chapter 7 climate model scenarios for global warming 7.1 greenhouse gases, aerosols and other...

Chapter 7Climate Model Scenarios for Global Warming

7.17.1 Greenhouse gases, aerosols and other climate forcingsGreenhouse gases, aerosols and other climate forcings

7.27.2 Global-average response to greenhouse warming scenariosGlobal-average response to greenhouse warming scenarios

7.37.3 Spatial patterns of the response to time-dependent Spatial patterns of the response to time-dependent warming scenarioswarming scenarios

7.47.4 Ice, sea level, extreme eventsIce, sea level, extreme events

7.57.5 Summary: the best-estimate prognosisSummary: the best-estimate prognosis

7.67.6 Climate change observed to dateClimate change observed to date

7.77.7 Emissions paths and their impactsEmissions paths and their impacts

Neelin, 2011. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge UPUP

7.17.1 Greenhouse gases, aerosols and other climate forcingsGreenhouse gases, aerosols and other climate forcings

7.1.a Scenarios, forcings and feedbacks7.1.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]


Spatial patterns of estimates of radiative forcingdue to effects of human activity

7.1.b Forcing by sulfate aerosols7.1.b Forcing by sulfate aerosols

Well mixed greenhouse gases Direct sulfate


Shin

e &

For

ster

, Glo

bal a

nd P

lane

tary

Cha

nge,

199

9

Figure 7.1

Radiative forcing as a function of time for various climate forcing scenarios

7.1.c Commonly used scenarios7.1.c Commonly used scenarios

SRES:• A1FI (fossil intensive), • A1T (green technology), • A1B (balance of these), • A2, B2 (regional economics) • B1 “greenest”• IS92a scenario used in manystudies before 2005

Top of the atmosphere radiative imbalance warming due to the net

effects of GHG and other forcings

from the Special Report on Emissions Scenarios

Figure 7.2


Adapted from Meehl et al., 2007 in Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report

SRES emissions scenarios, cont’d

A1 scenario family: assumes low population growth, rapid economic growth, reduction in regional income differences

A1FI : Fossil fuel IntensiveA1B: energy mix, incl. non-fossil fuel

A2: uneven regional economic growth, high income toward non-fossil, 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.


Radiative forcing and global average surface temperature response

Figure 7.3

Change in radiative forcing (Wm-2)

Change in temperature (K)

7.27.2 Global-average response to greenhouse warming Global-average response to greenhouse warming scenariosscenarios

Mitchell and Johns, 1997, J. ClimateNeelin, 2011. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge UPUP

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


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.


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] Figure 7.5

2010-2039

2040-2069

2070-2099

7.37.3 Spatial patterns of the Spatial patterns of the response to time-dependent response to time-dependent warming scenarioswarming 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

National Center for Atmospheric Research Community Climate

Simulation Model (NCAR_CCSM3)

Supplementary Figure

2010-2039

2040-2069

2070-2099


January and July surface temperature

from HadCM3 averaged 2040-2069 (SRES A2 scenario)

Figure 7.6

January

July


January and July surface temperature

from NCAR_CCSM3 averaged 2040-2069 (SRES A2 scenario)

Supplementary Figure January

July


Poleward amplification of warmingPoleward 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


30yr. avg annual surface air temperature response

for 3 climate models centered on 2055 relative

to the average during 1961-1990

Figure 7.7

GFDL-CM2.0

NCAR-CCSM3

MPI-ECHAM5


Comparing projections of Comparing projections of different climate modelsdifferent climate models

Comparing projections of different climate modelsComparing 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


NCAR-CCSM3

(mm/day)

Precipitation from 3 models for Jun.-Aug.

2070-2099 average minus 1961-90 avg (SRES A2 scenario)

Figure 7.8


MPI-ECHAM5

GFDL-CM2.0


Precipitation from 3 models for Dec.-Feb.

2070-2099 average minus 1961-90 avg (SRES A2 scenario)

NCAR-CCSM3

(mm/day)

MPI-ECHAM5

GFDL-CM2.0



Supplementary Figures

Precipitation from HadCM3 for Dec.-Feb. 2070-2099 avg. (SRES A2)

Supplementary figure



Precipitation from HadCM3 for Jun.-Aug. 2070-2099 avg. (SRES A2)


SRES A2 scenario 2070-2099 rel. to 1979-2000 Dec.-Feb. (DJF) Prec. Anom.

Multi-model ensemble average

North American West Coast Precipitation change under global warming

Compare to

individual models


Figure 7.9

Multi-model ensemble avg.

January and July precipitation change

for 10 model ensemble average

for 2070-2099 minus 1961-90 avg (SRES A2 scenario)

December – February

June – August


7.3.c Summary of spatial patterns of the response7.3.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)


7.3.c Summary of spatial patterns of the response (cont.)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).


Simulated ice fraction change (2070-99) minus (1961-90)as a percent of the base climatol. ice fraction

Figure 7.10

7.47.4 Ice, sea level, extreme eventsIce, sea level, extreme events7.4.a Sea ice and snow7.4.a Sea ice and snow

Echam5SRESA2

Sep. - Nov. Dec. - Feb.


Simulated change in ice fraction (% coverage) Sep.-Nov. (2040-69) minus (1961-90)

HadCM3SRESA2



Figure 7.11

Echam5 SRESA2

Sep. - Nov. Dec. - Feb.

Simulated snow fraction change (2070-99) minus (1961-90) as a percentof the base climatological snow amount (where base exceeds 1Kg/m3)


7.4.b,c Land ice & Sea level rise7.4.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


Breakup of the Larsen B Ice Shelf in Antarctica

Jan. 31, 2002

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.


MODIS images from NASA's Terra satellite, National Snow and Ice Data Center


Feb. 17, 2002

Minor retreat takes place




Feb. 23, 2002

Retreat continues (800 km2)




Mar. 5, 2002

Main collapse(~2600 km2), leaving thousands of icebergs

Figure 7.12



7.4.d Extreme events7.4.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

Mean change

Few events above 40C (104F)

(shaded area)

Much more frequent (shaded area many

times larger)


7.4.d Extreme events (cont.)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


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.


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.


Schematic summary of best-estimate

climate changes due to greenhouse

warming

Figure 7.14 Ada

pted

fro

m I

PC

C, T

hird

Ass

essm

ent R

epor

t, 20

01.


Figure 7.15 (will be expanded with supplementary figs. below)

• 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

7.57.5 Climate change observedClimate change observed to dateto date


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.


Annual and Decadal CRU 2m Tanom Area Avg.

(relative to 1961-1990 clim.)N. America

United States

Europe

Note axisscalechg.


Annual and Decadal CRU 2m Tanom

(relative to 1961-1990 clim., 5x5 degree avgs.)

Note axisscalechg.

Germany

~Beijing

~Washington D.C


January CRU 2m Tanom


Germany

~Beijing

~Washington D.C

Note axisscalechg.


7.6.b. Is the observed warming trend consistent with natural 7.6.b. Is the observed warming trend consistent with natural variability or anthropogenic forcing?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]


Observed 20th C. temperature for various averaging regions with climate model simulated range: natural only vs. natural + anthropogenic forcings

Figure 7.16

Observed warming

exceeds range that can occur

by natural variability in

models

After Hegerl et al., 2007, in IPCC Fourth Assessment Report


7.6.c. Sea ice, land ice, ocean heat storage and sea level rise7.6.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)

After Lemke et al., 2007, in IPCC Fourth Assessment Report

Figure7.17


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]

Shaded area = 90% confidence intervalVariations: natural variability and sampling error

Figure 7.18

After Bindoff et al., 2007, in IPCC Fourth Assessment Report; data from Levitus et al., 2005


Observed annual average anomalies of global mean sea level (mm)

Red reconstructed sea level fields rel. to 1961-90

[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

1961 to 2003 trend in global mean sea level rise est. ~ 13 to 23 mm/decade

Figure 7.19

After Bindoff et al., 2007, in IPCC Fourth Assessment Report, 2007



SRES:• A1FI (fossil intensive), • A1T (green technology), • A1B (balance of these), • A2, B2 (regional economics) • B1 “greenest”• IS92a scenario used in manystudies before 2005

Figure 7.2

Recall: emissions scenariosRecall: emissions scenarios

7.77.7 Emissions paths and their impactsEmissions paths and their impacts


Adapted from Meehl et al., 2007 in Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report


Recall: emissions scenariosRecall: 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

Focus on A2, A1B, B1

Adapted from Meehl et al., 2007 in Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment ReportNeelin, 2011. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge

UPUP

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 non-fossil, population 15 billion in 2100

B1: like A1 but resource-efficient technology. Emphasis on global economic, social, and environmental sustainability, equity.


SRES Multi-model mean surface warming projections

A2, A1B, B1Multi-model mean surface warming projections as a

continuation of 20th-century simulation

Shading +/- 1 standard deviation from individual

model ann. avgs.

Figure 7.20

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

After Solomon et al., 2007, IPCC Fourth Assessment Report


•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)


Precip change ~ proportional to large scale T changePrecip 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



Neelin et al., 2006, Neelin et al., 2006, Proc. Nat. Acad. Soc.Proc. Nat. Acad. Soc.

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 15-40% 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.


Impact of emissions pathway: A2, A1B and B1 scenarios’ effects onannual 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) Meehl et al., 2007, IPCC Fourth Assessment Report


Annual multi-model mean surface air temperature change


A2: 2011-2030

Meehl et al., 2007, IPCC Fourth Assessment Report




A1B: 2011-2030





B1: 2011-2030





A2: 2046-2065

1.5

2

2.5

2

22.5

2

2

2

3

3.53

3.5

2

45





A1B: 2046-2065

1.52

2.5

3

3.54

22

2.5

22.5

33.544.5

2

2

2.5

5





B1: 2046-2065

1.522.5

3

1.5

3

1.5

1.5

1.5

2

2.5

3.54

1





A2: 2080-2099

2

3

3.54

3

44.5

5

67

3

4.5 5

67

43

4

3.54

4.54

5

4





A1B: 2080-2099

2

3

4

3.5

3

4

3

5

6

3

3

43

4

4

567

3

3.5





B1: 2080-2099

2

3

4

2.5

2

3.5 3.53

45

2

2.52

2.5

2

2

2



Range for each

category shown as error bar in 2050

7.87.8 The road aheadThe road aheadMitigation 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)

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).

Val

ues

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7Figure 7.22


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.

Val

ues

cond

ense

d fr

om B

arke

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al.,

200

7


• Sidebar: greenhouse gas stabilization concentration, given as concentration of CO2 that would give equivalent radiative forcing;

• 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)

• Sidebar: rough estimate of stabilization global average surface temperature increase (relative to preindustrial) based on an approximate best-estimate 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.

Val

ues

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., 20

07.

Sim

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. 7.2

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2007


• 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 cost-benefit 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 yearsNeelin, 2011. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge

UPUP

Val

ues

con

den

sed

fro

m B

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. (20

07).

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.


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


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).


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)


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-megawatt-peak 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).


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).


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


When you start also matters

11 contributions, each growing to displace 1PgC in 50 years, starting in 2010 versus 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).

Starting in 2000

Starting in 2010




A2: 2080-2099

2

3

3.54

3

44.5

5

67

3

4.5 5

67

43

4

3.54

4.54

5

4



Repeat from Fig. 10.8



B1: 2080-2099

2

3

4

2.5

2

3.5 3.53

45

2

2.52

2.5

2

2

2



Repeat from Fig. 10.8

chapter 7 climate model scenarios for global warming 7.1 greenhouse gases, aerosols and other...

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