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Folie 1 Climate models, downscaling and uncertainties Hans von Storch, GKSS Research Centre, Geesthacht, and KlimaCampus clisap, University of Hamburg.

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Presentation on theme: "Folie 1 Climate models, downscaling and uncertainties Hans von Storch, GKSS Research Centre, Geesthacht, and KlimaCampus clisap, University of Hamburg."— Presentation transcript:

1 Folie 1 Climate models, downscaling and uncertainties Hans von Storch, GKSS Research Centre, Geesthacht, and KlimaCampus clisap, University of Hamburg Germany

2 Folie 2 Who is this? Hans von Storch (born 1949) Diploma in mathematics, PhD in meteorology Director of Institute for Coastal Research, GKSS Research Center, near Hamburg, Professor at the Meteorological Institute, KlimaCampus, University of Hamburg Works also with social and cultural scientists.

3 Folie 3 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 5.Regional scenarios

4 Folie 4 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

5 Folie 5 Components of the climate system. (Hasselmann, 1995)

6 Folie 6 Quasi-realistic climate models … … are dynamical models, featuring discretized equations of the type with state variables Ψ k and processes P i,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 non-described processes on the resolved dynamics, conditioned by the resolved state.

7 Folie 7 atmosphere

8 Folie 8 Dynamical processes in the atmosphere

9 Folie 9 Dynamical processes in a global atmospheric general circulation model

10 Folie 10 Results of a survey among climate modellers in 1996, 2003 and 2008 Bray and von Storch, 2010

11 Folie 11 Modell Beobachtet Klimazonen Klassifikation nach Koeppen Erich Roeckner, pers. Mitteilung

12 Folie 12 Observed Simulated Winter (DJF) Erich Roeckner, pers. Mitteilung Zyklogenese Sturmbahn- dichten

13 Folie 13 Precipitation in IPCC AR4 models Erich Roeckner, pers. Mitteilung

14 Folie 14 Free and forced simulations for reconstruction of historical climate

15 Folie 15 Different ways of running the model

16 Folie 16 Free Simulation: 1000 years no solar variability, no changes in greenhouse gas concentrations, no orbital forcing Temperature (at 2m) deviations from 1000 year average [K] Zorita, 2001

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

18 Folie 18

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

20 Folie 20 Global temperature anomaly

21 Folie 21 Climate change simulations

22 Folie 22

23 Folie 23 Scenarios of what? Climate = the statistics of weather, usually described by probability density functions, in particular by - their moments (e.g., mean, std deviation, covariances), - percentiles and return values, - spatial characteristics (e.g., EOFs), - temporal characteristics (autocovariance function, spectra)

24 Folie 24 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).

25 Folie 25 SRES Scenarios SRES = IPCC Special Report on Emissions Scenarios 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). A1 A2 B1 B2 IS92a IPCC, 2001

26 Folie 26 Scenario building 1.Simulation with global models, which describe several compartments of the global earth system – relatively coarse spatial grid resolution (e.g., 200 km) 2.Simulation with regional models, often with only one or a few compartments (mostly atmosphere) – relatively high spatial grid resolution (e.g., 50 km) 3.Simulation with impact models – a large variety of different systems, e.g., storm surges or ocean waves.

27 Folie 27 Scenario A2 Scenario B2 Danmarks Meteorologiske Institut Annual temperature changes [°C] (2071–2100) – (1961–1990)

28 Folie 28 Agreement among 7 out of a total of 9 simulations precipitation IPCC (2001) regional development scenarios A2 and B2. Giorgi et al., 2001

29 Folie 29 Typical global atmospheric model grid resolution with corresponding land mask. T42 used in global models. (courtesy: Ole Bøssing- Christensen)

30 Folie 30 global model Well resolved Insufficiently resolved Spatial scales variance

31 Folie 31 Downscaling

32 Folie 32 Globale development (NCEP) Dynamical Downscaling CLM Simulation with barotropic model of North Sea Empirical Downscaling Tide gauge St. Pauli Cooperation with a variety of governmental agencies and with a number of private companies Regional and local conditions – in the recent past and next century

33 Folie 33 Typical regional atmospheric model grid resolutions with corresponding land masks. 50 km grid used in regional models (courtesy: Ole Bøssing- Christensen)

34 Folie 34 Well resolved Insufficiently resolved Spatial scales variance regional model Added value

35 Folie 35 Concept of Dynamical Downscaling RCM Physiographic detail 3-d vector of state Known large scale state projection of full state on large-scale scale Large-scale (spectral) nudging

36 Folie 36 Example Extreme Events (Wind & Waves) 2, 5, and 25-year return values with 90% confidence limits based on Monte Carlo simulations each. (Weisse and Günther. 2006)

37 Folie 37 A set of model data of recent, ongoing and possible future coastal climate (hindcasts , reconstructions and scenarios for the future, e.g., ) 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) contact: Ralf Weisse What is coastDat?

38 Folie 38 - Ship design - Navigational safety - Offshore wind - Oils spill risk - Interpretation of measurements - Chronic Oil Pollution - Ocean Energy Wave Energy Flux [kW/m] Currents Power [W/m 2 ] Some applications of

39 Folie 39 Scenarios for Northern Germany

40 Folie 40 RCAOHIRHAM A2 - CTL: changes in 99 % - iles of wind speed (6 hourly, DJF): west wind sector selected (247.5 to deg) Scenarios for Woth, personal communication

41 Folie 41 North German Climate An institution set up to enable communication between science and stakeholders that is: making sure that science understands the questions and concerns of a variety of stakeholders that is: making sure that the stakeholders understand the scientific assessments and their limits. Typical stakeholders: Coastal defense, agriculture, off-shore activities (energy), tourism, water management, fisheries, urban planning

42 Folie 42 Online-Atlas Klimawandel Norddeutschland Darstellung unterschiedlicher Größen zum Klimawandel in Norddeutschland für die Zeiträume , und Darstellung der Differenz zu dem Kontrollzeitraum Darstellung unterschiedlicher Treibhausgasszenarien (nach dem IPCC) gerechnet mit verschiedenen regionalen Modellen norddeutscher-

43 Folie 43 Conclusions Global climate modeling allows the representation of global, continental and sub-continental scales. Global models fail on the regional and local scale. Global climates is varying because of both internal dynamics as well as external forcing. 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 the years with a 1-hourly resolution.

44 Folie 44 Background information on this issue: von Storch, H., S. Güss und M. Heimann, 1999: Das Klimasystem und seine Modellierung. Eine Einführung. Springer Verlag ISBN , 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 , 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 X

45 Folie 45 Weblog KLIMAZWIEBEL

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