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Publisher: Malden, MA : Wiley-Blackwell ×

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    Agent-based modeling to integrate elements from different disciplines for ambitious climate policy
    (Malden, MA : Wiley-Blackwell, 2022) Savin, Ivan; Creutzig, Felix; Filatova, Tatiana; Foramitti, Joël; Konc, Théo; Niamir, Leila; Safarzynska, Karolina; van den Bergh, Jeroen
    Ambitious climate mitigation policies face social and political resistance. One reason is that existing policies insufficiently capture the diversity of relevant insights from the social sciences about potential policy outcomes. We argue that agent-based models can serve as a powerful tool for integration of elements from different disciplines. Having such a common platform will enable a more complete assessment of climate policies, in terms of criteria like effectiveness, equity and public support. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Carbon Economy and Climate Mitigation > Policies, Instruments, Lifestyles, Behavior Policy and Governance > Multilevel and Transnational Climate Change Governance.
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    Climate information websites: an evolving landscape
    (Malden, MA : Wiley-Blackwell, 2017) Hewitson, Bruce; Waagsaether, Katinka; Wohland, Jan; Kloppers, Kate; Kara, Teizeen
    The climate change agenda is populated by actors and agencies with different objectives, values, and motivations, yet many seek decision scale climate information to inform policy and adaptation responses. A central element of this network of activity is the climate information website (CIW) that has seen a rapid and organic growth, yet with variable content and quality, and unfettered by any code of practice. This builds an ethical–epistemic dilemma that warrants assessment as the presence of CIWs contribute to real-world consequences and commitment. This study considers the context of CIW growth, and reviews a representative sample of CIWs to draw out key issues for consideration in CIW development. We assess content, function, and use-case value through a dual approach of a typology and user experience narratives to evaluate the general efficacy of a CIW. The typology reveals strong contrasts in content, complicated interfaces, and an overload of choice making it difficult to converge on a stable outcome. The narratives capture user experience and highlight barriers that include navigation difficulties, jargon laden content, minimal or opaque guidance, and inferred information without context about uncertainty and limits to skill. This illuminates four concerns: (1) the ethics of information provision in a context of real-world consequences; (2) interfaces that present barriers to achieving robust solutions; (3) weak capacity of both users and providers to identify information of value from the multimodel and multimethod data; and (4) inclusion of data that infer skill. Nonetheless, results provide a positive indication of a community of practice that is still maturing. WIREs Clim Change 2017, 8:e470. doi: 10.1002/wcc.470. For further resources related to this article, please visit the WIREs website.
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    S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts
    (Malden, MA : Wiley-Blackwell, 2018) Cohen, Judah; Coumou, Dim; Hwang, Jessica; Mackey, Lester; Orenstein, Paulo; Totz, Sonja; Tziperman, Eli
    The discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning.
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    Nanocarriers in photodynamic therapy—in vitro and in vivo studies
    (Malden, MA : Wiley-Blackwell, 2019) Sztandera, Krzysztof; Gorzkiewicz, Michał; Klajnert‐Maculewicz, Barbara
    Photodynamic therapy (PDT) is a minimally invasive technique which has proven to be successful in the treatment of several types of tumors. This relatively simple method exploits three inseparable elements: phototoxic compound (photosensitizer [PS]), light source, and oxygen. Upon irradiation by light with specified wavelength, PS generates reactive oxygen species, which starts the cascade of reactions leading to cell death. The positive therapeutic outcome of PDT may be limited due to several aspects, including low water solubility of PSs, hampering their effective administration and blood circulation, as well as low tumor specificity, inefficient cellular uptake and activation energies requiring prolonged illumination times. One of the promising approaches to overcome these obstacles involves the use of carrier systems modulating pharmacokinetics and pharmacodynamics of the PSs. In the present review, we summarized current in vitro and in vivo studies regarding the use of nanoparticles as potential delivery devices for PSs to enhance their cellular uptake and cytotoxic properties, and thus—the therapeutic outcome of PDT.