On estimating local long-term climate trends

Climate sensitivity is commonly taken to refer to the equilibrium change in the annual mean global surface temperature following a doubling of the atmospheric carbon dioxide concentration. Evaluating this variable remains of significant scientific interest, but its global nature makes it largely irrelevant to many areas of climate science, such as impact assessments, and also to policy in terms of vulnerability assessments and adaptation planning. Here, we focus on local changes and on the way observational data can be analysed to inform us about how local climate has changed since the middle of the nineteenth century. Taking the perspective of climate as a constantly changing distribution, we evaluate the relative changes between different quantiles of such distributions and between different geographical locations for the same quantiles. We show how the observational data can provide guidance on trends in local climate at the specific thresholds relevant to particular impact or policy endeavours. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. The mathematical basis is presented for two methods of extracting these local trends from the data. The two methods are compared first using surrogate data, to clarify the methods and their uncertainties, and then using observational surface temperature time series from four locations across Europe

From museum to memory institution: the politics of European culture online

Museums, libraries and archives have long been considered the retainers of some form of collective memory. Within the last twenty years, the term ‘memory institution’ has been coined to describe these entities, which is symptomatic of the fact that such places are increasingly linked through digital media and online networks. The concept of the memory institution is also part of the vocabulary used to promote broader cultural integration across nations, and appears in discussions of European heritage and in policy documents concerning the digitization of cultural heritage collections. To explore the relationship between cultural heritage, memory and digital technology further, this paper will examine the large-scale digitization project Europeana, under which museums, libraries and archives are re-defined as cultural heritage institutions or memory institutions. My purpose is to trace the conceptual trajectory of memory within this context, and to address how the idea of a European cultural memory structured by technology holds implications for institutions traditionally associated with practices of remembering

The nature and value of cultural policy research for early career researchers.

N/

On the physics of three integrated assessment models

Differing physical assumptions are embedded in an important class of integrated assessment models. Reverse-engineering a common description of their underlying physics facilitates inter-comparisons that separate economic and physical uncertainties. 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—DICE, FUND, and PAGE—when trying to balance the benefits and costs of climate action. Unpacking the physics of these IAMs accomplishes four things. Firstly, 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. Secondly, 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. Thirdly, 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 multi-model 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

An assessment of the foundational assumptions inhigh-resolution climate projections: the case of UKCP09

The United Kingdom Climate Impacts Programme’s UKCP09 project makes highresolution projections of the climate out to 2100 by post-processing the outputs of a large-scale global climate model. The aim of this paper is to describe and analyse the methodology used and then urge some caution. Given the acknowledged systematic, shared shortcomings in all current climate models, treating model outputs as decision relevant projections can be significantly misleading. In extrapolatory situations, such as projections of future climate change impacts, there is little reason to expect that postprocessing of model outputs can correct for the consequences of such errors. This casts doubt on our ability, today, to make trustworthy, high-resolution probabilistic projections out to the end of this century

Water Resource Planning Under Future Climate and Socioeconomic Uncertainty in the Cauvery River Basin in Karnataka, India

Decision-Making Under Uncertainty (DMUU) approaches have been less utilized in developing countries than developed countries for water resources contexts. High climate vulnerability and rapid socioeconomic change often characterize developing country contexts, making DMUU approaches relevant. We develop an iterative multi-method DMUU approach, including scenario generation, coproduction with stakeholders and water resources modeling. We apply this approach to explore the robustness of adaptation options and pathways against future climate and socioeconomic uncertainties in the Cauvery River Basin in Karnataka, India. A water resources model is calibrated and validated satisfactorily using observed streamflow. Plausible future changes in Indian Summer Monsoon (ISM) precipitation and water demand are used to drive simulations of water resources from 2021 to 2055. Two stakeholder-identified decision-critical metrics are examined: a basin-wide metric comprising legal instream flow requirements for the downstream state of Tamil Nadu, and a local metric comprising water supply reliability to Bangalore city. In model simulations, the ability to satisfy these performance metrics without adaptation is reduced under almost all scenarios. Implementing adaptation options can partially offset the negative impacts of change. Sequencing of options according to stakeholder priorities into Adaptation Pathways affects metric satisfaction. Early focus on agricultural demand management improves the robustness of pathways but trade-offs emerge between intrabasin and basin-wide water availability. We demonstrate that the fine balance between water availability and demand is vulnerable to future changes and uncertainty. Despite current and long-term planning challenges, stakeholders in developing countries may engage meaningfully in coproduction approaches for adaptation decision-making under deep uncertainty

Drug-mediated shortening of action potentials in LQTS2 hiPSC-cardiomyocytes

Cardiomyocytes (CMs) derived from human induced pluripotent stem cells (hiPSCs) are now a well-established modality for modeling genetic disorders of the heart. This is especially so for long QT syndrome (LQTS), which is caused by perturbation of ion channel function, and can lead to fainting, malignant arrhythmias and sudden cardiac death. LQTS2 is caused by mutations in KCNH2, a gene whose protein product contributes to IKr (also known as HERG), which is the predominant repolarizing potassium current in CMs. β-blockers are the mainstay treatment for patients with LQTS, functioning by reducing heart rate and arrhythmogenesis. However, they are not effective in around a quarter of LQTS2 patients, in part, because they do not correct the defining feature of the condition, which is excessively prolonged QT interval. Since new therapeutics are needed, in this report, we biopsied skin fibroblasts from a patient who was both genetically and clinically diagnosed with LQTS2. By producing LQTS-hiPSC-CMs, we assessed the impact of different drugs on action potential duration (APD), which is used as an in vitro surrogate for QT interval. Not surprisingly, the patient's own β-blocker medication, propranolol, had a marginal effect on APD in the LQTS-hiPSC-CMs. However, APD could be significantly reduced by up to 19% with compounds that enhanced the IKr current by direct channel binding or by indirect mediation through the PPARδ/protein 14-3-3 epsilon/HERG pathway. Drug-induced enhancement of an alternative potassium current, IKATP, also reduced APD by up to 21%. This study demonstrates the utility of LQTS-hiPSC-CMs in evaluating whether drugs can shorten APD and, importantly, shows that PPARδ agonists may form a new class of therapeutics for this condition

Weather, climate and the nature of predictability

The prediction and simulation of future weather and climate is a key ingredient in good weather risk management. This chapter briefly reviews the nature and underlying sources of predictability on timescales from hours-ahead to centuries-ahead. The traditional distinction between ‘weather’ and ‘climate’ predictions is described, and the role of recent scientific developments in driving a convergence of these two classic problems is highlighted. The chapter concludes by outlining and comparing the two main strategies used for creating weather and climate predictions, and discussing the challenges of using predictions in quantitative applications

Correction to: “Constraining climate forecasts: The role of prior assumptions”

N/

On quantifying the climate of the nonautonomous lorenz-63 model

The Lorenz-63 model has been frequently used to inform our understanding of the Earth's climate and provide insight for numerical weather and climate prediction. Most studies have focused on the autonomous (time invariant) model behaviour in which the model's parameters are constants. Here we investigate the properties of the model under time-varying parameters, providing a closer parallel to the challenges of climate prediction, in which climate forcing varies with time. Initial condition (IC) ensembles are used to construct frequency distributions of model variables and we interpret these distributions as the time-dependent climate of the model. Results are presented that demonstrate the impact of ICs on the transient behaviour of the model climate. The location in state space from which an IC ensemble is initiated is shown to significantly impact the time it takes for ensembles to converge. The implication for climate prediction is that the climate may, in parallel with weather forecasting, have states from which its future behaviour is more, or less, predictable in distribution. Evidence of resonant behaviour and path dependence is found in model distributions under time varying parameters, demonstrating that prediction in nonautonomous nonlinear systems can be sensitive to the details of time-dependent forcing/parameter variations. Single model realisations are shown to be unable to reliably represent the model's climate; a result which has implications for how real-world climatic timeseries from observation are interpreted. The results have significant implications for the design and interpretation of Global Climate Model experiments. Over the past 50 years, insight from research exploring the behaviour of simple nonlinear systems has been fundamental in developing approaches to weather and climate prediction. The analysis herein utilises the much studied Lorenz-63 model to understand the potential behaviour of nonlinear systems, such as the 5 climate, when subject to time-varying external forcing, such as variations in atmospheric greenhouse gases or solar output. Our primary aim is to provide insight which can guide new approaches to climate model experimental design and thereby better address the uncertainties associated with climate change prediction. We use ensembles of simulations to generate distributions which 10 we refer to as the \climate" of the time-variant Lorenz-63 model. In these ensemble experiments a model parameter is varied in a number of ways which can be seen as paralleling both idealised and realistic variations in external forcing of the real climate system. Our results demonstrate that predictability of climate distributions under time varying forcing can be highly sensitive to 15 the specification of initial states in ensemble simulations. This is a result which at a superficial level is similar to the well-known initial condition sensitivity in weather forecasting, but with different origins and different implications for ensemble design. We also demonstrate the existence of resonant behaviour and a dependence on the details of the \forcing" trajectory, thereby highlighting 20 further aspects of nonlinear system behaviour with important implications for climate prediction. Taken together, our results imply that current approaches to climate modeling may be at risk of under-sampling key uncertainties likely to be significant in predicting future climate