Bridging uncertainty concepts across narratives and simulations in environmental scenarios

Uncertainties in our understanding of current and future climate change projections, impacts and vulnerabilities are structured by scientists using scenarios, which are generally in qualitative (narrative) and quantitative (numerical) forms. Although conceptually strong, qualitative and quantitative scenarios have limited complementarity due to the lack of a fundamental bridge between two different concepts of uncertainty: linguistic and epistemic. Epistemic uncertainty is represented by the range of scenarios and linguistic variables within them, while linguistic uncertainty is represented by the translation of those linguistic variables via the fuzzy set approach. Both are therefore incorporated in the models that utilise the final quantifications. The application of this method is demonstrated in a stakeholder-led development of socioeconomic scenarios. The socioeconomic scenarios include several vague elements due to heterogeneous linguistic interpretations of future change on the part of stakeholders. We apply the so-called ‘Centre of Gravity’ (CoG) operator to defuzzify the quantifications of linguistic values provided by stakeholders. The results suggest that, in these cases, uniform distributions provide a close fit to the membership functions derived from ranges of values provided by stakeholders. As a result, the 90 or 95% intervals of the probability density functions are similar to the 0.1 or 0.05 degrees of membership of the linguistic values of linguistic variables. By bridging different uncertainty concepts (linguistic and epistemic uncertainties), this study offers a substantial step towards linking qualitative and quantitative scenarios

Toward quantification of the feasible potential of land-based carbon dioxide removal

Global climate-change overshoot scenarios, where warming exceeds Paris Agreement limits before being brought back down, are highly dependent on land-based carbon dioxide removal (CDR). In the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), such scenarios are supported by optimistic global assessments of the technical and economic potential for land-based CDR. However, a further type of potential—the ‘‘feasible’’ potential, which includes socio-cultural, environmental, and institutional factors—is noted in the AR6 but not quantified. Here, we set out research frameworks to work toward quantification of this feasible potential. We first argue that quantifying the feasible potential will substantiallyreduce current assessed CDR potential. Second, we demonstrate how transdisciplinary methods are improving understanding of feasibility constraints on land-based CDR. Third, we explore frameworks for synthesizing these advances during the next IPCC assessment process. We conclude that the research community should carefully consider the use of techno-economic CDR assessments in evidence for policymaker

Unraveling metastable Markovian open quantum systems

We analyze the dynamics of metastable Markovian open quantum systems by unraveling their average dynamics into stochastic trajectories. We use quantum reset processes as examples to illustrate metastable phenomenology, including a simple three-state model whose metastability is of classical type, and a two-qubit model that features a metastable decoherence-free subspace. In the three-state model, the trajectories exhibit classical metastable phenomenology: fast relaxation into distinct phases and slow transitions between them. This extends the existing correspondence between classical and quantum metastability. It enables the computation of committors for the quantum phases, and the mechanisms of rare transitions between them. For the two-qubit model, the decoherence-free subspace appears in the unraveled trajectories as a slow manifold on which the quantum state has a continuous slow evolution. This provides a classical (nonmetastable) analog of this quantum effect. We discuss the general implications of these results, and we highlight the useful role of quantum reset processes for analysis of quantum trajectories in metastable systems

The relative importance of subjective and structural factors for individual adaptation to climate change by forest owners in Sweden

A growing body of literature argues that subjective factors can more accurately explain individual adaptation to climate change than objective measurers of adaptive capacity. Recent studies have shown that personal belief in climate change and affect are much better in explaining climate awareness and action than income, education or gender. This study focuses on the process of individual adaptation to climate change. It assesses and compares the influence of cognitive, experiential and structural factors on individuals’ views and intentions regarding climate change adaptation. Data from this study comes from a survey with 836 forest owners in Sweden. Ordinal and binary logistic regression was used to test hypotheses about the different factors. Results show that cognitive factors—namely personal level of trust in climate science, belief in the salience of climate change and risk assessment—are the only statistically significant factors that can directly explain individuals’ intention to adapt to climate change and their sense of urgency. Findings also suggest that structural or socio-demographic factors do not have a statistically significant influence on adaptation decision-making among Swedish forest owners. The study also offers valuable insights for communication interventions to promote adaptation. Findings strongly suggest that communication interventions should focus more strongly on building trust and addressing stakeholders’ individual needs and experiences

Achievement of Paris climate goals unlikely due to time lags in the land system:Paris climate goals challenged by time lags in the land system

Achieving the Paris Agreement’s aim of limiting average global temperature increases to 1.5 °C requires substantial changes in the land system. However, individual countries’ plans to accomplish these changes remain vague, almost certainly insufficient and unlikely to be implemented in full. These shortcomings are partially the result of avoidable ‘blind spots’ relating to time lags inherent in the implementation of land-based mitigation strategies. Key blind spots include inconsistencies between different land-system policies, spatial and temporal lags in land-system change, and detrimental consequences of some mitigation options. We suggest that improved recognition of these processes is necessary to identify achievable mitigation actions, avoiding excessively optimistic assumptions and consequent policy failures

Improving the representation of adaptation in climate change impact models

Climate change adaptation is a complex human process, framed by uncertainties and constraints, which is difficult to capture in existing assessment models. Attempts to improve model representations are hampered by a shortage of systematic descriptions of adaptation processes and their relevance to models. This paper reviews the scientific literature to investigate conceptualisations and models of climate change adaptation, and the ways in which representation of adaptation in models can be improved. The review shows that real-world adaptive responses can be differentiated along a number of dimensions including intent or purpose, timescale, spatial scale, beneficiaries and providers, type of action, and sector. However, models of climate change consequences for land use and water management currently provide poor coverage of these dimensions, instead modelling adaptation in an artificial and subjective manner. While different modelling approaches do capture distinct aspects of the adaptive process, they have done so in relative isolation, without producing improved unified representations. Furthermore, adaptation is often assumed to be objective, effective and consistent through time, with only a minority of models taking account of the human decisions underpinning the choice of adaptation measures (14%), the triggers that motivate actions (38%) or the time-lags and constraints that may limit their uptake and effectiveness (14%). No models included adaptation to take advantage of beneficial opportunities of climate change. Based on these insights, transferable recommendations are made on directions for future model development that may enhance realism within models, while also advancing our understanding of the processes and effectiveness of adaptation to a changing climate

Emerging Opportunities for Landscape Ecological Modelling

Landscape ecological modelling provides a vital means for understanding the interactions between geographical, climatic, and socio-economic drivers of land-use and the dynamics of ecological systems. This growing field is playing an increasing role in informing landscape spatial planning and management. Here, we review the key modelling approaches that are used in landscape modelling and in ecological modelling. We identify an emerging theme of increasingly detailed representation of process in both landscape and ecological modelling, with complementary suites of modelling approaches ranging from correlative, through aggregated process based approaches to models with much greater structural realism that often represent behaviours at the level of agents or individuals. We provide examples of the considerable progress that has been made at the intersection of landscape modelling and ecological modelling, while also highlighting that the majority of this work has to date exploited a relatively small number of the possible combinations of model types from each discipline. We use this review to identify key gaps in existing landscape ecological modelling effort and highlight emerging opportunities, in particular for future work to progress in novel directions by combining classes of landscape models and ecological models that have rarely been used together

Human Cancer Long Non-Coding RNA Transcriptomes

Once thought to be a part of the ‘dark matter’ of the genome, long non-coding RNAs (lncRNAs) are emerging as an integral functional component of the mammalian transcriptome. LncRNAs are a novel class of mRNA-like transcripts which, despite no known protein-coding potential, demonstrate a wide range of structural and functional roles in cellular biology. However, the magnitude of the contribution of lncRNA expression to normal human tissues and cancers has not been investigated in a comprehensive manner. In this study, we compiled 272 human serial analysis of gene expression (SAGE) libraries to delineate lncRNA transcription patterns across a broad spectrum of normal human tissues and cancers. Using a novel lncRNA discovery pipeline we parsed over 24 million SAGE tags and report lncRNA expression profiles across a panel of 26 different normal human tissues and 19 human cancers. Our findings show extensive, tissue-specific lncRNA expression in normal tissues and highly aberrant lncRNA expression in human cancers. Here, we present a first generation atlas for lncRNA profiling in cancer