22-23 January 2004
STABILIZATION SCENARIOS WORKSHOP
Tsukuba, Japan

Haroon S. Kheshgi
Corporate Strategic Research
ExxonMobil Research & Engineering Company
1

22-23 January 2004
STABILIZATION SCENARIOS WORKSHOP
Tsukuba, Japan

Haroon S. Kheshgi
Corporate Strategic Research
ExxonMobil Research & Engineering Company
2

“STABILIZATION SCENARIOS” WORKSHOP• Objective: provide sound advice on near-term actions toaddress long-term risks– Key insights or spurious messages• Long-term scenarios– who is the consumer of the scenarios/what is the purpose• “What to stabilize”– “stabilization targets” paradigm+ Key insights or spurious messages– uncertainties+ Key insights or spurious messages– near-term metrics Vs long-term objectives+ Key insights or spurious messages

3

CLIMATE CHANGE SCIENCEPERSPECTIVE FOR THE IPCC
WORKSHOP ON DANGEROUS LEVELS
OF GHGs• Modeled equilibrium global temperature Vs CO2 and climatesensitivity• Quest for objective estimates of climate sensitivity• Abrupt climate change• Carbon cycle estimates of CO2 emissions for stabilization• Trajectories of CO2 concentration• Summary4MODELED EQUILIBRIUM GLOBALTEMPERATURE Vs CO2 AND CLIMATESENSITIVITY• Range of ∆T2x = 1.5 to
4.5 °C leads to a wide
range of modeled CO2levels for a specified
equilibrium
temperature– Other factors toconsider:+ Other GHGs
+ Aerosols+ Solar, volcanoes+ Variability

5

QUEST FOR OBJECTIVE ESTIMATES OF
CLIMATE SENSITIVITY• Approach: Theory and modeling– Obstacles: gaps in understanding, e.g., of cloud feedbacks• Approach: Ranges of model results– Obstacles: model validation/invalidation; no probability assigned to a given model
parameterization and structure– Characteristics: range of plausible model results must be contained in the rangeof uncertainty -- range is a lower bound on the width of the range of uncertainty• Approach: Climate sensitivity estimation through climate model calibration– Obstacles: gaps in understanding, e.g., of forcing (aerosol indirect effects, icecondensation nuclei etc.), and century time scale variability; limited observational
records (accumulating with time)• Approach: Paleo-analogues (deducing climate sensitivity from past climateepochs)– Obstacles: imperfect analogue for future (e.g. LGM and roles of sea ice, solar
insolation patterns, etc.), accuracy of reconstructions of past climate systems
6

ABRUPT CLIMATE CHANGE• Indications of rapid change in climate from paleo-records– Hypotheses for causes under active research -- currently difficult tosimulate abrupt behavior…far from predictable– Pre-Holocene changes may not be good analogies for future change– Causes were, of course, not anthropogenic• Potentially important mechanisms for abrupt change, forexample:– Shift in thermohaline circulation+ Response differs between models+ Could have strong regional effects+ Appropriate monitoring prudent

7

“SAFE LEVELS” SUMMARY• The commonly used range of climate sensitivity results in a widemodeled range of CO2 levels for a specified equilibrium
temperature• Fundamental obstacles for the scientific determination of theprobability distribution of climate sensitivity• Abrupt climate change could lead to serious impacts, but
research is at an early stage in determining mechanisms andwhat might trigger abrupt change, whether anthropogenic or not
Currently there is very little ability to makeprobabilistic forecasts of climate limiting
determination of safe levels of greenhouse gases.
Ability will improve over the time-scales ofconcern?

8

LONG-TERM CARBON CYCLECONSIDERATIONS• Carbon cycle characteristics• “Stabilization scenarios”

9

CARBON CYCLE ESTIMATESOF CO2 EMISSIONS FORSTABILIZATION
Kheshgi and Jain (GBC, 2003)• Arbitrary trajectories leading
monotonically to constant CO2 levels
specified (WRE trajectories)• Deduced net anthropogenic emissions
including modeled interactions withclimate– Based on responses of a range of models– Differences in responses due mostly tobiosphere response to changed CO2, andclimate– Long-term, the ocean sink dominates
natural uptake• Long-term, modeled temperature rise primarily
dependent on equilibrium climate sensitivityparameter• Factors in addition to CO2 could modify
results
10

Shaded area for 550ppm pathfor ISAM range or parametersCARBON CYCLE ESTIMATESOF CO2 EMISSIONS FORSTABILIZATION
Kheshgi and Jain (GBC, 2003)• Arbitrary trajectories leading
monotonically to constant CO2 levels
specified (WRE trajectories)• Deduced net anthropogenic emissions
including modeled interactions withclimateModels assume no substantialof plants
soils...ever.
– management
Based on responses
of a andrangeof models– Differences in responses due mostly tobiosphere response to changed CO2, andclimate– Long-term, the ocean sink dominates
natural uptake• Long-term, modeled temperature rise primarily
dependent on equilibrium climate sensitivity
parameter• Factors in addition to CO2 could modify
results
11

Shaded area for 550ppm pathfor ISAM range or parameters

EQUILIBRIUM PARTITIONING OF ADDED CO2 TO THEOCEAN/ATMOSPHERE SYSTEM(no sediment neutralization)

CO2(g) + H20 ⇔ H2CO3(aq) ⇔ HCO3- + H+ ⇔ CO32- + 2H+Total Carbon = TC(pCO2, Titration Alkalinity, Salinity, Temperature)
Kheshgi, Smith and Edmonds (MITI, in press 2004)

12

CARBON UPTAKE BY OCEAN/ATMOSPHERE SYSTEM:
WRE550 CASE
Kheshgi and Archer (JGR-Oceans, in press 2004)

Ocean sink decay timescale >1,000years

13CO2 EMISSIONS: LOGISTIC FUNCTIONS
Kheshgi (Energy, in press 2004)

24
Cumulative emissions = 5,000 GtC

224 ,000 GtCCO2 Emission, Gt C/yr

20
183 ,000 GtC

16142,000 GtC

1210

1,5 00 GtC

864214

Year

3000

2900

2800

2700

2600

2500

2400

2300

2200

2000

02100

WRE550TRAJECTORIES OF CO2 CONCENTRATION
Kheshgi, Smith and Edmonds (MITI, submitted 2003)• Example: two alternativetrajectories– Monotonic approach to 650 ppm– Non-monotonic trajectory
peaking near 650 ppm anddeclining to about 400 ppm• Deduced emissions for
ocean/atm system (biospheresources/sinks part of“emissions” in this case)– For monotonic approach to 650
ppm:+ Atm only accumulates carbon
prior to 650 ppm being reached+ Ocean sink persists for 1000+years– A linear decline in emissions
after 2100 results in the non15 monotonic trajectoryON STRATEGIES FOR REDUCING
GREENHOUSE GAS EMISSIONS
Bolin and Kheshgi, Proc Nat Academy Sci, 2001,• Target stabilization level notknown• Scenarios diverge overdecades• Vast differences in per capitaemissions• Lack of affordable energy formany -- development priority

16

ON STRATEGIES FOR REDUCING
GREENHOUSE GAS EMISSIONS
Bolin and Kheshgi, Proc Nat Academy Sci, 2001,• Target stabilization level notknown• Scenarios diverge overdecades

How can the advice• Vast differencesin percapita
enabled withtheseemissions
scenarios beimproved?
• Lack of affordable energy formany -- development priority

17

“STABILIZATION SCENARIOS” WORKSHOP• Objective: provide sound advice onnear-term actions to address longterm risks• Long-term scenarios– who is the consumer of thescenarios/what is the purpose• “What to stabilize”– “stabilization targets” paradigm– uncertainties– near-term metrics Vs long-termobjectives

18

“STABILIZATION SCENARIOS” WORKSHOP• Objective: provide sound advice onnear-term actions to address longterm risks• Long-term scenarios– who is the consumer of thescenarios/what is the purpose• “What to stabilize”– “stabilization targets” paradigm– uncertainties– near-term metrics Vs long-term
objectives

19⇐ modeling or decision making?

“STABILIZATION SCENARIOS” WORKSHOP• Objective: provide sound advice onnear-term actions to address longterm risks• Long-term scenarios– who is the consumer of thescenarios/what is the purpose• “What to stabilize”– “stabilization targets” paradigm ⇐– uncertainties– near-term metrics Vs long-termobjectives• model optimization objectives
confused with policy targets?
• Stabilization or long-termdevelopment?
• What’s included and what ishidden?

20• How is it communicated?

“STABILIZATION SCENARIOS” WORKSHOP• Objective: provide sound advice onnear-term actions to address longterm risks• Long-term scenarios– who is the consumer of thescenarios/what is the purpose• “What to stabilize”– “stabilization targets” paradigm– uncertainties– near-term metrics Vs long-term
objectives

21⇐ climate science, baselines,
existing/emerging technology
options ... What are the robustmessages

“STABILIZATION SCENARIOS” WORKSHOP• Objective: provide sound advice onnear-term actions to address longterm risks• Long-term scenarios– who is the consumer of thescenarios/what is the purpose• “What to stabilize”– “stabilization targets” paradigm– uncertainties– near-term metrics Vs long-term ⇐objectives• Near-term forecasts Vs long-termscenarios

22• observable metrics Vs model
objectives