The Art and Role of Climate Modeling

Transcription

1 Institute of Coastal Research, GKSS Research Centre Geesthacht, Hans von Storch The Art and Role of Climate Modeling

2 Overview: 1. Conceptual aspects of modelling 2. Conceptual models for the reduction of complex systems 3. Quasi-realistic climate models ( surrogate reality ) 4. Free simulations and forced simulations for reconstruction of historical and paleoclimate 5. Climate change simulations 6. Laboratory to test conceptual models

3 Conceptual aspects of modelling

4 1. Conceptual aspects of modelling 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.

5 1. Conceptual aspects of modelling 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.

6 Positive analog Neutral analog Application

7 1. Conceptual aspects of modelling Models are smaller than reality (finite number of processes, reduced size of phase space) simpler than 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)

8 1. Conceptual aspects of modelling

9 1. Conceptual aspects of modelling

10 1. Conceptual aspects of modelling

11 1. Conceptual aspects of modelling 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

12 1. Conceptual aspects of modelling Models can be shown to be consistent with observations, e.g. the known part of the phase space may reliably be reproduced.

13 1. Conceptual aspects of modelling 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.

14 1. Conceptual aspects of modelling Purpose of models reduction of complex systems understanding surrogate reality realism

15 Conceptual models for the reduction of complex systems

16 2. Models for reduction of complex systems 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 constitutes understanding, i.e. theory construction of hypotheses characteristics: simplicity idealisation conceptualisation fundamental science approach

17 Noise or deterministic chaos? Mathematical construct of randomness adequate concept for description of features resulting from the presence of many chaotic processes.

18 2. Models for reduction of complex systems Numerical experiment with ocean model: standard simulation with steady forcing (wind, heat and fresh water fluxes) plus random zero-mean forcing precipitation overlaid. Example for Stochastic Climate Model at work. response Mikolajewicz, U. and E. Maier-Reimer, 1990

19 2. Models for reduction of complex systems Energy balance at the surface of Earth (W/m²)

20 2. Models for reduction of complex systems Idealized energy balance

21 2. Models for reduction of complex systems Temperature dependent albedo (reflectivity)

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

23 Quasi-realistic Modelling

24 3. Quasi-realistic climate models Models as surrogate reality dynamical, process-based models, experimentation tool (test of hypotheses) design of scenario sensitivity analysis dynamically consistent interpretation and extrapolation of observations in space and time ( data assimilation ) forecast of detailed development (e.g. weather forecast) characteristics: complexity quasi-realistic mathematical/mechanistic engineering approach

25 3. Quasi-realistic climate models Components of the climate system. (Hasselmann, 1995)

26 quasi-realistic climate models

27 Dynamical processes in the atmosphere

28 Dynamical processes in a global atmospheric general circulation model

29 Dynamical processes in the ocean

30 Dynamical processes in a global ocean model

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32 validation ECHAM3/LSG ERA ECMWF

33 validation

34 How well are these processes represented in climate models? atmosphere ocean Bray and von Storch, 1999 Results of a survey among climate modellers

35 Roeckner & Lohmann, 1993 detailed parameterization Latitude-height distribution of temperature (deg C) Effect of black cirrus Difference black cirrus - detailed parameterization No cirrus Model as a constructive tool Difference no cirrus - detailed parameterization

36 Forcasting Data Analysis Scenarios Simulations Planning of everyday life Design of policy Generation of large, consistent data set Generation of large, consistent data set under controlled conditions Dynamical analysis Model application Practical knowledge Dynamical insightl Improvement of models hypotheses Hypothesis Testing

37 Free and forced simulations for reconstruction of historical climate

38 4. Free and forced model simulations Different ways of running the model "Free Simulation": Ψ t+ 1 = F( Ψ t ) "Forced Simulation": Ψ t+ 1 = F( Ψ ; η ) with η = greenhouse gas concentrations t t t or aerosol concentrations or solar output (incl.orbital configuration) or topography (e.g., ice sheets) or vegetation

39 Free Simulation: 1000 years no solar variability, no changes in greenhouse gas concentrations, no orbital forcing Model as a constructive tool Temperature (at 2m) deviations from 1000 year average [K] Zorita, 2001

40 Forced Simulation simulation Changing solar forcing and time variable volcanic aerosol load; greenhouse gases

41 4. Free and forced model simulations Climate model used Atmosphere: ECHAM4 horizontal resolution T30 ~ 300 km at mid latitudes Ocean: HOPE-G horizontal resolution T42 ~ 200 km at mid latitudes increased resolution in the tropics Model provided as community climate by Model & Data Group at MPI for Meteorology and run at German Climate Computing Centre (DKRZ) and computing facilities at FZ Jülich

42 4. Free and forced model simulations

43 4. Free and forced model simulations Temperature conditions in Switzerland according to Pfister s classification. From Luterbacher, 2001

44 validation Reconstruction from historical evidence, from Luterbacher et al. Late Maunder Minimum Model-based reconstuction vs

45 Global temperature anomaly Model as a constructive tool

46 Model as a constructive tool Simulated differences of ice coverage, in percent, during the LMM event and the long term mean

47 4. Free and forced model simulations Conclusions Free simulations are routinely done with GCMs; They reproduce most large-scale features of present climate in a satisfactorily manner. They exhibit a rich spectrum of variability. Forced simulations, with fully coupled atmosphereocean models, are also done. Changed factors are greenhouse gases, aerosols, vegetation, topography, orbit parameters... They generate just one of infinitely many consistent realizations of the forced state.

48 4. Free and forced model simulations Climate change simulations

49 5. Climate Change simulations

50 SRES Scenarios SRES = IPCC Special Report on Emissions Scenarios A1 A2 B1 B2 IS92a A world of rapid economic growth and rapid introduction of new and more efficient technology. A very heterogeneous world with an emphasis on family values and local traditions. A world of dematerialization and introduction of clean technologies. A world with an emphasis on local solutions to economic and environmental sustainability. business as usual scenario (1992). IPCC, 2001

51 Scenario A2 Annual temperature changes [ C] ( ) ( ) Scenario B2 Danmarks Meteorologiske Institut

52 TAR (2001) regional development scenarios A2 and B2. temperature Agreement among 7 out of a total of 9 simulations Giorgi et al., 2001

53 TAR (2001) regional development scenarios A2 and B2. precipitation Agreement among 7 out of a total of 9 simulations Giorgi et al., 2001

54 Inconsistency of model results on sub-regional scale temperature ( o C) Rossby Center model forced with ECHAM global scenario forced with HadCM global scenario precipitation (%) Bergström et al., 2001

55 Laboratory to test conceptual models

56 6. Laboratory to test conceptual models Example: Stommel model of the North Atlantic overturning F t, H t freshwater and heat flux F p, H p Subtropical Atlantic T t,s t Transport Subpolar Atlantic T p, S p t t ΔT = ΔH ΔS = ΔF m = k ΔH = γ * 2 m ΔT 2 m ΔS ( αδt βδs ) ( * ΔT ΔT )

57 6. Laboratory to test conceptual models Stommel s theory Rahmstorf s model * ΔF * ΔF Rahmstorf, 1995

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

59 Conclusions Models can be very different species The different species have different functional properties Models can hardly be verified For further reading, refer to: Müller, P., and H. von Storch, 2004: Computer Modelling in Atmospheric and Oceanic Sciences - Building Knowledge. Springer Verlag Berlin - Heidelberg - New York, 304pp, ISN X or von Storch, H., and G. Flöser (Eds.), 2001: Models in Environmental Research. Proceedings of the Second GKSS School on Environmental Research, Springer Verlag ISBN , 254 pp.

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