1. 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
echm5Tasa_djf2080s_ortho30.gif
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
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2. 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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3. 7.1.b Forcing by sulfate aerosols
Spatial patterns of estimates of radiative forcing due to effects of human activity
Well mixed greenhouse gases
Direct sulfate
Fig7-1_GHsulfgases_c.eps
Shine & Forster, Global and Planetary Change, 1999
Figure 7.1
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4. 7.1.c Commonly used scenarios
Radiative forcing as a function of time for various climate forcing scenarios
Top of the atmosphere radiative imbalance Þ warming due to the net effects of GHG and other forcings
4AR_scenario_forcings_c.jpg
from the Special Report on 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
Figure 7.2
Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report
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5. 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 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.
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6. Radiative forcing and global average surface temperature response
7.2 Global-average response to greenhouse warming scenarios
Radiative forcing and global average surface temperature response
Change in radiative forcing (Wm-2)
From glb.rad.frc_Tchng.eps (glb.rad.frc_Tchng_a.jpg, glb.rad.frc_Tchng_b.jpg)
Change in temperature (K)
Figure 7.3
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Mitchell and Johns, 1997, J. Climate

7. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
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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8. Fig. 7.4 Global average warming simulations in 11 climate models
Global avg. sfc. air temp. change
(ann. means rel. to base period)
Est. observed greenhouse gas + aerosol forcing, followed by
SRES A2 scenario (inset) in 21st century
(includes both GHG and aerosol forcing)
glbtmpanomtseries_ar4_c.eps
Data from the Program for Model Diagnosis and Intercomparison (PCMDI) archive.
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9. 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 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]
HdCMGSa1_ clr.jpg
Figure 7.5
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10. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Supplementary Figure
Response to the SRES A2 scenario GHG and sulfate aerosol forcing in surface air temperature relative to the average during from the National Center for Atmospheric Research Community Climate Simulation Model (NCAR_CCSM3)
CCSM3_4AR_tasa_ann c.jpg
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11. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
January
January and July surface temperature from HadCM3 averaged (SRES A2 scenario)
July
HadCM3_4AR_tasa_jan2050s_c.eps, HadCM3_4AR_tasa_jul2050s_c.eps (HadCM3_tasa_jan2050s_c.jpg, HadCM3_tasa_jul2050s_c.jpg)
Figure 7.6
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12. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Supplementary Figure
January
January and July surface temperature from NCAR_CCSM3 averaged (SRES A2 scenario)
July
From ccsm3_A2_tasa_janjul2050s.gif (ccsm3_A2_tasa_jan2050s.jpg, ccsm3_A2_tasa_jul2050s.jpg)
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13. 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
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14. Comparing projections of different climate models
GFDL-
CM2.0
30yr. avg annual surface air temperature response for 3 climate models centered on 2055 relative to the average during
NCAR-
CCSM3
3modccsm3_4AR_tasa_ann50s_c.jpg
MPI-
ECHAM5
Figure 7.7
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15. 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
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16. Comparing projections of different climate models
GFDL-
CM2.0
Precipitation from 3 models for Jun.-Aug average minus avg (SRES A2 scenario)
NCAR-
CCSM3
3modccsm_4AR_pr_jja80s_c.eps
MPI-
ECHAM5
Figure 7.8
(mm/day)
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17. Comparing projections of different climate models
GFDL-
CM2.0
Supplementary Figures
Precipitation from 3 models for Jun.-Aug average minus avg (SRES A2 scenario)
HadCM3
3mod_sup_4AR_pr_jja80s_c.jpg
MPI-
ECHAM5
(mm/day)
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18. Comparing projections of different climate models
GFDL-
CM2.0
Supplementary Figures
30yr. avg annual surface air temperature response for 3 climate models centered on 2055 relative to the average during
HadCM3
3modccsm3_4AR_tasa_ann50s_c.jpg with HadCM3_4AR_tasa_ann2050.c.jpg overlay
MPI-
ECHAM5
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19. Comparing projections of different climate models
GFDL-
CM2.0
Supplementary Figures
Precipitation from 3 models for Dec.-Feb average minus avg (SRES A2 scenario)
NCAR-
CCSM3
3modccsm3_4AR_pr_djf80s_c.eps
MPI-
ECHAM5
(mm/day)
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20. Precipitation from HadCM3 for Dec.-Feb. 2070-2099 avg. (SRES A2)
Supplementary figure
HadCM3_4ARpreca_djf2080s.eps
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21. Precipitation from HadCM3 for Jun.-Aug. 2070-2099 avg. (SRES A2)
Supplementary Figure
HadCM3_4ARpreca_jja2080s.eps
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22. North American West Coast Precipitation change under global warming
SRES A2 scenario rel. to
Dec.-Feb. (DJF) Prec. Anom.
Multi-model ensemble average
Compare to individual models
ens_preca_CA_7900djf_2080s.eps
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23. Multi-model ensemble avg.
December – February
January and July precipitation change for 10 model ensemble average for
minus avg (SRES A2 scenario)
June – August
Figure 7.9
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24. 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)
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25. 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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26. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.4 Ice, sea level, extreme events
7.4.a Sea ice and snow
Simulated ice fraction change ( ) minus ( ) as a percent of the base climatol. ice fraction
Sep. - Nov.
Dec. - Feb.
fig7-10_ech5_ice_sondjf2080.jpg
Echam5
SRESA2
Figure 7.10
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27. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Simulated change in ice fraction (% coverage) Sep.-Nov. ( ) minus ( )
HadCM3
SRESA2
From HadCM3_ice_sondjf2050s_c.eps
Supplementary Figure
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28. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Simulated snow fraction change ( ) minus ( ) as a percent of the base climatological snow amount (where base exceeds 1Kg/m3)
Echam5 SRESA2
Sep. - Nov.
Dec. - Feb.
From ech5_pred_snwice_sondjf2080.eps
Figure 7.11
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29. 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. ( to ; 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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30. 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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31. 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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32. 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
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33. 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
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34. 7.4.d Extreme events
If standard deviation of daily temperatures remains similar as mean temperature rises Þ more frequent occurrence of events currently considered extreme
e.g., heat waves
Figure 7.13
Few events above 40C (104F) (shaded area)
temp.probability.dist.eps
Much more frequent (shaded area many times larger)
Mean change
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35. 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
temp.probability.dist.eps
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36. 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 relative to 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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37. 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 relative to : for A m, B ; even if greenhouse gases are stabilized deep ocean warming creates ongoing sea level rise for centuries.
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38. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Schematic summary of best-estimate climate changes due to greenhouse warming
change_summary.eps
Adapted from IPCC, Third Assessment Report, 2001.
Figure 7.14
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39. Figure 7.15 (will be expanded with supplementary figs. below)
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
CRUtasa_NShemUSEurGerDC.jpg Orig. files in AS102/as102_obsTa_timeser_lab
Figure 7.15 (will be expanded with supplementary figs. below)
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40. Surface air temperature CRU
Surface air temperature CRU* 5 x 5 degree grid (with selected averaging regions)
CRU5x5glbgrid2.eps
*CRU= Climate Research Unit, U. of East Anglia

41. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual and Decadal CRU 2m Tanom Area Avg. (relative to clim.)
Global
N. Hem.
obs_Ann10yrtasa_Glb.NShem.jpg
From CRU 2m Ta data set /d/IPCC_AR4/data/OBS/rutem2v_ nc
S. Hem.
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42. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual and Decadal CRU 2m Tanom Area Avg. (relative to clim.)
Note axis
scale
chg.
N. America
United States
obs_Ann10yrtasa_USEurNA.jpg
Europe
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43. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual and Decadal CRU 2m Tanom (relative to clim., 5x5 degree avgs.)
Note axis
scale
chg.
Germany
obs_Ann10yrtasa6190_GerBeijDC.jpg
~Beijing
~Washington D.C
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44. January CRU 2m Tanom (relative to 1961-1990 clim.)
Note axis
scale
chg.
Germany
obs_janrtasa6190_GerBeijDC.jpg
~Beijing
~Washington D.C
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45. 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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46. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
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
AR4WG1_SPM-4.eps Hegerl et al chap 9, also SPM IPCC 2007
Figure 7.16
After Hegerl et al., 2007, in IPCC Fourth Assessment Report
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47. 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)
From glac.seaice.heatcont.sealev.eps (WG1AR4_glac.seaice.jpg) From AR4WG1_chap4 pg (b,e) Lemke et al 2007
Figure
7.17
After Lemke et al., 2007, in IPCC Fourth Assessment Report
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48. Observed global annual ocean heat content for 0 - 700m layer
Ocean heat content anomaly rel . to (black curve)
i.e. global upper ocean heat storage in response to accumulated heat flux imbalance (surface + exchange with lower layers)
Figure 7.18
[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 interval
Variations: natural variability and sampling error
From glac.seaice.heatcont.sealev.eps (WG1AR4_heatcont.jpg). Pg. 390 Fig 5.1 Bindoff et al 2007
Removed curves for clarity:
Red curve update rel. to (Ishii et al. 2006)
Green curve update rel. to Willis et al. (2004) (0 to 750m)
After Bindoff et al., 2007, in IPCC Fourth Assessment Report; data from Levitus et al., 2005
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49. 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
Figure 7.19
Red reconstructed sea level fields rel. to
[tide gauges avgd using spatial patterns from recent satellite data; Church & White, 2006]
Blue curve coastal tide gauge measurements [rel. to ; alt method; Holgate & Woodworth, 2004]
Black curve satellite altimetry rel. to
(After Bindoff et al 2007)
Error bars denote 90% confidence interval
From glac.seaice.heatcont.sealev.eps (WG1AR4_sealev.jpg). From AR4WG1_chap5 pg 510 Fig 5.13 Bindoff et al 2007
Refs: reconstructed sea level fields rel. to (updated from Church & White, 2006);
coastal time gauge measurements rel. to (from Holgate & Woodworth, 2004);
satellite altimetry rel. to (Leuliette et al., 2004)
~ 13 to 23 mm/decade*
[*> sum of est. trends from land ice and thermal expansion]
After Bindoff et al., 2007, in IPCC Fourth Assessment Report, 2007
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50. Recall: emissions scenarios
7.7 Emissions paths and their impacts
Radiative forcing as a function of time for various climate forcing scenarios
Recall: emissions scenarios
4AR_scenario_forcings_c.jpg
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
Figure 7.2
Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report
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51. 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
4AR_scenario_forcings_fade.jpg
Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report
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52. 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 data at
1990 fossil fuel emissions 6GtC (Gigatonnes of Carbon)
B1 levels TBA
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53. SRES Multi-model mean surface warming projections
A2, A1B, B1
Multi-model mean surface warming projections as a continuation of 20th-century simulation
Shading +/- 1 standard deviation from individual model ann. avgs.
Constant composition (2000 values) simulation, forcing kept at year 2000 level (gives global warming commitment)
+ Constant composition commitment simulations from A1B and B values
AR4WG1_sfcwmproj_SRESforce.eps fig TS-32, pg 79 of WG1 Technical Summary Solomon et al. 2007
Figure 7.20
After Solomon et al., 2007, IPCC Fourth Assessment Report
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54. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
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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55. Precip change ~ proportional to large scale T change
Amplitude of negative precip. change (rel to ) avg over tropics
versus tropical average surface air temperature
Supplementary Figure
Neelin, Munnich, Su, Meyerson and Holloway , 2006, PNAS ccsm3_prectas_jja_scat.jpg
(projection on late-21st century pattern)
Neelin et al., 2006, Proc. Nat. Acad. Soc.
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56. 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;
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;
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.
Possibly insert figure from Parry et al. (2007) estimates of ecosystem impacts
ranked by degrees above preindustrial of global averaged surface warming
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57. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Impact of emissions pathway: A2, A1B and B1 scenarios’ effects on annual avg. surface air temperature change (rel. to clim.; multi-model ensemble mean ) B1
AR4WG1_10.8_glb.proj.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007
A1B A2
Figure 7.21 (plus panel)
Meehl et al., 2007, IPCC Fourth Assessment Report
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58. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
A2:
AR4WG1_10.8_glb.projA2a.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

59. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
A1B:
AR4WG1_10.8_glb.projA1Ba.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

60. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
B1:
AR4WG1_10.8_glb.projB1a.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

61. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
A2: 5 4
3.5
3.5 3 3
2.5 2
2.5 2 2
1.5 2 2
AR4WG1_10.8_glb.projA2b.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007 2 2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

62. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
A1B: 4 5
4.5 4
3.5
3.5 3 3
2.5 2
2.5
2.5
1.5 2 2
2.5 2
AR4WG1_10.8_glb.projA1Bb.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007 2 2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

63. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
B1: 3 4
3.5 3
2.5
2.5 2 2
1.5
1.5
1.5
AR4WG1_10.8_glb.projB1b.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007
1.5 1
1.5
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

64. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
A2: 7 7 6 6
4.5 5 5 4
3.5 5
4.5 4 4
4.5 4 3
3.5 3
AR4WG1_10.8_glb.projA2c.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007 4 2 4 4 3 3
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

65. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
A1B: 6 7 6 5 5 4 4 3 4
3.5 4 3 3 3
AR4WG1_10.8_glb.projA1Bc.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007 4 3
3.5 3 3 2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

66. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
B1: 4 5 4
3.5
3.5 3 3
2.5 2
2.5 2
2.5 2 2
AR4WG1_10.8_glb.projB1c.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007 2 2 2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

67. Range for each category shown as error bar in 2050
7.8 The road ahead
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)
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).
Range for each category shown as error bar in 2050
Figure 7.22
Values condensed from Barker et al., 2007
Neelin, Climate Change and Climate Modeling, Cambridge UP

68. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
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 Þ ongoing increase of concentration;
Increasing emissions Þ concentration increases at ever faster rate;
Decreasing emissions Þ concentration increases but less quickly
Stabilization occurs for very low emissions.
If emissions are not brought down quickly enough, CO2 overshoots stabilization target Þ negative emissions are required, i.e. methods for actively removing CO2 (categories I-II). Alternative: bring down emissions sooner.
Neelin, Climate Change and Climate Modeling, Cambridge UP

69. Sidebar: greenhouse gas stabilization concentration, given as concentration of CO2 that would give equivalent radiative forcing;
greenhouse radiative forcing levels at stabilization W m−2 for category I through 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.
Values condensed from Barker et al., 2007.
Simplified fromFig. 7.20, after Bindoff et al., 2007
Neelin, Climate Change and Climate Modeling, Cambridge UP

70. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Climate impacts tend to roughly scale with the global average temperature, Þ 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 years
Neelin, Climate Change and Climate Modeling, Cambridge UP

71. 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, Climate Change and Climate Modeling, Cambridge UP

72. 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 but with currently unclear fate in the U.S. Senate): reduction targets for carbon emissions from large sources 17% below levels by 2020 and 83% below 2005 levels by 2050
European Parliament 80% reduction by 2050 target in a nonbinding resolution, February 2009
Neelin, Climate Change and Climate Modeling, Cambridge UP

73. 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, Climate Change and Climate Modeling, Cambridge UP

74. 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, Climate Change and Climate Modeling, Cambridge UP

75. 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 )
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).
Cutting in half the average mileage each car travels (e.g., replacing trips by low-emission transportation, telecommuting, etc.)
Energy-efficient buildings (reduce emissions associated with heating, cooling, lighting, refrigeration by 25% including in developing world).
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)
Wind power substituted for coal power, adding 2 million 1-megawatt- peak windmills (50 times current capacity).
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, Climate Change and Climate Modeling, Cambridge UP

76. Examples of scale-up (cont.):
Nuclear power substituted for 700 GW of coal power (a doubling of current capacity).
Biomass fuel production scaled to roughly 100 times the current Brazil or US ethanol production (requires about 1/6 of world cropland).
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 times the injection by Norway’s Sleipner project in the North Sea.
Decrease tropical deforestation completely plus double the current rate of new tree plantations.
Conservation tillage applied to all cropland (10 times current).
Neelin, Climate Change and Climate Modeling, Cambridge UP

77. 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 Þ 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, Climate Change and Climate Modeling, Cambridge UP

78. When you start also matters
11 contributions, each growing to displace 1PgC in 50 years, starting in 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
B1, A2 reminder next slides--- which world? We’ll see.
Starting in 2010
Neelin, Climate Change and Climate Modeling, Cambridge UP

79. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
A2:
Repeat from Fig. 10.8 7 7 6 6
4.5 5 5 4
3.5 5
4.5 4 4
4.5 4 3
3.5 3
AR4WG1_10.8_glb.projA2c.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007 4 2 4 4 3 3
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP

80. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change (relative to clim.)
B1:
Repeat from Fig. 10.8 4 5 4
3.5
3.5 3 3
2.5 2
2.5 2
2.5 2 2
AR4WG1_10.8_glb.projB1c.jpg. From AR4WG1_chap10 Pg. 766 Fig Meehl et al 2007 2 2 2
Meehl et al., 2007, IPCC Fourth Assessment Report
Neelin, Climate Change and Climate Modeling, Cambridge UP