International Institute for Applied Systems Analysis

About: International Institute for Applied Systems Analysis is a nonprofit organization based out in Laxenburg, Austria. It is known for research contribution in the topics: Population & Greenhouse gas. The organization has 1369 authors who have published 5075 publications receiving 280467 citations. The organization is also known as: IIASA.

Multiscale scenarios for nature futures

Abstract: Targets for human development are increasingly connected with targets for nature, however, existing scenarios do not explicitly address this relationship. Here, we outline a strategy to generate scenarios centred on our relationship with nature to inform decision-making at multiple scales.

Testing the vulnerability of the phylotypic stage: on modularity and evolutionary conservation.

Abstract: The phylotypic stage is the developmental stage at which vertebrates most resemble each other. In this study we test the plausibility of the hypotheses of Sander (1983) and Raff (1994) that the phylotypic stage is conserved due to the intense and global interactivity occurring during that stage. First, we test the prediction that the phylotypic stage is much more vulnerable than any other stage. A search of the teratological literature shows that disturbances at this stage lead to much higher mortality than in other stages, in accordance with the prediction. Second, we test whether that vulnerability is indeed caused by the interactiveness and lack of modularity of the inductions or, alternatively, is caused by some particularly vulnerable process going on at that time. From the pattern of multiple induced anomalies we conclude that it is indeed the interactiveness that is the root cause of vulnerability. Together these results support the hypotheses of Sander and Raff. We end by presenting an argument on why the absence of modularity in the inductive interactions may also be the root cause of the conservation of the much discussed temporal and spatial colinearity of the "Hox" genes.

Effective radiative forcing and adjustments in CMIP6 models

Abstract: The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.

Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: feedback from civil protection stakeholders

Abstract: The number of people affected by natural hazards is growing, as many regions of the world become subject to multiple hazards. Although volume of geophysical, sociological and economic knowledge is increasing, so are the losses from natural catastrophes. The slow transfer from theory to practice might lay in the difficulties of the communication process from science to policy-making, including perceptions by stakeholders from disaster mitigation practice regarding the usability of developed tools. As scientific evidence shows, decision-makers are faced with the challenge of not only mitigating against single hazards and risks, but also multiple risks, which must include the consideration of their interrelations. As the multi-hazard and risk concept is a relatively young area of natural risk governance, there are only a few multi-risk models and the experience of practitioners as to how to use these models is limited. To our knowledge, scientific literature on stakeholders' perceptions of multi-risk models is lacking. In this article we identify perceptions of two decision-making tools, which involve multi-hazard and multi-risk. The first one is a generic, multi-risk framework based on the sequential Monte Carlo method to allow for a straightforward and flexible implementation of hazard interactions, which may occur in a complex system. The second is a decision-making tool that integrates direct input from stakeholders by attributing weights to different components and constructing risks ratings. Based on the feedback from stakeholders, we found that interest in multi-risk assessment is high but that its application remains hampered by the complexity of processes involved.