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 The concept of climate simulations with quasi-realistic climate models is discussed and illustrated with examples. The relevant problem of deriving regional and local specifications is considered as well.

Overview: Hesse’s concept of positive, negative, and neutral analogs: the added value resides with the neutral analogs. Models describe a reduced, incomplete image of reality. Almost all models contain choices of modelers. In particular parameterisations. Purpose of models – what do we learn about the “real” world? – Understanding, analysis of data, experimentation.

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.

The constructive part of a model is in its neutral analogs. 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.

Dynamical processes in a global atmospheric general circulation model

variance Spatial scales Models represent only part of reality: Only part of contributing spatial and temporal scales are selected. 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) Insufficiently resolved Well resolved Spatial scales

A strict separation of scales is not possible 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.

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.

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.

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.

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

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: 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.