Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: David A. Stainforth x
  • Refine by Access: All Content x
Clear All Modify Search
Raphael Calel and David A. Stainforth

Abstract

Integrated assessment models (IAMs) are the main tools for combining physical and economic analyses to develop and assess climate change policy. Policy makers have relied heavily on three IAMs in particular—Dynamic Integrated model of Climate and the Economy (DICE), The Climate Framework for Uncertainty, Negotiation and Distribution (FUND), and Policy Analysis for the Greenhouse Effect (PAGE)—when trying to balance the benefits and costs of climate action. Unpacking the physics of these IAMs accomplishes four things. First, it reveals how the physics of these IAMs differ and the extent to which those differences give rise to different visions of the human and economic costs of climate change. Second, it makes these IAMs more accessible to the scientific community and thereby invites further physical expertise into the IAM community so that economic assessments of climate change can better reflect the latest physical understanding of the climate system. Third, it increases the visibility of the link between the physical sciences and the outcomes of policy assessments so that the scientific community can focus more sharply on those unresolved questions that loom largest in policy assessments. And finally, in making explicit the link between these IAMs and the underlying physical models, one gains the ability to translate between IAMs using a common physical language. This translation key will allow multimodel policy assessments to run all three models with physically comparable baseline scenarios, enabling the economic sources of uncertainty to be isolated and facilitating a more informed debate about the most appropriate mitigation pathway.

Full access
Marina Baldissera Pacchetti, Suraje Dessai, Seamus Bradley, and David A. Stainforth

Abstract

There are now a plethora of data, models, and approaches available to produce regional and local climate information intended to inform adaptation to a changing climate. There is, however, no framework to assess the quality of these data, models, and approaches that takes into account the issues that arise when this information is produced. An evaluation of the quality of regional climate information is a fundamental requirement for its appropriate application in societal decision-making. Here, an analytical framework is constructed for the quality assessment of science-based statements and estimates about future climate. This framework targets statements that project local and regional climate at decadal and longer time scales. After identifying the main issues with evaluating and presenting regional climate information, it is argued that it is helpful to consider the quality of statements about future climate in terms of 1) the type of evidence and 2) the relationship between the evidence and the statement. This distinction not only provides a more targeted framework for quality, but also shows how certain evidential standards can change as a function of the statement under consideration. The key dimensions to assess regional climate information quality are diversity, completeness, theory, adequacy for purpose, and transparency. This framework is exemplified using two research papers that provide regional climate information and the implications of the framework are explored.

Open access
Reto Knutti, Gerald A. Meehl, Myles R. Allen, and David A. Stainforth

Abstract

The estimated range of climate sensitivity has remained unchanged for decades, resulting in large uncertainties in long-term projections of future climate under increased greenhouse gas concentrations. Here the multi-thousand-member ensemble of climate model simulations from the climateprediction.net project and a neural network are used to establish a relation between climate sensitivity and the amplitude of the seasonal cycle in regional temperature. Most models with high sensitivities are found to overestimate the seasonal cycle compared to observations. A probability density function for climate sensitivity is then calculated from the present-day seasonal cycle in reanalysis and instrumental datasets. Subject to a number of assumptions on the models and datasets used, it is found that climate sensitivity is very unlikely (5% probability) to be either below 1.5–2 K or above about 5–6.5 K, with the best agreement found for sensitivities between 3 and 3.5 K. This range is narrower than most probabilistic estimates derived from the observed twentieth-century warming. The current generation of general circulation models are within that range but do not sample the highest values.

Full access