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This paper summarizes the main characteristics of the RCP8.5 scenario. The RCP8.5 combines assumptions about high population and relatively slow income growth with modest rates of technological change and energy intensity improvements, leading in the long term to high energy demand and GHG emissions in absence of climate change policies. Compared to the total set of Representative Concentration Pathways (RCPs), RCP8.5 thus corresponds to the pathway with the highest greenhouse gas emissions. Using the IIASA Integrated Assessment Framework and the MESSAGE model for the development of the RCP8.5, we focus in this paper on two important extensions compared to earlier scenarios: 1) the development of spatially explicit air pollution projections, and 2) enhancements in the land-use and land-cover change projections. In addition, we explore scenario variants that use RCP8.5 as a baseline, and assume different degrees of greenhouse gas mitigation policies to reduce radiative forcing. Based on our modeling framework, we find it technically possible to limit forcing from RCP8.5 to lower levels comparable to the other RCPs (2.6 to 6W/m2). Our scenario analysis further indicates that climate policy-induced changes of global energy supply and demand may lead to significant co-benefits for other policy priorities, such as local air pollution.
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RCP 8.5A scenario of comparatively high greenhouse
gas emissions
Keywan Riahi &Shilpa Rao &Volker Krey &
Cheolhung Cho &Vadim Chirkov &Guenther Fischer &
Georg Kindermann &Nebojsa Nakicenovic &Peter Rafaj
Received: 17 September 2010 /Accepted: 21 June 2011 /Published online: 13 August 2011
#The Author(s) 2011. This article is published with open access at
Abstract This paper summarizes the main characteristics of the RCP8.5 scenario. The
RCP8.5 combines assumptions about high population and relatively slow income growth
with modest rates of technological change and energy intensity improvements, leading in
the long term to high energy demand and GHG emissions in absence of climate change
policies. Compared to the total set of Representative Concentration Pathways (RCPs),
RCP8.5 thus corresponds to the pathway with the highest greenhouse gas emissions. Using
the IIASA Integrated Assessment Framework and the MESSAGE model for the
development of the RCP8.5, we focus in this paper on two important extensions compared
to earlier scenarios: 1) the development of spatially explicit air pollution projections, and 2)
enhancements in the land-use and land-cover change projections. In addition, we explore
scenario variants that use RCP8.5 as a baseline, and assume different degrees of greenhouse
gas mitigation policies to reduce radiative forcing. Based on our modeling framework, we
find it technically possible to limit forcing from RCP8.5 to lower levels comparable to the
other RCPs (2.6 to 6 W/m
). Our scenario analysis further indicates that climate policy-
induced changes of global energy supply and demand may lead to significant co-benefits
for other policy priorities, such as local air pollution.
1 Introduction
The Representative Concentration Pathways (RCPs) form a set of greenhouse gas concentration
and emissions pathways designed to support research on impacts and potential policy responses
to climate change (Moss et al. 2010; van Vuuren et al. 2011a). As a set, the RCPs cover the
range of forcing levels associated with emission scenarios published in the literature. The
Representative Concentration Pathway (RCP) 8.5 corresponds to a high greenhouse gas
emissions pathway compared to the scenario literature (Fisher et al. 2007;IPCC2008), and
hence also to the upper bound of the RCPs. RCP8.5 is a so-called baselinescenario that does
Climatic Change (2011) 109:3357
DOI 10.1007/s10584-011-0149-y
K. Riahi (*):S. Rao :V. Krey :C. Cho :V. Chirkov :G. Fischer :G. Kindermann :N. Nakicenovic :
P. Rafaj
International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
not include any specific climate mitigation target. The greenhouse gas emissions and
concentrations in this scenario increase considerably over time, leading to a radiative forcing
of 8.5 W/m
at the end of the century.
Underlying assumptions about main scenario drivers of the RCP8.5, such as
demographic and economic trends or assumptions about technological change are based
upon the revised and extended storyline of the IPCC A2 scenario published in Riahi et al.
(2007). Many scenario assumptions and outcomes of the RCP8.5 are thus derived directly
from the co-called A2r scenario (Riahi et al. 2007), which was selected from the literature
to serve as the basis for the RCP8.5 (for an overview of RCPs, see van Vuuren et al.
(2011a), and for the RCP process and selection see Moss et al. (2010), and IPCC (2008)).
While many scenario assumptions and results of the RCP8.5 are already well
documented, we review in this paper some of the main scenario characteristics with
respect to the relative positioning compared to the broader scenario literature. In addition,
we summarize main methodological improvements and extensions that were necessary to
make the RCP8.5 ready for its main purpose, i.e., to serve as input to the Coupled Model
Intercomparison Project Phase 5 (CMIP5) of the climate community. CMIP5 forms an
important element in the development of the next generation of climate projections for the
forthcoming IPCC Fifth Assessment Report (AR5). Finally, we use the RCP8.5 as a
baseline for developing scenarios that lead to similar forcing levels as the other RCPs
summarized in this SI (i.e. 2.6, 4.5 and 6.0 W/m
). For this purpose, we introduce
constraints on greenhouse gas emissions within the RCP8.5 storyline.
The main methodological improvements of the RCP8.5 since the original publication of
the A2r scenario of Riahi et al. (2007) include the explicit representation of present and
planned air quality legislation for the projection of regional air pollutant emissions; new
downscaling approaches for pollutant emissions that account for dynamic changes in spatial
relationships between exposure and mitigation; and finally, a more refined accounting of
land-use categories for the spatial representation of the land-transformation, including in
particular a new definition for grasslands.
The paper is structured as follows. Section 2presents an overview of the modeling
framework with primary focus on the new methodological enhancements. Section 3details
the results of both the RCP8.5 and a set of climate mitigation scenarios that lead the forcing
levels similar to the other RCPs. We first compare the main RCP trends to the broader
scenario literature, and then present implications for the energy-system, land-cover changes,
and emissions. Finally, Section 4provides a summary of the main findings.
2 Methodology
2.1 IIASA modeling framework
RCP8.5 was developed using the IIASA Integrated Assessment Modeling Framework that
encompasses detailed representations of the principal GHG-emitting sectorsenergy,
industry, agriculture, and forestry. The framework combines a careful blend of rich
disciplinary models that operate at different spatial resolutions that are interlinked and
integrated into an overall assessment framework (Fig. 1). Integration is achieved through a
The A2r scenario included some details of land use categories such as cultivated land, built-up land and
forests and grassland area (for further details see Tubiello and Fischer 2007).
34 Climatic Change (2011) 109:3357
series of hard and soft linkages between the individual components, to ensure internal
scenario consistency and plausibility (Riahi et al. 2007).
The three principal models of the IA framework (Fig. 1) are MESSAGEMACRO (Messner
and Strubegger 1995;RaoandRiahi2006), DIMA (Rokityanskiy et al. 2007)andAEZWFS
(Fischer et al. 2007) (see below for further details). The three models are driven by a set of
harmonized inputs at the regional, national, and grid (0.5×0.5°) level. For this purpose, the
regional population and GDP scenarios of the A2r scenario (see Section 3.1) are disaggregated
to the level of countries through a combination of decomposition and optimization methods. In
a subsequent second step, national results are further disaggregated to the grid-cell level, which
provides spatially explicit patterns of population and economic activities (Grubler et al. 2007).
The latter indicators are particularly important for the spatially explicit modeling of emissions
and land-cover changes in the forestry and agriculture sectors. They provide the basis for the
estimation of comparable indicators (such as relative land prices or population exposures to
pollutant emissions) that define e.g. the relative comparative advantages of agriculture- and
forestry-based activities or the stringency of spatial pollutant emissions reductions.
The MESSAGE model (Model for Energy Supply Strategy Alternatives and their General
Environmental Impact) stands at the heart of the integrated assessment framework. It is a
systems-engineering optimization model used for medium-to long-term energy system
Scenario Storyline
Mana gement
Agricu ltu ra l
Fra me work
Downscaling Tools
Spatially explicit (and national) projections of economic and demographic
growt h
Sys tems Engineerin g / Macro- Economic
Modelin g Framew ork (all GH Gs and all
Regional population &
econom ic projection s
MAGICC Climate Model
National, regional & spatially
explicit socio -econom ic dr ivers
Spatially explicit socio-economic
Consistency of land-cover changes
(spa tially explicit maps of
agricultural, urban, and forest land)
Poten tial and costs of
forest bioenergy and
sink s
Carbon and
biomass price
Feedba cks
Agricultural bioenergy
potentials and costs
Drivers for land- use related
non-CO2 emissions
Fig. 1 IIASA modeling framework (adapted from Riahi et al. 2007)
Climatic Change (2011) 109:3357 35
planning, energy policy analysis and scenario development. The model maps the entire energy
system with all its interdependencies from resource extraction, imports and exports, conversion,
transport and distribution to end-use services. The models current version provides global and
sub-regional information on the utilization of domestic resources, energy imports and exports
and trade-related monetary flows, investment requirements, the types of production or
conversion technologies selected (technology substitution), pollutant emissions, inter-fuel
substitution processes, as well as temporal trajectories for primary, secondary, final, and useful
energy. In addition to the energy system, the model includes a stylized representation of the forest
and agricultural sector and related GHG emissions mitigation potentials. It is a long-term global
model operating at the level of 11 world-regions and a time horizon of a century (19902100).
For each scenario the model calculates the least cost solution for the energy system given a set of
assumptions about main drivers such as energy demand, resources, technology performance and
environmental constraints.
The AEZWFS (Agro-Ecological ZoningWorld Food System) model framework projects
alternative development paths of the agriculture sector using three components: (i) a spatially
detailed agronomic module assessing crop suitability and land productivity (AEZ); (ii) an
applied general equilibrium model of the world food system (WFS); and (iii) a spatial
downscaling model allocating the aggregate WFS production levels and agricultural land use to
spatial biophysical resources. AEZ simulates land-resource availability, crop suitability, farm-
level management options, and crop production potentials as a function of climate, technology,
economic productivity, and other factors (for further details see Fischer et al. 2002,2009;
Fischer 2009). Land is broadly classified as built-up land, cultivated land, forests, grass/wood
land areas, including managed and natural grassland areas, and sparsely vegetated and other
land. WFS is an agro-economic model (Fischer et al. 2005,2009) that estimates regional
agricultural consumption, production, trade and land use. Applying the AEZ-WFS
framework, use and conversion of land is determined for food and feed production to meet
the global demand in accordance with agronomic requirements, availability of land resources,
and consistent with national incomes and lifestyles of consumers. Land for residential use and
transport infrastructure is assigned according to spatial population distribution and density.
The remaining land, i.e. part of grass/wood land, forest areas and sparsely vegetated areas, is
further evaluated in the DIMA model (see below) for possible use in dedicated bioenergy
systems and for forestry purposes (for additional details see Tubiello and Fischer 2007).
Agricultural residue supplies based on the agricultural land use are also available for energy
use and picked up where cost-effective. The delineation of pasture and unmanaged grasslands
is based on the projections of livestock numbers computed in the WFS model.
The DIMA model (Dynamic Integrated Model of Forestry and Alternative Land Use;
Rokityanskiy et al. 2007) is used to quantify the economic potential of global forests,
explicitly modeling the interactions and feedbacks between ecosystems and land use related
activities. Regional demand trajectories for timber and prices for carbon and bioenergy are major
drivers for the relevant estimates. Food security is maintained by introducing an exogenous
scenario-specific minimum amount of agricultural and urban land per grid cell as projected by
AEZ-WFS (and used as input by DIMA). The DIMA model is a spatial model operating on a
0.5×0.5° grid raster. It determines for each grid and time interval, which of the forestry
processes (afforestation, reforestation, deforestation, or conservation and management options)
would be applied in order to meet a specific regional timber demand and how much woody
As computational algorithm the model uses linear programming with a commercial solver (CPLEX) to
compute minimum discounted system costs over the entire time-frame. The time horizon is split into 5 year
time-steps between historical periods 19902010, and 10 year time periods between 2010 and 2100.
36 Climatic Change (2011) 109:3357
bioenergy and forest sink potential would be available for a given combination of carbon and
bioenergy prices. Main determinants of the land-use choices in each grid are assumptions about
the costs of forest production and harvesting, land-prices and productivity, age structure of
standing forest, and age-specific plant growth. Forest dynamics are thus a result of interactions
between demand-pull (price of bioenergy and carbon as well as timber demand), and inertia on
the supply-side (imputed through growth limitation of the forest). A schematic illustration of the
main linkages between the three principal models is shown in Fig. 1.
In the sequel of Section 2we discuss the main methodological improvements for the
RCP8.5. We particularly focus on those aspects most relevant for the development of spatial
land-cover and emissions projections, which serve as inputs to the climate modeling
community (see also Hurtt et al. 2011 and Lamarque et al. 2011 in this SI).
2.2 Spatial land use and land-cover change projections
The spatial land cover information of the RCP8.5 builds upon the dynamic land-use
projections available from the original A2r scenario as published in Fischer et al. 2007;
Tubiello and Fischer 2007, and Riahi et al. 2007. The categories comprise 1) built-up land
(residential plus infrastructure), 2) cultivated land (arable and permanent crops, separated by
irrigated and non-irrigated land), 3) forests (separated by managed and unmanaged forests), 4)
grassland/woodland/shrubland (GWS), and 5) other land (water, desert, rocks, and ice).
Major improvements of the RCP8.5 (compared to the original A2r scenario) include updates
with respect to the representation of base-year land-cover statistics, updates in the AEZ resource
inventory, as well as the split of the aggregated GWS category into pasture and natural
grasslands. The latter was done specifically as input for the climate modeling teams of the
IPCC-AR5 to represent dynamic land-cover changes in their future climate projections.
The base-year (2000) land inventory uses a continuous representation of different shares
of land-uses at 5 min latitude/longitude, i.e. each 5 min grid cell is characterized by shares
of the above classes.
Six geographic datasets were used for the compilation of an inventory of seven major land
cover/land use categories: (1) GLC2000 land cover, regional and global classifications at 30 arc-
seconds (JRC 2006); (2) IFPRI Agricultural Extent database, which is a global land cover
categorization providing 17 land cover classes at 30 arc-seconds (IFPRI 2002),basedona
reinterpretation of the Global Land Cover Characteristics Database (GLCCD 2001), EROS Data
Centre (EDC 2000); (3) The Global Forest Resources Assessment 2000 of FAO (FAO 2001)at
30 arc-seconds resolution; (4) Digital Global Map of Irrigated Areas (GMIA) version 4.0.1 of
(Siebert et al. 2007) at 5 arc-minute latitude/longitude resolution, providing by grid-cell the
percentage land area equipped with irrigation infrastructure; (5) IUCN-WCMC protected
areas inventory at 30-arc-seconds (, and (6)
Spatial population density inventory (30 arc-seconds) for year 2000 developed by FAO-
SDRN, based on spatial data of LANDSCAN 2003, LandScanTM Global Population Database
(, with calibration to UN 2000 population figures.
An iterative calculation procedure has been implemented to estimate land cover class
weights, consistent with aggregate FAO land statistics and spatial land cover patterns
obtained from (the above mentioned) remotely sensed data, allowing the quantification of
major land use/land cover shares in individual 5 arc-minute latitude/longitude grid-cells.
The estimated class weights define for each land cover class the presence of respectively
cultivated land and forest. Starting values of class weights used in the iterative procedure were
obtained by cross-country regression of statistical data of cultivated and forest land against land
cover class distributions obtained from GIS, aggregated to national level. The percentage of
Climatic Change (2011) 109:3357 37
urban/built-up land in a grid-cell was estimated based on presence of respective land cover
classes as well as regression equations, obtained using various sub-national statistical data,
relating built-up land with number of people and population density.
When land is spatially allocated to various uses in the AEZ-WFS model sequence, first
the conversion to built-up land is quantified, driven by changes in population numbers and
density. Second, changes in agricultural land simulated in WFS are spatially allocated,
simultaneously affecting the other land use types, except built-up land. Finally, other land
use changes (not driven by agriculture or built-up conversion), mainly between forest and
grass/wood land types, are accounted for. The conversion of agricultural land is allocated to
the spatial grid for 10-year time steps by solving a series of multi-criteria optimization
problems for each of the countries/regions of the world food system model.
The criteria used in the land conversion module depend on whether there is a decrease or
increase of cultivated land in a region. In the case of a decrease the main criteria include
demand for built-up land and abandonment of marginally productive agricultural land. In
case of increases of cultivated land, the land conversion algorithm takes land demand from
the world food system equilibrium and applies various constraints and criteria, including: (i)
the total amount of land converted from and to agriculture in each region of the world food
system model, (ii) the productivity, availability and current use of land resources in the
country/regions of the world food system model, (iii) agronomic suitability of land for
conversion to crop production, (iv) legal land use limitations, i.e. protection status, (iv)
spatial suitability/propensity of ecosystems to be converted to agricultural land, and (v) land
accessibility, i.e. in particular a grid-cells distance from existing agricultural activities.
The classification of GWS into areas that predominantly correspond to pastures vs.
natural GWS is based on spatial calculations of fodder supply versus livestock feed
requirements. For this purpose feed balance calculations were performed to compare
estimated feed requirements of livestock in a grid-cell to estimated feed supply from
grassland and cropland in each grid cell. Feed requirements were calculated as energy
requirements per unit of a reference livestock times number of ruminants (cattle, buffalo,
sheep, goat). Feed supply assumes a grass harvest index of 60% (on grass/wood land) and a
harvest index of 30% crop residues on crop land in the grid cell. These calculations were
done at 5 min latitude/longitude and aggregated later to 0.5×0.5° resolution of the RCP8.5.
By doing so the global grass/wood land cover was classified into four different categories.
For areas with no ruminants or a share of GWS <10% in a grid-cell, these grid-cells were
assigned to class 1; class 2 comprises areas with a ratio of feed requirements over feed
supply of less than 0.1; class 3 corresponds to calculated ratios of 0.1 to 0.5, and finally
class 4 corresponds to ratios greater than 0.5. The resulting global map of grazing intensity
is presented in Fig. 2.
2.3 Pollutant emissions
2.3.1 Base year estimates and environmental legislation
For the estimation of air pollutant emissions we rely on detailed technology activity data
and emissions coefficients from the Greenhouse Gas and Air Pollution Interactions and
Synergies model (GAINS, Amann et al. 2008a,b) and the recent assessment of
environmental legislation until 2030 (Cofala et al. 2007). The activity data including
improvements of emissions coefficients due to legislation was subsequently aggregated and
implemented into the MESSAGE modeling framework to derive projections for pollutant
gases, including sulfur-dioxide (SO
), nitrogen oxide (NO
), carbon monoxide (CO),
38 Climatic Change (2011) 109:3357
volatile organic compounds (VOCs), black and organic carbon aerosols (BC and OC).
Details of the methodology describing the linkage between MESSAGE and GAINS are
summarized in Rafaj et al. (2010).
The main sectors covered in our analysis include power plants, fossil fuel extraction, gas
flaring, waste and biomass burning (deforestation, savannah burning, and vegetation fires),
industry (combustion and process), domestic (residential and commercial sectors), and road
transport. We separately include estimates of air pollutants from international shipping and
aviation sectors, which have recently been identified as important sources of air pollutants.
Projections of emissions from international ships are based on the methodology described
in Eyring et al. (2005a,b) and reflect the implementation of recent updates of IMO
standards (amendments to the MARPOL Annex VI regulations). Lee et al. (2005) is used to
derive estimates of aviation fuel consumption and controls.
The main control policies and strategies for air pollutants until 2030 across different
sectors in both OECD & Non-OECD regions are detailed in Table 1.
For the medium to long term trends of RCP8.5 (beyond 2030) we assume a further
reduction in emissions intensity based on the assumption that higher environmental quality
will be associated with increasing welfare. To mimic this behavior, the Environmental
Kuznets Curve (EKC) theory is applied to derive changes in future emission coefficients
(see e.g. Dasgupta et al. 2001). Based on empirical observations, the EKC assumes first an
increase in emissions (with increasing economic activities) followed by a decrease. Many EKC
studies assume an income level between 5000 and 8000 $/cap as the turning point for the
introduction of stringent environmental controls. Recent evidence, however, suggests that in
many developing countries controls of air quality are introduced at faster rates than suggested by
the experience of industrialized countries in the past (see Dasgupta et al. 2001; Smith et al. 2005).
Increased environmental awareness and accelerated technological diffusion are major
contributors to this trend. The turning point of the EKC are likely to happen thus at lower
GDP/capita levels than assumed earlier. Consequently, we use in the RCP8.5 analysis an
income level of 5000$/capita as the threshold for increasing environmental consciousness
Fig. 2 Grazing intensity of grass, wood, and shrublands for the year 2000. Areas of moderate and intensive
grazing were classified as pasture, while other areas with lower grazing intensity as predominantly natural
The implementation of these policies and technologies vary across different regions.
Climatic Change (2011) 109:3357 39
triggering declines in emissions intensities.
For resulting development of emissions intensities
and overall emissions trends see Section 3on results.
As a final step in the development of the regional projections of the RCP, the MESSAGE
model results for all major air pollutant emissions and reactive GHGs were harmonized
with the historical and current inventories as described in Granier et al. (2011). A simple
harmonization algorithm was assumed, where emissions growth of the native MESSAGE
results were combined with the base-year values from Granier et al. (2011). For some
sectors, where the algorithm led to qualitative changes in the overall trends, a declining
offset over time was employed for the harmonization.
2.3.2 Downscaling of pollutant emissions
In addition to detailed representation of air-pollution legislation, another important improvement
of the RCP8.5 comprises the development of new downscaling algorithms for the spatially explicit
projections of pollutant emissions. These spatial air pollutant projections are important inputs to
the AR5 climate experiments, and related atmospheric chemistry models (Lamarque et al. 2011).
The vast majority of downscaling approaches have traditionally employed proportional
downscaling (van Vuuren et al. 2010), where emissions of individual grid-cells are scaled
following aggregate changes at the regional level. While proportional algorithms are simple
to implement and easy to reproduce, they generally do not account for important local
differences in efforts to reduce pollutant emissions. Empirical evidence, for example, shows
that efforts to reduce air-pollution have generally been stronger where the returns in terms
Table 1 Control measures for pollutant emissions (20002030)
Sector Control policies and strategies
Road Transport Directives on the SO
content in liquid fuels; directives on quality of petrol
and diesel fuels; adoption of pollution standards for light and heavy duty
cars after 2010 (EURO IIIIV, CARB, Tier II, other national equivalents)
Industry and Power Plants Use of high efficient electrostatic precipitators (ESP) in the power and
industrial sectors, increased use of low SO2 coal, increasing penetration
of flue gas desulphurization (FGD) after 2005 in new and existing plants,
primary measures for control of NO
Domestic Shift from solid biomass based fuels towards clean cooking fuels and
improved cooking stoves, standards on sulfur contents in domestic fuels
International Shipping Revised MARPOL Annex VI regulations
Others Reduced flaring, improved NO
controls in waste incinerators, decreased
agricultural waste burning, forest fire control approaching OECD
standards throughout the world, etc.
International Maritime Organization announced amendments to MARPOL Annex VI regulations which
include progressive reduction SO2 emissions from ships, progressively to 0.50%. Progressive reductions in
nitrogen oxide (NO
) emissions from marine engines were also agreed, with the most stringent controls on
so-called Tier IIIengines.
In order to explore uncertainties in the actual implementation of legislation beyond 2030, a sensitivity
analysis was carried out (Rafaj et al. 2010). Results indicate that the effect on the long-term pollutant
emissions depend on assumptions about further improvements in intensities beyond 2030. This effect was
found to be significant for NO
, but comparatively smaller for other emissions where technical shifts
dominate (CO, SO
). It is thus important to note that air pollutant emissions trends in RCP8.5 are the result
of dedicated policy interference. The trends should thus not be interpreted as autonomous developments in
absence of air pollution policies.
40 Climatic Change (2011) 109:3357
of health benefits have been the largest. In the past this has been particularly the case in
cities of todays industrialized countries, where dedicated urban air pollution legislation has
successfully reduced exposure and thus health impacts for millions of people (WEA 2000).
This trend is likely to continue in the future, particularly in the developing world, where
urban air quality is one of the prime concerns. We thus employ an exposure-driven spatial
algorithm for the downscaling of the regional air-pollutant emissions projection. By doing so,
we generate dynamic spatial maps at the resolution of 0.5×0.5° for all world regions and major
pollutant emissions (SO
, CO, BC, OC, VOCs). As a surrogate proxy for the spatial
distribution of exposure we compute population x emissionsof each grid-cell. The weight
of each individual cell in the aggregate regional exposure (i.e., the numerical sum of all
exposure values of the cells in the region) defines the allocation of emissions reductions for
each cell. As a result emissions are reduced most in those cells with the highest exposure. Vice
versa, in cells with either very low population or low emissions density the reductions are
comparatively smaller. Technically, we solve the problem by creating a rank-size distributionof
each region from the cells with the highest exposure to those with lowest. We start reducing
emissions first in thosecells that have the highest exposure.
Following a review of Air Quality
Monitoring Information of US cities (EPA 2008; see also UNEP and WHO 1996) we adopt a
maximum rate of reduction of up to 80% emissions reduction per decade for each grid-cell.
Obviously, the exposure driven algorithm is applied only if emissions are reduced
on the regional level due to increasing stringency of air pollution legislation. In the
case of regionally increasing emissions, we use spatial changes of economic activity
(GDP) as a proxy to allocate increasing emissions across grid-cells. I.e., we assume
that emissions increase proportionally to where economic activity is accelerating the
strongest. For the spatial distribution of population and GDP we rely on the
downscaled projections of the original scenario (A2r) as described in Grubler et al. 2007
(data can be downloaded at
Figure 3gives a schematic illustration of the effect of the exposure algorithm for SO
emissions in the Centrally Planned Asia region (including China) between 2020 and 2100.
The two important features are: 1) that top exposed cells corresponding to the Chinese mega-
cities improve air quality by about two orders of magnitudes by 2050, and 2) improvements
in cities are complemented by important distributional changes, shifting e.g. emissions
intensive activities to surrounding neighborhoods of cells with lower population density. For a
comparison see also resulting spatial maps of SO
emissions in Fig. 11 (Section 3).
2.4 Scenarios considered in this paper
The main scenario described in this paper is the RCP8.5. As indicated in the introduction,
however, we also use the MESSAGE model for the development of mitigation scenarios
that use the RCP8.5 as a baseline. As targets for the mitigation scenarios we adopt forcing
levels of 2.6, 4.5 and 6 W/m
by the end of the century, which corresponds to the same
radiative forcing levels as assumed by the other RCPs in this SI (see van Vuuren et al.
2011b; Thomson et al. 2011; Masui et al. 2011). For each mitigation scenario the
MESSAGE optimization model computes least-cost pathways to stay below the specified
target. This corresponds to the introduction of a cumulative GHG emissions budget and a
EPA (2008) reports on air pollution trends of US cities between 1990 and 2008. For CO, O
, and SO
most rapid air quality improvements among the US cities were between 60 and 80% per decade.
For example, if a grid-cell has 0.5% of the aggregated regional exposure at time t
, then 0.5% of the
regional emissions reductions between t
and t
are allocated to that specific cell.
Climatic Change (2011) 109:3357 41
globally uniform price vector for greenhouse gas emissions (assuming full temporal and
spatial flexibility in emission reductions across regions and gases).
3 Scenario assumptions and results
3.1 Storyline and main scenario drivers of RCP8.5
The RCP8.5 is based on the A2r scenario (Riahi et al. 2007), which provides an updated and
revised quantification of the original IPCC A2 SRES scenario storyline (Nakicenovic et al. 2000).
With a few exceptions, including an updated base year calibration (to 2005) and a
revised representation of short-term energy trends, especially in developing countries,
the RCP8.5 builds thus upon the socio-economic and demographic background,
resource assumptions and technological base of the A2r scenario.
The scenarios storyline describes a heterogeneous world with continuously increasing
global population, resulting in a global population of 12 billion by 2100. Per capita income
growth is slow and both internationally as well as regionally there is only little convergence
between high and low income countries. Global GDP reaches around 250 trillion US2005$
in 2100. The slow economic development also implies little progress in terms of efficiency.
Combined with the high population growth, this leads to high energy demands. Still,
international trade in energy and technology is limited and overall rates of technological
progress is modest. The inherent emphasis on greater self-sufficiency of individual
countries and regions assumed in the scenario implies a reliance on domestically available
resources. Resource availability is not necessarily a constraint but easily accessible
conventional oil and gas become relatively scarce in comparison to more difficult to harvest
unconventional fuels like tar sands or oil shale. Given the overall slow rate of technological
improvements in low-carbon technologies, the future energy system moves toward coal-
Fig. 3 SO
exposure (population x
emissions) of grid-cells with highest
exposure in Centrally Planned Asia
(CPA). Different colors indicate
changes in exposure over time
from 2020 to 2100. All cells are
ordered according to their rank-size
distribution in 2020
The MESSAGE model projects historical time periods from 1990 onwards, and is calibrated to reproduce
past trends up to the year 2005. As the harmonization of the RCPs was done for the year 2000, we show in
most of the figures historical trends up to 2000 only.
42 Climatic Change (2011) 109:3357
intensive technology choices with high GHG emissions. Environmental concerns in the A2
world are locally strong, especially in high and medium income regions. Food security is
also a major concern, especially in low-income regions and agricultural productivity
increases to feed a steadily increasing population.
Compared to the broader integrated assessment literature, the RCP8.5 represents thus a
scenario with high global population and intermediate development in terms of total GDP
(Fig. 4). Per capita income, however, stays at comparatively low levels of about 20,000 US
$2005 in the long term (2100), which is considerably below the median of the scenario
literature. Another important characteristic of the RCP8.5 scenario is its relatively slow
improvement in primary energy intensity of 0.5% per year over the course of the century.
This trend reflects the storyline assumption of slow technological change. Energy intensity
improvement rates are thus well below historical average (about 1% per year between 1940
and 2000). Compared to the scenario literature RCP8.5 depicts thus a relatively
conservative business as usual case with low income, high population and high energy
demand due to only modest improvements in energy intensity (Fig. 4).
3.2 Development of the energy system
3.2.1 Energy system of RCP8.5
As discussed earlier, the RCP 8.5 is a baseline scenario with no explicit climate policy,
representing the highest RCP scenario in terms of GHG emissions. In this section we will
first briefly describe the main energy system changes of the RCP 8.5 baseline. In addition to
baseline trends, we will congruently analyze also the required GHG emissions reductions in
order to limit radiative forcing to levels comparable to the other RCPs highlighted in this
SI. We primarily focus in this section on the transition of the energy system and move later
to results for land-use (Section 3.3) and GHG and pollutant emissions (Section 3.4).
A growing population and economy combined with assumptions about slow improve-
ments of energy efficiency lead in RCP8.5 to a large scale increase of primary energy
demand by almost a factor of three over the course of the century (Fig. 5). This demand is
primarily met by fossil fuels in RCP 8.5. There are two main reasons for this trend. First,
the scenario assumes consistent with its storyline a relatively slow pace for innovation in
advanced non-fossil technology, leading for these technologies to modest cost and
performance improvements (e.g., learning rates for renewables are below 10% per doubling
of capacity; see also Riahi et al. 2007 for further detail). Fossil fuel technologies remain
thus economically more attractive in RCP8.5. Secondly, availability of large amounts of
unconventional fossil resources extends the use of fossil fuels beyond presently extractable
reserves (BP 2010). The cumulative extraction of unconventional fossil resources lies,
however, within the upper bounds of theoretically extractable occurrences from the
literature (Rogner 1997; BGR 2009; WEC 2007).
Coal use in particular increases almost 10 fold by 2100 and there is a continued reliance
on oil in the transportation sector. This fossil fuel continuance does not necessarily mean a
complete lack of technological progress. In contrast to most other technologies, there are
significant improvements in existing fossil alternatives as well as the penetration of a
number of new advanced fossil technologies, thus increasing their efficiency and
For further details on the scenario storyline see Riahi et al. 2007.
In RCP8.5 unconventional natural gas extraction amounts to 17 ZJ and unconventional oil extraction to
about 21 ZJ over the course of the century.
Climatic Change (2011) 109:3357 43
performance in the longer-term. In the electricity sector, this results in a shift towards clean
coal technologies from current sub-critical coal capacities. In addition, with conventional
oil becoming increasingly scarce, a shift toward more expensive unconventional oil sources
takes place by 2050 and the subsequent increases in fossil fuel prices also leads an
increased penetration of syntheticfuels like coal-based liquids. The increase in fossil fuel
1940 1960 1980 2000 2020 2040 2060 2080 2100
World GDP (t rillio n US$ 20 05)
AR4 da tabase
RCP 8.5
AR4 database
1940 1960 1980 2000 2020 2040 2060 2080 2100
World population (billion)
AR4 da tabase
RCP 8.5
AR4 database
1940 1960 1980 2000 2020 2040 2060 2080 2100
World primary energy (EJ)
AR4 da tabase
RCP 8.5
AR4 database
1940 1960 1980 2000 2020 2040 2060 2080 2100
World primary energy intesity of GDP (MJ/US$2005)
AR4 da tabase
RCP 8.5
AR4 da tabase
Fig. 4 Global development of main scenario drivers in RCP 8.5 (red lines) compared to the range of scenarios
from the literature (grey areas: IPCC AR4 scenario database; Fisher et al. 2007; Nakicenovic et al. 2006). Right
hand vertical lines give the AR4 database range in 2100, including the 5th, 25th, 50th, 75th, and 95th percentile
of the AR4 scenario distribution
2000 2020 2040 2060 2080 2100
6W/m2 4.5W/m2 2.6W/m2
Hyd ro
Fig. 5 Development of global primary energy supply in RCP8.5 (left-hand panel) and global primary energy
supply in 2100 in the associated mitigation cases stabilizing radiative forcing at levels of 6, 4.5, and 2.6 W/m
(right-hand bars). Note that primary energy is accounted using the direct equivalent method
44 Climatic Change (2011) 109:3357
prices (about a doubling of both natural gas and oil prices by mid-century) triggers also
some growth for nuclear electricity and hydro power, especially in the longer-term. Overall,
however, fossil fuels continue to dominate the primary energy portfolio over the entire time
horizon of the RCP8.5 scenario (Fig. 5).
In terms of final energy, significant transformations occur in the manner in which energy
is used in RCP8.5 (Fig. 6). Particularly electricity continues its historical growth and
becomes the dominant mode of energy use mostly in the residential and partly also in the
industrial sector. In the long term (beyond 2050) electricity is provided in RCP8.5 to a large
extent from non-fossil sources (nuclear and biomass).
3.2.2 Impact of mitigation measures
The high energy demand and fossil intensity associated with RCP8.5 implies that achieving
climate stabilization will require a massive reduction of emissions and drastic energy
system transformations compared to the baseline. In fact, previous studies indicated that
achieving low climate stabilization levels from the A2r scenariothe predecessor of
RCP8.5may technically not be feasible (Rao et al. 2008). The earlier studies employed
though a qualitative criterion for target attainability that limited energy intensity
improvement of a given stabilization targets to stay within relatively narrow margins of
the baseline scenario storyline (see Riahi et al. 2007 and Rao et al. 2008). In our
assessment, however, we allow pronounced reductions in energy demand beyond this
criterion and observe that 2.6 W/m
target under a fossil intensive RCP8.5 scenario
would become feasible, if more rapid energy intensity improvements were possible to
In addition to responses in energy demand, our analysis considers a number of
options for reducing energy-related CO
emissions on the supply-side of the energy
system (see Riahi et al. 2007 for details). These include switching from fossil fuels to
renewable or nuclear power; fuel switching to low-carbon fossil fuels (e.g., from coal to
natural gas); and carbon capture and storage (both fossil and biomass based). Also
included in this analysis is the full basket of non-CO
gases and related mitigation options
(see Rao and Riahi 2006 for details), both energy related (e.g. extraction and transport of
2000 2020 2040 2060 2080 2100
6W/m2 4.5W/m2 2.6W/m2
Ethano l
On-site solar
Dist rict heat
Non-foss il electricity
Fos sil elect ricity
Oil pro ducts
Fig. 6 Development of global final energy in RCP8.5 (left-hand panel), and global final energy in 2100 in
the associated mitigation cases stabilizing radiative forcing at levels of 6, 4.5, and 2.6 W/m
Climatic Change (2011) 109:3357 45
coal, natural gas, and oil) and non-energy related (livestock, municipal solid waste,
manure management, rice cultivation, wastewater, and crop residue burning).
The primary energy mix of the climate mitigation scenarios (reaching 6, 4.5, and 2.6 W/m
radiative forcing by the end of the century) are illustrated in the right bars of Fig. 5.Inthe
short and medium term, transition options like fossil based CCS (in particular natural gas
with CCS) become particularly important while in the longer-term, dominant technological
options include energy conservation and efficiency improvements, nuclear, and
biomass with carbon capture (BECCS). This trend is robust across all analyzed
stabilization targets, but is obviously most pronounced in the low 2.6 W/m
scenario. While electricity from other renewables, like solar PV, increase their
contribution in the longer-term, the majority of the carbon free electricity comes from
centralized nuclear and biomass power plants. This technology choice reflects the
underlying storyline of the RCP8.5 and related technology assumptions, which favor
traditional centralized supply-options (including fossil CCS, nuclear and biomass). The
results highlight that in principle lower stabilization goals might be possible to reach
from high baselines as the RCP8.5, and that mitigation solutions would not necessarily
require a shift from large-scale centralized energy production to dispersed intermittent
sources (for a discussion of alternative mitigation paradigms with higher shares of
intermittent renewables see Riahi et al. 2007).
In terms of final energy, the pace of electrification is accelerated further in the
climate mitigation scenarios, where non-fossil electricity becomes a major driver of the
decarbonization, leading to electricity shares in final energy of up to about 60% by
2100 (compared to about 30% in RCP8.5). Oil use peaks around middle of the century
and declines in the longer term. In RCP8.5 the resulting gap for the supply of liquid
fuels is filled by other liquefaction processes like coal- and biomass-based liquids. In the
climate mitigation scenarios, hydrogen becomes an additional important long-term final energy
carrier in the transport sector. Important wide ranging consequences of the transformation away
from oil-products to electricity and hydrogen are at the one hand improvements of regional
energy security in terms of decreased oil dependency (oil imports). At the other hand the
transformation enables also major environmental improvements through decreasing pollutant
emissions, particularly in urban areas (see Section 3.5).
Figure 7compares the required pace of energy intensity and carbon intensity
improvements in the RCP8.5 and the mitigation scenarios that have been derived with
historical trends and selected scenarios from the literature (SRES B1 and B2). Reducing
GHG emissions requires both demand-side changes (improvements in energy intensity) as
well as supply-side structural changes (improvements in carbon intensity of the economy).
The required pace of the transition is particularly challenging in the case of the low target of
2.6 W/m
. In terms of carbon intensity the 2.6 W/m
scenario shows for example a six-fold
increase in the rate of decarbonization compared to the RCP8.5 baseline. This corresponds
also to a major trend-break and a five-fold acceleration of the decarbonization pace
compared to the long run historical improvement rate for the world (1940 to 2000). With
respect to energy intensity the 2.6 W/m
is less ambitious. It depicts improvement rates
roughly in line with historical trends between 1940 and 2000 of about 1% per year. This
rate is also comparable to assumptions for intermediate baseline scenarios in the literature
such as the B2 SRES (Fig. 7). While this improvement rate is quite modest considering the
Note that the mitigation scenarios assume full when and whereflexibility to reduce emissions, subject to
a global cumulative GHG emissions constraint for each radiative forcing level. Different measures are thus
deployed based on endogenous model decisions to derive a least-cost solution.
46 Climatic Change (2011) 109:3357
stringent climate target, it means nevertheless a drastic departure from the RCP8.5 baseline,
where energy intensity improves at only half this rate (0.5% per year). Our results thus also
indicate the importance of path dependency and conditionality of the transformation
strategy depending on the choice of the baseline and its underlying assumptions. Clearly, any of
the climate targets would have been achieved by a different mix of measures (and costs) if we
had used for example the sustainable SRES B1scenario with its relatively high rates of
improvements as the counterfactual of our analysis (see Fig. 7).
3.3 Land-use and land-cover change
Some 1.6 billion ha of land are currently used for crop production, with nearly 1 billion ha
under cultivation in the developing countries. During the last 30 years the worlds crop area
expanded by some 5 million ha annually, with Latin America alone accounting for 35% of
this increase. The potential for arable land expansion exists predominately in South
America and Africa where just seven countries account for 70% of this potential. There is
relatively little scope for arable land expansion in Asia, which is home to some 60% of the
worlds population. These constraints are also reflected by the land-use change dynamics of
the RCP 8.5 scenario. Projected global use of cultivated land in the RCP8.5 scenario
increases by about 185 million ha during 2000 to 2050 and another 120 million hectares
during 2050 to 2100. While aggregate arable land use in developed countries slightly
decreases, all of the net increases occur in developing countries. Africa and South America
together account for 85% of the increase. This strong expansion in agricultural resource use
is driven by the socio-economic context assumed in the underlying emission scenario with a
population increase to over 10 billion people in 2050 rising to 12 billion people by 2100.
Even then yield improvements and intensification are assumed to account for most of the
needed production increases: while global agricultural output in the scenario increases by
85% until 2050 and 135% until 2080, cultivated land expands respectively by 12% and
16% above year 2000 levels (Fig. 8).
An important characteristic of RCP8.5 are transformative changes the biomass use for
energy purposes from presently traditional (non-commercial) use in the developing world to
2.6 W/m2
4.5 W/m2
6.0 W/m2
8.5 W/m2
History (1940-
energy intensity improvement
(% per year)
carbon intensity impr oveme nt
(% per year improvement from 2000 levels)
Fig. 7 Long-term energy intensity
and carbon intensity improvement
rates between 2000 and 2100 for
RCP8.5, related mitigation
scenarios developed with the
MESSAGE model, and the
B2/B1 scenarios from SRES
(Nakicenovic et al. 2000). The
crossindicates the relative
position of historical intensity
improvements compared to future
developments of the scenarios
Climatic Change (2011) 109:3357 47
commercial use in dedicated bio-energy conversion facilities (for power and heat) in the
future. Globally the contribution of bioenergy is increasing in RCP8.5 from about 40 EJ in
2000 to more than 150 EJ by 2100. The vast majority of this biomass is harvested in
forests, resulting in increased land-requirements for secondary managed forests. While total
area of forests is declining in RCP8.5 (Fig. 8), the share of managed forests and harvested
areas for biomass are thus increasing considerably. The latter grows from about 17 million
ha to more than 26 million ha by 2100. Uncertainties in the interpretation of the underlying
land developments are nevertheless very large. Hurtt et al. (2011) for example estimate
about a factor of six higher land requirements for the same amount of wood harvest for
the year 2000. Differences between the estimates increase over time. The results
indicate the need for further harmonization of underlying data and definitions of carbon
harvest in forest models.
3.4 GHG emissions
3.4.1 GHG emissions in RCP8.5
GHG emissions of the RCP8.5 continue to rise as a result of the high fossil-intensity of the
energy sector as well as increasing population and associated high demand for food. The
development of main GHG emissions of RCP8.5 and the corresponding mitigation
scenarios is shown in Fig. 9. The RCP8.5 emissions are high, not only compared to the
overall emissions scenario literature, but also compared to the set of baseline scenarios. In
-eq. emissions more than double by 2050 and increase by three fold to about
120 GtCO
-eq. by 2100 (compared to 2000). Roughly about three quarter of this increase is
due to rising CO
emissions from the energy sector. The rest of the increase is mainly due to
increasing use of fertilizers and intensification of agricultural production, giving rise to the
main source of N
O emissions. In addition, increases in life-stock population, rice
production, and enteric fermentation processes drive emissions of methane (CH
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
milluon hectar
nonvegetated land
forest la nd
gras sla nd (extensi ve grazing)
grass land (intensive grazi ng,
Build-up land
Cropla nd
Fig. 8 Global land use by category in RCP8.5
48 Climatic Change (2011) 109:3357
The high GHG emissions in RCP8.5 imply the need of large-scale emissions reductions
to limit radiative forcing to levels comparable to the other RCPs. For the mitigation
potentials from livestock and agricultural sectors we rely on estimates from Rao and Riahi
(2006), which assumes no major technological breakthroughs in these sectors. Globally the
mitigation potential is thus limited to about 50% and 30% of the RCP8.5 baseline emissions
for CH
and N
O respectively. This explains also the comparatively limited role of CH
O emissions mitigation in our mitigation scenarios compared to the official RCP2.6,
RCP4.5, and RCP6 (see Fig. 9and papers on the other RCPs in this SI).
3.4.2 GHG Emissions in the mitigation scenarios
The comparatively limited potential for non-CO
mitigation options in RCP8.5 implies also
that the bulk of the emissions reductions in the longer term will need to come from CO
the energy sector (Fig. 9). Cumulative CO
emissions in RCP8.5 amount to about 7300
over the course of the entire century. In order to limit forcing to 6 W/m
about 40%
of these emissions would need to be avoided. The more stringent targets require further
emissions mitigation in the order of 60% and 87% of the RCP8.5 emissions to stay below
the 4.5 and 2.6 W/m2 target. The cumulative mitigation requirements have large
implications for the emissions pathways, which in all mitigation scenarios are characterized
by a peak and decline of CO
emissions. As indicated in Fig. 9, the peak of emissions in the
scenario leading to 6 W/m
occurs around middle of the century. If, however, emissions
2000 2020 2040 2060 2080 2100
Glob al CO2eq. emi ssio ns (GtCO2eq.)
RCP 8.5
RCP 4.5
2000 2020 2040 2060 2080 2100
Global CO2 emissions (GtCO2)
RCP 8.5
RCP 4.5
2000 2020 2040 2060 2080 2100
Glob al CH4 emissi ons (kton CH4)
RCP 8.5
RCP 4.5
2000 2020 2040 2060 2080 2100
Glob al N2O emissio ns (kton N2O)
RCP 8.5
RCP 4.5
Fig. 9 Development of global GHG emissions (CO2-eq., CO
, and N
O) in RCP8.5 and MESSAGE
mitigation scenarios of this study (brown lines). For a comparison the trends of the official RCPs described
elsewhere in this SI are shown as well (red = RCP6, blue = RCP4.5, green = RCP3-PD)
Note that RCP2.6 is often also referred to as RCP3-PD, indicating that its radiative forcing pathway is
peaking at about 3 W/m
and declining later to 2.6 W/m
. In the sequel of the paper we will refer to this RCP
as RCP2.6.
Climatic Change (2011) 109:3357 49
growth over the next decades is considerably slower than in our scenarios (as illustrated by
the official RCP6), the same target could be achieved with a later peaking date around
2080. Staying below 2.6 W/m
requires much more rapid emissions reductions, leading to
comparatively limited flexibility for the peak of emissions. Both the official RCP2.6 and our
2.6 W/m
scenario indicate the need of emissions to peak before around 2020. This finding is
also consistent with other assessments in the literature (e.g., van Vuuren and Riahi 2011).
There are nevertheless important differences between the CO
emissions pathways,
particularly with respect to the required negative emissions for limiting forcing to below
2.6 W/m
. As illustrated by Fig. 9, there is a considerably larger need for negative emissions
in our scenario than in the official RCP2.6. The main reason for this difference is the higher
emissions in our scenario, which are compensated by more pronounced negative
emissions compared to the official RCP2.6 in the long term (Fig. 9).
3.5 Emission of air pollutants
3.5.1 Air pollutants in RCP8.5
While RCP8.5 depicts baseline developments in absence of climate mitigation policies, air
quality legislation plays an important role for the scenariosprojection of pollutant
emissions. This reflects the fact that in contrast to climate policies, air quality measures
have already been introduced in many parts of the world. Specifically, RCP8.5 assumes the
successful implementation of present and planned environmental legislation over the next
two decades to 2030. Beyond 2030 we further assume that increasing affluence may lead to
tightening of pollutant legislation in the long term (see also Section 2.3.1).
RCP8.5 explicitly considers varying levels of legislation, economic growth and
technological progress across regions, resulting in regionally different developments for
emission intensities as illustrated in Fig. 10. Air quality standards are presently the highest
in the OECD region. Emission intensities in the OECD are thus already comparatively low,
and planned legislation is expected to reduce emissions intensities even further by 2030.
For economies in transition and regions with medium development,
current legislations
imply most significant declines across all regions by 2030. This trend reflects tightening of
policies particularly in the power sector (e.g., through application of flue gas desulfurization
or DENOx) and for vehicles (e.g., catalytic converters). Todays low income regions are
generally characterized by modest air quality controls. These regions show also the least
pronounced declines in emissions coefficients to 2030, reflecting the lack of concrete plans
for future legislation over the short term.
In RCP8.5 many regions exhibit a catch-up in economic levels beyond 2030 to income
levels greater than 5000$/capita (Fig. 10). After this point the regions follow the EKC
assumptions of declining emissions coefficients explained in Section 2.3.1. In addition, an
important trend in RCP8.5 is the pervasive shift in the energy system towards cleaner fuels
and advanced fossil technologies, which together with the EKC assumptions explain the
long-term decline in pollutant emissions intensities (Fig. 10). For example in the case of
emissions in the power sector, tightening of legislation results in emissions reductions
from end-of-the-pipe technologies, but at the same time a growing share of inherently
cleaner coal technologies (e.g., through gasification processes) fosters additional emissions
reductions through technology shifts.
The definitions of medium and low development are based on the GDP/capita assumptions of the modeled
region, and do not consider more complex indices like for instance the HDI (Human Development Index.
50 Climatic Change (2011) 109:3357
Assumptions about environmental legislations in combination with ongoing structural and
technological change imply thus in RCP8.5 that pollutant emissions decline significantly as
seen in the example of SO
emissions in Figs. 11 and 12. Growing regional environmental
concerns combined with the lack of a global climate change regime thus also imply a clear
decoupling of CO
emissions from pollutants. For example, the power sector remains a major
contributor to CO
emissions by the end of the century; although SO
emissions from this
sector are almost negligible due to increasing use of advanced coal technologies. Also in the
transport and residential sector, CO
emissions continue to rise globally while in most
developing regions, there is either a slowing down of growth of pollutants from this sector or
even a decline where air quality legislations are stringent enough to offset growing demand.
This is important as the RCP8.5 while representing the highest levels of GHG emissions
among the RCP set, is not necessarily a high pollutioncase as well.
Fig. 10 Illustrative examples for the development of emissions intensities for different pollutant emissions
and sectors. Current and planned environmental legislation drive improvements in emissions coefficients to
2030. Thereafter technology shifts and EKC assumptions explain further improvements. Colored ranges
depict sub-regional differences between regions at similar economic development stages (slow development,
medium development, and OECD)
An important caveat to note is that the RCP8.5 assumes the full implementation of present air quality
legislation in all regions. However if we took into account the uncertainty in implementation of present plans
for legislation, pollutant emissions might be higher than as depicted by the RCP8.5. In the longer term,
uncertainty in technological availability and controls may also lead to a higher emissions profile than
estimated here. For a sensitivity analysis of the impact of e.g., different EKC assumptions for long-term
pollutant emissions see Rafaj et al. 2010.
Climatic Change (2011) 109:3357 51
Fig. 11 Distribution of SO
Emissions in RCP8.5 for the years 2000, 2020, 2050, and 2100
52 Climatic Change (2011) 109:3357
While globally aggregated trends for pollutants show continues improvements and
declines in emissions, there are pronounced regional and spatial differences with local
implications for human health, environment, and climate change. The maps of Fig. 11
illustrate some of the main spatial dynamics for the evolution of SO
emissions in
RCP8.5. The spatial dynamics are similar for other pollutant emissions and to large extent
also for the mitigation scenarios. Initially, the majority of the reductions happen in OECD
countries whereas developing regions, in particular Asia, continues to grow in terms of
emissions, mainly due to growing energy demands (see map for 2020). This clearly
indicates that currently legislated environmental policies are most likely not sufficient in
offset the effects of control policies. This may particularly be the case in China and India.
In the longer-term, however, increasing affluence and technological shifts in these regions
(Fig. 10) imply in RCP8.5 that global emission levels decline significantly, leading to
reduced impacts from pollutants at global scale.
3.5.2 Air pollutants in the mitigation scenarios
With respect to the mitigation scenarios, we observe significant co-benefits from climate
mitigation for pollutant emissions. As explained earlier, the greenhouse gas emissions
reductions in the mitigation scenarios lead to major improvements of the carbon-
intensity and the energy-intensity compared to the RCP8.5 baseline. This switch to
carbon-free and non-fossil technologies is generally associated with lower pollutant
emissions. In addition, also the application of CCS requires cleaner combustion
processes, and thus reduces pollutant emissions in the climate mitigation scenarios
further. Perhaps most importantly, the higher rates of energy-intensity improvements in
the climate mitigation scenarios leads to pronounced energy savings, and each unit of
energy that is not consumed is obviously climate friendly as well as pollution free.
The co-benefit of climate mitigation for pollutants is particularly pronounced over
the short to medium-term (Fig. 12). For instance, the 2.6 W/m
scenario reduces
global SO
emissions by about 55% in 2030 compared to the year 2000. This steep
decline corresponds to roughly a doubling of pollutant emissions reductions compared
to the RCP8.5 baseline (25% reductions in 2030 compared to 2000). Or put in other
words, stringent climate mitigation may reduce pollutant emissions by about the
same order of magnitude as the entire legislated air pollution policy that is presently
in the pipe.
2000 8.5 6.0 4.5 2.6 8.5 6.0 4.5 2.6 8.5 6.0 4.5 2.6
Int. Shipping
2030 2050 2100
Fig. 12 Global SO
by sector in the RCP8.5 baseline
and the mitigation scenarios
for 6, 4.5, and 2.6 W/m
Climatic Change (2011) 109:3357 53
4 Discussion and conclusions
RCP8.5 depicts, compared to the scenario literature, a high-emission business as usual
scenario. Its socio-economic development pathway is characterized by slow rates of
economic development with limited convergence across regions, a rapidly rising population
to comparatively high levels, and relatively slow pace of technological change. The latter
assumption is reflected also by the scenarios modest improvement rates of energy intensity,
which drives energy demand towards the high end of the scenario literature. The primary
energy mix of RCP8.5 is dominated by fossil fuels, leading to the extraction of large
amounts of unconventional hydrocarbon resources well beyond presently extractable
reserves. GHG emissions grow thus by about a factor of three over the course of the
century, mainly as a result of both high demand and high fossil-intensity of the energy
sector as well as increasing population and associated high demand for food. The
resulting radiative forcing is the highest among the RCPs presented in this SI, with the
emissions profile of RCP8.5 being representative of high GHG emissions scenarios in
the literature.
For the development of RCP8.5 we employed new methodologies for the spatial
representation of land-cover changes as well as the improved representation of pollutant
emissions legislation, including spatial downscaling algorithms for exploring local
implications of regional/global air pollution trends. Our results indicate that successful
implementation of presently legislated pollutant control measures would reduce global
pollutant emissions significantly over the short term (e.g. global reductions of about
25% of SO
emissions between 2000 and 2030). This trend occurs despite the high GHG
intensity of RCP8.5, illustrating the possibility to decouple air pollutant emissions from
greenhouse gases through end-of-the-pipe technologies. In the long term additional
technological shifts to advanced fossil technologies reduce pollutant emissions further to
very low levels in RCP8.5.
The results from the mitigation analysis indicate that it would be technically
possible to reduce GHG emissions from RCP8.5 down to levels comparable to the
other RCPs presented in this SI. In contrast to earlier studies we found that this was
possible even for the most stringent radiative forcing target of 2.6 W/m
. This finding is
conditional, however, on the feasibility of massive changes in the energy system
compared to the RCP8.5 development path, accelerating energy intensity improvements
by a factor of two and carbon intensity even by a factor of about six over the entire
century. The mitigation scenarios would thus require a pronounced departure from the
original RCP8.5 storyline.
Finally, form the policy perspective, an important finding of our analysis is the
significant potential of climate mitigation to further reduce pollutant emissions. In the
case of the most stringent forcing target of 2.6 W/m
the co-benefit for air pollutants are
globally of the same order of magnitude as the effect of presently legislated pollutant
measures over the next two decades. The results thus indicate the importance of better
integration of local policy priorities, such as health and air pollution into the global
climate mitigation debate.
Open Access This article is distributed under the terms of the Creative Commons Attribution
Noncommercial License which permits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
54 Climatic Change (2011) 109:3357
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... No bias correction or nudging is applied to the HadGEM-driven WRF runs. In this work, we focus on historical data and the RCP8.5 scenario for future projections, which assumes the continued heavy use of fossil fuels at a similar, or greater, rate to the current concentrations of CO 2 and other greenhouse gases (GHGs) through the end of the century, leading to a radiative forcing of 8.5 W m −2 by 2100 (Riahi et al., 2011). For the historical time period, we focus on 1995-2004, and for the future time period, we focus on the late 21st century period (2085)(2086)(2087)(2088)(2089)(2090)(2091)(2092)(2093)(2094). ...
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This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.
... RCP 4.5 is a stabilization scenario in which total radiative forcing stabilizes shortly after 2100 while not exceeding the long-run radiative forcing target level [53]. RCP 8.5 is a high greenhouse gas emissions scenario that projects a significant increase in GHG emissions and concentrations over time, resulting in a radiative forcing of 8.5 W/m 2 at the end of the century [54]. The SWAT model was used to simulate streamflow for the reference period using observed daily meteorological data from 1970-2005. ...
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In Peninsular India, the Krishna River basin is the second largest river basin that is overutilized and more vulnerable to climate change. The main aim of this study is to determine the future projection of monthly streamflows in the Krishna River basin for Historic (1980–2004) and Future (2020–2044, 2045–2069, 2070–2094) climate scenarios (RCP 4.5 and 8.5, respectively), with the help of the Soil Water and Assessment Tool (SWAT). SWAT model parameters are optimized using SWAT-CUP during calibration (1975 to 1990) and validation (1991–2003) periods using observed discharge data at 5 gauging stations. The Cordinated Regional Downscaling EXperiment (CORDEX) provides the future projections for meteorological variables with different high-resolution Global Climate Models (GCM). Reliability Ensemble Averaging (REA) is used to analyze the uncertainty of meteorological variables associated within the multiple GCMs for simulating streamflow. REA-projected climate parameters are validated with IMD-simulated data. The results indicate that REA performs well throughout the basin, with the exception of the area near the Krishna River’s headwaters. For the RCP 4.5 scenario, the simulated monsoon streamflow values at Mantralayam gauge station are 716.3 m3/s per month for the historic period (1980–2004), 615.6 m3/s per month for the future1 period (2020–2044), 658.4 m3/s per month for the future2 period (2045–2069), and 748.9 m3/s per month for the future3 period (2070–2094). Under the RCP 4.5 scenario, lower values of about 50% are simulated during the winter. Future streamflow projections at Mantralayam and Pondhugala gauge stations are lower by 30 to 50% when compared to historic streamflow under RCP 4.5. When compared to the other two future periods, trends in streamflow throughout the basin show a decreasing trend in the first future period. Water managers in developing water management can use the recommendations made in this study as preliminary information and adaptation practices for the Krishna River basin.
... CMIP5's ACCESS 1.0 model incorporates long-term simulation data of the 20th century climate including solar, volcanic, stratospheric aerosol, anthropogenic aerosol, emissions, and greenhouse gas concentrations (Lewis 2013). RCP 8.5 is a future climate scenario that describes the expected baseline high greenhouse gas impact resulting from a lack of carbon emission mitigation policies (Riahi et al. 2011). ...
The greater U.S. Midwest is on the leading edge of tick and tick-borne disease (TBD) expansion, and tick and TBD encroachment into Illinois is occurring from both the northern and the southern regions. To assess historical and future habitat suitability of four ticks of medical concern within the state, we fit individual and mean-weighted ensemble species distribution models for Ixodes scapularis, Amblyomma americanum, Dermacentor variabilis, and a newly invading species, Amblyomma maculatum using a variety of landscape and mean climate variables for the periods of 1970-2000, 2041-2060, and 2061-2080. Ensemble models for the historical climate were consistent with known distributions of each species but predicted the habitat suitability of A. maculatum to be much greater throughout Illinois than what known distributions demonstrate. Proximity to wetlands and water bodies was important in predicting both I. scapularis and A. americanum presence. A. americanum occurrence was highly dependent on increasing forest cover, while A. maculatum habitat was more strongly predicted by open habitats. As the climate warmed, the expected distribution of all species became more strongly impacted by precipitation and temperature variables, particularly mean temperature of the wettest quarter and mean temperature of the driest quarter. By 2070, I. scapularis was expected to retract by as much as 60% from southern and central regions of the state as compared to historical climate distribution but remained concentrated in the Chicago metropolitan area. A. americanum was predicted to initially expand across parts of east- and west-central Illinois by 2050, but then largely retract in distribution to along rivers and water bodies by 2070. The ranges of D. variabilis and A. maculatum, however, were predicted to contract in the 2050 climate scenario, but then expand in the 2070 scenario. Predicting where ticks may invade and concentrate as the climate changes will be important to anticipate, prevent, and treat TBD in Illinois.
... The main important source of greenhouse gas (GHGs) emission is agriculture soil, and agricultural management strategies would have a significant influence on GHGs (Hamrani et al., 2020). Due to recent concerns on climatic changes, no-till and straw mulch are getting an increasingly higher attention in agricultural activities as the two important conservation agricultural strategies (Riahi et al., 2011). ...
Soil and water conservation are the important aspects in modern world, because of the scarcity of water, agricultural land degradation and soil loss mainly due to erosion. Mulching, a way to conserve both soil and water by covering it with different kinds of materials like organic (crop plant, compost, manures) or synthetic (paper, plastics, Aluminum foils). It controls evaporation rate and aids in managing soil and air microclimate. Through favorable microclimate, it improves soil physicochemical and biological properties. Mulches act as a soil cover to resist against erosion and provide congenial condition for plant growth. Mulching encourages soil and crop productivity, reduces the emergence of greenhouse gases and suppression of weeds. Plastic mulches are also becoming popular among farmers due to their low cost and easy handling. These materials have a greater importance than the organic ones as they are highly employable in controlled soil environment and could enhance soil-crop productivity. Mulching helps to balance hydro-thermal regimes by maintaining radiation flux, heat and water vapor transfer rate and soil heat capacity. Nowadays, biodegradable plastic mulches are employed which are relatively more sustainable as compared to conventional plastic mulches. The degradable nature of plastic mulches favors the microbial activities in soil, subsequently enhancing the productivity. The mulching could be effective in plant roots protection from hot, cold or drought conditions. This part covers the broader aspects related to application of mulches in maintaining microclimate and soil-plant productivity.KeywordsSoil and water conservationOrganic mulchesBiodegradable plasticsMicroclimateEnvironment
... Simulated daily temperature data of seven high-resolution Regional Climate Models (RCMs), participating in the Coordinated Regional Downscaling Experiment for Europe initiative-Euro-CORDEX 0.11° or EUR-11 (Jacob et al. 2013), were used as listed in Online Resource Table S7. Two Representative Concentration Pathways (RCPs) (Moss et al. 2010;O'Neill et al. 2014) for the future were considered, the mid-range climate change RCP4.5 (Wise et al. 2009) and the high-end climate change scenario RCP8.5 (Riahi et al. 2011). The above RCMs have been widely used for climate change impact assessment in the Mediterranean region for their fine spatial resolution and the existence of many model simulations (e.g., Mascaro et al., 2018;Grillakis et al. 2020;Molina et al. 2020). ...
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Climate change is expected to pose major challenges for olive cultivation in many Mediter-ranean countries. Predicting the development phases of olive trees is important for agro-nomic management purposes to foresee future climate impact and proactively act toward adaptation and mitigation strategies. In this study, a statistical model was developed based on winter chill accumulation and, in sequence, on heat accumulation to assess the changes in flowering occurrence for Olea europaea cv. Koroneiki, in the island of Crete, Greece. The model was based on and calibrated with long-term phenological observations and temperature data from four different sites in the island, spanning an elevation gradient between 45 and 624 m a.s.l. This model was used to assess the changes in flowering emergence under two Representative Concentration Pathway scenarios, RCP4.5 and RCP8.5, as projected by seven high-resolution Euro-CORDEX Regional Climate Models. Changes in chill accumulation were determined using the Dynamic Model. Reduction rates in chill accumulation for the whole chilling season ranged between 12.0 and 28.3% for the near future (2021-2060) and 22.7 and 70.9% for the far future (2061-2100), in comparison to the reference period of 1979-2019. Flowering was estimated to occur between 6 and 10 days earlier in the near future and between 12 and 26 days earlier in the far future, depending on the elevation and the climate change scenario.
... In the simulations, we use three different scenarios: RCP4.5 and RCP8.5 and the GeoMIP G4 scenario. RCP4.5 is a scenario that never exceeds a radiative forcing of 4.5 W m −2 (Thomson et al., 2011), while RCP8.5 is an unmitigated emissions scenario leading to a radiative forcing of 8.5 W m −2 at the end of the 21st century (Riahi et al., 2011). The GeoMIP experiment G4 specifies injection of sulfur dioxide into the equatorial lower stratosphere at a rate of 5 Mt yr −1 from 2020 to 2069 (Kravitz et al., 2011). ...
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We use four Earth system models (ESMs) to simulate climate under the modest greenhouse emissions RCP4.5 (Representative Concentration Pathway), the “business-as-usual” RCP8.5 and the stratospheric aerosol injection G4 geoengineering scenarios. These drive a 10 km resolution dynamically downscaled model (Weather Research and Forecasting, WRF) and a statistically bias-corrected (Inter-Sectoral Impact Model Intercomparison Project, ISIMIP) and downscaled simulation in a 450×330 km domain containing the Beijing Province, ranging from 2000 m elevation to sea level. The 1980s simulations of surface temperatures, humidities and wind speeds using statistical bias correction make for a better estimate of mean climate determined by ERA5 reanalysis data than does the WRF simulation. However correcting the WRF output with quantile delta mapping bias correction removes the offsets in mean state and results in WRF better reproducing observations over 2007–2017 than ISIMIP bias correction. The WRF simulations consistently show 0.5 ∘C higher mean annual temperatures than from ISIMIP due both to the better resolved city centres and also to warmer winter temperatures. In the 2060s WRF produces consistently larger spatial ranges of surface temperatures, humidities and wind speeds than ISIMIP downscaling across the Beijing Province for all three future scenarios. The WRF and ISIMIP methods produce very similar spatial patterns of temperature with G4 and are always cooler than RCP4.5 and RCP8.5, by a slightly larger amount with ISIMIP than WRF. Humidity scenario differences vary greatly between ESMs, and hence ISIMIP downscaling, while for WRF the results are far more consistent across ESMs and show only small changes between scenarios. Mean wind speeds show similarly small changes over the domain, although G4 is significantly windier under WRF than either RCP scenario.
Striga hermonthica (Del.) Benth is a parasitic weed that is damaging major cereal crops in sub‐Saharan Africa (SSA). Although Striga is recognised as an agricultural scourge, there is limited information available indicating the extent of its growth and spread as impacted by the changing climate in Kenya. This study investigated the impact of current climate conditions and projected future (2050) climate change on the infestation of Striga hermonthica in the western Kenya region. Specifically, the study aimed to predict Striga hermonthica habitat suitability in five counties in the western Kenya region through using the maximum entropy (MaxEnt) model and bioclimatic, soil, topographic and land use, and land cover (LULC) variables. Striga hermonthica geolocations were collected and collated and ecological niche models were developed to determine the habitat suitability. The results showed that approximately 1767 km2 (10% of the total study area) is currently highly suitable for Striga hermonthica occurrence. The future projections showed a range between 2106 km2 (19% of the total study area) and 2712 km2 (53% of the total study area) at the minimum carbon (RCP 2.6) and the maximum carbon emission scenarios (RCP 8.5) respectively. Elevation, annual precipitation, LULC, temperature seasonality and soil type were determined to be the most influential ecological predictor variables for Striga hermonthica establishment. The study revealed the importance of using climate, soil, topographic and LULC variables when evaluating agricultural production constraints such as Striga's prevalence. The methodology used in this study should be tested in other Striga affected areas.
Conference Paper
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By the end of the century, all countries must reduce carbon dioxide emissions to zero. As a result of deep decarbonization and the highest greenhouse gas emissions pathways, this study examines the effects of climate change on our planet. It is necessary to build renewable power plants in order to keep the global temperature well below 2 centigrade degrees. Despite this, building new renewable power plants costs almost twice as much as stranded fossil fuel power plants. Based on different emission reduction targets, this study compares the costs of different mitigation strategies. When comparing mitigation strategies, indirect consequences such as socioeconomic costs and environmental costs should be taken into account.
An important part of the health of the oceans depends on a good balance of the biogeochemical cycles. Both climate change (in its broadest sense, from the warming of the oceans to acidification) and the introduction of excess nutrients or heavy metals have caused, in many places, distortions in the balances between chemical elements, organisms and detritus. A series of scenarios have been created in which the excess or absence of certain components are distorting carbon fluxes or biomass accumulation. Such changes are not new at all, but now are accelerating and we have to be ready to understand and manage the repercussions that they may have locally and globally. An increase in nitrogen and phosphorus due to land changes in the Amazon, together with other local phenomena, are promoting an uncontrolled increase in Sargassum, which moves every year with the currents until it invades the Caribbean coast, for example. There is such inertia in the entry of these nutrients into the ocean that it becomes difficult to manage them, and even in areas where there is already a much more exhaustive control of the agricultural or industrial activities that promote them, the proliferation of micro and macro algae seems unstoppable. The microbial composition and also the seasonality are key points that have to be considered, especially when certain physical phenomena are weakened such as upwelling (and the related nutrient supply) or the ocean currents (and the related nutrient transport). Several models are based not only on temperature changes (which affect the availability of macro and micronutrients) but also on coastal morphology and local current dynamics. Such models are complex but very useful to understand, locally, what may happen with a cascade effect, such as the relationship of biogeochemical cycles with primary productivity and, in turn, with biomass production. Climate change is greatly affecting this nutrient availability, not only because the physical-chemical balance may be changing, but also because the organisms that process these nutrients are also changing and their ability to recycle may be affected. Acidification also enters this equation, which makes some microelements less available, or makes some species (for example, coccolithophorids) less capable of completing their life cycles, compete for nutrients or suffer more predation because they have more fragile structures. Latitude must also be taken into account in these changes, both due to the effects of climate change and the direct impacts of human activities that have profoundly transformed many ocean environments. In certain areas the predominance of the impact on biogeochemical cycle comes from the direct action of humans (e.g. fertilizers, farming, etc.), but in others the predominance comes from the warming or acidifying effect due to climate change. Thus, for example, the most accelerated changes in the Arctic are having very rapid effects on these biogeochemical cycles, both due to the increase in temperature and acidification and also due to the fact that the dynamics and coverage of the ice are changing. In this area, the direct impacts by pollution and eutrophication are replaced by climate change accelerating paths. Associated with these changes in nutrient cycles is the decrease in available oxygen that alters the physiological capacities of some organisms. The increase in temperature, the decrease in primary production and the slowdown in currents in various parts of the planet are affecting the response capacity of organisms, from benthic to pelagic. No less important is also the fact that stormy phenomena of different types are increasing in frequency and intensity. Storms and hurricanes are also responsible for the distortion of biogeochemical cycles, in some cases impoverishing biomass production and its quality for the following trophic levels. It is a very complex scenario in which the physiology and adaptability of many organisms is at stake, and which we will have to understand in order to properly manage marine resources in the near future.
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The intensity of extreme rainfall is expected to increase in a future climate at a rate close to 7%/°C estimated from the Clausius‐Clapeyron (CC) relationship, which represents the rate of change of the atmospheric water holding capacity with temperature. Previous studies using fixed‐interval extremes (e.g., hourly or daily) have shown that extreme rainfall can also respond to temperature increases at a rate larger than the CC scaling (super CC scaling). Temperature‐precipitation scaling rates (TPSR) were estimated through an event‐based analysis for the Northeastern North American (NNA) region, using a 50‐member large ensemble of climate simulations over the 1956–2099 period. Rainfall events (REs), in which 1‐hr annual maximums (AMs) are embedded, were analyzed. Results show that the TPSR of the RE peak intensity is determined by the duration of the RE in which they are embedded. Rainfall event duration, indicative of rainfall types (large‐scale or convective), therefore plays an essential role and should be considered when estimating the TPSR. Super CC scaling observed for 1‐hr AM in southern regions of the domain was explained by a change in the dominant rainfall type. This study also confirms previously reported results that the more extreme 1‐hr AM will be part of a shorter and probably more convective dominant RE in a future climate.
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Representative Concentration Pathway 6.0 (RCP6) is a pathway that describes trends in long-term, global emissions of greenhouse gases (GHGs), short-lived species, and land-use/land-cover change leading to a stabilisation of radiative forcing at 6.0 Watts per square meter (Wm−2) in the year 2100 without exceeding that value in prior years. Simulated with the Asia-Pacific Integrated Model (AIM), GHG emissions of RCP6 peak around 2060 and then decline through the rest of the century. The energy intensity improvement rates changes from 0.9% per year to 1.5% per year around 2060. Emissions are assumed to be reduced cost-effectively in any period through a global market for emissions permits. The exchange of CO2 between the atmosphere and terrestrial ecosystem through photosynthesis and respiration are estimated with the ecosystem model. The regional emissions, except CO2 and N2O, are downscaled to facilitate transfer to climate models.
This article assesses emissions scenarios in the literature, originally documented in the scenario database that was developed more than 7 years ago. The original scenario assessment and literature review has been used, among other things, as the basis for the quantification of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) reference scenarios and the IPCC Third Assessment Report (TAR) stabilization scenarios. In the meantime, a large number of new emissions scenarios have been developed and published. We have collected the relevant information about these new scenarios with the objective to assess the more recent perspectives about future global emissions and to assess the changes in the perspectives about future emissions and their driving forces that may have occurred since the publication of SRES and TAR scenarios. Our analysis goes beyond mere comparisons of emissions ranges. In particular, we explore the underlying drivers of emissions using the so-called IPAT identity (Impacts are proportional to the product of Population, Affluence, and Technology). When IPAT analysis refers to carbon emissions it is called the Kaya identity, where carbon dioxide (CO2) emissions are assumed to correspond to the product of population, per capita income, energy intensity of gross domestic product (GDP), and CO2 intensity of energy. Comparing the recent scenario literature with the scenarios developed before TAR shows that there are strong similarities for the main underlying tendencies in many of the scenario’s driving forces and results.
This report summarizes the findings and recommendations from the Expert Meeting on New Scenarios held in Noordwijkerhout, The Netherlands, 19-21 September 2007. It is the culmination of the combined efforts of the New Scenarios Steering Committee, an author team composed primarily of members of the research community, and numerous other meeting participants and external reviewers who provided extensive comments during the expert review process
We report here spatially explicit scenario interpretations for population and economic activity (GDP) for the time period 1990 to 2100 based on three scenarios (A2, B1, and B2) from the IPCC Special Report on Emissions Scenarios (SRES). At the highest degree of spatial detail, the scenario indicators are calculated at a 0.5 by 0.5 degree resolution. All three scenarios follow the qualitative scenario characteristics, as outlined in the original SRES scenarios. Two scenarios (B1 and B2) also follow (with minor adjustments due to scenario improvements) the original SRES quantifications at the level of 4 and 11 world regions respectively. The quantification of the original SRES A2 scenario has been revised to reflect recent changing perceptions on the demographic outlook of world population growth. In this revised “high population growth” scenario, A2r world population reaches some 12 billion by 2100 (as opposed to some 15 billion in the original SRES A2 scenario) and is characterized by a “delayed fertility transition” that is also mirrored in a delayed (economic) development catch-up, resulting in an initially stagnating and subsequently only very slow reduction in income disparities. The spatially explicit scenario interpretation proceeds via two steps. Through a combination of decomposition and optimization methods, world regional scenario results are first disaggregated to the level of 185 countries. In a subsequent second step, national results are further disaggregated to a grid cell level, taking urbanization and regional (rural–urban) income disparities explicitly into account. A distinguishing feature of the spatially explicit scenario results reported here is that both methodologies, as well as numerical assumptions underlying the “downscaling” exercise, are scenario-dependent, leading to distinctly different spatial patterns of population and economic activities across the three scenarios examined.