Introduction to climate modeling. ECOLMAS Course 1-4 April 2008

Transcription

1 Introduction to climate modeling ECOLMAS Course 1-4 April 2008

2 Course description Goal: To equipe you with a comfortable basic knowledge in the trade of climate modeling Course web site:

3 Recommended reading: General Hartmann, Dennis L.: Global Physical Climatology. Academic Press, Ruddiman, William F.: Earth s Climate. Past and Future. Freeman, 2001.

4 Recommended reading: Modeling Trenberth, Kevin E. (ed.): Climate System Modeling, Cambridge University Press, McGuffie, K. and A. Henderson-Sellers: Forty years of numerical climate modeling, International Journal of Climatology 21, (2001). Washington, Warren M. and Parkinson, Claire L.: An Introduction to Three-Dimensional Climate Modeling. 2nd edition. University Science Books, Sausalito, California, 2005.

5 What is climate? Climate s what we expect, but weather s what we get. (Larry Riddle)

6 What is climate? Climate is about the expected values of the meteorological elements ( climatic elements ) at a location during a certain month or season temperature, precipitation, wind, pressure, cloudiness, humidity usually at the surface of the Earth annual means, distribution through the year as well as interannual variability

7 What is the climate system?

8 Land surface Vegetation Ocean Atmosphere Ice From Apollo 17 flight, 7 December 1972

9 Conceptual model of the climate system Figure 1-5 (bottom) from Ruddiman (2001)

10 Conceptual model of the climate system Complexity of real climate system can be organized and simplified in a conceptual model Engineer s point of view: the climate system running as a machine But what are the connections between the various components it is made of?

11 Interactions between climate system components Energy exchange in different forms: Sensible heat flux Latent heat flux Related to evaporation/precipitation and water flux in the atmosphere, salt transport in the ocean Momentum flux E.g. associated with wind energy

12 What is a model? 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) (Hans von Storch)

13 Examples of models models build to scale (houses, cars, ) map, sketch or drawing numerical model conceptual quasi-realistic, surrogate of reality Models put numbers on ideas (W. F. Ruddiman)

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15 Why use climate models? To formulate and test hypothesis To have an independent way to test whether a particular theory can explain the (proxy-) data To understand past climates response to climate forcings couplings and feedbacks between climate system components To relate present climate to human activities To make predictions for the future

16 Types of models Atmospheric general circulation models (AGCMs) Ocean general circulation models (OGCMs) Sea-ice and land ice models Vegetation models Biogeochemical models Marine ecosystem models

17 First numerical model First description of a method for constructing a weather forecast by means of numerical calculation was published by Richardson (1922)

18 Basics of numerical models 1. State variables 2. Fundamental equations 3. Parameterization 4. Discretization 5. Numerical solution

19 State variables Many variables can be thought of as a concentration or property per unit volume. Fluxes then have dimensions of property per unit time and area.

20 Examples of state variables Ocean Atmosphere Temperature Temperature Salinity Density Pressure Humidity Current velocity Cloud water content Pressure Wind velocity

21 Fundamental equations Conservation of momentum (horizontal) velocity (winds, currents) Conservation of mass ( principle of continuity ) vertical velocity, humidity, salinity Conservation of energy ( first law of thermodynamics ) temperature Equation of state density (air, sea water)

22 Parameterization in climate models Sub-gridscale processes, or processes that cannot be derived from first principles, must be parameterized e.g. thundercloud formation, soil moisture transfer in the atmosphere, eddies and convection in the ocean

23 Discretization Most common in ocean models: Finite difference method in time Finite difference or finite volume method in space