Scales and Scaling in the Climate System

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The climate has traditionally been viewed as a composite of processes acting each over relatively narrow ranges of time scales. The most obvious are those associated with the daily and annual cycles, but there are many others: for example El Nino and the orbital (Milankovitch) cycles associated with the ice ages. The “in between” scales are are uninteresting “background noises”. However, most of the variability is in these wide scale range (“scaling”) processes: they are foreground, not background processes. This proposal is centred on re-establishing the primacy of these scaling processes and examining the consequences for understanding the climate of the past, present and future. Understanding the past climate is largely a question of interpreting the paleo “proxy” climate records from ice and ocean cores, to tree rings and lake sediments. This proposal will use new “scaling fluctuation analysis” techniques to systematically understand and model the variability over time scales from centuries to millions of years, over spatial scales from the size of the planet to hundreds of kilometers. This is needed in order to reconstruct the past. For the present climate, the proposal exploits the “butterfly effect” to reduce the weather to random noise driving a system with a long term memory. The butterfly effect refers to the fact that the atmosphere is so unstable that a butterfly flapping its wings can change its course. While the "butterfly effect" limits the accuracy of weather forecasts these have little skill beyond ten days at scales of months and longer the weather effectively gives random “nudges” to the state of the atmosphere and the statistics of the nudges combined with the huge system memory can be used to make long range forecasts. It turns out that the atmosphere's memory is so strong that we are still feeling the effects of century old fluctuations, and this memory can be exploited, thus improving the notoriously poor seasonal forecasts. The future climate depends not only on the natural variability associated with the long range memory and butterfly effect, it depends on the atmospheric composition especially greenhouse gases - land use and other anthropogenic changes. Currently, numerical climate models disagree significantly with each about the projections to 2050 or 2080. The existence of a new qualitatively different source of future projections is needed in order to give us confidence in future emission scenarios. In the next five years, for past, present and future climate we aim to a) produce new multiproxy reconstructions, b) produce new monthly, seasonal, annual and decadal forecasts, c) produce new temperature and precipitation projections for the 21st century. bypass the model uncertainties by making projections without the models by exploiting the past historical data.