22-23 January 2004 STABILIZATION SCENARIOS WORKSHOP Tsukuba, Japan - - PowerPoint PPT Presentation

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22 23 january 2004 stabilization scenarios workshop
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22-23 January 2004 STABILIZATION SCENARIOS WORKSHOP Tsukuba, Japan - - PowerPoint PPT Presentation

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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.

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22-23 January 2004 STABILIZATION SCENARIOS WORKSHOP Tsukuba, Japan Haroon S. Kheshgi Corporate Strategic Research ExxonMobil Research & Engineering Company 1

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2 22-23 January 2004 STABILIZATION SCENARIOS WORKSHOP Tsukuba, Japan Haroon S. Kheshgi Corporate Strategic Research ExxonMobil Research & Engineering Company

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“STABILIZATION SCENARIOS” WORKSHOP

  • Objective: provide sound advice on near-term actions to

address 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

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CLIMATE CHANGE SCIENCE PERSPECTIVE FOR THE IPCC WORKSHOP ON DANGEROUS LEVELS OF GHGs

  • Modeled equilibrium global temperature Vs CO2 and climate

sensitivity

  • Quest for objective estimates of climate sensitivity
  • Abrupt climate change
  • Carbon cycle estimates of CO2 emissions for stabilization
  • Trajectories of CO2 concentration
  • Summary

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MODELED EQUILIBRIUM GLOBAL TEMPERATURE Vs CO2 AND CLIMATE SENSITIVITY

  • Range of ∆T2x = 1.5 to

4.5 °C leads to a wide range of modeled CO2 levels for a specified equilibrium temperature

– Other factors to consider:

+ Other GHGs + Aerosols + Solar, volcanoes + Variability

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SLIDE 6

QUEST FOR OBJECTIVE ESTIMATES OF CLIMATE SENSITIVITY

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  • 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 range

  • f 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, ice condensation nuclei etc.), and century time scale variability; limited observational records (accumulating with time)

  • Approach: Paleo-analogues (deducing climate sensitivity from past climate

epochs)

– Obstacles: imperfect analogue for future (e.g. LGM and roles of sea ice, solar insolation patterns, etc.), accuracy of reconstructions of past climate systems

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SLIDE 7

ABRUPT CLIMATE CHANGE

  • Indications of rapid change in climate from paleo-records

– Hypotheses for causes under active research -- currently difficult to simulate 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, for

example:

– Shift in thermohaline circulation

+ Response differs between models + Could have strong regional effects + Appropriate monitoring prudent

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“SAFE LEVELS” SUMMARY

  • The commonly used range of climate sensitivity results in a wide

modeled range of CO2 levels for a specified equilibrium temperature

  • Fundamental obstacles for the scientific determination of the

probability distribution of climate sensitivity

  • Abrupt climate change could lead to serious impacts, but

research is at an early stage in determining mechanisms and what might trigger abrupt change, whether anthropogenic or not Currently there is very little ability to make probabilistic forecasts of climate limiting determination of safe levels of greenhouse gases. Ability will improve over the time-scales of concern?

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LONG-TERM CARBON CYCLE CONSIDERATIONS

  • Carbon cycle characteristics
  • “Stabilization scenarios”

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CARBON CYCLE ESTIMATES OF CO2 EMISSIONS FOR STABILIZATION

  • Arbitrary trajectories leading

monotonically to constant CO2 levels specified (WRE trajectories)

  • Deduced net anthropogenic emissions

including modeled interactions with climate

– Based on responses of a range of models – Differences in responses due mostly to biosphere response to changed CO2, and climate – 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

Kheshgi and Jain (GBC, 2003) Shaded area for 550ppm path for ISAM range or parameters

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CARBON CYCLE ESTIMATES OF CO2 EMISSIONS FOR STABILIZATION

  • Arbitrary trajectories leading

monotonically to constant CO2 levels specified (WRE trajectories)

  • Deduced net anthropogenic emissions

including modeled interactions with climate

– Based on responses of a range of models – Differences in responses due mostly to biosphere response to changed CO2, and climate – 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

Kheshgi and Jain (GBC, 2003) Shaded area for 550ppm path for ISAM range or parameters

Models assume no substantial management of plants and soils...ever.

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EQUILIBRIUM PARTITIONING OF ADDED CO2 TO THE OCEAN/ATMOSPHERE SYSTEM

(no sediment neutralization)

Kheshgi, Smith and Edmonds (MITI, in press 2004)

CO2(g) + H20 ⇔ H2CO3(aq) ⇔ HCO3

  • + H+ ⇔ CO3

2-

+ 2H+ Total Carbon = TC(pCO2, Titration Alkalinity, Salinity, Temperature)

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CARBON UPTAKE BY OCEAN/ATMOSPHERE SYSTEM: WRE550 CASE

Kheshgi and Archer (JGR-Oceans, in press 2004)

Ocean sink decay timescale >1,000 years

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SLIDE 14

CO2 EMISSIONS: LOGISTIC FUNCTIONS

Kheshgi (Energy, in press 2004)

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2 4 6 8 10 12 14 16 18 20 22 24 CO2 Emission, Gt C/yr 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 Year Cumulative emissions = 5,000 GtC 2,000 GtC 3 ,000 GtC 4 ,000 GtC 1,5 00 GtC

WRE550

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TRAJECTORIES OF CO2 CONCENTRATION

Kheshgi, Smith and Edmonds (MITI, submitted 2003)

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  • Example: two alternative

trajectories

– Monotonic approach to 650 ppm – Non-monotonic trajectory peaking near 650 ppm and declining to about 400 ppm

  • Deduced emissions for
  • cean/atm system (biosphere

sources/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 non- monotonic trajectory

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ON STRATEGIES FOR REDUCING GREENHOUSE GAS EMISSIONS

Bolin and Kheshgi, Proc Nat Academy Sci, 2001,

  • Target stabilization level not

known

  • Scenarios diverge over

decades

  • Vast differences in per capita

emissions

  • Lack of affordable energy for

many -- development priority 16

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ON STRATEGIES FOR REDUCING GREENHOUSE GAS EMISSIONS

  • Target stabilization level not

known

  • Scenarios diverge over

decades

  • Vast differences in per capita

emissions

  • Lack of affordable energy for

many -- development priority

Bolin and Kheshgi, Proc Nat Academy Sci, 2001,

How can the advice enabled with these scenarios be improved?

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“STABILIZATION SCENARIOS” WORKSHOP

  • Objective: provide sound advice on

near-term actions to address long- term risks

  • Long-term scenarios

– who is the consumer of the scenarios/what is the purpose

  • “What to stabilize”

– “stabilization targets” paradigm – uncertainties – near-term metrics Vs long-term

  • bjectives

18

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SLIDE 19

“STABILIZATION SCENARIOS” WORKSHOP

  • Objective: provide sound advice on

near-term actions to address long- term risks

  • Long-term scenarios

– who is the consumer of the scenarios/what is the purpose

  • “What to stabilize”

– “stabilization targets” paradigm – uncertainties – near-term metrics Vs long-term

  • bjectives

⇐ modeling or decision making?

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SLIDE 20

“STABILIZATION SCENARIOS” WORKSHOP

  • Objective: provide sound advice on

near-term actions to address long- term risks

  • Long-term scenarios

– who is the consumer of the scenarios/what is the purpose

  • “What to stabilize”

– “stabilization targets” paradigm – uncertainties – near-term metrics Vs long-term

  • bjectives

⇐

  • model optimization objectives

confused with policy targets?

  • Stabilization or long-term

development?

  • What’s included and what is

hidden?

  • How is it communicated?

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SLIDE 21

“STABILIZATION SCENARIOS” WORKSHOP

  • Objective: provide sound advice on

near-term actions to address long- term risks

  • Long-term scenarios

– who is the consumer of the scenarios/what is the purpose

  • “What to stabilize”

– “stabilization targets” paradigm – uncertainties – near-term metrics Vs long-term

  • bjectives

⇐ climate science, baselines, existing/emerging technology

  • ptions ... What are the robust

messages

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SLIDE 22

“STABILIZATION SCENARIOS” WORKSHOP

  • Objective: provide sound advice on

near-term actions to address long- term risks

  • Long-term scenarios

– who is the consumer of the scenarios/what is the purpose

  • “What to stabilize”

– “stabilization targets” paradigm – uncertainties – near-term metrics Vs long-term

  • bjectives

⇐

  • Near-term forecasts Vs long-term

scenarios

  • observable metrics Vs model
  • bjectives

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