1. Some fundamentals of numerical weather and climate prediction Robert Fovell Atmospheric and Oceanic Sciences University of California, Los Angeles rfovell@ucla.edu

2. Equations Navier-Stokes equations –Newton’s 2 nd law: real & apparent forces –Turbulence and mixing 1 st law of thermodynamics Ideal gas law Continuity equation Clausius-Clapeyron equation Discretize in space and time

3. Domain Local, regional or global in scale

4. Domain Local, regional or global in scale Discretize into grid volumes –virtual internal walls –boundary conditions Initialize each volume Make some forecasts…

5. Extrapolation Equations predict tendencies –Previous forecasts used to recalculate tendencies –Initial forecasts start with observations but subsequent forecasts based on forecasts –Success depends on quality of initialization and accuracy of tendencies

6. A simplistic example: temperature in your backyard

7. The model forecast will consider an enormous number of factors to estimate present tendency to project future value

8. In the absence of such information, you simply guess… and wait to see how good your guess was.

9. The model does not wait. It uses each forecast to recalculate the tendencies to make the next forecast

10. Verification of your forecast: OK since you didn’t project out too far

11. Forecast time steps cannot be too long. Tendencies have the tendency to CHANGE.

12. Forecasts will reflect… Radiative processes Surface processes Cloud development and microphysics Advection and mixing Convergence and divergence Ascent and subsidence Sea-breezes, cold and warm fronts Cyclones, anticyclones, troughs, ridges

13. Model resolution Things to try to resolve –Clouds, mountains, lakes & rivers, hurricane eyes and rainbands, fronts & drylines, tornadoes, much more What isn’t resolved is subgrid At least 2 grid boxes across a feature for model to even “see” it… and at least 6 to render it properly

14. Wave-like features are ubiquitous.

15. Models sample wave only at grid points. Example: 4 points across each wave.

16. Models “connect the dots”.

17. Suppose we have only 3 points across the wave… or just 2 points…

18. These do not look much like the actual wave at all. With only 2 points, wave may be invisible.

19. High resolution Hurricane Katrina satellite picture = composed of 1 km pixels. High resolution model = possibly accurate, definitely expensive, and extremely time-consuming

20. As model resolution increases, we see more, but have to DO more. Plus, the time step has to decrease, to maintain linear and nonlinear stability

21. Compromising on the resolution

22. 30 km resolution

23. At what point would we not be able to tell that’s a hurricane if we had not known it from the start?

24. This is the world as seen by global weather models not so long ago, and many climate models today.

25. Outlook is good… Models are improving –Faster computers –More and better input data (satellites) –Better ways of using those data –Progressively better numerical techniques –High resolution models… so less is missed (subgrid) –… but some things we can never capture

26. Parameterization Parameterization = an attempt to represent what we cannot see, based largely on what we can Parameterizations in a typical weather/climate model include - and are NOT limited to… –Boundary layer processes and subgrid mixing –Cloud microphysics [resolve cloud, can’t resolve drops] –Convective parameterizations [can’t resolve clouds] –Surface processes [heat, moisture fluxes] –Subsurface processes [soil model, ocean layers] –Radiative transfer, including how radiation interacts with clouds Here is an example…

27. Another view of Katrina... But focusing on roll clouds. Roll clouds accomplish boundary layer mixing.

28. How roll clouds form Consider the sun warming the land during the day

29. How roll clouds form With uneven heating, wind and vertical wind shear, roll-like circulations can start You can simulate roll formation in a higher resolution model, like DTDM

30. How roll clouds form As the land warms up, the rolls get deeper The rolls mix heat vertically –air is a lousy conductor Also mixing momentum, moisture

31. How roll clouds form If conditions are favorable, clouds will form above the roll updrafts These make the rolls visible

32. We see these roll clouds on satellite pictures In a sense, being able to simulate the roll clouds means we’ve done many things well - - radiation, winds, mixing, saturation processes

33. Parameterizing mixing… In many models, however, the clouds and the mixing that created them are subgrid But that mixing is important. It influences structure and stability of the atmosphere. If we cannot resolve it, we must parameterize it Weather/climate models are full of parameterizations, each a potential model shortcoming, each a possible source of problems, of error, of uncertainty regarding the future

34. Birth of Numerical Weather and Climate Prediction

35. Prof. Cleveland Abbe “There is a physical basis for all meteorological phenomena. There are laws of mechanics and heat that apply to the atmosphere, and as fast as we acquire the ability to discover and reason out their consequences, we shall perceive that LAW and ORDER prevail in all the complex phenomena of the weather and the climate.” (1901) First head of US Weather Bureau

36. Prof. Vilhelm Bjerknes 1904 vision on weather prediction Lamented the “unscientific” basis of meteorology Goal: to make meteorology a more exact science … by making predictions

37. Bjerknes’ vision Two key ingredients: –Sufficiently accurate knowledge of the state of the atmosphere at the initial time –Sufficiently accurate knowledge of the physical laws that govern how the atmospheric state evolves Identified 7 fundamental variables -- T, p, density, humidity, and three wind components -- and the equations that calculated their tendencies These are nasty equations, without simple solutions. Only numerical methods could be brought to bear on them.

38. Max Margules Austrian meteorologist Tried to predict surface pressure using continuity equation… and found it could produce very poor results In 1904, he declared this would be impossible and that weather forecasting was “immoral and damaging to the character of a meteorologist.”

39. Lewis Fry Richardson Among first to try to solve the weather/climate equations numerically Invented many concepts still used today Made the first numerical weather forecast Prediction was horribly wrong

40. Richardson’s technique Richardson laid out a grid, collected his observations, created novel numerical approximations for his equations and crunched his numbers, by hand and slide rule Actually, it was a hindcast that took laborious computations using old, tabulated data

41. Richardson’s grid Richardson used 5 vertical levels, including surface

42. Richardson’s forecast He predicted a surface pressure rise of 145 mb (about 14.5%) in only 6 h In reality the pressure hardly changed at all First forecast = first forecast failure What went wrong?

43. Richardson EXTRAPOLATED too far. His technique made monsters out of meaningless oscillations that happen as air wiggles up and down in a stable atmosphere.

45. Richardson’s book Richardson revealed the details of his blown forecast in “Weather Prediction by Numerical Process”, published in 1922. Undaunted, he imagined his pencil and paper technique applied to the entire global atmosphere… –In a huge circular ampitheatre in which human calculators would do arithmetic and, guided by a conductor at the center, pass results around to neighbors

46. Richardson’s forecasting theater

47. After Richardson Richardson’s confidence was not unfounded. Mathematicians and meteorologists realized the flaws of his techniques The digital computer was created The meteorological observation network was expanding rapidly It seemed only a matter of time until the vision of Abbe, the goal of Bjerknes, the dream of Richardson, became a reality… Then in 1962, Ed Lorenz did his little experiment…

48. Prof. Ed Lorenz MIT professor Landmark 1962 paper “Deterministic Nonperiodic Flow” Led to “chaos theory” and dynamical systems Coined “butterfly effect”

49. The Lorenz Experiment Lorenz’ model wasn’t a weather model, and didn’t even have grid points 3 simple equations, which can describe fluid flow in a cylinder with heated bottom and cooled top He called his variables X, Y and Z

50. The Lorenz Experiment X indicated the magnitude and direction of the overturning motion As X changed sign, the fluid circulation reversed

51. The Lorenz Experiment Y was proportional to the horizontal T gradient

52. The Lorenz Experiment And Z revealed the fluid’s stability

53. The Lorenz Model Three simple equations But in important ways they were like the equations we use in weather forecasting –They are coupled –They are nonlinear

54. Simulation similar to Lorenz’ original experiment. X = circulation strength & magnitude.

55. The model was started with unbalanced initial values for X, Y and Z, creating a shock. The model was seeking a suitable balance.

56. Next, a spin-up period with swings that grow until…

57. The fluid chaotically shifts from CW to CCW circulations in an nonperiodic fashion

58. This was Lorenz’ discovery… sensitive dependence on initial conditions

59. Dependence on initial conditions Caused by nonlinear terms Even if model is perfect, any error in initial conditions means forecast skill decreases with time Reality: models are far from perfect Long range weather prediction is impossible Lorenz: “We certainly had been successful at doing that anyway and now we had an excuse.”

60. Lorenz attractor 3D space with coordinates being the Lorenz variables, X Y and Z Predicted values can be plotted as a point in this space Start with an initial conditions that are slightly different…, … they diverge in this space Birth of chaos theory Poorly named: chaos ≠ random

61. If we cannot produce accurate weather forecasts for next week, how can we trust climate forecasts for next decade, or next century? The simple answer is: weather is not climate

62. Note weather is different but climate is similar.

63. Summary Weather and climate models = great tools for understanding our present and predicting our future Models initialized with data to compute tendencies, via extrapolation Models have limitations –Resolution –Unresolvable features and processes –Incomplete or erroneous initial conditions New forecasts based on previous ones, so error grows Limit to weather predictability (Lorenz) Weather ≠ climate

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