Weather, Macroweather and Climate: Big and Small, Fast and Slow, Our Random Yet Predictable Atmosphere

Weather, Macroweather and Climate: Big and Small, Fast and Slow, Our Random Yet Predictable Atmosphere

Shaun Lovejoy (McGill University Department of Physics)

Mar. 1, 2018 19:00

The slides from this talk can be found here


In cigarette smoke we can make out whirls only millimeters in size and on satellite photographs, we can see clouds that stretch across continents. The temperature and wind fluctuate thousands of times a second, and they’ve been doing it for billions of years, but never in quite the same way. How can we understand this mindboggeling variability?

The familiar treatments focus on a series of “scalebound” mechanisms each operating over a narrow range of scales ranging from meteorological fronts to convective cells, to storm systems - or from El Nino to global warming. Yet, in 2015, it was discovered that this conventional approach is in error by a factor of a quadrillion: a million billion. Helped by high level scaling laws operating over enormous ranges of scales from small to large, from fast to slow, I explain this new thing called “macroweather” and how it sits in between the weather and climate, finally settling the question: “What is the Climate”? I discuss how agriculture - and hence civilization itself - might be a result of freak macroweather.

I answer the old question: “Does the wind have a velocity?”, and the newer one “how big is a cloud?”. The answer turns out to explain why the dimension of atmospheric motions is D = 23/9 (=2.555…): more voluminous than the theoreticians’ flat value D = 2 yet less space filling than the human scale value D = 3. I show that Mars is our statistical twin and why this shouldn’t surprise us. I explain how the multifractal butterfly effect gives rise to events that are so extreme that they have been called “black swans”. I show how - even accounting for the black swans - we can close the climate debate by statistically testing and rejecting the skeptics’ Giant Natural Fluctuation hypothesis. I explain how the emergent scaling laws can make accurate monthly to decadal (macroweather) forecasts by exploiting an unsuspected but huge memory. I playfully imagine a 1909 International Committee for Projecting the Consequences of Coal Consumption (ICPCC) to show how a good scenario of economic development might have led – one hundred years in advance - to accurate projections of our current 1°C of global warming, and I’ll show how the same scaling approach can help to significantly reduce the large uncertainties in our current climate projections to 2050 and 2100.

The talk will have achieved its goal if it convinces the audience that the atmosphere is not what they thought.