EG1204: Earth Systems: an introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

IPCC

EG1204: Earth Systems: an introduction