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FROM MODELS TO MECHANISMS. FEEDBACK AND OPTIMIZATION IN CMIP5

IOAN MUNTEAN

THE REILLY CENTER FOR SCIENCE, TECHNOLOGY, AND VALUES

UNIVERSITY OF NOTRE DAME

IMUNTEAN@ND.EDU

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PREVIEW

Main issue: A transition from models to mechanisms in climate change

Argument for a mechanistic view in climate change

Feedback

Optimality

Control/ manipulation

Understanding

Arguments against mechanisms in climate science

Holism

Failed mechanisms in physical sciences

So what?

Not yet

Will never happen

Not needed

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WHAT IS UNDER SCRUTINY HERE?

The internal structure of climate models

Feedback in climate models

Mapping models to mechanism (M2M)

Many to one?

Many to many?

Optimality and plurality of models

Communicating results, metadata and mechanisms

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SOME TOPICS OF INTEREST IN CLIMATE SCIENCE

Social values (viz. Epistemic values) in creating models (Winsberg 2012)

Complexity of models and “analytic understanding” (Parker 2014)

Multiplicity/plurality of climate models, (Parker 2010a)

Uncertainty of models

Stability, reliability of models

Explanatory power and understanding of climate models

Modularity

Adapted from (Knutti and Sedláček 2012)

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ROLES OF MODELS

Climate change is mainly about building and assessing models

Climate models are mainly:

predicting tools

generate other models or hypotheses

Quantification of theories of climate change

“hybrid”: predict and explain

Do climate models really explain? How?

Do we have an IBE with climate models?

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OVERLAPPING MODELS IN MECHANISMS

Differenct communities focus on different parts

They do not necessarily look at the “coupled model”

Climate scientists are specialized

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SOME VIEWS ABOUT MODELS AND MECHANISMS

Models are not related to mechanisms

mathematical models exist in physics, without being related to any mechanism

some models summarize data (phenomenal models)

some other models predict (are phenomenally adequate) but do not explain

Models represent mechanisms

One task of model building is to represent the dynamics of mechanisms (Bechtel and Abrahamsen 2011)

Models needs mechanisms to be explanatory

Models are explanatory when they describe a mechanism (Craver 2006)

Models map to mechanisms (M2M)

Let us call these models “mechanistic models”

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MODEL ASSESSMENT IN CLIMATE SCIENCE

Confirmation of “the truth” of existing models (Lloyd 2010)

Adequacy-for-a-purpose: (Parker 2009)

Realism: accurate description of the actual climate system

Bayesian view

Possibilism (Katzav 2014)

Present focus: mapping models to a mechanism

How does model X map on the mechanism Y?

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THREE EXTREME PREDICTIONS

A. Where do I need to look in the sky to find the moon in London ON at 16:30 on 25.10.2044?

B. What will be Ioan’s state of health on 4:30 on 25.10.2044, given this and this constraints on the world and what we know of his diet, genes, etc.?

C. How far can I drive a Honda Civic car from London ON with a full tank of gas, in this direction, in the weather conditions, all things being equal?

A= one theory, simple simulation, simple data, perfect prediction

B= no theory no model, some mechanisms

C= one mechanism, no theory, some initial conditions, no need of models

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QUESTIONS 1 Are some (all?) climate models mechanistic?

2 Why explanation?

3 Can climate models explain without being “mechanistic”?

What advantages does a mechanistic view bring to climate science?

4 So what? Why do we need mechanistic explanation anyway?

1 yes, those in which feedback plays a role

2 We want to understand the “causal story” of the climate system. The understanding of why a phenomenon occurs (Parker 2014).

Question to Parker: is a mechanistic explanation better than causal explanation in improving our understanding of a phenomenon and of its question “why?”

3 yes, they can, but still mechanistic explanations can do better

4 Because with explanation, control, understanding and manipulation come!

4’ we can hope for the optimal model

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EXTRAPOLATING MECHANISMS

Universality: Model-building occurs anywhere in science

Neuroscience/cognitive science (empirical data and laws/equations)

Biology (empirical data)

Physics (laws, symmetries)

Life science, medicine

Scientific revolution can be read, charitably as a process building models, mechanisms, unifying, eliminating models, creating theories etc.

I think it makes sense to talk about:

“mechanisms & models (together) in climate science”

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A SIMPLIFIED VIEW

I. Convergence from a plurality of models to a limited number of models

Culling models

Coupling submodels

Constraint models

II. Mapping from a limited number of models to a limited number of mechanisms

III. Convergence of mechanisms to a theory (unification of mechanisms and models)

I think II deserves attention in the light of CMIP5

I am quietist about III. And I is already discussed in the literature

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ADVANTAGES OF MECHANISMS

Introduce new explanations

Integrate causal stories

Introduce levels

Facilitate communication between submodels and between subroutines

Can map elements of models to mechanisms and give them materiality

Cluster of different models into mechanisms based on the M2M

Move from statistical explanations/arguments close to what the layman wants to hear (not probabilities, but conditionals)

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“BOTTOMING OUT” MECHANISMS

Ignore the fundamental and fundamentality (deep physics)

Work at scales

Relative to a scale (time space energy)

Multiscale modeling

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FROM MODELS TO MECHANISMS

Why do we need mechanisms? A Kantian innuendo:

“Dynamical models without mechanistic grounding are empty, while mechanistic models without complex dynamics are blind.” (Bechtel and Abrahamsen 2011)

This suggests a relation among models and mechanisms.

Normatively: models and mechanism should be mapped one onto the other.

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DO MODELS EXPLAIN?

The Craver-Kaplan hypothesis (Kaplan and Craver 2011):

Models explain only when there is a model-to-mechanism mapping. M2M

Models needs to be modular in order to explain (Weber 2008)

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THE MECHANISM-MODEL MAPPING

Biologists discover mechanisms

Models resemble the mechanism

Some models are better, some are worse, in representing the mechanism

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MECHANISMS IN MODELS’ CLOTHING

Are mechanisms already in the climate science?

Try to identify in CMIP-5 the mechanistic mindset (not language)

Unveil their explanatory role

Explain the M2M mapping.

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SYNTHETIC MODELING

Mechanism complements the computational modeling

It is not a question of reinterpretation of what climate scientists already do

It is more or less a reconstruction based on M2M

It does bring in a clearly stated language of levels

Cycles of amplification are called amplifying mechanisms

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MECHANISTIC OPERATIONS IN THE MODELS

Decomposition is a procedure that happens in mechanisms

Switch on and off various components: Inhibition

Stimulation

Recomposition of the operation of the mechanisms (Bechtel, 2011)

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CLIMATE FEEDBACK 101

Feedback is never linear

Apply a forcing (CO2)

Temperature raise

Feedback changes

Look for mechanisms that are not switched off al low temperature

Once these processes go on, there is amplification or reducing of the temperature

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FEEDBACK

Feedback can be positive or negative

The net feedback from the combined effect of changes in water vapor, and differences between atmospheric and surface warming is almost surely positive.

The net radiative feedback due to all cloud types combined is positive.

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CMIP-5: A LOLLAPALOOZA OF FEEDBACK

AOGCM are not enough!

Earth System Models

Earth System Models of Intermediate Complexity

Includes cycles

Since AR4, the understanding of mechanisms and feedbacks of extreme in temperature improved

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FEEDBACK: M2M

Feedback can be captured by:

Non-linear equations

A cycle in a mechanism

Simple mechanisms are serial: start to finish. They contain only linear causal chains

Feedback loops complicate mechanisms.

They are non-sequential

Introduce timescale

Synchronization of feedback (makes them positive or negative, depending on phase factor)

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PRINCIPAL FEEDBACKS

The water vapor/lapse

Albedo

Cloud

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CARBON CYCLE IN CMIP5 AND FEEDBACK

Increased atmospheric CO2 increases land and ocean uptake

Limitations on plant growth imposed by nitrogen availability

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VAPOUR-CO2-CLIMATE

Vapour is a feedback not a Forcing of climate change

It is a fast and strong feedback (see Ch 8)

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HOW DO WE REACH OPTIMALITY?

Optimality does not belong to a model

Through mechanisms (Machamer, Darden, and Craver 2000)

Optimal mappings between models and mechanisms

Reduce uncertainty

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TIMESCALE MATTERS!

The effect of feedbacks is clear for longer timespans

Some feedbacks are delayed by centuries or millennia

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  Lifetime (years)   GWP20 GWP100 GTP20 GTP100

 

CH b 412.4a No cc fb 84 28 67 4

  With cc fb 86 34 70 11

HFC-134a 13.4 No cc fb 3710 1300 3050 201

  With cc fb 3790 1550 3170 530

CFC-11 45.0 No cc fb 6900 4660 6890 2340

  With cc fb 7020 5350 7080 3490

N2O 121.0a No cc fb 264 265 277 234

  With cc fb 268 298 284 297

CF4 50,000.0 No cc fb 4880 6630 5270 8040

  With cc fb 4950 7350 5400 9560

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ARGUMENTS AGAINST M2M IN CLIMATE SCIENCE Climate science is a physical science in which mechanisms do

not play the same role as in neuroscience/life science/ Some “disastrous” examples of mechanism thought in physics (ether,

phlogiston, Cartesian physics)

Climate models are mathematical models, unlike models in neuroscience

Climate science is holistic, in pursue of complexity, not reductionist. Emergence looms large

Climate science is more about statistical reasoning, not about discovering reality/mechanisms.

Climate modelers are partially blackboxing, or probably grey-boxing their object of study