INTERREG IIIB ASTRA PROJECT Report on Generation, Use and

ASTRA PROJECT
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INTERREG IIIB
ASTRA PROJECT
Report on Generation, Use and Problems of
Climate Change Scenarios:
Sharpening ASTRA’s profile!
NB! This report is an updated and extended version of a former report which was
prepared for the EC funded ARMONIA Framework 6 Project, Contract n° 511208.
Provided: J. Kropp et al., PIK, April 7th, 2006
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Table of contents
1
The IPCC Scenario Approach
1.1
IS92 Scenarios......................................................................................................................... 3
1.2
The SRES Scenarios ................................................................................................................ 4
1.2.1
2
3
3
SRES Reconsidered......................................................................................................... 6
Use of Scenarios in Climate Modelling
7
2.1
Climate Sensitivity................................................................................................................... 7
2.2
Downscaling............................................................................................................................ 8
2.2.1
Providing regional climate scenarios............................................................................... 8
2.2.2
Uncertainty ...................................................................................................................... 9
Which Underlying Storyline for ASTRA?
3.1
10
Selected Impacts/Changes in the Baltics............................................................................... 10
4
SRES Terminology
12
5
References
12
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1 The IPCC Scenario Approach
Climate scenarios are plausible representations of the future that are consistent
with assumptions about future emissions of greenhouse gases and other
pollutants and with our understanding of the effect of increased atmospheric
concentrations of these gases on global climate. It is important to emphasise
that climate scenarios are not predictions, like weather forecasts are. In contrast,
a climate scenario is a plausible indication of what the future could be like over
decades or centuries, given a specific set of assumptions. These assumptions
include future trends in energy demand, emissions of greenhouse gases, land
use change as well as assumptions about the behaviour of the climate system
over long time scales. It is largely the uncertainty surrounding these
assumptions which determines the range of possible scenarios.
1.1
IS92 Scenarios
In 1992 IPCC published six alternative development scenarios for greenhouse
gas emissions (IS92a - f). The are mainly based on business as usual
assumptions and do not include any kind of climate policies. The different worlds
that the scenarios imply, in terms of economic, social and environmental
conditions, vary widely and the resulting range of possible greenhouse gas
futures spans almost an order of magnitude. The assumptions for the IS92
scenarios came mostly from the published forecasts of major international
organisations or from published expert analyses and, i.e.
•
for the IS92a assumes that the population rises to 11.3 billion by 2100
and economic growth averages 2.3 % per annum between 1990 and 2100,
with a mix of conventional and renewable energy sources being used.
•
The IS92c propagates an eventually sinking of CO2 emissions below its
1990 starting level. It assumes that population first grows, then declines
by the middle of next century, that economic growth is low, and that there
are severe constraints on fossil fuel supply.
•
The IS92e produces the highest CO2 emissions. This scenario combines,
among other assumptions, moderate population growth, high economic
growth, high fossil fuel availability and eventual phase out of nuclear
power.
For the other scenarios and a summary of the main assumptions cf. the table 1
below.
In particular the IS92a has been widely adopted as a standard scenario for use in
impact assessments, although the IPCC recommended that all six IS92 emissions
scenarios shall be used in order to represent the range of uncertainty in
emissions.
Although the IS92 scenarios were path-breaking, they were the first global scenarios to
provide estimates of the full suite of greenhouse gases much has changed in the period
following the creation of the IS92 scenarios. For example, sulfur emissions have been
recognized as a more important radiative forcing factor than other non-CO2 greenhouserelated gases, and some regional control policies have been adopted. Restructuring in the
states of Eastern Europe and the Former Soviet Union has had far more powerful effects on
economic activity and emissions than were foreseen in the IS92 scenarios. For some regions
these scenarios are not representative of those found in the literature. Further, the advent of
integrated assessment (IA) models has made it possible to construct self-consistent
emissions scenarios that jointly consider the interactions between energy, economy, and
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land-use changes. Thus, the so-called SRES scenarios were developed (Alcamo et al. 1995,
cf. next section). Both scenario groups are used as input for GCM runs.
Scenario estimates
IS92 scenarios for 2100
1990
IS92a
IS92b
IS92c
IS92d
IS92e
IS92f
5.252
11.3
11.3
6.4
6.4
11.3
17.6
--
2.3
2.3
1.2
2.0
3.0
2.3
354
708
685
471
542
954
820
--
2.18
2.13
1.47
1.75
2.64
2.52
Range (ºC)(3)
--
1.5-3.14
1.46-3.06
1.29-2.18
1.18-2.56
1.83-3.73
1.74-3.59
Global mean sea-level
rise (cm) (2)
--
51
50
40
45
57
56
Range (cm)(3)
--
20-90
20-89
14-76
16-82
24-98
23-96
Population (billion)
Economic growth rate
(annual GNP;% p.a.)
CO2
concentration
(ppmv)(1)
Global mean
change (ºC)(2)
temp.
Table 1: Summary of the IS92 scenarios and their estimated environmental consequences. Note:
IS92 emissions used in calculations are taken from IPCC (1994). Model calculations are by the IPCC
SAR version of MAGICC (Wigley and Raper, 1992). Changes are with respect to the 1961-90
average. Aerosol effects are included. (1) best-guess assumptions; (2) assuming 2.5ºC climate
sensitivity; (3) based on 1.5ºC and 4.5ºC climate sensitivity range.
1.2 The SRES Scenarios
In the year 2000 the IPCC published a new set of scenarios for the Third Assessment Report
(often mentioned as IPCC-SRES scenarios (IPCC TAR 2001a, 2001b; IPCC SRES 2000)).
The SRES scenarios were developed between 1996 and 1999 and were constructed to
explore future developments in the global environment with special reference to the
production of greenhouse gases and aerosol precursor emissions. They are based on the
following three essentials:
Storyline: a narrative description of a scenario (or a family of scenarios), highlighting the
main scenario characteristics and dynamics, and the relationships between key driving
forces.
Scenario: projections of a potential future, based on a clear logic and a quantified storyline.
Scenario family: one or more scenarios that have the same demographic, politico-societal,
economic and technological storyline.
The SRES team defined four narrative storylines (see Figure 1), labelled A1, A2, B1 and B2,
describing the relationships between the forces driving greenhouse gas and aerosol
emissions and their evolution during the 21st century for large world regions and globally .
Each storyline represents different demographic, social, economic, technological, and
environmental developments that diverge in increasingly irreversible ways.
In simple terms, the four storylines combine two sets of divergent tendencies: one set varying
between strong economic values and strong environmental values, the other set between
increasing globalisation and increasing regionalisation (Nakicenovic et al., 2000):
•
A1: A future world of very rapid economic growth and successful economic
development in which regional average income per capita converge and current
distinctions between "poor" and "rich" countries could be dissolved; global population
that peaks in mid-century and declines thereafter; rapid introduction of new and more
efficient technologies; international mobility of people, ideas, and technology.
•
A2: represents very heterogeneous world with continuously increasing global
population and regionally oriented economic growth that is more fragmented and
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slower than in other storylines. The A2 world "consolidates" into a series of economic
regions. Less emphasis is directed on economic, social, and cultural interactions
between regions. Economic growth is uneven and the income gap between nowindustrialized and developing parts of the world does not narrow.
•
B1: a convergent world with the same global population as in the A1 storyline but with
rapid changes in economic structures toward a service and information economy,
with reductions in material intensity, and the introduction of clean and resourceefficient technologies, i.e. environmental and social consciousness combined with a
globally coherent approach to a more sustainable development.
•
B2: a world in which the emphasis is on local solutions to economic, social, and
environmental sustainability, with continuously increasing population (lower than A2)
and intermediate economic development. communities. International institutions
decline in importance, with a shift toward local and regional decision-making
structures and institutions. Human welfare, equality, and environmental protection all
have high priority, and they are addressed through community-based social solutions
in addition to technical solutions, although implementation rates vary across regions.
Economical
A1
A2
Global
Regional
B1
B2
Ecological
After determining the basic features of each of the four storylines, including quantitative
projections of major driving variables such as population and economic development taken
from reputable international sources (e,g, United Nations, World Bank and IIASA, etc.; for
details cf. SRES report). After determining the basic features and driving forces for each of
the four storylines, the SRES team began to model and quantify them. This resulted in 40
scenarios, each constituting an alternative interpretation and quantification of a storyline. All
the interpretations and quantifications associated with a single storyline are called a scenario
"family"
In all, six models were used to generate the 40 scenarios:
•
Asian Pacific Integrated Model (AIM) from the National Institute of Environmental Studies in
Japan (Morita et al., 1994);
•
Atmospheric Stabilization Framework Model (ASF) from ICF Consulting in the USA (Lashof
and Tirpak, 1990; Pepper et al., 1998; Sankovski et al., 2000);
•
Integrated Model to Assess the Greenhouse Effect (IMAGE) from the National Institute for
Public Health and Environmental Hygiene (RIVM) (Alcamo et al., 1998), used in connection
with the Dutch Bureau for Economic Policy Analysis (CPB) WorldScan model (de Jong and
Zalm, 1991), the Netherlands;
•
Multiregional Approach for Resource and Industry Allocation (MARIA) from the Science
University of Tokyo in Japan (Mori and Takahashi, 1999; Mori, 2000);
•
Model for Energy Supply Strategy Alternatives and their General Environmental Impact
(MESSAGE) from the International Institute of Applied Systems Analysis (IIASA) in Austria
(Messner and Strubegger, 1995; Riahi and Roehrl, 2000); and the
•
Mini Climate Assessment Model (MiniCAM) from the Pacific Northwest National Laboratory
(PNNL) in the USA (Edmonds et al., 1996a, 1996b).
All these models have different regional aggregations. Nevertheless the SRES team decided
to group the various global regions into four "macro-regions" (OECD, Asia, Africa & South
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America, Countries undergoing economic changes). However the scenario building
strategies have led to the following scenario structure (Fig.1)
Figure 1: Schematic illustration of SRES scenarios. Four qualitative storylines yield four sets of
scenarios called "families": A1, A2, B1, and B2. The set of scenarios consists of six scenario groups
drawn from the four families: one group each in A2, B1, B2, and three groups within the A1 family,
characterizing alternative developments of energy technologies: A1FI (fossil fuel intensive), A1B
(balanced), and A1T (predominantly non-fossil fuel). Within each family and group of scenarios, some
of them have "harmonized" driving forces and share the same pre-specified population and gross
world product. These are marked as "HS" for harmonized scenarios. "OS" denotes scenarios that
explore uncertainties in driving forces beyond those of the harmonized scenarios. The number of
scenarios developed within each category is shown (taken from IPCC SRES 2000).
1.2.1 SRES Reconsidered
In advance of the 4AR of the IPCC, which will be published and the end of 2007, several
efforts were already made in order to evaluate the existing SRES storylines and – if
necessary – to update some of these forcing scenarios (cf. e.g. van Vuuren & O’Neill 2006).
In principle it must be clear that any scenario cannot have an unlimited lifetime and
additionally several researchers has claimed that the SRES approach is already off-track
with respect to historical emission trends.
Van Vuuren & O’Neill (2006) have examined this in detail (cf. Table 1), and have found only
minor inconsistencies
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Table 1: Main inconsistencies found between the SRES scenarios and more recent scenarios and data (after van
Vuuren & O’Neill 2006).
With respect to CO2 emission it seems that these grow slightly slower than projected by the
SRES scenarios during the last decade (mostly caused by the decline in coal use in China),
but the overall difference between the measured and calculated absolute emissions is rather
small. As an important thing the authors point out that there exists a difference between the
prognoses of population development in the ALM region (Africa, Latin America, Middle East)
and the actual development, in particular for the A2 scenario and with respect to the global
economic growth also for the A1 scenario storyline. Overall now need was seen to develop
new storylines, corrections can be easily made, if necessary.
2 Use of Scenarios in Climate Modelling
As already mentioned the SRES storylines are state of the art input (GHG forcing) for climate
modelling purposes (they are translated into emission trajectories). The general idea is that
we can compare the results for the future climate (e.g. temperature trajectories, etc.) for the
same forcing scenario, e.g. A2, and different climate models. For this goal the IPCC has
established the coupled model inter-comparison project (CMIP; http://wwwpcmdi.llnl.gov/projects/cmip/index.php), where the outputs of the different model runs based
on the certain reference scenarios are accessible for public. New scenarios are under
development and will be included in the AR5 (forthcoming at the end of this decade).
2.1 Climate Sensitivity
If we are using climate change scenarios the question arises how large the influence of CO2
and the other emissions on the climate will be? We can also ask, if the radiation balance
(W/m2) will be changed, how large the temperature change will be? The standard concept
which tries to answer this question is the climate sensitivity (CS). The CS can be measured
as °C/(W/m2). Normally it is estimated for an equilibrium and for a doubling of CO2 (560ppm).
By several methods the (physically motivated ones and ensemble runs done by models) the
CS is estimated between 1.5 – 4.5 C (IPCC 2001b). Due to the variety of estimates a
probability function can be derived showing a most likely value of a global temperature
increase of approx. 3 °C in a case of a CO2 doubling. Why it is so important to talk about
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CS? CS tells us which temperature increase we can anticipate if greenhouse gas emissions
increase (CO2 equivalents).
2.2 Downscaling
Climate scenarios are based on the runs of general circulation models (GCM). They are not
available at a resolution suitable for the majority of regional impact assessments that may
concern the majority of decision makers. Downscaling techniques refers to techniques
making this results relevant to local decision makers and involves statistical methods for data
interpolation or local climate modelling using the output of GCMs. While the geographical
variability of such strategies may be improved, the accuracy of this regional climate
scenarios depend on the (largely unknown) accuracy of the underlying GCM, on the
suitability of the used statistical method, and on the accuracy of representation of specific
regional settings in the local climate models. Other strategies of downscaling include weather
generators and weather typing strategies, but they used mainly for some specific questions.
2.2.1 Providing regional climate scenarios
The main strategies used in order to provide regional climate change scenarios are model
nesting (dynamical) and regression based (statistical) downscaling.
The latter is mainly based on empirical relationships between local-scale variables and their
large scale predictors. These relationships can be based on a range of mathematical
functions and fitting routines (cf. Wilby et al. 2002, for an overview and, for example, Fig. 2).
Advantages of these methods are there computational efficiency, the possibility to apply
them easily to several GCMs and that these methods are also easily usable by communities
behind climate modelling community. One disadvantage of this method is that the modelled
relationships will not change in the future, feedback mechanism not always integrated, and a
lack in systematic evaluations exists. Therefore, the regional scenarios must be considered
with care. An advantage is that this downscaling method is fast and easy.
To date the assessment of potential impacts of climate change relies mainly on projections
of coarse resolution Atmospheric-Ocean General Circulation Models. The coarse resolution
precludes the simulation of realistic extreme events and the detailed spatial structure of
variables like temperature and precipitation over heterogeneous surfaces e.g. the Alps, the
Mediterranean or Scandinavia. Therefore local models are nested into this GCMs. This socalled dynamical downscaling methods are more costly, since the output of a GCM is fed into
the regional climate model using it as forcing.
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Figure 2: Precipitation changes (mm/day) of the 2080-2100s with respect to the pre-industrial level
(January and July), left GCM (HadCM2, resolution 2.5°×3.75°), middle downscaled via RCM
(horizontal resolution 50km) and right by a statistical method (for all shown on a 0.5°×0.5° grid). For
details cf. Murphy (2000).
Such a method is computationally demanding, since a lot of physical processes must defined
in a local model more explicitly. In addition the results of a local model is restricted to a
specific region under examination. A prominent example for such an approach is the
HIRHAM model. A combination of the HIRLAM weather forecast model, a cooperative project
of the national weather services in Denmark, Finland, Iceland, Ireland, the Netherlands,
Norway, Sweden and Spain (cf. http://www.knmi.nl/hirlam/) and the ECHAM GCM (cf.
http://www.dkrz.de). HIRHAM uses the physical parameterization package of the general
circulation model ECHAM4 (Roeckner et al., 1996). Physical parameterisations include:
radiation, cumulus convection, stratiform clouds, land surface processes, hydrology, sea
surface-, sea ice processes, planetary boundary layer and gravity wave drag. This model is
extensively used to provide regional climate scenarios for the arctic region and over Europe.
A common standard resolution of this models is 0.5° x 0.5°. In some specific cases also a bit
smaller resolutions are possible, although the general magnitude does not change.
In any case if we want to use local scenarios for ASTRA we have to calculate them, either on
the basis of GCM outputs combine with statistical downscaling routines or dynamical
downscaling methods (nested models). Considering the inherent uncertainty (cf. below) in
the scenarios I suggest to use existing scenarios for ASTRA purposes (based on most recent
data available from the IPCC data center). In any case, these model outputs must be
manipulated and combined with observed climate data before they are usable, for regional
impact assessments (IPCC TAR (2001b) WGI, p 743, Ch. 13). Downscaling methods and
regional model inter-comparison projects (e.g. PRUDENCE) are an hot issue of research.
Downscaling results of dynamical and statistical methods differs.
2.2.2 Uncertainty
Uncertainty is a very important issue, in particular on the regional scale when model
scenarios should used in impact assessment studies etc. In principle we have to consider
some kind of inherent uncertainties on any stage of an analysis.
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First there exists some uncertainties whether the underlying storylines are correct of not.
Since any model is only a generalization of reality, also the GCM/RCM models introduces
uncertainty. It is difficult to estimate the overall uncertainty quantitatively (although we have
seen improvements in this context recently), but in principle that uncertainty increases if we
change view from the global to the local scale (Fig.3, cf. e.g. New & Hulme 2000).
Figure 3: Schematic representation of uncertainty
propagation. The total uncertainty (illustrated by
the grey panel, but not to scale) expands as
individual uncertainties are combined. Adopted
after New & Hulme (2000).
Nevertheless, it must be mentioned here that the overall message of all climate
modelling efforts are the same, temperature will increase, precipitation will show
a tendency to more heavy rain, but parallel to changes in seasonality, etc.. Since
it is impossible to provide exact estimates for the future developments (limits of
prediction) the only strategy is to develop a bunch of (policy) options allowing a
“safe landing”, or in other words an adaptation. This is the scientific challenge
and a major focus of the ASTRA project. But this needs regional impact studies.
3 Which Underlying Storyline for ASTRA?
For ASTRA PIK suggests to use the A2 and - if necessary - the B2 storyline,
because these have a more regional focus. Furthermore, as shown during the
recent decades, it will be likely that we globally will develop to a more
fragmented world instead of to a “coherent block”, guaranteeing similar life
support for the whole world population. With respect to ASTRA’s issues it is more
important, to generate/use regional scenarios which an underlying storyline
which should be as realistic as possible. As mentioned an inherent uncertainty is
in any case unavoidable (cf. also above). Therefore it will be more interesting to
compare different model runs with respect to the same storyline instead of using
different ones.
3.1 Selected Impacts/Changes in the Baltics
In this section some potential impacts in the Baltics will be presented, in order to
make clear what we already know or what is possible if one follows a systematic
impact analysis. In particular, with respect to the first example (Fig. 4) a detailed
and systematic impact analysis was necessary to achieve the results as shown.
Anyway, the general message is always the same, climate change will have
effects for the Baltics. Policy and decision makers must learn to deal with
uncertainties and misconceptions of strategies are very valuable for mutual
learning (cf., for example Naess et al. 2005). A strategy “not yet no here” will be
not sustainable for the future. With respect to some specific issues the Baltic
region can be one of the winners of climate change, e.g. with respect to potential
grain yield (cf. Fig. 4).
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Figure 4: Agroclimatic yield grain maize in Europe (0.5deg grid), present and 2080 (t/ha). Source:
Fischer 2005, Fischer et al. 2002.
On the other hand there exist a lot of adverse effects which will be more
important. In particular with respect to lowland vulnerability if sea level rise
accelerates (Fig. 5).
Figure 5: Collapse of thermohaline Circulation
results in regional sea level rise of the order of
1m in north Atlantic and a sea level decline in
the southern Ocean (Source: Levermann et al.
2005). The effect add onto the increase due to
warming of the ocean and a melting of land
ice. It is likely that this will have some effects
also the Baltic Sea.
SweClim, for instance, prognoses that with respect to the reference climate the annual
average annual temperature will increase approx. 4 °C (HadCM/ECHAM, A2, Fig. 6). Parallel
the precipitation pattern will change with a significant increase in winter.
Figure 6: Temperature change in Europe with respect to 1961-1990. HadCM/ECHAM A2 runs.
Source: SweClim.
Anyway it is important to mention that ASTRA is designed as a project that should examine
impacts and develop strategies for adaptation on this basis. This implies that we have to
develop at least a rudimentary systematic approach, in order to evaluate what had happened
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in certain regions, what the prone inventories are and how institutions are currently are
designed or may possibly change (for instance, cf. Næss et al 2005, Kropp et al. 2006).
Data, in particular, say nothing if they are not related to specific exposure units. Therefore,
PIK sees the main issue of the project not in the provision of more ore less sophisticated
scenarios runs, since most of the knowledge which we need in this context is already on
hand (cf. ACCELERATES project for example). Reanalyses of empirical data will be done, if
they are necessary to address specific purposes (e.g. extreme value or trend assessment,
cf., for example, Kallache et al. 2005, Rebetez et al. 2006). I suggest again that should talk
about specific problem structures in the case study regions again (on the Klaipeda meeting)
and how we can approach them systematically.
4 SRES Terminology
•
Model: a formal representation of a system that allows quantification of relevant system
variables.
•
Storyline: a narrative description of a scenario (or a family of scenarios) highlighting the main
scenario characteristics, relationships between key driving forces, and the dynamics of the
scenarios.
•
Scenario: a description of a potential future, based on a clear logic and a quantified storyline.
•
Family: scenarios that have a similar demographic, societal, economic and technical-change
storyline. Four scenario families comprise the SRES: A1, A2, B1 and B2.
•
Group: scenarios within a family that reflect a variation of the storyline. The A1 scenario
family includes four groups designated by A1T, A1C, A1G and A1B that explore alternative
structures of future energy systems. The A1C and A1G groups have been combined into one
"fossil intensive" A1FI scenario group, thus reducing the number of groups constituting the A1
scenario family to three. The other three scenario families consist of one group each.
•
Category: scenarios are grouped into four categories of cumulative CO2 emissions between
1990 and 2100: low, medium-low, medium-high, and high emissions. Each category contains
scenarios with a range of different driving forces yet similar cumulative emissions.
•
Marker: a scenario that was originally posted on the SRES web site to represent a given
scenario family. A marker is not necessarily the median or mean scenario.
•
Illustrative: a scenario that is illustrative for each of the six scenario groups reflected in the
Summary for Policymakers of this report (after combining A1G and A1C into a single A1FI
group). They include four revised "scenario markers" for the scenario groups A1B, A2, B1 and
B2, and two additional illustrative scenarios for the A1FI and AIT groups.
•
Harmonized: harmonized scenarios within a family share common assumptions for global
population and GDP while fully harmonized scenarios are within 5% of the population
projections specified for the respective marker scenario, within 10% of the GDP and within
10% of the marker scenario's final energy consumption.
•
Standardized: emissions for 1990 and 2000 are indexed to have the same values.
•
Other scenarios: scenarios that are not harmonized.
•
Climate Sensitivity: equilibrium climate sensitivity refers to the equilibrium response in global
mean surface temperature following a doubling of the atmospheric (equivalent) CO2
concentration.
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Edmonds, J., M. Wise, H. Pitcher, R. Richels, T. Wigley, and C. MacCracken, 1996a: An integrated assessment
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ASTRA PROJECT
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