11-12 June 2017 - Third
International Symposium on
Climate and Earth System
Modeling, NUIST, 南京 (Nanjing)
On the added
value generated
by dynamical
models
Hans von Storch,
Geesthacht, Hamburg,
and Qingdao
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Overview:
1. Hesse’s concept of positive, negative, and neutral analogs:
the added value resides with the neutral analogs.
2. Models describe a reduced, incomplete image of reality.
Almost all models contain choices of modelers. In
particular parameterisations.
3. Purpose of models – what do we learn about the “real”
world? – Understanding, analysis of data, experimentation.
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Hesse’s concept of models
Reality and a model have attributes, some of which are consistent and others
are contradicting. Other attributes are unknown whether reality and model
share them.
The consistent attributes are positive analogs.
The contradicting attributes are negative analogs.
The “unknown” attributes are neutral analogs.
Hesse, M.B., 1970: Models and analogies in science. University of Notre Dame Press,
Notre Dame 184 pp.
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Validating the model means to determine the positive and negative
analogs.
Applying the model means to assume that specific neutral analogs are
actually positive ones.
The constructive part of a model is in its
neutral analogs.
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Dynamical processes in a
global atmospheric general circulation model
• Only part of contributing
spatial and temporal scales are
selected.
variance
Models represent only part of
reality:
Insufficiently
resolved
• Parameter range limited
• Subjective choice of the
researcher:
- Certain processes are
disregarded.
- Various processes are taken
into account by conditioning
their effect of the state of the
resolved scales
(parameterizations)
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Well resolved
Spatial scales
A strict separation of scales is not possible. Small scale processes, such as the
interaction of water droplets and radiation in clouds play an important role in
the pattern of warming and cooling on the general circulation of the
atmosphere.
The resolution of climate models is insufficient for describing the small scales
dynamics, but without considering them, the large-scales cannot be described
properly.
Thus, “parameterisations” are introduced: It is assumed that given a certain
configuration, which is resolved by the model, the unresolved processes will
generate a certain type of effect on the large scales. This “type of effect” may
take the form of a conditional random variable. When running the model,
either the conditional expectation is prescribed, or a randomized design is
chosen.
Obviously, the choice is not a matter of “right” or “wrong” but of “efficient” or
not. The naming of the set of parameterisations as “physics” is misleading.
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Validation =
determination of
positive analogs
Models can be shown to be
consistent with observations, e.g.
the known part of the phase
space may reliably be reproduced.
Validation teaches not about
reality but about models.
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Models can not be verified because reality is
open.
Coincidence of modelled and observed state may happen because
of model´s skill or because of fortuitous (unknown) external
influences, not accounted for by the model.
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Purpose of models
• reduction of complex systems
understanding
• surrogate reality
realism
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The issue of designing models is related to the
expected added value.
There is hardly a model „of something“ but mostly a model „for
studying / simulating something“.
Thus, models are conditioned upon the purpose of the model.
There is a problem is specifying what the expected added value of
„Earth System models“ is.
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Models for reduction of complex systems
• identification of significant, small subsystems and key processes
(cf. Hasselmann’s concepts of PIPs and POPs (1988))
• often derived through scale analysis
• often derived semi–empirically
• constitutes “understanding”, i.e. theory
• construction of hypotheses
characteristics:
simplicity
idealisation
conceptualisation
fundamental science approach
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Models as surrogate reality
• dynamical, process-based models,
• experimentation tool (test of hypotheses)
• sensitivity analysis; including scenarios
• dynamically consistent interpretation and extrapolation of
observations in space and time (“data assimilation”;
“analysis”)
•
forecast of detailed development (e.g. weather forecast)
characteristics:
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complexity
quasi-realistic
mathematical/mechanistic
engineering approach
Conclusions
• “Model” is a term with very many different meaning in different scientific
and societal quarters.
• Validation of models means to check positive and negative analogs.
Validation does not teach about functioning of the considered system but
about the considered model.
• The constructive part of models is in their neutral analogs with “reality”.
They represent possible “added value”.
• In climate science we have conceptual models – constituting understanding
– and quasi-realistic models, allowing for numerical experimentation and data
analysis.
• There is always the possibility that an identified neutral analog is a property
of the real world. What is considered added value may be a model artifact.
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