Observationally-constrained climate projections following the Paris Agreement

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The Paris Agreement, which recently came into force, commits signatories to holding the increase in global average temperature to well below 2C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5C above pre-industrial levels'. Since carbon dioxide is the dominant greenhouse gas, and since a certain total amount of carbon dioxide emissions gives the same amount of warming irrespective of whether it is emitted all at once or over a long period, a warming threshold of 1.5C can be associated with a particular total amount of carbon dioxide emissions, or carbon budget. Knowing the maximum amount of carbon dioxide which can be emitted while having a good chance of limiting global mean temperature to below the 1.5C and 2.0C thresholds is key to planning climate mitigation strategies and fulfilling the commitments made in the Paris Agreement. Carbon budgets presented in the last report of the United Nations Intergovernmental Panel on Climate Change (IPCC) were based directly on simulated emissions in all available climate models, and indicated that the best estimate of the 1.5C budget would be exceeded in less than ten years at current emissions levels, with a chance that this budget has already been exceeded. However, we know that historical carbon dioxide emissions, in conjunction with other climate drivers, have led to only about 1C global warming to date. Further, we know that on average the climate models considered in the last IPCC report on average warm more than observations. Can we use observations of past climate change to obtain better projections of future climate change? A number of ways of using observations to constrain projections have been proposed, including scaling simulated climate change up or down to best match observed changes, weighting models based on how well they agree with observations, or using observations to screen climate models in or out. This project will explore a range of approaches to observationally-constraining climate projections, with a focus on obtaining improved observationally-constrained carbon budgets and projections of global mean temperature. The application of observational constraints to other variables, such as regional temperature changes, rainfall changes and sea ice changes, will also be considered.