1. EG1204: Earth Systems: an introduction Meteorology and Climate Lecture 7 Climate: prediction & change

2. Topics we will cover Ancient climate prediction Weather vs Climate Short, Medium and Long Range prediction Statistical forecasting Chaos theory Dynamical forecasting Global climate change

3. Predictability is to prediction as romance is to sex Miyakoda, 1985

4. Ancient climate prediction The earliest attempts to predict the weather were by farmers and the military The Greeks successfully used predictions about the wind to defeat the Turkish during sea battles Predicting weather could make the difference between life and death for farmers

6. Weather vs Climate Weather forecasting is concerned with accurate descriptions of weather type for a short period of time Climate forecasting deals with how different future conditions may be from those expected in an average year Weather describes specific conditions (raining, wind speed and direction, dew-point etc). Climate discusses anomalies

7. Short, medium and long-range Short-range is between 3 and 72 hours Medium-range is between 3 days an a week Long-range is a month or more ahead Experimental-range (X-range) includes new seasonal forecasts up to 6 months ahead Global climate prediction looks at climate out to 50 to 100 years

8. Short, medium and long-range Range short long forecast accuracy good poor

9. Statistical Forecasting The oldest form of formal weather forecasting A statistical model is constructed from regression and correlation analyses Model is trained on past (historical) weather observations Model is given data relating to patterns of SST or other large-scale conditions prior to the period the weather changed

10. Statistical Forecasting The model thus learns what sets of conditions (certain SST pattern, persistence of pressure, timing of snowmelt etc) are associated with a particular weather regime To use a statistical model you enter details about large-scale conditions and it matches those with its historical database to give a prediction Drawback - can only “see” extremes encountered in training data

11. Chaos theory One of the most fundamental advances in the prediction of any natural process (climate and weather included) occurred after the discovery of chaos Chaos theory is an amalgamation of game theory, probability theory and fluid dynamics

13. Chaos theory Edward Lorenz realised that although the atmosphere behaved as a chaotic and random system, there were aspects of it which could be solved within a phase-space The strange attractor (Lorenz attractor) was his visualisation of this hyperspace and initialised fractal theory

15. Dynamical forecasting Dynamical forecasting is the most advanced and current method of weather/climate prediction Unlike a statistical forecast, it is based on the calculation of weather/climate conditions from first principles (Physics) Calculation is undertaken for each time-step for regularly spaced grid-points across the earth and up through the atmosphere

17. Dynamical forecasting A modern Atmospheric Global Circulation Model (AGCM) solves many equations for each grid-point for the earth surface, atmosphere and oceans This type of model requires extremely powerful computers (supercomputers) and the science of GCMs only developed after such computers became available

19. Dynamical forecasting A single model integration provides a deterministic solution A better approach (originally proposed by Lorenz) was to use a probabilistic ensemble approach Ensemble forecasting strategy allows greater uncertainty to be sampled

20. Dynamical forecasting 1) Define an “event” (e.g. rainfall above normal or presence/absence of high pressure) 2) Run the climate model over a period of days 3) Compare model output with event criteria

21. Dynamical forecasting A single model integration would provide only one outcome - which only allows us to say the event will occur or it will not A single integration only samples a small proportion of the overall probability distribution of the future state of the atmosphere

22. Dynamical forecasting By repeating the model integration many times we can sample more of the uncertainty and generate a probability estimate of our event occurring Models are thus initialised on separate days and then run forward in time Models are initialised with actual observations for that particular day Result is an ensemble of integrations - referred to as members

23. Dynamical forecasting Day rainfall quantity 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3

24. Dynamical forecasting Day rainfall quantity 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 Probability = f/n where f is number of members in a category where n is total number of integrations

25. Dynamical forecasting Day rainfall quantity 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 Probability = f/n where f is number of members in a category where n is total number of integrations 7 3

26. Dynamical forecasting Day rainfall quantity 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 Probability = f/n Prob of Rain = 0.3 (30%) Prob of NO rain = 0.7 (70%) 7 3

36. IPCC