EG4508: Issues in environmental science

1. EG4508: Issues in environmental science Meteorology and Climate Dr Mark Cresswell Oceans, Winds and Climate Forecasting

2. Properties of the oceans • The amount of water in the oceans is huge! • The average depth is 3,730 m - over 4 times the mean elevation of the world’s land above sea-level • The oceans cover 70.9% of the planet surface

3. Properties of the oceans • The total heat capacity of the entire atmosphere is equal to that of merely 3 metres of ocean depth • Both the atmosphere and the oceans are fluids and so behave in very similar ways

4. Ocean structure and circulation • At the ocean surface, winds produce a thermally mixed surface layer averaging a few tens of metres deep poleward of latitude 60º, 400 m at latitude 40º and 100-200 m at the equator

5. Ocean structure and circulation • Below the (relatively) warm, thermally mixed layer is the thermocline • The thermocline is a layer in which (vertically) temperature decreases and density increases • The thermocline layer exhibits a stable stratification which tends to inhibit vertical mixing and and acts as a barrier to warmer water above and colder water below

6. Ocean structure and circulation • The ocean currents of the world are thought to move in accordance with a conveyor belt paradigm • This deep ocean thermohaline circulation system has been accepted theory for some time • The theory has recently come under close scrutiny - and may soon be revised

8. Sea surface temperature • Sea surface temperatures (SSTs) were first mapped by Alexander von Humboldt in 1817 • SSTs vary from about -1.9ºC at the poles to over 30ºC in the Persian Gulf and the Red Sea in july. • Differences in temperature between oceans near the coast and areas inland lead to the creation of coastal breezes

9. Sea surface temperature • Isotherms of SST generally change as a function of latitude - although variations occur at coasts where the isotherms bend poleward on East coasts and equatorward on West coasts • Changes in SST greatly influence pressure, evaporation and hence wind and rainfall patterns • Sometimes relationships between SST patterns and pressure/precipitation anomalies occur over a great distance from each other and are called teleconnections

10. Sea surface temperature • The most well known SST anomaly is ENSO • Normally easterly trade winds prevents eastward movement of warm water from western Pacific to eastern Pacific • When trade winds weaken, warm water propagates eastward, the low pressure over Indonesia breaks down and so too does the Walker circulation with its downward branch over the eastern Pacific • It is essentially a change in SST brought about by a change in circulation

12. Salinity • There are normally about 34.5 grams of salts dissolved in each kilogram of sea water - written as 34.5%0 • The salinity affects climate by altering the density of the sea - thus changing the patterns of pressures which govern ocean currents and hence heat transport around the globe

13. The global winds • The driving force of ANY wind is the local pressure gradient expressed as: • ∆p/ ∆n where: • ∆p is the difference between the pressures at points separated horizontally by a distance ∆n

14. The global winds • Where there is a chain of centres of convergence associated with convective storms air moves vertically into the upper atmosphere • The raised air increases upper level pressure which creates poleward winds • The Coriolis deflection prevents the upper winds from reaching beyond 30º creating a belt of subtropical highs

15. The global winds • The accumulated air gradually cools, then sinks and creates surface high pressure • The descending air then replaces air moving equatorward at the surface to feed convection their - thus forming a circuit of motion we call a Hadley Cell

16. Hadley Cells

17. The global winds • Winds within 30º of the equator in the Atlantic and Indian oceans were first mapped by Edmund Halley in 1686 • The maps which developed from this time were invaluable for shipping and hence commercial and military strategies

19. The global winds • Winds between the tropics converge on a line called the Inter-Tropical Convergence Zone (ITCZ) • This line of convergence can be discerned on a map of streamlines and visualised on a satellite image from space

20. ITCZ (Inter-Tropical Convergence Zone)

21. The global winds • Winds are mainly easterly at latitudes between 10-30º - these are known as the trade winds or trades • Westerly winds prevail at about 35-60º and are known as the midlatitude winds • There are polar easterlies at latitudes above 60º

22. The global winds • The seasonal Monsoon circulation (West African, Asian and Northern Australian) result from moist south westerlies converging with dry north easterlies at special regions • Monsoon winds bring large amounts of rainfall to their respective regions • Desert winds can be vigourous - generating winds such as the Harmattan, Scirocco, Haboob and Willy willies

23. The local winds • Sea breezes: due to SST varying each day by only a degree or so - whilst surface air temperatures onshore change by around ten times as much • Land breeze: the opposite of a sea breeze happens at night when the land has cooled below that of SSTs • Mountain winds: the foehn effect, when the warming of winds blowing down mountains after there has been rainfall on the windward side • Katabatic and Anabatic winds: katabatic winds are downhill flows of cold air driven by gravity

24. The local winds • The daytime counterparts of katabatic winds are anabatic winds - they flow up sunny slopes fuelled by solar convectional heating and provide thermals for gliders • Anabatic winds are deeper and more gusty than katabatic winds

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

26. 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

28. 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

29. 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

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

31. 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

32. 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

33. 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

35. 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

37. 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

39. 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

41. 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

42. 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

43. 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 ensemble members

44. Dynamical forecasting • Example: Let us assume we wish to forecast the probability rainfall in Greater Manchester on 25th December 2009 – 1 month in advance • First, we initialise our model with actual observations TEN days before 25th November (15th) • We repeat this model initialisation on each successive day (16th, 17th...24th) • We end up with 10 model integrations - each one suggesting either rain will or will not occur • The proportion of members that agree with the event gives us our probability

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

46. 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

47. 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

48. 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

49. Dynamical forecasting • The UKMO and ECMWF utilise the ensemble forecasting approach to determine rainfall, temperature, El Niño and general weather patterns many weeks or even months in advance • El Niño forecasting is considered VERY important.......