1. Ch. 10 Global Climate Projections
Li Jian
Erik Swenson
Youkyoung Jang

2. Uncertainty in Global Climate Change Projections

3. Globally averaged Tsfc (air) after CO2 doubling
Equilibrium Climate Sensitivity
Response after equilibrium is reached
Indication of model feedbacks
Transient Climate Response (TCR)
1% yr-1 increase for 100 yrs
Result at time of doubling
TCR < Equilibrium
Ocean heat uptake causes lag in warming
Red - AOGCMs
Blue - EMICs
Green - perturbed physics
TCR more sensitive for low equilibrium climate sensitivities
Fig a

4. PDFs from Constraints likely range: TAR 1.50C to 4.50C
PDfs more
robust
30C most likely
Notice PDFs skewed to the left and extend far to the right (upper limit harder to constrain), lower limit of uncertainty more clear
a. Historic transient evolution of sfc. T, upper air T, ocean T, radiative forcing, satellite data, …
c. Perturbed physics
Box 10.2

5. Uncertainty from Internal Variability
Initial value problem
Chaos due to nonlinearity of dynamical system
Considered by taking ensembles within each model

6. Uncertainty with Emissions
Dependent on important socioeconomic drivers, technological development, and political decisions
Not addressed in AR4
6 SRES scenarios span different emission futures
Assume no mitigation, but wide variation
considered equally probable
Further uncertainty quantified after assuming a particular scenario will occur
What if we become concerned enough to make significant changes (negative feedback not addressed)
AR5 should look into this

7. Uncertainty with Physical Processes
Parameterization schemes
Ocean heat uptake / vertical mixing
Aerosol forcing
Clouds / precipitation
Multi-model ensembles attempt to sample all options
Individual studies play around with parameters
Water vapor feedback
Influences CH4 projections
Carbon cycle feedback (tune EMIC to AOGCM)
Model potential overestimation of ocean heat uptake (25% larger than observed)
+0.60 C change in TCR at most
Comparing observed thermal expansion to AR4 simulations indicates limited data coverage, uncertainty in data itself
Weak dependence on ocean mixing
TCR uncertainty: dominant source is cloud feedback, GCMs can’t produce low clouds (large scale subsidence marine boundary layer) => error in SW forcing
GCMs share fundamental inadequacies

8. * * *
Fig

9. * * *
A1B - rapid economic growth, population peaks then declines balanced reliance on energy sources
A2 - self-reliance and preservation of identity, continuous population growth
B1 - same population change as A1, but change towards information and service economy,
introduction of clean and resource efficient technologies
Fig

10. Time Dependence
Projections have more agreement first few decades (models & scenarios)
Over a longer time scale feedbacks become more important  more uncertainty
Fig

11. Emissions Commitment
Atmospheric concentration depends on sources and sinks
CO2 removal processes have multiple time scales (undefined lifetime)
CO2
120 yrs
lifetime
12 yrs
More than half removed in a century
20% remains for many millennia
Rate of emission currently exceeds rate of removal
FAQ 10.3, Fig. 1

12. Projected Changes Multi-model data set 1% increase equilibrium 2 x
2 x , 4 x atmospheric in the 1%
- change of emission in models --> radiative forcing change
--> climate response (temperature, precipitation, atmospheric
circulation)

13. Radiative forcing Change during 2000 ~ 2100
Long wave forcing
Short wave forcing

14. Instantaneous radiative forcing and flux change in vertical
Doubling in atmosphere --> specific humidity increase
Flux

15. Projected Change: Physical Climate System
A2(high), A1B(medium), B1(low) : prescribed concentrations and
resulting forcing relative to the SRES range

16. Projected Change: Physical Climate System
Temperature
Precipitation

17. Patterns of Change in 21st century:
zonal mean (2080~2099 relative to 1980~1999)

18. Patterns of Change : zonal mean of temperature

19. Patterns of Change: annual mean surface warming

20. Patterns of Change:
mean change for 2080~2099 from 1980~1999

21. Patterns of Change
: cloud fraction (important link to humidity and precipitation)
A1B:
2080~2099
Relative to
1980~1999

22. Patterns of Change: Precipitation and surface water
Annual mean, A1B: 2080~2099 relative to 1980~1999

23. Patterns of Change: sea ice extent

24. Changes in Meridional Overturning circulation
In high latitude: temperature and precipitation increase
--> less dense water
--> stable ocean
--> inhibit convective process

25. Changes in Atlantic meridional overturning circulation
1850~1999: 20th century climate in Coupled Model(20C3M)
1999~2100: SRES A1B emission scenario
~2200: forcing held constant at the values of year 2100

26. 3.6 weather and climate extremes
Precipitation extreme
Increased risk of drought (drought areas increase from 1% to 30% in A2)
Increased chance of intense precipitation and flooding
Temperature extreme
Raise probability of extreme warm seasons
Decline the frequency of cold air outbreaks in NH winter
Hurricanes and storms
Coarse resolution models :: more intense precipitation
High resolution models :: increase in peak wind speed and precipitation intensity, might globally decrease frequency
Polarward shift of storm track

27. Figure 10.18
Figure Changes in extremes based on multi-model simulations from nine global coupled climate models, adapted from Tebaldi et al. (2006). (a) Globally averaged changes in precipitation intensity (defined as the annual total precipitation divided by the number of wet days) for a low (SRES B1), middle (SRES A1B) and high (SRES A2) scenario. (b) Changes in spatial patterns of simulated precipitation intensity between two 20-year means (2080–2099 minus 1980–1999) for the A1B scenario. (c) Globally averaged changes in dry days (defined as the annual maximum number of consecutive dry days). (d) Changes in spatial patterns of simulated dry days between two 20-year means (2080–2099 minus 1980–1999) for the A1B scenario. Solid lines in (a) and (c) are the 10-year smoothed multi-model ensemble means; the envelope indicates the ensemble mean standard deviation. Stippling in (b) and (d) denotes areas where at least five of the nine models concur in determining that the change is statistically significant. Extreme indices are calculated only over land following Frich et al. (2002). Each model’s time series was centred on its 1980 to 1999 average and normalised (rescaled) by its standard deviation computed (after de-trending) over the period 1960 to The models were then aggregated into an ensemble average, both at the global and at the grid-box level. Thus, changes are given in units of standard deviations.

28. of standard deviations.
Figure Changes in extremes based on multi-model simulations from nine global coupled climate models, adapted from Tebaldi et al. (2006). (a) Globally averaged changes in the frost day index (defined as the total number of days in a year with absolute minimum temperature below 0°C) for a low (SRES B1), middle (SRES A1B) and high (SRES A2) scenario. (b) Changes in spatial patterns of simulated frost days between two 20-year means (2080–2099 minus 1980–1999) for the A1B scenario. (c) Globally averaged changes in heat waves (defined as the longest period in the year of at least five consecutive days with maximum temperature at least 5°C higher than the climatology of the same calendar day). (d) Changes in spatial patterns of simulated heat waves between two 20-year means (2080–2099 minus 1980–1999) for the A1B scenario. (e) Globally averaged changes in growing season length (defined as the length of the period between the first spell of five consecutive days with mean temperature above 5°C and the last such spell of the year). (f) Changes in spatial patterns of simulated growing season length between two 20-year means (2080–2099 minus 1980–1999) for the A1B scenario. Solid lines in (a), (c) and (e) show the 10-year smoothed multi-model ensemble means; the envelope indicates the ensemble mean standard deviation. Stippling in (b), (d) and (f) denotes areas where at least five of the nine models concur in determining that the change is statistically significant. Extreme indices are calculated only over land. Frost days and growing season are only calculated in the extratropics. Extremes indices are calculated following Frich et al. (2002). Each model’s time series was centred around its 1980 to 1999 average and normalised (rescaled) by its standard deviation computed (after de-trending) over the period 1960 to The models were then aggregated into an ensemble average, both at the global and at the grid-box level. Thus, changes are given in units of standard deviations.

29. 4.1 carbon cycle and vegetation feedback
Coupled climate-carbon cycle model intercomparison project (C4MIP, 11 models)
Results: Climate change will reduce the efficiency of land and ocean to absorb CO2
Positive feedback
CO2>Warm>Decrease absorption>CO2>warm…..
Two extreme models
CO2 concentration ppm ppm
Radiative forcing W/m W/m-2
Temperature oC oC

30. Figure 10.20 SRES A2 emission scenario C4MIP :: 2.4~5.6 oC
1020 ppm
SRES A2
emission scenario
Figure 10.20
836 ppm
730 ppm
C4MIP :: 2.4~5.6 oC
Figure (a) 21st-century atmospheric CO2 concentration as simulated by the 11 C4MIP models for the SRES A2 emission scenario (red) compared with the standard atmospheric CO2 concentration used as a forcing for many IPCC AR4 climate models (black). The standard CO2 concentration values were calculated by the BERN-CC model and are identical to those used in the TAR. For some IPCC-AR4 models, different carbon cycle models were used to convert carbon emissions to atmospheric concentrations. (b) Globally averaged surface temperature change (relative to 2000) simulated by the C4MIP models forced by CO2 emissions (red) compared to global warming simulated by the IPCC AR4 models forced by CO2 concentration (black). The C4MIP global temperature change has been corrected to account for the non-CO2 radiative forcing used by the standard IPCC AR4 climate models.
AR4 :: ~ 4.1 oC

31. Simulation w/o coupled climate carbon cycle
Goal
Simulation w/o coupled climate carbon cycle
Simulation w/ coupled climate carbon cycle
Figure (a) Atmospheric CO2 stabilisation scenarios SP1000 (red), SP750 (blue), SP550 (green) and SP450 (black). (b) Compatible annual emissions calculated by three models, the Hadley simple model (Jones et al., 2006; solid), the UVic EMIC (Matthews, 2005; dashed) and the BERN2.5CC EMIC (Joos et al., 2001; Plattner et al., 2001; triangles) for the three stabilisation scenarios without accounting for the impact of climate on the carbon cycle (see Table 8.3 for details of the latter two models). (c) As for (b) but with the climate impact on the carbon cycle accounted for. (d) The difference between (b) and (c) showing the impact of the climate-carbon cycle feedback on the calculation of compatible emissions.
Reduce more CO2 emission

32. 4.2 Ocean acidification Increasing CO2 lower ocean pH
By 2100, ocean pH is projected to decrease 0.3~0.4 unit. Affect ecosystem. (muti-model)
Deep ocean chemistry structure

33. Figure Multi-model median for projected levels of saturation (%) with respect to aragonite, a meta-stable form of calcium carbonate, over the 21st century from the Ocean Carbon-Cycle Model Intercomparison Project (OCMIP-2) models (adapted from Orr et al., 2005). Calcium carbonate dissolves at levels below 100%. Surface maps (left) and combined Pacific/Atlantic zonal mean sections (right) are given for scenario IS92a as averages over three time periods: 2011 to 2030 (top), 2045 to 2065 (middle) and 2080 to 2099 (bottom). Atmospheric CO2 concentrations for these three periods average 440, 570 and 730 ppm, respectively. Latitude-depth sections start in the North Pacific (at the left border), extend to the Southern Ocean Pacific section and return through the Southern Ocean Atlantic section to the North Atlantic (right border). At 100%, waters are saturated (solid black line - the aragonite saturation horizon); values larger than 100% indicate super-saturation; values lower than 100% indicate undersaturation. The observation-based (Global Ocean Data Analysis Project; GLODAP) 1994 saturation horizon (solid white line) is also shown to illustrate the projected changes in the saturation horizon compared to the present.
Figure Multi-model median for projected levels of saturation (%) with respect to aragonite, a meta-stable form of calcium carbonate, over the 21st century from the Ocean Carbon-Cycle Model Intercomparison Project (OCMIP-2) models (adapted from Orr et al., 2005). Calcium carbonate dissolves at levels below 100%. Surface maps (left) and combined Pacific/Atlantic zonal mean sections (right) are given for scenario IS92a as averages over three time periods: 2011 to 2030 (top), 2045 to 2065 (middle) and 2080 to 2099 (bottom). Atmospheric CO2 concentrations for these three periods average 440, 570 and 730 ppm, respectively. Latitude-depth sections start in the North Pacific (at the left border), extend to the Southern Ocean Pacific section and return through the Southern Ocean Atlantic section to the North Atlantic (right border). At 100%, waters are saturated (solid black line - the aragonite saturation horizon); values larger than 100% indicate super-saturation; values lower than 100% indicate undersaturation. The observation-based (Global Ocean Data Analysis Project; GLODAP) 1994 saturation horizon (solid white line) is also shown to illustrate the projected changes in the saturation horizon compared to the present.

34. Figure Changes in global average surface pH and saturation state with respect to aragonite in the Southern Ocean under various SRES scenarios. Time series of (a) atmospheric CO2 for the six illustrative SRES scenarios, (b) projected global average surface pH and (c) projected average saturation state in the Southern Ocean from the BERN2.5D EMIC (Plattner et al., 2001). The results for the SRES scenarios A1T and A2 are similar to those for the non-SRES scenarios S650 and IS92a, respectively. Modified from Orr et al. (2005).
Figure Changes in global average surface pH and saturation state with respect to aragonite in the Southern Ocean under various SRES scenarios. Time series of (a) atmospheric CO2 for the six illustrative SRES scenarios, (b) projected global average surface pH and (c) projected average saturation state in the Southern Ocean from the BERN2.5D EMIC (Plattner et al., 2001). The results for the SRES scenarios A1T and A2 are similar to those for the non-SRES scenarios S650 and IS92a, respectively. Modified from Orr et al. (2005).

35. 4.3 Future evolution of ozone
Coupled chemistry-climate models
Trend in upper-stratospheric ozone changes sign between 2000 and 2005
Anthropogenic activity,
Climate conditions,
Stratosphere-troposphere exchange
Troposphere ozone increase throughout 21st century
+20~25% from 2015~2050 (Grewe et al. 2001)
+40~60% at (Stevenson et al. 2001)
Ozone increases are largest in the tropics and subtropics

36. 4.4 Aerosol species Of 23 models for IPCC AR4
13 include aerosol species
7 have non-sulphate species interact with other physics
2 models treat Nitrates
Black and organic carbon :: highly simplified bulk parametrizations
Interaction of soil dust is under active investigation
Uncertainty also come from unpredictable natural forcing such as volcanic eruptions and solar variability

37. 6.1 sea level rise due to thermal expansion
Calculated from ocean temp.
17 models results available
Scenario
Year B1
A1B A2
2000 ~ 2020
mm/yr ~
2080 ~ 2100
mm/yr
mm/yr
mm/yr

38. Figure Projected global average sea level rise (m) due to thermal expansion during the 21st century relative to 1980 to 1999 under SRES scenarios A1B, A2 and B1. See Table 8.1 for model descriptions.
Figure Projected global average sea level rise (m) due to thermal expansion during the 21st century relative to 1980 to 1999 under SRES scenarios A1B, A2 and B1. See Table 8.1 for model descriptions.

39. 6.2 local change due to density and dynamics
Dynamic topography <-- local T & S & circulation
16 models (A1B)
Spatial patterns are different among models

40. Figure Local sea level change (m) due to ocean density and circulation change relative to the global average (i.e., positive values indicate greater local sea level change than global) during the 21st century, calculated as the difference between averages for 2080 to 2099 and 1980 to 1999, as an ensemble mean over 16 AOGCMs forced with the SRES A1B scenario. Stippling denotes regions where the magnitude of the multi-model ensemble mean divided by the multi-model standard deviation exceeds 1.0.
Figure Local sea level change (m) due to ocean density and circulation change relative to the global average (i.e., positive values indicate greater local sea level change than global) during the 21st century, calculated as the difference between averages for 2080 to 2099 and 1980 to 1999, as an ensemble mean over 16 AOGCMs forced with the SRES A1B scenario. Stippling denotes regions where the magnitude of the multi-model ensemble mean divided by the multi-model standard deviation exceeds 1.0.

41. 6.3 Glaciers and ice caps Mass balance sensitivity to T and P
Under A1B A2 and B1 -> 0.61mm/yr or 0.49mm/yr
Required increase P 20~50%/oC 29~41%/oC
Dynamic response and feedback
Volume lost-- area decline -- ablation decrease
Glacier and IC on Greenland and Antarctic
10~20% sea level rise contribution of G&IC in future decades

42. 6.4 Ice sheet Surface mass balance (SMB) Increase in accumulation
Antarctic SMB changes will contribute negatively to sea level
Greenland SMB represents a net positive contribution to sea level

43. Figure Projections and uncertainties (5 to 95% ranges) of global average sea level rise and its components in 2090 to 2099 (relative to 1980 to 1999) for the six SRES marker scenarios. The projected sea level rise assumes that the part of the present-day ice sheet mass imbalance that is due to recent ice flow acceleration will persist unchanged. It does not include the contribution shown from scaled-up ice sheet discharge, which is an alternative possibility. It is also possible that the present imbalance might be transient, in which case the projected sea level rise is reduced by 0.02 m. It must be emphasized that we cannot assess the likelihood of any of these three alternatives, which are presented as illustrative. The state of understanding prevents a best estimate from being made.

44. Long Term Commitment

45. Long Term Commitment EMICs A1B before 2100
Constant composition
Tsfc stabilizes relatively quickly
Sea level continues to rise
MOC shutdown?!
EMICs tuned to GCMs but don’t take into account full range of AOGCM sensitivities
5 include simple interactive carbon cycle,
For carbon cycle processes it has been shown that there is good agreement with more complex models
Sea level rise dependent on slow mixing of heat into deep ocean
If deep water formation is suppressed, ocean will warm up more for more thermal expansion
Fig

46. Long Term Commitment Path to CO2 stabilization at 750 ppm before 2100
Zero emissions
Model dependence
Notice carbon redistribution
Lasting impact of 21st century emissions, sfc. T drops slowly
ocean continues to take carbon
sea level rise is maximum at end of millennium
Redistribution of carbon is model dependent
System continues to warm for few decades
Parameterization of vertical mixing affecting ocean heat uptake
Also run idealized overshoot simulation, must avoid thresholds
Fig

47. Greenland Ice Sheet
Threshold for surface mass balance (SMB) of Greenland ice is 3.20 C C warming
Global average of 1.90 C C
Likely to be reached with A1B to 2100
Under constant 4*CO2, half of ice gone in ~1000 yrs
With less ice, Greenland’s climate is warmer (albedo feedback)  IRREVERSIBLE?
Much is not none about processes accelerating the melting process
Fig

48. Antarctic Ice Sheet Growing as a whole Concern is West Antarctic ice
Antarctic Ice Sheet
Growing as a whole
Concern is West Antarctic ice
shelves breaking off
Base melt rate  water T near base
Entire sheet  5m sea level rise!
“We are not able to relate this quantitatively to global warming with any confidence …”
May explain some part of sea level rise for last interglacial (4-6m)
Growth is negative contribution to sea level rise
Surface melting or thinning from basal melting
“We are not able to relate this quantitatively to global warming with any confidence, because the issue has so far received little attention, and current models may be inadequate to treat it because of limited resolution and poorly understood processes.”