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Models „for“ not „of“

Institute of Coastal Research, GKSS Research Centre Geesthacht, and Meteorological Institute, Hamburg University Hans von Storch. Models „for“ not „of“. Conceptual aspects of modelling. Hesse’s concept of models

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Models „for“ not „of“

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  1. Institute of Coastal Research, GKSS Research Centre Geesthacht, and Meteorological Institute,Hamburg UniversityHans von Storch Models „for“ not „of“

  2. Conceptual aspects ofmodelling 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.

  3. 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.

  4. Positive analog Neutral analog Application

  5. Models are • • • smallerthan reality (finite number of processes, reduced size of phase space) • • • simplerthan reality (description of processes is idealized) • • • closed,whereas reality is open(infinite number of external, unpredictable forcing factors is reduced to a few specified factors)

  6. Models represent only part of reality; • Subjective choice of the researcher; Certain processes are disregarded. • Only part of contributing spatial and temporal scales are selected. • Parameter range limited

  7. Models can not be verified because reality is open. Coincidence of modelled and observed state mayhappen because of model´s skill or because of fortuitous (unknown) externalinfluences, not accounted for by the model.

  8. Trivially: all models are “false” (= have negative analogs) Some are really garbage, but many are useful(= have sufficiently many positive analogs and relevant neutral analogs).

  9. Purpose of models # reduction of complex systems  “understanding” # surrogate reality  realism

  10. Models for reduction of complex systems • identification of significant, small subsystems and key processes • often derived through scale analysis • (Taylor expansion with some characteristic numbers) • often derived semi–empirically • characteristics:simplicity idealisation conceptualisation • fundamental science approach

  11. Models for reduction of complex systems • good for: • constitution of “understanding”, i.e. theory • construction of hypotheses • Validation: reproduces the gross features of key indicators of a phenomenon and the key processes supposedly relevant for the conceptualizing of the phenomenon; all other processes are disregarded.

  12. Constant orRandomized transmissivity α Idealized energy balance, with constant or dynamical albedo β

  13. no noise evolution from different initial values with constant transmissivity and temperature dependent albedo Integration of a zero–dimensional energy balance model with noise evolution with slightly randomized transmissivity

  14. Models as surrogate reality • dynamical, process-based models, • characteristics: • complexity quasi-realistic mathematical/mechanistic engineering approach

  15. Dynamical processes in the atmosphere Dynamical processes in a global atmospheric general circulation model

  16. These models are built to describe the dynamics for certain time and spatial scales and for a certain range of parameters. Validation means that positive analogs prevail for key processes (evolutions, statistics of key variables) Which? … depends in the purpose of the model

  17. The model can be validated only for that part of the “phase space”, which is sufficiently covered by observations.

  18. Applying the model outside the admissible domain means to exploit a neutral analog. e.g., climate change scenarios.

  19. Purposes • forecast of detailed development (e.g. weather forecast) – neutral analog: future development • dynamically consistent interpretation and extrapolation of observations in space and time (“data assimilation”) - neutral analog: space-time correlations • reconstruction of global past states and construction of scenarios - neutral analog: sensitivity to external forcings • reconstruction of regional past states - neutral analog: dynamical downscaling link • process sensitivity analysis – neutral analog: embedding of process in dynamics • experimentation tool (test of hypotheses) – neutral analog: all processes significant to the hypothesis are taken realistically into account.

  20. Concept of Dynamical Downscaling RCM 3-d vector of state Physiographic detail Known large scale state projection of full state on large-scale scale Large-scale (spectral) nudging

  21. PCC improvement/ deterioration RCM Nudge PCC DWD and NCEP PCC improvement/ deterioration RCM Standard Pattern correlations (%) Positive values show added value of the regional model. 95% significant deviations are marked by a *.

  22. detailed parameterization Latitude-height distribution of temperature (deg C) Effect of black cirrus Difference “black cirrus” - detailed parameterization Roeckner & Lohmann, 1993 No cirrus Difference “no cirrus” - detailed parameterization

  23. Testing the of multimodality of large scale atmospheric dynamics Berner and Branstator, pers. comm

  24. Testing the MBH “hockeystick method” • Simulating the process of “reconstructing” historical climate variations using the data from the 1000 year historical ECHO-G simulation. • Done by constructing “pseudo-proxies”. • Short-term (<20 yrs) variations about ok, but long-term variations (>100 years) severely underestimated. • MBH method methodically flawed.

  25. Conclusions „Models“ can be very different species The different species have different functional properties „Models“ can hardly be verified For further reading, refer to: von Storch, H., S. Güss und M. Heimann, 1999: Das Klimasystem und seine Modellierung. Eine Einführung. Springer Verlag ISBN 3-540-65830-0, 255 pp von Storch, H., and G. Flöser (Eds.), 2001: Models in Environmental Research. Proceedings of the Second GKSS School on Environmental Research, Springer Verlag ISBN 3-540-67862, 254 pp. Müller, P., and H. von Storch, 2004: Computer Modelling in Atmospheric and Oceanic Sciences - Building Knowledge. Springer Verlag Berlin - Heidelberg - New York, 304pp, ISN 1437-028X

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