Lecture

Climate Change Prediction: Linear Response Operators

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Description

This lecture explores predicting climate change through linear response operators, focusing on the nonequilibrium nature of the climate system, variability, and the interplay between climate response and variability. It delves into anthropogenic climate change, climate surprises, and tipping points, discussing the physics of climate variability and climate change. The lecture covers the hierarchy of models in climate sciences, advances in dynamical systems theory, and nonequilibrium statistical physics. It also addresses equilibrium climate sensitivity, transient climate response, and the challenges of climate predictability. The instructor discusses the construction of time-dependent measures using Ruelle Response Theory and the convergence of response formulas, emphasizing the importance of understanding climate response in different scenarios.

Official source

Ontological neighbourhood

Environmental science

Climate: Climate change

Related concepts (32)

Climate change

In common usage, climate change describes global warming—the ongoing increase in global average temperature—and its effects on Earth's climate system. Climate change in a broader sense also includes previous long-term changes to Earth's climate. The current rise in global average temperature is more rapid than previous changes, and is primarily caused by humans burning fossil fuels. Fossil fuel use, deforestation, and some agricultural and industrial practices increase greenhouse gases, notably carbon dioxide and methane.

Climate sensitivity

Climate sensitivity is a measure of how much Earth's surface will cool or warm after a specified factor causes a change in its climate system, such as how much it will warm for a doubling in the atmospheric carbon dioxide () concentration. In technical terms, climate sensitivity is the average change in global mean surface temperature in response to a radiative forcing, which drives a difference between Earth's incoming and outgoing energy.

Climate model

Numerical climate models use quantitative methods to simulate the interactions of the important drivers of climate, including atmosphere, oceans, land surface and ice. They are used for a variety of purposes from study of the dynamics of the climate system to projections of future climate. Climate models may also be qualitative (i.e. not numerical) models and also narratives, largely descriptive, of possible futures.

Attribution of recent climate change

Efforts to scientifically ascertain and attribute mechanisms responsible for recent global warming and related climate changes on Earth have found that the main driver is elevated levels of greenhouse gases produced by human activities, with natural forces adding variability. The likely range of human-induced surface-level air warming by 2010–2019 compared to levels in 1850–1900 is 0.8 °C to 1.3 °C, with a best estimate of 1.07 °C. This is close to the observed overall warming during that time of 0.9 °C to 1.

Climate variability and change

Climate variability includes all the variations in the climate that last longer than individual weather events, whereas the term climate change only refers to those variations that persist for a longer period of time, typically decades or more. Climate change may refer to any time in Earth's history, but the term is now commonly used to describe contemporary climate change. Since the Industrial Revolution, the climate has increasingly been affected by human activities.

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