Papers by David Stainforth
Climate Risk Management, 2016
Barriers and opportunities for robust decision making approaches to support climate change adapta... more Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world. Climate Risk Management, 14. pp. 1-10.
Climatic Change, Sep 14, 2013
It has recently been highlighted that the economic value of climate change mitigation depends sen... more It has recently been highlighted that the economic value of climate change mitigation depends sensitively on the slim possibility of extreme warming. This insight has been obtained through a focus on the fat upper tail of the climate sensitivity probability distribution. However, while climate sensitivity is undoubtedly important, what ultimately matters is transient temperature change. A focus on transient temperature change stresses the interplay of climate sensitivity with other physical uncertainties, notably effective heat capacity. In this paper we present a conceptual analysis of the physical uncertainties in economic models of climate mitigation, leading to an empirical application of the DICE model, which investigates the interaction of uncertainty in climate sensitivity and the effective heat capacity. We expand on previous results exploring the sensitivity of economic evaluations to the tail of the climate sensitivity distribution alone, and demonstrate that uncertainty about the system's effective heat capacity also plays a very important role. We go on to discuss complementary avenues of economic and scientific research that may help provide a better combined understanding of the physical and economic processes associated with a rapidly warming world.
Eurographics, May 19, 2004
Climateprediction.net aims to harness the spare CPU cycles of a million individual users' PCs to ... more Climateprediction.net aims to harness the spare CPU cycles of a million individual users' PCs to run a massive ensemble of climate simulations using an up-to-date, full-scale 3D atmosphere-ocean climate model. Although it has many similarities with other public-resource computing projects, it is distinguished by the complexity of its computational task, its system demands and the level of participant interaction, data volume and analysis procedures. For simulations running on individual PCs, there is a requirement for compelling visualizations that are readily grasped, since most users will be interested in the output from the model, but will have a limited level of scientific experience. This paper describes the design and implementation of these visualizations.
arXiv (Cornell University), Jul 13, 2020
Climate science employs a hierarchy of models, trading the tractability of simplified energy bala... more Climate science employs a hierarchy of models, trading the tractability of simplified energy balance models (EBMs) against the detail of Global Circulation Models. Since the pioneering work of Hasselmann, stochastic EBMs have allowed treatment of climate fluctuations and noise. However, it has recently been claimed that observations motivate heavy-tailed temporal response functions in global mean temperature to perturbations. Our complementary approach exploits the correspondence between Hasselmann's EBM and the original mean-reverting stochastic model in physics, Langevin's equation of 1908. We propose mapping a model well known in statistical mechanics, the Mori-Kubo Generalised Langevin Equation (GLE) to generalise the Hasselmann EBM. If present, long range memory then simplifies the GLE to a fractional Langevin equation (FLE). We describe the corresponding EBMs that map to the GLE and FLE, briefly discuss their solutions, and relate them to Lovejoy et al's new Fractional Energy Balance Model (FEBE).
Environmental Research Letters, Aug 1, 2022
Climate projections are highly uncertain; this uncertainty is costly and impedes progress on clim... more Climate projections are highly uncertain; this uncertainty is costly and impedes progress on climate policy. This uncertainty is primarily parametric (what numbers do we plug into our equations?) and structural (what equations do we use in the first place?). The former is straightforward to characterise in principle, though may be computationally intensive for complex climate models. The latter is more challenging to characterise and is therefore often ignored. We developed a Bayesian approach to quantify structural uncertainty in climate projections, using the idealised energybalance model representations of climate physics that underpin many economists' integrated assessment models (and therefore their policy recommendations). We define a model selection parameter, which switches on one of a suite of proposed climate nonlinearities and multidecadal climate feedbacks. We find that a temperature-dependent climate feedback is most consistent with global mean surface temperature observations, but that the sign of the temperaturedependence is opposite of what Earth system models suggest. This discrepancy is likely due to the assumption that the recent pattern effect can be represented as a temperature dependence. Moreover, the most likely model is less probable than the rest of the models combined, indicating that structural uncertainty is important for climate projections. Indeed, under shared socioeconomic pathways similar to current emissions reductions targets, structural uncertainty dwarfs parametric uncertainty in temperature. Consequently, structural uncertainty dominates overall non-socioeconomic uncertainty in economic projections of climate change damages, as estimated from a simple temperature-to-damages calculation. These results indicate that considering structural uncertainty is crucial for integrated assessment models in particular, and for climate projections in general.
Nature, Oct 26, 2022
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreeme... more Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Bulletin of the American Meteorological Society, Jun 1, 2017
We untangle differences in the physical assumptions embedded in three influential integrated asse... more We untangle differences in the physical assumptions embedded in three influential integrated assessment models. Separating the impact of physical and economic assumptions facilitates more interpretable model intercomparisons. 1 Another important class of IAMs is set up to find the least-cost way of achieving a prespecified target. This is, in some sense, a less demanding problem, and these models can therefore incorporate climate modules that are a great deal more complex. The IPCC's Fifth Assessment Report lists over 30 such IAMs in use (Clarke et al. 2014). Many of them use MAGICC to represent the physical climate (see online supplement). This article is licensed under a Creative Commons Attribution 4.0 license.
EGU General Assembly Conference Abstracts, Apr 1, 2017
Nature Communications, Oct 6, 2020
A number of influential assessments of the economic cost of climate change rely on just a small n... more A number of influential assessments of the economic cost of climate change rely on just a small number of coupled climate-economy models. A central feature of these assessments is their accounting of the economic cost of epistemic uncertainty-that part of our uncertainty stemming from our inability to precisely estimate key model parameters, such as the Equilibrium Climate Sensitivity. However, these models fail to account for the cost of aleatory uncertainty-the irreducible uncertainty that remains even when the true parameter values are known. We show how to account for this second source of uncertainty in a physically well-founded and tractable way, and we demonstrate that even modest variability implies trillions of dollars of previously unaccounted for economic damages.
Nature Climate Change, Feb 25, 2015
In the print version of this Perspective, the last sentence in Box 1 was cut off, and should have... more In the print version of this Perspective, the last sentence in Box 1 was cut off, and should have read "The model information of this specific case added with 'what-if ' scenarios of sea-level rise and on changes in extreme rainfall have been provided to water managers and now aid in designing adaptation measures in a realistic setting. " This error has been corrected in the online versions.
Climatic Change, Nov 3, 2012
Expert elicitation studies have become important barometers of scientific knowledge about future ... more Expert elicitation studies have become important barometers of scientific knowledge about future climate change (
Climate Dynamics, Sep 4, 2015
which every location can exhibit either strong cooling or rapid warming. However, the details of ... more which every location can exhibit either strong cooling or rapid warming. However, the details of the distribution are highly sensitive to the ocean initial condition chosen and particularly the state of the Atlantic meridional overturning circulation. On longer timescales, the warming signal becomes more clear and consistent amongst different initial condition ensembles. An ensemble using a range of different oceanic initial conditions produces a larger spread in temperature trends than ensembles using a single ocean initial condition for all lead times. This highlights the potential benefits from initialising climate predictions from ocean states informed by observations. These results suggest that climate projections need to be performed with many more ensemble members than at present, using a range of ocean initial conditions, if the uncertainty in nearterm regional climate is to be adequately quantified.
Philosophical Transactions of the Royal Society A, Jun 14, 2007
Over the last 20 years, climate models have been developed to an impressive level of complexity. ... more Over the last 20 years, climate models have been developed to an impressive level of complexity. They are core tools in the study of the interactions of many climatic processes and justifiably provide an additional strand in the argument that anthropogenic climate change is a critical global problem. Over a similar period, there has been growing interest in the interpretation and probabilistic analysis of the output of computer models; particularly, models of natural systems. The results of these areas of research are being sought and utilized in the development of policy, in other academic disciplines, and more generally in societal decision making. Here, our focus is solely on complex climate models as predictive tools on decadal and longer time scales. We argue for a reassessment of the role of such models when used for this purpose and a reconsideration of strategies for model development and experimental design. Building on more generic work, we categorize sources of uncertainty as they relate to this specific problem and discuss experimental strategies available for their quantification. Complex climate models, as predictive tools for many variables and scales, cannot be meaningfully calibrated because they are simulating a never before experienced state of the system; the problem is one of extrapolation. It is therefore inappropriate to apply any of the currently available generic techniques which utilize observations to calibrate or weight models to produce forecast probabilities for the real world. To do so is misleading to the users of climate science in wider society. In this context, we discuss where we derive confidence in climate forecasts and present some concepts to aid discussion and communicate the state-of-the-art. Effective communication of the underlying assumptions and sources of forecast uncertainty is critical in the interaction between climate science, the impacts communities and society in general.
Nature, Sep 1, 2012
The obituary of Andrew Fielding Huxley (Nature 486, 474; 2012) omitted to mention that Hugh Huxle... more The obituary of Andrew Fielding Huxley (Nature 486, 474; 2012) omitted to mention that Hugh Huxley and Jean Hanson also proposed a sliding-filament model of muscle contraction, based on myofibril experiments. Their work was published (Nature 173, 973; 1954) alongside A. F. Huxley's Nature paper.
<p>Probability distribution functions (PDFs) are widely used in projections... more <p>Probability distribution functions (PDFs) are widely used in projections of future climate, projections of the impacts of future climate, and by climate services aiming to provide information to support practical climate change adaptation. Furthermore they are often used as a means of connecting these different activities and linking the variety of disciplines involved in climate science and climate social science.</p><p>Here we present an assessment of when such probability distributions misrepresent our uncertainty and a discussion of how we might recognise when such misrepresentations occur [1]. We go on to provide a collection of alternatives to probability distributions for use in such situations.</p><p>We start by categorising the ways that probability distributions can misrepresent the state of our knowledge about future climate. Such misrepresentation is of importance because it may adversely affect practical societal decisions, particularly in regard to adaptation activities, as well as misdirecting other research efforts.</p><p>We follow this with a discussion of how we might identify such misrepresentations. Doing so would help us communicate climate information better and consequently provided better reasoned and more robust scientific conclusions and societal decisions. Such assessments are an important component in the evaluation of climate information provided by climate services: what aspects of the information can be described as actionable.</p><p>We consider two perspectives on these issues. On one, available theory and evidence in climate science essentially excludes using probability distributions to represent our uncertainty. On the other, which represents a significant strand of current practice, probability distributions can legitimately be provided by relying on appropriate expert judgement and the recognition of associated risks.  We discuss the reasoning behind each perspective, framed in terms of the analysis of climate models and expert judgement.</p><p>Finally we explore alternatives to the use of probability distributions. We describe two formal alternatives, namely imprecise probabilities and possibilistic distribution functions, as well as some informal possibilistic alternatives. We suggest that the possibilistic alternatives are preferable.</p><p> </p><p>[1] Katzav, Thompson, Risbey, Stainforth, Bradley and Frisch, <em>On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives</em>, Climatic Change, 2021.</p>
The literature contains a variety of definitions of climate, and the emphasis in these definition... more The literature contains a variety of definitions of climate, and the emphasis in these definitions has changed over time. Defining climate as a mean value is, of course, both limiting and misleading; definitions of climate based on averages have been deprecated as far back as 1931 [1]. In the context of current efforts to produce climate predictions for use in climate adaptation, it is timely to consider how well various definitions of climate serve the research for applications community.
Egu General Assembly Conference Abstracts, May 1, 2014
brings together international expertise on economics, as well as finance, geography, the environm... more brings together international expertise on economics, as well as finance, geography, the environment, international development and political economy to establish a worldleading centre for policy-relevant research, teaching and training in climate change and the environment. It is funded by the Grantham Foundation for the Protection of the Environment, which also funds the Grantham Institute for Climate Change at Imperial College London. More
Nature Climate Change, Jan 28, 2015
Society is vulnerable to extreme weather events and, by extension, to the human impact on future ... more Society is vulnerable to extreme weather events and, by extension, to the human impact on future events. As climate changes weather patterns will change. The search is on for more effective methodologies to aid decision-makers both in mitigation to avoid climate change and in adaptation to changes. The traditional approach employs ensembles of climate model simulations, statistical bias correction, downscaling to the spatial and temporal scales relevant to decision-makers, and then translation into quantities of interest. The veracity of this approach cannot be tested, and it faces in-principle challenges. Alternatively, numerical weather prediction models in an altered climate setting can provide tailored naritives of high-resolution simulations of high-impact weather in a future climate. This Tales of Future Weather approach will aid in the interpretation of lower resolution simulations. Arguably, it potentially provides a complementary and more realistic and more physically consistent pictures of what future weather might look like.
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Papers by David Stainforth