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Chapter 7
Climate Model Scenarios for Global Warming
7.1 Greenhouse gases, aerosols and other climate forcings
7.2 Global-average response to greenhouse warming scenarios
7.3 Spatial patterns of the response to time-dependent
warming scenarios
7.4 Ice, sea level, extreme events
7.5 Summary: the best-estimate prognosis
7.6 Climate change observed to date
7.7 Emissions paths and their impacts
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.1 Greenhouse gases, aerosols and other climate forcings
7.1.a Scenarios, forcings and feedbacks
•Climate model predictions for global warming respond to a forcing that
is continuously applied (e.g., radiative effects of greenhouse gases
(GHG)) as prescribed by a specified emissions scenario (section 7.1.c)
•Predictable: if forcing occurs, then response will occur—with range of
uncertainty (error bars)
•Natural variability unpredictable at long lead times
• Aerosols: particles (notably sulfate aerosols)
•Net cooling tendency by reflection of sunlight
•short residence times comp. to long-lived GHG
[aerosol indirect effects via cloud condensation nuclei may have similar magnitude
of cooling but big error bars]
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7.1.b Forcing by sulfate aerosols
Spatial patterns of estimates of radiative forcing
due to effects of human activity
Direct sulfate
Figure 7.1
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Shine & Forster, Global and Planetary Change, 1999
Well mixed greenhouse gases
7.1.c Commonly used scenarios
Radiative forcing as a function of time for various climate forcing scenarios
Top of the atmosphere
radiative imbalance 
warming due to the net
effects of GHG and other
forcings
SRES:
• A1FI (fossil intensive),
• A1T (green technology),
• A1B (balance of these),
• A2, B2 (regional economics)
• B1 “greenest”
• IS92a scenario used in many
studies before 2005
from the Special Report
on Emissions Scenarios
Figure 7.2
Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
SRES emissions scenarios, cont’d
A1 scenario family: assumes low population growth, rapid economic
growth, reduction in regional income differences
A1FI : Fossil fuel Intensive
A1B: energy mix, incl. non-fossil fuel
A2: uneven regional economic growth, high income toward nonfossil, population 15 billion in 2100
B1: like A1 but switch to information and service economy,
introduction of resource-efficient technology. Emphasis on global
solutions to economic, social, and environmental sustainability,
including improved equity.
•No explicit consideration of treaties
•Natural forcings e.g., volcanoes set to avg. from 20th C.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.2 Global-average response to greenhouse warming scenarios
Radiative forcing and global average
surface temperature response
Change in radiative forcing (Wm-2)
Change in temperature (K)
Figure 7.3
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Mitchell and Johns, 1997, J. Climate
Model names (a sample)
•CCMA_CGCM3.1, Canadian Community Climate Model
•CNRM_CM3, Meteo-France, Centre National de Recherches
Meteorologiques
•CSIRO_MK3.0, CSIRO Atmospheric Research, Australia
•GFDL_CM2.0, NOAA Geophysical Fluid Dynamics Laboratory
•GFDL_CM2.1, NOAA Geophysical Fluid Dynamics Laboratory
•GISS_ER, NASA Goddard Institute for Space Studies, ModelE20/Russell
•MIROC3.2_medres, CCSR/NIES/FRCGC, medium resolution
•MPI_ECHAM5, Max Planck Institute for Meteorology, Germany
•MRI_CGCM2.3.2a, Meteorological Research Institute, Japan
•NCAR_CCSM3.0, NCAR Community Climate System Model
•NCAR_PCM1, NCAR Parallel Climate Model (Version 1)
•UKMO_HADCM3, Hadley Centre for Climate Prediction, Met Office, UK
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Fig. 7.4 Global average warming simulations in 11 climate models
• Global avg. sfc.
air temp. change
• (ann. means rel.
to 1901-1960
base period)
• Est. observed
greenhouse gas
+ aerosol
forcing, followed
by
• SRES A2
scenario (inset)
in 21st century
• (includes both
GHG and
aerosol forcing)
Data from the Program for Model Diagnosis and Intercomparison (PCMDI) archive.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.3 Spatial patterns of the
response to time-dependent
warming scenarios
Response to the SRES
A2 scenario GHG and
sulfate aerosol forcing
in surface air
temperature relative to
the average during
1961-90 from the
Hadley Centre climate
model (HadCM3)
[choosing one model
simulation through the
21st century as an
example; later
compare models or
average results from
several models]
2010-2039
2040-2069
2070-2099
Figure 7.5
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Supplementary Figure
2010-2039
Response to the SRES
A2 scenario GHG and
sulfate aerosol forcing
in surface air
temperature relative to
2040-2069
the average during
1961-90 from the
National Center for
Atmospheric Research
Community Climate
Simulation Model
(NCAR_CCSM3)
2070-2099
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January
January and July
surface temperature
from HadCM3
averaged 2040-2069
(SRES A2 scenario)
Figure 7.6
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July
Supplementary Figure
January
January and July
surface temperature
from NCAR_CCSM3
averaged 2040-2069
(SRES A2 scenario)
July
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Poleward amplification of warming
1. The snow/ice feedback as described in chapter 6, operates in
these regions. The impacts are even larger regionally than they are
in the global average.
2. The lapse rate feedback. The lapse rate (rate of temperature
decrease with height) is larger at high latitudes than in the tropics.
This affects the greenhouse feedback between the atmospheric
temperature in the upper troposphere and the surface temperature
Also:
• Thinner sea ice, so greater heat transfer from ocean in winter
• Changes in very cold stable layer near surface in winter
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Comparing projections of
different climate models
GFDLCM2.0
30yr. avg annual surface
air temperature response
for 3 climate models
centered on 2055 relative
to the average during
1961-1990
NCARCCSM3
MPIECHAM5
Figure 7.7
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Comparing projections of different climate models
•Provides estimate of uncertainty
•Differences often occur with physical processes e.g., shift of jet stream,
reduction of soil moisture, …
•At regional scales (~size of country or state) more disagreement
•Precip challenging at regional scales
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Comparing projections of
different climate models
GFDLCM2.0
Precipitation from 3
models for Jun.-Aug.
2070-2099 average
minus 1961-90 avg
(SRES A2 scenario)
NCARCCSM3
MPIECHAM5
Figure 7.8
(mm/day)
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Comparing projections of
different climate models
GFDLCM2.0
Supplementary Figures
Precipitation from 3
models for Dec.-Feb.
2070-2099 average
minus 1961-90 avg
(SRES A2 scenario)
NCARCCSM3
MPIECHAM5
(mm/day)
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Precipitation from HadCM3 for Dec.-Feb. 2070-2099 avg. (SRES A2)
Supplementary figure
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Precipitation from HadCM3 for Jun.-Aug. 2070-2099 avg. (SRES A2)
Supplementary Figure
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North American West Coast
Precipitation change under global warming
SRES A2 scenario 2070-2099 rel. to 1979-2000
Dec.-Feb. (DJF) Prec. Anom.
Multi-model ensemble average
Compare
to
individual
models
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Multi-model ensemble avg.
January and July
precipitation change
for 10 model
ensemble average
for 2070-2099
minus 1961-90 avg
(SRES A2 scenario)
Figure 7.9
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
December – February
June – August
7.3.c Summary of spatial patterns of the response
• Poleward amplification of the warming is a robust feature. It is
partly due to the snow/ice feedback and partly to effects involving
the difference in lapse rate between high latitudes and the tropics.
• In time-dependent runs polar amplification is seen first in the
northern hemisphere. In the North Atlantic and Southern Ocean
effect of circulation to the deep ocean slows the warming.
• Continents generally tend to warm before the oceans.
• There is a seasonal dependence to the response. For instance,
winter warming in high latitudes is greater than in summer.
• The models tend to agree on continental scale and larger, but
there are many differences at the regional scale. Regional scale
predictions (e.g. for California) tend to have higher levels of
uncertainty, esp. for some aspects (e.g., precipitation)
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.3.c Summary of spatial patterns of the response (cont.)
• Natural variability will tend to cause variations about the forced
response, especially at the regional scale.
• Precipitation increase (about 5%-15%) on a global average; high
latitudes and tropical areas with high precipitation tend to have
precipitation increase but subtropical areas that currently have
low precipitation tend to decrease. However, regional aspects can
be quite variable between models, so there is uncertainty in which
areas will have the largest impacts. There is reason to believe that
regional changes are likely. Mid/high latitude wintertime
precipitation tends to increase.
• Summer soil moisture tends to decrease in some regions. This is
an example of an effect that would have implications for
agriculture. But soil moisture models depend on such things as
vegetation response, which are crudely modeled and have much
regional dependence (hence higher uncertainty).
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7.4 Ice, sea level, extreme events
7.4.a Sea ice and snow
Simulated ice fraction change (2070-99) minus (1961-90)
as a percent of the base climatol. ice fraction
Sep. - Nov.
Dec. - Feb.
Echam5
SRESA2
Figure 7.10
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Simulated change in ice fraction (% coverage)
Sep.-Nov. (2040-69) minus (1961-90)
HadCM3
SRESA2
Supplementary
Figure
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Simulated snow fraction change (2070-99) minus (1961-90) as a percent
of the base climatological snow amount (where base exceeds 1Kg/m3)
Echam5 SRESA2
Sep. - Nov.
Dec. - Feb.
Figure 7.11
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.4.b,c Land ice & Sea level rise
•Sea level rise due to thermal expansion in GCMs ~0.13 to 0.32 m in 21st
Cent. (1980-99 to 2090-99; A1B , similar for A2) (~13±7 mm/decade to 2020)
•Deep ocean warming continues, e.g., 1-4 m rise if stabilize at 4xCO2
•Warming impact on Greenland and Antarctic ice sheets poorly constrained
•[NOT relevant: all melt = mean sea level rise > 75 meters]
•Greenland eventual melting ~7m over millennial time scale
•Most of Antarctica cold enough to remain below freezing
•Ice sheet dynamics complicated: “calving” of icebergs affect pressure
on inland parts of ice sheet, flow rate
•Surprises: Larsen B ice shelf broke up in a period of months
• small but ice shelf retreats since 1974 ~ 13,000 km2
•Radar monitoring of ice thickness in coming decades
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Breakup of the Larsen B Ice Shelf in Antarctica
Late austral summer:
melt ponds on shelf.
Source: National Snow and Ice
Data Center, University of
Colorado, Boulder.
Images from the MODIS
(Moderate Resolution Imaging
Spectrometer) instrument on
NASA's Terra satellite.
Jan. 31, 2002
MODIS images from NASA's Terra satellite, National Snow and Ice Data Center
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Breakup of the Larsen B Ice Shelf in Antarctica
Minor retreat takes place
Feb. 17, 2002
MODIS images from NASA's Terra satellite, National Snow and Ice Data Center
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Breakup of the Larsen B Ice Shelf in Antarctica
Retreat continues
(800 km2)
Feb. 23, 2002
MODIS images from NASA's Terra satellite, National Snow and Ice Data Center
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Breakup of the Larsen B Ice Shelf in Antarctica
Main collapse
(~2600 km2), leaving
thousands of icebergs
Figure 7.12
Mar. 5, 2002
MODIS images from NASA's Terra satellite, National Snow and Ice Data Center
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.4.d Extreme events
• If standard deviation of daily temperatures remains similar as mean
temperature rises  more frequent occurrence of events currently
considered extreme
• e.g., heat waves
Figure 7.13
Few events above
40C (104F)
(shaded area)
Mean change
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Much more frequent
(shaded area many
times larger)
7.4.d Extreme events (cont.)
• Also applies to frost days (on low side), mid-winter thaws
• Precipitation events with higher mean moisture may act similarly
• e.g., hurricane models for ~2xCO2 avg. increase ~ ½ a category
on the 1-5 Saffir Simpson scale
• Tendency for increase in heavy rainfall events
• High natural variability in precip, implies precip effects of
warming will rise above natural variability more slowly than
temperature
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Summary of predicted climate change
Temperature • The lower atmosphere and Earth's surface warm (the
stratosphere cools).
• The surface warming at high latitudes is greater than the global
average in winter but smaller in summer. (In time dependent
simulations with a full ocean, there is less warming over the high
latitude southern ocean).
• surface warming smaller in the tropics, but can be large rel to
natural variability
• For equilibrium response to doubled CO2, global average
surface warming likely lies between +2C and +4.5C, with a most
likely value of 3C, based on models and fits to past variations.
• "Best-estimate” (IPCC 2007) temperature increase in 2090-99
relative to 1980-99 depends on future emissions. For A2 scenario
3.4C; B1 1.8C; A1B 2.8C,;A1FI 4.0C. Likely ranges est at 60% to
160% of these values (actual model ensemble ranges are smaller)
• Due to the thermal inertia of the ocean, the temperature would
increase for decades beyond whatever time stabilization of
greenhouse gases might be achieved.
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Summary of predicted climate change
Precipitation
• The global average increases (as does average evaporation);
the larger the warming, the larger the increase.
• Precipitation increases at high latitudes throughout the year;
for equilibrium response to doubled CO2, the average increase is
3 to 15%.
• The zonal mean value increases in the tropics although there
are areas of decrease. Shifts in the main tropical rain bands
differ from model to model, so there is little consistency between
models in simulated regional changes.
Soil Moisture
• Increases in high latitudes in winter.
• Decreases over northern mid-latitude continents in summer
(growing season).
Snow and
Sea-Ice
• The area of sea-ice and seasonal snow-cover diminish.
Sea Level
• Sea level increases excluding rapid changes in ice flow for
2090-99 relative to 1980-99: for A2 0.23-0.51m, B1 0.18-0.38;
even if greenhouse gases are stabilized deep ocean warming
creates ongoing sea level rise for centuries.
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Figure 7.14
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Adapted from IPCC, Third Assessment Report, 2001.
Schematic summary
of best-estimate
climate changes due
to greenhouse
warming
7.5 Climate change observed
to date
• Amplitude of natural variations
depends on the spatial and time
averages considered.
• much of weather/climate T
variability due to heat transport
anomalies; but these tend to
cancel in large regional averages
• anthropogenic trend in
temperature expected to have
large spatial scales; i.e. clearer
relative to noise in large-scale
avgs
Figure 7.15 (will be expanded with
supplementary figs. below)
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Surface air temperature CRU* 5 x 5 degree grid
(with selected averaging regions)
*CRU= Climate Research Unit, U. of East Anglia
Annual and Decadal CRU 2m Tanom Area Avg.
(relative to 1961-1990 clim.)
Global
N. Hem.
S. Hem.
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Annual and Decadal CRU 2m Tanom Area Avg.
(relative to 1961-1990 clim.)
Note
axis
scale
chg.
N. America
United States
Europe
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Annual and Decadal CRU 2m Tanom
(relative to 1961-1990 clim., 5x5 degree avgs.)
Note
axis
scale
chg.
Germany
~Beijing
~Washington D.C
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January CRU 2m Tanom
(relative to 1961-1990 clim.)
Note
axis
scale
chg.
Germany
~Beijing
~Washington D.C
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.6.b. Is the observed warming trend consistent with natural
variability or anthropogenic forcing?
• From observed time series, don’t have multiple examples of 50
or 100 year trends to establish range for decadal and centennial
scale natural variability
• Thus, compare to range from models
• Can do this for model runs with natural forcing only versus
runs that also have the observed 20th-century anthropogenic
forcing (GHG+aerosol) [Next slide]
• The range in the natural forcing runs comes both from specified
forcings (volcanoes, changes in solar input,…) and climate
variability (like El Niño or variations in the thermohaline
circulation) that occurs even for constant radiative forcing
• [More sophisticated “fingerprinting” techniques: use weighted spatial
averages associated with the spatial pattern predicted for the warming
rather than with spatial patterns of natural variability]
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Observed 20th C. temperature for various averaging regions with climate
model simulated range: natural only vs. natural + anthropogenic forcings
Observed
warming
exceeds range
that can occur
by natural
variability in
models
Figure 7.16
After Hegerl et al., 2007, in IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.6.c. Sea ice, land ice, ocean heat storage and sea level rise
• (a) Arctic sea ice extent anomalies (area with greater than 15% sea ice
coverage). Bars= yearly values; line= decadal average.
• (b) Global glacier mass balance. Bars=yearly mass balance.
Red line = cumulative global glacier mass balance (right axis)
Figure
7.17
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After Lemke et al., 2007, in IPCC Fourth Assessment Report
Observed global annual ocean heat content for 0 - 700m layer
Ocean heat content anomaly rel . to 1961-90 (black curve)
i.e. global upper ocean heat storage in response to accumulated heat flux
imbalance (surface + exchange with lower layers)
[Heat content anom. =
(temperature anom x
heat capacity x density),
integrated surface to
700m depth over global
ocean area]
[For refc: 1 Wm-2
surface heat flux anom. =
1.1x1022 J/yr over
3.6x1014m2 ocean]
Figure 7.18
Shaded area = 90%
confidence interval
Variations: natural
variability and sampling
error
After Bindoff et al., 2007, in IPCC Fourth Assessment Report; data from Levitus et al., 2005
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Observed annual average anomalies of global mean sea level (mm)
1961 to 2003 trend in global mean sea level rise est. ~ 13 to 23 mm/decade
Red reconstructed sea level
fields rel. to 1961-90
Figure 7.19
[tide gauges avgd using spatial
patterns from recent satellite
data; Church & White, 2006]
Blue curve coastal tide
gauge measurements [rel. to
1961-90; alt method; Holgate &
Woodworth, 2004]
Black curve satellite
altimetry rel. to 1993-2001
(After Bindoff et al 2007)
Error bars denote 90%
confidence interval
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
After Bindoff et al., 2007, in IPCC Fourth Assessment Report, 2007
7.7 Emissions paths and their impacts
Radiative forcing as a function of time for various climate forcing scenarios
Recall: emissions scenarios
SRES:
• A1FI (fossil intensive),
• A1T (green technology),
• A1B (balance of these),
• A2, B2 (regional economics)
• B1 “greenest”
• IS92a scenario used in many
studies before 2005
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Figure 7.2
Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report
Recall: emissions scenarios
Radiative forcing as a function of time for various climate forcing scenarios
Focus on A2, A1B, B1
SRES:
• A1FI (fossil intensive),
• A1T (green technology),
• A1B (balance of these),
• A2, B2 (regional economics)
• B1 “greenest”
• IS92a scenario used in
many studies before 2005
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Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report
Recall: Other emissions scenarios
A1 low population growth, rapid economic growth, reduction in
regional income differences A1B: energy mix, incl. non-fossil fuel
A2: uneven regional economic growth, high income toward nonfossil, population 15 billion in 2100
B1: like A1 but resource-efficient technology. Emphasis on global
economic, social, and environmental sustainability, equity.
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SRES Multi-model mean surface warming projections
A2, A1B, B1
Multi-model mean surface
warming projections as a
continuation of 20thcentury simulation
Constant composition (2000
values) simulation, forcing
kept at year 2000 level
(gives global warming
commitment)
+ Constant composition
commitment simulations
from A1B and B1 2100
values
Shading +/- 1 standard
deviation from individual
model ann. avgs.
Figure 7.20
After Solomon et al., 2007, IPCC Fourth Assessment Report
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•Warming approx. linearly related to radiative forcing (with lag)
– Lower emissions implies slower increase, smaller change
•Many other effects approx. prop. to warming
– Scaling of response: for many effects in the physical climate system,
changes approx. proportional to radiative forcing, e.g., surface
temperature increase, precipitation change.
Caveat: Some aspects of climate system may have threshold type responses
(e.g., thermohaline circulation), which are poorly known. For 21st Century
these have not been seen in physical climate models (but these models do not
include ice sheet dynamics or ecosystems).
Threshold response: disproportionate change as cross a certain value
(Threshold response may be more likely in ecosystem impacts)
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Precip change ~ proportional to large scale T change
• Amplitude of negative precip. change (rel to 1901-60) avg over tropics
• versus tropical average surface air temperature
Supplementary Figure
Neelin et al., 2006, Proc. Nat. Acad. Soc.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Not in this course but very much of interest --- the interface
between climate change science and societal/ecosystem impacts.
E.g., estimates of ecosystem impacts by degrees of global average
surface warming above preindustrial (Parry et al. 2007):
•1-2.5 C: polar ecosystems increasingly damaged, 10-15% of
species committed to extinction, coral reefs bleached, and major
loss of habitat or species in regions such as South Africa,
Queensland and the Amazon rainforest;
•2.5-3.5 C: coral reefs overgrown by algae, major changes in
polar systems, globally, 20-30% of species committed to
extinction, over 15% of global ecosystems transformed;
•3.5-4.5 C: over 40% of ecosystems transformed, extinction of 1540% of the endemic species in global biodiversity hotspots.
Sources of uncertainty in such estimates from the climate
modeling side include uncertainties in regional precipitation
change, changes in the distribution of extreme events, etc., but
the complexity of ecosystem response adds additional challenges.
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Impact of emissions pathway: A2, A1B and B1 scenarios’ effects on
annual avg. surface air temperature change
(rel. to 1980-1999 clim.; multi-model ensemble mean )
2011-2030
2046-2065
2080-2099
B1
A1B
A2
Figure 7.21 (plus 2011-2030 panel)
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Meehl et al., 2007, IPCC Fourth Assessment Report
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
A2: 2011-2030
Meehl et al., 2007, IPCC Fourth Assessment Report
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Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
A1B: 2011-2030
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
B1: 2011-2030
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
A2: 2046-2065
3
2.5
2
2.5
2
1.5
4
3.5
3
5
3.5
2
2
2
2
2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
A1B: 2046-2065
4
3.5
5
3
2.5
2
2.5
1.5
2.5
2
2.5
2
4.5 4
3.5
3
2
2
2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
B1: 2046-2065
3
4
2.5
2
3.5
3
2.5
2
1.5
1.5
1.5
1.5
1
1.5
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
A2: 2080-2099
7
6
7
5
4.5
4
5
4
4.5
4
3.5
3
2
4.5
4
3.5
6
5
3
4
4
3
4
3
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
A1B: 2080-2099
7
6
6
5
5
4
4
3
4
3.5
4
3
3
3
4
3
3.5
3
3
2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
B1: 2080-2099
4
5
3.5
3
2
3.5
3
2.5
2
2.5
4
2.5
2
2
2
2
2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.8 The road ahead
Range for
each
category
shown as
error bar
in 2050
Figure 7.22
Mitigation scenarios shown as center of a range of emissions for six
categories (CO2 emissions shown as a function of time; other
greenhouse gases follow a similar paths).
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Values condensed from Barker et al., 2007
Mitigation scenarios estimating greenhouse gas emissions as a
function of time (emissions pathways) that would lead to stabilization
of greenhouse gases, i.e., eventually bring emissions to low levels so
concentration stop increasing
(Climate change mitigation: actions aimed at limiting the size of the
climate change; Adaptation, actions that attempt to minimize the
impact of the climate change)
Values condensed from Barker et al., 2007
Categories IV-VI emissions continue to increase over the first decades
~ recent trends, modest societal action
Recall for long-lived gas,
• Constant emissions  ongoing increase of concentration;
• Increasing emissions concentration increases at ever faster rate;
• Decreasing emissions  concentration increases but less quickly
• Stabilization occurs for very low emissions.
• If emissions are not brought down quickly enough, CO2 overshoots
stabilization target negative emissions are required, i.e. methods
for actively removing CO2 (categories I-II). Alternative: bring down
emissions sooner.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
• Sidebar: rough estimate of stabilization global
average surface temperature increase (relative to
preindustrial) based on an approximate bestestimate 2xCO2 climate sensitivity of 3C
• (rough estimate of uncertainty: multiply the
temperature axis by a factor of 0.7 to 1.4 (for range
of equilibrium climate sensitivity in Table 6.2)
• Temperature evolution as a function of time
~similar to B1 in figure 7.20
but changing year and amplitude of stabilization &
add 0.6 C for change relative to pre-industrial.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Values condensed from Barker et al., 2007.
• greenhouse radiative forcing levels at stabilization
2.5-3.0 W m−2 for category I through 6.0-7.5 W m−2
for category VI (~A2 in 2100; B1 ~III-IV)
Simplified fromFig. 7.20, after Bindoff et al., 2007
• Sidebar: greenhouse gas stabilization
concentration, given as concentration of CO2 that
would give equivalent radiative forcing;
• Climate impacts tend to roughly scale with the global average
temperature, qualitative sense of how the costs of adaptation would
change among emissions pathways, but:
• Far from providing quantitative dollar values for an economic costbenefit analysis, and
• Impacts on ecosystems are difficult to quantify scientifically &
economically,
• Costs are likely to be unevenly distributed among regions and economic
groups.
• One rule of thumb, aim to limit warming to 2 C above preindustrial
temperatures (long discussed but supported, e.g., by Group of Eight
industrialized economies (G8) in 2009*)
• Emissions in 2050 provide a good indicator of whether such a goal is
likely to be met.
*L’Aquila, Italy summit, July 2009; European union has in principle been in favor of the 2C objective for several years
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Values condensed from Barker et al. (2007).
By 2050, global CO2 emissions relative to their values at the start of
the century:
- category I: 50% to 85% decrease,
- category II: 30% to 60% decrease;
-category III, 30% decrease to 5% increase;…
-category VI: 90% to 140% increase.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Examples of legislative emission targets:
• California legislation setting targets for reducing greenhouse
emissions to 1990 levels by 2020 (AB32), and to 80% below 1990 levels
by 2050 (CA Executive Order S-3-05),
• Waxman-Markey bill (passed US House of Representatives in June
2009 but with currently unclear fate in the U.S. Senate): reduction
targets for carbon emissions from large sources 17% below 2005
levels by 2020 and 83% below 2005 levels by 2050
• European Parliament 80% reduction by 2050 target in a nonbinding
resolution, February 2009
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Technologies that are expected to come into play in reducing emissions
(existing in some form, but requiring large scale up)
• Efficiency and conservation.
• Wind power (with geographical and energy storage constraints).
• Solar power (including solar thermal collectors and photovoltaic cells).
• Nuclear power (noting the environmental trade-off of nuclear waste
storage instead of CO2 emission).
• Hydroelectric power.
• Biofuels (including existing production of ethanol from sugarcane or
other crops or crop byproducts, and development of nonfood sources
such as perennial grasses or algae).
• Fossil fuel (primarily coal) power generation with carbon capture and
storage (in which CO2 is captured from the powerplant emissions stream,
compressed, and then injected back into geological formations, typically
coordinated with fossil fuel extraction).
• Ecosystem/agricultural management (including reduction of
deforestation and agricultural tillage, in which straw and other
agricultural byproducts are tilled into the soil to store carbon).
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
One way of visualizing contributions to the change in energy supply:
a “wedge” in which a low emission technology grows from small
contribution today to displace 1 PgC/yr of fossil fuel emissions 50 years
from now (Pacala & Socolow, 2004)
(25 PgC of emissions prevented overall)
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Examples of scale-up required to give this (Pacala & Socolow, 2004)
(each to displace 1 PgC/yr of fossil fuel emissions 50 years from now )
1. Doubling the fuel efficiency of cars (assuming there are 4 times as many
cars in 50 years, each traveling similar mileage to the average today).
2. Cutting in half the average mileage each car travels (e.g., replacing
trips by low-emission transportation, telecommuting, etc.)
3. Energy-efficient buildings (reduce emissions associated with heating,
cooling, lighting, refrigeration by 25% including in developing world).
4. Increase efficiency of coal-based electricity generation from 32% to
60% (assuming twice current capacity in 50 years, and that efficiency
increases to 40% would occur without carbon budget considerations)
5. Wind power substituted for coal power, adding 2 million 1-megawattpeak windmills (50 times current capacity).
6. Photovoltaic power increased to about 700 times the current capacity to
substitute for coal power (requires about 2-3 m2 of solar array per
person).
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Examples of scale-up (cont.):
7. Nuclear power substituted for 700 GW of coal power (a doubling of
current capacity).
8. Biomass fuel production scaled to roughly 100 times the current
Brazil or US ethanol production (requires about 1/6 of world
cropland).
9. Carbon capture and storage implemented for 800 GW of coal plants.
In terms of storage, this requires that CO2 injection increase to a
factor of 100 times today’s injection rates or the equivalent of 3500
times the injection by Norway’s Sleipner project in the North Sea.
10. Decrease tropical deforestation completely plus double the current
rate of new tree plantations.
11. Conservation tillage applied to all cropland (10 times current).
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Roughly how many of these contributions are required to move from
category VI emissions path to a lower emissions path?
Category VI emissions increase by between 7 and 8 PgC/year over 1st
50 years  7-8 of the above required just to keep emissions rates close
to present values (in face of increasingly energy intensive economies
and population growth)
Category I requires emissions to decrease ~ 4 to 5 PgC/year in 50 years
(~12 PgC/year relative to category VI) roughly 12 of the above items if
started in 2000 (11 shown)
Which approach?
All of the above plus more.
The 2C warming target is
already challenging
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
When you start also matters
11 contributions, each
growing to displace 1PgC in
50 years, starting in 2010
versus in 2000
Starting in 2000
-harder to get onto lower
emission path
+ CO2 concentrations have
the extra CO2 added over
the 10 years delay
Emissions path like category
III or IV (warming akin to
B1 scenario) also requires
substantial societal action,
compared to ongoing
emissions growth VI
(warming akin to A2).
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Starting in 2010
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
Repeat from Fig. 10.8
A2: 2080-2099
7
6
7
5
4.5
4
5
4
4.5
4
3.5
3
2
4.5
4
3.5
6
5
3
4
4
3
4
3
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
Repeat from Fig. 10.8
B1: 2080-2099
4
5
3.5
3
2
3.5
3
2.5
2
2.5
4
2.5
2
2
2
2
2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP