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Generation of added value with models Hans von Storch, GKSS Research Centre, Geesthacht, and KlimaCampus „clisap“, University of Hamburg Germany Folie 1 Overview: 1. Quasi-realistic climate models („surrogate reality“) 2. Free simulations and forced simulations for reconstruction of historical climate 3. Climate change simulations 4. Downscaling - Regional climate modelling Folie 2 Conceptual aspects of modelling Folie 3 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 Folie 4 pp. 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. Folie 5 Conceptual aspects of modelling Folie 6 Conceptual aspects of modelling Folie 7 Conceptual aspects of modelling Folie 8 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 Folie 9 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. Folie 10 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. Folie 11 Conceptual aspects of modelling Purpose of models • reduction of complex systems understanding • surrogate reality realism Folie 12 Conceptual models for the reduction of complex systems Folie 13 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 Folie 14 2. Models for reduction of complex systems Idealized energy balance Folie 15 2. Models for reduction of complex systems E =bE +aA with b = albedo a = transmissivity E = short wave solar radiation A = long wave thermal radiation = sT4 Teq 1 b E as 1 4 equilibrium without atmosphere a=1, with present atmosphere a=0.64, b= 0.30 : Teq = +15°C Folie 16 b= 0 : Teq = - 4°C Models for reduction of complex systems Temperature dependent albedo (reflectivity) Folie 17 Noise or deterministic chaos? Mathematical construct of randomness adequate concept for description of features resulting from the presence of many chaotic processes. Folie 18 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 Folie 19 Models for reduction of complex systems Numerical experiment with ocean model: standard simulation with steady forcing (wind, heat and fresh water fluxes) plus random forcing zero-mean precipitation overlaid. Example for Stochastic Climate Model at work. Folie 20 response Quasi-realistic modelling Folie 21 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: Folie 22 complexity quasi-realistic mathematical/mechanistic engineering approach Components of the climate system. (Hasselmann, 1995) Folie 23 Quasi-realistic climate models … … are dynamical models, featuring discretized equations of the type dΨ k Pi ,k (k ) dt i with state variables Ψk and processes Pi,k. The state variables are typically temperature of the air or the ocean, salinity and humidity, wind and current. … because of the limited resolution, the equations are not closed but must be closed by “parameterizations”, which represent educated estimates of the expected effect of nondescribed processes on the resolved dynamics, conditioned by the resolved state. Folie 24 atmosphere Folie 25 Dynamical processes in the atmosphere Folie 26 Dynamical processes in a global atmospheric general circulation model Folie 27 Dynamical processes in the ocean Folie 28 Dynamical processes in a global ocean model Folie 29 Bray and von Storch, 2010 Results of a survey among climate modellers in 1996, 2003 and 2008 Folie 30 Folie 31 Folie 32 validation 1880–2049 ECHAM3/LSG 1973–1993 ERA ECMWF Folie 33 Climatic Zones Modell Observed Classification following Koeppen Folie 34 Erich Roeckner, pers. communication Observed Simulated Winter (DJF) Cyclogenesis Density of stromtracks Erich Roeckner, pers. Comm. Folie 35 Typical different atmospheric model grid resolutions with corresponding land masks. T42 used in global models. (courtesy: Ole BøssingChristensen) Folie 36 variance global model Insufficiently resolved Well resolved Spatial scales Folie 37 Free and forced simulations for reconstruction of historical climate Folie 38 4. Free and forced model simulations Different ways of running the model " Free Simulation ": t 1 F(t ) " Forced Simulation ": t 1 F(t ; t ) with t greenhouse gas concentrat ions Folie 39 or aerosol concentrat ions or or or solar output (incl. orbital configurat ion) topography (e.g., ice sheets) vegetation Free Simulation: 1000 years Folie 40 Model as a constructive tool Zorita, 2001 Temperature (at 2m) deviations from 1000 year average [K] no solar variability, no changes in greenhouse gas concentrations, no orbital forcing Folie 41 validation Reconstruction from historical evidence, from Luterbacher et al. Late Maunder Minimum Model-based reconstuction 1675-1710 vs. 1550-1800 Folie 42 Global 1675-1710 temperature anomaly Folie 43 Model as a constructive tool Free and forced model simulations • 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 atmosphere-ocean models, are also done. Changed factors are greenhouse gases, aerosols, vegetation, topography, orbit parameters ... A simulation generates one of infinitely many consistent realizations of the forced state. Folie 44 Laboratory to test conceptual models Folie 45 Example: Stommel model of the North Atlantic overturning Laboratory to test conceptual models Ft, Ht freshwater and heat flux Fp, Hp Subtropical Atlantic Tt,St Subpolar Atlantic Tp, Sp Transport t T H 2 m T t S F * 2 m S m k aT bS H T T * Folie 47 Laboratory to test conceptual models Rahmstorf‘s model Stommel‘s theory F * F * Rahmstorf, 1995 Folie 48 Testing the of multimodality of large scale atmospheric dynamics Berner Folie 49 and Branstator, pers. comm 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 Folie 50 Difference “no cirrus” - detailed parameterization Deconstruction of recent climatic development °C simulation without anthropogenic drivers simulation with anthropogenic drivers vs. „observation“ (centered on 1960-1990 mean) Folie 51 Climate change simulations Folie 52 5. Climate Change simulations Folie 53 Scenario building • Construction of scenarios of emissions. • Construction of scenarios of concentrations of radiatively active substances in the atmosphere. • (Ok – not quite exact; aerosols …) • Simulation of climate as constrained by presence of radiatively active substances in the atmosphere (“prediction” of conditional statistics). Folie 54 “SRES” Scenarios SRES = IPCC Special Report on Emissions Scenarios A1 A world of rapid economic growth and rapid introduction of new and more efficient technology. A2 B1 A very heterogeneous world with an emphasis on family values and local traditions. A world of “dematerialization” and introduction of clean technologies. IS92a A world with an emphasis on local solutions to economic and environmental sustainability. “ business as usual ” scenario (1992). Folie 55 IPCC, 2001 B2 Scenario A2 Annual temperature changes [°C] (2071–2100) (1961–1990) – Scenario B2 Folie 56 Danmarks Meteorologiske Institut precipitation Folie 57 Agreement among 7 out of a total of 9 simulations Giorgi et al., 2001 TAR (2001) „sub-continental development“ scenarios A2 and B2. Downscaling Folie 58 Regional and local conditions – in the recent past and next century Simulation with barotropic model of North Sea Globale development (NCEP) Tide gauge St. Pauli Dynamical Downscaling CLM Cooperation with a variety of governmental agencies and with a number of private companies Folie 59 Empirical Downscaling Typical regional atmospheric model grid resolutions with corresponding land masks. 50 km grid used in regional models (courtesy: Ole BøssingChristensen) Folie 60 variance regional model Insufficiently resolved Well resolved Spatial scales Folie 61 Added value Concept of Dynamical Downscaling RCM Physiographic 3-d vector of state detail State space equation Ψ t 1 F(Ψ t ;ηt ) εt Observatio n equation d t G(Ψ t ) δt Known large scale statewit h t , t model and observatio n errors F dynamical model projection of full state on G observatio n model large-scale scale Ψ t*1 F(Ψ t ;ηt ) Forward integratio n : d t*1 G (t*1 ) Ψ t 1 Ψ t*1 K(d t*1 d t 1 ) with a suitable operator K . Folie 62 Large-scale (spectral) nudging Example Extreme Events (Wind & Waves) Wind [m /s] Years SON EUR K13 Hipocas xr90 2 5 25 2 5 25 2 5 25 24.38 25.86 28.44 22.50 23.76 25.67 23.29 24.89 26.68 xr 25.17 27.28 31.33 23.16 24.82 28.00 24.15 26.32 30.70 Waves [m ] Hipocas Observed Observed xr90 xr90 25.96 28.70 34.22 23.82 25.88 30.33 25.01 27.75 34.72 24.05 25.75 28.09 23.16 24.33 26.43 23.11 24.15 26.42 xr 25.21 27.64 32.77 24.03 25.94 29.75 24.03 25.94 29.75 xr90 26.37 29.53 37.45 24.90 27.55 33.07 24.95 27.73 33.08 xr90 xr xr90 7.12 7.49 7.86 7.84 8.44 9.04 8.99 10.35 11.71 5.89 6.15 6.41 6.34 6.83 7.32 6.90 8.20 9.50 6.78 7.06 7.34 7.37 7.79 8.21 8.04 9.03 10.02 xr90 xr 6.41 6.93 7.52 5.52 5.89 5.99 5.60 5.97 6.34 xr90 6.77 7.13 7.54 8.15 9.21 10.90 5.84 6.16 6.46 7.03 7.88 9.77 5.84 6.08 6.46 6.95 7.88 9.42 2, 5, and 25-year return values with 90% confidence limits based on 10.000 Monte Carlo simulations each. (Weisse and Günther. 2006) Folie 63 What is coastDat? A set of model data of recent, ongoing and possible future coastal climate (hindcasts 1948-2008, reconstructions and scenarios for the future, e.g., 2070-2100) Based on experiences and activities in a number of national and international projects (e.g. WASA, HIPOCAS, STOWASUS, PRUDENCE) Presently contains atmospheric and oceanographic parameter (e.g. near-surface winds, pressure, temperature and humidity; upper air meteorological data such as geopotential height, cloud cover, temperature and humidity; oceanographic data such as sea states (wave heights, periods, directions, spectra) or water levels (tides and surges) and depth averaged currents, ocean temperatures) Covers different geographical regions (presently mainly the North Sea and parts of the Northeast Atlantic; other areas such as the Baltic Sea, subarctic regions or E-Asia are to be included) http://www.coastdat.de, contact: Ralf Weisse ([email protected]) Folie 64 Some applications of - Ship design - Navigational safety - Offshore wind - Oils spill risk - Interpretation of measurements - Chronic Oil Pollution - Ocean Energy Currents Power [W/m2] Folie 65 Wave Energy Flux [kW/m] Scenarios for Northern Germany Folie 66 Conclusions • “Model” is a term with very many different meaning in different scientific and societal quarters. • The constructive part of models is in their neutral analogs with “reality”. • Validation of models means to check positive and negative analogs. • In climate science we have conceptual models – constituting understanding – and quasi-realistic models, allowing for numerical experimentation. • Quasi-realistic models may be used for testing hypothesis, for developing hypothesis, for the construction of a full 4-d state, forecasts and for scenarios. Folie 67 Conclusions • Global climate modeling allows the representation of global, continental and sub-continental scales. Global models fail on the regional and local scale. •Scenarios of future climate change hinge on the validity of economic scenarios. • Simulation of regional climate is a downscaling problem and not a boundary value problem. • Marine weather (winds, waves) have been successfully reconstructed for Northern Europe for the years 1958-97 with a 1-hourly resolution. (CoastDat@GKSS). Also, scenarios are available to this end. Folie 68 Background information on this issue: 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 3-54067862, 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 Folie 69