We present a suite of nine scenarios of future emissions trajectories of
anthropogenic sources, a key deliverable of the ScenarioMIP experiment within
CMIP6. Integrated assessment model results for 14 different emissions species
and 13 emissions sectors are provided for each scenario with consistent
transitions from the historical data used in CMIP6 to future trajectories using
automated harmonization before being downscaled to provide higher emissions
source spatial detail. We find that the scenarios span a wide range of
end-of-century radiative forcing values, thus making this set of scenarios ideal
for exploring a variety of warming pathways. The set of scenarios is bounded on
the low end by a 1.9 Wm-2 scenario, ideal for analyzing a world with
end-of-century temperatures well below 2 ∘C, and on the high end by a 8.5 Wm-2
scenario, resulting in an increase in warming of nearly 5 ∘C over pre-industrial
levels. Between these two extremes, scenarios are provided such that differences
between forcing outcomes provide statistically significant regional temperature
outcomes to maximize their usefulness for downstream experiments within CMIP6.
A wide range of scenario data products are provided for the CMIP6 scientific
community including global, regional, and gridded emissions datasets.
Introduction
Scenario development and analysis play a crucial role in linking socioeconomic
and technical progress to potential future climate outcomes by providing future
trajectories of various emissions species including greenhouse gases, aerosols,
and their precursors. These assessments and associated datasets allow for
wide-ranging climate analyses including pathways of future warming, localized
effects of pollution emissions, and impact studies, among others. By spanning a
wide range of possible futures, including varied levels of emissions mitigation,
pollution control, and socioeconomic development, scenarios provide a large
multivariate space of potential near-, medium-, and long-term outcomes for study
by the broader scientific community.
The results of scenario exercises have been used widely by national and
international assessment bodies and the global scientific community. They have
informed previous Assessment Reports by the Intergovernmental Panel on Climate
Change as well as reports on more topical issues including the
Special Report on Emissions Scenarios (SRES) . The SRES scenarios
were used extensively in the Coupled Model Intercomparison
Project Phase 3 (CMIP3) , whereas the following generation of scenarios
denoted the “Representative Concentration Pathways” (RCPs) were used to generate
emissions trajectories in CMIP5 . These emissions scenarios have been used by a broad audience,
including national governments (e.g., ) and climate scientists (e.g.,
).
As initially described in , a new framework has been utilized to
design scenarios that combine socioeconomic and technological development, named
the Shared Socioeconomic Pathways (SSPs), with future climate radiative forcing (RF)
outcomes (RCPs) in a scenario matrix architecture . This new structure provides two critical elements
to the scenario design space: first, it standardizes all socioeconomic
assumptions (e.g., population, gross domestic product, and poverty, among others) across modeled
representations of each scenario; second, it allows for more nuanced
investigation of the variety of pathways by which climate outcomes can be
reached. Five different SSPs exist, with model quantifications that span
potential futures of green or fossil-fueled growth (SSP1 , and
SSP5 ), high inequality between or within countries (SSP3
, and SSP4 ), and a “middle-of-the-road” scenario (SSP2 ). For each SSP, a number of different RF
targets can be met depending on policies implemented, either locally or
globally, over the course of the century .
Scenarios provide critical input for climate models through their description
and quantification of both land-use change as well as emissions trajectories. Of
the total population of newly available scenarios produced with integrated
assessment models (IAMs), nine have been chosen for inclusion for study in
ScenarioMIP, one of the dedicated CMIP6-endorsed model intercomparison projects (MIPs) . The
selection of scenarios is designed to allow investigation of two primary
scientific questions: “how does the Earth system respond to climate
forcing and how can we assess future climate changes given climate variability...and
uncertainties in scenarios?” . In order to support
an experimental design that can address these fundamental questions, scenarios
were chosen that explore a wide range of future climate forcings that both
complement and expand on prior work in CMIP5. While a given forcing pathway
could be met with potentially many different SSPs, a specific SSP is chosen for
each pathway according to three governing principles: “[maximizing] facilitation
of climate research, minimizing differences in climate between outcomes produced
by the [chosen] SSP, and ensuring consistency with scenarios that are most
relevant to the IAM and Impacts, Adaptation, and Vulnerability (IAV)
communities” p. 3469.
Selected scenarios sample a range of forcing outcomes (1.9–8.5 Wm-2,
calculated with the simple climate model MAGICC6; ),
with sufficient spacing between forcing outcomes to provide statistically
significant regional temperature outcomes . The nine selected scenarios can be divided into two
groups: four scenarios update the RCPs studied in CMIP5, achieving forcing
levels of 2.6, 4.5, 6.0, and 8.5 Wm-2, whereas five scenarios fill
gaps not previously studied in the RCPs, including a lower-bound 1.9 Wm-2
scenario corresponding to the most
optimistic interpretation of Article 2 of the Paris Agreement
. Additionally, a new “overshoot” scenario is included in the
Tier-2 set in which forcing peaks and then declines to 3.4 Wm-2 by 2100
in order to assess the climatic outcomes of such a pathway.
In order to provide historically consistent and spatially detailed emissions
datasets for other scientists collaborating in CMIP6, scenario results are
processed using methods of harmonization and downscaling. Harmonization refers to the alignment of model results with a
common historical dataset. Historical data consistency is paramount for use in
climate models which perform both historic and future runs, for which there must
be smooth transitions between the two sets of emissions
trajectories. Harmonization has been applied in previous studies (e.g., in
SRES – and the RCPs – ); however,
systematic harmonization for which common rules and algorithms are applied
across all models has not heretofore been performed . We
harmonize emissions trajectories, therefore, with a newly available methodology
and software (Aneris) in order to
address this need. We further downscale these results from their native model
region spatial dimension to individual countries using techniques which take
into account current and future emissions levels as well as socioeconomic
progress . An overview of the scenario selection and
processing steps that comprise this study as well as its contributions to the
broader CMIP6 community is shown in Fig. .
The role of ScenarioMIP in the CMIP6 ecosystem. From a population of over
40 possible SSPs, nine are downselected in order to span the climatic and
social dimensions of the ScenarioMIP SSP–RCP matrix. Emissions
trajectories developed from these scenarios then undergo harmonization to
a common and consistent historical dataset, downscaling, and gridding. The
resulting emissions datasets are then provided to the CMIP6 scientific
community, in conjunction with future scenarios of land use
, concentrations , and other
domain-specific datasets (e.g., VOC speciation and ozone concentrations).
The remainder of the paper is as follows. First, we discuss scenario selection,
historical data aggregation, harmonization, and downscaling methods in Sect. .
We then present harmonized model results, focusing on overall
emissions trajectories, climate response outcomes, and the spatial distribution
of key emissions species in Sect. . Finally, in Sect. ,
we discuss conclusions drawn from this study as well as
guidelines for using the results presented herein in further CMIP6 experiments.
Data and methodsSocioeconomic and climate scenarios
The global IAM community has developed a family of scenarios that describe a
variety of possible socioeconomic futures (the SSPs). The formation,
qualitative, and quantitative aspects of these scenarios have been discussed
widely in the literature . We briefly summarize here relevant narratives of the baseline
SSPs concerning socioeconomic development (see, e.g., Fig. ), energy systems , land use
, greenhouse gas (GHG) emissions , and air
pollution .
SSP1 and SSP5 describe worlds with strong economic growth via sustainable and
fossil fuel pathways, respectively. In both scenarios, incomes increase
substantially across the globe and inequality within and between countries is
greatly reduced; however, this growth comes at the expense of potentially large
impacts from climate change in the case of SSP5. Demand for energy- and resource-intensive agricultural commodities such as ruminant meat is significantly lower
in SSP1 due to changes in behavior and advances in energy efficiency. In both
scenarios, pollution controls are expanded in high-income economies with other
nations catching up relatively quickly with the developed world, resulting in
reductions in air pollutant emissions. SSP2 is a so-called middle-of-the-road
scenario with moderate population growth and slower convergence of income
levels across countries. In SSP2, food consumption, especially for
resource-intensive livestock-based commodities, is expected to increase and
energy generation continues to rely on fossil fuels at approximately the same
rates as today, resulting in continued growth of GHG emissions. Efforts at
curbing air pollution continue along current trajectories with developing
economies ultimately catching up to high-income nations, resulting in an
eventual decrease in pollutant emissions. Finally, SSP3 and SSP4 depict futures
with high inequality between countries (i.e., “regional rivalry”) and within
countries, respectively. Global gross domestic product (GDP) growth is low in both scenarios and
concentrated in currently high-income nations, whereas population increase is
focused in low- and middle-income countries. Energy systems in SSP3 see a
resurgence of coal dependence, whereas reductions occur in SSP4 as the high-tech
energy and economy sectors see increased developments and investments leading to
higher diversification of technologies . Policy making (either
regionally or internally) in areas including land-use regulation, air pollution
control, and GHG emissions limits is less effective. Thus policies vary
regionally in both SSPs with weak international institutions, resulting in the
highest levels of pollutant and aerosol emissions and potential effect on climate outcomes .
A matrix of socioeconomic–climate scenarios relevant to the broad scientific
community was created with SSPs on one axis and climate policy futures
(i.e., mitigation scenarios) delineated by end-of-century (EOC) RF on the other
axis (see Fig. ). The scenarios selected for inclusion in
ScenarioMIP, shown in Table , are comprised of both baseline
and mitigation cases, in which long-term climate policies are lacking or
included, respectively. They are divided into Tier-1 scenarios, which span a
wide range of uncertainty in future forcing and are utilized by other MIPs, and
Tier-2 scenarios, which enable more detailed studies of the effect of mitigation
and adaptation policies which fall between the Tier-1 forcing levels. Each
scenario is run by a single model within ScenarioMIP, comprised of the AIM/CGE,
GCAM4, IMAGE, MESSAGE-GLOBIOM, and REMIND-MAgPIE modeling teams. We provide a
short discussion here on their selection and refer the reader to
Sect. 3.2.2 for fuller discussion of the experimental design.
The Tier-1 scenarios include SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, designed to
provide a full range of forcing targets similar in both magnitude and
distribution to the RCPs as used in CMIP5. Each EOC forcing level is paired with
a specific SSP, which is chosen based on the relevant experimental coverage. For
example, SSP2 is chosen for the 4.5 Wm-2 experiment because of its high
relevance as a reference scenario to IAV communities as a scenario with
intermediate vulnerability and climate forcing and its median positioning of
land use and aerosol emissions (of high importance for DAMIP and DCPP), whereas
SSP3 is chosen for the 7.0 Wm-2 experiment as it allows for quantification of
avoided impacts (e.g., relative to SSP2) and has significant
emissions from near-term climate forcing (NTCF) species such as aerosols and
methane (also referred to as short-lived climate forcers, or SLCF).
The Tier-2 scenarios include SSP1-1.9, SSP3-LowNTCF, SSP4-3.4, SSP4-6.0, and
SSP5-3.4-Overshoot (OS), chosen to both complement and extend the types of
scenarios available to climate modelers beyond those analyzed in CMIP5. SSP1-1.9
provides the lowest estimate of future forcing matching the most ambitious goals
of the Paris Agreement (i.e., “pursuing efforts to limit the [global average]
temperature increase to 1.5 ∘C above pre-industrial levels”). The SSP3-LowNTCF
scenario provides an important experimental comparison to scenarios with high
NTCFs for use in AerChemMIP contrasting with SSP3-7.0
(see Appendix for more detail on differences in assumptions between SSP3-7.0 and SSP3-LowNTCF). Both
SSP4 scenarios fill gaps in Tier-1 forcing pathways and allow investigations of
impacts in scenarios with relatively strong land-use and aerosol climate effects
but relatively low challenges to mitigation. Finally, SSP5-3.4-OS allows for the
study of a scenario in which there is large overshoot in RF by
mid-century followed by the implementation of substantive policy tools to limit
warming in the latter half of the century. It is specifically designed to be
twinned with SSP5-8.5, following the same pathway through 2040, and support
experiments examining delayed climate action.
All scenarios and associated attributes used in the ScenarioMIP experiment ensemble.
TargetforcingScenariolevelScenarioContributingnameSSP(Wm-2)typeTierIAMto other MIPsSSP1-1.911.9Mitigation2IMAGEScenarioMIPSSP1-2.612.6Mitigation1IMAGEScenarioMIPSSP2-4.524.5Mitigation1MESSAGE-GLOBIOMScenarioMIP, VIACS AB, CORDEX,GeoMIP, DAMIP, DCPPSSP3-7.037Baseline1AIM/CGEScenarioMIP, AerChemMIP, LUMIPSSP3-LowNTCF36.3Mitigation2AIM/CGEScenarioMIP, AerChemMIP, LUMIPSSP4-3.443.4Mitigation2GCAM4ScenarioMIPSSP4-6.046Mitigation2GCAM4ScenarioMIP, GeoMIPSSP5-3.4-OS53.4Mitigation2REMIND-MAGPIEScenarioMIPSSP5-8.558.5Baseline1REMIND-MAGPIEScenarioMIP, C4MIP, GeoMIP, ISMIP6, RFMIPHistorical emissions data
We construct a common dataset of historical emissions for the year
2015
For sulfur emissions in China, we include values up to 2017, due
to a drastic reduction in these emissions in the most recently available
datasets.
, the transition year in CMIP6 between historic and future model
runs, using two primary sources developed for CMIP6. provide
data over 1750–2014 for anthropogenic emissions by country. They include a
detailed sectoral representation (59 sectors in total) which has been aggregated
into nine individual sectors (see Appendix Table ), including
agriculture, aircraft, energy, industry, international shipping, residential and
commercial, solvent production and application, transportation, and
waste. Values for 2015 were approximated by extending fossil fuel consumption
using aggregate energy statistics and trends in emissions factors
from the GAINS ECLIPSE V5a inventory . Sulfur
(SOx) emissions in China were trended from 2010 using values from
.
The study of provides data on historical emissions from open burning,
specifically including burning of agricultural waste on fields (AWB), forests,
grasslands, and peatlands out to 2015. Due to the high amount of inter-annual
variability in the historical data which is not explicitly modeled in IAMs, we
use a decadal mean over 2005–2014 to construct a representative value for 2015
(see, e.g., Fig. ). When used in conjunction with model
results, we aggregate country-level emissions to the individual model regions of
which they are comprised.
Emissions of N2O and fluorinated gas species were harmonized only at the
global level, with 2015 values from other data sources. Global N2O emissions
were taken from PRIMAP and global emissions of HFCs were
developed by . The HFC-23 and total PFC and SF6 emissions
were provided by Guus Velders, based on mixing ratios, and were
extended from 2012 to 2015 by using the average 2008–2012 trend.
Automated emissions harmonization
Emissions harmonization is defined as a procedure designed to match model
results to a common set of historical emissions trajectories. The goal of this
process is to match a specified base-year dataset while retaining consistency
with the original model results to the best extent possible while also providing
a smooth transition from historical trajectories. This non-disjoint transition
is critical for global climate models when modeling projections of climate
futures which depend on historical model runs, guaranteeing a smooth functional
shape of both emissions and concentration fields between the historical and
future runs. Models differ in their 2015 data points in part because the
historical emissions datasets used to calibrate the models differ (e.g.,
PRIMAP – ; EDGAR – ; CEDS –
). Another cause of differences is that 2015 is a projection
year for all of these models (the original scenarios were originally finalized
in 2015).
Harmonization can be simple in cases where a model's historical data are similar
to the harmonization dataset. However, when there are strong discrepancies
between the two datasets, the choice of harmonization method is crucial for
balancing the dual goals of accurate representation of model results and
reasonable transitions from historical data to harmonized trajectories.
The quantity of trajectories requiring harmonization increases the complexity of
the exercise. In this analysis, given the available sectoral representation of
both the historical data and models, we harmonize model results for 14
individual emissions species and 13 sectors as described in Table .
The majority of emissions–sector combinations are harmonized for
every native model region
Further information regarding the model
region definitions is available via the IAMC Wiki at
https://www.iamcdocumentation.eu (last access: 8 April 2019) and
.
(Table ). Global trajectories are harmonized for fluorinated species
and N2O, aircraft and international shipping sectors, and CO2
agriculture, forestry, and other land-use (AFOLU) emissions due to historical
data availability and regional detail. Therefore between 970 and 2776 emissions
trajectories require harmonization for any given scenario depending on the model used.
Harmonized species and sectors, adapted from with
permission of the authors. A mapping of original model variables (i.e.,
outputs) to ScenarioMIP sectors is shown in Appendix Table
.
a Global total trajectories are harmonized due to lack of detailed
historical data.
b Global sectoral trajectories are harmonized due to lack of detailed
historical data.
c A global trajectory for AFOLU CO2 is used; non-land-use sectors
are harmonized for each model region.
The number of model regions and total harmonized emissions
trajectories for each IAM participating in the study. The number of
trajectories is calculated from Table , including gas species
for which global trajectories are harmonized.
We employ the newly available open-source software Aneris in order to perform harmonization in a consistent and rigorous
manner. For each trajectory to be harmonized, Aneris chooses which harmonization
method to use by analyzing both the relative difference between model results
and harmonization historical data as well as the behavior of the modeled
emissions trajectory. Available methods include ratio and offset methods, which
utilize the quotient and difference of unharmonized and harmonized values,
respectively, as well as convergence methods, which converge to the original
modeled results at some future time period. We refer the reader to
for a full description of the harmonization methodology and
implementation.
Override methods can be specified for any combination of species, sectors, and
regions which are used in place of the default methods provided by
Aneris. Override methods are useful when default methods do not fully capture
either the regional or sectoral context of a given trajectory. Most commonly, we
observed this in cases where there are large relative differences in the
historical datasets, the base-year values are small, and there is substantial
growth in the trajectory over the modeled time period, thereby reflecting the
large relative difference in the harmonized emissions results. However, the
number of required override methods is small: 5.1 % of trajectories use override
messages for the IMAGE model, 5.6 % for MESSAGE-GLOBIOM, and 9.8 % for
REMIND. The AIM model elected not to use override methods, and GCAM uses a
relatively large number (35 %).
Finally, in order to provide additional detail for fluorinated gases (F gases)
we extend the set of reported HFC and CFC species based on exogenous
scenarios. We take scenarios of future HFCs from , which
provide detailed emissions trajectories for F gases. We downscale the global HFC
emissions reported in each harmonized scenario to arrive at harmonized emissions
trajectories for all constituent F gases, deriving the HFC-23 from the RCP
emissions pathway. We further include trajectories of CFCs as reported in
scenarios developed by the World Meteorological Organization (WMO)
, which are not included in all model results.
Region-to-country downscaling
Downscaling, defined here as distributing aggregated regional values to
individual countries, is performed for all scenarios in order to improve the
spatial resolution of emissions trajectories, and as a prelude to mapping to a
spatial grid (discussed in Appendix ). We developed
an automated downscaling routine that differentiates between two classes of
sectoral emissions: those related to AFOLU and those related to fuel combustion
and industrial and urban processes. In order to preserve as much of the original
model detail as possible, the downscaling procedures here begin with harmonized
emissions data at the level of native model regions and the aggregate sectors
(Table ). Here we discuss key aspects of the downscaling methodology
and refer the reader to the downscaling
documentation (https://github.com/iiasa/emissions_downscaling/wiki,
last access: 8 April 2019) for further details.
AFOLU emissions, including agricultural waste burning, agriculture, forest
burning, peat burning, and grassland burning, are downscaled using a linear
method. Linear downscaling means that the fraction of regional emissions in each
country stays constant over time. Therefore, the total amount of open-burning
emissions allocated to each country will vary over time as economies evolve into
the future, following regional trends from the native IAM. However there is no
subregional change in the spatial distribution of land-use related emissions over
time. This is in contrast to other anthropogenic emissions, where the impact, population, affluence, and
technology (IPAT)
method is used to dynamically downscale to the country level as discussed
above. Note that peat burning emissions were not modeled by the IAMs and are
constant into the future.
All other emissions are downscaled using the IPAT method developed by
, where population and GDP trajectories are taken from the SSP
scenario specifications . The overall philosophy
behind this method is to assume that emissions intensity values (i.e., the ratio
of emissions to GDP) for countries within a region will converge from a base
year, ti (2015 in this study), over the future. A convergence year, tf, is
specified beyond 2100, the last year for the downscaled data, meaning that
emissions intensities do not converge fully by 2100. The choice of convergence
year reflects the rate at which economic and energy systems converge toward
similar structures within each native model region. Accordingly, the SSP1 and
SSP5 scenarios are assigned relatively near-term convergence years of 2125,
while SSP3 and SSP4 scenarios are assigned 2200, and SSP2 is assigned an intermediate value
of 2150.
The downscaling method first calculates an emissions intensity, I, for the
base and convergence years using emissions level, E, and GDP.
It=EtGDPt
An emissions intensity growth factor, β, is then determined for each
country, “c”, within a model region, “R”, using convergence year
emissions
intensities, IR,tf, determined by extrapolating from the last 10 years
(e.g., 2090 to 2100) of the scenario data.
βc=IR,tfIc,ti1tf-ti
Using base-year data for each country and scenario data for each region, future
downscaled emissions intensities and patterns of emissions are then generated for
each subsequent time period.
Ic,t=βcIc,t-1Ec,t*=Ic,tGDPc,t
These spatial patterns are then scaled with (i.e., normalized to) the model
region data to guarantee consistency between the spatial resolutions, resulting
in downscaled emissions for each country in each time period.
Ec,t=ER,t∑c′∈REc′,t*Ec,t*
For certain countries and sectors the historical dataset has zero-valued
emissions in the harmonization year. This would result in zero downscaled future
emissions for all years. Zero emissions data occur largely for small countries,
many of them small island nations. This could be due to either lack of actual
activity in the base year or missing data on activity in those countries. In
order to allow for future sectoral growth in such cases, we adopt, for purposes
of the above calculations, an initial emissions intensity of one-third the
value of the lowest country in the same model region. We then allocate future
emissions in the same manner discussed above, which is consistent with our
overall convergence assumptions. Note that we exclude the industrial sector
(Table ) from this operation as it might not be reasonable to assume
the development of substantial industrial activity in these countries.
Finally, some scenarios include negative CO2 emissions at some point in the future
(notably from energy use). For CO2 emissions, therefore, we apply a linear
rather than exponential function to allow a smooth transition to negative
emissions values for both the emissions intensity growth factor and future
emissions intensity calculations. In such cases, Eqs. () and
() are replaced by Eqs. () and (),
respectively.
βc=(IR,tfIc,ti-1)1tf-tiIc,t=(1+βc)Ic,t-1
Results
Here we present the results of harmonization and downscaling applied to all nine
scenarios under consideration. We discuss in Sect. the relevance
of each selected scenario to the overall experimental design of ScenarioMIP,
focusing on their RF and mean global temperature pathways. In Sect. ,
we discuss general trends in global trajectories of important
GHGs and aerosols and their sectoral contributions over the modeled time
horizon. In Sect. , we explore the effect of harmonization on
model results and the difference between unharmonized and harmonized
results. Finally, in Sect. , we provide an overview of the
spatial distribution of emissions species at both regional and spatial grids.
Experimental design and global climate response
The nine ScenarioMIP scenarios were selected to provide a robust experimental
design space for future climate studies as well as IAV analyses with the broader
context of CMIP6. Chief among the concerns in developing such a design space
are
both the range and spacing of the global climate response within the portfolio
of scenarios . Prior work for the RCPs studied a range of
climate outcomes between ∼2.6 and 8.5 Wm-2 at EOC. Furthermore,
recent work finds that statistically significant regional
temperature outcomes (>5 % of half the land surface area) are observable with a
minimum separation of 0.3 ∘C, which is approximately equivalent to 0.75 Wm-2. Given the current policy context, notably the
recent adoption of the UN Paris Agreement, the primary design goal for the
ScenarioMIP scenario selection is thus twofold: span a wider range of possible
climate futures (1.9–8.5 Wm-2) in order to increase relevance to the global
climate dialogue and provide a variety of scenarios between these upper and
lower bounds such that they represent statistically significant climate
variations in order to support a wide variety of CMIP6 analyses.
We find that the selected scenarios meet this broad goal, as shown in
Fig. , by using the simple climate model MAGICC6 with central
climate-system and gas-cycle parameter settings for all scenarios to calculate
pathways of both RF and the resulting response of global mean
temperature (see Appendix Table for a listing of all EOC RF values).
We also present illustrative global mean temperature pathways. EOC temperature
outcomes span a large range, from 1.4 ∘C at the lower end to 4.9 ∘C for SSP5-8.5,
the scenario with the highest warming emissions trajectories. Notably, two scenarios
(SSP1-1.9, which reaches 1.4 ∘C by EOC, and SSP1-2.6, reaching 1.7 ∘C) can be used
for studies of global outcomes of the implementation of the UN Paris Agreement,
which has a desired goal of “[h]olding 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 above pre-industrial
levels” Article 2.1(a). The difference between scenario temperature
outcomes is statistically significant in nearly all cases, with a minimum
difference of 0.37 ∘C (SSP1-1.9 and SSP1-2.6) and maximum value of 0.77 ∘C (SSP3-7.0
and SSP5-8.5). The EOC difference between SSP4-3.4 and SSP5-3.4-OS is not
significant (0.07 ∘C); however global climate outcomes are likely sensitive to
the dynamics of the forcing pathway .
Trajectories of RF and global mean temperature (above
pre-industrial levels) are presented as are the contributions to RF for a
number of different emissions types native to the MAGICC6 model. The RF
trajectories are displayed with their RCP counterparts analyzed in
CMIP5. For those scenarios with direct analogues, trajectories are largely
similar in shape and match the same EOC forcing values.
A subset of four scenarios (SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5) was also
designed to provide continuity between CMIP5 and CMIP6 by providing similar
forcing pathways to their RCP counterparts assessed in CMIP5. We find that this
aspect of the scenario design space is also met by the relevant
scenarios. SSP2-4.5 and SSP5-8.5 track RCP4.5 and RCP8.5 pathways nearly
exactly. We observe slight deviations between SSP1-2.6 and RCP2.6 as well as
SSP4-6.0 and RCP6.0 at mid-century due largely to increased methane emissions in
the historic period (i.e., methane emissions broadly follow RCP8.5 trajectories
after 2000, resulting in higher emissions in the harmonization year of this
exercise; see Fig. below).
The remaining five scenarios were chosen to “fill gaps” in the previous RCP
studies in CMIP5 and enhance the potential policy relevance of CMIP6 MIP outputs
. SSP3-7.0 was chosen to provide a scenario with
relatively high vulnerability and land-use change with associated near-term
climate forcing (NTCF) emissions resulting in a high RF pathway. We find that it
reaches an EOC forcing target of ∼7.1Wm-2 and greater
than 4 ∘C mean global temperature increase. While contributions to RF from
CO2 in SSP3-7.0 are lower than that of SSP5-8.5, methane and aerosol
contributions are considerably higher (see, e.g.,
,
for a discussion on the effect of shortwave forcing on methane's contribution to
overall RF). A companion scenario, SSP3-LowNTCF, was also included in order to
study the effect of NTCF species in the context of AerChemMIP. Critically,
emissions factors of key NTCF species are assumed to develop similar to an SSP1
(rather than SSP3) scenario. SSP3-LowNTCF sees substantially fewer contributions
to EOC forcing from NTCF emissions (notably SOx and methane), resulting
in a forcing level of 6.3 Wm-2 and global mean temperature increase
of 3.75 ∘C by the end of the century. This significant reduction is largely due
to updating emissions coefficients for air pollutants and other NTCFs to match
the SSP1 assumptions. SSP4-3.4 was chosen to provide a scenario at the lower end
of the range of future forcing pathways. Reaching a EOC mean global temperature
between SSP2-4.5 and SSP1-2.6 (∼2.25∘C), it is an ideal scenario
for scientists to study the mitigation costs and associated impacts between
forcing levels of 4.5 and 2.6 Wm-2.
The final two scenarios, SSP1-1.9 and SSP5-3.4-OS, were chosen to study
policy-relevant questions of near- and medium-term action on climate
change. SSP1-1.9 provides a new low end to the RF pathway
range. It reaches an EOC forcing level of ∼1.9Wm-2 and an
associated global mean temperature increase of ∼1.4∘C (with
temperature peaking in 2040), in line with the goals of the Paris
Agreement. SSP5-3.4-OS, however, is designed to represent a world in
which action towards climate change mitigation is delayed but vigorously pursued
after 2050, resulting in a forcing and mean global temperature overshoot. A
peak temperature of 2.5 ∘C above pre-industrial levels is reached in 2060 after
which global mitigation efforts reduce EOC warming to ∼2.25∘C. In
tandem, and including SSP2-4.5 (which serves as a reference experiment in
ScenarioMIP; ), these scenarios provide a robust
experimental platform to study the effect of the timing and magnitude of global
mitigation efforts, which can be especially relevant to science-informed policy
discussions.
Global emissions trajectories
Emissions contributions to the global climate system are myriad but can broadly
be divided into contributions from greenhouse gases (GHGs) and aerosols. The
models used in this analysis explicitly represent manifold drivers and processes
involved in the emissions of various gas species. For a fuller description of
these scenario results see the original SSP quantification papers
. Here,
we focus on emissions species that most strongly contribute to changes in future
mean global temperature and scenarios with the highest relevance and uptake for
other MIPs within CMIP6, namely the Tier-1 scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0,
and SSP5-8.5. Where insightful, we provide additional detail on results from
other scenarios; however results for all scenarios are available in Appendix .
CO2 emissions have a large span across scenarios by the end of the century
(-20 to 125 Gt yr-1), as shown in Fig. . Scenarios can be
categorized based on characteristics of their trajectory profiles: those that
have consistent downward trajectories (SSP1, SSP4-3.4), those that peak in a
given year and then decrease in magnitude (SSP2-4.5 in 2040 and SSP4-6.0 in 2050),
and those that have consistent growth in emissions (SSP3). SSP5 scenarios, which
model a world with fossil-fuel-driven development, have EOC emissions which
bound the entire scenario set, with the highest CO2 emissions in SSP5-8.5 peaking
in 2080 and the lowest CO2 emissions in SSP5-3.4-OS resulting from the
application of stringent mitigation policies after 2040 in an attempt to
stabilize RF to 3.4 Wm-2 after overshooting this limit earlier in
the century. A number of scenarios exhibit negative net CO2 emissions before the
end of the century. SSP1-1.9, the scenario with the most consistent negative
emissions trajectory, first reports net negative emissions in 2060 with EOC
emissions of -14 Gt yr-1. SSP5-3.4-OS, SSP1-2.6, and SSP4-3.4 each cross the
zero-emissions threshold in 2070, 2080, and 2090, respectively.
Trajectories of CO2 and CH4, primary contributors to GHG emissions,
including both historical emissions, emissions analyzed for the RCPs, and
all nine scenarios covered in this study.
Global emissions trajectories for CO2 are driven largely by the behavior of
the energy sector in each scenario, as shown in Fig. . Positive emissions profiles are also greatly influenced by
the industry and transport sectors, whereas negative emissions profiles are
driven by patterns of agriculture and land-use as well as the means of energy
production. In SSP1-2.6, early to mid-century emissions continue to be dominated by
the energy sector with substantial contributions from industry and
transport. Negative emissions from land use are observed as early as 2030 due to
large-scale afforestation while net negative
emissions from energy conversion first occur in 2070. Such net negative
emissions are achieved when carbon dioxide removal from bioenergy from carbon
capture and storage
(CCS)
exceeds residual fossil CO2 emissions from the combustion of coal, oil, and
gas. Emissions contributions from the transport sector diminish over the century
as heavy- and light-duty transport fleets are electrified. Emissions from
industry peak and the decrease over time such that the residential and commercial
sector (RC) provides the majority of positive CO2 emissions by the end of the
century. SSP2-4.5 experiences similar trends among sectors but with smaller
magnitudinal changes and temporal delays. Negative emissions, for example, are
experienced in the land-use sector for the first time in 2060 and are not
experienced in the energy sector until the end of the century. Energy-sector
CO2 emissions continue to play a large role in the overall composition until
2080, at which point the industrial sector provides the plurality of
CO2. Emissions from the transport sector peak at mid-century, but are still a
substantive component of positive CO2 emissions at the end of the
century. Finally, the SSP5-8.5 scenario's emissions profile is dominated by the
fossil-fueled energy sector for the entirety of the century. Contributions from
the transport and industrial sectors grow in magnitude but are diminished as
the
share of total CO2 emissions, CO2 emissions from the AFOLU sector, decrease
steadily over time. By the end of the century, the energy sector comprises
almost 75 % of all emitted CO2 in this scenario relative to 50 % today.
The sectoral contributions to
CO2 and CH4 emissions for Tier-1 scenarios.
Methane (CH4) is an emissions species with substantial contributions to
potential future warming mainly due to its immediate GHG effect, but also
because of its influence on atmospheric chemistry, as a tropospheric ozone
precursor, and its eventual oxidation into CO2 in the case of CH4 from
fossil sources . At present, approximately 400 Mt yr-1 of
CH4 is emitted globally, and the span of future emissions developed in this
scenario set range from 100 to nearly 800 Mt yr-1 by the end of the
century. Global emissions of methane in SSP1 scenarios follow similar
trajectories to CO2, with large emissions reductions; SSP2 follows suit, with
emissions peaking in 2030 and then reducing throughout the rest of the century;
in SSP3's baseline scenario, emissions continue to grow while in the NTCF
scenario they are reduced drastically as policies are implemented to reduce
forcing from short-lived emissions species; SSP4 is characterized by growing
(SSP4-6.0) or mostly stable (SSP4-3.4) CH4 emissions until the middle of the
century which peak in 2060 and then decline; and finally SSP5's baseline
scenario sees a plateauing of CH4 emissions between 2050 and 2070 before
their eventual decline, while the overshoot scenario has drastic CH4
emissions reductions in 2040 corresponding to significant mid-century mitigation
efforts in that scenario.
Historically, CH4 emissions are dominated by three sectors: energy (due to
fossil fuel production and natural gas transmission), agriculture (largely enteric
fermentation from livestock and rice production), and waste (i.e.,
landfills). In each scenario, global emissions of CH4 are largely dominated by
the behavior of activity in each of these sectors over time. For example, in the
SSP1 scenarios, significant reductions in energy emissions are observed as
energy supply systems shift from fossil to renewable sources while agriculture
and waste-sector emissions see only modest reductions as global population
stabilizes around mid-century. In the SSP2 scenario, emissions from the energy
sector peak in 2040 as there is continued reliance on energy from natural gas
but large expansions in renewables in the future; however, emissions from the
agricultural and waste sectors are similar to today's levels by the end of the
century. Finally, CH4 emissions in SSP5's baseline scenario are characterized by
growth in the energy sector from continued expansion of natural gas and a peak and
reduction in agricultural emissions resulting in 20 % higher emissions at the end
of the century relative to the present as population grows in the near term
before contracting globally.
GHG emissions are broadly similar between the main scenarios in CMIP5 (RCPs) and
CMIP6 (SSPs). Notably, we observe that the SSPs exhibit slightly lower CO2
emissions in the 2.6 Wm-2 scenarios and higher emissions in the 8.5 Wm-2
scenarios due to lower and higher dependence on fossil fuels relative to their
RCP predecessors. CH4 emissions are largely similar at EOC for 2.6 and
4.5 Wm-2 scenarios between the RCPs and SSPs, with earlier values differing due to
continued growth in the historical period (RCPs begin in 2000 whereas SSPs begin
in 2015). The 8.5 Wm-2 scenario exhibits the largest difference in CH4
emissions between the RCPs and SSPs because of the SSP5 socioeconomic story line
depicting a world which largely develops out of poverty in less-developed
countries, reducing CH4 emissions from waste and agriculture. This contrasts
with a very different story line behind RCP8.5 .
In nearly all scenarios, aerosol emissions are observed to decline over the
century; however, the magnitude and speed of this decline are highly dependent on
the evolution of various drivers based on the underlying SSP story lines,
resulting in a wide range of aerosol emissions, as shown in
Fig. . For example, sulfur emissions (totaling 112 Mt yr-1 globally
in 2015) are dominated at present by the energy and industrial sectors. In SSP1,
where the world transitions away from fossil-fuel-related energy production
(namely coal in the case of sulfur), emissions decline sharply as the energy
sector transitions to non-fossil-based fuels and end-of-pipe measures for air
pollution control are ramped up swiftly. The residual amount of sulfur remaining
at the end of the century (∼10 Mt yr-1) is dominated by the
industrial sector. SSP2-4.5 sees a similar transition but with delayed action:
total sulfur emissions decline due primarily to the decarbonization of the
energy sector. SSP5 also observes declines in overall sulfur emissions led
largely by an energy mix that transitions from coal dependence to dependence on
natural gas, as well as strong end-of-pipe air pollution control efforts. These
reductions are similarly matched in the industrial sector, where natural gas is
substituted for coal use as well. Thus, overall reductions in emissions are
realized across the scenario set. Only SSP3 shows EOC sulfur emissions
equivalent to the present day, largely due to increased demand for industrial
services from growing population centers in developing nations with a heavy
reliance on coal-based energy production and weak air pollution control efforts.
Emissions trajectories for sulfur and black carbon (BC), for history, the
RCPs, and all nine scenarios analyzed in this study. SSP trajectories
largely track with RCP values studied in CMIP5. A notable difference lies
in BC emissions, which have seen relatively large increases in past years,
thus providing higher initial emissions for the SSPs.
Aerosols associated with the burning of traditional biomass, crop, and pasture
residues, as well as municipal waste, such as black carbon (BC) and organic
carbon (OC, see Appendix Fig. ), are affected most strongly by
the degree of economic progress and growth in each scenario, as shown in
Fig. . For example, BC emissions from the residential and
commercial sector comprise nearly 40 % of all emissions in the historical time
period with a significant contribution from mobile sources. By the end of the
century, however, emissions associated with crop and pasture activity comprise
the plurality of total emissions in SSP1, SSP2, and SSP5 due to a transition
away from traditional biomass usage based on increased economic development and
population stabilization and emissions controls on mobile sources. Only SSP3, in
which there is continued global inequality and the persistence of poor and
vulnerable urban and rural populations, are there continued quantities of BC
emissions across sectors similar to today. OC emissions are largely from biofuel
and open burning and follow similar trends: large reductions in scenarios with
higher income growth rates with a residual emissions profile due largely to
open-burning-related emissions. Other pollutant emissions (e.g., NOx, carbon
monoxide,
CO; and volatile organic carbon, VOC) also see a decline in total global
emissions at rates depending on the story line .
The sectoral contributions to sulfur
and black carbon emissions for Tier-1 scenarios.
The effects of harmonization
Harmonization, by definition, modifies the original model results such that
base-year values correspond to an agreed-upon historical source, with an aim for
future values to match the original model behavior as much as possible. Model
results are harmonized separately for each individual combination of model
region, sector, and emissions species. In the majority of cases, model results
are harmonized using the default methods described in Sect. ; however, it is possible for models to provide
harmonization overrides in order to explicitly set a harmonization method for a
given trajectory.
We assess the impact that harmonization has on model results by analyzing the
harmonized and unharmonized trajectories. Figure shows
global trajectories for each scenario of a selected number of emissions
species. Qualitatively, the CO2 and sulfur emissions trajectories match
relatively closely to the magnitude of model results due to general agreement
between historical sources used by individual models and the updated historical
emissions datasets. This leads to convergence harmonization routines being used
by default. In the case of CH4 and BC, however, there is larger disagreement
between model results and harmonized results in the base year. In such cases,
Aneris chooses harmonization methods that match the shape of a given trajectory
rather than its magnitude in order to preserve the relationship between driver
and emissions for each model.
Harmonized (solid) and unharmonized
(dashed) trajectories are shown are shown in Panels (a)–(d).
Panels (e)–(h) depict the distribution of differences
(harmonized and less unharmonized) for every modeled region. All box plots
show upper and lower quartiles as solid boxes, median values as solid
lines, and whiskers extending to the 10th and 90th percentiles. Median values
for all are near zero; however, the deviation decreases with time as
harmonized values begin to more closely match unharmonized model results
largely due to the use of convergence methods.
We find that across all harmonized trajectories the difference between
harmonized and unharmonized model results decreases over the modeled time
horizon. Panels (e–h) in Fig. show the
distribution of all 15 954 trajectories (unharmonized and less harmonized result)
for the harmonization year (2015) and two modeled years (2050 and 2100). Each
emissions species data population exhibits the same trend of reduced difference
between modeled and harmonized results. Not only does the deviation of result
distributions decrease over time, but the median value also converges toward zero
in all cases.
The trajectory behavior for a number of important emissions species is
dominated by certain sectors, as shown in Appendix Fig. . Notably, the energy sector tends to dominate
behavior of CO2 emissions, agriculture dominates CH4
emissions
trajectories, the industrial sector largely determines total sulfur emissions,
and emissions from the residential and commercial sectors tend to dominate BC
emissions across the various scenarios. Accordingly, we further analyzed the
harmonization behavior of these sector–species combinations. Importantly, we
again observe an overall trend towards convergence of results at the end of the
century; thus harmonized results largely track unharmonized results for these
critical emissions sectors. The deviation of distributions of differences
consistently decreases with time for all scenarios, and nearly all medians converge
consistently towards zero, save for energy-related CO2 SSP5-8.5, which
has a higher growth rate than convergence rate, thus larger differences in 2050
than 2015. Overall, we find the harmonization procedure successfully harmonized
results' historical base year and closely matches model results across the
scenarios by EOC.
Spatial distribution of emissions
The extent to which reductions or growth of emissions are distributed regionally
varies greatly among scenarios. The regional breakdown of primary contributors
to future warming potential, CO2 and CH4, is shown in
Fig. . While present-day CO2 emissions see near-equal contributions
from the Organization for Economic Cooperation and Development (OECD) and Asia, future CO2 emissions are governed largely by potential
developments in Asia (namely China and India). For SSP1-2.6, in which deep
decarbonization and negative CO2 emissions occur before the end of the century,
emissions in Asia peak in 2020 before reducing to zero by 2080. Mitigation
efforts occur across all regions, and the majority of carbon reduction is
focused in the OECD; however, all regions have net negative CO2 emissions by
2090. Asian CO2 emissions in SSP2-4.5 peak in 2030, and most other regions see
overall reductions except Africa, in which continued development and
industrialization results in emissions growth. Notably, Latin America is the
only region in which negative emissions occur in SSP2-4.5 due largely to
increased deployment of biomass-based energy production and carbon
sequestration. Sustained growth across regions is observed in SSP5-8.5, where
emissions in Asia peak by 2080, driving the global emissions peaking in the same
year. Other scenarios (see Appendix Fig. ) follow similar trends
with future CO2 emissions driven primarily by developments in Asia.
Regional emissions for five global regions
for CO2 and CH4 in each Tier-1
scenario.
CH4 emissions, resulting from a mix of energy use, food production, and waste
disposal, show a different regional breakdown across scenarios. In SSP1-2.6, CH4
emissions are reduced consistently across regions as energy systems transition
away from fossil fuel use (notably natural gas) and the husbandry of livestock
is curtailed globally. CH4 emissions in other scenarios tend to be dominated by
developments in Africa. In SSP5-8.5, for example, emissions in Africa begin to
dominate the global profile by mid-century, due largely to expansion of
fossil-fuel-based energy production. SSP3 and SSP4 see continued growth in
African CH4 emissions across the century, even when global emissions are reduced
as in the case of SSP4 scenarios.
CO2 and CH4 are well-mixed climate forcers and thus
their spatial variation has a higher impact from a political rather than
physical perspective. Aerosols, however, have substantive spatial variability
which directly impacts both regional climate forcing via scattering and
absorption of solar radiation and cloud formation as well as local and regional
air quality. Thus in order to provide climate models with more detailed and
meaningful datasets, we downscale emissions trajectories from model regions to
individual countries. In most cases, models explicitly represent countries with
large shares of emissions (e.g., USA, China, India). MESSAGE-GLOBIOM and
REMIND-MAGPIE are notable exceptions; however, their regional aggregations are
such that these important countries comprise the bulk of emissions in their
aggregate regions (e.g., the MESSAGE-GLOBIOM North American region comprises the
USA and Canada). For regions constituted by many countries, country-level
emissions are driven largely by bulk region emissions and country GDP in each
scenario (per Sect. ). Afterwards, country-level
emissions are subsequently mapped to spatial grids . We here
present global maps of two aerosol species with the strongest implications on
future warming, i.e., BC in Fig. and sulfur in
Fig. . We highlight three cases which have relevant aerosol
emissions profiles: SSP1-2.6, which has significantly decreasing emissions over
the century, SSP3-7.0, which has the highest aerosol emissions, and
SSP3-LowNTCF,
which has socioeconomic drivers similar to those of the SSP3 baseline but models the
inclusion of policies which seek to limit emissions of near-term climate forcing
species.
At present, BC has the highest emissions in China and India due largely to
traditional biomass usage in the residential sector and secondarily to
transport-related activity. In scenarios of high socioeconomic development and
technological progress, such as SSP1-2.6, emissions across countries decline
dramatically such that by the end of the century, total emissions in China, for
example, are equal to those of the USA today. In almost all countries, BC
emissions are nearly eradicated by mid-century while emissions in southeast Asia
reach similar levels by the end of the century. In SSP3-7.0, however, emissions
from southeast Asia and central Africa increase until the middle of the century
as populations grow while still depending on fossil-fuel-heavy energy supply
technologies, transportation, and cooking fuels. By the end of the century in
SSP3-7.0, global BC emissions are nearly equivalent to the present day (see,
e.g., Fig. ), but these emissions are concentrated largely
in central Africa, southeast Asia, and Brazil while they are reduced in North
America, Europe, and central Asia. By enacting policies that specifically target
near-term climate forcers in SSP3-LowNTCF, the growth of emissions in the
developing world is muted by mid-century and is cut by more than half of
today's levels (∼9 vs. ∼4 Mt yr-1) by the end of
the century. These policies result in similar levels of BC emissions in China as
in SSP1-2.6, while most of the additional emissions are driven by activity in
India and central Africa due to continued dependence on traditional biomass for
cooking and heating.
Downscaled and gridded emissions of black
carbon at present and in 2050 and 2100
for SSP1-2.6, SSP3-7.0, and SSP3-LowNTCF.
Downscaled and gridded emissions of
sulfur at present and in 2050 and 2100
for SSP1-2.6, SSP3-7.0, and SSP3-LowNTCF.
The spatial distribution of sulfur emissions varies from that of BC due to large
contributions from energy and industrial sectors, and is thus being driven by a
country's economic size and composition, as opposed to household
activity. Emissions today are largely concentrated in countries having large
manufacturing, industrial, and energy supply sectors with heavy reliance on
coal, such as China, India, the USA, Russia, and some parts of the Middle
East. Again, we observe in SSP1-2.6 a near elimination of sulfur emissions by the
end of the century with some continued reliance on sulfur-emitting technologies
in India and China in the middle of the century. In SSP3-7.0, although global
sulfur emissions over the course of the century peak slightly before reducing to
below current levels, increased emissions in southeast Asia offset reductions in
emissions elsewhere due to an expanding industrial sector with continued
reliance on coal. Notably, emissions in India peak around mid-century before
reducing to a magnitude lower than emissions levels today. In the SSP3-LowNTCF
scenario, NTCF policies have the added effect of reducing sulfur emissions,
resulting in more RF but fewer potential health impacts due to sulfur
pollution. By the end of the century in SSP3-LowNTCF, only India, China, and
Brazil have nontrivial quantities of emissions at significantly lower
magnitudes than today.
Conclusions
We present a suite of nine scenarios of future emissions trajectories of
anthropogenic sources, a key deliverable of the ScenarioMIP experiment within
CMIP6. IAM results for 14 different emissions species and 13 individual sectors
are provided for each scenario with consistent transitions from the historical
data used in CMIP6 to future trajectories using automated harmonization before
being downscaled to provide higher emissions source spatial detail. Harmonized
emissions at global, original native model region, and gridded resolution have
been delivered to participating climate teams in CMIP6 for further analysis and
study by a number of different MIPs.
Scenarios were selected from a candidate pool of over 40 different SSP
realizations such that a range of climate outcomes are represented which provide
sufficient spacing between EOC forcing to sample statistically significant
global and regional temperature outcomes. Of the nine scenarios, four were
selected to match forcing levels previously provided by the RCP scenarios used
in CMIP5. RF trajectories are largely comparable between two scenario sets. Five
additional scenarios were analyzed in order to enrich the possible studies of
physical and climate impact modeling teams as well as support the scientific
goals of specific MIPs. The additional scenarios provide both a variety of
statistically different EOC climate outcomes as well as enhanced policy and
scientific relevance of potential analyses.
These emissions data are now being used in a variety of multi-model climate model
studies (e.g., ), including ScenarioMIP. Identifying
sources of uncertainties is a critical component of the larger exercise of
CMIP6. As such, it is important that scientists using these datasets for further
model input and analysis take care when assessing the uncertainty not only
between scenarios but also between model results for a certain scenario. While each
scenario is presented by a single model in ScenarioMIP, models have also
provided a wider range of results as part of the SSP process.
A multi-model dispersion
Dispersion here is defined as the coefficient
of variation in model results. The coefficient of variation is defined here as
the ratio of the standard deviation to mean (absolute value) of a given
population of data. See further discussion in Appendix .
analysis is discussed in Appendix in order to provide
further insight into the robustness of results of emissions trajectories across
models for specific forcing targets. Notably, we observe large disagreement
between models for F-gas trajectories (>100 % dispersion by EOC in certain
cases); thus uncertainty for these species can be considered large by climate
modeling teams. We further observe small but non-negligible EOC dispersion
(>20 %) for certain aerosol emissions species, including CO, NH3, OC, and
sulfur. In general, dispersion between models of GHG species increases as EOC
RF decreases as the wide array of mitigation options chosen to
meet these lower climate targets can vary across models. The importance of this
measure of uncertainty is also scenario dependent. For example, models in
general report low emissions in SSP1 and high emissions in SSP3; thus, the
impact of dispersion may have a higher relevance to climate modelers in SSP3
than SSP1.
The ability for other IAM teams to generate and compare results with ScenarioMIP
scenarios is also of considerable importance in conjunction with CMIP6 and,
after its completion, for further scientific discovery and interpretation of
results. As such, we have striven to make openly available all of the tools used
in this exercise. The harmonization tool used in this study, Aneris, is
provided as an open-source software on GitHub as is the downscaling and gridding
methodology. Documentation for both is provided to users online. Such efforts
and standardizations not only make the efforts of ScenarioMIP robust and
reproducible, but can also prove useful for future exercises integrating a
variety of complex models.
Code and data availability
The harmonization tool used in this study,
Aneris, is available at https://github.com/iiasa/aneris (last access: 8 April 2019) and
documentation for using the tool is available at
http://software.ene.iiasa.ac.at/aneris/(last access:
8 April 2019). Similarly, the downscaling tool
used is available at https://github.com/iiasa/emissions_downscaling
(last access: 8 April 2019) and
its documentation can be found at
https://github.com/iiasa/emissions_downscaling/wiki
(last access: 8 April 2019). Model data, both
unharmonized and harmonized, are publicly available at the SSP database v1.1
(https://tntcat.iiasa.ac.at/SspDb, last access: 8 April 2019) via the “CMIP6 Emissions” tab while
gridded data are available via the ESGF Input4MIPs data repository
(https://esgf-node.llnl.gov/projects/input4mips/, last access: 8 April 2019).
Supplement figures
The primary socioeconomic assumptions associated with each SSP, including
population , urbanization , and GDP
. The figure is adapted from with
permission from the authors.
Historic values for land-burning emissions from 1990 until 2014. All
values for each emissions species are normalized to their value in
2005. The climatological mean window used for harmonization is shown in
grey. While decadal trends are present for some sectors, year-on-year
trends see large variation.
Supplement tables
The sectoral mapping used to aggregate historical data to a common
sectoral definition.
CEDS sectorsScenarioMIP sectors1A1a_Electricity-publicEnergy sector1A1a_Electricity-autoproducerEnergy sector1A1a_Heat-productionEnergy sector1A1bc_Other-transformationEnergy sector1A2a_Ind-Comb-Iron-steelIndustrial sector1A2b_Ind-Comb-Non-ferrous-metalsIndustrial sector1A2c_Ind-Comb-ChemicalsIndustrial sector1A2d_Ind-Comb-Pulp-paperIndustrial sector1A2e_Ind-Comb-Food-tobaccoIndustrial sector1A2f_Ind-Comb-Non-metallic-mineralsIndustrial sector1A2g_Ind-Comb-ConstructionIndustrial sector1A2g_Ind-Comb-transpequipIndustrial sector1A2g_Ind-Comb-machineryIndustrial sector1A2g_Ind-Comb-mining-quarryingIndustrial sector1A2g_Ind-Comb-wood-productsIndustrial sector1A2g_Ind-Comb-textile-leatherIndustrial sector1A2g_Ind-Comb-otherIndustrial sector1A3ai_International-aviationAircraft1A3aii_Domestic-aviationAircraft1A3b_RoadTransportation sector1A3c_RailTransportation sector1A3di_International-shippingInternational shipping1A3dii_Domestic-navigationTransportation sector1A3eii_Other-transpTransportation sector1A4a_Commercial-institutionalResidential commercial other1A4b_ResidentialResidential commercial other1A4c_Agriculture-forestry-fishingResidential commercial other1A5_Other-unspecifiedIndustrial sector1B1_Fugitive-solid-fuelsEnergy sector1B2_Fugitive-petr-and-gasEnergy sector1B2d_Fugitive-other-energyEnergy sector2A1_Cement-productionIndustrial sector2A2_Lime-productionIndustrial sector2A6_Other-mineralsIndustrial sector2B_Chemical-industryIndustrial sector2C_Metal-productionIndustrial sector2D_Degreasing-CleaningSolvent production and application2D3_Other-product-useSolvent production and application2D_Paint-applicationSolvent production and application2D3_Chemical-products-manufacture-processingSolvent production and application2H_Pulp-and-paper-food-beverage-woodIndustrial sector2L_Other-process-emissionsIndustrial sector3B_Manure-managementAgriculture3D_Soil-emissionsAgriculture3I_Agriculture-otherAgriculture3D_Rice-CultivationAgriculture3E_Enteric-fermentationAgriculture3F_Agricultural-residue-burning-on-fieldsBiomass burning11B_Forest-firesForest burning11B_Grassland-firesGrassland burning11B_Peat-firesPeat burning5A_Solid-waste-disposalWaste5E_Other-waste-handlingWaste5C_Waste-incinerationWaste6A_Other-in-totalIndustrial sector5D_Wastewater-handlingWaste7A_Fossil-fuel-firesEnergy sector
The sectoral mapping used to aggregate model output data to a common sectoral definition.
IAM variableScenarioMIP sectorsAFOLU|AgricultureAgricultureAFOLU|Biomass BurningAgricultural waste burningAFOLU|Land|Forest BurningForest burningAFOLU|Land|Grassland PasturesGrassland burningAFOLU|Land|Grassland BurningGrassland burningAFOLU|Land|WetlandsPeat burningEnergy|Demand|IndustryIndustrial sectorEnergy|Demand|Other SectorIndustrial sectorEnergy|Demand|Residential and Commercial and AFOFIResidential commercial otherEnergy|Demand|Transportation|AviationAircraftEnergy|Demand|Transportation|Road Rail and Domestic ShippingTransportation sectorEnergy|Demand|Transportation|Shipping|InternationalInternational shippingEnergy|SupplyEnergy sectorFossil Fuel FiresEnergy sectorIndustrial ProcessesIndustrial sectorOtherIndustrial sectorProduct Use|SolventsSolvents production and applicationWasteWaste
EOC RF values for unharmonized and harmonized scenario
results and differences between the two. The ScenarioMIP design
states that absolute differences must be within
±0.75Wm-2, for which all scenarios fall well within the acceptable
value.
The SSP3-LowNTCF scenario utilizes common assumptions with the SSP3-7.0 scenario except in the cases of assumptions
regarding near-term climate forcing (NTCF) species emission factors. These differences are designed to compare situations
within a SSP3 world in which NTCF-related policies are enacted in the absence of other GHG-related climate policies.
Here we list the assumptions additionally made to SSP3-7.0.
Regarding CH4, the CH4 emissions' reduction rates in SSP1-26 relative to
SSP1 baseline are adopted to SSP3-7.0. This implicitly assumes that SSP3-LowNTCF can
reduce CH4 as if SSP1's stringent climate mitigation policy is implemented in the SSP3 world.
For air pollutant species (sulfur, NOx, VOC, CO, NH3, BC, and OC), the
emissions factors assumed in SSP1 are adopted. This assumption implicitly assumes
that SSP1's air pollutant legislation and technological progress can be achieved in the SSP3 world.
Other species such as CFC, HFC, SF6, and C2H6 are identical to SSP3
baseline.
Along with these changes, CH4 emissions reduction further changes other air pollutants
and GHG emissions drivers. CH4 reduction generates emissions abatement costs, which changes
economic outputs in all sectors and household consumption in AIM/CGE. Consequently energy
consumption and CO2 emissions in all sectors are affected, which causes small differences
between SSP3-7.0 and SSP3-LowNTCF. Not only CO2 but also N2O, CH4, and air pollutants emissions
are also affected by these activity level changes, although this indirect effect is relatively minor.
Emissions gridding
Emissions data were mapped to a spatial grid generally following the
methodologies described in . A brief description is given
here, and a fuller discussion of the gridding process will be provided in
. For most anthropogenic sectors, emissions at the level of
country and aggregate sector are mapped to a 0.5 ∘C spatial grid by scaling the
2010 base-year country-level spatial pattern. Open-burning emissions from forest
fires, grassland burning, and agricultural waste burning on fields are mapped to
a spatial grid in the same manner, except that the spatial pattern is taken to
be the average from the last 10 years of the historical dataset (e.g.,
2005–2014). For each aggregate gridding sector the spatial pattern of emissions
within a country does not change over time in the future scenarios. This means
that, for example, the ratio of energy-sector NOx emissions from Shaanxi
and Beijing provinces in China is constant over time, even though total
NOx emissions from China vary over time. Because sectors are mapped to
the grid separately, however, total anthropogenic emissions (e.g., sum from all
sectors) from any two regions within a country will, in general, not have the
same time evolution.
International shipping and aircraft emissions are gridded globally such that the
global pattern does not change, only the overall emissions magnitude. One other
exception occurs for net negative CO2 emissions. Negative CO2
emissions occur in these models when biomass feedstocks are used
together with geologic carbon dioxide capture and storage (CCS). In this case,
physically, the emissions are taken out of the atmosphere at the locations where
biomass is grown, not at the point of energy consumption. In order to avoid
large, unphysical, net negative CO2 point source emissions, net negative
CO2 quantities are, therefore, summed globally and mapped to a spatial
grid corresponding to 2010 global cropland net primary production (NPP).
Global emissions
Emissions trajectories for all GHGs and all
scenarios analyzed in this study.
Sectoral breakdown for CO2 and
CH4 emissions per year for all scenarios
analyzed in this study.
Emissions trajectories for all
aerosols and all scenarios analyzed in this study.
Sectoral breakdown for sulfur and
BC emissions per year for all scenarios
analyzed in this study.
Harmonization
The relative difference
between harmonized and unharmonized trajectories
is
shown for the primary sectoral contributor for various emissions species in each
scenario. Boxes are comprised of the population of differences for all regions
in a given model–scenario combination (see, e.g., Table ). All
box plots show upper and lower quartiles as solid boxes, median values as solid
lines, and whiskers extending to the 10th and 90th percentiles. In general, the
largest deviations are observed in the base year. The spread of values
decreases
in time across almost all observations, with the convergence to zero or
near zero by EOC.
Regional emissions
Emissions for five global regions for all
other scenarios analyzed in this study.
Dispersion analysis
We here discuss the results of a dispersion analysis measuring the variation
in
emissions trajectories across models for a given scenario. Dispersion is a
measure of the spread of model values for a given global emissions value in a given
year. It is calculated in this context as the coefficient of variation (cv)
shown in Eq. (), which is defined as the ratio of the standard
deviation, σ, to mean, μ, of a given population of data.
cv=σ|μ|
In order to perform a consistent analysis, we select scenarios for which all
participating models provide results: SSP1-2.6, SSP2-4.5, and SSP3-7.0. Scenario
data are taken from the available SSP database at
https://tntcat.iiasa.ac.at/SspDb (last access: 8 April 2019) . Note that dispersion
has a nonzero value in the initial year of analysis due to model results not
being harmonized in this dataset. We show the dispersion for GHGs (with
aggregated F gases) in Fig. , individual F gases in
Fig. , and aerosols in Fig. .
Dispersion analysis results
for GHGs with aggregated F gases.
Dispersion analysis results for individual F gases.
Dispersion analysis results for aerosols.
Table shows gas species with the largest values of
dispersion. The highest dispersion occurs for F gases, notably C2F6,
SF6, and HFCs, implying that models generally do not agree on total
magnitudes for these gases. CO2 is also observed to have relatively high
dispersion in high-mitigation scenarios. Finally, aerosol species such as
NH3, sulfur, and OC show relatively high dispersion values (>30 %). In
almost every case, magnitudes of emissions with high dispersion decrease
substantially with time; thus this measure, while important for understanding
sources of error, may result in small total system error in climate
models. There are important scenario–species combinations to take account of,
however. First, CO2 dispersion in SSP1-2.6 can be of high consequence because
this is a scenario with substantial negative emissions at the end of
century. Additionally, users of the data should be aware of the dispersion for
aerosols in SSP3, as many aerosol species have large EOC magnitudes, thus
showing significant variation across models for these species–scenario
combinations.
The dispersion (cv) for the first modeled period and last modeled
period for scenarios with maximum model representation. Here we show the 10
highest EOC dispersion values for a given scenario–species combination.
MG, KR, SS, ShF, GL, EK, DV, MV, and DK contributed to the
harmonization process and provided extensive data validation efforts. LF led
the downscaling effort assisted by SS and MG. KC, JD, StF, OF, MH, TH, PH,
JeH, RH, JiH, AP, ES, and KT contributed scenario data. All authors contributed to
the writing of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank Joeri Rogelj for his thoughtful comments
during the development of this paper. This project has received funding from the
European Union's Horizon 2020 research and innovation program under grant
agreement no. 641816 (CRESCENDO).
Review statement
This paper was edited by Carlos Sierra and reviewed by three anonymous referees.
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