circulation changes .
In addition to the anthropogenic forcing, natural variability is a dominant
driver of the climate signal on multi-annual timescales for time-averaged
quantities such as mean temperature and precipitation change
and in particular for extreme weather events
. Thus, natural variability may mask an
already present climate change signal and consequently lead to a delayed
detection of the imprints of climate change . To overcome
this limitation, proposed a spatial aggregation approach
that allows for a robust detection of an anthropogenic footprint in climatic
extremes despite natural variability – an approach that has also been
successfully applied to the observational record .
Here we adopt and extend this spatial aggregation approach.
As in , we consider the distribution of changes in the
selected impact indicator at each grid point over the global land-mass
between 66∘ N and 66∘ S (henceforth referred to as global
land-mass) and additionally analyse changes for 26 world regions as
used insee Table for details. This yields
distributions for the indicators at 1.5 ∘C and 2 ∘C and for
each of the ensemble members and regions, where the sample size is given by
the number of grid points included in the respective regions. In a next step,
the statistical significance of differences between the 1.5 and
2 ∘C distributions is assessed for each region and ensemble member.
This is done using a two-sample Kolmogorov–Smirnov (KS) test with the null
hypothesis that both distributions for 1.5 ∘C and 2 ∘C are
drawn from the same probability distribution.
Overview of the world regions used as well as the respective
acronyms based on . Please note that the Central
American (CAM) region has been extended eastwards to also include the
Caribbean.
ALAAlaska, North-West CanadaNEBNorth-East BrazilAMZAmazonNEUNorthern EuropeCAMCentral America, Mexico, CaribbeanSAFSouth AfricaCASCentral AsiaSAHSaharaCEUCentral EuropeSASSouth AsiaCGIEast Canada, Greenland, IcelandSAUSouth Australia, New ZealandCNACentral North AmericaSEASouth-East AsiaEAFEast AfricaSSASouth-East South AmericaEASEast AsiaTIBTibetan PlateauENAEast North AmericaWAFWest AfricaMEDMediterraneanWASWest AsiaNASNorth AsiaWNAWest North AmericaNAUNorth AustraliaWSAWest Coast South America
A rejection of the test's null-hypothesis at a significance level of 95 % is
taken as a robust difference in projections between these two warming levels.
This pairwise test, based on the individual model ensemble members analysed,
allows for robust statements about differences between the two warming
levels, even if there is substantial overlap of uncertainty bands in the
model ensemble. For GCMs that provide multiple realizations, the
distributions are combined for each warming level leading to larger samples
and higher discriminatory power of the KS test. Please note that this
approach is only applied for the KS test and not for the ensemble
projections. For the latter, the averaged signal over multiple realizations
of a single GCM is included in the ensemble analysis ensuring equal weight to
all GCMs investigated (see SM Sect. 1 for further details on the methods and
the treatment of multiple realizations). A similar approach has been applied
recently to investigate the timing of anthropogenic emergence in simulated
climate extremes .
Based on the regionally specific distributions, cumulative density functions (CDF)
of changes in the impact indices over the land area of the respective
region are derived. As in , we fit a probability density
function to the empirical distribution of the climate signal using a Gaussian
kernel density estimator. Individual grid-cells are weighted according to
their latitude-dependent area. These CDFs are derived for each ensemble
member (GCM or GCM-impact model combination) and the ensemble median as well
the likely range (66 % of the ensemble members are within this range) are
given. This land-area focused approach allows to directly assess not only the
median change over a region, but also changes for smaller fractions of the
land area. At the same time, the uncertainty estimates based on the model
ensemble spread can be directly visualized.
Extreme weather events
There is a growing body of evidence showing that the frequency and intensity
of many extreme weather events has increased significantly over the last
decades as a result of anthropogenic climate change, but confidence in the
significance of the trend and attribution to anthropogenic origin differ
substantially between types of extreme weather events and regions
. With on-going warming, these trends are projected to
continue . Impacts of extreme weather events will
particularly, but not exclusively, affect the most vulnerable with the lowest
levels of adaptive capacity and represent one of the biggest threats posed by
climate change . In this Section, the difference in
impacts between a warming of 1.5 ∘C and 2 ∘C for four
different types of meteorological extreme event indices are assessed. Good
agreement between the CMIP5 model ensemble median estimates of extreme event
indices including the four indices investigated here and observational data
sets has been reported by . The indices used follow the
recommendations of the Expert Team on Climate Change Detection and Indices
and are derived on an annual basis:
Intensity of hot extremes (TXx): annual maximum value of daily
maximum temperature.
Warm spell duration indicator (WSDI): annual count of the longest
consecutive period in which the daily maximum temperature for each day
exceeds the 90 % quantile for this day over the reference period. The
minimum length is 6 consecutive days.
Dry spell length or consecutive dry days (CDD): annual maximum
number of consecutive days for which the precipitation is below 1 mm per day.
Heavy precipitation intensity or maximum accumulated 5-day
precipitation (RX5day): absolute annual maximum of consecutive 5-day precipitation.
Methods and data
Projected changes in climate extreme indices are assessed using an ensemble
of 11 CMIP5-models for TXx and WSDI and 14 for RX5day and CDD and follows the
methods outlined in Sect. . The model selection was done
based on data availability. All available GCMs are assessed on a uniform grid
with a 2.5∘× 1.9∘ resolution. Multiple realizations of
scenario runs for individual models are included when available, and in that
case allow to estimate CDFs for natural variability that are derived based on
pairwise realizations of model runs over the reference period (see SM Sect. 1.2
for further detail on the methodology applied).
Median changes of TXx (left panels) and WSDI (right panels) for a
warming of 2 ∘C (upper panels), 1.5 ∘C (middle panels) and
the difference between the two warming levels (lower panels). Changes in TXx
are given in ∘C, whereas changes in WSDI are given in
days.
We assess the changes in TXx and WSDI for a warming of 1.5 and
2 ∘C and derive changes of 20-year averages of extreme indices for
the model-dependent warming-level time-slices at each land grid point
relative to the 1986–2005 reference period. Changes in precipitation-related
indices are described as relative changes while we consider absolute changes
for the other indicators. For the CDF analysis for TXx, the absolute signal
is normalized by the standard deviation over the reference period.
ResultsHeat extremes
Substantial increases of 3 ∘C and more in TXx over large parts of
the Northern Hemisphere, central South America and South Africa as well as
increases in warm-spell durations (WSDI) of 3 months and more are projected
under a warming of 2 ∘C. Figure depicts changes
in TXx (left panels) and WSDI (right panels) for a 1.5 ∘C (top panels) and 2 ∘C
(middle panels) GMT temperature increase, as well as the differences between the two
warming levels (bottom panels) on a grid-cell basis. Particularly strong increases
in WSDI are found in some tropical coastal areas, which we attribute to a
large share of ocean surface in the respective grid cells that lead to an
amplification of the effect compared to pure land grid cells and should not
be over-interpreted. We correct for this potential spurious amplification by
excluding all grid-cells with a WSDI greater than 150 days under 2 ∘C
from the CDF analysis for the respective regions. The majority of GCMs
agree on a robust increase in these heat-related indices and show significant
differences between the two warming levels. The impacts are robustly smaller
at 1.5 ∘C warming in both cases (see results for the KS test listed in Table S2).
Globally and regionally resolved CDFs for TXx, normalized to the standard
deviation (σ) over the reference period, are given in
Fig. and median values are listed in Table S2. 50 % of
the global land-mass will experience a median TXx increase of more than
1.2 (1.8) SD (standard deviations) relative to the reference period for a warming of
1.5 ∘C (2 ∘C) above pre-industrial levels. The regional
assessments indicate that the tropical regions in Africa, South America and
South-East Asia are projected to experience the strongest increase in land
area covered by heat extremes relative to the regional natural variability,
where 3-σ events become the new normal under a 2 ∘C warming.
The pattern of a strong tropical signal is mainly due to the small natural
variability of TXx in tropical regions. This is also apparent for the WSDI
CDFs resolved in Fig. . For a warming of
1.5 ∘C, a median increase in WSDI length by about 1 month is
projected for 50 % of the global land area that increases by 50 % for a
2 ∘C warming. Since this index is derived relative to natural
variability over a reference period, the signal again is greatly amplified in
tropical regions, where a median WSDI of up to 3 months is projected for
Amazonia, East and West Africa and South-East Asia (see Table S2). Given that
the WSDI only measures the longest consecutive interval, such an increase can
be interpreted as entering a new climate regime for these tropical regions
.
CDFs for projected regional aggregated changes in TXx (relative to
the standard variation over the reference period) for the global land area
between 66∘ N and 66∘ S (lower left corner) as well as
resolved for 26 world regions separately (see Sect. for
further details). The graph axes are the same for all panels. Changes are
given relative to the standard deviation over the 1986–2005 reference
period. Note that a change in 2 (3) SD (standard deviations) implies that
events with a reference return time of several decades (centuries) become the
new normal, whereas a new normal of 4 σ refers to an event that would
be extremely unlikely to occur in a reference period climate. Region impact
overviews are provided in the Supplement.
Same as Fig. , but for WSDI in
days.
A meaningful assessment of impacts requires not only an assessment of
absolute changes, but these also have to be interpreted in the light of
regional climate conditions. It is the regional natural climate variability
that arguably determines a “climate normal” to which human systems as well
as ecosystems might be adapted to and . While this
may hold as a general assumption for a range of impacts concerning human
health as well as ecosystems, it is important to note that the severity of
certain climate impacts may also depend on the exceedance of absolute
thresholds, as has been shown for temperature effects on crop yields, for
example . The choice of an either relative or
absolute representation of changes in climate impacts thus has to be made in
light of the impact of interest. In addition, a normalization by the standard
deviation similar to the one applied here has been shown to introduce
statistical biases arising from a limited sample size of the reference period
that we do not account for in the results presented here.
Our findings are in line with previous assessments of projected changes in
extreme temperatures and heat-waves
and illustrate the substantial increase in the likelihood of heat extremes between 1.5 and
2 ∘C warming above pre-industrial levels, in particular when putting
these changes in perspective to regional natural climate variability .
Extreme precipitation and dry spells
Uncertainty in model projections of precipitation extremes is considerably
larger than that of temperature-related extremes.
Figure depicts the median projections for RX5day
(Maximum accumulated 5-day precipitation, left panels) and CDD (Dry spell length,
right panels), which exhibit contrasting patterns in terms of signal strength and
robustness. The KS test illustrates the additional merit of a regional
analysis of precipitation-related extremes (see Table S3). While all models in
the ensemble indicate a robust difference between a 1.5 and
2 ∘C warming for both indices for the global land mass, the analysis
for the separate world regions reveals different patterns.
Same as Fig. , but for RX5day and CDD. Hatched
areas indicate regions, where less than 66 % of the models in the ensemble
agree with the sign of change of the median projections.
Same as Fig. but for RX5day. Changes are given
relative to the 1986–2005 reference period.
A robust indication (more than 66 % of the models reject the null hypothesis
of the KS test at the 95 % significance level, see Table S3) of a difference
in RX5day is projected in particular for the high northern latitude regions,
East Asia, as well as East and West Africa. While the high northern latitudes
are also among those regions experiencing the largest increase in RX5day
between the assessed warming levels (up to 7 and 11 %, median estimates
for 1.5 ∘C and 2 ∘C, respectively), projections for other
regions that experience a considerable increase under a 1.5 ∘C
warming do not indicate a significant difference between the warming levels.
This is in particular noteworthy for the Amazon region and North-East Brazil,
where precipitation extremes are likely related to the South American monsoon
systems and to a lesser extent for West Africa (see
Fig. and Table S3).
A different picture emerges for CDD as an indicator for dry spell length. For
the majority of the global land area, little to no differences in CDD are
projected relative to the reference period (see
Fig. ). However, about 40 % of the global land area
in the subtropical and tropical regions experience an increase in CDD length,
including the Mediterranean, Central America, the Amazon as well as South
Africa (compare Fig. and
Fig. ). In these regions, the KS test also reveals
robust indications for differences in impacts between 1.5 and
2 ∘C. This difference is particularly pronounced for the
Mediterranean region, where the median CDD length increases from 7 % (likely
range 4 to 10 %) to 11 % (likely range 6 to 15 %) between 1.5 ∘C and 2 ∘C.
It is important to highlight that CDD is only an indicator for dry spell
length and does not account for changes in evapotranspiration and
soil-moisture related effects. It should hence not be interpreted as a direct
indicator for agricultural or hydrological (streamflow) drought
. Furthermore, CDD is a metric for short dry
spells, which represent only a snapshot of the overall changes in dryness
, while high-impact drought events like the Big Dry in
Australia or the recent California drought stretch
over months and potentially years . Nevertheless, CDD as
well as RX5day can be seen as proxies for the precipitation-related component
when assessing drought and flooding risks, respectively, and the results and
impacted regions identified here are broadly consistent with projections
based on more comprehensive indicators for droughts
and flooding risk alike.
Same as Fig. but for CDD. Changes are given
relative to the 1986–2005 reference period.
Water availability
Already today, water scarcity is among the biggest challenges for ecosystems
and human societies in many regions globally. To assess changes in water
availability (assessed here as the annual mean surface and subsurface runoff – QTOT)
at 1.5 ∘C and 2 ∘C, we follow the approach outlined
above in Sect. . Projections are based on 11 global
hydrological models (GHM) that participated in the ISI-MIP intercomparison
. These are forced with bias-corrected climate
simulations from five CMIP5 GCMs (HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM,
GFDL-ESM2M, and NorESM1-M, see for further details on the
bias-correction methodology applied). This GCM ensemble has been shown to
reproduce regional seasonal precipitation and temperature reasonably well
, which is further improved by applying a bias
correction . However, the bias correction method is not
designed to retain a physical consistent representation of extreme weather
events , and thereby the intercomparibility with the
quantitative results reported in Sect. is limited.
Each of the GCM-GHM combinations is treated as an individual ensemble member
resulting in a N= 55 ensemble as a basis for the KS tests described above.
Unlike the CMIP5 ensemble, only one realization of each experiment is
available and as a consequence the effect of natural variability cannot be
assessed. ISI-MIP impacts are assessed at a 0.5∘ by 0.5∘ resolution.
Median projections for changes in annual mean runoff for a warming
of 2 ∘C (upper panel), 1.5 ∘C (middle panel) and the
difference between both levels (lower panel) relative to the 1986–2005
reference period. The projections are based on the ISI-MIP GCM-GHM model
ensemble. Grid cells where less than 66 % of all GCM-GHM pairs agree with
the median sign of change are hatched out. Grid cells with an annual mean
runoff of less than 0.05 mm day-1 are masked white.
For a warming of 2 ∘C, reductions in water availability of up to
30 % are projected in several – mainly subtropical – regions, in
particular affecting the Mediterranean, South Africa, Central and southern
South America and South Australia (Fig. ). A relative
increase in runoff is projected in much of the high northern latitudes, as
well as in parts of India, East Africa and parts of the Sahel. While many of
these findings are consistent with earlier studies
, some may depend on the five GCMs chosen here
and may be less robust in larger CMIP5 GCM ensembles .
Same as Fig. but for total annual runoff.
Changes are given relative to the 1986–2005 reference period.
Figure (lower panel) and Fig.
illustrate the difference between a 1.5 ∘C and 2 ∘C warming.
Differences are most prominent in the Mediterranean region where the median
reduction in runoff almost doubles from about 9 % (likely range: 4.5–15.5 %)
at 1.5 ∘C to 17 % (8–28 %) at 2 ∘C. For
several other world regions such as Central America and Australia, there is
an increasing risk of substantial runoff reductions exceeding 30 % for the
upper limit of the 66 % quantile, although projections are highly uncertain
(Table S4 and Fig. ). The differences between
1.5 ∘C and 2 ∘C are smaller for many other regions, but the
KS-test reveals that they are statistically significant for all world regions
assessed (Table S4). These runoff results are also consistent with the
findings on precipitation-related extremes presented in Sect. .
In addition to changes in fresh water availability as a consequence of
changes in the hydrological cycle, saltwater intrusion resulting from rising
sea levels or extreme coastal flooding has to be considered
. Although strongly dependent on local circumstances
including regional water management and coastal protection, saltwater
intrusion might present a substantial challenge in particular for low-lying
coastal areas and small island states .
Crop yieldsMethods and data
We assess future agricultural crop yields in a 1.5 and
2 ∘C warmer world for the four major staple crops – maize, wheat, rice
and soy based on projections from the ISI-MIP Fast Track database
. Projections for agricultural
production depend on a complex interplay of a range of factors, including
physical responses to soils, climate and chemical processes, or nutrient and
water availability, but are also strongly determined by human development and
management. The representation of these processes differs strongly between
different agricultural models. While studies suggest an increase in
productivity for some crops as a result of higher
CO2 concentrations, large uncertainties remain with regard to
temperature sensitivity, nutrient and water limitations, differences in
regional responses and also the interactions between these different factors
. According to their metabolic pathways of carbon
fixation in photosynthesis, main crops can be categorized as C3 and C4 plants.
C4 plants such as maize, sorghum and sugar cane have a high
CO2 efficiency and as a consequence profit little from
increased CO2 concentrations, whereas for C3 plants including
wheat, rice and soy a positive CO2-fertilization effect is to
be expected. At the same time, increased CO2 concentrations may
lead to improved water use efficiency . However,
the effect of elevated CO2 concentrations on plant growth is
highly uncertain and the representation of this effect
greatly differs between different agricultural models. As a consequence, the
ISI-MIP protocol has been conducted with and without accounting for
CO2-fertilization effects (further referred to as the
CO2-ensemble and noCO2-ensemble, respectively).
Recent findings also underline the importance of elevated temperatures and
heat extremes , ozone concentrations
as well as the potential of increasing susceptibility to
disease as a consequence of elevated CO2 levels
for agricultural yields, which may counteract potential
yield gains by CO2-fertilization . Results
for the CO2 and noCO2-ensembles are presented
separately, showing the range of potential manifestations and the additional
risks of regional yield reductions, if effects of CO2-fertilization turn
out to be lower than estimated by the model ensemble.
The ISI-MIP ensemble contains simulations based on seven Global Gridded Crop
Models (GGCM) for wheat, maize and soy and six GGCM for rice, run with input
from five CMIP5 GCMs for further information see.
For the CO2-ensemble, all model combinations are available (35,
and 30 for rice), while for the noCO2-ensemble runs have been
provided for 23 (18 for rice) GGCM-GCM combinations. We restrict future crop
growing areas to present-day agricultural areas based
on and assume no change in management type, meaning that
“rainfed” and “irrigation” conditions are kept constant as well.
As in previous sections, the results presented here are based on 20-year time
slices from the RCP8.5 simulations and changes are given relative to the
1986–2005 reference period. The choice of displaying relative changes comes
with several advantages, but will also lead to a disproportional visual
amplification of minor absolute changes for regions with small present-day
yields, in particular in the high northern latitudes. An overview of the
regionally resolved present-day share in global production is given in Fig. S5.
Since agricultural impacts depend both on climatological changes and changes
in the atmospheric CO2 concentrations, the assumption of
time-independent impacts underlying the time-slice approach as discussed
above does not fully hold for agricultural projections accounting for the
effects of CO2-fertilization (the CO2-ensemble)
and will lead to increased inner-ensemble spread as a consequence. Please
note that the regional aggregation for agricultural yields is not based on
absolute yield change but on land area, as for the other indicators studied
above. Since societal impacts of changes in agricultural production go beyond
mere changes in yield, but also include for example local livelihood
dependencies , our assessment of local
yield changes (on a grid-cell level) supplements and extends previous
yield-centered analysis . Maps for the projected
differences of yield changes on a grid-cell basis are provided in the Supplement.
ResultsWheat
Our analysis reveals very small local median yield changes for 50 % of the
global land area for a 1.5 ∘C and 2 ∘C warming. However, the
uncertainties of these projections are substantial and reductions of about
6 and 8 % for 1.5 ∘C and 2 ∘C, respectively, mark the
lower end of the likely range (compare Table S5). For the
noCO2-ensemble, we find substantial median reductions in local
wheat yields of 14 % at 1.5 ∘C, with a statistically significant
higher decrease of 19 % at 2 ∘C and potential reductions of up to
20 % (1.5 ∘C) and 37 % (2 ∘C) as lower limits for the
likely range. The results of the KS-tests based on individual model
combinations are given in Table S5 and for the global level as well as most
regions, more than 83 % (90 %) of all ensemble members indicate a robust
difference between projected impacts at 1.5 ∘C and 2 ∘C for the CO2
(noCO2)-ensemble.
Best estimate local yield reductions are projected for the tropical region of
about 9 % (15 %) for 1.5 ∘C (2 ∘C) that are particularly
pronounced in West African – median reduction of 13 % (19 %). Under a
1.5 ∘C (2 ∘C) warming, reductions of up to 25 % (42 %) are
within the likely range of the CO2 ensemble projections and for
the noCO2-ensembles, median reductions of 28 % (35 %) would be projected.
Same as Fig. but for changes in wheat yields.
Changes are given relative to the 1986–2005 reference period and ensemble
projections excluding the effect of CO2-fertilization are shown
separately. The CDFs are derived only over the present-day growing areas of
the crop.
Maize
The effects of elevated CO2 concentrations affect maize yields
to a much lesser extent, as conditions are mostly saturated at present levels
see e.g.. Differences between runs are thus less
pronounced for maize yields, where yield reductions are projected for both
the CO2 and the noCO2-ensemble. As the number of
runs differ between the two ensembles (see Methods), the small differences
are likely due to the different ensemble size. Thus, we only discuss results
for the CO2-ensemble here. Differences between the warming
levels are significant (all ensemble members indicate a significant
difference for the global crop area, see Table S6), with median local yield
reductions experienced by 50 % of the global crop area of around 1.5 and
6 % for 1.5 ∘C and 2 ∘C warming, respectively. Risks of
reductions of up to 26 % at 1.5 ∘C and 38 % at 2 ∘C are
within the likely range globally (compare Fig. and Table S6).
As apparent in Fig. , the likely range is deferred
towards stronger reductions. Similar regional patterns compared to wheat
projections are apparent. Again, the highest relative median changes occur in
regions with a relatively low share of global production. For central North
America, where at present about 10 % of global maize is produced,
substantial differences between the two warming levels are projected, and
risks for a strong negative effect in this region more than double between
1.5 ∘C and 2 ∘C warming from 15.5 to 37 % (upper limit of
the 66 % range). Tropical regions such as Central America, the Amazon and
South-East Asia are projected to experience median local yield reductions
exceeding 5 % for 1.5 ∘C and up to and more than 10 % for
2 ∘C, while projections for the full tropical region do not differ
substantially from the global projections.
Soy
Projections of changes in soy yields between the two assessed warming levels
show robust differences (see Table S7). For the CO2-ensemble, a
median increase in global yields of 7 % is projected for 50 % of the global
area under a warming of 1.5 ∘C. This median increase vanishes for
2 ∘C. Global differences between warming levels for the
noCO2-ensemble are smaller but nonetheless robust, with median
reductions of 10 and 12 %, respectively.
Regionally, the differences for the noCO2-ensemble are more
pronounced, especially in those regions with a large share in present-day
global soy production. Median yields for the Amazon (AMZ) region, currently
producing about 7 % of global soy see also Fig. S5,
are projected to reduce from 15 % under 1.5 ∘C to 20 % under
2 ∘C warming. Similar robust differences in yield reductions between
1.5 ∘C and 2 ∘C warming are also projected for the major soy
producers in central North America and south-east South America. For North
Asia, where currently over 7 % of soy production takes place, median
increases in yields of 28 and 24 % are projected for a warming of
1.5 ∘C for the noCO2 and CO2 ensembles,
respectively. However, uncertainties for this region are high and a risk of
substantial reductions of 25 % (1.5 ∘C) and 20 % (2 ∘C) in
the CO2-ensemble are within the likely range of the ensemble projections.
Same as Fig. , but for changes in maize yields.
Same as Fig. , but for changes in soy yields.
Rice
Median changes in global rice yields for the CO2-ensemble do
not differ between the assessed warming levels, with projected increases of
about 7 % although the respective local yield change distributions are
significantly different (compare Table S8). The distribution of possible
developments indicates risk of substantial reductions of up to 17 and
14 % at 1.5 ∘C and 2 ∘C. For the noCO2-ensemble, reductions of
8 and 15 % are projected for the two warming levels.
The effects of CO2-fertilization consistently indicates yield
increases across regions for median projections. While differences between
warming levels are apparent for some regions and the
CO2-ensemble, global estimates are very similar between both
warming levels. For the noCO2-ensemble, robust differences
between 1.5 ∘C and 2 ∘C warming are apparent for all major
rice producing regions, including all Asian regions where a total of 40 % of
rice is produced today (EAS, SAS, SEA, TIB) as well as the Amazon, and South
American rice producers. Reductions are projected to double between the two
warming levels, for example in South Asia, south-east South America and the
Tibetan Plateau. For these regions, median projections are close to the lower
end of the likely range (compare Fig. and Table S8).
Same as Fig. but for changes in rice yields.
Discussion of crop yield projections
Our projections of local agricultural yields reveal substantial uncertainties
in global median regional yield changes (Figs.
to ) with a likely range (66 % – likelihood) comprising
zero. For wheat, rice and soy, our projections indicate differences between
the CO2 and noCO2 assessments, which are
generally much larger than those between a 1.5 ∘C and 2 ∘C
warming. While substantial uncertainty renders a differentiation between
impacts at 1.5 ∘C and 2 ∘C warming difficult in most world
regions, a clear signal emerges for the noCO2-ensemble, that
may serve as a high-risk illustration of potential climate impacts on
agricultural production. In the noCO2-ensemble, local yields
are projected to decrease between 1.5 ∘C and 2 ∘C for all crop types.
As discussed above, our crop-yield projections are subject to a range of
uncertainties also related to extreme weather events. Uncertainties in both
the bias-corrected climate model input as well as the
impact model representation of such events limit the confidence in the
projections of the effect of extreme weather events on crop yields.
Observational evidence, however, indicates substantial impacts of
specifically drought and extreme heat events on crop yields .
Given the pronounced increase in extreme heat events under global warming in
general and also specifically between 1.5 ∘C and 2 ∘C
(compare Figs. and , our
estimates of the absolute change in local crop yields as well as the
difference between 1.5 ∘C and 2 ∘C should be seen as a conservative estimate.
Our results indicate that risks are region and crop specific and are in line
with findings of previous model intercomparison studies
. While high-latitude regions may benefit,
median projections for local yields in large parts of the tropical land area
are found to be negatively affected already at 1.5 ∘C. Risks increase
substantially, if effects of CO2-fertilization are less
substantial or counter-acted by other factors such as extreme temperature
response, land degradation or nitrogen limitation
. In a statistical analysis
of climate impacts on wheat and barley yields in Europe,
report an overall negative contribution of climatic factors in line with
findings of a meta-analysis by , which questions the
positive effects projected in our CO2-ensemble for this region
and further support our approach of singling out noCO2-ensemble
projections. Given that a 1.5 ∘C warming might be reached already
around 2030, our findings underscore the risks of global crop yield
reductions due to climate impacts outlined by , while
giving further indications for the regional diversity of climate impacts with
tropical regions being a hot-spot for climate impacts on local agricultural
yields .
Upper panel: probabilistic GMT projections for illustrative emission
scenarios with a peak warming of 1.5 ∘C (left panels) and
2 ∘C (right panels) above pre-industrial levels during the
21st century. Lower panels: probabilistic projections of global sea-level
rise (SLR) for both scenarios relative to 1986–2005 levels. Uncertainty
bands indicate the likely range (66 % probability within this range) and
the very likely range (90 % probability),
respectively.
Sea-level riseMethods
Projections for sea-level rise (SLR) cannot be based on a time-slice approach
because of the importance of the time-lagged response of the ocean and
cryosphere to the warming signal. Therefore, we selected two multi-gas
scenarios illustrative of a 1.5 ∘C and 2 ∘C warming to assess
SLR impacts over the entire 21st century from a large emission
scenario ensemble created by . These scenarios were
created with the integrated assessment modelling framework MESSAGE the
Model for Energy Supply Strategy Alternatives and their General Environmental
Impact,. For both scenarios, temperature projections are
derived with the reduced complexity carbon cycle and climate model MAGICC
in a probabilistic setup ,
which has been calibrated to be in line with the uncertainty assessment of
equilibrium climate sensitivity of the IPCC AR5
. Each probabilistic setup ensemble consists of
600 individual scenario runs. The first scenario keeps GMT to below
2 ∘C relative to pre-industrial levels (1850–1875) during the
21st century with 50 % probability. The second scenario reduces
emissions sooner and deeper, and keeps warming to below 1.5 ∘C
relative to pre-industrial levels during the 21st century with about
50 % probability and returns end-of-century warming to below 1.5 ∘C
with about 70 % probability. See Fig. (upper panel)
for median temperature projections for the 2 and 1.5 ∘C
scenario and their associated uncertainty bands. Since the projections for
coral reef degradation include a time-dependent adaptation scenario, the same
approach is taken for the coral reef projections (see Sect. ).
SLR projections are based on , who developed a scaling
approach for the various SLR contributions according to an appropriately
chosen climate predictor – in this case GMT increase and ocean heat uptake.
Coupled with output from the MAGICC model, this allows us to emulate the
sea-level response of GCMs to any kind of emission scenario within the
calibration range of the method that is spanned by the RCPs.
Consistent with the relationship found in CMIP3 and CMIP5 GCMs, ocean thermal
expansion is assumed to be proportional to cumulative ocean heat uptake
. Mountain glacier melt is computed following a widely used
semi-empirical relationship between rate of glacier melt, remaining surface
glacier area, and temperature anomaly with respect to pre-industrial levels.
This approach assumes constant scaling between area and volume
, with parameters chosen to account for current
melt rate and known glacier volume Eq. 1 and Table 2
in. As already noticed by (their Fig. 5),
the surface mass balance (SMB) anomaly from the Greenland ice sheet can
be approximated with reasonable accuracy as a quadratic fit to global mean
temperature anomaly. Here we adopted the same functional form, but calibrated
it to more recent projections by . Following
, we scaled up these projections by 20 % ± 20 % to
account for missing dynamic processes (elevation feedback 10 % ± 5 %,
changes in ice dynamics 10 % ± 5 %, and ±10 % arising from the skill
of the SMB model to simulate the current SMB rate over Greenland). The
climate-independent land-water contribution has been added for all scenarios
following .
Projections for sea-level rise above year 2000 levels for two
illustrative 1.5 ∘C and 2 ∘C scenarios (see Fig. ).
Square brackets give the likely (66 %) range.
Beyond the scaling approach, the main advancement of our approach compared to
the IPCC AR5 stems from the inclusion of
scenario-dependent Antarctic ice-sheet projections following
. Linear response functions were derived from idealized
step-forcing experiments from the SeaRISE project
as a functional link between the rate of ice shelf melting and dynamical
contribution to SLR over four Antarctic sectors and various ice-sheet models.
further assume linear scaling between global surface
air warming, local ocean warming, and ice-shelf melting in each of the
sectors. They adopted a Monte Carlo approach with 50 000 samples to combine
the various parameter ranges, GCMs and ice-sheet models. To our knowledge,
this is the most comprehensive attempt to date to link climate warming and
Antarctic ice-sheet contributions to scenario-dependent sea-level rise over
the 21st century.
Results
For an illustrative 2 ∘C scenario, we project a median SLR of about
50 cm (36–65 cm, likely range) by 2100 and a rate of rise of 5.6 (4–7) mm yr-1
over the 2081–2100 period. Under our illustrative 1.5 ∘C
scenario, projected SLR in 2100 is about 20 % (or 10 cm) lower, compared to
the 2 ∘C scenario (see Table ). The corresponding reduction
in the expected rate of SLR over the 2081–2100 period is about 30 %. More
importantly, and in contrast to the projections for the 2 ∘C
scenario, the rate for the 1.5 ∘C scenario is projected to decline
between mid-century and the 2081–2100 period by about 0.5 mm yr-1, which
substantially reduces the multi-centennial SLR commitment .
The projected difference in SLR between the 1.5 ∘C and 2 ∘C
scenarios studied here is comparable to the difference between the RCP2.6 and
RCP4.5 scenarios , while the projected median
GMT difference between the two RCP scenarios is about 0.8 ∘C for
the 2081–2100 period. The relatively higher sensitivity of SLR in the
21st century to temperature increase at low climate warming
is probably related to the earlier peaking of GMT under such scenarios and
thus an already longer adjustment period for the time-lagged ocean and
cryosphere. This leads to a larger share of committed multi-centennial SLR to
occur in the 21st century. On multi-centennial timescales
these scenario-dependent differences are expected to vanish. A long-term
difference, however, may arise from contributions by mountain glacier melt,
which are particularly vulnerable to GMT increase and thus differences in
melted mountain glacier volume are higher for lower emission scenarios.
While SLR projections for the two illustrative 1.5 and
2 ∘C differ substantially, this effect is strongly scenario
dependent. In particular, most emission pathways labelled as 1.5 ∘C
scenarios allow for a temporal overshoot in GMT and a decline below
1.5 ∘C with a 50 % probability by 2100 , whereas
the illustrative 1.5 ∘C scenario used here does not allow for a GMT
overshoot, but stays below 1.5 ∘C over the course of the
21st century. For time-lagged climate impacts such as SLR
that depend on the cumulative heat entry in the system, the difference
between a scenario allowing for a GMT overshoot and one that does not will be significant.
Sea-level adjustment to climate warming has a timescale much larger than a
century as a result of slow ice-sheet processes and ocean heat uptake. This
means that in all emission scenarios considered, sea level will continue to
rise beyond 2100. have shown that on a 2000-year
timescale, sea-level sensitivity to global mean temperature increase is
about 2.3 m per ∘C. In addition to that, report
a steep increase in long-term SLR between 1.5 ∘C and 2 ∘C as
a result of an increasing risk of crossing a destabilizing threshold for the
Greenland ice-sheet . The disintegration process that
would lead to 5–7 m global SLR, however, is projected to happen on the timescale of several millennia.
Recent observational and modelling evidence indicates that a marine ice sheet
instability in the West Antarctic may have already been triggered, which
could lead to an additional SLR commitment of about 1 m on a multi-centennial
timescale. Spill-over effects of this destabilization on other drainage
basins and their relation to GMT increase are as yet little understood
, and there are indications that a
destabilization of the full West Antarctic ice-sheet could eventually be
triggered . Similarly, report a
potential marine ice-sheet instability for the Wilkens Basin in West
Antarctica containing 3–4 m of global SLR. The dynamics of these coupled
cryosphere-oceanic systems remain a topic of intense research. Current
fine-scale ocean models, suggest increased intrusion of warm deep water on
the continental shelf as a result of anthropogenic climate change and thus
indicate an increasing risk with increasing warming
. Given the risk of potentially triggering
multi-metre SLR on centennial to millennial timescales, this clearly calls
for a precautionary approach that is further underscored by evidence from
paleo-records, which reveals that past sea-levels might have about 6–9 m
above present day for levels for a GMT increase not exceeding 2 ∘C
above pre-industrial levels .
Coral reef systemsMethods
The projections of the degradation of coral reef sites uses the coral
bleaching model developed in based on the two
illustrative 1.5 ∘C and 2 ∘C global emission pathways
introduced in Sect. . The framework applies a
threshold-based bleaching algorithm by , which is based on
degree heating months (DHMs), to sea surface temperature (SST) pathways of
2160 individual geospatial locations of coral reef sites (see
http://www.reefbase.org) and generates as output the fraction of coral reef
locations subject to long-term degradation. DHMs are a measure for the
accumulated heat stress exerted on coral reefs due to elevated SST (see Fig. S6
for a graphical illustration of the methodology). Within a 4-month
moving window the monthly SST above a reference value (here the mean of
monthly maximal temperatures, MMM) are accumulated and compared to a
threshold value (critical DHM threshold) that is associated with mass coral
bleaching. The value of the critical DHM threshold depends on the scenario
assumptions (see below). In order to translate coral bleaching events into
long-term coral degradation, we refer to the assumption that reef recovery
from mass coral bleaching is usually very limited within the first 5 years
. Therefore, we assume a maximum tolerable probabilistic
frequency of 0.2 yr-1 for bleaching events causing
long-term degradation. The MMM is calculated from a 20-year climatological
reference period (1980–2000) individually for every coral location and SST
pathway. Thus, the MMM serves as an indicator of temperatures to which the
corals of a certain reef location are generally adapted. In order to generate
a scenario-independent description of coral reef response to different levels
of global warming (e.g. any given global mean air temperature pathway) we
apply the algorithm to a large number of SST pathways and reassign the
fraction of 2160 mapped coral reef locations subject to long-term degradation
back to global air temperature pathways. In total, we use the SST pathways of
19 Atmosphere-Ocean General Circulation Models (AOGCMs) from the multi-model
CMIP3 project and seven different emission scenarios leading to 30 728 model
years. We also used a wide range of critical DHMs (from 0 to 8∘), which
allows for the testing of risk scenarios with constant and variable critical
DHM thresholds (e.g. thermal adaptation).
The condensed output of the global coral bleaching assessment allows for the
implementation of different coral adaptation scenarios. In the standard
scenario (Constant) a constant DHM threshold of 2 ∘C is
assumed. This means that corals can resist a cumulative heat stress of
2 ∘C (accumulated over a 4-month period) above the long-term maximum
monthly mean (MMM) sea surface temperature for a given location. It has been
demonstrated that this value serves as a good proxy for severe mass coral
bleaching .
In addition to the constant scenario, an extremely optimistic scenario of
strong thermal adaptation of the corals is assessed (Adaptation).
Under this scenario, the critical DHM threshold constantly increases from
2 ∘C in the year 2000 up to 6 ∘C in 2100. The assumption of a
thermal adaptation of 0.4∘ per decade appears very ambitious given
the long creation times of reef-building corals and the consequently slow
rate at which evolutionary adaptation occurs. Furthermore, additional
environmental stressors such as ocean acidification and
disease spreading have to be expected to slow-down coral
growth and to reduce the adaptive capacity of tropical coral reefs. As a
consequence, this scenario should be seen as an absolute lower boundary for
degradation of coral reefs globally.
Finally, a third scenario takes the negative effect of the acidification of
the oceans into account which reduces the calcification rates of the corals
and thus promotes further degradation of coral reefs (Saturation). We
derived a transfer function based on atmospheric CO2 concentrations due to
the fact that tropical surface aragonite saturation levels are in equilibrium
with atmospheric CO2 concentrations on a timescale of years to decades
. With an assumption of the effect of the aragonite
saturation on the critical DHM threshold (see supporting material of
) this translates into a measurable increased stress to corals.
Results
Coral reef systems are slow-growing, complex ecosystems that are particularly
susceptible to the impacts of increased CO2 concentrations,
both through warming (and resulting coral bleaching) and ocean acidification
. Our analysis reiterates earlier findings that the risk
of coral reefs to suffer from long-term degradation eventually leading to an
ecosystem regime shift will be substantial as early as 2030
. We find that this risk
increases dramatically until the 2050s, where even under a 1.5 ∘C
scenario, 90 % and more of all global reef grid cells will be at risk of
long-term degradation under all but the most optimistic scenario assessed
(the Adaptation case, see Sect. ). However,
long-term risks towards the end of the century are reduced to about 70 % of
global coral reef cells under a 1.5 ∘C scenario but not under a
2 ∘C scenario (compare Fig. and Table ).
Our approach only includes the effects of increased
CO2-concentrations, but does not account for other stressors
for coral reef systems such as rising sea-levels, increased intensity of
ENSO-events , tropical cyclones ,
invasive species and disease spreading , and other local
anthropogenic stressors, which ranks our projections of long-term coral reef
degradation rather conservative. These projected losses will greatly affect
societies, which depend on coral reefs as a primary source of ecosystem
services, e.g. in the fishery and tourism sector .
estimate that about 25 % of the world's small-scale fishers
fish on coral reefs. report that a loss of less than 60 % of
global coral reef coverage, that could very well be reached already in the 2030s,
would inflict damages of more than USD 20 billion annually.
Probabilistic projections of the fraction of global tropical coral
reef cells suffering from long-term degradation under two illustrative
1.5 ∘C (upper panel) and 2 ∘C (lower panel) scenarios (see
Fig. , upper panel) for two different assumptions about
their adaptive capacity (see Sect. ). Median
projections and the 66 % range are shown. Note that uncertainties also
include uncertainties in the GMT response (see Fig. 13). See
Sect. for further details on the methodology. Only
the projections for the Constant and Adaptation scenario
are shown, since the projections for the Saturation scenario differ
only slightly from Constant. Table gives results
for all three scenarios assessed.
Discussion and conclusions
The findings of our analysis support the IPCC AR5 Working Group 2 RFC
assessment of differences in key impacts of climate change between warming of
1.5 and 2 ∘C above pre-industrial levels: we find that
under a 1.5 ∘ scenario, the fractions of coral reef cells at risk of
severe degradation are reduced significantly compared to a warming of
2 ∘C (RFC1), that the difference between 1.5 and
2 ∘C marks the transition between an upper limit of present-day
natural variability and a new climate regime in terms of heat extremes
globally (RFC2), and that changes in water availability and local
agricultural yields are already unevenly distributed between world regions at
1.5 ∘C and even more so at 2 ∘C (RFC3). Central findings
across the different indicators studied are summarized in Fig.
and regional summaries are given in the Supplement (Figs. S7–S33).
Water availability reduction and dry spell length (CDD) increase are found to
accelerate between 1.5 ∘C and 2 ∘C for several sub-tropical
regions, in particular in the Mediterranean, Central America and the
Caribbean, South Africa and Australia. Local agriculture production in
tropical regions is projected to be strongly affected by ongoing warming, and
even more so, if effects of CO2-fertilization do not play out
as current models project them or are counter-balanced by other factors such
as nitrogen and phosphor limitations or heat stress, which are not fully
included in the models investigated here. Given the substantial divergence in
projections of specifically extreme temperature events between 1.5 and
2 ∘C, this renders our estimates of respective crop yield differences
rather conservative.
Fraction of reef cells at risk of long-term degradation due to coral
bleaching in 2050 and 2100 for three different assumptions about the adaptive
capacity and susceptibility of corals to ocean acidification as described in
Sect. in percent. Median projections and the 66 %
range (in square brackets) are given, accounting also for uncertainties in
global mean temperature projections.
Summary of key differences in climate impacts between a warming of
1.5 ∘C and 2 ∘C above pre-industrial and stylized 1.5 ∘C and 2 ∘C
scenarios over the 21st century. Square brackets give the likely (66 %) range.
Our analysis of projected SLR reveals differences of about 10 cm in global
mean SLR between illustrative 1.5 ∘C and 2 ∘C scenarios by 2100.
In addition, the end-of-century rate of sea-level rise for 1.5 ∘C
is about 30 % lower than for a 2 ∘C pathway, indicating a
substantially lower long-term sea-level rise commitment .
Evidence from the paleo-record and modelling studies
further indicate that a multi-metre sea-level of
potentially up to 9 m cannot be ruled out under a 2 ∘C warming on
multi-millennial timescales.
Our assessment based on this limited set of indicators implies that
differences in climate impacts between 1.5 ∘C and 2 ∘C are
most pronounced for particularly vulnerable regions and societal groupings
with limited adaptive capacity . Under a 2 ∘C
warming, coastal tropical regions and islands may face the combined effects
of a near-complete loss of tropical coral reefs, which provide coastal
protection and are a main source of ecosystem services, on-going sea-level
rise above present-day rates over the 21st century and
increased threats by coastal flooding and inundation. The risks posed by
extreme heat and potential crop yield reductions in tropical regions in
Africa and South-East Asia under a 2 ∘C warming are particularly
critical given the projected trends in population growth and urbanization in
these regions . In conjunction with other development
challenges, the impacts of climate change represent a fundamental challenge
for regional food security and may trigger new poverty
traps for several countries or populations within countries .
Furthermore, the emergence of the Mediterranean region, including North
Africa and the Levant, as a hot-spot for reductions in water availability and
dry spell increases between 1.5 ∘C and 2 ∘C is of great
relevance given the specific vulnerability of this region to water scarcity
. The political instability in several countries in
this region may further exacerbate the vulnerability of societies to climatic
stresses, potentially increasing the risk of violent conflict outbreak .
Taken together, we provide a consistent and comprehensive assessment of
existing projections and a solid foundation for future work on refining our
understanding of the difference between impacts at 1.5 ∘C and 2 ∘C
warming. In particular, we illustrate how limiting warming to
1.5 ∘C would “significantly reduce the risks and impacts of climate
change” as stated in the Paris Agreement. However, our analysis can only be
a first step towards a more integrative post-Paris science agenda including
the assessment of below 1.5 ∘C impacts and requirements and costs of
energy system transformation pathways in line with limiting warming to below
1.5 ∘C .
The Supplement related to this article is available online at doi:10.5194/esd-7-327-2016-supplement.
Acknowledgements
We acknowledge the World Climate Research Programme's Working Group on
Coupled Modelling, which is responsible for CMIP, and we thank the climate
modelling groups for producing and making available their model output. For
CMIP, the US Department of Energy's Program for Climate Model Diagnosis and
Intercomparison provided coordinating support and led development of software
infrastructure in partnership with the Global Organization for Earth System
Science Portals. We would like to thank the modelling groups that
participated in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP).
The ISI-MIP Fast Track project underlying this paper was funded by the German
Federal Ministry of Education and Research with project funding reference
number 01LS1201A. The work was supported by the German Federal Ministry for
the Environment, Nature Conservation and Nuclear Safety (11-II-093-Global-A
SIDS and LDCs), within the framework of the Leibniz Competition
(SAW-2013-PIK-5), from EU FP7 project HELIX (grant no. FP7-603864-2), and by
the German Federal Ministry of Education and Research (BMBF, grant no. 01LS1201A1).
We would like to thank two anonymous reviewers and the handling editor for
their comments and suggestions that greatly helped to improve the manuscript.
Edited by: S. Smith
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